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`UNITED STATES PATENT AND TRADEMARK OFFICE
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`____________________
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`BEFORE THE PATENT TRIAL AND APPEAL BOARD
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`____________________
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`APPLE INC.
`Petitioner,
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`v.
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`ANDREA ELECTRONICS INC.,
`Patent Owner.
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`Patent No. 6,363,345
`____________________
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`IPR2017-00626
`__________________________________________________________________
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`Petitioner’s Opening Remand Brief
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`IPR2017-00626
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`Table of Contents
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`I.
`II.
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`Introduction .................................................................................................... 1
`Procedural Background ................................................................................ 1
`A.
`Summary of the Claims at Issue ........................................................ 1
`B.
`Scope of Issues to Be Decided ............................................................. 2
`III. Argument ........................................................................................................ 3
`A. Martin’s Noise Floor Tracking Algorithm Teaches the Elements
`of Claims 6-9 ........................................................................................ 3
`B. Martin Teaches the Future Minimum of Claim 4 ............................ 4
`C. Martin Teaches Setting a Current Minimum to a Future
`Minimum Value “Periodically,” as Required by Claim 6 ............... 8
`The Skilled Person Would Have Been Motivated to Set Hirsch’s
`Noise Threshold Using Martin’s Noise Tracking Algorithm .......... 9
`IV. Conclusion .................................................................................................... 10
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`D.
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`ii
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`IPR2017-00626
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`TABLE OF AUTHORITIES
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`Cases
`KSR Intern. Co. v. Teleflex Inc.,
`127 S. Ct. 1727 (2007) ........................................................................................ 10
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`Page(s)
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`I.
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`Introduction
`The sole question to be addressed in this remanded proceeding—whether
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`claims 6 to 9 of the ’345 Patent would have been obvious over Hirsch (Ex. 1005)
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`and Martin (Ex. 1006)—has been substantially narrowed by prior events in this
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`case. First, Andrea did not appeal the Board’s finding that the apparatus described
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`in Hirsch meets every requirement of claim 1 of the ’345 Patent. Second, Andrea
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`did not appeal the Board’s determination that claim 25 was obvious in view of
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`Hirsch and Martin, and now cannot dispute that a skilled person would have
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`considered Hirsch and Martin together or that “techniques such as those shown in
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`Hirsch and Martin are routinely combined.” Final Written Decision (FWD) at 16;
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`see also 949 F.3d 697, 703 (Fed. Cir. 2020) (“Hirsch refers to Martin as a ‘known’
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`approach ‘to avoid the problems of speech pause detection and to estimate the
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`noise characteristics just from a past segment of noisy speech.”). The obviousness
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`question thus distills down to a question of how Martin’s sound floor algorithm
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`works, and whether when integrated into Hirsch, the resulting device will meet the
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`requirements of claims 6 to 9. The answer, based on the arguments and evidence
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`in this record, is yes. The Board should find claims 6 to 9 unpatentable.
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`II.
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`Procedural Background
`Summary of the Claims at Issue
`A.
`Claims 6-9 ultimately depend from claim 1, which defines “[a]n apparatus
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`for canceling noise” by, inter alia, setting a threshold for each frequency bin using
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`a noise estimation process and detecting whether each bin’s magnitude is less than
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`the corresponding threshold. Ex. 1001, 9:34-46. Claim 4 depends from claim 1
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`and specifies “…set[ting] the threshold for each frequency bin in accordance with
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`a current minimum value of the magnitude of the corresponding frequency bin;
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`said current minimum value being derived in accordance with a future minimum
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`value of the magnitude of the corresponding frequency bin.” Id., 9:54-60. Claim 5
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`adds that the “future minimum value is determined as the minimum value of the
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`magnitude of the corresponding frequency bin within a predetermined period of
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`time.” Claim 6 depends from claim 5 and specifies that “said current minimum
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`value is set to said future minimum value periodically.”1 Id., 9:65-67. Claims 7
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`and 8 depend from claim 6, and address updating of the future minimum and
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`current minimum values, respectively, while claim 9 depends from claim 5, and
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`addresses updating of the future minimum value.
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`Scope of Issues to Be Decided
`B.
`The Board’s remand order (Paper 28) specifies that briefing is limited to
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`issues previously raised in the Response, Reply, or Observations on Cross. In
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`those papers, Andrea addressed Apple’s patentability challenges to claims 6-9 by
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`contending that Hirsch and Martin did not teach an apparatus as defined in claims
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`1 “Periodically” means a fixed interval of time. See 949 F.3d at 709.
