SRI International

Patent Trials and Appeals Board

Sep 30, 2021

2021002555 (P.T.A.B. Sep. 30, 2021)

UNITED STATES PATENT AND TRADEMARK OFFICE
UNITED STATES DEPARTMENT OF COMMERCE
United States Patent and Trademark Office
Address: COMMISSIONER FOR PATENTS
P.O. Box 1450
Alexandria, Virginia 22313-1450
www.uspto.gov
APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO.
15/625,578 06/16/2017 Sek M. Chai SRI7429-2 1038
14824 7590 09/30/2021
Moser Taboada/ SRI International
1030 Broad Street
Suite 203
Shrewsbury, NJ 07702
EXAMINER
KUO, CHARLES C
ART UNIT PAPER NUMBER
2126
NOTIFICATION DATE DELIVERY MODE
09/30/2021 ELECTRONIC
Please find below and/or attached an Office communication concerning this application or proceeding.
The time period for reply, if any, is set in the attached communication.
Notice of the Office communication was sent electronically on above-indicated "Notification Date" to the
following e-mail address(es):
docketing@mtiplaw.com
llinardakis@mtiplaw.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte SEK M. CHAI, DAVID C. ZHANG,
MOHAMED R. AMER, TIMOTHY J. SHIELDS,
ASWIN NADAMUNI RAGHAVAN, and
BHASKAR RAMAMURTHY
Appeal 2021-002555
Application 15/625,578
Technology Center 2100
Before JAMES R. HUGHES, STACEY G. WHITE, and
JOHN P. PINKERTON, Administrative Patent Judges.
HUGHES, Administrative Patent Judge.
DECISION ON APPEAL
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STATEMENT OF THE CASE
Claims 1–29 are pending1, stand rejected, are appealed by Appellant,
and are the subject of our decision under 35 U.S.C. § 134(a).2 See Final
Act. 1–2.3 We have jurisdiction under 35 U.S.C. § 6(b).
We AFFIRM-IN-PART.
CLAIMED SUBJECT MATTER
The claimed subject matter, according to Appellant, “relates generally
to computer processing systems, and more particularly, to the use of one or
more deep neural networks to control and/or inform computer
microarchitecture or otherwise manage computing operations.” Spec. ¶ 3.
Specifically, Appellant’s claims are directed to systems, computer-
readable media, and methods for optimizing operations of computing
devices. The method receives a first set of deep neural network inputs (for a
first set of deep neural networks (DNNs)), which include: sensor data related
to the operation of the computing devices, processing data corresponding to
processor instructions, and/or DNN output data. The method generates a

1 The Examiner references claim 29 as having been canceled. Final Act. 1–
2. Claim 29 is pending and appealed. See Appeal Br. 5; Appellant’s
Amendment filed April 16, 2020 at page 9. We correct the Examiner’s
listing of the claims for clarity and consistency of the record.
2 We use the term “Appellant” to refer to “applicant” as defined in 37 C.F.R.
§ 1.42. Appellant identifies the real party in interest as SRI International.
Appeal Br. 1.
3 We refer to Appellant’s Specification (“Spec.”), filed June 16, 2017
(claiming benefit of US 62/351,205, filed June 16, 2016); Appeal Brief
(“Appeal Br.”), filed Sept. 8, 2020; and Reply Brief (“Reply Br.”), filed
Mar. 1, 2021. We also refer to the Examiner’s Final Office Action (“Final
Act.”), mailed Feb. 21, 2020; and Answer (“Ans.”) mailed Dec. 31, 2020.
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first set of DNN outputs using one or more DNNs and the first set of inputs.
The DNNs (used to generate the outputs) include DNN parameters learned
using one or more computing workloads, and the output generation step
analyzes one or more relationships between the received inputs to generate
the outputs. The DNN outputs are then provided as: control signals for
managing the computing devices, predictions used to generate control
signals for managing the computing devices, warnings (indicating that a
predicted state a component of the computing device(s) deviates from an
acceptable state), and/or a second set of inputs to the first set of DNNs
and/or to a second set of DNNs. Spec. ¶¶ 6–45; Abstr. Claim 1 (directed to
a method), claim 17 (directed to a system), claim 28 (directed to a computer-
readable medium), and claim 29 (directed to a method) are independent.
