`________________
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`________________
`ARGOS USA LLC, DOLE FOOD COMPANY, INC., THE GILLETTE
`COMPANY, LLC, MILACRON LLC, PLY GEM INDUSTRIES, INC.,
`REVLON CONSUMER PRODUCTS CORPORATION, CALPINE
`CORPORATION, WATTS WATER TECHNOLOGIES, INC., LIBERTY
`MUTUAL INSURANCE COMPANY, INTERNATIONAL PAPER
`COMPANY, STATE INDUSTRIAL PRODUCTS CORP., BASSETT
`FURNITURE INDUSTRIES, INC.,
`Petitioners,
`
`v.
`
`GUADA TECHNOLOGIES LLC,
`Patent Owner.
`Case No. IPR2021-00771
`
`Patent No. 7,231,379
`
`_____________________________________________________________
`
`DECLARATION OF PADHRAIC SMYTH
`
`1
`
`EX1007
`
`
`
`TABLE OF CONTENTS
`
` BACKGROUND AND QUALIFICATIONS .................................................... 3
`
` LEGAL FRAMEWORK ................................................................................... 12
`
`A. Obviousness ................................................................................................. 12
`
` OPINION ........................................................................................................ 18
`
`A. Level of Skill of a Person Having Ordinary Skill in the Art ....................... 18
`
`B. Background of the Technology ................................................................... 20
`
`i. Navigation of Hierarchical Structures through “Jumping” ......................... 22
`
`ii. Using and Generating Key-term Thesauruses ......................................... 25
`
`C. Obvious to Apply Wesemann to the Claims of the ’379 Patent ................. 28
`
`D. Obvious to Combine Wesemann and Rajaraman ........................................ 46
`
`E. Obvious to Apply Fratkina to the Claims of the ’379 Patent ...................... 56
`
`F. Obvious to Combine Fratkina and Rajaraman ............................................... 65
`
` CONCLUSION .............................................................................................. 67
`
`
`
`
`
`
`
`
`2
`
`
`
`I, Padhraic Smyth, hereby declare the following:
`
`
`
`BACKGROUND AND QUALIFICATIONS
`1. My name is Padhraic Smyth and I am over 21 years of age and
`
`otherwise competent to make this Declaration. I make this Declaration based on facts
`
`and matters within my own knowledge and on information provided to me by others,
`
`and, if called as a witness, I could and would competently testify to the matters set
`
`forth herein.
`
`2.
`
`I am a Professor in the Department of Computer Science at the
`
`University of California, Irvine. I have been retained as a technical expert witness in
`
`this matter by Counsel for Petitioners Argos USA LLC, Dole Food Company, Inc.,
`
`The Gillette Company, LLC, Milacron LLC, Ply Gem Industries, Inc., Revlon
`
`Consumer Products Corporation, Calpine Corporation, Watts Water Technologies,
`
`Inc., Liberty Mutual Insurance Company, International Paper Company, State
`
`Industrial Products Corp., and Bassett Furniture Industries, Inc. (collectively,
`
`“Petitioners”) to provide my independent opinions on certain issues requested by
`
`Counsel for Petitioners relating to the accompanying petition for Inter Partes
`
`Review of U.S. Patent No. 7,231,379 (“the ’379 Patent”). My compensation in this
`
`matter is not based on the substance of my opinions or the outcome of this matter. I
`
`have no financial interest in Petitioners. I have been informed that Guada
`
`3
`
`
`
`Technologies LLC (“Guada”) is the purported owner of the ’379 Patent, and I note
`
`that I have no financial interest in Guada.
`
`3.
`
`I have summarized in this section my educational background, career
`
`history, and other qualifications relevant to this matter. I have also included a current
`
`version of my curriculum vitae as Ex. 1009.
`
`4.
`
`I received a bachelor’s degree in electronic engineering (B.E., first class
`
`honors) from the National University of Ireland, Galway, in 1984. I received a
`
`master’s degree (M.S.E.E.) and a Ph.D. in electrical engineering from the California
`
`Institute of Technology, Pasadena, CA, in 1985 and 1988, respectively. My Ph.D.
`
`thesis was focused on the use of hierarchical tree structures and rule-based methods
`
`for automated and efficient classification of objects into categories.
