IPR2017-01039 Smyth Declaration U.S. Patent 7,231,379 ......informed that Guada Technologies LLC...
Transcript of IPR2017-01039 Smyth Declaration U.S. Patent 7,231,379 ......informed that Guada Technologies LLC...
IPR2017-01039 Smyth Declaration U.S. Patent 7,231,379
UNITED STATES PATENT AND TRADEMARK OFFICE ____________
BEFORE THE PATENT TRIAL AND APPEAL BOARD ____________
UNIFIED PATENTS INC. Petitioner
v.
GUADA TECHNOLOGIES LLC Patent Owner
____________
IPR2017-01039 Patent 7,231,379 ____________
DECLARATION OF PADHRAIC SMYTH, PH.D.
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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 and Director of the UCI Data Science Initiative. I
have been retained as a technical expert witness in this matter by Counsel for
Petitioner Unified Patents, 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 Appendix A (Ex. 1009).
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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 at the intersection
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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 appointment in
the Department of Statistics 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, with an emphasis on developing new theories and algorithms
for automatically extracting useful information from very large volumes of data
across a wide variety of applications.
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,
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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 of text data, customer transactions, etc. Over the past 18 years I have
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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
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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 67 journal papers, 18 book chapters, 9 technical
magazine articles, and 109 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 40,000 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
5000 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 4700 citations, and has been translated into Chinese and
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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.
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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, and I was invited to serve as the
keynote speaker at the British International Conference on Databases in July 2015
(http://conferences.inf.ed.ac.uk/BICOD2015/).
16. As part of my work in connection with this proceeding, I have
reviewed the following materials:
• U.S. Patent 8,473,379 (Ex. 1001); • File History for U.S. Patent 8,473,379 (Ex. 1002) • U.S. Patent No. 6,731,724 to Wesemann (Ex. 1004) (“Wesemann”) • U.S. Patent No. 6,366,910 to Rajaraman (Ex. 1005) (“Rajaraman”) • U.S. Patent No. 7,539,656 to Fratkina (Ex. 1006) (“Fratkina”) • Hopcroft, John E., and Jeffrey D. Ullman. Data Structures and
Algorithms. Boston, MA, USA, Addison-Wesley, pp. 75-106, 155-197, 306-346, 1983 (Ex. 1010)
• Donald, B. Crouch, Carolyn J. Crouch, and Glenn Andreas, The use of cluster hierarchies in hypertext information retrieval, Hypertext ’89 Proceedings, ACM Press, pp. 225-237, 1989 (Ex. 1011)
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• Yvan Leclerc, Steven W. Zucker, Denis Leclerc, McGill University, A browsing approach to documentation, IEEE Computer, IEEE Press, pp 46-49, 1982 (Ex. 1012)
• Ricky E. Savage, James K. Habinek,Thomas W. Barnhart, The design, 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 (Ex. 1013)
• Ricardo Baeza-Yates, Berthier Ribiero-Neto, Modern Information Retrieval, pp. 24-40, ACM Press, 1999 (Ex. 1014)
• Daniel Cunliffe, Carl Taylor, and Douglas Tudhope, Query-based navigation in semantically indexed hypermedia, Proceedings of the Eighth ACM Conference on Hypertext, pp. 87-95, ACM Press, 1997 (Ex. 1015)
• Hornstein, Telephone Voice Interfaces on the Cheap at § 2.3, Proceedings of the UBLAB '94 Conference, 1994 (Ex. 1016)
• De Bra, Paul, et al., Information Retrieval in Distributed Hypertexts, in RIAO, pp. 481–493, 1995 (Ex. 1017)
• U.S. Pat. No. 6,198,939 to Holstrom (Ex.1018) • Karen Sparck Jones, A look back and a look forward, Proceedings of the
11th ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 13-29, ACM Press, 1988 (Ex. 1019)
• Gerard Salton, Anita Wong, and Chung-Shu Yang, A vector space model for automatic indexing, Communications of the ACM, 18(11): 613-620, 1975 (Ex. 1020)
• Jinxi Xu and W. Bruce Croft, Query expansion using local and global document analysis, Proceedings of the 19th ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 4-11. ACM, 1996 (Ex. 1021)
• Carolyn J. Crouch, A cluster-based approach to thesaurus construction, Proceedings of the 11th ACM SIGIR International Conference on Research and Development in Information Retrieval pp. 309-320. ACM, 1988 (Ex. 1022)
• Hinrich Schütze and Jan O. Pedersen, A cooccurrence-based thesaurus and two applications to information retrieval, 1 Intelligent Multimedia Information Retrieval Systems and Management, pp. 266-274, 1994 (Ex. 1023)
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• Güntzer et al., Automatic Thesaurus Construction by Machine Learning from Retrieval Sessions, 25 Information Processing & Management No. 3 pp. 265-273, 1998 (Ex. 1024)
• Mostafa et al., A Multilevel Approach to Intelligent Information Filtering: Model, System, and Evaluation, 15 ACM Transactions on Information Systems No. 4, pp. 368-399, 1997 (Ex.1025)
• U.S. Pat. No. 6,006,225 to Bowman et al. (Ex. 1026)
II. LEGAL FRAMEWORK
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
obviousness inquiry should not be done in hindsight. Instead, the obviousness
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inquiry should be done through the eyes of a PHOSITA at the time of the alleged
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
either explicitly or implicitly, in the specification. I believe that all of the
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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
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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
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
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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.
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
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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.
26. I am informed that even if a prima facie case of obviousness is
established, the final determination of obviousness must also consider “secondary
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considerations” if presented. In most instances, the patentee raises these secondary
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.
III. OPINION
A. Level of Skill of a Person Having Ordinary Skill in the Art
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28. I was asked to provide my opinion as to the level of skill of a person
having ordinary skill in the art (“PHOSITA”) of the ’379 Patent at the time of the
claimed invention, which counsel has informed me to assume is November 19,
2002, the filing date of the ’379 Patent. In determining the characteristics of a
hypothetical 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.
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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
B. Background of the Technology
31. 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 Ex. 1001 at 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. at 1:23-26; see also id. at 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
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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: Arranging nodes in a network, such as a menu having different categories of
information, in a hierarchical structure,2 allowing users to navigate between nodes
or vertexes using key terms or node descriptors,3 and automatically 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
1 Hopcroft, John E., and Jeffrey D. Ullman. Data Structures and Algorithms. Boston, MA, USA,
Addison-Wesley, pp. 75-106, 1983. 2 Donald, B. Crouch, Carolyn J. Crouch, and Glenn Andreas, The use of cluster hierarchies in hypertext
information retrieval, Hypertext ’89 Proceedings, ACM Press, pp. 225-237, 1989. 3 Yvan Leclerc, Steven W. Zucker, Denis Leclerc, McGill University, A browsing approach to
documentation, IEEE Computer, IEEE Press, pp. 46-49, 1982. 4 Ricky E. Savage, James K. Habinek,Thomas W. Barnhart, The design, simulation, and
evaluation of a menu driven user interface, Proceedings of the 1982 Conference on Human Factors in Computing Systems, ACM Press, pp 36-40, 1982
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are among the basic concepts that have been taught in introductory computer
science courses since the 1980’s and earlier.5
35. For example, one of the more well-known and efficient strategies for
searching a tree is the top-down depth-first approach.6 In the context of a user
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 leaf node (with no
children) the algorithm returns the result at the leaf node to the user, and
potentially additional result(s) found earlier along the search path. This is just one
potential version of a search algorithm to address the problem of a user searching
5 Hopcroft, John E., and Jeffrey D. Ullman. Data Structures and Algorithms. Boston, MA, USA:
Addison-Wesley, pp. 155-197, 306-346; 1983. 6 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.
