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 .
Response to Amendment
This Office action is in response to communications filed on 7/23/2026.
Claims 1, 3, 6-8, 11-15, and 17 have been amended.
Claim 16 has been cancelled.
Claims 1-15 and 17-19 are pending.
Response to Arguments
Applicant's arguments filed 7/23/2026 have been fully considered but they are not persuasive. In the response filed, applicant argues, in substance:
a) In pages 9-10 of the response filed, applicant argues that Wilde et al. (US 20240303415 A1, hereinafter Wilde) fails to teach or disclose the limitation “sending, from the server node, the AI-based digital asset to a plurality of client nodes participating in the collaboration session, allowing each respective client node of the plurality of the client nodes to display the AI-based digital asset in a digital display linked to the respective client node” because in ¶[0048] of Wilde, the “collaborative content” is provided “solely to the client that requested the specific data” (applicant arguments, page 9).
In response to argument (a), the examiner respectfully disagrees.
The examiner notes that Wilde in ¶[0048] teaches returning collaborative content to the client (underline for emphasis).
Wilde enables collaborative content to be content which is shared with other users (see ¶[0040], “While these examples show a single human collaborator, more than one human collaborator can contribute to the collaborative authoring session with the user and the AI” and ¶[0062], "the revised content provided by the GPT model 466 is presented to the user and/or any collaborators who are currently participating in the authoring session" – see also ¶[0040]).
Therefore, the content provided from the server is provided to all collaborators in Wilde.
Claim Objections
Claim 11 is objected to because of the following informalities:
The limitation "wherein the other prompt is at least one of a text-based a voice-based description" should be - - wherein the other prompt is at least one of a text-based and a voice-based description - -. Appropriate correction is required.
Claim Rejections - 35 USC § 103
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.
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.
Claim(s) 1-2, 6-8, 14-15, and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chanda (US 20220318755 A1) in view of Wilde et al. (US 20240303415 A1, hereinafter Wilde).
Regarding claim 1, Chanda discloses a method comprising:
sending, from a server node, at least a portion of a spatial event map that locates events in a virtual workspace at a client node participating in a collaboration session (¶[0100], "FIG. 4A is a flowchart 401 presenting high-level client-side process for starting a collaboration session"; Fig. 4A, "Start Collaboration Meeting and Receive Spatial Event Map"; ¶[0106], "The server can then send the spatial event map including the curation data identifying prioritization criterion, zoom level and workspace identifier linked to the meeting (operation 515)"; ¶[0068], "The spatial event map can include events comprising data specifying virtual coordinates of location within the workspace at which an interaction with the workspace is detected"; ¶[0109], "The client retrieves the spatial event map, or at least portions of it, from the collaboration server"),
the spatial event map comprising a specification of a dimensional location of a viewport in the virtual workspace (¶[0025], "The spatial event map allows for identification, for the client-side network nodes, of a local client viewport in the virtual workspace"; ¶[0067], "Display clients at participant client network nodes in the collaboration session can display a portion, or mapped area, of the workspace, where locations on the display are mapped to locations in the workspace. A mapped area, also known as a viewport within the workspace is rendered on a physical screen space (e.g., a local client screen space). Because the entire workspace is addressable in for example Cartesian coordinates, any portion of the workspace that a user may be viewing itself has a location, width, and height in Cartesian space. The concept of a portion of a workspace can be referred to as a “viewport” or “client viewport”"; ¶[0116], "The local copy of the spatial event map is traversed to gather display data for spatial event map entries that map to the displayable area for the local display. At this step the system traverses spatial event map to gather display data digital assets for spatial map events");
sending, from the server node, data to allow the client node to display, in a screen space of a display associated with the client node, a digital asset identified by events in the spatial event map that are associated with locations within a viewport of the client node (¶[0112], "The server will respond with all chunks (each its own section of time)"; ¶[0115], "The individual messages might include information like position on screen, color, width of stroke, time created etc."; ¶[0116], "The client then determines a viewport in the workspace, using for example a server provided focus point, and display boundaries for the local display [...] the client may gather additional data in support of rendering a display for spatial event map entries within a culling boundary defining a region larger than the displayable area for the local display, in order to prepare for supporting predicted user interactions such as zoom level and pan within the workspace. The display data can include virtual workspace attached to collaboration session. This data can also include coordinates indicating the boundary of the virtual workspace").
Chanda does not disclose receiving, from the client node, an input for a trained machine learning model wherein the input comprises at least one of (i) an identification of a digital asset selected by a user and (ii) a prompt, and wherein the prompt is at least one of a text-based and a voice-based description of desired features in an artificial intelligence (AI)-based digital asset; sending, from the server node, the input received from the client node to the trained machine learning model; receiving, at the server node, the AI-based digital asset as output by the trained machine learning model; and sending, from the server node, the AI-based digital asset to a plurality of client nodes participating in the collaboration session, allowing each respective client node of the plurality of the client nodes to display the AI-based digital asset in a digital display linked to the respective client node.
