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 response filed 03/13/2026.
Application filed: 04/03/2024.
Applicant’s PgPUB: 2025/0310413
Claims:
Claims 1-4, 6-11, 13, 15-17 and 21-26 are pending.
Claims 1, 8 and 15 are independent.
Claims 1, 2, 4, 6-9, 11, 13, 15 and 16 are amended.
Claims 5, 12, 14, 18-20 are canceled.
Claims 24-26 are new.
Claim 26 is objected to.
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Rejection under 35 U.S.C. 112 has been withdrawn due to Applicant’s amendment.
Examiner Note:
Terminal disclaimer filed and approved on 10/24/2025 for related application 18/624,477.
Response to Arguments
Applicant’s arguments, see Applicant’s response, filed 10/03/2025, with respect to the rejection(s) of claims 1-4, 6-11, 13, 15-17 and 21-23 under 35 U.S.C. 102 and 103 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. 2025/0045327 A1 to Avram et al. (“Avram”).
Allowable Subject Matter
Claim 26 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 1-4, 6, 8-11, 13, 15-17, 24 and 25 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. 2025/0045327 A1 to Avram et al. (“Avram”).
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 subsequent 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 the processing device 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 first historical data associated with a first plurality of 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).
Avram discloses what Thiemann and Gupta do not expressly disclose. However, Thiemann does teach determining the success of future recurring transactions (Abstract).
Avram discloses:
wherein the pattern of behavior comprises the first computing system executing the first plurality of interactions via a particular service (¶0003 – Avram teaches that based on similarity between the actions of the user and the actions of a candidate routine (or a “candidate routine template”), a routine can be suggested to the user, so that the user can select to perform the suggested routine in lieu of performing the steps individually and so the suggested routine can be performed with a reduced quantity of user input(s));
wherein the adjustment comprises performing a single interaction via the particular service via the particular service that combines the first plurality of interactions (¶0003 – Avram teaches that based on similarity between the actions of the user and the actions of a candidate routine (or a “candidate routine template”), a routine can be suggested to the user, so that the user can select to perform the suggested routine in lieu of performing the steps individually and so the suggested routine can be performed with a reduced quantity of user input(s));
configuring the particular service to automatically execute the single interaction at a particular repeating time (Fig. 5, ¶0003 – Avram teaches that based on similarity between the actions of the user and the actions of a candidate routine (or a “candidate routine template”), a routine can be suggested to the user, so that the user can select to perform the suggested routine in lieu of performing the steps individually and so the suggested routine can be performed with a reduced quantity of user input(s); ¶0079 – Avram teaches a routine suggestion is provided based on a time condition. As previously described, the user may have performed a routine at the same time for a number of times and a trigger that is associated with that time may be associated with the template routine.)
Thiemann, Gupta and Avram 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 consolidating user actions as discussed in Avram with ML modeling inputs as discussed in Gupta with ML modeling system as discussed in Thiemann by adding the functionality of Avram to the system/method of Thiemann and Gupta in order to provide to a user, that automates a sequence of actions previously performed by the user via multiple applications (Avram, ¶0003).
As to claim 2, Thiemann, Gupta and Avram discloses:
system of claim 1, and
Thiemann discloses:
wherein the first computing system further is configured to execute a second 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 further comprises adjusting the second interaction to be routed to a second service to execute the second 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 second 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 second 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, Gupta and Avram 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, Gupta and Avram discloses:
system of claim 1, and
Thiemann discloses:
wherein the pattern of behavior further comprises 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 further 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 6, Thiemann, Gupta and Avram 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 second historical data, the second historical data comprising historical patterns of behavior and historical scores associated with a second plurality of accounts (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 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 13, similar rejection as to claim 6.
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.
As to claim 24, Thiemann, Gupta and Avram discloses:
system of claim 1,
wherein the adjustment comprises
Avram discloses:
configuring the particular service to automatically execute the single interaction at the particular repeating time instead of executing the first plurality of interactions (Fig. 5, ¶0003 – Avram teaches that based on similarity between the actions of the user and the actions of a candidate routine (or a “candidate routine template”), a routine can be suggested to the user, so that the user can select to perform the suggested routine in lieu of performing the steps individually and so the suggested routine can be performed with a reduced quantity of user input(s); ¶0079 – Avram teaches a routine suggestion is provided based on a time condition. As previously described, the user may have performed a routine at the same time for a number of times and a trigger that is associated with that time may be associated with the template routine.),
Thiemann discloses:
wherein an amount of resources transferred by executing the single interaction corresponds to a total amount of resources transferred by executing the plurality of interactions (¶0054 – Thiemann teaches a recurring transaction is scheduled for November 15 for $100.).
The suggestion/motivation and obviousness rejection is the same as in claim 1.
As to claim 25, Thiemann, Gupta and Avram discloses:
system of claim 1, and
Thiemann discloses:
wherein the first computing system is further configured to execute a second repeating interaction,
wherein a configuration file for the first computing system dictates, prior to generating the adjustment, a first amount of resources to transfer for the second repeating interaction, and
wherein the adjustment further comprises updating the configuration file to dictate a second amount of resources to transfer for the repeating interaction, the second amount of resources being different from the first amount of resources.
(¶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),
Claim 7 is 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. 2025/0045327 A1 to Avram et al. (“Avram”) in further view of Chinese Patent Application Publication No. CN 107247786 A (Machine Translation) to Zezhong (“Zezhong”).
As to claim 7, Thiemann, Gupta and Avram discloses:
system of claim 1, and
Thiemann discloses:
wherein the set of usage data includes first 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), and
Zezhong discloses what Thiemann, Gupta and Avram do not expressly discloses.
Zezhong discloses:
wherein at least one of the first user characteristics is in common with a second user characteristic indicated by the second historical data associated with the plurality of accounts (¶0004 – Zezhong teaches clustering the users in the user set to be processed into a plurality of user clusters according to the label attribute characteristics and the geographic position information; and calculating the similarity between the target user and other users in the same user cluster based on the label attribute characteristics and determining the similar users of the target user).
Thiemann, Gupta, Avram and Zezhong are analogous arts because they are from the same field of endeavor with respect to user interactions and usage data.
Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to incorporate finding similar users as discussed in Zezhong with consolidating user actions as discussed in Avram with ML modeling inputs as discussed in Gupta with ML modeling system as discussed in Thiemann by adding the functionality of Zezhong to the system/method of Thiemann, Gupta and Avram in order to determine similar users (Zezhong, ¶0001).
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. 2025/0045327 A1 to Avram et al. (“Avram”) in further view of U.S. Patent Application Publication No. 2023/01964925 to Braud et al. (“Braud”).
As to claim 21, Thiemann, Gupta and Avram discloses:
system of claim 1,
Braud discloses what Thiemann, Gupta and Avram 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, Avram 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 consolidating user actions as discussed in Avram 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, Gupta and Avram in order to demonstrate how ML modeling can product various scoring results.
As to claim 22, Thiemann, Gupta and Avram discloses:
system of claim 1,
Braud discloses what Thiemann, Gupta and Avram 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, Gupta and Avram discloses:
system of claim 1,
Braud discloses what Thiemann, Gupta and Avram 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
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
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