UNITED STATES PATENT AND TRADEMARK OFFICE
`___________________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`___________________
`
`APPLE INC.
`Petitioner
`
`v.
`
`ANDREA ELECTRONICS CORPORATION
`Patent Owner
`___________________
`
`Case No. IPR2017-00627
`U.S. Patent 6,363,345
`___________________
`
`DECLARATION OF SCOTT C, DOUGLAS, PH.D. IN SUPPORT OF
`PATENT OWNER’S RESPONSE
`
`Patent Owner
`Andrea Electronics Corp.
`EXHIBIT 2002
`IPR2017-00627
`
`Page 1 of 100
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`

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`TABLE OF CONTENTS
`
`I.
`
`II.
`
`Introduction ..................................................................................................... 1
`A.
`Engagement .......................................................................................... 1
`B.
`Compensation and Prior Testimony ..................................................... 1
`C. Qualifications and Professional Experience ......................................... 2
`D.
`Summary of My Study ......................................................................... 5
`
`RELEVANT LEGAL STANDARDS ......................................................... 7
`A. Claim Construction ............................................................................ 7
`B. Anticipation ......................................................................................... 7
`C. Obviousness ......................................................................................... 8
`
`III. ONE OF ORDINARY SKILL IN THE ART .......................................... 10
`
`IV. BACKGROUND OF THE TECHNOLOGY ........................................... 11
`A. Audio Signals .................................................................................... 11
`B. Analyzing Audio Signals .................................................................. 15
`C. Adaptive Filtering............................................................................. 20
`
`V. OVERVIEW OF THE ’345 PATENT ...................................................... 20
`
`VI. Opinion On Claim Construction ................................................................... 22
`
`VII. Grounds Based on Helf Alone ...................................................................... 24
`A.
`Summary of Helf ................................................................................ 24
` 1.
`
`Helf’s Stationary Estimator ...................................................... 26
`2.
`Helf’s Running Minimum Estimator ....................................... 26
`Helf’s Use of the Two Noise Estimates ................................... 27
`3.
`Helf’s Noise Confidence Decisions ......................................... 27
`4.
`a)
`Helf’s Global Speech Detector ....................................... 28
`Helf’s Local Speech Detector ......................................... 28
`b)
`Setting the Gain and Attenuating Noise .................................. 29
`
`5.
`
`VIII. Claims 4-7, 9-11, 39-41 and 43Are Not Anticipated by Helf ...................... 30
`A. Neither Bk nor Mk are Minimum Values ............................................ 30
`B. Nk is Not Derived “In Accordance With” Mk .................................... 35
`C. Nk is not Set to Mk Periodically ......................................................... 36
`D. Mk Is Not Set to a Current Magnitude Value ..................................... 40
`E. Mk Is Not Set Periodically .................................................................. 41
`F. Method Claims ................................................................................... 41
`
`Page 2 of 100
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`

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`IX. Helf and The Relied Upon Secondary References Do Not Render
`obvious claims 6, 8-9, 12, 25, 42 and 46 of the ’345 patent ....................... 42
`A.
`Summary of the Asserted References ................................................ 42
`1.
` Martin ....................................................................................... 42
` Martin’s Techniques Allegedly Reduce Delay............... 46
`a)
`
` b)
`The Role of Subwindows in Martin’s Algorithm ........... 48
` Martin’s SNR Computation ............................................ 49
`c)
`Boll ........................................................................................... 50
` 2.
`
`Arslan ....................................................................................... 51
`
` 3.
`Uesugi ...................................................................................... 51
`
` 4.
`Petitioner’s Proposed Combination of Helf and Martin Does
`Not Render Obvious Claims 6, 8, 9, 12, 25, 42, or 46 ....................... 51
` 1.
`
`Petitioner’s Proposed Combination of Helf and Martin
`Does Not Meet Each and Every Limitation of Claims 6,
`8, or 9 ....................................................................................... 52
`a)
`Claim 6 ............................................................................ 52
`
`
` b)
`Claim 8 ............................................................................ 55
`Claim 9 ............................................................................ 58
`c)
`
` Method Claims ......................................................................... 59 2.
`
`
`A Skilled Artisan Would Not Have Been Motivated To
` 3.
`Combine Helf And Martin As Alleged .................................... 59
`Petitioner’s Proposed Combination of Helf and Boll Does Not
`Render Obvious Claims 17-20 and 47 ............................................... 64
`Petitioner’s Proposed Combination of Helf and Arslan Does
`Not Render Obvious Claims 15-16 .................................................... 66
`Petitioner’s Proposed Combination of Helf and Uesugi Does
`Not Render Obvious Claims 22 ......................................................... 68
`
`D.
`
`B.
`
`C.
`
`E.
`
`
`
`
`Page 3 of 100
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`

