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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 IPR2023-00037
`Patent No. 10,971,140
`____________________
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`DECLARATION OF DAVID ANDERSON, Ph.D. IN SUPPORT OF
`PATENT OWNER’S RESPONSE
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`I, David Anderson, Ph.D, do hereby declare as follows:
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`I.
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`Introduction
`A.
`Background and qualifications
`1.
`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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`2. 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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`3.
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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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`4.
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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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`perception. These courses and my research have covered many topics relevant to
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`the subject matter of the ’277 patent and the prior art cited therein.
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`5.
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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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`6.
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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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`“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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`7.
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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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`8.
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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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`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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`B.
`9.
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`Engagement
`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 Response to the
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`Petition in Inter Partes Review proceeding IPR2023-00037, with respect to U.S. Pat.
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`10,971,140. I am being compensated for my time spent on this matter. I have no
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`interest in the outcome of this proceeding and the payment of my fees is in no way
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`contingent on my providing any particular opinions.
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`10. 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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`11. The statements made herein are based on my own knowledge and
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`opinions.
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`C. Materials considered
`12.
`In the course of preparing my opinions, I have reviewed and am familiar
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`with the ’140 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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`Chen. 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 3 years of experience,
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`research, and education in the field of art.
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`II. Relevant legal standards
`13.
`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.
`14.
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`Person of ordinary skill in the art
`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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`art at the time of invention of the ’140 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 February 4, 2002,
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`for the challenged claims, Pet. 5, 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, but I note that obviousness must be viewed from the
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`perspective of one of ordinary skill in the field of speech recognition, and my
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`opinions are rendered on the basis of the definition of the ordinary artisan set forth
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`in the Petition in view of that further clarification. Throughout my declaration, my
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`statements as to the POSA’s capabilities and knowledge are based on the Petition’s
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`definition in conjunction with my statements about that person’s capabilities,
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`although I do not necessarily agree that the Petition has properly defined the POSA
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`in this context.
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`17.
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`I note that the field of electrical engineering encompasses many
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`specialties, including fiber optics, analog circuits, digital circuits, very large-scale
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`integration (VLSI), systems and controls, digital signal processing, bio-medical
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`sensors and systems, electrical energy, electromagnetics, nanotechnology,
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`telecommunications, computer
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`systems and
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`software, and others.
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`(see
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`https://ece.gatech.edu/research/tigs). At Georgia Tech, one of the top 5 electrical and
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`computer engineering schools in the country, students specialize in one of these
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`areas after taking some general courses. If a student desires to study speech
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`recognition, they would specialize in the area of digital signal processing (DSP) and
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`then pursue studies specific to the advanced subspecialty of speech recognition
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`within DSP in their graduate studies. On the other hand, a student who desires to
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`learn computer architecture would take a different set of core courses in one of the
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`computer engineering specialties. This structure is common, with only slight
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`variations, across all electrical and computer engineering programs. In this sense,
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`electrical and computer engineering can be compared with medicine. The engineers
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`each take basic math and physics classes as well as some introductory systems
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`classes before specializing. Medical students take general courses as well but then
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`may specialize into one of a variety of different subdisciplines such as cardiology,
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`neurosurgery, dermatology, orthopedic surgery, etc. It would be understood that if a
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`person was a specialist in orthopedic surgery they would not, under any ordinary
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`circumstances, also be a specialist in neurosurgery.
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`18. Mr. Schmandt’s proposed person of ordinary skill in the art was
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`specifically identified to have specialized in speech recognition. Any such person of
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`ordinary skill would not also be expected to have specialized in parallel processing
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`architectures and methods or high-performance computing in addition to speech
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`recognition. At Georgia Tech, I have a somewhat rare distinction of having been
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`hired to be a part of both the computer engineering group and the digital signal
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`processing group. This was due to my years of experience designing embedded
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`systems in addition to my Ph.D. studies in signal processing. In my role I teach
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`classes in both areas including courses on advanced digital signal processing, speech
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`recognition, machine learning, digital circuit design, and computer architecture.
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`However, I have never taught a course that would have equipped a masters-level
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`student in speech recognition to apply a parallel processing architecture like Chen’s
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`to known speech recognition techniques.
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`19. While a person with a master’s degree in one of the fields identified in
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`Mr. Schmandt’s POSA definition could be a person of ordinary skill in speech
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`recognition or a person of ordinary skill in high performance computing and parallel
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`processing, that person would not be both.
