Facebook, Inc.

Patent Trials and Appeals Board

May 21, 2021

2020000527 (P.T.A.B. May. 21, 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.
14/616,543 02/06/2015 Rituraj Kirti 2006.032US1 1171
160546 7590 05/21/2021
Mannava & Kang, P. C.
3201 Jermantown Road
Suite 525
Fairfax, VA 22030
EXAMINER
STROUD, CHRISTOPHER
ART UNIT PAPER NUMBER
3688
NOTIFICATION DATE DELIVERY MODE
05/21/2021 ELECTRONIC
Please find below and/or attached an Office communication concerning this application or proceeding.
The time period for reply, if any, is set in the attached communication.
Notice of the Office communication was sent electronically on above-indicated "Notification Date" to the
following e-mail address(es):
docketing@mannavakang.com
fb-pdoc@fb.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
____________________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________________

Ex parte RITURAJ KIRTI, SUE ANN HONG,
and
LEON R. CHO
____________________

Appeal 2020-000527
Application 14/616,543
Technology Center 3600
____________________

Before JOSEPH L. DIXON, DAVID M. KOHUT, and
JON M. JURGOVAN, Administrative Patent Judges.

JURGOVAN, Administrative Patent Judge.

DECISION ON APPEAL
Appellant1 seeks review under 35 U.S.C. § 134(a) from a final
rejection of claims 1, 3–10, 12–17, 19, and 20.2 We have jurisdiction under
35 U.S.C. § 6(b).
We AFFIRM.3

1 We use the word “Appellant” to refer to “applicant” as defined in
37 C.F.R. § 1.42. The real party in interest is Facebook, Inc. (Appeal Br. 2.)
2 Claims 2, 11, and 18 were canceled. (Final Act. 1.)
3 Our Decision refers to the Specification (“Spec.”) filed February 6, 2015,
Final Office Action (“Final Act.”) mailed March 14, 2019, the Appeal Brief
(“Appeal Br.”) filed July 25, 2019, the Examiner’s Answer (“Ans.”) mailed
August 29, 2019, and the Reply Brief (“Reply Br.”) filed October 29, 2019.
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CLAIMED INVENTION
The claims are directed to computer-implemented methods and
program products for “determining a number of cluster groups associated
with content identifying users eligible to receive the content” (e.g.,
advertisement content including text, images, videos, and/or animation), and
increasing a number of users eligible to be presented with the content.
(Spec. ¶ 26; Title (capitalization altered); Abstr.)
Independent claim 1, reproduced below, is illustrative of the claimed
subject matter:
1. A computer-implemented method comprising:
receiving an advertisement request including a first set of
targeting criteria and a second set of targeting criteria at an online
system, wherein the advertisement request further includes first
advertisement content associated with the first set of targeting
criteria and second advertisement content associated with the
second set of targeting criteria;
retrieving characteristics of a plurality of users of the
online system;
identifying a first targeting group that comprises a first
subset of users from the plurality of users of the online system
having one or more characteristics that satisfy the first set of
targeting criteria;
identifying a second targeting group that comprises a
second subset of users from the plurality of users of the online
system having one or more characteristics that satisfy the second
set of targeting criteria;
generating a first cluster group by applying a first cluster
model to the characteristics of at least some of the plurality of
users of the online system who are not in the first targeting group,
where the first cluster model is a machine learning model that is
trained to determine membership in the first cluster group using
the first subset of users as a training set, wherein the first cluster
model outputs a score for inclusion in the first cluster group
based on the users in the first targeting group, and wherein at
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least one of the at least some of the plurality of users of the online
system who are not in the first targeting group is added to the
first cluster group responsive to the score for inclusion output by
application of the first cluster model to characteristics of the at
least one user exceeding a threshold score;
generating a second cluster group by applying a second
cluster model to the characteristics of at least some of the
plurality of users of the online system who are not in the second
targeting group, where the second cluster model is a machine
learning model that is trained to determine membership in the
second cluster group using the second subset of users as a
training set, wherein the second cluster model outputs a score for
inclusion in the second cluster group based on the users in the
second targeting group wherein at least one of the at least some
of the plurality of users of the online system who are not in the
second targeting group is added to the second cluster group
responsive to the score for inclusion output by application of the
second cluster model to characteristics of the at least one user
exceeding a threshold score;
determining a first amount of overlap between the first
cluster group and the second cluster group, the first amount of
overlap based at least in part on a number of users included in
both the first cluster group and the second cluster group and
comprising at least one user included in both the first cluster
group and the second cluster group;
determining whether the first amount of overlap equals or
exceeds a first threshold amount of overlap;
responsive to the first amount equaling or exceeding the
first threshold amount of overlap:
combining the first and second cluster groups into an
overall cluster group; and
labeling each user in the overall cluster group with a
classifier, wherein users in the overall cluster group from
the first cluster group are each labeled with a first classifier
and users in the overall cluster group from the second
cluster group are each labeled with a second classifier; and
sending for presentation to at least one user in the overall
cluster group either the first advertisement content or the second
advertisement content based on the classifier labeling the at least
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one user, wherein users labeled with the first classifier are sent
the first advertisement content and users labeled with the second
classifier are sent the second advertisement content.

(Appeal Br. 21–23 (Claims App.).)

