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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 31, 2026, has been entered.
Response to Arguments
Applicant’s response to Office action was received on July 31, 2026.
In response to Applicant’s amendment of the claims, the corresponding prior art claim rejections, from the previous Office action, have been correspondingly amended, below in this Office action.
In response to Applicant’s amendment of the claims, the corresponding 101 claim rejections, from the previous Office action, have been correspondingly amended, below in this Office action.
Regarding the 101 rejections, Applicant first argues that the claims do not recite a mental process nor certain method(s) of organizing human activity. Examiner disagrees, on the basis that the claims at least recite certain method(s) of organizing human activity. The claims recite an AI assistant which makes suggestions that help users collaborate in a collaborative session. Thus, part of the claims recites a concept which helps users (which may be humans) collaborate with each other. This qualifies as certain method(s) of organizing human activity. Applicant references USPTO 101 Example 39, which trains a neural network for facial detection and is indicated as eligible. However, Examiner finds Applicant’s claims and Example 39 to be distinguishable, because the example’s analysis did not find a judicial exception recited in Example 39. Example 39 is different from Applicant’s claims, because Example 39 did not recite certain method(s) of organizing human activity. Therefore, Examiner does not find these Applicant arguments to be persuasive.
Applicant next argues that any alleged abstract idea is integrated into a practical application via an alleged technological/computing improvement brought about by feedback loop improvement to the AI/large language model. Examiner disagrees. This issue was essentially discussed earlier in prosecution. Specifically, regarding the argument that the updating of the AI/LLM using feedback helps with eligibility, Examiner directs Applicant’s attention to Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), p. 1212, which discusses how the requirements that a machine learning model be "iteratively trained" or dynamically adjusted do not represent a technological improvement. Therefore, Examiner does not find this Applicant argument to be persuasive.
Examiner has introduced the Holland reference into the 103 rejections to address Applicant’s arguments and concerns regarding the prior art rejections.
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.
Claim(s) 1-4, 6-11, 13-14, and 17-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per Claim(s) 1, 8, and 17, Claim(s) 1, 8, and 17 recite(s):
- training a large language model (LLM) for an assistant utilizing training datasets generated from collected data from various sources;
- receiving input data from a plurality of users;
- evaluating the input data from the plurality of users to interpret a context;
- personalized creative profiles, a personalized creative profile corresponding to a user of the plurality of users;
- generating a creative suggestion, wherein the generating makes the creative suggestion contextually relevant to the collaborative session using the context from the input data, wherein the generating makes the creative suggestion personalized to the user by further using at least one personalized creative profile corresponding to the user, and wherein the generating comprises analyzing divergent viewpoints in the input data from the plurality of users and generating the creative suggestion as a compromise solution combining one or more elements of the divergent viewpoints;
- refining the creative suggestion using a relevance filtering mechanism;
- collecting, from the user, user feedback relative to the creative suggestion;
- updating parameters of the LLM for the assistant, using the user feedback, to cause a change in the generating such that a future creative suggestion improves engagement of the user in the collaborative session.
Each of the above limitations falls within the abstract-idea category of “Certain methods of organizing human activity.” Specifically, those limitations relate to the following subject matter that is grouped into the category of “Certain methods of organizing human activity”:
- managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions): manages interactive communication experience between people.
To the extent that any of these limitations are recited alongside recitations of generic computer components, as described below in this rejection: If a claim limitation, under its broadest reasonable interpretation, covers subject matter recognized as certain methods of organizing human activity but for the recitation of generic computer components, then it falls within the “Certain method of organizing human activity” grouping of abstract ideas. Accordingly, the claim(s) recite an abstract idea.
This judicial exception is not integrated into a practical application because the additional elements when considered both individually and as an ordered combination do not integrate the abstract idea into a practical application. The claim(s) recite the following additional elements/limitations, each of which are addressed in the list below with the reason(s) why they do not integrate the abstract idea into a practical application:
- computer-implemented; a collaborative environment; participating in a collaborative session; maintaining a repository; AI; a computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a processor to cause the processor to perform operations; a computer system comprising a processor and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor to cause the processor to perform operations: These element(s)/limitation(s) amount to mere instructions to apply an exception. See MPEP 2106.05(f). In making this determination, examiners may consider whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Mere instructions to apply an exception is a consideration with respect to both integration of an abstract idea into a practical application and significantly more. MPEP 2106.05(f)(2) states: “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).” This is the case with these particular claim element(s)/limitation(s). Those elements/limitations do not meaningfully limit the claim because implementing an abstract idea on a generic computer does not integrate the abstract idea into a practical application, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. Therefore, these particular claim element(s)/limitation(s) do not integrate the abstract idea into a practical application for at least this reason.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Accordingly, the 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 claim(s) are directed to an abstract idea.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception, either individually or as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of computer-related components amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
The claim(s) are not patent eligible.