Claim 1, reproduced below, is illustrative of the claimed subject
matter:
1. A computer-implemented method for optimizing
operations of one or more computing devices having one or more
processing units, the method comprising:
receiving, as inputs to a first set of one or more deep neural
networks (DNNs), at least one of computing environment data
selected from a group consisting of:
1) sensor data corresponding with operation of one or
more of the computing devices,
2) processing data corresponding with a set of one or
more instructions received at one or more of the processing units
for execution by the one or more processing units,
3) DNN data corresponding with DNN outputs
generated by one or more DNNs involved in managing
operations of the one or more computing devices; and
4) any combination of 1), 2), and 3);
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applying one or more DNNs to the first set of inputs to
generate a first set of one or more DNN outputs based on one or
more relationships between the received inputs, the one or more
DNNs including DNN parameters learned using one or more
computing workloads; and
providing the first set of DNN outputs, generated by
application of the one or more DNNs, as one or more signals, at
least one signal selected from a group consisting of:
i) control signals for managing one or more operations
of one or more of the computing devices;
ii) predictions for use in generating control signals for
managing one or more operations of the computing devices, the
predictions corresponding to a future state of one or more of the
computing devices;
iii) warnings indicating that a predicted future state of
one or more components of the one or more computing devices
deviates more than a threshold deviation level from an acceptable
state;
iv) a second set of one or more inputs to the first set of
DNNs and/or to a second set of one or more DNNs involved in
managing operations of the one or more computing devices for
generation of a second set of one or more DNN outputs;
v) any combination of i), ii), iii), and iv);
whereby the one or more signals are provided to the one
or more computing devices to enhance performance, efficiency,
and/or security of one or more of the computing devices.
Appeal Br. 24 (Claims App.) (emphasis added).
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REFERENCES
The prior art relied upon by the Examiner is:
Name Reference Date
Bogojeska et al.
(“Bogojeska”)
US 2015/0178637 A1 June 25, 2015
W. Chedid and Y. Chansu, Survey on Power Management Techniques for
Energy Efficient Computer Systems, Cleveland State University (2002)
(“Chedid”).
C. Hughes and T. Li, Accelerating Multi-core Processor Design Space
Evaluation Using Automatic Multi-threaded Workload Synthesis, IEEE
Int’l Symposium on Workload Characterization, 163–172, (2008)
(“Hughes”).
R. Salakhutdinov and G. Hinton, Deep Boltzmann Machines, Proceedings
of the Twelfth Int’l Conf. on Artificial Intelligence and Statistics
(AISTATS), Journal of Machine Learning Research (JMLR) 5:448-455,
2009 (“Salakhutdinov”)
Sheikh et al., Transients of a Micro-Grid System with Multiple Distributed
Energy Resources, Sustainable Computing: Informatics and Systems Vol.
2, Issue 3, 151169 (Sept. 2012) (“Sheikh”).

REJECTIONS4
1. The Examiner rejected claims 1, 2, 4, 9–15, 17, 18, 21–25, and
27–29 under 35 U.S.C. § 103 as being unpatentable over Bogojeska and
Salakhutdinov. See Final Act. 8–33.
2. The Examiner rejected claims 3 and 5 under 35 U.S.C. § 103 as
being unpatentable over Bogojeska, Salakhutdinov, and Hughes. See Final
Act. 33–35.

4 The Leahy-Smith America Invents Act (“AIA”), Pub. L. No. 112–29, 125
Stat. 284 (2011), amended 35 U.S.C. § 103. Because the present application
has an effective filing date after the AIA’s effective date (March 16, 2013),
this decision refers to 35 U.S.C. § 103.
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3. The Examiner rejected claims 6–8, 19, and 20 under 35 U.S.C.
§ 103 as being unpatentable over Bogojeska, Salakhutdinov, and Chedid.