REJECTIONS4 & REFERENCES
Claims 1, 3–10, 12–17, 19, and 20 stand rejected under
35 U.S.C. § 112(a) for failing to comply with the written description
requirement. (Final Act. 2–3.)
Claims 1, 3–10, 12–17, 19, and 20 stand rejected under
35 U.S.C. § 101 as directed to non-statutory subject matter. (Final Act. 4–
7.)
Claims 1, 8–10, and 17 stand rejected under 35 U.S.C. § 103 based on
Zohar et al. (US 2014/0304066 A1, published Oct. 9, 2014) (“Zohar”),
Chang et al. (US 2011/0106611 A1, published May 5, 2011) (“Chang”), Joa
et al. (US 2014/0257933 A1, published Sept. 11, 2014) (“Joa”), and Li et al.
(US 2013/0124298 A1, published May 16, 2013) (“Li”). (Final Act. 8–12.)
Claims 3–7, 12–16, 19, and 20 stand rejected under 35 U.S.C. § 103
based on Zohar, Chang, Joa, Li, and Gunawardana et al. (US 2009/0327032
A1, published Dec. 31, 2009) (“Gunawardana”). (Final Act. 13–15.)

4 Claims 17, 19, and 20 were rejected under 35 U.S.C. § 112(b), as being
indefinite. (Final Act. 4.) However, this rejection was withdrawn in the
Examiner’s Advisory Action (mailed May 15, 2019), and is no longer
pending on appeal. (Advisory Act. 1.)
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ANALYSIS
Standard of Review
We undertake a limited de novo review of the appealed rejections 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
(BPAI 2010) (precedential).

Section 112(a) Rejection
Section 112(a) requires that
[t]he specification shall contain a written description of the
invention, and of the manner and process of making and using it,
in such full, clear, concise, and exact terms as to enable any
person skilled in the art to which it pertains . . . to make and use
the same.5

The written description must reasonably convey to those skilled in the
art that the inventor had possession of the claimed subject matter as of the
filing date. Ariad Pharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1351 (Fed.
Cir. 2010) (en banc); D Three Enterprises, LLC v. SunModo Corp., 890 F.3d
1042, 1047 (Fed. Cir. 2018).
The Examiner finds the Specification lacks written description support
for “a machine learning model that is trained to determine membership in
the first [or second] cluster group using the first [or second] subset of users
as a training set,” as recited in claim 1, and similarly in claims 10 and 17.

5 The Leahy-Smith America Invents Act, Pub. L. No. 112-29, 125 Stat. 284
(September 16, 2011) (“AIA”), included revisions to 35 U.S.C. § 112 that
became effective on September 16, 2012. Because the present patent
application was filed on February 6, 2015, AIA § 112 applies to this
application.
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(Final Act. 3; Ans. 3–5.) In particular, the Examiner asserts that Appellant’s
Specification “has not supplied the machine learning algorithm nor does it
state how the machine learning algorithm determines a score for determining
membership in a cluster” (Final Act. 3) and merely provides
a generic statement that basically says we can do cluster
modeling with machine learning. . . . mak[ing] reference to
required weighting, input, hidden, and output layers without
providing any information what so ever as to how these functions
would be achieved. One of ordinary skill in the art would simply
not be able to replicate the specific algorithm required to
implement the invention. While the concept of machine learning
may be well known to one of ordinary skill in the art, the
specifically claimed algorithm has not been supplied and thus it
has not been shown that applicant has possession of the invention

(Ans. 4).
Appellant argues the Specification provides written description
support under § 112 for the claimed “machine learning model that is trained
to determine membership in the first cluster group using the first subset of
users as a training set” and “machine learning model that is trained to
determine membership in the second cluster group using the second subset
of users as a training set.” (Appeal Br. 10–11 (citing Spec. ¶¶ 6, 53–54, 62–
65); Reply Br. 2–4.) In particular, Appellant argues the Specification
demonstrates Appellant possessed the claimed machine learning model(s)
because the Specification provides sufficient implementation details—
including the model’s training input, the desired model output (a prediction
of whether a user is in a cluster group), and how the model is used to cluster
users—for
one of skill in the art . . . to build the claimed cluster models. One
of skill in the art can select any appropriate type of model (e.g.,
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a neural network or a linear regression). The algorithms for
training known machine learning models (e.g., backpropagating
a neural network or fitting a linear regression) are well known.

(Appeal Br. 10–11; Reply Br. 4–5.) We agree with Appellant’s arguments.
As Appellant explains, machine learning algorithms are known in the art,
such algorithms known to input and fit training data and determine scores
for new data based on the training. (Appeal Br. 9–10; Reply Br. 4.) Claim 1
broadly recites a machine learning model that is trained to determine
membership using a set of users as a training set, and Appellant’s
Specification sufficiently discloses a machine learning model (see Spec.
¶¶ 70, 73, 74, “unsupervised machine learning algorithm,” “artificial neural
network”) that is trained (using, e.g., “weights of connections between input,
hidden, and output layers of the neural network,” “parameters . . .
determined using an iterative process to minimize a cost function over the
training data,” and “weights applied to various characteristics of a user,” see
Spec. ¶¶ 62, 70, 73) using training set(s) of users (defined by personal
characteristics/interests, see Spec. ¶¶ 20, 22, 53, 70, 72–74), to determine
membership (e.g., users’ affinity or interest for various content types, see
Spec. ¶¶ 6, 53, 62–63, 69, 74). (Appeal Br. 9–10; Reply Br. 3–4; see Fonar
Corp. v. Gen. Elec. Co., 107 F.3d 1543, 1549 (Fed. Cir. 1997) (“As a
general rule . . . writing code for [] software is within the skill of the art, not
requiring undue experimentation, once its functions have been disclosed. . . .
[and] flow charts or source code listings are not a requirement for adequately
disclosing the functions of software.”); Hybritech Inc. v. Monoclonal
Antibodies, Inc., 802 F.2d 1367, 1379–80, 1384 (Fed. Cir. 1986)
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(information that is conventional or well known in the art need not be
described in detail in the specification).)
We, therefore, find there is written description support for the claimed
machine learning model(s) trained to determine membership in clusters
using user groups as training sets, and do not sustain the Examiner’s
§ 112(a) rejection of independent claims 1, 10, and 17, and claims 3–9, 12–
16, 19, and 20, depending therefrom.

