CTNF 18/936,996 CTNF 84332 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 This office action is in response to application 18/936,996, which was filed 11/04/24. Claims 1-20 are pending in the application and have been considered. Specification 06-13 AIA The abstract of the disclosure is objected to because it is under 50 words and fails to describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details . Correction is required. See MPEP § 608.01(b). Claim Objections 07-29-01 AIA Claim s 5 and 14 are objected to because of the following informalities: in line 2, should “for the clusters:” be “for the clusters comprises:”? Appropriate correction is required. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim s 1, 6-8, 10, 15-17, and 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chan et al. (US 20250103636) . Consider claim 1, Chan discloses a method performed by one or more computers (computing device performs the methods, [0097]), the method comprising: obtaining a plurality of messages (digital communications 202-26, Fig. 2, [0045]); clustering the messages into a plurality of clusters (generating clusters by topic, [0047]); for each of the clusters (topic clusters, [0054]): selecting a plurality of the messages within the cluster (messages all related to a shared topic, [0048]); generating a first input prompt for a language model neural network from the selected messages (generating a prompt to provide to a summary generation model such as an LLM to generate summaries of the include communications, [0060-0061], Fig. 4); and processing the first input prompt using the language model neural network to generate a natural language summary of the cluster (processing the prompt and generating the summary using the LLM, [0060-0062]); generating data that associates, for each cluster, the natural language summary of the cluster with the messages within the cluster (ranking summaries and communications by topics identified by the clustering, [0054], [0064]); and providing, for presentation on a user device, a user interface that presents the natural language summaries for the clusters (summaries 506 and 518, Fig. 5, [0076]). Consider claim 10, Chan discloses a system (computing device 800, Fig. 8, [0108]) comprising: one or more computers (one or more computing devices, [0108]); and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations (processor executes instructions from memory, [0109]) comprising: obtaining a plurality of messages (digital communications 202-26, Fig. 2, [0045]); clustering the messages into a plurality of clusters (generating clusters by topic, [0047]); for each of the clusters (topic clusters, [0054]): selecting a plurality of the messages within the cluster (messages all related to a shared topic, [0048]); generating a first input prompt for a language model neural network from the selected messages (generating a prompt to provide to a summary generation model such as an LLM to generate summaries of the include communications, [0060-0061], Fig. 4); and processing the first input prompt using the language model neural network to generate a natural language summary of the cluster (processing the prompt and generating the summary using the LLM, [0060-0062]); generating data that associates, for each cluster, the natural language summary of the cluster with the messages within the cluster (ranking summaries and communications by topics identified by the clustering, [0054], [0064]); and providing, for presentation on a user device, a user interface that presents the natural language summaries for the clusters (summaries 506 and 518, Fig. 5, [0076]). Consider claim 19, Chan discloses one or more non-transitory computer-readable storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations (processor executes instructions from memory, [0109]) comprising: obtaining a plurality of messages (digital communications 202-26, Fig. 2, [0045]); clustering the messages into a plurality of clusters (generating clusters by topic, [0047]); for each of the clusters (topic clusters, [0054]): selecting a plurality of the messages within the cluster (messages all related to a shared topic, [0048]); generating a first input prompt for a language model neural network from the selected messages (generating a prompt to provide to a summary generation model such as an LLM to generate summaries of the include communications, [0060-0061], Fig. 4); and processing the first input prompt using the language model neural network to generate a natural language summary of the cluster (processing the prompt and generating the summary using the LLM, [0060-0062]); generating data that associates, for each cluster, the natural language summary of the cluster with the messages within the cluster (ranking summaries and communications by topics identified by the clustering, [0054], [0064]); and providing, for presentation on a user device, a user interface that presents the natural language summaries for the clusters (summaries 506 and 518, Fig. 5, [0076]). Consider claim 6, Chan discloses clustering the messages into a plurality of clusters comprises: generating a respective embedding of each of the plurality of messages (generating latent vector representations of the thread data, i.e. embeddings of each of the messages, [0047]); and clustering, using the respective embeddings of the plurality of messages, the messages into the plurality of clusters (clustering by distances between the latent vector representations, [0047]). Consider claim 7, Chan discloses: receiving a new message (detecting a new message from the data feeds of a user account, [0044]); generating a new embedding of the new message (generating a latent vector representations of the new message from the feed, i.e. an embedding, [0044], [0047]); adding, using