CTNF 18/740,220 CTNF 82150 DETAILED ACTION A. This action is in response to the following communications: Transmittal of New Application filed 06/11/21024. B. Claims 1-20 remains pending. 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 – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim(s) 1-6, 8-13 and 15-19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Knipfing, Jacob et al. (US Pub. 2024/0354555 A1), herein referred to as “Knipfing” . As for claims 1, 8 and 15, Knipfing teaches. A mobile device and corresponding method of claim 8 and non-transitory computer-readable device of claim 15, comprising: having instructions stored thereon that , when executed by at least one computing device, cause the at least one computing device to perform operations; a memory; and at least one processor coupled to the memory and configured to (par. 23 summary of the hardware system of the disclosure and depicted in figure 1; par. 117 hardware memory component; par. 252 computer system processor for executing; par. 258 non-transitory computer-readable storage medium): launch, at a mobile device, a mobile application including an integration component, wherein the integration component is in communication, through the mobile application, with a data service and a user interface (UI) service (par. 24 Each user system 102 may include multiple user devices, such as a mobile device 114 , head-wearable apparatus 116 , and a computer client device 118 that are communicatively connected to exchange data and messages; par. 25 An interaction client 104 interacts with other interaction clients 104 and with the interaction server system 110 via the network 108 . The data exchanged between the interaction clients 104 (e.g., interactions 120 ) and between the interaction clients 104 and the interaction server system 110 includes functions (e.g., commands to invoke functions) and payload data (e.g., text, audio, video, or other multimedia data)).; receive, at the integration component, a response including data and a data type from the data service generated by a large language model responsive to a natural language query (par. 68 trained algorithm and examples; par. 89 the trained machine-learning program, such as personalized Al agent system 232 may be a generative AI model. Generative AI is a term that may refer to any type of artificial intelligence that can create new content from training data. For example, generative AI can produce text, images, video, audio, code or synthetic data that are similar to the original data but not identical.; par. 165 Large Language Models (LLMs) is used by the intent component 612) and customize, by the integration component, an interface at the integration component using a rendering configuration received from the UI service to display the data, the rendering configuration generated by decomposing (par. 72 specific machine learning algorithm can be deployed which includes Matrix factorization is another type of machine learning algorithm used for recommender systems and other tasks. Matrix factorization decomposes a matrix into two or more matrices to uncover hidden patterns or relationships in the data; par. 73 Although several specific examples of machine learning algorithms are discussed herein, the principles discussed herein can be applied to other machine learning algorithms as well .) the data type into a predefined type (par. 193 Referring to FIG. 5 , the XR application generation system 590 implements an XR application that generates one or more XR objects using pre-defined templates. For example, the templates can define placeholders for images, text, video, graphics, behavior, segmentation models, conditions, and so forth. Par. 195 based upon sensor data (integration component) the template used/defined can update the graphical user interface according to the configurations made by a user in the XR application generation system 590 application; par. 196 the content items of the template can be populated based on information and/or data objects received from the tool components 512 ; par. 211-212 the first graphical user interface is displayed on a first users device that receives input to configure options 722 shown in a second user interface (e.g. menu frame), the XR application and present . As for claims 2, 9 and 16, Knipfing teaches. The mobile device of claim 1 and corresponding method of claim 8 and non-transitory computer-readable device of claim 15, wherein the at least one processor is further configured to: receive, at the integration component, an interaction with the data at the customized interface; receive, at the integration component, a recommended second mobile application based on the interaction, the data, and the data type, wherein the recommendation was generated by the large language model; and launch, by the integration component, a second mobile application in response to an interaction with the recommendation, wherein the integration component is configured to communicate data with the second mobile application (par. 196 The XR application generation system 590 can receive the first and second data objects and can populate the template portions for each of the first and second data objects to generate a populated template. Then, the XR application can be launched to present the message with an XR object generated based on a randomly selected template portion that includes the data object generated by the tool components 512 ; par. 198 the template can define a segmentation model that is associated with a given data object; par. 204 user utilizes the second application with recommendation template Disney princesses; par. 205 second user can go back to first application and define a new XR experience now related to a different subject (e.g. race car drivers); The tool components 512 can generate a list of data objects representing the list of racecar drivers and provide the list of data objects to the XR application generation system 590 . In some cases, the tool components 512 can obtain a multimodal memory 508 from the personal AI agent 502 to tailor selection of the data objects based on preferences of a given user). As for claims 3, 10 and 16, Knipfing teaches. The mobile device of claim 1 and corresponding method of claim 8 and non-transitory computer-readable device of claim 15, wherein the second mobile application is at least one of an email application, instant messaging application, an image capturing application, a video capturing application, an audio capturing application, a web browsing application, or a navigation application (par. 211 fig. 7 video and audio capturing application known as XR application that is launched from the XR application generation system 590; par. 162). As for claims 4, 11 and 17, Knipfing teaches. The mobile device of claim 1 and corresponding method of claim 8 and non-transitory computer-readable device of claim 15, wherein the customized interface includes a chatbot interface and wherein the at least one processor is further configured to: send a natural language request to update the received data, where the natural language request was input to the chatbot interface (par. 66 The artificial intelligence and machine learning system 230 may also provide chatbot functionality to message interactions 120 between user systems 102 and between a user system 102 and the interaction server system 110 ); receive, at the integration component, a