By: William D. Belanger
`Pepper Hamilton LLP
`125 High Street
`19th Floor, High Street Tower
`Boston, MA 02110
`(617) 204-5100 (telephone)
`(617) 204-5150 (facsimile)
`belangerw@pepperlaw.com
`
`
`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`___________________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`___________________
`
`APPLE INC.
`Petitioner
`
`v.
`
`ANDREA ELECTRONICS CORPORATION
`Patent Owner
`___________________
`
`Case No. IPR2017-00626
`U.S. Patent 6,363,345
`___________________
`
`
`
`
`
`PATENT OWNER’S PRELIMINARY RESPONSE
`
`

`

`TABLE OF CONTENTS
`
`IPR2017-00626
`Patent 6,363,345
`
`
`Page(s)
`
`Table of Authorities ................................................................................................. iii
`I. 
`INTRODUCTION .......................................................................................... 1 
`II.  OVERVIEW OF THE ’345 PATENT ........................................................... 3 
`III.  CLAIM CONSTRUCTION AND LEVEL OF ORDINARY SKILL
`IN THE ART .................................................................................................. 9 
`A.  A Person Having Ordinary Skill In The Art ........................................ 9 
`B. 
`Claim Construction ............................................................................ 10 
`IV.  THE ’626 PETITION FAILS TO DEMONSTRATE A
`REASONABLE LIKELIHOOD THAT CERTAIN CHALLENGED
`CLAIMS ARE OBVIOUS OVER HIRSCH AS ALLEGED ...................... 11 
`A.  Ground Based on the Combination of Hirsch and Martin ................. 12 
`Summary of Hirsch .................................................................. 12 
`1.
` 
`Summary of Martin .................................................................. 14 
`2.
` 
`Hirsch and Martin do not render obvious claims 4-11 and
`3.
` 
`39-42 ........................................................................................ 15 
`B.  Ground Based on the Combination of Hirsch and Boll ..................... 31 
`Summary of Boll ...................................................................... 31 
`1.
` 
`2.
`Apple Fails to Establish That A Skilled Artisan Would
` 
`Have Been Motivated to Combine Hirsch and Boll ................ 32 
`C.  Ground Based on the Combination of Hirsch, Martin, and Boll ....... 35 
`1.
`Apple Fails to Establish That A Skilled Artisan Would
` 
`Have Been Motivated to Combine Hirsch, Martin, and
`Boll ........................................................................................... 35 
`D.  Ground Based on the Combination of Hirsch, Boll, and Arslan ....... 37 
`Summary of Arslan .................................................................. 37 
`1.
` 
`2.
`Apple fails to establish that a skilled artisan would have
` 
`been motivated to combine Hirsch, Boll, and Arslan .............. 38 
`Grounds Based on the Combinations of Hirsch and Uesugi, and
`Hirsch, Martin, and Uesugi ................................................................ 42 
`
`E. 
`
`i
`
`

`

`1.
` 
`2.
` 
`
`IPR2017-00626
`Patent 6,363,345
`
`Summary of Uesugi ................................................................. 42 
`Apple fails to establish that a skilled artisan would have
`been motivated to combine Hirsch and Uesugi, or
`Hirsch, Martin, and Uesugi ..................................................... 42 
`THE ’626 PETITION OR ’627 PETITION SHOULD BE DENIED
`AS BEING REDUNDANT .......................................................................... 45 
`VI.  CONCLUSION ............................................................................................. 48 
`
`
`V. 
`
`ii
`
`

