Adobe Inc.

Patent Trials and Appeals Board

Mar 24, 2021

2020003694 (P.T.A.B. Mar. 24, 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.
14/623,248 02/16/2015 Moumita Sinha P4584-US 3152
108982 7590 03/24/2021
FIG. 1 Patents
116 W. Pacific Avenue
Suite 200
Spokane, WA 99201
EXAMINER
ANDERSON, SCOTT C
ART UNIT PAPER NUMBER
3694
NOTIFICATION DATE DELIVERY MODE
03/24/2021 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):
Fig1Docket@fig1patents.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte MOUMITA SINHA, KANDARP S. KHANDWALA,
HARVINEET SINGH, and D. P. TEJAS
Appeal 2020-003694
Application 14/623,248
Technology Center 3600
Before RICHARD M. LEBOVITZ, FRANCISCO C. PRATS, and
RACHEL H. TOWNSEND, Administrative Patent Judges.
TOWNSEND, Administrative Patent Judge.
DECISION ON APPEAL
Pursuant to 35 U.S.C. § 134(a), Appellant1 appeals from the
Examiner’s decision to reject claims to a method of quantifying and
displaying likelihood of future purchases of items for purchase through an
online store by at least one computing device. We have jurisdiction under
35 U.S.C. § 6(b).
We AFFIRM.

1 We use the term “Appellant” to refer to “applicant” as defined in
37 C.F.R. § 1.42. Appellant identifies the real party in interest as Adobe Inc.
(Appeal Br. 2.)
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STATEMENT OF THE CASE
Claim 1, reproduced below, is representative of the claimed subject
matter:
1. In a digital environment in which a user via their client
device selects items such as products or services for potential
purchase via an online store and an online shopping cart is
displayed to maintain the user-selected items, a method of
quantifying and displaying likelihood of future purchases of the
items by at least one computing device, the method comprising:
[a] identifying, by a processing system of the at least one
computing device, online store shopping sessions that client
device users have ended with unpurchased items in online
shopping carts, the online shopping carts enabling purchase of
items from the online store;
[b] collecting, via a network interface of the at least one
computing device, data that describes:
[i] interactions of the client device users with
content of the online store output by a client device;
[ii] attributes of both the unpurchased items
remaining in the online shopping carts and items that have been
purchased from the online shopping carts; and
[iii] cross-channel information describing
interaction of the client device users with different content
accessed through a content source other than the online store;
[c] determining, by the processing system, correlations
by performing a logistic regression analysis of the collected
data, the determined correlations being indicative, in part, of a
propensity of the interactions of the client device users to
purchase the items from the online shopping carts;
[d] generating, by the processing system, digital content
comprising a regression model indicative of the correlations
determined using the logistic regression analysis;
[e] identifying, by the processing system, return-to-
purchase thresholds for a plurality of customer types based on a
lift curve for the regression model, the return-to-purchase
thresholds identified as causing the lift curve to produce an
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optimum lift in correct determinations of the customer types
using the regression model;
[f] computing a likelihood using the regression model
that a subsequent client device user that ends an online store
shopping session with an unpurchased item in a corresponding
said online shopping cart returns to purchase the unpurchased
item;
[g] determining, by the processing system, a customer
type of the subsequent client device user based on the computed
likelihood and the return-to-purchase thresholds; and
[h] communicating, via the network interface, targeted
advertising content to the subsequent client device user for
output based on the determined customer type.
REJECTIONS
Claims 1–20 under 35 U.S.C. § 112(a) or 35 U.S.C. § 112 (pre-AIA),
first paragraph, as failing to comply with the written description
requirement.
Claims 1–20 under 35 U.S.C. § 101 as being directed to patent-
ineligible subject matter.

DISCUSSION
I. Written Description
The Examiner finds that the claims contain subject matter which, at
the time the application was filed, was not described in the specification in
such a way as to reasonably convey to one skilled in the relevant art that the
inventor had possession of the claimed invention. (Non-Final Action 3–4.)
The Examiner’s concern lies with the recitation in claim 1, and slight
variations in other claims, of “determining ‘return-to-purchase thresholds . . .
causing the lift curve to produce an optimum lift.’ (Id. at 3.) The Examiner
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explains that “[n]o hint [in the Specification] is provided as to how this is
done except for the broad phrase ‘using the regression model’” with no
disclosure of an algorithm or process which can perform the optimization.
(Id. at 3–4.) The Examiner notes that “simply restating the function” in the
Specification in the same terms as recited in the claim is not sufficient to
describe the claimed limitation. (Ans. 4.)
