Philippe Baumard

Patent Trials and Appeals Board

Apr 14, 2021

2019005569 (P.T.A.B. Apr. 14, 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.
14/660,519 03/17/2015 Philippe Baumard BAU.0003US 1085
21906 7590 04/14/2021
TROP, PRUNER & HU, P.C.
PO Box 41790
HOUSTON, TX 77241
EXAMINER
COUGHLAN, PETER D
ART UNIT PAPER NUMBER
2121
NOTIFICATION DATE DELIVERY MODE
04/14/2021 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):
tphpto@tphm.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte PHILIPPE BAUMARD
Appeal 2019-005569
Application 14/660,519
Technology Center 2100

Before MAHSHID D. SAADAT, ALLEN R. MacDONALD, and
NABEEL U. KHAN, Administrative Patent Judges.

SAADAT, Administrative Patent Judge.

DECISION ON APPEAL
STATEMENT OF THE CASE
Pursuant to 35 U.S.C. § 134(a), Appellant1 appeals from the
Examiner’s decision to reject claims 1–10 and 12–21. Claim 11 was
cancelled. We have jurisdiction under 35 U.S.C. § 6(b).
We affirm.

1 We use the word Appellant to refer to “applicant” as defined in 37 C.F.R.
§ 1.42(a). Appellant identifies the real party in interest as Philippe Baumard,
the inventor of the present application. Appeal Br. 1.
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Application 14/660,519
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CLAIMED SUBJECT MATTER
Appellant’s invention relates to the automated generation of
autonomous situational awareness and capability to detect incongruous
behaviors in a machine and/or interacting machines and within the behavior
of their components. Spec. ¶ 15.
Representative claim 1 under appeal read as follows;
1. A method comprising:
monitoring, by a system including a processor and sensors,
behavioral characteristics of at least a first machine component;
generating, by the system through unsupervised learning,
unsupervised Bayesian behavioral models at respective different
stages, the Bayesian behavioral models representing
interrelations between events and machine-to-machine
interactions between at least the first machine component and at
least a further machine component, the Bayesian behavioral
models at the respective different stages changed from one
another based on discovery of learned variables representing new
interactions between machine components and discovery of new
machine components;
predicting, by the system using the monitored behavioral
characteristics and a Bayesian behavioral model of the generated
Bayesian behavioral models, an incongruity of a behavior of at
least the first machine component and the machine-to-machine
interactions, wherein the incongruity is predicted based on
determining a singularity of the events and the machine-to-
machine interactions, and a discordance between a calculated
expectation of the system and the behavior and the machine-to-
machine interactions, the discordance assessing a dynamic
distortion and distance between the calculated expectation and
the behavior and the machine-to-machine interactions, and
wherein the predicting is performed without using a previously
built normative rule of behavior and machine-to-machine
interactions; and
performing, by the system, a counter-measure action
comprising controlling at least the first machine component to
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address a threat with respect to at least the first machine
component in response to the predicted incongruity.
REFERENCES AND REJECTIONS2
Claims 1, 4, 6, 8, 17, and 18 stand rejected under 35 U.S.C. § 103 as
being unpatentable over Duffield et al. (US 2010/0153316 A1; pub. June 17,
2010) (“Duffield”), Kapoor et al. (US 2012/0240185 A1; pub. Sept. 20,
2012) (“Kapoor”), and Miltonberger (US 2010/0094767 A1; pub. Apr. 15,
2010).3 See Final Act. 14–32.
Claims 2, 3, 5, 7, 14, and 19 stand rejected under 35 U.S.C. § 103 as
being unpatentable over Duffield, Kapoor, Miltonberger, and Chirashnya et
al. (US 2002/0019870 A1; pub. Feb. 14, 2002) (“Chirashnya”). See Final
Act. 32–39.
Claims 9 and 20 stand rejected under 35 U.S.C. § 103 as being
unpatentable over Duffield, Kapoor, Miltonberger, and Girouard et al.
(US 2005/0278178 A1; pub. Dec. 15, 2005) (“Girouard”). See Final
Act. 39–41.
Claims 12 and 13 stand rejected under 35 U.S.C. § 103 as being
unpatentable over Duffield, Kapoor, Miltonberger, and Kraemer
(US 2013/0111547 A1; pub. May 2, 2013). See Final Act. 42–45.
Claim 15 stands rejected under 35 U.S.C. § 103 as being unpatentable
over Duffield, Kapoor, Miltonberger, and Weidl et al.
(US 2005/0043922 A1; pub. Feb. 24, 2005) (“Weidl”). See Final Act. 45–
46.

