Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
2. The Amendment filed July 28, 2026 has been entered. Claim 7 is newly canceled. Claims 1-6 and 8-21 are pending and are rejected for the reasons set forth below.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-6 and 8-21 are rejected under 35 U.S.C. §101 because the claimed invention recites and is directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea) and does not include an inventive concept that is “significantly more” than the judicial exception under the January 2019 and October 2019 patentable subject matter eligibility guidance (2019 PEG) analysis which follows.
Step 1
5. Under the 2019 PEG step 1 analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter). Applying step 1 of the analysis for patentable subject matter to the claims, it is determined that the claims are directed to the statutory category of a process (claim 20), a machine (claims 1-6 and 8-19) and a manufacture (claim 21). Therefore, we proceed to step 2A, Prong 1.
Step 2A, Prong 1
6. Under the 2019 PEG step 2A, Prong 1 analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Claim 1 recites the abstract idea of:
receive a current objection inquiry document for a rate change request from an insurance regulator, the current objection inquiry document including (i) at least one first objection to the rate change request and (ii) at least one first request for additional information;
[[electronically]] parse the current objection inquiry document to identify a first model input including text describing the at least one first objection and the at least one first request for additional information;
enter the first model input into [[the large language model]] to generate a first output including an [[electronic]] response document for responding to the current objection inquiry for the rate change request; and
transmit the [[electronic]] response document to the insurance regulator to respond to the at least one first objection and the at least one first request for additional information included in the current objection inquiry document.
Here, the recited abstract idea falls within one or more of the three enumerated 2019 PEG categories of patent ineligible subject matter, to wit: certain methods of organizing human activity, which includes fundamental economic practices or principles and/or commercial interactions (e.g., processes for responding to inquiries related to insurance rate change requests).
Step 2A, Prong 2
7. Under the 2019 PEG step 2A, Prong 2 analysis, the identified abstract idea to which claim 1 is directed does not include limitations or additional elements that integrate the abstract idea into a practical application.
Besides reciting the abstract idea, the limitations of claim 1 also recite generic computer components (e.g., a computer system comprising: at least one processor; at least one memory device in communication with the at least one processor, machine learning, and AI tools including a large language model). In particular, the recited features of the abstract idea are merely being applied on a computer or computing device or via software programming that is simply being used as a tool (“apply it”) to implement the abstract idea. (See e.g., MPEP §2106.05(f)). Therefore, these additional elements are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components. In other words, the additional elements are simply used as tools to perform the abstract idea.
Claim 1 also recites the following limitations:
train, via machine learning, the large language model for identifying features of documents correlated with successful approval of rate change requests by using at least (a) the following input documents: (i) a plurality of historical rate change requests including a description of each historical rate change request, (ii) a plurality of historical objection inquiries to the plurality of historical rate change requests including one or more different objections and/or requests for information relating to each of the historical rate change requests, (iii) a plurality of historical responses to the plurality of historical objection inquiries including responses to each of the one or more objections and/or requests for information, and (iv) a plurality of historical decisions responding to the plurality of historical responses, and (b) data associated with each of the input documents indicating whether the input documents are associated with successful approval of a corresponding rate change request.
These limitations recite a process for building a large language model based on a variety of information (e.g., a plurality of input documents corresponding to historical rate change requests, and data associated with each of the input documents). However, the claim does not provide significant technical detail regarding how the model is trained and/or implemented to generate responses to the received inquiries. Rather, these limitations simply provide significant detail regarding the type of data used to train the model. Simply defining the type of data used to train the large language model does not provide an indication of an improvement to the functionality of the large language model itself, or any other technology or technological field. Therefore, such limitations amount to no more than merely applying generic machine learning technology and a generic large language model to implement the abstract idea on a computer.
Thus, claim 1 does not include any limitations or additional elements that integrate the abstract idea into a practical application. As a result, claim 1 is directed to an abstract idea.
