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` UNITED STATES PATENT AND TRADEMARK OFFICE
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`BEFORE THE PATENT TRIAL AND APPEAL BOARD
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`APPLE INC.,
`Petitioner,
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`v.
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`Zentian Limited,
`Patent Owner.
`____________________
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`Case IPR2023-00035
`Patent No. 10,062,377
`____________________
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`DECLARATION OF DAVID ANDERSON, Ph.D. IN SUPPORT OF
`PATENT OWNER’S PRELIMINARY RESPONSE
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`I.
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`I, David Anderson, Ph.D, do hereby declare as follows:
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`Introduction
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`Engagement
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`A.
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`1.
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`I have been retained by Patent Owner Zentian Limited (“Zentian” or
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`“Patent Owner”) to provide my opinions with respect to Zentian’s Preliminary
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`Response to the Petition in Inter Partes Review proceeding IPR2023-00035, with
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`respect to U.S. Pat. 10,062,377. I am being compensated for my time spent on this
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`matter. I have no interest in the outcome of this proceeding and the payment of my
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`fees is in no way contingent on my providing any particular opinions.
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`2.
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`As part of this engagement, I have also been asked to provide my
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`technical review, analysis, insights, and opinions regarding the materials cited and
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`relied upon by the Petition, including the prior art references and the supporting
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`Declaration of Mr. Schmandt.
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`3.
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`The statements made herein are based on my own knowledge and
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`opinions.
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`Background and qualifications
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`B.
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`4. My full qualifications, including my professional experience and
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`education, can be found in my Curriculum Vitae, which includes a complete list of
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`my publications, and is attached as Ex. A to this declaration.
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`IPR2023-00035
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`DECLARATION OF DAVID ANDERSON, PH.D. ISO
` PATENT OWNER’S PRELIMINARY RESPONSE
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`5.
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`I am a professor in the School of Electrical and Computer Engineering
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`at the Georgia Institute of Technology (“Georgia Tech”) in Atlanta, Georgia. I
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`have been a professor at Georgia Tech since 1999. In 2009 I served as a visiting
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`professor in the Department of Computer Science at Korea University in Seoul,
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`South Korea.
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`6.
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`I received my Ph.D. in Electrical and Computer Engineering from
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`Georgia Tech in 1999. I received my B.S. and M.S. in Electrical Engineering from
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`Brigham Young University in 1993 and 1994, respectively.
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`7.
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`In my employment prior to Georgia Tech as well as in my subsequent
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`studies and research, I have worked extensively in areas related to the research,
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`design, and implementation of speech and audio processing systems. I have also
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`taught graduate and undergraduate level courses at Georgia Tech on the
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`implementation of signal processing and embedded systems. For example, I have
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`taught courses on statistical machine learning, machine learning for speech, pattern
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`recognition, multimedia processing and systems, software design, computer
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`architecture, real-time signal processing systems, and applications of signal
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`processing (covering topics in audio processing and speech recognition). I have
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`also designed and taught a course on signal processing in the context of human
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`DECLARATION OF DAVID ANDERSON, PH.D. ISO
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`perception. These courses and my research have covered many topics relevant to
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`the subject matter of the ’377 patent and the prior art cited therein.
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`8.
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`I have served as principal investigator or co-principal investigator in
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`numerous multi-disciplinary research projects including “Blind Source Separation
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`for Audio,” “Audio Classification,” “Auditory Scene Analysis,” “Hearing Aid
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`Audio Processing,” “Speaker Driver Sound Enhancement,” “I-Vector Based Voice
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`Quality,” “Analysis of Voice Exercise Using Signal Processing,” and “Smart
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`Homes for Effective and Safe Remote Work During a Pandemic and Beyond.”
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`9.
