Software for Technological Patent Intelligence

Software for Technological Patent Intelligence

e4

Juan Carlos VergaraAlessandro ComaiJoaquín Tena Millán

2006

EMECOM Ediciones in collaboration with PUZZLE - Revista Hispana de la Inteligencia Competitiva

Evaluation of softwares and technological intelligence needs

ISBN-10 84-935178-0-1

EMECOM Ediciones in collaboration with PUZZLE - Revista Hispana de la Inteligencia Competitiva

Vergara, Com

ai y TenaSoftw

are for Technological Patent Intelligence

EMECOM

e4“What are the main functions used by professionals working in patent analysis? Howmuch importance do users attach to these functions? What software is available forpatent analysis and which software meets user requirements most satisfactorily?This pioneering report surveys the needs of intellectual property (IP) professionals, who exploit patents to produce intelligence, and surveys software product capabilitites.

Patents are a valuable source of information which, if analyzed, can help to generate knowledge about the relative positions of the different players or establish the state of the art in a given field. The report describes how patent professionals exploit and utilize software packages and it compares to the features of the evaluated software packages. It also shows the value attached to the characteristics provided by the producers. We think that this report, unique in his work, offers a framework for those working with intellectual property”

A unique survey of patent analysis software, detailing process steps and examining software features in the context of user need... also potentiallyuseful for future software developers.

Martha Matteo, Ph.D. Dr. Matteo is the former director of competitive technical intelligence at Boehringer Ingelheim Pharmaceuticals, Inc (ret.) and currently serves as the vice president for the Society of Competitive Intelligence Professionals (SCIP).

Eng - Cubierta Estudio.indd 11/07/2006, 12:281

Software for Technological Patent Intelligence

Evaluation of software and technological intelligence needs.

Juan Carlos VergaraAlessandro ComaiJoaquín Tena Millán

Software for Technological Patent IntelligenceEvaluation of software and technological intelligence needs. Vergara, Juan Carlos; Comai, Alessandro and Tena Millán, Joaquín

Published by:EMECOM Ediciones in collaboration withPUZZLE - Revista Hispana de la Inteligencia Competitiva (www.revista-puzzle.com).

EMECOM Consultores, S.L.Llacuna, 16208018 Barcelona - SpainTeléfono +34 93 401 98 [email protected]://www.emecom-ediciones.comNational book catalogue number: B-35363-2006ISBN-10 84-935178-0-1

Printed in Spain

© Copyright 2006: Juan Carlos Vergara, Alessandro Comai and Joaquín Tena Millán.

No part of this publication, including cover design, may be reproduced, stored in a retrieval system or transmitted, in any form or by any means, electronically or optically, without the prior written permission of the publisher.

TABLE OF CONTENTS

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- vii -

Table of Contents

Presentation and Acknowledgements

1. Main Findings

2. Introduction

3. Methodology

3.1 Definition of Application Characteristics

3.2 Analysis of supply

3.3 Definition of demand: use, relative needs and value

attached to the applications

4. Results of the study: demand, user

4.1 Profession

4.2 Sectors represented

4.3 Experience

4.4 Searching and Downloading

4.5 Filtering and Value adding

4.6 Local Analysis and Exploitation

4.7 Graphic Generation

4.8 Dissemination and Workgroup

4.9 Management of Tool

4.10 Importance

5. Comparison of Software: Supply

5.1 Program evaluated: Matheo Analyzer v3.0

5.2 Program evaluated: Matheo Patent v7.1

vii

xi

15

19

25

25

31

33

41

41

41

42

42

44

48

48

50

52

52

59

61

73

TABLE OF CONTENTS

Software for Technological Patent Intelligence- viii -

5.3 Program evaluated: PatentLabII v1.41

5.4 Program evaluated: PM Manager v1.4.0.3

5.5 Program evaluated: Vantage Point v4.0

5.6 Programs not evaluated

6. Conclusion and discussion

6.1 Results of comparison

6.2 Final thoughts

7. References and Autors

8. Annexes

8.1 Annex 1: Letter of invitation sent to software-

producing companies

8.2 Annex 2: Letter of Invitation sent to professional

individuals

8.3 Annex 3: 3rd. Letter of Invitation sent to

professional individuals

8.4 Annex 4: Letter of Invitation sent to professional

individuals (in Spanish)

8.5 Annex 5: Functions Table

8.6 Annex 6: Questionnaire

8.7 Annex 7: List of IP Organizations

83

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105

115

139

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145

151

157

157

159

160

161

163

165

175

PRESENTATION AND ACKNOWLEDGEMENTS

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We would like to thank all those individuals and companies who have taken part in our research, completing the questionnaire and attending to our requests for information. We would like to express our gratitude also to all the other individuals and companies we contacted in the course of this study, for the attention and time they have given us and for the interest they have shown.

We would also like to thank PUZZLE Magazine for providing us with the space and resources with which to prepare and carry out the poll.

PRESENTATION AND ACKNOWLEDGEMENTS

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SECTION ONEMain Findings

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- 15 -

Five companies took part and allowed us access to their patent analysis software, which we were then able to evaluate.

In order to evaluate the main functions and characteristics of patent analysis software, we prepared a model specifying the concepts into which said functions can be divided.

The model was applied to both supply and demand; in other words, our research looked, on the one hand, to experts in patent analysis for their evaluation of the functions and characteristics specified in the model, whilst on the other hand, we also assessed, according to the same model, the software created by individuals or companies to which we had access.

We worked on the supposition that the software assessed was representative of the software being offered on the market, although we accepted that our sample was limited and also biased by the eagerness of manufacturers to be included in our study.

We concluded that, of all the groups of functions, the section “Searching and Downloading” is the one which adapts least well, in general, to user demand. This weakness is also accentuated by the fact that users gave relative importance a higher rating than patent information searching and downloading compared to the other groups.

Only a few functions within this group, such as “Ability to import patent records” for instance, adapt to demand.

The group of functions which by and large lives up to user expectations is “Filtering and Value Adding”. In other words, the supply of functions slightly exceeds the use made of them.

According to the results of the study, “Local Analysis abd Exploitation” is a group of functions which does not meet user demand. The results obtained

1. MAIN FINDINGS

Software for Technological Patent Intelligence- 16 -

show that only two of the five software programs cover 50% of these functions.

“Graphic generation” shows positive global results, although there are several areas which are not dealt with quite as persistently as, for instance, graphic and statistical exploitation of the searches carried out. “Space or topographic representation of a patent collection – text mining analysis” or “Ability to use local databases to integrate new data and complete the patent analysis” are hence barely covered by the software studied, when they are in fact used relatively frequently.

“Dissemination and Workgroup” is another group of functions which is not given much space in the software analyzed in this study. Alerts, for instance, are not adequately covered despite the fact they are the functions most used by users.

There is a major weakness in “Management of Tool”. None of the software included in the study covers the seven functions described for this group in any satisfactory way. This low rating could be due to the fact that the professional individuals who replied are not application administrators (also referred to as webmaster). It is for this reason perhaps, that both the use, as well as the importance, of these functions has a relatively low rating.

Overall we have reached the following general conclusions:

- No patent-monitoring software fully covers the functions one would expect to find in software of this kind.

- Although existing patent-monitoring software meets user demands, there are major gaps in many functions.

SECTION TWOIntroduction

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2.1 Patent Analysis Software: a review of the situation

A bibliographical review of patent analysis software shows that no specialized work has been carried out as regards the evaluation of supply and demand of computer applications for patent analysis. An interesting work was proposed by Trippe 2003, who explored the added value of patent tools available in the market. Up until now, all that existed were a few reports dealing with issues relating mainly to copyright in general (“Patent Tools Survey”)1. Other minor work has focused on preparing lists of tools available in the market2.

The applications included in this study are used to obtain more advanced knowledge relating to copyright and they are used in Technological Watch or Competitive Technological Intelligence activities. In other words, patents are a valuable source of information which, if analyzed as a group, can help to generate the basic knowledge for creating theories on the relative positions of the different players in a given field. At the same time, the historical study of patents allows future projections to be made, by means of quantitative or statistical tools, or the trends within a sector or a specific company to be identified. Paap (2002) considers, for instance, that the following can be obtained from patent analysis:

- The main players - competitors and current and potential collaborators - and their focal areas.

- Movement in the interest of the aforementioned players to evaluate the greater or lesser importance given to a technology or a line of research and development.

- Organization of the technical endeavors and movement of personnel in time between departments.

- Patent strategies used by participants and the opportunities provided and threats posed by the strategies “surrounding the patents”.

A number of studies and articles in this field have shown the importance and benefits of carrying out more or less sophisticated patent analysis (Vergara,

2. INTRODUCTION


2004; Rodriguez, 2003; Lozano, 2003). Other work has focused on tackling patent exploitation (Paap, 2002; Adams, 2006) or how to organize a systematic collection process using patents as a primary source.

As we mentioned earlier, computer applications make the job of statistical analysis or the preparation of patent maps far simpler, thus giving rise to Competitive Technological Intelligence (CTI). It has been shown that computer applications can have a very large number of characteristics and functions aiding the work of experts in this area. For this reason, it is currently highly important that we be aware that applications do exist in the market and that we know which of them can best meet the needs of professional individuals working in CTI.

2.2 Software analysis studies for Competitive Technological Intelligence (CTI)

Competitive Technological Intelligence is a practice which specializes in scientific and technological tasks including several types of operations. Generally speaking, the basic process can be compared to the process used by Competitive Intelligence (CI). However, since the emphasis in this context is on technology, CTI uses specialized activities such as Patent Analysis3 (PA).

We should emphasize that it is perhaps due to this, that the software designed currently for CI (see for instance: Bouthiller and Shearer, 2003; Nikkel, 2003; Fuld&Company, 2004)4 do not yet include patent analysis, since this is a specialist area belonging to the department of R&D.

2.3 Purpose of the Study

This report looks at the supply and demand of software aimed at exploiting patent systems. As we saw earlier in the bibliographical review, no study has been made of how patent users exploit and utilize software packages. In this report, we made our assessment on the basis of two separate studies using the same base structure.

Our motives for carrying out this pioneering study can be summarized as follows:

- Non-existence of exhaustive studies comparing computer applications for PA.

- 21 -Introduction

- The lack of studies on the demand for software for patent analysis. That is, the lack of awareness regarding the use of and the value attached to the characteristics provided by the producers of this type of computer application.

- Non-existence of any comparison of applications together with a need expressed by users of said products.

- To obtain an assessment of the magnitude and growth of the supply. We recorded over 21 applications existing in the market5, which in our opinion, is an extensive supply for this specialized field. Another trend which stands out is the increase, if only marginal, in the number of this type of application.

- We are dealing with a wide range of available computer applications for PA. An application can have a very large number of functions. However, applications currently existing in the market include different groups of functions and it is, as a result, hard to make any kind of partial comparison of them. For this reason, we believe that it is necessary to standardize or have a uniform approach to the study of these applications in order to make the comparison valid.

All of this has led us to formulate several research questions:

1. Which are the main characteristics or functions used by professional individuals working in PA?

2. How much importance do PA users attach to each group of functions?

3. Which software is available for patent PA?4. Which software meets PA requirements most satisfactorily?

The answers to these key questions are given in the following chapters.

**********************************Footnotes

1 See: “PatentCafe’s Patent Software Tools survey” (http://tinyurl.com/96mja) [Consulted on August 11, 2005].2 See for instance Paap, J. (2002). Using technical intelligence to drive innovation and technical decisions. Workshop given at the Annual International SCIP conference in Cincinati, USA.3 It can be observed that the difference between CI and CTI does not occur only


in patent analysis. CTI also uses primary and secondary sources specializing in recovering technological information.4 See Assessing Competitive Intelligence Software by Bouthiller and Shearer (2003), Software Report 2004-2005 published by Fuld&Company (2004) (http://www.fuld.com/Products/ISR2004/HomePage.html) or How can We Determine which Competitive Intelligence Software Is Most Effective? By Nikkel (2003, p.163). Full references can be obtained at the end of the book (see page 151-152).5 See summary table 1.

SECTION THREEMethodology

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- 25 -

In order to answer the research questions posed above, two separate sections of the study were developed, using and integrating them in a joint framework.

a) Study of the patent CTI software available: this study focused on assessing the different application functions.

b) Study of the demand for patent CTI software: this study focused on assessing the subjective needs of users in terms of use and importance attached to the different application functions.

Both sections used the same framework for studying the application functions. In other words, the same groups of functions were studied from both a demand as well as a supply viewpoint (see the following section on this).

The study of supply was made separately - that is, it was “blind” - with no knowledge of the results of the study on demand. In this way, we tried to avoid any bias in the judgments made in both sections of the study.

3.1 Definition of Application Characteristics

In order to evaluate both demand as well as supply, we used a list of software characteristics or functions defined on the basis of:

- A review of literature in this field (Ashton y Klavans, 1997; APQC, 2001; Paap, 2002; Trippe, 2003; Dou, et al., 2005; Adamas, 2006).

- Analysis of the software available on the market. - and the personal experience of the authors.

The functions identified in this way (41 in total) were divided into 6 groups, as shown in the following table1:

3. METHODOLOGY


1.- Searching and Downloading

Ability to search in a set of online patent databases

Ability to search in other technical/grey literature online databases

Ability to search in local (intranet) databases

Ability to import patent records

Ability to import other records (not patents)

Ability to launch simultaneous searches in multiple databases

Ability to save search strategies

Ability to Schedule repetitive searches

Downloading and integration of patent legal status

Downloading and integration of graphics

Downloading and integration of pdf documents

2.- Filtering and Value Adding

Automatic duplicate detection and removal

Automatic grouping of patent families

Automatic generation of field indexes

Ability to define and build new indexes

Wizard for grouping and cleaning terms of indexes

Patent pertinence (user filled field)

Annotation of patents (user filled field)

Ability to define and edit patent groups

Links to other related documents

Taxonomies creation and edition

3.- Local Analysis and Exploitation

Automatic extraction of main keywords from patents

Automatic abstracts

Automatic clustering of patents

Automatic classification of patents using semantic filters

Full text searching capabilities

Semantic searching capabilities

- 27 -Methodology

4.- Graphic Generation

Cite Analysis (cited and citing patents in relation to a known patent)

Rankings - Analysis of one field.

Matrix or Bar graphs – Two field’s co-occurrence analysis

Network relations analysis – Two fields co-occurrence analysis

Space or topographic representation of a patent collection – text mining analysis

Ability to use local databases to integrate new data and complete the patent analysis

5.- Dissemination and Workgroup

Ability to publish the contents in the intranet / internet

Personalised alerts

Alerts to detect changes in the legal status of a patent

Automatic reports using templates

Ability to export data

Ability to create a poll and link a patent to a poll

Ability to link a patent to a forum

Ability to link a patent to an event in a shared agenda

6.- Management of Tool


Users access rights management

Multi-user access and edition

Access and search interface customisation

Multilanguage interface

Document collections access rights management

System utilisation statistics

Table 1 - Definition of Application Functions.

The following is a more detailed list of the functions.


