CTNF 18/459,233 CTNF 98440 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement The information disclosure statement(s) (IDS) submitted on 12/03/2024 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner. Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claims 1-5, 8, 1 1-14, 16, and 19 are rejec ted under 35 U.S.C. 103 as being unpatentable over BRADE A (US 20250068893 A1) in view of CHALLA (US 10978056 B1), hereafter BRADEA and CHALLA respectively. Regar ding Claim 1: BRADEA teaches: A method comprising: responsive to a first use of a first application by a first user, configuring, in a first prompt, at least one instruction based on first application context data and first user context data (BRADEA [0004] teaches: "In an example method, a personalization module includes a processing device that receives an indication that a user has interacted with content displayed on a web page. The personalization module then receives a set of attributes, including information about the user (i.e., first user context data ) and information about the content (i.e., first application context data ), as well as information about a segment to which the user belongs. The personalization module next receives a tuning parameter for a generative AI model, in which the tuning parameter controls the randomness of the output of the generative AI model.” BRADEA [0024] teaches: "In some examples, the generative AI may be periodically refined using previous prompts, previous responses including personalized content, information about the user interactions (i.e., first use of a first application by a first user ), and other information." BRADEA [0047] teaches: "For example, a typical example prompt generated based on the product description 170, the attributes 102, information about the events 103, the user segment information 104, and the tuning parameter 160 (i.e., configuring, in a first prompt, at least one instruction based on first application context data and first user context data ) may be: “Generate a new product description for a grey hooded fleece. The description is currently ‘Experience Comfort and Style with our Hooded Fleece—the Ultimate Hoodie for the Active Woman!’ The user is a 37 year old female. She is a Samsung phone user and enjoys mountain activities and trekking. She has added this item to her favorites (i.e., responsive to a first use of a first application by the first user, configuring, in a first prompt ). 3 other people have added this item to their favorites or purchased the item in the last month.” This prompt may be sent to the generative AI 150 along with instructions to use a specific temperature. For example, the prompt and temperature may be sent to an API provided by generative AI 150, in which both the prompt and the temperature are included as objects in a data structure, like a JSON object.”) wherein the first application context data is included in the first use of the first application by the first user ; (BRADEA [0047] teaches: "For example, a typical example prompt generated based on the product description 170, the attributes 102, information about the events 103, the user segment information 104, and the tuning parameter 160 may be: “Generate a new product description for a grey hooded fleece. The description is currently ‘Experience Comfort and Style with our Hooded Fleece—the Ultimate Hoodie for the Active Woman!’ The user is a 37 year old female. She is a Samsung phone user and enjoys mountain activities and trekking. She has added this item to her favorites.” BRADEA [0086] teaches: "The indication that the user 202 has interacted with content received by personalization module 120 may include a data structure that includes information about the events that have taken place on web page that correspond to actions taken by the user or users, as well as identifiers of the associated content." Examiner’s note: Specification paragraph [0043] states “ Application context data as used herein may refer to data logged during the user’s use of a particular application, such as data input, output, or interacted with, the timestamp at the user’s login in to the application, and actions taken by the user during the login session, including implicit and/or explicit user interactions with the application’s user interface elements.” Under BRI, first application context data encompass the interactions of the user during the current active session, such as adding an item to favorites, which is used to generate the prompt, and thus included in the use of the application by the user.) storing the first prompt in a memory that is accessible to the first application and a second application ; (BRADEA [0070] teaches: "The content generation subsystem 130 includes database 270. […] Database 270 may be used to store information about user segments, products, calculated temperature values, events, generated prompts (i.e., storing the first prompt in a memory ) and received responses, and so on.” BRADEA [0085] teaches: “For example, clicking the “Add to Cart” button may cause a web request to be sent to a particular application programming interface (API) hosted by the personalization module 120, which may then store information about the interactions of user 202.” BRADEA [0072] teaches: "Content generation subsystem 130 includes scheduling service 330 for periodically refining the generative AI 150. The scheduling service 330, at a configurable periodicity, can query database 270 for information about user segments, products, calculated temperature values, events, generated prompts and received responses, and other data indexed according to user or product." BRADEA [0020] teaches: “The personalization module next or in parallel receives a set of attributes, including information about the user and information about the content. […] For instance, the user attributes may be received from a user database based on identifying information about the user such as login metadata. The product attributes may be received an API provided by the e-commerce website (i.e., that is accessible to the first application ).” BRADEA [0025] teaches: "The personalization module displays (i.e., second application ) the personalized content in a dynamic content field associated with the content displayed on the web page. In this example, the product description is updated based on the personalized content returned by the generative AI.” BRADEA [0037] teaches: “The content generation subsystem 130 may be configured to cause a generative AI 150 to generate new or updated content based on prompts tailored to improve the personalization of content for a particular user or group of users.” Examiner’s note: Database 270 stores indexed information associated with a particular user and product information. The e-commerce website provides the attributes via an API, and the personalization module that includes the generation subsystem can receive information about the user, and thus accessible to database 270. Therefore, the e- commerce website ( first application ) and