`
`5.
`
`From 1988 to 1996, I was a technical staff member and technical group
`
`leader (from 1992 onwards) at the Jet Propulsion Laboratory (JPL) in Pasadena, CA.
`
`My role at JPL consisted of research and development in the areas of pattern
`
`recognition, machine learning, data mining, and expert systems, as well as leading
`
`projects involved in the application of these techniques to problems of interest to
`
`JPL and NASA.
`
`6.
`
`As part of my work, I published and presented papers during the period
`
`1988-1996 at multiple different conferences in the areas of pattern recognition,
`
`machine learning, and artificial intelligence. One example of my research work was
`
`4
`
`
`
`my involvement in the emerging research area of “knowledge discovery in
`
`databases” (KDD). This began as a small research workshop in 1989 and quickly
`
`evolved into a large annual international conference (with the first conference in
`
`2004 and continuing annually since then). The research area was somewhat unique
`
`in that it involved an interdisciplinary set of researchers working at the intersection
`
`of databases, statistics, and machine learning algorithms. I was involved with the
`
`KDD research field both as a researcher (writing and presenting papers), in the
`
`organization of the conference, and in co-editing the first text on knowledge
`
`discovery from databases (published by MIT Press in 1996). See discussion of
`
`publications below.
`
`7.
`
`In 1996, I moved from JPL to the University of California, Irvine, to
`
`take a position as an assistant professor in the Department of Computer Science. In
`
`1998, I was promoted to associate professor with tenure, and in 2003 I was promoted
`
`to the position of full professor. I also have joint faculty appointments in the
`
`Department of Statistics and in the Department of Education at UC Irvine. As a
`
`professor at UC Irvine since 1996, I have conducted research in the areas of pattern
`
`recognition, machine learning, and artificial intelligence.
`
`8.
`
`In 2007, I was also appointed as the founding director for the Center for
`
`Machine Learning and Intelligent Systems at UC Irvine. This Center has over 30
`
`affiliated faculty members at UC Irvine whom are all involved in research in areas
`
`5
`
`
`
`such as machine learning, database research, and artificial intelligence. In 2014, I
`
`was appointed as founding Director of the UC Irvine Data Science Initiative, a cross-
`
`campus research initiative involving computer scientists, statisticians, engineers,
`
`scientists, medical researchers, and more across the campus.
`
`9. My teaching duties have consisted of teaching both undergraduate and
`
`graduate courses in the Computer Science department, with a focus on courses in the
`
`areas of data mining and machine learning — titles of courses I have taught in the
`
`past few years include Data Mining, Introduction to Artificial Intelligence, Project
`
`in Artificial Intelligence, Applications of Probability for Computer Scientists, and
`
`Probabilistic Learning. These courses include material related to data structures such
`
`as hierarchical trees for automated decision-making and user navigation, design and
`
`evaluation of systems for information retrieval, and machine learning algorithms that
`
`can adapt and learn from data provided via user input.
`
`10.
`
`In addition to my duties at UC Irvine, I also consult with private
`
`industry in the areas of machine learning and pattern recognition. My consulting
`
`work often involves the development of mathematical models, algorithms, and
`
`software for companies who wish to develop and deploy operational systems that
`
`can autonomously interact with a user, such as recommending (on a Web site) the
`
`next item to a user from a large catalog of potential items they may wish to consider.
`
`These systems are typically constructed from large historical databases, consisting
`
`6
`
`
`
`of text data, customer transactions, etc. Over the past 18 years I have consulted in
`
`this manner with companies such as AT&T, Samsung, Nokia, First Quadrant,
`
`SmithKline Beecham, Yahoo!, eBay, and Netflix, as well as with a number of
`
`smaller startup companies. My involvement in consulting projects has given me the
`
`opportunity to develop expertise in the practical application of machine learning and
`
`information retrieval algorithms, and in particular, to develop an understanding of
`
`how these algorithms are deployed within real-world, large-scale software systems
`
`that allow users to interact with databases and websites.
