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 .
Claims 1-20 are presented for examination.
This Office action is Non-Final.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 09/24/2025 has been considered by the Examiner and made of record in the application file.
Obviousness Double Patenting
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 conflicting claims 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) 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 www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,455,914 (‘914). Although the claims at issue are not identical, they are not patentably distinct from each other because the patented ‘914 claims teaches a specific implementation of a sliding-window topic extraction algorithm. The instant claims encompass a broader implementation of the same inventive concept by omitting algorithmic details while still relying on the same agenda-item classification and topic-coverage determination framework. It would have been an obvious variation to generalize the patented implementation to encompass alternative sliding-window implementation because the omitted implementation details represents one known manner of performing the claimed topic extraction and do not confer a patentably distinct invention.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dai et al. discloses a conferencing software receives, from a device of a conference invitee, a request to join a virtual conference. The conference invitee is associated with an agenda item of the virtual conference. Based on the request to join the virtual conference, the conferencing software adds the device to a waiting room associated with the virtual conference. The conferencing software determines whether a start time of the agenda item is within a predefined threshold time from a current time. In response to determining that the start time of the agenda item is within a predefined threshold from a current time, the conferencing software removes the device from the waiting room and adds the device to the virtual conference.
Adlersberg et al. discloses automatic real-time moderation of meetings, by a computerized or automated moderation unit able to manage, steer and guide the meeting in real-time and able to selectively generate and convey real-time differential notifications and advice to particular participants. A Meeting Moderator Bot monitors audio conversations in a meeting, and analyzes their textual equivalent; detects topics that were skipped or that should be discussed, and notifies participants; detects double-talk or interferences and generates warnings accordingly; detects absence of participants that are relevant to particular topics; detects that the conversation should shift to another topic on the agenda; generates other meeting steering notifications; and monitors compliance of the meeting participants with such steering notifications.
Reshef et al. discloses a method for information processing includes receiving in a computer a corpus of recorded conversations, with two or more speakers participating in each conversation. Respective frequencies of occurrence of multiple words in each of a plurality of chunks in each of the recorded conversations are computed. Based on the frequencies of occurrence of the words over the conversations in the corpus, an optimal set of topics to which the chunks can be assigned is derived, such that the optimal set maximizes a likelihood that the chunks will be generated by the topics in the set. A recorded conversation from the corpus is segmented using the derived topics into a plurality of segments, such that each segment is classified as belonging to a particular topic in the optimal set.
Agapi et al. discloses signaling correspondence between a meeting agenda and a meeting discussion includes: receiving a meeting agenda specifying one or more topics for a meeting; analyzing, for each topic, one or more documents to identify topic keywords for that topic; receiving meeting discussions among participants for the meeting; identifying a current topic for the meeting in dependence upon the meeting agenda; determining a correspondence indicator in dependence upon the meeting discussions and the topic keywords for the current topic, the correspondence indicator specifying the correspondence between the meeting agenda and the meeting discussion; and rendering the correspondence indicator to the participants of the meeting.
Koay et al. discloses meeting minutes record any subject matters discussed, decisions reached and actions taken at meetings. The importance of minuting cannot be overemphasized in a time when a significant number of meetings take place in the virtual space. In this paper, we present a sliding window approach to automatic generation of meeting minutes. It aims to tackle issues associated with the nature of spoken text, including lengthy transcripts and lack of document structure, which make it difficult to identify salient content to be included in the meeting minutes. Our approach combines a sliding window and a neural abstractive summarizer to navigate through the transcripts to find salient content. The approach is evaluated on transcripts of natural meeting conversations, where we compare results obtained for human transcripts and two versions of automatic transcripts and discuss how and to what extent the summarizer succeeds at capturing salient content.
D.F.P. de Weerd presents a novel approach to detect agreement and disagreement moments between participants in meeting transcripts without relying on labeled data. We propose a model in which disagreement detection is defined as the process of first identifying argumentative theses relevant to a given corpus of text and then classifying all phrases in the text as being either in favor of, against or expressing no opinion on a given thesis. To identify relevant theses, we compare the performance of a latent Dirichlet allocation-based topic model against that of a diverse set of large language models. To classify the stance of a phrase with respect to a thesis, only large language models are used. We find that, while state-of-the-art large language models do not outperform topic modeling based approaches in extracting semantically relevant content, they are capable of presenting such content in a more concise and grammatically correct manner. We also find that state-of-the-art large language models are not capable of accurately performing stance classification as described above.
Kazi et al. discloses machine learning holds significant promise for automating and optimizing text data analysis. However, resource intensive tasks like data annotation, model training, and parameter tuning often limit its practicality for one-time data extraction, medium-sized datasets, or short-term projects. There are many community-fine-tuned large language models (CLLMs) that are fine-tuned on task-specific datasets and can demonstrate impressive performance on unseen data without further fine-tuning. Adopting a hybrid approach of leveraging CLLMs for rapid text data extraction and subsequently hand-curating the inaccurate outputs can yield high-quality results, workload balance, and improved efficiency. This project applies CLLMs to three tasks involving the analysis of open-ended survey responses: semantic text matching, exact answer extraction, and sentiment analysis. We present our overall process and discuss several seemingly simple yet effective techniques that we employ to improve model performance without fine-tuning the CLLMs on our own data. Our results demonstrate high precision in semantic text matching (0.92) and exact answer extraction (0.90), while the sentiment analysis model shows room for improvement (precision: 0.65, recall: 0.94, F1: 0.77). This study showcases the potential of CLLMs in open-ended survey text data analysis, particularly in scenarios with limited resources and scarce labeled data.
Allowable Subject Matter
Claims 1-20 would be allowed upon submitting a terminal disclaimer in order to obviate the above Obviousness Double Patent rejection.
The following is a statement of reasons for the indication of allowable subject matter:
The prior art of record teaches meeting transcription, meeting summarization, agenda tracking, transcript segmentation, topic detection, and semantic similarity techniques. However, the prior art fails to teach or suggest the claimed determination of agenda-item coverage by semantically correlating transcript content with agenda items in the particular claimed manner, including the claimed processing of agenda items and transcript portions to determine agenda coverage. The Examiner further notes that the cited references, whether considered individually or in combination, do not disclose or suggest the claimed arrangement of features recited in the independent claims.
When taken into context the claim as a whole were not uncovered in the prior art, even further, dependent claims 2-14, 16, 17, 19 and 20 would also be allowed as they depend upon the allowable independent claims 1, 15 and 18.
Conclusions/Points of Contacts
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORGE A CASANOVA whose telephone number is (571)270-3563. The examiner can normally be reached M-F: 9 a.m. to 6 p.m. (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aleksandr Kerzhner can be reached at (571) 270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORGE A CASANOVA/Primary Examiner, Art Unit 2165
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