Inter Partes Review of USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D. (Exhibit 2004)
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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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`ZTE USA, INC.
`Petitioner
`
`v.
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`SAINT LAWRENCE COMMUNICATIONS LLC
`Patent Owner
`
`Case: IPR2016-00704
`Patent No. 7,151,802
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`DECLARATION OF ODED GOTTESMAN, PH.D.
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`Mail Stop “PATENT BOARD”
`Patent Trial and Appeal Board
`U.S. Patent and Trademark Office
`P.O. Box 1450
`Alexandria, VA 22313-1450
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`SLC 2004
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`I.
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`Inter Partes Review of USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
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`TABLE OF CONTENTS
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`INTRODUCTION ........................................................................................... 1
`
`A.
`
`B.
`
`Background ........................................................................................... 1
`
`Qualifications ........................................................................................ 2
`
`II.
`
`LIST OF DOCUMENTS CONSIDERED IN FORMULATING MY
`OPINIONS ...................................................................................................... 6
`
`III. TECHNICAL BACKGROUND AND STATE OF THE ART AT
`THE TIME OF THE ALLEGED INVENTION ............................................. 9
`
`A.
`
`B.
`
`C.
`
`D.
`
`E.
`
`F.
`
`G.
`
`H.
`
`I.
`
`J.
`
`Speech coding and Linear Prediction Coding (LPC) analysis ........... 13
`
`Long Term Prediction (Pitch Prediction) ........................................... 18
`
`Quantization ........................................................................................ 20
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`Code Excited Linear Prediction (CELP) ............................................ 21
`
`Perceptual Weighting .......................................................................... 28
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`Long term pitch prediction and using adaptive codebook.................. 29
`
`CELP Decoding .................................................................................. 29
`
`Speech Bandwidth extension .............................................................. 30
`
`Speech Quality .................................................................................... 31
`
`Finite precision Considerations .......................................................... 31
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`IV. PERSON OF ORDINARY SKILL IN THE ART ........................................ 34
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`V. OVERVIEW OF THE ‘802 PATENT .......................................................... 35
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`VI. THE CLAIMS OF THE ‘802 PATENT ....................................................... 39
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`VII. LEGAL STANDARDS ................................................................................. 40
`
`A.
`
`B.
`
`Requirements of a Method and System Patent ................................... 40
`
`Obviousness ........................................................................................ 41
`
`VIII. CLAIM CONSTRUCTION .......................................................................... 46
`
`IX. SUMMARY OF PRIOR ART TO THE ’802 PATENT ALLEGED IN
`THIS PETITION (CASE IPR2016-00704) .................................................. 46
`
`A. A 13.0 kbit/s wideband speech codec based on SB-ACELP
`(“Schnitzler”) ...................................................................................... 46
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`Inter Partes Review of USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
`
`B.
`
`C.
`
`
`Reconstruction of Wideband Audio from Narrowband CELP
`Code (“Tasaki”) .................................................................................. 50
`
`16 kbit/s Wideband Speech Coding Based On Unequal
`Subbands (“Paulus”) ........................................................................... 51
`
`X. GROUNDS FOR PATENTABILITY FOR EACH CLAIM OF THE
`‘802 PATENT ............................................................................................... 52
`
`A. Ground 1: Schnitzler in View of Tasaki Does Not Render
`Obvious Claims 1-3, 8-11, 16, 25-27, 32-35, 40, 49, 50, 52,
`And 53 ................................................................................................. 52
`
`1.
`
`2.
`
`Schnitzler in view of Tasaki does not render obvious claims 1,
`9, 25, 33, 49, 50, 52, and 53 ..................................................... 52
`
`Schnitzler In View Of Tasaki Also Does Not Render Obvious
`Claims 2, 3, 8, 10, 11, 16, 26, 27, 32, 34, 35, And 40 .............. 55
`
`3.