In addition, the Examiner explains that “a regression model is a broad
class of mathematical structures that, without a great deal of further
elaboration than what is provided, solves nothing whatever.” (Id. at 3–4; see
also Ans. 4.) The Examiner then explains that “optimization” such as this is
“the subject of a great deal of research” and provides literature evidence
contemporaneous with the invention stating: “‘the main fact, which should
be known to any person dealing with optimization models, is that in general,
optimization problems are unsolvable’.” (Id. at 4 (citing Nesterov,
“Lectures on Convex Optimization,” 2nd Ed., Springer, 2018).)
The Examiner concludes that there is insufficient detail in the
Specification such “that one of ordinary skill in the art would understand
how the inventor intended the function to be performed.” (Ans. 5.) For this
reason, the Examiner concludes that one of ordinary skill in the art “would
not believe the inventor to be in possession of the claimed invention.” (Non-
Final Action 4; Ans. 5.)
We agree with the Examiner’s findings concerning the Specification’s
failure to provide an adequate written description for the claimed invention.
The Specification must describe the claimed invention in a manner
understandable to a person of ordinary skill in the art in a way that shows
that the inventor actually invented the claimed invention at the time of filing.
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Ariad Pharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1351 (Fed. Cir. 2010)
(en banc). “The purpose of [the written description requirement] is to ensure
that the scope of the right to exclude, as set forth in the claims, does not
overreach the scope of the inventor’s contribution to the field of art as
described in the patent specification.” Reiffin v. Microsoft Corp., 214 F.3d
1342, 1345 (Fed. Cir. 2000). The claims require identification of return-to-
purchase thresholds in a functional way. For example, in claim 1, the
identification is made where “the lift curve” is caused “to produce an
optimum lift in correct determinations of the customer types using the
regression model.”
Thus, we look to the Specification to determine whether it
“sufficiently describe[s] how the function is performed or the result is
achieved.” MPEP § 2161.01 (I); see also Vasudevan Software, Inc. v.
MicroStrategy, Inc., 782 F.3d 671, 682–83 (Fed. Cir. 2015).
We agree with the Examiner that the Specification does not describe
how to achieve this functional result in such a way that one of ordinary skill
in the art would understand the inventor actually invented the claimed
invention. Appellant’s argument that because (a) the limitation at issue is
expressly recited in the Specification in the same way it is recited in the
claims, and (b) the Specification describes that a lift curve is used to
determine the threshold values in paragraph 54, the limitation is adequately
described is not persuasive. (Appeal Br. 13–14.)
The Specification mentions that the threshold value is determined
based on a lift curve and provides the following statement as an example of
how that may occur:
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The customer classification model module 212 may, for
instance, use the lift curve to determine the threshold value so
that it results in an optimum lift in correct predictions of true
customers, as opposed to randomly classifying online-store
visitors as true customers.
(Spec. ¶ 54.) That description does not explain how “the lift curve” is
applied “to produce an optimum lift in correct determinations of the
customer types using the regression model.” Nor does the fact that the
Specification indicates that probabilities of an abandoner of a shopping cart
being a true customer is computed using a logistic regression such that the
output values are between zero and one. (Id. ¶¶ 53–54.)
Appellant’s argument that the Examiner has not properly analyzed the
claim according to the MPEP § 2163 (Appeal Br. 15; Reply Br. 3–4) is not
persuasive of error in the rejection. “[T]he examiner bears the initial burden,
on review of the prior art or on any other ground, of presenting a prima facie
case of unpatentability. If that burden is met, the burden of coming forward
with evidence or argument shifts to the applicant.” In re Oetiker, 977 F.2d
1443, 1445 (Fed. Cir. 1992). Here, the Examiner clearly communicated the
determination of why one of ordinary skill in the art would not have
recognized the inventor was in possession of the claimed invention in view
of the disclosure of the application as filed setting forth express findings,
including citation to contemporaneous literature support. Appellant offered
no convincing countervailing evidence for the reasons just discussed.
Thus, we affirm the Examiner’s rejection of claims 1–20 under
35 U.S.C. § 112(a) or 35 U.S.C. § 112 (pre-AIA), first paragraph, as failing
to comply with the written description requirement.

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II. Patent-Ineligible Subject Matter
Appellant addresses claims 1, 11, and 17 as a group, without
differentiating them. Thus, we address the Examiner’s rejection with respect
to exemplary claim 1. See 37 C.F.R. § 41.37(c)(1)(iv).
The Dispute
The Examiner finds that the claims recite method steps involved in the
abstract idea of “advertising, marketing or sales activities or behaviors.”
