CTNF 18/981,181 CTNF 86792 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. 12-151 AIA 26-51 12-51 Status of Claims This action is in reply to the communications filed on December 13, 2024. Claims 1-20 are currently pending and have been examined. Information Disclosure Statement The information disclosure statement filed December 13, 2024, has been considered by the Examiner. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claims 7, 15, 10, and 17 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 7 and 15: Claim 7 recites “at least part of the first training data set, user summary data, labelled context summary data, context formality data”. This limitation is unclear. First, it is unclear what is meant by “at least part of the first training data set.” To what particular data does this refer? Does this mean a portion of the entire data set or only specific types of data included in the set? Further, it is unclear what is meant by context formality data, as the specification does not appear to define it. For purposes of examination, the Examiner is interpreting this portion of claim 7 as reciting “a second training data set including various types of data." Claim 15 is rejected for similar reasons. Claims 10 and 17: Claim 10 recites “transmit a control signal to the computing device to reorganize icons corresponding to the ranked list of elements.” It is unclear what is meant by “reorganize.” Were icons previously displayed on the graphical user interface? If so, what was their previous arrangement such that they are now “reorganized”? For purposes of examination, the Examiner is interpreting this claim element as reciting “displaying icons corresponding to the ranked list of elements based on rankings of the elements in the ranked list.” Further, claim 10 recites “transmit in real-time an updated control signal to the computing device to reorganize icons corresponding to an updated ranked list of elements.” It is unclear what this updated ranked list of elements represents. Is this intended to mean that the updated behavior data is input into the system of claim 1 such that an updated ranked list of elements is obtained? If not, from where does this updated rank list of elements originate? Further, does this mean that the reranking and reorganizing occurs while the user is interacting with the ranked list, i.e., that after the user interacts with the list, the icons are immediately reorganized? For purposes of examination, the Examiner is interpreting this portion of claim 10 as reciting “transmit in real-time an updated control signal to the computing device to reorganize icons.” Claim 17 is rejected for similar reasons. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Independent claims 1, 11, and 18 are directed to a system, method, and a non-transitory computer readable medium. With respect to claim 11, claim elements obtaining current behavior data of a user, obtaining historical behavior data of the user, determining context data, and generating a ranked list, as drafted, illustrate steps that, under their broadest reasonable interpretation, cover a mental process. That is, nothing in the claim precludes the steps from practically being performed in the mind. Claims 1 and 18 recite similar limitations. The judicial exception is not integrated into a practical application. In particular, claims 1, 11, and 18 recite transmitting information. These limitations are considered to be insignificant extra-solution activity. Further, claim 1 recites a processor and a non-transitory memory. These elements are recited at a high level of generality, i.e., as generic computer components performing generic computer functions. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, claims 1, 11, and 18 recite transmitting information. Per MPEP 2106.05(d)(II), elements such as receiving or transmitting data over a network, using the Internet to gather data, and storing and retrieving information in memory are considered to be computer functions that are well-understood, routine, and conventional functions. See Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPG2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). Further, as discussed above, claim 1 recites a processor and a non-transitory memory. These elements are recited at a high level of generality (i.e., as generic computer components performing generic computer functions). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Thus, claims 1, 11, and 18 are directed to the abstract idea. Claims 2-10, 12-17, and 19-20 depend from claims 1, 11, and 18. Claim 2 is directed to the type of element and is further directed to the abstract idea. Claims 3, 12, and 19 are directed to generating prompts and inputting the prompts to language models and are further directed to the abstract idea. Claims 3 and 12 are further directed to retrieving (receiving) data which, as discussed above, is an activity that is considered well-understood, routine, and conventional. Claims 4, 13, and 20 are directed to determining context elements and query elements, generating embeddings, computing a cosine similarity, and determining context element data based on the cosine similarity and are further directed to the abstract idea. Claims 4, 13 and 20 are further directed to retrieving (receiving) data which, as discussed above, is an activity that is considered well-understood, routine, and conventional. Claims 5, 7, and 14-15 are directed to automatically generating the prompts and training the model and are further directed to the abstract idea. Claims 6 and 14 are directed to filtering data and types of filtered data and are further directed to the abstract idea. Claim 8 is directed to generating a user summary and a recall list and is further directed to the abstract idea. Claim 9 is directed to generating a prompt and inputting the prompt to a model and is further directed to the abstract idea. Claims 10 and 17 are directed to transmitting and receiving information which, as discussed above, are activities that are considered well-understood, routine, and conventional. The Examiner notes that the claims do not actually recite the reorganizing of the icons based on the updated rankings. The claims merely recite transmitting signals to the computing device. Thus, the claims are not patent eligible. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 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, 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1-3, 5-8, 11-12, 14-15, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over US 2026/0044538 A1 to Liu et al. (hereinafter “Liu”), in view of US 2022/0245209 A1 to Cho et al. (hereinafter “Cho”) . Claims 1, 11, and 18: Liu discloses a system and method for “dynamic user personalization using large language models” uses a conversation history of the user to personalize results to a user query. (See Liu, at least Abstract). Liu further discloses: a processor (See Liu, at least para. [0019], personalized LLM service includes a processor) ; and a non-transitory memory (See Liu, at least para. [0019], personalized LLM service includes a memory) storing instructions, that when executed, cause the processor to: obtain current behavior data of a user within a current user session (See Liu, at least para. [0028], request is received from a user for personalized LLM services and can include a prompt for an LLM and other data such as documents, media, hyperlinks, etc.) , obtain historical behavior data of the user during a past time period (See Liu, at least para. [0040], user messages are obtained for processing using a message analysis system and include conversation history with respect to a user including all messages sent/received by the user, unread messages received by the user, and messages mentioning the user) , determine, using at least one natural language model, context data that is relevant for predicting future behavior data of the user, wherein the context data is determined based on the current behavior data and the historical behavior data (See Liu, at least para. [0040], user messages can include a context window surrounding some or all of the messages; para. [0041], user messages are provided to the message analysis system to determine messages that are most likely to be relevant for determining a user’s interests and/or responsibilities; para. [0043], messages are ranked; para. [0044], relevant messages are determined based on the context windows of the messages; para. [0045], messages are provided to the LLM and the LLM determines the user’s responsibilities and/or interests; para. [0046], user’s responsibilities and/or interests are provided to an LLM; LLM outputs a personalized summary based on the user’s responsibilities and/or interests). Liu further discloses generate, using a prediction model,… [output] related to the future behavior data of the user based on the context data (See Liu, at least para. [0030], prompt received from the client includes a request to generate the output based on the user’s interests and/or responsibilities); and transmit the… [output] to a computing device associated with the current user session (See Liu, at least para. [0030], output is provided to the request processing module which can transmit the output as a response to the device that placed the request) . Liu does not expressly disclose that the output is a ranked list of elements. However, Cho discloses a personalization model that is “configured to generate personalized recall sets of search results for users”. (See Cho, at least Abstract). Cho further discloses a ranked list of elements (See Cho, at least para. [0132], personalized ranking model can generate item preference scores that are used to recommend the most relevant items to users, and to sort, rank, and/or order the search results in a manner that includes the most relevant items at or near the top of a listing of personalized search results) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the personalization system and method of Liu the ability that the output is a ranked list of elements as disclosed by Cho since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to do so in order to prevent users from having to “sift through a long listing of search results in attempting to identify the desired items.” (See Cho, at least para. [0004]). Claims 11 and 18 are rejected for similar reasons. Claim 2: The combination of Liu and Cho discloses all the limitations of claim 1 discussed above. Liu does not expressly disclose wherein each element in the ranked list includes at least one of: a product item, a product type, a payment method, a delivery method, or a store location. However, Cho discloses wherein each element in the ranked list includes at least one of: a product item, a product type, a payment method, a delivery method, or a store location (See Cho, at least para. [0049], database of products; para. [0132], personalized ranking model can generate item preference scores that are used to recommend the most relevant items to users, and to sort, rank, and/or order the search results in a manner that includes the most relevant items at or near the top of a listing of personalized search results) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the personalization system and method of Liu the ability wherein each element in the ranked list