Step 2B
8. Under the 2019 PEG step 2B analysis, the additional elements of claim 1 are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea. (i.e., an innovative concept). Here, the recited additional elements (e.g., a computer system comprising: at least one processor; at least one memory device in communication with the at least one processor, machine learning, and AI tools including a large language model), do not amount to an innovative concept since, as stated above in the Step 2A, Prong 2 analysis, the claims are simply using the additional elements as a tool to carry out the abstract idea (i.e., “apply it”) on a computer or computing device and/or via software programming (See e.g., MPEP §2106.05(f)). The additional elements are specified at a high level of generality such that they are being used in the claims to simply implement the abstract idea and are not themselves being technologically improved (See e.g., MPEP 2106.05(I)(A)); (See also applicant’s Specification at least Paragraphs 91-111).
Thus, claim 1 does not recite any additional elements that amount to “significantly more” than the abstract idea.
Additional Independent Claims
9. Independent claims 20 and 21 are similarly rejected under 35 U.S.C. 101 for the reasons described below:
Claim 20 recites limitations that are substantially similar to those recited in claim 1. However, the primary difference between claims 20 and 1 is that claim 20 is drafted as a method rather than as a system. Similarly, as described above regarding claim 1, claim 20 recites generic computer components (e.g., a computer system including at least one processor in communication with at least one memory device and artificial intelligence (AI) tools including a large language model) that are simply being used as a tool (“apply it”) to implement the abstract idea. Therefore, since the same analysis should be used for claims 1 and 20, claim 20 is not patent eligible (See Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014)).
Claim 21 recites limitations that are substantially similar to those recited in claim 1. However, the primary difference between claims 21 and 1 is that claim 21 is drafted as a computer-readable storage media rather than as a system. Similarly, as described above regarding claim 1, claim 21 recites generic computer components (e.g., at least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon, and at least one processor of a computer system) that are simply being used as a tool (“apply it”) to implement the abstract idea. Therefore, since the same analysis should be used for claims 1 and 21, claim 21 is not patent eligible (See Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014)).
Dependent Claims
10. Dependent claims 2-6 and 8-19 are also rejected under 35 U.S.C. 101 for the reasons described below:
Claim 2 simply refines the abstract idea because it recites process steps (e.g., receiving a decision document, parsing the decision document, generating a second output including a second response document, and transmitting the second response document to the insurance regulator) that fall under the category of organizing human activity, namely facilitating a transaction, as described above regarding claim 1. Additionally, merely stating that this process is performed by the large language model amounts to no more than merely applying generic computer components to implement the abstract idea on a computer.
Claim 3 simply provides further definition to the “second objection” recited in claim 3. Simply stating that the second representation the same as the first objection, different from the first objection, or a combination thereof, does not provide an indication of an improvement to any technology or technological field. Rather, this merely defines the type of objection received by the system.
Claim 4 simply provides further definition to the “second request for additional information” recited in claim 3. Simply stating that the second representation the same as the first request for additional information, different from the first request for additional information, or a combination thereof, does not provide an indication of an improvement to any technology or technological field. Rather, this merely defines the type of objection received by the system.
Claims 5 and 6 simply provides further definition to the “large language model” recited in claim 1. Simply stating that the large language model includes a generative AI large language model does not provide an indication of an improvement to any technology or technological field. Rather, this merely defines the type of large language model used by the system.
Claims 8 and 9 simply refine the abstract idea because they recite process steps (e.g., parsing the objection inquiry document, identifying key words, generating a query, and outputting a portion of text that responds to the first objection) that fall under the category of organizing human activity, namely facilitating a transaction, as described above regarding claim 1. Additionally, merely stating that this process is performed by the large language model and Natural Language Processing (NLP) tools amounts to no more than merely applying generic computer components to implement the abstract idea on a computer. The claims do not provide significant technical detail regarding how the large language model and the NLP tools operate to perform the claimed functions.
Claim 10 simply states that the large language model outputs a “transitional portion of the text” for transitioning between the first portion and the second portion. However, the claims do not provide significant technical detail regarding how the transitional portion of the text is generated and/or how it is applied to transition between the first portion and the second portion. Therefore, such limitations amount to no more than merely applying a generic large language model to implement the abstract idea on a computer.