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for information where the information is represented within a tree structure. A
PHOSITA by 2002 would have known that there were multiple different ways that
such algorithms could be constructed.
Navigation of Hierarchical Structures through “Jumping”
36. As the ’379 Patent recognizes, the comparison of input words with
keywords associated with a node to determine if the input matches the node was a
well-known concept before the time of the ’379 Patent. See, e.g., Ex. 1001 at 5:49-
60 (recognizing “fruit” at the “fruit” node). There were many known methods in
the art of performing a comparison between user input and keywords associated
with a node to determine if the input is sufficiently similar (exceeding some set
threshold similarity) to the keywords for the node. Examples of such methods that
were well-known in 2002 included Boolean matching, vector-space matching,
probabilistic matching, and fuzzy set matching, among others.7
37. Further, it was also well known that all of the nodes in a hierarchy
could be searched by comparing user input to keywords associated with all of the
nodes so as to determine a set of sufficiently similar nodes for further exploration
or to determine the most similar node (or, in the alternative, to determine that the
7 Ricardo Baeza-Yates, Berthier Ribiero-Neto, Modern Information Retrieval, pp. 24-40, ACM Press,
1999.
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input is not sufficiently similar to any node such that the input is considered not a
proper input).8
38. However, contrary to the ’379 Patent’s disclosure, it was not always
necessary in the prior art for users to navigate through each node/vertex of a
hierarchical menu tree by methodically providing a node selection (e.g., by
answering a question) at each level in the hierarchy so as to reach the goal
node/vertex of interest. Instead, prior art approaches allowed users to simply
“jump” to their goal node of interest. The concept of directly jumping to a node by
mapping user input to keywords associated with nodes was nothing new by 2002.9
By this time, hierarchical navigation systems were becoming increasingly complex
with a large potential number of nodes in a network. Persons of ordinary skill in
the art understood that to save time with such large amounts of data and large trees,
it was conventional for systems to allow users to navigate directly to non-adjacent
nodes upon receiving search terms containing key phrases or descriptions
associated with those nodes.10
8 Daniel Cunliffe, Carl Taylor, and Douglas Tudhope, Query-based navigation in semantically
indexed hypermedia, Proceedings of the Eighth ACM Conference on Hypertext, pp. 87-95, ACM Press, 1997.
9 Hornstein, Telephone Voice Interfaces on the Cheap at § 2.3, UBILAB Rep, Union Bank of Switzerland, Zurich, 1994; De Bra, Paul, et al., Information Retrieval in Distributed Hypertexts, in RIAO, pp. 481–493, 1995
10 Daniel Cunliffe, Carl Taylor, and Douglas Tudhope, Query-based navigation in semantically indexed hypermedia, Proceedings of the Eighth ACM Conference on Hypertext, pp. 87-95, ACM Press, 1997; Yvan Leclerc, Steven W. Zucker, Denis Leclerc, McGill University, A browsing approach to documentation, IEEE Computer, IEEE Press, pp 46-49, 1982
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39. Some menu navigation search systems were known in the art that,
upon receiving certain input from a user, would generate a suggested node or
nodes to which users may then choose to jump.11 However, other systems would
not require user input and would, instead, simply jump automatically to the node
best matching the input.12 In my opinion, any distinction between a hierarchical
navigation system that suggests different nodes for a user to choose where to
“jump” and a system which simply chooses a particular node to which it will
“jump” based on input received from the user and, for example a ranking of nodes
related to the input received from the user, would have been obvious to a
PHOSITA. Software to perform such functionality would have been well-
understood by persons having ordinary skill in the art. It would have been a mere
matter of design choice with respect to the particular application and desired user
interface as to whether the system automatically jumps to the node or whether the
system presents potential options that the user then chooses from to jump to the
selected node.
40. Given all of the above, it would have been obvious to a person or
ordinary skill in the art by 2002 to design a hierarchy navigation system that would
automatically “jump” users from one node to another node that is not directly
11 See, e.g., U.S. Pat. No. 6,198,939 to Holstrom. 12 See. e.g., U.S. Pat. No. 6,731,724 to Wesemann and U.S. Pat. No. 7,539,656 to Fratkina.
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connected by matching input words with keywords without requiring the user to
traverse through intermediate connecting nodes.
Using and Generating Key-term Thesauruses
41. There is a long history of the use of thesauruses in information
retrieval, going back to the 1960’s. 13 The key idea is that the specific word
provided by a user in their query to an information retrieval system, might not be
present in that specific form in any of the relevant documents or information
known to the system. However, relevant variants of the word provided by the user
might be present in the documents, variants such as synonyms or alternative
morphological forms of the word. Thus, it was known as early as the 1960’s, that
using thesauruses to capture synonyms and alternative variants of words could
significantly enhance the capability of an information retrieval system to match a
user query to a set of relevant documents or information.14
42. It was also well-known by 2002 that there were advantages to updating
or learning a thesaurus via automated analysis of text.15 There were a number of
different types of learning algorithms in 2002 that could be applied to the problem 13 Karen Sparck Jones, A look back and a look forward, Proceedings of the 11th ACM SIGIR
International Conference on Research and Development in Information Retrieval, pp. 13-29, ACM Press, 1988.
14 Gerard Salton, Anita Wong, and Chung-Shu Yang, A vector space model for automatic indexing, Communications of the ACM, 18(11): 613-620, 1975.
15 Jinxi Xu, W. Bruce Croft, Query expansion using local and global document analysis, Proceedings of the 19th ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 4-11. ACM, 1996.
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of learning a thesaurus. These algorithms typically relied on automatically finding
clusters of words (resulting in “concepts” for the learned thesaurus) that tend co-
occur frequently in sentences, paragraphs, and documents, using word co-
occurrences derived from documents known to the system.16
43. Another approach to learning a thesaurus, is to utilize logs of past user
searches in the system.17 The concept of learning new words given a user’s actions
after submitting a previously unrecognized query is a form of “query expansion”
and was already a known computer functionality for different search systems.18
44. Updating a thesaurus based on the inputs or searches of multiple users
would improve the thesaurus’s ability to learn new synonyms and would prevent
systems from returning no results to users searching a previously unknown term
for the first time. Persons of ordinary skill by 2002 would have recognized the
benefits of thesauruses storing new synonyms or keywords based on the learned 16 Carolyn J. 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; Hinrich Schütze and Jan O. Pedersen, A cooccurrence-based thesaurus and two applications to information retrieval, 1 Intelligent Multimedia Information Retrieval Systems and Management, pp. 266-274, 1994.
17 Güntzer et al., Automatic Thesaurus Construction by Machine Learning from Retrieval Sessions, 25 Information Processing & Management No. 3 pp. 265-273, 268, 1998 (“User searches allow an unambiguous conclusion to be drawn as regards a thesaurus content and this can accordingly be directly acquired without any query being directed at the user.”); 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, 396, 1997; Larry Fitzpatrick, Mei Dent, Automatic feedback using past queries: social searching?, 31 ACM SIGIR Forum, pp. 306-313. ACM, 1997.
18 Jinxi Xu, W. Bruce Croft, Query expansion using local and global document analysis, Proceedings of the 19th ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 4-11. ACM, 1996
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search behaviors of multiple users and would have known how to implement such
a system.19
45. Given all of the above, it would have been obvious to a PHOSITA by
2002 to design a hierarchy navigation system that would incorporate an evolving
thesaurus that would “learn” keywords based on, for example, observing new user
inputs and subsequent navigations and node selections within the hierarchical
network.