includes at least one of: a product item, a product type, a payment method, a delivery method, or a store location as disclosed by Cho since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to do so in order to prevent users from having to “sift through a long listing of search results in attempting to identify the desired items.” (See Cho, at least para. [0004]). Claims 3, 12, and 19: The combination of Liu and Cho discloses all the limitations of claims 1, 11, and 18 discussed above. Liu further discloses wherein: the at least one natural language model comprises multiple natural language models (See Liu, at least para. [0046], LLM may be one LLM or multiple LLMs) ; and the context data is determined based on: retrieving, from the historical behavior data, historical context data relevant to the current behavior data (See Liu, at least para. [0040], user messages can include a context window surrounding some or all of the messages) , generating a first prompt for a first natural language model of the multiple natural language models based on the historical context data (See Liu, at least para. [0041], user messages are provided to the message analysis system to determine messages that are most likely to be relevant for determining a user’s interests and/or responsibilities; para. [0043], messages are ranked; para. [0044], relevant messages are determined based on the context windows of the messages; para. [0045], messages are provided to the LLM and the LLM determines the user’s responsibilities and/or interests) , inputting the first prompt to the first natural language model to generate filtered context data (See Liu, at least para. [0045], messages are provided to the LLM and the LLM determines the user’s responsibilities and/or interests) , generating a second prompt for a second natural language model of the multiple natural language models based on the filtered context data (See Liu, at least para. [0046], user’s responsibilities and/or interests are provided to an LLM; LLM may be the same LLM or a different LLM) , and inputting the second prompt to the second natural language model to generate summarized context data (See Liu, at least para. [0046], user’s responsibilities and/or interests are provided to an LLM; LLM may be the same LLM or a different LLM; LLM outputs a personalized summary based on the user’s responsibilities and/or interests) . Claims 12 and 19 are rejected for similar reasons. Claim 5: The combination of Liu and Cho discloses all the limitations of claim 3 discussed above. Liu further discloses wherein: the first prompt is automatically generated based on the historical context data and a type of the elements to be included in the ranked list (See Liu, at least para. [0041], user messages are provided to the message analysis system to determine messages that are most likely to be relevant for determining a user’s interests and/or responsibilities; para. [0043], messages are ranked; para. [0044], relevant messages are determined based on the context windows of the messages) ; and the first natural language model is trained based on a first training data set including: element metadata, user metadata, labelled textual data, semantic data, context relevancy data (See Liu, at least para. [0025], LLMs of machine learning module may be trained using a large corpus of training data in which multiple different subject matter areas are represented). Claim 6: The combination of Liu and Cho discloses all the limitations of claim 5 discussed above. Liu further discloses wherein: the first natural language model filters out at least some of the historical context data to generate the filtered context data (See Liu, at least para. [0041], user messages are provided to the message analysis system to determine messages that are most likely to be relevant for determining a user’s interests and/or responsibilities; para. [0043], messages are ranked; para. [0044], relevant messages are determined based on the context windows of the messages) ; and the filtered context data includes a first list of context elements identified to be relevant for predicting the future behavior data of the user and includes insight data explaining relevancy of the first list of context elements for the predicting (See Liu, at least para. [0041], user messages are provided to the message analysis system to determine messages that are most likely to be relevant for determining a user’s interests and/or responsibilities; para. [0043], messages are ranked; para. [0044], relevant messages are determined based on the context windows of the messages) . Claims 7 and 15: The combination of Liu and Cho discloses all the limitations of claims 5 and 14 discussed above and below. Liu further discloses wherein: the second prompt is automatically generated based on the filtered context data and a user summary of the user (See Liu, at least para. [0045], messages are provided to the LLM and the LLM determines the user’s responsibilities and/or interests ; and the second natural language model is trained based on a second training data set including: at least part of the first training data set, user summary data, labelled context summary data, context formality data (See Liu, at least para. [0025], LLMs of machine learning module may be trained using a large corpus of training data in which multiple different subject matter areas are represented) . Claim 15 is rejected for similar reasons. Claim 8: The combination of Liu and Cho discloses all the limitations of claim 1 discussed above. Liu further discloses generate, using an intent understanding model, a user summary for the user based on the current behavior data (See Liu, at least