DETAILED ACTION
Notice of 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 .
Status of Claims
This action is in reply to the application filed on August 1, 2024.
Claims 1–20 are currently pending and have been examined.
Information Disclosure Statement
The Information Disclosure Statement filed on August 1, 2024 has been considered. An initialed copy of the Form 1449 is enclosed herewith.
Claim Rejections - 35 USC § 101
The following is a quotation of 35 U.S.C. 101:
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.
First of all, claims must be directed to one or more of the following statutory categories: a process, a machine, a manufacture, or a composition of matter. Claims 1–10 and 17–20 are directed to a machine (“A system” and “A non-transitory computer-readable medium”), and claims 11–16 are directed to a process (“A method”). Thus, claims 1–20 satisfy Step One because they are all within one of the four statutory categories of eligible subject matter.
Claims 1–20, however, are directed to an abstract idea without significantly more. For claim 1, the specific limitations that recite an abstract idea are:
obtain information associated with indicators related to fraudulent or illicit activity in P2P transactions;
receive textual information related to a P2P transaction between a sending user and a receiving user,
wherein the textual information related to the P2P transaction includes one or more of text that the sending user provided in a memo to accompany the P2P transaction or text that the receiving user provided to request the P2P transaction;
analyze the textual information related to the P2P transaction . . . to determine whether the P2P transaction includes one or more of the indicators related to fraudulent or illicit activity; and
trigger a remediation action for the P2P transaction based on the textual information including one or more of the indicators related to fraudulent or illicit activity.
Claims 1–10, therefore, recite determining and responding to transaction fraudulence, which is the abstract idea of certain methods of organizing human activity because they recite a commercial interaction and the fundament economic practice of mitigating risk.
For claim 11, the specific limitations that recite an abstract idea are:
obtaining . . . information associated with indicators related to fraudulent or illicit activity in P2P transactions;
receiving, from . . . a sending user, a request for a P2P transaction between the sending user and a receiving user;
analyzing . . . textual information related to the P2P transaction . . . to determine whether the P2P transaction includes one or more of the indicators related to fraudulent or illicit activity,
wherein the textual information related to the P2P transaction includes one or more of text that the sending user provided in a memo to accompany the P2P transaction or text that the receiving user provided to request the P2P transaction; and
processing . . . the request for the P2P transaction in accordance with whether the P2P transaction includes one or more of the indicators related to fraudulent or illicit activity, wherein processing the request for the P2P transaction includes:
triggering a remediation action for the P2P transaction based on the textual information including one or more indicators related to fraudulent or illicit activity; or
processing the P2P transaction based on the textual information lacking indicators related to fraudulent or illicit activity or including indicators of legitimate activity.
Claims 11–16, therefore, also recite determining and responding to transaction fraudulence, which is the abstract idea of certain methods of organizing human activity because they recite a commercial interaction and the fundament economic practice of mitigating risk.
For claim 17, the specific limitations that recite an abstract idea are:
obtain information associated with indicators related to fraudulent or illicit activity in P2P transactions;
receive . . . a request to assess a P2P transaction between a sending user and a receiving user for fraudulent or illicit activity and indicates information related to the P2P transaction,
wherein the information indicated in the request includes one or more of text that the sending user provided in a memo to accompany the P2P transaction, text that the receiving user provided to request the P2P transaction, a value of the P2P transaction, information related to one or more behavior patterns associated with an account of the sending user, or information related to one or more behavior patterns associated with an account of the receiving user;
analyze the information related to the P2P transaction . . . to determine whether the P2P transaction includes one or more of the indicators related to fraudulent or illicit activity; and
send . . . an indication of whether the P2P transaction includes one or more of the indicators related to fraudulent or illicit activity.
Claims 17–20, therefore, also recite determining and communicating transaction fraudulence, which is the abstract idea of certain methods of organizing human activity because they recite a commercial interaction and the fundament economic practice of mitigating risk.