(Non-Final Action 5.) In particular, the Examiner states the following are
the steps involved in the abstract idea of advertising activities:
The claim(s) recite(s) that a user “selects items such as products or
services for potential purchase”, identifying “shopping sessions” that
users have “ended with unpurchased items”, collecting data that
describes “interactions” of the users with the “content” of the store,
“attributes of both unpurchased items . . . and items that have been
purchased”, “cross-channel information describing interaction” of the
users “with different content accessed through a content source other
than” the presently-considered online store, determining “a customer
type” of a user and sending “targeted advertising content” to the user
“based on the determined customer type”.
(Id.) The Examiner further finds that the claims recite as the following
steps: (1) determining correlations by performing a logistic regression
analysis, (2) identifying thresholds based on a curve which is “a combination
of observation and evaluation” (3) identifying thresholds which produce an
optimum lift, i.e., “noting the highest value in the second column of a two-
column table and then observing the number to its left,” and (4) “computing
a likelihood, in no particular manner,” which includes trivial examples that
can be done mentally. (Id. at 5–6.)
The Examiner further finds that the judicial exception is not integrated
into a practical application because it simply recites the use of a generic
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computer “to perform the abstract steps, each in no particular way.” (Id. at
6.) The Examiner further explains that the manipulation of business and
mathematical data by the computer does not improve the functioning of a
computer or any other technology or technical field, and the computer does
not apply or use the abstract idea in some other meaningful way beyond
generally linking the idea to a particular technological environment. (Id.)
The Examiner also finds that the claims do not include additional
elements beyond the abstract idea that amount to significantly more than the
judicial exception. (Id. at 7.) In particular, the Examiner notes that the only
positively recited element that is additional to the abstract idea is the “at
least one computing device” and that it “only performs generic computer
functions of manipulating information and sharing information with persons
and/or other devices” which does not amount to significantly more. (Id. at
7–8.) In addition, explains the Examiner:
[t]he online shopping limitations are simply a field of use that
attempts to confine the invention to a particular technological
environment. The type of information being manipulated does
not impose meaningful limitations or render the idea less
abstract.
(Id. at 8.)
Despite acknowledging the claims include “[c]ommunicating targeted
advertising content” to a client device based on determining a customer
related to that client device meets a certain “type” criteria (Appeal Br. 22),
Appellant argues that the claimed invention is not an abstract idea because
(1) the Examiner “grossly oversimplifies the claimed features” by truncating
what is recited in the steps and (2) the “claimed subject matter is not
analogous to cases where the Court found claims to be ineligible for being
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directed to advertising, marketing or sales activities or behaviors” (id. at 20–
21).
Appellant also argues that the steps the Examiner identified as mental
processes “cannot practically be performed in the human mind,” because the
human mind cannot practically perform a logistic regression of the collected
data, nor identify the threshold as recited or the computation of likelihood of
return to purchase, and “the human mind is not equipped to generate digital
content, specifically digital content comprising a regression model.” (Id. at
24–25; see also id. at 27 “it would not be practical for a human mind to
determine customer types based on a logistic regression analysis or
otherwise generate digital content comprising a regression model as
claimed”).) In addition to the foregoing, Appellant argues that the “claims
represent a technological improvement” because “various aspects of the
claims clearly define how data is collected about . . . users” (id. at 27–28)
“clearly define how the data that is collected describes” certain interactions,
attributes, and information, “clearly define how [propensity] correlations are
determined . . ., how a regression model is generated that is indicative of the
correlations, how return-to-purchase thresholds are identified for . . .
customer types . . . , how a likelihood [return to purchase] is computed. . . ,
e.g. using the regression model, how a customer type is determined . . . and
how targeted advertising is communicated . . . for output depending on the
determined customer type.” (Id. at 28.) Appellant contends that these are a
“set of ‘rules’” and more than a “‘bare recitation of a generic computer’.”
(Id. at 29.) Appellant also urges that the generation of a regression model
using the particular data recited and the use of that model to identify return-
to-purchase thresholds meaningfully limits the claims and “prevents
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preemption of all processes for targeting advertising to shoppers that have
abandoned online shopping sessions with unpurchased items in their carts.”
(Id. at 30.)
Appellant additionally argues that the claims “are a practical
application of a solution addressing a digitally-rooted challenge.” (Id. at 36–
38.) According to Appellant, “the challenge” is “accurately predicting” a
likelihood that an online shopper who abandoned items in a shopping cart
would later return to that cart and purchase at least one of those items” and
thus enable “marketing systems to accurately classify customers relative to
whether they will return to purchase items held in persistent online shopping
carts, and thus also to deliver more effective advertising content” to such
customers. (Id. at 38, 39.) Appellant asserts that
This subject matter does not “merely recite the performance of
some business practice known from the pre-Internet world
along with the requirement to perform it on the Internet.
Instead, the claimed solution is necessarily rooted in computer
technology in order to overcome a problem specifically arising
in the realm of computer networks.”
