CTFR 18/819,864 CTFR 87425 DETAILED ACTION Introduction This Final Office Action is in response to amendments and remarks filed on March 3, 2026, for the application with serial number 18/819,864. Claims 1, 4, 9, 11, 14, and 18 are amended. Claims 1-20 are pending. Interview The Examiner acknowledges the interview conducted on January 22, 2026, in which proposed amendments and remarks were discussed. Response to Remarks/Amendments 35 USC §101 Rejections The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the claims solve a problem in machine learning by solving a cold start problem. See Remarks p. 15. In response, the Examiner submits that this appears to amount to a decision whether or not to “train” a model. No apparent improvement in machine learning is recited in the claims. The rejection for lack of subject matter eligibility is updated and maintained. 35 USC §112 Rejections In light of the Applicant’s amendments, the rejection of the claims under 35 USC §112 is withdrawn. 35 USC §103 Rejections Amendments to the claims changed the scope of the claims, necessitating further consideration of the prior art. The independent claims are obvious over Kumar in view of Adeli, Gao, and Mazzochi; as set forth, below. Merely stating that the embeddings or data are “dynamic” and “static” amounts to saying whether variables are held constant or not; and whether the model continues to be trained or not. This is obvious, as evidenced by the prior art cited in the rejections, below. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows. Claims 1-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Although claims(s) 1-20 are all directed to one of the four statutory categories of invention, the claims are directed to determining embeddings corresponding to entities (as evidenced by exemplary independent claim 1; “determining . . . approximate embeddings corresponding to the entity”), an abstract idea. Certain methods of organizing are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “accessing . . . historical transaction data . . . comprising entity-specific data;” “generating . . . one or more pseudo-objective models;” “extracting . . . entity-specific embeddings for each of the plurality of entities;” “generating . . . concatenated embeddings;” “generating . . . dynamic embeddings;” “aggregating . .. the dynamic embeddings;” “generating . . . static embeddings;” “generating . . . a task-specific model;” “accessing . . . new entity data associated with [a] new entity;” “determining . . . approximate embeddings corresponding to the new entity;” and “updating . . . one of the entity-specific embeddings based . . . on the approximate embeddings.” The steps are all steps for managing personal behavior related to the abstract idea of determining embeddings corresponding to entities that, when considered alone and in combination, are part of the abstract idea of determining embeddings corresponding to entities. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of determining embeddings corresponding to entities. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes profiling and clustering entities based on transaction data to optimize financial services offerings. Under step 2A of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a server in independent claim 1; a server system with a memory and a processor in independent claim 11; and a computer readable medium in independent claim 18). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims do recite the use of machine learning, including neural networks, but the abstract idea of determining embeddings corresponding to entities is generally linked to a machine learning environment with machine learning algorithms, such as neural networks, for implementation. See exemplary dependent claims 5 and 9. Therefore, the machine learning elements merely amount to a technological environment that does not provide a practical application. See MPEP §2106.05(h). The claims require no more than a generic computer (a server in independent claim 1; a server system with a memory and a processor in independent claim 11; and a computer readable medium in independent claim 18) to implement the abstract idea, which does not amount to significantly more than an abstract idea. See MPEP §2106.05(f). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1, 2, 4, 8, 11, 12, 14, 18, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20030135442 A1 to Kumar et al. (hereinafter ‘KUMAR’) in view of US 5815394 A to Adeli et al. (hereinafter ‘ADELI’), US 20230169587 A1 to Gao (hereinafter ‘GAO’), and US 10733612 B1 to Mazzochi et al. (hereinafter ‘MAZZOCHI’) Claim 1 (Currently Amended) KUMAR discloses a computer-implemented method (see abstract and ¶[0002]-[0004] and [0143]; a method using computer program code and using a computer) , comprising: accessing, by a server system (see ¶[0034]; a server) , historical transaction data from a database associated with the server system (see ¶[0041] and [0120]; credit performance information from purchases made using a credit card. Last date of purchase) , the historical transaction data comprising entity-specific data (see ¶[0041]; a merchant’s credit card) , cardholder-specific data (see again ¶[0041]; recipient’s whose last purchase was withing the last six months and who belong to a high spender category) , and transaction-specific data (see again ¶[0041]; transactions within the last six months) ; generating, by the server system, one or more pseudo-objective models (see ¶[0043] and [0097]-[0099]; an objective function) for each of a plurality of entities based, at least in part, on the historical transaction data and one or more pseudo-objectives (see ¶[0009]; data regarding the recipients offers, campaign requirements and objectives may be used to make a decision regarding which offer to provide to which recipient) , the plurality of entities comprising an acquirer (see ¶[0103]; segments of recipients) , a merchant (see ¶[0124]; offer ten percent off at a particular merchant. Brand the credit card with a merchant’s name) , and an issuer (see ¶[0003]-[004], [0036], and [0043]; the issuer may want to target promotional communications to most receptive customers. An entity providing a financial product may want to maximize incremental sales) ; wherein the one or more pseudo-objectives include tasks (see abstract and ¶[0003]-[0004]; promotional communications, advertisements, letters, and reminders about a credit card to satisfy a requirement or objective). KUMAR does not specifically disclose, but ADELI discloses, wherein each of the one or more pseudo-objective models is a machine learning model that is trained (see claim 1; apply a learning rule through application of a pseudo-objective function). KUMAR further discloses based on the historical transaction data to perform one of the tasks for one of the plurality of entities (see ¶[0041] and [0120]; credit performance information from purchases made using a credit card. Last date of purchase. See also ¶[0004]-[0005]; identify offers that comply with or satisfy a requirement or objective that the entity may have. Obtain the best results) ; extracting , by the server system, entity-specific embeddings for each of the plurality of entities from the one or more pseudo-objective models, based, at least in part, on the entity-specific data, the entity-specific embeddings comprising acquirer-specific embeddings (see ¶[0005]; a group or segment of recipients) , merchant-specific embeddings (see ¶[0041] and [0116] & claims 1 and 8; purchases made using a merchant’s credit card. A merchant may want to indicate potential recipients of the offer) , and issuer-specific embeddings (see abstract and ¶[0039]; an entity may have multiple offers the entity can make regarding a financial product. Satisfy entity requirements and objectives. An entity may have a maximum amount an entity can spend of a campaign or ROI minimum) ; and generating by the server system, concatenated embeddings by concatenating the entity- specific embeddings (see ¶[0041], [0155], and [0163]-[0187]; information is stored in a recipient information database. Maintain databases) . KUMAR does not specifically disclose, but GAO discloses, generating by the server system, dynamic embeddings based on the concatenated embeddings (see abstract; a multi-layer ML model framework that employs