The judicial exception recited above is not integrated into a practical application. The additional elements of the claims are various generic technologies and computer components to implement this abstract idea (“memories”, “processors”, “natural language processing (NLP)”, “machine learning”, “user device”, “requesting system”, “application program interface (API)”, “non-transitory computer-readable medium”, and “machine learning models”). These additional elements are not integrated into a practical application because the invention merely applies the abstract idea to generic computer technology, using the computer to analyze transaction information to determine fraud. Because the invention is using the computer simply as a tool to perform the abstract idea on, the judicial exception is not integrated into a practical application.
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements in combination are at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic components. Because merely “applying” the exception using generic computer components cannot provide an inventive concept, the additional elements do not recite significantly more than the judicial exception. Thus, claims 1, 11, and 17 is not patent eligible.
Dependent claims 2–10, 12–16, and 18–20 have been given the full two part analysis, analyzing the additional limitations both individually and in combination. The dependent claims, when analyzed individually and in combination, are also held to be patent ineligible under 35 U.S.C. 101.
For claims 2, 3, 9, 10, and 12, the additional recited limitations of these claims merely further narrow the abstract idea discussed above. These dependent claims only narrow the transaction fraud determination recited in claims 1 and 11 by further specifying the remediation action—“ block the P2P transaction”, “initiate a risk assessment workflow”, “triggered in connection with processing the request for the P2P transaction”, and “information to trigger . . . is sent to the requesting system”. The limitations of these claims fail to integrate the abstract idea into a practical application because these claims do not introduce additional elements other than the generic components discussed above (“user device”, “requesting system”, and “application program interface (API)”). These dependent claims, therefore, also amount to merely using a computer, in its ordinary capacity, as a tool to perform the abstract idea. Finally, the additional recited limitations of these dependent claims fail to establish that the claims provide an inventive concept because claims that merely use a computer, in its ordinary capacity, as a tool to perform the abstract idea cannot provide an inventive concept.
For claims 4, 8, and 13, the additional recited limitations of these claims merely further narrow the abstract idea discussed above. These dependent claims only narrow the transaction fraud determination recited in claims 1 and 11 by further specifying how the fraudulence is determined—“ analyze the transactional parameters related to the P2P transaction” and “analyze the textual information . . . includes one or more indicators of abusive behavior”. The limitations of these claims fail to integrate the abstract idea into a practical application because these claims do not introduce additional elements other than the generic components discussed above (“machine learning” and “NLP”). These dependent claims, therefore, also amount to merely using a computer, in its ordinary capacity, as a tool to perform the abstract idea. Finally, the additional recited limitations of these dependent claims fail to establish that the claims provide an inventive concept because claims that merely use a computer, in its ordinary capacity, as a tool to perform the abstract idea cannot provide an inventive concept.
For claims 5, 6, 14, 15, 18, and 19, the additional recited limitations of these claims merely further narrow the abstract idea discussed above. These dependent claims only narrow the transaction fraud determination recited in claims 1, 11, and 17 by further specifying the information associated with the indicators—“words, phrases, or communication tactics” and “transactional patterns or account usage patterns”.
For claims 7, 16, and 20, the additional recited limitations of these claims merely further narrow the abstract idea discussed above. These dependent claims only narrow the transaction fraud determination recited in claims 1, 11, and 17 by further specifying the indicators of fraud—“obfuscation techniques to mask one or more words or phrases or behavior patterns associated with structuring P2P transactions to evade detection”.
Claim Rejections - 35 USC § 102
In the event that the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 is incorrect, any correction of the statutory basis 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1–4, 6, 9–13, 15, 17, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ben Kimon et al., U.S. Patent App. No. 2020/0210849 (“Ben Kimon”).