3.1.1 Searching and Downloading

This section assesses all the characteristics relating to the process of information collection and its automation.The environment in which a typical user currently carries out his or her work might include access to patent databases as well as access to other bibliographical databases that are normally scientific and technological and which usually complement one another.

In addition to this, these databases can be located in a local network (in a private database for instance or in a commercial CDROM-based database) as well as in a website, which means that changes must be implemented in the program in order to allow access to each of these options.

Wherever the program included an interface for information searching in a website, we also considered the option for saving the search strategy and for programming its periodic implementation, since these are basic tasks for the Technological Observation function.

Another very basic characteristic is the ability to import the results of the searches carried out in any information source, normally in csv format (comma separated values), in text format delimited by fields or in XML format.

Lastly, the ability to integrate other information relating to patents in order to add to their value was also assessed. This information is normally in the form of graphics or .pdf documents, but it can also be, for instance: legal information which could increase or remove the value of a patent, or economic information relating to a company or a technology.

3.1.2 Filtering and Value Adding

This section covers a whole list of tasks all of which have in common the fact that, when they are carried out, the information becomes far cleaner and better organized and assessed, making subsequent analysis far easier and, in addition, resulting in much firmer conclusions.

In patent analysis, it is important to define the information “unit” to be analyzed. Generally speaking, analysts work with “patent families” which group together in one single record all the documents generated from the same priority number. The deletion of duplicate patents and the grouping of patents by families is a task which should be carried out either prior to loading the information into the software or once it has been loaded.

Automatic generation of indexes, the ability to easily generate new indexes from elements contained in different information fields (for instance terms

- 29 -Methodology

included in the title), is also examined.Once the indexes have been created, the data included therein must be

checked for any possible errors and, if necessary, these must be corrected, so that the analyses are correct. The most typical example is the name of an inventor or a company, which may vary where abbreviations are used.

Lastly, it is important for users to be able to assess the contents of the patents as they read them. In this way, new information fields are generated which can be analyzed subsequently, such as groups based on specific user interests, links with other valuable documents, or comments on the contents of each patent. Each of these operations adds value to the group of patents to be analyzed, making group work and the making of a final decision on a group of patents easier.

3.1.3 Local Analysis and Exploitation

In this section, we assess the basic abilities of the software to manage the information accumulated (filters and advanced searches, classification of results by different criteria, etc.).

Other more advanced abilities are also assessed, such as the generation of automatic abstracts for each patent, the extraction of the most representative concepts of each document, the automated “clustering” of the patents into different categories or semantic searching. These abilities already have some relation to text mining.

3.1.4 Graphic Generation

This section itemizes the graphics most used by current applications. In general, they can be divided into the following types:

- Single-dimension ratings or classifications, normally represented by a line or by bar charts showing one single variable (terms which appear in one field).

- Bar graphs or matrixes showing the relationship between two variables (two analyzed fields). These graphics show the number of times two terms appear simultaneously (co-occurrences), each in one information field.

- Relationship networks: this graphic allows a fair number of variants. Generally speaking, each term appears as a node in a bi-dimensional space, with lines leading to other terms to which the initial term


is related. These links can be shown in a way which is more or less vivid or using different colors or associated numbers according to the number of co-occurrences existing between both terms. In addition to this, each node can be larger or smaller or a different color depending on the total number of patents in which it appears. Each node can also be associated to a different icon according to other parameters. Lastly, the position of the nodes in this space and their close proximity to each other may be set by the software in accordance with specific algorithms or they may just float, allowing the user to move them around resulting in a clearer image.

- Topographic representations: images in 2 or 3 dimensions which can be complemented by different color tones, showing the more representative concepts, the main classifications or the most important companies in a group of patents. They provide an intuitive vision of the information available in said group and allow analysis to be focused on specific sections of said topographic representation.

- Cite analysis: a special kind of representation of relationships between patents, in which the links express the existence of a cite between one patent and another previous one.

3.1.5 Dissemination and Workgroup

This section includes a list of the different tasks and functions which can be automated in order to reinforce collaborative work. Firstly, the initial idea is that each user should have his, or her, own information profile and receive any alerts corresponding to said profile. From here, the ability of each user to generate reports using predefined templates in his or her specialty is assessed.

In addition, the aim is to generate new knowledge among different individuals by means of discussion and joint analysis of different multidisciplinary issues dealing with said shared information. The possibilities for group work considered were: the generation of polls, the generation of forum discussion and the existence of a shared agenda for the work to be carried out.

Several options for exporting information to standard formats, allowing their use in other software, were also listed.

- 31 -Methodology

3.1.6 Management of Tool

This section cites a group of functions that assess the ease with which the software adapts to the requirements of different users (for instance: different languages) with different rights (edition or creation, for instance), simultaneous work or whether or not the information can be published in an intranet.

The ability to generate system use statistics is included, so that an assessment can be made as to how the use of the software is developing and in short, as to how efficient it is.

3.2 Analysis of supply

A review of applications available on the market allowed an initial approach to be put forward with regard to specialized software for patent analysis.

3.2.1 Criteria and selection of patent analysis software

The initial software selection was carried out using the following criteria:

- the software carries out some kind of analysis.- the supplier is available to deliver a complete copy for assessment.- it must be possible to install the software in the client company’s

server.2 - we received a positive answer to our suggestion regarding participation

in the study.

The following table summarizes the selection criteria, organized in such a way that each criterion eliminates a specific number of applications from our study.


CriterionOrder

Calculation Selection Criteria Number of Softwares

1 + Analysis of existing software open to study

33

2 - Those which do not carry out any kind of analysis

12

3 = Those which can be assessed and installed in a server for testing

21

4 - Those which did not receive an invitation from us

2

5 - Those which decided not to join the study

2

6 - Do not confer software license and prefer to bring documentation

1

7 - Those which did not reply to our invitation to take part in the study

11

8 = Assessed software 5

Table 3 – Selection of Patent Analysis Softwares and results of invitation process.

By way of an example, we would like to cite two packages which were discarded and which were on the initial list of patent analysis software (see point 1 of the previous table):

- BizInt – This package only reformats records obtained from very specific patent databases (Dialog or Questel, for instance). That is, it creates tables and repositions the fields in tables, but it does not carry out any kind of analysis. We did not include this application because the assessment would be pretty well invalid in almost all sections.

- Kaliwatch (Pro and Server) is a generic technological observation tool. It has some interesting functions for cooperation between users but it does not focus on analysis, and even less on patent analysis (it does not even mention patents as an information source).

- 33 -Methodology

We did not, therefore, think it appropriate to include this software program in our list.

3.2.2 Invitation to participate

On the basis of prior analysis, 21 companies were identified as being suitable to partake in the study. They were invited to do so by electronic mail during the first week of July, 2005. The follow-up subsequent to the invitation was carried out by telephone.

All of the messages were sent with an acknowledgement of receipt. Very few of these were returned to us and we received a reply stating that the message had been deleted. In addition, the MAPIT mail server produced an error and was not therefore included in our research. Despite the fact that the number of acknowledgements was low, we assumed that a total of 19 companies received our invitation correctly.

3.3 Definition of demand: use, relative needs and value attached to the applications

In order to evaluate the situation as regards demand in relation to the characteristics or functions of the software, we carried out a poll among IP professional individuals and users interested in monitoring and analyzing patents and also copyright in general. The questions for which this part of the study hopes to find answers are:

1. Which characteristics or functions do professional individuals use mainly?

2. How much importance do they attach to each group of functions?

These two questions establish a starting point for patent analysis software demand. In other words, we wish to identify the potential needs of patent users, without actually specifically defining the real current and future needs of professional individuals.

Having carefully pondered the possibilities for studying user needs, we came up with a simple and representative approach. In effect, the approach we suggest in our study focuses on the “use” of the functions described in Table 1 of our poll.

In addition, we introduced a section in which we assess the degree of


relative importance attached by users to the different functions. This exercise allowed us to determine:

1. The degree to which each function is used.2. The importance attached to each group of functions4.

The main focus of our study is the situation as regards software currently available and whether this software lives up to user expectations. The assessment of user needs and the definition of the future of the software or the determination of the “value” attached to each function incorporated in existing software and included in our sample are not the main focus of this study.

Reality does not always match the wishes of users. Despite the fact that a user may wish for a function and value it highly, as long as he has no software program which offers said function, he will have to reply in the questionnaire that he does not use this function. This gives rise to a simplification of the questions.

3.3.1 Instrument

The instrument used to collect data was an online questionnaire, accessible on a webpage specializing in information gathering and data processing5.The questionnaire included 41 functions divided into 6 sections and an additional page where groups were assessed separately (see Annex 8.6).

3.3.2 Assessment of use and relative importance of functions

The assessment of the use of the 41 functions (items) divided into 6 groups described in the questionnaire was carried out by requesting information on the following aspects (see Annex 8.5 or page 26):

1. Function use - The degree of use was assessed using a 7-point Likert scale.

2. Relative importance of functions - It was suggested to those responding to the questionnaire that they use an evaluation system in which they awarded 1 point to the factor which was least important to them when compared with the other factors. This method allowed us to define which groups of functions users consider most important6.

3.3.3 Sample

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In our study of demand, we used two databases containing data on professional individuals and patent users. There are currently two main sources amassing the vast majority of professional individuals involved in patent and copyright issues. We discarded other distribution lists such as those originating in the east (see Annex 8.7, for instance) since this project is focused mainly on western populations. These two databases allowed us to reach a significant number of professional individuals. A detailed list of these is given below:

- The association PIUG has approximately 600 active members, according to its web page (http://www.piug.org), from 22 countries including the United States of America. The majority of members are from the United States, Europe and Japan. The professional profile of the members includes lawyers specializing in patents, patent agents, people who grant licenses, patent information researchers, patent information salespeople and experts in patent information and documentation.

- The mailing list “EPO Mailing List” (http://www.european-patent-office.org/mail.htm).

The total number of individuals to be studied is approximately 600 + 800. It should be remembered, that these figures are not exact due to the fact that these distribution lists are voluntary and free and may therefore fluctuate considerably over time7. In addition, it was impossible to obtain any demographic information on subscribers from either PIUG or EPO since they are anonymous lists open to all8.

3.3.4 Sending of the invitation

The invitation to participate in this initial study was sent by electronic mail. A total of three invitations were sent:

1. On July 11, 2005, the official invitation9 to participate in the poll was sent. This invitation resulted in around 35 replies to the questionnaire. In view of the relatively poor result, we decided to send a second invitation.

2. On July 18, 2005, a second invitation was sent. This produced 53 additional questionnaires.

3. On July 27, 2005, the third and final invitation was sent, the result


of which was a total of 102 replies. This final call set a deadline at July 31, 2005.

Despite the fact that a major increase was observed in the number of replies obtained each time a new invitation was sent, we considered that three invitations gave a sufficiently satisfactory result.

3.3.5 Results

The end results of the poll were 102 valid questionnaires. The following table summarizes the results of the PIUG and EPO listing.

Ref. Operation Action Number %

1 + Invitations sent 140010 100

2 - Mail deleted 1 -

3 - Unread mail 1 -

4 = Results: net invitations 1398 99.99

5 - Did not reply 1296 92.72

6 = Questionnaires opened 102 7.28

7 - Incomplete questionnaires 0 0

8 = People who replied 102 7.2811

Table 4 – People involved and final sample of users.

*******************************************Footnotes

1 This characteristic is not a selection criteria but it did significantly reduce the number of applications assessed. 2 The profile of those polled and of how the sample was chosen is described in later sections.3 This part of the poll is relatively simplified. We have taken a simple approach, however, since an assessment of the relevant functions would have become excessively complex otherwise.4 The scale was as follows: “Not at all”, “Very little”, “Little”, “Sometimes”, “Often”, “Almost every time”, “Always”. “Not Applicable (N/A)” was also added.5 For further information on the provider used, see Surveymonkey.com (http://

- 37 -Methodology

www.surveymonkey.com).6 Personal comunication; Juan Manel Batista (ESADE, Barcelona, Spain). To see an application which uses this method, see Comai, A (2005) “Factores y Contingencias en la Inteligencia Competitiva: Resultado en un estudio piloto,” PUZZLE - Revista Hispana de la Inteligencia Competitiva, 4(18):12-15. (see http://www.revista-puzzle.com/puzzle_sum_18.htm).7 By way of an example, it can be observed that the total number of confirmations of having read or rejections of the invitations we sent by electronic mail exceeded 230. It should also be noted that these readings were made long after the questionnaire expired. 8 We contacted both PIUG as well as EPO in order to obtain this information. The replies, however, were negative. In other words, they had no information on the subject. For PIUG, see “http://piug.org/list.html#Majordomo%20Commands%20-%20How%20to%20Join%20the%20Discussion%20List” and for EPO see “http://www.european-patent-office.org/mail.htm”.9 See Annex 8.110 Estimation (see section Sample, page 35).11 Approximation (see section Sample, page 35).

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SECTION FOURResults of the study: demand, users

- 40 -

- 41 -

This chapter gives the results in detail of the poll carried out among patent users.

Social and demographic information

This section provides a general overview of the 102 responses we had to the questionnaire. As we mentioned earlier, neither the PIUG nor the EPO list contains any social or demographic information and we have therefore considered it appropriate to include a profile of the IP experts polled.

4.1 Profession

There is some variety in the profession of those polled. The questionnaire included three clusters or groups of activity: R+D Manager (6.3%), Librarian (5.3%), and technicians (10.5%). These three activities accounted for less than 23% of the total number of individuals polled but constitute an important minority. The professions identified as most common were “Patent specialist or searcher” (18/102) “Patent attorney” (12/102) and “Copyright manager” (7/102). Nevertheless, lawyers, patent experts and company directors also completed the questionnaire (see Figure 1).

4.2 Sectors represented

The sectors represented in this sample are also very varied (see Figure 2). However, with regard to profession, it can be observed that 56% of those polled come from the pharmaceutical, chemical, electronics, computer and engineering and mechanics sectors. There is also a certain homogeneity within this group in which, excepting electronics, the four remaining sectors account for very similar percentages (between 10.3% and 14.4%).

4. RESULTS OF THE STUDY: DEMAND, USERS


In the rest of the sample (43.3%) several sectors are represented, including consumer goods, biotechnology, cosmetics, software design, as well as consultants, the government and universities. The consultancy group contains 16 companies.1

4.3 Experience

We inferred from the sample, that the patent experts have notably extensive experience in everything relating to their work. Despite the fact that we did not specifically ask in the questionnaire whether their experience related exclusively to patents or whether it was wider-ranging, we are inclined to think that their experience in the former is very extensive. In fact, the vast majority of the sample (75.5%) reports having 6 years experience and 53.1%, over 11 years. Figure 3 gives a breakdown of the sample in terms of experience in the field of patents.

These results support those obtained for our research, due to the fact that extensive experience in the field of patent analysis is very relevant to an adequate response to our questionnaire.

Information on the functions by area of interest

This section discusses the results relating to the functions included in the 6 key characteristics of patent analysis software plus a final section analyzing the relative importance of said characteristics.

