UNITED STATES PATENT AND TRADEMARK OFFICE
`________________
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`________________
`BLOOMREACH, INC.
`Petitioner,
`
`v.
`
`GUADA TECHNOLOGIES LLC
`Patent Owner
`________________
`Patent 7,231,379
`________________
`
`DECLARATION OF PADHRAIC SMYTH, PH.D.
`
`PETITIONERS
`EXHIBIT 1007, Page 1
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`
`________________
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`________________
`BLOOMREACH, INC.
`Petitioner,
`
`v.
`
`GUADA TECHNOLOGIES LLC
`Patent Owner
`________________
`Patent 7,231,379
`________________
`
`DECLARATION OF PADHRAIC SMYTH, PH.D.
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`PETITIONERS
`EXHIBIT 1007, Page 1
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`TABLE OF CONTENTS
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`I.
`II.
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`III.
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`IV.
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`BACKGROUND AND QUALIFICATIONS .............................................. 1
`LEGAL FRAMEWORK .............................................................................. 9
`Obviousness ....................................................................................... 9
`A.
`OPINION .................................................................................................... 16
`Level of Skill of a Person Having Ordinary Skill in the Art ........... 16
`A.
`Background of the Technology ........................................................ 17
`B.
`Obvious to Apply Wesemann to the Claims of the ’379 Patent ...... 25
`C.
`Obvious to Combine Wesemann and Rajaraman ............................ 42
`D.
`Obvious to Apply Fratkina to the Claims of the ’379 Patent ........... 51
`E.
`Obvious to Combine Fratkina and Rajaraman ................................. 59
`F.
`CONCLUSION .......................................................................................... 62
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`-i-
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`I, Padhraic Smyth, hereby declare the following:
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`I.
`
`BACKGROUND AND QUALIFICATIONS
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`1.
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`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 BloomReach, Inc. to provide my independent opinions on certain issues
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`requested by Counsel for Petitioner relating to the accompanying petition for Inter
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`Partes Review of U.S. Patent No. 7,231,379 (“the ’379 Patent”). My compensation
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`in this matter is not based on the substance of my opinions or the outcome of this
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`matter. I have no financial interest in Petitioner. I have been informed that Guada
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`Technologies LLC (“Guada”) is the purported owner of the ’379 Patent, and I note
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`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 current
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`version of my curriculum vitae as Appendix A (Ex. 1009).
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`4.
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`I received a bachelor’s degree in electronic engineering (B.E., first class
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`honors) from the National University of Ireland, Galway, in 1984. I received a
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`master’s degree (M.S.E.E.) and a Ph.D. in electrical engineering from the California
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`Institute of Technology, Pasadena, CA, in 1985 and 1988, respectively. My Ph.D.
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`thesis was focused on the use of hierarchical tree structures and rule-based methods
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`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, CA.
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`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.
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`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 was
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`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. In
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`1998, I was promoted to associate professor with tenure, and in 2003 I was promoted
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`to the position of full professor. I also have a joint faculty appointments in the
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`Department of Statistics and in the Department of Education at UC Irvine. As a
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`professor at UC Irvine since 1996, I have conducted research in the areas of pattern
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`recognition, machine learning, and artificial intelligence.
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`8.
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`In 2007, I was also appointed as the founding director for the Center for
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`Machine Learning and Intelligent Systems at UC Irvine. This Center has over 30
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`affiliated faculty members at UC Irvine whom are all involved in research in areas
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`such as machine learning, database research, and artificial intelligence. In 2014, I
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`was appointed as founding Director of the UC Irvine Data Science Initiative, a cross-
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`campus research initiative involving computer scientists, statisticians, engineers,
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`scientists, medical researchers, and more across the campus.
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`9.
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`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 the
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`areas of data mining and machine learning — titles of courses I have taught in the
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`past few years include Data Mining, Introduction to Artificial Intelligence, Project
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`in Artificial Intelligence, Applications of Probability for Computer Scientists, and
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`Probabilistic Learning. These courses include material related to data structures
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`such as hierarchical trees for automated decision-making and user navigation, design
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`and evaluation of systems for information retrieval, and machine learning algorithms
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`that can adapt and learn from data provided via user 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 consider.
