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 . This Office Action is responsive to the communications filed on 23 June 2026. Claim 1-6, 8-14, and 16-19 are pending.
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.
Claims 1-6, 8-14, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Saleh (US 2025/0181215 A1) in view of Ben-Ishay et al. (Hereinafter, Ben-Ishay, US 2020/0042606 A1).
Per claim 1, Saleh discloses a computing system for generating context-aware content (e.g., Computing device 110 as shown in Fig. 1; Abstract; paragraph[0064]), the computing system comprising:
one or more processors(e.g., processing system 902 as shown in Fig. 9; paragraph[0101], “Referring still to FIG. 9, processing system 902 may comprise a micro-processor and other circuitry that retrieves and executes software 905 from storage system 903 …”); and
one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors(e.g., storage system 903 as shown in Fig. 9; paragraph [0102], “Storage system 903 may comprise any computer readable storage media readable by processing system 902 and capable of storing software 905 …”), cause the computing system to perform operations, the operations comprising:
providing a user interface (e.g., step 201 as shown in Fig. 2; paragraph [0050], ”The computing device displays a content canvas which is populated with content (step 201)…”; user interface 121 as shown in Fig. 1; paragraph [0064], “… Computing device 110 runs application 120 including causing a local user experience to be displayed via user interface 121 ... “) to a user computing system (e.g., Computing device 110 as shown in Fig. 1; paragraph [0064]), the user interface including a first content generation environment (e.g., board 132 as shown in Fig. 1; paragraph [0065], “In operational environment 100, application 120 displays board 132 including content items 136 in user interface 121. A user interacts with application 120 to generate content for board 132 ..”.) and a model interaction environment (e.g., suggest button 133 as shown in Fig. 1; paragraph [0065], “… Application 120 displays Suggest button 133 which invokes virtual assistant 122 to display suggestions generated by foundation model 150 based on the board state of board 132. “) within a first item of a first application(e.g., application 120 as shown in Fig. 1; paragraph [0064]);
providing first content associated with the first application automatically to a generative model(e.g., steps 203-205 as shown in Fig. 2; paragraph [0017], “… In an implementation, the application prompts a foundation model to provide suggestions for enhancing or improving the content of a content board or canvas. Rather than waiting for a user to request assistance with his or her content, the application automatically and proactively captures information from the canvas and supplies it to the foundation model to generate suggestions ....”; paragraph [0052], “ The computing device captures a state of the content canvas (step 203). In an implementation, the computing device captures the canvas or board state (i.e., contextual information from the canvas or board) for a prompt for the foundation model ... “; paragraph [0053], “Continuing with process 200, the computing device generates a prompt for a foundation model for follow-on prompts for generating enhancements to the content based on contextual information (step 205)….”; Examiner’s Note: Saleh discloses proactively and continually prompting a generative model to suggest content. );
receiving context-aware content generated by the generative model based at least in part on the first content(e.g., step 207 as shown in Fig. 2; paragraph [0058], “The computing device displays suggestion components corresponding to the follow-on prompts in the user interface (step 207). In an implementation, the computing device displays natural language suggestions for each of the follow-on prompts generated by the foundation model …” ); and
presenting the context-aware content using the model interaction environment of the user interface(e.g., steps 209-211 as shown in Fig. 2; paragraph [0060], “When the computing device receives user input selecting a suggestion component, the computing device sends the corresponding follow-on prompt to the foundation model (step 209)…”; . paragraph [0062], “When the computing device receives the reply from the foundation model, the computing device populates the whiteboard with the enhancement (step 211)…”; Examiner’s Note: Saleh discloses displays Suggest button 133 which invokes virtual assistant 122 to display suggestions generated by generative or foundation model 150 based on the context of board 132. ).
Saleh does not expressly disclose:
wherein the context-aware content is generated by the generative model using a reference to a source; and
presenting the reference to the source using the model interaction environment of the user interface, wherein the source is a second item of a second application, the first application and the second application being different applications that are part of a productivity suite.
