Verint Systems Ltd.

Patent Trials and Appeals Board

Apr 27, 2021

2020004525 (P.T.A.B. Apr. 27, 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.
14/610,232 01/30/2015 Yair Horesh 10171-436US1 2629
75158 7590 04/27/2021
Verint Systems, Inc.
Meunier Carlin & Curfman, LLC
999 Peachtree Street NE
Suite 1300
Atlanta, GA 30309
EXAMINER
BYRD, UCHE SOWANDE
ART UNIT PAPER NUMBER
3624
NOTIFICATION DATE DELIVERY MODE
04/27/2021 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):
KCarroll@mcciplaw.com
docketing@mcciplaw.com
PTOL-90A (Rev. 04/07)

UNITED STATES PATENT AND TRADEMARK OFFICE
____________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________

Ex parte YAIR HORESH
and RONI ROMANO

Appeal 2020-004525
Application 14/610,232
Technology Center 3600
____________

Before MURRIEL E. CRAWFORD, ANTON W. FETTING, and
MATTHEW S. MEYERS, Administrative Patent Judges.

MEYERS, Administrative Patent Judge.

DECISION ON APPEAL

STATEMENT OF THE CASE
Pursuant to 35 U.S.C. § 134(a), the Appellant1 appeals from the
Examiner’s final decision to reject claims 1, 5, 6, 8–10, 14, 15, and 17–28,
which are all of the pending claims. 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. Appellant identifies the real party in interest as Verint
Systems Ltd. Appeal Br. 3.
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CLAIMED INVENTION
Appellant’s claimed invention generally relates to automated trend
identification within conversational communication data. Spec. ¶ 1.
Claims 1, 10, and 19 are the independent claims on appeal. Claim 1,
reproduced below, with modified formatting and bracketed notations, is
illustrative of the claimed subject matter.
1. A method of automated trend identification, the method
comprising:
[a] receiving, by a processing system executing software,
conversational communication data;
[b] receiving, by the processing system, at least one
modularity selection, the modularity selection defining a feature;
[c] identifying, by the processing system, counts of
instances of the feature in temporal intervals in the
communication data;
[d] receiving, by the processing system, a time interval,
wherein the counts of instances of the feature are identified
within the time interval of the communication data;
[e] normalizing, by the processing system, the identified
counts of instances of the feature in the communication data with
an amount of the received communication data to produce
normalized counts of identified instances of the feature;
[f] receiving, by the processing system, a report selection
of one or more reports, each of the reports represents a different
type of trend, and each of the reports is associated with a
particular statistical model that is used to evaluate the
corresponding report, wherein the reports include a plurality of:
1) a general trends report that identifies the most significant
trends for the feature in the received time interval, wherein the
particular statistical model associated with the general trends
report is a linear regression and significance tests; 2) a correlation
report that identifies significant correlations or anti-correlations
between the feature and another feature, wherein the particular
statistical model associated with the correlation report is a
Pearson Correlations Test; 3) a week-day, week, or month report
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that identifies whether the feature is significantly over or under
expressed during a specific week day, week, or month compared
to other week days, weeks, or months, respectively, wherein the
particular statistical model associated with the week-day, week,
or month report is a t-test; 4) a daily spike report that identifies
the most significant daily spikes in the feature, wherein the
particular statistical model associated with the daily spike report
is a Chauvenet’s Criterion; or 5) a weekly or monthly periodic
pattern report that identifies whether the feature significantly
behave in a weekly or monthly periodic cycle, respectively,
wherein the particular statistical model associated with the
weekly or monthly periodic pattern report is a standard deviation
ratio;
[g] for each of the one or more reports selected, producing,
by the processing system, a statistical measure of the normalized
counts of identified instances of the feature based on the
particular statistical model associated with the corresponding
report;
[h] evaluating, by the processing system, the statistical
measure by comparison of the statistical measure to a
predetermined threshold indicative of a trend of interest, wherein
the predetermined threshold is specific to the particular statistical
model associated with the corresponding report; and
[i] identifying, by the processing system, a particular trend
of interest from the evaluation of the statistical measure, wherein
the statistical measure of the normalized identified instances of
the feature for the particular trend of interest is greater than the
predetermined threshold.
Appeal Br. 29–30 (Claim App.).
REJECTIONS
1. Claims 1, 5, 6, 8–10, 14, 15, and 17–28 stand rejected under
35 U.S.C. § 101 as being directed to a judicial exception without
significantly more.
2. Claims 1, 5, 6, 8–10, 14, 15, and 17–28 are rejected under 35
U.S.C. § 103(a) as being unpatentable over Parnaby (US 2013/0018838 A1,
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pub. Jan. 17, 2013), Shilman (US 2009/0319342 A1, pub. Dec. 24, 2009),
Adams (US 2015/0161633 A1, pub. June 11, 2015), and MacGregor (US
7,194,465 B1, iss. Mar. 20, 2007).