`

`IPR2017-00626
`Patent 6,363,345
`
`
`TABLE OF AUTHORITIES
`
`
`CASES
`Belden Inc. v. Berk-Tek LLC, 805 F.3d 1064 (Fed. Cir. 2015) ................... 19, 39, 41
`
`Page(s)
`
`Canon Inc. v. Intellectual Ventures I LLC, IPR2014-00535,
`Paper 9 (PTAB Sep. 24, 2014) ........................................................................... 46
`
`CFMT, Inc. v. Yieldup Int’l Corp., 349 F.3d 1333 (Fed. Cir. 2003) ....................... 11
`
`Dominion Dealer Solutions, LLC v. Autoalert, Inc., IPR2013-00220,
`Paper 8 (PTAB Aug. 5, 2013) ...................................................................... 22, 40
`
`Endo Pharmaceuticals v. Depomed, IPR2014-00652,
`Paper 12 (PTAB Sep. 29, 2014) ................................................................... 11, 12
`
`Intelligent Bio-Systems, Inc. v Illumina Cambridge Ltd., 821 F.3d 1359,
`(Fed. Cir. 2016) ................................................................................................... 12
`
`Intelligent Bio-Systems, Inc. v. Illumina Cambridge Ltd., IPR2013-00324,
`Paper 19 (PTAB Nov. 21, 2013) .................................................................. 45, 46
`
`InTouch Techs., Inc. v. VGO Communications, Inc., 751 F.3d 1327
`(Fed. Cir. 2014) ..................................................................................................... 2
`
`KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398 (2007) .........................................passim
`
`LG Display, Ltd. v. Innovative Display Technologies LLC., IPR2014-01094,
`Paper 10 (PTAB Jan. 13, 2015) .......................................................................... 44
`
`LG Display, Ltd. v. Innovative Display Technologies LLC., IPR2014-01094,
`Paper 18 (PTAB April 9, 2015) .......................................................................... 44
`
`LG Electronics, Inc. v. ATI Technologies, IPR2015-00327,
`Paper 13 (PTAB Jul. 10, 2015) ........................................................................... 45
`
`Liberty Mutual Ins. Co. v. Progressive Casualty Ins. Co., CBM2012-00003,
`Paper 7 (PTAB Oct. 25, 2012) .................................................................. 3, 46, 47
`
`iii
`
`

`

`Medtronic, Inc. v. Robert Bosch Healthcare Sys., Inc., IPR2014-00436,
`Paper 17 (PTAB June 19, 2014) ......................................................................... 46
`
`IPR2017-00626
`Patent 6,363,345
`
`
`Oracle v. Clouding IP, LLC, IPR2013-00075,
`Paper 15 (PTAB June 13, 2013) ......................................................................... 48
`
`Personal Web Techs., LLC v. Apple, Inc., 848 F.3d 987 (Fed. Cir. 2017) .......passim
`
`In re Royka, 490 F.2d 981 (CCPA 1974) ................................................................ 11
`
`In re Translogic Tech., Inc., 504 F.3d 1249 (Fed. Cir. 2007) ................................... 9
`
`TRW Automotive U.S., LLC v. Magna Electronics, Inc., IPR2015-00949,
`Paper 7 (PTAB Sep. 17, 2015) ............................................................................. 9
`
`Unilever, Inc. d/b/a Unilever v. Proctor & Gamble Co., IPR2014-00506,
`Paper 17 (PTAB July 7, 2014) ............................................................................ 46
`
`In re Van Os, 844 F.3d 1359 (Fed. Cir. 2017) ................................................... 34, 36
`
`STATUTES
`
`35 U.S.C. § 313 .......................................................................................................... 1
`
`35 U.S.C. § 314 ........................................................................................................ 45
`
`35 U.S.C. § 325 .......................................................................................................... 3
`
`OTHER AUTHORITIES
`
`37 C.F.R. § 42.1 ....................................................................................................... 45
`
`37 C.F.R. § 42.100 ..................................................................................................... 9
`
`37 C.F.R. § 42.104 ................................................................................................... 34
`
`37 C.F.R. § 42.107 ..................................................................................................... 1
`
`37 C.F.R. § 42.108 ..................................................................................................... 3
`
`Office Patent Trial Practice Guide, 77 Fed. Reg. 48,756 (Aug. 14, 2012) ......... 9, 20
`
`iv
`
`