`
`No Motivation to Combine Schnitzler with Tasaki. ................ 59
`
`Ground 2: Schnitzler In View Of the Knowledge of a Person of
`Skill in the Art Does Not Render Obvious Claims 1, 9, 25, 33,
`49, 50, 52, And 53............................................................................... 59
`
`Ground 3: Schnitzler In View Of the Knowledge of a Person of
`Skill in the Art Further In View Of Paulus Does Not Render
`Obvious Claims 2, 3, 8, 10, 11, 16, 26, 27, 32, 34, 35, And 40. ........ 66
`
`1.
`
`No Motivation To Combine Schnitzler With Paulus ............... 66
`
`B.
`
`C.
`
`D.
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`Summary of Invalidity Analysis ......................................................... 67
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`XI. CONCLUSION ............................................................................................. 68
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`BIBLIOGRAPHY ................................................................................................... 70
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`DR. ODED GOTTESMAN – CURRICULUM VITAE ......................................... 72
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
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`I, Oded Gottesman, hereby declare as follows:
`
`I.
`
`INTRODUCTION
`
`A. Background
`
`1. My name is Oded Gottesman. I am a researcher and consultant
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`working in areas related to speech and audio coding and enhancement, digital
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`signal processing, telecommunications, networks, and location and positioning
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`systems.
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`2.
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`I have been retained to act as an expert witness on behalf of SAINT
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`LAWRENCE COMMUNICATIONS Inc. (“Patent Owner”) in connection with the
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`above captioned Petition for Method and System Review of U.S. Patent No.
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`7,151,802 (“Petition”) submitted by ZTE USA, INC. (“Petitioner”). I understand
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`that this proceeding involves U.S. Patent No. 7,151,802 (“the ‘802 Patent”), titled
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`“High frequency Content Recovering Method and Device for Over-Samples
`
`Synthesized Wideband Signal.” The ‘802 Patent is provided as Exhibit 1001.
`
`3.
`
`I understand that Petitioner challenges the validity of Claims 1-3, 8-
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`11, 16, 25-27, 32-35, 40, 49, 50, 52, and 53 of the ‘802 Patent (the “challenged
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`claims”).
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`4.
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`I have reviewed and am familiar with the ‘802 Patent as well as its
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`prosecution history. The ‘802 prosecution history is provided as Exhibit 1003.
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`Additionally, I have reviewed materials identified in Section III.
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
`
`5.
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`As set forth below, I am familiar with the technology at issue as of the
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`effective filing date of the ‘802 patent. I have been asked to provide my technical
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`review, analysis, insights, and opinions regarding the prior art references that form
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`the basis for the Petition. In forming my opinions, I have relied on my own
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`experience and knowledge, my review of the ‘802 Patent and its file history, and of
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`the prior art references cited in the Petition.
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`6. My opinions expressed in this Declaration rely to a great extent on my
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`own personal knowledge and recollection. However, to the extent I considered
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`specific documents or data in formulating the opinions expressed in this
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`Declaration, such items are expressly referred to in this Declaration.
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`7.
`
`I am being compensated for my time in connection with this covered
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`patent review at my standard consulting rate, which is $525 per hour. My
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`compensation is not contingent upon and in no way affects the substance of my
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`testimony.
`
`B. Qualifications
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`8.
`
`I am a citizen of the United States, and I am currently employed as the
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`Chief Technology Officer (“CTO”) of Compandent, Inc.
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`9. My curriculum vitae, including my qualifications, a list of the
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`publications that I have authored during my career, and a list of the cases in which,
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`during the previous four years, I have testified as an expert at trial or by deposition,
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`2
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
`
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`is attached to this report as Exhibit A. I expect to testify regarding my background,
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`qualifications, and experience relevant to the issues in this investigation.
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`10.
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`I earned my Bachelor of Science degree in Electrical Engineering
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`from Ben-Gurion University in 1988.
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`11.
