DETAILED ACTION
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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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/04/2025 and 06/02/2026 are being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
Page 1, para 0001; the status of the application no. 18/618,519 should be updated.
Appropriate correction is required.
Claim Objections
Claims 1 - 24 are objected to because of the following informalities:
Regarding claims 1, 9 and 17; the first occurrences of the acronym “API” should be spelled out.
Claims 2 – 8, 10 – 16 and 18 – 24 are dependent claims and thus, also objected.
Regarding claims 7, 15 and 23; the limitation “the processing the integration data object” lacks proper antecedent basis.
Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/forms/. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1 - 24 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1 – 2, 4 – 8, 10 – 14 and 16 - 18 of U.S. Patent No. 11,972,311 B2. Although the conflicting claims are not identical, they are not patentably distinct from each other because the pending claims are substantially covered by patented claims as shown below;
Instant Application No. 19/289,421
US Patent 11,972,311 B2
1. A method comprising: receiving an integration data object associated with a request for a payment processing system to facilitate a financial transaction between a payor device and a payee device, the integration data object comprising information about an invoice or a bill; determining, based at least on the integration data object and an integration machine learning model, one or more integration features associated with the integration data object; and generating, based at least upon the one or more integration features, an API model corresponding to the integration data object, the API model being configured to provide an API generation data object corresponding to the API model to facilitate generation of an API associated with the request to facilitate the financial transaction.
1. A computer-implemented method comprising: receiving, by an apparatus, from a payment processor device, an integration data object, the integration data object being associated with a payment processing request to initiate or facilitate a transaction between a payor device and a payee device, the payor device being associated with a payor, the payee device being associated with a payee, the integration data object comprising one or more of: security requirement information associated with the payment processor device or the payee device, functionalities of the payment processor device or the payee device, a structure or architecture of the payment processor device or the payee device, an account statement, an invoice, a bill, a payment receipt, a payment processor device type, or a payee device type; processing, using at least one processor of the apparatus, based at least in part on an integration machine learning model, the integration data object in order to identify one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processor device or the payee device is operating, computer language capabilities associated with the payment processor device or the payee device, data structure requirements associated with the payment processor device or the payee device, or system protocols associated with the payment processor device or the payee device; programmatically generating, using the at least one processor, based at least in part on the one or more integration features associated with the integration data object, an API model corresponding to the integration data object; generating, using the at least one processor, an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processor device for execution by the payment processor device to generate an API for communication between the payment processor device and the payee device.
2. The method of claim 1, wherein the one or more integration features comprises one or more of: an indication of a predicted API type associated with the API, a language associated with the request, a country in which the payment processing system is operating, a country in which the payor device is operating, a country in which the payee device is operating, one or more computer language capabilities associated with the payment processing system, one or more computer language capabilities associated with the payor device, one or more computer language capabilities associated with the payee device, or system.
1. A computer-implemented method comprising: receiving, by an apparatus, from a payment processor device, an integration data object, the integration data object being associated with a payment processing request to initiate or facilitate a transaction between a payor device and a payee device, the payor device being associated with a payor, the payee device being associated with a payee, the integration data object comprising one or more of: security requirement information associated with the payment processor device or the payee device, functionalities of the payment processor device or the payee device, a structure or architecture of the payment processor device or the payee device, an account statement, an invoice, a bill, a payment receipt, a payment processor device type, or a payee device type; processing, using at least one processor of the apparatus, based at least in part on an integration machine learning model, the integration data object in order to identify one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processor device or the payee device is operating, computer language capabilities associated with the payment processor device or the payee device, data structure requirements associated with the payment processor device or the payee device, or system protocols associated with the payment processor device or the payee device; programmatically generating, using the at least one processor, based at least in part on the one or more integration features associated with the integration data object, an API model corresponding to the integration data object; generating, using the at least one processor, an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processor device for execution by the payment processor device to generate an API for communication between the payment processor device and the payee device.
3. The method of claim 1, further comprising: generating, based at least on the API model, the API generation data object; and causing generation of the API associated with the request based at least on the API generation data object.
1. A computer-implemented method comprising: receiving, by an apparatus, from a payment processor device, an integration data object, the integration data object being associated with a payment processing request to initiate or facilitate a transaction between a payor device and a payee device, the payor device being associated with a payor, the payee device being associated with a payee, the integration data object comprising one or more of: security requirement information associated with the payment processor device or the payee device, functionalities of the payment processor device or the payee device, a structure or architecture of the payment processor device or the payee device, an account statement, an invoice, a bill, a payment receipt, a payment processor device type, or a payee device type; processing, using at least one processor of the apparatus, based at least in part on an integration machine learning model, the integration data object in order to identify one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processor device or the payee device is operating, computer language capabilities associated with the payment processor device or the payee device, data structure requirements associated with the payment processor device or the payee device, or system protocols associated with the payment processor device or the payee device; programmatically generating, using the at least one processor, based at least in part on the one or more integration features associated with the integration data object, an API model corresponding to the integration data object; generating, using the at least one processor, an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processor device for execution by the payment processor device to generate an API for communication between the payment processor device and the payee device.
4. The method of claim 1, further comprising: generating, based at least on the API model, the API generation data object; and sending the API generation data object corresponding to the API model to a payment processing device for execution by the payment processing device to generate the API for communication between the payment processing device and the payee device.
1. A computer-implemented method comprising: receiving, by an apparatus, from a payment processor device, an integration data object, the integration data object being associated with a payment processing request to initiate or facilitate a transaction between a payor device and a payee device, the payor device being associated with a payor, the payee device being associated with a payee, the integration data object comprising one or more of: security requirement information associated with the payment processor device or the payee device, functionalities of the payment processor device or the payee device, a structure or architecture of the payment processor device or the payee device, an account statement, an invoice, a bill, a payment receipt, a payment processor device type, or a payee device type; processing, using at least one processor of the apparatus, based at least in part on an integration machine learning model, the integration data object in order to identify one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processor device or the payee device is operating, computer language capabilities associated with the payment processor device or the payee device, data structure requirements associated with the payment processor device or the payee device, or system protocols associated with the payment processor device or the payee device; programmatically generating, using the at least one processor, based at least in part on the one or more integration features associated with the integration data object, an API model corresponding to the integration data object; generating, using the at least one processor, an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processor device for execution by the payment processor device to generate an API for communication between the payment processor device and the payee device.
