IPR2017-01039 Smyth Declaration
`U.S. Patent 7,231,379
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`UNITED STATES PATENT AND TRADEMARK OFFICE
`____________
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`BEFORE THE PATENT TRIAL AND APPEAL BOARD
` ____________
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`UNIFIED PATENTS INC.
`Petitioner
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`v.
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`GUADA TECHNOLOGIES LLC
`Patent Owner
`____________
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`IPR2017-01039
`Patent 7,231,379
` ____________
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`DECLARATION OF PADHRAIC SMYTH, PH.D.
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`IPR2017-01039 Smyth Declaration
`U.S. Patent 7,231,379
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`I, Padhraic Smyth, hereby declare the following:
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`I.
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`BACKGROUND AND QUALIFICATIONS
`1. My name is Padhraic Smyth and I am over 21 years of age and
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`otherwise competent to make this Declaration. I make this Declaration based on
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`facts and matters within my own knowledge and on information provided to me by
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`others, and, if called as a witness, I could and would competently testify to the
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`matters set forth herein.
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`2.
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`I am a Professor in the Department of Computer Science at the
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`University of California, Irvine and Director of the UCI Data Science Initiative. I
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`have been retained as a technical expert witness in this matter by Counsel for
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`Petitioner Unified Patents, Inc. to provide my independent opinions on certain
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`issues requested by Counsel for Petitioner relating to the accompanying petition for
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`Inter Partes Review of U.S. Patent No. 7,231,379 (“the ’379 Patent”). My
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`compensation in this matter is not based on the substance of my opinions or the
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`outcome of this matter. I have no financial interest in Petitioner. I have been
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`informed that Guada Technologies LLC (“Guada”) is the purported owner of the
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`’379 Patent, and I note that I have no financial interest in Guada.
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`3.
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`I have summarized in this section my educational background, career
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`history, and other qualifications relevant to this matter. I have also included a
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`current version of my curriculum vitae as Appendix A (Ex. 1009).
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`I received a bachelor’s degree in electronic engineering (B.E., first
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`4.
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`class honors) from the National University of Ireland, Galway, in 1984. I received
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`a master’s degree (M.S.E.E.) and a Ph.D. in electrical engineering from the
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`California Institute of Technology, Pasadena, CA, in 1985 and 1988, respectively.
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`My Ph.D. thesis was focused on the use of hierarchical tree structures and rule-
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`based methods for automated and efficient classification of objects into categories.
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`5.
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`From 1988 to 1996, I was a technical staff member and technical group
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`leader (from 1992 onwards) at the Jet Propulsion Laboratory (JPL) in Pasadena,
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`CA. My role at JPL consisted of research and development in the areas of pattern
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`recognition, machine learning, data mining, and expert systems, as well as leading
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`projects involved in the application of these techniques to problems of interest to
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`JPL and NASA.
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`6. As part of my work, I published and presented papers during the period
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`1988-1996 at multiple different conferences in the areas of pattern recognition,
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`machine learning, and artificial intelligence. One example of my research work
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`was my involvement in the emerging research area of “knowledge discovery in
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`databases” (KDD). This began as a small research workshop in 1989 and quickly
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`evolved into a large annual international conference (with the first conference in
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`2004 and continuing annually since then). The research area was somewhat unique
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`in that it involved an interdisciplinary set of researchers working at the intersection
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`of databases, statistics, and machine learning algorithms. I was involved with the
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`KDD research field both as a researcher (writing and presenting papers), in the
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`organization of the conference, and in co-editing the first text on knowledge
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`discovery from databases (published by MIT Press in 1996, see discussion of
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`publications below).
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`7.
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`In 1996, I moved from JPL to the University of California, Irvine, to
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`take a position as an assistant professor in the Department of Computer Science.