multiple layers for different ML models that process different features. The features in one layer and ML model may process data for static features, where an output from this layer may be used as an input with data for dynamic feature that provide a predictive score or output for the input data. The static features may only be required to be processed once or a few times in the first layer and may not be required to be further processed again at later times. With the second layer, the data for the dynamic features may change, and thus the second layer may process new data without being required to reprocess the static features) and the transaction-specific data (see ¶[0013]; electronic transaction processing) ; and aggregating by the server system, the dynamic embeddings to generate aggregated dynamic embeddings (see ¶[0001]; determine predictive outputs based on static and dynamic data aggregated over one or more time periods); generating, by the server system via a static entity model, static embeddings (see abstract; a multi-layer ML model framework that employs multiple layers for different ML models that process different features. The features in one layer and ML model may process data for static features, where an output from this layer may be used as an input with data for dynamic feature that provide a predictive score or output for the input data. The static features may only be required to be processed once or a few times in the first layer and may not be required to be further processed again at later times. With the second layer, the data for the dynamic features may change, and thus the second layer may process new data without being required to reprocess the static features). KUMAR further discloses based, at least in part, on the issuer-specific embeddings (see abstract and ¶[0039]; an entity may have multiple offers the entity can make regarding a financial product. Satisfy entity requirements and objectives. An entity may have a maximum amount an entity can spend of a campaign or ROI minimum) and the cardholder-specific data (see ¶[0005]; a group or segment of recipients) ; KUMAR does not specifically disclose, but ADELI discloses, generating, by the server system, a task-specific model based, at least in part, on the aggregated dynamic embeddings and the static embeddings, wherein the task-specific model is a machine learning model (see claim 1; apply a learning rule through application of a pseudo-objective function. See also col 5, ln 14-48; a pseudo-objective function that is an optimization problem. See also col 4, ln 38-col 5, ln 13; learning through supervised and unsupervised algorithms, including a neural dynamics model). KUMAR does not specifically disclose, but MAZZOCHI discloses, upon receiving a request to induct a new entity, accessing, by the server system, new entity data associated with the new entity from the database, the new entity data comprising transaction data associated with the new entity (see col 1, ln 32-55 and col 10, ln 29-38; determine the likelihood of an individual supplier to accept a credit card for efficient onboarding of the customer’s suppliers. Account payable information includes transaction data) ; determining, by the server system, approximate embeddings corresponding to the new entity based, at least in part, on the new entity data and the entity-specific embeddings (see again col 1, ln 32-55 and col 10, ln 29-38; determine the likelihood of an individual supplier to accept a credit card for efficient onboarding of the customer’s suppliers. Account payable information includes transaction data. Include information on suppliers with annual transaction amounts in different ranges) ; and updating, by the server system, one of the entity-specific embeddings based, at least in part, on the approximate embeddings (see col 12, ln 56-col 13, ln 4 & claim 1; update credit card acceptance information based on the suppliers response. Project responses of suppliers in the future. See again col 1, ln 32-55 and col 10, ln 29-38; determine the likelihood of an individual supplier to accept a credit card for efficient onboarding of the customer’s suppliers. Account payable information includes transaction data. Include information on suppliers with annual transaction amounts in different ranges) . KUMAR discloses determining an optimal offer regarding a financial product including credit cards that includes using an objective function to determine offers using a model (see ¶[0079]). ADELI discloses optimization that includes a pseudo-objective function with learning rules. It would have been obvious to include the learning rules as taught by ADELI in the system executing the method of KUMAR with the motivation to determine optimal offers using a model. KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers based on customer, merchant, and issuing entity information to meet issuing entity objectives (see ¶[0039]). GAO discloses progressive predictions in data pipeline management using transaction data that uses dynamic and static models of features to consider features that change over time. It would have been obvious for one of ordinary skill in the art at the time of invention to include the dynamic and static models as taught by GAO in the system executing the method of KUMAR to consider static features and features that change over time. KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers based on customer, merchant, and issuing entity information to meet issuing entity objectives (see ¶[0039]). MAZZOCHI discloses a commercial credit card system that includes classifying customer suppliers based on transaction data including annual transaction amounts. It would have been obvious to include the onboarding based on annual transaction amounts as taught by MAZZOCHI in the system executing the method of KUMAR with the motivation to onboard customers likely to spend an expected amount to meet issuing entity objectives. Claim 2 (Original) The combination of KUMAR, ADELI, GAO, and MAZZOCHI discloses the computer-implemented method as claimed in claim 1. KUMAR further discloses wherein generating the one or more pseudo-objective models for each of the plurality of entities, further comprises: determining, by the server system, an entity category of each of the plurality of entities based, at least in part, on the historical transaction data (see ¶[0005]; (see ¶[0005]; a group or segment of recipients) ; determining, by the server system, a desired model type for each of the one or more pseudo-objective models based, at least in part, on the entity category and each of the one or more pseudo-objectives (see ¶[0043]-[0046]; a linear programming model may be based on target segments of the population. See also ¶[0012]; each of a plurality of segments has an associated response rate) ; and generating, by the server system, the one or more pseudo-objective models for each of the plurality of entities based, at least in part, on the desired model type and the historical transaction data (see ¶[0099]-[0103]; a linear programming analyses establishes constraints used in the objective function. Segment and offer combinations below ROI should not be considered) . Claim 4 The combination of KUMAR, ADELI, GAO, and MAZZOCHI discloses the computer-implemented method as claimed in claim 1. KUMAR does not specifically disclose, but GAO discloses, further comprising: generating, by the server system via a dynamic entity model, the dynamic embeddings (see abstract; a multi-layer ML model framework that employs multiple layers for different ML models that process different features. The features in one layer and ML model may process data for static features, where an output from this layer may be used as an input with data for dynamic feature that provide a predictive score or output for the input data. The static features may only be required to be processed once or a few times in the first layer and may not be required to be further processed again at later times. With the second layer, the data for the dynamic features may change, and thus the second layer may process new data without being required to reprocess the static features) corresponding to each of a plurality of transactions (see ¶[0013]; electronic transaction processing). KUMAR further discloses based, at least in part, on the acquirer-specific embeddings (see ¶[0005]; a group or segment of recipients) , the merchant-specific embeddings (see ¶[0041] and [0116] & claims 1 and 8; purchases made using a merchant’s credit card. A merchant may want to indicate potential recipients of the offer) , and the transaction-specific data (see abstract and ¶[0039]; an entity may have multiple offers the entity can make regarding a financial product. Satisfy entity requirements and objectives. An entity may have a maximum amount an entity can spend of a campaign or ROI minimum) ; generating, by the server system, the aggregated dynamic embeddings corresponding to a plurality of transactions based, at least in part, on aggregating each of the dynamic embeddings corresponding to each of the plurality of transactions (see ¶[0157] and Fig. 4; segment information database and other databases). KUMAR does not specifically disclose, but GAO discloses, generating, by the server system via the static entity model, the static embeddings (see abstract; The features in one layer and ML model may process data for static features, where an output from this layer may be used as an input with data for dynamic feature that provide a predictive score or output for the input data. The static features may only be required to be processed once or a few times in the first layer and may not be required to be further processed again at later times. With the second layer, the data for the dynamic features may change, and thus the second layer may process new data without being required to reprocess the static features). KUMAR further discloses based, at least in part, on the issuer-specific embeddings (see abstract and ¶[0039]; an entity may have multiple offers the entity can make regarding a financial product. Satisfy entity requirements and objectives. An entity may have a maximum amount an entity can spend of a campaign or ROI minimum) and the cardholder-specific data (see again ¶[0041]; recipient’s whose last purchase was withing the last six months and who belong to a high spender category) ; and generating, by the server system, the task-specific model based, at least in part, on the aggregated dynamic embeddings and the static embeddings (see abstract and ¶[0043]; make an offer that complies with objectives in an objective function) . KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers based on customer, merchant, and issuing entity information to meet issuing entity objectives (see ¶[0039]). GAO discloses progressive predictions in data pipeline management using transaction data that uses dynamic and static models of features to consider features that change over time. It would have been obvious for one of ordinary skill in the art at the time of invention to include the dynamic and static models as taught by GAO in the system executing the method of KUMAR to consider static features and features that change over time. Claim 8 (Original) The combination of KUMAR, ADELI, GAO, and MAZZOCHI discloses the computer-implemented method as claimed in claim 1. KUMAR does not specifically disclose, but ADELI discloses, further comprising: determining, by the server system, the one or more pseudo-objectives for a task to be performed by the one or more pseudo-objective models based, at least in part, on a set of predefined rules (see claim 1; apply a learning rule through application of a pseudo-objective function) . KUMAR discloses determining an optimal offer regarding a financial product including credit cards that includes using an objective function to determine offers using a model (see ¶[0079]). ADELI discloses optimization that includes a pseudo-objective function with learning rules. It would have been obvious to include the learning rules as taught by ADELI in the system executing the method of KUMAR with the motivation to determine optimal offers using a model. Claim 11 (Currently Amended) KUMAR discloses a server system (see ¶[0034]; a server) , comprising: a memory configured to store instructions; a communication interface; and a processor in communication with the memory and the communication interface, the processor configured to execute the instructions stored in the memory (see ¶[0013]-[0014]; a memory, processor, and computer program product providing instructions) and thereby cause the server system to perform, at least in part, to: access historical transaction data from a database associated with the server system (see ¶[0041] and [0120]; credit performance information from purchases made using a credit card. Last date of purchase) , the historical transaction data comprising entity-specific data (see ¶[0041]; a merchant’s credit card) , cardholder-specific data (see again ¶[0041]; recipient’s whose last purchase was withing the last six months and who belong to a high spender category) , and transaction-specific data (see again ¶[0041]; transactions within the last six months) ; generate one or more pseudo-objective models (see ¶[0043] and [0097]-[0099]; an objective function) for each of a plurality of entities based, at least in part, on the historical transaction data and one or more pseudo-objectives (see ¶[0009]; data regarding the recipients offers, campaign requirements and objectives may be used to make a decision regarding which offer to provide to which recipient) , the plurality of entities comprising an acquirer (see ¶[0103]; segments of recipients) , a merchant (see ¶[0124]; offer ten percent off at a particular merchant. Brand the credit card with a merchant’s name) , and an issuer (see ¶[0003]-[004], [0036], and [0043]; the issuer may want to target promotional communications to most receptive customers. An entity providing a financial product may want to maximize incremental sales) ; wherein the one or more pseudo-objectives include tasks (see abstract and ¶[0003]-[0004]; promotional communications, advertisements, letters, and reminders about a credit card to satisfy a requirement or objective). KUMAR does not specifically disclose, but ADELI discloses , wherein each of the one or more pseudo-objective models is a machine learning model that is trained (see claim 1; apply a learning rule through application of a pseudo-objective function). KUMAR further discloses based on the historical transaction data to perform one of the tasks for one of the plurality of entities (see ¶[0041] and [0120]; credit performance information from purchases made using a credit card. Last date of purchase. See also ¶[0004]-[0005]; identify offers that comply with or satisfy a requirement or objective that the entity may have. Obtain the best results) ; extract entity-specific embeddings for each of the plurality of entities from the one or more pseudo-objective models, based, at least in part, on the entity-specific data, the entity-specific embeddings comprising acquirer-specific embeddings (see ¶[0005]; a group or segment of recipients) , merchant-specific embeddings (see ¶[0041] and [0116] & claims 1 and 8; purchases made using a merchant’s credit card. A merchant may want to indicate potential recipients of the offer) , and issuer-specific embeddings (see abstract and ¶[0039]; an entity may have multiple offers the entity can make regarding a financial product. Satisfy entity requirements and objectives. An entity may have a maximum amount an entity can spend of a campaign or ROI minimum) ; generate by the server system, concatenated embeddings by concatenating the entity- specific embeddings (see ¶[0041], [0155], and [0163]-[0187]; information is stored in a recipient information database. Maintain databases) . KUMAR does not specifically disclose, but GAO discloses, generate by the server system, dynamic embeddings based on the concatenated embeddings (see abstract; a multi-layer ML model framework that employs multiple layers for different ML models that process different features. The features in one layer and ML model may process data for static features, where an output from this layer may be used as an input with data for dynamic feature that provide a predictive score or output for the input data. The static features may only be required to be processed once or a few times in the first layer and may not be required to be further processed again at later times. With the second layer, the data for the dynamic features may change, and thus the second layer may process new data without being required to reprocess the static