5. The method of claim 1, wherein the integration machine learning model comprises a trained supervised machine learning model that is trained based at least in part on a plurality of historical integration data objects.
5. The computer-implemented method of claim 1, wherein the integration machine learning model comprises a trained supervised machine learning model that is trained based at least in part on a plurality of historical integration data objects.
6. The method of claim 1, further comprising: periodically sending requests for integration information for updating and/or refining the API model.
4. The computer-implemented method of claim 1, further comprising: subsequent to programmatically generating the API model, periodically sending requests for integration information for updating and/or refining the API model.
7. The method of claim 1, wherein the processing the integration data object comprises performing textual analysis on at least a portion of the integration data object.
6. The computer-implemented method of claim 1, wherein processing, using the at least one processor, the integration data object comprises performing textual analysis on at least a portion of the integration data object.
8. The method of claim 1, wherein the API generation data object is configured to facilitate one or more of: generating the API, modifying the API, generating one or more API-based data objects, or modifying the one or more API-based data objects.
2. The computer-implemented method of claim 1, wherein the API generation data object is configured to facilitate generation and/or modification of an API and/or one or more API-based data objects.
9. An apparatus comprising: at least one processor; and at least one memory storing instructions thereon that, when executed by the at least one processor, cause the apparatus to perform at least: receiving an integration data object associated with a request for a payment processing system to facilitate a financial transaction between a payor device and a payee device, the integration data object comprising information about an invoice or a bill; determining, based at least on the integration data object and an integration machine learning model, one or more integration features associated with the integration data object; and generating, based at least upon the one or more integration features, an API model corresponding to the integration data object, the API model being configured to provide an API generation data object corresponding to the API model to facilitate generation of an API associated with the request to facilitate the financial transaction.
7. An apparatus comprising: at least one processor; and at least one memory storing instructions thereon that, when executed by the at least one processor, cause the apparatus to perform at least: receiving, from a payment processor device, an integration data object, the integration data object being associated with a payment processing request to initiate or facilitate a transaction between a payor device and a payee device, the payor device being associated with a payor, the payee device being associated with a payee, the integration data object comprising one or more of: security requirement information associated with the payment processor device or the payee device, functionalities of the payment processor device or the payee device, a structure or architecture of the payment processor device or the payee device, an account statement, an invoice, a bill, a payment receipt, a payment processor device type, or a payee device type; processing, based at least in part on an integration machine learning model, the integration data object in order to identify one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processor device or the payee device is operating, computer language capabilities associated with the payment processor device or the payee device, data structure requirements associated with the payment processor device or the payee device, or system protocols associated with the payment processor device or the payee device; programmatically generating, based at least in part on the one or more integration features associated with the integration data object, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processor device for execution by the payment processor device to generate an API for communication between the payment processor device and the payee device.
10. The apparatus of claim 9, wherein the one or more integration features comprises one or more of: an indication of a predicted API type associated with the API, a language associated with the request, a country in which the payment processing system is operating, a country in which the payor device is operating, a country in which the payee device is operating, one or more computer language capabilities associated with the payment processing system, one or more computer language capabilities associated with the payor device, one or more computer language capabilities associated with the payee device, or system.
7. An apparatus comprising: at least one processor; and at least one memory storing instructions thereon that, when executed by the at least one processor, cause the apparatus to perform at least: receiving, from a payment processor device, an integration data object, the integration data object being associated with a payment processing request to initiate or facilitate a transaction between a payor device and a payee device, the payor device being associated with a payor, the payee device being associated with a payee, the integration data object comprising one or more of: security requirement information associated with the payment processor device or the payee device, functionalities of the payment processor device or the payee device, a structure or architecture of the payment processor device or the payee device, an account statement, an invoice, a bill, a payment receipt, a payment processor device type, or a payee device type; processing, based at least in part on an integration machine learning model, the integration data object in order to identify one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processor device or the payee device is operating, computer language capabilities associated with the payment processor device or the payee device, data structure requirements associated with the payment processor device or the payee device, or system protocols associated with the payment processor device or the payee device; programmatically generating, based at least in part on the one or more integration features associated with the integration data object, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processor device for execution by the payment processor device to generate an API for communication between the payment processor device and the payee device.
11. The apparatus of claim 9, wherein the instructions stored on the at least one memory, when executed by the at least one processor, further cause the apparatus to perform at least: generating, based at least on the API model, the API generation data object; and causing generation of the API associated with the request based at least on the API generation data object.
7. An apparatus comprising: at least one processor; and at least one memory storing instructions thereon that, when executed by the at least one processor, cause the apparatus to perform at least: receiving, from a payment processor device, an integration data object, the integration data object being associated with a payment processing request to initiate or facilitate a transaction between a payor device and a payee device, the payor device being associated with a payor, the payee device being associated with a payee, the integration data object comprising one or more of: security requirement information associated with the payment processor device or the payee device, functionalities of the payment processor device or the payee device, a structure or architecture of the payment processor device or the payee device, an account statement, an invoice, a bill, a payment receipt, a payment processor device type, or a payee device type; processing, based at least in part on an integration machine learning model, the integration data object in order to identify one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processor device or the payee device is operating, computer language capabilities associated with the payment processor device or the payee device, data structure requirements associated with the payment processor device or the payee device, or system protocols associated with the payment processor device or the payee device; programmatically generating, based at least in part on the one or more integration features associated with the integration data object, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processor device for execution by the payment processor device to generate an API for communication between the payment processor device and the payee device.
12. The apparatus of claim 9, wherein the instructions stored on the at least one memory, when executed by the at least one processor, further cause the apparatus to perform at least: generating, based at least on the API model, the API generation data object; and sending the API generation data object corresponding to the API model to a payment processing device for execution by the payment processing device to generate the API for communication between the payment processing device and the payee device.