Claim 11 simply states that the large language model outputs a “response header” for indicating a party who the electronic document is to be addressed to. However, the claims do not provide significant technical detail regarding how the response header is generated and/or how it is applied to indicate a party who the electronic document is to be addressed to. Therefore, such limitations amount to no more than merely applying a generic large language model to implement the abstract idea on a computer.
Claim 12 simply provides further definition to the “first output” recited in claim 1. Simply stating that the first output comprises a combination of the response header, the first portion, the second portion and the transitional portion does not provide an indication of an improvement to any technology or technological field. Rather, this merely defines the type of information included in the first output.
Claims 13 and 14 simply refine the abstract idea because they recite process steps (e.g., parsing the current decision document, identifying key words, generating a query, and outputting a portion of text that responds to the first objection and the first request for additional information) that fall under the category of organizing human activity, namely facilitating a transaction, as described above regarding claim 1. Additionally, merely stating that this process is performed by the large language model and Natural Language Processing (NLP) tools amounts to no more than merely applying generic computer components to implement the abstract idea on a computer. The claims do not provide significant technical detail regarding how the large language model and the NLP tools operate to perform the claimed functions.
Claim 15 simply states that the large language model outputs a “transitional portion of the text” for transitioning between the third portion and the fourth portion. However, the claims do not provide significant technical detail regarding how the transitional portion of the text is generated and/or how it is applied to transition between the third portion and the fourth portion. Therefore, such limitations amount to no more than merely applying a generic large language model to implement the abstract idea on a computer.
Claim 16 simply states that the large language model outputs a “response header” for indicating a party who the electronic document is to be addressed to. However, the claims do not provide significant technical detail regarding how the response header is generated and/or how it is applied to indicate a party who the electronic document is to be addressed to. Therefore, such limitations amount to no more than merely applying a generic large language model to implement the abstract idea on a computer.
Claim 17 simply provides further definition to the “second output” recited in claim 1. Simply stating that the first output comprises a combination of the response header, the first portion, the second portion and the transitional portion does not provide an indication of an improvement to any technology or technological field. Rather, this merely defines the type of information included in the first output.
Claim 18 simply states that the system is programmed to automatically track the progress of the various request, inquiries, and decisions described in claims 1 and 2. However, the claims do not provide any technical detail regarding how the progress of these features is tracked. Therefore, such limitations simply further refine the abstract idea.
Claim 19 simply states that the progress of each document is displayed on a dashboard for the user tot rack and follow up as needed. However, the claims do not provide significant technical detail regarding how the progress is displayed and/or how the user interacts with the interface. Therefore, this limitation amounts to no more than merely outputting/displaying data, which is a form of insignificant extra-solution activity (See MPEP 2016.05(g): OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)). As stated in MPEP 2106.05(d), a factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity (Berkheimer v. HP, Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018)). In view of this requirement set forth by Berkheimer, this limitation does not integrate the abstract idea into a practical application, or amount to significantly more than the abstract idea, because the courts have found the concept of merely outputting/displaying data to be well-understood, routine, and conventional activity (See MPEP 2106.05(d): OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)).
Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Response to Arguments
11. Applicant’s arguments filed July 28, 2026 have been fully considered.
Arguments Regarding 35 U.S.C. 101
12. Applicant’s arguments (Amendment, Pgs. 10-14) concerning the prior rejection of the claims under 35 USC §101, including supposed deficiencies in the rejection, are not persuasive for the following reasons. Under the prior and current 101 analysis under 2019 PEG, the amended claims recite and are directed to a patent ineligible abstract idea, without something significantly more, for the reasons given above after consideration of the claimed features and elements. The abstract idea has been restated herein in line with the 2019 PEG guidance and the amended claims. Applicant is directed to the above full Alice/Mayo analysis in the 101 rejection.