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`In 1992, I earned my Master of Science degree in Electrical and
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`Computer Engineering from Drexel University, which included performing
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`research at AT&T Bell Labs, Murray Hill, at the time considered the world “holy
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`grail” of speech processing research. My research was in the area of wideband
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`speech coding, and titled “Algorithm Development and Real-Time Implementation
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`of High-Quality 32kbps Wideband Speech Low-Delay Code-Excited Linear
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`Predictive (LD-CELP) Coder”. The work continued a prior research by E.
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`Ordentlich, and Y. Shoham who was also my M.Sc. research advisor. As a part of
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`my work, I have also implemented that algorithm in DSP Assembly Language on
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`two DSPs running in parallel. I subsequently co-authored and published two
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`articled about this work.
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`12.
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`I earned my Doctorate of Philosophy in Electrical and Computer
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`Engineering from the University of California at Santa Barbara in 2000.
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`13.
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`I have worked in the field of digital signal processing (“DSP”) for
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`over 25 years, and have extensive experience in DSP research, design, and
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`development, as well as the design and development of DSP-related software and
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
`
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`hardware. Presently, I am the CTO of Compandent, a technology company that
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`develops and provides telecommunication and DSP-related algorithms, software
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`and hardware products, real-time DSP systems, speech coding and speech
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`enhancement-related projects, and DSP, software, and hardware-related services.
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`While at Compandent, I have contributed to a speech coding algorithm and noise
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`canceller algorithm that has been adopted for secure voice communication by the
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`U.S. Department of Defense & NATO. Currently, I am supporting the DoD’s and
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`NATO’s use of these algorithms, and am performing real-time implementation
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`projects for DoD and NATO vendors, as well as the Defense Advanced Research
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`Projects Agency (DARPA).
`
`14.
`
`I have worked for numerous different companies in the field of digital
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`signal processing during my career. I am very familiar with most, if not all, speech
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`coding, speech enhancement, audio coding, and video coding techniques for
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`various applications. As part of my work, I have developed real-time DSP
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`systems, DSP software for telephony applications, various serial communication
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`software and hardware, and Internet communication software. I have led real-time
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`DSP and speech coding engineering groups in two high-tech companies before my
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`present company (Comverse Technology, Inc. and Optibase Ltd.), and, at DSP
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`Communications, Inc., I was involved with echo cancellation, noise cancellation,
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`and the creation of state-of-the-art chipsets for cellular telephones.
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
`
`15.
`
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`I have been working with, and have written programs for, personal
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`computers since around 1986. Initially on DOS, and later on Windows 3.1,
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`Windows 96, Windows 98, and Windows 2000, as well as Apple computers.
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`Much of my programming concerned digital signal processing, particularly speech,
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`audio and image coding, and communications.
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`16. From 2001, I also have been providing expert technology services in
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`patent disputes. My biography and experience relevant to my work in these
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`matters is more fully detailed in Exhibit A.
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`17.
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`I have been the co-recipient, with Dr. Allen Gersho, of the Ericsson-
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`Nokia Best Paper Award for the paper: “Enhanced Waveform Interpolative Coding
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`at 4 kbps,” IEEE Workshop on Speech Coding, Finland, 1999. The IEEE
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`Workshop on Speech Coding is an exclusive workshop for speech-coding
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`researchers from around the world.
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`18.
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`I have authored and co-authored approximately eight journal
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`publications, in addition to conference proceedings, technical articles, technical
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`papers, book chapters, and technical presentations concerning a broad array of
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`signal processing technology. I have also developed and taught many courses
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`related to digital signal processing and signal processing systems. These courses
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`have included introductory level and advanced courses.
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`
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
`
`19.
`
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`I have several international patents related to the field of audio signal
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`enhancement, including U.S. Patent Nos. 6,614,370; 7,643,996; 7,010,482; and
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`7,584,095.
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`20.
