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
This action is in reply to the claims filed on 16 January 2026.
Claims 1, 3-5, 7-8, 10-12, 14-15, and 17-20 have been amended.
Claims 1-20 are currently pending and have been examined.
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 a judicial exception (i.e., an abstract idea) without significantly more.
Step 1: Claims 1-7 is/are drawn to a method (i.e., a process), claims 8-14 is/are drawn to a system (i.e., a machine), and claims 15-20 is/are drawn to a non-transitory machine-readable storage medium (i.e., a manufacture). As such, claims 1-20 is/are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Representative Claim 1:
receiving at least one regulatory document from at least one regulatory source;
analyzing the at least one regulatory document;
retrieving historical mappings of a plurality of internal procedures and policies related to the at least one regulatory document based on a result of the analyzing, wherein the historical mappings of the plurality of internal procedures and policies are retrieved and are related to the at least one entity;
identifying at least one first procedure of the at least one entity related to the at least one regulatory document from among the historical mappings of the plurality of internal procedures and policies, wherein the identifying of the at least one first procedure includes evaluating and filtering the plurality of internal procedures and policies to remove errors and unwanted noise, based on predetermined criteria;
drafting an obligation for the at least one entity based on a result of the analyzing of the at least one regulatory document and the identifying of the at least one first procedure;
mapping an information in the obligation to the at least one first procedure of the at least one entity;
updating the at least one first procedure of the at least one entity to maintain a legal compliance with the obligation; and
updating to remediate and refine the procedural data, based on the updating of the at least one first procedure.
As noted by the claim limitations above, the independent claimed invention is directed to managing regulatory compliance. This is considered to be an abstract idea because it is managing personal behavior of people, which falls within the category of “certain methods of organizing human activity.”
See MPEP 2106.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: This judicial exception is not integrated into a practical application. In particular, claim 1 recites the following additional element(s): at least one processor, a trained model, wherein the trained model is a large language model (LLM) that includes an application programming interface (API) communicably coupled to a hallucination filter, and wherein the trained model is trained based on procedural data contained within a database of at least one entity; updating the database to remediate and refine the procedural data used for training the trained model. This/these additional elements individually or in combination do not integrate the exception into a practical application because they do no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, these additional element(s) do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claim 1 is directed to an abstract idea.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: Claim 1 does 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 element(s) do no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not render a claim as being significantly more than the judicial exception. Accordingly, claim 1 is ineligible.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claim 1 is not eligible subject matter under 35 USC 101.
Dependent claim(s) 2 and 4-7 merely further limit the abstract idea and do not recite any additional elements beyond those already recited in claim 1. Therefor claim(s) 2 and 4-7 are ineligible.
Dependent claim(s) 3 further recite(s) the additional element(s): the trained model is further trained. This/these additional element(s) alone or in ordered combination do no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim(s) into a practical application nor does it render a claim as being significantly more than the abstract idea. Accordingly, claim(s) 3 is/are ineligible.
Claim 8 is parallel in nature to claim 1. Claim 8 recites an abstract idea similar in nature to claim 1. Furthermore, claim 8 recites the following additional elements: a computing device, a processor, a memory, a communication interface coupled to each of the processor and the memory, a trained model, wherein the trained model is a large language model (LLM) that includes an application programming interface (API) communicably coupled to a hallucination filter, and wherein the trained model is trained based on procedural data contained within a database of at least one entity; update the database to remediate and refine the procedural data used for training the trained model. These additional elements do no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim into a practical application nor does it render a claim as being significantly more than the abstract idea.
Dependent claim(s) 9 and 11-14 merely further limit the abstract idea and do not recite any additional elements beyond those already recited in claim 8. Therefor claim(s) 9 and 11-14 are ineligible.
Dependent claim(s) 10 further recite(s) the additional element(s): the trained model is further trained using data. This/these additional element(s) alone or in ordered combination does no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim(s) into a practical application nor does it render a claim as being significantly more than the abstract idea. Accordingly, claim(s) 10 is/are ineligible.
Claim 15 is parallel in nature to claim 1. Claim 15 recites an abstract idea similar in nature to claim 1. Furthermore, claim 15 recites the following additional elements: a non-transitory computer readable medium storing instructions for regulatory compliance management, the storage medium comprising executable code which is executed by a processor, a trained model, wherein the trained model is a large language model (LLM) that includes an application programming interface (API) communicably coupled to a hallucination filter, and wherein the trained model is trained based on procedural data contained within a database of at least one entity; update the database to remediate and refine the procedural data used for training the trained model. These additional elements do no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim into a practical application nor does it render a claim as being significantly more than the abstract idea.