`

`Pursuant to 37 C.F.R. § 42.107(a), Andrea Electronics Corporation
`
`IPR2017-00626
`Patent 6,363,345
`
`
`(“Andrea” or “Patent Owner”) hereby submits the following Preliminary Response
`
`to the Petition assigned number IPR2017-00626 (“the ’626 Petition) seeking inter
`
`partes review of U.S. Patent No. 6,363,345 (“the ’345 Patent”). This filing is
`
`timely under 35 U.S.C. § 313 and 37 C.F.R. § 42.107, as it is being filed within
`
`three months of the mailing date of the Notice of Filing Date Accorded to the
`
`Petition (Paper 5), mailed January 31, 2017.
`
`I.
`
`INTRODUCTION
`
`Apple filed two IPR petitions against Andrea’s ’345 Patent. The second,
`
`assigned number IPR2016-00627 (“the ’627 Petition”), was filed on January 9,
`
`2017. This paper responds to the first ’626 Petition, which was also filed on the
`
`same day.
`
`In the ’626 Petition, Apple challenges claims of the ’345 Patent on the
`
`following grounds:
`
`1.
`
`Claims 1-3, 12, 13, 21, 23, and 38 as being anticipated by an article by
`
`H. Hirsch et al., entitled “Noise estimation techniques for robust
`
`speech recognition” (Ex. 1005, “Hirsch”);
`
`2.
`
`Claims 4-11, 25, 39-42, and 46 as being obvious over Hirsch in view
`
`of an article by R. Martin, entitled “An efficient algorithm to estimate
`
`instantaneous SNR of speech signals” (Ex. 1006, “Martin”);
`
`1
`
`

`

`3.
`
`4.
`
`5.
`
`Claims 13, 14, 17-21, 23, and 47 as being obvious over Hirsch in
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`IPR2017-00626
`Patent 6,363,345
`
`
`view of an article by S. Boll, entitled “Suppression of acoustic noise
`
`in speech using spectral subtraction” (Ex. 1009, “Boll”);
`
`Claim 43 as being obvious over Hirsch, Martin, and Boll;
`
`Claims 15, 16, and 24 as being obvious over Hirsch and Boll and
`
`further in view of U.S. Patent No. 5,706,395 of Arslan et al. (Ex.
`
`1011, “Arslan”);
`
`6.
`
`Claim 22 as being obvious over Hirsch in view of U.S. Patent No.
`
`5,459,683 of Uesugi et al. (Ex. 1015, “Uesugi”); and
`
`7.
`
`Claims 44 and 45 as being obvious over Hirsch and Martin and
`
`further in view of Uesugi.
`
`As discussed in detail below, Apple’s reasoning with respect to the
`
`obviousness of claims 4-11, 13-24, 39-42, 44, 45, and 47 fails to establish a
`
`reasonable likelihood that a person skilled in the art would have been motivated to
`
`combine Hirsch with any of the asserted prior art to arrive at the claimed
`
`inventions. Apple’s arguments fail to provide an “articulated reasoning with some
`
`rational underpinning” to combine the references as required to demonstrate that “a
`
`skilled artisan not only could have made but would have been motivated to make
`
`the combinations or modifications of prior art to arrive at the claimed invention.”
`
`KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 418 (2007) and InTouch Techs., Inc. v.
`
`2
`
`

`

`VGO Communications, Inc., 751 F.3d 1327, 1352 (Fed. Cir. 2014), respectively.
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`IPR2017-00626
`Patent 6,363,345
`
`
`Moreover, in the case of the ground based on Hirsch and Martin, Apple fails to
`
`establish that the combination discloses each and every limitation of certain of the
`
`challenged claims. For at least these reasons, trial should not be instituted with
`
`respect to claims 4-11, 13-24, 39-42, 44, 45, and 47 of the ’345 Patent on the
`
`obviousness grounds. See 37 C.F.R. § 42.108(a),(c).
`
`Second, the ’626 Petition should be denied because it presents “the same, or
`
`substantially the same, prior art and arguments” as the ’627 Petition. See 35
`
`U.S.C. § 325(d). Across the two petitions, Apple relies on two primary references,
`
`presenting them as “distinct and separate alternatives.” Liberty Mutual Ins. Co. v.
`
`Progressive Casualty Ins. Co., CBM2012-00003, Paper 7 at 3 (Oct. 25, 2012).
`
`Grounds based on these primary references are therefore “horizontally redundant,”
`
`and Apple makes no effort to explain how any one of the primary references is
`
`better than the other. Id.
`
`Should the Board decide to institute a trial, Patent Owner reserves the right
`
`to present additional arguments as to the patentability of the claims for which trial
`
`is instituted.
`
`II. OVERVIEW OF THE ’345 PATENT
`The ’345 Patent, entitled “System, Method and Apparatus for Cancelling
`
`Noise,” is generally directed to the processing of audio signals to cancel or reduce
`
`3
`
`