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`I am being compensated at the rate of $525 per hour for my work in
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`connection with this matter. The compensation is not dependent in any way on the
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`contents of this report, the substance of any further opinions or testimony that I
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`may provide, or the ultimate outcome of this matter.
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`II. LIST OF DOCUMENTS CONSIDERED IN FORMULATING MY
`OPINIONS
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`21.
`
`In formulating my opinions, I have reviewed and considered all of the
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`following documents:
`
`EXHIBIT
`NO.
`1001
`1002
`1003
`1004
`
`1005
`
`1006
`
`1007
`
`1008
`
`1009
`
`DESCRIPTION
`
`U.S. Patent No. 7,151,802
`File history of U.S. Patent No. 7,151,802
`Petition For Inter Partes Review Of U.S. Patent No. 7,151,802
`J. Schnitzler, A, “13.0 KBIT/S Wideband Speech Codec Based
`on SBACELP,” IEEE, pp. 157-160 (1998)
`J. Paulus and J. Schnitzler, “16 KBIT/S Wideband Speech
`Coding Based on Unequal Subbands,” IEEE, pp. 255-258
`(1996)
`J. Paulus and J. Schnitzler, “Wideband Speech Coding for the
`GSM Fullrate Channel,” ITG-Fachtagung
`prachkommunikation, pp. 11-14, September 1996
`ITU-T Recommendation G.729, “Coding of Speech at 8 kbit/s
`Using Conjugate-Structure Algebraic-Code-Excited Linear-
`Prediction (CSACELP),” March 1996
`GSM Enhanced Full Rate (EFR) Speech Transcoding (GSM
`06.60) (1996)
`Honkanen, T., et al., “Enhanced Full Rate Speech Codec for
`
`
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`6
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`

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`
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`1010
`1011
`1012
`
`1013
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`1014
`1015
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`1016
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`1017
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`1018
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`1019
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`1020
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`1021
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`1022
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`1023
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`1024
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
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`
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`IS-136
`Digital Cellular System,” IEEE, pp. 731-734 (1997)
`U.S. Pat. No. 5,455,888 (“Iyengar”)
`U.S. Pat. No. 5,797,120 (“Ireton”)
`Atal, B. and Remde, J., “A New Model of LPC Extension for
`Producing Natural-Sounding Speech at Low Bit Rates,” IEEE,
`pp. 614-617 (1982).
`Spanias, A., “Speech Coding: A Tutorial Review,” Proceedings
`of the IEEE, 82(10):1539-82 (1994)
`Federal Coding Standard 1016 (February 14, 1991)
`ITU-T Recommendation G.728, “Coding of Speech at 16 kbit/s
`Using Low-Delay Code Excited Linear Prediction,” September
`1992
`Schroeder, M. and Atal, B, “Code-Excited Linear Prediction
`(CELP): High Quality Speech at Very Low Bit Rates,” IEEE,
`pp. 937-940 (1985)
`Tasaki, H., et al., “Reconstruction of Wideband Audio from
`Narrowband CELP Code,” Acoustical Society of Japan, pp.
`249-252 (1994)
`ITU G.722.2 Series G: Transmission Systems and Media,
`Digital Systems and Networks (2002)
`Oppenheim and Schafer, Discrete-Time Signal Processing, pp.
