DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 have been examined in this Final Rejection. Claims 1-20 are currently pending.
Priority
Application 19/418,868 filed 12/12/2025 is a continuation of patented application 19/185,195 filed 04/21/2025, which is a continuation-in-part of patented application 19/182,588 filed 04/18/2025, which is a continuation-in-part of patented application 19/050,084 filed 02/10/2025, which is a continuation-in-part of patented application 18/736,407 filed 06/06/2024.
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 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual 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 www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim(s) 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim(s) 1-19, and 21 of U.S. Patent No. 12,499,454. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 of the instant application are anticipated by patent claims 1-19, and 21. See chart below:
Instant Application 19/418,868
U.S. Patent No. 12,499,454
1. A computer-implemented method for executing interactive review services of one or more development services to dynamically generate formatted export artifacts via a plurality of interactive visual representations, the computer-implemented method comprising:
1. A computer-implemented method for dynamically generating formatted export artifacts for validating authorization schemas based on select attributes extracted from applicable digital artifacts using a plurality of coordinated machine learning models, the method comprising:
Receiving, via a first interactive visual representation at a user interface, a request to evaluate authorization of a development service that corresponds to a digital artifact comprising a content embedding for an artifact attribute associated with tracked actions of the development service;
receiving, via a first interactive visual representation at the user interface, a request to evaluate authorization of at least one development service that comprises a digital artifact set, each digital artifact in the digital artifact set comprising: a content embedding for an artifact attribute set representing tracked development actions of the at least one development service;
inputting the content embedding and at least one reference embedding of at least one available authorization schema into a first artificial intelligence (AI) model to identify, from the at least one available authorization schema, an applicable authorization schema for authorizing the development service, comparison of the content embedding of the digital artifact and a reference embedding of the applicable authorization schema satisfying a similarity threshold value;
Invoking a first machine learning model trained on input, content embeddings of sample digital artifacts and corresponding input reference embeddings of sample authorization schemas to output reference identifiers of applicable authorization schemas for the sample digital artifacts, the invocation causing the first machine learning model to generate output reference identifiers of an applicable authorization schema subset from the authorization schema set based on input comprising the content embeddings of the digital artifact set and the reference embeddings of the authorization schema set, wherein each applicable authorization schema in the subset is associated with the output reference identifiers generated by the first machine learning model and mapped to at least one digital artifact of the at least one development service, and wherein the reference embedding of the applicable authorization schema and the content embedding of the at least one digital artifact satisfy a similarity threshold
retrieving one or more historical artifact attribute associated with tracked actions for prior development services authorized via the applicable authorization schema that is mapped to one or more digital artifacts to the development service;
retrieving, from a remote database, a historical artifact attribute set representing tracked development actions for prior development services authorized via the applicable authorization schema that is associated with the output reference identifiers generated by the first machine learning model and mapped to the at least one digital artifact;
inputting the one or more historical artifact attribute, the artifact attribute, and attribute threshold values of the applicable authorization schema into a second AI model to generate, for the development service, an authorization status indicating whether the artifact attribute satisfies the attribute threshold values of the applicable authorization schema;
Invoking a second machine learning model trained on input, artifact attributes for sample digital artifacts and corresponding input artifact attribute thresholds of sample applicable authorization schemas to output authorization status of development services associated with the sample digital artifacts, the invocation causing the second machine learning model to generate an authorization status for the at least one development service based on input comprising the historical artifact attribute set and the artifact attribute set of the applicable authorization schema associated with the output reference identifiers generated by the first machine learning model and mapped to the at least one digital artifact, wherein the authorization status indicates whether the required artifact attribute thresholds of the applicable authorization schema are satisfied;
automatically generating for display, at the user interface, a second interactive visual representation and a third interactive visual representation, wherein the second interactive visual representation visualizes mapping of the applicable authorization schema to the one or more digital artifacts of the development service, and wherein the third interactive visual representation visualizes a formatted export artifact based on an artifact template comprising a required field query that corresponds to an empty input field; and
automatically generating for display, at the user interface, a second interactive visual representation that visualizes at least one applicable authorization schema and the mapped at least one digital artifact of the at least one development service, wherein the authorization status of the at least one applicable authorization schema indicates satisfaction of the required artifact attribute thresholds; automatically generating for display, at the user interface, a third visual representation of the formatted export digital artifact based, in part, on an artifact template comprising a required field query set, each field query within the required field query set corresponding to an empty input field;
responsive to receiving a positive user indication for authorizing the development service using the applicable authorization schema via the second interactive visual representation, updating the third interactive visual representation for the required field query of the formatted export artifact by: inputting the artifact attribute and the one or more historical artifact attribute into a third AI model to generate a human-readable narrative for the required field query; and
and responsive to receiving a positive user indication for the at least one applicable authorization schema via the second interactive visual representation of the user interface, updating the third visual representation for each required field query of the formatted export digital artifact by: invoking a third machine learning model trained on input sample field queries and corresponding input artifact attributes of sample digital artifacts to output human-readable narrative entries for the sample field queries, the invocation causing the third machine learning model to generate a human-readable narrative for the required field query based on input comprising the artifact attribute set and the historical artifact attribute set associated for the at least one digital artifact mapped to the at least one applicable authorization schema associated with the output reference identifiers generated by the first machine learning model and mapped to the at least one digital artifact;
automatically updating, at the user interface, the second interactive visual representation of the formatted export artifact to populate the empty input field of the required field query with the human-readable narrative.
