CTNF 18/771,207 CTNF 87645 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. DETAILED ACTION This action is responsive to patent application as filed on 7/12/2024 This action is made Non-Final. Claims 1 – 20 are pending in the case. Claims 1, 8, and 15 are independent claims. Information Disclosure Statement The information disclosure statement (IDS) submitted on 2/17/2026, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Drawings The drawings filed on 7/12/2024 have been accepted by the Examiner. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15-aia AIA Claim(s) 1-20 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Hanson (USPUB 20220199079 A1 from IDs filed 2/17/2026) . Claim 1: Hanson discloses A contextualized action recommendation virtual assistant (Fig 1 and 0042) comprising: a user system comprising a display to display content to a user, one or more sensors to capture input data, and a virtual assistant application (Fig 1, 0042-43 and 0063) ; an Al action recommendation system that is associated with a large language model and includes a virtual assistant engine that is cooperative with the virtual assistant application of the user system to implement the virtual assistant (Figs 1, 13, 14, and 0042, 0208 and 0217: “a user at a client system...may use the assistant application...to interact with the assistant system...the assistant system...may accordingly analyze the user input, generate a response based on the analysis of the user input...the assistant system...may make the interactions between users and the assistant system...more engaging and interesting by using a chit-chat bot to supplement responses from the assistant system...when receiving a user input, the assistant system...may feed the user input and dialog context to two bots including a task bot and a chit-chat bot. The task bot may be the standard assistant xbot that responds to task requests and generates normal task responses”) ; one or more processors; and one or more memories accessible to the one or more processors, the one or more memories storing a plurality of instructions executable by the one or more processors, the plurality of instructions comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform processing (Fig 8 and 0164-168) comprising: collecting input data comprising personal information data of the user that includes at least one high-level goal of the user, and user context data from the one or more sensors of the user system (0208-210: the assistant system 140 may make the interactions between users and the assistant system 140 more engaging and interesting by using a chit-chat bot to supplement responses from the assistant system 140. When receiving a user input, the assistant system 140 may feed the user input and dialog context to two bots including a task bot and a chit-chat bot. The task bot may be the standard assistant xbot that responds to task requests and generates normal task responses. The chit-chat bot may generate personalized chit-chat responses based on the dialog context (e.g., knowledge about the user). Additionally, the chit-chat bot may also consider other information such as user context, multimodal context, or other auxiliary information when generating the chit-chat responses...The user may say “I'm looking for a concert in Vancouver.”... The existing dialogue corpora and models may be designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e.g., booking hotels), open-domain chat-bots aim at making socially engaging conversations) ; generating, using the input data, a prompt for the large language model ; inputting the prompt to the large language model (0208 and 0217: When receiving a user input, the assistant system 140 may feed the user input and dialog context to two bots including a task bot and a chit-chat bot) ; generating, by the large language model, a contextualized action recommendation for the user based on the prompt, wherein the contextualized action recommendation is predicted to help the user achieve the at least one high-level goal (0208-209: Each bot may generate one or more output strings. The output strings from each bot may be fed to a composition model including an arranger and a rewriter. The arranger may take the outputs from the bots and decide how they should be arranged (e.g., [task response, chit-chat response] versus [chit-chat response, task response]). The rewriter may take the outputs of the bots and synthesize them and generate a completely new natural language output... The user may say “I'm looking for a concert in Vancouver.” The assistant system 140 may reply “I found an event for the Boy Band at Pacific Amphitheatre.” The user may ask “when does the event start, and what's the event category?” The assistant system 140 may reply “it's a Pop event starting at 6:30 pm.” In addition, the assistant system 140 may generate a chit-chat response as “it's a great way to kick off the summer!”) ; and presenting the contextualized action recommendation to the user via a virtual assistant user interface on the display of the user system (Fig 13 and 0044: The rendering device...may be configured to render outputs generated by the assistant system...to the user). Claims 2, 9 and 16: Hanson discloses the user system comprises a portable