Ben-Ishay discloses:
wherein the context-aware content is generated by the generative model using a reference to a source (e.g., blocks 332 to 334 as shown in Fig. 4B; Abstract; paragraph [0034]; paragraph [0049]; paragraphs [0083-0084]; Examiner’s Note: Ben-Ishay teaches tracking the source of ingested content by generating a link to ingested content; generating restructured output; and integrating the output with an active application. Ben-Ishay teaches a system for dynamic, application-aware content ingestion that maintains live data synchronization between a source document and a generated document. ); and
presenting the reference to the source using the model interaction environment of the user interface (e.g., block 336 as shown Fig. 4B;paragraph [0085], “User interface logic 150 is then used to display the restructured content, using the content generation application. This is indicated by block 336. At the same time, conversational UI logic 180, and feedback logic 204, generate a conversational user interface display that requests user feedback on the restructured content. This is indicated by block 338.” ), wherein the source is a second item of a second application (Abstract; paragraph [0007]), the first application and the second application being different applications that are part of a productivity suite (e.g., content generation computing system 102 as shown in Fig. 1; paragraph [0004];paragraph [0025]; paragraph [0034], “The word processing application illustratively includes the add-in logic 158 which allows user 116 to specify source content generated using a different type of content generating application (such as content from a slide presentation document) to be ingested into the word processing document that user 116 is generating. In doing so, content ingestion and transformation add-in logic 158 generates a link to the source slide presentation document which contains the source content that is ingested into the word processing document being created by user 116….”; paragraph [0048]; paragraph [0111]; Examiner’s Note: Ben-Ishay teaches a system where a word processing application uses an add-in to pull content from an external source (e.g., a slide presentation) and keep it synchronized. ).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use the cross-application ingestion of Ben-Ishay in the content generation device of Saleh to improve content generation as suggested by Ben-Ishay (paragraph[0130]).
Per claim teaches 2, Saleh and Ben-Ishay disclose the computing system of claim 1,
wherein the first content comprises user-generated content within the first content generation environment (Saleh, e.g., step 201 as shown in Fig. 2;paragraph [0027]; paragraph [0050]; paragraph [0086]; Examiner’s Note: Saleh discloses an application allowing a user to add content items to a board, i.e., a first content generation environment.), and
wherein the context-aware content comprises a summary of the first content (Saleh paragraph [0062], “… If the foundation model returns a summary of the contents of the canvas, the computing device may display the summary in a specially formatted and labeled textbox on the project canvas …. “).
Per claim 3, Saleh and Ben-Ishay disclose the computing system of claim 1,
wherein the first content comprises at least one of user-generated content within the first content generation environment (Saleh e.g., content items 136 as shown in Fig. 1 as shown in Fig. 1; paragraph [0046], “ A brief operational scenario of operational environment 100 follows. A user of computing device 110 interacts with application 120 hosting board 132 displayed in user interface 121. User experiences 131(a), 131(b), and 131(c) include Suggest button 133 and content items 136 …. “) or functionalities of the first application (Saleh e.g., suggest button 133 as shown in Fig. 1; paragraph [0065], “ … Application 120 displays Suggest button 133 which invokes virtual assistant 122 to display suggestions generated by foundation model 150 based on the board state of board 132. “ ), and
wherein the context-aware content comprises a suggested prompt submittable to the generative model upon request by a user (Saleh, paragraph [0066], “Virtual assistant 122 prompts foundation model 150 to generate follow-on prompts which will task foundation model 150 with generating content for enhancing or improving the content of board 132…”).
