DETAILED ACTION
This Non-Final Office Action is in response to the application filed on 12/24/2024.
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 § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 4, 7 and 11-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved or (2) a broad genus claim is presented but the disclosure only describes a narrow species with no evidence that the genus is contemplated. See Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349-50 (Fed. Cir. 2010) (en banc).
While the Applicant specifies in claim(s) 4 that “calculate a fraud score using artificial intelligence based on transaction data including recipient information, contact details, and IP addresses”, there is no written content as to how or what specific process of determination are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to calculate the fraud score using generically recited artificial intelligence. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claim(s) 7 that “identify unusual patterns in transaction frequency”, there is no written content as to how or what specific process of determination are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to identify unusual patterns in transaction frequency. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claim(s) 11 that “analyzing transaction data using artificial intelligence to calculate the fraud score based on recipient information”, there is no written content as to how or what specific process of determination are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to calculate the fraud score using generically recited artificial intelligence. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claim(s) 12 that “analyzing patterns across payment types, amounts, and frequencies using machine learning classification models trained on historical transaction data”, there is no written content as to how or what specific process of determination are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to analyzing patterns across payment types, amounts, and frequencies using unspecified machine learning classification models. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claim(s) 13 that “processing natural language content of messages and communications for fraud indicators; evaluating similarity between current messages and known fraudulent communication patterns; and analyzing context information provided by the vulnerable adult explaining transactions”, there is no written content as to how or what specific process of determination are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to analyze natural language content of messages and communication for fraud indicators and to evaluate similarity between current messages and known fraudulent communication patterns. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claim(s) 14 that “identifying relationships between transaction parameters using pattern recognition networks; comparing current activity against historical baseline patterns for the vulnerable adult”, there is no written content as to how or what specific process of determination are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to identify relationships between transaction parameters using unspecified pattern recognition networks. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claim(s) 15 that “monitoring for deviations from normal transaction patterns using anomaly detection systems”, there is no written content as to how or what specific process of determination are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to monitor for deviation from normal transaction pattern using unspecified anomaly detection systems. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claim(s) 17 that “processing multiple data points simultaneously using neural networks to: calculate comprehensive fraud scores; learn and adapt to new fraud patterns over time; and integrate fraud detection data from multiple third-party applications”, there is no written content as to how or what specific process of determination are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to calculate the comprehensive fraud score using unspecified neural networks. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
The written description requirement can be satisfied if the particular steps, i.e., algorithm, necessary to perform the claimed function were “described in the specification.” In re Hayes Microcomputer Prods, Inc. Patent Litigation, 982 F.2d 1527, 1533-34, 25 USPQ2d 1241, (Fed. Cir. 1992).
As such, claims 4, 7 and 11-20 are rejected as failing the written description requirement.
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 1-20 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.
The term “vulnerable adult” in claims 1 and 11 is a relative term which renders the claim indefinite. The term “vulnerable” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Specification paragraph 0011 defined vulnerable adult as “such as an aging parent or other adult with diminished capacities that impact the adult's ability to make financial decisions”. However, both “aging parent” and “adult with diminished capacities” are relative terms that do not provide a standard for ascertaining whether a given adult is considered vulnerable.
The term “appropriate approval workflows” in claim 17 is a relative term which renders the claim indefinite. The term “appropriate” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As an initial matter, the claims as a whole are to a system and method, which falls within one or more statutory categories. (Step 1: YES) The recitation of the claimed invention is then further analyzed as follow, in which the abstract elements are boldfaced.
Claim 1 recites:
A computer system for controlling finances for a vulnerable adult, comprising: one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to generate:
a people module programmed to limit communications between the vulnerable adult and untrusted individuals;
a payments module programmed to limit transactions with third parties by the vulnerable adult; and
a reporting module programmed to provide a history of the communications and the transactions.
Claim 2 recites:
wherein the payments module is further programmed to interface with software on a computing device accessed by the vulnerable adult to control payment vehicles.
Claim 3 recites:
wherein the people module is further programmed to: maintain a database of trusted contacts; and require approval from one or more overseers before delivering messages from untrusted individuals to the vulnerable adult.
