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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 IPR2023-00033
`Patent No. 7,587,319
`____________________
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`DECLARATION OF DELIANG WANG, Ph.D., IN SUPPORT OF
`PATENT OWNER’S PRELIMINARY RESPONSE
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`Case IPR2023-00033
`DECLARATION OF DELIANG WANG, PH.D
`TABLE OF CONTENTS
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`Introduction ...................................................................................................... 1
`I.
`A.
`Engagement ........................................................................................... 1
`B.
`Background and qualifications .............................................................. 1
`C. Materials considered.............................................................................. 3
`Relevant legal standards .................................................................................. 4
`II.
`A.
`Person of ordinary skill in the art .......................................................... 4
`B.
`Burden of proof ..................................................................................... 6
`C.
`Claim construction ................................................................................ 6
`D. Obviousness ........................................................................................... 7
`III. Overview of the ’319 Patent ............................................................................ 8
`IV. The ’319 Patent’s limitations 46(b) and (d) .................................................... 9
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`I.
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`I, DeLiang Wang, Ph.D., do hereby declare as follows:
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`Introduction
`A.
`Engagement
`1.
`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 IPR2023-00033, with
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`respect to U.S. Pat. 7,587,319. 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
`B.
`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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`5.
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`I have spent my professional and academic career as a researcher in
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`the field of speech processing and machine learning (including deep learning). I
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`am currently a University Distinguished Scholar and Professor in the Department
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`of Computer Science and Engineering at Ohio State University.
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`6.
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`I received B.S. in 1983 and M.S. in 1986 from Peking (Beijing)
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`University, both in computer science. I received a Ph.D. in computer science in
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`1991 from the University of Southern California.
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`7.
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`I have received numerous awards and honors, including the U.S.
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`Office of Naval Research Young Investigator Award, the Best Paper Awards from
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`the Institute of Electrical and Electronics Engineers (“IEEE”) Computational
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`Intelligence Society and the IEEE Signal Processing Society, and the Helmholtz
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`Award from the International Neural Network Society. I am Co-Editor-in-Chief of
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`Neural Networks, a premier journal in the field of neural networks and deep
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`learning, and also served as President of the International Neural Network Society.
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`8.
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`I am an IEEE Fellow and an ISCA Fellow. I have published 175
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`articles in major scientific journals and more than 250 papers in leading conference
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`proceedings. In addition, I have supervised 29 graduate students who earned their
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`PhDs in computer science and engineering, including those currently employed by
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`leading IT companies preforming ASR and related work. More details are given in
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`the attached curriculum vitae.
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`9.
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`I am a recognized expert in the field of robust ASR (automatic speech
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`recognition) technology, including scientific methods, and algorithm development
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`and testing. Robust ASR aims to develop ASR algorithms that can suppress, or
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`remain unaffected by, background interference (such as noise). ASR algorithms
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`developed in my laboratory have been recognized as some of the best in the world;
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`our algorithms achieved the highest recognition rate in the CHiME-2 challenge in
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`2016, in the CHiME-4 challenge in 2020, and the LibriCSS challenge in 2021. My
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`research contributions and achievements in the fields of speech processing were
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`featured in the March 2017 issue of IEEE Spectrum, the most circulated technical
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`magazine in the world. I am one of the most published authors in peer-reviewed
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`scientific journals in the fields of speech and audio processing.
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`C. Materials considered
`In the course of preparing my opinions, I have reviewed and am familiar
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`with the ’319 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 Thelen, Bailey,
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`and Chen. I have also reviewed the materials cited in this declaration. My opinions
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`are based on my review of these materials as well as my 30 years of experience,
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`research, and education in the field of art.
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`II. Relevant legal standards
`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.
`12.
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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 ’319 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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`13.
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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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`14.
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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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`15.
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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 been
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`a 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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`16. 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.
`17.
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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
`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
`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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`20.
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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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`21.
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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 ’319 Patent
`22. U.S. Patent 7,587,319, titled “Speech recognition circuit using parallel
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`processors,” is directed to an improved speech recognition circuit that “uses parallel
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`processors for processing the input speech data in parallel.” Ex. 1001, 1:4-6. The
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`’319 patent teaches multiple processors “arranged in groups or clusters,” with each
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`group or cluster of processors connected to one of several “partial lexical memories”
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`that “contains part of the lexical data.” Ex. 1001, 2:64-3:3. “Each lexical tree
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`processor is operative to process the speech parameters using a partial lexical
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`memory and the controller controls each lexical tree processor to process a lexical
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`tree corresponding to partial lexical data in a corresponding partial lexical memory.”