Additionally, on page 11 of their remarks, the applicant argues, “Applicant respectfully submits that, even if the current claim limitations were considered to involve an exception, these claim limitations do not recite any human activity. For example, amended Claim 1 recites a processor configured to… These recitations, which relate to training and/or executing an LLM, do not include any type of commercial interaction or other form of human activity.” The examiner respectfully disagrees. Specifically, the examiner notes that the recitation of additional elements in the claim does not prevent the claim from reciting an abstract idea. As noted in the 101 rejection above, claim 1 recites limitations corresponding to processes for responding to inquiries related to insurance rate change requests. Such limitations fall under the category of certain methods of organizing human activity (e.g., fundamental economic practices and/or commercial interactions). The mere fact that the claim recites limitations that fall outside of this category of abstract ideas (e.g., training the machine learning model) does not necessarily indicate that the claim does not also recite an abstract idea.
Additionally, on page 12 of their remarks, the applicant argues, “One path to establishing a practical application is to show an improvement to another technology. See MPEP § 2106.04(d)(1). For example, existing LLMs generally are not capable of accurately generating responses to objection inquiry document for a rate change request, because LLMs generally do not consider correlations between features of the various documents relating to a rate change request and a successful outcome of the rate change request. Claim 1 solves this problem by utilizing machine learning to train an LLM to recognize correlations between documents associated with historical rate change requests and historical outcomes associated with these documents.” The examiner respectfully disagrees. As noted in the 101 rejection above, simply defining the thy of data used to train a large language model does not amount to an improvement to the functionality of the model itself. The claims do not provide significant technical detail regarding how the model is trained using the various types of data. Therefore, such limitations amount to no more than merely applying generic machine learning techniques to train the model, and perform the abstract idea on a computer.
Additionally, on page 13 of their remarks, the applicant argues, “In the instant Application, the pending claims clearly recite more than well-understood, routine, or conventional functionality LLM systems, at least with respect to the at least one processor configured to… The Office Action provides no indication that these recitations of the pending claims (alone or as an ordered combination) are well understood, routine, or conventional in computer systems.” Additionally, on page 14 of their remarks, the applicant argues, “The fact that the pending claims overcome the cited art strengthens the conclusion that these steps are not well understood, routine, and conventional.” The examiner respectfully disagrees. Specifically, it is not required for the examiner to provide Berkheimer evidence for each limitation recited in the claim. Rather, this evidence is only required when the examiner identifies a limitation as being well-understood, routine, and conventional (e.g., when identifying a limitation as reciting insignificant extra-solution activity). The examiner has not claimed that the limitations for training of the large language model are well-understood, routine, and conventional. Rather, the examiner has asserted that these limitations amount to no more than merely applying generic computer-related technology to perform the abstract idea on a computer. These are separate analyses. As stated in MPEP2106.07(a)(III), “At Step 2A Prong Two or Step 2B, there is no requirement for evidence to support a finding that the exception is not integrated into a practical application or that the additional elements do not amount to significantly more than the exception unless the examiner asserts that additional limitations are well-understood, routine, conventional activities in Step 2B.”
Therefore, for these reasons and the reasons given above, the rejection of these claims under 35 U.S.C. §101 is maintained.
Citation of Pertinent Prior Art
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Weiner (U.S. Pre-Grant Publication No. 20250272758): Describes systems and methods for generating insurance pricing engines using machine learning. An insurance provider may be required to file various documents with any number of administrative bodies, such as administrative body. The administrative body may be any person and/or entity who may receive rate filing documents from insurance providers, including, but not limited to, a state regulator, a federal regulator, a local regulator, and other administrative bodies.
Sardanopoli (U.S. Patent No. 12217271): Describes systems and methods that leverage artificial intelligence in identifying and potentially resolving compliance issues (e.g., with regulatory requirements, client-specified requirements, certification conditions, etc.), or preventing violations of law, rules and regulations.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM D NEWLON whose telephone number is (571)272-4407. The examiner can normally be reached Mon - Fri 8:30 - 4:30.
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/WILLIAM D NEWLON/Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
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