Wilde discloses receiving, from the client node, an input for a trained machine learning model wherein the input comprises at least one of (i) an identification of a digital asset selected by a user and (ii) a prompt, and wherein the prompt is at least one of a text-based and a voice-based description of desired features in an artificial intelligence (AI)-based digital asset (¶[0045], "The request processing unit 432 is configured to receive requests from the client-side interface 412 that include prompts for the GPT model 466 to produce collaborative content. The prompts can include a textual prompt, such as the textual prompt entered into the prompt field 215 and/or a prompt associated with an action tile as discussed in the preceding examples. The prompts may also include collaborative content that has been revised by the user, such as the textual content of the content pane 240 discussed in the preceding examples");
sending, from the server node, the input received from the client node to the trained machine learning model (¶[0046], "service interface unit 464 of the AI services 460 receives the request from the request processing unit 432 and provides the textual prompt and/or the revised collaborative content to the GPT model 466 as an input. The GPT model 466 analyzes these inputs and outputs collaborative content based on these inputs");
receiving, at the server node, the AI-based digital asset as output by the trained machine learning model (¶[0047], "the service interface unit 464 provides the AI-generated output to the request processing unit 432 for processing"); and
sending, from the server node, the AI-based digital asset to a plurality of client nodes participating in the collaboration session, allowing each respective client node of the plurality of the client nodes to display the AI-based digital asset in a digital display linked to the respective client node (¶[0048], "if the collaborative content passes the moderation checks, the request processing unit 432 sends the collaborative content to the client-side interface 412, and the client-side interface 412 provides the collaborative content to the collaboration application 414 for presentation to the user. As discussed in the preceding examples, the collaborative content may be presented to the user in the content pane 240 of the user interface 225"; ¶[0051], "functionality provided by the collaboration platform 410 is implemented by a native application installed on the client devices 405a, 405b, 405c, and 405d, and the client devices 405a, 405b, 405c, and 405d communicate directly with the collaboration platform 410 over a network connection"; ¶[0062], "the revised content provided by the GPT model 466 is presented to the user and/or any collaborators who are currently participating in the authoring session").
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Chanda in view of Wilde for receiving, from the client node, an input for a trained machine learning model wherein the input comprises at least one of (i) an identification of a digital asset selected by a user and (ii) a prompt, and wherein the prompt is at least one of a text-based and a voice-based description of desired features in an artificial intelligence (AI)-based digital asset; sending, from the server node, the input received from the client node to the trained machine learning model; receiving, at the server node, the AI-based digital asset as output by the trained machine learning model; and sending, from the server node, the AI-based digital asset to a plurality of client nodes participating in the collaboration session, allowing each respective client node of the plurality of the client nodes to display the AI-based digital asset in a digital display linked to the respective client node.
One of ordinary skill in the art would have been motivated because it would "user experience when generating content (Wilde, ¶[0001]) by providing "an opportunity to actively coauthor content with the AI and/or other users" (Wilde, ¶[0015]).
Regarding claim 2, the combined system of Chanda and Wilde discloses the invention substantially as applied to claim 1, above, wherein the trained machine learning model is trained to generate, as output, the AI-based digital asset (Wilde, ¶[0015], pre-trained model (e.g., GPT)) in dependence upon at least one of:
a similarity of the identified digital asset and the AI-based digital asset, such that the trained machine learning model is trained to maximize the similarity of a model input and a model output; and a match between a feature of the AI-based digital asset and one or more of the desired features within a prompt (Wilde, ¶[0015], "generated content includes other types of content, such as but not limited to diagrams, that may be edited by a human user before being sent back to the generative model for further refinement"; ¶[0016], " The edited content is provided as an input to the generative model and provided to the LLM for additional refinement [...] the user edits the content generated by the AI directly rather than attempting to refine the content generated by the AI by refining a prompt to the AI to generate the desired content. Thus, these techniques provides a more natural approach to content generation using AI, because the user can edit the AI content in much the same way that the users would edit such content when collaborating with human users" (i.e., if all desired features were present in the output (a match to the prompt), no further refinement would be necessary)).