`
`11. As part of my real-world consulting projects over the past 18 years (at
`
`Samsung, Yahoo!, eBay, and others), I have had direct experience with the
`
`development of systems that use automated algorithms to assist a user who is
`
`interacting with a system and has a specific goal in mind, such as finding a specific
`
`item of information. This includes experience with methods and algorithms that can
`
`extract useful information from large databases of user clickstream and search query
`
`data, as well as from text documents related to customer reviews, customer emails,
`
`product descriptions, Web page content, and search query data. As part of this work
`
`I developed and adapted a variety of mathematical and statistical frameworks that
`
`allow a computer to automatically decide what item of information to show to a
`
`human user who is interacting with a system and who has a specific goal in mind
`
`such as making a purchase or finding information on a Web page. I developed
`
`7
`
`
`
`computer algorithms to apply these mathematical and statistical frameworks
`
`efficiently both to (a) user data (such as log files of clickstreams recording how users
`
`interact with a system) and (b) to text data (such as text from Web pages and
`
`documents). I also wrote and tested software code to implement those algorithms in
`
`software, and I ran and interpreted computational experiments to evaluate the
`
`effectiveness of different approaches.
`
`12.
`
`I have published 82 journal papers, 18 book chapters, 9 technical
`
`magazine articles, and 119 peer-reviewed conference papers related to my research.
`
`Several of these publications are among the most highly-cited papers in the general
`
`areas of data mining and artificial intelligence — my papers have approximately
`
`55,790 citations in total according to Google Scholar. Four of my conference papers
`
`received best paper awards at the Association for Computing Machinery (ACM)
`
`Conference on Knowledge Discovery and Data Mining, the leading annual
`
`international conference on data mining.
`
`13.
`
`I co-edited the book Advances in Knowledge Discovery and Data
`
`Mining (AAAI/MIT Press, 1996), which is considered the first book published on
`
`the topic of automated extraction of information from large databases and has over
`
`15,000 citations according to Google Scholar. I also co-authored Principles of Data
`
`Mining (MIT Press, 2001), which is widely used as a graduate textbook in data
`
`mining courses, has over 7,400 citations, and has been translated into Chinese and
`
`8
`
`
`
`Polish editions. This text contains material describing the general principles of (a)
`
`hierarchical tree structures (chapter 9 on tree-structured clustering, and chapter 10
`
`on tree-structured classification) and (b) information retrieval and text analysis
`
`(chapter 14). I also co-authored the text Modeling the Internet and the Web:
`
`Probabilistic Methods and Algorithms (Wiley, 2003). This text contains material
`
`describing the general principles of text analysis and information retrieval (Chapter
`
`4), tree and graph-based models for representing text information (Chapter 5), and
`
`analysis of how human users interact with information retrieval systems (Chapter 7).
`
`14.
`
`I have been elected as a Fellow of the Association of Computing
`
`Machinery (ACM), in 2013, and also elected a Fellow of the Association for
`
`Advancement of Artificial Intelligence (AAAI), in 2010. I received the Innovation
`
`Award from ACM SIGKDD (Special Interest Group in Knowledge Discovery and
`
`Data Mining) in 2009. This award is given annually by ACM to an individual who
`
`has made significant contributions to the research and practice of data mining. The
`
`award is the most prestigious annual award given to a researcher in the data mining
`
`research field. I also received a National Science Foundation CAREER award, a
`
`Google Faculty Research Award, an IBM Faculty Partnership Award, and the Lew
`
`Allen Award for Research at JPL.
`
`15.
`
`I have served as an associate editor for the Journal of the American
`
`Statistical Association, the IEEE Transactions on Knowledge and Data Engineering,
`
`9
`
`
`
`and the Machine Learning journal. In addition, I was a founding editorial board
`
`member for the journals Bayesian Analysis, the Journal of Machine Learning
`
`Research, and the Data Mining and Knowledge Discovery Journal. I have served as
`
`a reviewer of papers for all of the major conferences and journals in my field as well
`
`a reviewer of proposals for the National Science Foundation and for NASA. I served
`
`as program chair for the annual ACM SIGKDD Conference on Knowledge
`
`Discovery and Data Mining in 2011.