§ 101 Rejection
Patent eligibility is a question of law that is reviewable de novo.
Dealertrack, Inc. v. Huber, 674 F.3d 1315, 1333 (Fed. Cir. 2012).
Accordingly, we review the Examiner’s § 101 determinations concerning
patent eligibility under this standard.
Patentable subject matter is defined by 35 U.S.C. § 101, as follows:
Whoever invents or discovers any new and useful process,
machine, manufacture, or composition of matter, or any new and
useful improvement thereof, may obtain a patent therefor, subject
to the conditions and requirements of this title.
In interpreting this statute, the Supreme Court emphasizes that patent
protection should not preempt “the basic tools of scientific and technological
work.” Gottschalk v. Benson, 409 U.S. 63, 67 (1972) (“Benson”); Mayo
Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 71 (2012)
(“Mayo”); Alice Corp. v. CLS Bank Int’l, 573 U.S. 208, 217–18 (2014)
(“Alice”). The rationale is that patents directed to basic building blocks of
technology would not “promote the progress of science and useful arts”
under the U.S. Constitution, Article I, Section 8, Clause 8, but instead would
impede it. Accordingly, laws of nature, natural phenomena, and abstract
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ideas, are not patent-eligible subject matter. Thales Visionix Inc. v. United
States, 850 F.3d 1343, 1346 (Fed. Cir. 2017) (citing Alice, 573 U.S. at 216–
17).
The Supreme Court set forth a two-part test for subject matter
eligibility in Alice (573 U.S. at 217–19). The first step is to determine
whether the claim is directed to a patent-ineligible concept. Id. (citing
Mayo, 566 U.S. at 76–77). If so, then the eligibility analysis proceeds to the
second step of the Alice/Mayo test in which we “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 (quoting Mayo, 566 U.S. at 72, 79). There is no need
to proceed to the second step, however, if the first step of the Alice/Mayo
test yields a determination that the claim is directed to patent eligible subject
matter.
The Patent Office has revised its guidance for how to apply the
Alice/Mayo test in the 2019 Revised Patent Subject Matter Eligibility
Guidance, 84 Fed. Reg. 50 (January 7, 2019) (the “Revised Guidance”).
Under the Revised Guidance, we first look to whether the claim recites:
(1) any judicial exceptions, including certain groupings of
abstract ideas (i.e., mathematical concepts, mental processes, or
certain methods of organizing human activity such as a
fundamental economic practice or managing personal behavior
or relationships or interactions between people); and
(2) additional elements that integrate the judicial exception into
a practical application (see Manual of Patent Examining
Procedure (“MPEP”) § 2106.05(a)–(c), (e)–(h)). 84 Fed. Reg. at
51–52, 55.
A claim that integrates a judicial exception into a practical application
applies, relies on, or uses the judicial exception in a manner that imposes a
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meaningful limit on the judicial exception, such that the claim is more than a
drafting effort designed to monopolize the judicial exception. Revised
Guidance, 84 Fed. Reg. at 54. When the judicial exception is so integrated,
then the claim is not directed to a judicial exception and is patent-eligible
under § 101. Revised Guidance, 84 Fed. Reg. at 54. Only if a claim (1)
recites a judicial exception and (2) does not integrate that exception into a
practical application, do we then evaluate whether the claim provides an
inventive concept. Revised Guidance, 84 Fed. Reg. at 56; Alice, 573 U.S. at
217–19, 221. Evaluation of the inventive concept involves consideration of
whether an additional element or combination of elements (1) adds a specific
limitation or combination of limitations that are not well-understood,
routine, conventional activity in the field, which is indicative that an
inventive concept may be present; or (2) simply appends well-understood,
routine, conventional activities previously known to the industry, specified
at a high level of generality, to the judicial exception, which is indicative
that an inventive concept may not be present.
USPTO Step 1 – Do the claims recite a statutory category of invention?
Claims 1, 3–10, and 12–16 recite computer-implemented methods that
qualify as “processes” under 35 U.S.C. § 101, so USPTO Step 1 of the
patent eligibility analysis is satisfied. Claims 17, 19, and 20 recite computer
program products which qualify as articles of manufacture under 35 U.S.C.
§ 101. Accordingly, the claims satisfy USPTO Step 1. We proceed in our
analysis to the first step of Alice/Mayo.