4.4 Searching and Downloading

This characteristic assembles the main patent searching functions in both commercial as well as in private databases. The functions relating to patent downloading/import or search strategy recording in this characteristic were considered at the same time (see Table 5).

- 43 -Results of the Study: demand, users

Figure 1 – Professions of those polled.

Figure 2 – Sectors represented in the sample.

Figure 3 – Years of experience in patent analysis of professional individuals polled.


1. Searching in complementary technical / grey literature online databases2. Searching in local (intranet) databases3. Importing patent records from other software4. Launching simultaneous searches in multiple databases5. Saving search strategies6. Scheduling repetitive searches7. Downloading and integration of patent legal status8. Downloading and integration of graphics9. Downloading and linking of pdf documents

Table 5 – Searching and Downloading functions.

Of these functions, the one which is most used (frequent use)2 or which has obtained the highest average rating is “Saving search strategies”. This function stands out from the others due to the fact that around 30% of those polled maintain that they always use it.

Other functions obtained similar average ratings, such as “Downloading and linking of pdf documents”, “Launching simultaneous searches in multiple databases” or “Downloading and integration of patent legal status”, for instance, which are used with a frequency very close to “often”.

In a second group not far behind the first, we find less frequently used functions. For instance, “Scheduling repetitive searches”, “Searching in complementary technical / grey literature online databases”, “Searching in local (intranet) databases”, “Downloading and integration of patent legal status”, “Importing patent records from other software” are used “Sometimes”.

It should be observed, however, that the data for each function is somewhat dispersed. That is, users make quite different use of the functions. A maximum of 32 cases and a minimum of 5 users per type of use were recorded for all functions. Figure 4 shows this distribution.

4.5 Filtering and Value Adding

This group of functions, under the heading filters and value adding, allows patents to be managed in a very different way once they have been captured and filed in the company’s database. The functions included in this section are as follows:


Oft

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2. Searching in local (intranet)databases

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8. Downloading and integration ofgraphics

9. Downloading and linking of pdfdocuments

0

10

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1. Searching in complementarytechnical/grey literature online

databases

Figure 4 – Searching and Downloading.


1. Automatic patent duplicate detection and removal2. Automatic grouping of patent families3. Automatic generation of field indexes4. Definition and building of additional indexes5. Grouping and cleaning of index terms6. Evaluation of pertinence (user filled field)7. Annotation of patents (user filled field)8. Definition and edition of patent groups9. Linking to other related documents10. Creation and edition of taxonomies

Table 6 – Filtering and Value Adding functions.

The results of the poll show that there is some difference in the way in which these functions are used.

Firstly, it should be observed that the most frequently used functions are “Automatic grouping of patent families” and “Automatic patent duplicate detection and removal”. The use associated with these two functions is “often” (4.88 and 4.73 respectively). In addition, 18 and 20 experts in each case stated that they “always” use these functions.

On the other hand, the function which is least used is “Creation and edition of taxonomies”. Half of all users of this function report using it “little” (2.46).

Other functions, such as “Automatic generation of field indexes” (3.76), “Evaluation of pertinence” (3.70), “Annotation of patents” (3.64) or “Linking to other related documents” (3.60), for instance, have an average use ranging between “sometimes” and “often”, whilst use of the remaining functions, such as “Definition and building of additional indexes” (3.31), “Definition and edition of patent groups” (3.29), or “Grouping and cleaning of index terms” (3.26) appears to be closer to “sometimes”.

It should be emphasized, however, that although some functions obtained a specific average rating, responses were also extremely wide ranging. In other words, there are as many experts “always” using a specific function as those “never” using that same function. There are clear differences in the use by experts of function 3) “Automatic generation of field indexes”, shown by the fact that a sizeable number of experts in all use groups appeared on the scale of 7 points we established in the questionnaire. These findings can be seen in figure 5.


Figure 5 – Filtering and Value Adding.


4.6 Local Analysis and Exploitation

This group of functions is characterized by its ability to analyze and use patents in accordance with the concepts stated in the following table:

1. Automatic extraction of main keywords from patents2. Automatic abstracts3. Automatic clustering of patents4. Automatic classification of patents in pre-defined categories5. Full text indexing/searching6. Semantic indexing/searching7. Ability to use local databases to integrate new data and complete the patent

analysis

Table 7 – Local Analysis and Exploitation functions.

The results of the study show that the function which stands out most is “Full text indexing/searching” of patents, having ascertained that experts use this function “often”. It should be observed that almost 25% of experts (of the total 82 who answered this question) always use this function.

A second group includes the remaining functions with relatively similar ratings, an average use of between “little” and “sometimes). The function in this group which is most used is the obtaining of “Automatic abstracts” (3.73) and the least used is “Automatic classification of patents in pre-defined categories”. The other functions are situated between these two extremes (see Figure 6).

4.7 Graphic Generation

The applications used in patent analysis can show the information they are processing in graphic form, allowing an additional, visual exploitation which is somehow far more vivid than in the cases described above. The five functions for which information was requested in this study are as follows:


Figure 6 – Local Analysis and Exploitation.

2. Automatic abstracts

0

10

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40

3. Automatic clustering of patents 4. Automatic classification ofpatents in pre-defined categories

0

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5. Full text indexing/searching

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the patent analysis

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1. Automatic extraction of mainkeywords from patents


1. Cite analysis (cited and citing patents in relation to a known patent)2. Rankings – Analysis of one field3. Matrix or bar graphs – Two fields co-occurrence analysis4. Network relations analysis - Two fields co-occurrence analysis5. Space or topographic representation of a patent collection – text mining

analysis

Table 8 – Graphic Generation functions.

The two most used functions are “Rankings – Analysis of one field” and “Cite analysis”, with (4.03 and 3.97)2 respectively, suggesting that they are used “sometimes”.

These are followed by “Matrix or bar graphs – Two fields co-occurrence analysis” which is used from “little” to “sometimes” (3.59) and “Network relations analysis - Two fields co-occurrence analysis” which is used slightly more than “little” (3.19).

Lastly, “Space or topographic representation of a patent collection – text mining analysis” is seldom used, given that the majority of the responses we received fell into the categories “very little” and “little” (2.70) (see Figure 7).

4.8 Dissemination and Workgroup

This group contains the following functions:

1. Publish the contents in the intranet / internet.2. Customized alerts.3. Alerts with changes on the legal status.4. Automatic reports using templates.5. Export all the fields: .csv, .txt, xml, etc6. Link a patent to a poll with a key question.7. Link a patent to a forum and begin discussion.8. Link a patent to an event with a shared agenda.

Table 9 – Dissemination and Workgroup function.

The function “Customized alerts” (3.99)2 is used “often”. In second place, somewhere between “sometimes” and “often” comes “Export all the fields” and “Publish the contents in the intranet / internet” with 3.69 and 3.57 respectively.

Two functions, “Alerts with changes on the legal status” and “Automatic


Figure 7 – Graphic Generation.


reports using templates”, were identified as being used “sometimes”.Lastly, another three functions are used “little”. It should be noted that

these functions “Link a patent to an event with a shared agenda”, “Link a patent to a poll with a key question” and “Link a patent to a forum and begin discussion” are the ones which were rated lowest in this group, since over 50% of those polled (83) said that they do not use these functions (see Figure 8).

4.9. Management of Tool

This group contains the following functions:

1. Management of users access rights2. Management of Document collections access rights3. Simultaneous multi-user access and edition4. Customization of access and search interface5. Multilanguage interface6. System utilization statistics

Table 10 – Management of Tool functions.

The functions above refer to the ability of the software to manage the applications in such a way that user needs are met. It should be emphasized that the overall results obtained in this section are among the lowest in the poll.

As is shown in figure 9, user responses are fairly similar for all functions, with an average use which is close to “sometimes”. Although the responses are quite varied, the one which appears most frequently (from 24% to 38%) is “never”. The function which stands out as being least used is “Multilanguage interface”.

4.10. Importance

The importance attached to each of the functions included in this section is a relevant estimate of how highly they are rated. This is how we distinguish frequency of use from the relative value each user considers has been added to his work by each group of functions.


Figure 8 –Dissemination and Workgroup.


In order to assess this aspect of the software, we have used an approach which relates all the functions to the one given the lowest rating by users3. In this way, a comparison between all the functions can be established on a qualitative basis which is appropriate for our study4.

A total of 79 experts completed this section. In order to make the evaluation process easier, the questionnaire suggested an assessment scale of 1 to 3 with intervals of 0.25 points.

The results of the average user ratings obtained are shown in table 11. The table shows the average of the results obtained and the correction carried out in order to obtain the relative value.

Figure 9 – Management of Tool.


Groups Average Correction5

1. Searching and Downloading 2.65 2.25

2. Filtering and Value Adding 2.27 1.87

3. Local Analysis and Exploitation 1.86 1.46

4. Graphic Generation 1.59 1.19

5. Dissemination and Workgroup 1.59 1.19

6. Management of Tool 1.40 1

Table 11 – Relative importance of functions.

Firstly, “Searching and Downloading” is the characteristic most appreciated by users for analyzing patents. This group was rated highest, with results which reflect the fact that those polled attach an importance to it which is more than twice that attributed to “Management of Tool”. In addition to this, more than 65% of those polled gave this type of function maximum rating.

The second item, “Filtering and value adding” also stands out as being appreciated almost twice as much by those polled as the reference characteristic. This means that the functions associated with this characteristic rank highly among users. Nevertheless, only 28% of those polled expressed an opinion on this concept and so it definitely has less of an impact than in the case of the previous concept (“Searching”).

“Local Analysis and Exploitation” was rated at 1.46. Lastly, another two characteristics were given a similar degree of importance. Those polled rated “Graphic Generation” and “Dissemination and Workgroup” only just above “Management Tool”.

The data obtained allows priorities to be established, emanating from the opinions of users regarding which functions require more attention in terms of design and the improvement of the type of software being studied.

********************************************************

Footnotes

1 For instance: legal, copyright management consultants, analysis and assessment of the financial risk in copyright, lawyers and consultants or IP consultants. 2 The scale used was: (1) Not at all, (2) Very little, (3) Little, (4) Sometimes (5) Often, (6) Almost every time, (7) Always y (0) N/A.


3 Method suggested by Juan Manel Batista of ESADE Business School (Personal Communication).4 The questionnaire contains the following explanation for users interviewed: - NOTE: Assign “1” to the least important group of methods/techniques and rate ALL the others groups against it. 6 If you rate “1” it means that the group of methods are equivalent to the one being compared with. If you rate 1.5 then it means that the group of methods are 50% more important to the one being compared with. If you rate 2, that means that it is one time more important or dobble and if you rate 3 it is two time more important and so on... -5 The correction was carried out as follows: The lowest average rating was identified. In the case of “Software Management” it is 1.40. Since the system takes 1 as the lowest rating, we made the lowest rating the reference rating. That is, reducing 1.4 by 0.4. In this case, “Software Management” became the reference and thus took on the rating 1. The ratings of other items were reduced by the same amount in order to study the incremental ratings in terms of importance, in accordance with the evaluation system used in the questionnaire.

SECTION FIVEComparison of Software: Supply

- 58 -

- 59 -

5. COMPARISON OF SOFTWARE: SUPPLY

In this section the technical specifications for five of the softwares will be evaluated in depth in this study. However, information will also be added on a further ten softwares which have not been fully evaluated. The partial or complete description of the programs is carried out using the format below:

- Name of the program and the company’s details. - Evaluation table summarising feature according to the system adopted

by this study.- Description and details of the program’s features in six key areas.

The technical details of each program are presented in the following section in alphabetical order.

- 60 -

- 61 -

5.1 Program evaluated: Matheo Analyzer v3.0

Producer: IMCS8 rue Crillon13005 Marseille, FranceTelephone: +33 (0)491 082 882Fax: +33 (0) 491 783 906E-mail: [email protected]: http://www.matheo-software.com

Evaluation Table1

MATHEO ANALYZER v3.0 Evaluation

1 2 3 4 5



























Automatic abstracts








Matrix or Bar graphs – Two field’s co-occurrence analysis.


- 63 -Comparison of Software: Supply - Matheo Analyzer v3.0





Personalised alerts








Management of users access right

Management of Document collections access rights

Simultaneous multi-user access and edition

Customization of access and search interface


System utilization statistic

Table 12 – Benchmark for the “Matheo Analyzer v3.0”.

5.1.1 Definition of the software

Matheo Analyzer is a tool specialising in graphically visualising and analysing information retrieved from bibliographic databases. The purpose is to analyse all types of bibliographic references, retrieve this information from its


respective fields and give it the appropriate bibliometric treatment.

Figure 10 – “Matheo Analyzer v3.0” main screen.

5.1.2 Comments on the features studied

1) Searching and DownloadingConnection to databases: Matheo Analyzer does not have modules for carrying out database searches. Its starting points are the lists in text form which contain registers obtained from databases or other applications. For instance, it can use data accessed from exported Matheo Patent bibliographic files.

Matheo Analyzer contains an assistant to take the inexperienced user step-by-step through the importation of a list of files. When the process has finished, the correct steps for importing from such a source can be saved for later reference and use.

Matheo Analyzer allows the user to carry out various types of importation at any time: for a given project new fields, which had not been imported previously, may be entered. In addition,

The program can import new records differentially (records with “key” fields differing from those already loaded can be imported).

Matheo Analyzer is not designed to import or manage either graphics or attached pdf documents.


Figure 11 – “Matheo Analyzer v3.0” importation.

2) Filtering and Value AddingIdentification of duplicates: Matheo Analyzer detects duplicated patents by identifying a “key” field which cannot be copied.

Working with fields: Matheo Analyzer can create new subfields from existing ones and can create new linked indices. This operation is generally carried out in order to work later with subgroups of special interest; for example, the 10 main patentees of the first 5 classifications.

The selection of the terms which a user wishes to incorporate into a new subfield may be defined using various criteria: range of frequency, search using key terms, or direct selection from an index.

Standardisation and cleaning of indices: Matheo Analyzer automatically generates as many indices as there are fields defined in the importation process. The user may search through these and examine the patents classified with each term. All fields can be edited.

There are two methods to facilitate the creation of a particular field:

- Reference table (filtered): This consists of a tool for automatically deleting all the unwanted terms in a particular field. It is made up of a list of terms (or common expressions). This table is of interest for


cleaning an index in two very different ways: a) As a positive filter, if we are only interested in working with

specified terms.b) As a negative filter, if we are interested in deleting specified

terms. In this case, the reference table acts as a list of empty words.

The reference table can either be entered by hand or by selecting a text file with a list of the terms (one per line).

- Correspondence table: this consists of a tool used for automatically “standardising” terms in a particular field. The mechanism used is that of search and automatic substitution based on a double list of terms. The first list indicates the “non-standard” term while the second indicates the “standard” one which should substitute it.This table can be loaded and edited manually but the user can also create a text file, with a pair of terms on each line, and then load it automatically.

In both cases, the user can create as many crossed or reference tables (crossed tables) as they wish.

Figure 12 – “Matheo Analyzer v3.0” reference table.


Thesaurus: Matheo Analyzer does not contain a thesaurus to establish equivalence among terms.