For claim 1, Ben Kimon teaches:
A system for detecting fraudulent or illicit activity in peer-to-peer (P2P) transactions, the system comprising (¶ 50: example system):
one or more memories (¶ 51: memory); and
one or more processors, communicatively coupled to the one or more memories, configured to (¶ 51: processors connected with memory):
obtain information associated with indicators related to fraudulent or illicit activity in P2P transactions (¶ 33: features extracted from transactions indicating likelihood of fraud);
receive textual information related to a P2P transaction between a sending user and a receiving user (¶ 36: transaction between different users with features extracted),
wherein the textual information related to the P2P transaction includes one or more of text that the sending user provided in a memo to accompany the P2P transaction or text that the receiving user provided to request the P2P transaction (¶ 33: features include various transaction data contained within transaction);
analyze the textual information related to the P2P transaction using natural language processing (NLP) to determine whether the P2P transaction includes one or more of the indicators related to fraudulent or illicit activity (¶ 40: natural language processing applied; ¶ 33: likelihood of fraud); and
trigger a remediation action for the P2P transaction based on the textual information including one or more of the indicators related to fraudulent or illicit activity (¶ 39: remediation action performed).
For claim 2, Ben Kimon teaches all the limitations of claim 1 above and further teaches:
The system of claim 1, wherein the remediation action is to block the P2P transaction between the sending user and the receiving user (¶ 39: remediation action performed).
For claim 3, Ben Kimon teaches all the limitations of claim 1 above and further teaches:
The system of claim 1, wherein the remediation action is to initiate a risk assessment workflow to review the P2P transaction or monitor activity associated with one or more of the sending user or the receiving user (¶ 39: alert provided to operator).
For claim 4, Ben Kimon teaches all the limitations of claim 1 above and further teaches:
The system of claim 1, wherein the one or more processors are further configured to: receive transactional parameters related to the P2P transaction between the sending user and the receiving user, wherein the transactional parameters include one or more of a value of the P2P transaction, information related to one or more behavior patterns associated with an account of the sending user, or one or more behavior patterns associated with an account of the receiving user (¶ 33: features include transaction amounts and users’ average activity frequencies); and
analyze the transactional parameters related to the P2P transaction using machine learning techniques to determine whether the P2P transaction includes one or more of the indicators related to fraudulent or illicit activity in P2P transactions, wherein the remediation action is triggered based on the transactional parameters including one or more of the indicators related to fraudulent or illicit activity (¶ 29–32, 38–39: neural network trained and used to determine fraudulence and remediation).
For claim 6, Ben Kimon teaches all the limitations of claim 1 above and further teaches:
The system of claim 1, wherein the information associated with the indicators related to fraudulent or illicit activity in P2P transactions includes transactional patterns or account usage patterns associated with tactics used in known fraudulent schemes, illegal behaviors, or unlawful organizations (¶ 33: features include transaction activity frequency indicating fraudulence).
For claim 9, Ben Kimon teaches all the limitations of claim 1 above and further teaches:
The system of claim 1, wherein the one or more processors are further configured to: receive, from a user device associated with the sending user, a request for the P2P transaction between the sending user and the receiving user, wherein the remediation action is triggered in connection with processing the request for the P2P transaction (¶ 20: transaction request; ¶ 39: remediation action performed).
For claim 10, Ben Kimon teaches all the limitations of claim 1 above and further teaches:
The system of claim 1, wherein the one or more processors are further configured to: receive, from a requesting system, an application program interface (API) call that includes a request to assess the P2P transaction for fraudulent or illicit activity, wherein information to trigger the remediation action for the P2P transaction is sent to the requesting system (¶ 48: transaction may be over APIs).
For claim 11, Ben Kimon teaches:
A method for assessing a risk of fraudulent or illicit activity in peer-to-peer (P2P) transactions, comprising (¶ 23: example methods):
obtaining, by a system, information associated with indicators related to fraudulent or illicit activity in P2P transactions (¶ 33: features extracted from transactions indicating likelihood of fraud);
receiving, from a user device associated with a sending user, a request for a P2P transaction between the sending user and a receiving user (¶ 36: transaction between different users with features extracted);
analyzing, by the system, textual information related to the P2P transaction using natural language processing (NLP) to determine whether the P2P transaction includes one or more of the indicators related to fraudulent or illicit activity (¶ 40: natural language processing applied; ¶ 33: likelihood of fraud),
wherein the textual information related to the P2P transaction includes one or more of text that the sending user provided in a memo to accompany the P2P transaction or text that the receiving user provided to request the P2P transaction (¶ 33: features include various transaction data contained within transaction); and
processing, by the system, the request for the P2P transaction in accordance with whether the P2P transaction includes one or more of the indicators related to fraudulent or illicit activity, wherein processing the request for the P2P transaction includes (¶ 20: transaction request):
triggering a remediation action for the P2P transaction based on the textual information including one or more indicators related to fraudulent or illicit activity (¶ 39: remediation action performed); or
processing the P2P transaction based on the textual information lacking indicators related to fraudulent or illicit activity or including indicators of legitimate activity (¶ 38: transaction determined to be legitimate).
