International Business Machines Corporation et al.

Patent Trials and Appeals Board

Jan 21, 2021

2020005370 (P.T.A.B. Jan. 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.
14/862,656 09/23/2015 Suresh N. Chari YOR920150355US2 1368
35526 7590 01/21/2021
DUKE W. YEE
YEE & ASSOCIATES, P.C.
P.O. BOX 6669
MCKINNEY, TX 75071
EXAMINER
SHAIKH, MOHAMMAD Z
ART UNIT PAPER NUMBER
3694
NOTIFICATION DATE DELIVERY MODE
01/21/2021 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):
ptonotifs@yeeiplaw.com
PTOL-90A (Rev. 04/07)

UNITED STATES PATENT AND TRADEMARK OFFICE
____________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________

Ex parte SURESH N. CHARI, TED A. HABECK,
COENRAAD JAN JONKER, FRANK JÖRDENS,
IAN M. MOLLOY, YOUNGJA PARK,
CORNELIS VAN SCHAIK, and
MARK EDWIN WIGGERMAN

Appeal 2020-005370
Application 14/862,656
Technology Center 3600
____________

Before RICHARD M. LEBOVITZ, TAWEN CHANG, and
RACHEL H. TOWNSEND, Administrative Patent Judges.

LEBOVITZ, Administrative Patent Judge.

DECISION ON APPEAL
The Examiner rejected the claims under 35 U.S.C. § 103 as obvious
and under 35 U.S.C. § 101 as lacking patent eligibility. Pursuant to 35
U.S.C. § 134(a), Appellant1 appeals from the Examiner’s decision to reject
the claims. We have jurisdiction under 35 U.S.C. § 6(b).
We AFFIRM and set forth a new ground of rejection pursuant to 37
C.F.R. § 41.50(b).

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 International
Business Machines Corporation. Appeal Br. 2.
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STATEMENT OF THE CASE
The Examiner rejected claims 1, 2, 4–8, 10–12, and 18–20 in the Final
Office Action as follows:
Claims 1, 2, 4–8, 10–12, and 18–20 under 35 U.S.C § 103(a) as
obvious in view of Jastrebski et al. (US 2010/0169137 A1, published July 1,
2010) (“Jastrebski”) and Choudhuri et al. (US 2013/0024361 A1, published
Jan. 24, 2013) (“Choudhuri”). Final Act. 59.
Claims 10–12 under 35 U.S.C § 103(a) as obvious in view of
Jastrebski, Choudhuri, and Shi et al. (US Patent 9,563,921, B2, published
Feb. 7, 2017) (“Shi”). Final Act. 69.
Claims 1, 2, and 4–20 under 35 U.S.C. § 101 “because the claimed
invention is directed to an abstract idea without significantly more.” Final
Act. 53.
Claim 1 is representative and reproduced below, with annotations
added to number the steps in the claim:
1. A computer-implemented method for identifying fraudulent
transactions, the computer-implemented method comprising:
[1] obtaining, by a data processing system, transactions
data corresponding to a plurality of transactions between
accounts from one or more different transaction channels;
[2] generating, by the data processing system, at least one
graph of transaction payment relationships between the
accounts from the transaction data;
[3] extracting, by the data processing system, features
from the at least one graph of transaction payment relationships
between the accounts;
[4] generating, by the data processing system, a fraud
score for a current transaction based on the extracted features
from the at least one graph of transaction payment relationships
between the accounts;
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[5] comparing, by the data processing system, the
generated fraud score for the current transaction to a fraudulent
transaction threshold value to determine a level of suspicion
regarding the current transaction; and
[6] responsive to the data processing system determining
that the current transaction is fraudulent, blocking, by the data
processing system, the current transaction from being
completed.

The claimed computer-implemented method obtains data about
payment transactions (step [1]), generates a graph of the transactions (step
[2]; see Spec., Fig. 3 for an exemplary graph), and extracts features from the
graph of the payment transactions (step [3]). The features can be the number
of accounts involved in a payment transaction, the monetary value of a
payment transaction, and the amount of money flowing from one account to
another, etc. Spec. ¶ 77. In step [4] of the claim, a “fraud score” is generated
for a “current transaction” based on the extracted features. The score is
compared “to a fraudulent transaction threshold value to determine a level of
suspicion regarding the current transaction” (step [5]), and the transaction is
blocked when the transaction is determined to be fraudulent (step [6]).