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`I also have extensive experience with the practical implementation of
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`signal processing algorithms, information theory, signal detection, and related
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`topics through my research and consulting. I have published over 200 book
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`chapters and papers in reviewed journals and conferences. Topics include those
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`such as “Speech recognition using filter bank features,” “Speaker adaptation using
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`speaker similarity score on DNN features.” “Segmentation based speech
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`enhancement using auxiliary sensors,” “A framework for estimation of clean
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`speech by fusion of outputs from multiple speech enhancement systems,”
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`“Distributed acquisition and processing systems for speech and audio,” “A missing
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`data-based feature fusion strategy for noise-robust automatic speech recognition
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`using noisy sensors,” “Learning distances to improve phoneme classification,”
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`DECLARATION OF DAVID ANDERSON, PH.D. ISO
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`“Identification of voice quality variation using i-vectors,” “Varying time-constants
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`and gain adaptation in feature extraction for speech processing,” “Low bit-rate
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`coding of speech in harsh conditions using non-acoustic auxiliary devices,”
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`“Speech analysis and coding using a multi-resolution sinusoidal transform,”
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`“Biologically inspired auditory sensing system interfaces on a chip,” “Cascade
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`classifiers for audio classification,” and “Single acoustic channel speech
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`enhancement based on glottal correlation using non-acoustic sensors.” I have also
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`contributed book chapters for treatises such as “Independent Component Analysis
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`for Audio and Biosignal Applications,” and written a book on Fixed-Point Signal
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`Processing which is related to the practical implementation of systems for
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`processing sound and other signals.
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`10.
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`I am a named inventor on eight patents, including “Speech activity
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`detector for use in noise reduction system, and methods therefor” (U.S. Patent No.
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`6,351,731), and “Analog audio signal enhancement system using a noise
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`suppression algorithm” (U.S. Patent No. 7,590,250).
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`11.
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`I am a Senior Member of the Institute of Electrical and Electronics
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`Engineers (“IEEE”) and have been a Member since 1991. I am also a Member of
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`the IEEE Signal Processing Society. From 1994 to 2016, I was also a member of
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`the Acoustical Society of America. In 2003, I served as the Co-Chair for the NSF
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`DECLARATION OF DAVID ANDERSON, PH.D. ISO
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`Symposium on Next Generation Automatic Speech Recognition. In 2004, I
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`received the Presidential Early Career Award for Scientists and Engineers,
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`presented by then-President George W. Bush, for my work on ultra-low-power
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`signal processing system design.
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`C. Materials considered
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`12.
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`In the course of preparing my opinions, I have reviewed and am familiar
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`with the ’377 patent, including its written description, figures, and claims. I have
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`also reviewed and am familiar with the Petition in this proceeding, the supporting
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`Declaration of Mr. Schmandt, and the relied upon prior art, including Jiang and
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`Smyth. I have also reviewed the materials cited in this declaration. My opinions are
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`based on my review of these materials as well as my more than 30 years of
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`experience, research, and education in the field of art.
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`II. Relevant legal standards
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`13.
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`I am not an attorney. I offer no opinions on the law. But counsel has
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`informed me of the following legal standards relevant to my analysis here. I have
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`applied these standards in arriving at my conclusions.
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`A.
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`14.
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`Person of ordinary skill in the art
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`I understand that an analysis of the claims of a patent in view of prior
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`art has to be provided from the perspective of a person having ordinary skill in the
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`DECLARATION OF DAVID ANDERSON, PH.D. ISO
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`art at the time of invention of the ’377 patent. I understand that I should consider
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`factors such as the educational level and years of experience of those working in the
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`pertinent art; the types of problems encountered in the art; the teachings of the prior
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`art; patents and publications of other persons or companies; and the sophistication
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`of the technology. I understand that the person of ordinary skill in the art is not a
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`specific real individual, but rather a hypothetical individual having the qualities
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`reflected by the factors discussed above.
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`15.
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`I understand that the Petition applies a priority date of September 14,
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`2004 for the challenged claims, Pet. 4, and I apply the same date.
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`16.
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`I further understand that the Petition defines the person of ordinary skill
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`in the art at the time of the invention as having had a master’s degree in computer
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`engineering, computer science, electrical engineering, or a related field, with at least
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`two years of experience in the field of speech recognition, or a bachelor’s degree in
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`the same fields with at least four years of experience in the field of speech
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`recognition. The Petition adds that further education or experience might substitute
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`for the above requirements. I do not dispute the Petition’s assumptions at this time,
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`and my opinions are rendered on the basis of the same definition of the ordinary
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`artisan set forth in the Petition.
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`DECLARATION OF DAVID ANDERSON, PH.D. ISO
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`17.
