DETAILED ACTION
Status of Claims
This action is in reply to amendment and response filed on 1/2/26. Claims 1, 12 and 18 have been amended. Claims 1, 3-12, 14-18 and 20 are pending and examined.
Response to Arguments
101: Applicant's amendments and response have been fully considered but are not persuasive.
The Applicant essentially argues that the amended claims overcome the rejection.
The Examiner disagrees.
The claims recite an abstract idea because claim limitations include fraudulent financial transaction determination based on location and timing analysis associated with the transactions.
The additional elements do not integrate the abstract idea into a practical application. Specifically, the transaction filtering execution does not recite technological details but instead recites abstract idea as to its components which include value comparison assigned transaction information and calculating fraud scores and ranking the scored transactions which are all abstract idea. Furthermore, storing the financial information and various attributes associated with the fraud determination in a structured manner does not integrate the abstract idea into a practical application because it amounts to storage of the financial information by a computer, see MPEP 2106.05(f)(2).
As such, the rejection is maintained and an updated rejection addressing the amended claims is provided below.
Reasons why the references do not teach the claims
References of record include US 20210073817 A1 (Kallas) identifying points of compromise and US 20210019762 A1 (Bosnjakovic) identity resolution for fraud ring detection.
Kallas and Bosnjakovic, individually or in combination, teach,
receive, via the network interface and from one or more computing devices, data regarding a first set of transactions executed at a plurality of locations within a first time window and a second set of transactions executed at a plurality of locations within a second time window subsequent the first time window;
generate, from the data generated from the first set of transactions executed within the first time window, a first standard query language (SQL) table or R dataframe data structure.
Kallas and Bosnjakovic, individually or in combination, do not teach,
generate database data structure, wherein the first SQL table or R dataframe data structure comprises a first set of rows, each row corresponding to a different transaction of the first set of transactions and including, in separate columns of the SQL table or R dataframe data structure, an identification of a location, an identification of a card that performed the transaction at the location, and a date of the transaction at the location, and wherein a first subset of the first set of rows each comprise an indication of a fraudulent transaction executed subsequent to the date identified in the row;
for each of the plurality of locations, determine a ratio of a first count of rows of the first subset of rows that each include an indication of a fraudulent transaction executed subsequent the date of the transaction in the row and the identification of the location in a location column of separate columns of the SQL table or R dataframe to a total count of rows of the first set of rows that each contain the identification of the location;
generate a second SQL or R dataframe data structure comprising a second set of rows based on each row of the second set of rows corresponding to a transaction of the second set of transactions executed within the second time window subsequent the first time window at a location of a set of locations of the plurality of locations each corresponding to a different ratio exceeding a threshold determined from the first SQL table or R dataframe data structure, wherein a second subset of the second set of rows each comprise an indication of a fraudulent transaction executed subsequent to the date in the row of the second subset;
for each of the set of locations, determine a second count of rows of the second subset of the second set of rows for the location that each include an indication of a fraudulent transaction;
determine a location from the set of locations is a point of compromise based on the second count of rows for the location, wherein determining the location is a point of compromise comprises executing a multi-phase filtering process that reduces a number of rows processed compared to a dataset comprising the received data comprising:
in a first phase, comparing compromised card ratios across merchants to select initial point of compromise candidates;
in a second phase, for each candidate, calculating a compromise threshold by computing an average compromised card count over a plurality of prior time windows and adding a product of a predetermined multiplier (N) and the standard deviation of compromised card counts over the plurality of time windows;
in a third phase, identifying candidates having increases of compromised card counts for a subsequent time window that exceed the compromise threshold; and
in a fourth phase, ranking the identified candidates by frequency of threshold exceedances and selecting a defined number of locations from the identified candidates having highest frequency of threshold exceedances; and
generate a record comprising a stored association between an identification of the location and an identification indicating a point of compromise responsive to the determination.
Claim Rejections - 35 USC § 112
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, 3-12, 14-18 and 20 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.
With respect to claims 1, 12, and 18, it is unclear what is intended by the phrase “filtering process that reduces a number of rows processed”. There appears to be logical inconsistency in this language. As the claims only appear to recite one “process”, and that is the “filtering process”. So logically the “filtering process” cannot result in fewer rows getting processed, since any row removed during the filter must have been processed.
Dependent claims are also rejected by virtue of their dependency.
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, 3-12, 14-18 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. (Step 1) The claims recite an apparatus (claims 1, 3-11), a process (claims 12, 14-17) and an article of manufacture (claims 18 and 20). For the purposes of this analysis, representative claim 1 (from claims 1, 12 and 18) is addressed. (Step 2A, prong 1) Abstract ideas are in bold below, and represent organizing human activity as a method of location and time based determination of fraudulent transactions, as are all a form of commercial or legal interactions and managing personal behavior or relationships or interactions between people.
