FAIR ISAAC CORPORATION

Patent Trials and Appeals Board

Sep 11, 2020

2020002863 (P.T.A.B. Sep. 11, 2020)

UNITED STATES PATENT AND TRADEMARK OFFICE
UNITED STATES DEPARTMENT OF COMMERCE
United States Patent and Trademark Office
Address: COMMISSIONER FOR PATENTS
P.O. Box 1450
Alexandria, Virginia 22313-1450
www.uspto.gov
APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO.
15/239,657 08/17/2016 Scott Michael Zoldi 35006-784F01US 5643
76615 7590 09/11/2020
Mintz Levin/Fair Isaac
Mintz Levin Cohn Ferris Glovsky and Popeo, P.C.
One Financial Center
Boston, MA 02111
EXAMINER
NORMAN, SAMICA L
ART UNIT PAPER NUMBER
3697
NOTIFICATION DATE DELIVERY MODE
09/11/2020 ELECTRONIC
Please find below and/or attached an Office communication concerning this application or proceeding.
The time period for reply, if any, is set in the attached communication.
Notice of the Office communication was sent electronically on above-indicated "Notification Date" to the
following e-mail address(es):
IPDocketingBOS@mintz.com
IPFileroombos@mintz.com
mintzdocketing@cpaglobal.com
PTOL-90A (Rev. 04/07)

UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte SCOTT MICHAEL ZOLDI and HEMING XU
Appeal 2020-002863
Application 15/239,657
Technology Center 3600
Before ULRIKE W. JENKS, TAWEN CHANG, and
MICHAEL A. VALEK, Administrative Patent Judges.
CHANG, Administrative Patent Judge.
DECISION ON APPEAL
Pursuant to 35 U.S.C. § 134(a), Appellant1 appeals from the
Examiner’s decision to reject claims 1–18. We have jurisdiction under
35 U.S.C. § 6(b).
We AFFIRM.

1 We use the word “Appellant” to refer to “applicant” as defined in 37
C.F.R. § 1.42. Appellant identifies the real party in interest as Fair Issac
Corporation. Appeal Br. 3.
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STATEMENT OF THE CASE
As described in the Specification,
[u]nderstanding the spending patterns of customers is crucial to
rapidly detect fraudulent transactions so as to mitigate monetary
losses for both card issuers and merchants. The spending patterns are
established based upon customer’s behavior in the aspects of spending
time, merchant location, purchase amount and merchant category code
(MCC) etc. The patterns may be extracted from the ever-growing
volume of the historical transaction data with a variety of techniques.
The cardholder historical data include all the attributes of transactions
involving customers and merchants and transaction types etc. One
technique is to examine the customer spending history in large
databases and then dividing customers into different subsets based on
their spending characteristics of transactions. The underlying
assumption may be that the consumers in the same subset may have
similar behaviors or characteristics.
Spec. ¶ 3.