the personalization module (i.e., second application ) that includes the generation subsystem have access to the memory.) via the second application, presenting first output of a generative artificial intelligence (GAI) model to the first user, (BRADEA [0025] teaches: "The personalization module displays (i.e., via the second application ) the personalized content (i.e., presenting first output ) in a dynamic content field associated with the content displayed on the web page. In this example, the product description is updated based on the personalized content returned by the generative AI (i.e., first output of a generative artificial intelligence (GAI) model ).” BRADEA [0037] teaches: “The content generation subsystem 130 may be configured to cause a generative AI 150 to generate new or updated content based on prompts tailored to improve the personalization of content for a particular user (i.e., to the first user ) or group of users.”) wherein the first output is responsive to the first prompt (BRADEA [0024] teaches: "The response (i.e., the first output ) may be generated by the generative AI in the context of the received prompt (i.e., is responsive to the first prompt ) as well as previous prompts and responses from the same session.") and the first output is based on at least the first user context data; (BRADEA [0024] teaches: "The response may be generated by the generative AI in the context of the received prompt as well as previous prompts and responses from the same session." BRADEA [0091] teaches: “At block 508, the personalization module 120 determines a tuning parameter 160 for a generative AI 150, wherein the tuning parameter 160 controls the randomness of the output of the generative AI 150.” BRADEA [0092] teaches: “However, other data may be used to determine the tuning parameter 160 including the user or product attributes, segmentation data, historical prompt (i.e., first user context data ), response, and temperature data, and other behavioral data about the user 202.”) based on the first output of the GAI model, configuring at least one of a second use of the first application by the first user […] (BRADEA [0027] teaches: "Moreover, the results of using the generated content in the real world as measured by, for example, user interaction with the generated content (i.e., based on the first output of the GAI model ) can be used to generate still more content (i.e., configuring at least one of a second use of the first application by the first user ) in a positive feedback loop." BRADEA [0024] teaches: "Personalized content including graphical, audio, video, or other forms may be generated by suitable implementations of generative AIs." BRADEA [FIG. 4B] and [0080] teaches 4 possible AI generated updates to the product description of the content displayed on screen. The AI generated content changes the words in the product description, and thus under BRI, configuring at least one of a second use of the first application by the first user can be interpreted as the user using the e-commerce application with the updated content based output of the generative AI model.) However, BRADEA is not relied upon for teaching, but CHALLA teaches: based on the […] output […], configuring […] a first use of a third application by the first user , (CHALLA [col. 6, lines 59-61] teaches: "In particular embodiments, the assistant application 136 may be integrated into the social-networking application 134 or another suitable application (e.g., a messaging application) (i.e., a third application )." CHALLA [col. 16, lines 13-17] teaches: “In particular embodiments, the assistant xbot 215 may send the communication content to the messaging platform 205. The messaging platform 205 may further send the communication content to the client system 130 via the assistant application 136.” CHALLA [col. 11, lines 42-43] teaches: “The assistant xbot 215 may work as the user's portal to the assistant system 140.” CHALLA [col. 2, lines 26-29] teaches: “The assistant system may also assist the user to be more engaging with an online social network by providing tools that help the user interact with the online social network (e.g., creating posts, comments, messages).” CHALLA [col. 16, lines 23-27] teaches: “In particular embodiments, the assistant xbot 215 may interact with a proactive inference layer 280 without receiving a user input. The proactive inference layer 280 may infer user interests and preferences based on the user profile that is retrieved from the user context engine 225. In particular embodiments, the proactive inference layer 280 may further communicate with proactive agents 285 regarding the inference. The proactive agents 285 may execute proactive tasks based on the inference. As an example and not by way of limitation, the proactive tasks may comprise sending content objects or providing services to the user.” CHALLA [col. 17, lines 47-56 and lines 61-62] teaches: “When it is determined that sending the selected candidate entities is not interruptive to the user, the dialog engine 235 may send the selected candidate entities to the CU composer 270 to generate a personalized and context-aware communication content comprising the selected candidate entities, subject to the user's privacy settings. In particular embodiments, the CU composer 270 may send the communication content to the assistant xbot 215 which may then send it to the client system 130 via the messaging platform 205 or the TTS module 275. […] In particular embodiments, the proactive inference layer 280 may further communicate with proactive agents 285 regarding the inference. The proactive agents 285 may execute proactive tasks based on the inference. As an example and not by way of limitation, the proactive tasks may comprise sending content objects or providing services to the user.” Examiner’s note: Under BRI, configuring a first use of a third application by the first user can be interpreted as the assistant xbot sending communication content to the client’s device via the messaging platform (i.e., third application ) without user input. CHALLA [col. 16, lines 23-27] teaches that xbots interact with the proactive inference layer, and execute tasks or provide services using the proactive agents based on the inferred user interests and preferences by the proactive inference layer (i.e., based on the […] output […] ). Once the content is presented to the user, the user can use the messaging platform to make additional requests or engage in conversation with the xbot via the messaging platform (CHALLA [col. 17, lines 47-56 and lines 61-62]).) wherein the third application is different from the first application and the second application. (CHALLA [col. 6, lines 59-61] teaches: "In particular embodiments, the assistant application 136 may be integrated into the social-networking application 134 or another suitable application (e.g., a messaging application) (i.e., the third application is different from the first application and the second application ).") Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of BRADEA and CHALLA before them, to include CHALLA’s assistant system in BRADEA’s personalized content generation method using generative AI. One would have been motivated to make such a combination in order to execute tasks in a personalized and context-aware manner (CHALLA [col. 16, lines 40-41]). Regarding Claim 2: BRADEA in view of CHALLA teaches the elements of claim 1 as outlined above. BRADEA further teaches: configuring, in the first prompt, at least one instruction based on an intent of the first user, wherein the intent is extracted from the first use of the first application by the first user . (BRADEA [0039] teaches: “The content generation system 130 may also receive information about events 103 that have occurred with respect to the content, such as an indication that a user interacted with content displayed on a web page. Events 103 may include actions taken by users with respect to particular content, with each event 103 indicative of a positive predisposition towards the subject matter of the content. For instance, events like the addition of an item to a virtual shopping cart or wish list may be indicative of an inclination to purchase (i.e., wherein the intent is extracted from the first use of the first application by the first user ) the item and can thus be used as a signal for the generation of personalized content. In this example, events 103 corresponding to a positive predisposition are used, but any type of event or user behavior may be relevant to the development of personalized content, including neutral or negative events.” BRADEA [0041] teaches: “In some examples, an algorithm may use an accumulation of certain events 103 as a signal that the generated content is having a positive effect on conversion rates and that randomness should therefore be reduced (i.e., the temperature should be lowered).” BRADEA [0042] teaches: “The content generation subsystem 130 can generate a prompt based on some or all of these inputs (i.e., configuring, in the first prompt, at least one instruction based on an intent of the first user ).”) Regarding Claim 3: BRADEA in view of CHALLA teaches the elements of claim 1 as outlined above. BRADEA further teaches: configuring, in the first prompt, at least one instruction based on a content item presented to the first user via the first application during the first use of the first application by the first user. (BRADEA [0047] For example, a typical example prompt generated based on the product description 170, the attributes 102, information about the events 103, the user segment information 104, and the tuning parameter 160 may be: “Generate a new product description for a grey hooded fleece. The description is currently ‘Experience Comfort and Style with our Hooded Fleece—the Ultimate Hoodie for the Active Woman!’ The user is a 37 year old female.”) Regarding Claim 4: BRADEA in view of CHALLA teaches the elements of claim 1 as outlined above. BRADEA further teaches: configuring, in the first prompt, at least one instruction based on at least one of profile data associated with the first user by the first application or historical interaction data associated with the first user and the first application . (BRADEA [0047] For example, a typical example prompt generated based on the product description 170, the attributes 102, information about the events 103, the user segment information 104, and the tuning parameter 160 may be: “Generate a new product description for a grey hooded fleece. The description is currently ‘Experience Comfort and Style with our Hooded Fleece—the Ultimate Hoodie for the Active Woman!’ The user is a 37 year old female. She is a Samsung phone user and enjoys mountain activities and trekking (i.e., profile data associated with the first user by the first application ). She has added this item to her favorites. 3 other people have added this item to their favorites or purchased the item in the last month.” This prompt may be sent to the generative AI 150 along with instructions to use a specific temperature.” BRADEA [0091] teaches: “At block 508, the personalization module 120 determines a tuning parameter 160 for a generative AI 150, wherein the tuning parameter 160 controls the randomness of the output of the generative AI 150.” BRADEA [0092] teaches: “However, other data may be used to determine the tuning parameter 160 including the user or product attributes, segmentation data, historical prompt (i.e., historical interaction data associated with the first user and the first application ), response, and temperature data, and other behavioral data about the user 202.”) Regarding Claim 5: BRADEA in view of CHALLA teaches the elements of claim 1 as outlined above. BRADEA further teaches: configuring the first prompt in response to a signal received via a user interface element of the first application . (BRADEA [0085] teaches: “At block 502, the personalization module 120 receives an indication that a user 202 interacted with content displayed on a web page. For example, a user browsing the web page may take various types of actions that are indicative of a subjective reaction to the content on the web page. Some actions may correspond to events. For instance, the user 202 may click a button, like an “Add to Cart” button, which is indicative of a positive reaction.” BRADEA [0086] teaches: “The indication that the user 202 has interacted with content received by personalization module 120 may include a data structure that includes information about the events that have taken place on web page that correspond to actions taken by the user or users, as well as identifiers of the associated content.” BRADEA [0045] teaches: “For example, the generative AI 150 can be a component of the personalization module 120.” BRADEA [0047] teaches: “For example, a typical example prompt generated based on the product description 170, the attributes 102, information about the events 103, the user segment information 104, and the tuning parameter 160 may be: “Generate a new product description for a grey hooded fleece. The description is currently ‘Experience Comfort and Style with our Hooded Fleece—the Ultimate Hoodie for the Active Woman!’ The user is a 37 year old female. She is a Samsung phone user and enjoys mountain activities and trekking. She has added this item to her favorites. 