DETAILED ACTION
This Final Office Action is in response to the application filed on 12/24/2024 and the Amendment & Remark filed on 04/06/2026.
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 .
Status of Claims
Claims 3, 4, 12 and 15-18 are canceled.
Claims 1, 9, 11 and 13 are amended.
Claims 1-2, 5-11, 13-14 and 19-20 are pending.
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 1-2, 5-11, 13-14 and 19-20 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) 1 and 11 that “calculating a fraud score using machine learning models trained on historical transaction data, wherein the fraud score is based on transaction data including recipient information, contact details, and Internet Protocol addresses, and routing the transactions for approval based on the fraud score”, 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. The examiner noted that the description merely states result-only description such as “can be calculated by artificial intelligence”, “models can be used” or “NLP can examine context information” but is completely devoid of important “how”. 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 models to calculate the fraud score based on recipient information, including: analyzing patterns across payment types, amounts, and frequencies using the artificial intelligence models trained on historical transaction data, wherein the historical transaction data includes the recipient information, contact details, and Internet Protocol addresses; detecting unusual patterns in recurring payment behaviors; and processing multiple data points simultaneously using the artificial intelligence models 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 fraud score using generically recited artificial intelligence. The examiner noted that the description merely states result-only description such as “can be calculated by artificial intelligence”, “models can be used” or “NLP can examine context information” but is completely devoid of “how”. 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. The examiner noted that the description merely states result-only description such as “NLP can examine context information” but is completely devoid of “how”. 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. The examiner noted that the description merely states result-only description such as “can be calculated by artificial intelligence”, “models can be used” or “NLP can examine context information” but is completely devoid of “how”. 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 1-2, 5-11, 13-14 and 19-20 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 1-2, 5-11, 13-14 and 19-20 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 “vulnerable adult with a diminished capacity to make financial decisions” in claim 1 is a relative term which renders the claim indefinite. The term “diminished capacity to make financial decisions” 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 1-2, 5-11, 13-14 and 19-20 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 with a diminished capacity to make financial decisions, 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 by maintaining a database of trusted contacts and requiring approval from one or more overseers before delivering messages from untrusted individuals to the vulnerable adult;
a payments module programmed to limit transactions with third parties by the vulnerable adult by calculating a fraud score using machine learning models trained on historical transaction data, wherein the fraud score is based on transaction data including recipient information, contact details, and Internet Protocol addresses, and routing the transactions for approval based on the fraud score; and
a reporting module programmed to provide a history of the communications and the transactions by maintaining an audit trail of the transactions and the communications, and tracking approval workflows and decisions.
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 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: 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:
limiting communications between the vulnerable adult and untrusted individuals by maintaining a database of trusted contacts and requiring approval from one or more overseers before delivering messages from untrusted individuals to the vulnerable adult;
analyzing transaction data using artificial intelligence model to calculate the fraud score based on recipient information, including:
analyzing patterns across payment types, amounts, and frequencies using the artificial intelligence models trained on historical transaction data, wherein the historical transaction data includes the recipient information, contact details, and Internet Protocol addresses;
detecting unusual patterns in recurring payment behaviors; and
processing multiple data points simultaneously using the artificial intelligence models to calculate comprehensive fraud scores, learn and adapt to new fraud patterns over time, and integrate fraud detection data from multiple third-party applications;
routing transactions for approval based on the fraud score, including:
applying different risk thresholds for various payment types;
automating escalation processes when suspicious patterns are detected; and
routing the transactions through approval workflows; and
generating alerts when the fraud score meets or exceeds defined thresholds;
maintaining an audit trail of system decisions;
tracking effectiveness of fraud detection across the artificial intelligence models; and
updating the artificial intelligence models based on confirmed fraudulent activities
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 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 by maintaining a database of trusted contacts and requiring 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 to apply the Judicial Exception step of limiting communications between the vulnerable adult and untrusted individuals by maintain a list of trusted contacts and requiring approval from overseer before delivering messages;
the limitation “a payments module programmed to limit transactions with third parties by the vulnerable adult by calculating a fraud score using machine learning models trained on historical transaction data, wherein the fraud score is based on transaction data including recipient information, contact details, and Internet Protocol addresses, and routing the transactions for approval based on the fraud score” 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 by calculating a fraud score using trained machine learning models and routing the transaction for approval based on the fraud score;
the limitation “a reporting module programmed to provide a history of the communications and the transactions by maintaining an audit trail of the transactions and the communications, and tracking approval workflows and decisions” 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 by maintaining an audit trail of the transaction and communications and tracking approval workflows and decisions;
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 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 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 “limiting communications between the vulnerable adult and untrusted individuals by maintaining a database of trusted contacts and requiring 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 to apply the Judicial Exception step of limiting communications between the vulnerable adult and untrusted individuals by maintain a list of trusted contacts and requiring approval from overseer before delivering messages;
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 “analyzing patterns across payment types, amounts, and frequencies using the artificial intelligence models trained on historical transaction data, wherein the historical transaction data includes the recipient information, contact details, and Internet Protocol addresses; 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 multiple data points simultaneously using the artificial intelligence models 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 “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 “applying different risk thresholds for various payment types; automating escalation processes when suspicious patterns are detected; routing the transactions through 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 “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 “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 “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 “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 analyzing steps described only by a result-oriented solution with insufficient detail for how the models accomplish it.