(Id.)
Lastly, Appellant argues that the claims include an inventive concept
in the combination of the specific determining, generating, identifying,
computing, and communicating limitations recited. (Id. at 39–40.)

The Analysis
The Supreme Court has established a two-step framework for
“distinguishing patents that claim laws of nature, natural phenomena, and
abstract ideas from those that claim patent-eligible applications of those
concepts.” Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 217
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(2014). “First, we determine whether the claims at issue are directed to” a
patent-ineligible concept. Id. If so, “we consider the elements of each claim
both individually and ‘as an ordered combination’ to determine whether the
additional elements ‘transform the nature of the claim’ into a patent-eligible
application.” Id. (quoting Mayo Collaborative Servs. v. Prometheus Labs.,
Inc., 566 U.S. 66, 78–79 (2012)).
Applying the 2019 Revised Patent Subject Matter Eligibility Guidance
(“Guidance”), 84 Fed. Reg. 50–57 (Jan. 7, 2019), and in accordance with
judicial precedent, we agree with the Examiner’s conclusion that the claims
are addressed to patent-ineligible subject matter. We address representative
claim 1 in light of the fact that Appellant does not provide separate argument
for the claims.
STEP 2A, Prong One:
Under the Guidance, in determining what concept a claim is “directed
to” in step one of the Supreme Court’s two-step framework, we first look to
whether the claim recites any judicial exceptions, such as (a) mathematical
concepts, (b) methods of organizing human activity including fundamental
economic principles or practices (including hedging, insurance, mitigating
risk), and/or (c) mental processes including an observation, evaluation,
judgment, or opinion. Guidance, 84 Fed. Reg. at 52, 54 (Step 2A, Prong
One).
We agree with the Examiner that claim 1 recites abstract concepts.
One of them is a method of advertising to a particular type of customer in a
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digital environment, i.e., providing targeted advertising based on user
information, step [h] of claim 1.
In making this determination, we note that the Appellant’s
Specification explains that “conventional techniques for analyzing online
shopping carts do not produce information that is suitable for effectively
targeting advertising content to customers that have abandoned online
shopping carts,” i.e., “terminat[ed] a shopping session without purchasing
items held in an online shopping cart.” (Spec. ¶¶ 1–4.) In particular, the
Specification asserts that “merely sending a reminder email that states ‘You
have unpurchased products in your cart’, can lead to brand irritation and
potentially lost customers.” (Id. ¶ 4.) Appellant’s invention is directed at
controlling the marketing activities to avoid lost customers or brand
irritation, allowing advertisers “to concentrate their resources (e.g.,
advertising budgets) on the customers that are determined to be the most
receptive to advertising” and “convince a greater number of customers to
return to purchase items in abandoned online shopping carts than businesses
using conventional techniques.” (Id. ¶¶ 6, 25.) To provide such control, the
Specification explains that computer analysis of past shopping habits of
customers who have left unpurchased items in their online shopping carts,
i.e., data that “describes their interactions with the online store as well as
attributes of items in their online shopping carts” is used to build a model for
“comput[ing] a likelihood that a given customer that leaves an online store
with unpurchased items in an online shopping cart will return to purchase
the unpurchased items. (Spec. ¶ 5.) Appellant’s Specification explains that
Having some insight about “why” an online shopping cart is
abandoned enables advertisers to deliver more effective
advertising content, e.g., content that results in a greater number
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of customers returning to purchase items left in abandoned
shopping carts.
(Spec. ¶ 15.)
Once the model is built, it is used with data collected about a
subsequent customer(s), and based on the determined “likelihood,” the
marketing activities directed to the subsequent customer(s) is controlled.
(Id. ¶¶ 6, 18.) Appellant’s Specification states;
If, for example, it is determined that an online shopping cart has
been abandoned because the shipping costs are believed to be
too high, advertising content that enables the customer to get
free shipping may be delivered to the customer. However, it
may be beneficial for an advertiser to identify that a customer is
likely or not likely to return to purchase the items in an
abandoned shopping cart. This way the advertiser may avoid
annoying such customers with unwanted advertising content.
(Id. ¶ 15.)