CLAIMED SUBJECT MATTER
The claims are directed to methods to detect and systems for detecting
fraudulent transactions. Claim 1 is illustrative:
1. A method to detect fraudulent transactions comprising:
[(a)] receiving, by a merchant transaction computer
during a card-not-present (CNP) online transaction conducted
over a communications network, data representing a new
transaction from a customer's payment card,
[(b)] associating, by the merchant transaction computer,
the customer with an archetype distribution stored in an
electronic database, the archetype distribution being generated
by an archetype calculation engine based on past transactions
across a plurality of merchants by the customer and customer
attributes and computed by an issuer or processor of the
customer’s payment card;
[(c)] generating, by the archetype calculation engine, a
topic model based on one or more words and/or one or more
documents selected from the data representing the new
transaction and the past transactions, the topic model
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representing a similarity of the words and/or documents of the
new transaction with words and/or documents in the past
transactions represented by the archetypes;
[(d)] generating, by the archetype calculation engine,
archetype clusters based on archetype distribution vectors for
each document upon execution of the topic model, the
archetype clusters representing a similarity of documents in the
archetype space based on past transactions by the customer and
customer attributes;
[(e)] associating, by the merchant transaction computer,
the customer with an archetype cluster generated by the
archetype cluster calculation engine based on data representing
the past transactions from the customer or one or more other
customers, the archetype cluster representing a distribution of a
probability of attributes related to each of the past transactions
at the merchant;
[(f)] locating, by the merchant transaction computer from
the electronic database, an archetype distribution vector and the
archetype cluster generated by the archetype calculation engine
based on data representing the past transactions from the
customer or one or more other customers, the archetype cluster
representing a distribution of a probability of attributes related
to each of the past transactions at a multitude of merchants, the
archetype distribution vector representing the current archetype
distribution of the customer's transactions across the multitude
of merchants;
[(g)] generating, by the merchant transaction computer in
near real time to the CNP transaction, a first score representing
a likelihood of fraud associated with the new transaction based
on the calculated transaction risks associated with global
archetype cluster membership, merchant-specific archetype
cluster membership, and recurrence list positions of transaction
details;
[(h)] generating, by the merchant transaction computer
using a frequent-behavior sorted list method, a second score
based on a frequency of transactions associated with the
customer, the frequent behavior sorted list method comprising
generating a frequency table including non-fraudulent and
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fraudulent activity in different merchant-specific archetype
clusters;
[(i)] calculating, by the merchant transaction computer, a
risk score based on a weighted sum of the first score and the
second score;
[(j)] determining, in response to the risk score exceeding
a preset threshold, that the new transaction is fraudulent;
[(k)] performing, in response to the determining, an
action on the new transaction, and
[(l)] updating the topic model and the frequency table
with the new transaction.
Appeal Br. 22–23 (Claims App.) (annotations added).

REJECTION
Claims 1–18 are rejected under 35 U.S.C. § 101 as being directed to
an abstract idea without significantly more.

OPINION
A. Issue
The Examiner concludes that “[t]he claimed limitations, under its
broadest reasonable interpretation, are based upon mathematical relations,
formulations or calculations and a fundamental economic principles or
practice,” which are abstract ideas and thus fall within one of the judicial
exceptions to patent eligibility. Ans. 3. In addition, the Examiner finds that
“[t]h[ese] judicial exception[s are] not integrated into a practical
application,” because the additional elements (i.e., “an archetype calculation
engine (software) and a merchant transaction computer”) do not use the
mathematical calculations and fundamental economic practice in a
sufficiently specific manner. Id. at 4. The Examiner further finds that “[t]he
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claims do not include additional elements that are sufficient to amount to
significantly more than the judicial exception.” Id.
Appellant contends that claim 1 is at most “based on or involves a
mathematical concept” and, thus, does not recite “a mathematical
relationship, a mathematical formula or equation, or a mathematical
calculation” under Prong One of Step 2A of the PTO’s 2019 Revised Patent
Subject Matter Eligibility Guidance (“Revised Guidance”). Appeal Br. 13
(citing PTO’s October 2019 Patent Eligibility Guidance Update (“Guidance
Update”)). Appellant further contends that “[c]laim 1 is directed to a
‘method to detect fraudulent transactions[,] which is not an enumerated
fundamental economic principle or practice and is not included in the
examples of MPEP 2106.04.” Id. at 14.
Appellant further contends that any abstract idea recited in claim 1 is
integrated into a practical application, because claim 1 provides an
improvement to a technology or technical field, e.g., “fraud detection in a
network.” Appeal Br. 15–16.
Finally, Appellant contends that, “[e]ven assuming, arguendo, that
Claim 1 is directed to an abstract idea . . ., Claim 1 recites significantly more
than the alleged abstract idea[s],” because “Claim 1 describes the
unconventional solution (enhanced fraud detection) of using the claimed
technique to solve a technical problem of providing risk scores using
temporal behavior maps.” Appeal Br. 17–19.
Appellant does not separately argue the claims. We therefore focus
our analysis on claim 1 as representative. The issue with respect to this
rejection is whether claim 1 is directed towards an abstract idea, without
significantly more.
