CTFR 18/934,877 CTFR 86594 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. Claims 1 – 20 are pending. Response to Arguments Applicant’s arguments with respect to the 35 USC 112b rejection of claim 19 have been fully considered and are persuasive in view of the amended claim language. The 35 USC 112b rejection of claim 19 has been withdrawn. Applicant's arguments with respect to the 35 USC 103 rejection of claim 1 have been fully considered but they are not persuasive. Wannerberg discloses the determination of which users submit which requests for objects to be found and inserted into the environment. Further, Wannerberg discloses weighting objects more heavily in the 3d environment based on preferences of the user profile of the user that requested the object be inserted into the environment. This is used to determine the context for the environment, which is used in determination of processing requests for finding of objects in response to requests from the users of the environment. Therefore, Wannerberg’s newly cited disclosure sufficiently discloses the current claim language as amended. Further clarification of the determination of the weights themselves and/or the role in the weight(s) for the assembling of the merged input prompt may help in overcoming the disclosure of Wannerberg. Further, the previously cited Shevchenko reference for claim 6 also discloses determination of users that provide input and determination of weight based on the user’s prior communication in the same or similar scenario (see Shevchenko: Para. 0080, 0195 – 0196, 0222). While this reference was not used in the current rejection below for claim 1, consideration of the disclosure within Shevchenko when making any further clarifications within the claim language may help in moving prosecution forward. 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. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim (s) 1 – 5 and 7 – 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2025/0252661 issued to Wannerberg et al (hereinafter referred to as Wannerberg) in view of U.S. Patent Application Publication No. 2025/0029170 issued to Chachek et al (hereinafter referred to as Chachek) . As to claim 1, Wannerberg discloses a method implemented using one or more processors, comprising: receiving a natural language query (first user input including natural language processing techniques, see Wannerberg: Para. 0151 – 0152, 0156 – 0157 and Fig. 12, see also Para. 0060 for input query being natural language input) ; determining that a first user is in a shared context with at least a second user (determining if multiple users are providing inputs into the 3d environment system, see Wannerberg: Para. 0152 and Fig. 12) ; determining which user, of the first user and the second user, provided the natural language query (in an environment with multiple users providing input, determine which user provided which request, see Wannerberg: Para. 0156) ; determining a first user prompt for the first user and a second user prompt for the second user, wherein the first user prompt conveys one or more known preferences of the first user and the second user prompt conveys one or more known preferences of the second user (determining first and second user provided inputs for 3d environment’s creation, including context and sub-contexts to create an overall context, see Wannerberg: Para. 0156 – 0157 and Fig. 12, and user preferences and/or historical selections for each user used as input for selection of 3d objects for the environment during creation, see Wannerberg: Para. 0048 – 0049, 0055, 0090 – 0091, 0154, 0165) ; determining, based on determining which user, of the first user and the second user, provided the natural language query, one or more weights for the first user prompt and/or the second user prompt (user preferences of a user profile are used in weighting the objects in response to requests for objects to be inserted into the environment, including based on previous requests by the user, see Wannerberg: Para. 0044, 0060, 0127, 0154) ; assembling, based on the one or more of the weights for the first user prompt and/or second user prompt, into a merged input prompt, data indicative of: the natural language query, the first user prompt, and the second user prompt (context for the environment are determined based on natural language descriptors of more heavily weighted objects based on preferences of a user profile of a user, see Wannerberg: Para. 0044, 0060, 0126, 0154, and query the 3D content library based on user inputs and overall context, see Wannerberg: Para. 0157 – 0159 and Fig. 12) ; processing the merged input prompt using one or more generative models to generate output that is conditioned on the first and second user prompts, and that includes content responsive to the natural language query (processing the input into the 3D content library to determine partially matched 3d objects and utilizing a generative machine learning model to generate modified versions of the partially matched object(s), see Wannerberg: Para. 0162 – 0163 and Fig. 12) ; and causing the content to be rendered at one or more output devices (generate 3d model of the modified version of the 3d object and provide for display of the modified 3d object, and inserting selected modified 3d object(s) into