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`I also note, however, that an ordinarily skilled engineer at the time of
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`the invention would have been trained in evaluating both the costs and benefits of a
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`particular design choice. Engineers are trained (both in school and through general
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`experience in the workforce) to recognize that design choices can have complex
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`consequences that need to be evaluated before forming a motivation to pursue a
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`particular design choice, and before forming an expectation of success as to that
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`design choice. In my opinion, anyone who did not recognize these realities would
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`not be a person of ordinary skill in the art. Thus, a person who would have simply
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`formed design motivations based only on the premise that a particular combination
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`of known elements would be possible would not be a person of ordinary skill
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`regardless of their education, experience, or technical knowledge. Likewise, a person
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`who would have formed design motivations as to a particular combination of known
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`elements based only on the premise that the combination may provide some benefit,
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`with no consideration of the relevance of the benefit in the specific context and in
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`relation to the costs or disadvantages of that combination, would also not have be a
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`person of ordinary skill in the art, regardless of their education, experience, or
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`technical knowledge. In my opinion, a person of ordinary skill in the art would have
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`been deliberative and considered, rather than impulsive.
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`DECLARATION OF DAVID ANDERSON, PH.D. ISO
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`18. Throughout my declaration, even if I discuss my analysis in the present
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`tense, I am always making my determinations based on what a person of ordinary
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`skill in the art (“POSA”) would have known at the time of the invention. Based on
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`my background and qualifications, I have experience and knowledge exceeding the
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`level of a POSA, and am qualified to offer the testimony set forth in this declaration.
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`B.
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`19.
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`Burden of proof
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`I understand that in an inter partes review the petitioner has the burden
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`of proving a proposition of unpatentability by a preponderance of the evidence.
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`C. Claim construction
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`20.
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`I understand that in an inter partes review, claims are interpreted based
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`on the same standard applied by Article III courts, i.e., based on their ordinary and
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`customary meaning as understood in view of the claim language, the patent’s
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`description, and the prosecution history viewed from the perspective of the ordinary
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`artisan. I further understand that where a patent defines claim language, the
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`definition in the patent controls, regardless of whether those working in the art may
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`have understood the claim language differently based on ordinary meaning.
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`D. Obviousness
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`21.
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`I understand that a patent may not be valid even though the invention
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`is not identically disclosed or described in the prior art if the differences between the
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`DECLARATION OF DAVID ANDERSON, PH.D. ISO
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`subject matter sought to be patented and the prior art are such that the subject matter
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`as a whole would have been obvious to a person having ordinary skill in the art in
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`the relevant subject matter at the time the invention was made.
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`22.
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`I understand that, to demonstrate obviousness, it is not sufficient for a
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`petition to merely show that all of the elements of the claims at issue are found in
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`separate prior art references or even scattered across different embodiments and
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`teachings of a single reference. The petition must thus go further, to explain how a
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`person of ordinary skill would combine specific prior art references or teachings,
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`which combinations of elements in specific references would yield a predictable
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`result, and how any specific combination would operate or read on the claims.
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`Similarly, it is not sufficient to allege that the prior art could be combined, but rather,
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`the petition must show why and how a person of ordinary skill would have combined
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`them.
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`23.
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`I understand that where an alleged motivation to combine relies on a
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`particular factual premise, the petitioner bears the burden of providing specific
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`support for that premise. I understand that obviousness cannot be shown by
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`conclusory statements, and that the petition must provide articulated reasoning with
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`some rational underpinning to support its conclusion of obviousness. I also
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`understand that skill in the art and “common sense” rarely operate to supply missing
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`DECLARATION OF DAVID ANDERSON, PH.D. ISO
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`knowledge to show obviousness, nor does skill in the art or “common sense” act as
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`a bridge over gaps in substantive presentation of an obviousness case.
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`III. Overview of the ’377 Patent
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`24. U.S. Patent 10,062,377, titled “Distributed pipelined parallel speech
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`recognition system,” is directed to an improved speech recognition circuit and
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`associated methods. Ex. 1001, 1:18-20.
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`25. The ’377 patent teaches that an “audio input for speech recognition”
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`may be input to the front end in the form of digital audio or analog audio that is
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`converted to digital audio using an analog to digital converter. Ex. 1001, 12:51-53.