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`Ex. 1001, 3:3-7. The ’319 patent further teaches that the invention “provides a circuit
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`in which speech recognition processing is performed in parallel by groups of
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`processors operating in parallel in which each group accesses a common memory of
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`lexical data.” Ex. 1001, 3:44-47.
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`IV. The ’319 Patent’s limitations 46(b) and (d)
`23. Limitation 46(b) recites “a plurality of lexical memories containing in
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`combination complete lexical data for word recognition, each lexical memory
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`containing part of said complete lexical data.” A person of ordinary skill would
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`understand limitation 46(b) to require (1) multiple lexical memories; (2) with each
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`lexical memory containing part of the “complete lexical data,” and (3) the lexical
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`memories together containing “complete lexical data for word recognition.”
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`24. Limitation 46(d) recites: “said [plurality of] processors being arranged
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`in groups of processors, each group of processors being connected to a lexical
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`memory.” A person of ordinary skill would understand limitation 46(d) to require
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`(1) multiple processors; (2) arranged in groups of processors; (3) with multiple
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`groups each containing said multiple processors; and (4) with each group of multiple
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`processors being connected to a lexical memory.
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`25. Together, limitations 46(b) and 46(d) require multiple groups of
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`processors, each group containing a plurality of processors, and each group
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`respectively connected to one of multiple lexical memories, with each lexical
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`memory containing part of the complete lexical data, and all of the lexical memories
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`combined containing all of the complete lexical data.
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`26. Figure 2 of the patent, annotated below, illustrates this architecture by
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`showing two groups of lexical tree processors, with each group containing multiple
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`processors 1-k, and each group of processors connected to a dedicated “acoustic
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`model memory,” such that there are at least two acoustic model memories for at least
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`two groups of processors. Fig. 2 (annotated).
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`I also note that the ’319 patent distinguishes that said architecture from
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`27.
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`two prior known alternative designs, which the patent describes as less
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`advantageous. The ’319 patent teaches that: “By providing a plurality of processors
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`in a group with a common memory, flexibility in the processing is provided without
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`being bandwidth limited by the interface to the memory that would occur if only a
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`single memory were used for all processors. The arrangement is more flexible than
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`the parallel processing arrangement in which each processor only has access to its
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`own local memory and requires fewer memory interfaces (i.e. chip pins).” Ex. 1001,
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`3:50-58. I understand from those teachings that the patent distinguishes its design
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`from (1) a one-memory-to-all-processors design, which it describes as bandwidth
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`(access) limited in the processor to memory interface; and (2) a one-memory-to-one-
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`processor design, which would require more memory interfaces and afford less
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`flexibility in controlling the extent of parallel processing of speech parameters.
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`28.
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`I understand that in order to meet the processor to memory architecture
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`of the challenged claims, the Petition relies on the combination of Thelen, Bailey,
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`and Chen. Pet. 23-33, 38-46.
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`29. The Petition acknowledges that Thelen’s recognizers 1-3 all access a
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`single memory storage 340. Pet at 24, 30. Thelen’s Figure 3 illustrates a single
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`storage 340 serving each of the recognizers (alleged processors) labeled as Rec 1,
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`Rec 2, Rec 3, and Testing Recognizer, and the single storage 340 containing all of
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`the “models” 341-348. Ex. 1030, Fig. 3 (Petition’s annotations).
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`30. As Thelen’s Fig 3 illustrates, Thelen neither teaches limitation 46(b)
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`nor 46(d), much less the two limitations combined.
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`31. With respect to limitation 46(b), Thelen does not teach multiple lexical
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`memories, with each lexical memory containing part of the complete lexical data,
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`and the lexical memories together containing “complete lexical data for word
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`recognition.” Instead, Thelen teaches the one-memory-to-all-processors (or one-
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`memory-to-all-recognizers) design that the ’319 patent expressly distinguished from
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`and improved upon.
