`_____________________________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`
`_____________________________
`
`ELASTIC N.V.,
`Petitioner,
`
`v.
`
`GUADA TECHNOLOGIES LLC,
`Patent Owner.
`
`_____________________________
`
`Patent No. 7,231,379
`_____________________________
`
`DECLARATION OF PADHRAIC SMYTH, PH.D.
`
`ELASTIC - EXHIBIT 1007
`
`
`
`TABLE OF CONTENTS
`
`I.
`II.
`
`BACKGROUND AND QUALIFICATIONS ................................................. 1
`LEGAL FRAMEWORK ............................................................................... 11
`A. Obviousness ......................................................................................... 11
`III. OPINION ....................................................................................................... 18
`A.
`Level of Skill of a Person Having Ordinary Skill in the Art .............. 18
`B.
`Background of the Technology ........................................................... 19
`C. Obvious to Apply Wesemann to the Claims of the ’379 Patent ......... 30
`D. Obvious to Combine Wesemann and Rajaraman ............................... 50
`E.
`Obvious to Apply Fratkina to the Claims of the ’379 Patent .............. 59
`F.
`Obvious to Combine Fratkina and Rajaraman .................................... 68
`IV. CONCLUSION .............................................................................................. 70
`
`
`-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 Petitioner 7, Inc. to provide my independent opinions
`
`on certain issues requested by Counsel for Petitioner 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 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.
`
`1
`
`
`
`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 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
`
`2
`
`
`
`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 a 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 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.
`
`3
`
`
`
`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 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
`
`4
`
`
`
`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
`
`5
`
`
`
`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
`
`56,399 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 7500 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
`
`6
`
`
`
`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, and the Machine Learning journal. In addition, I was a founding
`
`7
`
`
`
`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:
`
`(cid:120)
`
`(cid:120)
`
`(cid:120)
`
`(cid:120)
`
`(cid:120)
`
`(cid:120)
`
`(cid:120)
`
`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)
`
`8
`
`
`
`(cid:120)
`
`(cid:120)
`
`(cid:120)
`
`(cid:120)
`
`(cid:120)
`
`(cid:120)
`
`(cid:120)
`
`(cid:120)
`
`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)
`
`9
`
`
`
`(cid:120)
`
`(cid:120)
`
`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)
`
`(cid:120)
`
`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)
`
`(cid:120)
`
`(cid:120)
`
`(cid:120)
`
`(cid:120)
`
`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)
`
`10
`
`
`
`(cid:120)
`
`Salton, The Evaluation Of Automatic Retrieval Procedures— Selected
`
`Test Results Using the SMART System, American Documentation,
`
`16(3), 1965, at 209-222. (EX1029)
`
`(cid:120)
`
`Fitzpatrick et al., Automatic Feedback Using Past Queries: Social
`
`Searching?, 31 ACM SIGIR Forum 1997, at 306-313 (EX1030)
`
`II. LEGAL FRAMEWORK
`
`A. Obviousness
`
`17.
`
`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 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
`
`11
`
`
`
`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 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
`
`12
`
`
`
`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
`
`13
`
`
`
`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 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
`
`14
`
`
`
`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 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.
`
`15
`
`
`
`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 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.
`
`16
`
`
`
`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.
`
`17
`
`
`
`III. OPINION
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`A. Level of Skill of a Person Having Ordinary Skill in the Art
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`28.
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`I was asked to provide my opinion as to the level of skill of a person
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`having ordinary skill in the art (“PHOSITA”) of the ’379 patent at the time of the
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`claimed invention, which counsel has informed me to assume is November 19,
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`2002, the filing date of the ’379 patent. In determining the characteristics of a
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`hypothetical person of ordinary skill in the art of the ’379 patent at the time of the
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`claimed invention, I was told to consider several factors, including the type of
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`problems encountered in the art, the solutions to those problems, the rapidity with
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`which innovations are made in the field, the sophistication of the technology, and
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`the education level of active workers in the field. I also placed myself back in the
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`time frame of the claimed invention, and considered the colleagues with whom I
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`had worked at that time.
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`29.