See Final Act. 35–39.
4. The Examiner rejected claims 16 and 26 under 35 U.S.C. § 103
as being unpatentable over Bogojeska, Salakhutdinov, and Sheikh. See Final
Act. 39–41.
ANALYSIS
Obviousness
Appellant argues independent claims 1, 17, 28, and 29 together as a
group and does not separately argue the independent claims with
particularity. See Appeal Br. 5–15. We select, unless explicitly noted,
independent claim 1 as representative of Appellant’s arguments with respect
to independent claims 1, 17, 28, and 29. See 37 C.F.R. § 41.37(c)(1)(iv).
Appellant makes substantive arguments with respect to several dependent
claims. See Appeal Br. 15–23. We address Appellant’s substantive
arguments seriatim, unless otherwise noted.
Claims 1, 17, 28, and 29
The Examiner rejects independent claim 1 as being obvious in view of
Bogojeska and Salakhutdinov. See Final Act. 8–14; Ans. 3–12.
Specifically, the Examiner relies on Bogojeska to teach the recited features
of claim 1, except for explicitly teaching the disputed deep neural networks
(DNNs). See Final Act. 9–14. The Examiner relies on Salakhutdinov to
teach the DNNs. See Final Act. 14. In particular, the Examiner relies on
Bogojeska to teach a processor, processing unit (claim 1), and hardware
processing unit (claim 17); predictive modeler including a neural network;
neural network inputs including computing environment data; learned neural
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network parameters; and generated neural network outputs. See Final Act.
9–14; Ans. 3–12.
Appellant contends Bogojeska and Salakhutdinov do not teach certain
disputed features of claim 1 (and claim 17). Appeal Br. 5–15; Reply Br. 2–
11. Specifically, Appellant contends: i) Bogojeska does not teach a
processor (processing unit or hardware processor (claim 17)) because “the
broadest reasonable interpretation of ‘processor’ or ‘processing unit’ as
recited in the claims does not encompass a network of servers” (Reply Br. 2;
see Appeal Br. 5–7, 11–12; Reply Br. 3–4); ii) Bogojeska does not teach
DNNs that generate DNN outputs based on relationships between the
received inputs (see Appeal Br. 7–10; Reply Br. 5–7); iii) Bogojeska does
not teach learned DNN parameters (see Appeal Br. 10–11; Reply Br. 7–9);
iv) Bogojeska does not teach a DNN configured to receive DNN inputs (see
Appeal Br. 12–13; Reply Br. 9–10); and v) Salakhutdinov is not properly
combined with Bogojeska (see Appeal Br. 13–15; Reply Br. 10–11).
We adopt as our own (1) the findings and reasons set forth by the
Examiner in the action from which this appeal is taken (Final Act. 8–14) and
(2) the reasons set forth by the Examiner in the Examiner’s Answer (Ans. 3–
12) in response to Appellant’s Appeal Brief. We concur with the findings
and conclusions reached by the Examiner, and we provide the following
analysis for emphasis.
We begin by noting that Appellant does not dispute the Examiner’s
findings with respect to Salakhutdinov. See Appeal Br. 5–15; Reply Br. 2–
11. Appellant’s contentions instead focus on the combinability of
Salakhutdinov with Bogojeska or the Examiner’s findings with respect to
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Bogojeska (supra). We address each of Appellant’s contentions (supra), as
follows.
i) Processing Unit
Appellant’s arguments are not commensurate with the scope of the
claims (e.g., claim 1). Even if we accept Appellant’s interpretation of
Bogojeska—that “Bogojeska is concerned with ‘modifying server
configurations’ in a system that includes a plurality of servers” (Appeal Br 7
(quoting Bogojeska ¶ 2); see Appeal Br. 5–7, 11–12; Reply Br. 3–4)—
Bogojeska explicitly discloses a computer system including a processor (see
Bogojeska, Fig. 1 (elements 100, 105); Bogojeska ¶¶ 14–15). Appellant’s
claims do not preclude the recited “one or more computing devices having
one or more processing units” (claim 1) (Appeal Br. 24 (Claims App.)) from
including a server or multiple servers.
ii) Deep Neural Networks (DNNs) and Generating Outputs Based on
Input Relationships
Appellant contends Bogojeska does not teach DNNs that generate
DNN outputs based on relationships between the received inputs. See
Appeal Br. 7–10; Reply Br. 5–7. We disagree.