`17-33 (1989)
`Kroon, P., “Regular-Pulse Excitation – A Novel Approach to
`Effective and Efficient Multipulse Coding of Speech,” IEEE
`Transactions on Acoustics, Speech, and Signal Processing,
`34(5):1054-1063 (1986)
`Chan, W., et al., “Enhanced Multistage Vector Quantization by
`Joint Codebook Design,” IEEE Transactions on Acoustics,
`Speech, and Signal Processing, 40(11):1693-1697 (1992)
`Singhai, S. and A. Bishnu, “Improving Performance of Multi-
`Pulse LPC Coders at Low Bit Rates,” IEEE, pp. 1.3.1-1.3.4
`(1984)
`Ramachandran, R. and Kabal, R., “Pitch Prediction Filters in
`Speech Coding,” IEEE Transactions on Acoustics, Speech, and
`Signal Processing, 37(4):467-478 (1989)
`Atal, B. and Schroeder, M., “Predictive Coding of Speech
`Signals and Subjective Error Coding,” IEEE Transactions on
`Acoustics, Speech, and Signal Processing, 27(3):247-254
`
`7
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`

`1025
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`1026
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`1027
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`1028
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`1029
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`1030
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`1031
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`1032
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`1033
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`1034
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`1035
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`2001
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`2002
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
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`
`
`(1979)
`Rabiner, L., “On the Use of Autocorrelation Analysis for Pitch
`Detection,” IEEE Transactions on Acoustics, Speech, and
`Signal Processing, 25(1):24-33 (1977)
`Kleijn, W. and Ketchum, R., “Improved Speech Quality and
`Efficient Vector Quantization in SELP,” IEEE, pp. 155-158
`(1988)
`Marques, J, et al., “Improved Pitch Predication With Factional
`Delays in CELP Coding,” Filters in Speech Coding,” IEEE,
`665-668 (1990)
`Chen, J., and Gersho, A., “Adaptive Postfiltering for Quality
`Enhancement of Coded Speech,” IEEE Transactions on
`Acoustics, Speech, and Signal Processing, 3(1), pp. 59-71
`(1995)
`ITU G.722 – General Aspects of Digital Transmission Systems
`Terminal Equipments (1988)
`Cheng, Y., et al., “Statistical Recovery of Wideband Speech
`From Narrowband Speech,” IEEE Transactions on Acoustics,
`Speech, and Signal Processing, 2(4):544-548 (1994) (“Cheng”)
`Ordentlich, E. and Shoham, Y., “Low-Delay Code-Excited
`Linear-Predictive Coding of Wideband Speech at 32 KBPS,”
`IEEE, pp. 9-12 (1991)
`Avendano, C., et al., “Beyond Nyquist: Towards the Recovery
`of Broad-Bandwidth Speech from Narrow-Bandwidth Speech,”
`Eurospeech, 95 (1995)
`Foodeei, M, “Low-Delay Speech Coding at 16 kb/s and Below
`,” (May 1991) (“Foodeei”)
`Odentlich, E., “Low Delay – Code Excited Linear Predictive
`(LD- CELP) Coding of Wide Band Speech at 32kbits/sec,”
`Massachusetts Institute of Technology, pp. 1-132 (1990).
`Declaration of Michael T. Johnson, PhD, Case: IPR2016-00704
`Patent 7,151,802.
`P. Mermelstein, “G.722, A new CCITT Coding Standard for
`Digital Transmission of Wideband Audio Signals,” IEEE
`Comm. Mag., Vol. 26, No. 1, pp. 8-15, Jan. 1988.
`
`Fuemmeler et. al, “Techniques for the Regeneration of
`Wideband Speech from Narrowband Speech,” EURASIP
`Journal on Applied Signal Processing 2001:0, 1-9 (Sep. 2001).
`
`8
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
`
`2003
`
`2005
`
`
`C.H. Ritz et. al., “Lossless Wideband Speech Coding,” 10th
`Australian Int’l. Conference on Speech Science & Technology,
`p. 249 (Dec. 2004).
`
`“Discrete-Time Signal Processing,” by Alan V. Oppenheim,
`Ronald W. Schafer
`
`
`
`
`
`22.
`
`I have reviewed and am familiar with the response to Petition
`
`submitted on behalf pf Patent Owner for covered patent review submitted with this
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`Declaration and I agree with the technical analysis that underlies the positions set
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`forth in the response to Petition.
`
`23.