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Alice/Mayo—Step 1 (Abstract Idea)
USPTO Step 2A–Prongs 1 and 2 identified in the Revised Guidance

USPTO Step 2A—Prong 1 (Does the Claim Recite a Judicial Exception?)
Turning to the first step of the Alice inquiry (Step 2A, Prong 1 of the
Revised Guidance), the Examiner finds independent claim 1, and similarly,
independent claims 10 and 17, are directed to an abstract idea because the
claims recite
a process that would be performed precisely the same way with
or without a computer. The claims involve the steps of receiving
ad requests with targeting criteria, retrieving characteristics of
users, identifying a targeting group with those characteristics,
analyzing other users to see if they are similar enough to be put
in a cluster, examining clusters to see if there is a significant
amount of overlap in the clusters, combine the groups if enough
overlap exists, labeling the users of the cluster, and then
providing an advertisement to a member of the cluster based on
the identifier. All of these steps do not require the use of a
computer. They would be performed exactly the same with in the
human mind or with pencil and paper. Thus all of the grouping
and data analysis are in fact a mental process.

(Ans. 6.) The Examiner reasons that

[the claimed] process . . . under its broadest reasonable
interpretation, cover[sic] performance of the limitations in the
mind but for the recitation of a generic computing environment
of “an online system” and generic mention of “machine
learning.” Machine learning is in and of itself simply an iterative
process of reevaluating as more data comes in. In particular,
recited this generically it is little more than automating the same
process that can be done by a human in the exact same manner.
But for the recitation of the “online system” the steps can be
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performed mentally in the human mind or by a human analog
with pen and paper.

(Final Act. 6–7.)
Appellant contends the Examiner erred in rejecting claims 1, 10, and
17 under 35 U.S.C. § 101 as directed to non-statutory subject matter because
the claims are not directed to an abstract idea. (Appeal Br. 11–16
(referencing “Claims 1, 10, and 17” and submitting arguments for
limitations recited in claim 1); Reply Br. 5–7.) Particularly, Appellant
contends “[t]he claims . . . do not recite a mental process,” and “integrate the
machine learning technique into a practical application” for “addressing
overlap among cluster groups, which is a problem that arises due to the use
of machine learning to generate the expanded cluster.” (Appeal Br. 12, 15–
16; see also Reply Br. 5–6.) Appellant further argues “the claimed invention
is not merely ‘machine learning’ in the abstract, but rather an integration of
the concept into a practical application” that “uses machine learning to find
an expanded cluster group of users that are similar to an existing seed group
of users (a targeting group) . . . for multiple seed groups.” (Reply Br. 6–7.)
Appellant’s arguments do not persuade us that the Examiner erred in
finding claim 1 (and claims 10 and 17 reciting similar limitations) recites an
abstract idea and, therefore, we concur with the Examiner’s conclusion that
the claim recites an abstract idea of distributing targeted advertisements by a
process performable in the human mind or with pen and paper. (Final Act.
4–7; Ans. 6–8.) Under its broadest reasonable interpretation, claim 1 recites
an abstract mental process of determining user groups to which ads are to be
distributed. (Final Act. 6; Ans. 6–7.) In particular, claim 1 recites an
abstract mental process of grouping users to receive targeted ads by:
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collecting/gathering information (claimed “receiving an advertisement
request including a first set of targeting criteria and a second set of targeting
criteria” as well as “first advertisement content associated with the first set
of targeting criteria and second advertisement content associated with the
second set of targeting criteria,” and “retrieving characteristics of a plurality
of users”), analyzing the information (claimed “identifying a first targeting
group that comprises a first subset of users from the plurality of users . . .
having one or more characteristics that satisfy the first set of targeting
criteria,” “identifying a second targeting group that comprises a second
subset of users from the plurality of users . . . having one or more
characteristics that satisfy the second set of targeting criteria,” “generating a
first [or second] cluster group by applying a first [or second] cluster model
to the characteristics of at least some of the plurality of users . . . who are not
in the first [or second] targeting group” with the first/second cluster model
outputting “a score for inclusion in the first [or second] cluster group based
on the users in the first [or second] targeting group,” the first and second
cluster models being a model “trained to determine membership in the first
[or second] cluster group using the first [or second] subset of users as a
training set,” adding “at least one of the at least some of the plurality of
users . . . who are not in the first [or second] targeting group . . . to the first
[or second] cluster group” responsive to a score “output by application of the
first [or second] cluster model to characteristics of the at least one user
exceeding a threshold score,” “determining a first amount of overlap
between the first cluster group and the second cluster group,” “determining
whether the first amount of overlap equals or exceeds a first threshold
amount of overlap,” and “responsive to the first amount equaling or
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exceeding the first threshold amount of overlap: combining the first and
second cluster groups into an overall cluster group” and “labeling each user
in the overall cluster group with a classifier, wherein users in the overall
cluster group from the first cluster group are each labeled with a first
classifier and users in the overall cluster group from the second cluster group
are each labeled with a second classifier”), and providing results of the
collection and analysis (“sending for presentation to at least one user in the
overall cluster group either the first advertisement content or the second
advertisement content based on the classifier labeling” whereby “users
labeled with the first classifier are sent the first advertisement content and
users labeled with the second classifier are sent the second advertisement
content”).
That is, although claim 1 recites users of “an online system,”
advertisement requests received at “the online system,” and “a machine
learning model” for the first and second cluster models, the underlying
operations recited in the claim are acts that could be performed mentally and
by pen and paper, without the use of an online system or machine learning.
(Ans. 6.) For example, a person could (i) manually retrieve and analyze sets
of targeting criteria and characteristics of users, (ii) manually identify first
and second targeting groups, use cluster model(s) trained to determine
membership in cluster groups to generate first and second cluster groups by
applying the models to characteristics of some users that are not in particular
targeting group(s), and manually determine scores for inclusion in respective
cluster groups, (iii) manually add at least one user not in the first or second
targeting group to the first or second cluster group, based on the scores, (iv)
manually determine an overlap between cluster groups and, if the overlap
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equals or exceeds a threshold, manually generate an overall cluster group by
combining cluster groups, and manually label users in the overall cluster
group with classifiers based on the originating cluster group, and (v)
manually assign first or second ads to at least one user in the overall cluster
group, based on the user’s classifier. (See Ans. 6, 8; Final Act. 6.) Our
reviewing court has concluded that mental processes include similar
concepts of collecting, manipulating, and providing, data. See Intellectual
Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1340 (Fed. Cir.
2017) (the Federal Circuit held “the concept of . . . collecting data, . . .
recognizing certain data within the collected data set, and . . . storing that
recognized data in a memory” ineligible); and Electric Power Grp., LLC v.
Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (merely selecting
information, by content or source, for collection, analysis, and display does
nothing significant to differentiate a process from ordinary mental
processes). Claim 1’s “online system” merely automates data input and
output, and claim 1’s recitation of a “machine learning model” merely
references an automation of actions that are manually performable (e.g.,
actions of determining membership in clusters based on characteristics of
pre-selected training sets). However, mental processes remain unpatentable
even when automated to reduce the burden on the user of what once could
have been done with pen and paper. See CyberSource Corp. v. Retail
Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011) (“That purely mental
processes can be unpatentable, even when performed by a computer, was
precisely the holding of the Supreme Court in Gottschalk v. Benson.”).
Thus, Appellant’s arguments that claims 1, 10, and 17 recite elements
that cannot be characterized as mental steps or performed mentally, and that
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the claims recite more than an abstract idea, have not persuaded us the
Examiner erred in finding the claims also recite an abstract idea.6 We now
turn to USPTO Step 2A, Prong 2, of the Revised Guidance to determine
whether the abstract idea is integrated into a practical application. See
Revised Guidance, 84 Fed. Reg. at 54–55.