7. An apparatus comprising: at least one processor; and at least one memory storing instructions thereon that, when executed by the at least one processor, cause the apparatus to perform at least: receiving, from a payment processor device, an integration data object, the integration data object being associated with a payment processing request to initiate or facilitate a transaction between a payor device and a payee device, the payor device being associated with a payor, the payee device being associated with a payee, the integration data object comprising one or more of: security requirement information associated with the payment processor device or the payee device, functionalities of the payment processor device or the payee device, a structure or architecture of the payment processor device or the payee device, an account statement, an invoice, a bill, a payment receipt, a payment processor device type, or a payee device type; processing, based at least in part on an integration machine learning model, the integration data object in order to identify one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processor device or the payee device is operating, computer language capabilities associated with the payment processor device or the payee device, data structure requirements associated with the payment processor device or the payee device, or system protocols associated with the payment processor device or the payee device; programmatically generating, based at least in part on the one or more integration features associated with the integration data object, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processor device for execution by the payment processor device to generate an API for communication between the payment processor device and the payee device.
13. The apparatus of claim 9, wherein the integration machine learning model comprises a trained supervised machine learning model that is trained based at least in part on a plurality of historical integration data objects.
11. The apparatus of claim 7, wherein the integration machine learning model comprises a trained supervised machine learning model that is trained based at least in part on a plurality of historical integration data objects.
14. The apparatus of claim 9, wherein the instructions stored on the at least one memory, when executed by the at least one processor, further cause the apparatus to perform at least: periodically sending requests for integration information for updating and/or refining the API model.
10. The apparatus of claim 7, wherein the instructions stored on the at least one memory, when executed by the at least one processor, further cause the apparatus to perform at least: subsequent to programmatically generating the API model, periodically sending requests for integration information for updating and/or refining the API model.
15. The apparatus of claim 9, wherein the processing the integration data object comprises performing textual analysis on at least a portion of the integration data object.
12. The apparatus of claim 7, wherein processing the integration data object comprises performing textual analysis on at least a portion of the integration data object.
16. The apparatus of claim 9, wherein the API generation data object is configured to facilitate one or more of: generating the API, modifying the API, generating one or more API-based data objects, or modifying the one or more API-based data objects.
8. The apparatus of claim 7, the API generation data object is configured to facilitate generation and/or modification of an API and/or one or more API-based data objects.
17. A non-transitory computer-readable storage medium comprising instructions stored thereon that, when executed by at least one processor of an apparatus, cause the apparatus to perform at least: receiving an integration data object associated with a request for a payment processing system to facilitate a financial transaction between a payor device and a payee device, the integration data object comprising information about an invoice or a bill; determining, based at least on the integration data object and an integration machine learning model, one or more integration features associated with the integration data object; and generating, based at least upon the one or more integration features, an API model corresponding to the integration data object, the API model being configured to provide an API generation data object corresponding to the API model to facilitate generation of an API associated with the request to facilitate the financial transaction.
13. A non-transitory computer readable storage medium storing program instructions that, when executed by at least one processor, cause at least the following: receiving, from a payment processor device, an integration data object, the integration data object being associated with a payment processing request to initiate or facilitate a transaction between a payor device and a payee device, the payor device being associated with a payor, the payee device being associated with a payee, the integration data object comprising one or more of: security requirement information associated with the payment processor device or the payee device, functionalities of the payment processor device or the payee device, a structure or architecture of the payment processor device or the payee device, an account statement, an invoice, a bill, a payment receipt, a payment processor device type, or a payee device type; processing, based at least in part on an integration machine learning model, the integration data object in order to identify one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processor device or the payee device is operating, computer language capabilities associated with the payment processor device or the payee device, data structure requirements associated with the payment processor device or the payee device, or system protocols associated with the payment processor device or the payee device; programmatically generating, based at least in part on the one or more integration features associated with the integration data object, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processor device for execution by the payment processor device to generate an API for communication between the payment processor device and the payee device.
18. The non-transitory computer-readable storage medium of claim 17, wherein the one or more integration features comprises one or more of: an indication of a predicted API type associated with the API, a language associated with the request, a country in which the payment processing system is operating, a country in which the payor device is operating, a country in which the payee device is operating, one or more computer language capabilities associated with the payment processing system, one or more computer language capabilities associated with the payor device, one or more computer language capabilities associated with the payee device, or system.
13. A non-transitory computer readable storage medium storing program instructions that, when executed by at least one processor, cause at least the following: receiving, from a payment processor device, an integration data object, the integration data object being associated with a payment processing request to initiate or facilitate a transaction between a payor device and a payee device, the payor device being associated with a payor, the payee device being associated with a payee, the integration data object comprising one or more of: security requirement information associated with the payment processor device or the payee device, functionalities of the payment processor device or the payee device, a structure or architecture of the payment processor device or the payee device, an account statement, an invoice, a bill, a payment receipt, a payment processor device type, or a payee device type; processing, based at least in part on an integration machine learning model, the integration data object in order to identify one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processor device or the payee device is operating, computer language capabilities associated with the payment processor device or the payee device, data structure requirements associated with the payment processor device or the payee device, or system protocols associated with the payment processor device or the payee device; programmatically generating, based at least in part on the one or more integration features associated with the integration data object, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processor device for execution by the payment processor device to generate an API for communication between the payment processor device and the payee device.
19. The non-transitory computer-readable storage medium of claim 17, wherein the instructions stored thereon, when executed by the at least one processor, further cause the apparatus to perform at least: generating, based at least on the API model, the API generation data object; and causing generation of the API associated with the request based at least on the API generation data object.
13. A non-transitory computer readable storage medium storing program instructions that, when executed by at least one processor, cause at least the following: receiving, from a payment processor device, an integration data object, the integration data object being associated with a payment processing request to initiate or facilitate a transaction between a payor device and a payee device, the payor device being associated with a payor, the payee device being associated with a payee, the integration data object comprising one or more of: security requirement information associated with the payment processor device or the payee device, functionalities of the payment processor device or the payee device, a structure or architecture of the payment processor device or the payee device, an account statement, an invoice, a bill, a payment receipt, a payment processor device type, or a payee device type; processing, based at least in part on an integration machine learning model, the integration data object in order to identify one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processor device or the payee device is operating, computer language capabilities associated with the payment processor device or the payee device, data structure requirements associated with the payment processor device or the payee device, or system protocols associated with the payment processor device or the payee device; programmatically generating, based at least in part on the one or more integration features associated with the integration data object, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processor device for execution by the payment processor device to generate an API for communication between the payment processor device and the payee device.