Text-mining: Matheo Analyzer is not equipped with text-mining technology. Text fields, along with the title, a summary or various search requests, can be loaded, analysed or removed using the reference tables, but they are not analysed using semantic algorhythms.

User classification: Matheo Analyzer is not designed so that the user can classify information. It is assumed that this task has been carried out previously.

3) Local Analysis and ExploitationMatheo Analyzer contains three basic elements for exploiting information effectively:

- Forms: By selecting a field, the index in that field is displayed with all the terms contained within, and their frequency. This index forms the basis for carrying out other operations.

- Pairs: By selecting any two fields, an index with all combinations of pairs of terms and their frequency of co-occurrence is shown.

FFigure 13 – A “Matheo Analyzer v3.0” cluster.

- Clusters: By selecting a field (which, in most cases, will be a complex


text field with several classifications or descriptors) an analysis based on the K-means algorhythm is carried out. This classifies the patents into groups sharing certain features, which, in turn, differentiate them from the rest of the groups.

4) Graphic GenerationMatheo Analyzer 3.0 can create the following graph types:

Histograms: - Frequency histogram: this analyses the content of a field. The height

of each bar represents the number of patents corresponding to each term. This is the most commonly used type.

- Range histogram: this analyses the frequency of the terms used in a particular field. The height of each bar represents the number of terms in this field with a determined frequency.

- Indexing depth histogram: this analyses the lists with a defined number of terms in a particular field. It indicates to what extent this field is wide-ranging (many terms used to define this field) or extremely concentrated (very few terms used to define this field).

Figure 14 – “Matheo Analyzer v3.0” frequency histogram.

In all three cases a previous condition can be created (based on text, frequency


or selected terms from the index) linked to any field.In this case, the histogram (from any field) will only analyse the terms

within the limits of that previous condition.Network: a two-dimensional cartographic diagram of differing elements

(network nodes) and the relationships among them (links among nodes). Each element usually has an associated number indicating its frequency, and may also have links to any other element. In this case, the two elements form a “pair”. This pair also has an associated number indicating the frequency of the relationship between the two elements. By way of a contextual menu, every node in the network allows the visualisation of the patents included within it, as well as allowing the insertion of commentaries in these lists. There are four kinds of network graphics:

- Symmetric network: this corresponds with the analysis of the terms in a particular field (for instance, analysis of the co-operative relationship between companies or authors).

- Asymmetric network: this corresponds with the analysis of the terms from two fields (for instance, analysis of the relationship between companies and technical fields).

Figure 15 – “Matheo Analyzer v3.0” asymmetric network.

- Condorcet network: this corresponds with the analysis of the relationships among a group of patents, as they share a group of


terms from a particular field (for example, analysis of the relationships among various groups of patents).

- Propagation network: This corresponds to the maximum deployment of the Relationships among terms in the same field, based on one or more recognised terms (for example, the relationships among inventors, from “Li Ming” onwards).

Figure 16 – “Matheo analyzer v3.0” propagation network.

Matrix: This carries out an analysis of co-occurrences between two lists of terms. The result is a matrix of cells in which the number of co-occurrences appearing for each one is given. The greater the number of co-occurrences, the darker coloured the cells. They can be of two kinds:

- Simple: These can be binary, condorcet, symmetric and asymmetric. They consist of tables whose rows and columns contain the terms in each field or subfield (see figure 17).

- Evolved (MetaMatrix): This type of matrix contains the terms of a field or subfield in its columns, but in its rows it contains groups of terms (see figure 18).

5) Dissemination and WorkgroupMatheo Analyzer software is for individual use and does not permit groups of users to interact. Neither is it possible to publish information on the web for other users to search for information from another computer. Matheo


Analyzer can export matrices to a csv (comma separated values) formatted text document readable by any spreadsheet or database software.

Figure 17 – “Matheo Analyzer v3.0” asymmetric matrix.


Figure 18 – “Matheo Analyzer v3.0” meta-matrix.6) Management of ToolThe functions described in this section are applicable to software utilised by various people. These functions are not available in Matheo Analyzer.

- 73 -Comparison of Software: Supply - Matheo Patent v7.1

5.2 Program evaluated: Matheo Patent v7.1

Producer: IMCS8 rue Crillon13005 Marseille, FranceTelephone: +33 (0)491 082 882Fax: +33 (0) 491 783 906E-mail: [email protected]: http://www.matheo-software.com

MATHEO PATENT v.7.1 Evaluation

1 2 3 4 5



























Automatic abstracts















Personalised alerts








Management of users access rights





System utilization statistics

Table 13 – “Matheo Patent v7.1” Benchmark.

5.2.1. Definition of the software

Matheo Patent is a tool specifically designed for Technological Supervision and Management of Industrial Property, which allows the automization of the use of various sources of patents (Espacenet, USPTO requests and USPTO concessions). In addition, it manages the publishing, annotation, grouping


and consultation of patents found, as well as updating them. Finally, it is capable of analysing patents from a variety of viewpoints, generating matrices, histograms or relationship mapping.

Figure 19 – “Matheo Patent v7.1” main screen.

5.2.2. Comments on the features studied

1) Searching and DownloadingCDatabase connections: Matheo Patent contains a module/interface to carry out searches in Espacenet and USPTO. Matheo Patent breaks down the searches into time periods: year to year or even month to month. Thus, even with wide search strategies, it does not go beyond the visualisation limit of 500 patents imposed by Espacenet. It is therefore capable of retrieving all existing information. It can also download all the fields available for each patent (bibliographic file summary, search requests, graphics, first page and even a PDF file with the whole patent document). These fields can then be stored in a local database.

Matheo Patent also allows the user to carry out various complementary search strategies in various phases given that, once a particular set of search results has been downloaded, a new question can be asked and new patents can be downloaded. Patents already downloaded from an earlier search will


not be repeated. It also permits the selective loading of a specific list of patents of interest. At any given moment and for any patent, Matheo Patent permits the download of those fields which were not initially done so.

Figure 20 – Search form for “Matheo Patent v7.1”.

At any given moment, the user may carry out any of the above search strategies for the project and download the new patents published concerning that subject. Matheo Publisher detects the date on which this search was last run and runs the search only from that date onwards. All new patents are assigned an icon indicating that this information is pending revision.

2) Filtering and Value AddingIdentifying duplicates: Matheo Patent detects duplicated patents and does not download them.Working with fields: Matheo Patent cannot create new fields based on existing ones. Neither can it create new indices.

Matheo Patent has an internal engine allowing it to carry out advanced searches in all fields using Boolean Logic. This function facilitates the identification of patents fulfilling certain conditions and thus permits the creation of groups.

Standardizing and cleaning indices: Matheo Patent automatically generates


9 indices (inventor, applicant, year of priority, year of publication, number of family members, group, 4-digit IPC classification, complete IPC classification and ECLA classification) making them available to users so they may check them and examine the patents classified within each term.

Fields with information downloaded from the databases cannot be edited, except for ‘inventor’ and ‘applicant’.

There is no help facility for editing inventors and applicants. If a user wishes to purge an index, the non-standard entries must be corrected manually.

Figure 21 – “Matheo Patent v7.1” index.

Thesaurus: Matheo Patent doe not permit the creation of lists of key words, a thesaurus or lists of empty words.Text-mining: Matheo Patent does not dispose of text-mining technology. The title, summary and queries can be loaded but may not be analysed.

User classification: The user can give value to the information by adding notes to any patent. To do this, the “comments” field is used. It is also possible to evaluate a patent’s relevance (on a scale of 1 to 8) and create customized groups.

Users cannot create a link between each patent and its pdf document format, although the patent’s first page can be linked with the “mosaic” page, which contains the most representative graphics and diagrams.

3) Local Analysis and ExploitationMatheo Patent includes the “Report” function which creates a new document


in MS word, with a variety of options to choose from:- Quick Report: creates a document with the project title, the number

of families, the number of patents, the search strategies used and a range of calculations for each of the most significant fields.

- Global Report: this may include the following sections at the user’s discretion:

General: overall data on the project.Details (histograms): Main inventors, applicants, 4-digit IPC classifications, 7-digit CPIs, complete Consumer Price Indices and ECLA classifications.Statistics (main co-occurrences): Inventors/applicants, inventors/4-digit Consumer Price Indices (from now on, CPIs), applicants/4-digit IPCs.User information: Histogram with the number of patents per group and lists of the number of patents included in each group.

- IPC Report: Includes analysis focused on the IPC field:General: Overall project data.Details (histograms): 4-digit CPI, 7-digit CPI, complete CPI.Statistics (main matches): 4-digit CPI/Applicant, 4-digit CPI/Year of publication, 7-digit CPI/Applicant, 7-digit CPI/Year of publication, 7-digit CPI/7-digit CPI, complete CPI/ complete CPI.Matrices (tables): 4-digit CPI/Applicant, 4-digit CPI/Year of publication, 7-digit CPI/Applicant, 7-digit CPI/Year of publication.

- List: There are four kinds of lists:Inventors: list of inventors with their frequency.IPC Class 4 digits: lists the 4-digit CPI classifications and their frequency.IPC Class all digits: lists the 4-digit CPI classifications and their frequency.Patent assignee: lists the patentees and their frequency.

- Patent Assignee Report: includes analysis focused on the applicantfield:

General: Overall project data.Details: Histogram with main applicants and lists of all applicants by frequency.Statistics (main matches): Applicants/7-digit CPIs, applicants/


complete CPIs, applicants/year of publication.- Short Report: Includes the following analysis:

General: Overall project data.Statistics (main matches): Applicants/year, applicants/4-digit CPIs, applicants/complete CPIs, 4-digit CPIs/year of publication, complete CPI/year of publication.

4) Graphic GenerationMatheo Patent can create the following types of graphs:

Chart (histogram): Corresponds to the analysis of a field’s content. The vertical axis. Represents the number of patents which correspond to each term. The graph can easily be set to limit the minimum frequency of each term.

Figure 22 - “Matheo Patent v7.1” chart.

Matriice (Matrix): Carries out analysis of matches in two fields. The fields which can be used for this analysis are: inventor, applicant, year of priority, year of publication, number of family members, group, 4-diit CPI classification, complete IPC classification and ECLA classification.

The result is a matrix of cells in which the match number appears for each one. The cells’ colour becomes more intense the higher the number of matches.


Figure 23 – The “Matheo Patent v7.1” matrix.

Network: This is a 2-dimensional map diagram of the main elements in two fields and the relationship between them.

- Each element is shown as a coloured rectangle with its name. - Each element has a number indicating its frequency (the number of

patents in which it appears).

Every element can have a link to another one in another field. In this case, they form a “pair”. This pair has a reference number indicating its co-occurrence (the number of patents in which the two terms appear).

The user can create a network from one field (for instance, to analyse the relationship between inventors) or from two. In the latter case, the relationship between companies and the groups of patents they have created are shown.


Figure 24 – “Matheo Patent v7.1” network.

5) Dissemination and WorkgroupMatheo Patent is designed for personal use and does not have at its disposal functions enabling various users to interact. Neither is it possible to publish information through the web in order for others to perform a search for that information from other terminals.

Matheo Patent can, however, export any group of patents to MS Word, indicating the fields which a user wants to include. Bibliographical references can also be exported in text format (indicating the information about each field with a label) or in .xml format, so that they can be incorporated into another application on an intranet.

6) Management of ToolThe functions mentioned in this section refer to software accessed by various users at once. Matheo Patent does not have these functions.

- 83 -Comparison of Software: Supply - PatentLabII v1.41

5.3 Program evaluated: PatentLabII v1.41

Producer: Wisdomain Inc.2300 North Barrington Road, Suite 400Hoffman Estates, IL 60195Tel: 1.847.490.5310Fax: 1.847.885.7965Website: www.wisdomain.com

PATENTLAB II v1.41.0 + LaaMerger + LabViewer

Evaluation

1 2 3 4 5



























Automatic abstracts















Personalised alerts














Table 13 – “PatentLabIIv1.4l” Benchmark.

5.3.1. Definition of Software

PatentLab is a tool used for the statistical analysis and visualisation of registry details from patents obtained through the Delphion patent search service. This site offers the following collections of patents: PCT, European (request & concession), North American (request and concession), German, Japanese


and Inpadoc.5.3.2. Comments on the features studied

1) Searching and DownloadingConnection to databases: PatentLabII does not contain modules to connect and carry out searches in Delphion. When a search has been undertaken in Delphion, the results to be analysed have to be downloaded to a file (regardless of the number of registers) in “.laa” format in order to be used by this software.

If the user wishes to combine several “.laa” files obtained through various searches and then carry out an analysis on that group of patents, they need to use the LaaMerger/LabViewer program, allowing the merger of two “.laa” files.

Figure 25 – Laa Merger in “PatentLabIIv1.41”.

PPatentLab cannot connect to Delphion to check or complete the information. PatentLabII can load the majority of fields with text information from Delphion but is unable to load the graphics connected to each registry.

2) Filtering and Value AddingIdentification of duplicates: LaaMerger has a system for detecting duplicated patents from several “.laa” files. PatentLabII does not provide any system for detecting duplicated patents, nor for grouping patents from the same family in


a single registry. These operations should be undertaken before downloading the collection of patents.

Working with fields: PatentLabII cannot create new fields from existing ones, nor can it create new indices. The majority of the information in each registry can be edited. PatenLabIIv1.41 does not permit the search for and consequent editing of particular registers. It only allows searches which are part of the filtering process taking place before the analysis is carried out.

Standardization and cleaning of indices: PatentLabII has a feature which allows the detection of patentees with identical names. Furthermore, it permits the cutting and pasting of one name on top of another, considering this action as a “Non-normalized variant”.

Figure 26 – “PatentLabIIv1.41” edit assignee.

Thesaurus: PatentLabII does not allow the creation of lists of key words, thesauruses, empty words, etc.

Text-mining: PatentLabII does not have text-mining technology. The title, summary and enquiries can be loaded in PatentLabII but cannot be analysed.

User Classifications: There are four fields into which the user can download information of his choice. However, there is no tool to ease this task or to guarantee that the classifications are added without errors.


Figure 27 – Editing a registry-1 in “PatentLabIIv1.41”.

Figure 28 – Editing a registry-2 in “PatentLabIIv1.41”.

PatentLabII does not have specific fields for making notes, nor for the evaluation of a patent’s importance. However, the “fields defined by the user” can be used for this purpose. It is also not possible to include a link for each patent to its corresponding .pdf document.


3) Local Analysis and ExploitationPatentLabII v.1.41 has an assistant to create the most common matrices and graphics. This assistant does not offer the full range of combinations of fields. Nevertheless, it is capable of creating matrices and graphics manually selecting any pair of fields.

- The LabViewer facility allows the user to visualize all the registers from the database sequentially. It does not permit a search but allows the listing of the fields of most interest from a group of marked registers.

- There is a “report” function with various standard options:- Overall Summary: Creates tables with an analysis of the following

fields: patentee, Inventor, country, year and classification, showing the most significant ones.

