CTNF 19/324,451 CTNF 86914 DETAILED ACTION This Office Action is in response for Continuation Application # 19/324,451 filed on September 10, 2025 in which claims 1-14 are presented for examination. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 12-151 AIA 26-51 12-51 Status of claims Claims 1-14 are pending, of which claims 1-14 are rejected under 35 U.S.C. 101 and also claims 1-14 are rejected under Double Patenting. Obviousness Double Patenting 08-33 AIA 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 claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp. Claims 1-14 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14 of U.S. Patent 12,461,927 B2 (‘927). With respect to the above, the instant Application are performing an obvious variant of the features claimed in the ‘927 application. 19/324,451 U.S. 12,461,927 B2 Claim 1: Claim 1: A computer-implemented method for context reduction, comprising: identifying a context document relating to a query wherein the query regards domain-specific information that is not included in a training dataset of a lan g ua g e model, and wherein the context document is one of an academic paper, a user manual, a recent news story, or a private information base; training a policy using reinforcement learning to balance a token ratio and an accuracy difference, including a reward function: R=-(1-α) τ+α (2 r-r*) where T = t/T is a token ratio between a number of tokens t in the reduced context and a number of tokens T in the context document, r is a score representing accuracy of an output generated by the language model using the reduced context, r* is a score representing accuracy of an output generated by the language model using the context document, and a is a weighting parameter; determining a number of sentences of the context document to preserve, including applying the query and the context document to the policy to select a proportion of the context document to preserve; ranking the sentences of the context document according to respective similarities between the sentences and the query; generating a reduced context that preserves the determined number of highest ranked sentences of the context document and eliminates other sentences from the context document; and executing the query with a language model, including the reduced context in a prompt, to generate a response comprising the domain-specific information . A computer-implemented method for context reduction, comprising: identifying a context document relating to a query ; training a policy using reinforcement learning to balance a token ratio and an accuracy difference, including a reward function: R=-(1-α) τ+α (2 r-r*) where τ=t/T is a token ratio between a number of tokens t in the reduced context and a number of tokens T in the context document, r is a score representing accuracy of an output generated by the language model using the reduced context, r* is a score representing accuracy of an output generated by the language model using the context document, and α is a weighting parameter ; determining a number of sentences of the context document to preserve, including applying the query and the context document to the policy to select a proportion of the context document to preserve ; ranking the sentences of the context document according to respective similarities between the sentences and the query ; generating a reduced context that preserves the determined number of highest ranked sentences of the context document and eliminates other sentences from the context document ; and executing the query with a language model, including the reduced context in a prompt, to generate a response . Claim 8 Claim 8 A system for context reduction, comprising: a hardware processor; and a memory that stores a computer program which, when executed by the hardware processor, causes the hardware processor to: identify a context document relating to a query wherein the query regards domain-specific information that is not included in a training dataset of a language model, and wherein the context document is one of an academic paper, a user manual, a recent news story, or a private information base ; train a policy using reinforcement learning to balance a token ratio and an accuracy difference, including a reward function: R=-(1-α) τ+α (2 r-r*) where r = t/T is a token ratio between a number of tokens t in the reduced context and a number of tokens T in the context document, r is a score representing accuracy of an output generated by the language model using the reduced context, r* is a score representing accuracy of an output generated by the language model using the context document, and a is a weighting parameter; determine a number of sentences of the context document to preserve, including application of the query and the context document to the policy that selects a proportion of the context document to preserve: rank the sentences of the context document according to respective similarities between the sentences and the query; generate a reduced context that preserves the determined number of highest ranked sentences of the context document and eliminates other sentences from the context document; and execute the query with a language model, including the reduced context in a prompt, to generate a response comprising the domain-specific information . A system for context reduction, comprising: a hardware processor; and a memory that stores a computer program which, when executed by the hardware processor, causes the hardware processor to: identify a context document relating to a query ; train a policy using reinforcement learning to balance a token ratio and an accuracy difference, including a reward function : R=-(1-α) τ+α (2 r-r*) where τ=t/T is a token ratio between a number of tokens t in the reduced context and a number of tokens T in the context document, r is a score representing accuracy of an output generated by the language model using the reduced context, r* is a score representing accuracy of an output generated by the language model using the context document, and α is a weighting parameter ; determine a number of sentences of the context document to preserve, including application of the query and the context document to the policy that selects a proportion of the context document to preserve ; rank the sentences of the context document according to respective similarities between the sentences and the query ; generate a reduced context that preserves the determined number of highest ranked sentences of the context document and eliminates other sentences from the context document; and execute the query with a language model, including the reduced context in a prompt, to generate a response . “Omission of element and its function in combination is obvious expedient if theremaining elements perform same functions as before.” See In re Karlson (CCPA) 136USPQ 184, decide Jan 16, 1963, Appl. No. 6857, U.S. Court of Customs and Patent Appeals. It would have been obvious at the time the invention was made to implement domain specific information for query of having ordinary skill in the art to which said subject matter. Doing so would have enhanced the ‘927 patent. With respect to claims 2-7 and 9-14 they are also rejected since this claims are similar to 2-7 and 9-14 of the ‘927 patent. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-14 are rejected under 35 U.S.C. 101. because the claims are directed to an abstract idea; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.) Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—Claims 1-14 recite a method, device and readable medium respectively. The analysis of claims 1 and 8 are as follows: Step 2A, prong one: Does claims 1 and 8 recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations of “ identifying a context document relating to a query wherein the query regards domain-specific information that is not included in a training dataset of a language model , and wherein the context document is one of an academic paper, a user manual, a recent news story, or a private information base; training a policy using reinforcement learning to balance a token ratio and an accuracy difference, including a reward function: R=-(1-α) τ+α (2 r-r*) where τ = t/T is a token ratio between a number of tokens t in the reduced context and a number of tokens T in the context document, r is a score representing accuracy of an output generated by the language model using the reduced context, r* is a score representing accuracy of an output generated by the language model using the context document, and a is a weighting parameter; determining a number of sentences of the context document to preserve, including applying the query and the context document to the policy to select a proportion of the context document to preserve; ranking the sentences of the context document according to respective similarities between the sentences and the query; generating a reduced context that preserves the determined number of highest ranked sentences of the context document and eliminates other sentences from the context document; and executing the query with a language model , including the reduced context in a prompt, to generate a response comprising the domain-specific information” as drafted, are mental steps based on various processes can be performed in a human mind of context reduction for decision making using reward function “formulae” (acts of thinking, decision making). These limitations, therefore fall within the human mind processes group and with a pen & paper. Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application as just stated as related to the technical field of computer science . Although the claim recites that the recited functionality includes “system” and “method”, these computer components are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using generic computer component. In addition, the claim recites “ identifying a context document relating to a query wherein the query regards domain-specific information that is not included in a training dataset of a language model , and wherein the context document is one of an academic paper, a user manual, a recent news story, or a private information base; training a policy using reinforcement learning to balance a token ratio and an accuracy difference, including a reward function: R=-(1-α) τ+α (2 r-r*) where τ = t/T is a token ratio between a number of tokens t in the reduced context and a number of tokens T in the context document, r is a score representing accuracy of an output generated by the language model using the reduced context, r* is a score representing accuracy of an output generated by the language model using the context document, and a is a weighting parameter; determining a number of sentences of the context document to preserve, including applying the query and the context document to the policy to select a proportion of the context document to preserve; ranking the sentences of the context document according to respective similarities between the sentences and the query; generating a reduced context that preserves the determined number of highest ranked sentences of the context document and eliminates other sentences from the context document; and executing the query with a language model , including the reduced context in a prompt, to generate a response comprising the domain-specific information” are mere gathering data and applying process steps (i.e., context reduction for decision making using reward function “formulae”); the computers that perform those functions and the mental steps are recited at a high level of generality that do not impose a meaningful limitation on the judicial exception and are insufficient to integrate the mental steps into a practical application. Although the claim recites the additional functionality “ training a policy using reinforcement learning to balance a token ratio and an accuracy difference, including a reward function: R=-(1-α) τ+α (2 r-r*) where τ = t/T is a token ratio between a number of tokens t in the reduced context and a number of tokens T in the context document, r is a score representing accuracy of an output generated by the language model using the reduced context, r* is a score representing accuracy of an output generated by the language model using the context document, and a is a weighting parameter“, the identifying and training are also recited at a high level of generality and merely generally link to respective technological environments (e.g., generate response) and therefore likewise amounts to no more than a mere instructions to apply the exception using generic computer components and is insufficient to integrate the steps into a practical application. Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No— The recitation in the preamble is insufficient to transform a judicial exception to a patentable invention because the preamble elements are recited at a high level of generality that simply links to a field of use, see MPEP 2106.05(h). The claimed extra-solution of operation based on context reduction for decision making using reward function “formulae” is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)(i). Similarly, the gathering and identifying are also recited at a high level of generality and merely generally link to respective technological environments. The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Taken alone, their additional elements do not amount to significantly more than the above- identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. For the reasons above, claims 1 and 8 are rejected as being directed to non-patentable subject matter under §101. The analysis of claims 2-7 and 9-14 are as follows: Step 2A, prong one: Does claims 2-7 and 9-14 recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations of “claims 2 and 