C. Obvious to Apply Wesemann to the Claims of the ’379 Patent
46. Wesemann teaches a voice-enabled user interface that enables a user to
navigate systematically through a hierarchy of menu states. See Ex. 1004 at
Abstract, 2:45-65, 3:50-55, Fig. 6.
47. In my opinion, a PHOSITA at the time of the filing of the ’379 Patent
would have concluded that Wesemann is analogous art to the ’379 Patent. For
example, both Wesemann and the ’379 Patent are in the field of navigating in a
transaction processing system and, more specifically, navigating a system having a
hierarchical arrangement of nodes. See Ex. 1001 at Abstract, Field of Invention;
see also Ex. 1004 at 1:13-19, 3:6-14, 3:50-55. Therefore, Wesemann is in the same
field of endeavor as the ’379 Patent. Additionally, much like the ‘379 Patent,
Wesemann attempts to improve the efficiency of existing hierarchical navigation 19 See, e.g., U.S. Pat. No. 6,006,225 to Bowman, et al.
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systems by enabling users to jump to any level or menu state without requiring the
user to navigate through intermediate levels or menu states in the hierarchy.
Compare id. at Abstract, 3:10-14, 3:38-56, 10:13-18, 11:47-56, 11:65-12:12,
12:25-42, 12:65-13:2, Figs. 5 and 6; with Ex. 1001 at Abstract, 5:7-12, 12:49-56,
13:53-63, Fig. 6. Therefore, Wesemann is tied directly to a problem purportedly
addressed by the ’379 Patent. In my opinion, Wesemann would have logically
commended itself to the inventors of the ’379 Patent, particularly since Wesemann
addresses a problem the inventors allegedly faced in much the same way as the
inventors did. Therefore, in my opinion, Wesemann is analogous art because it is
reasonably pertinent to a problem faced by the inventors when they allegedly
conceived of the claimed invention.
48. Claim 1 of ’379 Patent recites a method related to navigating nodes
arranged hierarchically, including receiving input at a first node from a user related
to a keyword from among multiple keywords, identifying at least one other node
associated with the keyword, where the second node is not directly connected to
the first node, and jumping to that node.
49. As discussed, Wesemann teaches a voice-enabled navigation system
based on a hierarchy of “levels” or “menu states” that are mapped by a voice-
enabled user interface. See, e.g., Ex. 1004 at 8:35-39. A PHOSITA would
recognize that these levels or menu states are the same as the “vertices” or “nodes”
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described in the ’379 Patent, as these levels or menu states each represent choices
or options in the hierarchical network. In the system in Wesemann, a user may
navigate a telephone service system by, for example, speaking into a telephone or
similar device. See, e.g., Ex. 1004 at 3:28-29, 4:58-60, 6:56-58. Speech recognition
software receives and interprets the user’s input, and the input is compared against
acceptable user inputs stored in the template. Id. at 6:56-64, 8:37-63. As the ’379
Patent acknowledges, many such speech recognition techniques were already well
known. Ex. 1001 at 6:60-64. Once this process is complete, the system can
determine what level or menu state the user likely desires and directly “jump” the
user directly to that level or menu state in the hierarchy without traversing through
each “in-between,” or intermediate step in the hierarchy of nodes. See, e.g., id. at
10:13-18, 11:47-56, 11:65-12:12, 12:25-42. A translator is used to convert an
acceptable user input corresponding to the user input into DTMF signals. Id. at
6:56-7:10, 10:65-11:16.20 This translation step in Wesemann is meant to enable
backwards-compatibility with existing DTMF phone service systems, which are
only responsive to commands sent via DTMF signal. See, e.g., id. at 3:6-10, 3:47-
49, 10:10-20.
20 There appears to be a typo in Col. 10, Line 66 of Wesemann. It states, “assuming the input does already comprise a DTMF signal,” but a person of ordinary skill would have appreciated that this is supposed to say, “assuming the input does not already comprise a DTMF signal.”
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50. In my opinion, a PHOSITA would understand that the “acceptable
inputs and requests” mapped in the template to all of the menu states (nodes) of
Wesemann’s hierarchy constitute “keywords” within the meaning of the ’379
Patent. See Ex. 1001 at 12:13-24; see also id. at 10:65-11:16. I note that Petitioner
has proposed that the broadest reasonable interpretation of “keyword” in the ’379
Patent is “one or more words or pieces of information, such as a data pattern, that
is associated with at least one node or vertex” based on the patent’s description of a
“keyword.” Id. at 7:5-9 (“Note, there is no requirement for a [] ‘keyword’ to be a
single word, in some implementations, keywords could be single words, phrases of
two or more words, or even some other form of information like a specific data
pattern.”). The inclusion of “data patterns” within this definition of “keyword” is
consistent with the ’379 Patent’s description of embodiments that use an
interactive voice response (“IVR”) system in which a user responds vocally to
prompts and the user’s speech is compared to “keywords.” Id. at 6:63-7:9. A
PHOSITA would understand that the user’s speech is compared to the acceptable
responses mapped in the template by comparing the data patterns present in the
user’s speech with the data patterns representing the acceptable responses. This is
done to determine whether the user’s spoken words are sufficiently similar to any
acceptable response associated with a node (i.e., to “determine[] whether the input
is proper for any state of the telephone service system,” Ex. 1001 at 11:17-21) and,
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if so, then to identify the “most similar” node (or node that “most resembles” the
input). Ex. 1004 at 12:12-24, 7:3-14. For instance, using an example from
Wesemann, the user’s spoken input words of “refurbished laptop sales” are
compared to the acceptable responses contained in the template and are determined
to match with “refurbished laptop sales” acceptable input for node 672 in Fig. 6 of
Wesemann. Id. at 11:65-12:6; Fig. 6. If the voice-enabled interface determines that
the input is not sufficiently similar to any acceptable response (keyword)
associated with any node, then the input is deemed “not proper” and the system
may prompt the user for new input. Id. at 11:21-31. In addition, if the voice-
enabled interface cannot determine the most similar node to which the user’s input
corresponds (e.g., if it matches acceptable responses, i.e., keywords, for multiple
nodes), then the interface may prompt the users with a clarifying prompt asking
which node the user would like from among the matching nodes. Id. at 12:4-12.
51. A PHOSITA would have found Wesemann’s “template,” which maps
acceptable responses to each of Wesemann’s menu states (nodes) to be just like the
’379 Patent’s “index,” which maps keywords to each of the nodes in the ’379
Patent’s hierarchy. Ex. 1001 at 5:2-4 (“An index . . . associating these keywords
with the nodes containing them is then created.”); 6:7-11 (“[W]hen a response to a
verbal description is provided by a user, possible keywords are identified in the
response and used to search the index and identify any node to which the response
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may be directed … .”). Further, a PHOSITA would have found Wesemann’s
comparison of the user’s spoken words to the “acceptable inputs and requests”
stored in the template for an interactive voice response telephone system to be just
like the ’379 Patent comparison of a user’s spoken words to keywords stored in an
index for the ’379 Patent’s interactive voice response telephone system. The fact
that Wesemann’s interface also additionally translates the accepted user input into
a DTMF signal for purposes of communicating with the underlying telephone
service system, which is only responsive to DTMF signals, does not change this
conclusion, because this translation is merely a change in format that allows the
user’s command to be communicated to the telephone service system (e.g., for
purposes of causing the telephone service system to jump to the desired node).