Claim 4 recites:
wherein the payments module is further programmed to: calculate a fraud score using artificial intelligence based on transaction data including recipient information, contact details, and IP addresses; and route the transactions for approval based on the fraud score.
Claim 5 recites:
wherein the payments module is further programmed to: identify recurring payment transactions; monitor variations in recurring payment amounts; and automatically approve recurring payments within defined thresholds.
Claim 6 recites:
wherein the payments module is further programmed to: define different approval workflows based on payment type; require multiple approvals for the transactions exceeding defined thresholds; and implement escalation processes when approvers are unavailable.
Claim 7 recites:
wherein the payments module is further programmed to: monitor transaction velocity; identify unusual patterns in transaction frequency; and limit further transactions when velocity thresholds are exceeded.
Claim 8 recites:
wherein the payments module is further programmed to: integrate with third-party applications through application programming interfaces; apply transaction controls within the third-party applications; and share fraud detection data across integrated applications.
Claim 9 recites:
wherein the reporting module is further programmed to: maintain an audit trail of transactions and the communications; track approval workflows and decisions; and generate reports of suspicious activity patterns.
Claim 10 recites:
wherein the payments module is further programmed to: preauthorize transactions for specific events; define spending limits by category and time period; and implement time-based restrictions on certain transaction types.
Claim 11 recites:
A method of calculating a fraud score for financial transactions of a vulnerable adult, comprising: analyzing transaction data using artificial intelligence to calculate the fraud score based on recipient information; routing transactions for approval based on the fraud score; and generating alerts when the fraud score meets or exceeds defined thresholds.
Claim 12 recites:
analyzing patterns across payment types, amounts, and frequencies using machine learning classification models trained on historical transaction data; and detecting unusual patterns in recurring payment behaviors.
Claim 13 recites:
processing natural language content of messages and communications for fraud indicators; evaluating similarity between current messages and known fraudulent communication patterns; and analyzing context information provided by the vulnerable adult explaining transactions.
Claim 14 recites:
identifying relationships between transaction parameters using pattern recognition networks; comparing current activity against historical baseline patterns for the vulnerable adult; and maintaining a database of known fraudulent activities including websites, phone numbers, email addresses, and IP addresses associated with scams.
Claim 15 recites:
monitoring for deviations from normal transaction patterns using anomaly detection systems; flagging unusual payment amounts, frequencies, or recipients; and identifying suspicious changes in recurring payment behaviors.
Claim 16 recites:
applying different risk thresholds for various payment types; automating escalation processes when suspicious patterns are detected; and routing the transactions through appropriate approval workflows.
Claim 17 recites:
processing multiple data points simultaneously using neural networks to: calculate comprehensive fraud scores; learn and adapt to new fraud patterns over time; and integrate fraud detection data from multiple third-party applications.
Claim 18 recites:
maintaining an audit trail of system decisions; tracking effectiveness of fraud detection across different artificial intelligence components; and updating artificial intelligence models based on confirmed fraudulent activities.
Claim 19 recites:
preauthorizing transactions for specific events by: defining approved dollar amounts for specific locations and merchants; setting date ranges for expected transactions; and automatically approving the expected transactions that match predefined event parameters for travel, gifts, and other planned expenses.
Claim 20 recites:
implementing time-based transaction controls by: defining exclusion periods for specific transaction types; limiting transactions based on time of day and day of week; adjusting approval thresholds for seasonal payment variations; and modifying transaction limits based on recurring payment patterns for utilities and medications.
Based on the limitations above, the claims describe a process that covers transaction approval control. Controlling transaction approval manages commercial relationship between individuals and is considered to be a commercial interaction, which falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. As such, the claim(s) recite(s) a Judicial Exception. (Step 2A prong one: Yes)
This analysis then evaluates whether the claims as a whole integrates the recited Judicial Exception into a practical application of the exception. In particular, the claims recite the additional element(s) of “one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to generate … module programmed to …” and “using artificial intelligence” as a mere tool to perform the steps of the Judicial Exception, which encompasses no more than Mere Instruction to Apply.