`
`I have reviewed and am familiar with the Petition for covered patent
`
`submitted by Petitioner, and I disagree with some of it, and with its conclusions. I
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`have reviewed and considered Dr. Johnson Report submitted on behalf of
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`Petitioner, and I disagree with some of it, and with its conclusions.
`
`24.
`
`I may consider additional documents as they become available or
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`other that are necessary to form my opinions. I reserve the right to revise,
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`supplement, or amend my opinions based on new information and on my
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`continuing analysis.
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`III. TECHNICAL BACKGROUND AND STATE OF THE ART AT THE
`TIME OF THE ALLEGED INVENTION
`
`25. Speech signal is generated by air flow emanating from the lungs,
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`passed through the vocal chords which may vibrate to create quasi-periodic air
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
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`pulses, which are then passed through the vocal tracts (throat cavity, mouth cavity
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`and nasal cavity) and radiates outside though the lips and/or the nose. When the
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`vocal chords vibrate the generated quasi-periodic speech is called “voiced”, and
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`when they are at rest the generated speech is more noise like and called
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`“unvoiced”. A classical model for speech production (“Digital Processing of
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`Speech Signals, L. R. Rabiner and R. W. Schafer, Prentice Hall, 1978) is illustrated
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`below. This early model includes a switch for selecting between the “voiced”
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`component generated by the periodic source and “unvoiced” component generated
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`by the noise source, each multiplied by an appropriate gain, to form the excitation
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`signal, which is then passed through a system modeling the vocal tract and uses
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`time varying parameters, and the resulted signal is finally passed though the lips
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`radiation model to form the generated speech. The fundamental period of the
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`vocal chords vibration is known as the pitch period. The vocal tract is typically
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`modeled by time varying filter implemented by set of parameters (or coefficients).
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
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`26. Speech signal is much richer than simply voiced and unvoiced, for
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`example buzzing sounds like “z” and “v” include both periodic and noise
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`components, and therefore in later models, such as the one below, the switch was
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`replaced by a mixer that combined timed varying mixture. For simplicity let’s
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`consider the following simplified diagram. For producing good quality speech, the
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`vocal tract parameters, representing the vocal tract resonance, are sufficiently
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`updated every 20-30 ms, while the excitation components are sufficiently updated
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`every 5-7 ms. It can be shown that the vocal tract effect adds correlation among
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
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`neighboring samples, which is often treated as short-term prediction or
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`redundancy. The vocal chords quasi periodicity is often treated as long-term
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`prediction or redundancy.
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`Figure 1. Block diagram of simplified speech production model
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`27. Speech coding is used for representing speech signals in a compact
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`form for transmission or storage. The compression is typically achieved by first
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`capturing and removing the redundancies in forms of short-term prediction and
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`long-term prediction that exist in the speech signal, and forming a residual signal
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`having a much smaller dynamic range and energy. Then the residual signal is
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`quantized, a process of representing it by a finite number of bits and limited
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`resolution. The difference between the reduced resolution signal and the original
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`signal is considered quantization noise (or quantization error). The number of bits
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`12
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`Periodic
`component’s gain
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`Vocal chords
`periodic component
`(pitch)
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`Aspiration
`air flow
`(Noise)
`
`x
`
`x
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`Noisy component’s
`gain
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`+
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`Excitation
`Signal
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`(5-7 ms
`intervals)
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`Vocal Tract model
`(20-30ms intervals)
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`Speech
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
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`used for the quantization governs the amount of quantization noise, or alternatively
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`the overall quality of the coded speech.
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`A.
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`Speech coding and Linear Prediction Coding (LPC) analysis
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`28. The linear Predictive Coding (LPC) is a well known analysis for
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`calculating the short term prediction coefficients that are used to model the vocal
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`tract. It is typically applied directly to the input speech signal and LPCs are
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`encoded (or quantized) once per frame at of 20-30 ms, which is an adequate
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`interval based on the vocal tract change speed.