Every step recited in the method of claim 1 is for the purpose of being
able to communicate targeted advertising content to a client device user who
is of a particular customer type. As can be seen from the description of the
invention in Appellant’s Specification, steps [a] – [e] of claim 1 are
undertaken to build a model “that enables the likelihood-to-return
computation” of a future customer and assignment of customer-type based
on that likelihood computation applied to a customer is set forth in steps [f]
and [g]. (Spec. ¶¶ 18–24.) The model enables categorization of potential
customers on whom to spend advertising dollars. (See id. ¶ 66 (“By
segmenting subsequent customers that abandon items in online shopping
carts in this way, advertisers may spend advertising money on the
“prospects” rather than “true customers” and “true abandoners.”).) Steps [f]
and [g] determine how subsequent customers, those shopping at present,
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rather than historically, are likely to behave and in what category they
belong according to the model, i.e., the implementation of the model so as to
deliver targeted advertising. (Id. ¶¶ 23–25.) Step [h] is implementation of
the targeted advertising based on customer assessments according to the
model in steps [f] and [g]. As the Specification explains:
By delivering advertising content to customers that are
classified in this way, advertisers are able to concentrate their
resources (e.g., advertising budgets) on the customers that are
determined to be most receptive to advertising. Furthermore,
the customers to which the targeted advertising content is
delivered may be expanded or decreased based on an
advertising budget.
(Id. ¶ 25.) Moreover, the Specification explains that “[b]y convincing more
customers to return to purchase items in abandoned shopping carts, these
businesses also therefore generate more revenue, e.g., customers spend more
money at the online store.” (Id. ¶ 66)
Although the claim is directed to a computer being able to discern
customer type in an online shopping environment, such does not negate the
fact that the purpose of the claimed method steps is to predict customer
behavior so as to provide targeted advertising content to a potential
customer, which provision of advertising is also the final recited step of the
claim. Thus, we agree with the Examiner that the claim is concerned with
the commercial activity of advertising. Consequently, the claim does recite
a method of organizing human activity that is an abstract idea.
At least for the foregoing reasons, we do not find persuasive
Appellant’s argument that the Examiner’s determination that the claim
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recites a judicial exception is in error because it “grossly oversimplifies the
claimed features.” (Appeal Br. 20.)
As our reviewing court has stated:
Targeted marketing is a form of “tailoring information based on
[provided] data,” which we have previously held is an abstract
idea. Intellectual Ventures I LLC v. Capital One Bank ( USA),
792 F.3d 1363, 1369 (Fed. Cir. 2015). The concept is a
“fundamental practice” that dates back to newspaper
advertisements. Id. (ellipsis omitted).
Bridge & Post, Inc. v. Verizon Commc’ns, Inc., 778 F. App’x 882, 887 (Fed.
Cir. 2019).
In light of the foregoing, we also do not find persuasive Appellant’s
argument that “Appellant’s claimed subject matter is not analogous to cases
where the Court found claims to be ineligible for being directed to
advertising, marketing or sales activities or behaviors” (Appeal Br. 21).
And, we do not find persuasive, Appellant’s argument that
Communicating targeted advertising content to a subsequent
client device user that has ended an online shopping session
with an unpurchased item in an online shopping cart and
doing so based on a customer type that is determined using a
computed likelihood to return and return-to-purchase
thresholds simply is not analogous to pricing a product for
sale.
(Id. at 22.) Similar to the claims at issue in Bridge & Post which recited
placing directed media on a web site requested by a user that is customized
to the user based on the user profile, the recited steps of collecting and
analyzing historic data (steps [a] and [b] of claim 1), analyzing that collected
information to create a model that can predict on-line shopping behavior of
future customers, including by customer-type, (steps [c] – [g] of claim 1)
and communicating targeted advertising content based on customer type
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(step [h] of claim 1) is “nothing more than a computer implementation of
targeted marketing over the Internet.” Bridge & Post, 778 F. App’x at 887.
Besides reciting providing targeted advertising, the method steps
which lead to being able to provide the targeted advertising recite
mathematical concepts; and, mathematical concepts are another category of
abstract ideas. In particular, to “quantify” likelihood of future purchases of
items abandoned in an online shopping cart, the claim recites using a logistic
regression analysis of the collected data to determine correlations that are at
least indicative of a “propensity” to purchase items (step [c]), and to then
generate “a regression model indicative of the correlations determined using
the logisitic regression analysis” (step [d]). (Spec. ¶ 22 (“To enable
computation of the likelihood, data that describes the subsequent
abandoner’s interactions with the online store, attributes of the items left in
the abandoned shopping cart, and interactions with the cross-channel
information is collected.”) (Emphasis added); see also id. ¶¶ 51–52.) Once
the model is generated, it is used to identify certain classifications of
customers based on threshold values determined from a “lift curve” (step [e]
of claim 1). (Id. ¶ 54.) The Specification explains:
the customer classification model module 212 defines the "true
customers" as those customers whose value returned from the
logistic regression is above a certain threshold value. . . . The
customer classification model module 212 determines this
threshold value based on a lift curve (e.g., a lift curve
reference). The customer classification model module 212
may, for instance, use the lift curve to determine the threshold
value so that it results in an optimum lift in correct predictions
of true customers, as opposed to randomly classifying online-
store visitors as true customers.