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`These systems are typically constructed from large historical databases, consisting
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`of text data, customer transactions, etc. Over the past 18 years I have consulted in
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`this manner with companies such as AT&T, Samsung, Nokia, First Quadrant,
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`SmithKline Beecham, Yahoo!, eBay, and Netflix, as well as with a number of
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`smaller startup companies. My involvement in consulting projects has given me the
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`opportunity to develop expertise in the practical application of machine learning and
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`information retrieval algorithms, and in particular, to develop an understanding of
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`how these algorithms are deployed within real-world, large-scale software systems
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`that allow users to interact with databases and 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 can
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`extract useful information from large databases of user clickstream and search query
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`data, as well as from text documents related to customer reviews, customer emails,
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`product descriptions, Web page content, and search query data. As part of this work
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`I developed and adapted a variety of mathematical and statistical frameworks that
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`allow a computer to automatically decide what item of information to show to a
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`human user who is interacting with a system and who has a specific goal in mind
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`such as making a purchase or finding information on a Web page. I developed
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`computer algorithms to apply these mathematical and statistical frameworks
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`efficiently both to (a) user data (such as log files of clickstreams recording how users
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`interact with a system) and (b) to text data (such as text from Web pages and
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`documents). I also wrote and tested software code to implement those algorithms in
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`software, and I ran and interpreted computational experiments to evaluate the
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`effectiveness of different approaches.
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`12.
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`I have published 70 journal papers, 18 book chapters, 9 technical
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`magazine articles, and 116 peer-reviewed conference papers related to my research.
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`Several of these publications are among the most highly-cited papers in the general
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`areas of data mining and artificial intelligence — my papers have approximately
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`47,700 citations in total according to Google Scholar. Four of my conference papers
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`received best paper awards at the Association for Computing Machinery (ACM)
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`Conference on Knowledge Discovery and Data Mining, the leading annual
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`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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`15,000 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 6000 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 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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`15.
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`I have served as an associate editor for the Journal of the American
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`Statistical Association, the IEEE Transactions on Knowledge and Data Engineering,
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`and the Machine Learning journal. In addition, I was a founding editorial board
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`member for the journals Bayesian Analysis, the Journal of Machine Learning
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`Research, and the Data Mining and Knowledge Discovery Journal. I have served as
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`a reviewer of papers for all of the major conferences and journals in my field as well
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`a reviewer of proposals for the National Science Foundation and for NASA. I served
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`as program chair for the annual ACM SIGKDD Conference on Knowledge
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`Discovery and Data Mining in 2011.
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`16. As part of my work in connection with this proceeding, I have reviewed
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`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”)
`Hoperoft, 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)
`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,
`interface,
`simulation, and evaluation of a menu driven user
`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)
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`8
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`
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`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
`thesaurus
`to
`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)
`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)
`
`
`II. LEGAL FRAMEWORK
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`A. Obviousness
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`17.
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`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 art.
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`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 claim
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`obvious, counsel has informed me that I can consider the scope and content of the
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`prior art, including the fact that one of skill in the art would regularly look to the
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`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 analysis,
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`the reference must be “analogous art” to the claimed invention. I have been informed
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`that a reference is analogous art to the claimed invention if: (1) the reference is from
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`the same field of endeavor as the claimed invention (even if it addresses a different
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`problem); or (2) the reference is reasonably pertinent to the problem faced by the
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`inventor (even if it is not in the same field of endeavor as the claimed invention). In
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`order for a reference to be “reasonably pertinent” to the problem, it must logically
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`have commended itself to an inventor’s attention in considering his problem. In
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`determining whether a reference is reasonably pertinent, one should consider the
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`problem faced by the inventor, as reflected either explicitly or implicitly, in the
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`specification. I believe that all of the references I considered in forming my opinions
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`in this IPR are well within the range of references a PHOSITA would have consulted
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`to address the type of 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 the
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`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 there
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`was an apparent reason for one of ordinary skill in the art, at the time of the invention,
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`to combine the prior art, which can include, but is not limited to, any of the following
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`rationales: (A) combining prior art methods according to known methods to yield
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`predictable results; (B) substituting one known element for another to obtain
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`predictable results; (C) using a known technique to improve a similar device in the
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`same way; (D) applying a known technique to a known device ready for
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`improvement to yield predictable results; (E) trying a finite number of identified,
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`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 art;
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`or (G) identifying an explicit teaching, suggestion, or motivation in the prior art that
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`would have led one of ordinary skill to modify the prior art reference or to combine
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`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 demands
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`known to the technological community or present in the marketplace, and the
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`background knowledge possessed by a person having ordinary skill in the art. All
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`of these issues may be considered to determine whether there was an apparent reason
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`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 precise
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`teaching directed to the specific subject matter of the challenged claim need not be
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`sought out because it is appropriate to take account of the inferences and creative
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`steps that a PHOSITA would employ. The prior art considered can be directed to
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`any need or problem known in the field of endeavor at the time of invention and can
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`provide a reason for combining the elements of the prior art in the manner claimed.
