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
Response to Amendment
This is in response to applicant’s amendment/response filed on 04/07/2026, which has been entered and made of record. Claims 1, 7, 10, 11, 17, 19 and 20 have been amended. Claim 2, 12 have been cancelled. No claim has been added. Claims 1, 3-11, 13-20 are pending in the application.
Response to Arguments
Applicant’s arguments on 04/07/2026 have been fully considered but are moot because the arguments do not apply to any of the references being used in the current rejection.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 3-11, 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Piira et al. (US Pub 2025/0150494 A1) in view of Lebaredian et al. (US Pub 2025/0045996 A1).
As to claim 1, Piira discloses receiving, at a computing device, a natural language (NL) input associated with a world, a scene or an asset (Piira, ¶0015, “receiving, from the client node, an input for a trained machine learning model and a prompt. The input comprises the identification of the digital asset selected by a user and the prompt is a text-based and/or a voice-based description of desired features in an AI-based digital asset or layout of digital assets.”);
transmitting the NL input and a context to an AI agent configured to generate one or more instructions corresponding to the NL input, each of the one or more instructions executable by one or more computer processors, the context comprising one or more of at least instruction format data, translations of NL inputs to instructions, or segments of programs (¶0015, “sending, from the server node, the input received from the client node to the trained machine learning model and receiving, at the server node, the AI-based digital asset or layout of digital assets as output by the trained machine learning model.” ¶0031, “A key-phrase can comprise at least one of a text-based keyword, one or more spoken words, a portion of an image, a brief description or a sentence such as taken from a document, selected lines of code from a computer program, portions of three-dimensional models, etc.” ¶0033, “a user prompt may be a text or voice-based audio input such as “hurricane helene news and forecasts,” “a Doppler radar tracking the hurricane, a news article reporting on the hurricane, and so on.” ¶0057, “the generative AI model can receive a digital asset or an identifier of a digital asset with one or more labels for the digital assets as recommended by a user. ¶0074, “One or more digital assets can be selected by users in a collaboration session for providing as input to a trained machine learning model for generating a scene that can include the selected digital assets and AI-based image features corresponding to a text and/or a voice prompt.” “the technology disclosed includes connectors or APIs to connect to third party digital asset management (DAM) systems that comprise repositories of digital assets. The technology disclosed can access digital assets from DAM systems and provide those as input to the trained machine learning model.” Selected digital asset is considered as a context.);
receiving, from the AI agent, a set of generated instructions corresponding to the NL input, the set of generated instructions configured to update at least one of the world, the scene or the asset (¶0015, “sending, from the server node, the AI-based digital asset or layout of digital assets to the plurality of client nodes, allowing the client nodes to display the AI-based digital asset or layout of digital assets in respective digital displays linked to the plurality of client nodes.” ¶0031, “The digital assets can be curated and shared with other users. The user can start a collaboration session with other users to review search results and perform further searching and curation of digital assets.” “The digital asset may be a newly generated element (e.g., an image, video, document, text, data visualization, etc.) created by the generative AI model, based on one or more selected input digital assets (and/or a prompt) provided by a user, or a digital asset with coordinates within the virtual workspace.”);
generating a set of finalized instructions based on the set of generated instructions (¶0033, “After the generative AI model has generated the Operations Center dashboard layout within a collaborative workspace, populated with the digital assets retrieved in response to the provided prompt, multiple users are able to access the Operations Center dashboard and collaboratively interact with the digital assets within the dashboard.” ¶0036, “The output from the trained machine learning model is an AI-generated layout of curated digital assets that can be positioned in the workspace for further review and collaboration amongst the participants of the collaboration session.” ¶0039, “The digital assets can be arranged in canvases (also referred to as sections or containers). Multiple canvases can be placed on a workspace. The digital assets can be arranged in canvases based on various criteria. For example, digital assets can be arranged in separate canvases based on their respective source of digital asset or based on digital asset management system from where the digital asset has been accessed. The digital assets can be arranged in separate canvases based on users or participants.”); and
updating the world, the scene or the asset by executing the set of finalized instructions (¶0031, “The search results or an AI-generated digital asset based on the search results can be automatically placed in the virtual workspace and can be curated into different canvases based on pre-defined criteria or based on sources of search results.” ¶0036, “the display of one or more client devices corresponding to respective participants in a collaboration session may be automatically updated, in response to a particular AI-assisted action, to force display (to some or all users) of a region of the workspace in which the AI-based asset is located.”).