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`20. Having studied, taught, and researched multiple aspects of computer
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`architecture and the circuit-level implementation of signal processing systems, I am
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`conversant with the basic concepts, trade-offs, and challenges that would be
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`encountered in designing a system such as that claimed in the ’140 patent. An
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`ordinarily skilled engineer at the time of the invention would have been trained in
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`evaluating both the costs and benefits of a particular design choice. Engineers are
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`trained (both in school and through general experience in the workforce) to
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`recognize that design choices can have complex consequences that need to be
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`evaluated before forming a motivation to pursue a particular design choice, and
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`before forming an expectation of success as to that design choice. In my opinion,
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`anyone who did not recognize these realities would not be a person of ordinary skill
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`in the art. Thus, a person who would have simply formed design motivations based
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`only on the premise that a particular combination of known elements would be
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`possible would not be a person of ordinary skill regardless of their education,
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`experience, or technical knowledge. Likewise, a person who would have formed
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`design motivations as to a particular combination of known elements based only on
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`the premise that the combination may provide some benefit, with no consideration
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`of the relevance of the benefit in the specific context and in relation to the costs or
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`disadvantages of that combination, would also not have been a person of ordinary
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`skill in the art, regardless of their education, experience, or technical knowledge. In
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`my opinion, a person of ordinary skill in the art would have been deliberative and
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`considered, rather than impulsive.
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`21. 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.
`22.
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`Burden of proof
`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
`23.
`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
`24.
`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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`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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`25.
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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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`26.
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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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`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 ’140 Patent
`27. U.S. Patent 10,971,140, titled “Speech recognition using parallel
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`processors,” is directed to an improved speech recognition circuit that “uses
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`parallel processors for processing the input speech data in parallel.” Ex. 1001,
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`1:18-20. The ’140 patent teaches multiple processors “arranged in groups or
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`clusters,” with each group or cluster of processors connected to one of several
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`“partial lexical memories” that “contains part of the lexical data.” Ex. 1001, 3:13-
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`18. “Each lexical tree processor is operative to process the speech parameters using
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`a partial lexical memory and the controller controls each lexical tree processor to
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`process a lexical tree corresponding to partial lexical data in a corresponding
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`partial lexical memory.” Ex. 1001, 3:19-24. The ’140 patent further teaches that
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`the invention “provides a circuit in which speech recognition processing is
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`performed in parallel by groups of processors operating in parallel in which each
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`group accesses a common memory of lexical data.” Ex. 1001, 3:62-66.
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`28. The specification of the ’140 patent thus provides: “[T]he present
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`invention provides a circuit in which speech recognition processing is performed in
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`parallel by groups of processors operating in parallel in which each group accesses
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`a common memory of lexical data. . . . Each processor within a group can access
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`the same lexical data as any other processor in the group. The controller can thus
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`control the parallel processing of input speech parameters in a more flexible
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`manner. For example, it allows more than one processor to process input speech
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`parameters using the same lexical data in a lexical memory. This is because the
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`lexical data is segmented into domains which are accessible by multiple
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`processors.” Ex. 1001, 3:62-4:18.
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`29. Figure 2 of the patent, annotated below, further illustrates that
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`architecture by showing two groups of lexical tree processors, with each group
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`containing multiple processors 1-k, and each group of processors connected to a
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`dedicated “acoustic model memory,” such that there are least two acoustic model
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`memories for at least two groups of processors. Ex. 1001, Fig. 2 (annotated).
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`30. Moreover, the ’140 patent expressly distinguishes its novel
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`architecture from two prior known alternative designs, which the patent describes
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`as less advantageous. In particular, the ’140 patent teaches that: “By providing a
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`plurality of processors in a group with a common memory, flexibility in the
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`processing is provided without being bandwidth limited by the interface to the
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`memory that would occur if only a single memory were used for all processors.
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`The arrangement is more flexible than the parallel processing arrangement in
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`which each processor only has access to its own local memory and requires
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`fewer memory interfaces (i.e. chip pins).” Ex. 1001, 4:1-9. The patent thus
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`distinguishes its design from (1) a one-memory-to-all-processors design, which it
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`describes as bandwidth limited in the processor to memory interface; and (2) a one-
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`memory-to-one-processor design, which would require more memory interfaces
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`and would be less flexible.
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`IV. The POSA would not have had a reasonable expectation of success with
`respect to the Petition’s combination of Jiang and Chen
`23.
`I understand the Petition and Mr. Schmandt propose that the ordinary
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`artisan would have found it obvious “to configure a computing platform comprising
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`clusters of processors as taught by Chen to perform the speech recognition
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`techniques of Jiang.” Ex. 1003 ¶ 68. I disagree for the reasons below.
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`24.
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`Jiang does not enable performing its speech recognition techniques
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`using the particular clustered processor embodiment the Petition has selected from
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`Chen, and Chen does not enable using that clustered processor embodiment to
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`perform Jiang’s speech recognition techniques. Mr. Schmandt
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`admitted those facts at his deposition. Ex. 2017 at 86:9-17.