20. The non-transitory computer-readable storage medium of claim 17, wherein the instructions stored thereon, when executed by the at least one processor, further cause the apparatus to perform at least: generating, based at least on the API model, the API generation data object; and sending the API generation data object corresponding to the API model to a payment processing device for execution by the payment processing device to generate the API for communication between the payment processing device and the payee device.
13. A non-transitory computer readable storage medium storing program instructions that, when executed by at least one processor, cause at least the following: receiving, from a payment processor device, an integration data object, the integration data object being associated with a payment processing request to initiate or facilitate a transaction between a payor device and a payee device, the payor device being associated with a payor, the payee device being associated with a payee, the integration data object comprising one or more of: security requirement information associated with the payment processor device or the payee device, functionalities of the payment processor device or the payee device, a structure or architecture of the payment processor device or the payee device, an account statement, an invoice, a bill, a payment receipt, a payment processor device type, or a payee device type; processing, based at least in part on an integration machine learning model, the integration data object in order to identify one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processor device or the payee device is operating, computer language capabilities associated with the payment processor device or the payee device, data structure requirements associated with the payment processor device or the payee device, or system protocols associated with the payment processor device or the payee device; programmatically generating, based at least in part on the one or more integration features associated with the integration data object, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processor device for execution by the payment processor device to generate an API for communication between the payment processor device and the payee device.
21. The non-transitory computer-readable storage medium of claim 17, wherein the integration machine learning model comprises a trained supervised machine learning model that is trained based at least in part on a plurality of historical integration data objects.
17. The non-transitory computer readable storage medium of claim 13, wherein the integration machine learning model comprises a trained supervised machine learning model that is trained based at least in part on a plurality of historical integration data objects.
22. The non-transitory computer-readable storage medium of claim 17, wherein the instructions stored thereon, when executed by the at least one processor, further cause the apparatus to perform at least: periodically sending requests for integration information for updating and/or refining the API model.
16. The non-transitory computer readable storage medium of claim 13, wherein the instructions, when executed by the at least one processor, are further operable for causing at least: subsequent to programmatically generating the API model, periodically sending requests for integration information for updating and/or refining the API model.
23. The non-transitory computer-readable storage medium of claim 17, wherein the processing the integration data object comprises performing textual analysis on at least a portion of the integration data object.
18. The non-transitory computer readable storage medium of claim 13, wherein processing the integration data object comprises performing textual analysis on at least a portion of the integration data object.
24. The non-transitory computer-readable storage medium of claim 17, wherein the API generation data object is configured to facilitate one or more of: generating the API, modifying the API, generating one or more API-based data objects, or modifying the one or more API-based data objects.
14. The non-transitory computer readable storage medium of claim 13, wherein the API generation data object is configured to facilitate generation and/or modification of an API and/or one or more API-based data objects.
Claims 1 - 24 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1 – 2, 4 – 6, 8 – 9, 11 – 13, 15 – 16 and 18 - 20 of U.S. Patent No. 12,405,847 B2. Although the conflicting claims are not identical, they are not patentably distinct from each other because the pending claims are substantially covered by patented claims as shown below;
Instant Application No. 19/289,421
US Patent 12,405,847 B2
1. A method comprising: receiving an integration data object associated with a request for a payment processing system to facilitate a financial transaction between a payor device and a payee device, the integration data object comprising information about an invoice or a bill; determining, based at least on the integration data object and an integration machine learning model, one or more integration features associated with the integration data object; and generating, based at least upon the one or more integration features, an API model corresponding to the integration data object, the API model being configured to provide an API generation data object corresponding to the API model to facilitate generation of an API associated with the request to facilitate the financial transaction.
1. A method comprising: receiving, from a payment processing device, an integration data object associated with a payment processing service being carried out by a payment processor device, the integration data object being configured to initiate or facilitate a communication between the payment processor device and a client device, the integration data object comprising one or more of: security layers of the payment processing service, protocols of the payment processing device, integration requirements of the payment processing device, target functionalities of the payment processing device, an account statement associated with the client device, an invoice associated with the client device, or a bill associated with the client device; determining, based at least upon the integration data object, based at least in part on an integration machine learning model, one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processing device or the client device is operating, computer language capabilities associated with the payment processing device or the client device, data structure requirements associated with the payment processing device or the client device, or system protocols associated with the payment processing device or the client device; generating, based at least in part on the one or more integration features associated with the integration data objection, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processing device for execution by the payment processing device to generate an API for communication between the payment processing device and the client device.
2. The method of claim 1, wherein the one or more integration features comprises one or more of: an indication of a predicted API type associated with the API, a language associated with the request, a country in which the payment processing system is operating, a country in which the payor device is operating, a country in which the payee device is operating, one or more computer language capabilities associated with the payment processing system, one or more computer language capabilities associated with the payor device, one or more computer language capabilities associated with the payee device, or system.
2. The method of claim 1, wherein the integration machine learning model comprises a trained supervised machine learning model that is trained based at least in part on a plurality of historical integration data objects.
3. The method of claim 1, further comprising: generating, based at least on the API model, the API generation data object; and causing generation of the API associated with the request based at least on the API generation data object.
1. A method comprising: receiving, from a payment processing device, an integration data object associated with a payment processing service being carried out by a payment processor device, the integration data object being configured to initiate or facilitate a communication between the payment processor device and a client device, the integration data object comprising one or more of: security layers of the payment processing service, protocols of the payment processing device, integration requirements of the payment processing device, target functionalities of the payment processing device, an account statement associated with the client device, an invoice associated with the client device, or a bill associated with the client device; determining, based at least upon the integration data object, based at least in part on an integration machine learning model, one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processing device or the client device is operating, computer language capabilities associated with the payment processing device or the client device, data structure requirements associated with the payment processing device or the client device, or system protocols associated with the payment processing device or the client device; generating, based at least in part on the one or more integration features associated with the integration data objection, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processing device for execution by the payment processing device to generate an API for communication between the payment processing device and the client device.