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(Id.) Mathematically speaking, a curve is a plot of variables that best
expresses the apparent relationship suggested by the data. The Specification
describes various calculations involved in categorizing types of customers.
(See id. ¶¶ 53– 54 (describing the customer classification module that
computes probabilities, ¶¶ 55–58 (describing calculation for dividing non-
customers into prospects and true abandoners).) Thus, the identification of
customer types using a lift curve to determine thresholds, involves
application of mathematical concepts.
The model is also used for “computing a likelihood” that another
online shopper will return to an abandoned shopping cart to purchase the
unpurchased items (step [f] of claim 1). (Id. ¶ 22 (“The data collected for
the subsequent customer then serves as input to the model and the likelihood
is returned as output.”); see also id. ¶¶ 31; 33, ¶¶ 60–63, ¶¶ 77–79). Next, a
customer type is determined based on the computed likelihood and “return-
to-purchase thresholds” (step [g] of claim 1). That determination also
involves calculations. (See id. ¶¶ 63–65, ¶ 80.)
In light of the above, we conclude that claim steps [c]–[g] recite
mathematical concepts, which are abstract ideas. See Guidance, 84 Fed.
Reg. at 52.
While Appellant may be correct that these calculations cannot
practically be carried out in a person’s mind (Appeal Br. 23–25; Ans. 7), that
is a factual question which we need not resolve, because irrespective of
whether such is true, the steps recite mathematical concepts, which is a
category of abstract ideas under the Guidance.
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Thus, we agree with the Examiner that Appellant’s claim 1 recites
abstract concepts, and thus, we proceed to the next step of the eligibility
analysis under the Guidance.
STEP 2A, Prong Two:
Following the Guidance, we next consider whether “the claim as a
whole integrates the recited judicial exception into a practical application of
the exception,” i.e., 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.” Guidance, 84 Fed. Reg. at 54. This analysis includes
“[i]dentifying whether there are any additional elements recited in the claim
beyond the judicial exception(s)” and “evaluating those additional elements
individually and in combination to determine whether they integrate the
exception into a practical application.” Id. at 54–55.
Here, there are two steps that are additional to the abstract concepts.
They are steps [a] and [b]; the abstract concepts being the mathematical
concepts in steps [c]-[g], and advertising which is at a minimum step [h].
Steps [a] and [b] are data gathering steps. First, in step [a] historical
online shopping sessions of a particular type are identified and then in step
[b] data is mined concerning those shopping sessions. That data is then
subjected to the mathematical analysis described above in order to determine
to whom targeted advertising is sent. Data gathering steps cannot make an
otherwise nonstatutory claim statutory. See Mayo, 566 U.S. at 79
(concluding that additional element of measuring metabolites of a drug
administered to a patient was insignificant extra-solution activity); OIP
Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)
(holding that mere data gathering is insufficient to confer patent eligibility).
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That is because collecting and manipulating data itself is an abstract idea.
See, e.g., Intellectual Ventures I LLC v. Capital One Fin. Corp., 850 F.3d
1332, 1340 (Fed. Cir. 2017); SAP Am., Inc. v. InvestPic, LLC, 898 F.3d
1161, 1167 (Fed. Cir. 2018) (holding that “analyzing information . . . by
mathematical algorithms, without more” is an abstract idea).
“[E]ven if a process of collecting and analyzing information is
‘limited to particular content’ or a particular ‘source,’ that limitation does
not make the collection and analysis other than abstract.” In re Rosenberg,
813 F. App’x. 594, 597 (Fed. Cir. 2020) (citing SAP, 898 F.3d at 1168).
Furthermore “even a highly specific method for implementing an
abstract idea is, at step 1 of the Alice test, still directed to that abstract idea.”
Bridge & Post, 778 F. App’x at 889. And that is so even if the analysis
involves a new algorithm. SAP, 898 F.3d at 1163 (“No matter how much of
an advance in the finance field the claims recite, the advance lies entirely in
the realm of abstract ideas, with no plausibly alleged innovation in the non-
abstract application realm. An advance of that nature is ineligible for
patenting.”); Diamond v. Diehr, 450 U.S. 175, 188–89 (1981) (“The
‘novelty’ of any element or steps in a process, or even of the process itself, is
of no relevance in determining whether the subject matter of a claim falls
within the § 101 categories of possibly patentable subject matter.”); Mayo
Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 88–90 (2012)
(the patent eligibility of an abstract idea does not depend on its alleged
novelty or nonobviousness).