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`25. Mr. Schmandt and the Petition do not provide any explanation as to
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`how the ordinary artisan could have “configure[d] a computing platform comprising
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`clusters of processors as taught by Chen to perform the speech recognition
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`techniques of Jiang.” Ex. 1003 ¶ 68. Nor do they provide any evidence that ordinary
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`artisans at the time of the ’140 patent could have done so.
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`26. Mr. Schmandt’s declaration simply states the “modifications necessary,
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`such as configuring Chen’s circuitry to specifically recognize speech pursuant to
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`Jiang would have required only software programming and well-known computing
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`techniques and structures, and thus would have led a POSITA to a reasonable
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`expectation of success.” Ex. 1003 ¶ 68. Mr. Schmandt acknowledged at his
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`deposition, however, that his declaration does not specify what software
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`programming, computer techniques, or structures the POSA would need to utilize to
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`configure Chen’s circuitry to recognize speech pursuant to Jiang. Ex. 2017 at 48:13-
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`21. Moreover, I find it significant that Mr. Schmandt has never built the processor
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`to memory architecture for any of the systems identified in the background of his
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`declaration, nor has he ever supervised anyone involved in the process of mapping
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`a speech recognition model to a clustered processor and memory architecture like
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`Chen’s. Ex. 2017 at 34:24-35:5, 32:3-9, 146:20-24.
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`27.
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`In my opinion, based on my experience, implementing Jiang’s speech
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`recognition techniques on Chen’s clustered processor and memory architecture
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`would have been highly complex, and, without the benefit of the teachings of the’140
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`Patent, far outside the skill level of the ordinary artisan in the field of speech
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`recognition as defined by the Petition, without the benefit of the ’140 Patent. In
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`particular, moving Jiang’s speech recognition techniques to Chen’s clustered,
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`parallel processors and memories would have required coordinating multiple caches,
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`avoiding memory conflicts, controlling task sharing in an efficient manner, resolving
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`synchronous bottlenecks, and addressing communication bandwidth and latency
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`issues among the various hardware components, and developing a messaging
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`strategy to coordinate information sharing between and within clusters, among other
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`challenges. Such tasks were not within the skill level of the Petition’s ordinary
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`artisan in the field of speech recognition prior to the ’140 Patent.
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`28. As noted earlier, the ordinary artisan in the field of speech recognition
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`would have specialized in the area of digital signal processing (DSP), and would
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`have further pursued studies specific to speech recognition, an advanced
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`subspecialty of DSP, in their graduate studies. The POSA in the field of speech
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`recognition would not have also been a high-performance computing or parallel
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`processing specialist, and would not have been capable of addressing the complex
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`challenges of combining Chen with Jiang in the manner the Petition has proposed
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`any more than a dermatologist could be expected to also perform open heart surgery.
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`29. Moreover, speech recognition requires evaluating the comparative
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`likelihoods of many possible outcomes for each incoming frame of samples. Jiang
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`teaches the use of the Viterbi decoding algorithm. Ex. 1004 at 1:19-39, 2:1-46. The
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`Viterbi algorithm compares the likelihoods of all possible sequences for a given
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`input frame, given the most likely outcomes from the previous frame. Id. To perform
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`these comparisons requires extensive communication between computational
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`components that can have a drastic impact on the system design and performance.
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`By contrast, Chen teaches that the memory associated with one cluster is not directly
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`or adjacently accessible by the processors and memories in each other cluster. See
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`Ex. 1003 ¶ 95; Ex. 1005 at 9:10-39. As a result, performing Viterbi decoding when
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`the information needed by each node may be in a cluster that is not directly
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`accessible, as in Chen, is not likely to be successful in a practical speech recognition
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`system.
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`30. As explained by Hennesy & Patterson, communication between
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`parallel processing nodes is one of several considerations that can determine the
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`feasibility of a particular parallel architecture for use with a particular problem. Ex.
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`2019 at 534-535. Hennesy & Patterson also points out that the private memory model
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`in which memory is connected only within a cluster may be suitable “for
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`applications that require little or no communication.” Id. at Ex. 2019 at 533. Chen
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`implements such a private memory architecture but the speech recognition search
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`stage is an application that requires significant communication. Therefore, based on
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`teachings at the time of the ’140 Patent, the POSA would not expect the proposed
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`combination of Chen with Jiang to be successful.
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`31. My own experience in supervising a team that attempted to implement
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`an application within a parallel processing environment is illustrative. In addition to
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`my support, this team of engineers worked in close collaboration with a DSP
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`application expert at Texas Instruments for six months in order to implement a
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`custom computer vision application on a multi-processor system supplied by Texas
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`Instruments (Texas Instrument’s multi-processor daVinci platform). Even with
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`training, expert oversight, and extensive efforts on behalf of the team, the inter-
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`processor communication details prevented the successful implementation of their
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`system. Rather, the final product was only able to do a simple tracking of a black
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`ball on a white background—something that could have more easily been performed
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`on a single processor. While this system was not a speech recognition system, it was
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`a signal processing system that was simpler than a speech recognition system in
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`terms of complexity (only a few hundreds of lines of code). The failure of this design
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`example demonstrates that transitioning a signal processing system to any particular
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`multi-processing architecture is not necessarily trivial or practical, and that even
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`those beyond the level of ordinary skill in the field of speech recognition could not
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`necessarily expect success with respect to far simpler combinations than the one the
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`Petition has proposed.
