Hewlett Packard Enterprise Development LP

Patent Trials and Appeals Board

Mar 23, 2020

15201171 - (D) (P.T.A.B. Mar. 23, 2020)

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.
15/201,171 07/01/2016 Philipp Reinecke 90214803 7524
146568 7590 03/23/2020
MICRO FOCUS LLC
500 Westover Drive
#12603
Sanford, NC 27330
EXAMINER
JACKSON, JENISE E
ART UNIT PAPER NUMBER
2439
NOTIFICATION DATE DELIVERY MODE
03/23/2020 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):
software.ip.mail@microfocus.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
____________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________

Ex parte PHILIPP REINECKE, MARCO CASASSA MONT, and
YOLANTA BERESNA
____________

Appeal 2019-005645
Application 15/201,171
Technology Center 2400
____________

Before MICHAEL J. STRAUSS, JAMES B. ARPIN, and
AMBER L. HAGY, Administrative Patent Judges.

ARPIN, Administrative Patent Judge.

DECISION ON APPEAL
Appellant1 appeals under 35 U.S.C. § 134(a) the final rejection of
claims 1–20, all of the pending claims. Final Act. 2.2 We have jurisdiction
under 35 U.S.C. § 6(b).

1 “Appellant” here refers to “applicant” as defined in 37 C.F.R. § 1.42.
Appellant identifies the real party-in-interest as EntIT Software LLC, a
subsidiary of Micro Focus International Plc. Appeal Br. 3.
2 In this Decision, we refer to Appellant’s Appeal Brief (“Appeal Br.,” filed
October 15, 2018) and Reply Brief (“Reply Br.,” filed July 15, 2019); the
Final Office Action (“Final Act.,” mailed June 15, 2018) and the Examiner’s
Answer (“Ans.,” mailed May 15, 2019); and the originally-filed
Specification (“Spec.,” filed July 1, 2016). Rather than repeat the
Examiner’s findings and determinations and Appellant’s contentions in their
entirety, we refer to these documents.
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We affirm-in-part, and we enter a new ground of rejection with
respect to claim 1 under 35 U.S.C. § 101.
STATEMENT OF THE CASE
Appellant’s claimed subject matter relates to “devices and methods
. . . used to identify computer attacks at various stages, e.g., using symptoms
or signatures of known attacks.” Spec. ¶ 1. In particular,
[c]omputer attack models may be used to drive detection,
tracking, and prediction of malware and other malicious attacks
on a computing system. The behavior of attacks on computer
systems, whether a single computer or a large network of many
computing devices, can be modeled at a high level. For example,
the high level behavior of a data exfiltration attack - where an
attack attempts an unauthorized export of some type of data from
a computing system - can be modeled in a way that captures most
data exfiltration attacks. E.g., such an attack may begin with an
infection phase, followed by a discovery phase, then a lateral
movement phase, and finally a data exfiltration phase. An attack
model may include a variety of information that describes
potential actions that could be taken in any given phase, or state,
of an attack.”
Id. ¶ 8 (emphasis added). Referring to Figure 2, the Specification explains
[t]he example attack model specifies, for each of four phases,
attack actions that may occur during a data exfiltration attack,
e.g., an attack designed to transfer data from a target computing
system. Phase 1 202, the infection phase, specifies three actions
that may occur when an attack tries to infect a computing system:
i) install remote access Trojan (RAT), ii) establish a control
channel, and iii) ongoing command and control communication.
Id. ¶ 17.
As noted above, claims 1–20 stand rejected. Claims 1, 8, and 15 are
independent. Appeal Br. 26 (claim 1), 28 (claim 8), 30 (claim 15) (Claims
App.). Claims 2–7 depend directly or indirectly from claim 1, claims 9–14
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depend directly or indirectly from claim 8, and claims 16–20 depend directly
from claim 15. Id. at 26–31.
Claims 1–3, reproduced below with disputed elements emphasized,
are representative.
1. A computing device for computer attack model
management, the computing device comprising:
a hardware processor; and
a data storage device storing instructions that, when
executed by the hardware processor, cause the hardware
processor to:
identify a first set of attack models, wherein a computing
system is susceptible to a particular attack on the computing
system and each attack model in the first set of attack models
specifies a behavior of the particular attack on the computing
system;
obtain, for each attack model in the first set, performance
data that indicates at least one measure of attack model
performance for a previous use of the attack model in
determining whether the particular attack occurred on the
computing system; and
update the first set of attack models based on the
performance data to add an attack model to the first set of attack
models or remove an attack model from the first set of attack
models.
2. The computing device of claim 1, wherein the
performance data includes at least one of:
resource usage measurements that indicate computing
resources used to execute actions specified by the corresponding
attack model; or
analytics results data that indicates a frequency with which
the corresponding attack model successfully detected the
particular attack.
3. The computing device of claim 1, wherein the
instructions, when executed by the hardware processor, cause the
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hardware processor to update the first set of attack models in
response to a triggering event.
Id. at 26 (emphases added). Independent claims 8 and 15 recite
limitations corresponding to the disputed limitations of claims 1–3 (id.
at 28, 30).
REFERENCES AND REJECTIONS
The Examiner relies upon the following references in rejecting the
claims:
Name3 Number Publ’d/Issued Filed
Di Pietro US 2016/0028750 A1 Jan. 28, 2016 July 23, 2014
Yen US 9,378,361 B1 June 28, 2016 Dec. 31, 2012
Lin US 2017/0228659 A1 Aug. 10, 2017 Mar. 28, 2016
Specifically, the Examiner rejects claims 1–13, 15–17, 19, and 20 under 35
U.S.C. § 103 as obvious over the combined teachings of Di Pietro and Yen
(Final Act. 10–16); and claims 14 and 18 under 35 U.S.C. § 103 as obvious
over the combined teachings of Di Pietro, Yen, and Lin (id. at 16–17).4
Appellant contests the obviousness rejection of independent claims 1,
8, and 15 (Appeal Br. 13–19), and relies on similar deficiencies in the
rejection of each independent claim to overcome the rejections of dependent
claims 4–6, 9–11, 14, and 17–19 (id. at 24–25). Appellant contests
separately the rejection of dependent claims 2, 3, 7, 12, 13, 16, and 20. Id. at
20–24. Because we determine that affirmance of the rejection of the