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B. Analysis
We analyze this case under the framework the Supreme Court set
forth in Mayo Collaborative Services v. Prometheus Laboratories, Inc., 566
U.S. 66 (2012) and applied in Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573
U.S. 208 (2014). As the Supreme Court explained in Alice:
In Mayo . . . , we set forth a framework for distinguishing
patents that claim laws of nature, natural phenomena, and
abstract ideas from those that claim patent-eligible
applications of those concepts. First, we determine
whether the claims at issue are directed to one of those
patent-ineligible concepts. . . . If so, we then ask,
“[w]hat else is there in the claims before us?” . . . To
answer that question, we consider the elements of each
claim both individually and “as an ordered combination”
to determine whether additional elements “transform the
nature of the claim” into a patent-eligible application.
. . . We have described step two of this analysis as a
search for an “‘inventive concept’”—i.e., an element or
combination of elements that is “sufficient to ensure that
the patent in practice amounts to significantly more than
a patent upon the [ineligible concept] itself.”
Alice, 573 U.S. at 217–218.
Whether Claim 1 Is Directed to Patent-Ineligible Concept
We begin with the first step of the Mayo test, namely whether a claim
is “directed to” a patent-ineligible concept. On January 7, 2019, the Director
of the USPTO issued the “2019 Revised Patent Subject Matter Eligibility
Guidance” (“Revised Guidance”), which provides further details regarding
how the Patent Office analyzes patent-eligibility questions under 35 U.S.C.
§ 101. 84 Fed. Reg. 50–57 (Jan. 7, 2019). Under the Revised Guidance, the
first step of the Mayo test (i.e., Step 2A of the Revised Guidance) is “a two-
pronged inquiry.” Id. at 54. In prong one, we evaluate whether the claim
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recites a judicial exception, such as laws of nature, natural phenomena, or
abstract ideas. Id. If the claim recites a judicial exception, the claim is
further analyzed under prong two, which requires “evaluat[ion of] whether
the claim recites additional elements that integrate the exception into a
practical application of that exception.” Id. The Revised Guidance explains
that, “[i]f the recited exception is integrated into a practical application of
the exception, then the claim is eligible at Prong Two of . . . Step 2A [of the
Revised Guidance].” Id.
Prong One of Step 2A of Revised Guidance
Following the Revised Guidance, we first consider whether claim 1
recites a judicial exception such as an abstract idea.
Courts have held that patent-ineligible abstract ideas include certain
methods of organizing human activity, such as fundamental economic
practices, commercial or legal interactions, and managing personal behavior
or relationships or interactions between people. See, e.g., Alice, 573 U.S. at
219–20; Bilski v. Kappos, 561 U.S. 593, 611 (2010). Likewise,
“[m]athematical concepts – mathematical relationships, mathematical
formulas or equations, mathematical calculations” – are abstract ideas that
fall within the judicial exceptions to patent-eligibility. SAP America, Inc. v.
InvestPic, LLC, 898 F.3d 1161, 1163 (Fed. Cir. 2018) (holding that claims to
a ‘‘series of mathematical calculations based on selected information’’ are
directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for
Imaging, Inc., 758 F.3d 1344, 1350 (Fed. Cir. 2014) (holding that claims to a
‘‘process of organizing information through mathematical correlations’’ are
directed to an abstract idea). Finally, abstract ideas also include mental
processes, including subject matter that covers performance in the mind but
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for the recitation of generic computer components. Gottschalk v. Benson,
409 U.S. 63, 69 (1972); Mortg. Grader, Inc. v. First Choice Loan Servs.
Inc., 811 F.3d 1314, 1324 (Fed. Cir. 2016) (holding that computer-
implemented method for “anonymous loan shopping” was an abstract idea
because it could be “performed by humans without a computer).
We find that the steps of claim 1, collectively as an ordered
combination, recite a method of organizing human activity similar to other
concepts that have been identified by the courts as abstract. See, e.g.,
FairWarning IP, LLC v. Iatric Systems, Inc., 839 F.3d 1089, 1093 (Fed. Cir.
2016) (holding claims “‘directed to or drawn to the concept of analyzing
records of human activity to detect suspicious behavior’” to be directed to an
abstract idea).