Piira does not disclose the Al agent is selected from a plurality of available Al agents based on determining one of at least an intent associated with the NL input, a request scope associated with the NL input, or a topic associated with the NL input, the plurality of available Al agents including an Al model expert in generating commands in an application-specific language.
Lebaredian discloses the Al agent is selected from a plurality of available Al agents based on determining one of at least an intent associated with the NL input, a request scope associated with the NL input, or a topic associated with the NL input, the plurality of available Al agents including an Al model expert in generating commands in an application-specific language (Lebaredian, ¶0007, “different AI agents may be leveraged for different domains, where the particular AI agent may be selected by request (e.g., by name) and/or based on audible, textual, and/or visual input from a user (e.g., an analysis of the conversation may aid in determining which AI agent instance to render). As a result, the visual appearance of the AI agent may provide context as to the domain in which the AI agent operates, and any number of different AI agents may be rendered for any particular application and/or at any one time.” ¶0040, “The AI engine 112 may support any number of AI agents. For example, different AI agents may be programmed for different domains or skills. As such, a user may request a specific AI agent, or a particular AI agent may be selected by the AI engine 112 based on the incoming data (e.g., where a request is for weather, a weather AI agent may be instantiated, where a request is for finance, a financial AI agent may be instantiated, where a request is for a purchase, a shopping assistant AI may be generated, etc.). As a result of the AI agent corresponding to a particular domain(s), communications between users and the AI agent may be more successful as the requests, commands, questions, inquiries, etc., are more likely to be routed to the proper response or conversational logic and tools for that domain.” ¶0045, “an AI agent—or the AI agent device(s) 102—may have a corresponding email handle, calendaring application connection, etc., that may allow the AI agent device(s) 102 to connect the AI agent to the conference. As such, similar to how a user 130 may join the video conference session via the user device(s) 104—e.g., using a link from an email or meeting invite, going to a URL, entering a meeting code, etc.—the AI agent device(s) 102 may connect the AI agent to the video conference session using any means of access.” ¶0052, “a music AI agent, a map AI agent, an in-vehicle control AI agent (e.g., for climate controls, activating lights, making a phone call, sending a text, etc.), a news AI agent, an in-vehicle entertainment AI agent, etc.” An in-vehicle control AI agent can be one example of “generating commands in an application-specific language”. Other agents such as conference agent and weather agent also can be mapped to the claim.)
Piira and Lebaredian are considered to be analogous art because all pertain to Artificial Intelligence collaboration. It would have been obvious before the effective filing date of the claimed invention to have modified Pirra with the features of “the Al agent is selected from a plurality of available Al agents based on determining one of at least an intent associated with the NL input, a request scope associated with the NL input, or a topic associated with the NL input, the plurality of available Al agents including an Al model expert in generating commands in an application-specific language” as taught by Lebaredian. The suggestion/motivation would have been as a result of the AI agent corresponding to a particular domain(s), communications between users and the AI agent may be more successful as the requests, commands, questions, inquiries, etc., are more likely to be routed to the proper response or conversational logic and tools for that domain (Lebaredian, ¶0040).
As to claim 3, claim 1 is incorporated and the combination of Piira and Lebaredian discloses the context transmitted to the AI agent further comprises one of at least a plurality of previous NL inputs, or a plurality of pairs, each pair comprising a previous NL input and a set of instructions (Piira, ¶0044, “events can also be generated when a user provides feedback to the generative AI model to update the workspace. As such, the spatial event map provides a historical record of which digital assets have been modified by AI, what has been modified, and the prompts to which the modifications are responsive (e.g., for auditing or human validation purposes), thereby improving the explainability of the generative AI model with said historical record.”).
As to claim 4, claim 1 is incorporated and the combination of Piira and Lebaredian discloses determining NL input information comprising one of at least an intent of the NL input, a request scope of the NL input, or a topic of the NL input; generating the context based on the determined NL input information (Piira, ¶0051, “events including text and/or voice prompts describing the features desired in an AI-based dashboard workspace or digital asset for presentation within the workspace” “identifying one or more digital assets or portions of one or more digital assets for providing as input to a trained machine learning model for generating the AI-based dashboard workspace or digital asset for presentation within the workspace” ¶0057, “The generative AI model 110 can provide the text-based or the voice-based prompt to a trained machine learning model for generating the AI-based digital asset(s) (and optionally, the arrangement and visual presentation of the digital asset(s) such as within an Operations Center dashboard).” “The generative AI model 110 can also include logic to process selected digital asset for adding them to the training data for training machine learning models. For example, the generative AI model can receive a digital asset or an identifier of a digital asset with one or more labels for the digital assets as recommended by a user.”).