USPTO Step 2A—Prong 2 (Integration into Practical Application)
Under Revised Step 2A, Prong Two of the Revised Guidance, we
discern no additional element (or combination of elements) recited in
Appellant’s claims 1, 10, and 17 that may have integrated the judicial
exception into a practical application. See Revised Guidance, 84 Fed. Reg.
at 54–55. We recognize that claim 1 (and similarly, claims 10 and 17)
includes additional elements such as an operational connection to an “online
system,” and the use of cluster models that are “a machine learning model
that is trained to determine membership in the . . . cluster group using the
first [or second] subset of users as a training set.” Furthermore, our review
of Appellant’s Specification finds that the terms “online system” and
“machine learning model” are nominal. Appellant’s Specification indicates
that the “online system” and “machine learning model” (see Spec. ¶¶ 4, 19,
28, 44, 70, 73–74) of claim 1 do not recite specific types of additional
elements or their operations, thereby requiring no improvements to
computing or data processing technologies. For example, Appellant’s
claimed additional elements (“online system,” and cluster models that are

6 As the Examiner additionally notes, claim 1 also recites an abstract idea
within a grouping of “Certain methods of organizing human activity”
described in the Revised Guidance. (See Revised Guidance, 84 Fed. Reg. at
52; Ans. 6, Final Act. 6–7.)
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“machine learning model[s]”) do not: (1) improve the functioning of a
computer or other technology; (2) are not applied with any particular
machine (except for generic computing elements); (3) do not effect a
transformation of a particular article to a different state; and (4) are not
applied in any meaningful way beyond generally linking the use of the
judicial exception to a particular technological environment, such that the
claim as a whole is more than a drafting effort designed to monopolize the
exception. (See MPEP §§ 2106.05(a)–(c), (e)–(h); Ans. 7–8; Final Act. 7.)
As a result, the claim’s recited additional elements are not indicative of
“integration into a practical application,” and are not enough to distinguish
the steps of claim 1 from describing a mental process. See Revised
Guidance, 84 Fed. Reg. at 54–55.
Appellant argues claim 1 is not directed to a mental process because
the additional [claim] elements of “generating a first cluster
group . . . ,” “generating a second cluster group . . . ,”
“determining a first amount of overlap . . . ,” “determining
whether the first amount of overlap equals . . . ,” “combining the
first and second cluster groups . . . ,” and “labeling each user . . .”
integrate the machine learning technique into a practical
application.

(Appeal Br. 15.) Appellant asserts the “practical application [is] for
addressing overlap among cluster groups, which is a problem that arises due
to the use of machine learning to generate the expanded cluster.” (Id.)
Appellant further argues
[n]o one with any understanding of neural networks would
suggest that a neural network of any kind could be practically
performed in the human mind. . . . Similarly, no one with any
understanding of machine learning in general would suggest that
the steps of the present claims are practically performable in the
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human mind.