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`32. With respect to limitation 46(d), Thelen does not teach a plurality of
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`processors “arranged in groups of processors, each group of processors being
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`connected to a lexical memory.” Rather, Thelen teaches individual “recognizers,”
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`which are never arranged in groups, and which are all connected to a single complete
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`storage memory 340, rather than one of several lexical memories containing partial
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`lexical data, as the claims require. It is useful to keep in mind that Thelen’s teachings
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`mainly targeted huge vocabulary speech recognition, and the main innovation was
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`to employ a plurality of large vocabulary speech recognizers, each targeting a subset
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`of the huge vocabulary.
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`33.
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`I understand that the Petition also relies on two embodiments from
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`Bailey—the embodiments of Bailey’s Figures 3A and 3B. Pet. 30-33, 36-38.
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`34.
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`In the embodiment of Bailey’s Fig. 3A (below), each individual
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`processor (or pattern recognition engine, labeled PRE) is connected to its own
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`dedicated memory (labeled MEM) such that there is one processor to one memory.
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`Ex. 1031, Fig. 3 (Petition’s annotated version).
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`35. Bailey teaches, “each PR engine 525a-525c [is] interfaced with its own
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`private memory 615a-615c.” Ex. 1031, 11:1-3. The embodiment of Bailey’s Fig. 3A
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`is thus the one-memory-to-one-processor design distinguished by the ’319 patent.
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`Accordingly, while Bailey’s Fig. 3A teaches a “plurality” of memories, it does not
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`teach the memory architecture of the ’319 patent, but rather teaches the prior art
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`design that the ’319 patent explicitly distinguished as inferior.
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`36. The design of Bailey’s Figure 3B is identical to the other design
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`distinguished by the ’319 patent. Bailey’s Figure 3B illustrates a design in which all
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`of the processors PRE are connected to a single memory MEM 615. Ex. 1031, Fig.
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`3B, 11:1-4.
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`37. The design of Bailey’s Figure 3B is thus the same as Thelen’s, i.e., one
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`in which “only a single memory [is] used for all processors,” as distinguished by the
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`’319 patent.
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`38. As the ’319 patent explained, the design of Thelen and Bailey’s Fig. 3B
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`is bandwidth limited compared to the ’319 patent’s claimed architecture. Ex. 1001,
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`3:44-58. Thus, while Bailey’s Fig. 3B (like Thelen) teaches connecting a plurality
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`of processors to one memory, Bailey’s does not teach the design of the challenged
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`claims. Bailey’s Fig 3B does not teach multiple groups (clusters) of processors, each
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`group containing multiple processors, and each group of processors connected to a
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`shared lexical memory that contains a portion of the overall lexical data, with the
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`combined lexical memories containing all of the lexical data.
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`39. Thus, neither of Bailey’s two embodiments teaches the claimed
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`innovative processor-to-memory architecture of the challenged claims.
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`40.
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`I understand the Petition also relies on Chen for the disclosure of
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`multiple clusters of processors, each cluster containing multiple processors
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`connected to a cluster shared memory. Pet. at 38-40.
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`41.
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`I note that Chen is not a reference in the field of speech recognition.
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`Rather, Chen teaches a general-purpose parallel-computing architecture, designed to
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`provide high performance for all sorts of computational tasks. Chen has no teachings
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`directed to speech recognition systems; in particular, Chen provides no teachings
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`regarding “lexical memories,” much less “lexical memories containing in
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`combination complete lexical data for word recognition,” with “each lexical memory
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`containing part of said complete lexical data.” Pet. 38-40 (admitting Chen does not
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`teach lexical memories or lexical data). Thus, Chen also does not teach the claimed
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`design of limitations 46(b) and (d).
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`42.
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`I understand that the Petition relies on a series of modifications to each
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`of Thelen, Bailey, and Chen to arrive at what the Petition calls “a common sense
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`extension” of the actual teachings of those three references. Pet. 44. According to
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`the Petition, Thelen, Bailey, and Chen would have been reconfigured such that:
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`(1) Thelen’s recognizers would be modified to be in “groups,” Pet. 40;
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`(2) Each “group” of Thelen’s recognizers would consist of multiple
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`recognizers for the same subject matter context, e.g., multiple recognizers for
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`“sports,” and multiple recognizers for “health,” etc., Pet. 42-43;
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`(3) Each newly created “group” of Thelen’s recognizers would be connected
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`to a dedicated shared memory, such that there would be multiple dedicated shared
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`memories in total, Pet. 40;
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`(4) Each of the dedicated shared group memories would be a lexical
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`memory, Pet. 40;
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`(5) Each dedicated shared group memory would only store the recognition
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`model for the same specific “context” as the recognizers in the group, Pet. 42-43;
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`and
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`(6) The shared group memories together would contain complete lexical
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`data, Pet. 40, 43.