`

`undesired noise present in those signals. See Ex. 1001 at 1:19-21. Prior art
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`IPR2017-00626
`Patent 6,363,345
`
`
`techniques estimated the level of noise in the signal by measuring the magnitude of
`
`the signal “during non-speech time intervals” and then subtracting that estimate
`
`from the whole signal. Id. at 1:60-64. The problem with this approach is that the
`
`thresholds used to distinguish non-speech intervals were inaccurate. Id. at 2:45-58.
`
`The inventions of the ’345 Patent address these shortcomings by decomposing the
`
`signal into frequency bins and then using a threshold detector to identify non-
`
`speech segments for each frequency bin. Id. at 3:28-31.
`
`In accordance with certain aspects of the ’345 Patent, an input audio signal
`
`can be digitized and conditioned in order to generate a frequency spectrum of the
`
`signal. Id. at 4:65-5:10. In one particularly preferred embodiment, the frequency
`
`spectrum is generated using a Fast Fourier Transform (“FFT”). Id. at 5:10-12. In
`
`such aspects, the FFT can utilize a window of 512 points, consisting of 256 new
`
`points and 256 points from the previous window. Id. at 4:65-5:1; see also id. at
`
`5:12-14 (noting that other lengths of FFT samples such as 256 or 1024 can be
`
`used). Before applying the FFT, a shading window can be applied “to smooth
`
`transients between two processed blocks” and “to reduce the side lobes in the
`
`frequency domain and hence prevent the masking of low energy tonals by high
`
`energy side lobes.” Id. at 5:4-10. “The shaded results are converted to the
`
`frequency domain through an FFT (Fast Fourier Transform).” Id. at 5:10-12.
`
`4
`
`

`

`Although the FFT is said to be a preferred embodiment, the ’345 Patent also
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`IPR2017-00626
`Patent 6,363,345
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`
`contemplates that “other transforms may be applied to the present invention to
`
`obtain the spectral noise signal.” Id. at 5:30-33.
`
`Again with reference to the preferred embodiment of the ’345 Patent
`
`depicted in FIG. 2, the frequency bins are sent through the noise processing block,
`
`where the magnitude of each frequency bin can be estimated:
`
`FIG. 2 is a detailed description of the noise processing block
`200(112). First, each frequency bin(n) 202 magnitude is estimated.
`The straight forward approach is to estimate the magnitude by
`calculating:
`
`Y(n) = ((Real(n))2 + (Imag(n))2)-2
`In order to save processing time and complexity the signal magnitude
`(Y) is estimated by an estimator 204 using an approximation formula
`instead:
`Y(n) = Max[|Real(n),Imag(n)|] + 0.4*Min[|Real(n),Imag(n)|]
`In order to reduce the instability of the spectral estimation, which
`typically plagues the FFT Process (ref[2] Digital Signal Processing,
`Oppenheim Schafer, Prentice Hall P. 542545), the present invention
`implements a 2D smoothing process. Each bin is replaced with the
`average of its value and the two neighboring bins’ value (of the same
`time frame) by a first averager 206. In addition, the smoothed value
`of each smoothed bin is further smoothed by a second averager 208
`using a time exponential average with a time constant of 0.7 (which is
`the equivalent of averaging over 3 time frames).
`
`5
`
`

