DETAILED ACTION
1. Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Status of Application and Claims
Claims 1, 4, 7-10, 13 and 16-18 are pending.
Claims 5, 6, 14 and 15 were cancelled in the Applicant’s filing on 9/10/2025.
Claims 1, 4, 7, 10, 13 and 16 were amended or newly added in the Applicant’s filing on 9/10/2025.
This office action is being issued in response to the Applicant's filing(s) on 09/10/2025.
3. Claim Interpretation
The subject matter of a properly construed claim is defined by the terms that limit its scope when given their broadest reasonable interpretation. see MPEP §2013(I)(C). Specifically, the “broadest reasonable construction ‘in light of the specification as it would be interpreted by one of ordinary skill in the art.’” See MPEP §2111, citing Phillips v. AWH Corp., 75 USPQ2d 1321, 1329 (Fed. Cir. 2005). However, “[t]hough understanding the claim language may be aided by explanations contained in the written description, it is important not to import into claim limitations that are not part of the claim.” See MPEP §2111.01, citing Superguide Corp. v. DirecTV Enterprises, Inc., 69 USPQ2d 1865, 1868 (Fed. Cir. 2004). Construing claims broadly during prosecution is not unfair to the applicant, because the applicant has the opportunity to amend the claims to obtain more precise claim coverage. See MPEP §2111, citing In re Yamamoto, 222 USPQ 934, 936 (Fed. Cir. 1984).
As a general matter, grammar and the plain meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. See MPEP §2013(I)(C). Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. See MPEP §2013(I)(C).
As such, claim limitations that contain statement(s) such as “if, may, might, can, could” are treated as containing optional language. See MPEP §2013(I)(C). As matter of linguistic precision, optional claim elements do not narrow claim limitations, since they can always be omitted. See MPEP §2013(I)(C).
Similarly, a method step exercised or triggered upon the satisfaction of a condition, where there remains the possibility that the condition was not satisfied under the broadest reasonable interpretation, is an optional claim limitation. see MPEP §2111.04(II). As the Applicant does not address what happens should the optional claim limitations fail, Examiner assumes that nothing happens (i.e., the method stops). An alternate interpretation is that merely the claim limitations based upon the condition are not triggered or performed.
In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See MPEP §2143.03, citing Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298 (Fed. Cir. 2009);
Language in a method or system claim that states only the intended use or intended result, but does not result in a manipulative difference in the steps of the method claim nor a structural difference between the system claim and the prior art, fails to distinguish the claims from the prior art. In other words, if the prior art structure is capable of performing the intended use, then it meets the claim.
The following types of claim language may raise a question as to its limiting effect (this list is not exhaustive):
Statements of intended use or field of use, including statements of purpose or intended use in the preamble. See MPEP §2111.02;
Clauses such as “adapted to”, “adapted for”, “wherein”, and “whereby.” See MPEP §2111.04;
Contingent limitations. See MPEP §2111.04(II);
Printed matter. See MPEP §2111.05; and
Functional language associated with a claim term. See MPEP §2181.
As such, while all claim limitations have been considered and all words in the claims have been considered in judging the patentability of the claimed invention, the following italicized, underlined and emboldened language is interpreted as not further limiting the scope of the claimed invention.
Additionally, the following italicized, underlined and emboldened language is not necessarily an exhaustive list of claim language that is interpreted as not further limiting the scope of the claimed invention. Applicant should review all claims for additional claim interpretation issues.
Claim 1 recites a method comprising:
receiving, by a distributed fraud detection computer program that is part of a fraud management network comprising a plurality of distributed fraud detection computer programs;
The method is being performed by a (singular) distributed fraud detection computer program. The fact that that there is a plurality of distributed fraud detection programs that are not utilized in the recited method has no bearing on the scope of the method performed by the distributed detection computer program.
Claim 10 has similar issues.
Claim 1 recites a method comprising:
receiving, by a distributed fraud detection computer program that is part of a fraud management network comprising a plurality of distributed fraud detection computer programs, a data set comprising data including merchant data, acquirer data, issuer data, and payment network data from a plurality of participating merchants;
formatting, by the distributed fraud detection computer program, the data to a format for a trained machine learning model;
Claim elements (i.e. merchant data, acquirer data, issuer data, and payment network data) pertain to nonfunctional descriptive material and are not functionally involved in the steps recited. The data set is received and formatted. The data set is not recited as being utilized. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See MPEP §2111.05 (III).