the 3d environment, see Wannerberg: Para. 0163 – 0165 and Fig. 12) . While Wannerberg discloses multiple user devices connected to and interacting with a 3d environment through communications between the devices, however, this is not explicitly based on the signals provided by the one or more computing devices and may just be directed to receiving the signals at the 3d computing environment server (see Wannerberg: 0036). Therefore, Wannerberg does not explicitly disclose determining, based on one or more signals provided by one or more computing devices, that the first user is in a shared context with at least a second user. Chachek teaches determining, based on one or more signals provided by one or more computing devices, that the first user is in a shared context with at least a second user (determining a plurality of users hold and operate a respective plurality of end-user-devices within the store based on device location communication, see Chachek: Para. 0056 – 0060) . Chachek and Wannerberg are analogous for their disclosure of generating virtual environments for multiple users. Therefore, it would have been obvious to one of ordinary skill in the art to modify Wannerberg’s use of providing a 3d environment and generating modified 3d objects based on users’ input and context, such as through virtual reality (VR) metaverses, with Chachek’s use of providing augmented reality (AR) and VR environments in real-life locations based on user device signals and user preferences in order to provide real-time information about products depicted in the images, VLM-generated shopping assistance, and VLM-generated in-store navigation guidance (see Chachek: Abstract). As to claim 2, Wannerberg modified by Chachek discloses the method of claim 1, wherein the shared context comprises a shared physical environment (the plurality of users are in the same store/location, see Chachek: Para. 0056 – 0060) . As to claim 3, Wannerberg modified by Chachek discloses the method of claim 2, wherein the one or more signals comprise a wireless signal generated by a mobile device carried by the first or second user (devices being connected to the same Wi-Fi network, GPS, Wi-Fi signal strength, see Chachek: Para. 0056 – 0060) . As to claim 4, Wannerberg modified by Chachek discloses the method of claim 2, wherein the one or more signals comprise contemporaneous detection of one or more biometrics of the first user and one or biometrics of the second user (users’ facial recognition, travel paths and time periods at locations within the store from multiple users in heat maps used to determine recommended navigation route(s) to a user based on their preferences, see Chachek: Para. 0072 – 0073, see also voice commands/queries by users, see Chachek: Para. 0063 – 0064, 0089, 0091, 0198 – 0199 ) . As to claim 5, Wannerberg modified by Chachek discloses the method of claim 1, wherein the shared context comprises a multi-participant message exchange thread in which the first and second users are participants (3d environment including a dialog between user and the system to create and interact with the environment, see Wannerberg: Para. 0059, and multi-user 3d environment, see Wannerberg: Para. 0152) . As to claim 7, Wannerberg modified by Chachek discloses the method of claim 1, wherein the first user prompt comprises one or more natural language statements that convey one or more of the known preferences of the first user (natural language processing of input by user to determine preferences, including processing of user profile, interaction history, or dialog with the system, see Wannerberg: Para. 0048 – 0049, 0060, 0064, 0090 – 0091, 0120 – 0121) . As to claim 8, Wannerberg modified by Chachek discloses the method of claim 1, further comprising: retrieving one or more digital files created or interacted with by the first user (retrieving a previously compiled description file based on prior user interaction, see Wannerberg: Para. 0091) ; assembling, into a user preference generation prompt, data indicative of or derived from the one or more digital files (taking metadata from the description file to determine 3d object features to be migrated to a second 3d content library, see Wannerberg: Para. 0091) ; and processing the user preference generation prompt using one or more of the generative models to generate data indicative of the first user prompt (retrieve 3d object details for the second 3d content library, see Wannerberg: Para. 0091, and modifying 3d objects retrieved from the 3d content library using a generative machine learning model, see Wannerberg: Para. 0099, 0120 – 0123, 0163) . As to claim 9, Wannerberg modified by Chachek discloses the method of claim 8, wherein one or more of the digital files comprises a digital image, digital audio, or digital video (the 3d content library including image data that may be searched using image analysis and/or metadata search, see Wannerberg: Para. 0094, 0097, 0099, 0120 – 0121) . As to claim 10, Wannerberg modified by Chachek discloses the method of claim 1, further comprising: assembling, into a user preference generation prompt, data indicative of or derived from one or more past natural language queries issued by the first user (natural language processing of input by user to determine preferences, including processing of