Regarding claim 6, the combined system of Chanda and Wilde discloses the invention substantially as applied to claim 1, above, further including: receiving, from the client node, a feedback input for the trained machine learning model wherein the feedback input comprises at least one of (i) an identification of another digital asset selected by the user and (ii) a feedback prompt wherein the feedback prompt is at least one of a text-based and a voice-based description of desired features in a refined AI-based digital asset (Wilde, ¶[0036], "The user can collaborate with the AI to refine the collaborative content in a couple of ways. First, the user may directly edit the collaborative content included in the content pane 240, and the edited content is provided as a subsequent prompt to the generative model to further refine the generated content. Second, the user may enter a textual prompt in the prompt field 215, in manner similar to that shown in the user interface 205. As will be shown in the examples which follow, the user may both edit the collaborative content and provide a textual prompt in the prompt field 215 to provide as an input to the generative model. Once the user is satisfied with their edits to the collaborative content of the content pane 240 and/or the textual prompt, the user can click on or otherwise activate the submit option 220 to cause the revised collaborative content and/or the textual prompt from the prompt field 215 to be submitted to the generative model"; ¶[0040], "FIG. 3D shows an example in which the user has edited the collaborative content in the content pane 240 to add additional text. The user submits this as a prompt to the generative model, and the history section of the user interface 225 is updated to add the prompt 230d indicating that the user has edited the collaborative content. The revised collaborative content is shown in the content pane 240");
sending, from the server node, the feedback input received from the client node to the trained machine learning model (Wilde, ¶[0036], "provided as a subsequent prompt to the generative model"; Fig. 4, the generative model is accessible from the client via the server, thus communicating the prompt through the server is suggested); and
receiving, at the server node, the refined AI-based digital asset as output by the trained machine learning model, wherein the refined AI-based digital asset is an updated version of the AI-based digital asset based on the feedback input (Wilde, ¶[0040], "FIG. 3D shows an example in which the user has edited the collaborative content in the content pane 240 to add additional text. The user submits this as a prompt to the generative model, and the history section of the user interface 225 is updated to add the prompt 230d indicating that the user has edited the collaborative content. The revised collaborative content is shown in the content pane 240"; Fig. 4, the generative model is connected to the client via the server, thus communicating updates through the server is suggested).
Regarding claim 7, the combined system of Chanda and Wilde discloses the invention substantially as applied to claim 1, above, further including sending, from the server node, at least a portion of the spatial event map identifying a particular event associated with the AI-based digital asset (Chanda, ¶[0106], "The server can then send the spatial event map including the curation data identifying prioritization criterion, zoom level and workspace identifier linked to the meeting (operation 515)"; ¶[0068], "The spatial event map can include events"), the particular even including at least one of:
data specifying virtual coordinates within the virtual workspace of the AI-based digital asset; data specifying at least one of a parameter and an input of the trained machine learning model associated with generating the AI-based digital asset; data identifying a time corresponding to the particular event; and data identifying an action associated with the AI-based digital asset, the data identifying the action including at least one of a generation, an update, and a deletion of the AI-based digital asset within the virtual workspace (Chanda, ¶[0068], "The spatial event map can include events comprising data specifying virtual coordinates of location within the workspace at which an interaction with the workspace is detected"; ¶[0109], "The client retrieves the spatial event map, or at least portions of it, from the collaboration server").
Regarding claim 8, the combined system of Chanda and Wilde discloses the invention substantially as applied to claim 1, above, wherein the AI-based digital asset is at least one of a text element, a graphical element, an uploaded file, a programmable window of a third-party application, a webpage, and a three-dimensional model (Wilde, ¶[0015], "The generated content can include textual content. The textual content includes formatted textual content in some implementations, such as but not limited to lists and tables. In some implementations, generated content includes other types of content, such as but not limited to diagrams").
Regarding claim 14, the combined system of Chanda, Wilde and Xu discloses the invention substantially as applied to claim 11, above, further including sending, from the server node, at least a portion of the spatial event map identifying a particular event associated with a digital asset of the plurality of digital assets (Chanda, ¶[0106], "The server can then send the spatial event map including the curation data identifying prioritization criterion, zoom level and workspace identifier linked to the meeting (operation 515)"; ¶[0068], "The spatial event map can include events"), the particular event including at least one of:
data specifying virtual coordinates within the virtual workspace of the digital asset associated with the particular event; data specifying at least one of a parameter and an input of the trained machine learning model associated with generating or arranging of the digital asset associated with the particular event; data identifying a time associated with the particular event; and data identifying an action including at least one of a generation, an update, and a deletion of the digital asset associated with the particular event within the virtual workspace (Chanda, ¶[0068], "The spatial event map can include events comprising data specifying virtual coordinates of location within the workspace at which an interaction with the workspace is detected"; ¶[0109], "The client retrieves the spatial event map, or at least portions of it, from the collaboration server").