Appellant does not address the Examiner’s findings with respect to
Salakhutdinov (supra). As explained by the Examiner, Salakhutdinov
describes deep Boltzmann machines, i.e., deep neural networks. See Appeal
Br. 14; Ans. 4; Salakhutdinov, Abstr. (p. 1 (5:448)) and Salakhutdinov Sec.
4.1 (p. 6 (5:553)).
As further explained by the Examiner, Bogojeska describes a neural
network—i.e., a predictive modeler that includes a neural network. See
Final Act. 10; Ans. 7–8 (citing Bogojeska ¶ 31). The Examiner relied on the
combination of Bogojeska and Salakhutdinov for the teaching of DNNs
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(supra). We address Appellant’s combinability arguments in greater detail,
infra, but Appellant’s contentions do not persuade us of Examiner error with
respect to the DNNs.
With respect to the generating outputs based on input relationships,
the Examiner finds that Bogojeska describes the operation of its predictive
modeler (including a neural network) generating outputs based on inputs that
have a relationship and, accordingly, that the outputs are generated based on
a relationship between the inputs. See Final Act. 10–12; Ans. 6–8;
Bogojeska ¶¶ 32, 36–37. As explained by the Examiner (id.), Bogojeska’s
predictive modeler can be trained to predict the quantity and severity of
incidents related to a particular feature (such as feature extracted from a
hardware specification (see Bogojeska ¶¶ 30)) using historical data related to
the feature (e.g., hardware performance data). See Bogojeska ¶ 32.
Bogojeska’s predictive modeler then predicts whether a feature is potentially
problematic (or unproblematic) and how it affects future operation.
Bogojeska ¶¶ 32, 36–37. That is, Bogojeska describes how changes in
certain features (e.g., features related to hardware configurations) affect
operation of the system. Thus, we find Bogojeska would have at least
suggested generating outputs based on input relationships. Appellant’s
contentions do not persuade us of Examiner error with respect to generating
outputs based on input relationships.
iii) DNN Parameters
Appellant also contends Bogojeska does not teach learned DNN
parameters because the recited DNN parameters are learned using
computing workloads, which Appellant contends must be “task[s] . . . in
which the processor is engaged” (Appeal Br. 10), and the structure of the
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neural network in Bogojeska’s predictive modeler is a “black box” that is
not described by Bogojeska (see Appeal Br. 10–11). See Appeal Br. 10–11;
Reply Br. 7–9. Contrary to Appellant’s contentions, and as explained by the
Examiner (see Final Act. 11–12; Ans. 8–10), Bogojeska describes training
the predictive modeler (including the neural network) using historical data,
such as hardware performance data to predict future operation of the system.
Bogojeska ¶¶ 32, 36–37. Accordingly, Bogojeska describes training the
predictive modeler (neural network) to predict operation of the system for
certain inputs related to hardware performance. Although Bogojeska does
not explicitly describe a computing workload and learned parameters, we
find Bogojeska at least suggests that training the predictive modeler would
cause “parameters” to be learned and that the learning (learned parameters)
would be used in the predicting functions. Further, Salakhutdinov describes
training DNNs (learning algorithms for DNNs) at length. See Salakhutdinov
Abstr., Salakhutdinov et seq. Thus, the combination of Bogojeska and
Salakhutdinov would have at least suggested learned DNN parameters.
Further, Appellant’s claim 1 (as well as claim 28) do not require the
training information (computing workload) to be processor tasks, or specific
processor activity. We agree with the Examiner that Bogojeska’s historical
training data may include software and hardware performance data, and that
a computing workload can be broadly, but reasonably interpreted to include
such historical performance data. See Final Act. 11–12; Ans. 8–9;
Bogojeska ¶¶ 32, 36–37.