3 All reference citations are to the first named inventor only.
4 The Examiner rejected claims 1–20 as directed to an abstract idea without
significantly more (Final Act. 7–9), but withdrew that rejection (Ans. 3).
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independent claims is dispositive, except for our ultimate decision, we do
not discuss the merits of the rejections of claims 4–6, 9–11, 17, and 19
further herein. We address separately Appellant’s contentions with respect
to claims 2, 3, 7, 12–14, 16, 18, and 20.
We review the appealed rejections of the claims for error based upon
the issues identified by Appellant, and in light of the arguments and
evidence produced thereon. Ex parte Frye, 94 USPQ2d 1072, 1075 (BPAI
2010) (precedential). Arguments not made are waived. See 37 C.F.R.
§ 41.37(c)(1)(iv). Unless otherwise indicated, we adopt the Examiner’s
findings in the Final Action and the Answer with respect to the affirmed
rejections as our own and add any additional findings of fact for emphasis.
We address the rejections of the separately argued claims below.
ANALYSIS
1. Obviousness Over Di Pietro and Yen
a. Independent Claim 1
The Examiner finds that Di Pietro teaches or suggests almost all of the
limitations of independent claim 1. Final Act. 10–11. The Examiner finds,
however, that, although Di Pietro teaches or suggests an attack model (id. at
11 (citing Di Pietro, Fig. 9 (step 915: “PROVIDE [EXPECTED TRAFFIC]
MODEL TO NODE(S)”)); see Di Pietro ¶ 75; see also Spec. ¶¶ 8, 17
(defining an “attack model”)), “Di Pietro does not explicitly disclose a first
set of attack models” (Final Act. 11).
Nevertheless, the Examiner finds that Yen teaches or suggests a first
set of attack models. Id. (citing Yen, 3:40–46, 4:27–31, 5:38–41). In
particular, the Examiner finds Yen discloses building a plurality of
templates. Yen explains,
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[t]he major component of the threat detection system 18 is the
monitor/analyzer 22. As described more below, it employs both
top-down and bottom-up components. The top-down component
builds and utilizes templates based on known information about
current and prior [advanced persistent threats (APT)] attacks,
and these templates are used in analysis for detecting behavior
that may be indicative of such attacks.
Yen, 3:40–46 (emphasis added). Further, Yen explains,
[s]tarting from the general APT model, different classes of
attacks can be defined and the classes used to guide template
creation. Examples of attack classes include attackers that
propagate through social media, attackers that gather data to a
central location and exfiltrate the data, attackers that exfiltrate
from multiple machines in the enterprise. Templates for such
different classes of attack can be built and deployed for use. The
templates can also be refined during use as more knowledge is
gathered about attacker behavior.
Id. at 4:22–30 (emphases added); cf. Spec. ¶ 17 (describing “data exfiltration
attack”).
Yen teaches, “[a] framework is used in which templates are defined
by a human analyst for the correlations implemented by the correlators.”
Yen, 2:29–31. Di Pietro similarly teaches the following:
In some aspects, the update to the classifier may leverage the
recommendation of an external expert, such as a network
administrator, etc. On one hand, the techniques herein may
improve the reliability of a learning machine-based attack
detection mechanism, such as when [a machine learning
classifier (e.g., an Artificial Neural Networks (ANN))] is used.
Di Pietro ¶ 52; cf. Spec. ¶ 24 (describing “user feedback”). The Examiner
concludes that a person of ordinary skill in the relevant art would have had
reason to combine the teachings of Di Pietro and Yen to modify the
teachings of Di Pietro to include a first set of attack models, as taught by
Yen. Final Act. 11.
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Appellant contends that the Examiner erred in rejecting claim 1 for at
least four reasons. Appeal Br. 13–17. For the reasons given below, we
disagree.
First, Appellant contends, “neither Di Pietro’s traffic model nor its
attack detector/classifier may be considered the attack model of claim 1.”
Id. at 15. In particular, Appellant contends
Di Pietro discusses an expected traffic model (See, for example,
paragraph number [0045] of Di Pietro) and an attack detection
classifier. As explained in paragraph number [0018] of Di Pietro,
“according to one or more embodiments of the disclosure, a
device in a network generates an expected traffic model based on
a trained set of data used to train a machine learning attack
detector.”
In paragraph number [0068], Di Pietro discusses
evaluating the performance of the attack detector, or classifier,
and based on the performance, the expected traffic model may be
updated (if the performance of the classifier is “tolerable”), or
“otherwise, if the current version of the attack detector is unable
to satisfy the required performance, [signature generation
process (SGE)] 402 may also update the attack 14 detector by
adding the traffic samples to the training data set for the relevant
class and recomputing the attack detector.” Di Pietro, para. no.
[0068].
Appeal Br. 14–15.
Initially, we note that the Specification defines “attack model”
broadly. In particular, the Specification states, “[a]n attack model may
include a variety of information that describes potential actions that could be
taken [by an attacker] in any given phase, or state, of an attack.” Spec. ¶ 8;
see id. ¶ 17. We interpret “attack model” accordingly.
In response to Appellant’s contentions, the Examiner finds Di Pietro
teaches that a machine learning attack detector is updated, so that it is able to
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recognize observed traffic behavior as an attack if the traffic behavior occurs
again. Ans. 4 (citing Di Pietro ¶ 53). Further, observing particular traffic
behavior, the attack detector may label the observed traffic behavior as an
attack. Id. (citing Di Pietro ¶ 48); see Di Pietro ¶¶ 71, 72 (describing
automatic assessment of unexpected behavior and distinguishing between
“normal traffic” and “a new attack class”). We agree with the Examiner.
Therefore, we disagree that Di Pietro’s traffic model or its attack
detector/classifier fails to teach or suggest the attack model of claim 1.
Second, Appellant contends:
Claim 1 recites, “each attack model in the first set of attack
models specifies the particular attack on the computing system”
(emphasis added). Di Pietro fails to disclose or render obvious
updating a set of attack detectors, where each detector specifies
behavior of the same particular attack on a computer system
based on performance data that indicates measure(s) of attack
model performance use of the attack model in determining
whether the particular attack occurred.
Appeal Br. 16 (italics added); see Reply Br. 1–2. As the Specification
explains, however, “[i]n some implementations, each model in the first set is
for the same type of computer attack.” Spec. ¶ 23 (emphases added).
The Examiner finds Appellant improperly reads limitations from the
Specification into the claims. Ans. 6. The term: “the particular attack” takes
antecedent basis from the term: “a particular attack,” which we interpret to
mean one or more particular attacks. E.g., Spec. ¶¶ 8 (“The behavior of
attacks on computer systems, whether a single computer or a large network
of many computing devices, can be modeled at a high level. For example,
the high level behavior of a data exfiltration attack - where an attack
attempts an unauthorized export of some type of data from a computing
system - can be modeled in a way that captures most data exfiltration
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attacks.”), 21 (“Other data exfiltration attack models, aside from the example
data exfiltration attack model 200, may be used for data exfiltration attacks.
Other data exfiltration attack models may specify different attack actions
and/or different phases; attack models need not include all actions or phases
of a given attack. In addition, attack models may exist for many different
types of attacks, such as [distributed denial of service (DDOS)]5 attacks,
attacks designed to destroy rather than export data, ransomware attacks,
etc.”); see Harari v. Lee, 656 F.3d 1331, 1341 (Fed. Cir. 2011); Baldwin
Graphic Systems, Inc. v. Siebert, Inc., 512 F.3d 1338, 1342–43 (Fed. Cir.
2008). Because we agree with the Examiner that Appellant’s contentions
are not based on the language of claim 1, we do not find those contentions
persuasive of error.
Third, Appellant contends, “Di Petro fails to disclose or render
obvious updating a first set of attack models based on performance data by
adding an attack model to or removing an attack model from the first set of
attack models,” and Yen fails to cure this deficiency. Appeal Br. 16–17. In
particular, Appellant contends
Yen fails to disclose or render obvious updating a first set of
attack models based on performance data (which indicates at
least a measure of an attack model performance from previous
use in the attack model in determining whether the same
particular attack occurred on the computing system), regardless
of whether Yen is considered singularly or in combination with
Di Pietro.
Id. at 17 (emphasis added). We disagree.
Initially, we note Appellant relies on the same inaccurate
characterization of the language of claim 1 as in its previous contention. See

