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 .
Claim(s) 1-15 is/are pending and has/have been examined.
Drawings
The drawings are objected to because of the following informalities: Figs. 1, 2, and 6 - some elements are pixelated, making them difficult to read. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 11 is objected to because of the following informalities: the claim recites “a user” in the last line. The Examiner suggests amending the claim(s) to recite –the user-- in order to maintain clear antecedent basis. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 10-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 10, 11, and 12 recite “a summary-text”, (claim 10) “summary-text”, and (claims 11 and 12) “related summary-text”. There is unclear antecedent basis for these terms in the claims. Claim 4, upon which all 3 claims ultimately depend, recites receiving a regenerated summary-text and performing operations (ii)-(v) to result in the same scores for the regenerated summary-text. As such, it is unclear whether the processes in claims 10-12 are referring to the summary-text as originally recited in claim 1, the regenerated summary text of claim 4, or another new summary-text. The Examiner suggests amending the claims to clarify which summary-text is being referred to.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim(s) 1, the limitation(s) of receiving, operating, measuring, operating, and operating, as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind and/or with pen and paper but for the recitation of generic computer components. More specifically, the mental process of a human reading a document and summary that was written by another person using specific input information, using an understanding of language to re-write the document and summary text into a specific format, using a specific set of evaluation criteria to determine scores for the re-written text, performing a normalization calculation on each score from the set of scores, and performing another calculation on the set of scores to determine a final score. The LLM and NLP modules read to a human understanding language rules for how to read, understand, evaluate, and provide written text. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind and/or with pen and paper but for the recitation of generic computer components, then it falls within the --Mental Processes-- grouping of abstract ideas. Accordingly, the claim(s) recite(s) an abstract idea.
The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim(s) is/are not patent eligible.
With respect to claim(s) 2, the claim(s) recite(s) operating tokenization, operating lemmatization, and operating named entity recognition, which reads on a human parsing the text in specific ways and recognizing entities within the text by identifying the a category for each segment of text. No additional limitations are present.
With respect to claim(s) 3, the claim(s) recite(s) sorting, assigning, and dividing, which reads on a human performing specific steps and/or calculations to perform normalization of the scores. No additional limitations are present.
With respect to claim(s) 4, the claim(s) recite(s) providing feedback details, receive a regenerated summary-text, and perform analysis operations, which reads on a human determining when the scores are below a certain quality level, telling another person what the results of the analysis were, the other person changing the summarization instructions and redoing the summary, and giving the new summary to the human so that they can perform the analysis on the new summary. This judicial exception is not integrated into a practical application because the recitation of a computerized device reads to generalized computer components, based upon the claim interpretation wherein the structure is interpreted using [0034] in the specification. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim(s) is/are directed to an abstract idea.
The claim(s) do(es) 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 element of using generalized computer components to provide, receive, and perform, amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible.
With respect to claim(s) 5-8, the claim(s) recite(s) specific characteristics of the feedback-details, which reads on a human providing specific information to the other person. No additional limitations are present.
With respect to claim(s) 9, the claim(s) recite(s) feedback details are not provided, which reads on a human recognizing when not to provide the results to the other person. No additional limitations are present.
With respect to claim(s) 10 and 12, the claim(s) recite(s) storing the interpreted final-quality score and (claim 12) other specific information, which reads on a human writing the score and other specific information down on a piece of paper for future reference. The database reads to a generalized computer component as per [0034] in the specification.
With respect to claim(s) 11, the claim(s) recite(s) retrieving, which reads on a human looking up specific information and writing it out to show to the other person. The display unit reads to a generalized computer component as per [0034] and [0162] in the specification.
With respect to claim(s) 13 and 14, the claim(s) recite(s) performing specific calculations on the metric scores, which reads on a human performing the specified calculations on the different scores. No additional limitations are present.
With respect to claim(s) 15, the claim(s) recite(s) identifying a context used to determine the adjusted-weight of each normalized score, which reads on a human using a specific characteristic of the text to determine a weight for the calculations to be performed. No additional limitations are present.
These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
Allowable Subject Matter
Claims 1-15 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, and any additional respective rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
The closest prior art of Mukherjee (US 2023/0122609) teaches the evaluation of automatic text summarizers by comparing the performance of candidate summarizers to a base summarizer, where the summarizers are trained and ranked based on Rouge-L f-measure values for each summary. However, Mukherjee does not teach the generation of the summary by a GPT-based LLM provided an original text and a prompt, processing the texts with NLP to yield processed-texts, the rank-based normalization of each metric score from a plurality of metrics that are measured based on the processed original and summary texts, performing an aggregation based on weighted hierarchical ranking of the normalized scores to yield a final-quality score, or the plurality of metrics scored for a summary text.
Gao et al. (2023) teaches evaluating text summarization using ChatGPT using a set of automatic evaluation metrics. However, Gao does not teach the generation of the summary by a GPT-based LLM provided an original text and a prompt, processing the texts with NLP to yield processed-texts, the rank-based normalization of each metric score from a plurality of metrics that are measured based on the processed original and summary texts, performing an aggregation based on weighted hierarchical ranking of the normalized scores to yield a final-quality score, or the plurality of metrics scored for a summary text.
Kryscinski et al. (2019) teaches evaluating summarization models using a set of evaluation metrics. However, Kryscinski does not teach the generation of the summary by a GPT-based LLM provided an original text and a prompt, processing the texts with NLP to yield processed-texts, the rank-based normalization of each metric score from a plurality of metrics that are measured based on the processed original and summary texts, performing an aggregation based on weighted hierarchical ranking of the normalized scores to yield a final-quality score, or the plurality of metrics scored for a summary text.
Bhandari et al. (2020) teaches evaluating text summarization systems and summaries using a comparison of metrics scores. However, Bhandari does not teach the generation of the summary by a GPT-based LLM provided an original text and a prompt, processing the texts with NLP to yield processed-texts, the rank-based normalization of each metric score from a plurality of metrics that are measured based on the processed original and summary texts, performing an aggregation based on weighted hierarchical ranking of the normalized scores to yield a final-quality score, or the plurality of metrics scored for a summary text.
Fabbri et al. (2021) teaches evaluating text summaries by a range of models using automatic metrics. However, Fabbri does not teach the generation of the summary by a GPT-based LLM provided an original text and a prompt, processing the texts with NLP to yield processed-texts, the rank-based normalization of each metric score from a plurality of metrics that are measured based on the processed original and summary texts, performing an aggregation based on weighted hierarchical ranking of the normalized scores to yield a final-quality score, or the plurality of metrics scored for a summary text.
None of Mukherjee, Gao, Kryscinski, Bhandari, and Fabbri, either alone or in combination, teaches or makes obvious the generation of a summary by a GPT-based LLM provided an original text and a prompt, processing the texts with NLP to yield processed-texts, the rank-based normalization of each metric score from a plurality of metrics that are measured based on the processed original and summary texts, performing an aggregation based on weighted hierarchical ranking of the normalized scores to yield a final-quality score, or the plurality of metrics scored for a summary text. Therefore, none of the cited prior art either alone or in combination, teaches or makes obvious the combination of limitations as recited in the independent claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICOLE A K SCHMIEDER whose telephone number is (571)270-1474. The examiner can normally be reached 8:00 - 5:00 M-F.
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/NICOLE A K SCHMIEDER/Primary Examiner, Art Unit 2659