Per claim 4, Saleh and Ben-Ishay disclose the computing system of claim 3, the operations further comprising:
receiving a request from the user via the user interface to submit the suggested prompt to the generative model(Saleh, e.g., step 201 as shown in Fig. 2; paragraph [0050], ”The computing device displays a content canvas which is populated with content (step 201)…”; user interface 121 as shown in Fig. 1; paragraph [0064], “… Computing device 110 runs application 120 including causing a local user experience to be displayed via user interface 121 ... “; Examiner’s Note: Saleh proactively and continually captures contextual information and sends a prompt to a generative model as the user interacts with content in the user interface.);
providing the suggested prompt to the generative model (Saleh, e.g., steps 203-205 as shown in Fig. 2; paragraph [0017], “… In an implementation, the application prompts a foundation model to provide suggestions for enhancing or improving the content of a content board or canvas. Rather than waiting for a user to request assistance with his or her content, the application automatically and proactively captures information from the canvas and supplies it to the foundation model to generate suggestions ....”; paragraph [0052], “ The computing device captures a state of the content canvas (step 203). In an implementation, the computing device captures the canvas or board state (i.e., contextual information from the canvas or board) for a prompt for the foundation model ... “; paragraph [0053], “Continuing with process 200, the computing device generates a prompt for a foundation model for follow-on prompts for generating enhancements to the content based on contextual information (step 205)….”; Examiner’s Note: Saleh discloses proactively and continually prompting a generative model to suggest content. );
receiving responsive content generated by the generative model in response to providing the suggested prompt to the generative model(Saleh, e.g., step 207 as shown in Fig. 2; paragraph [0058], “The computing device displays suggestion components corresponding to the follow-on prompts in the user interface (step 207). In an implementation, the computing device displays natural language suggestions for each of the follow-on prompts generated by the foundation model …” ); and
presenting the responsive content via the user interface(Saleh, e.g., steps 209-211 as shown in Fig. 2; paragraph [0060], “When the computing device receives user input selecting a suggestion component, the computing device sends the corresponding follow-on prompt to the foundation model (step 209)…”; . paragraph [0062], “When the computing device receives the reply from the foundation model, the computing device populates the whiteboard with the enhancement (step 211)…”; Examiner’s Note: Saleh discloses displays Suggest button 133 which invokes virtual assistant 122 to display suggestions generated by generative or foundation model 150 based on the context of board 132. ).
Per claim 5, Saleh and Ben-Ishay disclose the computing system of claim 1, the operations further comprising:
receiving a user-generated prompt via the model interaction environment of the first application(Saleh, e.g., user clicks the Suggest button 521 as shown in Fig. 5A; paragraph [0080], “In user experience 510(a) of FIG. 5A, a user clicks the Suggest button 521 to display titles of the follow-on prompts in the form of natural language suggestions for enhancing the content of a project displayed on board 522….” ); and
submitting the user-generated prompt to the generative model (Saleh, paragraph [0081], ” The foundation model is also prompted to generate titles for each follow-on prompt which will be displayed to the user. In various implementations, the follow-on prompts are not displayed to the user, so the user's selection is based on the titles. The foundation model may be tasked with generating multiple different follow-on prompts, (e.g., at least three but no more than five) and to limit the size (e.g., character or word length) of the titles for display.”), (e.g., user experience 510(b) as shown in FIG. 5B; paragraph [0083]),
wherein providing the first content associated with the first application automatically to the generative model comprises providing functionalities of the first application associated with the first application automatically to the generative model(Saleh, e.g., menu 134 as shown in Fig. 1; paragraph [0046], “… When the user clicks or selects Suggest button 133, virtual assistant 122 causes menu 134 to be displayed which includes suggestion components labeled with titles that were generated by foundation model 150 based on contextual information of board 132, such as the text content of content items 136. In menu 134, the user can select a suggestion which will cause virtual assistant 122 to prompt foundation model 150 to create content in accordance with the suggestion. The user can also delete suggestion components which the user deems unsuitable. “).
Per claim 6, Saleh and Ben-Ishay disclose the computing system of claim 5, wherein providing the first content associated with the first application automatically to the generative model comprises providing the first content associated with the first application automatically to the generative model when submitting the user-generated prompt to the generative model (Saleh, paragraph [0079], “FIGS. 5A-5E illustrate user experiences of operational scenario 500 for a proactive prompting for content generation via a foundation model integration of an application, such as a project planning application, in an implementation. In user experience 510(a) of FIG. 5A, the application generates a prompt for submission to a foundation model which tasks the foundation model with suggesting follow-on prompts which task the model with generating suggestions or ideas for enhancing the content of board 522.”).
Per claim 8, Saleh and Ben-Ishay disclose the computing system of claim 7, wherein the user-generated prompt specifies the source (Saleh, paragraph [0020], “The board state includes information and/or metadata for notes (e.g., virtual sticky notes), posts, or other content items on the board, such as the text content of the content items, the authors of the various content items, a record of modifications to the content items, and reactions of other users (e.g., emojis) to the content items.”; paragraph [0088]).