ANALYSIS
Patent-Ineligible Subject Matter
Appellant argues claims 1, 5, 6, 8–10, 14, 15, and 17–28 as a group.
Appeal Br. 13–27. We select independent claim 1 as representative. The
remaining claims stand or fall with claim 1. See 37 C.F.R. § 41.37(c)(1)(iv).
35 U.S.C. § 101 Framework
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
abstract ideas” are not patentable. E.g., Alice Corp. 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)
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(“Claims 1 and 4 in petitioners’ application explain the basic concept of
hedging, or protecting against risk.”).
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)). Concepts
determined to be patent eligible include physical and chemical processes,
such as “molding rubber products” (Diamond v. Diehr, 450 U.S. 175, 191
(1981)); “tanning, dyeing, making water-proof cloth, vulcanizing India
rubber, smelting ores” (id. at 182 n.7 (quoting Corning v. Burden, 56 U.S.
252, 267–68 (1853))); and manufacturing flour (Benson, 409 U.S. at 69
(citing Cochrane v. Deener, 94 U.S. 780, 785 (1876))).
In 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.”
Diehr, 450 U.S. at 187; see also id. at 191 (“We view respondents’ claims as
nothing more than a process for molding rubber products and not as an
attempt to patent a mathematical formula.”). 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. (citing Benson and
Flook); see, e.g., id. at 187 (“It 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.”).
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If the claim is “directed to” an abstract idea, we turn to the second
step of the Alice and Mayo framework, where “we 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.
USPTO Section 101 Guidance
We are also guided by U.S. Patent and Trademark Office (“USPTO”)
Guidance, as set forth in the 2019 Revised Patent Subject Matter Eligibility
Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019) (“Guidance”), incorporated into
the MANUAL OF PATENT EXAMINING PROCEDURE §§ 2104–06, Rev. 10.2019
(“MPEP”) in June 2020. “The guidance sets out agency policy with respect
to the USPTO’s interpretation of the subject matter eligibility requirements
of 35 U.S.C. [§] 101 in view of decisions by the Supreme Court and the
Federal Circuit.” Guidance, 84 Fed. Reg. at 51.2 Although “[a]ll USPTO
personnel are, as a matter of internal agency management, expected to
follow the guidance,” the Guidance “does not create any right or benefit,

2 In response to received public comments, the Office issued further
guidance on October 17, 2019, clarifying the 2019 Revised Guidance, 84
Fed. Reg. USPTO, October 2019 Update: Subject Matter Eligibility (the
“October 2019 Update”) (available at https://www.uspto.gov/sites/default/
files/documents/peg_oct_update.pdf).
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substantive or procedural, enforceable by any party against the USPTO.” Id.
The Guidance, by its terms, applies to all applications, and to all patents
resulting from applications, filed before, on, or after January 7, 2019. Id.
at 50.
Under USPTO Guidance and October 2019 Update, we first look to
whether the claim recites: (1) any judicial exceptions, including laws of
nature, natural phenomena, and products of nature (see MPEP
§§ 2106.04(II)(A)(1), 2106.04(b)) (“Step 2A, Prong One”); and (2)
additional elements that integrate the judicial exception into a practical
application (see id. §§ 2106.04(II)(A)(2), 2106.04(d)) (“Step 2A, Prong
Two”).3
Only if a claim (1) recites a judicial exception and (2) does not
integrate that exception into a practical application, do we then look, under
Step 2B, to whether the additional elements, individually or in combination,
provide an inventive concept. See MPEP §§ 2106(III), 2106.05. “An
inventive concept ‘cannot be furnished by the unpatentable law of nature (or
natural phenomenon or abstract idea) itself.’” Id. § 2106.05 (quoting
Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376 (Fed. Cir. 2016)).
Among the considerations in determining whether the additional elements,
individually or in combination, amount to significantly more than the