`

`Id. at 5:34-54.
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`IPR2017-00626
`Patent 6,363,345
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`The 2-D smoothed magnitude value can then be used by the “noise
`
`estimation processor” to set a separate adaptive threshold for each frequency bin.
`
`Id. at 5:55-58 and 5:66-6:22. The ’345 Patent explains:
`
`The logic behind this method is that, for each syllable, the energy may
`appear at different frequency bands. At the same time, other frequency
`bands may contain noise elements. It is therefore possible to apply a
`non-sensitive threshold for the noise and yet locate many non-speech
`data points for each bin, even within a continuous speech case.
`
`Id. at 6:14-19 (emphasis added).
`
`In various aspects, the noise estimation process uses a “current minimum
`
`value” to set the threshold. Id. at 6:46-48. The current minimum value is set by
`
`setting it to the minimum magnitude value over a period of time. Id. at 6:34-39.
`
`The current minimum value is refreshed with a future minimum value at the
`
`beginning of each period. Id. at 6:34-39. This process “ensures a tight and quick
`
`estimation of the noise value . . . while preventing [] too high an estimation of the
`
`noise.” Id. at 6:42-45.
`
`The magnitude of each signal is continuously compared to the threshold in
`
`order to estimate the level of noise in each frequency bin. Id. at 6:49-53. If the
`
`magnitude of the frequency bin is less than the threshold, the noise estimate is
`
`updated using that magnitude value. Id. at 6:48-52. The subtraction processor
`
`6
`
`

`

`uses subtraction or filter multiplication to subtract the estimated noise from the
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`IPR2017-00626
`Patent 6,363,345
`
`
`frequency bin. Id. at 6:58-7:33.
`
`In accordance with various aspects of the above exemplary teachings, the
`
`challenged claims of the ’345 Patent are directed to apparatuses and methods for
`
`cancelling noise. Independent claim 1, for example, recites:
`
`1.
`
`An apparatus for canceling noise, comprising:
`an input for inputting an audio signal which includes a noise
`signal;
`a frequency spectrum generator for generating the frequency
`spectrum of said audio signal thereby generating frequency bins of
`said audio signal; and
`a threshold detector for setting a threshold for each frequency
`bin using a noise estimation process and for detecting for each
`frequency bin whether the magnitude of the frequency bin is less than
`the corresponding threshold, thereby detecting the position of noise
`elements for each frequency bin.
`
`Id. at 9:35-46. Challenged claims 2-25, which depend directly or indirectly from
`
`claim 1, recite various additional characteristics of the noise canceling apparatus
`
`recited in claim 1. Claim 4, for example, recites that the “threshold detector sets
`
`the threshold for each frequency bin in accordance with a current minimum value
`
`of the magnitude of the corresponding frequency bin; said current minimum value
`
`being derived in accordance with a future minimum value of the magnitude of the
`
`corresponding frequency bin.” Id. at 9:54-60.
`
`7
`
`

`

`Independent claim 38 recites a method for canceling noise from an audio
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`IPR2017-00626
`Patent 6,363,345
`
`
`signal, which in relevant aspects substantially tracks the limitations of the
`
`apparatus of claim 1 with the additional recitation of the final “subtraction” step:
`
`38. A method for driving a computer processor for generating a
`noise canceling signal for canceling noise from an audio signal
`representing audible sound including a noise signal representing
`audible noise, said method comprising the steps of:
`inputting said audio signal which includes said noise signal;
`generating the frequency spectrum of said audio signal thereby
`generating frequency bins of said audio signal;
`setting a threshold for each frequency bin using a noise
`estimation process;
`detecting for each frequency bin whether the magnitude of the
`frequency bin is less than the corresponding threshold, thereby
`detecting the position of noise elements for each frequency bin; and
`subtracting said noise elements detected in said step of
`detecting from said audio signal to produce an audio signal
`representing said audible sound substantially without said audible
`noise.
`
`Id. at 12:4-23. Challenged claims 39-47, which depend directly or indirectly from
`
`claim 38, recite various additional characteristics of the method recited in claim 38.
`
`Claim 39, for example, recites that “said setting step sets the threshold for each
`
`frequency bin in accordance with a current minimum value of the magnitude of the
`
`corresponding frequency bin; said current minimum value being derived in
`
`8
`
`