Per claim 10, Saleh discloses a computer-implemented method for generating context-aware content, the computer-implemented method comprising:
providing, by a computing system, a user interface (Saleh, e.g., step 201 as shown in Fig. 2; paragraph [0050], ”The computing device displays a content canvas which is populated with content (step 201)…”; user interface 121 as shown in Fig. 1; paragraph [0064], “… Computing device 110 runs application 120 including causing a local user experience to be displayed via user interface 121 ... “) to a user computing system (e.g., Computing device 110 as shown in Fig. 1; paragraph [0064]), the user interface including a first content generation environment (e.g., board 132 as shown in Fig. 1; paragraph [0065], “In operational environment 100, application 120 displays board 132 including content items 136 in user interface 121. A user interacts with application 120 to generate content for board 132 ..”.) and a model interaction environment (e.g., suggest button 133 as shown in Fig. 1; paragraph [0065], “… Application 120 displays Suggest button 133 which invokes virtual assistant 122 to display suggestions generated by foundation model 150 based on the board state of board 132. “) within a first item of a first application(e.g., application 120 as shown in Fig. 1; paragraph [0064]);
providing first content associated with the first application automatically to a generative model(Saleh, e.g., steps 203-205 as shown in Fig. 2; paragraph [0017], “… In an implementation, the application prompts a foundation model to provide suggestions for enhancing or improving the content of a content board or canvas. Rather than waiting for a user to request assistance with his or her content, the application automatically and proactively captures information from the canvas and supplies it to the foundation model to generate suggestions ....”; paragraph [0052], “ The computing device captures a state of the content canvas (step 203). In an implementation, the computing device captures the canvas or board state (i.e., contextual information from the canvas or board) for a prompt for the foundation model ... “; paragraph [0053], “Continuing with process 200, the computing device generates a prompt for a foundation model for follow-on prompts for generating enhancements to the content based on contextual information (step 205)….”; Examiner’s Note: Saleh discloses proactively and continually prompting a generative model to suggest content. );
receiving, by a computing system, context-aware content generated by the generative model based at least in part on the first content(Saleh, e.g., step 207 as shown in Fig. 2; paragraph [0058], “The computing device displays suggestion components corresponding to the follow-on prompts in the user interface (step 207). In an implementation, the computing device displays natural language suggestions for each of the follow-on prompts generated by the foundation model …” ); and
presenting, by a computing system, the context-aware content using the model interaction environment of the user interface(e.g., steps 209-211 as shown in Fig. 2; paragraph [0060], “When the computing device receives user input selecting a suggestion component, the computing device sends the corresponding follow-on prompt to the foundation model (step 209)…”; . paragraph [0062], “When the computing device receives the reply from the foundation model, the computing device populates the whiteboard with the enhancement (step 211)…”; Examiner’s Note: Saleh discloses displays Suggest button 133 which invokes virtual assistant 122 to display suggestions generated by generative or foundation model 150 based on the context of board 132. ).
Saleh does not expressly disclose:
wherein the context-aware content is generated by the generative model using a reference to a source; and
presenting the reference to the source using the model interaction environment of the user interface, wherein the source is a second item of a second application, the first application and the second application being different applications that are part of a productivity suite.
Ben-Ishay discloses:
wherein the context-aware content is generated by the generative model using a reference to a source (e.g., blocks 332 to 334 as shown in Fig. 4B; Abstract; paragraph [0034]; paragraph [0049]; paragraphs [0083-0084]; Examiner’s Note: Ben-Ishay teaches tracking the source of ingested content by generating a link to ingested content; generating restructured output; and integrating the output with an active application. Ben-Ishay teaches a system for dynamic, application-aware content ingestion that maintains live data synchronization between a source document and a generated document. ); and
presenting, by the computing system, the reference to the source using the model interaction environment of the user interface (e.g., block 336 as shown Fig. 4B;paragraph [0085], “User interface logic 150 is then used to display the restructured content, using the content generation application. This is indicated by block 336. At the same time, conversational UI logic 180, and feedback logic 204, generate a conversational user interface display that requests user feedback on the restructured content. This is indicated by block 338.” ), wherein the source is a second item of a second application (Abstract; paragraph [0007]), the first application and the second application being different applications that are part of a productivity suite (e.g., content generation computing system 102 as shown in Fig. 1; paragraph [0004];paragraph [0025]; paragraph [0034], “The word processing application illustratively includes the add-in logic 158 which allows user 116 to specify source content generated using a different type of content generating application (such as content from a slide presentation document) to be ingested into the word processing document that user 116 is generating. In doing so, content ingestion and transformation add-in logic 158 generates a link to the source slide presentation document which contains the source content that is ingested into the word processing document being created by user 116….”; paragraph [0048]; paragraph [0111]; Examiner’s Note: Ben-Ishay teaches a system where a word processing application uses an add-in to pull content from an external source (e.g., a slide presentation) and keep it synchronized. ).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use the cross-application ingestion of Ben-Ishay in the content generation device of Saleh to improve content generation as suggested by Ben-Ishay (paragraph[0130]).
