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`BEFORE THE PATENT TRIAL AND APPEAL BOARD
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`OHIO FARMERS INSURANCE COMPANY and PREGIS LLC
`Petitioners,
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`v.
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`GUADA TECHNOLOGIES LLC,
`Patent Owner.
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`Patent No. 7,231,379
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`DECLARATION OF PADHRAIC SMYTH, PH.D.
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`PETITIONERS - EXHIBIT 1007
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`IPR2022-00217
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`TABLE OF CONTENTS
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`I.
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`II.
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`BACKGROUND AND QUALIFICATIONS .................................................................... 1
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`LEGAL FRAMEWORK .................................................................................................... 8
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`A.
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`Obviousness ............................................................................................................ 8
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`III.
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`OPINION .......................................................................................................................... 14
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`A.
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`B.
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`C.
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`D.
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`E.
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`F.
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`Level of Skill of a Person Having Ordinary Skill in the Art ................................ 14
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`Background of the Technology ............................................................................. 15
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`Obvious to Apply Wesemann to the Claims of the ‘379 Patent ........................... 21
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`Obvious to Combine Wesemann and Rajaraman ................................................. 37
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`Obvious to Apply Fratkina to the Claims of the ‘379 Patent ................................ 44
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`Obvious to Combine Fratkina and Rajaraman ...................................................... 51
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`IV.
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`CONCLUSION ................................................................................................................. 53
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`i
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`I, Padhraic Smyth, hereby declare the following:
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`I.
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`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 otherwise
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`competent to make this Declaration. I make this Declaration based on facts and matters within
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`my own knowledge and on information provided to me by others, and, if called as a witness, I
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`could and would competently testify to the 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 University of
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`California, Irvine. I have been retained as a technical expert witness in this matter by Counsel for
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`Petitioners Ohio Farmers Insurance Company and Pregis LLC to provide my independent
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`opinions on certain issues requested by Counsel for Petitioners relating to the accompanying
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`petition for 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 outcome of this
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`matter. I have no financial interest in either 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 that I have no
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`financial interest in Guada.
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`3.
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`I have summarized in this section my educational background, career history, and
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`other qualifications relevant to this matter. I have also included a current version of my
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`curriculum vitae as EX1009.
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`4.
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`I received a bachelor’s degree in electronic engineering (B.E., first class honors)
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`from the National University of Ireland, Galway, in 1984. I received a master’s degree
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`(M.S.E.E.) and a Ph.D. in electrical engineering from the California Institute of Technology,
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`Pasadena, CA, in 1985 and 1988, respectively. My Ph.D. thesis was focused on the use of
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`hierarchical tree structures and rule- based methods for automated and efficient classification of
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`objects into categories.
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`1
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`
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`5.
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`From 1988 to 1996, I was a technical staff member and technical group leader
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`(from 1992 onwards) at the Jet Propulsion Laboratory (JPL) in Pasadena, CA. My role at JPL
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`consisted of research and development in the areas of pattern recognition, machine learning, data
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`mining, and expert systems, as well as leading projects involved in the application of these
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`techniques to problems of interest to 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 1988-
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`1996 at multiple different conferences in the areas of pattern recognition, machine learning, and
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`artificial intelligence. One example of my research work was my involvement in the emerging
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`research area of “knowledge discovery in databases” (KDD). This began as a small research
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`workshop in 1989 and quickly evolved into a large annual international conference (with the first
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`conference in 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 of databases,
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`statistics, and machine learning algorithms. I was involved with the KDD research field both as a
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`researcher (writing and presenting papers), in the organization of the conference, and in co-
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`editing the first text on knowledge discovery from databases (published by MIT Press in 1996,
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`see discussion of publications below).
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`7.
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`In 1996, I moved from JPL to the University of California, Irvine, to take a
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`position as an assistant professor in the Department of Computer Science. In 1998, I was
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`promoted to associate professor with tenure, and in 2003 I was promoted to the position of full
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`professor. I also have joint faculty appointments in the Department of Statistics and in the
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`Department of Education at UC Irvine. As a professor at UC Irvine since 1996, I have conducted
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`research in the areas of pattern recognition, machine learning, and artificial intelligence.