3 This evaluation is performed by (a) identifying whether there are any
additional elements recited in the claim beyond the judicial exception, and
(b) evaluating those additional elements individually and in combination to
determine whether the claim as a whole integrates the exception into a
practical application. See Guidance - Section III(A)(2), 84 Fed. Reg.
at 54–55; MPEP § 2106.04(d).
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exception itself, we look to whether they add a specific limitation beyond the
judicial exception that is not “well-understood, routine, conventional” in the
field or simply append well-understood, routine, conventional activities
previously known to the industry, specified at a high level of generality, to
the judicial exception. MPEP § 2106.05(II); Guidance, 84 Fed. Reg. at 56.
Step One of the Mayo/Alice Framework
Under the first step of the Mayo/Alice framework and Step 2A, Prong
One of USPTO Guidance, the Examiner determines that exemplary
independent claim 1 recites broadly a series of steps “for managing trend
identification (e.g. receiving communication data, evaluating reports,
identifying trends).” Final Act. 4. According to the Examiner, the
“limitations of representative claim 1 recite concepts that amount to (i)
Certain Methods of Organizing Human Activity (e.g. based on managing
steps for a user to normalize questions and answers) and (ii) concepts
performed in the human mind (e.g. based on receiving data, normalizing
data, and evaluating data).” Id. Under Prong Two of USPTO Guidance, the
Examiner determines that the identified judicial exception is not integrated
into a practical application because
claim 1 merely includes instructions to implement an abstract
idea on a computer (or merely uses a computer as a tool to
perform an abstract idea) while additional elements do no more
than generally link the use of a judicial exception to a particular
technological environment or field of use.
Id. at 5.
In response, Appellant asserts that the Examiner improperly
characterizes exemplary claim 1 as being directed to a “Certain Method[] of
Organizing Human Activity” because the claim does not recite “managing
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steps for a user to normalize questions and answers.” Appeal Br. 15.
Appellant also asserts that the Examiner improperly characterizes claim 1 as
being directed to a mental process because ‘“[t]he present disclosure is
related to the field of automated data analysis’” (id. at 16 (quoting Spec.
¶ 1), and as such, “it is clear that the pending claims cannot practically be
performed in the human mind.” Id.
Under the first step of the Mayo/Alice framework and Step 2A of the
USPTO Guidance, we first determine to what claim 1 is directed, i.e.,
whether claim 1 recites an abstract idea and if so, whether claim 1 is directed
to that abstract idea. The Federal Circuit has explained that “the ‘directed
to’ inquiry applies a stage-one filter to claims, considered in light of the
specification, based on whether ‘their character as a whole is directed to
excluded subject matter.’” Enfish, LLC v. Microsoft Corp., 822 F.3d 1327,
1335 (Fed. Cir. 2016) (quoting Internet Patents Corp. v. Active Network,
Inc., 790 F.3d 1343, 1346 (Fed. Cir. 2015)). It asks whether the focus of the
claim is on a specific improvement in relevant technology or on a process
that itself qualifies as an “abstract idea” for which computers are invoked
merely as a tool. See Enfish, 822 F.3d at 1335–36. Here, it is clear from the
Specification, including the claim language, that claim 1 focuses on an
abstract idea, and not on any improvement to technology and/or a technical
field.
Reciting a Judicial Exception
In making this determination, we note that the Specification is titled
“SYSTEM AND METHOD OF TREND IDENTIFICATION,” and is
related generally “to the field of automated data analysis,” and more
specifically, “the identification of trends in communication data.” Spec. ¶ 1.
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According to the Specification, “communication data [are] exemplarily data
of interpersonal communication . . . wherein customer service interaction
communication data [are] acquired, large amounts of communication data
can be acquired daily, and therefore automated analysis tools are required in
order to be able to practically analyze such data on an ongoing basis.” Id.
¶ 2. The Specification recognizes that “[o]ne such technique for automated
analysis is the identification of trends within the communication data,” and
that “[c]urrent approaches will identify occurrences of specific words in the
communication data and calculate differences with which those words occur
in the communication data versus a stored reference corpus of historical
communication data or against previously calculated historical averages of
word occurrences.” Id. ¶ 3. However, the Specification identifies that these
common “techniques generally rely on heuristics to evaluate whether a word
frequency calculated from the communication data is within or outside of
expected norms.” Id. The Specification also points out that these known
techniques are “difficult to implement” because “differences in the historical
averages or a set of communication data used to arrive at the historical
averages can impact the trend result and further such results are often
insensitive to periodically recurring or slow developing trends.” Id. To
address these drawbacks, the present invention “provide[s] automated
analysis tools for more refined trend analysis and evaluation of identified
trends.” Id. ¶ 4.
Consistent with this disclosure, independent claim 1 recites “[a]
method of automated trend identification” including steps for
“identifying . . . counts of instances of the feature in temporal intervals in the
communication data” (limitation [c]); “normalizing . . . the identified counts
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of instances of the feature” (limitation [e]); “producing . . . a statistical
measure of the normalized counts” (limitation[g]); “evaluating . . . the
statistical measure by comparison of the statistical measure to a
predetermined threshold indicative of a trend of interest” (limitation [h]);
and “identifying . . . a particular trend of interest from the evaluation of the
statistical measure, wherein the statistical measure . . . is greater than the
predetermined threshold” (limitation [i]). The method additionally includes
steps to receive various data in order to perform the trend identification, e.g.,
“conversational communication data” (limitation [a]), “modularity selection
defining a feature” (limitation [b]); “a time interval” (limitation [d]); “a
report selection of one or more reports” (limitation [f]). Appeal Br. 29–30
(Claim App.).
When considered collectively and under the broadest reasonable
interpretation, independent claim 1, as summarized above, recites
performing an automated trend identification based on a series of steps for
receiving information and analyzing information by comparing selected
features to communication data; normalizing identified feature instances;
performing statistical analysis, and comparing a statistical measure to a
predetermined threshold to identify trends of interest.4 This is an abstract
idea which may be characterized as a “[m]ental process[]—[a] concept
performed in the human mind (including an observation, evaluation,
judgement, opinion).” Guidance, 84 Fed. Reg. at 52; MPEP