5 See Di Pietro ¶ 3 (“a distributed DoS (DDoS) attack”).
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Ans. 6, 7–8. Further, the Examiner relies on Di Pietro, not Yen, to teach the
“update” limitation of claim 1. Id. at 8–9; see Final Act. 11.
Di Pietro discloses:
When a new attack type is identified by ground truth 606,
SGE 402 may create a new attack class and add the observed
traffic samples exhibiting the unexpected behavior to the training
data set for the attack classifier, with a correct label identifying
the new attack. SGE 402 may then update the attack detector
using the updated training data set and the new classification.
For example, the entire ANN classifier or a portion thereof may
be recomputed using the updated training data. Subsequently,
SGE 402 may then send an updated version of the attack detector
that can now recognize the new attack to any or all of the
nodes/devices 104. In addition, SGE 402 may generate and send
an updated expected traffic model that incorporates the
previously unexpected behavior into the training data set.
Di Pietro ¶ 67 (emphasis added); see id. ¶ 93. The Specification defines
“performance data” broadly, explaining that
the performance data 152 includes resource usage
measurements, analytics results data, and/or user feedback. The
resource usage measurements may indicate the computing
resources used to execute analytics for actions specified by the
corresponding attack model, e.g., number and usage rate of data
processors, volatile memory usage, long-term memory usage,
computing time, personnel involvement time, etc. The analytics
results data may indicate whether the corresponding attack model
successfully detected the particular attack, e.g., the frequency
with which attack action detection was successful, how many
devices the attack actions were confirmed on, the time it took to
confirm an attack action occurred and/or the entire attack
occurred.
Spec. ¶ 24 (emphasis added). In view of this broad definition of
performance data, we are persuaded the Examiner has shown Di Pietro, in
combination with Yen, teaches or suggests “instructions that . . . update the
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first set of attack models based on the performance data to add an attack
model to the first set of attack models or remove an attack model from the
first set of attack models.” Final Act. 11; Ans. 6–7; see Appeal Br. 26
(Claims App.) (quoting claim 1).
Fourth, Appellant contends Examiner relies on improper hindsight in
combining the teachings of Di Pietro and Yen to achieve the device of
claim 1. Appeal Br. 17.
Any judgment on obviousness is in a sense necessarily a
reconstruction based upon hindsight reasoning, but so long as it
takes into account only knowledge which was within the level of
ordinary skill [in the art] at the time the claimed invention was
made and does not include knowledge gleaned only from
applicant’s disclosure, such a reconstruction is proper.
In re McLaughlin, 443 F.2d 1392, 1395, (CCPA 1971). Nevertheless, in
view of the evidence supporting the Examiner’s finding that a person of
ordinary skill in the art would have had reason to combine the teachings of
Di Pietro and Yen to achieve the recited device and Appellant’s inaccurate
characterization of the limitations of claim 1, we are not persuaded that the
Examiner relied on improper hindsight in combining the teachings of
Di Pietro and Yen to achieve the devices of claim 1. Final Act. 11.
We are not persuaded that the Examiner erred in the obviousness
rejection of claim 1, and we sustain that rejection. Because the rejection of
claims 4–6 is not contested separately, we also sustain that rejection.
b. Independent Claim 8
As with independent claim 1, the Examiner finds that Di Pietro
teaches or suggests almost all of the limitations of independent claim 8.
Final Act. 13–14. As with claim 1, the Examiner finds that, although
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Di Pietro teaches or suggests an attack model, Yen teaches or suggests a first
set of attack models (id. at 14 (citing Yen, 3:40–46)); and a person of
ordinary skill in the art would have had reason to combine the teachings of
Di Pietro and Yen to achieve the methods of claim 8 (id.).
Independent claim 8 recites limitations corresponding to the
limitations discussed above with respect to claim 1. Appeal Br. 28 (Claims
App.). Appellant raises substantially the same contentions regarding those
limitations with respect to independent claim 8. See Appeal Br. 18–19. We
are equally unpersuaded by these contentions with respect to the
corresponding limitations of claim 8, as we are with respect to those of
claim 1.
Nevertheless, claim 8 recites
obtaining, for each attack model in the first set,
performance data that indicates at least one measure of attack
model performance for a previous use of the attack model in
determining whether the particular attack occurred on the
computing system, the performance data including:
resource usage measurements that indicate computing
resources used to execute actions specified by the corresponding
attack model; and
analytics results data that indicates whether the
corresponding attack model successfully detected the particular
attack; and
in response to a triggering event, update the first set of
attack models based on the performance data.
Appeal Br. 28 (Claims App.) (emphases added). Appellant contends the
Examiner fails to show that Di Pietro teaches or suggests the recited
“resource usage measurements” and “a triggering event” resulting in
updates. For the reasons given below, we disagree.
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First, Appellant contends “[c]laim 8 recites obtaining performance
data that includes resource usage measurements that indicate computing
resources that are used to execute actions that are specified by the
corresponding attack model.” Reply Br. 3. As noted above, we interpret the
“actions that are specified by the corresponding attack model” to be actions
of the attacker. Spec. ¶¶ 8, 17; cf. Yen 4:22–30.
Di Pietro discloses
traffic behavior is observed by the node . . . . For example, the
node may observe the number of requests, the bandwidth usage,
the amount of delays, the amount of jitter, etc. of the traffic. In
some cases, the behavior may be observed directly by the node
(e.g., the traffic is flowing through the node itself). In other
cases, the node may observe the traffic indirectly (e.g., by
receiving traffic records from one or more other nodes in the
network).
Di Pietro ¶ 91. Each of these behaviors is a measurement of resource usage
resulting from the actions of an attacker. See id. ¶¶ 92–94. The Examiner
finds that Di Pietro teaches or suggests “resource usage measurements,” as
recited in claim 8. Ans. 10–11. We agree.
Second, the Examiner finds that “Di Pietro discloses a triggering
event, because Di Pietro discloses determin[ing] whether the observed traffic
behavior is unexpected, the node may compare the observed traffic behavior
to the expected traffic model, to determine whether or not the difference
between the two exceeds an anomaly threshold.” Ans. 11 (emphasis added).
Appellant contends, “neither Di Pietro nor Yen discloses or renders obvious
in response to a triggering event, updating a first set of attack models based
on performance data.” Appeal Br. 19; Reply Br. 3–4. In particular,
Appellant contends:
Even assuming, arguendo, that an “anomaly threshold” is used
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for the deviation determination, this still fails to disclose or
render obvious the expressly-recited elements of claim 8. In this
manner, Di Pietro states that the analytics results data indicates
whether the corresponding attack model successfully detected
the particular attack. In paragraph number [0092], Di Pietro
discusses determining whether the observed traffic behavior is
different from the behavior used to train the attack detector.
Measurements pertaining to observed traffic behavior fails to
disclose or render obvious measurements that indicate
computing resources used to execute actions specified by an
attack model.
Reply Br. 4. Thus, Appellant contends that Di Pietro does not teach or
suggest the recited “triggering event” because Di Pietro’s “anomaly
threshold” is used for a different purpose.
Nevertheless, claim 8 recites “in response to a triggering event,
updat[ing] the first set of attack models based on the performance data.”
Appeal Br. 28 (Claim App.) (emphasis added). Interpreting this limitation in
view of the other claims and the Specification, we determine that to “update”
may include “to add . . . or remove” an attack model to or from the first set
of attack models. See Appeal Br. 26 (claim 1), 29 (claims 11, 12, 14),
30 (claim 15) (Claims App.); Spec. ¶ 25 (“In some implementations, the set
of attack models is updated by adding, removing, or changing an attack
model in the first set.” (emphasis added)); see Rexnord Corp. v. Laitram
Corp., 274 F.3d 1336, 1342 (Fed. Cir. 2001); In re Morris, 127 F.3d 1048,
1053 (Fed. Cir. 1997); Vitronics Corp. v. Conceptronic, Inc., 90 F.3d 1576,
1582 (Fed. Cir. 1996). Thus, we are persuaded the Examiner has adequately
shown that Di Pietro teaches or suggests the “updat[ing]” limitation.
We are not persuaded that the Examiner erred in the obviousness
rejection of claim 8, and we sustain that rejection. Because the rejection of
claims 9–11 is not contested separately, we also sustain that rejection.
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c. Independent Claim 15
As with independent claims 1 and 8, the Examiner finds that Di Pietro
teaches or suggests almost all of the limitations of independent claim 15
Final Act. 16. As with claims 1 and 8, the Examiner finds that Yen teaches
the missing limitations of claim 15, and a person of ordinary skill in the art
would have had reason to combine the teachings of Di Pietro and Yen to
achieve the computer-readable media storing instructions of claim 15. Id.
Independent claim 15 recites limitations corresponding to the
limitations discussed above with respect to claims 1 and 8. Appeal Br. 30
(Claims App.). Appellant raises substantially the same contentions
regarding those limitations with respect to independent claim 15. See
Appeal Br. 19. We are equally unpersuaded by these contentions with
respect to the corresponding limitations of claim 15, as we are with respect
to those of claims 1 and 8.
For the reasons given above, we are not persuaded that the Examiner
erred in the obviousness rejection of claim 15, and we sustain that rejection.
Because the rejection of claims 17 and 19 is not contested separately, we
also sustain that rejection.
d. Dependent Claims 2 and 16
In the devices of claim 1, claim 2 recites “the performance data
includes at least one of: resource usage measurements that indicate
computing resources used to execute actions specified by the corresponding
attack model; or analytics results data that indicates a frequency with which
the corresponding attack model successfully detected the particular attack.”
Appeal Br. 26 (Claims App. (emphasis added)). Claim 16 recites
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substantially the same limitation in the computer-readable media of
claim 15. Id. at 30. Appellant relies on substantially the same contentions it
presented with respect to independent claim 8 to overcome the rejection to
claims 2 and 16. Id. at 17–19, 20.
For the reasons given above with respect to the “resource usage