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`In my opinion, a person having ordinary skill in the art of the ’379
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`patent at the time of its filing would have been a person having the equivalent of a
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`bachelor’s degree in computer science, electrical engineering, or a similar
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`discipline, and at least one year of experience working with technology related to
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`information retrieval and database searching, or an equivalent amount of similar
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`work experience or education, with additional education substituting for
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`experience and vice versa. Such a person of ordinary skill in the art would have
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`18
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`been capable of understanding the ’379 patent and the prior art references
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`discussed herein.
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`30. Based on my education, training, and professional experience in the
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`field of the claimed invention, I am familiar with the level and abilities of a person
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`of ordinary skill in the art at the time of the claimed invention. Additionally, I met
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`at least these minimum qualifications to be a person having ordinary skill in the art
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`as of the time of the claimed invention of the ’379 patent.
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`B.
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`31.
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`Background of the Technology
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`I was asked to briefly summarize the background of the prior art from
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`the standpoint of the knowledge of a PHOSITA prior to November 19, 2002, the
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`filing date of the ’379 patent.
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`32. As the ’379 patent shows in its “Background of the Invention,” the
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`concept of using keywords to navigate nodes or vertices arranged hierarchically,
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`such as in a graph structure, in a network was well known long before 2002. The
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`’379 patent also recognizes that one “familiar” application of such a hierarchical
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`system is an automated telephone voice response system. See EX1001, 1:40-45.
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`The ’379 patent also acknowledges that “travers[ing] the network in the most
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`efficient manner possible” is a desirable feature of hierarchical navigation systems.
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`Id., 1:23-26; see also id., 2:9-18.
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`33. The representation of interconnected nodes in a hierarchical network,
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`such as a tree structure like that described in the ’379 patent, has long been well
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`known in the art and has long been a subject covered in introductory computer
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`science courses well before the ’379 patent’s November 2002 filing date.1 In
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`particular, all of the following would have been familiar to a PHOSITA before
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`2002: Organizing a network as a hierarchical structure, such as a network-based
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`menu having different nodes representing categories of information,2 allowing
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`users to navigate between nodes or vertexes using key terms or node descriptors,3
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`and automatically searching a tree to direct a user to a node or vertex associated
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`1 Hoperoft. John E., and Jeffrey D. Ullman. Data Structures and Algorithms.
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`Boston, MA, USA, Addison-Wesley, pp. 75-106, 1983.
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`2 Donald, B. Crouch, Carolyn J. Crouch, and Glenn Andreas, The use of cluster
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`hierarchies in hypertext information retrieval, Hypertext `89 Proceedings,
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`ACM Press, pp. 225-237, 1989.
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`3 Yvan Leclerc, Steven W. Zucker, Denis Leclerc, McGill University, A browsing
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`approach to documentation, IEEE Computer, IEEE Press, pp. 46-49, 1982.
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`with a key term or descriptor associated with that node without traversing through
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`other intervening nodes in the hierarchy.4
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`34.
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`In such hierarchical tree structures, where the root node contains all
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`objects, the children of the root node contain disjoint subsets of objects, and so on,
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`down to leaf nodes. By 2002, a PHOSITA would have known that there are
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`multiple different ways that an algorithm can search a tree to identify nodes of
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`interest and perform other operations. The different types of tree search algorithms
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`are among the basic concepts that have been taught in introductory computer
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`science courses since the 1980s and earlier.5
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`
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`4 Ricky E. Savage, James K. Habinek, Thomas W. Barnhart, The design,
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`simulation, and evaluation of a menu driven user interface, Proceedings of the
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`1982 Conference on Human Factors in Computing Systems, ACM Press, pp 36-
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`40, 1982
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`5 Hoperoft. John E.. and Jeffrey D. Ullman. Data Structures and Algorithms.
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`Boston, MA, USA: Addison-Wesley, pp. 155-197, 306-346; 1983.
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`35. For example, one of the more well-known and efficient strategies for
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`searching a tree is the top-down depth-first approach.6 In the context of a user
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`searching for information where the information is represented by a tree structure,
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`the algorithm begins the search by evaluating the similarity of the item to be
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`matched (such as input from a user in the form of a keyword or query) to a set of
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`possible matches (represented as branches) at the root node of the tree. If one or
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`more branches match the input, the best such match is selected, the algorithm
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`descends the tree to the child node, and the matching process is repeated at the
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`child node, potentially requesting additional information from the user along the
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`path. If there is no adequate match at the root node, or at a subsequent child node
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`along the search path, the search algorithm can halt and return the best result(s)
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`found to that point or n