`
`16. As part of my work in connection with this proceeding, I have reviewed
`
`the following materials:
`
`17. U.S. Patent 7,231,379 (Ex. 1001 (’379 Patent))
`18. File History for U.S. Patent 7,231,379 (Ex. 1002)
`19. U.S. Patent No. 6,731,724 to Wesemann (Ex. 1004 (Wesemann))
`(“Wesemann”)
`20. U.S. Patent No. 6,366,910 to Rajaraman (Ex. 1005 (Rajaraman))
`(“Rajaraman”)
`21. U.S. Patent No. 7,539,656 to Fratkina (Ex. 1006 (Fratkina))
`(“Fratkina”)
`22. JOHN E. HOPCROFT, JEFFREY D. ULLMAN & ALFRED V. AHO, DATA
`STRUCTURE AND ALGORITHMS 75–106, 155–197, 306–346 (Addison-
`Wesley 1983) (Ex. 1010)
`23. Donald, B. Crouch, Carolyn J. Crouch & Glenn Andreas, The Use Of
`Cluster Hierarchies in Hypertext Info. Retrieval, HYPERTEXT ‘89
`PROC., ACM PRESS, at 225-237, 1989 (Ex. 1011)
`24. Yvan Leclerc, Steven W. Zucker, Denis Leclerc, A Browsing Approach
`to Documentation, IEEE COMPUTER, IEEE PRESS, June 1982, at 46–49
`(Ex. 1012)
`25. Ricky E. Savage, James K. Habinek, Thomas W. Barnhart, The Design,
`Simulation, and Evaluation of a Menu Driven User Interface, PROC. OF
`
`10
`
`
`
`THE 1982 CONF. ON HUMAN FACTORS IN COMPUTING SYS., ACM PRESS,
`March 1982, at 36–41. (Ex. 1013)
`26. RICARDO BAEZA-YATES, BERTHIER RIBIERO-NETO, MODERN INFO.
`RETRIEVAL 24-40 (ACM Press 1999) (Ex. 1014)
`27. Daniel Cunliffe, Carl Taylor, and Douglas Tudhope, Query-Based
`Navigation in Semantically Indexed Hypermedia, PROC. OF THE EIGHTH
`ACM CONF. ON HYPERTEXT, ACM PRESS, 1997, at 87–95 (Ex. 1015)
`28. Hornstein, Telephone Voice Interfaces on the Cheap, PROCEEDINGS OF
`THE UBLAB ‘94 CONF., 1994, at 134-147. (Ex. 1016)
`29. Paul De Bra, et al., Info. Retrieval in Distrib. Hypertexts, RIAO 1994,
`at 481–491, 1995 (Ex. 1017)
`30. U.S. Pat. No. 6,198,939 to Holmstrom (Ex.1018)
`31. Karen Sparck Jones, A Look Back And A Look Forward, PROCEEDINGS
`OF THE 11TH ACM SIGIR INT’L CONF. ON RSCH. AND DEV. IN INFO.
`RETRIEVAL ACM Press, 1988, 14 pages (Ex. 1019)
`32. Gerard Salton, Anita Wong, and Chung-Shu Yang, A Vector Space
`Model For Automatic Indexing, COMMC’NS OF THE ACM, 1975 18(11),
`at 613–620 (Ex. 1020)
`33. Jinxi Xu and W. Bruce Croft, Query Expansion Using Local And
`Global Document Analysis, PROCEEDINGS OF THE 19TH ACM SIGIR
`INT’L CONF. ON RSCH AND DEV. IN INFO. RETRIEVAL ACM, 1996, at 4–
`11 (Ex. 1021)
`34. Carolyn J. Crouch, A Cluster-Based Approach
`to Thesaurus
`Construction, PROC. OF THE 11TH ACM SIGIR INT’L CONF. ON RSCH
`AND DEV. IN INFO. RETRIEVAL, ACM, 1988, at 309–320 (Ex. 1022)
`35. Hinrich Schütze and Jan O. Pedersen, A Cooccurrence-Based
`Thesaurus And Two Applications to Information Retrieval, 1
`INTELLIGENT MULTIMEDIA INFO. RETRIEVAL SYS. AND MGMT., , 1994
`at 266–274 (Ex. 1023)
`36. Güntzer et al., Automatic Thesaurus Construction by Machine
`Learning from Retrieval Sessions, 25 INFO. PROC. & MGMT. No. 3,
`1998, at 265–273, 1998 (Ex. 1024)
`37. Mostafa et al., A Multilevel Approach to Intelligent Information
`Filtering: Model, Sys., and Evaluation, 15 ACM TRANSACTIONS ON
`INFO. SYS. NO. 4, 1997, at 368–399, 1997 (Ex.1025)
`
`11
`
`
`
`38. U.S. Pat. No. 6,006,225 to Bowman et al. (Ex. 1026)
`39. Gerald Salton, The Evaluation Of Automatic Retrieval Procs.—
`the SMART Sys., AMERICAN
`Selected Test Results Using
`DOCUMENTATION, 16(3), 1965, at 209-222. (Ex. 1027)
`40. Larry Fitzpatrick, Mei Dent, Automatic Feedback Using Past Queries:
`Social Searching?, 31 ACM SIGIR FORUM 1997, at 306-313 (Ex. 1028)
`41. U.S. Patent No. 6,453,315 to Weissman et al. (Ex. 1029)
`
`
`
` LEGAL FRAMEWORK
`A. Obviousness
`I am a technical expert and do not offer any legal opinions. However,
`
`42.