For claim 12, Ben Kimon teaches all the limitations of claim 11 above and further teaches:
The method of claim 11, wherein the remediation action includes blocking the P2P transaction between the sending user and the receiving user or initiating a risk assessment workflow to review the P2P transaction or monitor activity associated with one or more of the sending user or the receiving user (¶ 39: remediation action performed or alert provided to operator).
For claim 13, Ben Kimon teaches all the limitations of claim 11 above and further teaches:
The method of claim 11, further comprising: analyzing transactional parameters related to the P2P transaction using machine learning techniques to determine whether the P2P transaction includes one or more of the indicators related to fraudulent or illicit activity in P2P transactions (¶ 29–32, 38–39: neural network trained and used to determine fraudulence and remediation),
wherein the transactional parameters include one or more of a value of the P2P transaction, information related to one or more behavior patterns associated with an account of the sending user, or one or more behavior patterns associated with an account of the receiving user (¶ 33: features include transaction amounts and users’ average activity frequencies).
For claim 15, Ben Kimon teaches all the limitations of claim 11 above and further teaches:
The method of claim 11, wherein the information associated with the indicators related to fraudulent or illicit activity in P2P transactions includes transactional patterns or account usage patterns associated with tactics used in known fraudulent schemes, illegal behaviors, or unlawful organizations (¶ 33: features include transaction activity frequency indicating fraudulence).
For claim 17, Ben Kimon teaches:
A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising (¶ 53: non-transitory computer readable medium storing instructions):
one or more instructions that, when executed by one or more processors of a system, cause the system to (¶ 52: processor executes instructions):
obtain information associated with indicators related to fraudulent or illicit activity in P2P transactions (¶ 33: features extracted from transactions indicating likelihood of fraud);
receive, from a requesting system, an application program interface (API) call that includes a request to assess a P2P transaction between a sending user and a receiving user for fraudulent or illicit activity and indicates information related to the P2P transaction (¶ 48: transaction may be over APIs; ¶ 36: transaction between different users with features extracted),
wherein the information indicated in the request includes one or more of text that the sending user provided in a memo to accompany the P2P transaction, text that the receiving user provided to request the P2P transaction, a value of the P2P transaction, information related to one or more behavior patterns associated with an account of the sending user, or information related to one or more behavior patterns associated with an account of the receiving user (¶ 33: features include various transaction data contained within transaction, such as transaction amounts and users’ average activity frequencies),
analyze the information related to the P2P transaction using one or more machine learning models to determine whether the P2P transaction includes one or more of the indicators related to fraudulent or illicit activity (¶ 40: natural language processing applied; ¶ 33: likelihood of fraud); and
send, to the requesting system, an indication of whether the P2P transaction includes one or more of the indicators related to fraudulent or illicit activity (¶ 39: alert provided to operator).
For claim 19, Ben Kimon teaches all the limitations of claim 17 above and further teaches:
The non-transitory computer-readable medium of claim 17, wherein the information associated with the indicators related to fraudulent or illicit activity in P2P transactions includes transactional patterns or account usage patterns associated with tactics used in known fraudulent schemes, illegal behaviors, or unlawful organizations (¶ 33: features include transaction activity frequency indicating fraudulence).
Claim Rejections - 35 USC § 103
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 5, 7, 8, 14, 16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ben Kimon et al., U.S. Patent App. No. 2020/0210849 (“Ben Kimon”) in view of Sivajothi et al., U.S. Patent App. No. 2026/0010907 (“Sivajothi”).