Regarding claim 15, Chanda discloses a method comprising:
receiving, at a client node, at least a portion of a spatial event map that locates events in a virtual workspace at the client node (¶[0106], "The server can then send the spatial event map including the curation data identifying prioritization criterion, zoom level and workspace identifier linked to the meeting (operation 515)"; ¶[0068], "The spatial event map can include events comprising data specifying virtual coordinates of location within the workspace at which an interaction with the workspace is detected"; ¶[0109], "The client retrieves the spatial event map, or at least portions of it, from the collaboration server"),
the spatial event map comprising a specification of a dimensional location of a viewport in the virtual workspace (¶[0025], "The spatial event map allows for identification, for the client-side network nodes, of a local client viewport in the virtual workspace"; ¶[0067], "Display clients at participant client network nodes in the collaboration session can display a portion, or mapped area, of the workspace, where locations on the display are mapped to locations in the workspace. A mapped area, also known as a viewport within the workspace is rendered on a physical screen space (e.g., a local client screen space). Because the entire workspace is addressable in for example Cartesian coordinates, any portion of the workspace that a user may be viewing itself has a location, width, and height in Cartesian space. The concept of a portion of a workspace can be referred to as a “viewport” or “client viewport”"; ¶[0116], "The local copy of the spatial event map is traversed to gather display data for spatial event map entries that map to the displayable area for the local display. At this step the system traverses spatial event map to gather display data digital assets for spatial map events");
receiving, at the client node, data to allow the client node to display, in a screen space of a display associated with the client node, a digital asset identified by events in the spatial event map that are associated with locations within a viewport of the client node (¶[0112], "The server will respond with all chunks (each its own section of time)"; ¶[0115], "The individual messages might include information like position on screen, color, width of stroke, time created etc."; ¶[0116], "The client then determines a viewport in the workspace, using for example a server provided focus point, and display boundaries for the local display [...] the client may gather additional data in support of rendering a display for spatial event map entries within a culling boundary defining a region larger than the displayable area for the local display, in order to prepare for supporting predicted user interactions such as zoom level and pan within the workspace. The display data can include virtual workspace attached to collaboration session. This data can also include coordinates indicating the boundary of the virtual workspace");
receiving, at the client node, at least a portion of the spatial event map identifying a particular event associated with the AI-based digital asset (¶[0106], "The server can then send the spatial event map including the curation data identifying prioritization criterion, zoom level and workspace identifier linked to the meeting (operation 515)"; ¶[0068], "The spatial event map can include events"), the particular event including at least one of:
data specifying virtual coordinates within the virtual workspace of the AI-based digital asset; data specifying at least one of a parameter and an input of the trained machine learning model associated with the AI-based digital asset; data identifying a time of the particular event; and data identifying an action including at least one of a generation, an update, and a deletion of the AI-based digital asset within the virtual workspace (¶[0068], "The spatial event map can include events comprising data specifying virtual coordinates of location within the workspace at which an interaction with the workspace is detected"; ¶[0109], "The client retrieves the spatial event map, or at least portions of it, from the collaboration server").
Chanda does not disclose sending, to a server node, an input for a trained machine learning model, wherein the input comprises at least one of (i) an identification of a digital asset selected by a user and (ii) a prompt, and wherein the prompt is at least one of a text-based or a voice-based description of desired features in an artificial intelligence (AI)-based digital asset; and receiving, at the client node, the AI-based digital asset, allowing the client node to display the AI-based digital asset in a digital display linked to the client node.
Wilde discloses sending, to a server node, an input for a trained machine learning model, wherein the input comprises at least one of (i) an identification of a digital asset selected by a user and (ii) a prompt, and wherein the prompt is at least one of a text-based or a voice-based description of desired features in an artificial intelligence (AI)-based digital asset (¶[0045], "The request processing unit 432 is configured to receive requests from the client-side interface 412 that include prompts for the GPT model 466 to produce collaborative content. The prompts can include a textual prompt, such as the textual prompt entered into the prompt field 215 and/or a prompt associated with an action tile as discussed in the preceding examples. The prompts may also include collaborative content that has been revised by the user, such as the textual content of the content pane 240 discussed in the preceding examples"); and
receiving, at the client node, the AI-based digital asset, allowing the client node to display the AI-based digital asset in a digital display linked to the client node (¶[0048], " if the collaborative content passes the moderation checks, the request processing unit 432 sends the collaborative content to the client-side interface 412, and the client-side interface 412 provides the collaborative content to the collaboration application 414 for presentation to the user. As discussed in the preceding examples, the collaborative content may be presented to the user in the content pane 240 of the user interface 225").
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Chanda in view of Wilde for sending, to a server node, an input for a trained machine learning model, wherein the input comprises at least one of (i) an identification of a digital asset selected by a user and (ii) a prompt, and wherein the prompt is at least one of a text-based or a voice-based description of desired features in an artificial intelligence (AI)-based digital asset; and receiving, at the client node, the AI-based digital asset, allowing the client node to display the AI-based digital asset in a digital display linked to the client node.