`

`accordance with a future minimum value of the magnitude of the corresponding
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`IPR2017-00626
`Patent 6,363,345
`
`
`frequency bin.” Id. at 12:24-29.
`
`III. CLAIM CONSTRUCTION AND LEVEL OF ORDINARY SKILL IN
`THE ART
`
`In an inter partes review, claim terms in an unexpired patent are interpreted
`
`according to their broadest reasonable construction in light of the specification of
`
`the patent in which they appear. 37 C.F.R. § 42.100(b); Office Patent Trial
`
`Practice Guide, 77 Fed. Reg. 48,756, 48,766 (Aug. 14, 2012). Under this standard,
`
`a claim term is given its ordinary and customary meaning as it would be
`
`understood by one of ordinary skill in the art. In re Translogic Tech., Inc., 504
`
`F.3d 1249, 1257 (Fed. Cir. 2007); TRW Automotive U.S., LLC v. Magna
`
`Electronics, Inc., IPR2015-00949, Paper 7 at 9 (Sep. 17, 2015).
`
`A. A Person Having Ordinary Skill In The Art
`Apple alleges that a hypothetical person of ordinary skill in the field of the
`
`’345 Patent at the time of the invention 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.” ’626 Petition at 12.
`
`9
`
`

`

`For the purposes of this paper, Patent Owner applies Apple’s proposed
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`IPR2017-00626
`Patent 6,363,345
`
`
`standard without prejudice. Should a trial be instituted, Patent Owner reserves the
`
`right to present evidence and arguments as to the above definition or an alternative
`
`definition as to the level of ordinary skill in the art.
`
`B. Claim Construction
`Apple addresses the construction of the following claim terms: “magnitude”
`
`(independent claims 1 and 38); “frequency spectrum generator”/ “generating the
`
`frequency spectrum” (independent claims 1 and 38); “threshold detector for setting
`
`a threshold … and for detecting” (independent claim 1); and “generating a noise
`
`canceling signal for canceling noise” (independent claim 38).1
`
`For purposes of this paper, Andrea applies Apple’s proposed constructions
`
`without prejudice, but reserves its rights to present evidence and arguments as to
`
`the proper construction of the claim terms within the meaning of the ’345 Patent in
`
`this or any other proceeding.
`
`
`1 With respect to the remaining claim terms, Apple purportedly relies on its
`
`proffered declarant in applying the “ordinary meaning of the words being used in
`
`those claims from the perspective of a person of ordinary skill in the art in light of
`
`the specification.” See e.g., ’626 Petition at 12 and Ex. 1003 at ¶¶ 90, 93.
`
`10
`
`

`

`IV. THE ’626 PETITION FAILS TO DEMONSTRATE A REASONABLE
`LIKELIHOOD THAT CERTAIN CHALLENGED CLAIMS ARE
`OBVIOUS OVER HIRSCH AS ALLEGED
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`IPR2017-00626
`Patent 6,363,345
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`
`The ’626 Petition’s grounds allege that various of claims 4-11, 13-25, 39-42,
`
`and 44-47 are obvious over Hirsch in combination with one or more additional
`
`references. In particular, the ’626 Petition alleges that claims 4-11, 25, 39-42, and
`
`46 are obvious over Hirsch in view Martin, and that claim 43 is obvious over
`
`Hirsch and Martin and further in view of Boll. Additionally, the ’626 Petition
`
`alleges that claims 13, 14, 17-21, 23, and 47 are obvious over Hirsch and Boll and
`
`that claims 15, 16, and 24 are obvious over Hirsch and Boll and further in view of
`
`Arslan. Further, Petitioner alleges that claim 22 is obvious over Hirsch in view of
`
`Uesugi. Finally, Petitioner alleges that claims 44 and 45 are obvious over Hirsch
`
`and Martin and further in view of Uesugi. Apple’s proposed combinations are not
`
`supported with sufficient reasoning necessary to establish a reasonable likelihood
`
`that claims 4-11, 13-24, 39-42, 44, 45, and 47 are unpatentable, as alleged.
`
`“To establish obviousness of a claimed invention, all the claim limitations
`
`must be taught or suggested by the prior art.” Endo Pharmaceuticals v. Depomed,
`
`IPR2014-00652, Paper 12 at 10 (Sep. 29, 2014) (citing CFMT, Inc. v. Yieldup Int’l
`
`Corp., 349 F.3d 1333 (Fed. Cir. 2003) and In re Royka, 490 F.2d 981 (CCPA
`
`1974)). “[A] patent claim composed of several elements, however, is not proved
`
`obvious merely by demonstrating that each of its elements was known,
`
`11
`
`