For claim 5, Ben Kimon teaches all the limitations of claim 1 above. Ben Kimon does not teach: wherein the information associated with the indicators related to fraudulent or illicit activity in P2P transactions includes words, phrases, or communication tactics associated with known fraudulent schemes, illegal behaviors, or unlawful organizations.
Sivajothi, however, teaches:
The system of claim 1, wherein the information associated with the indicators related to fraudulent or illicit activity in P2P transactions includes words, phrases, or communication tactics associated with known fraudulent schemes, illegal behaviors, or unlawful organizations (¶ 112: factors include keywords and phrases associated with fraudulent activities).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the fraud detection in Ben Kimon by adding language analysis from Sivajothi. One of ordinary skill in the art would have been motivated to make this modification for the purpose of providing a more real-time and comprehensive fraud detection—a benefit explicitly disclosed by Sivajothi (¶ 13: need for more comprehensive fraud detection; ¶ 14: invention provides fraud detection that analyzes and authenticates intent using machine learning) and desired by Ben Kimon (¶ 13, 15: need for improved accuracy and efficiency in fraud detections).
For claim 7, Ben Kimon teaches all the limitations of claim 1 above. Ben Kimon does not teach: wherein the indicators related to fraudulent or illicit activity in P2P transactions include one or more of obfuscation techniques to mask one or more words or phrases or behavior patterns associated with structuring P2P transactions to evade detection.
Sivajothi, however, teaches:
The system of claim 1, wherein the indicators related to fraudulent or illicit activity in P2P transactions include one or more of obfuscation techniques to mask one or more words or phrases or behavior patterns associated with structuring P2P transactions to evade detection (¶ 112: factors include suspicious speech patterns and inconsistencies).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the fraud detection in Ben Kimon by adding language analysis from Sivajothi. One of ordinary skill in the art would have been motivated to make this modification for the purpose of providing a more real-time and comprehensive fraud detection—a benefit explicitly disclosed by Sivajothi (¶ 13: need for more comprehensive fraud detection; ¶ 14: invention provides fraud detection that analyzes and authenticates intent using machine learning) and desired by Ben Kimon (¶ 13, 15: need for improved accuracy and efficiency in fraud detections).
For claim 8, Ben Kimon teaches all the limitations of claim 1 above. Ben Kimon does not teach: analyze the textual information related to the P2P transaction using NLP to determine whether the textual information includes one or more indicators of abusive behavior, wherein the remediation action is triggered for the P2P transaction based on the textual information including the one or more indicators of abusive behavior.
Sivajothi, however, teaches:
The system of claim 1, wherein the one or more processors are further configured to: analyze the textual information related to the P2P transaction using NLP to determine whether the textual information includes one or more indicators of abusive behavior, wherein the remediation action is triggered for the P2P transaction based on the textual information including the one or more indicators of abusive behavior (¶ 112: factors include threat patterns).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the fraud detection in Ben Kimon by adding language analysis from Sivajothi. One of ordinary skill in the art would have been motivated to make this modification for the purpose of providing a more real-time and comprehensive fraud detection—a benefit explicitly disclosed by Sivajothi (¶ 13: need for more comprehensive fraud detection; ¶ 14: invention provides fraud detection that analyzes and authenticates intent using machine learning) and desired by Ben Kimon (¶ 13, 15: need for improved accuracy and efficiency in fraud detections).
For claim 14, Ben Kimon teaches all the limitations of claim 11 above. Ben Kimon does not teach: wherein the information associated with the indicators related to fraudulent or illicit activity in P2P transactions includes words, phrases, or communication tactics associated with known fraudulent schemes, illegal behaviors, or unlawful organizations.
Sivajothi, however, teaches:
The method of claim 11, wherein the information associated with the indicators related to fraudulent or illicit activity in P2P transactions includes words, phrases, or communication tactics associated with known fraudulent schemes, illegal behaviors, or unlawful organizations (¶ 112: factors include keywords and phrases associated with fraudulent activities).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the fraud detection in Ben Kimon by adding language analysis from Sivajothi. One of ordinary skill in the art would have been motivated to make this modification for the purpose of providing a more real-time and comprehensive fraud detection—a benefit explicitly disclosed by Sivajothi (¶ 13: need for more comprehensive fraud detection; ¶ 14: invention provides fraud detection that analyzes and authenticates intent using machine learning) and desired by Ben Kimon (¶ 13, 15: need for improved accuracy and efficiency in fraud detections).
