DETAILED ACTION
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 .
General Remarks
This communication is considered fully responsive to Applicant’s application filed 10/03/2025.
Application filed: 04/03/2024.
Applicant’s PgPUB: 2025/0310413
Claims:
Claims 1-17 and 21-23 are pending.
Claims 1, 8 and 15 are independent.
Claims 1, 8 and 15 are amended.
Claims 21-23 are new.
Continuity/Priority Data:
This Application is a Continuation of Non-Provisional Application No. 18/624,477 filed 04/02/2024
Previous Office Action:
Rejection under 35 U.S.C. 112 has been withdrawn due to Applicant’s amendment.
Response to Arguments
Applicant’s arguments, see Applicant’s response, filed 10/03/2025, with respect to the rejection(s) of claim(s) 1-17 under 35 U.S.C. 102 have been fully considered and are persuasive to overcome the prior rejection. However, upon further consideration, a new ground(s) of rejection is made in view of U.S. Patent Application Publication No. 2024/0013263 A1 to Gupta et al. (“Gupta”) and U.S. Patent Application Publication No. 2023/01964925 to Braud et al. (“Braud”).
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 may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1-17 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. Patent Application Publication No. 2025/0173730 to Thiemann et al. (“Thiemann”) in view of U.S. Patent Application Publication No. 2024/0013263 A1 to Gupta et al. (“Gupta”).
As to claim 1, Thiemann discloses:
a system comprising:
a processing device (Fig. 5, 504, Processor of Thiemann); and
a memory including instructions that are executable by the processing device for causing the processing device to (Fig. 5, 506, Main Memory of Thiemann):
generate, by executing a second trained machine-learning model and based on a second input comprising the first output, a second output comprising an adjustment to an interaction performable by the first computing system, the adjustment to the interaction being usable to increase the score for the pattern of behavior (Fig. 2, 240-250, ¶0052-¶0067 – Thiemann teaches executing a second machine-learning model (second MLM) to predict a second score which corresponds to the likelihood that the first score will increase; ¶0054 - Thiemann teaches changing hold timestamps based on results from the first MLM to determine if pre-authoriation is ok’d; ¶0057 – Thiemann teaches the transaction server can determine the second timestamp based on the account associated with the user. The time of the pre-authorization can correspond to various attributes of the user and or their account. For instance, the time of the pre-authorization may be based on previously successful or unsuccessful transactions, times of transactions, previous pre-authorizations (e.g., if a user has multiple other holds on their account), previous spend or credit limits (e.g., users with lower credit limits), among others (i.e., pattern of behavior)); and
automatically configure the first computing system to fulfill the interaction according to the adjustment (Fig. 2, 252, ¶0066 - Thiemann teaches the transaction server (i.e, first computing system) to message the user for a secondary payment (i.e., fulfill the interaction according to the adjustment)).
Gupta discloses what Thiemann does not expressly disclose.
Gupta discloses:
generate, by executing a first trained machine-learning model and based on a first input comprising a set of usage data associated with an account, a first output comprising (i) a pattern of behavior identified from the set of usage data and (ii) a score for the pattern of behavior, the set of usage data including historical data associated with interactions between a first computing system (Fig. 5, 510, 520, 525, -¶0057-¶0060 – Gupta teaches use of user interactions (i.e., usage data) as input into a first ML model that produces a lead score (i.e., score of behavior) to create a subset of interactions (i.e., pattern of behavior)));
Thiemann and Gupta are analogous arts because they are from the same field of endeavor with respect to ML modeling.
Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to incorporate ML modeling inputs as discussed in Gupta with ML modeling system as discussed in Thiemann by adding the functionality of Gupta to the system/method of Thiemann in order to optimize the communications between the online system and users that are likely to convert, thus reducing the amount of processing power used for acting upon the leads and reducing the bandwidth used for following up on the leads (Gupta, ¶0001).