2 The Examiner has withdrawn the rejection of claims 1–10 and 12–21 under
35 U.S.C. § 101. See Ans. 5.
3 The statement of the rejection omits claim 8, but the body of the rejection
addresses claim 8. See Final Act. 20.
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Claims 10, 16, and 21 stand rejected under 35 U.S.C. § 103 as being
unpatentable over Duffield, Kapoor, Kraemer, Miltonberger, and Gilbert et
al. (US 7,530,105 B2; iss. May 5, 2009) (“Gilbert”). See Final Act. 46–54.
ANALYSIS
We have reviewed the Examiner’s rejections under 35 U.S.C. § 103 in
light of Appellant’s contentions and the evidence of record. We agree with
the Examiner and highlight the following for emphasis.
Independent Claims 1, 8, and 17
The Examiner found Kapoor discloses, inter alia, “the Bayesian
behavioral models at the respective different stages changed from one
another based on discovery of learned variables representing new
interactions between machine components and discovery of new machine
components,” as recited in independent claim 1. See Final Act. 16–17
(citing Kapoor ¶¶ 168, 556). As found by the Examiner, Kapoor teaches
performing real-time updates to parameters of a self-organizing map of
artificial neurons in order to recognize threats to a computer network, where
the real-time updating includes adjusting weight vectors of the artificial
neurons, and detecting an anomalous data flow from a new or unknown
source. See Ans. 10–11 (citing Kapoor ¶¶ 556, 590).
Appellant contends Kapoor describes a flow processing facility that
uses a set of artificial neurons for pattern recognition, such as a self-
organizing map, where pattern recognition relates to stationary learning, i.e.,
based on use of stationary models. See Appeal Br. 10 (citing Kapoor ¶¶ 14,
27, 46). In contrast, according to Appellant, the claimed “Bayesian
behavioral models” of claim 1 are non-stationary models that are “changed
from one another based on discovery of learned variables representing new
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interactions between machine components and discovery of new machine
components,” where learned variables are new variables. See Appeal Br. 10;
see also Reply Br. 8–9. In support of its argument, Appellant further cites a
Rule 132 Declaration by the inventor Philippe Baumard submitted during
prosecution of the present application. See Appeal Br. 7, 10; see also
Declaration Under 37 C.F.R. § 1.132, submitted August 16, 2018 (“Rule 132
Declaration”).
We are not persuaded by this argument because it is not
commensurate with the scope of claim 1. Neither claim 1, nor Appellant’s
Specification, limits the claimed “Bayesian behavioral models” to non-
stationary models. Similarly, neither claim 1, nor Appellant’s Specification,
limits the claimed “learned variables representing new interactions between
machine components and discovery of new machine components” to new
variables. As the Examiner correctly found, Kapoor teaches performing
real-time updates to a self-organizing map of artificial neurons in order to
recognize threats to a computer network, where the real-time updating
including adjusting weight vectors of artificial neurons and detecting an
anomalous data flow from a new or unknown source. See Kapoor ¶¶ 14, 27,
46, 168, 556. Because Appellant does not persuasively distinguish the
claimed “Bayesian behavior models” of claim 1 from Kapoor’s self-
organizing map, we agree with the Examiner that Kapoor’s adjustment of
weight vectors and detection of an anomalous data flow teaches or suggests
“the Bayesian behavioral models at the respective different stages changed
from one another based on discovery of learned variables representing new
interactions between machine components and discovery of new machine
components,” as recited in claim 1.
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Furthermore, Appellant’s Rule 132 Declaration appears to proffer
arguments regarding Kapoor that are similar to Appellant’s arguments
described above. See Rule 132 Decl. ¶¶ 3–5. For the reasons previously
described, we agree with the Examiner that the arguments proffered in the
Rule 132 Declaration are not persuasive of Examiner error. See Ans. 7.
The Examiner further found Miltonberger discloses, inter alia, “a
discordance between a calculated expectation of the system and the behavior
and the machine-to-machine interactions, the discordance assessing a
dynamic distortion and distance between the calculated expectation and the
behavior and the machine-to-machine interactions, and wherein the
predicting is performed without using a previously built normative rule of
behavior and machine-to-machine interactions,” as recited in claim 1. See
Final Act. 18–19 (citing Miltonberger ¶ 85). As found by the Examiner,
Miltonberger teaches determining and representing thresholds corresponding