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`2
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`8.
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`In 2007, I was also appointed as the founding director for the Center for Machine
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`Learning and Intelligent Systems at UC Irvine. This Center has over 30 affiliated faculty
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`members at UC Irvine who are all involved in research in areas such as machine learning,
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`database research, and artificial intelligence. Since 2014 I have been an Associate Director of the
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`Center. In 2014, I was appointed as founding Director of the UC Irvine Data Science Initiative, a
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`cross-campus research initiative involving computer scientists, statisticians, engineers, scientists,
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`medical researchers, and more across the campus. Since 2018 I have been an Associate Director
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`of the Initiative.
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`9.
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`My teaching duties have consisted of teaching both undergraduate and graduate
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`courses in the Computer Science department, with a focus on courses in the areas of data mining
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`and machine learning — titles of courses I have taught include Data Mining, Introduction to
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`Artificial Intelligence, Project in Artificial Intelligence, Applications of Probability for Computer
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`Scientists, and 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 and
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`evaluation of systems for information retrieval, and machine learning algorithms that can adapt
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`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 industry in the
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`areas of machine learning and pattern recognition. My consulting work often involves the
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`development of mathematical models, algorithms, and software for companies who wish to
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`develop and deploy operational systems that can autonomously interact with a user, such as
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`recommending (on a Web site) the next item to a user from a large catalog of potential items they
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`may wish to consider. These systems are typically constructed from large historical databases,
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`consisting of text data, customer transactions, etc. Over the past 22 years or so I have consulted
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`3
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`
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`in this manner with companies such as AT&T, Samsung, Nokia, First Quadrant, SmithKline
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`Beecham, Yahoo!, eBay, and Netflix, as well as with a number of smaller startup companies. My
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`involvement in consulting projects has given me the opportunity to develop expertise in the
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`practical application of machine learning and information retrieval algorithms, and in particular,
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`to develop an understanding of how these algorithms are deployed within real-world, large-scale
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`software systems that allow users to interact with databases and websites.
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`11.
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`As part of my real-world consulting projects over the past 18 years (at Samsung,
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`Yahoo!, eBay, and others), I have had direct experience with the development of systems that
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`use automated algorithms to assist a user who is interacting with a system and has a specific goal
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`in mind, such as finding a specific item of information. This includes experience with methods
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`and algorithms that can extract useful information from large databases of user clickstream and
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`search query 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 I developed
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`and adapted a variety of mathematical and statistical frameworks that allow a computer to
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`automatically decide what item of information to show to a human user who is interacting with a
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`system and who has a specific goal in mind such as making a purchase or finding information on
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`a Web 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 recording how
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`users interact with a system) and (b) to text data (such as text from Web pages and documents). I
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`also wrote and tested software code to implement those algorithms in software, and I ran and
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`interpreted computational experiments to evaluate the effectiveness of different approaches.
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`12.
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`I have published 82 journal papers, 18 book chapters, 13 technical magazine
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`articles, and 120 peer-reviewed conference papers related to my research. Several of these
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`4
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`publications are among the most highly-cited papers in the general areas of data mining and
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`artificial intelligence — my papers have 58,366 citations in total according to Google Scholar.
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`Four of my 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 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 Mining
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`(AAAI/MIT Press, 1996), which is considered the first book published on the topic of automated
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`extraction of information from large databases and has over 18,000 citations according to Google
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`Scholar. I also co-authored Principles of Data Mining (MIT Press, 2001), which is widely used
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`as a graduate textbook in data mining courses, has over 7800 citations, and has been translated
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`into Chinese and Polish editions. This text contains material describing the general principles of
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`(a) hierarchical tree structures (chapter 9 on tree-structured clustering, and chapter 10 on tree-
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`structured classification) and (b) information retrieval and text analysis (chapter 14). I also co-
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`authored the text Modeling the Internet and the Web: Probabilistic Methods and Algorithms
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`(Wiley, 2003). This text contains material describing the general principles of text analysis and
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`information retrieval (Chapter 4), tree and graph-based models for representing text information
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`(Chapter 5), and analysis of how human users interact with information retrieval systems
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`(Chapter 7).