4 We note that “[a]n abstract idea can generally be described at different
levels of abstraction.” Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1240
(Fed. Cir. 2016). The Board’s “slight revision of its abstract idea analysis
does not impact the patentability analysis.” Id. at 1241.
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§ 2106.04(a)(2)(III). This is consistent with the Examiner’s
characterization. Final Act. 4; Ans. 5.
The courts have held similar concepts to be abstract. For example, the
Federal Circuit has held abstract the concepts of “selecting certain
information, analyzing it using mathematical techniques, and reporting or
displaying the results of the analysis” in SAP America, Inc. v. InvestPic,
LLC, 898 F.3d 1161, 1167 (Fed. Cir. 2018), “considering historical usage
information while inputting data” in BSG Tech LLC v. Buyseasons, Inc., 899
F.3d 1281, 1287, 1289 (Fed. Cir. 2018) (“‘[S]ummary comparison usage
information’ covers any information concerning the relative frequency at
which different parameters and values have been used”), and Elec. Power
Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1351–54 (Fed. Cir. 2016) (Claim
for detecting events by receiving, detecting, analyzing, displaying,
accumulating, and updating data, and deriving a composite indicator from
that data was directed to the abstract idea of “collecting information,
analyzing it, and displaying certain results of the collection and analysis.”).
Having concluded that claim 1 recites a judicial exception, i.e., an
abstract idea, in determining whether the claim is directed to this abstract
idea, we next consider whether the claim recites additional elements that
integrate the judicial exception into a practical application.
Integration into a Practical Application
We look to whether the claim “appl[ies], rel[ies] on, or use[s] the
judicial exception in a manner that imposes a meaningful limit on the
judicial exception, such that the claim is more than a drafting effort designed
to monopolize the judicial exception,” i.e., “integrates a judicial exception
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into a practical application.” Guidance, 84 Fed. Reg. at 54; MPEP
§ 2106.04(d). Here, the Examiner identifies that claim 1 does “not include
additional elements that are sufficient to amount to significantly more than
the judicial exception because the computer as recited is a generic computer
component that performs functions.” Ans. 4; see also Spec. ¶ 16
(“[P]rocessing system 206 include general purpose central processing units,
application specific processors, and logic devices, as well as any other type
of processing device, combinations of processing devices, or variations
thereof.”). The Examiner also notes that the processing system is
insufficient to integrate the judicial exception identified in claim 1 into a
practical application because it “merely includes instructions to implement
an abstract idea on a computer (or merely uses a computer as a tool to
perform an abstract idea).” Final Act. 5. We agree with the Examiner.
As is clear from the Specification, there is no indication that the
processes recited in claim 1 require any specialized computer hardware or
other inventive computer components, i.e., a particular machine, invoke any
asserted inventive programming, or that the claimed invention is
implemented using other than generic computer components to perform
generic computer functions. See DDR Holdings, LLC v. Hotels.com, L.P.,
773 F.3d 1245, 1256 (Fed. Cir. 2014) (“[A]fter Alice, there can remain no
doubt: recitation of generic computer limitations does not make an otherwise
ineligible claim patent-eligible.”); see also Spec. ¶¶ 14–21. Independent
claim 1, unlike the claims found non-abstract in prior cases, uses generic
computer technology to receive and analyze information for the purpose of
“evaluation of identified trends” Spec. ¶ 4, and does not recite an
improvement to a particular computer technology. Cf. McRO, Inc. v. Bandai
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Namco Games Am. Inc., 837 F.3d 1299, 1314–15 (Fed. Cir. 2016) (Finding
claims not abstract because they “focused on a specific asserted
improvement in computer animation.”).
Reproducing large portions of limitations [a]–[e] of claim 1 (Appeal
Br. 17), Appellant argues that these additional elements “reflect an
improvement in the functioning of a computer or an improvement to another
technology or technical field” by “provid[ing] an improvement in the
accuracy of automatically identifying trends in conversational
communication data.” Id. at 18.
However, we agree with the Examiner that the receiving steps
identified as additional elements are insignificant extra-solution activity.
Ans. 6. The step of receiving data (i.e., limitations [a], [d]) is an extra-
solution activity of data gathering. See Elec. Power, 830 F.3d at 1353–54;
In re Bilski, 545 F.3d 943, 963 (Fed. Cir. 2008) (en banc), aff’d sub nom
Bilski v. Kappos, 561 U.S. 593 (2010) (characterizing data gathering steps as
insignificant extra-solution activity). The step of selecting a particular data
source or type of data to be manipulated (i.e., limitations [b], [f]) has also
been found to be insignificant extra-solution activity. See Elec. Power, 830
F.3d at 1355 (“[M]erely selecting information . . . for collection, analysis
and display does nothing significant to differentiate process from ordinary
mental processes.”).
Furthermore, there is no such improvement to the functioning of a
computer, another technology, or technical field. The claimed method of
receiving data, selecting features to be investigated for trends over a
particular time interval, selecting reports, and then analyzing the received
information using various known statistical models is recited without any
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technological details on how the steps are performed technologically other
than using a conventional computer in its ordinary capacity to perform data
analysis. In this regard, the Specification identifies that the present
invention is directed to “provid[ing] automated analysis tools for more