A system for efficiently detecting points of compromise with reduced computational resources by using non-matrix data structures, comprising:
a network interface; and
a processor coupled to memory, the processor configured to:
receive, via the network interface and from one or more computing devices, data regarding a first set of transactions executed at a plurality of locations within a first time window and a second set of transactions executed at a plurality of locations within a second time window subsequent the first time window;
generate, from the data generated from the first set of transactions executed within the first time window, a first standard query language (SQL) table or R dataframe data structure, wherein the first SQL table or R dataframe data structure comprises a first set of rows, each row corresponding to a different transaction of the first set of transactions and including, in separate columns of the SQL table or R dataframe data structure, an identification of a location, an identification of a card that performed the transaction at the location, and a date of the transaction at the location, and wherein a first subset of the first set of rows each comprise an indication of a fraudulent transaction executed subsequent to the date identified in the row;
for each of the plurality of locations, determine a ratio of a first count of rows of the first subset of rows that each include an indication of a fraudulent transaction executed subsequent the date of the transaction in the row and the identification of the location in a location column of separate columns of the SQL table or R dataframe to a total count of rows of the first set of rows that each contain the identification of the location;
generate a second SQL or R dataframe data structure comprising a second set of rows based on each row of the second set of rows corresponding to a transaction of the second set of transactions executed within the second time window subsequent the first time window at a location of a set of locations of the plurality of locations each corresponding to a different ratio exceeding a threshold determined from the first SQL table or R dataframe data structure, wherein a second subset of the second set of rows each comprise an indication of a fraudulent transaction executed subsequent to the date in the row of the second subset;
for each of the set of locations, determine a second count of rows of the second subset of the second set of rows for the location that each include an indication of a fraudulent transaction;
determine a location from the set of locations is a point of compromise based on the second count of rows for the location, wherein determining the location is a point of compromise comprises executing a multi-phase filtering process that reduces a number of rows processed compared to a dataset comprising the received data comprising:
in a first phase, comparing compromised card ratios across merchants to select initial point of compromise candidates;
in a second phase, for each candidate, calculating a compromise threshold by computing an average compromised card count over a plurality of prior time windows and adding a product of a predetermined multiplier (N) and the standard deviation of compromised card counts over the plurality of time windows;
in a third phase, identifying candidates having increases of compromised card counts for a subsequent time window that exceed the compromise threshold; and
in a fourth phase, ranking the identified candidates by frequency of threshold exceedances and selecting a defined number of locations from the identified candidates having highest frequency of threshold exceedances; and
generate a record comprising a stored association between an identification of the location and an identification indicating a point of compromise responsive to the determination.
(Step 2A prong 2) The additional elements are as follows:
"A system …, comprising", "a network interface" and "a processor coupled to memory, the processor configured", "[receive], via the network interface and from one or more computing devices, []". These additional elements do not integrate the abstract idea into a practical application as they are no more than “apply it” because they are mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2).
"generate, […], a first standard query language (SQL) table or R dataframe data structure, wherein the first SQL table or R dataframe data structure comprises […], in separate columns of the SQL table or R dataframe data structure, […]". These additional elements do not integrate the abstract idea into a practical application as they are no more than “apply it” because they are mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2).
"[determine a location …, wherein determining … comprises] executing a multi-phase filtering process that reduces a number of rows processed compared to a dataset comprising the received data comprising […]". These additional elements do not integrate the abstract idea into a practical application as they are no more than “apply it” because they are mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2). Reduction of the rows in the datastructure does not integrate the abstract idea into a practical application as it is no more than “apply it” because the claims fail to recite the technological details of how rows of the datastructure are reduced, see MPEP 2106.05(f)(1), but instead merely recite additional abstract idea as a solution achieving the reduction.
"[in a second phase, for each candidate, calculating a compromise threshold by] computing [an average compromised card count …]". Computing does not integrate the abstract idea into a practical application as it is no more than “apply it” because it is mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2).
(Step 2B) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration into a practical application, the additional elements do no more than provide mere instructions to apply the abstract idea using “a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2), and/or the claim fails to recite the technological details of “how a solution to a problem is accomplished”, see MPEP 2106.05(f)(1). Therefore, the claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea of location and time based determination of fraudulent transactions.
Continuing the analysis with dependent claims, claims 3-11, 14-17 and 20, recite additional details which only further narrow the abstract idea and do not add any additional features, alone or in combination, that would provide a practical application or provide significantly more.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BROCK E TURK whose telephone number is (571)272-5626. The examiner can normally be reached Monday-Friday 9AM-5PM EST.
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/BROCK E TURK/Examiner, Art Unit 3692
/RYAN D DONLON/Supervisory Patent Examiner, Art Unit 3692 October 8, 2026