The intended use or purpose of the data must result in a structural and/or functional difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use or fulfilling said purpose (i.e., a format usable for a trained machine learning model), then it meets the claim. See MPEP §2114(II), citing Ex parte Masham, 2 USPQ2d 1647 (BPAI 1987).
Examiner notes that the claims, as written, do not recite training the machine learning model with the formatted data just that the motivation of the formatting is to use it with a trained machine learning model.
Claim 10 has similar issues.
Claim 1 recites a method comprising:
detecting, by the distributed fraud detection computer program, high-risk fraud attributes from the validated extracted attributes by comparing validated extracted attributes to a threshold, wherein the threshold is based on historical data.
Method claims are defined by the method steps being actively performed (i.e., comparing the attributes to a threshold), not method steps performed in the past (i.e., the process by which the threshold was generated or selected). Claiming method steps in the past tense can be interpreted as the method steps performed in the past are outside the scope of the claimed method. Alternatively stated, the scope of the claimed method are the active method steps which are building off a pre-existing state. The method steps performed for creation of the pre-existing state are outside the scope of the claimed invention.
Claim 10 has similar issues.
Claim 7 recites a method comprising:
enriching, by the distributed fraud detection computer program, the extracted attributes with historical data relating to an issuer, a merchant, an acquirer, and/or a type of financial instruments wherein the enriching improves a value of the extracted attributes in identifying fraud.
Method claims are defined by the method steps being actively performed (i.e., enriching extracted attributes with historical data), not intended benefit(s) or purpose(s) of the performance (i.e., wherein the enriching improves a value of the extracted attributes in identifying fraud). The intended use or purpose of the data must result in a structural and/or functional difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use or fulfilling said purpose (i.e., enriching), then it meets the claim. See MPEP §2114(II), citing Ex parte Masham, 2 USPQ2d 1647 (BPAI 1987).
Claim 16 has similar issues.
4. Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 4, 7-10, 13 and 16-18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1
The claimed invention falls within one of the four statutory categories of invention (i.e., process, machine, manufacture and composition of matter). See MPEP §2106.03.
STEP 2A – PRONG ONE
The claim(s) recite(s) a method, a system to perform a method and/or computer-readable medium containing instructions, when executed, perform a method comprising:
receiving, …, a data set comprising data including merchant data, acquirer data, issuer data, and payment network data from a plurality of participating merchants;
formatting, …, the data to a format for a … model;
[developing], …, a … model to extract attributes based on a historical significance of the attributes in detecting fraud;
receiving, …, a transaction from one of the participating merchants;
extracting, … using the *** model, attributes from the transaction;
validating, …, the extracted attributes using historical dispute data, auxiliary fraud data, and card scheme dispute data;
detecting, …, high-risk fraud attributes fraud from the validated extracted attributes by comparing the validated extracted attributes to a threshold, wherein the threshold is based on historical data; and
notifying, …, the participating merchant of a fraud event based on the detection.
These limitations, as drafted, under its broadest reasonable interpretation, covers a series of steps instructing how to detect financial fraud which is a fundamental economic practice, a sub-category of certain method(s) of organizing human activity, an enumerated grouping of abstract ideas. See MPEP §2106.04(a)(2)(II)(A).
Examiner notes that detecting financial fraud is mitigation of financial risk and that the mitigation of financial risk is a court-provided example of a fundamental economic practice. See MPEP §2106.04(a)(2)(II)(A), citing Alice Corp. v. CLS Bank, 573 U.S. 208, 218, 110 USPQ2d 1976, 1982 (2014).
Additionally, these limitations, as drafted, under its broadest interpretation, covers a series of steps that can be practically performed in the human mind (e.g., observations, evaluations, judgments and opinions) which are mental process, a second enumerated grouping of abstract ideas. See MPEP §2106.04(a)(2)(III).
Examiner notes that “’collecting information, analyzing it, and displaying certain results of the collection and analysis,’ where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind” is a mental process. See MPEP §2106.04(a)(2)(III)(A) citing Electric Power Group v. Alstom, SA. (Fed. Cir. 2016).