features) and the transaction-specific data (see ¶[0013]; electronic transaction processing) ; and aggregate by the server system, the dynamic embeddings to generate aggregated dynamic embeddings (see ¶[0001]; determine predictive outputs based on static and dynamic data aggregated over one or more time periods) ; generate, by the server system via a static entity model, static embeddings (see abstract; a multi-layer ML model framework that employs multiple layers for different ML models that process different features. The features in one layer and ML model may process data for static features, where an output from this layer may be used as an input with data for dynamic feature that provide a predictive score or output for the input data. The static features may only be required to be processed once or a few times in the first layer and may not be required to be further processed again at later times. With the second layer, the data for the dynamic features may change, and thus the second layer may process new data without being required to reprocess the static features). KUMAR further discloses based, at least in part, on the issuer-specific embeddings (see abstract and ¶[0039]; an entity may have multiple offers the entity can make regarding a financial product. Satisfy entity requirements and objectives. An entity may have a maximum amount an entity can spend of a campaign or ROI minimum) and the cardholder-specific data (see ¶[0005]; a group or segment of recipients) ; KUMAR does not specifically disclose, but ADELI discloses, generating, by the server system, a task-specific model based, at least in part, on the aggregated dynamic embeddings and the static embeddings, wherein the task-specific model is a machine learning model (see claim 1; apply a learning rule through application of a pseudo-objective function. See also col 5, ln 14-48; a pseudo-objective function that is an optimization problem. See also col 4, ln 38-col 5, ln 13; learning through supervised and unsupervised algorithms, including a neural dynamics model). KUMAR does not specifically disclose, but MAZZOCHI discloses, upon receiving a request to induct a new entity, access new entity data associated with the new entity from the database, the new entity data comprising transaction data associated with the new entity (see col 1, ln 32-55 and col 10, ln 29-38; determine the likelihood of an individual supplier to accept a credit card for efficient onboarding of the customer’s suppliers. Account payable information includes transaction data) ; determine approximate embeddings corresponding to the new entity based, at least in part, on the new entity data and the entity-specific embeddings (see again col 1, ln 32-55 and col 10, ln 29-38; determine the likelihood of an individual supplier to accept a credit card for efficient onboarding of the customer’s suppliers. Account payable information includes transaction data. Include information on suppliers with annual transaction amounts in different ranges) ; and update one of the entity-specific embeddings based, at least in part, on the approximate embeddings (see col 12, ln 56-col 13, ln 4 & claim 1; update credit card acceptance information based on the suppliers response. Project responses of suppliers in the future. See again col 1, ln 32-55 and col 10, ln 29-38; determine the likelihood of an individual supplier to accept a credit card for efficient onboarding of the customer’s suppliers. Account payable information includes transaction data. Include information on suppliers with annual transaction amounts in different ranges) . KUMAR discloses determining an optimal offer regarding a financial product including credit cards that includes using an objective function to determine offers using a model (see ¶[0079]). ADELI discloses optimization that includes a pseudo-objective function with learning rules. It would have been obvious to include the learning rules as taught by ADELI in the system executing the method of KUMAR with the motivation to determine optimal offers using a model. KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers based on customer, merchant, and issuing entity information to meet issuing entity objectives (see ¶[0039]). GAO discloses progressive predictions in data pipeline management using transaction data that uses dynamic and static models of features to consider features that change over time. It would have been obvious for one of ordinary skill in the art at the time of invention to include the dynamic and static models as taught by GAO in the system executing the method of KUMAR to consider static features and features that change over time. KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers based on customer, merchant, and issuing entity information to meet issuing entity objectives (see ¶[0039]). MAZZOCHI discloses a commercial credit card system that includes classifying customer suppliers based on transaction data including annual transaction amounts. It would have been obvious to include the onboarding based on annual transaction amounts as taught by MAZZOCHI in the system executing the method of KUMAR with the motivation to onboard customers likely to spend an expected amount to meet issuing entity objectives. Claim 12 (Original) The combination of KUMAR, ADELI, GAO, and MAZZOCHI discloses the server system as claimed in claim 11. KUMAR further discloses wherein to generate the one or more pseudo-objective models for each of the plurality of entities, the server system is further caused, at least in part, to: determine an entity category of each of the plurality of entities based, at least in part, on the historical transaction data (see ¶[0005]; (see ¶[0005]; a group or segment of recipients) ; determine a desired model type for each of the one or more pseudo-objective models based, at least in part, on the entity category and each of the one or more pseudo-objectives (see ¶[0043]-[0046]; a linear programming model may be based on target segments of the population. See also ¶[0012]; each of a plurality of segments has an associated response rate) ; and generate the one or more pseudo-objective models for each of the plurality of entities based, at least in part, on the desired model type and the historical transaction data (see ¶[0099]-[0103]; a linear programming analyses establishes constraints used in the objective function. Segment and offer combinations below ROI should not be considered) . Claim 14 (Currently Amended) The combination of KUMAR, ADELI, GAO, and MAZZOCHI discloses the server system as claimed in claim 11. KUMAR does not specifically disclose, but GAO discloses, wherein the server system is further caused, at least in part, to: generate via a dynamic entity model, the dynamic embeddings (see abstract; a multi-layer ML model framework that employs multiple layers for different ML models that process different features. The features in one layer and ML model may process data for static features, where an output from this layer may be used as an input with data for dynamic feature that provide a predictive score or output for the input data. The static features may only be required to be processed once or a few times in the first layer and may not be required to be further processed again at later times. With the second layer, the data for the dynamic features may change, and thus the second layer may process new data without being required to reprocess the static features). corresponding to each of a plurality of transactions (see ¶[0013]; electronic transaction processing). KUMAR further discloses based, at least in part, on the acquirer-specific embeddings (see ¶[0005]; a group or segment of recipients) , the merchant-specific embeddings (see ¶[0041] and [0116] & claims 1 and 8; purchases made using a merchant’s credit card. A merchant may want to indicate potential recipients of the offer) , and the transaction-specific data (see abstract and ¶[0039]; an entity may have multiple offers the entity can make regarding a financial product. Satisfy entity requirements and objectives. An entity may have a maximum amount an entity can spend of a campaign or ROI minimum) ; generate the aggregated dynamic embeddings corresponding to a plurality of transactions based, at least in part, on aggregating each of the dynamic embeddings corresponding to each of the plurality of transactions (see ¶[0157] and Fig. 4; segment information database and other databases). KUMAR does not specifically disclose, but GAO discloses, generate via the static entity model, the static embeddings (see abstract; The features in one layer and ML model may process data for static features, where an output from this layer may be used as an input with data for dynamic feature that provide a predictive score or output for the input data. The static features may only be required to be processed once or a few times in the first layer and may not be required to be further processed again at later times. With the second layer, the data for the dynamic features may change, and thus the second layer may process new data without being required to reprocess the static features). KUMAR further discloses based, at least in part, on the issuer-specific embeddings (see abstract and ¶[0039]; an entity may have multiple offers the entity can make regarding a financial product. Satisfy entity requirements and objectives. An entity may have a maximum amount an entity can spend of a campaign or ROI minimum) and the cardholder-specific data (see again ¶[0041]; recipient’s whose last purchase was withing the last six months and who belong to a high spender category) ; and generate the task-specific model based, at least in part, on the aggregated dynamic embeddings and the static embeddings (see abstract and ¶[0043]; make an offer that complies with objectives in an objective function) . KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers based on customer, merchant, and issuing entity information to meet issuing entity objectives (see ¶[0039]). GAO discloses progressive predictions in data pipeline management using transaction data that uses dynamic and static models of features to consider features that change over time. It would have been obvious for one of ordinary skill in the art at the time of invention to include the dynamic and static models as taught by GAO in the system executing the method of KUMAR to consider static features and features that change over time. Claim 18 (Currently Amended) KUMAR discloses a non-transitory computer-readable storage medium comprising computer-executable instructions that, when executed by at least a processor of a server system (see ¶[0013]-[0014]; a memory, processor, and computer program product providing instructions. See also ¶[0034]; a server) , cause the server system to perform a method comprising: accessing historical transaction data from a database associated with the server system (see ¶[0041] and [0120]; credit performance information from purchases made using a credit card. Last date of purchase) , the historical transaction data comprising entity-specific data (see ¶[0041]; a merchant’s credit card) , cardholder-specific data (see again ¶[0041]; recipient’s whose last purchase was withing the last six months and who belong to a high spender category) , and transaction-specific data (see again ¶[0041]; transactions within the last six months) ; generating one or more pseudo-objective models (see ¶[0043] and [0097]-[0099]; an objective function) for each of a plurality of entities based, at least in part, on the historical transaction data and one or more pseudo-objectives (see ¶[0009]; data regarding the recipients offers, campaign requirements and objectives may be used to make a decision regarding which offer to provide to which recipient) , the plurality of entities comprising an acquirer (see ¶[0103]; segments of recipients) , a merchant (see ¶[0124]; offer ten percent off at a particular merchant. Brand the credit card with a merchant’s name) , and an issuer (see ¶[0003]-[004], [0036], and [0043]; the issuer may want to target promotional communications to most receptive customers. An entity providing a financial product may want to maximize incremental sales) ; wherein the one or more pseudo-objectives include tasks (see abstract and ¶[0003]-[0004]; promotional communications, advertisements, letters, and reminders about a credit card to satisfy a requirement or objective). KUMAR does not specifically disclose, but ADELI discloses, wherein each of the one or more pseudo-objective models is a machine learning model that is trained (see claim 1; apply a learning rule through application of a pseudo-objective function). KUMAR further discloses based on the historical transaction data to perform one of the tasks for one of the plurality of entities (see ¶[0041] and [0120]; credit performance information from purchases made using a credit card. Last date of purchase. See also ¶[0004]-[0005]; identify offers that comply with or satisfy a requirement or objective that the entity may have. Obtain the best results) ; extract entity-specific embeddings for each of the plurality of entities from the one or more pseudo-objective models, based, at least in part, on the entity-specific data, the entity-specific embeddings comprising acquirer-specific embeddings (see ¶[0005]; a group or segment of recipients) , merchant-specific embeddings (see ¶[0041] and [0116] & claims 1 and 8; purchases made using a merchant’s credit card. A merchant may want to indicate potential recipients of the offer) , and issuer-specific embeddings (see abstract and ¶[0039]; an entity may have multiple offers the entity can make regarding a financial product. Satisfy entity requirements and objectives. An entity may have a maximum amount an entity can spend of a campaign or ROI minimum) ; generating by the server system, concatenated embeddings by concatenating the entity- specific embeddings (see ¶[0041], [0155], and [0163]-[0187]; information is stored in a recipient information database. Maintain databases) . KUMAR does not specifically disclose, but GAO discloses, generating by the server system, dynamic embeddings based on the concatenated embeddings (see abstract; a multi-layer ML model framework that employs multiple layers for different ML models that process different features. The features in one layer and ML model may process data for static features, where an output from this layer may be used as an input with data for dynamic feature that provide a predictive score or output for the input data. The static features may only be required to be processed once or a few times in the first layer and may not be required to be further processed again at later times. With the second layer, the data for the dynamic features may change, and thus the second layer may process new data without being required to reprocess the static features) and the transaction-specific data (see ¶[0013]; electronic transaction processing) ; aggregating by the server system, the dynamic embeddings to generate aggregated dynamic embeddings (see ¶[0001]; determine predictive outputs based on static and dynamic data aggregated over one or more time periods); generating, by the server system via a static entity model, static embeddings (see abstract; a multi-layer ML model framework that employs multiple layers for different ML models that process different features. The features in one layer and ML model may process data for static features, where an output from this layer may be used as an input with data for dynamic feature that provide a predictive score or output for the input data. The static features may only be required to be processed once or a few times in the first layer and may not be required to be further processed again at later times. With the second layer, the data for the dynamic features may change, and thus the second layer may process new data without being required to reprocess the static features). KUMAR further discloses based, at least in part, on the issuer-specific embeddings (see abstract and ¶[0039]; an entity may have multiple offers the entity can make regarding a financial product. Satisfy entity requirements and objectives. An entity may have a maximum amount an entity can spend of a campaign or ROI minimum) and the cardholder-specific data see ¶[0005]; a group or segment of recipients) ; KUMAR does not specifically disclose, but ADELI discloses, generating, by the server system, a task-specific model based, at least in part, on the aggregated dynamic embeddings and the static embeddings, wherein the task-specific model is a machine learning model (see claim 1; apply a learning rule through application of a pseudo-objective function. See also