3 other people have added this item to their favorites or purchased the item in the last month.” This prompt may be sent to the generative AI 150 along with instructions to use a specific temperature.”) Regarding Claim 8: BRADEA in view of CHALLA teaches the elements of claim 1 as outlined above. BRADEA further teaches: responsive to a second use of the first application by the first user, configuring, in a third prompt, at least one instruction based on at least one interaction of the first user with a content item presented to the first user via the first application during the second use of the first application by the first user ; (BRADEA [0024] teaches: "In some examples, the generative AI may be periodically refined using previous prompts, previous responses including personalized content (i.e., configuring, in a third prompt, at least one instruction based on at least one interaction of the first user with a content item presented to the first user via the first application during the second use of the first application by the first user ), information about the user interactions, and other information." BRADEA [0085] teaches: “At block 502, the personalization module 120 receives an indication that a user 202 interacted with content displayed on a web page. For example, a user browsing the web page may take various types of actions that are indicative of a subjective reaction to the content on the web page. Some actions may correspond to events. For instance, the user 202 may click a button, like an “Add to Cart” button, which is indicative of a positive reaction. JavaScript code may be used to monitor for and tally such events locally to each client device, or they may be tracked on an external server. For example, clicking the “Add to Cart” button may cause a web request to be sent to a particular application programming interface (API) hosted by the personalization module 120, which may then store information about the interactions of user 202.” BRADEA [0027] teaches: "Moreover, the results of using the generated content in the real world as measured by, for example, user interaction with the generated content can be used to generate still more content in a positive feedback loop.” BRADEA [FIG. 4B] and [0080] teaches 4 possible AI generated updates to the product description of the content displayed on screen. The AI generated content changes the words in the product description. Under BRI, responsive to a second use of the first application by the first user, configuring, in a third prompt can be interpreted as generating the prompt based on the user interacting (i.e., “Add to Cart”) with the e-commerce application after the e-commerce application has been updated with content generated based on output of the generative AI model.) storing the third prompt in the memory that is accessible to the first application and the second application ; (BRADEA [0037] teaches: "Personalization module 120 includes a content generation subsystem 130.” BRADEA [0070] teaches: "The content generation subsystem 130 includes database 270. […] Database 270 may be used to store information about user segments, products, calculated temperature values, events, generated prompts (i.e., storing the third prompt in a memory ) and received responses, and so on. In some examples, stored information may be indexed to identifiers associated with a particular user 202, user segments, events, or some combination thereof." BRADEA [0072] teaches: "Content generation subsystem 130 includes scheduling service 330 for periodically refining the generative AI 150. The scheduling service 330, at a configurable periodicity, can query database 270 for information about user segments, products, calculated temperature values, events, generated prompts and received responses, and other data indexed according to user or product." BRADEA [0020] teaches: “The personalization module next or in parallel receives a set of attributes, including information about the user and information about the content. […] For instance, the user attributes may be received from a user database based on identifying information about the user such as login metadata. The product attributes may be received an API provided by the e-commerce website (i.e., that is accessible to the first application ).” Examiner’s note: Database 270 stores indexed information associated with a particular user and product information. The e-commerce website provides the attributes via an API, and the personalization module that includes the generation subsystem can receive information about the user, and thus has access to database 270. Therefore, the e-commerce website (i.e., first application ) and the personalization module (i.e., second application ) that includes the generation subsystem have access to the memory.) via the second application, presenting third output of the GAI model to the first user , (BRADEA [0025] teaches: "The personalization module displays (i.e., via the second application ) the personalized content (i.e., presenting third output ) in a dynamic content field associated with the content displayed on the web page. In this example, the product description is updated based on the personalized content returned by the generative AI (i.e., third output of a generative artificial intelligence (GAI) model ).” BRADEA [0037] teaches: “The content generation subsystem 130 may be configured to cause a generative AI 150 to generate new or updated content based on prompts tailored to improve the personalization of content for a particular user (i.e., to the first user ) or group of users.”) wherein the third output is responsive to the third prompt (BRADEA [0024] teaches: "The response may be generated by the generative AI in the context of the received prompt as well as previous prompts and responses from the same session.") and the third output is based on the at least one interaction of the first user with the content item presented to the first user via the first application during the second use of the first application by the first user . (BRADEA [0024] teaches: "In some examples, the generative AI may be periodically refined using previous prompts, previous responses including personalized content (i.e., based on at least one interaction of the first user with a content item presented to the first user via the first application during the second use of the first application by the first user ), information about the user interactions, and other information.") Regarding Claim 11: The claim recites similar limitations as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. Additionally, BRADEA teaches: A system comprising: at least one processor; and at least one memory coupled to the at least one processor, wherein the at least one memory comprises instructions that, when executed by the at least one processor, cause the at least one processor to perform at least one operation comprising : (BRADEA [0099] teaches: “Any suitable computer system or group of computer systems can be used for performing the operations described herein. For example, FIG. 6 depicts an example of a computer system 600. The depicted example of the computer system 600 includes a processor 602 communicatively coupled to one or more memory devices 604. The processor 602 executes computer-executable program code stored in a memory device 604, accesses information stored in the memory device 604, or both.”) Regarding Claim 12: BRADEA in view of CHALLA teaches the elements of claim 11 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale. Additionally, BRADEA teaches: wherein the instructions, when executed by the at least one processor, cause the at least one processor to perform at least one operation