REJECTION BASED ON SECTION 103
The Examiner found that Jastrebski describes a computer-
implemented method for identifying fraudulent activity in an account using a
“graph of transaction payment relationships between the accounts” as recited
in step 2 of claim 1, extracting features from the graph to identify a fraud
score, and comparing to a threshold to determine “good” and “bad”
transactions. Final Act. 59–61. The Examiner found that Jastrebski does not
disclose step 6 of the claim which recites “determining that the current
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transaction is fraudulent, [and then] blocking, by the data processing system,
the current transaction from being completed.” However, the Examiner
found that Choudhuri describes this step of the claim. Final Act. 61. The
Examiner found it would have been obvious to one of ordinary skill in the
art to modify Jastrebski
to include responsive to the data processing system determining
that the current transaction is fraudulent, blocking, by the data
processing system, the current transaction from being
completed in order to ensure that the multi stage filtering
process allows the server capacity to be dynamically adjusted
for supporting fraud detection services [as described in
Choudhuri].
Final Act. 61.
Appellant argues that the Examiner erred in finding that Jastrebski
describes generating a fraud score for a “current transaction” as required by
steps 4–6 of claim 1. Appeal Br. 35. Instead, citing specific disclosure in
Jastrebski, Appellant explains that the fraud score is for a graph of
transactions in accounts and that the analysis described in Jastrebski to
obtain that score is performed on transactions that have already taken place.
Id. at 38.
The Examiner responds by citing paragraphs 36, 38, 44, 60, and 86
from Jastrebski, asserting that these disclosures generate a score for a current
transaction. Ans. 53–54.
We have reviewed these passages, and as explained by Appellant, all
the disclosures cited by the Examiner are for the construction of a graph of
past transactions in accounts, i.e., transactions that have already occurred,
and then generating a score for the graph of these past transactions. For
example, Jastrebski discloses:
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The node 880 is connected to the other nodes with edges 892,
894, 896, 898, and 900. The edges 892, 894, and 896 are
directed edges and represent transactions. For example, the
edge 892 represents a flow of money from the node 880 to node
882. The width of the edge visually highlights a greater amount
of money in comparison with the edges 894 and 896. For
example, the edge 892 may represent a transfer of $100.00
USD. Further for example, the edge 894 represents a flow of
money from the node 884 to the node 880. The width of the
edge visually highlights a lesser amount of money in
comparison with the edge 892. For example, the edge 894 may
represent a transfer of $50.00 USD. The edge 896 represents a
flow of money from the node 880 to the node 886. The width of
the edge visually highlights a lesser amount of money in
comparison with the edge 892 and the edge 894. For example,
the edge 896 may represent a transfer of $25.00 USD. The edge
898 and 900 represent links that respectively connect the node
880 the nodes 890 and 888.
Jastrebski ¶ 86.
Jastrebski describes generating a score for the graph: “Further, the
machine learning engine 634 may generate a score 690 shown in FIG. 8B for
the graph being analyzed based on the presence of a matching graph in the
un-ranked graph criteria 702.” Jastrebski ¶ 62.
It is clear from this disclosure Jastrebski is graphing transactions that
have already taken place. Thus, we agree with Appellant that the Examiner
erred in finding that Jastrebski describes generating a fraud score for a
current transaction as recited in step 4 of claim 1.
However, as discussed by the Examiner, Choudhuri describes
analyzing current transactions for fraud. Final Act. 61. Choudhuri
specifically discloses:
In accordance with embodiments of the invention, the term
“filtration” or “filter” refers to the means or the process of
analyzing aspects of a financial transaction to evaluate the
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possibility of fraud associated with the transaction. A “filtration
stage” is a stage in a multi-stage process wherein the possibility
of fraud associated with a transaction is evaluated. After each
filtration stage, the transaction is either deemed to be
acceptable to process, the transaction is evaluated by a
subsequent filtration stage, the transaction is declined, or other
fraud related actions are taken to mitigate additional fraud in the
future.
Choudhuri ¶ 42 (emphasis added).

In accordance with embodiments of the invention, a “fraud
alert” is a notification that the multi-stage fraud detection
process has deemed a transaction to be potentially fraudulent.
The alert may be a notification of any type to any party
including the financial institution customer, an employee of the
financial institution, the potential payee of the transaction, etc.
The term “fraud-detection action” is an action that may be taken
when a transaction is identified as fraudulent and/or potentially
fraudulent. The action may include sending a fraud alert,
flagging a transaction as fraudulent, preventing a transaction
from occurring, accessing additional information to further
filter the transaction for potential fraud, etc.
Choudhuri ¶ 43 (emphasis added).
The difference between Jastrebski and Choudhuri is that Jastrebski
uses a graph of relationships (see Jastrebski, Figs. 13A–C, Fig. 15) to extract
features to determine whether there was past fraud in the account (Jastrebski
¶ 38: “In one embodiment, the graph engine may utilize account metrics to
identify the status of the account. If the graph engine identifies the account is
‘GOOD,’ then the graph engine may effectively prune off a part of a graph
from further analysis.”). Choudhuri does not use a graph, but Choudhuri
does use past transactions to determine whether a current transaction is
fraudulent, but does not map the transactions to a graph as does Jastrebski.
Choudhuri teaches:
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The fraud investigator filtering process 230 may be the same as
or similar to the pre-filtering stage or stages. However, instead
of pre-filtering the fraud investigator may set up specialized
rules to examine the potentially fraudulent transaction on a
customer level instead of a transaction level. For example, the
fraud investigator may set up rules to look at all of the events
related to the customer, such as transaction information (i.e.
amount, the payee, the location, the channel, the date and/or
time, etc.), customer behavior information (i.e. customer
history, velocity data, etc.), account events (i.e. non-monetary
account data, etc.), customer information (i.e. customer profile
information, customer fraud preferences, type of customer,
preferred customer, etc.). In this way the fraud investigator may
determine if potential fraud exists, the likelihood that fraud
exists, etc. based on the experience of the fraud investigator and
an omniscient view of the customer as a whole as opposed to a
narrow view of just the transaction.
Choudhuri ¶ 68 (emphasis added).
Choudhuri blocks a transaction identified as fraudulent:
Once the fraud investigator filtering process 230 analyzes the
transaction in view of the overall customer, the process 200
moves to decision block 225. If the transaction is deemed to not
likely be fraudulent, the transaction related to the customer is
processed, as illustrated at block 250. . . . However, if the
transaction is determined to be fraudulent, the transaction may
be declined, as represented at block 260 and a fraud alert may
be generated as noted at block 270.
Choudhuri ¶ 69.
It would have been obvious to one of ordinary skill in the art to apply
Jastrebski’s relationship graphing analysis to detect whether a current
transaction is fraudulent, in place of the filtering process described in
Choudhuri, for its expected benefit in identifying a current fraudulent
transaction. Choudhuri explicitly describes looking at “all of the events
related to the customer” (Choudhuri ¶ 68; as reproduced above) in
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identifying whether a current transaction is fraudulent, making it obvious to
apply Jastrebski’s graphing analysis of looking at customer accounts to the
detection of fraudulent activity of a current transaction. Jastrebski
specifically looks for “risky” transactions in the graph (at ¶ 81) and uses “the
account metric” to “indicate an unusual purchase, unusual amount of
purchase, or an unusual location of purchase” (at ¶ 106) and thus looks at
individual transactions in the graph, providing further reason to look at a
current transaction, as described in Choudhuri, using Jastrebski graphing
technique.

