UNITED STATES PATENT AND TRADEMARK OFFICE
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`OHIO FARMERS INSURANCE COMPANY and PREGIS LLC
`Petitioners,
`
`v.
`
`GUADA TECHNOLOGIES LLC,
`Patent Owner.
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`Patent No. 7,231,379
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`DECLARATION OF PADHRAIC SMYTH, PH.D.
`
`
`
`PETITIONERS - EXHIBIT 1007
`
`IPR2022-00217
`
`

`

`TABLE OF CONTENTS
`
`I.
`
`II.
`
`BACKGROUND AND QUALIFICATIONS .................................................................... 1
`
`LEGAL FRAMEWORK .................................................................................................... 8
`
`A.
`
`Obviousness ............................................................................................................ 8
`
`III.
`
`OPINION .......................................................................................................................... 14
`
`A.
`
`B.
`
`C.
`
`D.
`
`E.
`
`F.
`
`Level of Skill of a Person Having Ordinary Skill in the Art ................................ 14
`
`Background of the Technology ............................................................................. 15
`
`Obvious to Apply Wesemann to the Claims of the ‘379 Patent ........................... 21
`
`Obvious to Combine Wesemann and Rajaraman ................................................. 37
`
`Obvious to Apply Fratkina to the Claims of the ‘379 Patent ................................ 44
`
`Obvious to Combine Fratkina and Rajaraman ...................................................... 51
`
`IV.
`
`CONCLUSION ................................................................................................................. 53
`
`
`
`i
`
`

`

`I, Padhraic Smyth, hereby declare the following:
`
`I.
`
`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 Ohio Farmers Insurance Company and Pregis LLC 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 either Petitioner. I have been informed that Guada
`
`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 EX1009.
`
`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.
`
`1
`
`

`

`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 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.
`
`
`
`2
`
`

`

`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 who are all involved in research in areas such as machine learning,
`
`database research, and artificial intelligence. Since 2014 I have been an Associate Director of the
`
`Center. 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. Since 2018 I have been an Associate Director
`
`of the Initiative.
`
`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 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 of text data, customer transactions, etc. Over the past 22 years or so I have consulted
`
`
`
`3
`
`

`

`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 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, 13 technical magazine
`
`articles, and 120 peer-reviewed conference papers related to my research. Several of these
`
`
`
`4
`
`

`

`publications are among the most highly-cited papers in the general areas of data mining and
`
`artificial intelligence — my papers have 58,366 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 18,000 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 7800 citations, and has been translated
`
`into Chinese and 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
`
`
`
`5
`
`

`

`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, the Lew Allen Award for
`
`Research at JPL, and a Qualcomm Faculty Award.
`
`15.
`
`I have served as an associate editor for the Journal of the American Statistical
`
`Association, the IEEE Transactions on Knowledge and Data Engineering, 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:
`
`• U.S. Patent No. 7,231,379 to Parikh et al. (EX1001);
`
`• File History for U.S. Patent No. 7,231,379 (EX1002)
`
`• U.S. Patent No. 6,731,724 to Wesemann et al. (EX1004) (“Wesemann”)
`
`• U.S. Patent No. 6,366,910 to Rajaraman et al. (EX1005) (“Rajaraman”)
`
`• U.S. Patent No. 7,539,656 to Fratkina et al. (EX1006) (“Fratkina”)
`
`• Hoperoft et al.. Data Structures and Algorithms. Boston, MA, USA, Addison-
`
`Wesley, pp. 75-106, 155-197, 306-346, 1983 (EX1010)
`
`• Crouch et al., The use of cluster hierarchies in hypertext information retrieval,
`
`Hypertext `89 Proceedings, ACM Press, pp. 225-237, 1989 (EX1011)
`
`
`
`6
`
`