4. The method of claim 1, further comprising: generating, based at least on the API model, the API generation data object; and sending the API generation data object corresponding to the API model to a payment processing device for execution by the payment processing device to generate the API for communication between the payment processing device and the payee device.
1. A method comprising: receiving, from a payment processing device, an integration data object associated with a payment processing service being carried out by a payment processor device, the integration data object being configured to initiate or facilitate a communication between the payment processor device and a client device, the integration data object comprising one or more of: security layers of the payment processing service, protocols of the payment processing device, integration requirements of the payment processing device, target functionalities of the payment processing device, an account statement associated with the client device, an invoice associated with the client device, or a bill associated with the client device; determining, based at least upon the integration data object, based at least in part on an integration machine learning model, one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processing device or the client device is operating, computer language capabilities associated with the payment processing device or the client device, data structure requirements associated with the payment processing device or the client device, or system protocols associated with the payment processing device or the client device; generating, based at least in part on the one or more integration features associated with the integration data objection, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processing device for execution by the payment processing device to generate an API for communication between the payment processing device and the client device.
5. The method of claim 1, wherein the integration machine learning model comprises a trained supervised machine learning model that is trained based at least in part on a plurality of historical integration data objects.
2. The method of claim 1, wherein the integration machine learning model comprises a trained supervised machine learning model that is trained based at least in part on a plurality of historical integration data objects.
6. The method of claim 1, further comprising: periodically sending requests for integration information for updating and/or refining the API model.
4. The method of claim 1, further comprising: subsequent to generating the API model, periodically sending, to the payment processing device or the client device, one or more requests for integration information for updating or refining the API model.
7. The method of claim 1, wherein the processing the integration data object comprises performing textual analysis on at least a portion of the integration data object.
5. The method of claim 1, wherein processing the integration data object comprises performing textual analysis on at least a portion of the integration data object.
8. The method of claim 1, wherein the API generation data object is configured to facilitate one or more of: generating the API, modifying the API, generating one or more API-based data objects, or modifying the one or more API-based data objects.
6. The method of claim 1, wherein the API generation data object is configured to facilitate one or more of: generation of the API, modification of the API, generation of one or more API-based data objects, or modification of one or more API-based data objects.
9. An apparatus comprising: at least one processor; and at least one memory storing instructions thereon that, when executed by the at least one processor, cause the apparatus to perform at least: receiving an integration data object associated with a request for a payment processing system to facilitate a financial transaction between a payor device and a payee device, the integration data object comprising information about an invoice or a bill; determining, based at least on the integration data object and an integration machine learning model, one or more integration features associated with the integration data object; and generating, based at least upon the one or more integration features, an API model corresponding to the integration data object, the API model being configured to provide an API generation data object corresponding to the API model to facilitate generation of an API associated with the request to facilitate the financial transaction.
8. An apparatus comprising: at least one processor; and at least one memory comprising instructions stored thereon that, when executed by the at least one processor, cause the apparatus to perform at least: receiving, from a payment processing device, an integration data object associated with a payment processing service being carried out by a payment processor device, the integration data object being configured to initiate or facilitate a communication between the payment processor device and a client device, the integration data object comprising one or more of: security layers of the payment processing service, protocols of the payment processing device, integration requirements of the payment processing device, target functionalities of the payment processing device, an account statement associated with the client device, an invoice associated with the client device, or a bill associated with the client device; determining, based at least upon the integration data object, based at least in part on an integration machine learning model, one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processing device or the client device is operating, computer language capabilities associated with the payment processing device or the client device, data structure requirements associated with the payment processing device or the client device, or protocols associated with the payment processing device or the client device; generating, based at least in part on the one or more integration features associated with the integration data objection, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processing device for execution by the payment processing device to generate an API for communication between the payment processing device and the client device.
10. The apparatus of claim 9, wherein the one or more integration features comprises one or more of: an indication of a predicted API type associated with the API, a language associated with the request, a country in which the payment processing system is operating, a country in which the payor device is operating, a country in which the payee device is operating, one or more computer language capabilities associated with the payment processing system, one or more computer language capabilities associated with the payor device, one or more computer language capabilities associated with the payee device, or system.
8. An apparatus comprising: at least one processor; and at least one memory comprising instructions stored thereon that, when executed by the at least one processor, cause the apparatus to perform at least: receiving, from a payment processing device, an integration data object associated with a payment processing service being carried out by a payment processor device, the integration data object being configured to initiate or facilitate a communication between the payment processor device and a client device, the integration data object comprising one or more of: security layers of the payment processing service, protocols of the payment processing device, integration requirements of the payment processing device, target functionalities of the payment processing device, an account statement associated with the client device, an invoice associated with the client device, or a bill associated with the client device; determining, based at least upon the integration data object, based at least in part on an integration machine learning model, one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processing device or the client device is operating, computer language capabilities associated with the payment processing device or the client device, data structure requirements associated with the payment processing device or the client device, or protocols associated with the payment processing device or the client device; generating, based at least in part on the one or more integration features associated with the integration data objection, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processing device for execution by the payment processing device to generate an API for communication between the payment processing device and the client device.
11. The apparatus of claim 9, wherein the instructions stored on the at least one memory, when executed by the at least one processor, further cause the apparatus to perform at least: generating, based at least on the API model, the API generation data object; and causing generation of the API associated with the request based at least on the API generation data object.
8. An apparatus comprising: at least one processor; and at least one memory comprising instructions stored thereon that, when executed by the at least one processor, cause the apparatus to perform at least: receiving, from a payment processing device, an integration data object associated with a payment processing service being carried out by a payment processor device, the integration data object being configured to initiate or facilitate a communication between the payment processor device and a client device, the integration data object comprising one or more of: security layers of the payment processing service, protocols of the payment processing device, integration requirements of the payment processing device, target functionalities of the payment processing device, an account statement associated with the client device, an invoice associated with the client device, or a bill associated with the client device; determining, based at least upon the integration data object, based at least in part on an integration machine learning model, one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processing device or the client device is operating, computer language capabilities associated with the payment processing device or the client device, data structure requirements associated with the payment processing device or the client device, or protocols associated with the payment processing device or the client device; generating, based at least in part on the one or more integration features associated with the integration data objection, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processing device for execution by the payment processing device to generate an API for communication between the payment processing device and the client device.