Summary
Because we find that the Examiner erred in finding that Jastrebski
describes generating a fraud score for a current transaction as recited in
claim 1 (e.g., step 4), we reverse the obviousness rejections as expressed by
the Examiner of all the claims. However, as explained above, we find, for
different reasons than the Examiner, that the combination of Jastrebski and
Choudhuri makes claim 1 obvious. A new ground of rejection under 37
C.F.R. § 41.50(b) is therefore set forth for claim 1. We leave it to the
Examiner to determine whether the rejection is applicable to the other
independent and dependent claims.

REJECTION BASED ON 101
Principles of Law
Under 35 U.S.C. § 101, an invention is patent-eligible if it claims a
“new and useful process, machine, manufacture, or composition of matter.”
However, not every discovery is eligible for patent protection. Diamond v.
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Diehr, 450 U.S. 175, 185 (1981). “Excluded from such patent protection are
laws of nature, natural phenomena, and abstract ideas.” Id. The Supreme
Court articulated a two-step analysis to determine whether a claim falls
within an excluded category of invention. Alice Corp. v. CLS Bank Int’l, 573
U.S. 208 (2014); Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566
U.S. 66, 75–77 (2012).
In the first step, it is determined “whether the claims at issue are
directed to one of those patent-ineligible concepts.” Alice, 573 U.S. at 217. If
it is determined that the claims are directed to an ineligible concept, then the
second step of the two-part analysis is applied in which it is asked “[w]hat
else is there in the claims before us?” Id. (alteration in original). The Court
explained that this step involves
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 217–18 (alteration in original) (citing from Mayo, 566
U.S. at 75–77).
Alice, relying on the analysis in Mayo of a claim directed to a law of
nature, stated that in the second part of the analysis, “the elements of each
claim both individually and ‘as an ordered combination’” must be
considered “to determine whether the additional elements ‘transform the
nature of the claim’ into a patent-eligible application.” Alice, 573 U.S. at
217.
The PTO published revised guidance on the application of 35 U.S.C.
§ 101, USPTO’s January 7, 2019 Memorandum, 2019 Revised Patent
Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019) (“2019
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Eligibility Guidance”) (we cite to Federal Register where the 2019
Eligibility Guidance was published). This guidance provides additional
direction on how to implement the two-part analysis of Mayo and Alice.
Step 2A, Prong One, of the 2019 Eligibility Guidance looks at the
specific limitations in the claim to determine whether the claim recites a
judicial exception to patent eligibility. In Step 2A, Prong Two, the claims are
examined to identify whether there are additional elements in the claims that
integrate the exception in a practical application, namely, whether there is a
“meaningful limit on the judicial exception, such that the claim is more than
a drafting effort designed to monopolize the judicial exception.” 84 Fed.
Reg. 54 (Prong Two).
If the claim recites a judicial exception that is not integrated into a
practical application, then as in the Mayo/Alice framework, Step 2B of the
2019 Eligibility Guidance instructs us to determine whether there is a
claimed “inventive concept” to ensure that the claims define an invention
that is significantly more than the ineligible concept itself. 84 Fed. Reg. 56.
With these guiding principles in mind, we proceed to determine
whether the claimed subject matter in this appeal is eligible for patent
protection under 35 U.S.C. § 101.
Discussion
Claim 1 is directed to a computer-implemented method for identifying
fraudulent transactions. A method is also a “process,” one of the statutory
categories of patent-eligible subject matter under 35 U.S.C. § 101. Because
the claim falls into one of the statutory categories of patent-eligible subject
matter, we proceed to Step 2A, Prong One, of the Eligibility Guidance.
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Step 2A, Prong One
In Step 2A, Prong One, of the 2019 Eligibility Guidance, the specific
limitations in the claim are examined to determine whether the claim recites
a judicial exception to patent eligibility, namely whether the claim recites an
abstract idea, law of nature, or natural phenomenon.
The Examiner found that the claims cover the performance of “certain
methods of organizing human activity,” which is one of the groupings of
abstract ideas listed in the 2019 Eligibility Guidance. Final Act. 54. The
Examiner specifically characterized the steps in the claim as a fundamental
economic practice of “mitigating transaction risk” by generating a fraud
score “to determine a level of suspicion regarding . . . [a] current
transaction” (step [5]). Final Act. 54–55. The 2019 Eligibility Guidance,
citing Alice, 573 U.S. at 219–20, identifies risk mitigation as a fundamental
economic practice and an abstract idea. 84 Fed. Reg. 52.
Appellant contends that the Examiner erred in characterizing the
claim as a method of organizing human activity. Appeal Br. 9. Appellant
argues that “methods of organizing human activity must somehow entail
actual activities of humans, and the ‘organizing’ of such human activities.”
Id. Appellant states that this is “clearly seen when reviewing the various
fundamental economic practice examples given in the 2019 Guidance —
which all involve human activities.” Id.
This argument is not persuasive. The “transactions” which occur
between accounts (step [1]), the “current transaction” for which a “fraud