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`43.
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`I note, however, that each modification in the above theory relies on an
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`arrangement that is not taught in Thelen, Bailey, or Chen.
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`44. First, Thelen does not teach “groups” of multiple recognizers for the
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`same subject matter, as the Petition’s steps (1) and (2) above require. Rather, Thelen
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`teaches an embodiment in which “the number of recognition models corresponds to
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`the number of recognizers; each recognizer being associated with an exclusive
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`recognition model in a fixed one-to-one relationship.” Ex. 1030, 7:43-46, 7:30-33.
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`In this embodiment, there is only one recognizer for each subject matter, not multiple
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`recognizers per subject matter, as the Petition’s theory requires.
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`45. Alternatively, Thelen teaches a “preferred” embodiment in which “the
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`system comprises more models than active recognizers[.]” Ex. 1030, 7:47-48. In this
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`“preferred” embodiment, there is one recognizer for multiple recognition models,
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`not multiple recognizers for one subject matter, as the Petition’s theory requires.
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`Thus, Thelen’s preferred embodiment teaches the opposite of what the Petition’s
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`theory would require, i.e., an architecture in which multiple recognition models are
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`grouped to one processor (recognizer), rather than multiple processors being
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`grouped to one recognition model (handling a single subject matter) as described in
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`the Petition’s step (2).
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`46. Second, as I stated earlier, none of Thelen, Bailey, or Chen teaches a
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`design in which multiple groups of speech recognition processors, with multiple
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`processors per group, are each connected to one dedicated lexical memory, as steps
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`(3) and (4) of the Petition’s theory (above) require. Rather, Thelen and Bailey merely
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`teach the prior art designs the ’319 patent expressly distinguished, and Chen
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`provides no teachings regarding speech recognition or lexical memories at all.
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`47. Third, none of Thelen, Bailey, or Chen teaches storing one recognition
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`model per lexical memory, with multiple lexical memories combined storing all
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`recognition models, as steps (5) and (6) of the Petition’s theory (above) require.
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`Rather, Thelen teaches storing all recognition models in one storage memory 340.
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`With respect to the embodiment of Fig. 3B, Bailey also teaches storing all lexical
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`data in a single storage memory. As for the embodiment of Fig. 3A, Bailey also
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`teaches storing all of its lexical data in each of the dedicated private memories, such
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`that each private memory 615a, b, and c contains all of the lexical data, not part of
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`the lexical data. Ex. 1031, 13:12-13 (describing “the private memory 615 which
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`holds all the prototype patterns.”); id. 11:22-23 (“Each PR engine contains its own
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`library of prototype patterns 615a to 615c respectively.”); id. at 11:34-37 (“Refer to
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`FIG. 3(B) which illustrates the system substantially as described with reference to
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`FIG. 3(A) bus [sic] wherein each separate PR engine shares the same prototype
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`library 615.”). Bailey never teaches that the private memories in Fig. 3A contain
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`portions of the overall lexical data. And Chen teaches nothing regarding lexical
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`memories or lexical data at all.
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`48. Accordingly, each aspect of the Petition’s combination theory relies on
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`arrangements and modifications that are not taught or suggested by any of Thelen,
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`Bailey, or Chen.
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`49. Furthermore, I disagree with the Petition’s statement that arriving at the
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`design claimed in limitations 46(b) and (d) would have been a “common sense
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`extension” of Thelen, Bailey, and Chen. As explained above, the Petition’s theory
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`requires modifying numerous aspects of each of Thelen, Bailey, and Chen to arrive
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`at a design that is not disclosed in any of those references. In my experience,
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`modifying the teachings of three different references to arrive at a design that is not
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`taught by any of them is not typically the product of “common sense.”