12. The apparatus of claim 9, wherein the instructions stored on the at least one memory, when executed by the at least one processor, further cause the apparatus to perform at least: generating, based at least on the API model, the API generation data object; and sending the API generation data object corresponding to the API model to a payment processing device for execution by the payment processing device to generate the API for communication between the payment processing device and the payee device.
8. An apparatus comprising: at least one processor; and at least one memory comprising instructions stored thereon that, when executed by the at least one processor, cause the apparatus to perform at least: receiving, from a payment processing device, an integration data object associated with a payment processing service being carried out by a payment processor device, the integration data object being configured to initiate or facilitate a communication between the payment processor device and a client device, the integration data object comprising one or more of: security layers of the payment processing service, protocols of the payment processing device, integration requirements of the payment processing device, target functionalities of the payment processing device, an account statement associated with the client device, an invoice associated with the client device, or a bill associated with the client device; determining, based at least upon the integration data object, based at least in part on an integration machine learning model, one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processing device or the client device is operating, computer language capabilities associated with the payment processing device or the client device, data structure requirements associated with the payment processing device or the client device, or protocols associated with the payment processing device or the client device; generating, based at least in part on the one or more integration features associated with the integration data objection, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processing device for execution by the payment processing device to generate an API for communication between the payment processing device and the client device.
13. The apparatus of claim 9, wherein the integration machine learning model comprises a trained supervised machine learning model that is trained based at least in part on a plurality of historical integration data objects.
9. The apparatus of claim 8, wherein the integration machine learning model comprises a trained supervised machine learning model that is trained based at least in part on a plurality of historical integration data objects.
14. The apparatus of claim 9, wherein the instructions stored on the at least one memory, when executed by the at least one processor, further cause the apparatus to perform at least: periodically sending requests for integration information for updating and/or refining the API model.
14. The apparatus of claim 8, wherein the one or more integration features comprises one or more of: a data structure, a predicted API type, a country, or a language.
15. The apparatus of claim 9, wherein the processing the integration data object comprises performing textual analysis on at least a portion of the integration data object.
12. The apparatus of claim 8, wherein processing the integration data object comprises performing textual analysis on at least a portion of the integration data object.
16. The apparatus of claim 9, wherein the API generation data object is configured to facilitate one or more of: generating the API, modifying the API, generating one or more API-based data objects, or modifying the one or more API-based data objects.
13. The apparatus of claim 8, wherein the API generation data object is configured to facilitate one or more of: generation of the API, modification of the API, generation of one or more API-based data objects, or modification of one or more API-based data objects.
17. A non-transitory computer-readable storage medium comprising instructions stored thereon that, when executed by at least one processor of an apparatus, cause the apparatus to perform at least: receiving an integration data object associated with a request for a payment processing system to facilitate a financial transaction between a payor device and a payee device, the integration data object comprising information about an invoice or a bill; determining, based at least on the integration data object and an integration machine learning model, one or more integration features associated with the integration data object; and generating, based at least upon the one or more integration features, an API model corresponding to the integration data object, the API model being configured to provide an API generation data object corresponding to the API model to facilitate generation of an API associated with the request to facilitate the financial transaction.
15. A non-transitory computer-readable storage medium comprising instructions stored thereon that, when executed by at least one processor of an apparatus, cause the apparatus to perform at least: receiving, from a payment processing device, an integration data object associated with a payment processing service being carried out by a payment processor device, the integration data object being configured to initiate or facilitate a communication between the payment processor device and a client device, the integration data object comprising one or more of: security layers of the payment processing service, protocols of the payment processing device, integration requirements of the payment processing device, target functionalities of the payment processing device, an account statement associated with the client device, an invoice associated with the client device, or a bill associated with the client device; determining, based at least upon the integration data object, based at least in part on an integration machine learning model, one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processing device or the client device is operating, computer language capabilities associated with the payment processing device or the client device, data structure requirements associated with the payment processing device or the client device, or protocols associated with the payment processing device or the client device; generating, based at least in part on the one or more integration features associated with the integration data objection, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processing device for execution by the payment processing device to generate an API for communication between the payment processing device and the client device.
18. The non-transitory computer-readable storage medium of claim 17, wherein the one or more integration features comprises one or more of: an indication of a predicted API type associated with the API, a language associated with the request, a country in which the payment processing system is operating, a country in which the payor device is operating, a country in which the payee device is operating, one or more computer language capabilities associated with the payment processing system, one or more computer language capabilities associated with the payor device, one or more computer language capabilities associated with the payee device, or system.
15. A non-transitory computer-readable storage medium comprising instructions stored thereon that, when executed by at least one processor of an apparatus, cause the apparatus to perform at least: receiving, from a payment processing device, an integration data object associated with a payment processing service being carried out by a payment processor device, the integration data object being configured to initiate or facilitate a communication between the payment processor device and a client device, the integration data object comprising one or more of: security layers of the payment processing service, protocols of the payment processing device, integration requirements of the payment processing device, target functionalities of the payment processing device, an account statement associated with the client device, an invoice associated with the client device, or a bill associated with the client device; determining, based at least upon the integration data object, based at least in part on an integration machine learning model, one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processing device or the client device is operating, computer language capabilities associated with the payment processing device or the client device, data structure requirements associated with the payment processing device or the client device, or protocols associated with the payment processing device or the client device; generating, based at least in part on the one or more integration features associated with the integration data objection, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processing device for execution by the payment processing device to generate an API for communication between the payment processing device and the client device.
19. The non-transitory computer-readable storage medium of claim 17, wherein the instructions stored thereon, when executed by the at least one processor, further cause the apparatus to perform at least: generating, based at least on the API model, the API generation data object; and causing generation of the API associated with the request based at least on the API generation data object.