(Reply Br. 6.)
Appellant’s arguments are unpersuasive for the following reasons.
The claimed “generating a first cluster group,” “generating a second cluster
group,” “determining a first amount of overlap,” “determining whether the
first amount of overlap equals or exceeds a first threshold,” “combining the
first and second cluster groups,” and “labeling each user in the overall
cluster group” are operations readily performable in the human mind or with
pen and paper, and are part of the abstract idea (a mental process, as
discussed supra at USPTO Step 2A, Prong 1). (See Ans. 6, 8; Final Act. 6–
7.) With respect to the claimed limitations requiring the first and second
cluster models to be “a machine learning model that is trained to determine
membership in the first [or second] cluster group using the first [or second]
subset of users as a training set,” we agree with the Examiner that these
limitations are insufficient to integrate the abstract idea into a practical
application. (See Ans. 7–8; Final Act. 7.) As explained by the Examiner,
the machine learning model limitations in Appellant’s claims are not novel,
and can be reasonably seen as the conventional application of well-known
machine learning concepts to build and train a model using a training set.
(Ans. 7–8; Final Act. 7.) We agree with the Examiner that claim 1 does not
recite a specific implementation of machine learning that “ties it to a
technological process”; rather, the claim recites the generic use of machine
learning to train a model to determine group membership based on training
sets, and
[the] generic recitation of “machine learning” amounts to no
more than an iterative process in which a model is updated as
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new information is obtained and analyzed. . . . under broadest
reasonable interpretation a generic use of machine learning
implies nothing more than updating a model as more data
becomes available. . . . Further a generic recitation of “machine
learning” is equivalent to the words “apply it” on a computer or
simply instruct the computer to implement the abstract idea.

(Ans. 6–7.)
In addition, Appellant’s Specification describes the use of machine
learning in general terms, without explaining how Appellant’s “machine
learning model” would be different from known machine learning and
artificial neural network algorithms. (See, e.g., Spec. ¶¶ 70, 73–74.)
Because Appellant’s Specification provides a high-level and general
description of machine learning requiring no specialized implementation, we
agree with the Examiner that the “machine learning” limitations in claims 1,
10, and 17 do not evidence any improvements to the functioning of a
computer or any other technology or technical field. (Ans. 6–8; see MPEP
§ 2106.05(a).)
As noted above, Appellant also argues the claims “integrate the
machine learning technique into a practical application” for “addressing
overlap among cluster groups, which is a problem that arises due to the use
of machine learning to generate the expanded cluster.” (See Appeal Br. 15.)
As noted by the Examiner, however, “the problem addressed has nothing
[whatsoever] to do with machine learning or computer related issues. The
problem alleged by appellant is not a machine learning issue, it is a grouping
issue which is related to the abstract idea.” (Ans. 7.) Appellant’s
Specification does not explain why the problem of cluster group overlap
would be an exclusive result of using machine learning, or “how [this]
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problem is unique or a result of machine learning.” (Ans. 7–8.) In addition,
Appellant’s asserted improvement (addressing overlap among cluster groups
subjected to targeted advertising) is not an improvement to machine learning
or other technology, rather, it is an improvement to targeted advertising and
the use of advertising resources. (See Spec. ¶¶ 54–55.)
Appellant also seeks to analogize claim 1 to a hypothetical claim
identified as eligible in the Office’s Patent Eligibility Example 39 (Method
for Training a Neural Network for Facial Detection).7 (Appeal Br. 12–13;
Reply Br. 5–6.) As the Examiner points out, however, “[t]here are several
major differences between example 39 and the current claims.” (Ans. 5.) In
particular,
example 39 is directed to a process of digital facial detection
from images. This is a process rooted in computers similar to the
findings in McRo. . . . the claims in example 39 could not be
considered a mental process as the process was for digital facial
recognition and could not be considered organizing human
activities because it would not be performed in the same manner
if a human being were to attempt the task without a computer.
In stark contrast, the current claims provide a process that
would be performed precisely the same way with or without a
computer. . . . With regard to the added buzzword of “machine
learning,” appellant has not provided any meaningful limitation
that adds any significance to its use that ties it to a technological
process. . . . A generic recitation of “machine learning” amounts
to no more than an iterative process in which a model is updated
as new information is obtained and analyzed.

(Ans. 5–6 (emphasis added).) We agree with the Examiner’s findings.

7 Located at:
https://www.uspto.gov/sites/default/files/documents/101_examples_37to42_
20190107.pdf
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Appellant further argues that claim 1 recites additional elements that
integrate a judicial exception into a practical application because the claim is
similar to a hypothetical claim 1 identified as eligible in the Office’s Patent
Eligibility Example 42 (Method for Transmission of Notifications When
Medical Records Are Updated).8 (Appeal Br. 15–16; Reply Br. 6.) That
hypothetical claim, however, recited a method comprising steps for
converting non-standardized information dependent on the hardware and
software platform used by a user providing the information, into a
standardized format, storing the standardized updated information about a
patient’s condition in a collection of medical records in the standardized
format, and automatically generating a message containing the updated
information about the patient’s condition by a content server whenever
updated information has been stored. Thus, unlike Appellant’s claim 1
(which recites a generic use of machine learning for clustering data, e.g.,
user descriptors), the hypothetical method recites a combination of
additional elements integrating a practical application—one that provides a
specific improvement over prior art medical records management systems,
by allowing remote users to share information in real time in a standardized
format regardless of the format in which the information was input by users.
As noted by the Examiner, “[u]nlike example 42 the claimed invention
provides no improvements over prior systems and the problem addressed has
nothing what so ever to do with machine learning or computer related
issues” as the “problem alleged by appellant is not a machine learning issue,