score” is generated (step [4]), and the “blocking” of the “current transaction”
when it is determined to be fraudulent (step [6]) are each an activity that is
initiated by or otherwise involves a human. As explained in the
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Specification, the transactions are those of a “payer” and can be a transaction
between a customer and merchant for the payment of a good. Spec. ¶¶ 2,
137, 138. The purpose of the claim is to identify a payment fraud by a
“payer” in a transaction. Spec. ¶ 2. The claim therefore is monitoring a
human’s payment transactions to determine whether they are fraudulent,
which is clearly involving a human activity.
Appellant’s statement that the claim does not recite any “human
activity” may be strictly correct in the sense that the steps recite performance
by a “data processing system,” but the claim organizes the “human activity”
of a payment transaction, e.g., between a customer and merchant, to mitigate
the risk that the customer’s current transaction with the merchant is
fraudulent by generating a fraud score (step [4]) and comparing it to a
“threshold value to determine a level of suspicion regarding the current
transaction” (step [5]), and then blocking the customer’s transaction if it is
determined to be fraudulent (step [6]). The human activity is the “plurality of
transactions between accounts from one or more different transaction
channels” that is recited in the first step [1] of the claim. While a human is
not “obtaining” the transaction data (step [1]), “generating . . . transaction
payment relationships between the accounts from the transaction data” (step
[2]), etc., the claim is managing a human’s payment transactions.
Appellant takes the position that a human must actually be an actor in
a claim for the claim to be a method of organizing human activity. Appellant
states that the “fundamental economic practice examples” cited in the
Eligibility Guidance “all involve human activities.” Appeal Br. 9–10. In
contrast, Appellant argues that the steps of the claim are accomplished
“without any human involvement.” Id. at 10.
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We do not agree with Appellant. For example, in Inventor Holdings,
LLC v. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1374 (Fed. Cir. 2017) cited
in footnote 13 of the 2019 Eligibility Guidance, the claims of the ’582 patent
involved a method of processing a payment for purchase of goods from a
seller. The steps comprised transmitting orders to a “remote seller,” using
“codes” associated with the orders, and making payments to the seller. Id.
None of the steps actually recited a human actor, but the claim was still
considered to be a patent-ineligible fundamental business practice. As
explained by the court:
the claims of the ’582 patent are manifestly directed to an
abstract idea, which the district court accurately described as
“local processing of payments for remotely purchased goods.”
Inventor Holdings, 123 F.Supp.3d at 561 (citing ‘582 patent
col. 1 ll. 6–10). The idea that a customer may pay for items
ordered from a remote seller at a third-party’s local
establishment is the type of fundamental business practice that,
when implemented using generic computer technology, is not
patent-eligible under Alice, 134 S.Ct. at 2355.
Inventor Holdings, 876 F.3d at 1378.
Claim 1 in this appeal is no different because it involves making a
determination that a payment transaction is fraudulent using generic
computer technology.
The 2019 Eligibility Guidance expressly lists “commercial or legal
interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors)” as
falling in the grouping of “Certain methods of organizing human activity”
(84 Fed. Reg. 52); there is no indication in this list that an actual human
actor must participate in the claim for it be classified as such. Rather, a claim
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involves organizing human activity when a step in it includes a purchase,
sale, etc. that a human ordinarily makes.
Appellant also quotes from the 2019 PEG Update,2 as supporting their
argument about the requirement for human activity. Appeal Br. 11–12. The
disclosure cited by Appellant is as follows:
Finally, the sub-groupings encompass both activity of a single
person (for example, a person following a set of instructions or
a person signing a contract online) and activity that involves
multiple people (such as a commercial interaction), and thus,
certain activity between a person and a computer (for example a
method of anonymous loan shopping that a person conducts
using a mobile phone) may fall within the “certain methods of
organizing human activity” grouping.
2019 PEG Update 5.
Appellant did not quote the full discussion in the 2019 PEG Update.
Preceding the disclosure reproduced by Appellant in the Appeal Brief, the
2019 PEG Update explained the scope of the abstract idea of “certain
methods of organizing human activity”:
The term “certain” qualifies the “certain methods of organizing
human activity” grouping as a reminder of several important
points. First, not all methods of organizing human activity are
abstract ideas (e.g., “a defined set of steps for combining
particular ingredients to create a drug formulation” is not a
“certain method of organizing human activity”). Second, this
grouping is limited to activity that falls within the enumerated
sub-groupings of fundamental economic principles or practices,
commercial or legal interactions, managing personal behavior,
and relationships or interactions between people, and is not to
be expanded beyond these enumerated sub-groupings except in

2 Available at
https://www.uspto.gov/sites/default/files/documents/peg_oct_2019_update.p
df (last accessed Dec. 31, 2020) (“2019 PEG Update”).