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`2
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`4 and 6.2 As Andrea did not present separate arguments regarding claims 5 and 7-
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`9, it should not be permitted to do so now. See Reply, 3 n.2.
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`Andrea also did not appeal several of the Board’s findings in its Final
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`Written Decision which are now finally decided and cannot be reconsidered.
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`Those include the Board’s finding that a skilled person would have considered
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`Hirsch and Martin together. See FWD at 16 (citing Apple’s explanations that
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`“‘because Hirsch not only cites to Martin, but identifies relevant benefits it
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`provides,’ and ‘[a] skilled person would also have recognized that techniques such
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`as those shown in both Hirsch and Martin are routinely combined.’”). Andrea thus
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`cannot now dispute that a skilled person would have considered the combined
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`teachings of Hirsch and Martin and that such teachings were routinely combined.
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`III. Argument
`A. Martin’s Noise Floor Tracking Algorithm Satisfies Claims 6-9
`Andrea has maintained in these proceedings that Martin does not describe or
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`suggest (i) the “future minimum” element of claim 4, from which claims 6-9
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`depend or (ii) the future minimum setting requirement of claim 6. The full record
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`of this proceeding refutes both of Andrea’s contentions.
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`2 Andrea did not appeal the Board’s finding in IPR2017-00627 that claims 4-5 and
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`10-11 are unpatentable.
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`3
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`1. Martin Teaches the Future Minimum of Claim 4
`As Apple explained in the original briefing, Martin teaches a noise
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`estimation algorithm that determines the noise floor of an audio signal by tracking
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`its minimum value within successive windows of time. In Martin’s technique,
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`each window has a defined duration (e.g., of 0.625 seconds) and is comprised of L
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`digital samples. Pet., 33, 40; Ex. 1003, ¶¶135-39; Ex. 1006 at 1093. Martin
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`explains that each window can be divided into an arbitrary number of sub-windows
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`W, each of the same length M (i.e., L = M x W), and illustrates its algorithm with an
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`example using 4 sub-windows (W=4). Ex. 1003, ¶¶135, 139; Ex. 1023, ¶20.
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`Martin uses the variable PMmin to track the signal’s minimum value within a
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`sub-window. This variable is the “future minimum” of the claims. Pet., 38-39, 42.
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`At the end of a sub-window, PMmin is
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`used to update the noise floor Pn(i). The
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`variable Pn(i) is the “current minimum.”
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`Martin describes two ways of
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`updating the noise floor based on the
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`nature of the signal in the window of
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`samples L. If the signal is monotonically
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`increasing (i.e., its minimum value
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`increases across successive sub-
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`windows), then Martin’s algorithm sets Pn(i) based on the value of PMmin observed
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`in each sub-window. That has the effect of setting the noise floor Pn(i) (“current
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`minimum”) based on the value of PMmin (“future minimium”) in the last sub-
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`window of the window (e.g., W=4). If the signal is not monotonically increasing,
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`Martin’s algorithm sets Pn(i) (“current minimum”) to the smallest PMmin (“future
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`minimum”) value observed in the entire window of L samples (e.g., across the 4
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`sub-windows in Martin’s illustration). See Ex. 1006, 1094; Ex. 1003, ¶¶135-36;
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`Ex. 1023, ¶¶16-20. Martin also makes clear that signal characteristics dictate
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`which option is used—if the PMmin value over the past W sub-windows is
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`monotonically increasing, option (1) is used, otherwise option (2) is used. Ex.
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`1006, 1094; Fig. 2. Martin’s algorithm thus tracks a current minimum (i.e., Pn(i))
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`and derives it in accordance with a future minimum (PMmin) as claim 4 specifies.
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`Andrea previously contended that Martin does not teach claim 4’s “future
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`minimum value” because PMmin is the minimum of a sub-window and not of an
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`entire window. Resp., 25. This, according to Andrea, means that Martin’s
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`algorithm supposedly does not set the current minimum to the lowest observed
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`value within a window of L samples when the sound is monotonically increasing.
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`Id. Andrea also contended that when the sound is not monotonically increasing,
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`Martin’s algorithm sets the future minimum value in a different way than its
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`putative invention. Resp., 26-27.