electronic device selected from the group consisting of a desktop computer, a notebook or laptop computer, a netbook, a tablet computer, an e-book reader, a global positioning system (GPS) device, a personal digital assistant, a smartphone, a wearable extended reality device, and combinations thereof (0044). Claim 3: Hanson discloses the one or more sensors of the user system include one or more of a motion sensor, an image capturing device, an input and/or output audio transducer, a GPS transceiver, and a user system orientation sensor (0042, 0043 and 0063). Claim 4: Hanson discloses the Al action recommendation system includes a recommendation engine comprising: a context detector component configured to determine a current user context from the user context data collected from the one or more sensors of the user system, and wherein the current user context includes one or more of a location of the user, places of interest near the location of the user, a time of day, a day of week, weather conditions, movement of the user, and tools available to the user to complete the action of the action recommendation; a goal parser component configured to receive the personal information data of the user and to divide the at least one high-level goal of the user into a plurality of sub-goals; and the large language model, wherein the large language model is configured to receive, relative to generating the contextualized action recommendation, the user context from the context detector component and the plurality of sub-goals from the goal parser component (0088, 0106-107: the capability of signals intelligence may enable the assistant system 140 to, for example, determine user location, understand date/time, determine family locations, understand users' calendars and future desired locations, integrate richer sound understanding to identify setting/context through sound alone, and/or build signals intelligence models at runtime which may be personalized to a user's individual routines... the dialog state tracker 218 may communicate with the action selector 222 about the dialog intents and associated content objects. In particular embodiments, the action selector 222 may rank different dialog hypotheses for different dialog intents. The action selector 222 may take candidate operators of dialog state and consult the dialog policies 360 to decide what actions should be executed. In particular embodiments, a dialog policy 360 may a tree-based policy, which is a pre-constructed dialog plan. Based on the current dialog state, a dialog policy 360 may choose a node to execute and generate the corresponding actions. As an example and not by way of limitation, the tree-based policy may comprise topic grouping nodes and dialog action (leaf) nodes. In particular embodiments, a dialog policy 360 may also comprise a data structure that describes an execution plan of an action by an agent 228. A dialog policy 360 may further comprise multiple goals related to each other through logical operators. In particular embodiments, a goal may be an outcome of a portion of the dialog policy and it may be constructed by the dialog manager 216. A goal may be represented by an identifier (e.g., string) with one or more named arguments, which parameterize the goal. As an example and not by way of limitation, a goal with its associated goal argument may be represented as {confirm artist, args: {artist: “Madonna”}}. In particular embodiments, goals may be mapped to leaves of the tree of the tree-structured representation of the dialog policy... The general policy 362 may be used for actions that are not specific to individual tasks. The general policy 362 may be used to determine task stacking and switching, proactive tasks, notifications, etc. The general policy 362 may comprise handling low-confidence intents, internal errors, unacceptable user response with retries, and/or skipping or inserting confirmation based on ASR or NLU confidence scores. The general policy 362 may also comprise the logic of ranking dialog state update candidates from the dialog state tracker 218 output and pick the one to update (such as picking the top ranked task intent). Claims 5, 10 and 17: Hanson discloses the contextualized action recommendation is a natural language contextualized action recommendation (Fig 13 and 0209). Claim 6: Hanson discloses a remote system communicatively coupled to the Al action recommendation system, the remote system storing a user profile that includes the personal information data of the user (0048 and 0100). Claims 7 and 19: Hanson discloses the virtual assistant user interface is a chat interface (Fig 13 and 0209). Claim 8: Hanson discloses A computer implemented method comprising: implementing a virtual assistant through a user system (Fig 1 and 0042) comprising a display that displays content to a user, one or more sensors that capture input data, and a virtual assistant application (Fig 1, 0042-43 and 0063) ; in combination with an Al action recommendation system that is associated with a large language model and includes a virtual assistant engine that cooperates with the virtual assistant application of the user system to implement the virtual assistant (Figs 1, 13, 14, and 0042, 0208 and 0217: “a user at a