And automatically updating, at the user interface, the third visual representation of the formatted export digital artifact to populate the empty input field of the required field query with the generated human-readable narrative.
2. The computer-implemented method of claim 1 further comprising: responsive to receiving a negative user indication for the applicable authorization schema: automatically generating a model prediction training sample comprising an input data based on the historical artifact attribute and the artifact attribute of the digital artifact and an output label based on the authorization status of the applicable authorization schema;
2. The computer-implemented method of claim 1, further comprising: responsive to receiving a negative user indication for the at least one applicable authorization schema via the user interface: automatically generating a model prediction training sample comprising an input data based on the historical artifact attribute set and the artifact attribute set of the at least one digital artifact and an output label based on the authorization status of the at least one applicable authorization schema;
accessing a stored model prediction training sample corresponding to predicted authorization statuses of prior applicable authorization schemas;
accessing, from the remote database, a stored model prediction training sample set, each model prediction training sample corresponding to predicted authorization statuses of prior applicable authorization schemas;
and retraining, using the stored model prediction training sample and the model prediction training sample, the first AI model, the second AI model, the third AI model, or a combination thereof.
And retraining, using the stored model prediction training sample set and the generated model prediction training sample, the first machine learning model, the second machine learning model, the generative machine learning model, or a combination thereof.
3. The computer-implemented method of claim 1 further comprising: responsive to receiving a negative user indication identifying an attribute threshold subset of the attribute threshold values not satisfied by the artifact attribute:
3. The computer-implemented method of claim 1, wherein the user interface is a first user interface, and wherein the method further comprises: responsive to receiving, via the first user interface, a negative user indication for the at least one applicable authorization schema, the indication identifying a required artifact attribute threshold subset not satisfied by the artifact attribute set:
inputting into a fourth AI model, the historical artifact attribute, the artifact attribute, and the attribute threshold subset to generate an adjusted artifact attribute satisfying the attribute threshold subset;
inputting into a third machine learning model, the historical artifact attribute set, the artifact attribute set, and the required artifact attribute threshold subset to generate an adjusted artifact attribute set for the at least one digital artifact that satisfy the required artifact attribute threshold subset;
and configuring for transmission, to an authorized editor of the digital artifact, the attribute threshold subset and the adjusted artifact attribute.
and configuring for display, at a second user interface, the identified required artifact attribute threshold subset and the adjusted artifact attribute set, the second user interface corresponding to an authorized editor of the at least one digital artifact.
4. The computer-implemented method of claim 1, wherein the representation is configured to transmit the artifact attribute of the digital artifact associated with the tracked actions of the development service.
4. The computer-implemented method of claim 1, wherein the visual representation is configured to display the artifact attribute set of the at least one digital artifact that represent the tracked development actions of the at least one development service.
5. The computer-implemented method of claim 4 further comprising: configuring for transmission a distinct visual marking over an artifact attribute subset that corresponds to artifact attributes satisfying the attribute threshold values of the applicable authorization schema.
5. The computer-implemented method of claim 4, wherein the method further comprises: configuring for display, at the user interface, a distinct visual marking over an artifact attribute subset that corresponds to artifact attributes satisfying the required artifact attribute thresholds of the applicable authorization schema.
6. The computer-implemented method of claim 4, wherein the authorization status further indicates whether the attribute threshold values of the applicable authorization schema is partially satisfied.
6. The computer-implemented method of claim 4, wherein the authorization status further indicates whether the required artifact attribute thresholds of the applicable authorization schema are partially satisfied.
7. The computer-implemented method of claim 6 further comprising: configuring for transmission a distinct visual marking over an artifact attribute subset that corresponds to artifact attributes partially satisfying the attribute threshold values of the applicable authorization schema.
7. The computer-implemented method of claim 6, further comprising: configuring for display, at the user interface, a distinct visual marking over an artifact attribute subset that corresponds to artifact attributes partially satisfying the required artifact attribute thresholds of the applicable authorization schema.
8. The computer-implemented method of claim 7 further comprising: causing the third AI model to generate a human-readable recommendation for adjusting one or more artifact attributes from the artifact attribute subset to satisfy the attribute threshold values of the applicable authorization schema; and configuring for transmission the human-readable recommendation.
8. The computer-implemented method of claim 7, further comprising: causing the generative machine learning model to generate a human-readable recommendation for adjusting at least one artifact attribute from the displayed artifact attribute subset to satisfy the required artifact attribute thresholds of the applicable authorization schema; and configuring for display, at the user interface, the generated human-readable recommendation.
9. The computer-implemented method of claim 1, wherein the applicable authorization schema comprises regulatory policies, predetermined evaluation rulesets, narrative guidelines, fiscal procedures, or any combination thereof.
9. The computer-implemented method of claim 1, wherein each authorization schema in the authorization schema set for the at least one development service comprises regulatory policies, predetermined evaluation rulesets, narrative guidelines, fiscal procedures, or any combination thereof.
10. The computer-implemented method of claim 1, wherein the representation of the applicable authorization schema is further configured to transmit a comparative diagram that maps a first mapping of content similarities between the historical artifact attribute and the artifact attribute and a second mapping of content differences between the historical artifact attribute and the artifact attribute.