further comprising : (BRADEA [0099] teaches: “Any suitable computer system or group of computer systems can be used for performing the operations described herein. For example, FIG. 6 depicts an example of a computer system 600. The depicted example of the computer system 600 includes a processor 602 communicatively coupled to one or more memory devices 604. The processor 602 executes computer-executable program code stored in a memory device 604, accesses information stored in the memory device 604, or both.”) Regarding Claim 13: BRADEA in view of CHALLA teaches the elements of claim 11 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale. Additionally, BRADEA teaches: wherein the instructions, when executed by the at least one processor, cause the at least one processor to perform at least one operation further comprising : (BRADEA [0099] teaches: “Any suitable computer system or group of computer systems can be used for performing the operations described herein. For example, FIG. 6 depicts an example of a computer system 600. The depicted example of the computer system 600 includes a processor 602 communicatively coupled to one or more memory devices 604. The processor 602 executes computer-executable program code stored in a memory device 604, accesses information stored in the memory device 604, or both.”) Regarding Claim 14: BRADEA in view of CHALLA teaches the elements of claim 11 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale. Additionally, BRADEA teaches: wherein the instructions, when executed by the at least one processor, cause the at least one processor to perform at least one operation further comprising : (BRADEA [0099] teaches: “Any suitable computer system or group of computer systems can be used for performing the operations described herein. For example, FIG. 6 depicts an example of a computer system 600. The depicted example of the computer system 600 includes a processor 602 communicatively coupled to one or more memory devices 604. The processor 602 executes computer-executable program code stored in a memory device 604, accesses information stored in the memory device 604, or both.”) Regarding Claim 16: The claim recites similar limitations as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. Additionally, BRADEA teaches: At least one non-transitory machine-readable storage medium comprising instructions that, when executed by at least one processor, cause the at least one processor to perform at least one operation comprising : (BRADEA [0100] teaches: “The memory device 604 includes any suitable non-transitory computer-readable medium for storing program code 606, or both. A computer-readable medium can include any electronic, optical, magnetic, or other storage device capable of providing a processor with computer-readable instructions or other program code.”) Regarding Claim 19: BRADEA in view of CHALLA teaches the elements of claim 16 as outlined above. The claim recites similar limitations as corresponding claim 8 and is rejected for similar reasons as claim 8 using similar teachings and rationale. Additionally, BRADEA teaches: wherein the instructions, when executed by the at least one processor, cause the at least one processor to perform at least one operation further comprising : (BRADEA [0099] teaches: “Any suitable computer system or group of computer systems can be used for performing the operations described herein. For example, FIG. 6 depicts an example of a computer system 600. The depicted example of the computer system 600 includes a processor 602 communicatively coupled to one or more memory devices 604. The processor 602 executes computer-executable program code stored in a memory device 604, accesses information stored in the memory device 604, or both.”) 07-21-aia AIA Claim s 6, 15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over BRADEA in view of CHALLA as applied respectively above to claims 1, 11, and 16, and further in view of GRAY (US 20240220735 A1) and KANNAN ("Smart Reply: Automated Response Suggestion for Email"), hereafter GRAY and KANNAN respectively . Regarding Claim 6: BRADEA in view of CHALLA teaches the elements of claim 5 as outlined above. BRADEA in view of CHALLA is not relied upon for teaching, but GRAY teaches: [ …] responsive to determining that a content item presented to the first user during the first use of the first application by the first user satisfies at least one criterion related to the GAI model , (GRAY [0004] teaches: “Implementations disclosed herein are directed to at least selectively utilizing an LLM in generating an NL based summary to be rendered (e.g., audibly and/or graphically) in response to a query (e.g., a submitted query or an automatically generated query).” GRAY [0036] teaches: “In various implementations, the client device 110 can include a context engine 113 that is configured to determine a context (e.g., current or recent context) of the client device 110 and/or of a user of the client device 110. In some of those implementations, the context engine 113 can determine a context utilizing current or recent interaction(s) via the client device 110, a location of the client device 110, profile data of a profile of a user of the client device 110”. GRAY [0118] teaches: "For example, the system can process content of the query using a machine learning classifier to classify the query into one or more classifications, and the generative model(s) corresponding to those classification(s) can be determined to be most appropriate. [...] For example, if the top N result(s) include certain type(s) of results and/or result(s) that include certain type(s) of content, the system can determine an informational LLM should be used (i.e., satisfies at least one criterion related to the GAI model )." GRAY [0042] teaches: "The LLM selection engine 132 can, in response to receiving a query, determine which, if any, of multiple generative model(s) (LLM(s) and/or other generative model(s)) to utilize in generating response(s) to render responsive to the query. For example, the LLM selection engine 132 can select none, one, or multiple generative model(s) to utilize in generating response(s) to render responsive to a query." GRAY [0072] teaches: "In such an instance, search results can optionally be rendered or transmitted only in response to satisfaction of one or more conditions, such as the user interacting with a selectable graphical user interface element that requests display of search results and/or passage of a threshold duration of time since rendering of the NL based summary (e.g., a fixed threshold duration of time, or one dynamically determined based on a length of the NL based summary). [...] As yet another example, if measure(s) for portion(s) and/or a confidence measure for the NL based summary as a whole fails to satisfy lower threshold(s) less indicative of confidence, transmission and/or rendering of the NL based summary can be suppressed completely, and only additional search results transmitted and rendered.” GRAY [0033] teaches: “The client device 110 can execute one or more applications, such as application 115, via which queries can be submitted and/or NL based summaries and/or other response(s) to the query can be rendered (e.g., audibly and/or visually). […] For example, the application 115 can be a web browser installed on top of the operating system, or can be an application that is integrated as part of the operating system functionality.”) Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of BRADEA, CHALLA, and GRAY before them, to include GRAY’s criteria for determining LLM use for responding to queries in BRADEA and CHALLA’s personalized content generation method using generative AI. One would have been motivated to make such a combination in order to determine to not utilize any of the candidate generative models based on one or more objective criteria indicating that such utilization is not needed to satisfy the needs of the query, thereby conserving resources that would otherwise be consumed by utilization of the generative model(s) (GRAY [0115]). BRADEA in view of CHALLA and GRAY is not relied upon for teaching, but KANNAN teaches: via the first application, responsive to determining […] linking the user interface element with the content item . (KANNAN [page 961, section 6. TRIGGERING] teaches: "The triggering module is the entry point of the Smart Reply system. It is responsible for filtering messages that are bad candidates for suggesting responses. This includes emails for which short replies are not appropriate (e.g., containing open-ended questions or sensitive topics), as well as emails for which no reply is necessary at all (e.g., promotional emails and auto-generated updates). The module is applied to every incoming email just after the preprocessing step. If the decision is negative, we finish the execution and do not show any suggestions […]. If the score is above some threshold, we trigger and run the LSTM scoring." Examiner's note: KANNAN [page 955, Figure 1] illustrates the Smart Reply suggestions of "Yes, it's done", "Working on it now.", or "No, I haven't yet." for the content displayed on the screen. Under broadest reasonable interpretation, linking the user interface element with the content item can be interpreted as the Smart Reply being suggested for the specific content displayed on screen when the user opens an item that satisfies the condition for generating the Smart Reply.”) Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of BRADEA, CHALLA, GRAY, and KANNAN before them, to include KANNAN’s response suggestion to specific content on screen in BRADEA, CHALLA, and GRAY’s personalized content generation method using generative AI. One would have been motivated to make such a combination in order to suggest brief responses when appropriate with just one tap (i.e., KANNAN [page 955, section 1. Introduction]). Regarding Claim 15: BRADEA in view of CHALLA teaches the elements of claim 11 as outlined above. Additionally, the claim recites similar limitations as corresponding claims 5 and 6 and is rejected for similar reasons as claims 5 and 6 using similar teachings and rationale. Additionally, BRADEA teaches: wherein the instructions, when executed by the at least one processor, cause the at least one processor to perform at least one operation further comprising : (BRADEA [0099] teaches: “Any suitable computer system or group of computer systems can be used for performing the operations described herein. For example, FIG. 6 depicts an example of a computer system 600. The depicted example of the computer system 600 includes a processor 602 communicatively coupled to one or more memory devices 604. The processor 602 executes computer-executable program code stored in a memory device 604, accesses information stored in the memory device 604, or both.”) Regarding Claim 17: BRADEA in view of CHALLA teaches the elements of claim 16 as outlined above. The claim recites similar limitations as corresponding claims 2-6 and is rejected for similar reasons as claims 2-6 using similar teachings and rationale. Additionally, BRADEA teaches: wherein the instructions, when executed by the at least one processor, cause the at least one processor to perform at least one operation further comprising : (BRADEA [0099] teaches: “Any suitable computer system or group of computer systems can be used for performing the operations described herein. For example, FIG. 6 depicts an example of a computer system 600. The depicted example of the computer system 600 includes a processor 602 communicatively coupled to one or more memory devices 604. The processor 602 executes computer-executable program code stored in a memory device 604, accesses information stored in the memory device 604, or both.”) 07-21-aia AIA Claim s 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over BRADEA in view of CHALLA as applied respectively above to claims 1 and 16, and further in view of LIN (US 20120131456 A1), hereafter LIN . Regarding Claim 7: BRADEA in view of CHALLA teaches the elements of claim 1 as outlined above. BRADEA further teaches: storing the […] prompt in a memory that is accessible to […] the second application (BRADEA [0070] teaches: "Database 270 may be used to store information about user segments, products, calculated temperature values, events, generated prompts (i.e., storing the […] prompt in a memory ) and received responses, and so on.” BRADEA [0027] teaches: "Moreover, the results of using the generated content in the real world as measured by, for example, user interaction with the generated content can be used to generate still more content in a positive feedback loop." BRADEA [0025] teaches: "The personalization module displays (i.e., second application ) the personalized content in a dynamic content field associated with the content displayed on the web page. In this example, the product description is updated based on the personalized content returned by the generative AI.”) […] the GAI model […] (BRADEA [0037] teaches: “The content generation subsystem 130 may be configured to cause a generative AI 150 to generate new or updated content based on prompts tailored to improve the personalization of content for a particular user (i.e., to the first user ) or group of users.”)