The examiner further noted generic computer affixes such as “automatically” “automating” or “automated” are appended to abstract elements such as “escalation” 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 1-2, 5-11, 13-14 and 19-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
Previous rejection under 35 USC 103 is withdrawn in view of the Amendment and Remark filed on 04/06/2026.
Response to Arguments
Applicant's arguments filed on 04/06/2026 have been fully considered but they are not persuasive.
Regarding the applicant’s argument that Specification paragraphs 0062-0063, 0066, 0068 provides adequate written description support for the fraud score calculation limitation rejected under 35 USC 112(a), the examiner respectfully disagrees. The examiner noted that the description merely states result-only description such as “can be calculated by artificial intelligence” or “models can be used” but is completely devoid of “how”. Alleging an unspecified generic model (such as the artificial intelligence model invoked in the claims) to perform a specific function (such as calculating a fraud score based particularly on transaction data including recipient information, contact details, and Internet Protocol addresses) without providing technological detail of how the calculation is accomplished does not objectively demonstrate possession of such AI models. The examiner further noted that a generic reference of Artificial Intelligence or Machine Learning models is no more technically informative than a reference to a “black box” that produces a desired output. It provided no structural description of the model architecture, no disclosure of the training methodology, or hyperparameter selection that would yield a model capable of performing the specific analysis in the claims. Such a disclosure is functional equivalent of claiming “use a computer” to solve a problem – a form of disclosure long held to be insufficient. See Aristocrat Techs. Australia Pty Ltd. v. Int’l Game Tech. The written description is not satisfied by simply drafting claim language that a skilled artisan could theoretically implement using generic AI/ML tools available in the art. The question is whether this Specification conveys that the inventor(s) actually possessed the specific solution claimed. As such, the argument is not persuasive.
Regarding the applicant’s argument that Specification paragraphs 0067-0068 provides adequate written description support for the pattern recognition limitation rejected under 35 USC 112(a), the examiner respectfully disagrees. The examiner noted that the description merely states result-only description such as “NLP can examine context information” but is completely devoid of “how”. Alleging an unspecified generic model (such as the Natural Language Processing model invoked in the claims) to perform a specific function (such as evaluating similarity between current messages and known fraudulent communication patterns; and analyzing context information provided by the vulnerable adult explaining transactions) without providing technological detail of how the analysis is accomplished does not objectively demonstrate possession of such NPL models. As such, the argument is not persuasive.
Regarding the applicant’s argument that Specification paragraphs 0065 provides adequate written description support for the analyzing text content of message limitation rejected under 35 USC 112(a), the examiner respectfully disagrees. The examiner noted that the description merely states result-only description such as “artificial intelligence could also be implemented, such as pattern recognition networks” but is completely devoid of “how”. Alleging an unspecified generic model (such as the pattern recognition networks invoked in the claims) to perform a specific function (such as identifying relationships between transaction parameters using pattern recognition networks) without providing technological detail of how the analysis is accomplished does not objectively demonstrate possession of such pattern recognition models. As such, the argument is not persuasive.
Regarding the applicant’s argument that appending “with a diminished capacity to make financial decisions” to the relative term vulnerable adult would have sufficient definiteness, the examiner respectfully disagrees. The examiner noted that the term “diminished capacity” is nonetheless as much a relative term as “vulnerable”. The Specification provides no definitive standard as to what would be considered “diminished capacity to make financial decisions” or diminished from what level of capacity. As such, the argument is not persuasive.
Regarding the applicant’s argument that the claims integrate the Judicial Exception into practical application and amounting to an inventive concept by reflecting an improvement to technology, the examiner respectfully disagrees. The applicant contended that the claims reflect an improvement to computer security and fraud detection technology. However, the examiner noted that the claims at best provide improvement to the commercial interaction of transaction fraud detection. It must be noted that mere usage of AI/ML in applying a Judicial Exception does not improve the technology AI/ML. The examiner acknowledges that Ex Parte Desjardins supports eligibility where claims are directed to a concrete improvement in the functioning of machine learning technology itself, but notes that Desjardins does not stand for the broader proposition that any claim invoking a machine learning model is thereby directed to an improvement to machine learning technology. In fact, merely invoking a generic machine learning model as a tool to execute the analytical steps of the Judicial Exception does not improve the machine learning technology. Here, the claims recite “calculating a fraud score using machine learning models trained on historical transaction data” and “analyzing patterns across payment types, amounts, and frequencies using the artificial intelligence models trained on historical transaction data” without specifying any particular model architecture, training methodology, or technical mechanism by which machine learning technology itself is improved. The applicant has not identified, and the examiner does not find, any limitation in the claims that specifies how the machine model is improved, what specific technical problem in machine learning is solved, or why the claimed approach represents an advancement over conventional machine learning techniques. As such, the argument is not persuasive.
Applicant’s arguments, see Remark, filed 04/06/2026, with respect to the rejection under 35 USC 103 have been fully considered and are persuasive. The rejection has been withdrawn.
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 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
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