As per dependent claim(s) 2-4, 6-7, 9-11, 13-14, and 18-22, these claim(s) incorporate the above abstract idea via their dependencies on the respective independent claim(s). The additional element(s)/limitation(s) of the respective independent claim(s) do not integrate the abstract idea into a practical application, nor do they add significantly more, with respect to those dependent claim(s), under the same reasoning as above with respect to the respective independent claim(s).
Those dependent claim(s) add the following generic computer components, which do not integrate the abstract idea into a practical application, nor add significantly more, under the same reasoning as given above with respect to generic computer components in the independent claim(s). Those additional generic computer components and their corresponding dependent claim(s) are as follows:
- real time (claims 7, 14, and 22);
- wherein the program instructions are stored in a computer readable storage device in a server data processing system, and wherein the program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising: program instructions (claim 16).
The remaining added elements/limitations of those dependent claim(s) do not integrate the abstract idea into a practical application nor add significantly more because they all merely add further functional step(s) and/or detail to the abstract idea; as part of the abstract idea, they cannot integrate into a practical application or be significantly more than the abstract idea of which they are a part. For example, claim 2 merely adds detail to the input data.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application, nor add significantly more. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Claim(s) 1-4, 6-11, 13-14, and 17-22 are therefore not drawn to eligible subject matter as they are directed to an abstract idea that is not integrated into a practical application and is without significantly more.
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.
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.
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.
Claim(s) 1-4, 6-11, 13-14, and 17-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhuk, US 20200287736 A1, in view of Shoemaker, US 7885902 B1, in further view of Vasylyev, US 20240412720 A1, in further view of Holland, US 20030101151 A1.
As per Claims 1, 8, and 17, Zhuk discloses:
- a computer-implemented method (paragraph [0024] (“Some portions of the detailed descriptions that follow are presented in terms of procedures, methods, flows, logic blocks, processing, and other symbolic representations of operations performed on a computing device or a server.”));
- receiving input data from a plurality of users participating in a collaborative session in the collaborative environment (paragraph [0054] (“The method comprises receiving text data for one or more users of the collaboration environment.”); paragraph [0055] (“receiving text data for one or more users of the collaboration environment”));
- evaluating the input data from the plurality of users to interpret a context for the collaborative session (paragraph [0051] (“Traditionally, a user receives digital communication information and acts upon the content of the information.”); paragraph [0053] (“The presently described approaches seek to address these shortcomings by using machine learning (ML) to evaluate the content of the information, create an overall communication context, and determined target action items that are user-specific.”); paragraph [0097] (“In an embodiment, the extraction 702 model outputs participant data 704, named entity data 706, and/or conceptual type data 708.”; “Conceptual type data 708 is data pertaining to contextual categories or types of specific discussions. For example, discussions may be related to: politics, travel, business, project discussions, scheduling events, problem descriptions such as a customer complaint, hate speech, casual welcome luncheons, or any other conceptual types. In an embodiment, the extraction 702 is done using three different AI models that are configured to extract the participant data 704, named entity data 706, and conceptual type data 708, respectively. Any number of AI models may be used to extract the relevant information.”));
- generating a creative suggestion, wherein the generating makes the creative suggestion contextually relevant to the collaborative session using the context from the input data (paragraphs [0133]-[0134] (context considered); paragraph [0189] (“In an embodiment, the system 300 may access John's knowledge base on a specific topic, such as a list of frequently asked questions across all collaboration systems and chat groups. The system 300 may suggest relevant documents, text, excerpts, answers, and other retained parts of communication based on John's current activity.”));
- refining the creative suggestion using a relevance filtering mechanism (paragraph [0071] (whole paragraph));
- collecting, from the user, user feedback relative to the creative suggestion (paragraph [0071] (whole paragraph));
- updating parameters of the AI assistant, using the user feedback, to cause a change in the generating such that a future creative suggestion improves engagement of the user in the collaborative session (paragraph [0071] (whole paragraph));
- a computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a processor to cause the processor to perform operations (paragraph [0056]);
- a computer system comprising a processor set and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor set to cause the processor set to perform operations (paragraph [0056]).