To the extent Appellant contends that Bogojeska does not teach
“DNN parameters corresponding to processor behavior” (claim 28) (Appeal
Br. 29 (Claims App.)), as discussed, supra, Bogojeska describes how
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changes in certain features related to hardware configurations affect
operation of the system. Thus, Bogojeska (in combination with
Salakhutdinov) would have at least suggested learning DNN parameters that
correspond to hardware (e.g., processor) behavior. Appellant’s contentions
do not persuade us of Examiner error with respect to learned DNN
parameters.
iv) Received DNN Inputs
Appellant further contends Bogojeska does not teach a DNN
configured to receive DNN inputs because, according to Appellant,
Bogojeska’s “‘Inputs’ that have been standardized, stored in a database, and
fed through a feature extractor are not inputs that are received by a DNN.”
Appeal Br. 12; see Appeal Br. 12–13; Reply Br. 9–10. As explained by the
Examiner (supra), Bogojeska (in combination with Salakhutdinov) describes
receiving inputs to the predictive modeler (neural network) related to
hardware performance. See Final Act. 10–12; Ans. 6–8, 11; Bogojeska
¶¶ 32, 36–37. The computing environment data (including, e.g., sensor data)
recited in the claims can be broadly, but reasonably interpreted to include
such hardware performance data. Additionally, Salakhutdinov describes the
operation of, and inputs to, DNNs in detail. See Salakhutdinov Abstr.,
Salakhutdinov et seq. Thus, Bogojeska (in combination with Salakhutdinov)
would have at least suggested receiving DNN inputs that correspond to the
recited computing environment data. Appellant’s contentions do not
persuade us of Examiner error with respect to a DNN receiving DNN inputs.
iv) Combinability
Finally, Appellant contends the Examiner did not properly combine
Bogojeska and Salakhutdinov. See Appeal Br. 13–15; Reply Br. 10–11.
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Appellant’s generalized contentions that Salakhutdinov is concerned with a
different purpose or problem (“improves the learning procedure,” which
“deals with model creation, not model use” (Appeal Br. 13)), and that the
Examiner has not “establish[ed] that the cited combination would lead to
predictable results with a reasonable expectation of success” (Appeal Br. 14)
do not persuade us that the Examiner erred. See Appeal Br. 13–15; Reply
Br. 10–11.
The Examiner cited Salakhutdinov to show that deep neural networks
and their operation were known. We agree with the Examiner that it would
have been within the skill of one of ordinary skill in the art to combine such
known techniques of using a deep neural network, as taught by
Salakhutdinov, with the system and predictive modeler (neural network)
taught by Bogojeska. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417
(2007) (“[I]f a technique has been used to improve one device, and a person
of ordinary skill in the art would recognize that it would improve similar
devices in the same way, using the technique is obvious unless its actual
application is beyond his or her skill”). Appellant has not presented
evidence sufficient to persuade us that combining the respective familiar
elements of the cited references in the manner proffered by the Examiner
would have been “uniquely challenging or difficult for one of ordinary skill
in the art” at the time of Appellant’s invention. Leapfrog Enters., Inc. v.
Fisher-Price, Inc., 485 F.3d 1157, 1162 (Fed. Cir. 2007) (citing KSR, 550
U.S. at 418).
Thus, Appellant does not persuade us of error in the Examiner’s
obviousness rejection of representative claim 1. Independent claims 17, 28,
and 29 include limitations of commensurate scope (except as noted, supra).
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Therefore, we affirm the Examiner’s obviousness rejection of representative
claim 1 and claims 17, 28, and 29 not separately argued with particularity
(supra). See 37 C.F.R. § 41.37(c)(1)(iv).
Claims 9 and 22
The Examiner rejects dependent claims 9 and 22 as being obvious in
view of Bogojeska and Salakhutdinov. See Final Act. 15–16, 23–24; Ans.