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`In 1998, I was promoted to associate professor with tenure, and in 2003 I was
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`promoted to the position of full professor. I also have a joint faculty appointment in
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`the Department of Statistics at UC Irvine. As a professor at UC Irvine since 1996, I
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`have conducted research in the areas of pattern recognition, machine learning, and
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`artificial intelligence, with an emphasis on developing new theories and algorithms
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`for automatically extracting useful information from very large volumes of data
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`across a wide variety of applications.
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`8.
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`In 2007, I was also appointed as the founding director for the Center
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`for Machine Learning and Intelligent Systems at UC Irvine. This Center has over
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`30 affiliated faculty members at UC Irvine whom are all involved in research in
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`areas such as machine learning, database research, and artificial intelligence. In
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`2014, I was appointed as founding Director of the UC Irvine Data Science
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`Initiative, a cross-campus research initiative involving computer scientists,
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`statisticians, engineers, scientists, medical researchers, and more across the
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`campus.
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`9. My teaching duties have consisted of teaching both undergraduate and
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`graduate courses in the Computer Science department, with a focus on courses in
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`the areas of data mining and machine learning – titles of courses I have taught in
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`the past few years include Data Mining, Introduction to Artificial Intelligence,
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`Project in Artificial Intelligence, Applications of Probability for Computer
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`Scientists, and Probabilistic Learning. These courses include material related to
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`data structures such as hierarchical trees for automated decision-making and user
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`navigation, design and evaluation of systems for information retrieval, and
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`machine learning algorithms that can adapt and learn from data provided via user
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`input.
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`10.
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`In addition to my duties at UC Irvine, I also consult with private
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`industry in the areas of machine learning and pattern recognition. My consulting
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`work often involves the development of mathematical models, algorithms, and
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`software for companies who wish to develop and deploy operational systems that
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`can autonomously interact with a user, such as recommending (on a Web site) the
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`next item to a user from a large catalog of potential items they may wish to
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`consider. These systems are typically constructed from large historical databases,
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`consisting of text data, customer transactions, etc. Over the past 18 years I have
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`consulted in this manner with companies such as AT&T, Samsung, Nokia, First
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`Quadrant, SmithKline Beecham, Yahoo!, eBay, and Netflix, as well as with a
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`number of smaller startup companies. My involvement in consulting projects has
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`given me the opportunity to develop expertise in the practical application of
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`machine learning and information retrieval algorithms, and in particular, to
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`develop an understanding of how these algorithms are deployed within real-world,
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`large-scale software systems that allow users to interact with databases and
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`websites.
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`11. As part of my real-world consulting projects over the past 18 years (at
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`Samsung, Yahoo!, eBay, and others), I have had direct experience with the
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`development of systems that use automated algorithms to assist a user who is
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`interacting with a system and has a specific goal in mind, such as finding a specific
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`item of information. This includes experience with methods and algorithms that
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`can extract useful information from large databases of user clickstream and search
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`query data, as well as from text documents related to customer reviews, customer
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`emails, product descriptions, Web page content, and search query data. As part of
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`this work I developed and adapted a variety of mathematical and statistical
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`frameworks that allow a computer to automatically decide what item of
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`information to show to a human user who is interacting with a system and who has
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`a specific goal in mind such as making a purchase or finding information on a Web
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`page. I developed computer algorithms to apply these mathematical and statistical
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`frameworks efficiently both to (a) user data (such as log files of clickstreams
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`recording how users interact with a system) and (b) to text data (such as text from
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`Web pages and documents). I also wrote and tested software code to implement
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`those algorithms in software, and I ran and interpreted computational experiments
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`to evaluate the effectiveness of different approaches.
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`12.
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` I have published 67 journal papers, 18 book chapters, 9 technical
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`magazine articles, and 109 peer-reviewed conference papers related to my
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`research. Several of these publications are among the most highly-cited papers in
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`the general areas of data mining and artificial intelligence – my papers have
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`approximately 40,000 citations in total according to Google Scholar. Four of my
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`conference papers received best paper awards at the Association for Computing
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`Machinery (ACM) Conference on Knowledge Discovery and Data Mining, the
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`leading annual international conference on data mining.