One of ordinary skill in the art would have been motivated because it would "user experience when generating content (Wilde, ¶[0001]) by providing "an opportunity to actively coauthor content with the AI and/or other users" (Wilde, ¶[0015]).
Regarding claim 17, Chanda discloses a server node, the server node comprising a processor configured with logic to implement operations (¶[0106], "server"; ¶[0098], "The flowcharts illustrate logic executed by clients (network nodes), a server (collaboration server) or both. The logic can be implemented using processors") comprising:
sending, from a server node, at least a portion of a spatial event map that locates events in a virtual workspace at a client node (¶[0100], "FIG. 4A is a flowchart 401 presenting high-level client-side process for starting a collaboration session"; Fig. 4A, "Start Collaboration Meeting and Receive Spatial Event Map"; ¶[0106], "The server can then send the spatial event map including the curation data identifying prioritization criterion, zoom level and workspace identifier linked to the meeting (operation 515)"; ¶[0068], "The spatial event map can include events comprising data specifying virtual coordinates of location within the workspace at which an interaction with the workspace is detected"; ¶[0109], "The client retrieves the spatial event map, or at least portions of it, from the collaboration server"),
the spatial event map comprising a specification of a dimensional location of a viewport in the virtual workspace (¶[0025], "The spatial event map allows for identification, for the client-side network nodes, of a local client viewport in the virtual workspace"; ¶[0067], "Display clients at participant client network nodes in the collaboration session can display a portion, or mapped area, of the workspace, where locations on the display are mapped to locations in the workspace. A mapped area, also known as a viewport within the workspace is rendered on a physical screen space (e.g., a local client screen space). Because the entire workspace is addressable in for example Cartesian coordinates, any portion of the workspace that a user may be viewing itself has a location, width, and height in Cartesian space. The concept of a portion of a workspace can be referred to as a “viewport” or “client viewport”"; ¶[0116], "The local copy of the spatial event map is traversed to gather display data for spatial event map entries that map to the displayable area for the local display. At this step the system traverses spatial event map to gather display data digital assets for spatial map events");
sending, from the server node, data to allow the client node to display, in a screen space of a display associated with the client node, a digital asset identified by events in the spatial event map that are associated with locations within a viewport of the client node (¶[0112], "The server will respond with all chunks (each its own section of time)"; ¶[0115], "The individual messages might include information like position on screen, color, width of stroke, time created etc."; ¶[0116], "The client then determines a viewport in the workspace, using for example a server provided focus point, and display boundaries for the local display [...] the client may gather additional data in support of rendering a display for spatial event map entries within a culling boundary defining a region larger than the displayable area for the local display, in order to prepare for supporting predicted user interactions such as zoom level and pan within the workspace. The display data can include virtual workspace attached to collaboration session. This data can also include coordinates indicating the boundary of the virtual workspace");
sending, from the server node, at least a portion of the spatial event map identifying a particular event associated with the AI-based digital asset (¶[0106], "The server can then send the spatial event map including the curation data identifying prioritization criterion, zoom level and workspace identifier linked to the meeting (operation 515)"; ¶[0068], "The spatial event map can include events"), the particular event including at least one of:
data specifying virtual coordinates within the virtual workspace of the AI-based digital asset; data specifying at least one of a parameter and an input of the trained machine learning model associated with the AI-based digital asset; data identifying a time corresponding to the particular event; and data identifying an action associated with the AI-based digital asset, the data identifying the action including at least one of a generation, an update, and a deletion of the AI-based digital asset within the virtual workspace (¶[0068], "The spatial event map can include events comprising data specifying virtual coordinates of location within the workspace at which an interaction with the workspace is detected"; ¶[0109], "The client retrieves the spatial event map, or at least portions of it, from the collaboration server").