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`I, Scott C. Douglas, Ph.D., do hereby declare:
`
`I.
`
`INTRODUCTION
`
`A. Engagement
`I have been retained by counsel for Andrea Electronics Corporation as
`1.
`
`an expert witness to render opinions on certain issues concerning Inter Partes
`
`Review No. IPR2017-00627 of U.S. Patent No. 6,363,345 to Joseph Marash et al.
`
`(Ex. 1001, “the ’345 Patent”). This is my written report.
`
`B. Compensation and Prior Testimony
`I am being compensated at a standard rate of $575 per hour for my
`2.
`
`study and preparation of this declaration. I am also being reimbursed for
`
`reasonable and customary expenses associated with my work and testimony in this
`
`study. This compensation is not dependent on my opinions or testimony or the
`
`outcome of this matter.
`
`3.
`
`I have previously testified as an expert in the following matters, which
`
`also involved the ’345 Patent: U.S. International Trade Commission Investigation
`
`Nos. 337-TA-949 and 337-TA-1026 on behalf of Andrea Electronics Corp. During
`
`the previous four years, I have additionally testified as an expert in the following
`
`matters: Ericsson Inc. v. Apple Inc., E.D.Tx., 2:15-cv-288-JRG-RSP; and Masimo
`
`v. Covidien, U.S. Patent and Trademark Office, Interference No. 105875.
`
`Page 4 of 100
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`

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`C. Qualifications and Professional Experience
`I am currently a professor in the Department of Electrical Engineering
`4.
`
`at the Bobby B. Lyle School of Engineering at Southern Methodist University. I
`
`have been a professor in the Department of Electrical Engineering at Southern
`
`Methodist University since August 1998. I have taught, and continue to teach,
`
`courses to undergraduate and graduate level students in the areas of signal
`
`processing, including adaptive filtering and adaptive arrays. My research at
`
`Southern Methodist University is focused in the areas of acoustic signal
`
`processing, active noise control, adaptive filtering, array processing, multichannel
`
`blind deconvolution and source separation.
`
`5.
`
`Prior to my position at Southern Methodist University, I was an
`
`assistant professor in the Department of Electrical Engineering at the University of
`
`Utah. I taught courses to undergraduate and graduate level students in the areas of
`
`signal processing, including digital signal processing, adaptive filtering, and active
`
`noise control. In addition to teaching, I also performed research in the areas of
`
`adaptive filtering, active noise control, multichannel blind deconvolution and
`
`source separation, and hardware implementations of adaptive signal processing
`
`systems.
`
`Page 5 of 100
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`

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`6.
`
`I have been a member of the Institute of Electrical and Electronics
`
`Engineers since 1988, and am currently a Senior Member. I have been an
`
`Associate Editor of the IEEE Transactions on Signal Processing and IEEE Signal
`
`Processing Letters. I have had leadership roles in IEEE organizational activities,
`
`including conference and workshop organization, and I have served on three
`
`Technical Committees of the IEEE Signal Processing Society and held leadership
`
`positions of Secretary or Chair of some of these committees. In 2010, I was the
`
`General Chair and the organizer of the IEEE International Conference on
`
`Acoustics, Speech, and Signal Processing, the premier yearly IEEE conference
`
`series on all aspects of signal processing theory, methods, and applications, and I
`
`have published in and attended this conference every year it has been offered since
`
`1990. I was the recipient of the Best Paper Award in Audio and Electroacoustics
`
`of the IEEE Signal Processing Society in 2003.
`
`7.
`
`I have written several book chapters related to adaptive filters,
`
`microphone arrays, blind deconvolution, and source separation. I was section
`
`editor of the Adaptive Filters portion of The Digital Signal Processing Handbook,
`
`Vijay Madisetti and Douglas Williams, eds. (Boca Raton, FL: CRC/IEEE Press,
`
`1998), and authored one chapter and co-authored another chapter on adaptive
`
`filters for this text. I co-authored, with Shun-ichi Amari, the book chapter entitled
`
`“Natural Gradient Adaptation,” in Unsupervised Adaptive Filtering, Vol. I: Blind
`
`Page 6 of 100
`
`