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`13.
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`I co-edited the book Advances in Knowledge Discovery and Data
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`Mining (AAAI/MIT Press, 1996), which is considered the first book published on
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`the topic of automated extraction of information from large databases and has over
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`5000 citations according to Google Scholar. I also co-authored Principles of Data
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`Mining (MIT Press, 2001), which is widely used as a graduate textbook in data
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`mining courses, has over 4700 citations, and has been translated into Chinese and
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`Polish editions. This text contains material describing the general principles of (a)
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`hierarchical tree structures (chapter 9 on tree-structured clustering, and chapter 10
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`on tree-structured classification) and (b) information retrieval and text analysis
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`(chapter 14). I also co-authored the text Modeling the Internet and the Web:
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`Probabilistic Methods and Algorithms (Wiley, 2003). This text contains material
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`describing the general principles of text analysis and information retrieval (Chapter
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`4), tree and graph-based models for representing text information (Chapter 5), and
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`analysis of how human users interact with information retrieval systems (Chapter
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`7).
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`14.
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`I have been elected as a Fellow of the Association of Computing
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`Machinery (ACM), in 2013, and also elected a Fellow of the Association for
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`Advancement of Artificial Intelligence (AAAI), in 2010. I received the Innovation
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`Award from ACM SIGKDD (Special Interest Group in Knowledge Discovery and
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`Data Mining) in 2009. This award is given annually by ACM to an individual who
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`has made significant contributions to the research and practice of data mining. The
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`award is the most prestigious annual award given to a researcher in the data mining
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`research field. I also received a National Science Foundation CAREER award, a
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`Google Faculty Research Award, an IBM Faculty Partnership Award, and the Lew
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`Allen Award for Research at JPL.
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`I have served as an associate editor for the Journal of the American
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`15.
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`Statistical Association,
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`the IEEE Transactions on Knowledge and Data
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`Engineering, and the Machine Learning journal. In addition, I was a founding
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`editorial board member for the journals Bayesian Analysis, the Journal of Machine
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`Learning Research, and the Data Mining and Knowledge Discovery Journal. I have
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`served as a reviewer of papers for all of the major conferences and journals in my
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`field as well a reviewer of proposals for the National Science Foundation and for
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`NASA. I served as program chair for the annual ACM SIGKDD Conference on
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`Knowledge Discovery and Data Mining in 2011, and I was invited to serve as the
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`keynote speaker at the British International Conference on Databases in July 2015
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`(http://conferences.inf.ed.ac.uk/BICOD2015/).
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`16. As part of my work in connection with this proceeding, I have
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`reviewed the following materials:
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`• U.S. Patent 8,473,379 (Ex. 1001);
`• File History for U.S. Patent 8,473,379 (Ex. 1002)
`• U.S. Patent No. 6,731,724 to Wesemann (Ex. 1004) (“Wesemann”)