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`In other words, the prior art need not be directed towards solving the same specific
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`problem as the problem addressed by the patent. Further, the individual prior art
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`references themselves need not all be directed towards solving the same problem. I
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`am informed that, under the KSR obviousness standard, common sense is important
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`and should be considered. Common sense teaches that familiar items may have
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`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 is
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`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 obvious
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`techniques or combinations, and it often may be the case that market demand, rather
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`than scientific literature or knowledge, will drive the design of an invention. I am
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`informed that an invention that is a combination of prior art must do more than yield
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`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) the
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`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 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 invention
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`would not have been obvious in view of these considerations, which include: (a)
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`commercial success of a product due to the merits of the claimed invention; (b) a
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`long-felt, but unsatisfied need for the invention; (c) failure of others to find the
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`solution provided by the claimed invention; (d) deliberate copying of the invention
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`by others; (e) unexpected results achieved by the invention; (f) praise of the
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`invention by others skilled in the art; (g) lack of independent simultaneous invention
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`within a comparatively short space of time; (h) teaching away from the invention in
`
`the prior art.
`
`27.
`
`I am further informed that secondary-considerations evidence is only
`
`relevant if the offering party establishes a connection, or nexus, between the
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`15
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`PETITIONERS
`EXHIBIT 1007, Page 17
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`
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`evidence and the claimed invention. The nexus cannot be based on prior art features.
`
`The establishment of a nexus is a question of fact. While I understand that the Patent
`
`Owner here has not offered any secondary considerations at this time, I will
`
`supplement my opinions in the event that the Patent Owner raises secondary
`
`considerations during the course of this proceeding.
`
`III. OPINION
`
`A. Level of Skill of a Person Having Ordinary Skill in the Art
`
`28.
`
`I was asked to provide my opinion as to the level of skill of a person
`
`having ordinary skill in the art (“PHOSITA”) of the ’379 Patent at the time of the
`
`claimed invention, which counsel has informed me to assume is November 19, 2002,
`
`the filing date of the ’379 Patent. In determining the characteristics of a hypothetical
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`person of ordinary skill in the art of the ’379 Patent at the time of the claimed
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`invention, I was told to consider several factors, including the type of problems
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`encountered in the art, the solutions to those problems, the rapidity with which
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`innovations are made in the field, the sophistication of the technology, and the
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`education level of active workers in the field. I also placed myself back in the time
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`frame of the claimed invention, and considered the colleagues with whom I had
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`worked at that time.
`
`29.
`
`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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`16
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`PETITIONERS
`EXHIBIT 1007, Page 18
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`bachelor’s degree in computer science, electrical engineering, or a similar discipline,
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`and at least one year of experience working with technology related to information
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`retrieval and database searching, or an equivalent amount of similar work experience
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`or education, with additional education substituting for experience and vice versa.
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`Such a person of ordinary skill in the art would have been capable of understanding
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`the ’379 patent and the prior art references discussed herein.
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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.
`
`B.
`
`31.
`
`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.
`
`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,
`
`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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`17
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`PETITIONERS
`EXHIBIT 1007, Page 19
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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.
`
`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 2002:
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`Organizing a network as a hierarchical structure, such as a network-based menu
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`having different nodes representing categories of information,2 allowing users to
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`navigate between nodes or vertexes using key terms or node descriptors,3 and
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`automatically searching a tree to direct a user to a node or vertex associated with a
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`key term or descriptor associated with that node without traversing through other
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`intervening nodes in the hierarchy.4
`
`
`1 Hoperoft. 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, IEEE Press, pp. 46-49, 1982.
`4 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
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`18
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`PETITIONERS
`EXHIBIT 1007, Page 20
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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 multiple
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`different ways that an algorithm can search a tree to identify nodes of interest and
`
`perform other operations. The different types of tree search algorithms are among
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`the basic concepts that have been taught in introductory computer science courses
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`since the 1980’s and earlier.5
`
`35. For example, one of the more well-known and efficient strategies for
`
`searching a tree is the top-down depth-first approach.6 In the context of a user
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`searching for information where the information is represented by a tree structure,
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`the algorithm begins the search by evaluating the similarity of the item to be matched
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`(such as input from a user in the form of a keyword or query) to a set of possible
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`matches (represented as branches) at the root node of the tree. If one or more
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`branches match the input, the best
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