refined trend analysis and evaluation of identified trends,” which “increase
trend identification accuracy by specifically tailoring the methods and
algorithms as described in further detail herein to a specific report or reports
to be used.” Spec. ¶¶ 4, 27. The claims reflect the proposed improvement to
the abstract idea, not in the functioning of the computer, technological
environment, or technical field. Cf. OIP Techs., Inc. v. Amazon.com, Inc.,
788 F.3d 1359, 1363 (Fed. Cir. 2015) (Holding that claims are directed to
“the abstract idea of offer-based price optimization.”); Enfish, 822 F.3d at
1336 (Focusing on whether the claim is “an improvement to [the] computer
functionality itself, not on economic or other tasks for which a computer is
used in its ordinary capacity.”). See also Bridge & Post, Inc. v. Verizon
Commc’ns, Inc., 778 F. App’x 882, 893 (Fed. Cir. 2019) (“The ability to run
a more efficient advertising campaign, even if novel, and even if aided by
conventional computers, is an advance ‘entirely in the realm of abstract
ideas,’ which we have repeatedly held to be ineligible.”).
Thus, we are not persuaded of error in the Examiner’s determination
that the additional elements of claim 1 do not integrate the judicial exception
into a practical application, as the term is used in USPTO Guidance, and we
are not persuaded of error in the Examiner’s determination that claim 1 is
directed to an abstract idea.
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Step Two of the Mayo/Alice Framework
Under the second step in the Alice framework and USTPO Guidance
Step 2B, we “[e]valuat[e] additional elements to determine whether they
amount to an inventive concept [which] requires considering them both
individually and in combination to ensure that they amount to significantly
more than the judicial exception itself.” MPEP § 2106.05(I). Here, the
Examiner determines that “taken individually or as a whole the additional
elements of claim 1 do not provide an inventive concept (i.e. they do not
amount to ‘significantly more’ than the exception itself).” Final Act. 6. See
also Elec. Power, 830 F.3d at 1355 (gathering, sending, monitoring,
analyzing, selecting, and presenting information does not transform the
abstract process into a patent-eligible invention); OIP Techs, 788 F.3d at
1363 (Claims reciting, inter alia, sending messages over a network,
gathering statistics, using a computerized system to automatically determine
an estimated outcome, and presenting offers to potential customers found to
merely recite “‘well-understood, routine conventional activit[ies],’ either by
requiring conventional computer activities or routine data-gathering steps.”)
(alteration in original); SAP Am., 898 F.3d at 1167–68 (“[S]electing certain
information, analyzing it using mathematical techniques, and reporting or
displaying the results of the analysis” were “basic functions” of a
computer.).
Appellant asserts that the “features of . . . automated trend
identification by a processing system executing software, the conversational
communication data, and the modularity selection” together in combination
“tie the pending claims to a processor’s ability to automatically process
conversational communication data.” Appeal Br. 19. Appellant further
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argues that claim 1 “increase[s] trend identification accuracy” because it
identifies different trends that are “specific to the particular statistical model
associated with the corresponding report,” and as such, independent claim 1
“improve[s] the functioning of a computer or an improvement to another
technology or technical field.” Appeal Br. 19.
However, as discussed above, these steps amount to insignificant
extra-solution activity or the abstract idea itself rather than an improvement
to the functioning of a computer or an improvement to another technology or
technical field. There is no indication that the steps require any specialized
computer or inventive computer components; the Specification indicates just
the opposite. See, e.g., Spec. ¶¶ 15–19. Instead, the improvement is to the
abstract idea of trend analysis of communication data. See Elec. Power, 830
F.3d at 1355 (gathering, sending, monitoring, analyzing, selecting, and
presenting information does not transform the abstract process into a patent-
eligible invention); Trading Techs. Int’l, Inc. v. IBG LLC, 921 F.3d
1084, 1093 (Fed. Cir. 2019) (data gathering and displaying are well-
understood, routine, and conventional activities); OIP Techs, 788 F.3d at
1363 (claims reciting, inter alia, sending messages over a network, gathering
statistics, using a computerized system to automatically determine an
estimated outcome, and presenting offers to potential customers found to
merely recite “‘well-understood, routine conventional activit[ies],’ either by
requiring conventional computer activities or routine data-gathering steps”
(alteration in original)).
Viewed as a whole, independent claim 1 recites a series of steps for
providing automated trend identification by receiving and selecting data of
interest, analyzing the data, and identifying a particular trend of interest
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based on the received data. The steps of the method are performed by a
conventional computer that merely includes instructions to implement an
abstract idea on a computer (or merely uses a computer as a tool to perform
an abstract idea). Instead of improving the computer or technology or
technical field, independent claim 1 amounts to nothing significantly more
than an instruction to apply the abstract idea using generic computing
elements, which, under our precedents, is not enough to transform an
abstract idea into a patent-eligible invention. See Alice, 573 U.S. at 226.
We are not persuaded, on the present record, that the Examiner erred
in rejecting independent claim 1 under 35 U.S.C. § 101. Therefore, we
sustain the Examiner’s rejection of independent claims 1, 10, and 19 and
claims 5, 6, 8, 9, 14, 15, 17, 18, and 20–28, which fall with independent
claim 1.