12–13. Claim 9 recites, in pertinent part, “DNN parameters are learned via
at least one bottom-up pass followed by at least one top-down pass; in the
bottom-up pass, all hidden nodes of a hidden layer of the DNN are sampled
in parallel; and in the top-down pass, all visible nodes are sampled in
parallel.” Appeal Br. 24 (Claims App.). Appellant concedes that
Salakhutdinov “mention[s] ‘bottom up and top down pass,’” but contends
“Salakhutdinov does not mention” that “in the bottom-up pass, all hidden
nodes of a hidden layer of the DNN are sampled in parallel; and in the top-
down pass, all visible nodes are sampled in parallel.” Appeal Br. 15; see
Reply Br. 11–12. The Examiner counters that:
Salakhutdinov teaches these limitations, including training using
contrastive divergence (sec. 2, p. 2), learning via at least one
bottom-up pass followed by at least one top-down pass (sec. 3,
p. 3), all hidden nodes on a bottom-up pass (sec. 3.1, p. 5)
sampled (sec. 2, p. 2) in parallel (sec. 2.1, p. 3), all visible nodes
in a top-down pass (sec. 3, p. 3) sampled (sec. 2, p. 2) in parallel
(sec. 2.1, p. 3).
Ans. 15.
The Examiner’s broad citations to Salakhutdinov (supra) do not
clearly explain how Salakhutdinov teaches that all the hidden nodes in the
hidden layers are sampled in parallel. The Examiner has not sufficiently
shown that Salakhutdinov teaches performing the parallel hidden node
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sampling as required by these claims. Consequently, we are constrained by
the record before us to find that the Examiner erred in finding Bogojeska and
Salakhutdinov render obvious Appellant’s claims 9 and 22. Thus, we do not
sustain the Examiner’s obviousness rejection of claims 9 and 22.
Claims 10, 11, and 23
The Examiner rejects dependent claims 10, 11, and 23 as being
obvious in view of Bogojeska and Salakhutdinov. See Final Act. 16–17, 24;
Ans. 13. Claim 10 recites, in pertinent part, “the DNN parameters are a first
set of DNN parameters, and wherein the method further comprises receiving
a second set of one or more DNN parameters to be applied to received
inputs.” Appeal Br. 26 (Claims App.). Appellant contends that the claims
require two distinct sets of parameters and Bogojeska does not teach two
distinct sets of parameters being applied to the same inputs. See Appeal Br.
15–17; Reply Br. 12–13.
Appellant’s arguments are persuasive of Examiner error. Claim 10
(as well as claims 11 and 23) requires two sets of parameters that can
operate on the same inputs. As explained by Appellant, even if we accept
the Examiner’s interpretation of Bogojeska, at best Bogojeska describes two
distinct neural networks operating on the inputs, not two sets of parameters
(in the same neural network). See Appeal Br. 15–17; Reply Br. 12–13.
The Examiner has not sufficiently shown that Bogojeska teaches the
two sets of parameters. Consequently, we are constrained by the record
before us to find that the Examiner erred in finding Bogojeska and
Salakhutdinov render obvious Appellant’s claims 10, 11, and 23, and we do
not sustain the Examiner’s obviousness rejection of these claims.
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Claims 12 and 25
The Examiner rejects dependent claims 12 and 25 as being obvious in
view of Bogojeska and Salakhutdinov. See Final Act. 17, 25; Ans. 13–15.
Claim 12 recites, in pertinent part, “one or more of the DNNs in the first set
of DNNs is implemented using circuitry in one or more of the processing
units.” Appeal Br. 26 (Claims App.). Appellant contends, similar to claim 1
(supra), that Bogojeska doesn’t teach processing units (or processors), much
less implementing DNNs in processing unit circuitry. See Appeal Br. 17–
18; Reply Br. 13.
Appellant does not persuade us of error in the Examiner’s rejection of
claims 12 and 25 for the same reasons as claim 1 (supra). Bogojeska
discloses a computer system including a processor. See Bogojeska, Fig. 1
(elements 100, 105); Bogojeska ¶¶ 14–15. Accordingly, we affirm the
Examiner’s rejection of claims 12 and 25.