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`Initially, neither of Andrea’s supposed distinctions are based on the claims.
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`Nothing in claims 6 to 9 requires the sample being processed to have any minimum
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`duration, and nothing in the claims imposes any requirements on how the future
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`minimum value is derived from a current minimum.
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`Andrea also ignores that Martin explicitly teaches what Andrea says it does
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`not—that if the signal is not monotonically increasing, then Martin’s algorithm
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`will set the Pn(i) variable (the “current minimum”) to the minimum value in the
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`entire window of samples L. Ex. 1023, ¶¶19-20. As Martin states: “[i]n case of
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`non monotonic power the noise power estimate is set to the minimum of the
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`length L window...” Ex. 1006, 1094. Andrea’s only response here is its assertion
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`that Martin uses a variable, “min_vec”, to temporarily store the PMmin values of
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`each subwindow. See Resp., 27 (arguing that the noise floor Pn(i) is never set
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`equal to PMmin because it is set to min_vec instead). But Andrea ignores the role of
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`min_vec in Martin’s algorithm—that it is used temporarily to store the PMmin values
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`of each sub-window, which enables the Martin algorithm to determine what the
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`lowest value is within the entire window L of samples, and to thereby set the noise
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`floor Pn(i) to the smallest PMmin value for that entire window of samples L.3 Ex.
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`1006, 1094 (showing “min_vec(r*M)=PMmin”); Ex. 1023, ¶19.
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`3 If W=4, Martin will set Pn(i) to the smallest of the past 4 PMmin values in min_vec.
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`Andrea also cannot dispute this is what Martin’s algorithm does. Andrea’s
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`expert, Dr. Douglas confirmed at his deposition that the min_vec variable stores the
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`PMmin values, and that Martin sets the noise floor Pn(i) to be equal to the smallest of
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`those PMmin values:
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`Q And the min vec values are the PMmin values, correct?
`A They are the PMmin values.
`Q So this assignment in Figure 2 of Martin assigns Pn(i) to the
`smallest PMmin in the data window, correct?
`A Yes.
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`Ex. 1030, 177:5-16; see also Ex. 1026, 87:15-20, 103:7-104:12.
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`Andrea also argued that, in the scenario where a signal is monotonically
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`increasing, Martin only describes setting the noise floor Pn(i) to be the PMmin value
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`of the most recent sub-window, which will not be the minimum of the entire
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`window L, and therefore is not a “future minimum.” Resp., 25-26. But nothing in
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`the claims requires use of the lowest value in the entire window L to derive the
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`“future minimum”—claim 4 imposes no minimum duration of the sample, nor does
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`it restrict how the determination is to be made. See Ex. 1023, ¶18. The only
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`reference to time in the claims is found in claim 5, which depends from claim 4,
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`and specifies simply that the “future minimum” is determined “over a
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`predetermined period of time.” Because the sub-windows in Martin’s algorithm
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`have a fixed interval of time, they satisfy that requirement. Ex. 1003, ¶¶138, 141.
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`Importantly, Andrea also has admitted that Martin’s variable PMmin tracks the
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`minimum value during each sub-window, which is a predetermined period of M
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`samples. Resp., 24 (“In the monotonically increasing case, PMmin will always
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`represent the most recent subwindow minimum…”). That admission, coupled with
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`the absence of any claim requirements on duration of the signal being processed, is
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`fatal to Andrea’s argument. Nothing in claim 4 (or any other claim) requires use of
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`a predetermined period of a particular length (e.g., L) or prohibits tracking the
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`minimum of a sub-window. Thus, in the scenario where a signal is monotonically
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`increasing, Martin also satisfies the claims. Ex. 1023, ¶18.
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`B. Martin Teaches Setting a Current Minimum to a Future
`Minimum Value “Periodically,” as Required by Claim 6
`With respect to claim 6, Andrea contended that Martin does not teach
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`updating the current minimum “periodically,” incorrectly asserting that Martin sets
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`Pn(i) (“current minimum”) equal to PMmin (“future minimum”) only “sporadically.”