user profile, interaction history, or dialog with the system, see Wannerberg: Para. 0048 – 0049, 0060, 0064, 0090 – 0091, 0120 – 0121) ; and processing the user preference generation prompt using one or more of the generative models to generate data indicative of the first user prompt (processing the input into the 3D content library to determine partially matched 3d objects and utilizing a generative machine learning model to generate modified versions of the partially matched object(s), see Wannerberg: Para. 0163 and Fig. 12) . As to claim 11, Wannerberg modified by Chachek discloses the method of claim 1, further comprising: assembling, into a user preference generation prompt, data indicative of or derived from one or more past search engine queries issued by the first user (library search engine returns results based on user input and user preferences, see Wannerberg: Para. 0072 – 0074, 0091 – 0093, 0097, 0160, see also/alternatively: semantic engine, see Wannerberg: Para. 0079, 0084, 0112 ) ; and processing the user preference generation prompt using one or more of the generative models to generate data indicative of the first user prompt (processing the input into the 3D content library to determine partially matched 3d objects and utilizing a generative machine learning model to generate modified versions of the partially matched object(s), see Wannerberg: Para. 0163 and Fig. 12) . As to claim 12, Wannerberg modified by Chachek discloses the method of claim 1, further comprising: determining one or more device prompts for one or more computing devices available in the shared context, wherein the one or more device prompts convey one or more attributes of the one or more computing devices available in the shared context (user device(s) where input is received by user, see Wannerberg: Para. 0129 – 0131, and determining first and second user provided inputs for 3d environment’s creation, including context and sub-contexts to create an overall context, see Wannerberg: Para. 0156 – 0157, and query the 3D content library based on user inputs and overall context, see Wannerberg: Para. 0157 – 0159) ; and assembling, into the merged input prompt, data indicative of the one or more device prompts (query the 3D content library based on user inputs and overall context, see Wannerberg: Para. 0157 – 0159, and processing the input into the 3D content library to determine partially matched 3d objects and utilizing a generative machine learning model to generate modified versions of the partially matched object(s), see Wannerberg: Para. 0163) . As to claim 13, Wannerberg modified by Chachek discloses the method of claim 12, wherein the one or more attributes comprise one or more of : one or more preferences for operating one or more of the computing devices available in the shared context to render content; one or more states of one or more sensors of one or more of the computing devices available in the shared context; or one or more resource constraints of one or more of the computing devices available in the shared context (device input including audio, remote control device input, text input, etc. see Wannerberg: Para. 0129 – 0131) . As to claim 14, Wannerberg modified by Chachek discloses the method of claim 1, further comprising determining respective weights for the first and second user prompts, wherein the assembling is based on the respective weights (more heavily weighting certain 3d objects requested from the received input, see Wannerberg: Para. 0044, 0060, 0068, 0084, alternatively: machine learning models may be weighted and weights adjusted based on user input for generating search strings of 3d content libraries, see Wannerberg: Para. 0124) . As to claim 15, Wannerberg modified by Chachek discloses the method of claim 14, wherein the respective weights for the first and second user prompts are determined based on relative proximities of the first and second users to a shared audio or vision sensor (scoring positions based on location to nearby objects/landmarks using probability scoring, see Chachek: Para. 0163 – 0166, and determining probability of user location relative to an item using visual/image capture, OCR, etc., see Chachek: Para. 0215 – 0220, probability scoring is a weight ) . As to claim 16, Wannerberg modified by Chachek discloses the method of claim 15, wherein the respective weights for the first and second user prompts are determined based on which of the first or second user issued the natural language query (more heavily weighting certain 3d objects requested from the received input, see Wannerberg: Para. 0044, 0060, 0068, 0084, alternatively: machine learning models may be weighted and weights adjusted based on user input for generating search strings of 3d content libraries, see Wannerberg: Para. 0124) . As to claim 17, Wannerberg modified by Chachek discloses the method of claim 16, wherein the assembling comprises allocating different numbers of tokens to each of the first and second user prompts based on the respective weights (user historical interactions/searches are used for generating tags/keywords/metadata for 3d objects, see Wannerberg: Para. 0064, and analyzing keywords of the users’ inputs/contexts to determine candidate context specifications for each input, see Wannerberg: Para. 0067 – 0068, 0070 – 0071, 0112, 