Claim 14
The Examiner rejects dependent claim 14 as being obvious in view of
Bogojeska and Salakhutdinov. See Ans. 15–16. Claim 14 recites, in
pertinent part, “the one or more DNNs are applied using a first core of a
processing unit, and the at least one signal includes at least one control
signal for a second core of the processing unit.” Appeal Br. 26 (Claims
App.). Appellant contends that “Bogojeska certainly does not say anything
about how different cores of a processing unit may be used to implement one
or more DNNs.” Appeal Br. 18; see Reply Br. 14.
Appellant’s arguments are persuasive of Examiner error. The
Examiner does not address claim 14 in the Final Office Action and simply
states in the Examiner’s Answer that “[d]ue to similarity of language of
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claims 13 and 14, the limitations of claim 14 are rejected under the same
reasons as claim 13.” Ans. 15. Although claims 13 and 14 recite similar
features, claim 14 requires a multi-core processor and using one (first) core
to implement the DNNs.
The Examiner has not sufficiently shown that Bogojeska teaches
using one core of a multi-core processor to implement the DNNs.
Consequently, we are constrained by the record before us to find that the
Examiner erred in finding Bogojeska and Salakhutdinov render obvious
Appellant’s claim 14, and we do not sustain the Examiner’s obviousness
rejection of claim 14.
Claim 21
The Examiner rejects dependent claim 21 as being obvious in view of
Bogojeska and Salakhutdinov. See Final Act. 23; Ans. 16. Claim 21 recites,
in pertinent part, “the system is further configured to provide a warning
signal . . . if one or more unexpected branches, faults, or exceptions in one or
both of the first and second processing units is predicted.” Appeal Br. 28
(Claims App.). Appellant contends the Examiner’s determinations that the
predictive modeler may predict whether a feature is problematic or
unproblematic “does not arrive at the specific limitation of claim 21,” i.e., “a
warning . . . if one or more unexpected branches, faults, or exceptions in one
or both of the first and second processing units is predicted,” and that
“[t]here is no discussion at all in Bogojeska of ‘unexpected branches, faults,
or exceptions.” Appeal Br. 19; see Reply Br. 15.
We agree with Appellant’s arguments. The Examiner does not
explain in any detail how Bogojeska describes the delineated predictions that
trigger the warning. The Examiner has not sufficiently shown that
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Bogojeska teaches the disputed predictions or the DDN output including a
warning. Consequently, we are constrained by the record before us to find
that the Examiner erred in finding Bogojeska and Salakhutdinov render
obvious Appellant’s claim 21. Appellant persuades us that the Examiner
erred in rejecting claim 21, and we do not sustain the Examiner’s
obviousness rejection of claim 21.
Claims 2, 4, 13, 15, 18, 24, and 27
The Examiner rejects dependent claims 2, 4, 13, 15, 18, 24, and 27 as
being obvious in view of Bogojeska and Salakhutdinov. See Final Act. 14–
15, 18–19, 22–23, and 25–26. Appellant does not separately argue the
rejection of claims 2, 4, 13, 15, 18, 24, and 27 with particularity. See Appeal
Br. 5–19.
For the same reasons as claim 1, Appellant does not persuade us of
error in the Examiner’s obviousness rejection of claims 2, 4, 13, 15, 18, 24,
and 27, not separately argued with particularity. See 37 C.F.R.
§ 41.37(c)(1)(iv). Therefore, we affirm the Examiner’s obviousness
rejection of claims 2, 4, 13, 15, 18, 24, and 27.
Claims 3 and 5
The Examiner rejects dependent claims 3 and 5 as being obvious in
view of Bogojeska, Salakhutdinov, and Hughes. See Final Act. 33–35; Ans.
17–20. Claim 3 recites, in pertinent part, “one or more of the DNNs in the
first set of DNNs is implemented within a tool for creating an optimal
processor design.” Appeal Br. 25 (Claims App.). Appellant contends,
similar to claim 1 (supra), that the Examiner has not provided a sufficient
rationale for combining Bogojeska, Salakhutdinov, and Hughes. See Appeal
Br. 19–20; Reply Br. 15–16.