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rare circumstances as explained in Section III(C) of the 2019
PEG.
PEG Update 4–5 (footnote omitted).
The discussion in the 2019 PEG Update of activities between humans
or a human and a computer was not exclusive, but simply another
subgrouping considered to fall within the category of certain methods of
organizing human activity. The 2019 PEG Update specifically lists Inventor
Holdings as an example a fundamental economic practice (2019 PEG
Update 5), and as explained above, the claims in Inventor Holdings were
similar to those at issue in this appeal because they involved transactions
between a customer and a merchant.
Because we find that the Examiner correctly found that claim 1 recites
an abstract idea, we proceed to Step 2A, Prong Two, of the analysis.

Step 2A, Prong Two
Prong Two of Step 2A under the 2019 Eligibility Guidance asks
whether there are additional elements that integrate the exception into a
practical application. We must look at the claim elements individually and
“as an ordered combination” to determine whether the additional elements
integrate the recited abstract idea into a practical application. As explained
in the 2019 Eligibility Guidance, integration may be found when an
additional element “reflects an improvement in the functioning of a
computer, or an improvement to other technology or technical field” or
“applies or uses the judicial exception in some other 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.” Id. at 55. The 2019
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PEG Update further explains that “the specification should be evaluated to
determine if the disclosure provides sufficient details such that one of
ordinary skill in the art would recognize the claimed invention as providing
an improvement.” PEG Update 12.
Appellant contends that the “technological improvement” provided by
the claims is “that particular types of network traffic are conditionally
blocked in order to mitigate the use of fraudulent transactions.” Appeal Br.
12. Appellant states that “transaction data from one or more channels of
transactions is utilized to score transactions, and such transaction score is
then utilized to identify and block fraudulent transactions.” Id. Appellant
argues that the “automated payment relationship-based graph generation”
and “automated transaction blocking” are additional elements which
integrate “the alleged judicial exception into a practical application.” Id. at
13.
Appellant also asserts that the claims are patent-eligible because “the
features of Claim 1 do not come anywhere close [to] tying up the science or
technology of ‘mitigating transaction risk’ (the alleged abstract idea, per the
last two lines on page 54 of the final Office Action).” Id. at 7. Specifically,
Appellant argues that “fraudulent transactions could be identified without
using a graph-based process” and features extracted from it. Id. at 8.
The improvement and additional elements described by Appellant
correspond to steps 2 through 6 of claim 1. In step 2, a graph of transaction
payment relationships between accounts is generated. Features from the
graph are extracted (step 3) and a fraud score for a “current transaction” is
generated based on the extracted features (step 4). The score for the current
transaction is compared to a “fraudulent transaction threshold value to
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determine a level of suspicion regarding the current transaction” in step 5 of
the claim. When the current transaction is determined to be fraudulent in
step 5, the current transaction is blocked from being completed (step 6).
Steps 2–5 of the claim constitute the rules which are followed in order
to determine whether a current payment transaction should be blocked as
being fraudulent. The question before us is whether these rules are a
technological improvement to a process of identifying fraudulent
transactions or whether they are the abstract idea, itself. We address these
claim limitations below.
The rules recited in the claim are performed by a “data processing
system.” The Specification describes a “data processing system” as “a
network of computers and other devices,” pointing to Figure 1 which shows
servers, a storage device, and client devices connected to a “network.” Spec.
¶ 28. All the steps recited in the claims are performed on this system.
Features from the graph recited in step 2 are extracted and used to
determine a fraud score in steps 3 and 4. The graph and the extraction of
features from it do not impart “an improvement in the functioning of a
computer, or an improvement to other technology or technical field” (84
Fed. Reg. 55), but rather provide an improvement to how a fraud score is
computed by the data processing system, which is integral to the
fundamental practice of mitigating risk of a fraudulent transaction. For this
reason, we are not persuaded that the graph and the feature extraction are
additional elements of the claim; rather, we find them to be part of the
abstract idea of determining a fraud score. Our reasoning is explained in
more detail below by contrasting the rejected claims with claims in which a
software improvement was found to confer patent-eligibility.
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Specifically, we find that the rejected claims in this appeal are
distinguishable from the claims in Enfish, LLC v. Microsoft Corp., 822 F.3d
1327 (Fed. Cir. 2016) and Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299
(Fed. Cir. 2018), each of which involved software improvements
implemented on a computer.
For example, in Enfish, 822 F.3d at 1339, a self-referential table
recited in the claims was found by the court to be “a specific type of data
structure designed to improve the way a computer stores and retrieves data
in memory.” In Finjan, 879 F.3d at 1304, the court found that the claim of
the patent was directed to a “behavior-based” computer virus scan in which
the claimed method “scans a downloadable and attaches the virus scan
results to the downloadable in the form of a newly generated file: a ‘security
profile that identifies suspicious code in the received Downloadable.’” The