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`29. The LPC are used to capture and remove the short-term prediction
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`from the input speech, where the LPC coefficients are used as weighted sum of the
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`most recent 10 samples to predict the present sample, which is essentially a
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`filtering operation. The short-term predicted sample is subtracted from the input
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`sample to generate the prediction error also known as the residual signal sample
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`having a much smaller dynamic range and energy than the input speech. For this
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`reason the residual signal is more desirable for quantization than the speech, since
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`thanks to its smaller energy and dynamic range, its quantization error (or noise) is
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`also smaller.
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`13
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
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`Figure 2. Block diagram of open-loop short-term prediction removal
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`
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`example using LPC analysis
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`
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`30. The removal of the short-term prediction from the input speech to
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`generate the residual signal is essentially a decomposition of the speech signal into
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`its slowly varying vocal tract characteristics (i.e. the LPC coefficients) and its
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`rapidly changing excitation characteristic (i.e. the residual signal). Such a
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`decomposition allows for applying slower LPC quantization at a frame intervals of
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`typically 20-30 ms, and a faster residual encoding at a subframe rate of typically 5-
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`7 ms. Examples of speech signals and their corresponding residual signals (scaled
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`up in the figure for better view) is illustrated at Id Fig 8.5. As can be seen, the
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`residual signals exhibits less short-term correlation among neighboring samples,
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`looks more like noise and comb pulses, but still exhibits long-term correlation
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`between neighboring pitch periods.
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`14
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`Input Speech
`sample
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`Residual
`signal sample
`
`-
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`Stored recent
`speech samples
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`LPC Prediction
`(Short-Term
`Prediction)
`
`Predicted
`speech sample
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`LPC Analysis
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`LPC Quantization
`(every 20-30 ms)
`
`LPC
`coefficients
`
`Quantized LPC
`coefficients
`
`Quantized LPC code
`transmitted every
`20-30 ms
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`

`

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`
`
`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
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`
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`31.
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`In the frequency domain, the vocal tract shapes the speech spectral
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`envelope, while the excitation component shapes the fine spectral structure about
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`that envelope. The figure below illustrates from top to bottom time domain
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`sampled speech segment, the corresponding residual signal that contains much less
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`short-term prediction and energy, the speech signal’s (log) spectrum and the
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`corresponding spectral envelope represented by the LPC, and the residual signal’s
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`(log) spectrum which is flat since the spectral envelope captured by the LPC was
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`removed from the speech. As illustrated, the speech was decomposed to the
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`15
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`

`

`
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
`
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`spectral envelope - represented by the LPC, and the fine spectral variations around
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`the spectral envelope - represented by the residual signal. As illustrated, during
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`voiced speech segments, the residual signal exhibits flat comb-like harmonic
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`structure at the fundamental frequency’s (pitch) multiple frequencies (harmonics).
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`During buzzy sounds like “v” and “z”, and during transitions, the harmonic
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`structure appears more prominent in the lower frequency range, and the higher
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`frequency range (e.g. 4-5kHz in the figure below) becomes less structured and
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`more noise like. The LPC only captures the spectral envelope, and does not
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`capture the harmonic structure. As illustrated, by removing the LPC spectral
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`envelope from the speech in (c), the residual signal (d) has a flat spectrum that
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`exhibits harmonic structure (during voiced speech).
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`16
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`
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
`
`
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`32. This also illustrates why some statements in the ‘802 petition are
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`completely wrong, for example in p.7 “using harmonic structure represented the
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`encoded LPC analysis…” Dr. Johnson is also flatly wrong about that, and the
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`following statements in his reports reflect much confusion (Ex. 1035 regarding
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`‘802 patent and Ex. 1036 regarding ‘521 patent, p.23 section.55) “such frequency
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`17
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
`
`
`extension can be accomplished in a simple manner by simply using the harmonic
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`structure represented by the encoded LPC analysis…” Person of ordinary skills in
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`the art knows that the LPC analysis does not represent the harmonic structure and
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`cannot be used for that, rather than the residual signal does so. The LPC represents
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`merely the spectral envelope, its resonances (formants) and valleys.