In particular, claim 1 on appeal is similar to the claims at issue in
Bilski and Alice, in that it is directed to a computer-implemented method for
carrying out a process that was widespread long before computers, the
Internet, and electronic commerce: i.e., determining the likelihood that a
transaction is fraudulent based on comparison with past transactions of an
individual and similarly situated individuals. Detecting fraudulent
transactions falls within, at least, commercial interactions such as sales
activities. 84 Fed. Reg. at 52 (identifying commercial interactions including
sales activities as falling within “[c]ertain methods of organizing human
activity” categorized as abstract ideas).
The steps performed by the computer-readable program code of claim
1 correspond to the electronic versions of:
• “receiving . . . new transaction from a customer’s payment card”
(step (a));
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• categorizing the customer based on data relating to (1) customer
attributes; (2) attributes of the current transaction, and (3) attributes
of past transactions of the customer and one or more other
customers at a plurality of merchants, including the merchant
receiving new transaction (steps (b)–(f)):
o “associating . . . the customer with an archetype
distribution . . . based on past transactions across a
plurality of merchants by the customer and customer
attributes” (step (b));
o “generating . . . a topic model based on one or more
words and/or . . . documents . . . from . . . new and past
transactions, the topic model representing a similarity of
the words and/or documents of the new transaction with
words and/or documents in the past transactions
represented by the archetypes” (step (c));
o “generating . . . archetype clusters based on archetype
distribution vectors for each document upon execution of
the topic model, the archetype clusters representing a
similarity of documents in the archetype space based on
past transactions by the customer and customer
attributes” (step (d));
o “associating . . . the customer with an archetype cluster
. . . based on . . . the past transactions from the customer
or one or more other customers, the archetype cluster
representing a distribution of a probability of attributes
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related to each of the past transactions at the merchant”
(step (e));
o “locating . . . an archetype distribution vector and the
archetype cluster . . . based on . . . past transactions from
the customer or one or more other customers, the
archetype cluster representing a distribution of a
probability of attributes related to each of the past
transactions at a multitude of merchants, the archetype
distribution vector representing the current archetype
distribution of the customer's transactions across the
multitude of merchants” (step (f));
• determining the risk that the transaction is fraudulent based on
fraud risk associated with customer’s membership in particular
customer categories and attributes of the current transaction and
the customer’s past transactions:
o “generating . . . a first score representing a likelihood of
fraud associated with the new transaction based on the
calculated transaction risks associated with global
archetype cluster membership, merchant-specific
archetype cluster membership, and recurrence list
positions of transaction details” (step (g));
o “generating . . . a second score based on a frequency of
transactions associated with the customer, the frequent
behavior sorted list method comprising generating a
frequency table including non-fraudulent and fraudulent
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activity in different merchant-specific archetype clusters”
(step (h));
o “calculating . . . a risk score based on a weighted sum of
the first score and the second score” (step (i));
• “determining, in response to the risk score exceeding a preset
threshold, that the new transaction is fraudulent” (step (j));
• “performing, in response to the determining, an action on the new
transaction” (step (k)); and
• updating the relevant datasets with the new transaction (i.e.,
“updating the topic model and the frequency table with the new
transaction” (step (l)).
In addition to reciting a method of organizing human activity, we note
that claim 1 steps also recites steps that may be performed entirely in the
human mind. For instance, under the broadest reasonable interpretation, step
(a) recites receiving information; steps (b)–(f) recite evaluating, generating,
and/or categorizing information; and steps (g)–(j) recites assessing, and
making a determination based on, information, which are all actions that can
be performed in the mind. 84 Fed. Reg. at 52 (identifying mental processes
as a category of abstract ideas). Likewise, under the broadest reasonable
interpretation, steps (b)–(i), drawn in whole or in part to generating or
calculating steps, recite mathematical concepts. Id.

Prong Two of Step 2A of Revised Guidance
Although claim 1 recites an abstract idea, it would still be patent-
eligible if “the claim as a whole integrates the recited judicial exception into
a practical application of the exception”; i.e., whether the claim “appl[ies],
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rel[ies] on, or use[s] the judicial exception in a manner that imposes a
meaningful limit on the judicial exception.” 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.