Appellant’s claim 1 also recites that various steps are performed by a
processing system (steps [a], [c], [d], [e], [g]) or a network interface (steps
[b] and [h]). The claim does not require any specific configuration of the
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recited processing system or network interface that might reflect an
improvement in the functioning of a computer. Consistent with the claim
language, the Specification does not indicate there are special requirements
for the processing system or network interface recited in claim 1. (See Spec.
¶¶ 84– 97.) “[A]fter Alice, there can remain no doubt: recitation of generic
computer limitations does not make an otherwise ineligible claim patent-
eligible.” FAIRWARNING IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1097
(Fed. Cir. 2016).
We do not find persuasive Appellant’s arguments that “[t]he features
recited in claim 1 are a practical application of a solution addressing a
digitally-rooted challenge” like the claims in DDR. Holdings, LLC v.
Hotels.com, 773 F.3d 1245 (Fed. Cir. 2014). (Appeal Br. 35; see also Reply
Br. 8.) The claimed invention in DDR created a hybrid web page that
combined advantageous elements from two web pages, bypassing the
expected manner of sending a visitor to another party’s web page when
clicking on an advertisement, in order to solve the internet-centric problem
of retaining website visitors on a host website. DDR Holdings, 773 F.3d at
1257–59. Although the claims addressed “a business challenge (retaining
website visitors), it [was] a challenge particular to the Internet” and the
claims used an inventive functioning of Internet hyperlink protocol, not
simply using computers to serve a conventional business purpose. Id. at
1257.
Here, the claim is concerned with solving the problem of targeting
prospective customers based on historical data of one’s customers, which is
an age-old business endeavor. The computer implementation of data
collection and mining the collection to determine an algorithm to predict
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future customer behavior such that the algorithm can be used to send
targeted advertising to a certain type of customer is simply the use of
computers to serve a conventional business purpose. Appellant’s “solution”
to its advertising problem lies in its creation of a mathematical model to
describe customer behavior and then to use that to project future behavior of
prospects. The solution is the mathematical model, itself derived from
mathematical concepts, all of which are abstract ideas; all of which employ
one or more computers in their conventional capacities, i.e., analyzing data.
“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 [the Federal Circuit has] repeatedly held to be
ineligible.” Bridge & Post, 778 F. App’x at 893.
For the foregoing reasons, we do not find persuasive Appellant’s
argument that the claim is an improvement in technology. (Appeal Br. 41.)
As Appellant acknowledges, “[t]he claims recite a particular way of
predicting” a customer’s propensity to behave in a particular way with
respect to merchandise left in a shopping cart. (Id.) That way is embodied
by particular mathematical concepts as just discussed, not in any particular
improvement to technology. “[S]oftware can make patent-eligible
improvements to computer technology, and related claims are eligible as
long as they are directed to non-abstract improvements to the functionality
of a computer or network platform itself.” Uniloc USA, Inc. v. LG
Electronics USA, Inc., 957 F.3d 1303, 1309 (Fed. Cir. 2020); see also
TecSEC, Inc. v. Adobe Inc., 978 F.3d 1278, 1293 (Fed. Cir. 2020).
Appellant’s claim does neither.
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We also do not find persuasive Appellant’s argument that because the
claims recite limitations that involve generation of a regression model based
on a logistic regression analysis on specific types of data, and uses that
regression model in a particular manner, it recites specific rules and should
be found patent eligible as was the case in McRO, Inc. v. Bandai Namco
Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016). (Appeal Br. 29–34.) The
claimed method in McRO “allow[ed] computers to produce ‘accurate and
realistic lip synchronization and facial expressions in animated characters'
that previously could only be produced by human animators,” providing “an
improved technological result in conventional industry practice.” Id. at
1313, 1316. As the court explained in McRO, the recited rules “are limiting
in that they define morph weight sets as a function of the timing of phoneme
sub-sequences.” Id. at 1313. “The claimed process uses a combined order of
specific rules that renders information into a specific format that is then used
and applied to create desired results: a sequence of synchronized, animated
characters.” Id. at 1315 (emphasis added). Thus, the claims were found to
be directed to a “technological improvement over the existing, manual 3-D
animation techniques.” Id. at 1316. In Appellant’s claim, the result of
providing target advertising content to a customer is accomplished by an
improvement in the abstract idea; the advertising content, itself, is not
changed in any way and is therefore not analogous to McRO where there
was a specific technological improvement to how the resulting characters are
animated.
Appellant’s preemption argument (Appeal Br. 30) is also
unpersuasive. In particular, preemption is not a stand-alone test for patent
eligibility. Although preemption “‘might tend to impede innovation more
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than it would tend to promote it,’ thereby thwarting the primary object of the
patent laws,” Alice, 573 U.S. at 216 (quoting Mayo, 566 U.S. at 71), “the
absence of complete preemption does not demonstrate patent eligibility,”
Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379 (Fed. Cir.