Accordingly, the claimed invention recites an abstract idea.
STEP 2A – PRONG TWO
The claimed invention recites additional elements (i.e., computer elements) of a computer program (Claim(s) 1 and 10), a trained machine learning model (Claim(s) 1 and 10) and a computer network (Claim(s) 1 and 10).
The claimed invention does not include additional elements that integrate the judicial exception into a practical application of the exception because the claims do not provide improvements to another technology or technical field; improvements to the functioning of the computer itself; are not applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; are not applying the judicial exception with or by use of a particular machine; are not effecting a transformation or reduction of a particular article to a different state or thing; and are not applying the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. See MPEP §2106.04(d).
The additional elements are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. See MPEP §2106.05(f). Alternately, the additional elements amount to no more than generally linking the exception to a particular technological environment or field of use. See MPEP §2106.05(h). Accordingly, these additional element(s), when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, the claimed invention is directed to an abstract idea without a practical application.
STEP 2B
Upon reconsideration of the indicia noted under Step 2A in concert with the Step 2B considerations, the additional claim element(s) amounts to (i) adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, (ii) adding insignificant extra-solution activity to the judicial exception, and/or (iii) generally linking the use of judicial exception to a particular technological environment or field of use. See MPEP §2106.07(a)(II). The same analysis applies in Step 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claim does not provide an inventive concept significantly more than the abstract idea.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
DEPENDENT CLAIMS
Dependent Claim(s) 4, 7-9, 13 and 16-18 recite claim limitations that further define the abstract idea recited in respective independent Claim(s) 1 and 10. As such, the dependent claims are also grouped an abstract idea utilizing the same rationale as previously asserted against the independent claims.
Dependent Claim(s) recite additional elements (i.e., computer elements) of a data repository (Claim(s) 8 and 17) and a distributed ledger network (Claim(s) 9 and 18).
In each case, the additional element(s) are recited at a high level of generality such that these additional element(s) amount to no more than mere instructions to apply the exception using a generic computer component.
The dependent claims do not include any additional elements that integrate the abstract idea into a practical application of the judicial exception or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination utilizing the same rationale as previously asserted against the independent claims.
Accordingly, the dependent claim(s) are also not patent eligible.
Appropriate correction is requested.
Additionally, Claims 10, 13 and 16-18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter.
The preamble of Claims 10, 13 and 16-18 indicates the claims pertain to a system. However, the claims recite no system elements (i.e., no hardware or computer elements) to enable performance of the recited method steps. The claims, as written, recite that the method steps as being performed by “a fraud detection computer program” (i.e., software).
Software and computer programs are not physical "things." They are neither computer components nor statutory processes, as they are not "acts" being performed. Such claimed computer programs do not define any structural and functional interrelationships between the computer program and other claimed elements of a computer which permit the computer program's functionality to be realized. Thus, Claims 10, 13 and 16-18 are deemed to be non-statutory.
Appropriate correction is requested.
5. Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 4, 7-10, 13 and 16-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites a method comprising:
training, by the distributed fraud detection computer program, a machine learning model to extract attributes based on a historical significance of the attributes in detecting fraud.
Claim 1, as written, contain terms that are subjective or determinations of whether the claim limitations are satisfied are subjective. Specifically, said claims contain terminology such as “a historical significance”. Claims are indefinite in circumstances where a claim contains a term that is completely dependent on a person’s subjective opinion. See MPEP §2173.05(b)(IV). As such, claims containing the cited claim limitations are rejected under §112, 2nd paragraph.
Claim 10 is rejected on similar grounds.
Claim 4 recites a method comprising:
normalizing, by the fraud detection computer program, the data by …
What is “the data” being normalized?
Claim 1, upon which Claim 4 is dependent upon, recites “a data set comprising data including merchant data, acquirer data, issuer data, and payment network data” and “historical data, auxiliary fraud data, and card scheme dispute data.”
Claim 13 is rejected on similar grounds.
Appropriate correction is requested.
6. Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 7-10 and 14-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Drapeau (US PG Pub. 2023/0298031) in view of Gold (US PG Pub. 2019/0121673).