court asked “whether this behavior-based virus scan in the ’844 patent
constitutes an improvement in computer functionality” and answered the
question in the affirmative. Id. As the court explained, “the method of claim
1 employs a new kind of file that enables a computer security system to do
things it could not do before.” Id. at 1305.
The graph in the claim identified by Appellant as an “additional
element” does not add a new functionality to a computer as it did in Enfish
and Finjan, but rather is a representation of the payment transactions to
facilitate the computation of the fraud score by the data processing system
without imparting any change to the system, itself. Figure 3 of the
Specification, copied below, shows an example of a payment relationship
graph:
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Figure 3, reproduced above, shows transaction payment relationship
graph 300 showing various transactions that occur between accounts. For
example, as explained in the Specification, “Source account vertex 302
represents account ‘1234’ and destination account vertex 304 represents
account ‘5678’. Accounts ‘1234’ and ‘5678’ have multiple transactions 306
performed between them.” Spec. ¶ 68. The transaction payment relationship
graph 300 “also shows transaction 312 between account ‘5678’ and a point-
of-sale terminal, which corresponds to point-of-sale terminal vertex 314.”
Spec. ¶ 69.
The transaction payment relationship graph therefore depicts
transactions that occur between accounts to facilitate observing/viewing
which of these transactions have a level of suspicion. The data processing
system is being used as tool to “draw” the graph; this is not a technological
improvement, but rather is a representation of the transaction data of step 1
to facilitate the extraction of features from it. In SAP Am., Inc. v. InvestPic,
LLC, 898 F.3d 1161, 1167 (Fed. Cir. 2018), where “the focus of the claims”
was on “selecting certain information, analyzing it using mathematical
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techniques, and reporting or displaying the results of the analysis,” the court
identified the claimed steps involving the manipulation and display of the
data as an abstract principle.
The claims in this appeal are also distinguishable from those in
McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315 (Fed.
Cir. 2016). In McRO, the claim recited a series of steps that “produce[d] lip
synchronization and facial expression control of said animated characters.”
McRO found that the recited steps “are limiting in that they define morph
weight sets as a function of the timing of phoneme sub-sequences.” Id. at
1313. The court found that 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. For this reason, the claimed process was found to be
patent-eligible under 35 U.S.C. § 101. Thus, as explained in SAP, the claims
in McRO were “directed to the creation of something physical—namely, the
display of ‘lip synchronization and facial expressions’ of animated
characters on screens for viewing by human eyes.” SAP, 898 F.3d at 1167.
Here, the result of generating the graph and extracting features from it to
generate a fraud score for a transaction (steps 2–4) is to know when to block
a transaction from being completed. There is no change to a physical process
and no change to the process in which the transaction is even blocked. The
asserted “additional element” is an integral part of the underlying
computation to determine when a transaction is fraudulent to mitigate the
risk of fraudulent payment.
The claims are also ineligible for similar reasons as in Parker v.
Flook, 437 U.S. 584 (1978). In Flook, the claim was directed to a “method
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for updating the value of at least one alarm limit on at least one process
variable involved in a process comprising the catalytic chemical conversion
of hydrocarbons.” Flook, 437 U.S. at 596–97 (Appendix to Opinion). The
steps comprised determining a new alarm base using a mathematical
algorithm, using the alarm base to update an alarm limit, and then adjusting
the alarm limit to the updated value. Id. The alarm limit was used in a
catalytic chemical process. The Court held that the “application simply
provides a new and presumably better method for calculating alarm limits”
which did not confer patent eligibility on the claim. Id. at 595.
Here, we find the same deficiency. The rules recited in the claim of
generating a graph to determine a fraudulent transaction is a part of the
abstract idea, and not the process in which the transaction is blocked from
completion.
The claim’s ineligibility is also clear when contrasted with Diehr, in
which the claims were directed to a method of operating a rubber-molding
press to mold a compound by curing it in a mold cavity. 450 U.S. at 179, n.
5. The temperature in the mold during the rubber-molding process was
constantly determined and provided to a digital computer. Id. The computer
calculated the Arrhenius equation for the reaction time during the cure to
determine when the compound was cured and to automatically open the
press. Id. Although the claim recited a mathematical algorithm, the
Arrhenius equation, the Court held that the claim was eligible for a patent.
[W]hen a claim containing a mathematical formula implements
or applies that formula in a structure or process which, when
considered as a whole, is performing a function which the
patent laws were designed to protect (e.g., transforming or
reducing an article to a different state or thing), then the claim
satisfies the requirements of § 101.
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Diehr, 450 U.S. at 192–93.
In this case, the graph, and the step of extracting features from it, is
not used to control or change the operation of a non-abstract process. While
the steps in the rejected claim instruct how to determine a fraud score and a
fraudulent transaction, the steps at best improve the abstract process, in
contrast to how the mathematical formula in Diehr was used to inform when
to open the mold to obtain the cured product. In our view, as explained in
Diehr, the patent laws were not designed to protect an improvement to the
fundamental economic practice of mitigating risk of a fraudulent transaction