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`Resp., 28. According to Andrea, Martin sets Pn(i) to PMmin only in the
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`monotonically increasing scenario, and thus, because the signal sometimes
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`monotonically increases and sometimes does not, this assignment occurs randomly
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`not periodically. Id. Andrea’s argument, however, rests on its flawed position
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`that, in the non-monotonically increasing case, Martin’s use of min_vec to
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`determine the smallest PMmin values in a data window means that the noise floor
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`Pn(i) is not set equal to a PMmin value. As explained above, Andrea’s distinction
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`between min_vec and PMmin is illusory, as Dr. Douglas admitted. Ex. 1030, 177:5-
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`16 (“Q So this assignment in Figure 2 of Martin assigns Pn(i) to the smallest
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`PMmin in the data window, correct? A Yes.”); Ex. 1026, 87:15-20, 103:7-104:12.
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`What Martin shows instead is that it updates the value Pn(i) at a fixed
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`interval—after every M samples, as Dr. Douglas admitted. Ex. 1026, 77:16-80:19.
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`Thus, whether the signal is monotonically increasing or not, Pn(i) is set to PMmin at
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`the end of every sub-window of M samples, as shown in the blue box of annotated
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`Figure 2 of Martin (reproduced above). Ex. 1023, ¶20; Ex. 1003, ¶¶138, 141.
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`Because Martin sets Pn(i) equal to PMmin every M samples, it does so “at regular
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`intervals of time” consistent with the Federal Circuit’s construction of
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`“periodically” in claim 6.
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`C. The Skilled Person Would Have Been Motivated to Set Hirsch’s
`Noise Threshold Using Martin’s Noise Tracking Algorithm
`Given the Board’s findings supporting its determination that claim 25 was
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`obvious and Andrea’s failure to appeal that decision, Andrea cannot now dispute
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`that a skilled person would have considered Hirsch and Martin together, or that
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`“techniques such as those shown in Hirsch and Martin are routinely combined.”
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`FWD at 16. Those findings, moreover, rest on substantial evidence and Apple’s
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`prior arguments. See, e.g., Pet., 34-38 and Ex. 1003, ¶¶125-32 (explaining the
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`skilled person would have found it obvious to modify Hirsch’s adaptive threshold4
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`calculation “to use Martin’s noise floor algorithm instead of Hirsch’s running
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`average algorithm” as it would improve operation of Hirsch in non-stationary
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`environments and because Hirsch cites Martin as a known technique); Ex. 1003,
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`¶¶131-32 (in audio signal processing, “[i]t was standard to attempt to optimize
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`system performance by swapping algorithms or tuning parameters”); Ex. 2005,
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`129:1-23 (a skilled artisan would have been motivated to try adding Martin’s
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`algorithm to Hirsch to determine whether it performed better); see also, e.g., KSR
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`Intern. Co. v. Teleflex Inc., 127 S. Ct. 1727, 1740 (2007) (“…if a technique has
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`been used to improve one device, and a [POSA] would recognize that it would
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`improve similar devices in the same way, using the technique is obvious unless its
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`actual application is beyond his or her skill.”). As explained above, modifying
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`Hirsch’s adaptive threshold to use Martin’s noise floor algorithm would yield a
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`system that meets claim 4’s requirement of “set[ting] the threshold… in
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`accordance with a current minimum”). Ex. 1003, ¶127; Pet., 36-37, 47.
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`IV. Conclusion
`Apple respectfully submits that the Board should cancel claims 6-9.
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`4 Hirsch uses the adaptive threshold to distinguish between speech and noise;
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`signals above the threshold are speech, below are noise.
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`Dated: April 22, 2020
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`Respectfully Submitted,
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`/Jeffrey P. Kushan/
`Jeffrey P. Kushan
`Registration No. 43,401
`Sidley Austin LLP
`1501 K Street NW
`Washington, DC 20005
`jkushan@sidley.com
`(202) 736-8914
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`Lead Counsel for Petitioner
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`CERTIFICATE OF SERVICE
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`I hereby certify that on this 22nd day of April, 2020, copies of this
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`Petitioner’s Opening Remand Brief, and Exhibits have been served in its entirety
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`by email on the following counsel of record for Patent Owner:
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`William D. Belanger, belangerw@pepperlaw.com
`Frank D. Liu, liuf@pepperlaw.com
`Andrew P. Zappia, zappiaa@pepperlaw.com
`BN_IPR-Andrea@pepperlaw.com
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`Respectfully submitted,
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`/Jeffrey P. Kushan/
`Jeffrey P. Kushan
`Reg. No. 43,401
`Attorney for Petitioner
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`Dated:
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`April 22, 2020
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