`• U.S. Patent No. 6,366,910 to Rajaraman (Ex. 1005) (“Rajaraman”)
`• U.S. Patent No. 7,539,656 to Fratkina (Ex. 1006) (“Fratkina”)
`• Hopcroft, John E., and Jeffrey D. Ullman. Data Structures and
`Algorithms. Boston, MA, USA, Addison-Wesley, pp. 75-106, 155-197,
`306-346, 1983 (Ex. 1010)
`• Donald, B. Crouch, Carolyn J. Crouch, and Glenn Andreas, The use of
`cluster hierarchies in hypertext information retrieval, Hypertext ’89
`Proceedings, ACM Press, pp. 225-237, 1989 (Ex. 1011)
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`• Yvan Leclerc, Steven W. Zucker, Denis Leclerc, McGill University, A
`browsing approach to documentation, IEEE Computer, IEEE Press, pp
`46-49, 1982 (Ex. 1012)
`• Ricky E. Savage, James K. Habinek,Thomas W. Barnhart, The design,
`simulation, and evaluation of a menu driven user interface, Proceedings
`of the 1982 Conference on Human Factors in Computing Systems, ACM
`Press, pp 36-40, 1982 (Ex. 1013)
`• Ricardo Baeza-Yates, Berthier Ribiero-Neto, Modern Information Retrieval,
`pp. 24-40, ACM Press, 1999 (Ex. 1014)
`• Daniel Cunliffe, Carl Taylor, and Douglas Tudhope, Query-based navigation
`in semantically indexed hypermedia, Proceedings of the Eighth ACM
`Conference on Hypertext, pp. 87-95, ACM Press, 1997 (Ex. 1015)
`• Hornstein, Telephone Voice Interfaces on the Cheap at § 2.3,
`Proceedings of the UBLAB '94 Conference, 1994 (Ex. 1016)
`• De Bra, Paul, et al., Information Retrieval in Distributed Hypertexts, in
`RIAO, pp. 481–493, 1995 (Ex. 1017)
`• U.S. Pat. No. 6,198,939 to Holstrom (Ex.1018)
`• Karen Sparck Jones, A look back and a look forward, Proceedings of the
`11th ACM SIGIR International Conference on Research and
`Development in Information Retrieval, pp. 13-29, ACM Press, 1988 (Ex.
`1019)
`• Gerard Salton, Anita Wong, and Chung-Shu Yang, A vector space model
`for automatic indexing, Communications of the ACM, 18(11): 613-620,
`1975 (Ex. 1020)
`• Jinxi Xu and W. Bruce Croft, Query expansion using local and global
`document analysis, Proceedings of the 19th ACM SIGIR International
`Conference on Research and Development in Information Retrieval, pp.
`4-11. ACM, 1996 (Ex. 1021)
`• Carolyn J. Crouch, A cluster-based approach to thesaurus
`construction, Proceedings of the 11th ACM SIGIR International
`Conference on Research and Development in Information Retrieval pp.
`309-320. ACM, 1988 (Ex. 1022)
`• Hinrich Schütze and Jan O. Pedersen, A cooccurrence-based thesaurus
`and two applications to information retrieval, 1 Intelligent Multimedia
`Information Retrieval Systems and Management, pp. 266-274, 1994 (Ex.
`1023)
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`• Güntzer et al., Automatic Thesaurus Construction by Machine Learning
`from Retrieval Sessions, 25 Information Processing & Management No.
`3 pp. 265-273, 1998 (Ex. 1024)
`• Mostafa et al., A Multilevel Approach to Intelligent Information
`Filtering: Model, System, and Evaluation, 15 ACM Transactions on
`Information Systems No. 4, pp. 368-399, 1997 (Ex.1025)
`• U.S. Pat. No. 6,006,225 to Bowman et al. (Ex. 1026)
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`II. LEGAL FRAMEWORK
`A. Obviousness
`17.
`I am a technical expert and do not offer any legal opinions. However,
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`counsel has informed me as to certain legal principles regarding patentability and
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`related matters under United States patent law, which I have applied in performing
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`my analysis and arriving at my technical opinions in this matter.
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`18.
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`I have been informed that a person cannot obtain a patent on an
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`invention if the differences between the invention and the prior art are such that the
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`subject matter as a whole would have been obvious at the time the invention was
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`made to a person having ordinary skill in the art. I have been informed that a
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`conclusion of obviousness may be founded upon more than a single item of prior
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`art. I have been further informed that obviousness is determined by evaluating the
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`following factors: (1) the scope and content of the prior art, (2) the differences
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`between the prior art and the claim at issue, (3) the level of ordinary skill in the
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`pertinent art, and (4) secondary considerations of non-obviousness. In addition, the
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`obviousness inquiry should not be done in hindsight. Instead, the obviousness
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`inquiry should be done through the eyes of a PHOSITA at the time of the alleged
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`invention.