Dependent claim(s) 16 and 18-20 merely further limit the abstract idea and do not recite any additional elements beyond those already recited in claim 8. Therefor claim(s) 16 and 18-20 are ineligible.
Dependent claim(s) 17 further recite(s) the additional element(s): the trained model is further trained using data. This/these additional element(s) alone or in ordered combination does no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim(s) into a practical application nor does it render a claim as being significantly more than the abstract idea. Accordingly, claim(s) 17 is/are ineligible.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under the 35 U.S.C. 101 rejection set forth in this Office action.
The present invention is directed towards regulatory compliance management. Independent claims 1, 8, and 15 teaches the novel and non-obvious features of:
receiving, by the at least one processor, at least one regulatory document from at least one regulatory source;
analyzing, by the at least one processor using a trained model, the at least one regulatory document, wherein the trained model is a large language model (LLM) that includes an application programming interface (API) communicably coupled to a hallucination filter, and wherein the trained model is trained based on procedural data contained within a database of at least one entity;
retrieving, by the at least one processor, historical mappings of a plurality of internal procedures and policies related to the at least one regulatory document based on a result of the analyzing, wherein the historical mappings of the plurality of internal procedures and policies are retrieved from the database and are related to the at least one entity;
identifying, by the at least one processor via the trained model, at least one first procedure of the at least one entity related to the at least one regulatory document from among the historical mappings of the plurality of internal procedures and policies, wherein the identifying of the at least one first procedure includes evaluating and filtering the plurality of internal procedures and policies to remove errors and unwanted noise, based on predetermined criteria;
drafting, by the at least one processor, an obligation for the at least one entity based on a result of the analyzing of the at least one regulatory document and the identifying of the at least one first procedure;
mapping, by the at least one processor, an information in the obligation to the at least one first procedure of the at least one entity;
updating, by the at least one processor, the at least one first procedure of the at least one entity to maintain a legal compliance with the obligation; and
updating, by the at least one processor, the database to remediate and refine the procedural data used for training the trained model, based on the updating of the at least one first procedure.
The Examiner notes that particular way the Large Language model is being used to analyze the regulatory documents, retrieve historical mappings, and draft an obligation for the at least one entity distinguishes the claimed invention from the prior art. While using natural language processing to analyze regulatory documentation and identify an obligation for a procedure of an entity is known, the specification that the invention analyzes regulatory documents using large language models trained based on procedural data and including an API coupled to a hallucination filter; retrieves historical mappings of internal policies and procedures related to the findings of the analysis; identifies via the LLM procedures of the entity related to the regulatory document from among the historical mappings of the plurality of internal procedures and policies, wherein the identifying of the at least one first procedure includes evaluating and filtering the plurality of internal procedures and policies to remove errors and unwanted noise, based on predetermined criteria; drafts an obligation for the at least one entity based on a result of the analyzing of the at least one regulatory document and the identifies of the at least one first procedure; and updates the database to remediate and refine the procedural data used for training the trained model, based on the updating of the at least one first procedure makes the invention novel.
Moreover, even assuming arguendo that the features of the claims exist individually, the combination of features as claimed would not have been obvious to one of the ordinary skill in the art because any combination of the evidence obtained to reach the combination of features as claimed would require a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
The following references have been identified as the most relevant prior art to the claimed invention:
Nair (US 20200219111 A1) teaches using natural language processing to analyze a regulatory document from a regulatory source to determine what entities are linked to obligations in the regulatory document. Nair is silent to applying the obligations to procedure of the entities and using a large language model.
Paknad (US 9830563 B2) teaches determining procedures to be followed by an entity in order to comply with compliance rules determined by a mapping of policies to entities, and managing the implementation of those procedures. Paknad is silent to using a large language model and determining from regulatory documents from regulatory sources how various obligations link to entity procedures.
Neither Nair nor Paknad anticipate or render obvious:
analyzing, by the at least one processor using a trained model, the at least one regulatory document, wherein the trained model is a large language model (LLM) that includes an application programming interface (API) communicably coupled to a hallucination filter, and wherein the trained model is trained based on procedural data contained within a database of at least one entity;
retrieving, by the at least one processor, historical mappings of a plurality of internal procedures and policies related to the at least one regulatory document based on a result of the analyzing, wherein the historical mappings of the plurality of internal procedures and policies are retrieved from the database and are related to the at least one entity;
identifying, by the at least one processor via the trained model, at least one first procedure of the at least one entity related to the at least one regulatory document from among the historical mappings of the plurality of internal procedures and policies, wherein the identifying of the at least one first procedure includes evaluating and filtering the plurality of internal procedures and policies to remove errors and unwanted noise, based on predetermined criteria;
drafting, by the at least one processor, an obligation for the at least one entity based on a result of the analyzing of the at least one regulatory document and the identifying of the at least one first procedure; and
updating, by the at least one processor, the database to remediate and refine the procedural data used for training the trained model, based on the updating of the at least one first procedure.