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`50. For the same reasons, the Petition’s combination would not have been
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`expected to yield “predictable result,” as persons of ordinary skill in the art would
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`not have found “predictable” the outcome of rearranging and redesigning Thelen,
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`Bailey, and Chen in ways that are not taught or even hinted in any of those
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`references.
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`51.
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`I am aware that the Petition contends that its proposed combination
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`would have improved Thelen’s “efficiency.” I note, however, that the proposed
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`modification would have required far more hardware—more processors and more
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`memories—than what either Thelen or Bailey teaches. An ordinary artisan would
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`have known that this would have come at significant additional cost and complexity
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`compared to Thelen’s and Bailey’s existing systems. The Petition does not account
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`for that consideration—or indeed, any costs or trade-offs implied by its combination.
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`52.
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` Finally, in my opinion the Petition’s rearrangement of Thelen, Bailey,
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`and Chen goes well beyond the level of ordinary skill in the art. In the Petition’s
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`combination, the artisan would have needed the skill and a reason to (1) dedicate
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`multiple recognizers to each of Thelen’s recognition models, creating multiple
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`groups each containing multiple recognizers, contrary to Thelen’s own teachings and
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`motivations; (2) associate each group of recognizers with a dedicated memory,
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`which was not taught by Thelen or Bailey; (3) ensure that each dedicated memory
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`contained part of the lexical data, contrary to Bailey and Thelen’s teachings; and (4)
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`ensure that the sum of the dedicated memories together contained the complete
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`lexical data, which was not even remotely taught in Thelen or Bailey. In my
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`experience, an ordinary artisan with the qualifications and work experience
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`described by the Petition would not have arrived at those choices through ordinary
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`creativity. In particular, I note that the Petition identifies no logical path or
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`motivation that would have led the ordinary artisan, using ordinary creativity, to start
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`from Thelen, Bailey, and Chen and arrive at the design of limitations 46(b) and (d).
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`53.
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`I hereby declare that all statements made herein of my own knowledge
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`are true and that all opinions expressed herein are my own; and further that these
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`statements were made with the knowledge that willful false statements and the like
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`are punishable by fine or imprisonment, or both, under Section 1001 of Title 18 of
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`the United States Code.
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`DeLiang Wang, Ph.D.
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`Executed on March 15, 2023
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`22
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`EXHIBIT A
`EXHIBIT A
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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.
`____________________
`
`Case IPR2023-00037
`Patent No. 10,971,140
`____________________
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`
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`DECLARATION OF DELIANG WANG, Ph.D., IN SUPPORT OF
`PATENT OWNER’S PRELIMINARY RESPONSE
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`Case IPR2023-00037
`DECLARATION OF DELIANG WANG, PH.D
`TABLE OF CONTENTS
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`Introduction
`Engagement
`Background and qualifications
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`I.
`1
`A.
`1
`B.
`1
`C. Materials considered
`3
`II. Relevant legal standards
`4
`III. Overview of the ’140 Patent
`8
`IV. Replacing Jiang’s generic processor with Chen’s supercomputer processing
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`V. Using each of Chen’s shared cluster memories as an acoustic model memory
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`VI. It would not have been obvious to configure each of Chen’s eight or more
`19
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`Person of ordinary skill in the art
`A.
`Burden of proof
`B.
`Claim construction
`C.
`D. Obviousness
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`4
`6
`6
`7
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`architecture would not have been obvious
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`for storing acoustic model data would not have been obvious
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`processors “to compute a probability” as recited in the challenged claims
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`- i -
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`I, DeLiang Wang, Ph.D, do hereby declare as follows:
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`I.
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`Introduction
`A.
`Engagement
`1.
`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 IPR2023-00037, with
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`respect to U.S. Pat. 10,971,140. 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
`B.
`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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`5.
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`I have spent my professional and academic career as a researcher in
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`the field of speech processing and machine learning (including deep learning). I
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`am currently a University Distinguished Scholar and Professor in the Department
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`of Computer Science and Engineering at Ohio State University.
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`6.
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`I received B.S. in 1983 and M.S. in 1986 from Peking (Beijing)
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`University, both in computer science. I received a Ph.D. in computer science in
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`1991 from the University of Southern California.
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`7.
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`I have received numerous awards and honors, including the U.S.
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`Office of Naval Research Y
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