`
`counsel has informed me as to certain legal principles regarding patentability and
`
`related matters under United States patent law, which I have applied in performing
`
`my analysis and arriving at my technical opinions in this matter.
`
`43.
`
`I have been informed that a person cannot obtain a patent on an
`
`invention if the differences between the invention and the prior art are such that the
`
`subject matter as a whole would have been obvious at the time the invention was
`
`made to a person having ordinary skill in the art. I have been informed that a
`
`conclusion of obviousness may be founded upon more than a single item of prior art.
`
`I have been further informed that obviousness is determined by evaluating the
`
`following factors: (1) the scope and content of the prior art, (2) the differences
`
`between the prior art and the claim at issue, (3) the level of ordinary skill in the
`
`pertinent art, and (4) secondary considerations of non-obviousness. In addition, the
`
`obviousness inquiry should not be done in hindsight. Instead, the obviousness
`
`12
`
`
`
`inquiry should be done through the eyes of a PHOSITA at the time of the alleged
`
`invention.
`
`44.
`
`In considering whether certain prior art renders a particular patent claim
`
`obvious, counsel has informed me that I can consider the scope and content of the
`
`prior art, including the fact that one of skill in the art would regularly look to the
`
`disclosures in patents, trade publications, journal articles, conference papers,
`
`industry standards, product
`
`literature and documentation,
`
`texts describing
`
`competitive technologies, requests for comment published by standard setting
`
`organizations, and materials from industry conferences, as examples. I have been
`
`informed that for a prior art reference to be proper for use in an obviousness analysis,
`
`the reference must be “analogous art” to the claimed invention. I have been informed
`
`that a reference is analogous art to the claimed invention if: (1) the reference is from
`
`the same field of endeavor as the claimed invention (even if it addresses a different
`
`problem); or (2) the reference is reasonably pertinent to the problem faced by the
`
`inventor (even if it is not in the same field of endeavor as the claimed invention). In
`
`order for a reference to be “reasonably pertinent” to the problem, it must logically
`
`have commended itself to an inventor’s attention in considering his problem. In
`
`determining whether a reference is reasonably pertinent, one should consider the
`
`problem faced by the inventor, as reflected either explicitly or implicitly, in the
`
`specification. I believe that all of the references I considered in forming my opinions
`
`13
`
`
`
`in this IPR are well within the range of references a PHOSITA would have consulted
`
`to address the type of problems described in the Challenged Claims.
`
`45.
`
`I have been informed that, in order to establish that a claimed invention
`
`was obvious based on a combination of prior art elements, a clear articulation of the
`
`reason(s) why a claimed invention would have been obvious must be provided.