col 5, ln 14-48; a pseudo-objective function that is an optimization problem. See also col 4, ln 38-col 5, ln 13; learning through supervised and unsupervised algorithms, including a neural dynamics model). KUMAR does not specifically disclose, but MAZZOCHI discloses, upon receiving a request to induct a new entity, accessing new entity data associated with the new entity from the database, the new entity data comprising transaction data associated with the new entity (see col 1, ln 32-55 and col 10, ln 29-38; determine the likelihood of an individual supplier to accept a credit card for efficient onboarding of the customer’s suppliers. Account payable information includes transaction data) ; determining approximate embeddings corresponding to the new entity based, at least in part, on the new entity data and the entity-specific embeddings (see again col 1, ln 32-55 and col 10, ln 29-38; determine the likelihood of an individual supplier to accept a credit card for efficient onboarding of the customer’s suppliers. Account payable information includes transaction data. Include information on suppliers with annual transaction amounts in different ranges) ; and updating one of the entity-specific embeddings based, at least in part, on the approximate embeddings (see col 12, ln 56-col 13, ln 4 & claim 1; update credit card acceptance information based on the suppliers response. Project responses of suppliers in the future. See again col 1, ln 32-55 and col 10, ln 29-38; determine the likelihood of an individual supplier to accept a credit card for efficient onboarding of the customer’s suppliers. Account payable information includes transaction data. Include information on suppliers with annual transaction amounts in different ranges) . KUMAR discloses determining an optimal offer regarding a financial product including credit cards that includes using an objective function to determine offers using a model (see ¶[0079]). ADELI discloses optimization that includes a pseudo-objective function with learning rules. It would have been obvious to include the learning rules as taught by ADELI in the system executing the method of KUMAR with the motivation to determine optimal offers using a model. KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers based on customer, merchant, and issuing entity information to meet issuing entity objectives (see ¶[0039]). GAO discloses progressive predictions in data pipeline management using transaction data that uses dynamic and static models of features to consider features that change over time. It would have been obvious for one of ordinary skill in the art at the time of invention to include the dynamic and static models as taught by GAO in the system executing the method of KUMAR to consider static features and features that change over time. KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers based on customer, merchant, and issuing entity information to meet issuing entity objectives (see ¶[0039]). MAZZOCHI discloses a commercial credit card system that includes classifying customer suppliers based on transaction data including annual transaction amounts. It would have been obvious to include the onboarding based on annual transaction amounts as taught by MAZZOCHI in the system executing the method of KUMAR with the motivation to onboard customers likely to spend an expected amount to meet issuing entity objectives. Claim 19 (Original) The combination of KUMAR, ADELI, GAO, and MAZZOCHI discloses the non-transitory computer-readable storage medium as claimed in claim 18. KUMAR further discloses wherein for generating the one or more pseudo-objective models for each of the plurality of entities, the method further comprises: determining an entity category of each of the plurality of entities based, at least in part, on the historical transaction data (see ¶[0005]; (see ¶[0005]; a group or segment of recipients) ; determining a desired model type for each of the one or more pseudo-objective models based, at least in part, on the entity category and each of the one or more pseudo-objectives (see ¶[0043]-[0046]; a linear programming model may be based on target segments of the population. See also ¶[0012]; each of a plurality of segments has an associated response rate) ; and generating the one or more pseudo-objective models for each of the plurality of entities based, at least in part, on the desired model type and the historical transaction data (see ¶[0099]-[0103]; a linear programming analyses establishes constraints used in the objective function. Segment and offer combinations below ROI should not be considered) . 07-22-aia AIA Claim (s) 3, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20030135442 A1 to KUMAR et al. in view of US 5815394 A to ADELI et al., US 20230169587 A1 to GAO, and US 10733612 B1 to MAZZOCHI et al . as applied to claim 1 above, and further in view of US 20110258118 A1 to Ciurea (hereinafter ‘CIUREA’) . Claim 3 (Original) The combination of KUMAR, ADELI, GAO, and MAZZOCHI discloses the computer-implemented method as claimed in claim 1. The combination of KUMAR, ADELI, GAO, and MAZZOCHI does not specifically disclose, but CIUREA discloses, wherein determining the approximate embeddings corresponding to the new entity, further comprises: determining, by the server system, a new entity category of the new entity based, at least in part, on the new entity data, the new entity category being one of a new acquirer, a new merchant, and a new issuer (see ¶[0029], [0035], and [0094]; a user cardholder and profile information. Profile may include total amount by category and spend amounts by time of year) ; determining, by the server system, a geo-location of the new entity based, at least in part, on the new entity data (see ¶[0077] and [0093]-[0094]; information provided may include a user address. Transaction records include merchant address and spend amounts by location) ; determining, by the server system, one or more neighboring entities with an identical entity category to the new entity category from the plurality of entities based, at least in part, on the transaction-specific data (see ¶[0094]; spending profiles includes multiple peer group identifications) ; extracting, by the server system, one or more entity-specific embeddings associated with the one or more neighboring entities from the entity-specific embeddings of each of the plurality of entities (see again ¶[0094]; multiple peer group identifications and comparisons) ; and computing, by the server system, an average of the one or more entity-specific embeddings associated with the one or more neighboring entities to determine the approximate embeddings corresponding to the new entity (see ¶[0094]; spending profile may include average spend amounts by time of year, location, or category, or multiple peer group identifications and comparisons) . KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers based on customer, merchant, and issuing entity information to meet issuing entity objectives (see ¶[0039]). CIUREA discloses using transaction records with spending amounts by category, address, and peer comparisons. It would have been obvious for one of ordinary skill in the art at the time of invention to include the profile information as taught by CIUREA in the system executing the method of KUMAR with the motivation to determine offers for credit cards based on customer profile data. Claim 13 (Original) The combination of KUMAR, ADELI, GAO, and MAZZOCHI discloses the server system as claimed in claim 11. The combination of KUMAR, ADELI, GAO, and MAZZOCHI does not specifically disclose, but CIUREA discloses, wherein to determine the approximate embeddings corresponding to the new entity, the server system is further caused, at least in part, to: determine a new entity category of the new entity based, at least in part, on the new entity data, the new entity category being one of a new acquirer, a new merchant, and a new issuer (see ¶[0029], [0035], and [0094]; a user cardholder and profile information. Profile may include total amount by category and spend amounts by time of year) ; determine a geo-location of the new entity based, at least in part, on the new entity data (see ¶[0077] and [0093]-[0094]; information provided may include a user address. Transaction records include merchant address and spend amounts by