client system...may use the assistant application...to interact with the assistant system...the assistant system...may accordingly analyze the user input, generate a response based on the analysis of the user input...the assistant system...may make the interactions between users and the assistant system...more engaging and interesting by using a chit-chat bot to supplement responses from the assistant system...when receiving a user input, the assistant system...may feed the user input and dialog context to two bots including a task bot and a chit-chat bot. The task bot may be the standard assistant xbot that responds to task requests and generates normal task responses”) ; collecting input data comprising personal information data of the user that includes at least one high-level goal of the user, and user context data from the one or more sensors of the user system (0208-210: the assistant system 140 may make the interactions between users and the assistant system 140 more engaging and interesting by using a chit-chat bot to supplement responses from the assistant system 140. When receiving a user input, the assistant system 140 may feed the user input and dialog context to two bots including a task bot and a chit-chat bot. The task bot may be the standard assistant xbot that responds to task requests and generates normal task responses. The chit-chat bot may generate personalized chit-chat responses based on the dialog context (e.g., knowledge about the user). Additionally, the chit-chat bot may also consider other information such as user context, multimodal context, or other auxiliary information when generating the chit-chat responses...The user may say “I'm looking for a concert in Vancouver.”... The existing dialogue corpora and models may be designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e.g., booking hotels), open-domain chat-bots aim at making socially engaging conversations) ; generating, using the input data, a prompt for the large language model; inputting the prompt to the large language model (0208 and 0217: When receiving a user input, the assistant system 140 may feed the user input and dialog context to two bots including a task bot and a chit-chat bot) ; generating, by the large language model, a contextualized action recommendation for the user based on the prompt, wherein the contextualized action recommendation is predicted to help the user achieve the at least one high-level goal (0208-209: Each bot may generate one or more output strings. The output strings from each bot may be fed to a composition model including an arranger and a rewriter. The arranger may take the outputs from the bots and decide how they should be arranged (e.g., [task response, chit-chat response] versus [chit-chat response, task response]). The rewriter may take the outputs of the bots and synthesize them and generate a completely new natural language output... The user may say “I'm looking for a concert in Vancouver.” The assistant system 140 may reply “I found an event for the Boy Band at Pacific Amphitheatre.” The user may ask “when does the event start, and what's the event category?” The assistant system 140 may reply “it's a Pop event starting at 6:30 pm.” In addition, the assistant system 140 may generate a chit-chat response as “it's a great way to kick off the summer!”) ; and presenting the contextualized action recommendation to the user via a virtual assistant user interface on the display of the user system (Fig 13 and 0044: The rendering device...may be configured to render outputs generated by the assistant system...to the user). Claim 11: Hanson discloses the personal information data of the user is retrieved from a user profile associated with the user (0048 and 0100). Claim 12: Hanson discloses the user profile is selected from the group consisting of a network accessible social media user profile, a user profile stored in a datastore communicatively coupled to the Al action recommendation system, and a user profile stored on the user system (0006, 0055, 0057). Claim 13: Hanson discloses privacy rules that determine an extent of the personal information data of the user that is shareable with the Al action recommendation system (0006 and 0055). Claims 14 and 20: Hanson discloses the virtual assistant is persistent, such that at least some of the user context data is collected at times when the user is not actively engaged with the virtual assistant (0063). Claim 15: Hanson discloses A non-transitory computer-readable memory storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to: implement a virtual assistant through a user system (Fig 1 and 0042) comprising a display to display content to a user, one or more sensors to capture input data, and a virtual assistant application (Fig 1, 0042-43 and 0063) ; in combination with an Al action recommendation system that is associated with a large language model and includes a virtual assistant engine that is cooperative with the virtual assistant application of the user system to implement the virtual assistant (Figs 1, 13, 14, and 0042, 0208 and 0217: “a user at a client system...may use the assistant application...to interact with the assistant system...the assistant system...may accordingly analyze the user input, generate