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`14.
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`I have been elected as a Fellow of the Association of Computing Machinery
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`(ACM), in 2013, and also elected a Fellow of the Association for Advancement of Artificial
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`Intelligence (AAAI), in 2010. I received the Innovation Award from ACM SIGKDD (Special
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`Interest Group in Knowledge Discovery and Data Mining) in 2009. This award is given annually
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`by ACM to an individual who has made significant contributions to the research and practice of
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`5
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`
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`data mining. The award is the most prestigious annual award given to a researcher in the data
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`mining research field. I also received a National Science Foundation CAREER award, a Google
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`Faculty Research Award, an IBM Faculty Partnership Award, the Lew Allen Award for
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`Research at JPL, and a Qualcomm Faculty Award.
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`15.
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`I have served as an associate editor for the Journal of the American Statistical
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`Association, the IEEE Transactions on Knowledge and Data Engineering, and the Machine
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`Learning journal. In addition, I was a founding editorial board member for the journals Bayesian
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`Analysis, the Journal of Machine Learning Research, and the Data Mining and Knowledge
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`Discovery Journal. I have served as a reviewer of papers for all of the major conferences and
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`journals in my 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 Knowledge
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`Discovery and Data Mining in 2011.
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`16.
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`As part of my work in connection with this proceeding, I have reviewed the
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`following materials:
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`• U.S. Patent No. 7,231,379 to Parikh et al. (EX1001);
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`• File History for U.S. Patent No. 7,231,379 (EX1002)
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`• U.S. Patent No. 6,731,724 to Wesemann et al. (EX1004) (“Wesemann”)
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`• U.S. Patent No. 6,366,910 to Rajaraman et al. (EX1005) (“Rajaraman”)
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`• U.S. Patent No. 7,539,656 to Fratkina et al. (EX1006) (“Fratkina”)
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`• Hoperoft et al.. Data Structures and Algorithms. Boston, MA, USA, Addison-
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`Wesley, pp. 75-106, 155-197, 306-346, 1983 (EX1010)
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`• Crouch et al., The use of cluster hierarchies in hypertext information retrieval,
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`Hypertext `89 Proceedings, ACM Press, pp. 225-237, 1989 (EX1011)
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`
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`6
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`
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`• Leclerc et al., A browsing approach to documentation, IEEE Computer, IEEE
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`Press, pp 46-49, 1982 (EX1012)
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`• Savage et al., The design, simulation, and evaluation of a menu driven user
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`interface, Proceedings of the 1982 Conference on Human Factors in Computing
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`Systems, ACM Press, pp 36-40, 1982 (EX1013)
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`• Baeza-Yates et al., Modern Information Retrieval, pp. 24-40, ACM Press, 1999
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`(EX1014)
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`• Cunliffe et al., Query-based navigation in semantically indexed hypermedia,
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`Proceedings of the Eighth ACM Conference on Hypertext, pp. 87-95, ACM Press,
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`1997 (EX1015)
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`• Hornstein, Telephone Voice Interfaces on the Cheap, §2.3, Proceedings of the
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`UBLAB `94 Conference, 1994 (EX1016)
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`• De Bra et al., Information Retrieval in Distributed Hypertexts, in RIAO, pp. 481-
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`493, 1995 (EX1017)
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`• U.S. Pat. No. 6,198,939 to Holmström et al. (EX1018)
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`• Jones, A look back and a look forward, Proceedings of the 11th ACM SIGIR
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`International Conference on Research and Development in Information Retrieval,
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`pp. 13-29, ACM Press, 1988 (EX1019)
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`• Salton et al., A vector space model for automatic indexing, Communications of
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`the ACM, 18(11): 613-620, 1975 (EX1020)
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`• Xu et al., Query expansion using local and global document analysis, Proceedings
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`of the 19th ACM SIGIR International Conference on Research and Development
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`in Information Retrieval, pp. 4-11. ACM, 1996 (EX1021)
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`
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`7
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`
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`• Crouch, A cluster-based approach to thesaurus construction, Proceedings of the
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`11th ACM SIGIR International Conference on Research and Development in
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`Information Retrieval pp. 309-320. ACM, 1988 (EX1022)
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`• Schiitze et al., A cooccurrence-based thesaurus and two applications to
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`information retrieval, 1 Intelligent Multimedia Information Retrieval Systems and
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`Management, pp. 266-274, 1994 (EX1023)
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`• Giintzer et al., Automatic Thesaurus Construction by Machine Learning from
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`Retrieval Sessions, 25 Information Processing & Management No. 3 pp. 265-273,
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`1998 (EX1024)
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`• Mostafa et al., A Multilevel Approach to Intelligent Information Filtering: Model,
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`System, and Evaluation, 15 ACM Transactions on Information Systems No. 4, pp.