measurements” limitation of independent claim 8, we are not persuaded that
the Examiner erred in the obviousness rejection of claims 2 and 16, and we
sustain that rejection. See Ans. 10–12.
e. Dependent Claim 3
In the devices of claim 1, claim 3 recites “the instructions, when
executed by the hardware processor, cause the hardware processor to update
the first set of attack models in response to a triggering event.” Appeal
Br. 26 (Claims App.) (emphasis added). Appellant relies on substantially
the same contentions it presented with respect to independent claim 8 to
overcome the rejection to claim 3. Id. at 21.
For the reasons given above with respect to the “triggering event”
limitation of independent claim 8, we are not persuaded that the Examiner
erred in the obviousness rejection of claim 3, and we sustain that rejection.
See Ans. 12.
f. Dependent Claims 7, 12, and 20
In the devices of claim 1, claim 7 recites
wherein the instructions further cause the hardware processor to:
determine, based on the performance data that a particular
attack model in the first set performed worse than at least one
other attack model included in the first set; and
in response to the determination, remove or change the
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particular attack model.
Appeal Br. 27 (Claims App.) (emphasis added). Claims 12 and 20 depend
from independent claims 8 and 15, respectively, and recite substantially the
same limitation. Id. at 29, 31.
Di Pietro discloses
SGE 402 or another device may evaluate the performance of the
attack detector on these samples labeled by the expert. If the
performance of the classifier is tolerable (e.g., satisfies a recall
or precision value set by a user), this means that the classifier is
already capable of correctly identifying the behavior as
indicative of an attack. . . . Otherwise, if the current version of
the attack detector is unable to satisfy the required performance,
SGE 402 may also update the attack detector by adding the
traffic samples to the training data set for the relevant class and
recomputing the attack detector.
Di Pietro ¶ 68 (emphasis added). Thus, the Examiner finds
[Di Pietro’s] attack detector performance will be worse if it is
unable to detect an unexpected behavior. As far as determin[ing]
an attack model from another attack model, the prior art of Yen
discloses the attack models can be put into different classes, and
the templates can be used [or] refined during use as more
knowledge is gathered about attack behavior.
Ans. 13 (emphasis added; citing Yen, 4:22–35).
Appellant contends that the Examiner fails to show either Di Pietro or
Yen, alone or in combination, teaches or suggests comparing attack models
to identify one that performed “worse” than another did. Appeal Br. 22–23.
We agree. Neither the cited portion of Di Pietro nor that of Yen teaches or
suggests comparing the performance of attack models, as opposed to
identifying inadequate or different attack models. See Di Pietro ¶¶ 67–72:
Yen, 4:25–35.
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For the reasons given above, we are persuaded that the Examiner
erred in the obviousness rejection of claims 7, 12, and 20, and we do not
sustain that rejection.
g. Dependent Claim 13
In the methods of claim 8, claim 13 recites, “clustering attack models
included in the first set to create at least two subsets of attack models, the
clustering being based on at least one of performance or attack model
characteristics of the attack models in the first set.” Appeal Br. 29 (Claims
App.). In the Final Action, the Examiner relies solely on Di Pietro to teach
or suggest this limitation (Final Act. 15–16 (citing Di Pietro ¶¶ 43, 44));
however, in the Answer, the Examiner relies on Di Pietro and Yen to teach
or suggest this limitation (Ans. 14 (citing Yen, 4:22–29; Di Pietro ¶¶ 56,
68)).
The Examiner finds
Yen discloses clustering attack models in the first set to create at
least two subsets of attack models, because Yen discloses
different classes of attacks can be defined and the classes used to
guide template creation/model. Yen discloses templates for such
different classes of attack can be built and deployed for use.
Id. (citing Yen, 4:22–24). Thus, the Examiner concludes that “Yen
disclose[s], ‘clustering attack models included in the first set to create at
least two subsets of attack models.’” Id. We agree.
The Examiner further finds “Di Pietro discloses a GMM (Gaussian
Mixture Model) or cluster-based model may be used for the expected traffic
model (Di Pietro: para. 0056). The attack model, attack detector, can be
evaluated based on performance of the attack detector (Di Pietro: para.
0068).” Ans. 14. Thus, the Examiner concludes, “Di Pietro discloses the
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clustering being based on the performance of the attack models in the first
set.” Id. However, we understand that a Gaussian Mixture Model (GMM)
or cluster-based model, as described in Di Pietro, is a method of analyzing
data sets, and not a method for organizing models, such as attack models.
See, http://leap.ee.iisc.ac.in/sriram/teaching/MLSP_16/refs/GMM_Tutorial
_Reynolds.pdf. Consequently, we are not persuaded the Examiner shows
that Di Pietro teaches or suggests “clustering being based on at least one of
performance or attack model characteristics of the attack models in the first
set,” as recited in claim 13.
For the reasons given above, we are persuaded that the Examiner
erred in the obviousness rejection of claim 13, and we do not sustain that
rejection.
2. Obviousness Over Di Pietro, Yen, and Lin
The Examiner rejects claims 14 and 18 as obvious over the combined
teachings of Di Pietro, Yen, and Lin. Final Act. 16–17. Claim 14 depends
from independent claim 8 via intervening claim 13, and claim 20 depends
directly from independent claim 15. Appeals Br. 29, 30. For the reasons
given above, we sustain the rejection of claims 8 and 15, but not the
rejection of claim 13. Because we do not sustain the Examiner’s rejection of
claim 13, we also do not sustain the Examiner’s rejection of claim 14.
Nevertheless, because Appellant does not contest the rejection of claims 14
and 18 separately (Appeal Br. 25), Appellant waives any arguments
regarding the Examiner’s findings concerning the teachings of Lin. For this
reason, we sustain the rejection of claim 18.
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NEW GROUND OF REJECTION OF CLAIM 1 UNDER
37 C.F.R. § 41.50(B)
In the Final Rejection, the Examiner rejected the pending claims as
directed to a judicial exception to patent eligibility, without significantly
more, and made findings supporting that rejection. See Final Act. 2–4, 7–9.
Nevertheless, the Examiner withdrew the rejection in the Answer “due to
new guidelines regarding 101.” Ans. 3. “More specifically, [the Examiner
found] independent claims 1, 8, and 15 have a practical application, because
the attack model performance is used to determine whether to update the
attack model by adding or [re]moving [an attack model to/from a first set of
attack models].” Id. Although the Examiner withdrew Final Action’s
rejection under Section 101, we are not persuaded that the Examiner’s
finding of a practical application as a reason for withdrawing the rejection is
consistent with the Office’s guidance on Section 101, and, therefore, we
consider here whether claim 1 is patent eligible under the Office’s guidance.
We cite to the Examiner’s original findings where applicable.
We make the following new ground of rejection:
• Claim 1 is rejected under 35 U.S.C. § 101 as directed to an abstract
idea without reciting additional elements amounting to
significantly more than the abstract idea.
Patent Ineligible Claim
A. Section 101
An invention is patent-eligible if it claims a “new and useful process,
machine, manufacture, or composition of matter.” 35 U.S.C. § 101.
However, the U.S. Supreme Court has long interpreted 35 U.S.C. § 101 to
include implicit exceptions: “[l]aws of nature, natural phenomena, and
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abstract ideas” are not patentable. E.g., Alice Corp. Pty. Ltd. v. CLS Bank
Int’l, 573 U.S. 208, 216 (2014).
In determining whether a claim falls within an excluded category, we
are guided by the Court’s two-part framework, described in Mayo and Alice.
Alice, 573 U.S. at 217–18 (citing Mayo Collaborative Servs. v. Prometheus
Labs., Inc., 566 U.S. 66, 75–77 (2012)). In accordance with that framework,
we first determine what concept the claim is “directed to.” See Alice, 573
U.S. at 219 (“On their face, the claims before us are drawn to the concept of
intermediated settlement, i.e., the use of a third party to mitigate settlement
risk.”); see also Bilski v. Kappos, 561 U.S. 593, 611 (2010) (“Claims 1 and 4
in petitioners’ application explain the basic concept of hedging, or protecting
against risk.”). According to the Court, concepts determined to be abstract
ideas and, thus, patent ineligible, include certain methods of organizing
human activity, such as fundamental economic practices (Alice, 573 U.S. at
219–20; Bilski, 561 U.S. at 611); mathematical formulas (Parker v. Flook,
437 U.S. 584, 594–95 (1978)); and mental processes (Gottschalk v. Benson,
409 U.S. 63, 67 (1972)).
In Diamond v. Diehr, the claim at issue recited a mathematical
formula, but the Court held that “a claim drawn to subject matter otherwise
statutory does not become nonstatutory simply because it uses a
mathematical formula.” Diamond v. Diehr, 450 U.S. 175, 187 (1981).
Having said that, the Court also indicated that a claim “seeking patent
protection for that formula in the abstract . . . is not accorded the protection
of our patent laws, and this principle cannot be circumvented by attempting
to limit the use of the formula to a particular technological environment.”
Id. at 191 (citing Benson and Flook). Nevertheless, the Court noted that “[i]t
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is now commonplace that an application of a law of nature or mathematical
formula to a known structure or process may well be deserving of patent
protection.” Id. at 187; see also BASCOM Glob. Internet Servs., Inc. v.
AT&T Mobility LLC, 827 F.3d 1341, 1352 (Fed. Cir. 2016) (Even if the
individual components were known, “an inventive concept can be found in
the ordered combination of claim limitations that transform the abstract idea
of filtering content into a particular, practical application of that abstract
idea” (emphasis added).).
If the claim is “directed to” an abstract idea, we next “must examine
the elements of the claim to determine whether it contains an ‘inventive
concept’ sufficient to ‘transform’ the claimed abstract idea into a patent-
eligible application.” Alice, 573 U.S. at 221 (quotation marks omitted). “A
claim that recites an abstract idea must include ‘additional features’ to
ensure ‘that the [claim] is more than a drafting effort designed to
monopolize the [abstract idea].’” Id. (alterations in original) (quoting Mayo,
566 U.S. at 77). “[M]erely requir[ing] generic computer implementation[]
fail[s] to transform that abstract idea into a patent-eligible invention.” Id.
B. Office Patent Eligibility Guidance
In an effort to achieve clarity and consistency in how the Office
applies the Court’s two-part framework, the Office published revised
guidance on the application of § 101. See 2019 Revised Patent Subject
Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019).6 In Step One of