9 recites wherein generating the reduced context includes eliminating sentences before a first of the highest ranked sentences and sentences after a last of the highest ranked sentences in the context document. Claims 3 and 10 recites wherein generating the reduced context further includes performing text reduction on sentences other than the highest ranked sentences that occur after a first of the highest ranked sentences and before a last of the highest ranked sentences in the context document. Claims 4 and 11 recites wherein generating the reduced context includes preserving an order of the highest-ranked sentences from the context document. Claims 5 and 12 recites wherein ranking the sentences includes determining a similarity score between the query and each respective sentence. Claims 6 and 13 recites wherein determining the similarity score includes determining a cosine similarity between embeddings of the query and each respective sentence. Claims 7 and 14 recites wherein the context document includes subject matter that was not used in training the language model” as drafted, are mental steps based on various processes can be performed in a human mind of context reduction for decision making using reward function “formulae” (acts of thinking, decision making). These limitations, therefore fall within the human mind processes group and with a pen & paper. Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application as just stated as related to the technical field of computer science . Although the claim recites that the recited functionality includes “system” and “method”, these computer components are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using generic computer component. In addition, the claim recites “claims 2 and 9 recites wherein generating the reduced context includes eliminating sentences before a first of the highest ranked sentences and sentences after a last of the highest ranked sentences in the context document. Claims 3 and 10 recites wherein generating the reduced context further includes performing text reduction on sentences other than the highest ranked sentences that occur after a first of the highest ranked sentences and before a last of the highest ranked sentences in the context document. Claims 4 and 11 recites wherein generating the reduced context includes preserving an order of the highest-ranked sentences from the context document. Claims 5 and 12 recites wherein ranking the sentences includes determining a similarity score between the query and each respective sentence. Claims 6 and 13 recites wherein determining the similarity score includes determining a cosine similarity between embeddings of the query and each respective sentence. Claims 7 and 14 recites wherein the context document includes subject matter that was not used in training the language model” are mere gathering data and applying process steps (i.e., context reduction for decision making using reward function “formulae”); the computers that perform those functions and the mental steps are recited at a high level of generality that do not impose a meaningful limitation on the judicial exception and are insufficient to integrate the mental steps into a practical application. Although the claim recites the additional functionality “ training a policy using reinforcement learning to balance a token ratio and an accuracy difference, including a reward function: R=-(1-α) τ+α (2 r-r*) where τ = t/T is a token ratio between a number of tokens t in the reduced context and a number of tokens T in the context document, r is a score representing accuracy of an output generated by the language model using the reduced context, r* is a score representing accuracy of an output generated by the language model using the context document, and a is a weighting parameter“, the identifying and training are also recited at a high level of generality and merely generally link to respective technological environments (e.g., generate response) and therefore likewise amounts to no more than a mere instructions to apply the exception using generic computer components and is insufficient to integrate the steps into a practical application. Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No— The recitation in the preamble is insufficient to transform a judicial exception to a patentable invention because the preamble elements are recited at a high level of generality that simply links to a field of use, see MPEP 2106.05(h). The claimed extra-solution of operation based on context reduction for decision making using reward function “formulae” is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)(i). Similarly, the gathering and identifying are also recited at a high level of generality and merely generally link to respective technological environments. The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Taken alone, their additional elements do not amount to significantly more than the above- identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. For the reasons above, claims 2-7 and 9-14 are rejected as being directed to non-patentable subject matter under §101. Allowable Subject Matter Claims 1-14 are allowed over prior-art. Applicant need to resolve 35 U.S.C. 101 and Obviousness double patenting rejection for allowability. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure . Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAVAN MAMILLAPALLI whose telephone number is (571)270-3836. The examiner can normally be reached on M-F. 8am - 4pm, EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J Lo can be reached on (571) 272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /PAVAN MAMILLAPALLI/ Primary Examiner, Art Unit 2159 Application/Control Number: 19/324,451 Page 2 Art Unit: 2159 Application/Control Number: 19/324,451 Page 3 Art Unit: 2159 Application/Control Number: 19/324,451 Page 4 Art Unit: 2159 Application/Control Number: 19/324,451 Page 5 Art Unit: 2159 Application/Control Number: 19/324,451 Page 6 Art Unit: 2159 Application/Control Number: 19/324,451 Page 7 Art Unit: 2159 Application/Control Number: 19/324,451 Page 8 Art Unit: 2159 Application/Control Number: 19/324,451 Page 9 Art Unit: 2159 Application/Control Number: 19/324,451 Page 10 Art Unit: 2159 Application/Control Number: 19/324,451 Page 11 Art Unit: 2159 Application/Control Number: 19/324,451 Page 12 Art Unit: 2159 Application/Control Number: 19/324,451 Page 13 Art Unit: 2159 Application/Control Number: 19/324,451 Page 14 Art Unit: 2159 Application/Control Number: 19/324,451 Page 15 Art Unit: 2159 Application/Control Number: 19/324,451 Page 16 Art Unit: 2159 Application/Control Number: 19/324,451 Page 17 Art Unit: 2159 Application/Control Number: 19/324,451 Page 18 Art Unit: 2159 Application/Control Number: 19/324,451 Page 19 Art Unit: 2159 Application/Control Number: 19/324,451 Page 20 Art Unit: 2159
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