2015); see also OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362–
63 (Fed. Cir. 2015) (“[T]hat the claims do not preempt all price optimization
or may be limited to price optimization in the e-commerce setting do not
make them any less abstract.”).
In sum, we find that the claimed invention does not integrate the
abstract idea into a “practical application,” as that phrase is used in the
Guidance. See Guidance, 84 Fed. Reg. at 55. Thus, we conclude that the
claim is directed to an abstract idea. We next turn to the second step of the
Alice analysis, i.e., whether the claim includes an “inventive concept.”

STEP 2B
Step 2B requires that we look to whether the claim, that we have
determined above to set forth the judicial exception of an abstract idea,
“[a]dds a specific limitation [beyond the judicial exception that is] not well-
understood, routine, conventional in the field.” Guidance 84 Fed. Reg. at
56; MPEP § 2106.05(d)).
As discussed above, the only steps beyond the judicial exceptions
recited in the claim (i.e., mathematical concepts and advertising), are data
gathering steps and the use of a computer processing system and network
interface. Appellant does not argue that these data gathering steps are
inventive or not well-understood. Instead, Appellant argues that the claimed
method steps [c]-[g] as a whole do not represent well-understood, routine, or
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conventional activity. (Appeal Br. 40.) However, as already noted these
steps are the mathematical concepts and advertising, not steps beyond the
judicial exceptions. What is needed to be sufficient to remove a claim from
the class of subject matter ineligible for patenting at this stage “is an
inventive concept in the non-abstract application realm.” SAP, 898 F.3d at
1168. Appellant’s argument confirms that such is not the case here.
With respect to the required computer elements, we note that using
generic computer components to perform abstract ideas does not provide the
necessary inventive concept. See Alice, 573 U.S. at 223 (“[T]he mere
recitation of a generic computer cannot transform a patent-ineligible abstract
idea into a patent-eligible invention.”).
Appellant’s argument that the Examiner’s review of the claims fails to
comply with the “Berkheimer Memo” (Appeal Br. 40–41) is not persuasive
of error in the rejection. That is because, consistent with the Berkheimer
case itself, Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018), the
memo directs the well-understood, routine, and conventional analysis to be
applied to additional elements beyond the abstract ideas. See Robert W.
Bahr, Memo to Patent Examining Corp: “Changes in Examination
Procedure Pertaining to Subject Matter Eligibility, Recent Subject Matter
Eligibility Decision”, at 2–3, April 19, 2018 (“Berkheimer Memo”);
Berkheimer, 881 F.3d at 1369–1370 (claim 1 was determined patent-eligible
because “[t]he limitations [of claim 1] amount to no more than performing
the abstract idea of parsing and comparing data with conventional computer
components,” whereas claims 4–7, in contrast, contained a limitation that, at
least from a summary judgment standpoint, was arguably a technological
improvement, i.e., an unconventional asset management system that stored
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object structures in a new way that improved operating efficiency and
reduced storage costs). The memo explains that if there are multiple
additional elements beyond the abstract ideas, they are to be considered not
just individually, but also in combination. Berkheimer Memo at 3. Thus,
that the Examiner did not demonstrate that “the features of ‘determining . . .
correlations . . . ’‘generating . . . a regression model . . . ’ ‘identifying . . .
return-to-purchase thresholds . . . ’ and ‘computing a likelihood using the
regression model’,” which are the steps reciting mathematical concepts, are
“well-understood, routine, conventional activity” is not evidence of error.
In light of the foregoing, we affirm the Examiner’s rejection of claim
1 as being directed to ineligible subject matter under 35 U.S.C. § 101.
Appellant’s only argument as to why claims 2–10, 12–16, and 18–20 are
patent eligible is because they “depend from independent claims 1, 11, or 17,
and thus are allowable at least because they depend from an allowable base
claim.” (Appeal Br. 42.) Accordingly, the argument for these claims is not
substantively different from those made in regard to claim 1, and which we
do not find persuasive. These claims thus stand or fall with claim 1.
37 C.F.R. § 41.37(c)(1)(iv). Thus, we affirm the Examiner’s rejection of
claim 2–10, 12–16, and 18–20 as being directed to ineligible subject matter
under 35 U.S.C. § 101 for the same reasons we affirm the rejection as to
claim 1.

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DECISION SUMMARY
Claim(s)
Rejected
35 U.S.C. § Reference(s)/Basis Affirmed Reversed
1–20 112(a) Written
Description
1–20
1–20 101 1–20
Overall
Outcome
1–20

RESPONSE
No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a). See 37 C.F.R.
§ 1.136(a)(1)(iv).
AFFIRMED