10. The computer-implemented method of claim 1, wherein the visual representation of the at least one applicable authorization schema is further configured to display a comparative diagram that maps a first mapping of content similarities between the historical artifact attribute set and the artifact attribute set and a second mapping of content differences between the historical attribute set and the artifact attribute set.
11. The computer-implemented method of claim 1 further comprising: obtaining a first sequence of intermediary logic operations executed during operation of the first AI model and a second sequence of intermediary logic operations executed during operation of the second AI model; causing the third AI model to generate, using the first sequence of intermediary logic operations and the second sequence of intermediary logic operations, a human-readable narrative explaining a logical sequence resulting in the authorization status of the applicable authorization schema; and configuring for transmission the human-readable narrative alongside the representation of the applicable authorization schema.
11. The computer-implemented method of claim 1, further comprising: obtaining a first sequence of intermediary logic operations executed during operation of the first machine learning model and a second sequence of intermediary logic operations executed during operation of the second machine learning model; causing the generative machine learning model to generate, using the first and the second sequence of intermediary logic operations, a human-readable narrative explaining a logical sequence resulting in the authorization status of the displayed at least one applicable authorization schema; and configuring for display, at the user interface, the generated human-readable narrative alongside the visual representation of the at least one applicable authorization schema and the mapped at least one digital artifact.
12. The computer-implemented method of claim 1, further comprising: monitoring one or more intermediary logic operations from invocation of the first AI model to generate output reference identifiers of the applicable authorization schema; and invoking, during the invocation of the first AI model, a generative AI model to output at least one human-readable explanation based on input comprising the one or more intermediary logic operations, the at least one human-readable explanation indicating incremental logic for the one or more intermediary logic operations.
21. The computer-implemented method of claim 1, further comprising: monitoring one or more intermediary logic operations from the invocation of the first machine learning model to generate output reference identifiers of the applicable authorization schema subset from the authorization schema set; invoking, during invocation of the first machine learning model, a generative machine learning model to output at least one human-readable explanation based on input comprising the one or more intermediary logic operations, the at least one human-readable explanation indicating incremental logic for the intermediary logic operations.
13. A system for executing interactive review services to dynamically generate formatted export artifacts via a plurality of interactive visual representations, of one or more development services, the system comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, causes the system to:
12. A system for dynamic generation of formatted export artifacts for validating authorization schemas based on select attributes extracted from applicable digital artifacts using a plurality of coordinated machine learning models, the system comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, causes the system to:
receive, via a first interactive visual representation at a user interface, a request to evaluate authorization of a development service that corresponds to a digital artifact comprising a content embedding for an artifact attribute associated with tracked actions of the development service;
receive, via a first interactive visual representation, a request to evaluate authorization of a development service that comprises a digital artifact set, each digital artifact in the digital artifact set comprising: a content embedding for an artifact attribute set representing tracked development actions of the at least one development service;
input the content embedding and at least one reference embedding of at least one available authorization schema into a first artificial intelligence (AI) model to identify an applicable authorization schema for authorizing the development service, comparison of the content embedding of the digital artifact and a reference embedding of the applicable authorization schema satisfying a similarity threshold value;
invoke a first machine learning model trained on input content embeddings of sample digital artifacts and corresponding input reference embeddings of sample authorization schemas to output reference identifiers of applicable authorization schemas for the sample digital artifacts, the invocation causing the first machine learning model to generate output reference identifiers of an applicable authorization schema from the authorization schema set based on input comprising the content embeddings of the digital artifact set and the reference embeddings of the authorization schema set, wherein the applicable authorization schema is associated with the output reference identifiers generated by the first machine learning model and mapped to at least one digital artifact of the development service, and wherein the reference embedding of the applicable authorization schema and the content embedding of the at least one digital artifact satisfy a similarity threshold;
retrieve one or more a historical artifact attribute associated with tracked actions for prior development services authorized via the applicable authorization schema that is mapped to one or more digital artifacts to the development service;
retrieve a historical artifact attribute set representing tracked development actions for prior development services authorized via the applicable authorization schema that is associated with the output reference identifiers generated by the first machine learning model and mapped to the at least one digital artifact;
input the one or more historical artifact attribute, the artifact attribute, and attribute threshold values of the applicable authorization schema into a second AI model to generate an authorization status indicating whether the artifact attribute satisfies the attribute threshold values of the applicable authorization schema;
invoke a second machine learning model trained on input, artifact attributes for sample digital artifacts and corresponding input artifact attribute thresholds of sample applicable authorization schemas to output authorization status of development services associated with the sample digital artifacts, the invocation causing the second machine learning model to generate an authorization status for the development service based on input comprising the historical artifact attribute set and the artifact attribute set of the applicable authorization schema associated with the output reference identifiers generated by the first machine learning model and mapped to the at least one digital artifact, wherein the authorization status indicates whether the required artifact attribute thresholds of the applicable authorization schema are satisfied;
automatically generate for display, at the user interface, a second interactive visual representation and a third interactive visual representation, wherein the second interactive visual representation visualizes mapping of the applicable authorization schema to the one or more digital artifacts of the development service, and wherein the third interactive visual representation visualizes a formatted export artifact based on an artifact template comprising required field query that corresponds to an empty input field;
automatically generating for display, at the user interface, a second interactive visual representation that visualizes at least one applicable authorization schema and the mapped at least one digital artifact of the at least one development service, wherein the authorization status of the at least one applicable authorization schema indicates satisfaction of the required artifact attribute thresholds; automatically generating for display, at the user interface, a third visual representation of the formatted export digital artifact based, in part, on an artifact template comprising a required field query set, each field query within the required field query set corresponding to an empty input field;
and responsive to receiving a positive user indication for authorizing the development service using the applicable authorization schema, via the second interactive visual representation, update the third interactive visual representation for the required field query of the formatted export artifact by: input the artifact attribute and the historical artifact attribute into a third AI model to generate a human-readable narrative for the required field query;
and responsive to receiving a positive user indication for the at least one applicable authorization schema via the second interactive visual representation of the user interface, updating the third visual representation for each required field query of the formatted export digital artifact by: invoking a third machine learning model trained on input sample field queries and corresponding input artifact attributes of sample digital artifacts to output human-readable narrative entries for the sample field queries, the invocation causing the third machine learning model to generate a human-readable narrative for the required field query based on input comprising the artifact attribute set and the historical artifact attribute set associated for the at least one digital artifact mapped to the at least one applicable authorization schema associated with the output reference identifiers generated by the first machine learning model and mapped to the at least one digital artifact;
and automatically update, at the user interface, the second interactive visual representation of the formatted export artifact to populate the empty input field of the required field query with the human-readable narrative.