Regarding Claim 1, Drapeau discloses a method for fraud detection and management comprising:
receiving, by the distributed fraud detection computer program (distributed configuration or distributed across different servers) that is part of a plurality of a fraud management network comprising a plurality of distributed fraud detection computer programs, a dataset (data) comprising data including merchant data (data from a merchant system), acquirer data (bank data), issuer data (card number), and/or payment network data (IP address). (see para. 21, 26, 27, 33, 38, 44 and 57);
training, by the distributed fraud detection computer program, a machine learning model to extract attributes (transaction features or transaction attributes) based on a historical significance of the attributes (transaction features or transaction attributes from previous results) in detecting fraud. (see para. 31 and 44-45);
receiving, by the distributed fraud detection program, a transaction from one of the participating merchants. (see fig. 3; para. 52);
extracting, by the distributed fraud detection computer program and using a trained machine learning model, attributes (initial transaction attribute data). (see para. 53);
validating (comparing), by the distributed fraud detection computer program, the extracted attributes (initial transaction attribute data) using (transaction attribute data contained in) historical dispute data, auxiliary fraud data and card scheme dispute data. (see para. 44, 54 and 57);
detecting, by the distributed fraud detection computer program, high-risk fraud attributes from the validated extracted attributes by comparing validated extracted attributes to a threshold (fraud threshold), wherein the threshold is based on historical data. (see fig. 3; para. 55);
rejecting, by the distributed fraud detection computer program, the participating merchant (from completing transaction) of the fraud event (submitted fraudulent transaction) based on the detection. (see para. 49).
Drapeau does not explicitly teach a method comprising notifying (rather than just rejecting payment to) the participating merchant of the fraud event, although Drapeau discloses a method comprising notifying, by the fraud detection computer program, the participating merchant of a non-fraud event (successful payment). (see para. 55)
It would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified Drapeau by incorporating merchant notification, as disclosed by Drapeau, about a fraud detection, thereby ensuring that the merchant was properly informed about issues pertaining to unsuccessful payments.
Drapeau does not explicitly teach a method comprising formatting, by the distributed fraud detection computer program, the data to a format for a trained machine learning model, although Drapeau does disclose a method comprising generating, by the distributed fraud detection computer program, data for a trained machine learning model. (see para. 31 and 32). However, Examiner asserts that, if Drapeau is generating data for a machine learning model, Drapeau is generating data in the format for the trained machine learning model. The formatting of the data is inherently performed via the generation of the data to allow the trained machine learning model to use the generated format.
Regardless, Gold discloses a method comprising formatting, by the distributed computer program, the data to a format (second format) for a trained machine learning model. (see para. 266).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified Drapeau by incorporating the formatting of data, as disclosed by Gold, thereby ensuring data consistency and customer privacy.
Regarding Claim 7, Drapeau discloses a method comprising:
enriching (augmenting), by the distributed fraud detection computer program, the extracted attributes with historical data relating to an issuer, a merchant, an acquirer and/or a type of financial instrument wherein the enriching improves a value of the extracted attributes in identifying fraud. (see para. 59).
Regarding Claim 8, Drapeau discloses a method wherein the data is received from a common data repository (transaction data store). (see para. 27).
Regarding Claim 9, Drapeau does not teach a method wherein the data is received from a distributed ledger network, although Drapeau discloses a method wherein the data received is transaction data. (see abstract).
Gold discloses a method wherein transaction data is stored in a distributed ledger. (see para. 143).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to have modified Drapeau and Gold by incorporating a distributed ledger network, as disclosed by Gold, as a distributed ledger network is a standard and conventional technology in which to store data.
Regarding Claims 10 and 14-18, such claim recites substantially similar limitations as claimed in previously rejected claims and, therefore, would have been obvious based upon previously rejected claims
Claim(s) 4 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Drapeau and Gold, as applied to Claims 1 and 10 above, and further in view of Khailany (US PG Pub 2022/0067530).
Regarding Claim 4, Drapeau does not explicitly teach a method comprising normalizing, by the fraud detection computer program, the data by converting, by the distributed fraud detection computer program, the data into one or more vectors, although Drapeau discloses a method wherein the fraud detection computer program utilizes support vector machine models (see para. 23).
Drapeau does not teach a method comprising scaling, by the distributed fraud detection computer program, the one or more vectors to a standardized unit that represents a highest derived accuracy.