6 “All USPTO personnel are, as a matter of internal agency management,
expected to follow the guidance.” 2019 Revised Patent Subject Matter
Eligibility Guidance, 84 Fed. Reg. at 51; see also October 2019 Update:
Subject Matter Eligibility, 1 (Oct. 17, 2019) (“Note, the feedback received
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our analysis, we look to see whether the claims, as written, fall within one of
the four statutory categories identified in § 101. Id. at 53 (“Examiners
should determine whether a claim satisfies the criteria for subject matter
eligibility by evaluating the claim in accordance with the criteria discussed
in MPEP7 § 2106, i.e., whether the claim is to a statutory category (Step 1)
and the Alice/Mayo test for judicial exceptions (Steps 2A and 2B)”).
Under the guidance, we then look to whether the claim recites:
(1) Step 2A – Prong One: any judicial exceptions, including
certain groupings of abstract ideas (i.e., mathematical concepts,
certain methods of organizing human activity, such as a
fundamental economic practice, or mental processes); and
(2) Step 2A – Prong Two: additional elements that integrate the
judicial exception into a practical application (see MPEP
§§ 2106.05(a)–(c), (e)–(h)).
See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg.
at 54–55 (“Revised Step 2A”).
Only if a claim (1) recites a judicial exception and (2) does not
integrate that exception into a practical application, do we then look to
whether the claim:
(3) adds a specific limitation beyond the judicial exception that
is not “well-understood, routine, conventional” in the field (see
MPEP § 2106.05(d)); or
(4) simply appends well-understood, routine, conventional
activities previously known to the industry, specified at a high
level of generality, to the judicial exception.