and automatically update the third visual representation of the formatted export digital artifact to populate the empty input field of the required field query with the generated human-readable narrative.
14. The system of claim 13 further caused to: responsive to receiving a negative user indication for the applicable authorization schema: automatically generate a model prediction training sample comprising an input data based on the historical artifact attribute and the artifact attribute of the digital artifact and an output label based on the authorization status of the applicable authorization schema; access a stored model prediction training sample corresponding to predicted authorization statuses of prior applicable authorization schemas; and retraining, using the stored model prediction training sample and the model prediction training sample, the first AI model, the second AI model, the third AI model, or a combination thereof.
13. (Original) The system of claim 12 further caused to: responsive to receiving a negative user indication for the applicable authorization schema: automatically generate a model prediction training sample comprising an input data based on the historical artifact attribute set and the artifact attribute set of the at least one digital artifact and an output label based on the authorization status of the applicable authorization schema; access a stored model prediction training sample set, each model prediction training sample corresponding to predicted authorization statuses of prior applicable authorization schemas; and retraining, using the stored model prediction training sample set and the generated model prediction training sample, the first machine learning model, the second machine learning model, the generative machine learning model, or a combination thereof.
15. The system of claim 13 further caused to: responsive to receiving a negative user indication identifying an attribute threshold subset of the attribute threshold values not satisfied by the artifact attribute: input into a fourth AI model, the historical artifact attribute, the artifact attribute, and the attribute threshold subset to generate an adjusted artifact attribute satisfying the attribute threshold subset; and configure for transmission, to an authorized editor of the digital artifact, the attribute threshold subset and the adjusted artifact attribute.
14. (Original) The system of claim 12 further caused to: responsive to receiving, via a first user interface, a negative user indication for the applicable authorization schema, the indication identifying a required artifact attribute threshold subset not satisfied by the artifact attribute set: input into a third machine learning model, the historical artifact attribute set, the artifact attribute set, and the required artifact attribute threshold subset to generate an adjusted artifact attribute set for the at least one digital artifact that satisfy the required artifact attribute threshold subset; and configure for display, at a second user interface, the identified required artifact attribute threshold subset and the adjusted artifact attribute set, the second user interface corresponding to an authorized editor of the at least one digital artifact.
16. The system of claim 15 further caused to: obtain a first sequence of intermediary logic operations executed during operation of the first AI model and a second sequence of intermediary logic operations executed during operation of the second AI model; cause the third AI model to generate, using the first sequence of intermediary logic operations and the second sequence of intermediary logic operations, a human-readable narrative explaining a logical sequence resulting in the authorization status of the applicable authorization schema; and configure for transmission a human-readable narrative alongside the representation of the applicable authorization schema.
15. (Original) The system of claim 14 further caused to: obtain a first sequence of intermediary logic operations executed during operation of the first machine learning model and a second sequence of intermediary logic operations executed during operation of the second machine learning model; cause the generative machine learning model to generate, using the first and the second sequence of intermediary logic operations, a human-readable narrative explaining a logical sequence resulting in the authorization status of the displayed applicable authorization schema; and configure for display the generated human-readable narrative alongside the visual representation of the applicable authorization schema and the mapped at least one digital artifact.