Assuming arguendo that the features of the claims exist individually, the combination of features as claimed would not have been obvious to one of ordinary skill in the art because any combination of the evidence obtained to reach the combination of features as claimed would require a substantial reconstruction of Applicant's claimed invention relying on improper hindsight bias.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Turaga (see attached NPL)- CO2 (Co-Compliance Officer): An LLM-Based Ontology-Driven Methodology for Generating Knowledge Graphs and AI Compliance Checking
Response to Arguments
Applicant's arguments, see Page(s) 12-16, filed 16 January 2026, with respect to the 35 USC § 101 rejection(s) of claim(s) 1-20 have been fully considered but they are not persuasive. Applicant argues 1) the claims are not directed to an abstract idea; 2) the claims are integrated into a practical application; and 3) the claims recite significantly more than the abstract idea. The Examiner respectfully disagrees.
Regarding argument 1, the Applicant argues the claims are not directed to an abstract idea. The Examiner respectfully disagrees. MPEP 2106.04(a)(2)(II) states:
The phrase "methods of organizing human activity" is used to describe concepts relating to:
fundamental economic principles or practices (including hedging, insurance, mitigating risk);
commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and
managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
The preamble of claim 1 recites “A method for regulatory compliance management”. Regulatory compliance relates to managing whether an entity is complying with legal policies and procedures. This includes entities following rules or instructions. Following rules or instructions falls under managing personal behavior or interactions between people. Therefore, the claims recite an abstract idea. The Examiner properly following the Alice/Mayo eligibility test to come to this conclusion.
Regarding argument 2, the Applicant argues the claim are integrated into a practical application due to the amendments filed 16 January 2026 providing the improvement of accurately and efficiently updating systems and ensuring compliance with constantly changing regulatory policies. The Examiner respectfully disagrees. MPEP 2106.05(f) recites:
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
By applying a generic computer using a generic large language model to the abstract idea of managing regulatory compliance, the Applicant is claiming the speed and efficiency inherent with applying the abstract idea on a generic computer using a large language model (see MPEP 2106.05(f)). The claims recite no active training step, but even if they did recite an active training step, the training that is recited currently is high level. For example, claim 1 recites “the trained model is trained based on procedural data contained within a database of at least on entity”. Generic LLMs are trained using data from databases, the Applicant is simply applying the generic LLM to the abstract idea of managing regulatory compliance. Applying a generic LLM to managing regulatory compliance would inherently provide the efficiency that comes with applying a generic computer to an abstract idea. Since the use of the LLM does not go beyond the identified judicial exception, the improvement is not considered to be technical in nature. Therefore the invention is an improvement to the abstract idea and not to a specific technical problem. The Applicant argues their claims are similar to the claims from Ex parte Desjardins et al. Ex parte Desjardins et al was eligible because the claims provided a specific improvement to the memory retention of the machine learning model, therefore providing an improvement to a the technology of machine learning. Unlike Ex parte Desjardins, the Applicants claims do not provide an improvement to the large language model because they claim an inherent improvement to an abstract idea. An improvement to an abstract idea is not considered technical in nature. Therefore, the Examiner maintains the amendments to the claims do not integrate the claims into a practical application.
Regarding argument 3, the Applicant argues the claims recite significantly more than the abstract idea because of the reasons listing in argument 2. The Examiner respectfully disagrees because of the reasons listed in argument 2. The Examiner maintains the amendments to the claims does not provide significantly more than the abstract idea.
Applicant’s arguments, see Pages 16-18, filed 16 January 2026, with respect to the 103 rejection of claims 1-20 have been fully considered and are persuasive due to the amendments filed 16 January 2026. Assuming arguendo that the features of the claims exist individually, the combination of features as claimed would not have been obvious to one of ordinary skill in the art because any combination of the evidence obtained to reach the combination of features as claimed would require a substantial reconstruction of Applicant's claimed invention relying on improper hindsight bias. The 103 rejection of claims 1-20 has been withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIELLE ELIZABETH ZEVITZ whose telephone number is (703)756-1070. The examiner can normally be reached Mo-Th 10am-6pm.
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/DANIELLE ELIZABETH ZEVITZ/Examiner, Art Unit 3628 /GEORGE CHEN/Primary Examiner, Art Unit 3628
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