location) ; determine one or more neighboring entities with an identical entity category to the new entity category from the plurality of entities based, at least in part, on the transaction-specific data (see ¶[0094]; spending profiles includes multiple peer group identifications) ; extract one or more entity-specific embeddings associated with the one or more neighboring entities from the entity-specific embeddings of each of the plurality of entities (see again ¶[0094]; multiple peer group identifications and comparisons) ; and compute an average of the one or more entity-specific embeddings associated with the one or more neighboring entities to determine the approximate embeddings corresponding to the new entity (see ¶[0094]; spending profile may include average spend amounts by time of year, location, or category, or multiple peer group identifications and comparisons) . KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers based on customer, merchant, and issuing entity information to meet issuing entity objectives (see ¶[0039]). CIUREA discloses using transaction records with spending amounts by category, address, and peer comparisons. It would have been obvious for one of ordinary skill in the art at the time of invention to include the profile information as taught by CIUREA in the system executing the method of KUMAR with the motivation to determine offers for credit cards based on customer profile data. Claim 20 (Original) The combination of KUMAR, ADELI, GAO, and MAZZOCHI discloses the non-transitory computer-readable storage medium as claimed in claim 19. The combination of KUMAR, ADELI, GAO, and MAZZOCHI does not specifically disclose, but CIUREA discloses, wherein for determining the approximate embeddings corresponding to the new entity, the method further comprises: determining a new entity category of the new entity based, at least in part, on the new entity data, the new entity category being one of a new acquirer, a new merchant, and a new issuer (see ¶[0029], [0035], and [0094]; a user cardholder and profile information. Profile may include total amount by category and spend amounts by time of year) ; determining a geo-location of the new entity based, at least in part, on the new entity data (see ¶[0077] and [0093]-[0094]; information provided may include a user address. Transaction records include merchant address and spend amounts by location) ; determining one or more neighboring entities with an identical entity category to the new entity category from the plurality of entities based, at least in part, on the transaction-specific data (see ¶[0094]; spending profiles includes multiple peer group identifications) ; extracting one or more entity-specific embeddings associated with the one or more neighboring entities from the entity-specific embeddings of each of the plurality of entities (see again ¶[0094]; multiple peer group identifications and comparisons) ; and computing an average of the one or more entity-specific embeddings associated with the one or more neighboring entities to determine the approximate embeddings corresponding to the new entity (see ¶[0094]; spending profile may include average spend amounts by time of year, location, or category, or multiple peer group identifications and comparisons) . KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers based on customer, merchant, and issuing entity information to meet issuing entity objectives (see ¶[0039]). CIUREA discloses using transaction records with spending amounts by category, address, and peer comparisons. It would have been obvious for one of ordinary skill in the art at the time of invention to include the profile information as taught by CIUREA in the system executing the method of KUMAR with the motivation to determine offers for credit cards based on customer profile data . 07-22-aia AIA Claim (s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20030135442 A1 to KUMAR et al. in view of US 5815394 A to ADELI et al., US 20230169587 A1 to GAO, and US 10733612 B1 to MAZZOCHI et al . as applied to claim s 1 and 4 above, and further in view of US 20060015373 A1 to Cuypers (hereinafter ‘CUYPERS’) . Claim 9 (Currently Amended) The combination of KUMAR, ADELI, GAO, and MAZZOCHI discloses the computer-implemented method as claimed in claim 4 . The combination of KUMAR, ADELI, GAO, and MAZZOCHI does not specifically disclose, but CUYPERS discloses, wherein the dynamic entity model and the static entity model are recurrent neural network (RNN). KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers using a model that includes temporal data (see ¶[0039]-[0041] and [0185]-[0186]). CUYPERS discloses experience rating to model a portfolio or risks (see ¶[0010]) that includes static and dynamic neural networks chosen to replicate data is sufficient quality (see ¶[0047]). It would have been obvious for one of ordinary skill in the art at the time of invention to include dynamic and static neural networks as taught by CUYPERS in the system executing the method of KUMAR with the motivation to choose model structures that ensure sufficient quality of data . 07-22-aia AIA Claim (s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20030135442 A1 to KUMAR et al. in view of US 5815394 A to ADELI et al., US 20230169587 A1 to GAO, and US 10733612 B1 to MAZZOCHI et al . as applied to claim 1 above, and further in view of US 11715129 B2 to Srinivasan (hereinafter ‘SRINIVASAN’) . Claim 10 (Original) The combination of KUMAR, ADELI, GAO, and MAZZOCHI discloses the computer-implemented method as claimed in claim 1. The combination of KUMAR, ADELI, GAO, and MAZZOCHI does not specifically disclose, but SRINIVASAN discloses, wherein the server system is a payment server associated with a payment network (see abstract and col 5, ln 48-col 6, ln 39; a payment processing server) . KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers based transaction data (see ¶[0120]). SRINIVASAN discloses transaction data acquired by a payment processing server. It would have been obvious to include the payment processing server as taught by SRINIVASAN in the system executing the method of MAZZOCHI with the motivation to acquire transaction data . 07-22-aia AIA Claim (s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20030135442 A1 to KUMAR et al. in view of US 5815394 A to ADELI et al., US 20230169587 A1 to GAO, and US 10733612 B1 to MAZZOCHI et al . as applied to claim s 1 and 4 above, and further in view of US 20230222578 A1 to Plante (hereinafter ‘PLANTE’) . Claim 5 (Original) The combination of KUMAR, ADELI, GAO, and MAZZOCHI discloses the computer-implemented method as claimed in claim 4. The combination of KUMAR, ADELI, GAO, and MAZZOCHI does not specifically disclose, but PLANTE discloses, wherein aggregating each of the dynamic embeddings corresponding to each of a plurality of transactions is performed via at least one of a recurrent neural network (RNN) and a long short-term memory network (LSTM) model (see ¶[0030]; a dynamic modeling system with a machine learning model including a recurrent neural network) . KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers based on customer, merchant, and issuing entity information to meet issuing entity objectives (see ¶[0039]). PLANTE discloses dynamically determining credit value using a model that includes a dynamic modeling system that may include a recurrent neural network. It would have been obvious to include the recurrent neural network as taught by PLANTE in the system executing the method of KUMAR with the motivation to evaluate credit accounts for customers. Claim 15 (Original) The combination of KUMAR, ADELI, GAO, and MAZZOCHI discloses the server system as claimed in claim 14. The combination of KUMAR, ADELI, GAO, and MAZZOCHI does not specifically disclose, but PLANTE discloses, wherein aggregating each of the dynamic embeddings corresponding to each of a plurality of transactions is performed via at least one of a recurrent neural network (RNN) and a long short-term memory network (LSTM) model (see ¶[0030]; a dynamic modeling system with a machine learning model including a recurrent neural network) . KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers based on customer, merchant, and issuing entity information to meet issuing entity objectives (see ¶[0039]). PLANTE discloses dynamically determining credit value using a model that includes a dynamic modeling system that may include a recurrent neural network. It would have been obvious to include the recurrent neural network as taught by PLANTE in the system executing the method of KUMAR with the motivation to evaluate credit accounts for customers . 