52. I was also asked to consider a scenario in which I was asked to assume,
in the alternative, that the “acceptable inputs and responses” mapped to the nodes
in Wesemann’s template were not “keywords” per se within the meaning of the
’379 Patent. Even if they were not (which I disagree with as discussed in the
preceding two paragraphs), in my opinion, at a bare minimum, it would have been
exceedingly obvious to a PHOSITA to implement the “acceptable inputs and
responses” as keywords. It would have been obvious, because using keywords to
search a hierarchy of nodes to determine nodes having associated keywords
matching a user’s input had long been well known well before 2002, as discussed
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above (see paragraphs 36, 39-40). Indeed, most conventional computer operating
systems at that time (and still today) used file systems allowing users to search for
files or folders of interest in a hierarchy of folders using keywords. Because of
their common usage, using keywords to perform the comparison of the user’s input
with the nodes in the tree would have been natural to a PHOSITA, and a
PHOSITA could have achieved predictable results in implementing this
functionality using keywords, without any undue experimentation. Keywords, at
their base level, can be represented in numerical or binary form, in relation to
which, other keywords (also represented in numerical or binary form) can be
compared using a similarity function defined using the numerical or binary forms,
to determine whether there is sufficient similarity so as to constitute a “match.” See
Ex. 1001 at 3:35-43, 9:13-16, 21:36-48. It would have been a matter of mere
design choice as to the precise algorithm that were used to perform the comparison
between user input and the set of acceptable responses/inputs, and using keywords
would have been a well-known and obvious way to do so. Further, Wesemann’s
express teaching of “acceptable inputs and responses” would have, at a minimum,
at least been highly suggestive to a PHOSITA to use keywords and would have
also motivated a PHOSITA to use keywords.
53. In my opinion, a PHOSITA by November 2002 would have concluded
that the limitations of claim 1 read on, or at a minimum, are at least obvious
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variations of, the teachings of Wesemann, both individually and as a whole. For
example, Wesemann teaches receiving user inputs containing words, such as
speech, at a given menu state (node). Ex. 1004 at 3:28-29, 4:58-60, 6:56-58. If the
input is proper (i.e., matches or is sufficiently similar to an acceptable input or
response, or keyword, mapped in the template), a translator provides a DTMF
translation of the closest acceptable user input in the template for transmission to a
legacy telephone service system, which in turn transmits data to a user or performs
a requested process, such as transitioning by “jumping” to a different menu state.
See id. at 10:65-11:16; see also id. at 12:13-19, 3:35-46. The system does this by
having the telephone service system transition between the different menu states
without the knowledge of the user and without requiring any effort on the part of
the user. See id. at 12:53-13:2.
54. Additionally, to the extent that claim 1 requires that multiple keywords
be associated with a single node, Wesemann discloses this concept as well. For
example, Wesemann teaches that “[a]dditional data can also be stored in the
template to associate common correlating inputs with the acceptable inputs and
requests.” Id. at 12:19-21. A PHOSITA would have understood this to mean that
menu states could be mapped to more than one possible acceptable input, or
keyword.
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55. The teachings of Wesemann are readily understood through its
exemplary disclosures. In one example, which references the hierarchical menu
tree displayed in Figure 6, Wesemann teaches that a user may input “123” from
main menu state 610 (i.e., receiving a user input at a first node). See id. at 11:52-
56. The input “123” is identifiable with at least one keyword of another node,
“extension 123” among multiple keywords shown in Figure 6. See id. at 11:52-56,
12:30-32, Fig. 6. Although extension 123 is not directly connected to main menu
state 610, upon receiving the input “123,” the system will “identify the user input
as a valid input for another state of the menu hierarchy,” (i.e., identifying a node
other than the first node) and the user will “automatically be transferred” by the
system to extension 123, without forcing the user to traverse through the
intermediate node, “Directory of Personnel” 640 (i.e., jumping the user to the other
node). See id. at 11:33-56, 12:25-32. This example demonstrates “vertical”
jumping.
56. In a second example, Wesemann illustrates each limitation of claim 1
in a “lateral” jumping context. See id. at 12:25-36. Again with reference to Figure
6, a user begins at home laptop sales 652 (i.e. a first node). By saying, “home
computer support,” the system will identify this is a proper input for home
computer support 646 because each menu state is mapped in a template (i.e.
receiving an input at a first node, the input containing at least one word identifiable
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with at least one keyword from among multiple keywords). Id.; see also id. at
12:13-24. Upon identifying the desired menu state based on the user input and
associated acceptable input - home computer support - in the template, the system
will “jump” from home laptop sales 652 to home computer support 646 without
requiring the user to traverse intermediate steps (i.e., identifying at least one node
other than the first node associated with the keyword and jumping to that node). Id.
57. Claim 2 depends from claim 1 and adds that the method includes
providing a verbal description associated with the at least one node to the user. The
’379 Patent discloses that “verbal descriptions” may include prompts or questions
designed to elicit further response from a user. See, e.g., Ex. 1001 at 3:35-43, 5:49-
60, 18:34-45. I understand that Petitioner has proposed that the broadest reasonable
interpretation of “verbal description” is “a set of words relating to the subject
matter whether presented audibly or in written form” based on the patent’s
description of this term. Id. at 1:50-52. A PHOSITA would understand that
Wesemann teaches that each of its menu states (nodes) have such associated
“verbal descriptions.” Specifically, Wesemann teaches providing menu prompts
corresponding to each of the menu states—and these prompts may be provided as
pre-recorded messages the user. See, e.g., Ex. 1004 at 5:62-67, 12:13-16, 13:3-22;
see also id. at 11:12-16 (“After transmitting requested data or performing a
requested process, the telephone service system waits for additional user input. The
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telephone service system may also prompt the user for additional input.”). For
example, these verbal menu prompts may include “press 1 followed by # for
sales,” “press 2 followed by # for computer support,” “or press 3 followed by # for
a directory of personnel.” Id. at 11:33-38. As other examples, these prompts may
also include prompts for “home computer sales,” “business computer sales,” or
“refurbished computer sales,” for each of those particular nodes. Id. at Fig. 6,
11:65-12:6. Wesemann also teaches a system that provides a “clarifying prompt”
when a user mistakenly enters an incorrect or ambiguous input. See, e.g., id. at
11:65-12:12. Given these disclosures in Wesemann, in my opinion, a PHOSITA
would have understood that Wesemann teaches “providing a verbal description
associated with the at least one node to the user,” as in claim 2. I note that “the at
least one node” in claim 2 refers to the node in claim 1 to which the system has
jumped. A PHOSITA would understand that, as with all of the nodes, after the user
jumps to a node, the user is presented with the verbal description for that node. For
example, if the user jumps to the sales node 630, the user would be presented with
a prompt asking the user whether they would like home computer sales 650,
business computer sales 660, or refurbished computer sales 670.
58. Claim 7 of the ’379 Patent recites a method performed in connection
with an arrangement of nodes representable as a hierarchical graph containing
vertices and edges connecting at least two of the vertices. The method includes
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receiving an input from a user as a response to a verbal description associated with
a first vertex, analyzing the input to identify a meaningful term that can be
associated with at least one keyword, and selecting a second vertex not connected
by an edge to the first vertex, based upon an association between the meaningful
term and the at least one keyword and a correlation between the at least one
keyword and the vertex; and jumping to the second vertex.
59. In my opinion, a PHOSITA would have concluded that the limitations
of claim 7 read on, or at least are obvious variations of, the teachings of
Wesemann, both individually and as a whole. For example, similar to the ’379
Patent, Wesemann teaches an arrangement of nodes representable as a hierarchical
graph containing vertices and edges connecting at least two vertices. For
comparison, Figure 6 of each of Wesemann and the ’379 Patent are “menu trees,”
an example of such a graph representation. See, e.g., Ex. 1001 at 3:5-28.