8 Located at:
https://www.uspto.gov/sites/default/files/documents/101_examples_37to42_
20190107.pdf
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it is a grouping issue which is related to the abstract idea.” (Ans. 7–8.) We
agree with the Examiner.
Accordingly, under Step 2A, Prong 2, we conclude that claim 1, and
similarly worded independent claims 10 and 17 argued for the same reasons
do not recite “additional elements that integrate the [judicial] exception into
a practical application,” and are directed to an abstract idea in the form of a
mental process. Revised Guidance, 84 Fed. Reg. at 52, 54. Therefore, we
proceed to USPTO Step 2B, The Inventive Concept.

Alice/Mayo—Step 2 (Inventive Concept)
USPTO Step 2B identified in the Revised Guidance

USPTO Step 2B of the Alice two-step framework requires us to
determine whether any element, or combination of elements, in the claim is
sufficient to ensure that the claim amounts to significantly more than the
judicial exception. Alice, 573 U.S. at 221; see also Revised Guidance, 84
Fed. Reg. at 56. As discussed above, claim 1 includes additional elements
such as an “online system” and cluster models that are “machine learning
model[s].” We agree with the Examiner’s findings that the additional
elements of claim 1 (and similar ones in claims 10 and 17), when considered
individually and in an ordered combination, correspond to nothing more than
generic and well-known components and operations used to implement the
abstract idea. (See Final Act. 7; Ans. 7–8.) In other words, we find that the
additional elements, as claimed, are well-understood, routine, and
conventional and “behave exactly as expected according to their ordinary
use.” (See In re TLI Commc’ns LLC Patent Litig., 823 F.3d 607, 615 (Fed.
Cir. 2016); Final Act. 7; Ans. 7–8.) Thus, implementing the abstract idea
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with these generic and well-known elements “fail[s] to transform that
abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 221.
Therefore, we agree with the Examiner that claims 1, 10, and 17 do not
provide significantly more than the abstract idea itself.
Therefore, because claims 1, 10, and 17 are directed to the abstract
idea of a mental process, and do not provide significantly more than the
abstract idea itself, we agree with the Examiner that claims 1, 10, and 17 are
ineligible for patenting. We, therefore, sustain the Examiner’s § 101
rejection of independent claims 1, 10, and 17, and dependent claims 3–9,
12–16, 19, and 20 not separately argued. See 37 C.F.R. § 41.37(c)(1)(iv)
(“the failure of appellant to separately argue claims which appellant has
grouped together shall constitute a waiver of any argument that the Board
must consider the patentability of any grouped claim separately.”).

§ 103 Rejection
Title 35, section 103, provides:
A patent for a claimed invention may not be obtained,
notwithstanding that the claimed invention is not identically
disclosed as set forth in section 102, if the differences between
the claimed invention and the prior art are such that the claimed
invention as a whole would have been obvious before the
effective filing date of the claimed invention to a person having
ordinary skill in the art to which the claimed invention pertains.

The question of obviousness is resolved on the basis of underlying factual
determinations including: (1) the scope and content of the prior art; (2) any
differences between the claimed subject matter and the prior art; (3) the level
of ordinary skill in the art; and (4) where present, objective evidence of
nonobviousness. Graham v. John Deere Co., 383 U.S. 1, 17–18 (1966).
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With respect to independent claim 1, the Examiner finds Zohar’s
segments (including behavioral segments, segment combinations, and
unified combinations of segments) teach the claimed “first targeting group”
and “second targeting group.” (Final Act. 9 (citing Zohar ¶¶ 16, 84–87, 92–
93, 105).) The Examiner also finds Zohar teaches the claimed “determining
a first amount of overlap between the first cluster group and the second
cluster group . . . based at least in part on a number of users included in both
the first cluster group and the second cluster group,” because Zohar
determines a percentage overlap between users included in a behavioral
segment and users included in a unified combination. (Final Act. 9–10
(citing Zohar ¶¶ 114–117, 181–183) (emphasis omitted)). The Examiner
acknowledges “Zohar does not expressly teach generating cluster groups
including users not in targeting segments” but finds Chang’s expansion of
targeted segments by complementary user segments teaches the claimed
generation of first and second cluster groups by applying cluster models to
characteristics of at least some users who are not in first or second targeting
groups. (Final Act. 10 (citing Chang ¶¶ 14, 26–28); Ans. 8–9.) The
Examiner asserts “[i]f you combine the groups from Zohar with the
expansion of groups from Chang you have the cluster groups of the claims,”
as
[i]t would have been obvious to one of ordinary skill in the
art at the time of filing of the invention to include other members
in a group that do not meet the targeting criteria as taught by
Chang in order to expand the reach of targeted advertisements to
other potential interested groups. . . . Further, since each element
of the combination performs the same functions individually as
it does in combination one of ordinary skill in the art would find
the combination to yield predictable results.