As to claim 5, claim 4 is incorporated and the combination of Piira and Lebaredian discloses the intent associated with the NL input is determined to be one of a select intent, create intent, add intent, update intent, move intent or delete intent (Piira, ¶0057, “the generative AI model can receive a digital asset or an identifier of a digital asset with one or more labels for the digital assets as recommended by a user.”);
the request scope of the NL input is determined to be associated with the asset or the scene (Piira, ¶0057, “process selected digital asset for adding them to the training data for training machine learning models.”); and
generating the context based on the determined NL input information further comprises adding to the context the instruction format data, or the translations of NL inputs to instructions (Piira, ¶0057, “the generative AI model can receive a digital asset or an identifier of a digital asset with one or more labels for the digital assets as recommended by a user. The generative AI model 110 can add the selected digital asset along with the label to a training data set. The generative AI model 110 can also add new labels for digital assets already saved in the training data set.”).
As to claim 6, claim 4 is incorporated and the combination of Piira and Lebaredian discloses the intent associated with the NL input is determined to be a create intent (Piira, ¶0057, “process selected digital asset for adding them to the training data for training machine learning models.”);
the request scope of the NL input is determined to be associated with the world (Piira, ¶0057, “generating the AI-based digital asset(s) (and optionally, the arrangement and visual presentation of the digital asset(s) such as within an Operations Center dashboard)”); and
generating the context based on the determined NL input information further comprises adding to the context the segments of programs (Piira, ¶0057, “the generative AI model can receive a digital asset or an identifier of a digital asset with one or more labels for the digital assets as recommended by a user. The generative AI model 110 can add the selected digital asset along with the label to a training data set. The generative AI model 110 can also add new labels for digital assets already saved in the training data set.”).
As to claim 7, claim 1 is incorporated and the combination of Piira and Lebaredian discloses generating the set of finalized instructions further comprises:
transmitting an input and a second context to the AI agent, the input comprising one or more of the instructions in the set of generated instructions, the second context comprising verification information associated with the one or more instructions (Piira, ¶0034, “the user can provide feedback to the generative AI model to request that the organizational layout or visual presentation of the digital assets is modified to meet certain criteria (e.g., making a particular digital asset or category of digital assets larger or more centered, or rearranging the digital assets based on a user-specified categorization schema).” ¶0139, “During a collaboration session, the participants can add a variety of content to the workspace such as images, videos, 3D models, documents, user interface designs, architectural designs, etc., as previously described. The participants of a collaboration session may provide their input by adding comments and/or annotations, or write questions, or make corrections, etc. to content presented on the workspace, such as within an AI-generated dashboard. The collaboration system includes logic that allows participants to provide such input by adding first-party elements (or first-party content) to a collaboration whiteboard (also referred to as a workspace), manually or via the generative AI model. Examples of first-party elements include annotations, comments, sticky notes, connectors, various types of geometric shapes, lines, etc. The participants may enter content to the whiteboard at any location.” The user’s feedback or selection to the additional contents is considered as verification information.);
receiving, from the AI agent, a second set of generated instructions (Piira, ¶0034, “the user can select a particular digital asset and prompt the generative AI model to replace with a different digital asset, either based on similarity or optionally based on further specifics provided by the user. In yet another implementation, the user can select a particular digital asset and request that the generative AI model update the dashboard to include additional digital assets that are similar in content and/or format to the selected digital asset.” A rearranging or replacement of digital asset is considered as a second set of generated instructions.);
generating the set of finalized instructions based on the set of generated instructions and the second set of generated instructions (Piira, ¶0057, “the generative AI model can receive a digital asset or an identifier of a digital asset with one or more labels for the digital assets as recommended by a user. The generative AI model 110 can add the selected digital asset along with the label to a training data set. The generative AI model 110 can also add new labels for digital assets already saved in the training data set.” ¶0122, “Users can perform annotations on the programmable windows, add comments, attach sticky notes, resize, move, etc. Because of state synchronization, all of these updates to the programmable window application instances are visible in real time to all users in the collaboration session.” ¶0121, “The user's team has been previously collaborating on a slide deck summarizing predictive data by state for the election. In addition to other relevant digital assets responsive to the user prompt, the generative AI model can also access, from memory linked to the collaboration server, a file containing the current version of the slide deck. The generative AI model can retrieve the file and instantiate a programmable window for the slide creation application (e.g., PowerPoint) with the slide deck open within the programmable window. This enables multiple participants within a session to collaboratively work on the slide deck, including full access to the functionality of the slide creation algorithm”).