`

`• Leclerc et al., A browsing approach to documentation, IEEE Computer, IEEE
`
`Press, pp 46-49, 1982 (EX1012)
`
`• Savage et al., 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 (EX1013)
`
`• Baeza-Yates et al., Modern Information Retrieval, pp. 24-40, ACM Press, 1999
`
`(EX1014)
`
`• Cunliffe et al., Query-based navigation in semantically indexed hypermedia,
`
`Proceedings of the Eighth ACM Conference on Hypertext, pp. 87-95, ACM Press,
`
`1997 (EX1015)
`
`• Hornstein, Telephone Voice Interfaces on the Cheap, §2.3, Proceedings of the
`
`UBLAB `94 Conference, 1994 (EX1016)
`
`• De Bra et al., Information Retrieval in Distributed Hypertexts, in RIAO, pp. 481-
`
`493, 1995 (EX1017)
`
`• U.S. Pat. No. 6,198,939 to Holmström et al. (EX1018)
`
`• 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 (EX1019)
`
`• Salton et al., A vector space model for automatic indexing, Communications of
`
`the ACM, 18(11): 613-620, 1975 (EX1020)
`
`• Xu et al., 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 (EX1021)
`
`
`
`7
`
`

`

`• Crouch, A cluster-based approach to thesaurus construction, Proceedings of the
`
`11th ACM SIGIR International Conference on Research and Development in
`
`Information Retrieval pp. 309-320. ACM, 1988 (EX1022)
`
`• Schiitze et al., A cooccurrence-based thesaurus and two applications to
`
`information retrieval, 1 Intelligent Multimedia Information Retrieval Systems and
`
`Management, pp. 266-274, 1994 (EX1023)
`
`• Giintzer et al., Automatic Thesaurus Construction by Machine Learning from
`
`Retrieval Sessions, 25 Information Processing & Management No. 3 pp. 265-273,
`
`1998 (EX1024)
`
`• 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 (EX1025)
`
`• U.S. Pat. No. 6,006,225 to Bowman et al. (EX1026)
`
`• Salton, The Evaluation Of Automatic Retrieval Procedures— Selected Test
`
`Results Using the SMART System, American Documentation, 16(3), 1965, at 209-
`
`222. (EX1029)
`
`• Fitzpatrick et al., Automatic Feedback Using Past Queries: Social Searching?, 31
`
`ACM SIGIR Forum 1997, at 306-313 (EX1030)
`
`II.
`
`LEGAL FRAMEWORK
`
`A.
`
`17.
`
`Obviousness
`
`I am a technical expert and do not offer any legal opinions. However, counsel has
`
`informed me as to certain legal principles regarding patentability and related matters under
`
`
`
`8
`
`

`

`United States patent law, which I have applied in performing my analysis and arriving at my
`
`technical opinions in this matter.
`
`18.
`
`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 inquiry should be done through the eyes of a
`
`PHOSITA at the time of the alleged invention.
`
`19.
`
`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
`
`
`
`9
`
`

`

`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 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.
`
`20.
`
`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 would have led one of ordinary
`
`
`
`10
`
`

`

`skill to modify the prior art reference or to combine the prior art references to arrive at the
`
`claimed invention.
`
`21.
`
`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.
`
`22.
`
`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 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
`
`
`
`11
`
`

`

`considered. Common sense teaches that familiar items may have obvious uses beyond their
`
`primary purposes.
`
`23.
`
`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.
`
`24.
`
`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 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.
`
`25.
`
`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
`
`
`
`12
`
`

`

`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.
`
`26.
`
`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 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.
`
`27.
`
`I am further informed that secondary-considerations evidence is only relevant if
`
`the offering party establishes a connection, or nexus, between the 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.
`
`
`
`13
`
`

`

`III. OPINION
`
`A.
`
`28.
`
`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 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 person of ordinary skill in the art of
`
`the ‘379 patent at the time of the claimed invention, I was told to consider several factors,
`
`including the type of problems encountered in the art, the solutions to those problems, the
`
`rapidity with which innovations are made in the field, the sophistication of the technology, and
`
`the education level of active workers in the field. I also placed myself back in the time frame of
`
`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
`
`experience working with technology related to information retrieval and database searching, or
`
`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
`
`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
`
`standpoint of the knowledge of a PHOSITA prior to November 19, 2002, the filing date of the
`
`‘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
`
`one “familiar” application of such a hierarchical system is an automated telephone voice
`
`response system. See EX1001, 1:40-45. The ‘379 patent also acknowledges that “travers[ing] the
`
`network in the most efficient manner possible” is a desirable feature of hierarchical navigation
`
`systems. Id., 1:23-26; see also id., 2:9-18.
`
`33.
`
`The representation of interconnected nodes in a hierarchical network, such as a
`
`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
`
`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
`
`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
`
`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
`
`tree is the top-down depth-first approach.6 In the context of a user searching for information
`
`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
`
`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
`
`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