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`“The audio input is divided into time frames, each time frame typically being on the
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`order of 10 ms.” Ex. 1001, 12:53-55. “For each audio input time frame, the audio
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`signal is converted into a feature vector. This may be done by splitting the audio
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`signal into spectral components,” such as, for instance, 13 components plus their
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`first and second derivatives, creating a total of 39 components. Ex. 1001, 12:56-58.
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`The feature vector thus “represents a point in an N-dimensional space,” where N is
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`generally in the range of 20 to 39. Ex. 1001, 13:19-23.
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`26. Each feature vector is then passed to the calculating circuit, or distance
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`calculation engine, which calculates a distance indicating the similarity between a
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`feature vector and one or more predetermined acoustic states of an acoustic model.
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`Ex. 1001, 5:63-6:2, 25:33-35 (“Each feature vector is transferred to a distance
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`calculation engine circuit 204, to obtain distances for each state of the acoustic
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`model.”). “The distance calculator stage of the recognition process computes a
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`probability or likelihood that a feature vector corresponds to a particular state.” Ex.
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`1001, 13:24-26. “The likelihood of each state is determined by the distance between
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`the feature vector and each state.” Ex. 1001, 13:1-2. The distance calculation may
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`be a Mahalanobis distance using Gaussian distributions. Ex. 1001, 4:20-33. “The
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`MHD (Mahalanobis Distance) is a distance between two N-dimensional points,
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`scaled by the statistical variation in each component.” Ex. 1001, 13:13-15. The ’377
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`patent teaches calculating the distance between a feature vector and 8,000 states,
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`“i.e. one distance for each of the 8,000 states,” Ex. 1001, 13:59-61, which it teaches
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`“gives the best recognition results when used with a language model.” Id. at 13:11-
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`12, 14:2-4 (“Each state is also a 39 dimensional vector, having the same spectral
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`components as the feature vector.”). “Due to the 10 ms frame length, a feature vector
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`arrives at the MHD engine,” i.e., the distance calculation engine or calculating
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`circuit, “every 10 ms.” Ex. 1001, 13:65-66.
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`27. The distance calculation engine or calculating circuit “may be included
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`within an accelerator,” Ex. 1001, 3:59-61, which may be a “loosely bound co-
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`processor for a CPU running speech recognition software,” and which “has the
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`advantage of reducing computational load on the CPU, and reducing memory
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`bandwidth load for the CPU.” Id. at 24:17-20; see Figs. 17-23. “Each time a feature
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`vector is loaded into the accelerator, the accelerator computes the distances for all
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`states for that feature vector[.]” Ex. 1001, 26:6-10.
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`28.
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`“The distances calculated by the distance calculation engine are then
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`transferred to the search stage 106 of the speech recognition circuit, which uses
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`models such as one or more word models and/or language models to generate and
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`output recognised text.” Ex. 1001, 24:5-11.
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`IV. Jiang
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`29. U.S. Pat. 6,374,219, titled “System for using silence in speech
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`recognition,” (“Jiang”), is directed to “computer speech recognition performed by
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`conducting a prefix tree search of a silence bracketed lexicon.” Ex. 1004, 1:15-18.
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`“Possible words represented by the input data stream are provided as a prefix tree
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`including a plurality of phoneme branches connected at nodes.” Ex. 1004, 4:8-12.
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`Speech is input into system 60 in the form of audible voice signal provided by the
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`user to a microphone 62, which converts the audible speech into an analog electric
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`signal, which is converted by A/D converter 64 into a sequence of digital signals that
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`are then provided to feature extraction module 66. Ex. 1004, 6:45-52. Feature
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`extraction module 66 divides the digital signal into frames, each approximately 10
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`ms in duration. Id. at 6:62-65.
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`30.
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`“The frames are then preferably encoded by feature extraction module
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`66 into a feature vector reflecting the spectral characteristics for a plurality of
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`frequency bands.” Ex. 1004, 6:65-7:1. “In the case of discrete and semi-continuous
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`hidden Markov modeling, feature extraction module 66 also preferably encodes the
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`feature vectors into one or more codewords using vector quantization techniques and
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`a codebook derived from training data.” Id. at 7:1-5.