para. [0028], user request may include a document to be summarized by the LLM; para. [0058], personalized summary is provided to the user) . Liu does not expressly disclose generate a recall list of elements based on the user summary and historical behavior data of a plurality of users. However, Cho discloses generate a recall list of elements based on the user summary and historical behavior data of a plurality of users (See Cho, at least para [0110], implicit learning model includes a natural language learning model that is configured to generate similarity scores between attribute-item pairs; these similarity scores can be utilized to infer users' preferences for attributes (and to select corresponding items) in situations where explicit affinity scores are not available; para. [0125], both explicit and implicit affinity scores are used to optimize generation of the personalized search results provided to users; paras. [0143]-[0144], search engine recall component determines item preference scores based on the affinity scores; scoring information is sent with the recall set and user ID to a search engine ranking component; search engine ranking component uses the scoring information to run the recall set of search results, thereby generating personalized search results that are customized to the user’s preferences). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the personalization system and method of Liu the ability to generate a recall list of elements based on the user summary and historical behavior data of a plurality of users as disclosed by Cho since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to do so in order to prevent users from having to “sift through a long listing of search results in attempting to identify the desired items.” (See Cho, at least para. [0004]). Claim 14: The combination of Liu and Cho discloses all the limitations of claim 12 discussed above. Liu further discloses wherein: the first prompt is automatically generated based on the historical context data and a type of the elements to be included in the ranked list (See Liu, at least para. [0041], user messages are provided to the message analysis system to determine messages that are most likely to be relevant for determining a user’s interests and/or responsibilities; para. [0043], messages are ranked; para. [0044], relevant messages are determined based on the context windows of the messages) ; the first natural language model is trained based on a first training data set including: element metadata, user metadata, labelled textual data, semantic data, context relevancy data (See Liu, at least para. [0025], LLMs of machine learning module may be trained using a large corpus of training data in which multiple different subject matter areas are represented) ; the first natural language model filters out at least some of the historical context data to generate the filtered context data (See Liu, at least para. [0041], user messages are provided to the message analysis system to determine messages that are most likely to be relevant for determining a user’s interests and/or responsibilities; para. [0043], messages are ranked; para. [0044], relevant messages are determined based on the context windows of the messages) ; and the filtered context data includes a first list of context elements identified to be relevant for predicting the future behavior data of the user and includes insight data explaining relevancy of the first list of context elements for the predicting (See Liu, at least para. [0041], user messages are provided to the message analysis system to determine messages that are most likely to be relevant for determining a user’s interests and/or responsibilities; para. [0043], messages are ranked; para. [0044], relevant messages are determined based on the context windows of the messages). . Potentially Allowable Subject Matter Claims 4, 9-10, 13, 16-17, and 20 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), 2nd paragraph, and 35 USC 101 set forth in this Office action and to include all of the limitations of the base claim and any intervening claims. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNE MARIE GEORGALAS whose telephone number is (571)270-1258 E.S.T. . The examiner can normally be reached on Monday-Friday 8:30am-5:00pm. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Anne M Georgalas/ Primary Examiner, Art Unit 3689 Application/Control Number: 18/981,181 Page 2 Art Unit: 3689 Application/Control Number: 18/981,181 Page 3 Art Unit: 3689 Application/Control Number: 18/981,181 Page 4 Art Unit: 3689 Application/Control Number: 18/981,181 Page 5 Art Unit: 3689 Application/Control Number: 18/981,181 Page 6 Art Unit: 3689 Application/Control Number: 18/981,181 Page 7 Art Unit: 3689 Application/Control Number: 18/981,181 Page 8 Art Unit: 3689 Application/Control Number: 18/981,181 Page 9 Art Unit: 3689 Application/Control Number: 18/981,181 Page 10 Art Unit: 3689 Application/Control Number: 18/981,181 Page 11 Art Unit: 3689 Application/Control Number: 18/981,181 Page 12 Art Unit: 3689 Application/Control Number: 18/981,181 Page 13 Art Unit: 3689 Application/Control Number: 18/981,181 Page 14 Art Unit: 3689 Application/Control Number: 18/981,181 Page 15 Art Unit: 3689 Application/Control Number: 18/981,181 Page 16 Art Unit: 3689 Application/Control Number: 18/981,181 Page 17 Art Unit: 3689 Application/Control Number: 18/981,181 Page 18 Art Unit: 3689 Application/Control Number: 18/981,181 Page 19 Art Unit: 3689 Application/Control Number: 18/981,181 Page 20 Art Unit: 3689 Application/Control Number: 18/981,181 Page 21 Art Unit: 3689
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