Obviousness
Independent claims 1, 10, and 19, and dependent claims 5, 6, 8,
9, 14, 15, 17, 18, and 20–28
We are not persuaded of error by Appellant’s argument that the
combination of Parnaby, Shilman, Adams, and MacGregor fails to disclose
or suggest limitations [b], [e], and [f], as recited by independent claim 1, and
similarly recited by independent claims 10 and 19. Appeal Br. 20–27.
Instead, we agree with the Examiner that the combination of Parnaby,
Shilman, Adams, and MacGregor discloses the argued limitations. See Final
Act. 7–25, 32 (citing Parnaby ¶¶ 10, 22, 35, 37, 39–41, 47, 60, 64, 68, 98,
112, 122, claim 13; Shilman ¶¶ 6, 27, 28, 31–34, 38, 45, 49, 51; Adams ¶¶ 3,
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15, 16, 25–28, 45–47, 54, 59, 61, 63; MacGregor 6: 8–25, 40–55, 10: 63–67,
11: 38–60, 16: 59–67, 17: 1–55, 19:1–35); Ans. 6–10.
In making this determination, we note that Adams is directed to trend
identification and reporting which includes “gathering data from multiple
social media sources.” Adams ¶ 3. Adams describes that “[t]rending data is
identified based at least in part on an analysis of the gathered data. The
trending data is classified into categories. Data similarity between the
trending data in a respective category is measured to create groups.” Id.
Adams discloses that reporting groups can include “controls for exploring
top trending issues, groupings, [and] top trending issues in groups,” and can
also include “presenting trend data for one or more issues including metrics
for determining how far outside a predetermined normal distribution a
specific bigram associated with an issue occurred.” Id. ¶ 4.5 Adams
describes a control “that is selectable by a user from a support service screen
504a. The user may select the control 502 to become informed of new or
existing trends, including to obtain information about the size and scope of a
trending issue.” Id. ¶ 42. More particularly, Adams describes “specific
categories of trending information can be selectable by any of the
categories.” Id. ¶ 43. Adams discloses a statistics area that “can present
various statistics associated with the identified trends.” Id. ¶ 47.
MacGregor is directed to a system for identifying patterns in a multi-
dimensional database that defines “a plurality of statistical tests
corresponding to a plurality of patterns, applying the plurality of statistical
tests to a plurality of data vectors comprising the multi-dimensional