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`31.
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`“Upon receiving the codewords from feature extraction module 66, and
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`the boundary detection signal provided by silence detection module 68, tree search
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`engine 74 accesses information stored in the phonetic speech unit model memory
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`72.” Ex. 1004, 7:30-34. “Based upon the HMMs stored in memory 72, tree search
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`engine 74 determines a most likely phoneme represented by the codeword received
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`from feature extraction module 66, and hence representative of the utterance
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`received by the user of the system.” Ex. 1004, 7:37-42.
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`V.
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`Smyth
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`32. U.S. Pat. 5,819,222, titled “Task-constrained connected speech
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`recognition of propagation of tokens only if valid propagation path is present,”
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`(“Smyth”), is directed to “task-constrained connected word recognition where the
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`task, for example, might be to recognise one of a set of account numbers or product
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`codes.” Ex. 1005, 1:15-21.
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`VI. The ’377 Patent’s claimed distance calculations
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`33. All challenged claims of the ’377 patent recite “a second programmable
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`device programmed to calculate distances indicating the similarity between a feature
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`vector and a plurality of acoustic states of an acoustic model,” see Pet. 77, limitation
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`1(c) (emphasis added), and “wherein said identification of spoken words uses one or
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`more distances calculated from a first feature vector.” Pet. 77, limitation 1(g)
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`(emphasis added).
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`34.
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`In my opinion, an ordinary artisan would understand the above claim
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`language to require making distance calculations using the feature vectors
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`themselves, and this is particularly clear in view of the language of limitation 1(g).
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`35.
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`I also note that each embodiment of the ’377 patent is consistent with
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`that understanding. For instance, Fig. 20 of the patent, included below, shows feature
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`vectors (FVs) extracted at the front end, and then passed to the distance calculation
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`Accelerator, which “calc[ulates] all dist[ances] for” each depicted feature vector. Ex.
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`1001, FIG 20.
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`36. Figures 18, 19, 21, 22, and 23 likewise contain the same teaching. Ex.
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`1001.
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`37. The ’377 patent’s written description likewise teaches that “[t]he
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`distance calculator computes the distance in the N-dimensional space from the
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`Feature Vector to the probability distribution for each state.” Ex. 1001, 13:27-30.
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`The patent likewise states: “The FV registers 209 hold the feature vector whose
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`distances are currently bring [sic] computed by the distance calculation engine 204.”
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`Ex. 1001, 25:9-11 (emphasis added). Moreover, “[e]ach feature vector is transferred
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`to distance calculation circuit 204, to obtain distances for each state of the acoustic
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`model[,]” Ex. 1001 at 25:33-35 (emphasis added), and “[e]ach time a feature vector
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`is loaded into the accelerator, the accelerator computes distances for all states for
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`that feature vector, and stores the results alternately in the A or B Results Memory.”
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`Id. at 26:6-10 (emphasis added), see also id. at 35:5-8. As these passages illustrate,
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`in each instance the patent teaches the direct use of the feature vectors themselves
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`for performing distance calculations. Moreover, although the ’377 patent
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`contemplates the use of certain different types of distance calculations, including
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`Mahalanobis Distances, it clearly teaches that any distance calculation must use the
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`feature vectors themselves, not other values. For instance, the patent states: “The
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`feature vector is used to calculate 8,000 MHD [Mahalanobis Distances] for each
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`time frame, i.e., one distance for each of the 8,000 states.” Ex. 1001, 13:59-61.
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`VII. Jiang does not teach the ’377 Patent’s claimed distance calculations
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`38.
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`I understand that the Petition and Mr. Schmandt present two theories
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`with respect to the distance calculation requirement of the challenged claims. Pet. at
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`18-19. The Petition alleges that Jiang alone teaches the limitation, Pet. 23-27, and
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`alternatively alleges that the limitation would have been obvious by modifying Jiang
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`DECLARATION OF DAVID ANDERSON, PH.D. ISO
` PATENT OWNER’S PRELIMINARY RESPONSE
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`in view of Smyth. Pet. 40-41. I address Jiang below and the modification of Jiang in
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`view of Smyth in the following section.