_ CHALLA further teaches: responsive to the first use of the third application by the first user, configuring, in a second prompt, at least one instruction based on third application context data , (CHALLA [col. 11, lines 45-51] teaches: “In particular embodiments, the assistant xbot 215 may send the textual user input (i.e., third application context data ) to a natural-language understanding (NLU) module 220 to interpret the user input. In particular embodiments, the NLU module 220 may get information from a user context engine 225 and a semantic information aggregator (SIA) 230 to accurately understand the user input.” CHALLA [col. 12, lines 4-6] teaches: “In particular embodiments, an intent may be an output of the NLU module 220 if the user input comprises a text/speech input.” Examiner’s note: Under BRI, a second prompt can be interpreted as the output (e.g., user intent) of the natural-language understanding (NLU) module, which is responsive to the user inputting text to the assistant xbot, as disclosed in CHALLA [col. 11, lines 45-51]. Additionally, CHALLA [col. 11, lines 42-43] teaches that the xbot is the portal for the user to access the assistant application (i.e., third application ).” Examiner’s note: Specification paragraph [0043] states “ Application context data as used herein may refer to data logged during the user’s use of a particular application, such as data input, output, or interacted with, the timestamp at the user’s login in to the application, and actions taken by the user during the login session, including implicit and/or explicit user interactions with the application’s user interface elements.” Under BRI, third application context data can be interpreted as the textual input from the user to the assistant xbot.) wherein the third application context data is included in the first use of the third application by the first user ; (CHALLA [col. 11, lines 45-51] teaches: “In particular embodiments, the assistant xbot 215 may send the textual user input (i.e., third application context data ) to a natural-language understanding (NLU) module 220 to interpret the user input.” Examiner’s note: Under BRI, third application context data is included in the first use of the third application by the first user can be interpreted as the user inputting text data during to the assistant xbot, which can be integrated into the messaging platform (i.e., third application ).) storing the second prompt in a memory that is accessible to the third application […] (CHALLA [col. 6, lines 59-61] teaches: "In particular embodiments, the assistant application 136 may be integrated into the social-networking application 134 or another suitable application (e.g., a messaging application) (i.e., a third application ).” CHALLA [col. 53, lines 26-30] teaches: “processor 1002 may retrieve (or fetch) the instructions from an internal register, an internal cache, memory 1004, or storage 1006; decode and execute them; and then write one or more results to an internal register, an internal cache, memory 1004 […].” Examiner’s note: Under BRI, a second prompt can be interpreted as the output (e.g., user intent) of the natural-language understanding (NLU) module, which is responsive to the user inputting text to the assistant xbot, as disclosed in CHALLA [col. 11, lines 45-51]. The output of the NLU module is a result that can is written to the internal register cache memory (i.e., a memory accessible to the third application ).) via the second application, presenting second output of the […] model to the first user , (CHALLA [col. 16, lines 11-17] teaches: “In particular embodiments, the CU composer 270 may send the generated communication content to the assistant xbot 215. In particular embodiments, the assistant xbot 215 may send the communication content to the messaging platform 205. The messaging platform 205 may further send the communication content to the client system 130 (i.e., presenting the second output of the […] model to the first user ) via the assistant application 136 (i.e., via the second application ).” CHALLA [col. 5, lines 60-67] teaches: “a client system 130 may include a computer system such as a desktop computer, notebook or laptop computer, netbook, a tablet computer, e-book reader, GPS device, camera, personal digital assistant (PDA), handheld electronic device, cellular telephone, smartphone, smart speaker, virtual reality (VR) headset, augment reality (AR) smart glasses, other suitable electronic device, or any suitable combination thereof.”) wherein the second output is responsive to the second prompt and the second output is based on at least the third application context data. (CHALLA [col. 2, lines 26-29] teaches: “The assistant system may also assist the user to be more engaging with an online social network by providing tools that help the user interact with the online social network (e.g., creating posts, comments, messages). […] In particular embodiments, the assistant system may proactively execute tasks that are relevant to user interests and preferences based on the user profile without a user input (i.e., responsive to the first use of the third application by the first user ).” CHALLA [col. 6, lines 59-61] teaches: "In particular embodiments, the assistant application 136 may be integrated into the social-networking application 134 or another suitable application (e.g., a messaging application)." CHALLA [col. 15, lines 59-65] teaches: “In particular embodiments, the CU composer 270 may retrieve a user profile from the user context engine 225 when generating the communication content and determining the modality of the communication content. As a result, the communication content may be more natural, personalized, and context-aware for the user.” Examiner’s note: Under BRI, the second output is responsive to the second prompt can be interpreted as the user using the messaging application and the assistant system assisting the user by creating messages (i.e., second output ). The assistant system retrieves contextual information (i.e., third application context data ) from the user profile, which includes data aggregated from the messaging platform, and uses that information to generate personalized communication content for the user.) However, BRADEA in view of CHALLA is not relied upon for teaching, but LIN teaches: […] memory that is accessible to the […] application and the […] application . (LIN [0081] teaches: “The shared memory 320 is called that because it is shared by multiple applications (e.g., applications 302-306) via their input capture modules.”) Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of BRADEA, CHALLA, and LIN before them, to include LIN’s shared memory in BRADEA and CHALLA’s personalized content generation method using generative AI. One would have been motivated to make such a combination in order to control where and how the collected information is written to the shared memory 320 (LIN [0082]). Regarding Claim 18: BRADEA in view of CHALLA teaches the elements of claim 16 as outlined above. The claim recites similar limitations as corresponding claim 7 and is rejected for similar reasons as claim 7 using similar teachings and rationale. Additionally, BRADEA teaches: wherein the instructions, when executed by the at least one processor, cause the at least one processor to perform at least one operation further comprising : (BRADEA [0099] teaches: “Any suitable computer system or group of computer systems can be used for performing the operations described herein. For example, FIG. 6 depicts an example of a computer system 600. The depicted example of the computer system 600 includes a processor 602 communicatively coupled to one or more memory devices 604. The processor 602 executes computer-executable program code stored in a memory device 604, accesses