`

`Signal Separation, Simon Haykin, ed., (New York: Wiley, 2000), and I co-
`
`authored, with Simon Haykin, the book chapter entitled “Relationships Between
`
`Blind Deconvolution and Blind Source Separation,” in Unsupervised Adaptive
`
`Filtering, Vol. II: Blind Deconvolution, Simon Haykin, ed., (New York: Wiley,
`
`2000). I wrote the book chapter entitled, “Blind Separation of Acoustic Signals,”
`
`appearing in Microphone Arrays: Techniques and Applications, Michael
`
`Brandstein and Darren Ward, eds., (New York: Springer-Verlag, 2001). I co-
`
`authored, with Malay Gupta, the book chapter entitled, “Convolutive Blind Source
`
`Separation for Audio Signals,” in Blind Speech Separation, Shoji Makino, Te-Won
`
`Lee, and Hiroshi Sawada, eds. (New York: Springer, 2007).
`
`8.
`
`I received my bachelors degree (June 1988), masters degree (June
`
`1989), and doctorate degree (June 1992) in electrical engineering from Stanford
`
`University. For my doctorate degree, the focus of my studies were in the area of
`
`signal processing, adaptive filters, and statistical estimation and detecting.
`
`9.
`
`By virtue of the above experience, I have gained a detailed
`
`understanding of the technology that is at issue in this proceeding. I believe I am
`
`qualified to provide opinions about how one of ordinary skill in the art would have
`
`interpreted and understood the ’345 Patent and the art relied upon by the Petitioner
`
`at the time of the invention of the ’345 Patent.
`
`Page 7 of 100
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`

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`10. A copy of my curriculum vitae, appended hereto as Appendix A,
`
`further describes in detail my qualifications, responsibilities, employment history,
`
`honors, awards, publications, and professional associations.
`
`D.
`11.
`
`Summary of My Study
`I understand that the Petition for Inter Partes Review in IPR2017-
`
`00627 (Paper 1, “the ’627 Petition”) challenges the validity of certain claims of the
`
`’345 Patent, and that the Institution Decision (Paper 7, “the ’627 Decision”)
`
`instituted this proceeding on the following grounds:
`
`•
`
`Ground 1: Claims 1-7, 9-11, 13, 14, 21, 23, 38-41, and 43 as being
`
`anticipated by U.S. Patent No. 5,550,924 of Helf et al. (Ex. 1010,
`
`“Helf”);
`
`•
`
`Ground 2: Claims 1-7, 9-11, 13, 14, 21, 23, 38-41, and 43 as being
`
`obvious over Helf in view of the knowledge of a person of ordinary
`
`skill in the art;
`
`•
`
`Ground 3: Claims 6, 8, 9, 12, 25, 42, and 46 as being obvious over
`
`Helf in view of an article by R. Martin, entitled “An efficient
`
`algorithm to estimate instantaneous SNR of speech signals” (Ex.
`
`1006, “Martin”);
`
`Page 8 of 100
`
`

`

`•
`
`Ground 4: Claims 17-20 and 47 as being obvious over Helf in view
`
`of an article by S. Boll, entitled “Suppression of acoustic noise in
`
`speech using spectral subtraction” (Ex. 1009, “Boll”);
`
`Ground 5: Claims 15 and 16 as being obvious over Helf in view of
`
`U.S. Patent No. 5,706,395 of Arslan et al. (Ex. 1011, “Arslan”);
`
`Ground 6: Claim 24 as being obvious over Helf, Boll, and Arslan;
`
`Ground 7: Claim 22 as being obvious over Helf in view of U.S.
`
`Patent No. 5,459,683 of Uesugi et al. (Ex. 1015, “Uesugi”); and
`
`Ground 8: Claims 44 and 45 as being obvious over Helf, Martin, and
`
`•
`
`•
`
`•
`
`•
`
`Uesugi.
`
`12.
`
`In preparing this declaration , I have reviewed the ’345 Patent, the
`
`’627 Petition, the declaration of Dr. Bertand Hochwald (Ex. 1004, “the ’627
`
`Hochwald Report”), and the ’627 Decision, as well as each of Helf, Martin, Boll,
`
`Arslan, and Uesugi. I also reviewed relevant portions of the deposition transcript
`
`of Dr. Hochwald. My opinions are set forth below, and are based on my years of
`
`education, research, and experience, as well as my investigation and study of the
`
`materials identified above. I make these statements based upon facts and matters
`
`within my own knowledge or on information provided to me by others. All such
`
`facts and matters are true to the best of my knowledge and belief.
`
`Page 9 of 100
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`