`

`independently, in the prior art.” KSR, 550 U.S. at 419. “In that regard, for an
`
`IPR2017-00626
`Patent 6,363,345
`
`
`obviousness analysis it can be important to identify a reason that would have
`
`prompted one of skill in the art to combine prior art elements in the way the
`
`claimed invention does.” Endo Pharmaceuticals, IPR2014-00652, Paper 12 at 10.
`
`Thus, it is “[petitioner]’s burden to demonstrate both that a skilled artisan would
`
`have been motivated to combine the teachings of the prior art references to achieve
`
`the claimed invention, and that the skilled artisan would have had a reasonable
`
`expectation of success in doing so.” Intelligent Bio-Systems, Inc. v Illumina
`
`Cambridge Ltd., 821 F.3d 1359, 1363-64 (Fed. Cir. 2016) (internal quotations
`
`removed).
`
`A. Ground Based on the Combination of Hirsch and Martin
`As noted above, Apple alleges that claims 4-11, 25, 39-42, and 46 are
`
`rendered obvious by Hirsch in view of Martin. This ground should not be
`
`sustained at least with respect to claims 4-11 and 39-42.
`
`
`Summary of Hirsch
`1.
`Hirsch is directed to two techniques to estimate noise characteristics for
`
`noisy speech signals. Ex. 1005 at 153, Abstract. These techniques were designed
`
`to separate unwanted background noise from a speech signal, enhancing the speech
`
`signal and improving the performance of speech recognition systems.
`
`12
`
`

