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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1, claims are directed to at least one statutory category, a system and a method, respectively.
Under Step 2A, Prong 1, claim 1 or claim 11 is directed to an abstract idea of receive a transaction request; determine account related attributes or relationship related attributes of the transaction request; generate one or more graph embeddings based on the account related attributes or the relationship related attributes; generate one or more geo-spatial mappings based on the account related attributes or the relationship related attributes; apply an unsupervised statistical algorithm to the one or more graph embeddings and the one or more geo-spatial mappings by applying one or more of an Isolation Forest cluster, a k-means cluster, or a Gaussian cluster; apply one or more detection rules to the account related attributes or the relationship related attributes; identify anomalous behavior in the transaction request based on the unsupervised statistic algorithm and the one or more detection rules; generate a transaction anomaly score based on the identified anomalous behavior in the transaction request; recommend an action for the transaction request based on the transaction anomaly score exceeding a defined threshold; confirm the recommended action using an investigation layer, wherein the investigation layer analyzes the transaction request using a graph visualization tool; and execute the recommended action based on the confirmation. This concept of anomaly detection for mobile payment fund transfers falls under the abstract idea category of certain methods of organizing human activity, specifically commercial or legal interactions as it is directed to sales activities or behaviors.
Under Step 2A, Prong Two, the additional elements recited in claim 1 or claim 11 include: a server computer comprising a processor and a memory coupled to the processor, the memory storing thereon machine executable instructions that when executed cause the processor and processor. These additional limitations do not integrate the judicial exception into a practical application. In particular, the claimed computer components, receiving and transmitting data are amount to no more than mere instructions to apply the exception using a generic computer system, which is not indicative of integration into a practical application; see MPEP 2106.05(f). The original disclosure that describes the computer components merely generic components, [0002] the system including a server computer including a processor and a memory coupled to the processor, the memory storing thereon machine executable instructions that when executed cause the processor and {0047} "a device," "a server," "a processor," and/or the like, as used herein, may refer to a previously-recited device, server, or processor that is recited as performing a previous step or function, a different server or processor, and/or a combination of servers and/or processors. The additional element amount to no more than merely linking the general technology to the judicial exception without significantly more and, in the alternative, mere insignificant extra-solution activity to gather data used in the claimed system/method/non-transitory computer readable medium. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Under Step 2B, the claimed invention is considered as a whole whether the additional elements individually or as an ordered combination amount to an inventive concept. Upon further determination, the claims do not integration of the abstract idea into a practical application, the additional element of a server computer comprising a processor and a memory coupled to the processor, the memory storing thereon machine executable instructions that when executed cause the processor and processor is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer system, and recites the steps of data manipulation. Mere instructions to apply an exception using a generic computer system and/or adding insignificant extra-solution activity to the judicial exception is not indicative of an inventive concept. The sending and receiving data over a network have been determined by the courts to be well-known, conventional and routine functions, see MPEP 2106.05(d)(II)(i). Claims claim 11 recite similar limitations and are ineligible for similar rational. Therefore, claims 1 and 11 are not patent eligible.
As for dependent claims 2-10, these claims recite limitation that further define the same abstract idea noted in claim 1. Therefore, they are considered patent ineligible for the reasons given above.
As for dependent claims 12-20, these claims recite limitation that further define the same abstract idea noted in claim 11. Therefore, they are considered patent ineligible for the reasons given above.
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.
Claim(s) 1, 4, 11 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chari (US 2017/0140382), in view of Adjaoute (US 2021/0264433), further in view of Kruse et al. (US Publication Number: 2024/0013221 A1).