17. One or more non-transitory, computer-readable storage media comprising instructions recorded thereon, wherein the instructions when executed by at least one data processor of a system for executing interactive review services of one or more development service to dynamically generate formatted export artifacts via a plurality of interactive visual representations,, cause the system to:
16. (Currently Amended) One or more non-transitory, computer-readable storage media comprising instructions recorded thereon, wherein the instructions when executed by at least one data processor of a system for dynamic generation of formatted export artifacts for validating authorization schemas based on select attributes extracted from applicable digital artifacts using a plurality of coordinated machine learning models, the system, cause the system to:
Receive, via a first interactive visual representation at a user interface, a request to evaluate authorization of a development service that corresponds to a digital artifact comprising a content embedding for an artifact attribute associated with tracked actions of the development service;
receive, via a first interactive visual representation, a request to evaluate authorization of a development service that comprises a digital artifact set, each digital artifact in the digital artifact set comprising: a content embedding for an artifact attribute set representing tracked development actions of the at least one development service;
input the content embedding and at least one reference embedding of at least one available authorization schema into a first artificial intelligence (AI) model to identify an applicable authorization schema for authorizing the development service, comparison of the content embedding of the digital artifact and a reference embedding of the applicable authorization schema satisfying a similarity threshold value;
invoke a first machine learning model trained on input content embeddings of sample digital artifacts and corresponding input reference embeddings of sample authorization schemas to output reference identifiers of applicable authorization schemas for the sample digital artifacts, the invocation causing the first machine learning model to generate output reference identifiers of identify an applicable authorization schema from the authorization schema set based on input comprising the content embeddings of the digital artifact set and the reference embeddings of the authorization schema set, wherein the applicable authorization schema is associated with the output reference identifiers generated by the first machine learning model and mapped to at least one digital artifact of the development service, and wherein the reference embedding of the applicable authorization schema and the content embedding of the at least one digital artifact satisfy a similarity threshold;
retrieve one or more historical artifact attribute associated with tracked actions for prior development services authorized via the applicable authorization schema that is mapped to one or more digital artifacts to the development service;
retrieve a historical artifact attribute set representing tracked development actions for prior development services authorized via the applicable authorization schema that is associated with the output reference identifiers generated by the first machine learning model and mapped to the at least one digital artifact;
and input the one or more historical artifact attribute, the artifact attribute, and attribute threshold values of the applicable authorization schema into a second AI model to generate an authorization status indicating whether the artifact attribute satisfies the attribute threshold values of the applicable authorization schema.
and invoke a second machine learning model trained on input artifact attributes for sample digital artifacts and corresponding input artifact attribute thresholds of sample applicable authorization schemas to output authorization status of development services associated with the sample digital artifacts, the invocation causing the second machine learning model to generate an authorization status for the development service based on input comprising the historical artifact attribute set and the artifact attribute set of the applicable authorization schema associated with the output reference identifiers generated by the first machine learning model and mapped to the at least one digital artifact, wherein the authorization status indicates whether the required artifact attribute thresholds of the applicable authorization schema are satisfied.
automatically generate for display, at the user interface, a second interactive visual representation and a third interactive visual representation ,wherein the second interactive visual representation visualizes mapping of the applicable authorization schema to the one or more digital artifacts of the development service, and wherein the third interactive visual representation visualizes a formatted export artifact based on an artifact template comprising a required field query that corresponds to an empty input field;
automatically generating for display, at the user interface, a second interactive visual representation that visualizes at least one applicable authorization schema and the mapped at least one digital artifact of the at least one development service, wherein the authorization status of the at least one applicable authorization schema indicates satisfaction of the required artifact attribute thresholds; automatically generating for display, at the user interface, a third visual representation of the formatted export digital artifact based, in part, on an artifact template comprising a required field query set, each field query within the required field query set corresponding to an empty input field;
and responsive to receiving a positive user indication for authorizing the development service using the applicable authorization schema via the second interactive visual representation, updating the third interactive visual representation for the required field query of the formatted export artifact by: inputting the artifact attribute and the one or more historical artifact attributes into a third Al model to generate a human-readable narrative for the required field query; and
and responsive to receiving a positive user indication for the at least one applicable authorization schema via the second interactive visual representation of the user interface, updating the third visual representation for each required field query of the formatted export digital artifact by: invoking a third machine learning model trained on input sample field queries and corresponding input artifact attributes of sample digital artifacts to output human-readable narrative entries for the sample field queries, the invocation causing the third machine learning model to generate a human-readable narrative for the required field query based on input comprising the artifact attribute set and the historical artifact attribute set associated for the at least one digital artifact mapped to the at least one applicable authorization schema associated with the output reference identifiers generated by the first machine learning model and mapped to the at least one digital artifact;
automatically updating, at the user interface, the second interactive visual representation of the formatted export artifact to populate the empty input field of the required field query with the human-readable narrative.
And automatically updating, at the user interface, the third visual representation of the formatted export digital artifact to populate the empty input field of the required field query with the generated human-readable narrative.
18. The one or more non-transitory, computer-readable storage media of claim 17, wherein the instructions further cause the system to: configure for transmission a representation of the applicable authorization schema and the digital artifact of the development service, wherein the authorization status of the applicable authorization schema indicates satisfaction of the attribute threshold values.
17. (Original) The one or more non-transitory, computer-readable storage media of claim 16, wherein the instructions further cause the system to: configure for display a visual representation of the applicable authorization schema and the mapped at least one digital artifact of the development service, wherein the authorization status of the applicable authorization schema indicates satisfaction of the required artifact attribute thresholds.
19. The one or more non-transitory, computer-readable storage media of claim 17, wherein the instructions further cause the system to: responsive to receiving a positive user indication for the applicable authorization schema: automatically generate an export artifact based on an artifact template, the artifact template comprising at least one required field query; and input the artifact attribute and the historical artifact attribute into a fourth AI model to generate at least one human-readable narrative for the at least one required field query.