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`368-399, 1997 (EX1025)
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`• U.S. Pat. No. 6,006,225 to Bowman et al. (EX1026)
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`• Salton, The Evaluation Of Automatic Retrieval Procedures— Selected Test
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`Results Using the SMART System, American Documentation, 16(3), 1965, at 209-
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`222. (EX1029)
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`• Fitzpatrick et al., Automatic Feedback Using Past Queries: Social Searching?, 31
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`ACM SIGIR Forum 1997, at 306-313 (EX1030)
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`II.
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`LEGAL FRAMEWORK
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`A.
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`17.
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`Obviousness
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`I am a technical expert and do not offer any legal opinions. However, counsel has
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`informed me as to certain legal principles regarding patentability and related matters under
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`8
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`United States patent law, which I have applied in performing my analysis and arriving at my
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`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 invention if the
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`differences between the invention and the prior art are such that the subject matter as a whole
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`would have been obvious at the time the invention was made to a person having ordinary skill in
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`the art. I have been informed that a conclusion of obviousness may be founded upon more than a
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`single item of prior art. I have been further informed that obviousness is determined by
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`evaluating the 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 pertinent art, and
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`(4) secondary considerations of non-obviousness. In addition, the obviousness inquiry should not
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`be done in hindsight. Instead, the obviousness inquiry should be done through the eyes of a
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`PHOSITA at the time of the alleged invention.
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`19.
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`In considering whether certain prior art renders a particular patent claim obvious,
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`counsel has informed me that I can consider the scope and content of the prior art, including the
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`fact that one of skill in the art would regularly look to the disclosures in patents, trade
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`publications, journal articles, conference papers, industry standards, product literature and
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`documentation, texts describing competitive technologies, requests for comment published by
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`standard setting organizations, and materials from industry conferences, as examples. I have
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`been informed that for a prior art reference to be proper for use in an obviousness analysis, the
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`reference must be “analogous art” to the claimed invention. I have been informed that a reference
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`is analogous art to the claimed invention if: (1) the reference is from the same field of endeavor
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`as the claimed invention (even if it addresses a different problem); or (2) the reference is
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`reasonably pertinent to the problem faced by the inventor (even if it is not in the same field of
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`9
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`endeavor as the 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 considering his
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`problem. In 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 specification. I
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`believe that all of the references I considered in forming my opinions in this IPR are well within
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`the range of references a PHOSITA would have consulted to address the type of problems
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`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 was
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`obvious based on a combination of prior art elements, a clear articulation of the reason(s) why a
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`claimed invention would have been obvious must be provided. Specifically, I am informed that,
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`under the U.S. Supreme Court’s KSR decision, a combination of multiple items of prior art
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`renders a patent claim obvious when there was an apparent reason for one of ordinary skill in the
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`art, at the time of the invention, to combine the prior art, which can include, but is not limited to,
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`any of the following rationales: (A) combining prior art methods according to known methods to
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`yield predictable results; (B) substituting one known element for another to obtain predictable
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`results; (C) using a known technique to improve a similar device in the same way; (D) applying a
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`known technique to a known device ready for improvement to yield predictable results; (E)
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`trying a finite number of identified, predictable potential solutions, with a reasonable expectation
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`of success; (F) identifying that known work in one field of endeavor may prompt variations of it
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`for use in either the same field or a different one based on design incentives or other market
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`forces if the variations are predictable to one of ordinary skill in the art; or (G) identifying an
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`explicit teaching, suggestion, or motivation in the prior art that would have led one of ordinary
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`10
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`skill to modify the prior art reference or to combine the prior art references to arrive at the
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`claimed invention.