For example, the limitation “a people module programmed to limit communications between the vulnerable adult and untrusted individuals” encompasses no more than generically invoking a computing module to apply the Judicial Exception step of limiting communications between the vulnerable adult and untrusted individuals;
the limitation “a payments module programmed to limit transactions with third parties by the vulnerable adult” encompasses no more than generically invoking a computing module to apply the Judicial Exception step of limiting transactions with third parties by the vulnerable adult;
the limitation “a reporting module programmed to provide a history of the communications and the transactions” encompasses no more than generically invoking a computing module or artificial intelligence to apply the Judicial Exception step of providing the history of the communication and the transaction;
the limitation “wherein the payments module is further programmed to interface with software on a computing device accessed by the vulnerable adult to control payment vehicles” encompasses no more than generically invoking a computing module to apply the Judicial Exception step of controlling the vulnerable adult’s payment vehicles;
the limitation “wherein the people module is further programmed to: maintain a database of trusted contacts; and require approval from one or more overseers before delivering messages from untrusted individuals to the vulnerable adult” encompasses no more than generically invoking a computing module or artificial intelligence to apply the Judicial Exception step of maintaining a database of trusted contacts and requiring approval from overseer(s) before delivering messages from untrusted individuals;
the limitation “wherein the payments module is further programmed to: calculate a fraud score using artificial intelligence based on transaction data including recipient information, contact details, and IP addresses; and route the transactions for approval based on the fraud score” encompasses no more than generically invoking a computing module and artificial intelligence to apply the Judicial Exception step of calculating a fraud score and routing the transactions for approval based on the fraud score;
the limitation “wherein the payments module is further programmed to: identify recurring payment transactions; monitor variations in recurring payment amounts; and automatically approve recurring payments within defined thresholds” encompasses no more than generically invoking a computing module to apply the Judicial Exception step of identifying recurring payment transaction and monitoring variations and approving recurring payments within defined threshold;
the limitation “wherein the payments module is further programmed to: define different approval workflows based on payment type; require multiple approvals for the transactions exceeding defined thresholds; and implement escalation processes when approvers are unavailable” encompasses no more than generically invoking a computing module to apply the Judicial Exception step of defining different approval workflow based on payment type, requiring multiple approval for transactions exceed thresholds and implementing escalation process when approvers are unavailable;
the limitation “wherein the payments module is further programmed to: monitor transaction velocity; identify unusual patterns in transaction frequency; and limit further transactions when velocity thresholds are exceeded” encompasses no more than generically invoking a computing module to apply the Judicial Exception step of monitoring transaction velocity, identifying unusual patterns in transaction frequency and limiting further transaction when velocity threshold are exceeded;
the limitation “wherein the payments module is further programmed to: integrate with third-party applications through application programming interfaces; apply transaction controls within the third-party applications; and share fraud detection data across integrated applications” encompasses no more than generically invoking a computing module to apply the Judicial Exception step of interacting with third parties, applying transaction control to transaction involving the third parties and sharing fraud detection data with the third parties;
the limitation “wherein the reporting module is further programmed to: maintain an audit trail of transactions and the communications; track approval workflows and decisions; and generate reports of suspicious activity patterns” encompasses no more than generically invoking a computing module to apply the Judicial Exception step of maintain the audit trail, tracking approval workflows and decisions and generating reports of suspicious activity patterns;
the limitation “wherein the payments module is further programmed to: preauthorize transactions for specific events; define spending limits by category and time period; and implement time-based restrictions on certain transaction types” encompasses no more than generically invoking a computing module to apply the Judicial Exception step of preauthorizing transaction, defining spending limits by category and time period and implementing time-based restrictions on certain transaction types;
the limitation “analyzing transaction data using artificial intelligence to calculate the fraud score based on recipient information” encompasses no more than generically invoking artificial intelligence to apply the Judicial Exception step of analyzing transaction data to calculate a fraud score;
the limitation “routing transactions for approval based on the fraud score” encompasses no more than generically invoking artificial intelligence to apply the Judicial Exception step of routing transactions for approval based on the fraud score;