18. (Original) The one or more non-transitory, computer-readable storage media of claim 16, wherein the instructions further cause the system to: responsive to receiving a positive user indication for the applicable authorization schema: automatically generate an export digital artifact based on an artifact template, the artifact template comprising a required field query set; and cause a generative machine learning model to generate human-readable narratives for each required field query of the required field query set using the artifact attribute set and the historical artifact attribute set associated for the at least one digital artifact mapped to the applicable authorization schema.
20. The one or more non-transitory, computer-readable storage media of claim 18, wherein the representation is configured to display the artifact attribute of the digital artifact associated with the tracked actions of the development service, and wherein the instructions further cause the system to: configure for display a distinct visual marking over an artifact attribute subset that corresponds to artifact attributes satisfying the attribute threshold values of the applicable authorization schema.
19. (Original) The one or more non-transitory, computer-readable storage media of claim 17, wherein the visual representation is configured to display the artifact attribute set of the at least one digital artifact that represent the tracked development actions of the development service, and wherein the instructions further cause the system to: configure for display a distinct visual marking over an artifact attribute subset that corresponds to artifact attributes satisfying the required artifact attribute thresholds of the applicable authorization schema.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-20 are directed to a system, method, or product which are/is one of the statutory categories of invention. (Step 1: YES).
Claims 1, 13, and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a method and computing device for evaluating authorization of a development service. For Claims 1, 13, and 17 the limitations of (Claim 1 being representative):
receiving, via a first interactive visual representation […], to evaluate authorization of a development service that corresponds to a […] artifact comprising a content embedding for an artifact attribute associated with tracked actions of the development service;
inputting the content embedding and at least one reference embedding of at least one available authorization schema into a first […] model to identify, from the at least one available authorization schema, an applicable authorization schema for authorizing the development service, comparison of the content embedding of the […] artifact and a reference embedding of the applicable authorization schema satisfying a similarity threshold value;
retrieving one or more historical artifact attribute associated with tracked actions for prior development services authorized via the applicable authorization schema that is mapped to one or more […] artifacts to the development service;
inputting the one or more historical artifact attribute, the artifact attribute, and attribute threshold values of the applicable authorization schema into a second […] model to generate, for the development service, an authorization status indicating whether the artifact attribute satisfies the attribute threshold values of the applicable authorization schema;
automatically generating for display, […], a second interactive visual representation and a third interactive visual representation, wherein the second interactive visual representation visualizes mapping of the applicable authorization schema to the one or more […] artifacts of the development service, and wherein the third interactive visual representation visualizes a formatted export artifact based on an artifact template comprising a required field query that corresponds to an empty input field; and
responsive to receiving a positive user indication for authorizing the development service using the applicable authorization schema via the second interactive visual representation, updating the third interactive visual representation for the required field query of the formatted export artifact by:
inputting the artifact attribute and the one or more historical artifact attribute into a third […] model to generate a human-readable narrative for the required field query; and
automatically updating, […], the second interactive visual representation of the formatted export artifact to populate the empty input field of the required field query with the human-readable narrative.
The above limitations are reciting a process of receiving a request to evaluate authorization, inputting the content, retrieving historical attributes, inputting the historical attributes, generating representation of a export artifact, updating the representation in response to receiving a positive user indication for authorization, generating a narrative for a filed queried from the artifact attribute and historical artifact attribute, and updating the representation to populate empty input field. This is claiming a concept of determining authorization and is a certain method of organizing human activities type of abstract idea. As set forth in the specification, it is known that people in the form of accountants and/or auditors to determine authorization and likelihood of obtaining a material benefit. Determining authorization is a commercial practice that is a risk mitigation method and execution of steps required by legal agreements. For the above reasons, claims 1, 13, and 17 fall into the category of being an abstract idea of a certain method of organizing human activities. (Step 2A- Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. Claims 1, 13, and 17 recites the additional elements of a user interface (Claims 1, 13, and 17), digital artifact (Claims 1, 13, and 17), first AI model (Claims 1, 13, and 17), second AI model (Claims 1, 13, and 17), third AI model (Claims 1, 13, and 17), hardware processor(Claim 13 and 17), a non-transitory memory (Claim 13), and non-transitory, computer-readable storage media (Claim 13), that implements the identified abstract idea. These additional elements are not described by the applicant and are recited at a high-level of generality (i.e., one or more generic computers performing a generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer components. Accordingly, even in combination these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Claims 1, 13, and 17 are directed to an abstract idea. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a user interface (Claims 1, 13, and 17), digital artifact (Claims 1, 13, and 17), first AI model (Claims 1, 13, and 17), second AI model (Claims 1, 13, and 17), third AI model (Claims 1, 13, and 17), hardware processor(Claim 13 and 17), a non-transitory memory (Claim 13), and non-transitory, computer-readable storage media (Claim 13), to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, these additional elements do not provide significantly more. As such claims 1, 13, and 17 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Dependent Claims 2-12, 14-16 and 18-20 are similarly rejected because they either further define/narrow the abstract idea of independent claims 1, 13 and 17 as discussed above. Claim(s) 2, and 14 merely describe(s) in response to receiving a negative user indication for the applicable authorization schema: automatically generating a model prediction training sample comprising an input data based on the historical artifact attribute and the artifact attribute of the digital artifact and an output label based on the authorization status of the applicable authorization schema, and accessing a stored model prediction training sample corresponding to predicted authorization statuses of prior applicable authorization schemas. Claim(s) 3, and 15, merely describe(s) in response to receiving a negative user indication identifying an attribute threshold subset of the attribute threshold values not satisfied by the artifact attribute: inputting into a fourth […] model, the historical artifact attribute, the artifact attribute, and the attribute threshold subset to generate an adjusted artifact attribute satisfying the attribute threshold subset, and configuring for transmission, to an authorized editor of the digital artifact, the attribute threshold subset and the adjusted artifact attribute. Claim(s) 4 merely describe(s) wherein the representation is configured to transmit the artifact attribute of the digital artifact associated with the tracked actions of the development service. Claim(s) 5 merely describe(s) configuring for transmission a distinct visual marking over an artifact attribute subset that corresponds to artifact attributes satisfying the attribute threshold values of the applicable authorization schema. Claim(s) 6 merely describe(s) wherein the authorization status further indicates whether the attribute threshold values of the applicable authorization schema is partially satisfied. Claim(s) 7 merely describe(s) configuring for transmission a distinct visual marking over an artifact attribute subset that corresponds to artifact attributes partially satisfying the attribute threshold values of the applicable authorization schema. Claim(s) 8 merely describe(s) causing the third […] model to generate a human-readable recommendation for adjusting one or more artifact attributes from the artifact attribute subset to satisfy the attribute threshold values of the applicable authorization schema; and configuring for transmission the human-readable recommendation. Claim(s) 9 merely describe(s) wherein the applicable authorization schema comprises regulatory policies, predetermined evaluation rulesets, narrative guidelines, fiscal procedures, or any combination thereof. Claim(s) 10 merely describe(s) wherein the representation of the applicable authorization schema is further configured to transmit a comparative diagram that maps a first mapping of content similarities between the historical artifact attribute and the artifact attribute and a second mapping of content differences between the historical artifact attribute and the artifact attribute. Claim(s) 11, and 16 merely describe(s) obtaining a first sequence of intermediary logic operations executed during operation of the first […] model and a second sequence of intermediary logic operations executed during operation of the second […] model, causing the third […] model to generate, using the first sequence of intermediary logic operations and the second sequence of intermediary logic operations, a human-readable narrative explaining a logical sequence resulting in the authorization status of the applicable authorization schema; and configuring for transmission the human-readable narrative alongside the representation of the applicable authorization schema. Claim(s) 12 merely describe(s) monitoring one or more intermediary logic operations from invocation of the first […] model to generate output reference identifiers of the applicable authorization schema, and invoking, during the invocation of the first […] model, a generative […] model to output at least one human-readable explanation based on input comprising the one or more intermediary logic operations, the at least one human-readable explanation indicating incremental logic for the one or more intermediary logic operations. Claim(s) 18 merely describe(s) a representation of the applicable authorization schema and the digital artifact of the development service, wherein the authorization status of the applicable authorization schema indicates satisfaction of the attribute threshold values. Claim(s) 19 merely describe(s) in response to receiving a positive user indication for the applicable authorization schema: automatically generating an export artifact based on an artifact template, the artifact template comprising at least one required field query, and input the artifact attribute and the historical artifact attribute into a fourth […] model to generate at least one human-readable narrative for the at least one required field query. Claim(s) 20 merely describe(s) a distinct visual marking over an artifact attribute subset that corresponds to artifact attributes satisfying the attribute threshold values of the applicable authorization schema. Therefore claims 2-12, 14-16 and 18-20 are considered patent ineligible for the reasons given above.
Dependent Claim(s) 2, 3, 14, 15, and 19 recite limitations that further define the abstract idea noted in independent claims 1, 13 and 17. In addition, it recites the additional elements of retraining, using the stored model prediction training sample and the model prediction training sample, first AI model, second AI model, third AI model, and a fourth AI model. The retraining, using the stored model prediction training sample and the model prediction training sample, first AI model, second AI model, third AI model, and a fourth AI model, are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computing component. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself.
Dependent Claims 2, 8, 12, 14, and 16 include the additional elements of a first AI model, the second AI model, the third AI model. The first AI model, the second AI model, the third AI model are analyzed in the same manner as the first AI model, the second AI model, the third AI model in the independent claim and does not provide a practical application or significantly more for the same reasons above. Therefore claims 2-12, 14-16 and 18-20 are considered patent ineligible for the reasons given above.
Response to Arguments
Applicant's arguments filed 05/18/2026 with respect to the Claim Objections, have been fully considered and are persuasive. The Claim Objections are withdrawn in light of the amendments.
Applicant's arguments filed 05/18/2026 with respect to the Double Patenting Rejections, have been fully considered. Claims 1-20 remain rejected on the ground of non-statutory obviousness-type double patenting.
