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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 9/18/2026 has been entered.
Response to Amendment
This communication is responsive to the applicant’s amendments dated 9/18/2026. The applicant has amended claims 1, 4-10, 14-16, 19-22, and 24.
Response to Arguments
Applicant's arguments with respect to 35 U.S.C. 101 (See Remarks, pg. 10, line 16 – pg. 16, line 6) filed 9/18/2026 have been fully considered but they are not persuasive.
The applicant has amended the claims broadly but needs to provide greater detail that reflects the improvements to a technology as outlined in the cited paragraphs from the specification. For example, in paragraphs [0024-0025] the specification discusses multi-level summarization loss but the applicant generically claims the model is trained based on a loss function which is a mathematical calculation. Next, in paragraph [0071], the specification outlines how the model is trained. The last step involves updating the weights of the model in a direction on a gradient of the loss function to reduce the loss for the batch. This is not mentioned in the independent claims. Next, in paragraph [0042], the specification talks about using customized text summarization training data to fine tune the LLM weights for optimized text summarization. This is not shown in the independent claim. Lastly, in paragraph [0064], the specification discusses automated evaluation to obtain optimized LLM weights for text summarization, however this is not seen in the independent claims. The applicant is recommended to go into more detail of how the model is specifically trained to help overcome the 35 U.S.C. 101 rejection. Therefore, the 35 U.S.C. 101 rejection is maintained.
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 1,4-11,13-16 and 18-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claim 1 recites “access a collection of text samples, wherein the text samples include a body of text in natural language and an example summary in natural language of the body of text”, “fine-tune a large language model by adjusting one or more parameters of the large language model based on a loss function that performs a comparison of words shared between the example summary and a generated summary generated by the large language model, wherein the comparison of the words shared includes: evaluating recall and precision for words of more than one of corresponding sentences, paragraphs, or articles; and combining the evaluated recall and precision in the loss function”, “generate an evaluation score for performance of the tuned large language model as a text summarizer based on a second comparison of words shared between a reference summary and a second generated summary generated by the tuned large language model”, and “automatically signal that fine-tuning of the tuned large language model is complete in response to the evaluation score satisfying a threshold”.
The limitation of accessing text samples, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “one or more non-transitory computer-readable media”, nothing in the claim precludes the step from practically being performed in the mind. For example, but for the “one or more non-transitory computer-readable media” language, “access” in the context of this claim encompasses reviewing text, which a human can do in the mind. Next, the limitation of comparing words in summaries using a loss function, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a large language model”, nothing in the claim precludes the step from practically being performed in the mind. For example, but for the “large language model” language, “compares” in the context of this claim encompasses comparing summaries, which a human can do in the mind. Next, the limitation of generating a score of the summaries, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “large language model” language, “generate” in the context of this claim encompasses scoring summaries, which a human can do in the mind or with a pen and paper. Lastly, the limitation of signaling the completion of text summarization, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “large language model” language, “signal” in the context of this claim encompasses providing a completion indication, which a human can do in the mind or with a pen and paper.
The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements, using “one or more non-transitory computer-readable media” and “a large language model” to perform the recited limitations. These elements in these steps are recited at a high-level of generality such that is amounts no more than mere instructions to apply the exception using generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using “one or more non-transitory computer-readable media” and “a large language model” to perform the recited limitations amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Dependent claims 4 recites, “wherein the instructions further cause the computer system to compare the example summary and the generated summary at a sentence level by determining recall and precision of sentences of the generated summary for words appearing in corresponding sentences of the example summary”, corresponding to mental processes.
Dependent claims 5 recites, “wherein the instructions further cause the computer system to compare the example summary and the generated summary at a paragraph level by determining recall and precision of paragraphs of the generated summary for words appearing in corresponding paragraphs of the example summary”, corresponding to mental processes.
Dependent claims 6 recites, “wherein the instructions further cause the computer system to compare the example summary and the generated summary at an article level by determining recall and precision of the generated summary for words appearing in the example summary”, corresponding to mental processes.
Dependent claims 7 recites, “wherein the instructions further cause the computer system to compare the example summary and the generated summary at an N-sentence level by determining recall and precision of groups of N sentences of the generated summary for words appearing in corresponding groups of N sentences in the example summary”, corresponding to mental processes.