Chanda does not disclose receiving, from the client node, an input for a trained machine learning model, wherein the input comprises at least one of (i) an identification of a digital asset selected by a user and (ii) a prompt, and wherein the prompt is at least one of a text-based and a voice-based description of desired features in an artificial intelligence (AI)-based digital asset; sending, from the server node, the input received from the client node to the trained machine learning model; receiving, at the server node, the AI-based digital asset as output by the trained machine learning model; and sending, from the server node, the AI-based digital asset to the client node, allowing the client node to display the AI-based digital asset in a digital display linked to the client node; and
Wilde discloses receiving, from the client node, an input for a trained machine learning model, wherein the input comprises at least one of (i) an identification of a digital asset selected by a user and (ii) a prompt, and wherein the prompt is at least one of a text-based and a voice-based description of desired features in an artificial intelligence (AI)-based digital asset (¶[0045], "The request processing unit 432 is configured to receive requests from the client-side interface 412 that include prompts for the GPT model 466 to produce collaborative content. The prompts can include a textual prompt, such as the textual prompt entered into the prompt field 215 and/or a prompt associated with an action tile as discussed in the preceding examples. The prompts may also include collaborative content that has been revised by the user, such as the textual content of the content pane 240 discussed in the preceding examples");
sending, from the server node, the input received from the client node to the trained machine learning model (¶[0046], "service interface unit 464 of the AI services 460 receives the request from the request processing unit 432 and provides the textual prompt and/or the revised collaborative content to the GPT model 466 as an input. The GPT model 466 analyzes these inputs and outputs collaborative content based on these inputs");
receiving, at the server node, the AI-based digital asset as output by the trained machine learning model (¶[0047], "the service interface unit 464 provides the AI-generated output to the request processing unit 432 for processing");
sending, from the server node, the AI-based digital asset to the client node, allowing the client node to display the AI-based digital asset in a digital display linked to the client node (¶[0048], "if the collaborative content passes the moderation checks, the request processing unit 432 sends the collaborative content to the client-side interface 412, and the client-side interface 412 provides the collaborative content to the collaboration application 414 for presentation to the user. As discussed in the preceding examples, the collaborative content may be presented to the user in the content pane 240 of the user interface 225"; ¶[0051], " functionality provided by the collaboration platform 410 is implemented by a native application installed on the client devices 405a, 405b, 405c, and 405d, and the client devices 405a, 405b, 405c, and 405d communicate directly with the collaboration platform 410 over a network connection"; ¶[0062], "the revised content provided by the GPT model 466 is presented to the user and/or any collaborators who are currently participating in the authoring session"); and
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Chanda in view of Wilde for receiving, from the client node, an input for a trained machine learning model, wherein the input comprises at least one of (i) an identification of a digital asset selected by a user and (ii) a prompt, and wherein the prompt is at least one of a text-based and a voice-based description of desired features in an artificial intelligence (AI)-based digital asset; sending, from the server node, the input received from the client node to the trained machine learning model; receiving, at the server node, the AI-based digital asset as output by the trained machine learning model; and sending, from the server node, the AI-based digital asset to the client node, allowing the client node to display the AI-based digital asset in a digital display linked to the client node; and
One of ordinary skill in the art would have been motivated because it would "user experience when generating content (Wilde, ¶[0001]) by providing "an opportunity to actively coauthor content with the AI and/or other users" (Wilde, ¶[0015]).
Regarding claim 18, the combined system of Chanda and Wilde discloses a non-transitory computer-readable recording medium having a program recorded thereon, the program, when executed by a server node including a processor, causing the server node to perform the operations of claim 1 (¶[0120], "a computer system, or network node, which can be used to implement the client-side functions (e.g. computer system 110) or the server-side functions (e.g. server 107) in a distributed collaboration system. A computer system typically includes a processor subsystem 714 which communicates with a number of peripheral devices via bus subsystem 712. These peripheral devices may include a storage subsystem 724, comprising a memory subsystem 726 and a file storage subsystem 728"; ¶[0125], "The storage subsystem 724 when used for implementation of server-side network-nodes, comprises a product including a non-transitory computer readable medium storing a machine readable data structure").
Regarding claim 19, the combined system of Chanda and Wilde discloses a non-transitory computer-readable recording medium having a program recorded thereon, the program, when executed by a client node including a processor, causing the client node to perform the operations of claim 15 (¶[0120], "a computer system, or network node, which can be used to implement the client-side functions (e.g. computer system 110) or the server-side functions (e.g. server 107) in a distributed collaboration system. A computer system typically includes a processor subsystem 714 which communicates with a number of peripheral devices via bus subsystem 712. These peripheral devices may include a storage subsystem 724, comprising a memory subsystem 726 and a file storage subsystem 728"; ¶[0126], "The storage subsystem 724 when used for implementation of client side network-nodes, comprises a product including a non-transitory computer readable medium storing a machine readable data structure").
Claim(s) 3-5, 9-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chanda (US 20220318755 A1) in view of Wilde (US 20240303415 A1), as applied to claim 1, above, and further in view of Xu et al. (US 20250077765 A1, hereinafter Xu).
Regarding claim 3, the combined system of Chanda and Wilde discloses the invention substantially as applied to claim 1, above.
The combined system of Chanda and Wilde does not disclose that the AI-based digital asset is generated in dependence upon one or more digital assets within a digital asset storage accessible to the trained machine learning model.
Xu discloses that an AI-based digital asset is generated in dependence upon one or more digital assets within a digital asset storage accessible to the trained machine learning model (¶[0026], "using the prompt and parameters, the system searches a database that stores previously-generated content items, such as images, for content items that may satisfy the prompt, and displays the search result content items […] The system may repeat step 106 as desired until a final content item such as image 109 is chosen among search results or generated"; ¶[0043], "The system 201 can then receive a selection of one chosen image of the previously generated images to download").