by determining a fraud score as recited in the claims.
Appellant also argues that claim 1 does not tie up the technology of
mitigating the risk of fraudulent transactions and that such fraud could be
identified without the graph. Appeal Br. 7, 8. However, as explained above,
the steps of the claim of using a graph and extracting features from it to
determine a fraud score are part of the abstract idea and therefore do not
serve as an additional element that integrates the exception into a practical
application. Unlike in Diehr and McRO where the specific rules in the claim
were used to change how a process is accomplished — opening a mold in
Diehr and animating a character in McRO — claim 1 does not change how
the process of blocking the transaction is achieved. Rather, as in Flook, the
asserted improvement resides in the rules which embody the abstract idea of
risk mitigation, and not in the process of blocking the transaction.
Moreover, there is no specific detail in the claim describing how the
graph is generated or how the features are extracted. Thus, even if our
analysis above under Diehr and McRO is flawed, the claim still fails. As
explained in Dropbox, Inc. et al. v. Synchronism Technologies, Inc., 815
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Fed. Appx. 529, 533 (Fed. Cir. 2020) (nonprecedential), it has been
“consistently held that an ‘inventive concept’ exists when a claim ‘recite[s] a
specific, discrete implementation of the abstract idea’ where the ‘particular
arrangement of elements is a technical improvement over [the] prior art.’
[BASCOM Glob. Internet Servs. v. AT&T Mobility LLC, 827 F.3d 1341,
1350 (Fed. Cir. 2016) (“Bascom”)].” As further explained in that case,
The patent has to describe how to solve the problem in a
manner that encompasses something more than the “principle in
the abstract.” See [ChargePoint, Inc. v. SemaConnect, Inc., 920
F.3d 759, 769 (Fed. Circ. 2019)] (explaining that an invention
may not be patent eligible if the “claims ‘were drafted in such a
result-oriented way that they amounted to encompassing the
‘principle in the abstract’ no matter how implemented’”
(quoting Interval Licensing LLC v. AOL, Inc., 896 F.3d 1335,
1343 (Fed. Cir. 2018))).
Dropbox, 815 Fed. Appx. at 533.
The steps in the claim recite neither how the graph is made nor how
the features are extracted from it. Instead, the steps are recited in a “result-
oriented way” in which the result is described in the claim, but not how the
result is accomplished. The steps are therefore the “principle in the abstract”
and it has not been salvaged from ineligibility by limiting how the
determination is made. We contrast this to McRO where the claim recited a
series of steps that “produce[d] lip synchronization and facial expression
control of said animated characters.” The court in McRO found that the
recited steps “are limiting in that they define morph weight sets as a function
of the timing of phoneme sub-sequences.” 837 F.3d at 1313. The court
further found that 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
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characters.” Id. at 1315. For this reason, the claimed process was found to be
patent-eligible under 35 U.S.C. § 101. In contrast, claim 1 on appeal does
not recite how steps [2] and [3] of the claim are accomplished. The steps are
abstract and therefore cannot serve as the additional element which goes
beyond the judicial exception. 84 Fed. Reg. 54–55. Thus, we are not
persuaded by Appellant’s argument that an alleged technical improvement
integrates the recited abstract idea into a practical application.
Step 2B
Because we determined that the judicial exception is not integrated
into a practical application, we proceed to Step 2B of the Eligibility
Guidelines, which asks whether there is an inventive concept. In making this
Step 2B determination, we must consider whether there are specific
limitations or elements recited in the claim “that are not well-understood,
routine, conventional activity in the field, which is indicative that an
inventive concept may be present,” or whether the claim “simply appends
well-understood, routine, conventional activities previously known to the
industry, specified at a high level of generality, to the judicial exception,
indicative that an inventive concept may not be present.” Eligibility
Guidance, 84 Fed. Reg. 56 (footnote omitted). In this part of the analysis, 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.” Alice, 573 U.S. at
217.
Reciting all the specific steps in claim 1, Appellant states that the
“claim recites features that go well beyond merely implementing a
methodology of ‘mitigating transaction risk’ . . . using a generic computer
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process.” Appeal Br. 14. Appellant responds to the Examiner’s arguments,
but does not persuade us the ordered combination of steps in claim 1 provide
an inventive concept.
For example, Appellant argues that the Examiner improperly found
that step 1 of the claim is “mere data gathering.” Appeal Br. 15. We do not
agree. As explained in the 2019 Eligibility Guidance, “a mere data gathering
such as a step of obtaining information about credit card transactions so that
the information can be analyzed in order to detect whether the transactions
were fraudulent” is insignificant extra-solution activity which is insufficient
to integrate the judicial exception into a practical application. 84 Fed. Reg.
55 (fn. 31) (citing CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d
1366 (Fed. Cir. 2011). While Appellant refers to this step under Step 2B,
Appellant does not explain how this step in combination with the other steps
in the claim provide an inventive step. Appeal Br. 15.
The principal deficiency in Appellant’s arguments is that the steps in
the claim in which a graph is generated and used to determine a fraud score
are all steps of the abstract idea, and do not provide any additional element,
that in combination, provide an inventive step. The transaction relationship
graph, itself, is described in Jastrebski, as an approach to analyzing
transaction relationships between accounts.3 The graph is also use to identify