- Assignee Summary: Creates the following tables: patentee-year, patentee-country, Patentee-country-year, patentee-classification and patentee-main inventor.

- Assignee Detail – Patent Classification: Creates the following tables: Main Classification-patentee, main associated classification-patentee and patentee-original classification-crossed classification.

- Patent Classification Summary: Creates the following tables: Classification-year and Original classification-crossed classification.

- Country Summary: Creates the following tables: Classification-country, country-year and country-patentee.

4) Graphic GenerationPatentLabII can create graphics in two or three dimensions. The graphics in two dimensions correspond with the statistical analysis of a field’s content in which the height of each bar is proportional to the number of patents corresponding to each term. By clicking twice on each element, the list of patents corresponding to that term appears.

The three-dimensional graphics correspond to analysis of the co-occurrences between two fields. The height of the bar is proportional to the number of co-occurrences existing between each pair of terms. By clicking twice on each cell, the list of patents corresponding to these terms appears.


Figure 29 – 2-dimensional bar graph, “PatentLabIIv.1.41”.

Figure 30 – 3-dimensional bar graph “PatentLabIIv.1.41”.


Thanks to the existence of personalized fields, PatentLabII allows the creation of totally personalized graphs and matrices. In this case, the following fields have been created: risk of infringement of a patent (high/medium/low) and the difficulty of a technology (high/medium/low), leading to the following examples below in figures 31 and 32.

Figure 31 – Competitor – Risk of Infringement graph from “PatentLabIIv.1.41”.

Figure 32 – Technological Difficulty – Risk of Infringement from “PatentLabIIv.1.41”.


5) Dissemination and WorkgroupPatentLabII is designed for personal use and does not have functions enabling various users to interact. Neither is it possible to publish information via the web in order for others to visualize information from other terminals.

PatentLabII can directly export areas of interest from matrices to MS Excel and can also generate documents in HTML format, which can then be published directly on an intranet.

6) Management of ToolThe functions mentioned in this section are of relevance only to those programs designed for use by several people at once. PatentLabII does not offer these functions.

- 93 -Comparison of Software: Supply - PM Manager v1.4.0.3

5.4 Program Evaluated: PM Manager v1.4.0.3

Producer: WIPS Co. Ltd.93-45 Bookchang-Dong, Joong-Gu,Seoul 100-080Republic of KoreaTEL: +82-(0)2-726-1103/1109FAX: +82-(0)2-726-1001Email: [email protected] : www.wipsglobal.com

PM MANAGER v1.4.0.3 Evaluation

1 2 3 4 5



























Automatic abstracts















Personalised alerts














Table 14 – “PM Manager v1.4.0.3” Benchmark.

5.4.1 Definition and positioning of software

PM Manager has been developed as a complement to the WIPS Global patent search system. The present global reach of this system is: Patents in Korea, Japan, China, USA, Pat. Europe, Pat. PCT, Inpadoc and GPAT (Switzerland, France, Great Britain and Germany).


PM Manager is able to load files obtained from WIPS Global and then, classify, analyse and process information from patents by applying different viewpoints. PM Manager’s focus is on giving the user the capacity to modify, annotate and complete information by adding new personalized fields.

It then allows basic statistical analysis, advanced statistical analysis and other analysis tailor-made for the personalized fields created by the user.

5.4.2 Comments on the features studied

1) Searching and DownloadingConnection to databases: PM Manager does not dispose of modules to connect and run search in WIPS Global. Its starting point is the importation of a list of already existing registers in “.pmd” format, downloaded from WIPS Global.

Once a search has been run on WIPS Global the results can be downloaded (in groups of 200 registers at the most) in “.pmd” format to be treated by the program.

PM Manager is equipped to connect with WIPS Global to check for new information (e.g. whether a family of patents has changed).

Figure 33 – Main screen, “PM Manager v1.4.0.3”.


Importation: PM Manager can also import registers in electronic spreadsheet format (.xls)

PM Manager is able to load an associated graph in each register. If this information is not available, it can connect to WIPS Global to check whether a graph is available and if so, download it.

2) Filtering and Value Adding Identification of duplicates: New lists of patents in “.pmd” format may be added (merged). If there are equivalent patents, the system will detect them automatically and ask if the user wishes to keep the existing version, substitute it for the new one or complete the present fields with the new information obtained.

Families of patents: PM Manager can identify members of the same family through the priority number.

Working with fields: PM Manager cannot create new fields from existing ones, nor can it create new indices. Nevertheless, all information loaded can be edited.

Standardization and cleaning of indices: PM Manager has a feature allowing the selection of the names of “non-unified” patentee companies which are, in the user’s judgement, equivalent. The user can then key in the name “unified”, which is then saved for future use. It is also possible to import lists of unified companies from previous projects. Thus, the list grows as time passes.

Figure 34 – Index cleaner, “PM Manager v1.4.0.3”.


Thesaurus: PM Manager does not permit the creation of lists of key words, thesaurus or empty words.

Text mining: PM Manager does not have text mining technology.User Classifications: The software allows for the creation of a three-level

thematic classification which can be as extensive as required. In order to facilitate this task, this structure appears when the user wishes to classify the content of a patent.

PM Manager also permits the user to define up to 5 kinds of classifications to evaluate a patent, together with its variables. These classifications can be whatever the user wishes. As an example, the following can be cited:

- possibility of infraction (high/medium/low).- price of technology (expensive/mid/low).- difficulty of technology (complex/normal/simple).

Figure 35 – User classification, “PM Manager v1.4.0.3”.

PM Manager also contains another field to evaluate the importance of the patent (A/B/C/D), a “memo” field in which notes can be included and a “core patent” field, used to label a patent as “key”.


Last but not least, with each patent a range of links with documents and applications can be included.

Figure 36 – Edit key information in “PM Manager v1.4.0.3”.

3) Local Analysis and Exploitation PM Manager offers several functions to carry out all kinds of analysis:

- The “Create Technology development Map” function highlights one or several of the technological classifications of interest and indicates to the user how these technologies have evolved over time, showing for each year the number of patents, the titles and the patentees of the technologies (see Figure 37).

- The “Create Key Information List” function automatically creates a report in MS Word format with all registries of patents marked “key” including the “purpose of the patent”, the main claim”, “problems with the state of the previous technique and the “explanation of the graph”. This report is of value when evaluating and comparing technologies competing among themselves (see Figure 38).


Figure 37 – Technology Development Map, “PM Manager v1.4.0.3”.

Figure 38 – Key Information List, “PM Manager V1.4.0.3”.

4) Graphic GenerationMP Manager can create graphs in two or three dimensions. The two-dimensional graphs correspond to the statistical analysis of a field’s content, in


which the height is proportional to the number of patents corresponding to each term. By clicking twice on each element, the list of patents corresponding to the term appears (see Figure 40).

Figure 39 – 2-dimensional graph, “PM Manager V1.4.0.3”.

Figure 40 – Statistical analysis, “PM Manager V1.4.0.3”.


PM Manager allows for the creation of matrices, using three fields in the analysis including those created by a user. In this case, PM Manager permits the running of a co-occurrence analysis in two fields. If this co-occurrence exists, it shows the value of the third field. The usefulness of this analysis may be very different depending on the type of content within the two fields defined by the user.

Figure 41 – Analysis of the possibility of infringing competitors’ patents, “PM Manager v1.4.0.3”.

Figure 42 – Analysis of the probability of patent infringement in different technologies, “PM Manager V1.4.0.3”.

5) Dissemination and WorkgroupPM Manager is designed for individual use and does not have functions enabling various users to interact. Neither is it possible to publish information


via the web in order for others to visualize information from other terminals.It can interact with MS Excel and MS Word.

Apart from the report on the “Key Information List”, it can generate files in MS Excel from practically any analysis which has been carried out.

6) Management of ToolThe functions mentioned in this section are of relevance only to those programs designed for use by several people at once. PM Manager does not offer these functions.


- 105 -Comparison of Software: Supply - Vantage Point v4.0

5.5 Program Evaluated: Vantage Point v4.0

Producer: Search Technology, Inc.4960 Peachtree Industrial Blvd., Suite 230Norcross, GA 30071-1580 United StatesTelephone: +1 (770) 441-1457Fax: +1 (770) 263-0802Email: [email protected]: http://www.thevantagepoint.com

VANTAGE POINT V.4 Evaluation

1 2 3 4 5



























Automatic abstracts







Rankings - Analysis of one field








Personalised alerts














Table 15 – Benchmark, “Vantage Point v4.0”.

5.5.1. Brief definition and positioning of Vantage Point

Vantage Point is text mining software specializing in the analysis of (usually) bibliographical registers obtained from databases. Its purpose is to assist the user when surfing through large volumes of text-based information and to


offer new perspectives on the most important concepts discovered in that information.Vantage Point is able to analyse patents, although its brief is more general. The most common use of this software is for the analysis of bibliographical registries in technology-focused databases (technical magazines, patents or R&D projects).

Figure 43 – Main screen, “Vantage Point v4.0”.

1) Searching and DownloadingConnection to databases: Vantage Point v4.0 does not contain modules to connect to and utilize databases. Its starting point is the importation of an existing list of registers.

Importation: It can import registers in flat text (.dat, .txt, .csv, .trn, .xml) although it can also directly load files in spreadsheet format (MS Excel). Vantage Point is able to load one or more associated graphs in each register as long as these form a part of the list obtained from the original database. The importation process for registers can be carried out in two ways:

- Advanced users can build and edit import filters for a database editing the parameters of an “importation engine”. Any register obtained from any database can be considered as importable from Vantage Point.

- Elementary users have an assistant at their disposal, which will guide


them during the importation process.

The importation of information fields can be carried out in a standard way or by creating new fields, obtained through processing in the natural language of these fields’ content. For instance, it can import the field “title” and also create two new fields: one with the “words” contained in the title and the other with the “expressions” contained in the title.

Figure 44 – Module importation, “Vantage Point v4.0”.

2) Filtering and Value AddingIdentifying duplicates: The user can freely define when he considers that two registers are identical (for example, if the fields Publication Nº and Priority Nº are identical). Detection of identical registers is carried out immediately. Vantage Point has two specific functions to either eliminate duplicated registers or combine them (e.g. if they have been obtained from different sources and their contents are complementary).

Families of patents: It is simple to group registers by family. By simply


listing the variants in the Priority field and clicking on an entry, all members of that family can be seen.

Working with fields: Vantage Point is capable of carrying out a multitude of operations using field content: copying, merging existing fields to create new ones, or creating new indices from the contents of a group of terms.

Standardization and index-cleaning: Vantage Point has developed several diffuse logic programs to minimize the work involved in cleaning certain information fields. In particular, it is possible to indicate the system which automatically groups inventors and patentees in line with previously programd criteria. Furthermore, the user can manually check the indices and group synonymous terms using the “drag and drop” tool.

Figure 45 – Index-cleaning, “Vantage Point v4.0”.

Thesaurus: Lists of key words can be made, taken from the contents of one or more fields. They can then be edited or combined. There are also lists of empty words, used to clean the indices. These lists can be edited.

Text-mining: Vantage Point uses its NLP (Natural Language Processing) technology to analyse any non-structured text (title, summary, full text) written


in English with the purpose of extracting the most significant concepts. Firstly, it divides the text into sentences and labels each word as noun, adjective or verb. It then applies linguistic rules to identify “multiterms” composed of various words. If the user wishes to analyse text in another language, it can retrieve the text but cannot recognize verbs or adjectives. Thus, it cannot identify the “multiterms” made up of several words.

3) Local Analysis and ExploitationVantage Point offers several functions to carry out a wide range of analysis. It has a battery of scripts (programs which automate a determined sequence of functions) of use for specific purposes. Below are seven examples:

- Carry out sequential searches for fields and create a group with the results.

- Combine groups.- Export groups to MS Excel.- Export part of a matrix to MS Excel and create associated 3-D

graphs.- Export selected fields from a group of registers to MS Word.- Create a thesaurus from marked elements in a matrix.- Detect the terms which have appeared for the first time year after

year and Export them to MS Excel.

If a user wishes to carry out a particular systematic analysis, Vantage Point offers the option of modifying the existing scripts and creating new ones. In order to do this, VBScript language must be used for the programming.

4) Graphic GenerationVantage Point can create 2-dimensional matrices or maps. The matrices may be of various types:

- Co-occurrence matrix: Each cell contains the number of times that the two terms appear in the same register (see Figure 46).

- Self-correlation matrix: Each cell shows the degree of correlation between one term and the rest of terms in the same field, using a decimal number between 0 and 1.

- Cross correlation matrix: Each cell shows the degree of correlation between one term and those in another field.

- Factor matrix: This shows the result of applying an Analysis of Main Components to the terms in a list. The rows correspond with the


terms which have been analysed. The columns correspond to the groups or “factors” which the system has detected. Those terms displaying an especially high or low number (higher than 0.5 or lower than -0.5) and a marked similarity between each other form part of the same “cluster” (groups of terms having a special relationship among themselves).

Figure 46 – Co-occurrence matrix, “Vantage Point v4.0”.

The maps can be:

- Autocorrelation maps: Maps illustrating the main relationships among terms in a field.

- Cross-correlation maps: Maps illustrating the main relationships between terms from two fields (see Figure 47).

- Factor maps: Maps in which the main terms analysed are situated. The terms may be expressed as codes, company names or concepts


(see Figure 48).

To create these maps, the following technologies are used:

- Main Components Analysis, to group closely related terms.- Similitude Measurements, based on the correlation between terms.- Multidimensional Scale, to represent multi-dimensional data in 2

dimensions.- Algorhythms, for the generation of links based on similarity: to

prioritize certain links in the map over others.

Figure 47 – Cross-correlation map (Companies-IPC), “Vantage Point v4.0”.


Figure 48 – Factor map-title terms, “Vantage Point v4.0”.

5) Dissemination and WorkgroupVantage Point is for individual use and is not designed for various users to interact. Neither is it possible to publish information via the web for other users to access that information on other terminals. It can interact with other Windows programs through VBScript and can export information in various formats.

6) Management of ToolThe functions outlined in this section are of relevance for multiple-user software. Vantage Point does not have these functions.

- 115 -Comparison of Software: Supply - Programs not evaluated

5.6 Programs not evaluated

5.6.1 Program (not evaluated): Anacubis Desktop + Intellectual Property Analysis Add-in

Producer: I2The Visual Space, Capital Park,Fulbourne, Cambridge, CB1 5XH, United KingdomTel: 01223-728600Fax: [email protected]: http://www.i2.co.uk/anacubis/

Definition of softwareAnacubis Desktop is an application which creates an intuitive visual representation of information, showing all types of entities (people, companies, patents, etc.) as icons and indicating their relationships through links.

A differentiating feature of Anacubis is that it permits the users to consult various sources of information and combine them to instantaneously obtain a single all-encompassing view.

Anacubis Desktop has a specific complement for the treatment of patents. Its usefulness lies in that it allows the inexperienced user to carry out advanced analysis by following the step-by-step instructions of a guide.

Main FeaturesThe main analyses “Anacubis Desktop”, complemented by “Intellectual Property Analysis Add-in”, can carry out are the following:

Analysis of citations- A patentee’s citations: Creates an analysis of “who mentions whom”.