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(Ans. 9; Final Act. 11.) We do not agree.
We agree with Appellant that Zohar and Chang, alone or in
combination, fail to teach or suggest determining an overlap between first
and second cluster groups obtained by expanding membership to users that
are similar to, but outside of, user sets of first and second targeting groups,
as recited in claim 1. (Appeal Br. 17–19; Reply Br. 8–9.) Although the
Examiner has identified a teaching of generating cluster groups by
expanding initial target groups (in Chang reference), and a teaching of
determining an overlap between targeting groups (in Zohar), the Examiner
has not provided a reason to combine the teachings of Zohar and Chang to
(1) first generate cluster groups by expanding initial targeting groups, and
then (2) determine an overlap between such cluster groups obtained by
expanding targeting groups. (Appeal Br. 18–19; Reply Br. 8–9.)
Initially, we note that—contrary to the Examiner’s assertion that
Zohar teaches the claimed “determining a first amount of overlap
between the first cluster group and the second cluster group” (see Final
Act. 9)—Zohar does not determine an overlap between cluster groups like
those recited in claim 1. (Appeal Br. 18–19; Reply Br. 8–9.) Rather, Zohar
determines an overlap between a “behavioral segment” and a “unified
combination,” neither the “behavioral segment” nor the “unified
combination” being cluster groups obtained by expanding membership to
users that are similar to, but outside of, user sets of first and second
targeting groups (as required by claim 1). (See Zohar ¶¶ 117, 181–183;
Appeal Br. 18–19; Reply Br. 8–9.) Zohar’s behavioral segment is a simple
targeting group of users (e.g., users having a particular interest), and Zohar’s
unified combination is a combination of segments (e.g., groups of users
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having the same gender, age range, interests, etc.). (See Zohar ¶¶ 4–5, 21
(“combine two or more qualified [segment] combinations to create, for
example, a ‘unified combination’”), 84–88 (“[a] jazz interest segment may
be an example of a ‘behavioral’ segment and can be combined with other
types of segments, e.g. user location, or user age segments to form segment
combinations”), 90–91 (describing “segments definitions, such as behavioral
segments, e.g. ‘likes to swim’, or ‘surfs the Web in late evening’”).)
Thus, Zohar does not determine an overlap as claimed (i.e., between
cluster groups obtained by expanding a membership to users that are
similar to, but outside of, user sets of identified targeting groups), and
neither does Chang. (Appeal Br. 18–19; Reply Br. 8–9.) In addition, a
skilled artisan would not apply Zohar’s overlap calculation (between a
behavioral segment and a unified combination) to expanded cluster groups
(e.g., of users not in Chang’s initial targeted segments), because the goal of
Zohar’s overlap calculation is to check whether a behavioral segment’s
definition is outdated or non-relevant (e.g., does not create a user group with
predictable responses to targeted advertising) and should be deleted. (See
Zohar ¶¶ 7, 113, 116–117, 168, 181–185.) The Examiner’s findings that
“[i]f you combine the groups from Zohar with the expansion of groups from
Chang you have the cluster groups of the claims” and “one of ordinary skill
in the art would find the combination to yield predictable results” (see Ans.
9 and Final Act. 11) do not adequately explain why the references would
have been combined as in the disputed limitation of claim 1 (i.e., to
determine overlap between cluster groups obtained by expanding
membership to users that are similar to, but outside of, user sets of
previously identified targeting groups). See Belden Inc. v. Berk–Tek LLC,
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805 F.3d 1064, 1073 (Fed. Cir. 2015) (“[O]bviousness concerns whether a
skilled artisan not only could have made but would have been motivated to
make the combinations or modifications of prior art to arrive at the claimed
invention.”). The Examiner also has not shown that the additional teachings
of Joa, Li, and Gunawardana make up for the above-noted deficiencies of
Zohar and Chang.
As the Examiner has not shown that the asserted art combination
teaches or suggests the disputed “overlap” limitation of claim 1, we do not
sustain the Examiner’s obviousness rejection of independent claim 1, and
claims 3–9 dependent therefrom. We also do not sustain the Examiner’s
obviousness rejection of independent claims 10 and 17 (argued for
substantially the same reasons as claim 1 and reciting similar limitations),
and claims 12–16, 19, and 20 dependent therefrom. (Appeal Br. 18–19.)

DECISION SUMMARY
The Examiner’s rejection of claims 1, 3–10, 12–17, 19, and 20 under
35 U.S.C. § § 112(a) is REVERSED.
The Examiner’s rejection of claims 1, 3–10, 12–17, 19, and 20 under
35 U.S.C. § 101 is AFFIRMED.
The Examiner’s rejection of claims 1, 3–10, 12–17, 19, and 20 under
35 U.S.C. § 103 is REVERSED.

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In summary:
Claims
Rejected
35 U.S.C. § Reference(s)/
Basis
Affirmed Reversed
1, 3–10,
12–17, 19,
20
112(a) Written
Description
1, 3–10,
12–17, 19,
20
1, 3–10,
12–17, 19,
20
101 Eligibility 1, 3–10,
12–17, 19,
20

1, 8–10, 17 103 Zohar, Chang,
Joa, Li
1, 8–10, 17
3–7, 12–16,
19, 20
103 Zohar, Chang,
Joa, Li,
Gunawardana
3–7, 12–16,
19, 20
Overall
Outcome
1, 3–10,
12–17, 19,
20

Because we have affirmed at least one ground of rejection with
respect to each claim on appeal, the Examiner’s decision is affirmed. See
37 C.F.R. § 41.50(a)(1).
No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a). See 37 C.F.R.
§ 1.136(a)(1)(iv).

AFFIRMED