As to claim 8, claim 1 is incorporated and the combination of Piira and Lebaredian discloses selecting a second artificial intelligence (AI) agent configured to generate a second set of instructions corresponding to the NL input; and wherein generating the finalized set of instructions is further based on the second set of instructions (Lebaredian, ¶0035, “where the discussion is better suited for a different domain, in addition to the environment or location changing, the particular AI agent may also change. For example, where a user is asking for information about weather in the city of London, a weather-based AI agent may be represented within a rendered virtual environment corresponding to a skyline of London, and when the user asks a follow up question about the history of London, a history-focused AI agent may be represented within or proximate to a photograph or rendered image of a historical building in London.” ¶0052, “various different AI agent types may be accessible to the user 202, such as a music AI agent, a map AI agent, an in-vehicle control AI agent (e.g., for climate controls, activating lights, making a phone call, sending a text, etc.), a news AI agent, an in-vehicle entertainment AI agent, etc. As such, each AI agent may be represented by a different avatar, the same avatar, or a combination thereof. To activate a specific AI agent, the user 204E may request the AI agent by name, e.g., “Hey, Weather Agent,” and/or the AI engine 112 may determine the proper agent based on the received audio (e.g., captured using a microphone in the vehicle), video (e.g., captured using camera 216), and/or textual data (e.g., input by the user such as to a touch-sensitive surface of the display 214).”)
As to claim 9, claim 1 is incorporated and the combination of Piira and Lebaredian discloses receiving the NL input via a user interface (UI); and displaying the updated world, scene or asset via a second UI (Piira, ¶0077, “The AI-based image is generated using image and/or product selections from a user and includes features such as green walls, large windows based on text or voice prompts provided by the user. In a collaboration session, the AI-based images generated by the trained machine learning model can be saved in the workspace. The server node (or the collaboration server) can update the spatial event map so that all client nodes participating in a collaboration session can display the AI-based images.” For a collaboration session, a second client node can be considered as a second UI.).
As to claim 10, claim 1 is incorporated and the combination of Piira and Lebaredian discloses the NL input is received from a first user and the method further comprises:
receiving a second NL input from a second user, the second NL input being associated with the world, a second scene or a second asset (For a collaboration session, a second client node can be considered as a second user environment.);
transmitting the second NL input and a second context to the AI agent, the second context comprising one or more of at least a second set of instruction format data, a second set of translations of NL inputs to instructions, or a second set of segments of programs; receiving, from the AI agent, a second set of generated instructions corresponding to the second NL input, the second set of generated instructions configured to update the world, the second scene or the second asset; generating a second set of finalized instructions based on the second set of generated instructions; and updating, at the computing device, the world, the second scene or the second asset by executing the second set of finalized instructions (Piira, ¶0079, “The participants of the collaboration system can review and label the digital assets in a collaboration session.” ¶0080, “The users or participants of the collaboration search can then provide one or more search results (e.g., from the initial search results for chairs) to the trained machine learning model along with the prompt describing the desired features in an AI-based scene or an AI-based image.” ¶0098, “The digital assets included within a collaboration session can include third-party applications that can be added to a workspace by the generative AI model or by one or more participants of the collaborative session.” ¶0099, “Programmable windows technology allows placement of third-party applications in a collaboration workspace to provide real time collaboration between multiple users of a collaboration system.” ¶0102, “The programmable window applications allow simultaneous editing of content by multiple users in a collaboration session. The programmable window applications can be operated in a synchronous (such as in leader-follower mode, presentation mode, etc.) and asynchronous manner (in which users work independently in a collaboration session). Programmable window applications allow unstructured collaboration using annotations, use of note cards, addition and editing of comments, inclusion of snapshots, and linking content to other objects or applications in the workspace. Users can work in groups such as breakout groups and allows users to participate in voting.” Claim 10 is merely repeating the same features as claim 1 for a second user.).