information stored in the memory device 604, or both.”) 07-21-aia AIA Claim s 9-10 and 20, are rejected under 35 U.S.C. 103 as being unpatentable over BRADEA in view of CHALLA as applied respectively above to claims 1 and 16, and further in view of AHMAD-TAYLOR (US 20250061491 A1), hereafter AHMAD-TAYLOR . Regarding Claim 9: BRADEA in view of CHALLA teaches the elements of claim 1 as outlined above. BRADEA in view of CHALLA is not relied upon for teaching, but AHMAD-TAYLOR teaches: responsive to at least one interaction of the first user with the first output of the GAI model in the second application, storing, in the memory accessible to the first application and the second application, data associated with the at least one interaction of the first user with the first output of the GAI model in the second application ; (AHMAD-TAYLOR [0030] teaches: "The chatbot system can also use usage data and transaction details to enhance user profiles and generate analytics for vendors." AHMAD-TAYLOR [0041] teaches: "The interaction servers 124 are communicatively coupled to a database server 126, facilitating access to a database 128 that stores data associated with interactions processed by the interaction servers 124. Similarly, a web server 130 is coupled to the interaction servers 124 and provides web-based interfaces to the interaction servers 124.”) responsive to a second use of the first application by the first user, providing, to the first application, the data associated with the at least one interaction of the first user with the first output of the GAI model in the second application . (AHMAD-TAYLOR [0033] teaches: "The chatbot system updates the user profile with details extracted from the current query to personalize future recommendations.") Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of BRADEA, CHALLA, and AHMAD-TAYLOR before them, to include AHMAD-TAYLOR’s storing data associated with interactions in BRADEA and CHALLA’s personalized content generation method using generative AI. One would have been motivated to make such a combination in order to serve the user 320 better recommendations, search results, and other personalized experiences, thereby allowing the user profile to incrementally improve over time, becoming more reflective of the user's current interests and responsive to their changing needs. Their experience on the platform becomes more tailored as additional natural language interactions are captured (AHMAD-TAYLOR [0145]). Regarding Claim 10: BRADEA in view of CHALLA teaches the elements of claim 9 as outlined above. BRADEA in view of CHALLA is not relied upon for teaching, but AHMAD-TAYLOR teaches: configuring a preference of the first user in the first application based on the data associated with the at least one interaction of the first user with the first output of the GAI model in the second application . (AHMAD-TAYLOR [0132] teaches: "In some examples, the dynamic advertisement system 314 recommends additional products to the user 320 based on analysis of the user query and a user profile 310 stored in a data store of the interactive platform 352. The user profile 310 may be updated by various components of the interactive platform 352 including the chatbot system 300 storing user profile information 346 about conversations the chatbot 340 has with the user 320.”) Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of BRADEA, CHALLA, and AHMAD-TAYLOR before them, to include AHMAD-TAYLOR’s user profile updating in BRADEA and CHALLA’s personalized content generation method using generative AI. One would have been motivated to make such a combination in order to serve the user 320 better recommendations, search results, and other personalized experiences, thereby allowing the user profile to incrementally improve over time, becoming more reflective of the user's current interests and responsive to their changing needs. Their experience on the platform becomes more tailored as additional natural language interactions are captured (AHMAD-TAYLOR [0145]). Regarding Claim 20: BRADEA in view of CHALLA teaches the elements of claim 16 as outlined above. The claim recites similar limitations as corresponding claims 9 and 10 and is rejected for similar reasons as claims 9 and 10 using similar teachings and rationale. Additionally, BRADEA teaches: wherein the instructions, when executed by the at least one processor, cause the at least one processor to perform at least one operation further comprising : (BRADEA [0099] teaches: “Any suitable computer system or group of computer systems can be used for performing the operations described herein. For example, FIG. 6 depicts an example of a computer system 600. The depicted example of the computer system 600 includes a processor 602 communicatively coupled to one or more memory devices 604. The processor 602 executes computer-executable program code stored in a memory device 604, accesses information stored in the memory device 604, or both.”) Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Alvaro S Laham Bauzo whose telephone number is (571)272-5650. The examiner can normally be reached Mon-Fri 7:30 AM - 11:00 AM | 1:00 PM - 5:30 PM ET. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /A.S.L./Examiner, Art Unit 2146 /USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146 Application/Control Number: 18/459,233 Page 2 Art Unit: 2146 Application/Control Number: 18/459,233 Page 3 Art Unit: 2146 Application/Control Number: 18/459,233 Page 4 Art Unit: 2146 Application/Control Number: 18/459,233 Page 5 Art Unit: 2146 Application/Control Number: 18/459,233 Page 6 Art Unit: 2146 Application/Control Number: 18/459,233 Page 7 Art Unit: 2146 Application/Control Number: 18/459,233 Page 8 Art Unit: 2146 Application/Control Number: 18/459,233 Page 9 Art Unit: 2146 Application/Control Number: 18/459,233 Page 10 Art Unit: 2146 Application/Control Number: 18/459,233 Page 11 Art Unit: 2146 Application/Control Number: 18/459,233 Page 12 Art Unit: 2146 Application/Control Number: 18/459,233 Page 13 Art Unit: 2146 Application/Control Number: 18/459,233 Page 14 Art Unit: 2146 Application/Control Number: 18/459,233 Page 15 Art Unit: 2146 Application/Control Number: 18/459,233 Page 16 Art Unit: 2146 Application/Control Number: 18/459,233 Page 17 Art Unit: 2146 Application/Control Number: 18/459,233 Page 18 Art Unit: 2146 Application/Control Number: 18/459,233 Page 19 Art Unit: 2146 Application/Control Number: 18/459,233 Page 20 Art Unit: 2146 Application/Control Number: 18/459,233 Page 21 Art Unit: 2146 Application/Control Number: 18/459,233 Page 22 Art Unit: 2146 Application/Control Number: 18/459,233 Page 23 Art Unit: 2146 Application/Control Number: 18/459,233 Page 24 Art Unit: 2146 Application/Control Number: 18/459,233 Page 25 Art Unit: 2146 Application/Control Number: 18/459,233 Page 26 Art Unit: 2146 Application/Control Number: 18/459,233 Page 27 Art Unit: 2146 Application/Control Number: 18/459,233 Page 28 Art Unit: 2146 Application/Control Number: 18/459,233 Page 29 Art Unit: 2146