It can produce an analysis of all the patentees appearing in a document or, alternatively, a restricted analysis of several patentees receiving a minimum number of mentions.

- History of citations: Shows generations of citations, both forward and backward, based on selected patents.

- Patent citations: Shows who cites or is cited by whom for one or more key patents.


- Auto-citations: Shows the auto-citations of a patentee.

Analysis of Inventors- Company inventors: Creates a vision about who invents in a

company.- Common inventors: From a group of selected companies, it shows

those inventors who have or have had a connection with more than one of that company.

- Research groups: For a company, this shows “who is working with whom”.

Analysis of classifications and value- Classification of patentees: Produces an analysis of the main

classifications Associated with the patents of one or more patentees.- Indicates the value of a patent: Adds a bar to each patent in a group,

indicating its value. This value can then be transferred to a patentee, inventor or classification. The value assigned to each patent is based on a simple measure of number of citations received.

Seasonal analysis- Patent citations over time: Produces an analysis with all patents that

cite or are cited by one or more key patents in sequential order. Each patent is linked to the request made for it.

- Citations from patentees over time. This produces a timeline which illustrates the relationship of citations between one patentee and others selected by the user.

Merging with economic information- Anacubis Desktop can be used to merge economic information or

business figures with a patent analysis. This option allows for the enrichment of information being analysed. Anacubis can be set to store relevant fields; For example, number of employees, sales, etc. These fields can be accessed and completed in various ways.

- Manually.- Using on-line sources of information.- Using an MS Excel importer.


Figura 49 - Anacubis Desctop. Fuente:

Figure 49 – Anacubis desktop. Source: http://wwwi2.co.uk/anacubis/anacubisviewer/help/Step2.htm

(visited 24 Nov. 2005).


5.6.2 Program (not evaluated): Aureka v.9.2

Producer: Micropatent LLC (belonging to the Thomson group)250 Dodge AvenueEast Haven, CT 06512, United StatesTel: +1 (203) 466 5055Fax: +1 (203) 466 5054Website: http://www.micropatent.com

Definition of softwareAureka is a wide-ranging program focusing on Industrial property management within a company. It includes functions for searching for patent information, options for their validation and evaluation, the capability of adding a user’s own fields, advanced functions for analysing results (including text-mining) cartographic representation of groups of patents, management of warnings and functions for working in groups.

Main featuresSince Aurigin (original manufacturer of Aureka) was taken over by Micropatent, Aureka has become the application used by this company for the management and analysis of information sourced from its patent databases. At present, Micropatent offers access to the United States Patent Collection (requests and concessions), European Patents (requests and concessions), PCT Patents and patents from Germany, France, Great Britain and Japan.

Local or Distance ModeAureka is a client/server application, capable of dealing with interaction between different members of an organization. It can work either as an internal server for the company or can be stored in a Micropatent secure server.

MessagingThe collaboration or working groups functions are based on an e-mail messaging system among all users of the system. There may be several user categories (administrator, trainer, expert) and each has its own particular functions. This system is used for distributing the results of the searches, filters or analysis among the users.


NotesAureka allows each user to make notes on each patent and search notes for all patents.

Each user’s own fieldsEach user can add several personalized fields in order to incorporate internal data. For instance, classifications of certain technologies or markets, internal divisions in companies, names of connected companies and clients, degrees of relevance, dates and data relative to legal status, civil service entrance exams , or licences.

AlertsEach user may define weekly or monthly alerts which can be sent to other people or groups within the organization.

Technological mapsAureka has software for the textual analysis of information contained within a group of patents. The result is the generation of a topographical map in which the highest peaks represent an accumulation of documents sharing a particular concept.

The user can specify the fields whose text he wishes to analyse (his own fields may be included) before the generation of the map. Then, one part of the map can be selected in order to obtain a more detailed overview. Additionally, a search for key words can be carried out and the program is able to find the area of the map where the answers are concentrated.

These maps can be shared among selected groups of users so that they can comment on and discuss specific areas of interest and reach shared conclusions.

Citation “trees”Aureka also has an analysis of citations in which the citing and cited patents are visualized in their time sequence.

These analyses can be limited through certain key words or by demanding that patents fulfil certain requirements (including those in own fields).

Aureka allows for colour-coding of patents dependent on, for example, the patentee or the rate of inflation.


Figure 50 – Eureka. Source: http://www.micropatent.com/static/advanced.htm (visited 24 Nov. 2005).


5.6.3 Program (not evaluated): ClearForest Analytics – Patent Analysis

Producer: ClearForest Corp.950 Winter Street, Suite 1900Waltham, MA 02451, United StatesTel: +1 (781) 250 4300Fax: +1 (781) 250 4301Website: http://www.clearforest.com

Definition of softwareClearForest Analytics is text-mining software which converts non-structured information into structured and clearly related concepts and data. This allows users to concentrate on their key competencies: analysing data and taking decisions. The three objectives are:

- Improving the detection of early warning signals: using textual information to better direct the organizational responses.

- Discovering new perspectives, identifying trends, patterns and complex relationships in wide-reaching collections of text.

- Creating links to structured data: this allows for improvement in the Business intelligence process by showing relationships which had previously been impossible to visualize.

Main featuresClearForest Analytics is part of the ClearForest platform which includes ClearForest Tag and ClearForest Industry Modules. Once the labels for ClearForest Tag are entered, ClearForest Analytics extracts relevant concepts from the tagged terms, permits navigation by concept and detects trends empirically.

Patent Analysis is a module specifically for professionals working in the field of Industrial Property. This module has a “rule book” concerning patents, to extract specific information from patent databases.

It is designed to shorten the time required to take decisions concerning technology, to carry out more wide-ranging competitive analysis, to obtain an improved overview of competitors’ R&D activities and to reduce research costs. By examining patent databases, this module can discover the names of


the principal players, main researchers in a particular field or patents which appear to be key for a particular field. Its functions are:

- Advanced exploration of data: Permits interactive questioning concerning previously tagged non-structured content.

- Visualization of relationship maps: Represents collections of documents in graph form; shows links and connections between entities from the same or different categories.

- Analysis of indirect links: Identifies relationships between entities or concepts connected by other entities or concepts.

- Links to documents: Users can always have access to text files. They have an integrated document viewer which highlights tagged terms in complete text.

- Tracking of concepts and events over a period of time: Monitors concepts and their relationships with each other, showing changes and revealing general trends.

- Reports using assistants: ClearForest Analytics outlines a series of simple steps to produce reports. It guides the user through use of a series of questions, facilitating access to information.

- Simultaneous analysis of structured and unstructured data: ClearForest’s settings allow the user to include structured data from other Business Intelligence systems.

- Easy integration with other analytical solutions.- Business Intelligence solutions can be integrated through ClearForest’s

API web services, as those allow easy access to ClearForest Analytics. It is also possible to use .xml file format to input information on a relational database or directly to a Business Intelligence application.


5.6.4 Program (not evaluated): Derwent Analytics

Contact: Thomson Scientific – Europe, Middle East and Africa14 Great Queen StreetLondon WC2B 5DF, Great BritainPhone: +44 20 7344 2800Fax: +44 20 7344 2900Website: http://scientific.thomson.com/products/derwentanalytics/

Definition of softwareDerwent Analytics is a software program which is not sold independently but offered in combination with a subscription to the Derwent World Patents Index database. This has coverage in 41 countries and patent organizations. It offers exclusive fields and very high-quality content. The software itself is a personalized version of Vantage Point (see earlier section) which includes the programming of several specific macros to fully utilize the database.

Therefore, Derwent Analytics can be defined as text-mining software specializing in the analysis of registers obtained from the World Patents Index. Its aim is to analyse these registers to gain new perspectives on the most important concepts they contain.

Main featuresDerwent Analytics does not contain modules to connect to and use databases. Its starting point is the importation of a list of registers obtained from various hosts where Derwent WPI is sold by way of previously programd importation modules.

Working with fieldsDerwent Analytics can copy or merge exiting fields to create new ones.

During the import process, it can create new fields with the most significant concepts from those containing free text (for example, titles or summaries), identifying “multiterms” made up of several words.

Index cleaningInventors and Patentees can be automatically grouped, even though they may show small variations.


ThesaurusLists of key words can be created from the contents of one or more fields. They may then be edited or combined. The program also has lists of empty words which can be used to clean the indices. These lists are also editable.

Graphics generationDerwent Analytics can create 2-dimensional lists, matrices or maps.

Data analysisDerwent Analytics has 6 specifically programd macros for DWPI, so a variety of tasks and analyses are automated:

- It secures uniformity as regards the patentee, inventor, IPC and Derwent Classifications fields.

- It identifies the main patentees and inventors.- It produces a table characterizing the 20 most important patentees.- It produces an MS Excel table in which the entire group of current

patentees is displayed.- It carries out an analysis and produces 7 graphs showing the trends

for a technology over a period of time.- It analyses the trends in R & D for each of the 10 main patentees.

If a user wishes to carry out a particular analysis systematically, Derwent Analytics can create a new macro. To do this, it ha to be programd using VBScript language.


Figure 51 – Derwent Analytics. Source: http://scientific.thomson.com/products/derwentanalytics/ (visited 24 Nov. 2005).


5.6.5 Program (not evaluated): Goldfire Innovator 2.0

Contact: Invention Machine Corporation133 Portland StreetBoston, MA 02114, United StatesTel: +1 (617) 305-9250 Fax: +1 (617) 305-9255Email: [email protected]: http://www.invention-machine.com/prodserv/GFIN.cfm

Definition of SoftwareGoldfire Innovator uses patent analysis within a structured system aimed at improving the solving of invention problems.

It facilitates the identification of problems for users and capacitates them to solve the problem and generate solutions. The software:

- Improves the quantity and quality of ideas generated.- Improves the conversion rate of ideas into products.- Achieves improved manufacturing processes.- Achieves greater speed in access to markets.- Achieves a greater rate of return on investment in R&D.- Accelerates corporate growth.- Ensures a better definition and knowledge of problems.- Permits an in-depth analysis of the value of existing physical resources

and of production processes.- Defines and prioritizes engineering problems and solutions.- Facilitates the capture and sharing of personal and corporate

knowledge, eliminating duplicated effort and promoting the recycling of ideas.

- Eases competitive analysis, analysis of patents and analysis of technological trends.

- Generates an earlier and better knowledge of the market in the product development process.

In processes of new product conception, rectification of faults, designing modification of features on existing products, identification of technological trends and development of future products, protection of industrial property or in the improvement of production processes, Goldfire Innovator improves and accelerates engineering processes, marketing and production. It


methodically explores and validates system designs which are more efficient, cheaper more competitive and of higher quality. Main featuresGoldfire Innovator has three modules:

1. The Optimizer Workbench: This specializes in structuring the solution to invention problems. It focuses on and clarifies the definition of the problem and the analysis helps to generate innovative ideas as well as evaluating, validating and prioritizing solutions.

2. Researcher: This is a semantic engine which facilitates accurate searches, manages knowledge, and offers the capacity to analysis innovative trends.

3. Innovation intelligence: Offers scientific information on critical patents. Includes access to more than 15 million patents, a database with 8,000 scientific findings, and 3,000 scientific websites. This database helps the entire analytical process: problem definition, generation of concepts and the prioritization and validation of solutions.


5.6.6 Program (not evaluated): OmniViz

Contact: OmniViz, Inc.2 Clock Tower PlaceSuite 600Maynard, MA 01754, United StatesTel: +1 (978) 4611250Fax: +1 (978) 4611299Email: [email protected]: http://www.omniviz.com

Definition of softwareOmniViz is geared towards “visual intelligence”. That is, the visualization and analysis of large volumes of structured and unstructured information as an aid to decision-making. Its purpose is to offer an overall vision which can lead to rapid identification and interpretation of the most relevant details.

It can analyse numerical, categorical and complete text data (including patents), all within the same visual layout. It is also able to support the analysis of chemical structures and genome sequences. This allows the inclusion of all relevant information when taking decisions.

OmniViz is capable of integrating the analysis of experimental data with that of scientific literature, patents and marketing data sourced from press releases. Its fields of application are research, development, testing, control of processes, marketing, finance or legal information.

Main featuresThe software is suitable for use as much by beginners or elementary users as well as experts.

It can import data from any source or format. As well as accepting the automatic importation of common data types, it also has an interface to create reusable instructions to analyse other data formats. Additionally, it permits the creation of tailor-made data importers.

AnalysisThe program is flexible with different kinds of analysis. OmniViz can carry out simple analysis for inexperienced users, those who are not experts in data processing or in the standardization of data analysis. On the other hand, it is capable of controlling all analytical parameters and offers a wide range of


advanced tools for the expert user to analyse any problem. Furthermore, new methods of clustering or grouping data can be added.

IntegrationXML can be used to define which data to use, how to carry out analysis and which visualisation to use. OmniViz can be executed from other interfaces and therefore, it can be used by those who do not necessarily want or require advanced parameters. Automated analyses allow end users to see and evaluate their data rapidly, while still providing a platform which can be exploited by expert users to carry out further analysis.

OmniViz can share its data with other applications. It may both receive data and send it to other applications via .xml format. Moreover, data can be sent in personalized format for other applications.

OmniViz Platinum: Contains six interactive visualizations, designed to respond to fundamental questions concerning the relationships between registers and how certain attributes are distributed throughout a group of registers. The opportunity to analyse and visualize multiple data permits integrated analysis.

It also contains questioning tools, including dynamic questioning, three-dimensional graphic tools, various sophisticated statistics packages and many other unique features.

OmniViz Titanium: This package integrates further advanced modules such as the Barnard Chemical Information (BCI), the Ward clustering algorhythm or the Stanford SAM analytical algorhythm. These make possible the direct identification and grouping of chemical compounds from large volumes of data, as well as the identification of changes in large amounts of data.


Figure 52 – OmniViz. Source: http://www.omniviz.com/applications/omni_viz.htm (visited 24 Nov. 2005).


5.6.7 Program (not evaluated): PatList

Contact: Raytec Co., Ltd.Hayao Building 3F, 1-4-7,Kanda-Izumicho Chiyoda-KuTokyo 101-0024, JapanWebsite: http://www.raytec.co.jp/EngPages/IndexEng/1.htm

Definition of softwarePatList is used for the reformatting and statistical analysis of information on patents extracted from certain databases.

Main featuresImportation: Can import data from bibliographic reference lists taken from Derwent WPI or from IFI/Claims in the following hosts – Dialog, STN or Questel-Orbit.

AnalysisThis program permits different types of statistical analysis:

- Co-occurrence matrix: Creates a table with a combination of two fields. In the cells either an identity number for each patent can be included (patent number, priority, request) or the frequency of co-occurrences.

- Graphs: Can create various types of graphs (bar, cylinder, tape, line, circle, radar, portfolio and life-cycle), also based on the co-occurrences of two fields.

The table contents can be exported in csv format in order to visualize or work on them in MS Excel.