a response based on the analysis of the user input...the assistant system...may make the interactions between users and the assistant system...more engaging and interesting by using a chit-chat bot to supplement responses from the assistant system...when receiving a user input, the assistant system...may feed the user input and dialog context to two bots including a task bot and a chit-chat bot. The task bot may be the standard assistant xbot that responds to task requests and generates normal task responses collect input data comprising personal information data of the user that includes at least one high-level goal of the user, and data from the one or more sensors of the user system that indicates a user context (0208-210: the assistant system 140 may make the interactions between users and the assistant system 140 more engaging and interesting by using a chit-chat bot to supplement responses from the assistant system 140. When receiving a user input, the assistant system 140 may feed the user input and dialog context to two bots including a task bot and a chit-chat bot. The task bot may be the standard assistant xbot that responds to task requests and generates normal task responses. The chit-chat bot may generate personalized chit-chat responses based on the dialog context (e.g., knowledge about the user). Additionally, the chit-chat bot may also consider other information such as user context, multimodal context, or other auxiliary information when generating the chit-chat responses...The user may say “I'm looking for a concert in Vancouver.”... The existing dialogue corpora and models may be designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e.g., booking hotels), open-domain chat-bots aim at making socially engaging conversations) ; generate, using the input data, a prompt for the large language model; input the prompt to the large language model (0208 and 0217: When receiving a user input, the assistant system 140 may feed the user input and dialog context to two bots including a task bot and a chit-chat bot) ; generate, by the large language model, a contextualized action recommendation for the user based on the prompt, wherein the contextualized action recommendation is predicted to help the user achieve the at least one high-level goal (0208-209: Each bot may generate one or more output strings. The output strings from each bot may be fed to a composition model including an arranger and a rewriter. The arranger may take the outputs from the bots and decide how they should be arranged (e.g., [task response, chit-chat response] versus [chit-chat response, task response]). The rewriter may take the outputs of the bots and synthesize them and generate a completely new natural language output... The user may say “I'm looking for a concert in Vancouver.” The assistant system 140 may reply “I found an event for the Boy Band at Pacific Amphitheatre.” The user may ask “when does the event start, and what's the event category?” The assistant system 140 may reply “it's a Pop event starting at 6:30 pm.” In addition, the assistant system 140 may generate a chit-chat response as “it's a great way to kick off the summer!”) ; and present the contextualized action recommendation to the user via a virtual assistant user interface on the display of the user system (Fig 13 and 0044: The rendering device...may be configured to render outputs generated by the assistant system...to the user). Claim 18: Hanson discloses the personal information data of the user is retrievable from a user profile associated with the user, and the user profile is a network accessible social media user profile, a user profile stored in a datastore communicatively coupled to the AI action recommendation system, or a user profile stored on the user system (0006, 0048, 0055, 0057). Note The Examiner cites particular columns, line numbers and/or paragraph numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. See MPEP 2123 . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is listed in the attached PTOL-892 form . Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED-IBRAHIM ZUBERI whose telephone number is (571)270-7761. The examiner can normally be reached on M-Th 8-6 Fri: 7-12/OFF. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Steph Hong can be reached on (571) 272-4124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MOHAMMED H ZUBERI/ Primary Examiner, Art Unit 2178 Application/Control Number: 18/771,207 Page 2 Art Unit: 2178 Application/Control Number: 18/771,207 Page 3 Art Unit: 2178 Application/Control Number: 18/771,207 Page 4 Art Unit: 2178 Application/Control Number: 18/771,207 Page 5 Art Unit: 2178 Application/Control Number: 18/771,207 Page 6 Art Unit: 2178 Application/Control Number: 18/771,207 Page 7 Art Unit: 2178 Application/Control Number: 18/771,207 Page 8 Art Unit: 2178 Application/Control Number: 18/771,207 Page 9 Art Unit: 2178 Application/Control Number: 18/771,207 Page 10 Art Unit: 2178 Application/Control Number: 18/771,207 Page 11 Art Unit: 2178 Application/Control Number: 18/771,207 Page 12 Art Unit: 2178 Application/Control Number: 18/771,207 Page 13 Art Unit: 2178 Application/Control Number: 18/771,207 Page 14 Art Unit: 2178
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