Per claim 11, Saleh and Ben-Ishay disclose the computer-implemented method of claim 10,
wherein providing the first content comprises providing user-generated content within the first content generation environment(Saleh, e.g., step 201 as shown in Fig. 2;paragraph [0027]; paragraph [0050]; paragraph [0086]; Examiner’s Note: Saleh discloses an application allowing a user to add content items to a board, i.e., a first content generation environment.), and
wherein receiving the context-aware content comprises receiving a summary of the first content(Saleh, paragraph [0062], “… If the foundation model returns a summary of the contents of the canvas, the computing device may display the summary in a specially formatted and labeled textbox on the project canvas …. “).
Per claim 12, Saleh and Ben-Ishay disclose the computer-implemented method of claim 10,
wherein providing the first content comprises providing at least one of user-generated content within the first content generation environment (Saleh, e.g., content items 136 as shown in Fig. 1 as shown in Fig. 1; paragraph [0046], “ A brief operational scenario of operational environment 100 follows. A user of computing device 110 interacts with application 120 hosting board 132 displayed in user interface 121. User experiences 131(a), 131(b), and 131(c) include Suggest button 133 and content items 136 …. “) or functionalities of the first application, and
wherein receiving the context-aware content comprises receiving a suggested prompt submittable to the generative model upon request by a user (Saleh, paragraph [0066], “Virtual assistant 122 prompts foundation model 150 to generate follow-on prompts which will task foundation model 150 with generating content for enhancing or improving the content of board 132…”).
Per claim 13, Saleh and Ben-Ishay disclose the computer-implemented method of claim 12, further comprising:
receiving, by the computing system, a request from the user via the user interface to submit the suggested prompt to the generative model(Saleh, e.g., step 201 as shown in Fig. 2; paragraph [0050], ”The computing device displays a content canvas which is populated with content (step 201)…”; user interface 121 as shown in Fig. 1; paragraph [0064], “… Computing device 110 runs application 120 including causing a local user experience to be displayed via user interface 121 ... “; Examiner’s Note: Saleh proactively and continually captures contextual information and sends a prompt to a generative model as the user interacts with content in the user interface.);
providing, by the computing system, the suggested prompt to the generative model (Saleh, e.g., steps 203-205 as shown in Fig. 2; paragraph [0017], “… In an implementation, the application prompts a foundation model to provide suggestions for enhancing or improving the content of a content board or canvas. Rather than waiting for a user to request assistance with his or her content, the application automatically and proactively captures information from the canvas and supplies it to the foundation model to generate suggestions ....”; paragraph [0052], “ The computing device captures a state of the content canvas (step 203). In an implementation, the computing device captures the canvas or board state (i.e., contextual information from the canvas or board) for a prompt for the foundation model ... “; paragraph [0053], “Continuing with process 200, the computing device generates a prompt for a foundation model for follow-on prompts for generating enhancements to the content based on contextual information (step 205)….”; Examiner’s Note: Saleh discloses proactively and continually prompting a generative model to suggest content. );
receiving, by the computing system, responsive content generated by the generative model in response to providing the suggested prompt to the generative model(Saleh, e.g., step 207 as shown in Fig. 2; paragraph [0058], “The computing device displays suggestion components corresponding to the follow-on prompts in the user interface (step 207). In an implementation, the computing device displays natural language suggestions for each of the follow-on prompts generated by the foundation model …” ); and
presenting, by the computing system, the responsive content via the user interface(Saleh, e.g., steps 209-211 as shown in Fig. 2; paragraph [0060], “When the computing device receives user input selecting a suggestion component, the computing device sends the corresponding follow-on prompt to the foundation model (step 209)…”; . paragraph [0062], “When the computing device receives the reply from the foundation model, the computing device populates the whiteboard with the enhancement (step 211)…”; Examiner’s Note: Saleh discloses displays Suggest button 133 which invokes virtual assistant 122 to display suggestions generated by generative or foundation model 150 based on the context of board 132. ).