was primarily directed to examination procedures and, accordingly, this
update focuses on clarifying practice for patent examiners. However, all
USPTO personnel are expected to follow the guidance.”).
7 All Manual of Patent Examining Procedure (“MPEP”) citations herein are
to MPEP, Rev. 08.2017, January 2018.
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See id. at 56 (“Step 2B: If the Claim Is Directed to a Judicial Exception,
Evaluate Whether the Claim Provides an Inventive Concept.”).
C. Step One – Claim 1 Is Directed to a Statutory Category
Appellant’s independent claim 1 is directed to a device (i.e., a
“machine”). Appeal Br. 26 (Claims App.). Thus, claim 1 is directed to a
recognized statutory category.
D. Two-Part Alice/Mayo Analysis
1. Step 2A, Prong One – Claim 1 Recites an Abstract Idea
Applying the first part of the Alice/Mayo analysis (Step 2A), we
determine that claim 1 recites “Mental processes—concepts performed in the
human mind (including an observation, evaluation, judgment, opinion).”
2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. at 52.
Although claim 1 recites devices comprising a hardware processor and
a data storage device, the data storage device stores instructions that perform
processes when executed by the processor. See Appeal Br. 26 (Claims
App.). Claim 1 broadly recites (1) identifying a first set of attack models
(“identify a first set of attack models, wherein a computing system is
susceptible to a particular attack on the computing system and each attack
model in the first set of attack models specifies a behavior of the particular
attack on the computing system”); (2) obtaining performance data for each
attack model that indicates a measure of attack performance data for a
previous attack, from which it is determined the previous attack occurred
(“obtain, for each attack model in the first set, performance data that
indicates at least one measure of attack model performance for a previous
use of the attack model in determining whether the particular attack occurred
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on the computing system”); and (3) updating the first set of attack models
based on the performance data to add or remove an attack model (“update
the first set of attack models based on the performance data to add an attack
model to the first set of attack models or remove an attack model from the
first set of attack models”). See id. The Examiner finds “[t]he claims
reciting the steps of ‘identify’, ‘obtain’, and ‘update . . . are directed to an
abstract idea as the claims describe an abstract idea of collecting and
analyzing information.’” Final Act. 7.
The Office explains, “claims do recite a mental process when they
contain limitations that can practically be performed in the human mind,
including for example, observations, evaluations, judgments, and opinions.”
October 2019 Update: Subject Matter Eligibility at 7. For example, the
Office notes “a claim to ‘collecting information, analyzing it, and displaying
certain results of the collection and analysis,’ where the data analysis steps
are recited at a high level of generality such that they could practically be
performed in the human mind” recites a mental process. Id. (quoting Elec.
Power Grp., LLC v. Alstom, S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016)); see
Final Act. 7 (“The concept described in said claims is similar to the concepts
found by the courts to be abstract ideas of collecting and analyzing of
FairWarning IP, LLC v. Iatric Systems[, Inc., 839 F.3d 1089] (Fed. Cir.
2016 for details).”). Further, as the Office explains:
Claims can recite a mental process even if they are claimed as
being performed on a computer. . . . The courts have found claims
requiring a generic computer or nominally reciting a generic
computer may still recite a mental process even though the claim
limitations are not performed entirely in the human mind.
October 2019 Update: Subject Matter Eligibility at 8.
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Appellant contends claim 1 recites a “solution to improve computer
technology and more specifically, set forth a way to manage computer attack
models, which may be used to identify and handle security attacks on a
computing system.” Appeal Br. 8. In particular, Appellant contends:
The claims set forth a solution to identify a set of attack models
that are successful at detecting malware and are not overly
resource intensive. Using claim 1 as an example, this claim
recites that a computing system is susceptible to a particular
attack on the computing system, and each attack model in a first
set of attack models specifies a behavior of the particular attack
on the computing system. The computing device of independent
claim 1 recites that the hardware processor updates the first set
of attack models based on performance data. As recited in
claim 1, the “performance data . . . indicates at least one measure
of attack model performance for a previous use of the attack
model in determining whether the particular attack occurred in
the computer system.”
Id. at 9. We disagree.
In SRI International Inc. v. Cisco Systems Inc., the Federal Circuit noted
that:
Contrary to Cisco’s assertion, the claims are not directed
to just analyzing data from multiple sources to detect suspicious
activity. Instead, the claims are directed to an improvement in
computer network technology. Indeed, representative claim 1
recites using network monitors to detect suspicious network
activity based on analysis of network traffic data, generating
reports of that suspicious activity, and integrating those reports
using hierarchical monitors. The “focus of the claims is on the
specific asserted improvement in computer capabilities”—that
is, providing a network defense system that monitors network
traffic in real-time to automatically detect large-scale attacks.
930 F.3d 1295, 1303 (Fed. Cir. 2019) (emphasis added; citations omitted);
see October 2019 Update: Subject Matter Eligibility at 7 (citing SRI Int’l).
Unlike the claims in SRI International, claim 1 merely recites a process for
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identifying, gathering, and updating stored information about attacks.
Therefore, we are persuaded that claim 1 recites a mental process.
2. Step 2A, Prong Two – The Abstract Idea Recited in Claim 1 Is Not
Integrated Into a Practical Application.
The Examiner finds that claim 1 includes additional elements
integrating the recited abstract idea into a practical application. Ans. 3. In
particular, the Examiner finds that claim 1 has “a practical application,
because the attack model performance is used to determine whether to
update the attack model by adding or moving.” Id.; but see In re Comiskey,
554 F.3d 967, 979 (Fed. Cir. 2009) (“In Flook the patentee argued that his
claims did not seek to patent an abstract idea (an algorithm) because they
were limited to a practical application of that idea—updating ‘alarm limits’
for catalytic chemical conversion of hydrocarbons. The Court rejected the
notion that mere recitation of a practical application of an abstract idea
makes it patentable, concluding that ‘[a] competent draftsman could attach
some form of post-solution activity to almost any mathematical formula.’”);
Ex parte Gail et al., Appeal No. 2017-008600, 2019 WL 1776826, at *4
(PTAB Mar. 25, 2019) (“’Updating’ can be met by incorporating new
weather data either in the mind or by looking at new information and writing
or remembering the updated forecast.”) (Non-precedential). Integration into
a practical application is evaluated by identifying whether there are
additional elements, individually or in combination, that go beyond the
judicial exception. See 2019 Revised Patent Subject Matter Eligibility
Guidance, 84 Fed. Reg. at 54–55. However, the Examiner fails to show how
this limitation amounts to a practical application consistent with the Office’s
guidance. We disagree with the Examiner’s finding.
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Claim 1 recites two components: a processor and a storage device
coupled to the processor and storing instructions. Appeal Br. 26 (Claims
App.). The Specification makes clear that these are generic components
performing generic functions.8 E.g., Spec. ¶¶ 8 (“The behavior of attacks on
computer systems, whether a single computer or a large network of many
computing devices, can be modeled at a high level.”), 14 (“Computing
device 110 may be, for example, a personal computer, a server computer,
mobile computing device, network device, or any other similar electronic
device . . . .”), 15 (“Hardware processor 120 may be one or more central
processing units (CPUs), semiconductor-based microprocessors, and/or
other hardware devices suitable for retrieval and execution of instructions
stored in machine-readable storage medium, 130.”), 16 (“A machine-
readable storage medium, such as 130, may be any electronic, magnetic,
optical, or other physical storage device that contains or stores executable
instructions. Thus, machine-readable storage medium 130 may be, for
example, Random Access Memory (RAM), non-volatile RAM (NVRAM),
an Electrically Erasable Programmable Read-Only Memory (EEPROM), a
storage device, an optical disc, and the like.”). Moreover, the steps recited
in claim 1 describe generic functions of the processor and storage device.
Id. ¶¶ 1 “Analytical devices and methods are also used to identify computer