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Appellant does not persuade us of error in the Examiner’s rejection of
claims 3 and 5 for the same reasons as claim 1 (supra). Appellant has not
presented any persuasive evidence or argument that the Examiner’s rationale
for combining Bogojeska, Salakhutdinov, and Hughes was inadequate.
Accordingly, we affirm the Examiner’s rejection of claims 3 and 5.
Claims 6–8, 19, and 20
The Examiner rejects dependent claims 6–8, 19, and 20 as being
obvious in view of Bogojeska, Salakhutdinov, and Chedid. See Final Act.
35–39; Ans. 20–23. Appellant presents generalized arguments that the
Examiner mis-construed or mis-quoted the claims and that Bogojeska,
Salakhutdinov, and Chedid do not teach the various features of the claims.
See Appeal Br. 20–22; Reply Br. 16–17.
Appellant does not persuade us of error in the Examiner’s rejection of
claims 6–8, 19, and 20 because Appellant has not presented any persuasive
evidence or argument that the Examiner misconstrued the claims or that
Chedid in combination with Bogojeska and Salakhutdinov would not have at
least suggested the features of claims 6–8, 19, and 20. Accordingly, we
affirm the Examiner’s rejection of claims 6–8, 19, and 20.
Claims 16 and 26
The Examiner rejects dependent claims 16 and 26 as being obvious in
view of Bogojeska, Salakhutdinov, and Sheikh. See Final Act. 39–41; Ans.
23–24. Appellant contends, with respect to claim 16, that “in Sheikh, the
power level, not the temperature, is the input because a change in the power
level results in a change in GPU temperature” and, therefore, “Sheikh does
not suggest that a temperature of a processing unit can be received as an
input to a DNN.” Appeal Br. 23; see Appeal Br. 22–23; Reply Br. 17. With
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respect to claim 26, Appellant also generally contends that Bogojeska,
Salakhutdinov, and Sheikh are not properly combined or do not teach claim
26—“a general statement that Sheikh mentions a GPU is not sufficient . . . to
combine Sheikh with Salakhutdinov and Bogojeska to arrive at claim 26.”
Appeal Br. 23; see Reply Br. 17.
Appellant does not persuade us of error in the Examiner’s rejection of
claims 16 and 26 for the same reasons set forth by the Examiner. See Final
Act. 39–41; Ans. 23–24. Appellant’s generalized attorney argument does
not persuade us the Examiner erred in interpreting Sheikh or combining
Sheikh with Bogojeska and Salakhutdinov. Therefore, we affirm the
Examiner’s rejection of claims 16 and 26.
CONCLUSION
Appellant has not shown persuasively that the Examiner erred in
rejecting claims 1–8, 12, 13, 15–20, and 24–29 under 35 U.S.C. § 103.
Appellant has shown persuasively that the Examiner erred in rejecting
claims 9–11, 14, and 21–23 under 35 U.S.C. § 103. We, therefore, sustain
the Examiner’s rejections of claims 1–8, 12, 13, 15–20, and 24–29, but do
not sustain the Examiner’s rejection of claims 9–11, 14, and 21–23.
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DECISION SUMMARY
In summary:
Claim(s)
Rejected
35 U.S.C. § Reference(s)/Basis Affirmed Reversed
1, 2, 4, 9–
15, 17, 18,
21–25, 27–
29
103 Bogojeska,
Salakhutdinov
1, 2, 4, 12,
13, 15, 17,
18, 24, 25,
27–29
9–11, 14,
21–23
3, 5 103 Bogojeska,
Salakhutdinov,
Hughes
3, 5
6–8, 19, 20 103 Bogojeska,
Salakhutdinov,
Chedid
6–8, 19, 20
16, 26 103 Bogojeska,
Salakhutdinov,
Sheikh
16, 26
Overall
Outcome
1–8, 12, 13,
15–20, 24–
29
9–11, 14,
21–23
RESPONSE
No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a)(1)(iv). See
37 C.F.R. § 41.50(f).
AFFIRMED-IN-PART