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`19.
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`In considering whether certain prior art renders a particular patent
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`claim obvious, counsel has informed me that I can consider the scope and content
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`of the prior art, including the fact that one of skill in the art would regularly look to
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`the disclosures in patents, trade publications, journal articles, conference papers,
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`industry standards, product
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`literature and documentation,
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`texts describing
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`competitive technologies, requests for comment published by standard setting
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`organizations, and materials from industry conferences, as examples. I have been
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`informed that for a prior art reference to be proper for use in an obviousness
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`analysis, the reference must be “analogous art” to the claimed invention. I have
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`been informed that a reference is analogous art to the claimed invention if: (1) the
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`reference is from the same field of endeavor as the claimed invention (even if it
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`addresses a different problem); or (2) the reference is reasonably pertinent to the
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`problem faced by the inventor (even if it is not in the same field of endeavor as the
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`claimed invention). In order for a reference to be “reasonably pertinent” to the
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`problem, it must logically have commended itself to an inventor's attention in
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`considering his problem. In determining whether a reference is reasonably
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`pertinent, one should consider the problem faced by the inventor, as reflected
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`either explicitly or implicitly, in the specification. I believe that all of the
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`references I considered in forming my opinions in this IPR are well within the
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`range of references a PHOSITA would have consulted to address the type of
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`problems described in the Challenged Claims.
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`20.
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`I have been informed that, in order to establish that a claimed invention
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`was obvious based on a combination of prior art elements, a clear articulation of
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`the reason(s) why a claimed invention would have been obvious must be provided.
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`Specifically, I am informed that, under the U.S. Supreme Court’s KSR decision, a
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`combination of multiple items of prior art renders a patent claim obvious when
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`there was an apparent reason for one of ordinary skill in the art, at the time of the
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`invention, to combine the prior art, which can include, but is not limited to, any of
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`the following rationales: (A) combining prior art methods according to known
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`methods to yield predictable results; (B) substituting one known element for
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`another to obtain predictable results; (C) using a known technique to improve a
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`similar device in the same way; (D) applying a known technique to a known device
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`ready for improvement to yield predictable results; (E) trying a finite number of
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`identified, predictable potential solutions, with a reasonable expectation of success;
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`(F) identifying that known work in one field of endeavor may prompt variations of
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`it for use in either the same field or a different one based on design incentives or
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`other market forces if the variations are predictable to one of ordinary skill in the
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`art; or (G) identifying an explicit teaching, suggestion, or motivation in the prior
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`art that would have led one of ordinary skill to modify the prior art reference or to
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`combine the prior art references to arrive at the claimed invention.
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`21.
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`I am informed that the existence of an explicit teaching, suggestion, or
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`motivation to combine known elements of the prior art is a sufficient, but not a
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`necessary, condition to a finding of obviousness. This so-called “teaching-
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`suggestion-motivation” test is not the exclusive test and is not to be applied rigidly
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`in an obviousness analysis. In determining whether the subject matter of a patent
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`claim is obvious, neither the particular motivation nor the avowed purpose of the
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`patentee controls. Instead, the important consideration is the objective reach of the
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`claim. In other words, if the claim extends to what is obvious, then the claim is
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`invalid. I am further informed that the obviousness analysis often necessitates
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`consideration of the interrelated teachings of multiple patents, the effects of
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`demands known to the technological community or present in the marketplace, and
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`the background knowledge possessed by a person having ordinary skill in the art.
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`All of these issues may be considered to determine whether there was an apparent
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`reason to combine the known elements in the fashion claimed by the patent.
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`22.