8 We acknowledge that some of the considerations at Step 2A, Prong Two,
properly may be evaluated under the second part of the Alice/Mayo analysis
(Step 2B of the Office guidance). See Final Act. 7–8. For purposes of
maintaining consistent treatment within the Office, we evaluate those
considerations under first part of the Alice/Mayo analysis (Step 2A of the
Office guidance). See 2019 Revised Patent Subject Matter Eligibility
Guidance, 84 Fed. Reg. at 55 nn.25, 27–32.
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attacks at various stages, e.g., using symptoms or signatures of known
attacks.”), 14 (Computing device 110 is “capable of handling data, e.g., to
manage computer attack models.”), 15 (“Hardware processor 120 may fetch,
decode, and execute instructions, such as 132-136, to control processes for
computer attack model management.”). The components, considered
individually, do not link the abstract idea to a particular machine or
technology or describe an improvement to the functioning of a computer.
Final Act. 7. Viewed as an ordered combination, Applicant’s claim simply
recites the functions of data identification and collection and of updating the
stored data, as performed by a generic computer utilizing generic computer
components performing generic functions.9 See Final Act. 7–8; see Spec.
¶¶ 13, 42, 49.

9 It is well settled . . . that automating conventional activities
using generic technology does not amount to an inventive
concept. See Alice, 134 S.Ct. at 2358 (explaining that “if a
patent’s recitation of a computer amounts to a mere instruction
to implement an abstract idea on ... a computer, that addition
cannot impart patent eligibility”) (internal alteration, citation,
and quotations omitted); Intellectual Ventures [I LLC v. Capital
One Bank (USA)], 792 F.3d [1363,] 1367 [(Fed. Cir. 2015)]
(“claiming the improved speed or efficiency inherent with
applying the abstract idea on a computer [does not] provide a
sufficient inventive concept”); Bancorp Servs., L.L.C. v. Sun Life
Assur. Co. of Can. (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012)
(“[T]he fact that the required calculations could be performed
more efficiently via a computer does not materially alter the
patent eligibility of the claimed subject matter.” (emphasis
added)).
LendingTree, LLC v. Zillow, Inc., 656 F. App’x 991, 996 (Fed. Cir. 2016)
(emphases added).