5 Adams describes that “bigrams” as “significant terms.” Adams ¶ 4.
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database to identify the plurality of patterns.” MacGregor, 2:1–7.
MacGregor also discloses
The user-interface module 122 indicates one or more patterns
identified in the multi-dimensional database 110. The user-
interface module 122 provides reports or graphs to facilitate
understanding by a user. In addition to providing such reports or
graphs, the user-interface module 122 allows the user to specify
a variety of processing options used to identify one or more
patterns.
Id. at 3:35–53. MacGregor describes statistical tests associated with the
patterns include: Regression Analysis and Rank Correlation test and
includes Linear Regression Analysis (id. at 11:45–60); Tukeys test and
Standard Deviation test (id. at 6:50–55); and Runs test, Autocorrelation test,
and Mean Squared Successive Difference test (id. at 8:30–35), for example.
Shilman is directed to a system for aggregating and summarizing
product/topic sentiment, which includes the process of obtaining snippets of
text from collected documents which “are analyzed for relevance, sentiment,
credibility and other aspects.” Shilman Abstract. In Shilman, “[f]eature
vectors are computed for snippets” and “[s]tatistical analysis is performed on
the feature vectors.” Id. More specifically, Shilman discloses that “a query
is received 605 by the user interaction module 360 from a user. The query
provides terms from a topic. The relevance analyzer 335 identifies 610
snippets relevant to the topic,” (id. ¶ 47), and that “analysis subsystem 235
analyzes 420 the information aggregated 410 to compute quality metrics for
products and topics.” Id. ¶ 36. Shilman further discloses that the relevance
analyzer
uses one or more of these criteria for computing components of
feature vectors for each snippet: (1) Presence or absence of any
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of a set of one or more hand-specified regular expressions for
that topic. (2) Presence or absence of the most frequent K
unigrams, bigrams, and trigrams (K=10,000). (3) Presence or
absence of the most frequent K unigrams, bigrams, and trigrams
annotated with part-of-speech information, as computed using an
off-the-shelf part of speech tagger (K=300). (4) Matching of the
product metadata to any of a set of boolean predicates on product
metadata (“type=DSLR AND (price<1000 OR brand=Acme)”).
Other criteria can be considered for evaluating the relevance
score, for example, heuristics such as length of snippet, a scalar
value based on the length of the snippet, the number of instances
of a phrase in a snippet, a measure of the proximity of a phrase
to the start or the end of the snippet, the value of product
attributes. In general, any boolean expression on the comparison
of any scalar feature to a predefined threshold, set predicates on
product metadata, presence or absence of phrases in the body of
the text, part of speech tags, parse tree tags, and so on.
Id. ¶ 49.
Shilman also discloses that “[r]elevant pieces of the information are
extracted from the data retrieved from the diverse set of sources and stored,”
and that “[p]roduct information gathered by aggregation may be normalized
into a single unified representation.” Shilman ¶ 27. More particularly,
Shilman discloses that a “normalized data store 305 contains a cleaned
representation of the data acquired from the web suitable for consumption by
the analysis subsystem 235,” and that a “content extractor 320 extracts
content relevant to computing quality scores for products that may be
presented to the user.” Id. ¶ 31. Shilman describes “given a topic, a set of
products, a set of reviews (or any other text) that discusses those products,
and a set of metadata about the products such as prices and specifications,
the analysis determines a normalized score (e.g. ranging from 0 to 100) for
each product with respect to the topic.” Id. ¶ 38.
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Appellant first argues that the combination of Parnaby, Shilman,
Adams, and MacGregor fails to disclose or suggest “a report selection,
where each of the reports represent a different type of trend, and each of the
reports is associated with a particular statistical model that is used to
evaluate the corresponding report,” as recited by limitation [f] of
independent claim 1, and similarly recited by independent claims 10 and 19.
Appeal Br. 20–23 (emphasis omitted). Appellant acknowledges that Adams
teaches “various statistics associated with the identified trends” (id. at 21
(emphasis omitted)), but argues that Adams “only discloses identifying a
single type of trend,” and thus only “identifies whether a trend is occurring if
the frequency of occurrence of a significant term exceeds historical levels”
which is “substantially similar to solutions discussed” with respect to known
approaches in the art. Id. at 21–22.
However, we disagree with Appellant that Adams teaches only
reporting a single type of trend. Here, as the Examiner points out, Adams
teaches “gathering and reporting of different trends of data such as hourly
and other thresholds” and “reporting trends.” Ans. 7 (citing Adams ¶¶ 28,
59). We also note that Adams describes presenting an interface “in which
specific trending information can be selected for presentation” (Adams ¶ 61)
and presenting a screen in which “specific categories of trending information
can be selectable” and upon selection, “present specific trending information
related to the selected particular category of interest.” Id. ¶ 43. Adams
discloses “presenting trend data for one or more issues including metrics for
determining how far outside a predetermined normal distribution a specific
bigram associated with an issue occurred.” Id. ¶ 63. Adams further
discloses providing information for a subject of interest that includes a
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normal frequency which “can be a mean term frequency,” a current
frequency, a difference of frequencies, and category probability. Id. ¶ 46.
We also note that Adams was not relied upon solely for teaching the
report selection. The Examiner also relies on MacGregor (see, e.g.,
MacGregor, 6:8–25 and 40–55, 10:63–67, 11:38–60, 19:29–35) to address
reporting trends. Final Act. 23–24, Ans. 7. In this regard, MacGregor
describes providing the user with the ability to select patterns, which are
associated with particular statistical tests, to be searched for in the database