Per claim 14, Saleh and Ben-Ishay disclose the computer-implemented method of claim 10, further comprising:
receiving, by the computing system, a user-generated prompt via the model interaction environment of the first application(Saleh, e.g., user clicks the Suggest button 521 as shown in Fig. 5A; paragraph [0080], “In user experience 510(a) of FIG. 5A, a user clicks the Suggest button 521 to display titles of the follow-on prompts in the form of natural language suggestions for enhancing the content of a project displayed on board 522….” ); and
submitting, by the computing system, the user-generated prompt to the generative model (Saleh, paragraph [0081], ” The foundation model is also prompted to generate titles for each follow-on prompt which will be displayed to the user. In various implementations, the follow-on prompts are not displayed to the user, so the user's selection is based on the titles. The foundation model may be tasked with generating multiple different follow-on prompts, (e.g., at least three but no more than five) and to limit the size (e.g., character or word length) of the titles for display.”), (e.g., user experience 510(b) as shown in FIG. 5B; paragraph [0083]),
wherein providing the first content associated with the first application automatically to the generative model comprises providing functionalities of the first application associated with the first application automatically to the generative model(Saleh, e.g., menu 134 as shown in Fig. 1; paragraph [0046], “… When the user clicks or selects Suggest button 133, virtual assistant 122 causes menu 134 to be displayed which includes suggestion components labeled with titles that were generated by foundation model 150 based on contextual information of board 132, such as the text content of content items 136. In menu 134, the user can select a suggestion which will cause virtual assistant 122 to prompt foundation model 150 to create content in accordance with the suggestion. The user can also delete suggestion components which the user deems unsuitable. “).
Per claim 16, Saleh discloses one or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more computing devices (e.g., storage system 903 as shown in Fig. 9; paragraph [0102], “Storage system 903 may comprise any computer readable storage media readable by processing system 902 and capable of storing software 905 …”), cause the one or more computing devices to perform operations, the operations comprising:
providing a user interface (e.g., step 201 as shown in Fig. 2; paragraph [0050], ”The computing device displays a content canvas which is populated with content (step 201)…”; user interface 121 as shown in Fig. 1; paragraph [0064], “… Computing device 110 runs application 120 including causing a local user experience to be displayed via user interface 121 ... “) to a user computing system (e.g., Computing device 110 as shown in Fig. 1; paragraph [0064]), the user interface including a first content generation environment (e.g., board 132 as shown in Fig. 1; paragraph [0065], “In operational environment 100, application 120 displays board 132 including content items 136 in user interface 121. A user interacts with application 120 to generate content for board 132 ..”.) and a model interaction environment (e.g., suggest button 133 as shown in Fig. 1; paragraph [0065], “… Application 120 displays Suggest button 133 which invokes virtual assistant 122 to display suggestions generated by foundation model 150 based on the board state of board 132. “) within a first item of a first application(e.g., application 120 as shown in Fig. 1; paragraph [0064]);
providing first content associated with the first application automatically to a generative model(e.g., steps 203-205 as shown in Fig. 2; paragraph [0017], “… In an implementation, the application prompts a foundation model to provide suggestions for enhancing or improving the content of a content board or canvas. Rather than waiting for a user to request assistance with his or her content, the application automatically and proactively captures information from the canvas and supplies it to the foundation model to generate suggestions ....”; paragraph [0052], “ The computing device captures a state of the content canvas (step 203). In an implementation, the computing device captures the canvas or board state (i.e., contextual information from the canvas or board) for a prompt for the foundation model ... “; paragraph [0053], “Continuing with process 200, the computing device generates a prompt for a foundation model for follow-on prompts for generating enhancements to the content based on contextual information (step 205)….”; Examiner’s Note: Saleh discloses proactively and continually prompting a generative model to suggest content. );
receiving context-aware content generated by the generative model based at least in part on the first content(e.g., step 207 as shown in Fig. 2; paragraph [0058], “The computing device displays suggestion components corresponding to the follow-on prompts in the user interface (step 207). In an implementation, the computing device displays natural language suggestions for each of the follow-on prompts generated by the foundation model …” ); and
presenting the context-aware content using the model interaction environment of the user interface(e.g., steps 209-211 as shown in Fig. 2; paragraph [0060], “When the computing device receives user input selecting a suggestion component, the computing device sends the corresponding follow-on prompt to the foundation model (step 209)…”; . paragraph [0062], “When the computing device receives the reply from the foundation model, the computing device populates the whiteboard with the enhancement (step 211)…”; Examiner’s Note: Saleh discloses displays Suggest button 133 which invokes virtual assistant 122 to display suggestions generated by generative or foundation model 150 based on the context of board 132. ).