As to claim 11, the combination of Piira and Lebaredian discloses a system comprising: one or more computer processors; one or more computer memories; and a set of instructions stored in the one or more computer memories, the set of instructions configuring the one or more computer processors to perform operations, the operations comprising: receiving, at a computing device, a natural language (NL) input associated with a world, scene or asset; transmitting the NL input and a context to an AI agent configured to generate one or more instructions corresponding to the NL input, each of the one or more instructions executable by a computer, the context comprising one or more of at least instruction format data, translations of NL inputs to instructions, or segments of programs; wherein the AI agent is selected from a plurality of available AI agents based on determining one of at least an intent associated with the NL input, a request scope associated with the NL input, or a topic associated with the NL input, the plurality of available AI agents including an AI model expert in generating commands in an application-specific language; receiving, from the AI agent, a set of generated instructions corresponding to the NL input, the set of generated instructions configured to update at least one of the world, the scene or the asset; generating a set of finalized instructions based on the set of generated instructions; and updating the world, the scene or the asset by executing the set of finalized instructions (See claim 1 for detailed analysis.).
As to claim 13, claim 11 is incorporated and the combination of Piira and Lebaredian discloses the context transmitted to the AI agent further comprises one of at least a plurality of previous NL inputs, or a plurality of pairs, each pair comprising a previous NL input and a set of instructions (See claim 3 for detailed analysis.).
As to claim 14, claim 11 is incorporated and the combination of Piira and Lebaredian discloses determine NL input information comprising one of at least an intent of the NL input, a request scope of the NL input, or a topic of the NL input; generate the context based on the determined NL input information (See claim 4 for detailed analysis.).
As to claim 15, claim 14 is incorporated and the combination of Piira and Lebaredian discloses the intent associated with the NL input is determined to be one of a select intent, create intent, add intent, update intent, move intent or delete intent; the request scope of the NL input is determined to be associated with the asset or the scene; and generate the context based on the determined NL input information further comprises adding to the context the instruction format data or the translations of NL inputs to instructions (See claim 5 for detailed analysis.).
As to claim 16, claim 14 is incorporated and the combination of Piira and Lebaredian discloses the intent associated with the NL input is determined to be a create intent; the request scope of the NL input is determined to be associated with the world; and generate the context based on the determined NL input information further comprises adding to the context the segments of programs (See claim 6 for detailed analysis.).
As to claim 17, claim 11 is incorporated and the combination of Piira and Lebaredian discloses generating the set of finalized instructions further comprises: transmitting an input and a second context to the AI agent, the input comprising one or more of the instructions in the set of generated instructions, the second context comprising verification information associated with the one or more instructions; receiving, from the AI agent, a second set of generated instructions; generating the set of finalized instructions by combining the set of generated instructions and the second set of generated instructions (See claim 7 for detailed analysis.).
As to claim 18, claim 11 is incorporated and the combination of Piira and Lebaredian discloses receive the NL input via a user interface (UI); and display the updated world, scene or asset via a second UI (See claim 9 for detailed analysis.).
As to claim 19, claim 11 is incorporated and the combination of Piira and Lebaredian discloses the NL input is received from a first user and the operations further comprise: receiving a second NL input from a second user, the second NL input being associated with the world, a second scene or a second asset; transmitting the second NL input and a second context to the AI agent, the second context comprising one or more of at least a second set of instruction format data, a second set of translations of NL inputs to instructions, or a second set of segments of programs; receiving, from the AI agent, a second set of generated instructions corresponding to the second NL input, the second set of generated instructions configured to update the world, the second scene or the second asset; generating a second set of finalized instructions based on the second set of generated instructions; and updating, at the computing device, the world, the second scene or the second asset by executing the second set of finalized instructions (See claim 10 for detailed analysis.).
As to claim 20, the combination of Piira and Lebaredian discloses a non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising: receiving, at a computing device, a natural language (NL) input associated with a world, scene or asset; transmitting the NL input and a context to an AI agent configured to generate one or more instructions corresponding to the NL input, each of the one or more instructions executable by a computer, the context comprising one or more of at least instruction format data, translations of NL inputs to instructions, or segments of programs, wherein the AI agent is selected from a plurality of available AI agents based on determining one of at least an intent associated with the NL input, a request scope associated with the NL input, or a topic associated with the NL input, the plurality of available AI agents including an AI model expert in generating commands in an application-specific language; receiving, from the AI agent, a set of generated instructions corresponding to the NL input, the set of generated instructions configured to update at least one of the world, the scene or the asset; generating a set of finalized instructions based on the set of generated instructions; and updating the world, the scene or the asset by executing the set of finalized instructions (See claim 1 for detailed analysis.).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YU CHEN whose telephone number is (571)270-7951. The examiner can normally be reached on M-F 8-5 PST Mid-day flex.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xiao Wu can be reached on 571-272-7761. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YU CHEN/
Primary Examiner, Art Unit 2613
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