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Ex. 1001, Fig. 6 (describing a network of nodes in the airline industry context)
Ex. 1004, Fig. 6 (describing menu hierarchy for computer sales organization)
60. Wesemann teaches receiving an input from a user as a response to a
verbal description, such as menu prompts, associated with a first vertex. See, e.g.,
id. at 5:62-67, 12:13-16, 13:3-22; see also id. at 11:12-16 For example, if a user
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wanted to purchase refurbished laptops from a computer sales organization, for
which an exemplary graphical representation of a menu is provided in Figure 6, the
user could “systematically” enter input in response to menu prompts to step
through each of the vertices of sales 630 and refurbished computer sales 670 to
arrive at refurbished laptop sales. See id. at 11:65-12:4. A PHOSITA would
recognize that the user inputs in response to menu prompts at any one of these
nodes in the hierarchical menu of Wesemann would satisfy this limitation of claim
7. For example, at the main menu node 610, the system provides a menu prompt
asking the user if they would like computer support, sales, or a personnel directory.
Id. at 11:33-55, Fig. 6. While Wesemann states that prior art systems would have
only recognized acceptable/responses inputs associated with those three nodes in
response to this prompt at the main menu, Wesemann teaches that its system will
search the entire tree so as to recognize any acceptable response/input, such as the
input “123,” which will cause the system to jump directly to the node associated
with that acceptable response/input (i.e., jumping to extension 123 in the personnel
directory, in this example). Id. at 11:47-55. These verbal prompts provided by the
system to the user over the telephone would be understood by a PHOSITA to be a
verbal description (which Petitioner has construed as “a set of words relating to the
subject matter whether presented audibly or in written form” based on the patent’s
description of this term). A PHOSITA, therefore, would understand that Wesemann
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teaches receiving an input from a user as a response to a verbal description
associated with a first vertex.
61. A PHOSITA would understand Wesemann also teaches claim 7’s step
of “analyzing the input to identify a meaningful term that can be associated with at
least one keyword.” First, Wesemann teaches using speech recognition technology
and comparing the user’s spoken words/terms to the set of “acceptable responses
and inputs” that are mapped to all the menu states (nodes) in the Wesemann’s
template. See e.g., id. at 6:65-7:14, 8:37-63, 10:65-11:16, 12:13-24. The system
performs a process of “comparing the user input with the accepted inputs and
requests on file” to determine what the user is attempting to do or say. Id. at 12:13-
19. If an input includes any term that is mapped on the template, the system will
associate this to at least one acceptable response/input (keyword). See, e.g., id. at
11:52-56, 12:25-42, 6:56-7:22. Based on this analysis, the system will identify a
meaningful term which can be associated with an acceptable input/response,
which, as I have discussed above in paragraphs 51-52, would be understood by a
PHOSITA to be a keyword (“one or more words or pieces of information, such as
a data pattern, that is associated with at least one node or vertex”). Second,
Wesemann teaches that the template may also contain additional “common
correlating inputs” for each of the acceptable inputs/responses (keywords) to allow
the system to better determine what a user is attempting to input. Id. at 12:13-24. A
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PHOSITA would understand that these additional correlating inputs allows
additional meaningful terms from the user’s spoken input to be associated with an
acceptable response/input (keyword) used to identify a node to jump to. Third, in
examples where the input cannot be used to uniquely determine the most similar
acceptable response/input (keyword), such as if a user mistakenly says
“refurbished notebook sales,” for example, the system will identify at least one
meaningful term, such as the term “refurbished” that is associated with the
acceptable inputs/responses for “refurbished laptop sales” and “refurbished
desktop sales” based on their use of the word “refurbished.” Id. at 11:65-12:12.
The system will then provide the user with a clarifying prompt to suggest either
“refurbished laptop sales” or “refurbished desktop sales.” Id. at 11:65-12:12. A
PHOSITA, therefore, would understand that Wesemann teaches claim 7’s step of
“analyzing the input to identify a meaningful term that can be associated with at
least one keyword.” In addition, for the same reasons I have expressed in
paragraphs 52-53 above, it would have also been obvious to a PHOSITA to use
“keywords” in Wesemann to the extent one were to assume that Wesemann’s
“acceptable inputs and responses” were somehow not “keywords.”
62. Finally, a PHOSITA would conclude that Wesemann teaches the final
limitation of claim 7, which, like the final limitation of claim 1, requires that the
system “jump” from a first vertex to a non-connected vertex associated with the
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keyword and meaningful term input by the user at the first vertex. As discussed
above, Wesemann teaches the concept of jumping from a first vertex to a second,
non-directly-connected vertex based on key terms or meaningful terms of a user’s
input. See supra at paragraphs 49-56; see also Ex. 1004 at Abstract, 3:10-14, 3:38-
56, 10:13-18, 11:33-56, 11:65-12:12, 12:25-42, 12:65-13:2, Figs. 5 and 6. To
provide an example, Wesemann teaches comparing the user’s spoken words/terms,
such as “123” or “home computer sales” to the set of acceptable responses/inputs
(keywords) mapped to the vertices of the hierarchy in the template and then
jumping to the vertex having the acceptable response/input (keyword) that matches
the user’s input. Id. at 11:33-56, 12:25-13:2, Fig. 6. Wesemann provides examples
of both a lateral jump (from the “home computer sales” vertex to the “home
computer support” vertex) and a vertical jump (from the “main menu” vertex to the
“extension 123” vertex), as depicted in the annotated version of Fig. 6 below.
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A PHOSITA, therefore, would understand that Wesemann teaches claim 7.
63. In my opinion, a PHOSITA would have found that Wesemann teaches
the limitations of claims 1, 2, and 7 of the ’379 Patent. As discussed, any
distinctions whatsoever that could be made between Wesemann and these claims
of the ’379 Patent would have required at most very minor modifications that
would have yielded predictable results to a PHOSITA, and, thus would have been
obvious to such a person.
D. Obvious to Combine Wesemann and Rajaraman
64. Rajaraman teaches a “general purpose search” (GPS) method and
system that generates search results for classifications/nodes that, like the ’379
Patent, are arranged hierarchically. See, e.g., Ex. 1005 at Abstract, 1:5-7, 1:66-67,
2:9-22, 2:57-3:7, Figs. 4, 13:
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Ex. 1005 at Fig. 4.
65. As discussed above, the ’379 Patent also relates to methods for
navigating a hierarchical system of nodes. See supra, at ¶ 47. In my opinion,
because Rajaraman and the ’379 Patent are both in the field of navigation in a
transaction processing system, specifically navigating a system having a
hierarchical arrangement of nodes, Rajaraman is analogous art for falling within
the same field of endeavor of the ’379 Patent.
66. Rajaraman also attempts to address at least two problems identified in
the ’379 Patent. First, both the ’379 Patent and Rajaraman purportedly seek to
improve efficiency and prevent user frustration in the navigation of a hierarchy of
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nodes (referred to as categories or classifications and sub-classifications in
Rajaraman) to find an item of interest, and both the ’379 Patent and Rajaraman
allow keyword searching across nodes/classifications to address this issue.
Compare Ex. 1001 at 2:9-18 with Ex. 1005 at 1:42-2:22. Second, Rajaraman
teaches the use of a “special terms file” list of synonyms to expand search queries,
like the thesaurus of the ’379 Patent. A PHOSITA would have recognized that the
teachings in Rajaraman are reasonably pertinent to the problem addressed by the
’379 Patent related to improving efficiency of navigational systems by enabling
them to search synonyms of keywords, as well as assisting with the learning of
previously unknown keywords or synonyms. See Ex. 1001 at 3:44-48; see also Ex.
