DETAILED ACTION
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 .
2. This Office Action is in response to the application filed on 10/29/2024.
3. The IDS filed on 01/09/2025 is considered and entered into the application file.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
4. Claims 1, 9, 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen et al (US 20240160852 A1).
Chen et al (“Chen”) is directed to Method For Realizing Domain-specific Text Labeling Using Natural Language Inference Model, Involves Applying Few-shot Learning Model On Generated Second Text Corpus To Generate Third Text Corpus Associated With Domain.
As per claim 1, Chen discloses a computer-implemented method (see flowcharts of Figs. 5, 7-13 and 15) comprising:
generating, utilizing a large language model, atomic facts from a digital summary of a digital document ([0030]The electronic device 102 may apply the pre-trained natural language inference (NLI) model 114 on each of the received set of texts 110A and on each of the received set of hypothesis statements 112. The electronic device 102 may be further configured to generate a second text corpus associated with the domain, based on the application of the pre-trained NLI model 114. Herein, the generated second text corpus may correspond to a set of labels associated with the domain);
generating, utilizing a natural language inference model, localized relationship scores by comparing an atomic fact of the atomic facts with sentences of the digital document ([0095] At 906, the overall NLI score associated with the received set of texts may be obtained, based on the comparison of the determined NLI prediction score with the first predefined threshold. In an embodiment, the processor 204 may be configured to obtain the overall NLI score associated with the received set of texts, based on the comparison of the determined NLI prediction score with the first predefined threshold);
generating, utilizing the natural language inference model, granularity expanded relationship scores by comparing the atomic fact with a plurality of granularity expanded sentence combinations from the digital document ([0072] The intermediate NLI score associated with the selected first sentence may be determined based on the determined inference relation. The intermediate NLI score may be a tuple of probabilities of three labels, such as, an entailment relation label, a contradiction relation label, or a neutral relation label. For example, if the determined inference relation corresponds to the contradiction relation, then the intermediate NLI score of contradiction relation label might be highest among three possible labels. If the determined inference relation is the neutral relation, then the intermediate NLI score of neutral relation label might be highest among three possible labels. Further, if the determined inference relation is the entailment relation, then the intermediate NLI score of entailment relation label might be highest among three possible labels); and
generating a predicted document-summary consistency between the digital summary and the digital document from the localized relationship scores and the granularity expanded relationship scores ([0082] At block 702, an NLI prediction score of each sentence of the set of sentences, may be determined over each of a set of predefined NLI classes, based on the intermediate NLI score. In an embodiment, the processor 204 may be configured to determine, based on the intermediate NLI score, the NLI prediction score of each sentence of the set of sentences, over each of the set of predefined NLI classes. Details related to the determination of the NLI prediction score of each sentence of the set of sentences have been provided, for example, in FIG. 5 and FIG. 6).
As per a system claim 9, the system claim is also rejected under similar citations given to the method claim 1.
As per a non-transitory computer readable medium claim 15, the claim is also rejected under similar citations given to the method claim 1.
Allowable Subject Matter
5. Claims 2-8, 10-14 and 16-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to TADESSE HAILU whose telephone number is (571)272-4051; and the email address is Tadesse.hailu@USPTO.GOV. The examiner can normally be reached Monday- Friday 9:30-5:30 (Eastern time).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bashore, William L. can be reached (571) 272-4088. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TADESSE HAILU/ Primary Examiner, Art Unit 2174
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