Figure 53 – PatList. Source: http://www.raytec.co.jp/EngPages/Tour/WPIpatentMap/html#graph (visited 24 Nov. 2005).


5.6.8 Program (not evaluated): STN-AnaVist

Contact: FIZ-Karlsruhe – HelpDeskTel: +49 7247 808 555, GermanyEmail: [email protected]: http://www.cas.org/stnanavist/index.html

Definition of softwareSTN AnaVist is software used for visualization and interactive analysis which offers a wide variety of ways of analysing the results of searches in scientific and patent databases. It also visualizes trends in a research field. STN AnaVist can help to solve complex questions as well as providing information which can be used to take faster and better business decisions. STN AnaVist allows the user to:

- Analyse the competitive environment – determine who, what, where, when and why.

- Carry out Competitive Intelligence tracking – discover what competitors are doing.

- Discover new applications for existing technology.- Determine trends in a research field – localize whether a particular

field of research is gaining significance, remaining stable or in decline.

- Support the development of strategic planning.

Main featuresSTN AnaVist offers a unique combination of functions for gathering, analysing and interpreting information obtained from scientific and STN patent databases. Its main functions are:

- Flexible creation of groups of results: It is possible to import a group of registers created by STN Express Discover!™ and Analysis Edition, or use the search capacity of STN AnaVist.

- Integrating the content of multiple databases. It is possible to search, analyse and visualize data from diverse sources, including CaplusSM and the PCT and USA complete text databases.

- Find unique relationships between structured and unstructured data: in total, nine fields are analysed; For example, companies, inventors, years and concepts drawn from texts.


- Grouping and cleaning of data: The program has a thesaurus of company names which groups the large number of variants on company names before carrying out the analysis. It also allows the grouping of inventors’ names in order to reuse them at a later moment.

- Standardization of concepts: The application of CAS vocabulary standardizes terms among databases, reducing the number of synonymous terms, thus saving time and providing more significative results.

- Interactive relationships between data and tables: the visualization space allows the user to see relationships which intensify the data during analysis.

- Instant and understandable results: Immediately after the visualization, understanding of the subject of interest is improved.

Figure 54 – STN. Source: http://www.cas.org/stnanavist/vidual.html (visited 24 Nov. 2005).


5.6.9 Program (not evaluated): Tetralogie v6.0

Contact: I.R.I.T. / U.P.S. équipe SIG118, route de Narbonne31062 Toulouse Cedex 04, FranceTel : (+33) 5.61 55.67.81.Fax : (+33) 5.61.55.62.58.Email : [email protected] : http://atlas.irit.fr

Definition of softwareTetralogie is software aimed at analysis and graphic representation of large amounts of registers from databases. Its principal field of application is in Technological Monitoring and Economic Intelligence. Tetralogie is a tool for:

- Describing the state of the art in a research field.- Tracking the evolution of a technical, economic or juridical field.- Evaluating the relative position of an organization, company or

research group.- Evaluating the connections between a website and the rest of the

internet.- Evaluation research.- Detecting experts.

Main featuresTetralogie accesses its material from lists of bibliographical registers of databases available on-line, CD-ROMs or from any other source. These registers may be factual or may have fields in complete text. Tetralogie is a powerful tool in statistical, exploratory analysis and interactive cartography methods.

After a preliminary phase preparing the data, it carries out analysis of the information provided as a result of the appearance of focuses and knowledge which cannot be obtained through reading the information sequentially, such as:

- Identification of the main protagonists and their workplaces.- Evaluation of their importance, their relationships and mobility in

time.- Emergence and evolution of ideas and concepts.


- Most relevant terminology.- Main thematic areas.- Detection of key documents.- Detection of main sources of information.- Detection of main potential partners with whom co-operation is

possible.

In order to fulfil these aims, Tetralogie has:

- Tools for the manipulation of corpus (data, words, etc.) such as descriptor databases, descriptor structures, synonyms or filters and formats for data correction.

- Three-dimensional electronic spreadsheets to carry out operations such as table cleaning, retrieval of new tables, realineations, comparisons, classifications of varying types, groupings, detection of multiterms or emerging terms.

- Multidimensional methods of analysis.- Analysis of main components.- Factorial analysis of correspondence.- Visualization of factorial maps (2D, 3D & 4D).- Visualization of quantitive links in 4D.- CAH hierarchical ascendancy classification for navigation in this

classification.- CPP partitioned classification method.- Study of absolute evolution (trajectory)- Study of differential evolution (speed and acceleration).- Study of relative evolution (procrustean rotation).

SECTION SIXConclusion and discussion

- 138 -

- 139 -

6. CONCLUSION AND DISCUSSION

6. General Conclusion

6.1 Results of comparison

The study of supply and demand for patent analysis softwares, along with their comparison allows us to make several observations and draw certain conclusions. To begin with, the comparison in this study is the first of its kind to be carried out. It underlines not only the deficiencies of commercially available applications but also the overload of functions which are not strictly of use to regular users. Table 16 summarizes all the data gathered.

In the “supply” section of the table, values are listed for the items considered in the study for each program to which we have been given access. In the comparative study a value range of 0 to 7 was assigned to each function. A column has been added reflecting the average value of each function and each group of functions (average weighted value).

On the “demand” side, table 16 shows the result obtained in the study of supply. Those program users surveyed were asked to give a value to each function. Below, we outline some comments and conclusions to bear in mind for each section:

1. Searching and Downloading: This is the group of functions which generally least corresponds to the requirements of users. Moreover, this deficiency is accentuated by the fact that users have given greater value to the relative importance which the search and download of patent information has for them in comparison with other groups. Only a few functions in this group respond to the demand, such as the capacity to import patents (“Ability to import patent records”).

2. Filtering and Value Adding: In the large majority of cases, this group

of functions fulfils the requirements of users very satisfactorily. In


other words, the range of functions available slightly exceeds the use made of them.

3. Local Analysis and Exploitation: According to the results of the study, this function does not satisfy users’ needs. Results obtained show that only two of the five programs cover 50% of these functions.

4. Graphic Generation: Although the overall result is positive, there are some areas where it is not possible to work in any great depth. Functions such as “Space or topographical representation of a patent collection - text mining analysis” or “Ability to use local databases to integrate new data and complete the patent analysis” are poorly covered by the programs studied but are generally used frequently.

5. Dissemination and Workgroup: This is another group of functions which is not particularly noted for its presence in the programs analyzed in the study. For example, alerts, important tools for all users, are not sufficiently covereds.

6. Management of Tool: There is a serious lack in this area. Practically none of the programs analyzed in the study fulfil to an acceptable degree the seven functions described in this group. Their low value scores may be due to the fact that the professionals who have responded to the survey are not the application administrators (also known as webmaster). Therefore, perhaps, both the use and the importance of these functions have a relatively low value.

- 141 -Conclusion and discussion

Supply Demand

Mat

heo

Anal

yzer

v3.

0 - (

MA)

Mat

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Pate

nt v

7.1

- (M

P)

Pate

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- (P

L)

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Vant

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Poin

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VP)

Supp

ly (A

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y Fu

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Use

(Ave

rage

by

Func

tion)

Rela

tive

Impo

rtan

ce

(Ave

rage

)


1,51 4,42 2,23


7,04 1,4 1,75 4,46


4,4


4,2


7,0 2,8 4,2 2,8 7,0 4,8 3,9


7,0 7,0 2,8 3,9


4,5


7,0

1,4 5,0


4,4


7,0

1,4 4,7


7,0 7,0 4,2 3,6 4,0


4,2 1,4 1,1 4,9



MA

MP PL PM VP 3,9 3,7 1,9


7,0 7,0 7,0 7,0 5,6 4,7


7,0 7,0 2,8 4,9


7,0 7,0 7,0 7,0 5,6 3,8


5,0 7,0 2,8 3,3


2,8 4,2 2,8 7,0 3,4 3,3


7,0 7,0 7,0 4,2 3,7


7,0 7,0 7,0 7,0 5,6 3,6


7,0 7,0 7,0 5,6 5,3 3,3


1,4 7,0 1,4

2,0 3,6


7,0 1,4 1,1 2,5


MA

MP PL PM VP 1,3 3,8 1,5


1,4 1,4 0,6 3,7

Automatic abstracts

3,7


4,2 1,4 1,2 3,4


4,9


2,6 7,0 5,6 4,5 3,9 3,5


3,7



MA

MP PL PM VP 3,3 2,9 1,2


4,0


7,0 7,0 7,0 7,0 7,0 7,04,0


7,0 7,0 7,0 7,0 7,0 7,03,6


7,0 7,0 4,2 3,6

3,2


1,4 0,3

2,7


2,8 1,4

1,7


MA

MP PL PM VP 1,4 2,9 1,2

Publish the contents in the intranet / internet

1,4 2,8 0,8 3.8

Personaed alerts

4,4

Alerts to detect changes on the legal status of a patent

3,2


5,6 4,2 4,2 2,8

3,4 3,3

Ability to export data 4,2 7,0 4,2 7,0 5,9 5,7 3,7


1,4 0,7

0,4 1,9


1,4 0,7

0,4 2,0



1,4 0,7

0,4 2,0


MA

MP PL PM VP 0,0 3,4 1,0


3,8


3,6


3,8


3,4


2,6


2,9

Table 16 – Comparison of adjusted values for supply and demand.

The table above (table 16) summarizes the conclusions drawn about the previous groups of functions, with those functions not satisfying the demand marked in red and those which (to a greater or lesser extent) do marked in green. We can conclude that only two features fulfil the average expectations of program users.

The results from table 16 are summarized in table 17. Figure 56 offers a different visualization of the results obtained in table 17. The groups of factors have been compared with the average use of those factors. It can be seen that, in the majority of cases, there is a certain positive relationship between the frequency of use of those functions and the value given to them. This relationship is similar to that discovered in other studies using the same methodology (for example, see Comai, 2005).


Group of Functions

Aver

age V

alue

of

Sup

ply

Aver

age V

alue

of

Dem

and

Rela

tive

Impo

rtan

ce

1.- Searching and Downloading 1,5 4,4 2,2

2.- Filtering and Value Adding 3,9 3,7 1,9

3.- Local Analysis and Exploitation 1,3 3,8 1,5

4.- Graphic Generation 3,3 2,9 1,2

5.- Dissemination and Workgroup 1,4 2,9 1,2

6.- Management of Tool 0 3,4 1,0

Table 17 – Summary of the comparison between groups of functions and their priorities.

6.2 Final thoughts

The programs evaluated here do not totally address the 41 functions identified as being of special significance for patent tracking programs. These programs cover the range of areas with differing levels of intensity. Although this study only concerns itself with the five programs which kindly gave us permission to evaluate, we believe that the non-evaluated programs possess similar features therefore, they do not fully satisfy the functions examined in this study. The main findings of our report can be synthesized into the following statements:

1. None of the patent tracking programs totally cover the functions which one would expect to find in a program of this type.

2. Existing patent tracking programs satisfy users’ demands although there are serious deficiencies in some functions.

We believe that the results of this test show that there is still work to be done by the companies producing these softwares. Manufacturers need to increase


Figure 56 – Relative importance and Frequency in the use of groups of functions.

1 - SEARCHING ANDDOWNLOADING (4.4/2.2)

2 - FILTERING AND VALUE ADDING (3.7/1.9)

3 - LOCAL ANALYSIS ANDEXPLOITATION (3.8/1.5)

4 - GRAPHICGENERATION (2.9/1.2)

6 - MANAGEMENT OF TOOL (3.4/1.0)

5 - DISSEMINATION ANDWORKGROUP (2.9/1.2)

Frequency in the use

Rel

ativ

e Im

port

ance


their capacities in the areas underlined above in order to increase the added value of these programs. The main improvements in the softwares should be centred on the functions most commonly accessed by users, adopting the priorities suggested in table 17 as “Searching and Downloading”, “Local Analysis and Exploitation”, “Management of Tool” and “Dissemination and Workgroup”.

******************************************************

Footnotes

1 This value was calculated by taking the average of the adjusted values for all the functions in a class.2 The value reflects the average of the functions classified in a category.3 The value comes from table 11 (page 55).4 The value reflects the individual tables for each program in the study, presented in chapter 5 which have an original scale of 0 (no function in the program) to 5 (the program fully covers this function). This scale has been adjusted to 0 to 7 in order for supply and demand table to be directly comparable. See comparison tables and survey used in the indices. If the box indicates no mark and is not marked this means that the program does not fulfil the function in question.5 This value was obtained by taking the average of the associated values of the five programs for the same function.6 This value was calculated by taking the average of users’ responses to the programs in the study, obtained in the study of use of functions (see chapter 4).

- 148 -

SECTION SEVEN References and Authors

- 150 -

- 151 -

7.1 REFERENCES

APQC (2001). Using Science and Technology Intelligence to drive Business Results. APQC [http://www.apqc.org].

Adams, S. R. (2006). Information Sources in Patents. Saur.

Ashton, W. B. and R. A. Klavans, (1997). Keeping Abreast of Science and Technology: Technical Intelligence for Business. Battelle Press, Columbus.

Dou, H.; Levillé, V.; Manullang, S. and J. M. Dou, (2005). “Patent Analysis for Competitive Technicall Intelligence and Innovative Thinking,” Data Science Journal, 4:209-237.

Fuld&Company (2004). Intelligence Software Report 2004/2005. Published by Fuld&Company Inc. [http://www.fuld.com/Products/ISR2004/HomePage.html].

Lozano, I. P. (2003). “El análisis de patentes en el mundo de la inteligencia tecnológica competitiva,” PUZZLE - Revista Hispana de la Inteligencia Competitiva. 2(8):10-13.

Nikkel, P. (2003). “How can We Determine Which Competitive Intelligence Software Is Most Effective?” pp.163-175 in Fleisher, C. S. and Blenkhorn, D. L (editors) (2003). Controverises in Competitive Intelligence: The Enduring Issues. Praeger.

Paap, J. (2002). Using technical intelligence to drive innovation and enhance technical decisions. Workshop given at the annual International SCIP conference in Cincinati, USA.

Rodríguez, M. (2003). “Análisis de patentes en la inteligencia competitiva y tecnológica: el caso de los materiales avanzado,” PUZZLE - Revista Hispana de la Inteligencia Competitiva, 2(8):4-9.


Trippe, A. J. (2003). “Patinformatics: Tasks to tools,” World Patent Information. No.25:211-221.

Vergara, J. C. (2004). “Uso de las patentes en la práctica de la Vigilancia Tecnológica e Inteligencia Competitiva,” PUZZLE - Revista Hispana de la Inteligencia Competitiva. 3(10):4-10.