`

`The first technique utilizes an algorithm using a first order recursive system
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`that calculates the noise magnitude level in signal subbands using a weighted
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`average of past spectral magnitudes. Id. at 153. The first technique utilizes
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`adaptive thresholds to calculate the noise magnitude level. Id. The algorithm
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`estimates the noise magnitude level by taking a weighted sum of past spectral
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`magnitude values in each subband and multiplying that sum by an overestimation
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`factor to derive a threshold. The noise is estimated when a spectral component
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`first exceeds the noise threshold previously calculated, thus stopping the recursive
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`accumulation of past spectral magnitude values, resulting in an estimate of the
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`magnitude level of noise. Id. When a spectral value is less than the noise
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`threshold, speech is not detected, and said values are set to zero. Id.
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`The second technique disclosed in Hirsch employs an algorithm that
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`evaluates the histograms of past spectral magnitude values corresponding to noise
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`segments in signal subbands, taking the maximum as a noise magnitude level
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`estimation. Id. at 154. The noise threshold calculated in accordance with the first
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`technique described above is used to evaluate past spectral values that fall below
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`said threshold. Id. Past values identified as noise segments are evaluated to
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`determine the noise distribution in roughly forty frequency bins. Id. The
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`maximum of the noise distribution in each subband is used to estimate the noise
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`magnitude level. Id. These estimated noise magnitude values are smoothed over
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`time. Id.
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`Summary of Martin
`2.
`Martin is directed to an algorithm for estimating the instantaneous signal-to-
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`noise-ratio (“SNR”) of a noisy speech signal. Ex. 1006 at 1093, Abstract.
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`Martin’s algorithm attempts to gather noise statistics by tracking varying noise
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`levels during speech activity. Id. at 1093. Martin’s method is based on the
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`observation that peaks in the smoothed power estimate of a noisy speech signal
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`correspond to speech activity, while the valleys of the smoothed power estimate
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`can be used to obtain a noise floor. Id.
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`Martin’s algorithm estimates the noise floor, or minimum noise power (i.e.,
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`Pn(i)), by taking the minimum of a smoothed power estimate within a window of
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`finite length. The minimum noise power estimate for a given sample is found by
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`comparing the actual minimum and the smoothed power estimate. Id. After all of
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`the samples within a sub-window have been read, the minimum power of said
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`samples is stored and the actual minimum power is restored to its maximum value.
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`Id. The algorithm distinguishes two scenarios in this calculation: (1) slowly
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`varying noise power; and (2) rapidly varying noise power. Id. Where the
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`minimum power of the samples in the last sub-window monotonically increases,
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`rapid noise power variation is selected, and the noise power estimate equals the
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`power minimum of the samples. Id. In the case of non-monotonic power, the
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`noise power is set to be equal to the minimum power of the whole window. Id. If
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`the actual smoothed power is smaller than the estimated noise power, the noise
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`power is updated immediately. Id.
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`Finally, the signal-to-noise ratio, SNR(i), of the full-band signal x(i) at time i
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`is estimated based on the estimated minimum noise power as shown below, where
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`ofactor is a constant between 1.3 and 2:
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`
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`Martin selects a window length of 0.625 seconds as the optimal window
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`length for a fullband speech signal based on experimentation. Id.
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` Hirsch and Martin do not render obvious claims 4-11 and
`3.
`39-42
`Apple acknowledges that Hirsch does not disclose “maintaining the noise
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`threshold for each frequency bin using ‘current minimum,’ and ‘future minimum,’
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`values as specified in claims 4-11 and 39-42. . .” ’626 Petition at 31-32. In an
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`attempt to cure the conceded deficiencies of Hirsch with respect to claims 4-11 and
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`39-42, Apple relies on Martin. The combination of Hirsch and Martin, however,
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`still fails to disclose each and every limitation of claims 4-11 and 39-42. Further,
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`as discussed in detail below, Apple fails to demonstrate that a person skilled in the
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`art would have been motivated to combine Hirsch and Martin to arrive at the
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`invention as recited in claims 4 and 39 (from which claims 5-11 and 40-42
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`depend).
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` Hirsch and Martin fail to disclose the limitations of a.
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`claims 4-11 and 39-42
`Contrary to Apple’s assertions, Hirsch and Martin fail to disclose each of the
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`elements of claims 4-11 and 39-42. Specifically, neither Hirsch nor Martin
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`discloses the use of a current minimum value derived in accordance with a future
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`minimum value to set a threshold for each frequency bin where a threshold
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`detector detects the position of noise elements for each frequency bin, as recited in
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`claims 4 and 39 (from which claims 5-11 and 40-42 depend).
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`First, Apple concedes that Hirsch does not disclose setting or maintaining a
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`noise threshold for each frequency bin using “current minimum” and “future
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`minimum” values. ’626 Petition at 32. Apple asserts that Hirsch discloses setting
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`an adaptive threshold for each frequency bin based on noise level estimates
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`obtained by either of Hirsch’s two disclosed algorithms (i.e., one using weighted
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`averages of past spectral magnitude values and the other evaluating histograms of
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`past spectral magnitude values). See, e.g., ’626 Petition at 24. Apple does not
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`dispute that neither of Hirsch’s algorithms is utilized to set thresholds in
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`accordance with a current minimum value and a future minimum value.
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`Moreover, Apple does not even assert that Martin calculates, estimates, or
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`sets noise thresholds or utilizes a noise threshold detector that detects the position
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`of noise elements for each frequency bin. See ’626 Petition at 32-34, 38-42.
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`Instead, Apple alleges that for each period of M samples, Martin’s algorithm
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`observes power level, stores the minimum value as Pmin, and sets the estimated
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`noise floor (Pn(i)) to the minimum observed power level. Id. at 39. The minimum
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`value is reset and the process repeats for another sample. Id. If the observed
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`power level is less than the estimated noise floor, the noise floor is set equal to the
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`