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Thus, in view of Appellant’s claim recitations and Specification, we
are persuaded the rejected claim does not recite:
(i) an improvement to the functioning of a computer;
(ii) an improvement to another technology or technical field;
(iii) an application of the abstract idea with, or by use of, a
particular machine;
(iv) a transformation or reduction of a particular article to a
different state or thing; or
(v) other meaningful limitations beyond generally linking the
use of the abstract idea to a particular technological
environment.
See MPEP § 2106.05(a)–(c), (e)–(h); 2019 Revised Patent Subject Matter
Eligibility Guidance, 84 Fed. Reg. at 55. Thus, we conclude claim 1 does
not integrate the judicial exception into a practical application, and claim 1 is
directed to an abstract idea.
3. Step 2B – Claim 1 Does Not Recite Not Significantly More Than the
Abstract Idea.
Because we find that claim 1 recites an abstract idea and does not
integrate that abstract idea into a practical application, we now consider
whether claim 1 includes additional limitations, such that the claim amounts
to significantly more than the abstract idea. Applying the second part of the
Alice/Mayo analysis, the Examiner concludes:
The claims do not include additional elements that are sufficient
to amount to significantly more than the judicial exception
because the additional elements when considered both
individually and as an ordered combination do not amount to
significantly more than the abstract idea. It’s noted that the claim
recites some additional elements such as “hardware processor”,
and “data storage device”. However, said additional elements,
taken individually and as a combination, do not result in the
claim amounting to significantly more than the abstract idea
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because “hardware processor”, and “data storage device” are
recited as performing generic computer content distributing
functions routinely used in detecting fraud. Generic computer
components recited as performing generic computer functions
that are well understood, routine and conventional activities
amount to no more than implementing the abstract idea with a
computerized system. Therefore, the claim is directed to non-
statutory subject matter.
Final Act. 7–8.
Appellant contends the Examiner fails to demonstrate that the
additional components recited in the claims are well-understood, routine,
and conventional, as required by in Berkheimer v. HP Inc., 881 F.3d 1360
(Fed. Cir. 2018). Appeal Br. 11–12. Nevertheless, as noted above, the
Specification shows the components are well-understood, routine, and
conventional. Spec. ¶¶ 1, 8, 14–16; see Changes in Examination Procedure
Pertaining to Subject Matter Eligibility, Recent Subject Matter Eligibility
Decision (Berkheimer v. HP., Inc.), 3–4 (Apr. 19, 2018) (noting that the
specification may evidence that components are well-understood, routine,
and conventional); see also In re TLI Commc’ns LLC Patent Litigation, 823
F.3d 607, 614 (Fed. Cir. 2016) (citing to specification as showing a server
that receives data, extracts classification information from the received data,
and stores the digital images is insufficient to add an inventive concept).
Consequently, we agree with the Examiner’s assessment of these
components.
As noted above, claim 1 recites the computer device performs the
steps of identifying a first set of attack models, obtaining performance data
for each attack model in the first set, and updating the first set of attack
models based on the performance data to add or remove an attack model to
the first set of attack models. The claim recites these computer functions at
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a high level of generality. Further, the Specification does not describe that
these recited steps are anything but generic computer functions, as are
performed by any computer processing data. E.g., Spec. ¶¶ 23 (identifying a
first set of attack models), 24 (obtaining performance data), 25 (updating the
first set of attack models based on the performance data), 47–50 (describing
general methods of accomplishing the recited functions).
Our reviewing court has identified such functions as insufficient to
show an inventive concept. In Electric Power Group, LLC v. Alstom S.A.,
our reviewing court noted:
The claims in this case do not even require a new source
or type of information, or new techniques for analyzing it. As a
result, they do not require an arguably inventive set of
components or methods, such as measurement devices or
techniques, that would generate new data. They do not invoke
any assertedly inventive programming. Merely requiring the
selection and manipulation of information—to provide a
“humanly comprehensible” amount of information useful for
users,—by itself does not transform the otherwise-abstract
processes of information collection and analysis.
830 F.3d at 1355 (citations omitted); see Content Extraction and
Transmission LLC v. Wells Fargo Bank, Nat. Ass’n, 776 F.3d 1343, 1348
(Fed. Cir. 2014) (“At most, CET’s claims attempt to limit the abstract idea
of recognizing and storing information from hard copy documents using a
scanner and a computer to a particular technological environment.”);
Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716 (Fed. Cir. 2014)
(“Adding routine additional steps such as updating an activity log, requiring
a request from the consumer to view the ad, restrictions on public access,
and use of the Internet does not transform an otherwise abstract idea [of
showing an advertisement before delivering content] into patent-eligible
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subject matter.” (emphasis added)). Moreover, the ordering of these steps is
entirely well-understood, conventional, and routine; performance data is
gathered and used to update a previously identified set of attack models.
On this record, we determine that claim 1 recites generic computer
components performing generic computer functions, which, considered
individually or as an ordered combination, are well-understood, routine, and
conventional; and claim 1 does not recite “significantly more” than the
identified abstract idea. Final Act. 7–8. Therefore, we reject independent
claim 1 under 35 U.S.C. § 101.
We have not reviewed independent claims 8 and 15 or the dependent
claims. When prosecution resumes, the Examiner should consider the patent
eligibility of at least claims 7, 12–14, and 20 in light of the Office’s 2019
Revised Patent Subject Matter Eligibility Guidance and this new ground of
rejection of claim 1. We leave it to the Examiner to ascertain the
appropriateness of any further rejections. Our decision not to enter a new
ground of rejection for all or other pending claims should not be considered
as any indication regarding the appropriateness of further rejection or
allowance of the other pending claims. See MPEP § 1213.03.
DECISIONS
1. The Examiner did not err in rejecting claims 1–6, 8–11, and 15–19 as
obvious over the combined teachings of Di Pietro and Yen, alone or in
combination with Lin.
2. The Examiner erred in rejecting claims 7, 12–14, and 20 as obvious
over the combined teachings of Di Pietro and Yen, alone or in
combination with Lin.
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3. We determine that claim 1 is unpatentable under 35 U.S.C. § 101, as
directed to patent ineligible subject matter, without significantly more.
4. Thus, on this record, claims 1–6, 8–11, and 15–19 are not patentable;
and claims 7, 12–14, and 20 are not unpatentable.
CONCLUSION
For the above reasons, we affirm the Examiner’s decision rejecting
claims 1–6, 8–11, and 15–19; reverse the Examiner’s decision rejecting
claims 7, 12–14, and 20; and enter a new ground rejecting claim 1.
In summary:
Claims
Rejected
35 U.S.C.
§
References Affirmed Reversed New
Ground
1–13, 15–
17, 19, 20
103 Di Pietro, Yen 1–6, 8–11,
15–17, 19
7, 12, 13,
20

14, 18 103 Di Pietro, Yen,
Lin
18 14
1 101 Eligibility 1
Overall
Outcome
1–6, 8–11,
15–19,
7, 12–14,
20
1
This decision contains new grounds of rejection pursuant to 37 C.F.R.
§ 41.50(b). 37 C.F.R. § 41.50(b) provides “[a] new ground of rejection
pursuant to this paragraph shall not be considered final for judicial review.”
37 C.F.R. § 41.50(b) also provides that Appellant, WITHIN TWO
MONTHS FROM THE DATE OF THE DECISION, must exercise one of
the following two options with respect to the new ground of rejection to
avoid termination of the appeal as to the rejected claims:
(1) Reopen prosecution. Submit an appropriate amendment of
the claims so rejected or new Evidence relating to the claims so
rejected, or both, and have the matter reconsidered by the
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examiner, in which event the prosecution will be remanded to the
examiner. . . .
(2) Request rehearing. Request that the proceeding be reheard
under § 41.52 by the Board upon the same Record. . . .
Further guidance on responding to a new ground of rejection can be found in
the Manual of Patent Examining Procedure § 1214.01. No time for taking
any action connected with this appeal may be extended under 37 C.F.R.
§ 1.136(a)(1).

AFFIRMED-IN-PART; 37 C.F.R. § 41.50(b)