For claim 16, Ben Kimon teaches all the limitations of claim 11 above. Ben Kimon does not teach: wherein the indicators related to fraudulent or illicit activity in P2P transactions include one or more of obfuscation techniques to mask one or more words or phrases or behavior patterns associated with structuring P2P transactions to evade detection.
Sivajothi, however, teaches:
The method of claim 11, wherein the indicators related to fraudulent or illicit activity in P2P transactions include one or more of obfuscation techniques to mask one or more words or phrases or behavior patterns associated with structuring P2P transactions to evade detection (¶ 112: factors include suspicious speech patterns and inconsistencies).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the fraud detection in Ben Kimon by adding language analysis from Sivajothi. One of ordinary skill in the art would have been motivated to make this modification for the purpose of providing a more real-time and comprehensive fraud detection—a benefit explicitly disclosed by Sivajothi (¶ 13: need for more comprehensive fraud detection; ¶ 14: invention provides fraud detection that analyzes and authenticates intent using machine learning) and desired by Ben Kimon (¶ 13, 15: need for improved accuracy and efficiency in fraud detections).
For claim 18, Ben Kimon teaches all the limitations of claim 17 above. Ben Kimon does not teach: wherein the information associated with the indicators related to fraudulent or illicit activity in P2P transactions includes words, phrases, or communication tactics associated with known fraudulent schemes, illegal behaviors, or unlawful organizations.
Sivajothi, however, teaches:
The non-transitory computer-readable medium of claim 17, wherein the information associated with the indicators related to fraudulent or illicit activity in P2P transactions includes words, phrases, or communication tactics associated with known fraudulent schemes, illegal behaviors, or unlawful organizations (¶ 112: factors include keywords and phrases associated with fraudulent activities).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the fraud detection in Ben Kimon by adding language analysis from Sivajothi. One of ordinary skill in the art would have been motivated to make this modification for the purpose of providing a more real-time and comprehensive fraud detection—a benefit explicitly disclosed by Sivajothi (¶ 13: need for more comprehensive fraud detection; ¶ 14: invention provides fraud detection that analyzes and authenticates intent using machine learning) and desired by Ben Kimon (¶ 13, 15: need for improved accuracy and efficiency in fraud detections).
For claim 20, Ben Kimon teaches all the limitations of claim 17 above. Ben Kimon does not teach: wherein the indicators related to fraudulent or illicit activity in P2P transactions include one or more of obfuscation techniques to mask one or more words or phrases or behavior patterns associated with structuring P2P transactions to evade detection.
Sivajothi, however, teaches:
The non-transitory computer-readable medium of claim 17, wherein the indicators related to fraudulent or illicit activity in P2P transactions include one or more of obfuscation techniques to mask one or more words or phrases or behavior patterns associated with structuring P2P transactions to evade detection (¶ 112: factors include suspicious speech patterns and inconsistencies).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the fraud detection in Ben Kimon by adding language analysis from Sivajothi. One of ordinary skill in the art would have been motivated to make this modification for the purpose of providing a more real-time and comprehensive fraud detection—a benefit explicitly disclosed by Sivajothi (¶ 13: need for more comprehensive fraud detection; ¶ 14: invention provides fraud detection that analyzes and authenticates intent using machine learning) and desired by Ben Kimon (¶ 13, 15: need for improved accuracy and efficiency in fraud detections).
Prior Art Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. Those prior art references are as follows:
Opedal, U.S. Patent App. No. 2024/0362640, discloses fraud detection through machine learning models.
Gu et al, U.S. Patent App. No. 2023/0237493, discloses detecting fraudulent activity through machine learning.
Conclusion
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/DIVESH PATEL/Examiner, Art Unit 3696
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