the limitation “generating alerts when the fraud score meets or exceeds defined thresholds” encompasses no more than generically invoking artificial intelligence to apply the Judicial Exception step of generating alerts when the fraud score meets or exceeds defined thresholds;
the limitation “analyzing patterns across payment types, amounts, and frequencies using machine learning classification models trained on historical transaction data; and detecting unusual patterns in recurring payment behaviors” encompasses no more than generically invoking artificial intelligence to apply the Judicial Exception step of analyzing patterns across payment types, amounts, and frequencies and detecting unusual patterns in recurring payment behaviors;
the limitation “processing natural language content of messages and communications for fraud indicators; evaluating similarity between current messages and known fraudulent communication patterns; and analyzing context information provided by the vulnerable adult explaining transactions” encompasses no more than generically invoking artificial intelligence to apply the Judicial Exception step of processing the content of messages and communications for fraud indicators, evaluating similarity with known fraud patterns and analyzing context information provided by the vulnerable adult;
the limitation “identifying relationships between transaction parameters using pattern recognition networks; comparing current activity against historical baseline patterns for the vulnerable adult; and maintaining a database of known fraudulent activities including websites, phone numbers, email addresses, and IP addresses associated with scams” encompasses no more than generically invoking artificial intelligence to apply the Judicial Exception step of identifying relationship between transaction parameters, comparing current activity against baseline patterns and maintaining a database of know fraudulent activities;
the limitation “monitoring for deviations from normal transaction patterns using anomaly detection systems; flagging unusual payment amounts, frequencies, or recipients; and identifying suspicious changes in recurring payment behaviors” encompasses no more than generically invoking artificial intelligence to apply the Judicial Exception step of monitoring for deviation from normal transaction patterns, flagging unusual transaction and identifying suspicious changes;
the limitation “applying different risk thresholds for various payment types; automating escalation processes when suspicious patterns are detected; and routing the transactions through appropriate approval workflows” encompasses no more than generically invoking artificial intelligence to apply the Judicial Exception step of applying different risk threshold for various payment types, escalating when suspicious patterns are detected and routing the transaction through appropriate approval workflows;
the limitation “processing multiple data points simultaneously using neural networks to: calculate comprehensive fraud scores; learn and adapt to new fraud patterns over time; and integrate fraud detection data from multiple third-party applications” encompasses no more than generically invoking artificial intelligence to apply the Judicial Exception step of calculating comprehensive fraud scores, learning and adapting to new fraud patterns and integrating fraud detection data from multiple third parties;
the limitation “maintaining an audit trail of system decisions; tracking effectiveness of fraud detection across different artificial intelligence components; and updating artificial intelligence models based on confirmed fraudulent activities” encompasses no more than generically invoking artificial intelligence to apply the Judicial Exception step of maintain an audit trail of decision, tracking effectiveness of fraud detection and updating fraud detection models based on confirmed fraudulent activities;
the limitation “preauthorizing transactions for specific events by: defining approved dollar amounts for specific locations and merchants; setting date ranges for expected transactions; and automatically approving the expected transactions that match predefined event parameters for travel, gifts, and other planned expenses” encompasses no more than the Judicial Exception step of preauthorizing expected transactions by defining approved amounts and date ranges and approving the expected transaction;
the limitation “implementing time-based transaction controls by: defining exclusion periods for specific transaction types; limiting transactions based on time of day and day of week; adjusting approval thresholds for seasonal payment variations; and modifying transaction limits based on recurring payment patterns for utilities and medications” encompasses no more than the Judicial Exception step of implementing time-based transaction controls by: defining exclusion periods, limiting transactions based on time of day and day of week, adjusting approval thresholds for seasonal payment variations and modifying transaction limits based on recurring payment patterns for utilities and medications.
Other than being generally linked to the steps of the Judicial Exception, the additional elements in the above step(s) is/are recited at a high-level of generality, without technological detail of how the particular steps are performed technologically.
The additional element(s) of “memory”, “database” and/or “non-transitory storage medium” are generically recited to store data and/or instructions of the Judicial Exception.