Dependent claims 8 recites, “wherein the instructions to generate an evaluation score for performance of the tuned large language model as a text summarizer further cause the computer system to: generate a word count of the second generated summary; and generate a word count score that indicates an extent to which the word count complies with a cap on length for summaries, wherein the word count score is incorporated in the evaluation score”, corresponding to mental processes.
Dependent claims 9 recites, “wherein the instructions to generate an evaluation score for performance of the tuned large language model as a text summarizer further cause the computer system to: prompt an additional large language model to score the second generated summary for a specified criterion; and further base the evaluation score on a response generated by the additionalspecified criterion”, corresponding to mental processes.
Dependent claims 21 recites, “wherein the instructions for fine-tuning the large language model further cause the computer system to: in response to the evaluation score failing to satisfy the threshold, further adjust the one or more parameters of the large language model based on the loss function”, corresponding to mental processes.
Independent claim 10 recites, “accessing a collection of text samples, wherein the text samples include a body of text in natural language and an example summary in natural language of the body of text”, “fine-tuning a large language model by: executing the large language model on the body of text to generate a generated summary; and adjusting one or more parameters of the large language model
based on a loss function that performs a comparison of words shared between the example summary and the generated summary, wherein the comparison of the words shared includes: evaluating recall and precision for words of more than one of corresponding sentences, paragraphs, or articles; and combining the evaluated recall and precision in the loss function”, “generating an evaluation score for performance of the tuned large language model as a text summarizer based on a second comparison of words shared between a reference summary and a second generated summary generated by the tuned large language model”, and “automatically determining to deploy the tuned large language model to a text summarization task in response to the evaluation score satisfying a threshold”
The limitation of accessing text samples, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind. Nothing in the claim precludes the step from practically being performed in the mind. For example, “access” in the context of this claim encompasses reviewing text, which a human can do in the mind. Next, the limitation of comparing words in summaries using a loss function, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a large language model”, nothing in the claim precludes the step from practically being performed in the mind. For example, but for the “large language model” language, “compares” in the context of this claim encompasses comparing summaries, which a human can do in the mind. Next, the limitation of generating a score of the summaries, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “large language model” language, “generating” in the context of this claim encompasses scoring summaries, which a human can do in the mind or with a pen and paper. Lastly, the limitation to deploy a text summarization task, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “large language model” language, “deploy” in the context of this claim encompasses providing an indication, which a human can do in the mind or with a pen and paper.
The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements, using “a large language model” to perform the recited limitations. These elements in these steps are recited at a high-level of generality such that is amounts no more than mere instructions to apply the exception using generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using “a large language model” to perform the recited limitations amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Dependent claims 11 recites, “wherein the example summary and generated summary are compared at each of a sentence level, a paragraph level, and an article level”, corresponding to mental processes.
Dependent claims 13 recites, “wherein the comparison of words shared between the example summary and the generated summary further comprises: evaluating the recall and the precision for the words of the corresponding sentences of the example summary and the generated summary to produce a sentence-level loss; evaluating the recall and the precision for the words of the corresponding paragraphs of the example summary and the generated summary to produce a paragraph-level loss; evaluating the recall and the precision for the words of the example summary and the generated summary to produce an article-level loss; and combining the sentence-level loss, the paragraph-level loss, and the article- level loss in the loss function”, corresponding to mental processes.
Dependent claims 14 recites, “wherein generating the evaluation score further comprises: generating a word count of the second generated summary; and generating a word count score based on the word count that indicates an extent to which the generated summary complies with a cap on length for summaries”, corresponding to mental processes.
Dependent claims 15 recites, “wherein generating an evaluation score for performance of the tuned large language model as a text summarizer further comprises: prompting an additional large language model to score the second generated summary for a specified criterion, wherein the specified criterion is one of repetition, human-readability, relevance, or conciseness; and further basing the evaluation score on a response generated by the additionalspecified criterion”, corresponding to mental processes.
Dependent claims 22 recites, “further comprising repeating the fine-tuning of the large language model and the generating of the evaluation score across further training epochs until the evaluation score satisfies the threshold”, corresponding to mental processes.
Dependent claims 23 recites, “in response to the evaluation score failing to satisfy the threshold, automatically initiating an additional training epoch for the large language model”, corresponding to mental processes.
Dependent claims 24 recites, “wherein in response to automatically determining to deploy the tuned large language model, the method further comprises: serializing the tuned large language model; and automatically updating an application programming interface endpoint to direct incoming text summarization requests to the tuned large language model”, corresponding to mental processes.