to component risk scores of a parameter of an event (e.g., login), where a
probability of a parameter is specific for a particular user based on data
specific to that particular user, as opposed to a previously built normative
rule of behavior. See Ans. 31–32 (citing Miltonberger ¶ 85).
Appellant next contends Miltonberger also refers to use of stationary
models and would not have led to use of a non-stationary model as recited in
claim 1. See Appeal Br. 16. More specifically, according to Appellant,
Miltonberger describes a structure of a predictive user model (“PUM”) as
pre-formulated so that there is no requirement to discover the structure of the
model, but rather the requirement is to estimate unknown parameters of the
model. See Appeal Br. 16–17 (citing Miltonberger ¶ 59); see also Reply
Br. 22–23. Appellant further argues, because Miltonberger discloses the
structure of the PUM as pre-formulated, Miltonberger teaches the set of
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parameters of the PUM is stationary. See Appeal Br. 17. In support of its
argument, Appellant further cites its Rule 132 Declaration. See id.
This argument is not persuasive either because it is also not
commensurate with the scope of claim 1. Similar to its argument regarding
Kapoor, Appellant’s argument focuses on Miltonberger’s alleged use of a
stationary model as opposed to a non-stationary model, but neither claim 1,
nor Appellant’s Specification, limits the claimed “Bayesian behavioral
models” to non-stationary models. As the Examiner correctly found,
Miltonberger describes a system that determines and represents thresholds
corresponding to component risk scores of a parameter of an event (e.g., user
login), where the probability of the parameter is specific for a particular user
based on data specific to that particular user. See Miltonberger ¶ 85. We
agree with the Examiner that Miltonberger’s disclosure of the probability of
a parameter of an event teaches or suggests the claimed “discordance
between a calculated expectation of the system and the behavior and the
machine-to-machine interactions.” See Final Act. 18–19. We further agree
that because Miltonberger’s parameter is specific for a particular user based
on data specific to that particular user, Miltonberger further teaches or
suggests that the calculation of the probability is performed “without using a
previously built normative rule of behavior and machine-to-machine
interactions.” See Ans. 31–32. Appellant’s argument does not persuasively
explain how Miltonberger’s disclosure is distinct from the relevant limitation
of claim 1.
Further, Appellant’s Rule 132 Declaration appears to proffer
arguments regarding Miltonberger that are similar to Appellant’s arguments
described above. See Rule 132 Decl. ¶ 6. For the reasons previously
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described, we agree with the Examiner that the arguments proffered in the
Rule 132 Declaration are not persuasive of Examiner error. See Ans. 7.
Thus, we are not persuaded that the Examiner erred in finding the
combination of Duffield, Kapoor, and Miltonberger teaches or suggests all
the elements of claim 1. No separate arguments are presented for
independent claims 8 and 17. See Appeal Br. 17. Thus, we are also not
persuaded that the Examiner erred in finding the combination of Duffield,
Kapoor, and Miltonberger teaches or suggests all the elements of claims 8
and 17.
Dependent Claim 19
The Examiner found the combination of Miltonberger and Chirashnya
discloses, inter alia, “predicting performed without using a previously built
normative rule that is pre-established by a human, an application, or a
machine,” as recited in claim 19. See Final Act. 18, 38–39 (citing
Miltonberger ¶ 85; Chirashnya ¶ 7). As found by the Examiner,
Miltonberger teaches “predicting performed without using a previously built
normative rule” as previously described above. See Final Act. 18–19 (citing
Miltonberger ¶ 85); see also Ans. 31–32 (citing Miltonberger ¶ 85). As
further found by the Examiner, Chirashnya discloses diagnostic methods for
large complex switched networks utilizing Bayesian networks, where one or
more probabilities of a malfunction of a given module are compared with a
predetermined threshold, where the predetermined threshold teaches “a
previously built normative rule that is pre-established by . . . an application.”
See Final Act. 38–39 (citing Chirashnya ¶ 7); see also Ans. 32 (citing
Chirashnya ¶¶ 27, 47, 63).
Appellant contends Chirashnya refers to Bayesian network models
that declare a fault in response to a measure exceeding a certain threshold.
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See Appeal Br. 18 (citing Chirashnya ¶¶ 10, 27, 47, 63). According to
Appellant, such thresholds are examples of a previously built normative rule
of behavior that is pre-established by a human, an application, or a machine,