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`33.
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`Interestingly, the Ozawa is directed toward a heuristic preselection
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`election of the long term prediction path for modeling the harmonic structure for
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`the frame, based on the calculated LPC, and given that preselected single path the
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`long-term prediction is determined for each subframe within the frame.
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`B.
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`Long Term Prediction (Pitch Prediction)
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`34. As explained above, the vocal chords “pitch” periodicity generates
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`long term correlation between neighboring speech periods. For encoding purposes,
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`such long-term correlation can be modeled and captured by means of long-term
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`prediction. The parameters that are typically used to encode the long-term
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`prediction are the pitch period which may be integer or fractional, and the gain
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`used to predict the present period from the past period. Such parameters may be
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`viewed as describing a comb filter, where the comb pulses are spaced by the pitch
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`period, and their exponential progression is given by the gain. A simple scheme is
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`illustrated below. In this scheme the past residual signal is stored and used to
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`generate a predicted residual sample which is then subtracted from the input
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`18
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`
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`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
`
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`residual signal to form a long-term prediction error often referred to as the remnant
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`signal.
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`
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`Figure 3. Block diagram of simplified open-loop long-term prediction
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`removal example
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`35. The remnant signal is noise like signal that exhibits almost no short
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`term and no long-term correlation, and its energy and dynamic range are much
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`smaller than the input speech. It is very hard to encode since it has no particular
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`structure, and it is hard to tell how much its quantization error would affect the
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`overall generated speech quality at the decoder. In other words, the simple
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`encoding scheme described so far was performed in open-loop and did not really
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`consider the speech signal generated by the decoder. We will discuss below better
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`coding scheme that operate in closed-loop and consider the synthesized speech.
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`19
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`Residual signal
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`Residual sample
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`Remnant
`signal sample
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`-
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`Stored previous
`period residual
`samples
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`pitch Prediction
`(long-Term
`Prediction)
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`Predicted
`residual sample
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`Pitch (long-term)
`Analysis
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`Pitch Quantization
`(evvery 5-7 ms)
`
`pitch
`coefficients
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`Quantized pitch
`coefficients
`
`Quantized pitch
`code transmitted
`every 5-7 ms
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`

`

`
`
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`C. Quantization
`
`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
`
`36. Quantization is the process of reducing signal resolution, and
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`representing the reduced resolution signal by some code to be stored and
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`transmitted. It can be done on a sample by sample basis, using table of
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`quantization levels, where the quantization level is selected by the encoder (scalar
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`quantizer) using for example nearest-neighbor criterion. Quantization can also be
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`performed on a set of values often called vector (e.g. set of LPC parametes or set
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`of consecutive signal samples), where quantized vectors are selected from multi-
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`dimensional tables also referred to as codebooks, again the quantized vector is
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`selected by the encoder (vector quantizer, VQ) using for example nearest-neighbor
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`criterion. For a given number of bits, vector quantizer produces much better
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`quality than scalar quantizer, at the cost of increased computational complexity.
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`37. Codebooks (and quantizers) are usually computed by training data
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`gathered during system operation, such that the generated trained codebook
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`minimizes some distortion measure over all the training data generated by the
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`system. Once the system is changed, e.g. its operation order is changed,
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`computation elements are added, removed or modified, the codebook would no
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`longer operate optimally, and new training process is needed to be performed to
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`achieve adequate performance. In other words, systems having quantizers and/or
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`20
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`
`
`Inter Partes Review USPN 7,151,802
`Declaration of Oded Gottesman, Ph.D.
`
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`codebooks may operate unpredictably once they are changed or combined with
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`other systems.
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`38. Similarly, person of ordinary skills in the art knows that one cannot
`
`com