Saleh does not expressly disclose:
wherein the context-aware content is generated by the generative model using a reference to a source; and
presenting the reference to the source using the model interaction environment of the user interface, wherein the source is a second item of a second application, the first application and the second application being different applications that are part of a productivity suite.
Ben-Ishay discloses:
wherein the context-aware content is generated by the generative model using a reference to a source (e.g., blocks 332 to 334 as shown in Fig. 4B; Abstract; paragraph [0034]; paragraph [0049]; paragraphs [0083-0084]; Examiner’s Note: Ben-Ishay teaches tracking the source of ingested content by generating a link to ingested content; generating restructured output; and integrating the output with an active application. Ben-Ishay teaches a system for dynamic, application-aware content ingestion that maintains live data synchronization between a source document and a generated document. ); and
presenting the reference to the source using the model interaction environment of the user interface (e.g., block 336 as shown Fig. 4B;paragraph [0085], “User interface logic 150 is then used to display the restructured content, using the content generation application. This is indicated by block 336. At the same time, conversational UI logic 180, and feedback logic 204, generate a conversational user interface display that requests user feedback on the restructured content. This is indicated by block 338.” ), wherein the source is a second item of a second application (Abstract; paragraph [0007]), the first application and the second application being different applications that are part of a productivity suite (e.g., content generation computing system 102 as shown in Fig. 1; paragraph [0004];paragraph [0025]; paragraph [0034], “The word processing application illustratively includes the add-in logic 158 which allows user 116 to specify source content generated using a different type of content generating application (such as content from a slide presentation document) to be ingested into the word processing document that user 116 is generating. In doing so, content ingestion and transformation add-in logic 158 generates a link to the source slide presentation document which contains the source content that is ingested into the word processing document being created by user 116….”; paragraph [0048]; paragraph [0111]; Examiner’s Note: Ben-Ishay teaches a system where a word processing application uses an add-in to pull content from an external source (e.g., a slide presentation) and keep it synchronized. ).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use the cross-application ingestion of Ben-Ishay in the content generation device of Saleh to improve content generation as suggested by Ben-Ishay (paragraph[0130]).
Per claim 17, Saleh and Ben-Ishay disclose the one or more non-transitory computer-readable media of claim 16,
wherein the first content comprises user-generated content within the first content generation environment (Saleh, e.g., step 201 as shown in Fig. 2;paragraph [0027]; paragraph [0050]; paragraph [0086]; Examiner’s Note: Saleh discloses an application allowing a user to add content items to a board, i.e., a first content generation environment.), and
wherein the context-aware content comprises a summary of the first content (Saleh, paragraph [0062], “… If the foundation model returns a summary of the contents of the canvas, the computing device may display the summary in a specially formatted and labeled textbox on the project canvas …. “).