As to claim 2, Thiemann and Gupta discloses:
system of claim 1, and
Thiemann discloses:
wherein the first computing system is configured to execute the interaction via a first service prior to generating the adjustment (Fig. 2, 232 – Thiemann shows conducting a transaction which indicates it is configured to execute by the transaction server (i.e., first computing system)), wherein the adjustment comprises adjusting the interaction to be routed to a second service to execute the interaction (Fig. 2, 260), the second service being different from the first service and executed by a third-party computing system (Fig. 1, 120a-e), and wherein the memory further includes instructions that are executable by the processing device for causing the processing device to automatically configure the first computing system to fulfill the interaction by (Fig. 2, Steps 210-250; Fig. 5, 504, Processor of Thiemann; Fig. 5, 506, Main Memory of Thiemann):
adjusting a configuration file for the account such that a subsequent interaction is automatically routed to the third-party computing system to be executed by the second service (Fig. 2, 260 – Thiemann teaches instructing second server to conduct and pre-auth for the future transaction at the second time stamp (i.e., second repeating date))
As to claim 3, Thiemann and Gupta discloses:
system of claim 2, and
Thiemann discloses:
wherein the first service is a wire service (Fig. 2, 232, Conduct transaction of Thiemann), and wherein the second service is a real-time service (Fig. 2, 260, pre-auth; 280, message user of Thiemann).
As to claim 4, Thiemann and Gupta discloses:
system of claim 1, and
Thiemann discloses:
wherein the pattern of behavior is an automatic interaction that is repeated, wherein the account comprises a configuration file dictating that the automatic interaction is executed on a first repeating date, wherein the adjustment comprises executing the automatic interaction on a second repeating date that is different than the first repeating date (¶0054 – Thiemann teaches a recurring transaction is scheduled for November 15 for $100. On November 1, the transaction server may execute the first machine learning model and determine that the transaction will likely fail (75% chance of failure). As a result, the transaction server may execute the second machine learning model. The second machine learning model predicts that the transaction will succeed if a hold of $60 is placed on the user's credit card on November 12. The $60 may be a dynamic amount that may depend on the user's account activity. For instance, if the user conducts a transaction of $1500 before November 12), and wherein the memory further includes instructions that are executable by the processing device for causing the processing device to automatically configure the first computing system to execute the automatic interaction by (Fig. 2, Steps 210-250; Fig. 5, 504, Processor of Thiemann; Fig. 5, 506, Main Memory of Thiemann):
adjusting the configuration file for the account to cause the automatic interaction to be executed on the second repeating date (Fig. 2, 260 – Thiemann teaches instructing second server to conduct and pre-auth for the future transaction at the second time stamp (i.e., second repeating date)).
As to claim 5, Thiemann and Gupta discloses:
system of claim 1, and
Thiemann discloses:
wherein the set of usage data includes historical data relating to a plurality of interactions executed by a particular service, wherein the adjustment comprises performing a single interaction with the particular service that combines the plurality of interactions (Fig. 1, 120, second server) (Fig. 2, 210-230, ¶0038-¶0050 – Thiemann teaches executing a first machine-learning model (first MLM) to predict a first score which uses input which includes historical transaction data, user ID, etc. which predicts the first score which corresponds to a likelihood of success for future transactions), and wherein the memory further includes instructions that are executable by the processing device for causing the processing device to automatically configure the first computing system to fulfill the interaction by (Fig. 2, Steps 210-250; Fig. 5, 504, Processor of Thiemann; Fig. 5, 506, Main Memory of Thiemann):
configuring the particular service to automatically execute the single interaction at a particular repeating time (Abstract – Thiemann teaches determining the success of future recurring transactions).