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`39. U.S. Patent No. 6,374,219 to Jiang (“Jiang”), is directed to a “System
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`for Using Silence in Speech Recognition,” Ex. 1004, Title, and particularly to using
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`silence detection to indicate a word boundary to a tree search engine as part of a
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`speech recognition system. Ex. 1004, 7:17-28.
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`40.
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`Jiang teaches that “speech is input into system 60 in the form of an
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`audible voice signal provided by the user to microphone 62,” which “converts the
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`audible speech signal into an analog electronic signal which is provided to A/D
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`converter 64. A/D converter 64 converts the analog speech signal into a sequence of
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`digital signals which is provided to feature extraction module 66.” Ex. 1004, 6:46-
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`53. “Feature extraction module 66 divides the digital signal received from A/D
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`converter 64 into frames which include a plurality of digital samples. Each frame is
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`approximately 10 milliseconds in duration. The frames are then preferably encoded
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`by feature extraction module 66 into a feature vector reflecting the spectral
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`characteristics for a plurality of frequency bands. In the case of discrete and semi-
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`continuous hidden Markov modeling, feature extraction module 66 also preferably
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`encodes the feature vectors into one or more codewords using vector quantization
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`techniques and a codebook derived from training data. Thus, feature extraction
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`IPR2023-00035
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`DECLARATION OF DAVID ANDERSON, PH.D. ISO
` PATENT OWNER’S PRELIMINARY RESPONSE
`
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`module 66 provides, as its output the feature vectors (or codewords) for each spoken
`
`utterance.” Ex. 1004, 6:62-7:7. “Upon receiving the codewords from feature
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`extraction module 66, and the boundary detection signal provided by silence
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`detection module 68, tree search engine 74 accesses information stored in the
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`phonetic speech unit models, such as hidden Markov models, which represent
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`speech units to be detected by system 60.” Ex. 1004, 7:29-35. “Based upon the
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`HMMs stored in memory 72, tree search engine 74 determines a most likely
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`phoneme represented by the codeword received from feature extraction module 66,
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`and hence representative of the utterance received by the user of the system.” Ex.
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`1004, 7:37-42.
`
`41.
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`Jiang further teaches that “[a]s the tree search engine traverses tree 77,
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`it preferably computes a score, for each phoneme branch considered in tree 77,
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`wherein the score represents the likelihood that the particular phoneme encoded by
`
`the codeword corresponds to the phoneme for the branch under consideration.” Ex.
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`1004, 8:28-33. “As tree search engine 74 traverses tree 77, it preferably assigns a
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`score to each node in tree 77 which is based on the likelihood that the present
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`codeword (output probability distributions) under analysis is represented by the
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`phoneme corresponding to the branch in tree 77 then being considered, and based
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`on the score assigned to nodes further up the tree which are connected by phoneme
`
`
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`18
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`
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`IPR2023-00035
`
`DECLARATION OF DAVID ANDERSON, PH.D. ISO
` PATENT OWNER’S PRELIMINARY RESPONSE
`
`
`branches to the present node. This is all done in a known manner.” Ex. 1004, 8:43-
`
`51.
`
`42.
`
`In view of Jiang’s teachings above, an ordinary artisan would have
`
`understood that Jiang teaches clustering feature vectors into similar codewords,
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`where each feature vector was vector quantized encoded into a codeword, and then
`
`using the codewords to compute a score that represents the likelihood that the
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`particular phoneme encoded by the codeword corresponds to the phoneme for the
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`branch under consideration. Jiang then determines a most likely phoneme
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`represented by the codeword received from feature extraction module 66. I
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`understand the Petition provides the same understanding of Jiang. Pet. 21, 23.
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`43. An ordinary artisan would have known that determining likelihood
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`scores based on codewords and vector quantization, as taught in Jiang, is an entirely
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`different technique in the art of speech recognition from calculating distances based
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`on the feature vectors themselves, as recited in the ’377 patent’s challenged claims.
`
`44. Vector quantization is a lossy data compression technique that is “used
`
`to code a spectral vector into one of a fixed number of discrete symbols in order to
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`reduce the computation required in a practical system.” Ex. 1015, Part 1, at 28.