In this case, the additional elements recited in claim 1 beyond the
judicial exceptions are (1) “a merchant transaction computer,” (2) “a card-
not-present (CNP) online transaction conducted over a communications
network,” (3) “an electronic database,” and 4) “an archetype calculation
engine.” Thus, other than the limitations reciting the abstract idea, the
invention is claimed at a very high level of generality.
In particular, most of the additional elements the claim recites are only
generic hardware and/or software elements (i.e., “transaction computer,”
“communications network,” “electronic database,” and “calculation engine”).
Moreover, the recited functions performed by these elements – “receiving . . .
data”; “associating” objects (e.g., associating customer and archetype
distribution or archetype cluster); organizing data into particular forms (e.g.,
“generating” archetype distributions, topic models and archetype clusters
based on customer attributes and current and past transactions, and
“calculating” probabilities such as “likelihood of fraud” and “risk score”);
and “updating” information – are all conventional functions of a computer.
Thus, claim 1 essentially implements an abstract idea on a computer,
which does not suffice to integrate the abstract idea into a practical
application. 84 Fed. Reg. at 55; see also buySAFE, Inc. v. Google, Inc., 765
F.3d 1350, 1355 (Fed. Cir. 2015) (stating that “[t]he Court in Alice made
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clear that a claim directed to an abstract idea does not move into section 101
eligibility territory by ‘merely requir[ing] generic computer
implementation’”) (alteration in original).
Likewise, “an additional element [that] adds insignificant extra-
solution activity to the judicial exception” or “does no more than generally
link the use of a judicial exception to a particular technological environment
or field of use” does not integrate a judicial exception into a practical
application. 84 Fed. Reg. at 55. The “card-not-present (CNP) online
transaction” limitation recited in claim 1 does not integrate a judicial
exception into a practical application, because it merely link the use of the
recited abstract ideas, discussed above, to a particular type of transaction or
field of use and/or add insignificant extra-solution activity to the judicial
exception by providing the data needed for the performance of the abstract
idea. Id.; see also CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d
1366, 1370 (Fed. Cir. 2011) (explaining that “mere ‘[data-gathering] step[s]
cannot make an otherwise nonstatutory claim statutory’”).
In summary, claim 1 recites an abstract idea and does not integrate the
abstract idea into a practical application. Therefore, claim 1 is directed to an
abstract idea.
Whether Claim 1 Amounts to “Significantly More”
Finally, the Revised Guidance directs us to consider whether claim 1
includes “additional elements . . . [that] provide[] ‘significantly more’ than
the recited judicial exception.” 84 Fed. Reg. at 56. The Revised Guidance
states that an additional element that “simply appends well-understood,
routine, conventional activities previously known in the industry, specified
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at a high level of generality, to the judicial exception, . . . is indicative that
an inventive concept may not be present.” Id.
Here, as discussed, the only elements recited in claim 1, other than the
abstract idea itself, are generic computer components (i.e., “a merchant
transaction computer,” “a communications network,” “an electronic
database,” and “an archetype calculation engine”) and “a card-not-present
(CNP) online transaction conducted over [the] communications network.”
The Specification further makes clear that all of these elements used
to implement the invention may be generic and are thus well-understood,
routine, and conventional. For instance, the Specification states that
various aspects or features [of the subject matter described herein] can
include implementation in one or more computer programs that are
executable and/or interpretable on a programmable system including
at least one programmable processor, which can be special or general
purpose, coupled to receive data and instructions from, and to transmit
data and instructions to, a storage system, at least one input device,
and at least one output device.
Spec. ¶ 74; see also id. ¶¶ 9, 75–77. The Specification states that
“[m]erchant losses [due to fraud] occur mainly on the card-not-present
(CNP) transactions on the web according to the fraud statistics,” which
indicates that such transactions are well-understood, routine, and
conventional. Id. ¶ 4.
