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 .
DETAILED ACTION
1. This is in response to application filed on 07/29/2024 in which claim 1-20 are presented for examination.
Status of Claims
2. Claims 1-20 are pending, of which claim 1, 9 and 17 are in independent form.
Allowable Subject Matter
3. Claims 7-8 and 15-16 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.
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.
4. Claims 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to
non-statutory subject matter
Claims 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 17 recites “A computer-readable storage medium” It appears that the medium recited in the claims are not described in the specification as including a non-transitory tangible medium in a manner which enables it to act as a computer component to realize the computer program’s functionality. Therefore, when the claims are interpreted broadly as transmission medium or signal, the claims appear to be non-statutory because they are not tangibly embodied in a manner so as to be executable. Applicant should add the “non-transitory" to the preamble.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
5. Claims 1-6, 9-14 and 17-20 are rejected under 35 U.S.C 103 as being unpatentable over Rama (US PG Pub 2020/0311122) published on October 01, 2020 in view of Nguyen et al. (US 11972463) filed on March 30, 2021.
As per claim 1, 9 and 17, Rama teaches A computer implemented method for generating multi-granularity summarizations of a transcript of a conference, the method comprising:
receiving, from a user through a user interface, a segmentation granularity value indicating a number of events in the transcript to be included in a summary(Para0021-0025] generate personalized meeting summary transcript corresponding meeting, as taught by Rama) ;
generating, by a summarizer model that includes a re-trained language model, respective summaries, one for each event, of a portion of the transcript corresponding to the event(Para[0034-0041] Machine learning system 224 apply other types of machine learning to train model 226 to identify relevant meeting items for different users. The machine learning system 224 may discover a user's interest in a particular topic based on search behavior and transcript corresponding to meeting based on the discovery, as taught by Rama); and
providing the respective summaries as an overall summary of the transcript(fig 1-3 [0021-0025][0034-0041][0072] Computing system 102 may output respective indications of relevance to the user for one or more of the meeting item summaries to provide a personalized summary of the meeting to the user, as taught by Rama).
Rama does not explicitly teach extracting, by a ranker model and from the transcript, a number of hints equal to the number of events;
On the other hand Nguyen teaches, extracting, by a ranker model and from the transcript, a number of hints equal to the number of events(abstract: Col 17 Ln 01-45 fig 1 and 7 at least one machine learned model may generate a first summary of the plurality of text descriptions. In some examples, the first summary may be relevant to a first category of the list of categories. In further examples, the at least one machine learned model may generate a second summary of the plurality of text descriptions. The second summary may be relevant to a second category of the list of categories. First and second summary can be considered as hints that corresponds to events, which corresponds to categories, as taught by Nguyen);
It would have been obvious to one of ordinary skill in the art before the filing date of the
invention to modify Rama invention with the teaching of Nguyen because doing so would
result in increased efficiency by allowing the user easily read the transcript of a conversation.
As per claim 2, 10 and 18, the combination of Rama and Nguyen teaches further comprising: receiving, from the user through the user interface, a summary granularity value indicating a length of each of the respective summaries(fig 1 shows summary with different length, as taught by Nguyen); and wherein the respective summaries are generated, by the summarizer model and based on the summary granularity value, to have a length consistent with the summary granularity value(fig 1 Col 16-17 Ln 1-45, as taught by Nguyen).
As per claim 3, 11 and 19, the combination of Rama and Nguyen teaches further comprising: receiving, from the user through the user interface, topic data indicating one or more events to be summarized(Para[0037-0038], as taught by Rama); and wherein the respective summaries are generated, by the summarizer model and based on the topic data, to cover the events indicated by the topic data(Para[0037-0038] then summary personalization unit 110 may determine the meeting item containing these topics is relevant to the specific user., as taught by Rama);.
As per claim 4, 12 and 20, the combination of Rama and Nguyen teaches further comprising: receiving, from the user through the user interface, speaker data indicating one or more speakers to be summarized(fig 1 Para[0051-0052], as taught by Rama); and
wherein the respective summaries are generated, by the summarizer model and based on the speaker data, to cover utterances made by the one or more speakers indicated by the speaker data(fig 1-2 Para[0019-0022][0051-0052], as taught by Rama).
As per claim 5 and 13, the combination of Rama and Nguyen teaches further comprising: receiving, from the user through the user interface, readability data indicating how fluent the overall summary is to be(Col 2 Ln 20-55, as taught by Nguyen); and
wherein the respective summaries are generated, by the summarizer model, to be readable at a level indicated by the readability data(col 5 Ln 15-45, as taught by Nguyen).
As per claim 6 and 14, the combination of Rama and Nguyen teaches wherein the readability data indicates whether to remove filler words by identification and masking and whether to segment the transcript based on a ranking of the events(Para[0024-0025], as taught by Rama).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAYEEZ R CHOWDHURY whose telephone number is (571)270-3069. The examiner can normally be reached Monday-Friday 9AM-6:30PM EST.
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/RAYEEZ R CHOWDHURY/Primary Examiner, Art Unit 2174 Friday, July 10, 2026
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