0151, 0157) . As to claim 18, Wannerberg modified by Chachek discloses the method of claim 17, wherein the assembling comprises: assembling, into a summarization input prompt, data indicative of the first user prompt and a target length constraint, wherein the target length constraint is selected based on one or more of the respective weights for the first and second user input prompts (determine a threshold number of words for querying the 3d library based on user input, see Wannerberg: Para. 0048, 0076, 0160 – 0163) ; and processing the summarization input prompt using one or more of the generative models to generate a summary of the first user prompt that satisfies the target length constraint (determine matches or partial matches based on the number of common terms and using generative machine learning model to produce modified versions of partially matching 3d objects, see Wannerberg: Para. 0160 – 0163) . As to claim 19, Wannerberg modified by Chachek discloses the method of claim 14, further comprising assembling, into the merged input prompt, data indicative of relative priorities to be assigned to known preferences conveyed in the first and second user prompts, wherein the relative priorities are determined based on the respective weights (more heavily weighting certain 3d objects requested from the received input, see Wannerberg: Para. 0044, 0060, 0068, 0084, 0154, and ranking or presenting more prominently recommended objects higher based on inputs and predicted likelihood of insertion, see Wannerberg: Para. 0044, 0050, 0055 – 0057, 0098, 0154) . As to claim 20, Wannerberg modified by Chachek discloses the method of claim 1, wherein the assembling comprises: assembling, as a prompt merging input prompt, data indicative of: the first and second user prompts, and a request to combine the first and second user prompts into the merged input prompt while resolving any conflicts between the first and second user prompts (merging of user input and contexts into an overall context, see Wannerberg: Para. 0104 – 0106 and 0156) ; and processing the prompt merging input prompt using one or more of the generative models to generate at least a portion of the merged input prompt (processing the input into the 3D content library to determine partially matched 3d objects and utilizing a generative machine learning model to generate modified versions of the partially matched object(s), see Wannerberg: Para. 0162 – 0163 and Fig. 12) . 07-22-aia AIA Claim (s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wannerberg modified by Chachek as applied to claim 5 above, and further in view of U.S. Patent Application Publication No. 2025/0053735 issued to Shevchenko et al (hereinafter Shevchenko) . As to claim 6, Wannerberg modified by Chachek discloses the method of claim 5, however, Wannerberg modified by Chachek does not explicitly disclose wherein the multi-participant message exchange thread comprises a text messaging thread. Shevchenko teaches wherein the multi-participant message exchange thread comprises a text messaging thread (multiple users in a group’s context determined using communication between users in the group including previous communications in the same thread, previous threads involving the same users, etc., see Shevchenko: Para. 0172 – 0175) . Shevchenko, Chachek and Wannerberg are analogous for their disclosure of generating virtual environments for multiple users. Therefore, it would have been obvious to one of ordinary skill in the art to modify Wannerberg and Chachek’s use of providing a 3d environment and generating modified 3d objects based on users’ input and context, such as through virtual reality (VR) metaverses, with Shevchenko’s use of past text threads between users for determining user context for responding to user requests in order to more efficiently and effectively access the vast knowledge captured in enterprise-specific documents for use in enterprise-specific prompts and responses (see Shevchenko: Para. 0007). Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK E HERSHLEY whose telephone number is (571)270-7774. The examiner can normally be reached M-F: 9am-6pm. 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, Amy Ng can be reached at (571) 270-1698. 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. /MARK E HERSHLEY/Primary Examiner, Art Unit 2164 Application/Control Number: 18/934,877 Page 2 Art Unit: 2164 Application/Control Number: 18/934,877 Page 3 Art Unit: 2164 Application/Control Number: 18/934,877 Page 4 Art Unit: 2164 Application/Control Number: 18/934,877 Page 5 Art Unit: 2164 Application/Control Number: 18/934,877 Page 6 Art Unit: 2164 Application/Control Number: 18/934,877 Page 7 Art Unit: 2164 Application/Control Number: 18/934,877 Page 8 Art Unit: 2164 Application/Control Number: 18/934,877 Page 9 Art Unit: 2164 Application/Control Number: 18/934,877 Page 10 Art Unit: 2164 Application/Control Number: 18/934,877 Page 11 Art Unit: 2164 Application/Control Number: 18/934,877 Page 12 Art Unit: 2164 Application/Control Number: 18/934,877 Page 13 Art Unit: 2164 Application/Control Number: 18/934,877 Page 14 Art Unit: 2164 Application/Control Number: 18/934,877 Page 16 Art Unit: 2164 Application/Control Number: 18/934,877 Page 17 Art Unit: 2164
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