the new embedding, the new message to a particular one of the plurality of clusters (using distance in latent feature space to cluster the message, [0044], [0047]); and updating the data to include the new message with the particular cluster and associate the new message with the respective natural language summary for the particular cluster (combining the thread data that share a topic into an aggregate summary, [0054]). Consider claim 8, Chan discloses selecting a plurality of the messages within the cluster comprises: ranking the messages within the cluster according to one or more signals (using a ranking algorithm to determine messages less relevant within the topic, [0056]); and selecting a fixed number of highest-ranked messages in the ranking (only including the more relevant digital communications in the aggregate summary generation process, [0056]). Consider claim 15, Chan discloses clustering the messages into a plurality of clusters comprises: generating a respective embedding of each of the plurality of messages (generating latent vector representations of the thread data, i.e. embeddings of each of the messages, [0047]); and clustering, using the respective embeddings of the plurality of messages, the messages into the plurality of clusters (clustering by distances between the latent vector representations, [0047]). Consider claim 16, Chan discloses: receiving a new message (detecting a new message from the data feeds of a user account, [0044]); generating a new embedding of the new message (generating a latent vector representations of the new message from the feed, i.e. an embedding, [0044], [0047]); adding, using the new embedding, the new message to a particular one of the plurality of clusters (using distance in latent feature space to cluster the message, [0044], [0047]); and updating the data to include the new message with the particular cluster and associate the new message with the respective natural language summary for the particular cluster (combining the thread data that share a topic into an aggregate summary, [0054]). Consider claim 17, Chan discloses selecting a plurality of the messages within the cluster comprises: ranking the messages within the cluster according to one or more signals (using a ranking algorithm to determine messages less relevant within the topic, [0056]); and selecting a fixed number of highest-ranked messages in the ranking (only including the more relevant digital communications in the aggregate summary generation process, [0056]) . Claim Rejections - 35 USC § 103 07-20-aia AIA 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 of this title, 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. 07-21-aia AIA Claim s 2-5, 11-14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chan et al. (US 20250103636) in view of Khabiri et al. (“Summarizing User-Contributed Comments”. Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, 2011) . Consider claim 2 Chan does not, but Khabiri discloses each of the plurality of messages is a respective user comment associated with a particular content item (user comments regarding a YouTube video, page 535). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chan such that each of the plurality of messages is a respective user comment associated with a particular content item in order to overcome the challenge of quickly ascertaining the overall themes and thrusts of user content, predictably helping users focus their attention, as suggested by Khabiri (page 1, Introduction). The references cited are analogous art in the same field of natural language processing. Consider claim 3 Chan does not, but Khabiri discloses the particular content item is a media content item hosted on a media sharing platform (videos on YouTube, page 535). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chan such that the particular content item is a media content item hosted on a media sharing platform for reasons similar to those for claim 2. Consider claim 4 Chan does not, but Khabiri discloses the particular content item is a video (videos on YouTube, page 535). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chan such that the particular content item is a video for reasons similar to those for claim 2. Consider claim 5, Chan discloses providing, for presentation to a user, a user interface that presents the natural language summaries for the clusters comprises: providing, for presentation to the user on a user device, a user interface that displays the particular content item and the natural language summaries of the clusters and that includes, for each natural language summary, a control element that, when selected by the user, modifies the user interface to display at least a portion of the messages within the corresponding cluster (data feed 12 includes thread data used to generate the aggregate summary 506, and aggregated interface system 102 provides the data feed based on selecting the data feed as the channel for replying to a topic reflected by the aggregate summary, [0075], Fig 5). Consider claim 11 Chan does not, but Khabiri discloses each of the plurality of messages is a respective user comment associated with a particular content item (user comments regarding a video, page 535). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chan such that each of the plurality of messages is a respective user comment associated with a particular content item for reasons similar to those for claim 2. Consider claim 12 Chan does not, but Khabiri discloses the particular content item is a media content item hosted on a media sharing platform (videos on YouTube, page 535). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chan such that the particular content item is a media content item hosted on a media sharing platform for reasons similar to those for claim 2. Consider claim 13 Chan does not, but Khabiri discloses the particular content item is a video (videos on YouTube, page 535). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chan such that the particular content item is a video for reasons similar to those for claim 2. Consider claim 14, Chan discloses providing, for presentation to a user, a user interface that presents the natural language summaries for the clusters comprises: providing, for presentation to the user on a user device, a user interface that displays the particular content item and the natural language summaries of the clusters and that includes, for each natural language summary, a control element that, when selected by the user, modifies the user interface to display at least a portion of the messages within the corresponding cluster (data feed 12 includes thread data used to generate the aggregate summary 506, and aggregated interface system 102 provides the data feed based on selecting the data feed as the channel for replying to a topic reflected by the aggregate summary, [0075], Fig 5). Consider claim 20, Chan does not, but Khabiri discloses each of the plurality of messages is a respective user comment associated with a particular content item (user comments regarding a video, page 535). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chan such that each of the plurality of messages is a respective user comment associated with a particular content item for reasons similar to those for claim 2 . 07-21-aia AIA Claim s 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chan et al. (US 20250103636) in view of Koh et al. (“Generating Images with Multimodal Language Models”. arXiv:2305.17216v3 [cs.CL] 13 Oct 2023) . Consider claim 9, Chan discloses, for each cluster: generating a second input prompt for neural network from (i) the messages within the cluster, (ii) the natural language summary of the cluster, or (iii) both (Takeaway 608 button generates a prompt to large language model using aggregate summary 604, noting the language “or” in the claim, [0081], Fig. 6); and processing the second input prompt using the neural network describe the cluster (LL process the prompt to provide highlights, [0081]). Chan does not specifically mention an image generation neural network to generate an image. Koh discloses an image generation neural network to generate an image (Fig. 3, page 4). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chan by generating a second input prompt as in Chan for an image generation neural network as in Koh from the natural language summary of the cluster of Chan, and processing the second input prompt using the image generation neural network to generate an image as in Koh that describes the cluster of Chan in order to leverage the capabilities that LLMs learn during large scale text-only pretraining to effectively process longer context, as suggested by Koh (page 1, Section 1), predictably improving effectiveness of the LLM on vision and language tasks, as suggested by Koh (page 1, Section 1). The references cited are analogous art in the same field of natural language processing. Consider claim 18, Chan discloses, for each cluster: generating a second input prompt for neural network from (i) the messages within the cluster, (ii) the natural language summary of the cluster, or (iii) both (Takeaway 608 button generates a prompt to large language model using aggregate summary 604, noting the language “or” in the claim, [0081], Fig. 6); and processing the second input prompt using the neural network describe the cluster (LL process the prompt to provide highlights, [0081]). Chan does not specifically mention an image generation neural network to generate an image. Koh discloses an image generation neural network to generate an image (Fig. 3, page 4). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chan by generating a second input prompt as in Chan for an image generation neural network as in Koh from the natural language summary of the cluster of Chan, and processing the second input prompt using the image generation neural network to generate an image as in Koh that describes the cluster of Chan for reasons similar to those for claim 9 . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20230229288 Sicora discloses graphical user interfaces for presenting content summaries from user generated comments US 11620455 Norton discloses intelligently summarizing and presenting textual response with machine learning US 11853340 Kumaresan discloses clustering using natural language processing US 11785429 Rubin discloses semantic clustering of messages US 11769017 Gray discloses generative summaries for search results Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jesse Pullias whose telephone number is 571/270-5135. The examiner can normally be reached on M-F 8:00 AM - 4:30 PM. The examiner’s fax number is 571/270-6135. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Andrew Flanders can be reached on 571/272-7516. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Jesse S Pullias/ Primary Examiner, Art Unit 2655 05/19/26 Application/Control Number: 18/936,996 Page 2 Art Unit: 2655 Application/Control Number: 18/936,996 Page 4 Art Unit: 2655 Application/Control Number: 18/936,996 Page 5 Art Unit: 2655 Application/Control Number: 18/936,996 Page 6 Art Unit: 2655 Application/Control Number: 18/936,996 Page 7 Art Unit: 2655 Application/Control Number: 18/936,996 Page 8 Art Unit: 2655 Application/Control Number: 18/936,996 Page 9 Art Unit: 2655 Application/Control Number: 18/936,996 Page 10 Art Unit: 2655 Application/Control Number: 18/936,996 Page 11 Art Unit: 2655 Application/Control Number: 18/936,996 Page 12 Art Unit: 2655
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