`

`II. RELEVANT LEGAL STANDARDS
`
`13. My understanding of the relevant legal standards is based on
`
`information provided to me by Patent Owner’s counsel.
`
`A. Claim Construction
`I understand that in an inter partes review proceeding, the claims of a
`14.
`
`non-expired patent are construed from the perspective of one of ordinary skill in
`
`the art at the time of the claimed invention and are given their broadest reasonable
`
`construction consistent with the specification.
`
`B. Anticipation
`I understand that a claim is anticipated if a single prior art reference
`15.
`
`discloses, explicitly or inherently, all limitations of the invention arranged or
`
`combined in the same way as in the claim. I further understand that inherency may
`
`not be established by probabilities or possibilities, and the fact that one of ordinary
`
`skill in the art understands that the missing limitation could exist under certain
`
`circumstances is not sufficient. Instead, I understand that the party claiming
`
`inherency must prove that the missing matter is necessarily present and that it
`
`would be so recognized by a person of ordinary skill in the relevant art. I
`
`understand that whether the inherent disclosure was recognized at the time of the
`
`reference is immaterial.
`
`Page 10 of 100
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`16.
`
`I further understand that the disclosure of an anticipatory reference
`
`must describe the claimed invention to a degree adequate to enable a person of
`
`ordinary skill in the art to not only comprehend the invention, but also to make, or
`
`in the case of a method, use, the claimed invention without undue experimentation.
`
`Provided that the reference asserted is enabling, it is my understanding that it need
`
`not disclose any independent use or utility to anticipate a claimed invention.
`
`C. Obviousness
`It is my understanding that an invention is unpatentable if the
`17.
`
`differences between the invention and the prior art are such that the subject matter
`
`of the invention as a whole would have been obvious at the time the invention was
`
`made to a person having ordinary skill in the art. I further understand that
`
`obviousness is determined by evaluating: (1) the scope and content of the prior art,
`
`(2) the differences between the prior art and the claim, (3) the level of ordinary
`
`skill in the art, and (4) secondary considerations of nonobviousness. To establish
`
`obviousness based on a combination of prior art references, it is my understanding
`
`that a petitioner must identify a specific combination that teaches all limitations
`
`and establish that a person of ordinary skill in the art at the time of the claimed
`
`invention would have found it obvious to make that combination.
`
`18. To guard against hindsight and an unwarranted finding of
`
`obviousness, I understand that an important component of any obviousness inquiry
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`is whether the petitioner has identified any teaching, suggestion, or motivation that
`
`would have prompted a person of ordinary skill in the art to make the claimed
`
`combination and have a reasonable expectation of success in doing so. I
`
`understand that this test should not be rigidly applied, but can be an important tool
`
`to avoid the use of hindsight in the determination of obviousness.
`
`19.
`
`I further understand that the teaching, suggestion, or motivation may
`
`be found explicitly or implicitly: (1) in the prior art; (2) in the knowledge of those
`
`of ordinary skill in the art that certain references, or disclosures in those references,
`
`are of special interest or importance in the field; or (3) from the nature of the
`
`problem to be solved. Additionally, I understand that the legal determination of
`
`the motivation to combine references allows recourse to logic, judgment, and
`
`common sense. In order to resist the temptation to read into prior art the teachings
`
`of the invention in issue, however, it should be apparent that “common sense”
`
`should not be conflated with what appears obvious in hindsight.
`
`20.
`
`I understand that it is improper to combine references where the
`
`references teach away from their combination. I understand that a reference may
`
`be said to teach away when a person of ordinary skill in the relevant art, upon
`
`reading the reference, would be discouraged from following the path set out in the
`
`reference, or would be led in a direction divergent from the path that was taken by
`
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`