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`I also am informed that in conducting an obviousness analysis, a
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`precise teaching directed to the specific subject matter of the challenged claim
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`need not be sought out because it is appropriate to take account of the inferences
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`and creative steps that a PHOSITA would employ. The prior art considered can be
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`directed to any need or problem known in the field of endeavor at the time of
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`invention and can provide a reason for combining the elements of the prior art in
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`the manner claimed. In other words, the prior art need not be directed towards
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`solving the same specific problem as the problem addressed by the patent. Further,
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`the individual prior art references themselves need not all be directed towards
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`solving the same problem. I am informed that, under the KSR obviousness
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`standard, common sense is important and should be considered. Common sense
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`teaches that familiar items may have obvious uses beyond their primary purposes.
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`23.
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`I also am informed that the fact that a particular combination of prior
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`art elements was “obvious to try” may indicate that the combination was obvious
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`even if no one attempted the combination. If the combination was obvious to try
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`(regardless of whether it was actually tried) or leads to anticipated success, then it
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`is likely the result of ordinary skill and common sense rather than innovation. I am
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`further informed that in many fields it may be that there is little discussion of
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`obvious techniques or combinations, and it often may be the case that market
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`demand, rather than scientific literature or knowledge, will drive the design of an
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`invention. I am informed that an invention that is a combination of prior art must
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`do more than yield predictable results to be non-obvious.
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`24.
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`I am informed that for a patent claim to be obvious, the claim must be
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`obvious to a PHOSITA at the time of the alleged invention. I am informed that the
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`factors to consider in determining the level of ordinary skill in the art include (1)
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`the educational level and experience of people working in the field at the time the
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`invention was made, (2) the types of problems faced in the art and the solutions
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`found to those problems, and (3) the sophistication of the technology in the field.
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`25.
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`I am informed that it is improper to combine references where the
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`references teach away from their combination. I am informed that a reference may
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`be said to teach away when a PHOSITA, upon reading the reference, would be
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`discouraged from following the path set out in the reference, or would be led in a
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`direction divergent from the path that was taken by the patent applicant. In general,
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`a reference will teach away if it suggests that the line of development flowing from
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`the reference’s disclosure is unlikely to be productive of the result sought by the
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`patentee. I am informed that a reference teaches away, for example, if (1) the
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`combination would produce a seemingly inoperative device, or (2) the references
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`leave the impression that the product would not have the property sought by the
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`patentee. I also am informed, however, that a reference does not teach away if it
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`merely expresses a general preference for an alternative invention but does not
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`criticize, discredit, or otherwise discourage investigation into the invention
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`claimed.
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`26.
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`I am informed that even if a prima facie case of obviousness is
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`established, the final determination of obviousness must also consider “secondary
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`considerations” if presented. In most instances, the patentee raises these secondary
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`considerations of non-obviousness. In that context, the patentee argues an
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`invention would not have been obvious in view of these considerations, which
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`include: (a) commercial success of a product due to the merits of the claimed
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`invention; (b) a long-felt, but unsatisfied need for the invention; (c) failure of
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`others to find the solution provided by the claimed invention; (d) deliberate
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`copying of the invention by others; (e) unexpected results achieved by the
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`invention; (f) praise of the invention by others skilled in the art; (g) lack of
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`independent simultaneous invention within a comparatively short space of time;
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`(h) teaching away from the invention in the prior art.
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`27.
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` I am further informed that secondary-considerations evidence is only
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`relevant if the offering party establishes a connection, or nexus, between the
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`evidence and the claimed invention. The nexus cannot be based on prior art
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`features. The establishment of a nexus is a question of fact. While I understand that
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`the Patent Owner here has not offered any secondary considerations at this time, I
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`will supplement my opinions in the event that the Patent Owner raises secondary
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`considerations during the course of this proceeding.
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`III. OPINION
`A. Level of Skill of a Person Having Ordinary Skill in the Art
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`IPR2017-01039 Smyth Declaration
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`I was asked to provide my opinion as to the level of skill of a person
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`28.