Khailany discloses a method comprising:
converting, by the computer program, the data (parameters) into one or more vectors (vectors). (see abstract); and
scaling, by the computer, the one or more vectors to a standardized unit (via a shared scale factor or fine-grained per-vector scale factor) that represents a highest derived accuracy. (see abstract).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to have modified Drapeau and Gold by converting data into vectors, as disclosed by Khailany, as said conversion is standard and conventional in machine learning technologies.
Regarding Claims 13, such claim recites substantially similar limitations as claimed in previously rejected claims and, therefore, would have been obvious based upon previously rejected claims
7. Response to Arguments
Applicant's arguments filed 9/10/2025 have been fully considered but they are not persuasive.
§101 Rejection
Applicant argues that the claimed invention recites a practical application, specifically “an improvement in the functioning of a computer, or an improvement to other technology or technical field,” and, as such, satisfies Step 2A Prong Two of the §101 Guidelines. See Arguments, pp. 7-9.
Specifically, Applicant argues:
Applicant respectfully submits that the amended claims integrate the alleged judicial exception into a practical application by improving the operation of a fraud detection system. First, as recognized by the Office Action (see Office Action, page 12), providing a distributed fraud detection computer program that is part of a fraud management network comprising a plurality of distributed fraud detection computer programs improves the flexibility and scalability of the system. Further, the claim recites the use of a machine learning engine that is trained to identify attributes based on a historical significance of the attributes in detecting fraud, and then to use these attributes to detect fraud. This provides fraud detection at a more granular level than simply using a machine learning engine, such as is disclosed by the Drapeau. See Arguments, pp. 8-9.
The Examiner respectfully disagrees.
MPEP §2106.05(a) recites:
If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. – emphasis added.
However, the specification does not provide any evidence that the claimed invention results in an improvement to the functioning of a computer, an improvement to conventional technology or technological processes, or is addressing a technology-based problem.
Additionally, the specification does not provide any evidence that there is even a technical (i.e., technology-based) problem to be solved. For example, the specification does not provide any evidence that existing technology was incapable of distributed computing or that machine learning engines were incapable to be trained to identify attributes but for the claimed technical solution.
Additionally, MPEP §2106.05(f)(1) recites:
Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743 – emphasis added.
Even assuming there was a technical problem, the claims, as written, fail to recite the details of how a technical solution to the technical problem was accomplished.
If there was a technical problem (e.g., existing technology was incapable of performing the claimed functions) then the claims should recite the details of the technical solution (e.g., how existing technology was improved to overcome this inability). However, the claims, as written, provide no such details and merely recite that the claimed functions (i.e., the outcome) are being performed.
Examiner asserts that the claimed invention is analogous to Electric Power Group LLC v. Alstom SA (Fed. Cir. 2016) wherein the court stated:
The claims here are unlike the claims in Enfish. There, we relied on the distinction made in Alice between, on one hand, computer-functionality improvements and, on the other, uses of existing computers as tools in aid of processes focused on “abstract ideas” (in Alice, as in so many other § 101 cases, the abstract ideas being the creation and manipulation of legal obligations such as contracts involved in fundamental economic practices). Enfish, 822 F.3d at 1335-36; see Alice, 134 S. Ct. at 2358-59. That distinction, the Supreme Court recognized, has common-sense force even if it may present line-drawing challenges because of the programmable nature of ordinary existing computers. In Enfish, we applied the distinction to reject the § 101 challenge at stage one because the claims at issue focused not on asserted advances in uses to which existing computer capabilities could be put, but on a specific improvement—a particular database technique—in how computers could carry out one of their basic functions of storage and retrieval of data. Enfish, 822 F.3d at 1335-36; see Bascom, 2016 U.S. App. LEXIS 11687, 2016 WL 3514158, at *5; cf. Alice, 134 S. Ct. at 2360 (noting basic storage function of generic computer). The present case is different: the focus of the claims is not on such an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools. see Electric Power Group LLC v. Alstom SA, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) – emphasis added.
The claimed invention is not an improvement to computer technology (e.g., distributed computing or machine learning models) or computer functionality. Rather, the claimed invention is applying a computer’s existing capabilities to implement a particular abstract idea. As in Electric Power Group, the focus of the claimed invention is not on an improvement in computers as tools but on improving an abstract idea (i.e., detecting fraud) that uses computers as tools.