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`the applicant. I further understand that if the teachings of a prior art reference
`
`would lead a person of ordinary skill in the art to make a modification that would
`
`render another prior art device inoperable, then such a modification would
`
`generally not be obvious. I also understand that if a proposed modification would
`
`render the prior art invention being modified unsatisfactory for its intended
`
`purpose, then there would have been no suggestion or motivation to make the
`
`proposed modification.
`
`III. ONE OF ORDINARY SKILL IN THE ART
`
`21.
`
`I understand that Petitioner and the ’626 Hochwald Declaration have
`
`alleged that a person of ordinary skill in the art in the field of the invention claimed
`
`in the ’345 Patent would have had “a good working knowledge of digital signal
`
`processing techniques and their applications” gained through “an undergraduate
`
`education in electrical engineering or a comparable field, in combination with
`
`either a graduate degree (or two years of graduate work) in electrical engineering
`
`or a comparable field, or through two years of practical work experience, where
`
`such graduate education or work experience focused on or involved the use of
`
`digital signal processing techniques.” Petition at 12; Ex. 1003 at ¶37.
`
`22.
`
`I believe I am qualified based on my education and experience,
`
`discussed above, to render opinions from the perspective of a person of ordinary
`
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`

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`skill in the art according to Petitioner’s proposed definition, and for the purposes of
`
`my opinions herein, I have used Petitioner’s definition. In particular, I have read
`
`the ’345 Patent, Hirsch, Martin, Boll, Arslan, and Uesegi, and have considered
`
`their disclosures from the perspective of such a person of ordinary skill at the time
`
`of the invention of the ’345 Patent. Unless otherwise stated, my statements herein
`
`refer to the knowledge of one of ordinary skill in the field of the invention claimed
`
`in the ’345 Patent.
`
`IV. BACKGROUND OF THE TECHNOLOGY
`
`23. The ’345 Patent, entitled “System, Method and Apparatus for
`
`Cancelling Noise,” is generally directed to systems, methods, and apparatuses for
`
`processing audio signals, and more specifically to the processing of audio signals
`
`so as to cancel noise components. See Ex. 1001 at 1:19-21.
`
`A. Audio Signals
`24. Audio signals are a representation of sound. Sound is a vibration that
`
`propagates as a pressure wave through a medium (e.g., air). These physical sound
`
`waves can be represented in terms of electrical voltage, for example, when picked
`
`up by a microphone. A microphone typically includes a membrane which vibrates
`
`when the sound wave (vibrations) impact the membrane. The vibrations of the
`
`membrane are converted into electrical energy and measured in terms of electrical
`
`voltage. When a loud sound (which carries more energy) hits the membrane, it
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`results in a greater vibration in the membrane, which translates into a higher
`
`measure of electrical voltage. Likewise, when a soft sound (which carries less
`
`energy) hits the membrane, it results in a smaller vibration in the membrane, which
`
`translates into a lower measure of electrical voltage. These measures of electrical
`
`voltage, collected over time, correspond to the audio signal, and they can be
`
`plotted to show the waveform of the audio signal. Below is an example of the
`
`waveform of an audio signal, with the oscillations in the waveform corresponding
`
`to the physical vibration of the membrane in a microphone (the audio signal shown
`
`is an example of a sinusoidal signal, and is sometimes called a sine wave).
`
`
`
`
`25. Typically, the audio signals generated from a microphone are analog
`
`
`
`signals. Analog signals are unsampled, continuous signals that vary over time.
`
`These analog signals can be analyzed and modified using analog systems (e.g.,
`
`usually consisting of analog amplifiers, resistors, capacitors, and other electronic
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`elements). Analog systems, however, are relatively limited in function and often
`
`require substantial hardware redesign to incorporate additional functionality.
`
`Digital systems allow for much greater functionality, as they can be implemented
`
`using general purpose computational systems programmed with software to
`
`achieve the desired functionality. As such, audio signals are typically processed
`
`using digital systems when such processing hardware is available.
`
`26.
`
`In order to process audio signals in digital systems, they must first be
`
`converted into digital signals. The conversion of an analog signal into a digital
`
`signal is accomplished by use of an analog-to-digital converter (A/D converter).
`
`The A/D converter samples the input signal by taking measurements of the
`
`amplitude of the analog signal at regular time intervals, i.e., by “sampling.” This
`
`sampling is illustrated, for example, in the diagram below:
`
`27. Unlike analog signals, digital signals allow for the representation of a
`
`signal using a discrete and finite number of points. Because of the discrete nature
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`