response including updated data and a data type from the data service generated by a large language model responsive to the natural language request (par. 66 The artificial intelligence and machine learning system 230 may also provide personalized Al agent system 232 functionality to message interactions 120 between user systems 102 and between a user system 102 and the interaction server system 110 .; and update, by the integration component, the interface at the integration component using the rendering configuration from the UI service to display the updated data (par. 67 The code segments can be generated based one a prompt that is defined or generated at least partially based on a user query that defines attributes or desired content of the XR application and one or more templates associated with the XR application. Once the code segments are received from the one or more machine learning models, the templates of the XR application or other application are populated and used to generate and provide unique and engaging XR experiences to an end user of the interaction client 104 .). As for claims 5, 12 and 18, Knipfing teaches. The mobile device of claim 1 and corresponding method of claim 8 and non-transitory computer-readable device of claim 15, wherein the at least one processor is further configured to: authenticate a user associated with the mobile application via an authentication mechanism executed by the integration component (par. 263 authentication methods used for user authentication). As for claims 6, 13 and 19, Knipfing teaches. The mobile device of claim 5 and corresponding method of claim 12 and non-transitory computer-readable device of claim 18, wherein the authentication mechanism is configured to use a username, password, fingerprint, facial recognition, voiceprint, security question, or a reverse Turing test (par. 263 authentication methods such as biometric) . Claim Rejections - 35 USC § 103 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. 07-20-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 Claim (s) 7, 14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Knipfing in view of Maschmeyer, Russ et al. (US Pub. 2024/0338902 A1), herein referred to as “Maschmeyer” . As for claims 7, 14 and 20, Knipfing teaches. The mobile device of claim 1 and corresponding method of claim 8 and non-transitory computer-readable device of claim 15, wherein the integration component is configured to input sensor data collected by the mobile device, the sensor data including GPS data (par. 109 GPS), image data, video data, audio data (par. 117 camera component; image data, video data, audio data sensor information used as input), accelerometer data, gyroscope data (par. 269 acceleration sensor and gyroscope sensor information used as input), barometer data (par. 270 pressure sensor e.g. barometer), proximity data (par. 270 proximity sensor components (e.g. infrared), ambient light data (par. 270 illumination sensor (e.g. photometer)), magnetometer data (par. 273 orientation sensor components e.g. magnetometers). Knipfing teaches paragraph 270 and 284 Infrared sensors and emitters used to detect nearby objects and as known in the art Lidar uses infrared light in many systems but infrared itself is a type of electromagnetic radiation, while lidar is a technology that uses laser pulses to measure distance; Knipfing does not specifically mention the technology “LIDAR”; however in the same field of endeavor Maschmeyer teaches augmented reality (par. 30) and the use of LIDAR data (par. 46 The optical sensors 238 may include any type of sensor device that captures optical information and generates the media data containing the optical information by converting light rays into electronic signals and binary data. Non-limiting examples of the optical sensors 238 may include a camera, LIDAR sensor, and a light sensor, among others). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Maschmeyer into Knipfing because Maschmeyer suggests in paragraph 13 conventional AR functions may enhance a user's experience by overlaying information about a product within an AR-enhanced graphical user interface , but do not provide comparable information of two or more products. (Note :) It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275, 277 (CCPA 1968)) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Displaying Query Results From A Previous Query When Accessing A Panel Document ID US 11636128 B1 Date Published 2023-04-25 Abstract Systems and methods are disclosed for allowing a user to view query results associated with a time range that is different from a time range indicated by a query. For example, a user interface (UI) data manager can receive a request for a panel of a workbook with a query that identifies the first time range. The UI data manager can obtain the panel, including the query and query results, and cause display of a panel view corresponding to the panel. The displayed query results may not correspond to the same time range as the literal time range indicated by the query. Rather, the query results may be the query results generated during an earlier time range that corresponds to the last time the query was run. Systems And Methods For Automating Benchmark Generation Using Neural Networks For Image Or Video Selection Document ID US 20220198779 A1 Date Published 2022-06-23 Abstract A method includes accessing a web-based property over a network; storing a plurality of images or videos from the web-based property and associations between the plurality of images or videos and a target audience identifier responsive to the web-based property having a stored association with the target audience identifier; retrieving the plurality of images or videos from the database responsive to each of the plurality of images or videos having stored associations with the target audience identifier; executing a neural network to generate a performance score for each of the plurality of images or videos; calculating a target audience benchmark; executing the neural network to generate a first performance score for a first image or video and a second performance score for a second image or video; comparing the first performance score and the second performance score to the benchmark; and generating a record identifying the first image or video. Inquires 07-100 AIA Any inquiry concerning this communication should be directed to NICHOLAS AUGUSTINE at telephone number (571)270-1056 . 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. PNG media_image1.png 213 559 media_image1.png Greyscale /NICHOLAS AUGUSTINE/Primary Examiner, Art Unit 2178 June 11, 2026 Application/Control Number: 18/740,220 Page 2 Art Unit: 2178 Application/Control Number: 18/740,220 Page 3 Art Unit: 2178 Application/Control Number: 18/740,220 Page 4 Art Unit: 2178 Application/Control Number: 18/740,220 Page 5 Art Unit: 2178 Application/Control Number: 18/740,220 Page 6 Art Unit: 2178 Application/Control Number: 18/740,220 Page 7 Art Unit: 2178 Application/Control Number: 18/740,220 Page 8 Art Unit: 2178 Application/Control Number: 18/740,220 Page 10 Art Unit: 2178 Application/Control Number: 18/740,220 Page 11 Art Unit: 2178 Application/Control Number: 18/740,220 Page 12 Art Unit: 2178 Application/Control Number: 18/740,220 Page 13 Art Unit: 2178 Application/Control Number: 18/740,220 Page 14 Art Unit: 2178
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