MPEP §2106.04(d) recites:
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); [and]
Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
Examiner asserts that the additional elements amount to merely (1) including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, or alternatively, (2) merely links the use of a judicial exception to a particular technological environment or field of use.
§103 Rejection
Applicant argues that the previously asserted prior art (Drapeau and Blumberg) fail to teach or suggest “training a machine learning model to identify attributes that have a historical significance in detecting fraud.” See Arguments, p. 10.
The Examiner respectfully disagrees.
As a preliminary matter, the claims, as amended, recite training a model to extract attributes not to identify attributes and the term “historical significance” raises §112(b) issues due to subjective terminology.
Regardless, Drapeau recites:
In embodiments, the models used by ML engine(s) 214A and 214B can at least partially be created offline using features extracted from identities, as well as traditional user-based features, and transaction records associated with prior fraud detection. In embodiments, ML engine(s) 214A and 214B can be trained using training data based on various user and identity based features, and may further be refined over time based future transactions for which no fraud was detected and no fraud existed, no fraud was detected but fraud did exists, fraud was detected and no fraud existed, fraud was detected and fraud did exist. In embodiments, such training data may be gathered from a transaction data store and identity feature data. In embodiments, one or more ML training techniques appropriate for a given model may be executed by ML engine(s) 214A and 214B periodically as new/additional training data becomes available, as well as in real-time using, for example, session data and transaction data as transactions occur. See para. 45 – emphasis added.
Drapeau discloses a method comprising training a machine learning model (ML engine) to identify attributes (identity based features) that have a historical significance in detecting fraud (identity based features in prior fraud detections). See para. 45.
Examiner notes that the broad claim language allows for multiple interpretations.
The machine learning model is trained to determine whether an attribute, in the abstract, has historical significance in detecting fraud. For example, the machine learning model determines that an account holder’s name is historically significant in detecting fraud.
An attribute has been determined to be historically significant outside the machine learning model. The machine learning model is trained to identify (or extract) attributes in processed data to identify occurrences of that attribute. For example, a human decides that an account holder’s name is historically significant in detecting fraud. The machine learning model is then trained to identify (or extract) an account holder’s name from the processed data.
Applicant argues that the previously asserted prior art (Drapeau and Blumberg) fail to teach or suggest “detecting, by the distributed fraud detection computer program, high-risk fraud attributes from the validated extracted attributes by comparing the validated extracted attributes to a threshold, wherein the threshold is based on historical data.” See Arguments, p. 10.
The Examiner respectfully disagrees.
As a preliminary matter, the claims, as amended, do not recite a method for determining the threshold based on historical data. The claims, as amended, just recite the basis of the threshold which does not have patentable weight.
Regardless, Drapeau recites:
In embodiments, identity based fraud detection system 115 may then employ the improved models during transactions to take advantage of the improved fraud detection techniques. For example, a transaction may be received from a user system 130, via merchant system 120, where the transaction has certain attributes (e.g. card number, email address, IP address, tracking cookie, etc.). One or more transaction attributes, such as card number, may be used to identify the associated identity for the transaction. Then, once identified, the identity based fraud detection features, as well as traditional fraud detection features, may be generated by the identity based fraud detection system 115. The fraud detection features with the transaction parameters may then be fed into one or more ML fraud detection models to determine a likelihood of fraud associated with the current transaction. If the ML models indicate that a fraud detection threshold is satisfied (e.g., a transaction is likely associated with fraud), the transaction may be rejected. Thus, the use of ML models trained with identity features improves fraud detection during transactions. See para. 33 – emphasis added.
Drapeau discloses a method comprising detecting, by the distributed fraud computer program, high-risk fraud attributes (i.e., attributes of the transaction that indicate that the transaction is likely associated with fraud) from the validated extracted attributes (i.e., the attributes identified or extracted from the transaction, such as card number) by comparing the validated extract attributes (i.e., the attributes identified or extracted from the transaction, such as card number) against a threshold (that a transaction is likely associated with fraud). See para. 33.
8. Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Jason M. Borlinghaus/Primary Examiner, Art Unit 3692 January 15, 2026
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