As to claim 6, Thiemann and Gupta discloses:
system of claim 1, and
Thiemann discloses:
wherein the second trained machine-learning model is configured to generate the second output comprising the adjustment to the pattern of behavior by comparing the pattern of behavior and the score to historical data, the historical data comprising historical patterns of behavior and historical scores (Fig. 2, 240-250, ¶0052-¶0067 – Thiemann teaches executing a second machine-learning model (second MLM) to predict a second score which corresponds to the likelihood that the first score will increase; ¶0054 - Thiemann teaches changing hold timestamps based on results from the first MLM to determine if pre-authorization is ok’d; ¶0057 – Thiemann teaches the transaction server can determine the second timestamp based on the account associated with the user. The time of the pre-authorization can correspond to various attributes of the user and or their account. For instance, the time of the pre-authorization may be based on previously successful or unsuccessful transactions, times of transactions, previous pre-authorizations (e.g., if a user has multiple other holds on their account), previous spend or credit limits (e.g., users with lower credit limits), among others (i.e., pattern of behavior)).
As to claim 7, Thiemann and Gupta discloses:
system of claim 1, and
Thiemann discloses:
wherein the set of usage data includes user characteristics of a user associated with the account (¶0087 – Thiemann teaches The variety of data can include customer information 305 (e.g., location, an IP address, a user identifier, etc.), historic payments 310 (e.g., historic transactions of the customer or of a multitude of customers), and/or a payment method 350 (e.g., a type of credit card vendor, a credit limit amount, the expiration date of the credit card, or other payment identifiers).
As to claim 8, similar rejection as to claim 1.
As to claim 9, similar rejection as to claim 2.
As to claim 10, similar rejection as to claim 3.
As to claim 11, similar rejection as to claim 4.
As to claim 12, similar rejection as to claim 5.
As to claim 13, similar rejection as to claim 6.
As to claim 14, similar rejection as to claim 7.
As to claim 15, similar rejection as to claim 1.
As to claim 16, similar rejection as to claim 2.
As to claim 17, similar rejection as to claim 3.
Claims 21-23 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. Patent Application Publication No. 2025/0173730 to Thiemann et al. (“Thiemann”) in view of U.S. Patent Application Publication No. 2024/0013263 A1 to Gupta et al. (“Gupta”) in further view of U.S. Patent Application Publication No. 2023/01964925 to Braud et al. (“Braud”).
As to claim 21, Thiemann and Gupta discloses:
system of claim 1,
Braud discloses what Thiemann and Gupta do not expressly disclose.
Braud discloses:
wherein the score comprises
a computational efficiency for the first computing system in executing the interactions (Fig. 2, Fig. 3, Network Coverage Improvement Metric, Provider Device Utilization Score, Response time score).
Thiemann, Gupta and Braud are analogous arts because they are from the same field of endeavor with respect to ML modeling.
Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to incorporate ML modeling scores as discussed in Braud with ML modeling inputs as discussed in Gupta with ML modeling system as discussed in Thiemann by adding the functionality of Gupta to the system/method of Thiemann and Gupta in order to demonstrate how ML modeling can product various scoring results.
As to claim 22, Thiemann and Gupta discloses:
system of claim 1,
Braud discloses what Thiemann and Gupta do not expressly disclose.
Braud discloses:
wherein the score comprises
a resource consumption efficiency score for the first computing system in executing the interactions (Fig. 2, Fig. 3, Network Coverage Improvement Metric, Provider Device Utilization Score, Response time score). The suggestion/motivation and obviousness rejection is the same as in claim 21.
As to claim 23, Thiemann and Gupta discloses:
system of claim 1,
Braud discloses what Thiemann and Gupta do not expressly disclose.
Braud discloses:
wherein the score comprises
a latency efficiency score for the first computing system in executing the interactions (Fig. 2, Fig. 3, Network Coverage Improvement Metric, Provider Device Utilization Score, Response time score). The suggestion/motivation and obviousness rejection is the same as in claim 21.
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 TAYLOR A ELFERVIG whose telephone number is (571)270-5687. The examiner can normally be reached Monday (10:00 AM CST) - Friday (4:00 PM CST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Oscar Louie can be reached at (571) 270-1684. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TAYLOR A ELFERVIG/Primary Examiner, Art Unit 2445