15. A non-transitory computer-readable storage medium comprising instructions stored thereon that, when executed by at least one processor of an apparatus, cause the apparatus to perform at least: receiving, from a payment processing device, an integration data object associated with a payment processing service being carried out by a payment processor device, the integration data object being configured to initiate or facilitate a communication between the payment processor device and a client device, the integration data object comprising one or more of: security layers of the payment processing service, protocols of the payment processing device, integration requirements of the payment processing device, target functionalities of the payment processing device, an account statement associated with the client device, an invoice associated with the client device, or a bill associated with the client device; determining, based at least upon the integration data object, based at least in part on an integration machine learning model, one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processing device or the client device is operating, computer language capabilities associated with the payment processing device or the client device, data structure requirements associated with the payment processing device or the client device, or protocols associated with the payment processing device or the client device; generating, based at least in part on the one or more integration features associated with the integration data objection, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processing device for execution by the payment processing device to generate an API for communication between the payment processing device and the client device.
20. The non-transitory computer-readable storage medium of claim 17, wherein the instructions stored thereon, when executed by the at least one processor, further cause the apparatus to perform at least: generating, based at least on the API model, the API generation data object; and sending the API generation data object corresponding to the API model to a payment processing device for execution by the payment processing device to generate the API for communication between the payment processing device and the payee device.
15. A non-transitory computer-readable storage medium comprising instructions stored thereon that, when executed by at least one processor of an apparatus, cause the apparatus to perform at least: receiving, from a payment processing device, an integration data object associated with a payment processing service being carried out by a payment processor device, the integration data object being configured to initiate or facilitate a communication between the payment processor device and a client device, the integration data object comprising one or more of: security layers of the payment processing service, protocols of the payment processing device, integration requirements of the payment processing device, target functionalities of the payment processing device, an account statement associated with the client device, an invoice associated with the client device, or a bill associated with the client device; determining, based at least upon the integration data object, based at least in part on an integration machine learning model, one or more integration features associated with the integration data object, the one or more integration features comprising one or more of: a predicted application programming interface (API) model type, a country in which the payment processing device or the client device is operating, computer language capabilities associated with the payment processing device or the client device, data structure requirements associated with the payment processing device or the client device, or protocols associated with the payment processing device or the client device; generating, based at least in part on the one or more integration features associated with the integration data objection, an API model corresponding to the integration data object; generating an API generation data object corresponding to the API model; and sending the API generation data object corresponding to the API model to the payment processing device for execution by the payment processing device to generate an API for communication between the payment processing device and the client device.
21. The non-transitory computer-readable storage medium of claim 17, wherein the integration machine learning model comprises a trained supervised machine learning model that is trained based at least in part on a plurality of historical integration data objects.
16. The non-transitory computer-readable storage medium of claim 15, wherein the integration machine learning model comprises a trained supervised machine learning model that is trained based at least in part on a plurality of historical integration data objects.
22. The non-transitory computer-readable storage medium of claim 17, wherein the instructions stored thereon, when executed by the at least one processor, further cause the apparatus to perform at least: periodically sending requests for integration information for updating and/or refining the API model.
18. The non-transitory computer-readable storage medium of claim 15, wherein the instructions stored thereon, when executed by the at least one processor, further cause the apparatus to perform at least: subsequent to generating the API model, periodically sending, to the payment processing device or the client device, one or more requests for integration information for updating or refining the API model.
23. The non-transitory computer-readable storage medium of claim 17, wherein the processing the integration data object comprises performing textual analysis on at least a portion of the integration data object.
19. The non-transitory computer-readable storage medium of claim 15, wherein processing the integration data object comprises performing textual analysis on at least a portion of the integration data object.
24. The non-transitory computer-readable storage medium of claim 17, wherein the API generation data object is configured to facilitate one or more of: generating the API, modifying the API, generating one or more API-based data objects, or modifying the one or more API-based data objects.
20. The non-transitory computer-readable storage medium of claim 15, wherein the API generation data object is configured to facilitate one or more of: generation of the API, modification of the API, generation of one or more API-based data objects, or modification of one or more API-based data objects.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 4 – 9, 13 – 17 and 21 - 24 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by the prior art of record, BAHRAMI et al., (US 2019/0196811 A1) (hereinafter “BAHRAMI”) (submitted by the applicant via IDS filed 09/04/2025).
BAHRAMI discloses;
Regarding claim 1, a method [i.e., method 200 (see figure 2), (page 3, para 0036)] comprising:
receiving an integration data object associated with a request for a payment processing system to facilitate a financial transaction between a payor device and a payee device, the integration data object comprising information about an invoice or a bill [i.e., obtain a plain language API documentation (see reference 205 of figure 2), (page 3, para 0037) i.e., obtain an API documentation (page 1, para 0019), (see figure 1)];
determining based at least on the integration data object and an integration machine learning model, one or more integration features associated with the integration data object [i.e., the API specification 130 include a computer-readable notation to implement the API resource of the API document 120 (page 3, para 0030), (page 4, para 0044)];
generating, based at least upon the one or more integration features, an API model corresponding to the integration data object [i.e., produce an API specification 130 for the API resources of the API documentation 120 (page 1, para 0019), (see figure 1 and reference 240 of figure 2), (page 4, para 0044)]; the API model being configured to provide an API generation data object corresponding to the API model to facilitate generation of an API associated with the request to facilitate the financial transaction [i.e., the API specification is used by the computing device to implement and generate calls to the API resources (page 4, para 0044), (page 3, para 0030)].
Regarding claim 5, the method of claim 1, wherein the integration machine learning model comprises a trained supervised machine learning model that is trained based at least in part on a plurality of historical integration data objects [i.e., additional API specifications is utilized to make the model more robust (page 2, para 0027)].
Regarding claim 6, the method of claim 1, further comprising: periodically sending for integration information for updating and/or refining the API model [i.e., any content extraction technique to obtain the tables or the functions (page 3, para 0038), (see references 205 – 210 of figure 2)].
Regarding claim 7, the method of claim 1, wherein the processing the integration data object comprises performing textual analysis on at least a portion of the integration data object [i.e., at block 230, one or more of the extracted tables is applied to the machine learning models of the block 225 to detect a type of each of the extracted tables (page 3, para 0037 - page 4, para 0042), (see references 205 - 230 of figure 2) i.e., the output of such an extraction process include one or more functions, a description of each of the one or more functions (page 2, para 0020), (page 3, para 0028)].