Claim 1. Chari teaches a system for detecting anomalies in mobile payment transactions ([0002]), the system comprising:
a server computer comprising a processor and a memory coupled to the processor, the memory storing thereon machine executable instructions that when executed cause the processor to ([0021]-[0022]):
receive a transaction request ([0033] “client devices 110, 112, and 114 also may include other devices, such as, for example, automated teller machines, point-of-sale terminals, kiosks, laptop computers, handheld computers, smart phones, smart watches, personal digital assistants, gaming devices, or any combination thereof. Users of client devices 110, 112, and 114 may use client devices 110, 112, and 114 to perform financial transactions, such as, for example, transferring monetary funds from a source or paying financial account to a destination or receiving financial account to complete a financial transaction; determine account related attributes or relationship related attributes of the transaction request ([0071] “the vertex link prediction is based on features of the two endpoint vertices of a particular financial transaction. The features of the two endpoint vertices (e.g., the source and destination account vertices) may include features, such as, for example, the type of accounts corresponding to the vertices, the geographic locations of the accounts, and the type of merchant. Illustrative embodiments may train the machine learning classifier to determine if an account with a first set of features will pay another account with a second set of features.”); generate one or more graph embeddings based on the account related attributes or the relationship related attributes (abstract; [0005], [0008]-[0010], [0031], [0035] and [0071]); generate one or more geo-spatial mappings based on the account related attributes or the relationship related attributes (abstract; [0005], [0008]-[0010], [0031], [0035], [0063] and [0071]); apply an unsupervised statistical algorithm to the one or more graph embeddings and the one or more geo-spatial mappings (abstract; [0005], [0008]-[0010], [0031], [0035], [0063], [0071] and [0097]); apply one or more detection rules to the account related attributes or the relationship related attributes (abstract; [0005], [0008]-[0010], [0061], [0071] and [0081]-[0082]); identify anomalous behavior in the transaction request based on the unsupervised statistic algorithm and the one or more detection rules (Fig 9, [0081]-[0082], [0097] and [0076] “Illustrative embodiments may cluster an edge adjacency matrix and calculate the vertex link prediction proportional to an edge cluster density value.”) using an unsupervised statistical algorithm, and wherein the rule layer applies detection rules to the account related attributes or relationship related attributes (Fig 9; [0076]; [0097] “illustrative embodiments may utilize an unsupervised machine learning system for the fraud scoring function S( ). An unsupervised machine learning system, such as, for example, a one-class support vector machine, can find transactions that are unusual or different from other transactions. Here, illustrative embodiments may require domain knowledge to give the system a hint on how certain features affect the fraudulent transaction scores, such as positively or negatively.”); generate a transaction anomaly score based on the identified anomalous behavior in the transaction request ([0094] Transaction scoring component 422 uses the vertex link prediction to generate fraudulent transaction score 424. Transaction scoring component 422 may be, for example, transaction scoring component 230 in FIG. 2. Fraudulent transaction score 424 indicates whether current transaction 412 is fraudulent or not.); recommend an action for the transaction requestion based on the transaction anomaly score exceeding a defined threshold ([0094] Transaction scoring component 422 uses the vertex link prediction to generate fraudulent transaction score 424. Transaction scoring component 422 may be, for example, transaction scoring component 230 in FIG. 2. Fraudulent transaction score 424 indicates whether current transaction 412 is fraudulent or not. A fraudulent transaction evaluation component, such as fraudulent transaction evaluation component 230 in FIG. 2, may block current transaction 412, or otherwise mitigate current transaction 412, when fraudulent transaction score 424 is greater than or equal to a predefined fraudulent transaction threshold score.); confirm the recommended action using an investigation layer, wherein the investigation layer analyzes the transaction request using a graph visualization tool (abstract; Figs 2-5 and 9; [0031], [0035], [0047], [0076]; [0097]); and execute the recommended action based on the confirmation (abstract; Figs 2-5 and 9; [0031], [0035], [0047], [0063], [0076], [0097], [0130]).
Chari does not teach generating a transaction. However, Adjaoute teaches generating a transaction (Fig. 6) by clustering transaction data and generating a transaction anomaly score (“decision score” by each predictive model [0123]) based on the identified anomalous behavior of the current mobile payment transaction ([0129] “Method 600 works with at least two of the predictive models from steps 128, 132, 136, 140, 144, and 148 (of FIG. 1). The predictive models each simultaneously produce a score and a score-confidence level in parallel sets, all from a particular record in a plurality of enriched-data records.”).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to combine the teachings of Adjaoute with the invention of Chari because as Adjaoute suggests using multiple models and/or customizable rules allows decisions to be more accurate and reduces uncertainty for good customers [0246].
Chari does not teach generating by applying one or more of an Isolation Forest cluster, a k-means cluster, or a Gaussian cluster. However, Kruse et al. teaches by applying one or more of an Isolation Forest cluster, a k-means cluster, or a Gaussian cluster ([0099]-[0100]).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to combine the teachings of Kruse et al. with the invention of Chari because as Kruse et al. suggests determine whether the inputted data is accurate and satisfies the required data field inputs [00096] and/or behavioral pattern analysis in validating an incoming transaction [0101].