In short, the additional elements of claim 1, individually or in
combination, only require using a generic computer system and the
Internet to perform routine communication and analysis functions, and
“the mere recitation of a generic computer cannot transform a patent-
ineligible abstract idea into a patent-eligible invention.” Alice, 573
U.S. at 223. Thus, the combination of elements recited in claim 1
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does not amount to significantly more than the judicial exception
itself, and under 35 U.S.C. § 101 the claim is ineligible for patenting.

Appellant’s Arguments
Appellant argues that, under a broadest reasonable interpretation of
the claims in light of the Specification, claim 1 does not recite an abstract
idea. Appellant first argues that claim 1 does not recite a fundamental
economic principle or practice, because “[a]ccording to the 2019 PEG,
‘fundamental economic principles or practices’ include hedging,
insurance, and mitigating risk and ‘describe subject matter relating to
the economy and commerce,’” whereas “[c]laim 1 is directed to a ‘method
to detect fraudulent transactions’ which is not an enumerated fundamental
economic principle or practice and is not included in the examples of MPEP
2106.04.” Appeal Br. 14; see also Reply Br. 8–11, 17. Appellant further
appears to argue that, because “not a single user or person is involved in the
recited steps,” claim 1 “does not recite a method of organizing human
activity for at least falling outside the enumerated subgroupings.” Reply Br.
10–11.
We are not persuaded. As an initial matter, a claim does not need to
explicitly recite a user or a person in order for the claim to recite a method of
organizing human activity. For example, the claim in Alice, which the
Supreme Court held to be directed to a fundamental economic principle or
practice, also does not explicitly recite any user or person in its steps. Alice,
573 U.S. at 213 n. 2, 219. As the Alice court explained, “mere recitation of a
generic computer cannot transform a patent-ineligible abstract idea into a
patent-eligible invention.” Id. at 223. The fact that claim 1 cites computers,
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rather than a user or person, as performing its steps, therefore, does not
render the claim non-abstract.
Indeed, as already discussed above, we find that claim 1 recites a
method of organizing human activity constituting an abstract idea, at least
because the claim recites commercial interactions such as sales activities.
The first step of claim 1, for example, recites part of a sales activity, i.e.,
“receiving, by a merchant transaction computer during a card-not-present
(CNP) online transaction conducted over s communications network, data
representing a new transaction from a customer’s payment card.” Appeal
Br. 22 (Claims App (emphasis added).). The remaining steps recite a
method for determining whether such sales activity involves a fraudulent
transaction.
Appellant similarly argues that, while claim 1 may be “based on or
involves a mathematical concept,” it does not recite such a concept
because it does not recite “a mathematical relationship, a mathematical
formula, or a mathematical calculation.” Appeal Br. 13; see also Reply Br.
7–8, 17.
We are not persuaded. Appellant points to the examples of
mathematical relationships provided in the Guidance Update – e.g., “a ratio
between force and area, a relationship between a reaction rate and a
temperature, and a conversion between binary coded decimal and pure
binary numerals” – and contends that “[c]laim 1 does not recite any of these
examples nor describes a mathematical relationship, a mathematical formula
or equation, or a mathematical calculation.” Reply Br. 7.
As the Guidance Update makes clear, however, no particular words
need to be used when reciting mathematical concepts, and a mathematical
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relationship expressed in words rather than mathematical symbols are
nevertheless abstract. Guidance Update 3–4. In this case, steps (b) through
(i) are all directed to generating models of, computing, and/or calculating
either (1) the relationship between a current transaction and/or a customer
and a larger dataset of past transactions and/or customers or (2) the
likelihood of fraud associated with a particular transaction. These steps
recite mathematical concepts because, as with the claims found ineligible in
Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350
(Fed. Cir. 2014), they “employ[] mathematical algorithms to manipulate
existing information to generate additional information.” Id. at 1351. Step
(e), for instance, describes generation of an archetype cluster that
“represent[s] a distribution of a probability of attributes related to each of
the past transactions at the merchant.” Appeal Br. 22 (Claims App.)
(emphasis added). Similarly, the Specification describes step (j),
“calculating . . . a risk score based on a weighted sum of the first score and
the second score,” in terms of a mathematical formula:
logodds(R) = a * logoddds(Ra) + (1 – a) * logodds(Rf),
where a (0