`of digital signals, the digitized samples may not exactly match the values of the
`
`analog signals, as shown for example in the figure above, which shows that the
`
`digital samples do not line up exactly with the analog counterpart. But it is the
`
`discrete nature of digital signals that allows each point of the digital signal to be
`
`stored into discrete memory locations in the digital system.
`
`28. Higher sampling rates and more accurate digitization of the samples
`
`can be employed within the A/D converter to provide for a more accurate
`
`representation of the analog signal in digital form. However, using a higher
`
`sampling rate and/or a more-accurate digitizer requires increased memory (to store
`
`the additional measurements of the higher-quality samples of the analog signal)
`
`and processing power (to process the additional data).
`
`29. Lower sampling rates can also be used, but too low of a sampling rate
`
`may result in aliasing. When aliasing occurs, the sampled signal becomes
`
`indistinguishable from other signals and can cause unwanted distortions or artifacts
`
`in the signal when it is converted back into analog form. An example of an aliased
`
`signal is shown below, in which the blue signal corresponds to the original signal,
`
`while the red signal represents an aliased signal that can result from using too low
`
`of a sampling rate:
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`
`
`
`30. To avoid aliasing, the signal should be sampled at a sampling rate that
`
`is at least two times that of the highest frequency component that appears in the
`
`signal. For example, if the highest frequency component that appears in the signal
`
`has a frequency of 10 kHz, the signal should be sampled at a rate of at least 20 kHz
`
`to avoid aliasing. This rule is referred to in the art as the Nyquist theorem or the
`
`sampling theorem.
`
`B. Analyzing Audio Signals
`31. Audio signals can be analyzed with respect to time. Such an analysis
`
`is referred to in the art as “time domain” analysis. The properties of a periodic
`
`signal (e.g., a sinusoid) can be readily ascertained in the time domain. Take, for
`
`example, the following sinusoid:
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`
`32. The period of the signal is the amount of time it takes for a signal to
`
`repeat itself. The period of the sinusoidal signal is illustrated below:
`
`
`
`
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`
`33. From the period of the sinusoidal signal, one can specify its
`
`frequency, which is another property of the sinusoidal signal. The frequency is
`
`defined as the inverse of the period, or frequency =
`
`1period .
`
`34. Another property of a periodic signal is its amplitude. The amplitude
`
`is a characteristic property that describes the general size or height of a signal.
`
`One way to characterize the amplitude is the peak amplitude, which is the highest
`
`value of the signal in the given period. This is illustrated in the diagram below:
`
`
`
`
`35. Another way to characterize the amplitude is the peak-to-peak
`
`
`
`amplitude which describes the amount of change between the lowest value and the
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`Page 20 of 100
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`highest value of the signal in the given period. So in the figure above, the peak-to-
`
`peak amplitude would be the range from -1 to 1, or a value of 2.
`
`36.
`
`In the real world, however, signals are more complex, and as such, the
`
`properties of such signals can be much more difficult to ascertain. Take the
`
`following exemplary signal, which represents a chord - a particular combination of
`
`sine waves:
`
`
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`
`37. This chord is actually the sum result of three sinusoids at different frequencies, as
`illustrated by the diagram below:
`
`
`
`
`38. As one can see, once the chord is broken down into its individual
`
`frequency components, the signal becomes easier to analyze over time. Analyzing
`
`signals with regard to frequency is referred to in the art as “frequency domain”
`
`analysis. In frequency domain analysis, the signal is represented in terms of its
`
`frequency components.
`
`39. One way of extracting the frequency components of a signal is by
`
`using a Discrete Fourier Transform (DFT). The DFT represents a signal as a
`
`weighted sum of sine and cosine waves of increasing frequency. The DFT
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`computes the set of coefficients for each of the sine and cosine components to
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`indicate how much of each sine and cosine component is present in the signal. For
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`example, an 8-point DFT takes 8 samples (points) of the digital input signal, and
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`outputs 8 complex pairs, representing the “real” and “imaginary” part of each input
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`sample. The “real” part of the complex pair represents the magnitude of the
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`sample, while the “imaginary” part represents the phase information. The Fast
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`Fourier Transform (“FFT”) is an algorithm that runs a DFT computation quickly.
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`C. Adaptive Filtering
`40. An adaptive filter is a digital signal processing system that, in its
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`simplest form, models the relationship between an input signal and a desired
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`response or main signal. Adaptive filters are used for many different applications
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`in signal processing, including noise removal, signal enhancement, beam forming,
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`and echo cancellation.
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`V. OVERVIEW OF THE ’345 PATENT
`41. The ’345 patent is generally directed to systems, methods, and
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`apparatuses for processing audio signals, and more specifically to the processing of