`
`Specifically, I am informed that, under the U.S. Supreme Court’s KSR decision, a
`
`combination of multiple items of prior art renders a patent claim obvious when there
`
`was an apparent reason for one of ordinary skill in the art, at the time of the invention,
`
`to combine the prior art, which can include, but is not limited to, any of the following
`
`rationales: (A) combining prior art methods according to known methods to yield
`
`predictable results; (B) substituting one known element for another to obtain
`
`predictable results; (C) using a known technique to improve a similar device in the
`
`same way; (D) applying a known technique to a known device ready for
`
`improvement to yield predictable results; (E) trying a finite number of identified,
`
`predictable potential solutions, with a reasonable expectation of success; (F)
`
`identifying that known work in one field of endeavor may prompt variations of it for
`
`use in either the same field or a different one based on design incentives or other
`
`market forces if the variations are predictable to one of ordinary skill in the art; or
`
`(G) identifying an explicit teaching, suggestion, or motivation in the prior art that
`
`14
`
`
`
`would have led one of ordinary skill to modify the prior art reference or to combine
`
`the prior art references to arrive at the claimed invention.
`
`46.
`
`I am informed that the existence of an explicit teaching, suggestion, or
`
`motivation to combine known elements of the prior art is a sufficient, but not a
`
`necessary, condition to a finding of obviousness. This so-called “teaching-
`
`suggestion-motivation” test is not the exclusive test and is not to be applied rigidly
`
`in an obviousness analysis. In determining whether the subject matter of a patent
`
`claim is obvious, neither the particular motivation nor the avowed purpose of the
`
`patentee controls. Instead, the important consideration is the objective reach of the
`
`claim. In other words, if the claim extends to what is obvious, then the claim is
`
`invalid. I am further informed that the obviousness analysis often necessitates
`
`consideration of the interrelated teachings of multiple patents, the effects of demands
`
`known to the technological community or present in the marketplace, and the
`
`background knowledge possessed by a person having ordinary skill in the art. All of
`
`these issues may be considered to determine whether there was an apparent reason
`
`to combine the known elements in the fashion claimed by the patent.
`
`47.
`
`I also am informed that in conducting an obviousness analysis, a precise
`
`teaching directed to the specific subject matter of the challenged claim need not be
`
`sought out because it is appropriate to take account of the inferences and creative
`
`steps that a PHOSITA would employ. The prior art considered can be directed to
`
`15
`
`
`
`any need or problem known in the field of endeavor at the time of invention and can
`
`provide a reason for combining the elements of the prior art in the manner claimed.
`
`In other words, the prior art need not be directed towards solving the same specific
`
`problem as the problem addressed by the patent. Further, the individual prior art
`
`references themselves need not all be directed towards solving the same problem. I
`
`am informed that, under the KSR obviousness standard, common sense is important
`
`and should be considered. Common sense teaches that familiar items may have
`
`obvious uses beyond their primary purposes.
`
`48.
`
`I also am informed that the fact that a particular combination of prior
`
`art elements was “obvious to try” may indicate that the combination was obvious
`
`even if no one attempted the combination. If the combination was obvious to try
`
`(regardless of whether it was actually tried) or leads to anticipated success, then it is
`
`likely the result of ordinary skill and common sense rather than innovation. I am
`
`further informed that in many fields it may be that there is little discussion of obvious
`
`techniques or combinations, and it often may be the case that market demand, rather
`
`than scientific literature or knowledge, will drive the design of an invention. I am
`
`informed that an invention that is a combination of prior art must do more than yield
`
`predictable results to be non-obvious.
`
`49.
`
`I am informed that for a patent claim to be obvious, the claim must be
`
`obvious to a PHOSITA at the time of the alleged invention. I am informed that the
`
`16
`
`
`
`factors to consider in determining the level of ordinary skill in the art include (1) the
`
`educational level and experience of people working in the field at the time the
`
`invention was made, (2) the types of problems faced in the art and the solutions
`
`found to those problems, and (3) the sophistication of the technology in the field.
`
`50.
`
`I am informed that it is improper to combine references where the
`
`references teach away from their combination. I am informed that a reference may
`
`be said to teach away when a PHOSITA, upon reading the reference, would be
`
`discouraged from following the path set out in the reference, or would be led in a
`
`direction divergent from the path that was taken by the patent applicant. In general,
`
`a reference will teach away if it suggests that the line of development flowing from
`
`the reference’s disclosure is unlikely to be productive of the result sought by the
`
`patentee. I am informed that a reference teaches away, for example, if (1) the
`
`combination would produce a seemingly inoperative device, or (2) the references
`
`leave the impression that the product would not have the property sought by the
`
`patentee. I also am informed, however, that a reference does not teach away if it
`
`merely expresses a general preference for an alternative invention but does not
`
`criticize, discredit, or otherwise discourage investigation into the invention claimed.