- 153 -References and Authors

7.2 AUTHORS

Juan Carlos Vergara

Is an Industrial Engineer from the University of Navarra with a postgraduate degree, from the University of Barcelona, in both the Spanish and the European Patent. He is the co-founder of “CDE-Centro de Vigilancia Normas y Patentes”, a company which is part of the CDE Group, where he is Technical Director. He has extensive experience in the creation of Automated Observation Services in Internet using his CDETracker. He provides training in and advice on the implementation of Technological Observation and Competitive Intelligence Systems in organizations of all kinds. He created Sector-based Technological Observation Services, assisting in areas such as definition, configuration and start-up. He is a consultant in Intellectual Property, from both the technological (analysis of patent rights status, identification of competition’s lines of research, technological leaders and their specialties) as well as from the legal viewpoint, in order to prevent and find solutions for conflict between patents (analysis of patentability, analysis of the risk of patent infringement in a country, analysis of the legal status of a patent, etc.) He is a member of SCIP and is an expert/collaborator with Vizcaya City Council’s Zaintek Servicios (Technological Observation for SMEs in Vizcaya) and Navarra Innovation Agency’s Navactiva. Contact: [email protected]

Alessandro Comai

Ph.D. candidate at ESADE business school (URL), he holds a BSc (Honor) in Engineering (Coventry University) and an MBA from the University of Pompeu Fabra, Barcelona. At the moment he is an associate professor of the University of Pompeu Fabra, (Barcelona, Spain) and was a visiting professor at Tampere University of Technology (Tampere, Finland) during 2005. He researches and teaches competitive intelligence (CI). He has written several articles and papers about CI and he is involved in several research projects in this field. He has also given several workshops and


presentations about CI at international conferences. Co-author of several books (“Inteligencia Competitiva y Vigilancia Tecnológica: Experiencias de Implantación en España y Latinoamérica”, Barcelona: Emecom, 2006) and studies, he is currently the director of the Spanish Competitive Intelligence Magazine “PUZZLE - Revista Hispana de la Inteligencia Competitiva” and is a member of the editorial board of the Journal of Competitive Intelligence and Management. He is also a member of SCIP (Society of Competitive Intelligence Professionals). Contact: [email protected]

Joaquín Tena Millán

Has a degree and a doctorate in Business Administration from the Autonomous University of Barcelona and a Master of Business Administration from the University of California in Los Angeles. Full Professor at Pompeu Fabra University (UPF). Director of the MBApt for the Diploma in Business Management and co-director (with Alessandro Comai) of the online Course in Competitive Intelligence (CICOL) at the Institute of Further Education of the UPF. Director of the Business School at UPF. Author of, among other titles, “Inteligencia Competitiva y Vigilancia Tecnológica: Experiencias de Implantación en España y Latinoamérica”, Barcelona: Emecom, 2006, “El Entorno de la Empresa”, Barcelona: Editions 2000, 1992, “Organización de la Empresa: Teoría y Aplicaciones”, Editions 2000, 1989, “Análisis y Formulación de Estrategia Empresarial” with J.D. Grima. Barcelona: Hispano Europea, 1984. Co-director of PUZZLE and member of the Academy of Management, the Strategic Management Society and SCIP. Contact: [email protected]

SECTION EIGHTAnnexes

- 156 -

- 157 -

8. ANNEXES

8.1 Annex 1: Letter of invitation sent to software-producing companies

Dear Sir,

We are inviting you to participate in our study.

PUZZLE, The Hispanic Magazine of Competitive Intelligence (www.revista-puzzle.com) is working on a research project to establish the characteristics of patent analysis software.

This study seeks to understand the usage of patents analysis software and make a comparison between several softwares. The benchmark evaluation will be done for each software against a defined list of characteristics which will be validated from a survey we are doing with practitioners. This survey will define the relevance and importance of different methods or techniques within the profession.

We are interested in installing your software “Company Brand Name” and making the test with results obtained from “Company Brand Name”Host. We would like to ask if you could send us a 1-month limited copy of your software “Company Brand Name” and if you could allow us limited access to “Company Brand Name” so that we can test the software.

If this were not possible, we would appreciate if you could send us the documentation and other relevant material that would allow us to study the software. In this specific case the conclusion of the software benchmark will be classified in a special group of products that have not been tested.

As a small token of our appreciation, we will provide a summary of our findings for each stage of the research. The full report, which provides several


patent analysis software programs, and the results will be published at the beginning of November 2005.

If you have any questions, please do not hesitate to contact us.

Sincerely,

Juan Carlos VergaraTechnical Adviser

- 159 -Annexes

8.2 Annex 2: Letter of Invitation sent to professional individuals

Dear Sir,

PUZZLE, The Hispanic Magazine of Competitive Intelligence (www.revista-puzzle.com) is working on a research project to establish the characteristics of patent analysis software.

We are seeking your expert assistance and your participation is critical in this research. This study seeks to understand the usage of patents analysis software. Therefore, we are particularly interested in defining the relevance and importance of different methods or techniques within the profession.

We would very much appreciate it if you could complete the questionnaire online at:

http://www.surveymonkey.com/s.asp?u=72931084818

The deadline will be tJuly 15, 2005. As a small token of our appreciation, we will provide a summary of our findings for each stage of the research. The full report, which provides several patent analysis software programs, and the results will be published at the beginning of November 2005.


Sincerely,

Alessandro ComaiDirector of PUZZLE and Ph.D. candidate (ESADE Business School)[email protected]


8.3 Annex 3: 3rd. Letter of Invitation sent to professional individuals

Dear Sir,

I am sending a 3rd. and last call for participating in our study. This study seeks to understand the usage of patent analysis software. After the 2nd call we have added 19 valid questionnaires to the initial 34. Therefore, we have achieved 53 valid responses at the moment. We would very much appreciate it if you could complete the questionnaire online at: http://www.surveymonkey.com/s.asp?u=72931084818

The deadline will be July 31, 2005. As a small token of our appreciation, we will provide a summary of our findings for each stage of the research.


Sincerely,

Alessandro ComaiDirector of PUZZLE and Ph.D. candidate (ESADE Business School)[email protected]

- 161 -Annexes

8.4 Annex 4: Letter of Invitation sent to professional individuals (in Spanish)

Estimado/a Sr/a,

PUZZLE, la revista hispana de Inteligencia Competitiva, (www.revista-puzzle.com) está llevando a cabo un trabajo de investigación para establecer las características más valoradas de los Software de Análisis de Patentes y a continuación hacer una comparativa entre ellos.

Buscamos su opinión como experto en patentes y consideramos que su participación es crítica en esta investigación. Este estudio busca entender el uso que se hace del software de análisis de patentes. Por tanto, estamos particularmente interesados en definir la relevancia y la importancia de diferentes métodos o técnicas de análisis en el desempeño de su profesión.

Le agradeceremos que dedique unos minutos en responder a un cuestionario disponibe online en:

http://www.surveymonkey.com/s.asp?u=648091192001

La fecha para la recopilación de los resultados es el 29 de Julio de 2005. Como muestra de agradecimiento a los participantes, les enviaremos un resumen con las conclusiones de cada etapa de investigación. El informe que incluye una comparación de varios paquetes de análisis de patentes se publicará en Noviembre de 2005.

Le agradeceremos que re-envie este mensaje a otros colegas que puedan responder al cuestionario.

Si Ud. tiene alguna pregunta, por favor no dude en contactarnos.

Sinceramente,

Alessandro ComaiDirector de PUZZLE y doctorando (ESADE)[email protected]


Joaquín TenaCo-Director de [email protected]

Juan Carlos VergaraAsesor de [email protected]

- 163 -Annexes

8.5 Annex 5: Functions Table


























Automatic abstracts















Personalised alerts









Users access rights management

Multi-user access and edition

Access and search interface customisation


Document collections access rights management


- 165 -Annexes

8.6 Annex 6: Questionnaire

The questionnaire used in the poll was a form consisting of a total of 10 pages in HTML format, accessible only through the internet (see example screens below).

The entire contents of the questionnaire are shown below although the format of the document is slightly different to the one which appears in the internet.


Patent Analysis Software

1. Purpose

This study seeks to understand the usage of patents analysis software. We are particularly interested in defining the relevance and importance of different methods or techniques with respect to your profession.

The questionnaire is divided in two parts. The first will explore what methods or techniques are most commonly used. The second one distinguishes the importance of the methods.

2. Demographic Information Confidentiality: We assure you that your identity and that of your company will be treated as strictly confidential. The information you provide will not be shared with any other person and all references to your company’s data will be blinded in any report resulting from this research.

1. Position: R&D / Director / Libraian / Technician / Other (please specify)

2. Industry/Sector: Pharmacy / Veterinary / Chemical / Petrochemical / Automotive / Electronic / Metal / Plastic / Medical / Engineering or Mechanical / Computer / Other (please specify) 3. Years of Experience: ______

If you are interested in receiving an executive copy of the full study, please fill the following information:

4. Name: 5. Surname: 6. e-mail

- 167 -Annexes

3. Search

7. Please rate the extent to which you use the following methods/techniques to SEARCH patents?

Not

at

all

Very

litt

le

Litt

le

Som

etim

es

Oft

en

Alm

ost

ever

y tim

e

Alw

ays

N/A

1. Search in complementary technical/grey literature online databases

2. Search in local (intranet) databases

3. Import patent records from other software

4. Launch simultaneous searches in multiple databases

5. Save search strategies

6. Schedule repetitive searches

7. Download and integrate of patent legal status

8. Download and integrate of graphics

9. Download and link of pdf documents


4. Filter and value add

8. Please rate the extent to which you use the following methods/techniques to FILTER & VALUE ADD patents?

Not

at

all

Very

litt

le

Litt

le

Som

etim

es

Oft

en

Alm

ost

ever

y tim

e

Alw

ays

N/A

1. Automatic patent duplicate detection and removal

2. Automatic grouping of patent families

3. Automatic generation of field indexes

4. Definition and building of additional indexes

5. Group and clean of index terms

6. Evaluation of pertinence (user filled field)

7. Annotation of patents (user filled field)

8. Definition and edition of patent groups

9. Link to other related documents

10. Creation and edition of Taxonomies

- 169 -Annexes

5. Local Analysis and Exploitation

9. Please rate the extent to which you use the following methods/techniques to ANALYSE & EXPLOIT patents?

Not

at

all

Very

litt

le

Litt

le

Som

etim

es

Oft

en

Alm

ost

ever

y tim

e

Alw

ays

N/A

1. Automatic extraction of main keywords from patents

2. Automatic abstracts

3. Automatic clustering of patents

4. Automatic classification of patents in pre-defined categories

5. Full text indexing/searching

6. Semantic indexing/searching

7. Ability to use local databases to integrate new data and complete the patent analysis


6. Graphic Generation

10. Please rate the extent to which you use the following methods/techniques to GENERATE GRAPHICS from patents?

Not

at

all

Very

litt

le

Litt

le

Som

etim

es

Oft

en

Alm

ost

ever

y tim

e

Alw

ays

N/A

1. Cite Analysis (cited and citing patents in relation to a known patent)

2. Rankings - Analysis of one field.

3. Matrix or Bar graphs – Two fields co-occurrence analysis.

4. Network relations analysis – Two fields co-occurrence analysis

5. Space or topographic representation of a patent collection – text mining analysis

- 171 -Annexes

7. Dissemination and Workgroup

11. Please rate the extent to which you use the following methods/techniques to DISSEMINATE patents?

Not

at

all

Very

litt

le

Litt

le

Som

etim

es

Oft

en

Alm

ost

ever

y tim

e

Alw

ays

N/A

1. Publish the contents in the intranet / internet

2. Customised alerts

3. Alerts with changes on the legal status

4. Automatic reports using templates

5. Export all the fields (csv, xml, etc)

6. Link a patent to a poll with a key question

7. Link a patent to a forum and begin a discussion

8. Link a patent to an event in a shared agenda


8. Management of Tool

12. Please rate the extent to which you use the following characteristics to MANAGE Patent SOFTWARE?

Not

at

all

Very

litt

le

Litt

le

Som

etim

es

Oft

en

Alm

ost

ever

y tim

e

Alw

ays

N/A

1. Management of users access rights

2. Management of Document collections access rights

3. Simultaneous multi-user access and edition

4. Customisation of Access and search interface

5. Multilanguage interface

6. System utilisation statistics

- 173 -Annexes

9. Please rate how important you think the following group of methods/techniques for analyzing patents is

NOTE: Assign “1” to the least important group of methods/techniques and rate ALL the others groups against it. If you rate “1”, it means that the group of methods is equal to the group they are being compared against. If you rate “1.5”, then it means that the group of methods is 50% more important than the one they are being compared against. If you rate “2” then it means that the group is 100% more important or twice as important and if you rate “3” then it is 200% more important or three times as important and so on.

Relative Importance:

1.00

1.25

1.50

1.75

2.00

2.25

2.50

2.75

3.00

N /

A

1. Search

2. Filter and value add

3. Local Analysis and Exploitation

4. Graphic generation

5. Dissemination and Workgroup

6. Software Management


10. Conclusion

Thank you for completing this Survey. We appreciate your input. If you have any questions, please send an e-mail to:

Mr. Alessandro Comai,Ph.D. Candidate ESADE and director of the magazine PUZZLE.

[email protected]

- 175 -Annexes

8.7 Annex 7: List of IP Organizations

World Intellectual Property Organization (WIPO)

http://www.wipo.org

Office for Harmonization in the Internal Market (OAMI)

http://oami.eu.int

International Intellectual Property Institute (IIPI)

http://www.iipi.org

European Patent Office (EPO) http://www.european-patent-office.org

EPI - Institute of Professional Representatives before the European Patent Office (EPO)

http://www.patentepi.com

The Institute of Trade Mark Attorneys (ITMA)

http://www.itma.org.uk

The Eurasian Patent Organization (EAPO) http://www.eapo.org

Asia-Pacific Industrial Property Center (APIC)

http://www.apic.jiii.or.jp

Japan Patent Information Organization (JAPIO)

http://www.japio.or.jp

Institute of Intellectual Property (IIP) http://www.iip.or.jp

Domain Name Dispute Resolution Service http://arbiter.wipo.int/domains

American Intellectual Property Law Association (AIPLA)

http://www.aipla.com

Austin Intellectual Property Law Association (Austin-IPLA)

http://www.austin-ipla.org

International Association for the Protection for Industrial Property (IAPPI)

http://www.aippi.org

Intellectual Property Owners Associations (IPO)

http://www.ipo.org

Japan Intellectual Property Association (JIPA)

http://www.jipa.or.jp

International Federation of Intellectual Property Attorneys (FICPI)

http://www.ficpi.org


Interamerican Association of Industrial Property (ASIPI)

http://www.asipi.org

American Bar Association - IP Law http://www.abanet.org/intelprop

Japan Patent Attorneys Association (JPAA) http://www.jpaa.or.jp

The Institute of Patent and Trade Mark Attorneys of Australia (IPTA)

http://www.ipta.com.au

Patent Information Users Group (PIUG) http://www.piug.org

Boston Patent Law Association (BPLA) http://www.bpla.org

National Association of Patent Practioners (NAPP)

http://www.napp.org

Patent and Trademark Office Society (PTOS)

http://www.ptos.org

International Trademark Association (INTA)

http://www.inta.org

European Communities Trademark Association (ECTA)

http://www.ecta.org

The Association of European Trademark Owners (MARQUES)

http://www.martex.co.uk/marques/index.htm

The Domain Name Rights Coalition (DNRC)

http://www.domain-name.org

- 177 -Annexes

- 178 -

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