1005 at 7:22-8:25. In my opinion, given the problems faced by the inventors in the
’379 patent, a PHOSITA would have found that Rajaraman logically would have
commended itself to the inventors. Therefore, Rajaraman is also analogous art by
virtue of being reasonably pertinent to problems faced by the inventors of the ’379
patent.
67. Claims 3 through 6 of the ’379 Patent relate to a “thesaurus,” where
the thesaurus is implemented in software to correlate keywords with synonyms. I
note that claim 3 itself recites “a thesaurus correlating keywords with synonyms,”
and the ’379 Patent describes its thesaurus as “equating” synonyms with keywords,
so that input of a synonym by a user can also cause the claimed “jumping” to the
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desired node. ’379 Patent at 8:3-32. The patent states that this “equating” can be
done in “any of a myriad of different ways” and gives examples such as correlating
words, correlating synonyms to nodes, etc. Id. at 8:28-41. But the ’379 Patent notes
that “the exact implementation details of which re [sic] irrelevant to the invention.”
Id. at 8:28-32.
68. Rajaraman teaches of a “special terms file” that “lists various words
(i.e., ‘Good Terms’) that are synonymous with classification names.” See Ex. 1005
at 7:22-26, Fig. 7. Rajaraman’s “index builder” accesses the special terms file
(thesaurus) during a search and assigns synonymous terms, which it calls “Good
Terms,” a priority of zero so as to include classifications having synonymous terms
in the search results. Id. at 8:26-30 and Fig. 9; see also id. at 9:7-45 and Fig. 11. In
my opinion, PHOSITA would have concluded that the special terms file taught in
Rajaraman, including the list of “Good Terms,” is the same as the “thesaurus”
identified in claims 3-6 of the ’379 Patent because it serves to correlate synonyms
with classification names (i.e., keywords).
69. Claim 3 is dependent on claim 1, and adds “searching a thesaurus
correlating keywords with synonyms.” Claim 4 depends on claim 3 and adds that
the searching comprises identifying “the at least one word as synonymous with the
at least one keyword.” Rajaraman discloses both of these limitations. With respect
to claims 3 and 4, Rajaraman teaches that its searching system uses an “index
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builder” that goes through the “special terms file” (thesaurus) and assigns the
synonymous “Good Terms” a priority of 0 for search purposes (however,
Rajaraman recognizes that a different priority may be used). By giving Good
Terms 0-priority, Rajaraman ensures that search results include classifications
indicated in the special terms file as being synonymous with Good Terms, but that
they do not supersede the original classification terms (i.e., “keywords”), which are
assigned a priority of 1. Id. at 8:26-30 and Fig. 9; see also id. at 9:7-45 and Fig. 11.
As an example, Rajaraman teaches making “blouse” a “Good Term,” or synonym,
for “women’s shirts.” Id. at 7:22-26, Fig. 7. When a user searches for women’s
shirts the search will also produce results that have hits on the term “blouses” as
well, and if a user searches for “sole,” results related to the classification “shoes”
may be produced. See id. at 7:25-26, 7:43-62, 9:7-52, Figs. 7-11. As I discuss in
further detail below in paragraph 76, it would have been obvious to a PHOSITA to
incorporate this searching of synonyms of keywords, as taught by Rajaraman, in
Wesemann’s system.
70. Claim 5 depends from claim 1 and adds that the system would
determine that the user input word is neither a keyword or a synonym and learning
the meaning for the word so that the word will be treated as a learned synonym for
at least one particular keyword. Claim 6 depends from claim 5 and comprises
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adding the word to a thesaurus so that when the word is input by a subsequent user,
it will be treated as synonymous.
71. Rajaraman teaches that its system maintains a log of search requests
and search results that may also log which search results are selected by the user,
so as to monitor user search behavior and to allow a determination of whether
synonyms should be added for a search term. Id. at 7:63-8:19. A “log analyzer” is
provided to help determine when to add synonyms by performing a statistical
analysis of search requests, such as those requests that “resulted in no results” or in
very few classifications appearing in the results. Id. Thus, a PHOSITA would
understand that Rajaraman teaches determining that the at least one-word input by
the user is neither a keyword nor a synonym of any keyword.
72. Rajaraman also teaches learning a meaning for the word so that it will
be treated as a learned synonym for at least one particular keyword of the multiple
keywords, as in claim 5, and it teaches learning the meaning by having the search
term added to the special terms list (i.e., the “thesaurus”) as a synonym, as in claim
6. For example, the log analyzer produces statistical results that can, for example,
be reviewed by an analyst to determine whether the search term should be added to
a special terms list. See id. at 7:63-8:25. Similar to the teachings of the ’379 Patent,
subsequent actions by the user after the unknown term is entered, such as
subsequent successful searches (i.e., searches that return results) may be provided
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to help determine what classifications or keywords would be synonymous with the
previously unknown word. See id. at 8:16-19; see also, e.g., Ex. 1001 at 9:65-
11:12.
73. Rajaraman teaches that “Good Terms” added to the special terms list
do not always need to be literal synonyms. For example, it may be useful for the
system to treat common misspellings or often-misused homonyms of words as
synonyms. Rajaraman offers the term “aparel” as a potentially common
misspelling of the word, “apparel” and the word “sole” as homonym for “soul” that
may be misused. Ex. 1005 at 7:63-8:25. If these terms were often used but turn up
no-to-few search results, then this would be revealed by the log analyzer’s
statistical analysis.
74. In my opinion, a PHOSITA at the time of the filing of the ’379 patent
would have readily been able to substitute the log analyzer described in Rajaraman
with software algorithms to perform the function of adding synonyms to the
special terms list. As discussed in paragraphs 41-45, automatic thesaurus-updating
was already well known by the time of the ’379 Patent, and persons having
ordinary skill would have been able to automate the process of the “analyst”
described in Rajaraman to use the log analyzer to automatically add learned
synonyms determined from an analysis of user’s search behavior. Such automation
of these steps would have required the application of existing computer technology
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using well-known methods of programming that would have been within the level
of ordinary skill in the art. A PHOSITA would have also appreciated the benefit in
allowing automatic addition of synonyms for keywords to avoid the well-known
issues with labor-intensive manual updating of keyword thesauruses, as also
discussed above in paragraphs 42-43.
75. With respect to claim 6’s reference to “a subsequent user” being able
to have the benefit of having the learned word added to the thesaurus for
subsequent searching, I note that the ’379 Patent refers to a subsequent caller/user
being “either the same person or a different person.” Ex. 1001 at 14:32-38.
Nonetheless, counsel has asked me to consider an assumption that “a subsequent
user” in claim 6 must be a different user. Even under this assumption, a PHOSITA
would understand that Rajaraman satisfies such an interpretation. Rajaraman does
not limit its thesaurus to only a specific user, but instead bases it on the behavior of
multiple users and even on homonyms and alternate spellings, as discussed. As
Rajaraman states: “For example, users may enter the search term ‘aparel,’ rather
than ‘apparel.’ Because the term ‘aparel’ is not in the product database and not in
the classification hierarchy, the search result will be empty. Therefore, it would be
useful to add the term ‘aparel’ as a synonym of ‘apparel.’” Rajaraman at 7:67-8:5
(emphasis added); see also id. at 8:22-25 (“For example, if users enter the search
request ‘sole’ and the search results relate only to shoes, the analyst may want to
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indicate that ‘sole’ is a synonym for ‘soul,’ as in music.”) (emphasis added).