`
`51.
`
`I am informed that even if a prima facie case of obviousness is
`
`established, the final determination of obviousness must also consider “secondary
`
`considerations” if presented. In most instances, the patentee raises these secondary
`
`17
`
`
`
`considerations of non-obviousness. In that context, the patentee argues an invention
`
`would not have been obvious in view of these considerations, which include: (a)
`
`commercial success of a product due to the merits of the claimed invention; (b) a
`
`long-felt, but unsatisfied need for the invention; (c) failure of others to find the
`
`solution provided by the claimed invention; (d) deliberate copying of the invention
`
`by others; (e) unexpected results achieved by the invention; (f) praise of the
`
`invention by others skilled in the art; (g) lack of independent simultaneous invention
`
`within a comparatively short space of time; (h) teaching away from the invention in
`
`the prior art.
`
`52.
`
`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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`evidence and the claimed invention. The nexus cannot be based on prior art features.
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`The establishment of a nexus is a question of fact. While I understand that the Patent
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`Owner here has not offered any secondary considerations at this time, I will
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`supplement my opinions in the event that the Patent Owner raises secondary
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`considerations during the course of this proceeding.
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` OPINION
`A. Level of Skill of a Person Having Ordinary Skill in the Art
`I was asked to provide my opinion as to the level of skill of a person
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`53.
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`having ordinary skill in the art (“PHOSITA”) of the ’379 Patent at the time of the
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`18
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`
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`claimed invention, which counsel has informed me to assume is November 19, 2002,
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`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.
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`54.
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`In my opinion, a person having ordinary skill in the art of the ’379
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`Patent at the time of its filing would have been a person having the equivalent of a
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`bachelor’s degree in computer science, electrical engineering, or a similar 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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`55. 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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`19
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`
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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.
`Background of the Technology
`I was asked to briefly summarize the background of the prior art from
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`56.
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`the standpoint of the knowledge of a PHOSITA prior to November 19, 2002, the
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`filing date of the ’379 Patent.
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`57. As the ’379 Patent shows in its “Background of the Invention,” the
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`concept of using keywords to navigate nodes or vertices arranged hierarchically,
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`such as in a graph structure, in a network was well known long before 2002. The
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`’379 Patent also recognizes that one “familiar” application of such a hierarchical
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`system is an automated telephone voice response system. See Ex. 1001 (’379 Patent),
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`at 1:40-45. The ’379 Patent also acknowledges that “travers[ing] the network in the
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`most efficient manner possible” is a desirable feature of hierarchical navigation
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`systems. Id. at 1:23-26; see also id. at 2:9-18.
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`58. 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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`
`1 JOHN E. HOPCROFT, JEFFREY D. ULLMAN & ALFRED V. AHO, DATA STRUCTURE AND
`ALGORITHMS 75–106 (Addison-Wesley 1983).
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`20
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`
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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 information2; 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
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`59.
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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
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`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 1980s and earlier.5
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`2 Donald, B. Crouch, Carolyn J. Crouch & Glenn Andreas, The Use Of Cluster Hierarchies in
`Hypertext Info. Retrieval, HYPERTEXT ‘89 PROC., ACM PRESS, at 225-237, 1989.
`3 Yvan Leclerc, Steven W. Zucker, Denis Leclerc, A Browsing Approach to Documentation, IEEE
`COMPUTER, IEEE PRESS, June 1982, at 46–49.
`4 Ricky E. Savage, James K. Habinek, Thomas W. Barnhart, The Design, Simulation, and
`Evaluation of a Menu Driven User Interface, PROC. OF THE 1982 CONF. ON HUMAN FACTORS IN
`COMPUTING SYS., ACM PRESS, March 1982, at 36–40.
`5 Hoperoft. John E. and Jeffrey D. Ullman. Data Structures and Algorithms. Boston, MA, USA:
`Addison-Wesley, pp. 155