Applicant's arguments filed 05/18/2026 with respect to 35 U.S.C. § 101, have been fully considered but they are not persuasive. Applicant argues under Step 2A that the claims are directed to a novel technical improvement in the operation of interactive computer systems for digital artifact authorization and export and that the technical problem addressed by the claims concerns the inefficiency and inaccuracy of conventional digital artifact authorizations processes which often rely on manual review or static rule-based systems, further arguing that the claimed properties of features (1)-(7) recite specific properties for each machine learning model and demonstrate a specific improvement of the functioning of the interactive computer system on which they are implemented and integrate any alleged abstract idea into a practical application. The Applicant further argues that the claimed system performs a cascading analysis in which each stage incrementally narrows the scope of data under consideration, thereby reducing computational overhead, improving accuracy, and preventing the indiscriminate processing of irrelevant information therefore enhancing the efficiency and reliability of the authorization proves and integrating the abstract idea into a practical application. The Examiner respectfully disagrees. The improvements are at the level of an abstract idea, improving accuracy and preventing the indiscriminate processing of irrelevant information is merely an improvement to how “risk mitigation” of a commercial practice is carried out. MPEP 2106.05(a) states "Notably, the court did not distinguish between the types of technology when determining the invention improved technology. However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. data display based on authorization) is not an improvement in technology." Even when considering that the claim features where the "architecture ensures that only those digital artifacts and authorization schemas identified as relevant by the first model are subsequently analyzed for authorization status and narrative generation" this is merely an improvement to the abstract idea lent by a generic implementation of machine learning to train the data. In order for a machine learning related improvement to result in a practical application or significantly more, the field of machine learning itself must be improved upon through an improvement machine learning technique. Therefore, the applicant's arguments that the claims integrate the abstract idea into a practical application because it is a “marked departure from conventional systems that apply machine learning models to broad, undifferentiated datasets” is not persuasive because a general purpose computer carrying a generic machine learning model would inherently result in the improvement to the abstract idea.
The Applicant further argues that the claim recites additional technical features that demonstrate an unconventional improvement in automatic and dynamic generation of formatted data structures based on validated components of non-standard data formats for actively populating and updating the user interface with generative information. The Examiner respectfully disagrees. The fact that claimed features are “automatically” occurring based on validated data does not go beyond “certain method of organizing human activity”, especially when the analysis steps are recited at a high level of generality. The examiner notes that improvements to the abstract idea itself do not qualify as improvements that integrate the abstract idea into a practical application because the improvement must be lent by the additional elements. See MPEP 2106.05(a). Therefore, the applicant’s argument that the claim recites technical features that demonstrate an unconventional improvement in automatic and dynamic generation of formatted data structures based on validated components of non-standard data formats is not persuasive because the improvements are to the abstract idea, and they are being “applied to” a general purpose computer. No improvements to computer functionality, a technology, or a technical field would have been apparent to a person of ordinary skill in the art when reviewing the original disclosure or the claims as reflected.
Applicant further argues that the claims are analogous to Example 42. The Examiner respectfully disagrees. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. Example 42 is an illustration of this. Example 42 describes a technical problem (i.e., a problem caused by the technology. The claimed invention then solved this problem (a technical solution) by storing information in a standardized format, providing remote access over a network so any user can update the record in real time through the interface, where the information is in a non- standardized format, converting the non-standardized format into a standardized format, storing the standardized updated information, and transmitting a notification to all the users in real time so each user has access to the information, thus integrating the abstract idea into a practical application. Unlike Example the Applicant's argued problem is not a technological problem caused by the general-purpose computer to which the invention is claimed. The Examiner acknowledges the Applicant’s argument that the claim addresses the problem of sharing information in a standardized format regardless of the format in which the information was input by the user, however, the Applicant has not identified nor can the Examiner locate any physical improvement to the functioning of the generic computing component that results from the implementation of Applicant’s claim. There is no indication that the generic computing component is made to run faster, more efficiently, or utilize less power. In fact, the computer may be caused to operate slower and less efficiently through the implementation of Applicant’s claimed invention; we do not know and therefore it can’t be asserted that the problem indicated by the Applicant was a problem caused by the computer, and therefore it appears to be a problem that existed and/or exists regardless of whether a computer is involved in the process. At best, Applicant's identified problem is a business / administrative problem. Because no technological problem is present, the claims do not provide a practical application.
Applicant further argues that under Step 2B that the elements, either separately or in combination, have not been shown to be well-understood, routine, or conventional, and are therefore eligible under Step 2B. The Examiner respectfully disagrees. The rejection does not rely on an assertion that the additional elements are well-understood, routine, or conventional. MPEP 2106.05(d) states, "If the additional element (or combination of elements) is a specific limitation other than what is well- understood, routine and conventional in the field, for instance because it is an unconventional step that confines the claim to a particular useful application of the judicial exception, then this consideration favors eligibility. If, however, the additional element (or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility." Since the rejection does not rely on this consideration to show that the claims are ineligible, the applicant's arguments are not persuasive because the consideration is based on the additional elements and it overlaps with the improvement consideration (MPEP 2106.05(a), and mere instructions to apply an exception (MPEP 2106.05(f)). Therefore, none of the applicant’s arguments over 35 U.S.C. 101 are persuasive and claims 1-20 remain rejected for being directed to an abstract idea without significantly more.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Sloane (US-20230077289-A1)
DeFoor (US-11972223-B1)
Rao (US-20220253592-A1)
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.
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/E.M.K./Examiner, Art Unit 3626
/EMMETT K. WALSH/Primary Examiner, Art Unit 3626
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