Therefore, 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 combined system of Chanda and Wilde in view of Xu so that the AI-based digital asset is generated in dependence upon one or more digital assets within a digital asset storage accessible to the trained machine learning model.
One of ordinary skill in the art would have been motivated because it would "reduce the iterations of prompting and generation, as well as the resource demand for AI generation computer systems" (Xu, ¶[0009]).
Regarding claim 4, the combined system of Chanda, Wilde and Xu discloses the invention substantially as applied to claim 3, above, wherein the trained machine learning model identifies and extracts a preexisting digital asset from the digital asset storage for use as the AI-based digital asset (Xu, ¶[0026], "using the prompt and parameters, the system searches a database that stores previously-generated content items, such as images, for content items that may satisfy the prompt, and displays the search result content items […] The system may repeat step 106 as desired until a final content item such as image 109 is chosen among search results or generated").
Regarding claim 5, the combined system of Chanda, Wilde and Xu discloses the invention substantially as applied to claim 3, above, wherein the trained machine learning model identifies one or more digital assets from the digital asset storage and generates the AI-based digital asset with features in dependence on the one or more identified digital assets from the digital asset storage (Xu, ¶[0026], "The system may then update the search of the database with the information that the closest match 104a is similar to the searched-for image 109. It may accordingly merge metadata connected with the closest match 104a with the original prompt 102 and generation parameters, and again execute a search at step 105. The system may then again display content items resulting from the updated search of the database of previously generated content items using the merged metadata [...] The system may repeat step 106 as desired until a final content item such as image 109 is chosen among search results or generated").
Regarding claim 9, the combined system of Chanda and Wilde discloses the invention substantially as applied to claim 1, above.
The combined system of Chanda and Wilde does not disclose that the trained machine learning model generates the AI-based digital asset in further dependence upon an Internet-based data source.
Xu discloses that the trained machine learning model generates the AI-based digital asset in further dependence upon an Internet-based data source (¶[0008], "websites provide image search functionality for AI-generated images. Some websites only provide image results with corresponding prompts, and some provide results including also the model name and parameters used to generate the results. These websites provide visual feedback of AI-generated images and corresponding prompts, and those prompts can generate new images using the text-to-image model" where AI inherently involves a trained machine learning model).
Therefore, 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 combined system of Chanda and Wilde in view of Xu so that the trained machine learning model generates the AI-based digital asset in further dependence upon an Internet-based data source.
One of ordinary skill in the art would have been motivated because use of internet available tools could simplify system requirements.
Regarding claim 10, the combined system of Chanda and Wilde discloses the invention substantially as applied to claim 1, above.
The combined system of Chanda and Wilde does not disclose that the AI-based digital asset is stored in a training database for later use in subsequent training of a machine learning model.
Xu discloses that an AI-based digital asset is stored in a training database for later use in subsequent training of a machine learning model (¶[0047], "It may also store the new image with its metadata to previously generated image database 203"; ¶[0043], "the system 201 searches a database or store of previously generated images 203 for images with metadata matching the provided search elements including the prompt").
Therefore, 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 combined system of Chanda and Wilde in view of Xu so that the trained machine learning model generates the AI-based digital asset in further dependence upon an Internet-based data source.
One of ordinary skill in the art would have been motivated because it would "reduce the iterations of prompting and generation, as well as the resource demand for AI generation computer systems" (Xu, ¶[0009]).
Regarding claim 11, the combined system of Chanda and Wilde discloses the invention substantially as applied to claim 1, above, further including receiving, from the client node, another input for the trained machine learning model wherein the other input comprises at least one of (i) the identification of the digital asset selected by a user and (ii) another prompt, and wherein the other prompt is at least one of a text-based a voice-based description of desired features (¶[0036], "The user can collaborate with the AI to refine the collaborative content in a couple of ways. First, the user may directly edit the collaborative content included in the content pane 240, and the edited content is provided as a subsequent prompt to the generative model to further refine the generated content. Second, the user may enter a textual prompt in the prompt field 215, in manner similar to that shown in the user interface 205. As will be shown in the examples which follow, the user may both edit the collaborative content and provide a textual prompt in the prompt field 215 to provide as an input to the generative model. Once the user is satisfied with their edits to the collaborative content of the content pane 240 and/or the textual prompt, the user can click on or otherwise activate the submit option 220 to cause the revised collaborative content and/or the textual prompt from the prompt field 215 to be submitted to the generative model"; ¶[0040], "FIG. 3D shows an example in which the user has edited the collaborative content in the content pane 240 to add additional text. The user submits this as a prompt to the generative model, and the history section of the user interface 225 is updated to add the prompt 230d indicating that the user has edited the collaborative content. The revised collaborative content is shown in the content pane 240").
The combined system of Chanda and Wilde does not disclose an AI-based layout of a plurality of digital assets.