for the reports provided. MacGregor, 3:35–53. Thus, one of ordinary skill
would recognize that the combination of references teaches receiving a
report selection of one or more reports, each of the reports represents a
different type of trend, and each of the reports is associated with a particular
statistical model that is used to evaluate the corresponding report.
Appellant next argues that the combination of Parnaby, Shilman,
Adams, and MacGregor fails to disclose or suggest “a plurality of the recited
reports,” as recited by limitation[f] of independent claim 1, and similarly
recited by independent claims 10 and 19. Appeal Br. 23 (emphasis omitted).
More particularly, Appellant argues that the asserted combination fails to
disclose or suggest that the reports include a plurality of: “5) a weekly or
monthly periodic pattern report . . . wherein the particular statistical model . .
. is a standard deviation ratio.” Final Act. 23 (emphasis omitted). Although
we agree with Appellant that the statistical test to define a periodic pattern is
the Autocorrelation test (citing MacGregor, 14:55–59), MacGregor
nonetheless discloses that a “general trends report is a linear regression and
significance tests.” Final Act. 23 (citing MacGregor, 11:38–60, 19:29–35).
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Appellant also argues that none of the applied art teaches a modularity
selection defining a feature. Appellant acknowledges that
Shilman discloses that “In one embodiment, sentiment and
relevance analysis are combined into a single process. ...In
another embodiment sentiment analysis is computed as a
separate process... Separating the two processes has practical
benefits, for example, the relevance analysis can be performed
for each topic, whereas the sentiment analysis can be performed
for a category of topics or at a global level... The sentiment
analyzer 340 can perform sentiment analysis at different levels
of granularity: (1) for each topic, (2) for a topic category, (3) for
all topics at a global level, (4) combinations of the first three
model so as to get the best approach for a given context.”
Appeal Br. 25–26 (citing Shilman ¶ 51). However, Appellant argues that the
“disclosure of performing an analysis at different levels of granularity does
not provide any teaching of receiving a modularity selection defining a
feature,” and that “Shilman does not provide any teaching of receiving a
selection at all.” Id. at 26. We disagree.
Instead, we agree with the Examiner that Shilman “describes
modularity selection for defining a feature by analyzing data, by separating
and combining data for modeling.” Final Act. 14–15. We also note that
Shilman describes receiving a query providing “terms from a topic” and
identifying “snippets relevant to the topic.” Shilman ¶ 47. Shilman
discloses that a “snippet of text corresponds to a portion of the text
describing a product with respect to the topic,” and that
[a] feature vector representing the relevance of snippet with
respect to the topic is computed for each identified snippet. A
relevance score for each identified snippet is determined based
on statistical analysis of the feature vectors associated with the
snippets. In some embodiments, the feature vector components
are computed by matching patterns describing the topic.
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Id. ¶ 6; cf. Spec. ¶ 23 (“The modularity selection may include the selecting
of one or more features which will be investigated for trends . . . example of
the features include relations, group clusters, and micro patterns” and
“[s]cripts are strings of multiple terms that are standardized . . .
[m]icropatterns are flexible templates that capture a relatively short
concept.”). Shilman also discloses performing statistical analysis for the
snippet “for example, using classification or regression techniques.”
Shilman ¶ 52. Thus, we agree with the Examiner that Shilman discloses
argued limitation [b].
Appellant last argues that the combination of Parnaby, Shilman,
Adams, and MacGregor fails to disclose or suggest “normalizing counts of
instances of the feature with an amount of communication data,” as recited
by limitation [e] of independent claim 1, and similarly recited by
independent claims 10 and 19. Appeal Br. 26 (emphasis omitted).
Appellant asserts that the disclosure including the word “normalized” is
irrelevant to the claim limitation because what is being normalized in
Shilman is the identification of a product. Appeal Br. 26–27. However, we
agree with Examiner that Shilman describes “normalized instances in the
normalized data store that has a normalized representation instance for all
products and related data used as input by the analysis subsystem and
display subsystem.” Final Act. 15–17. Shilman discloses a system that
normalizes information gathered from a diverse set of sources. Shilman
¶ 27. Shilman also discloses a “normalized data store 305 contains a cleaned
representation of the data acquired from the web suitable for consumption by
the analysis subsystem 235 and display subsystem 240” (id. ¶ 31), and a
content extractor that performs normalization in which the “normalized
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representation of all product and related data used as input by the analysis
subsystem 235 and display subsystem 240 is stored in the normalized data
store 305.” Id. ¶ 37. Shilman further describes the analysis subsystem
includes a relevance analyzer, a sentiment analyzer, and a quality score
computation module, (id. ¶ 32) and that the “analysis subsystem 235
analyzes 420 the information aggregated 410 to compute quality metrics for
products and topics.” Id. ¶ 36. We also note that Shilman describes
computing a quality score. Id. ¶ 64. Thus, we agree with the Examiner that
Shilman discloses argued limitation [e].
In view of the foregoing, we sustain the Examiner’s rejection of
independent claims 1, 10, and 19. We also sustain the rejections of
dependent claims 5, 6, 8, 9, 14, 15, 17, 18, and 20–28 because Appellant has
not argued the separate patentability of these claims.

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CONCLUSION
In summary:
Claim(s)
Rejected
35 U.S.C. § Reference(s)/Basis Affirmed Reversed
1, 5, 6, 8–
10, 14, 15,
17–28
101 Eligibility 1, 5, 6, 8–10,
14, 15, 17–28

1, 5, 6, 8–
10, 14, 15,
17–28
103 Parnaby, Shilman,
Adams, MacGregor
1, 5, 6, 8–10,
14, 15, 17–28

Overall
Outcome
1, 5, 6, 8–10,
14, 15, 17–28

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).

AFFIRMED