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`“[T]he basic idea of VQ is to reduce the information rate of the speech signal to a
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`low rate through the use of a codebook with a relatively small number of code
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`
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`19
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`
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`IPR2023-00035
`
`DECLARATION OF DAVID ANDERSON, PH.D. ISO
` PATENT OWNER’S PRELIMINARY RESPONSE
`
`
`words.” Ex. 1015, Part 3 at 162. Stated otherwise, vector quantization converts the
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`actual feature vectors themselves into “a much smaller set of vector quantized (VQ)
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`feature signals.” Ex. 2005 at 16:1-7; Ex. 1015, Part 3, at 155. Using vector
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`quantization comes “at the cost of increased error in signal representation but with
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`the benefit of significantly reduced computation in the recognition process.” Ex.
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`1015, Part1, at 34. Notably, when a vector quantization and codeword approach is
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`used, the need to calculate actual distances between the feature vectors and acoustic
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`states of an acoustic model is eliminated and “this spectral similarity computation is
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`often reduced to a table lookup of similarities between pairs of codebook vectors.”
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`Ex. 1015, Part 3, at 154.
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`45. By contrast, the ’377 patent teaches that “[e]ach feature vector is
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`transferred to distance calculation circuit 204, to obtain distances for each state of
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`the acoustic model[,]” Ex. 1001 at 25:33-35 (emphasis added), and “[e]ach time a
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`feature vector is loaded into the accelerator, the accelerator computes distances for
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`all states for that feature vector, and stores the results alternately in the A or B Results
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`Memory.” Id. at 26:6-10 (emphasis added), see also id. at 35:5-8.
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`46.
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`Jiang’s codeword-based teachings are thus significantly different than
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`the ’377 patent’s claimed distance calculations, and an ordinary artisan would have
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`readily recognized that fact. Simply put, the ordinary artisan would have understood
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`
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`20
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`
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`IPR2023-00035
`
`DECLARATION OF DAVID ANDERSON, PH.D. ISO
` PATENT OWNER’S PRELIMINARY RESPONSE
`
`
`that Jiang’s codewords are not the multi-dimensional real “feature vector” that the
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`challenged claims require for distance calculations.
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`47.
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`I understand that the Petition and Mr. Schmandt argue that the
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`challenged claims are met so long as a distance calculation “indicates a similarity
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`between a feature vector and” one or more acoustic states of an acoustic model, and
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`that Jiang teaches the claimed distance calculation because its codeword-based
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`likelihood scores allegedly indicate such a similarity. Pet. 24-25; Ex. 1003, at
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`paragraphs 142-45.
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`48.
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`I disagree with that assessment for three reasons.
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`49. First, limitation 1(g) expressly requires “distances calculated from a
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`first feature vector.” Thus, regardless of the “indicating a similarity” language of
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`limitation 1(c), limitation 1(g) precludes satisfying the challenged claims based on
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`codeword-based likelihood scores, which are not “distances calculated from a first
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`feature vector.”
`
`50. Second, even where it discusses the use of distance calculations to
`
`determine the “similarity” between a feature vector and the acoustic states, the ’377
`
`patent specifically teaches using the feature vectors themselves to make the distance
`
`calculation. For instance, the patent expressly teaches that “the Mahalanobis distance
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`between the feature vector and each state is calculated, to determine similarity of the
`
`
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`21
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`IPR2023-00035
`
`DECLARATION OF DAVID ANDERSON, PH.D. ISO
` PATENT OWNER’S PRELIMINARY RESPONSE
`
`
`feature vector to each state.” Ex. 1001, 13:15-18 (emphasis added). Indeed, in every
`
`disclosed embodiment of the distance calculation step, the ’377 patent teaches
`
`calculating distances by directly using the feature vectors, not a different value such
`
`as a codeword derived from vector quantization. Accordingly, an ordinary artisan
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`would have known that the phrase “indicating a similarity” as recited in the claims
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`permits a choice as to the method of distance calculation that can be used (so long
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`as that method uses the feature vectors themselves and “indicates a similarity
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`between a feature vector and” the acoustic states), but does not permit so-called
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`distance calculations that do not use the feature vectors themselves.
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`51. Third, an ordinary artisan would have known that vector quantization
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`and codewords are not used to “calculate distances” as that term is used in the field
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`of art. Rather, it was well known prior at
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