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`32.
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`In my opinion, based on actual experience, the POSA would not have
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`“been capable of simply substituting” Chen’s clustered processors to implement
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`Jiang’s speech recognition techniques, as Mr. Schmandt’s declaration simplistically
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`presumes.
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`V. The Petition and Mr. Schmandt fail to prove a motivation to combine
`Jiang with Chen
`33. Apple and Mr. Schmandt allege that the POSA would have been
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`motivated to combine Jiang with Chen due to the alleged benefits of improved speed
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`and power, a relaxed pruning threshold, and reduced cost. Ex. 1003 ¶¶ 67, 74.
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`34. Separately, Mr. Schmandt contends that “the flexibility and scalability
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`afforded to the MARS machine by its clustered architecture [ ] would have motivated
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`a POSITA to implement clustered architecture in other speech recognition circuits.”
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`Ex. 1003 ¶ 71.
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`35.
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` Mr. Schmandt’s declaration assumes that the clustered processor and
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`memory arrangement of Chen would have necessarily improved the speed and
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`power of Jiang’s speech recognition, and thus allowed for relaxed pruning
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`threshold. Ex. 1003 ¶¶ 67, 74. Mr. Schmandt’s declaration does not
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`undertake any quantitative or other particularized analysis to demonstrate why
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`Chen would allegedly speed up Jiang and relax its pruning threshold, or by how
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`much. Rather, Mr. Schmandt seems to simply assume the conclusion that more
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`processors would have been better.
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`36. Mr. Schmandt’s assumptions are not correct in the context of his
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`proposed combination. Mathew I provides a detailed report on computer architecture
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`modifications intended to improve the speed of a speech recognizer, and in particular
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`through the addition of more processors. Ex. 2021 at 11. Matthew I explains that it
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`developed “a parallel version” of the Sphinx speech recognition system in which the
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`distance calculation (GAU, or Gaussian probability estimation) was run on a
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`separate processor from the search stage (HMM processing) with parallelization. Id.
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`This two-processor parallel processing design was only 1.67x faster than the
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`“original sequential version,” not 1.97x faster as the authors had predicted based on
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`Amdahl’s law. Id. The authors then further modified the search stage (HMM phase)
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`“to use 4 processors instead of 1,” thus moving from a two processor system (one
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`for distance calculation and one for search) to a five processor system (one processor
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`for the calculation stage, and four for search). Id. As Mathew I reports, “the resulting
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`5 processor version was slower than the 2 processor version due to the high
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`synchronization overhead.” Id. (emphasis added).
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`37. Mathew I demonstrates that the ordinary artisan could not have
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`assumed that Chen’s complex, clustered parallel processing architecture with
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`numerous processors in each cluster and multiple distributed memories would have
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`improved the speed of Jiang’s speech recognition system or relaxed its pruning
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`threshold in view of the high synchronization overhead and other complications that
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`such a system would have implicated.
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`38. Mr. Schmandt’s declaration testimony as to the alleged cost-based
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`motivations for the combination are likewise unsupported. As Mr. Schmandt
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`admitted at his deposition, his declaration opines that the combination of Jiang and
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`Chen would have been less expensive, but never states what the combination
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`would have been less expensive than, or on what basis.
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` Ex. 2017 at 61:14-62:9. Mr. Schmandt also made clear that his declaration
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`testimony was not based on a cost comparison between Chen’s clustered processing
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`architecture and any other allegedly alternative clustered processing architectures.
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`Ex. 2017 at 63:8-15. And Mr. Schmandt likewise admitted that his declaration
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`provides no quantitative cost comparison between Jiang’s unmodified architecture
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`and his proposed modified architecture in combination with Chen. Ex. 2017 at
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`40:23-41:4.
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`39. Mr. Schmandt thus provides no basis for a cost-driven motivation to
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`combine Jiang with Chen.
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`40. Mr. Schmandt’s “flexibility and scalability” motivation theory is also
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`lacking evidentiary support. Ex. 1003 ¶¶ 71, 72. Mr. Schmandt’s declaration cites
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`Hon as purportedly teaching that clustered processing architectures in general
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`present the benefits of flexibility and scalability, and that this would have motivated
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`the combination of Jiang and Chen. Id. But Mr. Schmandt’s deposition testimony
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`clarifies that Hon’s cited passages in
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