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`21.
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`I am informed that the existence of an explicit teaching, suggestion, or motivation
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`to combine known elements of the prior art is a sufficient, but not a necessary, condition to a
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`finding of obviousness. This so-called “teaching- suggestion-motivation” test is not the exclusive
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`test and is not to be applied rigidly in an obviousness analysis. In determining whether the
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`subject matter of a patent claim is obvious, neither the particular motivation nor the avowed
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`purpose of the 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 invalid. I am
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`further informed that the obviousness analysis often necessitates consideration of the interrelated
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`teachings of multiple patents, the effects of demands known to the technological community or
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`present in the marketplace, and the background knowledge possessed by a person having
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`ordinary skill in the art. All of these issues may be considered to determine whether there was an
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`apparent 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 precise teaching
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`directed to the specific subject matter of the challenged claim need not be sought out because it
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`is appropriate to take account of the inferences and creative steps that a PHOSITA would
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`employ. The prior art considered can be directed to any need or problem known in the field of
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`endeavor at the time of invention and can provide a reason for combining the elements of the
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`prior art in the manner claimed. In other words, the prior art need not be directed towards solving
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`the same specific problem as the problem addressed by the patent. Further, the individual prior
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`art references themselves need not all be directed towards solving the same problem. I am
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`informed that, under the KSR obviousness standard, common sense is important and should be
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`11
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`considered. Common sense teaches that familiar items may have obvious uses beyond their
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`primary purposes.
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`23.
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`I also am informed that the fact that a particular combination of prior art elements
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`was “obvious to try” may indicate that the combination was obvious even if no one attempted the
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`combination. If the combination was obvious to try (regardless of whether it was actually tried)
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`or leads to anticipated success, then it is likely the result of ordinary skill and common sense
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`rather than innovation. I am further informed that in many fields it may be that there is little
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`discussion of 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 invention. I am
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`informed that an invention that is a combination of prior art must do more than yield predictable
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`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 obvious to
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`a PHOSITA at the time of the alleged invention. I am informed that the factors to consider in
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`determining the level of ordinary skill in the art include (1) the educational level and experience
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`of people working in the field at the time the invention was made, (2) the types of problems
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`faced in the art and the solutions found to those problems, and (3) the sophistication of the
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`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 references
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`teach away from their combination. I am informed that a reference may be said to teach away
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`when a PHOSITA, upon reading the reference, would be discouraged from following the path set
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`out in the reference, or would be led in a direction divergent from the path that was taken by the
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`patent applicant. In general, a reference will teach away if it suggests that the line of
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`development flowing from the reference’s disclosure is unlikely to be productive of the result
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`12
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`sought by the 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 leave the
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`impression that the product would not have the property sought by the patentee. I also am
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`informed, however, that a reference does not teach away if it merely expresses a general
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`preference for an alternative invention but does not criticize, discredit, or otherwise discourage
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`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 established, the
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`final determination of obviousness must also consider “secondary considerations” if presented.
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`In most instances, the patentee raises these secondary considerations of non-obviousness. In that
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`context, the patentee argues an invention would not have been obvious in view of these
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`considerations, which include: (a) commercial success of a product due to the merits of the
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`claimed invention; (b) a long-felt, but unsatisfied need for the invention; (c) failure of others to
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`find the solution provided by the claimed invention; (d) deliberate copying of the invention by
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`others; (e) unexpected results achieved by the invention; (f) praise of the invention by others
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`skilled in the art; (g) lack of independent simultaneous invention within a comparatively short
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`space of time; (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 relevant if
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`the offering party establishes a connection, or nexus, between the evidence and the claimed
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`invention. The nexus cannot be based on prior art features. The establishment of a nexus is a
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`question of fact. While I understand that the Patent Owner here has not offered any secondary
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`considerations at this time, I will supplement my opinions in the event that the Patent Owner
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`raises secondary considerations during the course of this proceeding.