Zhuk fails to disclose maintaining a repository of personalized creative profiles, a personalized creative profile in the repository corresponding to a user of the plurality of users; wherein the generating makes the creative suggestion personalized to the user by further using from the repository at least one personalized creative profile corresponding to the user. Shoemaker discloses maintaining a repository of personalized creative profiles, a personalized creative profile in the repository corresponding to a user of the plurality of users (column 3, lines 56-60 (“Generally speaking, embodiments of the invention apply scientific analysis in the form of machine learning to real-world profile data from users and to feedback data from past user interactions in order to provide more satisfactory recommendations to individual users.”)); wherein the generating makes the creative suggestion personalized to the user by further using from the repository at least one personalized creative profile corresponding to the user (column 2, line 51, through column 3, line 4; column 3, lines 56-60 (“Generally speaking, embodiments of the invention apply scientific analysis in the form of machine learning to real-world profile data from users and to feedback data from past user interactions in order to provide more satisfactory recommendations to individual users.”)). 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 Zhuk such that the invention maintains a repository of personalized creative profiles, a personalized creative profile in the repository corresponding to a user of the plurality of users; and the generating makes the creative suggestion personalized to the user by further using from the repository at least one personalized creative profile corresponding to the user, as disclosed by Shoemaker, 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.
The modified Zhuk fails to disclose training a large language model (LLM) for an AI assistant utilizing training datasets generated from collected data from various sources within a collaborative environment; wherein the generating comprises analyzing divergent viewpoints in the input data from the plurality of users and generating the creative suggestion as a compromise solution; wherein the AI assistant uses an LLM. Vasylyev discloses training a large language model (LLM) for an AI assistant utilizing training datasets generated from collected data from various sources within a collaborative environment (paragraph [0033] (“Useful examples of such NLPs include but are not limited to advanced Transformer-Based Models (TBMs), Large Language Models (LLMs), and/or other known forms or combinations of generative AI technology. The LLMs may be trained on a large corpus of text and utilize a neural network with a transformer-based architecture, such as a Generative Pretrained Transformer (GPT) style model that uses self-attention mechanisms.”); paragraph [0121] (“Imagine the user is engaged in an AI-assisted, fast-paced, interactive conversation with a customer support representative, and this conversation is monitored, analyzed, and augmented on-the-fly by assistant system 2.”); paragraphs [0176]-[0182] (training data from various sources in collaborative environment)); wherein the generating comprises analyzing divergent viewpoints in the input data from the plurality of users and generating the creative suggestion as a compromise solution (paragraph [0443] (“Assistant system 2 may be further trained to support collaborative decision making among a group of users where the system summarizes different viewpoints expressed during a discussion, suggests compromises, or helps to organize voting or consensus-building exercises. Assistant system 2 may also be configured to make proactive suggestions to the meeting participants based on the current context of the conversation and real-time processing, for example, suggesting related data, files, images, documents, or even strategy points based on the ongoing discussion.”)); wherein the AI assistant uses an LLM (paragraph [0033] (much of paragraph)). 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 the modified Zhuk such that the invention trains a large language model (LLM) for an AI assistant utilizing training datasets generated from collected data from various sources within a collaborative environment; the generating comprises analyzing divergent viewpoints in the input data from the plurality of users and generating the creative suggestion as a compromise solution; and the AI assistant uses an LLM, as disclosed by Vasylyev, 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.
The modified Zhuk fails to disclose wherein a compromise solution combines one or more elements of the divergent viewpoints. Holland discloses wherein a compromise solution combines one or more elements of the divergent viewpoints (paragraph [0296] (much of paragraph)). 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 the modified Zhuk such that a compromise solution combines one or more elements of the divergent viewpoints, as disclosed by Holland, 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.
As per Claims 2, 9, and 18, Zhuk further discloses wherein the input data comprises at least one of a user preference of the user, a historical creative work of the user, and contextual information related to the collaborative session (paragraph [0160]).
As per Claims 3, 10, and 19, Zhuk further discloses wherein the context is based on a conversation history in the collaborative session (paragraph [0051]; paragraphs [0053]-[0055]).
As per Claims 4, 11, and 20, the modified Zhuk fails to disclose wherein the personalized creative profile of the user comprises at least one of data describing a feedback history of the user. Shoemaker further discloses wherein the personalized creative profile of the user comprises at least one of data describing a feedback history of the user (column 3, line 56, through column 4, line 9). 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 the modified Zhuk such that the personalized creative profile of the user comprises at least one of data describing a feedback history of the user, as disclosed by Shoemaker, 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.
As per Claims 6, 13, and 21, Zhuk further discloses wherein collecting the user feedback further comprises: iteratively optimizing, using a reinforcement learning algorithm, an output of the AI assistant (paragraph [0071]).
As per Claims 7, 14, and 22, Zhuk further discloses wherein the updating is based on a real-time context in the collaborative session (paragraph [0051]; paragraphs [0053]-[0055]; paragraph [0071]; paragraph [0088]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Penrose, US 20200076746 A1 (managing content in a collaboration environment).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATHAN ERB whose telephone number is (571)272-7606. The examiner can normally be reached M - F, 11:30 AM - 8 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JEFFREY ZIMMERMAN can be reached at (571) 272-4602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NATHAN ERB/Primary Examiner, Art Unit 3628
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