Independent claim 16 recites, “access a collection of text samples, wherein the text samples include a body of text in natural language and an example summary in natural language of the body of text”, “retrain a large language model by executing the large language model on the body of text to generate a generated summary; and adjusting one or more parameters of the large language model
based on a loss function that performs a comparison of words shared between the example summary and the generated summary, wherein the comparison of the words shared includes: evaluating recall and precision for words of more than one of corresponding sentences, paragraphs, or articles; and combining the evaluated recall and precision in the loss function”, “generate an evaluation score for performance of the tuned large language model as a text summarizer based on a second comparison of words shared between a reference summary and a second generated summary generated by the tuned large language model”, “automatically determine to deploy the tuned large language model to a text summarization task in response to the evaluation score satisfying a threshold”.
The limitation of accessing text samples, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “one or more non-transitory computer-readable media” and “processor connected to at least one memory”, nothing in the claim precludes the step from practically being performed in the mind. For example, but for the “one or more non-transitory computer-readable media” and “processor connected to at least one memory” language, “access” in the context of this claim encompasses reviewing text, which a human can do in the mind. Next, the limitation of comparing words in summaries using a loss function, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a large language model”, nothing in the claim precludes the step from practically being performed in the mind. For example, but for the “large language model” language, “compares” in the context of this claim encompasses comparing summaries, which a human can do in the mind. Next, the limitation of generating a score of the summaries, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “large language model” language, “generate” in the context of this claim encompasses scoring summaries, which a human can do in the mind or with a pen and paper. Lastly, the limitation to deploy a text summarization task, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “large language model” language, “deploy” in the context of this claim encompasses providing an indication, which a human can do in the mind or with a pen and paper.
The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements, using “a large language model”, “one or more non-transitory computer-readable media” and “processor connected to at least one memory” to perform the recited limitations. These elements in these steps are recited at a high-level of generality such that is amounts no more than mere instructions to apply the exception using generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using “a large language model”, “one or more non-transitory computer-readable media” and “processor connected to at least one memory” to perform the recited limitations amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Dependent claims 18 recites, “wherein when executed, the instructions for the comparison of words shared between the example summary and the generated summary further cause the computing system to: evaluate the recall and the precision for the words of the corresponding sentences of the example summary and the generated summary to produce a sentence-level loss; evaluate the recall and the precision for the words of the corresponding paragraphs of the example summary and the generated summary to produce a paragraph-level loss; evaluate the recall and the precision for the words of the example summary and the generated summary to produce an article-level loss; and combine the sentence-level loss, the paragraph-level loss, and the article-level loss in the loss function”, corresponding to mental processes.
Dependent claims 19 recites, “wherein when executed, the instructions for generation of the evaluation score further cause the computing system to: generate a word count of the second generated summary; and generate a word count score based on the word count that indicates an extent to which the generated summary complies with a cap on length for summaries”, corresponding to mental processes.
Dependent claims 20 recites, “wherein when executed, the instructions for generation of the evaluation score further cause the computing system to: prompt an additional large language model to score the second generated summary for a specified criterion, wherein the specified criterion is one of repetition, human-readability, relevance, or conciseness; and further base the evaluation score on a response generated by the additional large language model regarding the specified criterion”, corresponding to mental processes.
Allowable Subject Matter
Claims 1,4-11,13-16 and 18-24 would be allowable if rewritten or amended to overcome the rejection under 35 U.S.C. 101, set forth in this Office action. There are no pending prior art rejections.
The following is an examiner’s statement of reasons for allowance in terms of the prior art:
The closest piece of prior the examiner found was Gharebagh et al. (US 20230259544 A1) which teaches features such as “generate an evaluation score for performance of the tuned large language model as a text summarizer based on a second comparison of a reference summary and a second generated summary generated by the tuned large language model”, however upon further search and consideration the examiner deems the prior art of record whether taken alone or in combination fails to teach “wherein the comparison of the words shared includes: evaluating recall and precision for words of more than one of corresponding sentences, paragraphs, or articles; and combining the evaluated recall and precision in the loss function” in combination with the other claim features, therefore claims 1, 4-11, 13-16 and 18-24 as a whole are allowable in terms of the prior art.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZEESHAN SHAIKH whose telephone number is (703)756-1730. The examiner can normally be reached Monday-Friday 7:30AM-5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at (571) 272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ZEESHAN MAHMOOD SHAIKH/Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658