The additional element(s) of “interface with software on a computing device” and “integrate with … applications through application programming interfaces” are generically recited to perform communication steps such as receiving and transmitting.
The additional element(s) of “using artificial intelligence”, “using machine learning classification models”, “using pattern recognition networks”, “using neural networks”, “learn and adapt to new … patterns over time”, “updating artificial intelligence models based on confirmed … activities” are generically recited to perform input/output steps described only by a result-oriented solution with insufficient detail for how the model accomplish it.
The examiner further noted generic computer affixes such as “automatically” or “automated” are appended to abstract elements such as “escalating” or “approving transaction”, but found that to be mere instructions to implement the Judicial Exception idea on a computer.
Indeed, the instant claims (1) attempted to cover a solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result; (2) used of a computer or other machinery in its ordinary capacity for economic or other tasks or simply added a general purpose computer or computer components after the fact to the Judicial Exception and (3) generally applied the Judicial Exception to a generic computing environment without limitation indicative of practical application (See MPEP 2106.04(d)I). Thus, the claims are no more than Mere Instruction to Apply the Judicial Exception (See MPEP 2106.05(f)) or adding insignificant extra-solution activity to the judicial exception (See MPEP 2106.05(g)), which do not integrate the cited Judicial Exception into practical application (Step 2A prong two: No) The claims are directed to a Judicial Exception.
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 of the abstract idea into a practical application, the additional element of using computer module and generically described artificial intelligence to control transaction approval amounts no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. No additional element currently recited in the claims amount the claims to be significantly more than the cited abstract idea. (Step 2B: No)
Therefore, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claim(s) 1-9 and 11-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al (US 20200005310) in view of Mergenthaler (US 2006/0225140)
As per claim 1, Kumar teaches a system comprising:
one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to generate:
a payments module programmed to limit transactions with third parties by an individual; (See Kumar Paragraph 0027, 0038, 0045, 0083) and
a reporting module programmed to provide a history of the transactions. (See Kumar Paragraph 0020, 0025-0026 and 0061)
Kumar does not teach a people module programmed to limit communications between the vulnerable adult and untrusted individuals; a reporting module programmed to provide a history of the communications.
However, Mergenthaler teaches limit communications between the vulnerable adult and untrusted individuals (See Mergenthaler Paragraph 0032-0033, 0037 and 0040) and a reporting module programmed to provide a history of the communications. (See Mergenthaler Paragraph 0030, 0035 and 0041)
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify the transaction approval taught by Kumar with teaching from Mergenthaler to limit and track communication with the vulnerable adult. One of ordinary skill in the art would have been motivated as signs of fraudulent activity may be presented in the communication, limiting and tracking the communication reduce chance of successful fraud.
As per claim 2, Kumar in view of Mergenthaler teaches:
wherein the payments module is further programmed to interface with software on a computing device accessed by the vulnerable adult to control payment vehicles. (See Kumar Paragraph 0035-0036)
As per claim 3, Kumar in view of Mergenthaler teaches:
wherein the people module is further programmed to: maintain a database of trusted contacts; and require approval from one or more overseers before delivering messages from untrusted individuals to the vulnerable adult. (See Mergenthaler Paragraph 0032-0033, 0037 and 0040)
As per claim 4, Kumar in view of Mergenthaler teaches:
wherein the payments module is further programmed to: calculate a fraud score using artificial intelligence based on transaction data including recipient information, contact details, and IP addresses; and route the transactions for approval based on the fraud score. (See Kumar Paragraph 0022, 0027, 0057 and 0072)
As per claim 5, Kumar in view of Mergenthaler teaches:
wherein the payments module is further programmed to: identify recurring payment transactions; monitor variations in recurring payment amounts; and automatically approve recurring payments within defined thresholds. (See Kumar Paragraph 0027, 0070 and 0073)
As per claim 6, Kumar in view of Mergenthaler teaches:
wherein the payments module is further programmed to: define different approval workflows based on payment type; require multiple approvals for the transactions exceeding defined thresholds; and implement escalation processes when approvers are unavailable. (See Mergenthaler Paragraph 0032-0033, 0037 and 0040)
As per claim 7, Kumar in view of Mergenthaler teaches:
wherein the payments module is further programmed to: monitor transaction velocity; identify unusual patterns in transaction frequency; and limit further transactions when velocity thresholds are exceeded. (See Kumar Paragraph 0027, 0070 and 0073)
As per claim 8, Kumar in view of Mergenthaler teaches:
wherein the payments module is further programmed to: integrate with third-party applications through application programming interfaces; apply transaction controls within the third-party applications; and share fraud detection data across integrated applications. (See Kumar Paragraph 0024-0026)
As per claim 9, Kumar in view of Mergenthaler teaches:
wherein the reporting module is further programmed to: maintain an audit trail of transactions and the communications; track approval workflows and decisions; and generate reports of suspicious activity patterns. (See Kumar Paragraph 0029-0030, 0038 and 0052)
As per claim 11, Kumar teaches a method comprising:
analyzing transaction data using artificial intelligence to calculate the fraud score based on recipient information; (See Kumar Paragraph 0022, 0027, 0057 and 0072)
routing transactions for approval based on the fraud score; (See Kumar Paragraph 0022, 0027, 0057 and 0072) and
generating alerts when the fraud score meets or exceeds defined thresholds. (See Kumar Paragraph 0029)
Kumar does not teach financial transaction for a vulnerable adult.
However, Mergenthaler teaches limit communications between the vulnerable adult and untrusted individuals (See Mergenthaler Paragraph 0032-0033, 0037 and 0040)
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify the transaction approval taught by Kumar with teaching from Mergenthaler to analyze transaction data for financial transaction of vulnerable adult. One of ordinary skill in the art would have been motivated as analyzing transaction data for vulnerable adults reduces chance of successful fraud.
As per claim 12, Kumar in view of Mergenthaler teaches:
analyzing patterns across payment types, amounts, and frequencies using machine learning classification models trained on historical transaction data; and detecting unusual patterns in recurring payment behaviors. (See Kumar Paragraph 0035-0036)
As per claim 13, Kumar in view of Mergenthaler teaches:
processing natural language content of messages and communications for fraud indicators; evaluating similarity between current messages and known fraudulent communication patterns; and analyzing context information provided by the vulnerable adult explaining transactions. (See Kumar Paragraph 0022, 0027, 0057 and 0072)
As per claim 14, Kumar in view of Mergenthaler teaches:
identifying relationships between transaction parameters using pattern recognition networks; comparing current activity against historical baseline patterns for the vulnerable adult; and maintaining a database of known fraudulent activities including websites, phone numbers, email addresses, and IP addresses associated with scams. (See Kumar Paragraph 0022, 0027, 0057 and 0072)
As per claim 15, Kumar in view of Mergenthaler teaches:
monitoring for deviations from normal transaction patterns using anomaly detection systems; flagging unusual payment amounts, frequencies, or recipients; and identifying suspicious changes in recurring payment behaviors. (See Kumar Paragraph 0022, 0027, 0057 and 0072)
As per claim 16, Kumar in view of Mergenthaler teaches:
applying different risk thresholds for various payment types; automating escalation processes when suspicious patterns are detected; and routing the transactions through appropriate approval workflows (See Kumar Paragraph 0029-0030, 0038 and 0052)
As per claim 17, Kumar in view of Mergenthaler teaches:
processing multiple data points simultaneously using neural networks to: calculate comprehensive fraud scores; learn and adapt to new fraud patterns over time; and integrate fraud detection data from multiple third-party applications. (See Kumar Paragraph 0029-0030, 0038 and 0052)
As per claim 18, Kumar in view of Mergenthaler teaches:
maintaining an audit trail of system decisions; tracking effectiveness of fraud detection across different artificial intelligence components; and updating artificial intelligence models based on confirmed fraudulent activities. (See Kumar Paragraph 0029-0030, 0038 and 0052)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHO KWONG whose telephone number is (571)270-7955. The examiner can normally be reached 9am - 5pm EST M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MICHAEL W ANDERSON can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHO YIU KWONG/Primary Examiner, Art Unit 3693