Further, Rajaraman’s thesaurus (i.e., “the special terms file”) is part of
Rajaraman’s overall system, which is used by all users of the system. A PHOSITA,
therefore, would understand that Rajaraman is not limiting its thesaurus to only a
particular user, and, instead, subsequent users also benefit from the thesaurus of
synonyms, which is developed from the statistical analysis of multiple users’
search behaviors. Id. at 4:55-5:9, Fig. 2, 7:22-42, Fig. 7. It would have also been
obvious to a PHOSITA for the thesaurus to be applied to other users, because it
was well known in the art that thesauruses of synonyms for keyword searching are
improved when they are based on the learned search behaviors of multiple users,
which yields a more robust thesaurus functionality.
76. As mentioned above, claims 3, 4, 5, and 6 all depend at least
indirectly from claim 1. Wesemann teaches each limitation of claim 1. See supra at
paragraphs 48-56. Both Rajaraman and Wesemann relate to the field of navigation
of hierarchical networks. In my opinion, it would have been obvious for a
PHOSITA to incorporate Rajaraman’s use of a “special terms file” (thesaurus) for
searching a hierarchy of classifications, as well as Rajaraman’s log analyzer, in
Wesemann’s system for matching words in a user’s input with keywords
associated with nodes to jump to a node within a hierarchy. As discussed above in
paragraphs 41-45, use of such thesaurus functionality to search synonyms in
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addition to keywords was well known by 2002, and a PHOSITA would have
appreciated that implementing the thesaurus functionality taught in Rajaraman
would have improved the system of Wesemann by making it more user-friendly
and increasing the efficiency with which the user could navigate through the
hierarchical network. Further, given that both Wesemann and Rajaraman relate to
searching a hierarchy of nodes and Wesemann already teaches inclusion of
additional data for “common correlating inputs” for the “acceptable responses and
inputs” (keywords) in Wesemann’s template, such modifications to the system in
Wesemann would have required only minor modifications in software and would
have yielded predictable results to a PHOSITA. These conclusions are supported
by the fact that the system in Wesemann expressly teaches adding “common
correlating inputs,” for acceptable user inputs into the template. See Ex. 1004 at
12:19-24. This express teaching of Wesemann would have, at a minimum,
provided a suggestion or motivation to a PHOSITA to use synonymous terms, such
as taught by Rajaraman’s “special terms files,” as the “common correlating inputs”
associated with the acceptable responses and inputs (keywords) of Wesemann,
such that user inputs corresponding to those synonymous terms would be deemed
by the system to be a match for their assigned keyword(s). Further, a PHOSITA
would have also recognized that the application of methods related to searching
keyword synonyms contained in a thesaurus, which were well known at that time,
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would have yielded predictable results when applied in the system of Wesemann
and would not render any features of Wesemann, such as the ability to jump
between nodes, inoperable. Therefore, in my opinion, a PHOSITA would have
been motivated to combine the teachings of Wesemann and Rajaraman to render
claims 3-6 obvious.
E. Obvious to Apply Fratkina to the Claims of the ’379 Patent
77. Fratkina discloses a system for providing an automated, multi-step
dialogue with users of the system. Fratkina discloses taxonomies of parent, child,
and sibling nodes through which a user may navigate with the assistance of a
dialogue engine that processes user inputs. See Ex. 1006, 4:42-5:19, 6:65-7:5,
14:42-67. These taxonomies may be arranged hierarchically. See id.; see also id. at
14:47-67, 22:19-29, 26:26-27:27, Figs. 4, 5, 10-12. Like the ’379 Patent, Fratkina
allows the user’s input words to be spoken words that are recognized using voice
recognition software in an interactive voice response (IVR) system. Id. at 13:15-
29.
78. In my opinion, Fratkina is analogous art to the ’379 Patent. Both
Fratkina and the ’379 Patent relate to the navigation of transaction processing
systems, specifically in a hierarchical fashion. See id. at 14:47-67, 22:19-29, 26:26-
27:27, Figs. 4, 5, 10-12; see also Ex. 1001 at Abstract, Field of Invention.
Additionally, both the ’379 Patent and Fratkina disclose the use of “interactive
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voice response” (“IVR”) systems. See Ex. 1006 at Abstract, 2:56-62, 13:15-17; see
also Ex. 1001 at 6:66-67. Therefore, Fratkina is within the field of endeavor of the
’379 Patent.
79. Fratkina is also reasonably pertinent to at least one problem addressed
in the ’379 Patent – improving the efficiency of navigating menu systems. Id. at
2:12-14, 40:30-33. Additionally, like the ’379 Patent, Fratkina teaches the use of
“jumping” between nodes to accomplish efficient navigation. See id. at 34:32-53;
see also id. at 27:25-43; see also id. at 37:54-63, Fig. 15. In my opinion, Fratkina
would have logically commended itself to the problems purportedly addressed of
the ’379 Patent. Therefore, Fratkina is also analogous art because it is reasonably
pertinent to the problems faced by the inventors of the ’379 Patent.
80. Claim 1 of ’379 Patent recites a method related to navigating nodes
arranged hierarchically, including receiving input at a first node from a user related
to a keyword from among multiple keywords, identifying at least one other node
associated with the keyword, where the second node is not directly connected to
the first node, and jumping to that node. As discussed above, Fratkina teaches a
system in which navigable nodes are arranged hierarchically. See Ex. 1006 at Figs.
4, 5, 10-12, 20.
81. Fratkina teaches limitation [1(a)]; specifically, Fratkina discloses
receiving input from a user at a node, where the input contains at least one word
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that, when processed by a dialog engine, may be identifiable with at least one “tag”
(i.e., keyword), such as the name of the node. Id. at 13:15-29 (regarding user
input); 5:13-28 (“keywords”), 14:27-31 (regarding tags); see also id. at 26:26-
27:27. Multiple tags may be used for different nodes on the hierarchy. Id at 5:13-
28, 14:27-3, 26:26-27:27.
82. An example of Fratkina’s teachings is provided in the form of a meal
menu. See, e.g., id. at Figs. 10-12, 20, 21. Figure 11 of Fratkina is provided for
illustration:
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83. In navigating this menu, the system may ask the user about what they
are seeking, and the user may answer using “keywords or natural language,” such
as by speaking, which can be recognized by voice recognition software in
Fratkina’s “interactive voice response (IVR)” implementation. See id. at 26:50-57,
22:19-20; see also id. at 5:24-29, 13:15-29. For example, a user may input “eggs”
at the breakfast node to move to the eggs node. Id. at 26:50-57; see also id. at Fig.
10, 22:19-29. A PHOSITA would understand that Fratkina teaches a system
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where, at a first node, a user may input a command/response containing one or
more words identifiable with at least one or more keywords for navigating the
taxonomies.
84. Fratkina also teaches limitation [1(b)], i.e., “identifying at least one
node, other than the first node, that is not directly connected to the first node but is
associated with the at least one keyword, and jumping to the at least one node.” For
example, Fratkina states that while a user would “typically” advance through the
menu along the edges of the taxonomy graph, the system may allow users to
“jump” to a node “more than one edge away from the previous focus” (i.e., the first
node the user was occupying). Id. at 27:25-43; see also id. at 34:32-53, 37:54-63.