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`having ordinary skill in the art (“PHOSITA”) of the ’379 Patent at the time of the
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`claimed invention, which counsel has informed me to assume is November 19,
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`2002, the filing date of the ’379 Patent. In determining the characteristics of a
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`hypothetical person of ordinary skill in the art of the ’379 Patent at the time of the
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`claimed invention, I was told to consider several factors, including the type of
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`problems encountered in the art, the solutions to those problems, the rapidity with
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`which innovations are made in the field, the sophistication of the technology, and
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`the education level of active workers in the field. I also placed myself back in the
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`time frame of the claimed invention, and considered the colleagues with whom I
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`had worked at that time.
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`29.
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`In my opinion, a person having ordinary skill in the art of the ’379
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`Patent at the time of its filing would have been a person having the equivalent of a
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`bachelor’s degree in computer science, electrical engineering, or a similar
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`discipline, and at least one year of experience working with technology related to
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`information retrieval and database searching, or an equivalent amount of similar
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`work experience or education, with additional education substituting for
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`experience and vice versa. Such a person of ordinary skill in the art would have
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`been capable of understanding the ’379 patent and the prior art references
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`discussed herein.
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`IPR2017-01039 Smyth Declaration
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`30. Based on my education, training, and professional experience in the
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`field of the claimed invention, I am familiar with the level and abilities of a person
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`of ordinary skill in the art at the time of the claimed invention. Additionally, I met
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`at least these minimum qualifications to be a person having ordinary skill in the art
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`as of the time of the claimed invention of the ’379 Patent
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`B.
`31.
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`Background of the Technology
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`I was asked to briefly summarize the background of the prior art from
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`the standpoint of the knowledge of a PHOSITA prior to November 19, 2002, the
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`filing date of the ’379 Patent.
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`32. As the ’379 Patent shows in its “Background of the Invention,” the
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`concept of using keywords to navigate nodes or vertices arranged hierarchically,
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`such as in a graph structure, in a network was well known long before 2002. The
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`’379 Patent also recognizes that one “familiar” application of such a hierarchical
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`system is an automated telephone voice response system. See Ex. 1001 at 1:40-45.
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`The ’379 Patent also acknowledges that “travers[ing] the network in the most
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`efficient manner possible” is a desirable feature of hierarchical navigation systems.
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`Id. at 1:23-26; see also id. at 2:9:18.
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`33. The representation of interconnected nodes in a hierarchical network,
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`such as a tree structure like that described in the ’379 Patent, has long been well
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`known in the art and has long been a subject covered in introductory computer
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`science courses well before the ’379 Patent’s November 2002 filing date. 1 In
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`particular, all of the following would have been familiar to a PHOSITA before
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`2002: Arranging nodes in a network, such as a menu having different categories of
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`information, in a hierarchical structure,2 allowing users to navigate between nodes
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`or vertexes using key terms or node descriptors,3 and automatically searching a tree
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`to direct a user to a node or vertex associated with a key term or descriptor
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`associated with that node without traversing through other intervening nodes in the
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`hierarchy.4
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`34.
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`In such hierarchical tree structures, where the root node contains all
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`objects, the children of the root node contain disjoint subsets of objects, and so on,
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`down to leaf nodes. By 2002, a PHOSITA would have known that there are
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`multiple different ways that an algorithm can search a tree to identify nodes of
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`interest and perform other operations. The different types of tree search algorithms
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`
`1 Hopcroft, John E., and Jeffrey D. Ullman. Data Structures and Algorithms. Boston, MA, USA,
`Addison-Wesley, pp. 75-106, 1983.
`2 Donald, B. Crouch, Carolyn J. Crouch, and Glenn Andreas, The use of cluster hierarchies in hypertext
`information retrieval, Hypertext ’89 Proceedings, ACM Press, pp. 225-237, 1989.
`3 Yvan Leclerc, Steven W. Zucker, Denis Leclerc, McGill University, A browsing approach to
`documentation, IEEE Computer, I