As per claim 4, Chari, Adjaoute and Kruse et al. teach the system of claim 1 described above. Chari further teaches wherein the identified anomalous behavior is at least one of a fraudulent activity, client abuse activity, or potential laundering activity (Abstract “Identifying fraudulent transactions is provided.”).
Claim 11 is rejected for similar reasons as described above with regard to claim 1.
Claim 14 is rejected for similar reasons as described above with regard to claim 4.
Response to Arguments
Applicant's arguments filed 3/30/2026 have been fully considered but they are not persuasive.
The rejections under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph of claims 1 and 11 have been withdrawn in view of applicant’s claims amendments.
The applicant’s arguments have been considered, but are deemed not persuasive. The applicant amended the claims, the examiner has updated the 35 U.S.C. §101 base on applicant’s amendment.
In response to applicant’s argument with respect to the claims are not directed to abstract idea under step 2A prong one, the examiner respectfully disagrees. Claim 1 or claim 11 is directed to an abstract idea of receive a transaction request; determine account related attributes or relationship related attributes of the transaction request; generate one or more graph embeddings based on the account related attributes or the relationship related attributes; generate one or more geo-spatial mappings based on the account related attributes or the relationship related attributes; apply an unsupervised statistical algorithm to the one or more graph embeddings and the one or more geo-spatial mappings by applying one or more of an Isolation Forest cluster, a k-means cluster, or a Gaussian cluster; apply one or more detection rules to the account related attributes or the relationship related attributes; identify anomalous behavior in the transaction request based on the unsupervised statistic algorithm and the one or more detection rules; generate a transaction anomaly score based on the identified anomalous behavior in the transaction request; recommend an action for the transaction request based on the transaction anomaly score exceeding a defined threshold; confirm the recommended action using an investigation layer, wherein the investigation layer analyzes the transaction request using a graph visualization tool; and execute the recommended action based on the confirmation. This concept of anomaly detection for mobile payment fund transfers falls under the abstract idea category of certain methods of organizing human activity, specifically commercial or legal interactions as it is directed to sales activities or behaviors. Therefore, the applicant’s argument is not persuasive.
In response to applicant’s argument in regard to Step 2A Prong Two the claims are directed to a practical application, the examiner respectfully disagrees. Under Step 2A, Prong Two, the additional elements recited in claim 1 or claim 11 include: a server computer comprising a processor and a memory coupled to the processor, the memory storing thereon machine executable instructions that when executed cause the processor and processor. These additional limitations do not integrate the judicial exception into a practical application. In particular, the claimed computer components, receiving and transmitting data are amount to no more than mere instructions to apply the exception using a generic computer system, which is not indicative of integration into a practical application; see MPEP 2106.05(f). The original disclosure that describes the computer components merely generic components, [0002] the system including a server computer including a processor and a memory coupled to the processor, the memory storing thereon machine executable instructions that when executed cause the processor and {0047} "a device," "a server," "a processor," and/or the like, as used herein, may refer to a previously-recited device, server, or processor that is recited as performing a previous step or function, a different server or processor, and/or a combination of servers and/or processors. The additional element amount to no more than merely linking the general technology to the judicial exception without significantly more and, in the alternative, mere insignificant extra-solution activity to gather data used in the claimed system/method/non-transitory computer readable medium. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, the applicant’s argument is not persuasive.
In response to applicant’s argument in regard to 35 § 101 technological improvement, the examiner respectfully disagrees. The applicant’s claims do not describe technological improvements, or a specific improvement to the way server computer, processor and memory. Rather, Applicant’s specification and claims describe anomaly detection. The claims do not to improve the performance of computers or any underlying technology; instead, the focus is to use generic server computer, processor and memory. Therefore, the applicant’s argument is not persuasive.
Applicant’s arguments with respect to claim(s) 1, 4, 11 and 14 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to I JUNG LIU whose telephone number is (571)270-1370. The examiner can normally be reached Monday-Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christine Behncke can be reached at (571)272-8103. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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I JUNG LIU
Examiner
Art Unit 3695
/I JUNG LIU/ Primary Examiner, Art Unit 3695
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