3 “In some example embodiments, systems and methods are illustrated that
allows users to analyze data using a graph. In some instances that data may
be descriptive of accounts and relationships between those accounts in real
time. Further, the accounts may be represented as nodes in a graph and the
relationships between these accounts may be represented as edges
connecting these accounts.” Jastrebski ¶ 36.
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fraud.4,5 Thus, the Examiner properly found that it was well-known and
conventional to generate a graph in step 2 of the claim and use features of it
to determine fraud.

Dependent claims
Appellant provides separate arguments for claims 2, 4–16, and 20.
The arguments are all basically the same, namely, that a fraud score could be
generated that does not use the specific limitations recited in the claim and
therefore the abstract idea of mitigating the risk of a fraudulent transaction is
not tied up. As illustrative of the claims, claims 14 and 16 are reproduced
below:
14. The computer-implemented method of claim 13, wherein
the data processing system determines whether the current
transaction is fraudulent based on one of the data processing
system determining a probability of the current transaction
being fraudulent inversely proportional to the shortest path
between the source account vertex and the destination account
vertex in the at least one graph of transaction payment
relationships or the data processing system determining that the
current transaction is fraudulent in response to the shortest path
being greater than a defined length and determining that the

4 “For example, the graph engine may use a set of rules to identify an
account that as suspected of fraud. . . . The graph engine may further
generate a set of graph metrics for the graph and a score for the graph . . .
The agent may review the graph to identify whether the graph is suspected
of fraudulent activity or includes other interesting activity. The agent may
further review the graph to identify whether the graph includes accounts that
are suspected of fraudulent activity” Jastrebski ¶ 37.
5 “The graph is highlighted. For example, new activity in the form of
additional transactions, nodes or links, may be highlighted on the graph.
Also for example, transactions that are risky may be highlighted on the
graph” Jastrebski ¶ 81.
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current transaction is not fraudulent in response to the shortest
path being less than or equal to the defined length.

16. The computer-implemented method of claim 5, wherein the
data processing system utilizes monetary flow between the
source account vertex and the destination account vertex in the
at least one graph of transaction payment relationships between
the accounts for transaction fraud scoring, and wherein the data
processing system determines that the current transaction is
fraudulent based on one of the data processing system
determining a probability of the current transaction being
fraudulent inversely proportional to a maximum monetary flow
between the source account vertex and the destination account
vertex corresponding to the current transaction or the data
processing system determining that the monetary flow between
the source account vertex and the destination account vertex in
the at least one graph of transaction payment relationships is
less than a monetary flow threshold value.

We agree with Appellant that at least claims 14 and 16 recite a
specific ways in which the fraudulent transactions are determined. However,
as discussed above, that the rules recited in these claims are abstract and a
part of the fundamental economic practice of mitigating risk by determining
which transactions are fraudulent. Specifically, the steps in the claim
identify the features in the graph to determine the fraud score. Unlike in
McRO where the computations performed in the claim were used to animate
characters, here, the computations are a part of the fundamental economic
practice of mitigating risk by determining whether a transaction is
fraudulent.
For the foregoing reasons, we affirm the rejection of claims 1, 2, and
14–20 for lack of patent eligibility. Claims not argued separately fall with
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claim 1, etc., because separate arguments for their patentability were not
made. 37 C.F.R. § 41.37(c)(1)(iv).

CONCLUSION
In summary:
Claims
Rejected
35
U.S.C.
§
Reference(s)/Basis Affirmed Reversed New
Ground
1, 2, 4–8,
10–12,
18–20
103 Jastrebski,
Choudhuri
1, 2, 4–8,
10–12,
18–20

10–12 103 Jastrebski,
Choudhuri
10–12
1 103 Jastrebski,
Choudhuri
1
1, 2, 4–
20
101 Eligibility 1, 2, 4–20
Overall
Outcome
1, 2, 4–20 1

TIME PERIOD FOR RESPONSE
This decision contains a new ground of rejection pursuant to 37
C.F.R. § 41.50(b). Section 41.50(b) provides “[a] new ground of rejection
pursuant to this paragraph shall not be considered final for judicial review.”
Section 41.50(b) also provides:
When the Board enters such a non-final decision, the appellant,
within two months from the date of the decision, must exercise
one of the following two options with respect to the new ground
of rejection to avoid termination of the appeal as to the rejected
claims:
(1) Reopen prosecution. Submit an appropriate
amendment of the claims so rejected or new Evidence relating to
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the claims so rejected, or both, and have the matter reconsidered
by the examiner, in which event the prosecution will be
remanded to the examiner. The new ground of rejection is
binding upon the examiner unless an amendment or new
Evidence not previously of Record is made which, in the opinion
of the examiner, overcomes the new ground of rejection
designated in the decision. Should the examiner reject the claims,
appellant may again appeal to the Board pursuant to this subpart.
(2) Request rehearing. Request that the proceeding be
reheard under §41.52 by the Board upon the same Record. The
request for rehearing must address any new ground of rejection
and state with particularity the points believed to have been
misapprehended or overlooked in entering the new ground of
rejection and also state all other grounds upon which rehearing
is sought.

Further guidance on responding to a new ground of rejection can be
found in the MPEP § 1214.01.

No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a)(1). See 37 C.F.R.
§§ 41.50(f), 41.52(b).

AFFIRMED; 37 C.F.R. § 41.50(b)