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`audio signals so as to cancel noise components. See Ex. 1001 at 1:19-21.
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`42. The ’345 patent breaks the signal into its constituent frequency
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`components, called “frequency bins,” using a “frequency spectrum generator,” for
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`example, an FFT. Ex. 1001 at 2:11-19. The claimed invention has the ability to
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`detect and cancel noise on a frequency bin-by-frequency bin basis by setting an
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`adaptive threshold for each frequency bin. Ex. 1001 at 3:24-45.
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`43. Once the frequency bins are generated, the ’345 patent teaches that the
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`sample values within each frequency bin can be optionally smoothed. Ex. 1001 at
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`5: 45-58. After the smoothing process, there are still a collection of sample values
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`in each bin.
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`44. An adaptive threshold is set for each frequency bin based on the
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`magnitude characteristics of the samples within the bin. Ex. 1001 at 6:10-22. The
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`’345 patent sets the threshold using a cascading minimum determination process.
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`First, a “future minimum” is determined as the minimum magnitude of the
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`frequency bin for a given period of time, for example, five seconds. Ex. 1001 at
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`6:23-32. After this five-second period elapses, the “future minimum” value is used
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`to initiate the “current minimum” parameter. Ex. 1001 at 6:33-41. The initiated
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`“current minimum” is then compared to each sample in the frequency bin over the
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`next five second period, and its value is updated whenever the magnitude of a
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`sample in the frequency bin is less than the “current minimum’s” value. Id. Thus,
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`the “current minimum” is ultimately determined as the smallest magnitude value
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`within the frequency bin. After the five second period has elapsed, the “current
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`minimum” value is then used to set the threshold. In the preferred embodiment,
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`the minimum is scaled by a factor of 4, and this scaled minimum “4*Min” serves
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`as the threshold. Ex. 1001 at 6:46-57; see also, id. at Fig 3.
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`45. Once the threshold is set (e.g., 4*Min), the threshold detector
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`compares the threshold to the magnitude of the signal. Id. Where the magnitude
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`of the signal is less than the threshold, the noise estimate is updated using the
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`magnitude value (“New Data”). See, e.g., id. at Fig. 3. The updated noise estimate
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`is then used in the subtraction process to cancel noise from the signal. Ex. 1001 at
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`6:58-7:27.
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`VI. OPINION ON CLAIM CONSTRUCTION
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`46.
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`I understand that claim construction is the common terminology used
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`to describe the interpretation of claim terms. It is also my understanding that in
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`this inter partes review proceeding, the claim terms of an unexpired patent are to
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`be given their broadest reasonable interpretation consistent with the specification
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`and file history of the ’345 Patent, as understood by one of ordinary skill in the art.
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`47.
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`It is also my understanding that Petitioner has proposed constructions
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`for the terms “magnitude” (independent claims 1 and 38); “frequency spectrum
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`generator”/ “generating the frequency spectrum” (independent claims 1 and 38);
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`“threshold detector for setting a threshold … and for detecting” (independent claim
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`1); “generating a noise canceling signal for canceling noise” (independent claim
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`38); “current minimum” (claims 4, 6, 7, 10, 11, and 39); and “future minimum”
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`(claims 4-7, 9, and 39-41). Petition at 13-18. I have been instructed to apply, and
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`have applied, Petitioner’s construction of these terms for the purposes of my
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`opinions.
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`48. Additionally, I provide the following comments on the term
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`“periodically” as recited in claims 6 and 9 of the ’345 Patent, and which the ’627
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`Petition alleges encompasses a meaning of “from time to time” based on a non-
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`technical definition within Merriam-Webster Dictionary. I initi