07-22-aia AIA Claim (s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20030135442 A1 to KUMAR et al. in view of US 5815394 A to ADELI et al., US 20230169587 A1 to GAO, and US 10733612 B1 to MAZZOCHI et al . as applied to claim s 1 and 4 above, and further in view of US 20130018719 A1 to Abraham et al. (hereinafter ‘ABRAHAM’) . Claim 6 (Original) The combination of KUMAR, ADELI, GAO, and MAZZOCHI discloses the computer-implemented method as claimed in claim 4. The combination of KUMAR, ADELI, GAO, and MAZZOCHI does not specifically disclose, but ABRAHAM discloses, wherein aggregating each of the dynamic embeddings corresponding to each of a plurality of transactions is performed via a geometric decay network. (see ¶[0016] and [0127]; weighting values for temporal data of exposures determined by a geometric decay factor). KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers using a model that includes temporal data (see ¶[0039]-[0041] and [0185]-[0186]). ABRAHAM discloses analyzing the effects of advertising with a model that weights temporal values according to a geometric decay factor. It would have been obvious to include the geometric decay factor as taught by ABRAHAM in the system executing the method of KUMAR with the motivation to model temporal data. Claim 16 (Original) The combination of KUMAR, ADELI, GAO, and MAZZOCHI discloses the server system as claimed in claim 14. The combination of KUMAR, ADELI, GAO, and MAZZOCHI does not specifically disclose, but ABRAHAM discloses, wherein aggregating each of the dynamic embeddings corresponding to each of a plurality of transactions is performed via a geometric decay network (see ¶[0016] and [0127]; weighting values for temporal data of exposures determined by a geometric decay factor). KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers using a model that includes temporal data (see ¶[0039]-[0041] and [0185]-[0186]). ABRAHAM discloses analyzing the effects of advertising with a model that weights temporal values according to a geometric decay factor. It would have been obvious to include the geometric decay factor as taught by ABRAHAM in the system executing the method of KUMAR with the motivation to model temporal data . 07-22-aia AIA Claim (s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20030135442 A1 to KUMAR et al. in view of US 5815394 A to ADELI et al., US 20230169587 A1 to GAO, US 10733612 B1 to MAZZOCHI et al., and US 20130018719 A1 to ABRAHAM et al . as applied to claim s 1 and 6 above, and further in view of US 6018723 A to Siegel et al. (hereinafter ‘SIEGEL’) . Claim 7 (Original) The combination of KUMAR, ADELI, GAO, MAZZOCHI, and ABRAHAM discloses the computer-implemented method as claimed in claim 6. The combination of KUMAR, ADELI, GAO, MAZZOCHI, and ABRAHAM does not specifically disclose, but SIEGEL discloses, wherein aggregating each of the dynamic embeddings corresponding to each of a plurality of transactions, further comprises: computing, by the server system via the geometric decay network, a weighted sum of the dynamic embeddings corresponding to each of the plurality of transactions to generate the aggregated dynamic embeddings based, at least in part, on a geometric decay factor (see col 8, ln 47-58; computer a weighted sum of transaction data that includes a decay constant) . KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers using a model that includes temporal data (see ¶[0039]-[0041] and [0185]-[0186]). ABRAHAM discloses analyzing the effects of advertising with a model that weights temporal values according to a geometric decay factor. SIEGEL discloses determining patterns for assessing credit risks that includes using a weighted sum of transaction data that includes a decay factor. It would have been obvious to include the weighted sum of transactions with a decay factor as taught by SIEGEL in the system executing the method of KUMAR with the motivation to determine on offer for a financial product including credit cards. Claim 17 (Original) The combination of KUMAR, ADELI, GAO, MAZZOCHI, and ABRAHAM discloses the server system as claimed in claim 16 The combination of KUMAR, ADELI, GAO, MAZZOCHI, and ABRAHAM does not specifically disclose, but SIEGEL discloses, wherein to aggregate each of the dynamic embeddings corresponding to each of a plurality of transactions, the server system is further caused, at least in part, to: compute via the geometric decay network, a weighted sum of the dynamic embeddings corresponding to each of the plurality of transactions to generate the aggregated dynamic embeddings based, at least in part, on a geometric decay factor (see col 8, ln 47-58; computer a weighted sum of transaction data that includes a decay constant) . KUMAR discloses determining an offer regarding a financial product including credit cards that includes using an objective function to determine offers using a model that includes temporal data (see ¶[0039]-[0041] and [0185]-[0186]). ABRAHAM discloses analyzing the effects of advertising with a model that weights temporal values according to a geometric decay factor. SIEGEL discloses determining patterns for assessing credit risks that includes using a weighted sum of transaction data that includes a decay factor. It would have been obvious to include the weighted sum of transactions with a decay factor as taught by SIEGEL in the system executing the method of KUMAR with the motivation to determine on offer for a financial product including credit cards. Conclusion 07-39 AIA THIS ACTION IS MADE FINAL. 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 RICHARD N SCHEUNEMANN whose telephone number is (571)270-7947. The examiner can normally be reached M-F 9am-5pm EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached at 571-270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /RICHARD N SCHEUNEMANN/Primary Examiner, Art Unit 3624 Application/Control Number: 18/819,864 Page 2 Art Unit: 3624 Application/Control Number: 18/819,864 Page 3 Art Unit: 3624 Application/Control Number: 18/819,864 Page 4 Art Unit: 3624 Application/Control Number: 18/819,864 Page 5 Art Unit: 3624 Application/Control Number: 18/819,864 Page 7 Art Unit: 3624 Application/Control Number: 18/819,864 Page 8 Art Unit: 3624 Application/Control Number: 18/819,864 Page 9 Art Unit: 3624 Application/Control Number: 18/819,864 Page 10 Art Unit: 3624 Application/Control Number: 18/819,864 Page 11 Art Unit: 3624 Application/Control Number: 18/819,864 Page 12 Art Unit: 3624 Application/Control Number: 18/819,864 Page 13 Art Unit: 3624 Application/Control Number: 18/819,864 Page 14 Art Unit: 3624 Application/Control Number: 18/819,864 Page 15 Art Unit: 3624 Application/Control Number: 18/819,864 Page 16 Art Unit: 3624 Application/Control Number: 18/819,864 Page 17 Art Unit: 3624 Application/Control Number: 18/819,864 Page 18 Art Unit: 3624 Application/Control Number: 18/819,864 Page 19 Art Unit: 3624 Application/Control Number: 18/819,864 Page 20 Art Unit: 3624 Application/Control Number: 18/819,864 Page 21 Art Unit: 3624 Application/Control Number: 18/819,864 Page 22 Art Unit: 3624 Application/Control Number: 18/819,864 Page 23 Art Unit: 3624 Application/Control Number: 18/819,864 Page 24 Art Unit: 3624 Application/Control Number: 18/819,864 Page 25 Art Unit: 3624 Application/Control Number: 18/819,864 Page 26 Art Unit: 3624 Application/Control Number: 18/819,864 Page 27 Art Unit: 3624 Application/Control Number: 18/819,864 Page 28 Art Unit: 3624 Application/Control Number: 18/819,864 Page 29 Art Unit: 3624 Application/Control Number: 18/819,864 Page 30 Art Unit: 3624 Application/Control Number: 18/819,864 Page 32 Art Unit: 3624 Application/Control Number: 18/819,864 Page 33 Art Unit: 3624 Application/Control Number: 18/819,864 Page 34 Art Unit: 3624 Application/Control Number: 18/819,864 Page 35 Art Unit: 3624 Application/Control Number: 18/819,864 Page 36 Art Unit: 3624 Application/Control Number: 18/819,864 Page 37 Art Unit: 3624 Application/Control Number: 18/819,864 Page 38 Art Unit: 3624
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