Xu discloses an AI-based layout of a plurality of digital assets (¶[0026], "At step 104, using the prompt and parameters, the system searches a database that stores previously-generated content items, such as images, for content items that may satisfy the prompt, and displays the search result content items. In response to displaying or providing the result content items, the system may receive an indication that a content item, such as an image, of the search results is selected as a closest match 104a. The system may then update the search of the database with the information that the closest match 104a is similar to the searched-for image 109. It may accordingly merge metadata connected with the closest match 104a with the original prompt 102 and generation parameters, and again execute a search at step 105. The system may then again display content items resulting from the updated search of the database of previously generated content items using the merged metadata, and receive a second closest match selection").
Therefore, 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 combined system of Chanda and Wilde in view of Xu to include an AI-based layout of a plurality of digital assets.
One of ordinary skill in the art would have been motivated because it would "reduce the iterations of prompting and generation, as well as the resource demand for AI generation computer systems" (Xu, ¶[0009]).
Regarding claim 12, the combined system of Chanda, Wilde and Xu discloses the invention substantially as applied to claim 11, above, wherein the plurality of digital assets, of the AI-based layout, are selected and arranged within the AI-based layout based on at least one of: a similarity of the identified digital asset and a particular digital asset of the plurality of digital assets, such that the trained machine learning model is trained to maximize the similarity of a model input and a model output; and a match between a feature of the AI-based digital asset and one or more of the desired features within a prompt (Xu, ¶[0039], "The image search engine 205 may return the top ranked images from the generated-image database 203 according to their ranking scores, which measures how similar an image is to the input prompt"; ¶[0042], "system 201 ranks the returned images 504 based on a similarity score, which may be a combination of several different components: a first component may be the similarity score between the input prompt embedding vector and the generated image embedding vector (i.e., a comparison of an analysis of a prompt to that of a content item); a second component may be the similarity score between the input prompt and the prompts used to generate the images in the database using their respective embedding vectors (i.e., a comparison of analyses of a given prompt and an earlier prompt in a database); a third component may be an image quality score, measured by Fréchet inception distance (FID) or other equivalent quality metric. Other components can contribute to the overall ranking such as image popularity, measured as the number of times that particular image received a “like” or selection for download").
Regarding claim 13, the combined system of Chanda, Wilde and Xu discloses the invention substantially as applied to claim 11, above, further including: receiving, from the client node, a feedback input for the trained machine learning model wherein the feedback input comprises at least one of (i) an identification of another digital asset selected by the user and (ii) a feedback prompt, and wherein the feedback prompt is a text-based or a voice-based description of desired features in a refined AI-based digital asset (Wilde, ¶[0036], "The user can collaborate with the AI to refine the collaborative content in a couple of ways. First, the user may directly edit the collaborative content included in the content pane 240, and the edited content is provided as a subsequent prompt to the generative model to further refine the generated content. Second, the user may enter a textual prompt in the prompt field 215, in manner similar to that shown in the user interface 205. As will be shown in the examples which follow, the user may both edit the collaborative content and provide a textual prompt in the prompt field 215 to provide as an input to the generative model. Once the user is satisfied with their edits to the collaborative content of the content pane 240 and/or the textual prompt, the user can click on or otherwise activate the submit option 220 to cause the revised collaborative content and/or the textual prompt from the prompt field 215 to be submitted to the generative model"; ¶[0040], "FIG. 3D shows an example in which the user has edited the collaborative content in the content pane 240 to add additional text. The user submits this as a prompt to the generative model, and the history section of the user interface 225 is updated to add the prompt 230d indicating that the user has edited the collaborative content. The revised collaborative content is shown in the content pane 240");
sending, from the server node, the feedback input received from the client node to the trained machine learning model (Wilde, ¶[0036], "provided as a subsequent prompt to the generative model"; Fig. 4, the generative model is accessible from the client via the server, thus communicating the prompt through the server is suggested); and
receiving, at the server node, the refined AI-based digital asset as output by the trained machine learning model, wherein the refined AI-based digital asset is an updated version of the AI-based digital asset based on the feedback input (Wilde, ¶[0040], "FIG. 3D shows an example in which the user has edited the collaborative content in the content pane 240 to add additional text. The user submits this as a prompt to the generative model, and the history section of the user interface 225 is updated to add the prompt 230d indicating that the user has edited the collaborative content. The revised collaborative content is shown in the content pane 240"; Fig. 4, the generative model is connected to the client via the server, thus communicating updates through the server is suggested).
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BORIS D GRIJALVA LOBOS whose telephone number is (571)272-0767. The examiner can normally be reached M-F 10:30AM to 6:30PM EST.
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/BORIS D GRIJALVA LOBOS/ Primary Patent Examiner, Art Unit 2496
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