and thus, would not have led one of ordinary skill in the art to the claimed
limitation, “predicting performed without using a previously built normative
rule that is pre-established by a human, an application, or a machine,” as
recited in claim 19. See Appeal Br. 18; see also Reply Br. 25.
Appellant’s argument is not persuasive because it solely addresses
Chirashnya rather than the combination of Miltonberger and Chirashnya. As
described above, the Examiner relied upon Miltonberger for teaching
“predicting performed without using a previously built normative rule,” and
further relied upon Chirashnya for teaching “a previously built normative
rule that is pre-established by a human, an application, or a machine.”
Appellant’s argument does not address the Examiner’s combination of
Miltonberger and Chirashnya, and thus, is not persuasive. One cannot show
non-obviousness by attacking references individually when the rejection is
based on a combination of references. See In re Merck & Co., Inc., 800 F.2d
1091, 1097 (Fed. Cir. 1986); see also In re Keller, 642 F.2d 413, 425 (CCPA
1981). Thus, we are not persuaded that the Examiner erred in finding the
combination of Duffield, Kapoor, Miltonberger, and Chirashnya teaches or
suggests all the elements of claim 19.
Dependent Claim 10
The Examiner found Gilbert discloses, inter alia,
learn over time a persistence of incongruities and incongruous
behaviors of the machine components, to detect contrived
incongruities and deliberate acclimatization to planned
incongruous behavior in the system, the deliberate
acclimatization to the planned incongruous behavior in the
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system achieved by an entity introducing small variations of
incongruous behavior into the system such that the system
would not be able to detect the planned incongruous behavior,
as recited in claim 10. See Final Act. 47–48 (citing Gilbert at Abstract). As
found by the Examiner, Gilbert teaches a platform that utilizes inexact
matching to identify, detect, or predict “attack variants” or variants of
previous attack strategies based on incomplete data. See Ans. 33 (citing
Gilbert 11:18–50).
Appellant contends Gilbert merely describes that tactical attacks can
be detected using a search of an input graph. See Appeal Br. 21–22 (citing
Gilbert at Abstract). Appellant specifically argues that there is nothing in
Gilbert that provides any teaching or suggestion of any deliberate
acclimatization to a planned incongruous behavior by introducing small
variations of incongruous behavior into a system such that the system would
not be able to detect the planned incongruous behavior. See Appeal Br. 22;
see also Reply Br. 27–28.
We are not persuaded by this argument either. As the Examiner
correctly found, Gilbert discloses a platform that utilizes inexact matching to
identify, detect, or predict “attack variants” (i.e., variants of previous attack
strategies based on incomplete data). See Gilbert 11:18–50. We agree with
the Examiner that Gilbert’s disclosure teaches or suggests “learn over time a
persistence of incongruities and incongruous behaviors of the machine
components.” See Ans. 33–34. With respect to, the claimed “deliberate
acclimatization to planned incongruous behavior in the system . . . achieved
by an entity introducing small variations of incongruous behavior into the
system such that the system would not be able to detect the planned
incongruous behavior,” Appellant’s argument does not persuasively explain
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how Gilbert’s disclosure is distinct from the aforementioned limitation of
claim 10. Thus, we are not persuaded that the Examiner erred in finding the
combination of Duffield, Kapoor, Kraemer, Miltonberger, and Gilbert
teaches or suggests all the elements of claim 10.
Remaining Dependent Claims
No separate arguments are presented for dependent claims 2–7, 9, 12–
16, 18, 20, and 21. See Appeal Br. 19–20, 22. Accordingly, we sustain the
rejections of those claims as well.
CONCLUSION
We affirm the Examiner’s decision to reject claims 1–10 and 12–21
under 35 U.S.C. § 103.
DECISION SUMMARY
In summary:
Claim(s)
Rejected
35 U.S.C.
§
Reference(s)/Basis Affirmed Reversed
1, 4, 6, 8,
17, 18
103 Duffield, Kapoor,
Miltonberger
1, 4, 6, 8,
17, 18

2, 3, 5, 7,
14, 19
103 Duffield, Kapoor,
Miltonberger,
Chirashnya
2, 3, 5, 7,
14, 19

9, 20 103 Duffield, Kapoor,
Miltonberger,
Girouard
9, 20
12, 13 103 Duffield, Kapoor,
Miltonberger,
Kraemer
12, 13
15 103 Duffield, Kapoor,
Miltonberger,
Weidl
15
10, 16, 21 103 Duffield, Kapoor,
Kraemer,
10, 16, 21
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Miltonberger,
Gilbert
Overall
Outcome
1–10, 12–21

TIME PERIOD FOR 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). See 37 C.F.R.
§ 1.136(a)(1)(iv).
AFFIRMED