Regarding claim 8, the method of claim 1, wherein the API generation data object is configured to facilitate one or more of: generating the API [i.e., produce an API specification 130 for the API resources of the API documentation 120 (page 1, para 0019), (see figure 1 and reference 240 of figure 2), (page 4, para 0044)].
Regarding claim 9, an apparatus comprising:
at least one processor; and
at least one memory storing instructions thereon that, when executed by the at least one processor, cause the apparatus to perform at least:
receiving an integration data object associated with a request for a payment processing system to facilitate a financial transaction between a payor device and a payee device, the integration data object comprising information about an invoice or a bill [i.e., obtain a plain language API documentation (see reference 205 of figure 2), (page 3, para 0037) i.e., obtain an API documentation (page 1, para 0019), (see figure 1)];
determining based at least on the integration data object and an integration machine learning model, one or more integration features associated with the integration data object [i.e., the API specification 130 include a computer-readable notation to implement the API resource of the API document 120 (page 3, para 0030), (page 4, para 0044)];
generating, based at least upon the one or more integration features, an API model corresponding to the integration data object [i.e., produce an API specification 130 for the API resources of the API documentation 120 (page 1, para 0019), (see figure 1 and reference 240 of figure 2), (page 4, para 0044)]; the API model being configured to provide an API generation data object corresponding to the API model to facilitate generation of an API associated with the request to facilitate the financial transaction [i.e., the API specification is used by the computing device to implement and generate calls to the API resources (page 4, para 0044), (page 3, para 0030)].
Regarding claim 13, the apparatus of claim 9, wherein the integration machine learning model comprises a trained supervised machine learning model that is trained based at least in part on a plurality of historical integration data objects [i.e., additional API specifications is utilized to make the model more robust (page 2, para 0027)].
Regarding claim 14, the apparatus of claim 9, wherein the instructions stored on the at least one memory, when executed by the at least one processor, further cause the apparatus to perform at least: periodically sending requests for integration information for updating and/or refining the API model [i.e., any content extraction technique to obtain the tables or the functions (page 3, para 0038), (see references 205 – 210 of figure 2)].
Regarding claim 15, the apparatus of claim 9, wherein the processing the integration data object comprises performing textual analysis on at least a portion of the integration data object [i.e., at block 230, one or more of the extracted tables is applied to the machine learning models of the block 225 to detect a type of each of the extracted tables (page 3, para 0037 - page 4, para 0042), (see references 205 - 230 of figure 2) i.e., the output of such an extraction process include one or more functions, a description of each of the one or more functions (page 2, para 0020), (page 3, para 0028)].
Regarding claim 16, the apparatus of claim 9, wherein the API generation data object is configured to facilitate one or more of: generating the an API [i.e., produce an API specification 130 for the API resources of the API documentation 120 (page 1, para 0019), (see figure 1 and reference 240 of figure 2), (page 4, para 0044)].
Regarding claim 17 a non-transitory computer readable storage medium storing program instructions that, when executed by at least one processor, cause at least the following [i.e., method 200 (see figure 2), (page 3, para 0036)]:
receiving an integration data object associated with a request for a payment processing system to facilitate a financial transaction between a payor device and a payee device, the integration data object comprising information about an invoice or a bill [i.e., obtain a plain language API documentation (see reference 205 of figure 2), (page 3, para 0037) i.e., obtain an API documentation (page 1, para 0019), (see figure 1)];
determining based at least on the integration data object and an integration machine learning model, one or more integration features associated with the integration data object [i.e., the API specification 130 include a computer-readable notation to implement the API resource of the API document 120 (page 3, para 0030), (page 4, para 0044)];
generating, based at least upon the one or more integration features, an API model corresponding to the integration data object [i.e., produce an API specification 130 for the API resources of the API documentation 120 (page 1, para 0019), (see figure 1 and reference 240 of figure 2), (page 4, para 0044)]; the API model being configured to provide an API generation data object corresponding to the API model to facilitate generation of an API associated with the request to facilitate the financial transaction [i.e., the API specification is used by the computing device to implement and generate calls to the API resources (page 4, para 0044), (page 3, para 0030)].
Regarding claim 21, the non-transitory computer readable storage medium of claim 17, wherein the integration machine learning model comprises a trained supervised machine learning model that is trained based at least in part on a plurality of historical integration data objects [i.e., additional API specifications is utilized to make the model more robust (page 2, para 0027)].
Regarding claim 22, the non-transitory computer readable storage medium of claim 17, wherein the instructions, when executed by the at least one processor, further cause the apparatus to perform at least: periodically sending requests for integration information for updating and/or refining the API model [i.e., any content extraction technique to obtain the tables or the functions (page 3, para 0038), (see references 205 – 210 of figure 2)].
Regarding claim 23, the non-transitory computer-readable storage medium of claim 17, wherein the processing the integration data object comprises performing textual analysis on at least a portion of the integration data object [i.e., at block 230, one or more of the extracted tables is applied to the machine learning models of the block 225 to detect a type of each of the extracted tables (page 3, para 0037 - page 4, para 0042), (see references 205 - 230 of figure 2) i.e., the output of such an extraction process include one or more functions, a description of each of the one or more functions (page 2, para 0020), (page 3, para 0028)].
Regarding claim 24, the non-transitory computer-readable storage medium of claim 17, wherein the API generation data object is configured to facilitate one or more of: generation the API [i.e., produce an API specification 130 for the API resources of the API documentation 120 (page 1, para 0019), (see figure 1 and reference 240 of figure 2), (page 4, para 0044)].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Oros (US 2020/0134374 A1) discloses listening for an update request for an artificial intelligence (AI)/machine learning (ML) model, by a digital process executing on a computing system; and when the update request is received to update the AWL model, reinitializing or re-instantiating the digital process to call an updated version of the AWL model and listening for another update request, by the digital process executing on the computing system, wherein the updating of the AI/ML, model occurs during runtime of the digital process.
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/SYED A RONI/Primary Examiner, Art Unit 2432