The system is able to identify the non-adjacent node to which the user would like
to go through “autocontexualization.” Id. at 34:32-53 (“Autocontextualization can
be used to jump to a specific place in the taxonomy and the dialog designer can
explicitly specify a place to jump to.”). A PHOSITA would recognize these
teachings to render obvious the identifying and jumping limitation of claim 1. A
PHOSITA would have, for example, recognized immediately how the jumping
functionality provided by Fratkina’s autocontextualization process could be
applied to the breakfast menu example. If a user input “scrambled eggs,” for
example, while at the “breakfast node,” a PHOSITA would have understood that
the system would be able to recognize where the user desired to go and transfer the
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user to the scrambled eggs node, without traversing through the “eggs” node
between the “breakfast” and “egg” nodes. Fratkina’s express teachings regarding
use of autocontextualization to “jump” to non-connected nodes would have
motivated a PHOSITA to allow such jumping within the menu hierarchy to
provide for a more natural and conversational dialogue, and, ultimately, a more
efficient flow in Fratkina’s system. Therefore, in my opinion, claim 1 is obvious
over Fratkina.
85. Claim 2 depends from claim 1 and adds that the method includes
providing a verbal description associated with the at least one node to the user. The
’379 Patent discloses that verbal descriptions may include prompts or questions
designed to elicit further response from a user. See, e.g., Ex. 1001 at 3:35-43, 4:32-
45, 5:49-60, 18:34-45. Fratkina teaches providing users with menu prompts in
response to user inputs associated with the at least one node (i.e., the second node
identified in claim 1). For example, Fratkina discloses prompting users for
information regarding whether they would like eggs or pancakes at a breakfast
node, and then whether the user would like poached or scrambled eggs upon the
user selecting eggs. Id. at 26:46-60, Fig. 11, Fig. 21; see also id. at 13:15-24 (“The
dialog engine 232 response is passed to a text-to-speech system that turns it into a
vocal response to the user.”), 26:34-45. Thus, in my opinion, a PHOSITA would
understand that Fratkina teaches the limitation added by claim 2 of the ’379 Patent.
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86. Claim 7 of the ’379 Patent recites a method performed in connection
with an arrangement of nodes representable as a hierarchical graph containing
vertices and edges connecting at least two of the vertices. The method includes
receiving an input from a user as a response to a verbal description associated with
a first vertex, analyzing the input to identify a meaningful term that can be
associated with at least one keyword, and selecting a second vertex not connected
by an edge to the first vertex, based upon an association between the meaningful
term and the at least one keyword and a correlation between the at least one
keyword and the vertex; and jumping to the second vertex.
87. Fratkina discloses an arrangement of nodes that can be represented in a
graph containing vertices and edges connecting at least two of the vertices:
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See, e.g., id. at Figs. 10, 20; see also id. at Figs. 11, 12.
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88. Fratkina also discloses the second limitation of claim 7 related to
receiving an input from a user as a response to a verbal description. As just one
example, with reference to the menu example, Fratkina discloses that a user may
input “scrambled” as a response to the question at the breakfast node, “How would
you like your eggs prepared?” See id. at 26:34-60. Such questions align with the
teachings of the ’379 Patent regarding a “verbal description.” Thus, a PHOSITA
would understand that Fratkina teaches this limitation.
89. As discussed with respect to claim 1, Fratkina also discloses the
“jumping” concepts of the final limitation of claim 7. Using autocontextualization,
the system in Fratkina can select a vertex in a graph that is not connected to a first
vertex and, based on the user’s input and a keyword or “tag” associated with the
second vertex, jump from the first vertex to the second vertex. 34:32-53; see also
id. at 27:25-43; see also id. at 37:54-6. Although Fratkina does not apply its
teachings of jumping to a concrete example, one having skill in the art would
understand from Fratkina’s disclosure how to apply the teachings of Fratkina to
claim 7. For example, as with the breakfast menu example provided above for
claim 1 (see paragraph 84, limitation [1(b)]), a PHOSITA would understand that
the system would be able to jump from the “breakfast node” to the “scrambled”
node, skipping the “eggs node” altogether, upon receiving a user input such as
“scrambled eggs.” Thus, Fratkina taches all of the limitations of claim 7. Further, it
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would have been obvious to a PHOSITA that Fratkina would be able to operate in
this way based on Fratkina’s express teachings with regard to how
autocontextualization can cause the system to jump to non-connected nodes. That
is, as discussed above for claim 1 (see paragraph 84, limitation [1(b)]), Fratkina’s
express teachings regarding use of autocontextualization to “jump” to non-
connected nodes would have motivated a PHOSITA to allow such jumping within
the menu hierarchy to provide for a more natural and conversational dialogue, and,
ultimately, a more efficient flow in Fratkina’s system.
90. In my opinion and based on the foregoing, claims 1, 2, and 7 would
have been obvious to persons having ordinary skill in the art over the teachings and
disclosures in Fratkina.
F. Obvious to Combine Fratkina and Rajaraman
91. As discussed in Section E, supra, claim 1 is obvious over Fratkina.
Additionally, as discussed in Section D, supra, Rajaraman teaches and/or renders
obvious the limitations of claims 3, 4, 5, and 6.
92. In my opinion, it would have been obvious for one of ordinary skill in
art to incorporate Rajaraman’s use of a “special terms file” (thesaurus) for
searching a hierarchy of classifications, as well as Rajaraman’s log analyzer, into
Fratkina’s system. As discussed above in paragraphs 41-45, use of such thesaurus
functionality to search synonyms in addition to keywords was well known by that
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time, and a PHOSITA would have appreciated that implementing the thesaurus
functionality taught in Rajaraman would have improved the system of Fratkina by
making it more user-friendly and increasing the efficiency with which the user
could navigate through the hierarchical network.
93. Further, I note that Fratkina already expressly teaches that user inputs
can be processed using a “thesaurus” functionality: “Text disambiguation queries
(DAQs): Identified by characteristics of the user’s text, such as misspellings or
ambiguity of words in the text (as defined by an external machine-readable
dictionary, thesaurus, or lexicon, e.g., WordNet), or by a lack of information in the
autocontextualization engine about words in the text.” Fratkina at 7:47-52. And I
note that Fratkina expressly teaches that its dialog engine “is designed to work with
any prior art search/retrieval engines to produce a search space.” Id. at 8:51-60.
These express teachings would have motivated a PHOSITA to combine Fratkina
with the thesaurus functionality of Rajaraman’s search engine. Given that both
Fratkina and Rajaraman relate to searching a hierarchy of nodes and Fratkina
already teaches inclusion of thesaurus functionality, such modifications to the
system in Fratkina would have required only minor modifications in software and
would have yielded predictable results to a PHOSITA. This express “thesaurus”
teaching of Fratkina would have, at a minimum, provided a suggestion or
motivation to a PHOSITA to use synonymous terms, such as taught by
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Rajaraman's "special terms file" for Fratkina's "thesaurus" function, such that user
inputs co1Tesponding to synonymous terms would be deemed by the system to be a
match for their assigned keyword(s). Further, a PHOSITA would have also
recognized that the application of methods related to searching keyword synonyms
contained in a thesaurus, which were well known at that time, would have yielded
predictable results when applied in the system of Fratkina and would not render
any features of Fratkina, such as the ability to jump between nodes, inoperable.
Therefore, in my opinion, a person having skill in the art would have been
motivated to combine the teachings of Fratkina and Rajaraman to render claims 3-
6 obvious.
IV. CONCLUSION
94. I declare that all statements made herein of my knowledge are true, and
that all statements made on information and belief are believed to be true, and that
these statements were made with the knowledge that willful false statements and
the like so made are punishable by fine or imprisonment, or both, under Section
1001 of Title 18 of the United States Code.
Date: MtA.Wl 21 2017
By: �� Pad�Smyth, Ph.D.
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