Per claim 18, Saleh and Ben-Ishay disclose the one or more non-transitory computer-readable media of claim 16,
wherein the first content comprises at least one of user-generated content within the first content generation environment (Saleh, e.g., content items 136 as shown in Fig. 1 as shown in Fig. 1; paragraph [0046], “ A brief operational scenario of operational environment 100 follows. A user of computing device 110 interacts with application 120 hosting board 132 displayed in user interface 121. User experiences 131(a), 131(b), and 131(c) include Suggest button 133 and content items 136 …. “) or functionalities of the first application (e.g., suggest button 133 as shown in Fig. 1; paragraph [0065], “ … Application 120 displays Suggest button 133 which invokes virtual assistant 122 to display suggestions generated by foundation model 150 based on the board state of board 132. “ ), and
wherein the context-aware content comprises a suggested prompt submittable to the generative model upon request by a user (Saleh, paragraph [0066], “Virtual assistant 122 prompts foundation model 150 to generate follow-on prompts which will task foundation model 150 with generating content for enhancing or improving the content of board 132…”).
Per claim 19, Saleh and Ben-Ishay disclose the one or more non-transitory computer-readable media of claim 16, the operations further comprising:
receiving a user-generated prompt via the model interaction environment of the first application(Saleh, e.g., user clicks the Suggest button 521 as shown in Fig. 5A; paragraph [0080], “In user experience 510(a) of FIG. 5A, a user clicks the Suggest button 521 to display titles of the follow-on prompts in the form of natural language suggestions for enhancing the content of a project displayed on board 522….” ); and
submitting the user-generated prompt to the generative model (Saleh, paragraph [0081], ” The foundation model is also prompted to generate titles for each follow-on prompt which will be displayed to the user. In various implementations, the follow-on prompts are not displayed to the user, so the user's selection is based on the titles. The foundation model may be tasked with generating multiple different follow-on prompts, (e.g., at least three but no more than five) and to limit the size (e.g., character or word length) of the titles for display.”), (e.g., user experience 510(b) as shown in FIG. 5B; paragraph [0083]),
wherein providing the first content associated with the first application automatically to the generative model comprises providing functionalities of the first application associated with the first application automatically to the generative model(Saleh, e.g., menu 134 as shown in Fig. 1; paragraph [0046], “… When the user clicks or selects Suggest button 133, virtual assistant 122 causes menu 134 to be displayed which includes suggestion components labeled with titles that were generated by foundation model 150 based on contextual information of board 132, such as the text content of content items 136. In menu 134, the user can select a suggestion which will cause virtual assistant 122 to prompt foundation model 150 to create content in accordance with the suggestion. The user can also delete suggestion components which the user deems unsuitable. “).
Conclusion
Statement of the Interview
Examiner acknowledges applicant’s statements regarding the telephone interview on June 8, 2026.
Independent Claim 1
Applicant's arguments filed 23 June 2026 have been fully considered but they are not persuasive.
On pages 7-9, Applicant argues, with respect to claim 1, that “Saleh does not teach or suggest "receiving context- aware content generated by the generative model based at least in part on the first content, wherein the context-aware content is generated by the generative model using a reference to a source; and presenting the context-aware content and the reference to the source using the model interaction environment of the user interface, wherein the source is a second item of a second application, the first application and the second application being different applications that are part of a productivity suite,” since “the system of Saleh is limited to a single application instead of extending across multiple applications.”
Examiner disagrees since Saleh was not relied upon to disclose “wherein the context-aware content is generated by the generative model using a reference to a source; and presenting the context-aware content and the reference to the source using the model interaction environment of the user interface, wherein the source is a second item of a second application, the first application and the second application being different applications that are part of a productivity suite” in the above rejection of the amended claims.
Independent Claims 10 and 16
Independent claims 10 and 16 have been amended to recite similar limitations to amended claim 1. Examiner submits that, for at least the reasons discussed above with respect to claim 1, claims 10 and 16 are not patentably distinct from the cited art. Thus, for at least the reasons set forth above with respect to claim 1, claims 10 and 16 are not patentable over Saleh in view of Ben-Ishay. Examiner respectfully submits that claims 10 and 16 are not patentably distinct from the cited art and are allowable.
Dependent Claims
Examiner further submits that for at least the reasons relating to corresponding
independent claims, the pending dependent claims are not in condition for allowance.
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 DARRIN HOPE whose telephone number is (571)270-5079. The examiner can normally be reached Mon-Thr - 6:45-4:15, Fri - 6:45-3:15, Alt. Fri Off.
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DARRIN HOPE
Examiner
Art Unit 2178
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