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`
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`13
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`
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`III. OPINION
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`A.
`
`28.
`
`Level of Skill of a Person Having Ordinary Skill in the Art
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`I was asked to provide my opinion as to the level of skill of a person having
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`ordinary skill in the art (“PHOSITA”) of the ‘379 patent at the time of the claimed invention,
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`which counsel has informed me to assume is November 19, 2002, the filing date of the ‘379
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`patent. In determining the characteristics of a hypothetical person of ordinary skill in the art of
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`the ‘379 patent at the time of the claimed invention, I was told to consider several factors,
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`including the type of problems encountered in the art, the solutions to those problems, the
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`rapidity with 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 time frame of
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`the claimed invention, and considered the colleagues with whom I had worked at that time.
`
`29.
`
`In my opinion, a person having ordinary skill in the art of the ‘379 patent at the
`
`time of its filing would have been a person having the equivalent of a bachelor’s degree in
`
`computer science, electrical engineering, or a similar discipline, and at least one year of
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`experience working with technology related to information retrieval and database searching, or
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`an equivalent amount of similar work experience or education, with additional education
`
`substituting for experience and vice versa. Such a person of ordinary skill in the art would have
`
`been capable of understanding the ‘379 patent and the prior art references discussed herein.
`
`30.
`
`Based on my education, training, and professional experience in the field of the
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`claimed invention, I am familiar with the level and abilities of a person of ordinary skill in the art
`
`at the time of the claimed invention. Additionally, I met at least these minimum qualifications to
`
`be a person having ordinary skill in the art as of the time of the claimed invention of the ‘379
`
`patent.
`
`
`
`14
`
`
`
`B.
`
`31.
`
`Background of the Technology
`
`I was asked to briefly summarize the background of the prior art from the
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`standpoint of the knowledge of a PHOSITA prior to November 19, 2002, the filing date of the
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`‘379 patent.
`
`32.
`
`As the ‘379 patent shows in its “Background of the Invention,” the 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 ‘379 patent also recognizes that
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`one “familiar” application of such a hierarchical system is an automated telephone voice
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`response system. See EX1001, 1:40-45. The ‘379 patent also acknowledges that “travers[ing] the
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`network in the most efficient manner possible” is a desirable feature of hierarchical navigation
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`systems. Id., 1:23-26; see also id., 2:9-18.
`
`33.
`
`The representation of interconnected nodes in a hierarchical network, such as a
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`tree structure like that described in the ‘379 patent, has long been well known in the art and has
`
`long been a subject covered in introductory computer science courses well before the ‘379
`
`patent’s November 2002 filing date.1 In particular, all of the following would have been familiar
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`to a PHOSITA before 2002: Organizing a network as a hierarchical structure, such as a network-
`
`based menu 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 automatically
`
`
`
`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.
`
`
`
`15
`
`
`
`searching a tree to direct a user to a node or vertex associated with a key term or descriptor
`
`associated with that node without traversing through other intervening nodes in the hierarchy.4
`
`34.
`
`In such hierarchical tree structures, where the root node contains all objects, the
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`children of the root node contain disjoint subsets of objects, and so on, down to leaf nodes. By
`
`2002, a PHOSITA would have known that there are multiple 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 the basic concepts that have been taught in introductory
`
`computer science courses since the 1980s and earlier.5
`
`35.
`
`For example, one of the more well-known and efficient strategies for searching a
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`tree is the top-down depth-first approach.6 In the context of a user searching for information
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`where the information is represented by a tree structure, the algorithm begins the search by
`
`evaluating the similarity of the item to be matched (such as input from a user in the form of a
`
`keyword or query) to a set of possible matches (represented as branches) at the root node of the
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`tree. If one or more branches match the input, the best such match is selected, the algorithm
`
`descends the tree to the child node, and the matching process is repeated at the child node,
`
`potentially requesting additional information from the user along the path. If there is no adequate
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`match at the root node, or at a subsequent child node along the search path, the search algorithm
`
`can halt and return the best result(s) found to that point or no result. If the algorithm reaches a
`
`
`
`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