CTFR 18/731,055 CTFR 101704 DETAILED ACTION The action is in response to the original filing on May 31, 2024 and the Remarks and Amendments filed on April 20, 2026. Claims 1-20 are pending and have been considered below. Claims 1-3, 10-12 & 18-20 are amended accordingly. 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. 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 1-2, 5-11, & 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Jones in view of Comer Michael (Pat. Pub. No. WO-2019213220-A1, herein after “Michael”) . Regarding claims 1, 10, & 18 , Jones teaches [a] method comprising: identifying characteristic data indicating one or more physical characteristics of an object “At block 320, a template three-dimensional (3D) mesh associated with an object is obtained. In some implementations, a category of the object may be determined based on an image analysis of the provided 2D image” (Jones, ¶ [0098]) where determining an image category through image matching compares identified characteristic data ; providing the characteristic data as an input to an artificial intelligence (AI) model “At block 310, a two-dimensional (2D) image of an object is provided as input to a trained machine learning model” (Jones, ¶ [0097]) where the 2D image is to be compared with other 2D images to generate a 3D model, the 2D image features are interpreted as characteristic data ; obtaining one or more outputs of the AI model, “In some aspects, the described systems and methods provide for one or more models to generate a three-dimensional reconstruction from a two-dimensional image of an object.” (Jones, ¶ [0047]) where one or more models can be generated, wherein the one or more outputs comprise articulation data indicating a stationary state and a dynamic state of the stationary state and the dynamic state based on the physical characteristics of the object “In some embodiments, the variation in the distinct views and scenes for different versions of scenarios may be data-driven. Distinct views and scenes may be generated for different versions of scenarios using the logic associated with potential animals, plants, and terrain features in ways that adhere to human expectations.” (Jones, ¶ [0035]) where the generation of a model is considered a stationary state ; and updating, based on articulation data, a model file associated with a virtual asset corresponding to the object, wherein the model file, when executed, creates a rendering of the virtual asset in a virtual scene associated with a three-dimensional (3D) graphics platform according to at least one of the stationary state or the dynamic state “The rig may be utilized to animate the character and to simulate motion and action by the character. The 3D model may be represented as a data structure, and one or more parameters of the data structure may be modified to change various properties of the character …” (Jones, ¶ [0086]) where the model, or virtual asset, is represented as a data structure, based on the information provided to and assessed by the artificial intelligence “FIG. 2 is a diagram of an example system architecture to generate 3D meshes from 2D images in an online gaming context” (Jones, ¶ [0061]) where stored data can be pulled and executed by a rendering engine . PNG media_image1.png 858 721 media_image1.png Greyscale Jones does not explicitly teach articulation data indicating a stationary state and a dynamic state of at least one component of the object relative to another component of the object, the stationary state and the dynamic state reflecting how the at least one component and the other component interact based on the physical characteristics of the object, Specifically that the articulation data indicates the states of at least one component relative to another component. Michael teaches articulation data indicating a stationary state and a dynamic state of at least one component of the object relative to another component of the object “The physical subject can have various points of articulation. In the case of a human model, the points of articulation can be the anatomical joints, such as the shoulders, elbows, wrists, hips, knees, ankles, spinal joints, and others” (Michael, ¶ [0163]) which teaches points of articulation. The points of articulation are used to determine how objects are to be posed and how joints move. “the vertices which make up a given articulation segment can be considered to change position and/or orientation substantially together as a group if at least 85% of the vertices in the articulation segment translate in a particular direction, or rotate about a particular axis, by an amount that is within 30% of the translation and/or rotation of the articulation segment as a whole in that particular direction, or about that particular axis, from one pose to another” (Michael, ¶ [0175]) which determines if an articulation segment is dynamic , the stationary state and the dynamic state reflecting how the at least one component and the other component interact based on the physical characteristics of the object “An articulation segment can consist essentially of those vertices which pertain to a given body part.; or which change position and/or orientation substantially together as a group from one pose to another with articulation of a given anatomical joint or set of anatomical joints by the physical subject” (Michael, ¶ [0175]) where the physical characteristics of the body part are reflected by the articulation segment, such as their appearance or movement . “Each of the points of articulation can have a range of movement, sometimes including complex motions in multiple planes” (Michael, ¶ [0163]) which teaches the dynamic capabilities, or movements associated with the stationary state, or physical appearance, such as how the arm components interact by bending at the elbow. Michael additionally teaches obtaining one or more outputs of the AI model, wherein the one or more outputs comprise articulation data “The object recognitions can additionally or alternatively be performed by a variety of machine learning algorithms” (Michael ¶ [0117]), and updating, based on articulation data, a model file “Upon completion of the alignment operation, a motion sequence file can be generated for the body part articulations” (Michael, ¶ [0173]). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of virtual asset generation using AI, and storing it to later be rendered by Jones with the use of articulation data taught by Michael to produce an AI model capable of outputting articulation data regarding the object’s stationary and dynamic state, as well as how the components of the object interact based on their physical characteristics, and to update a model file corresponding to the object based on the articulation data. The suggestion/motivation to do so would have been to utilize data regarding the object’s physical appearance and capabilities. In regards to claim 10 , claim 1 is substantially similar to claim 10, hence the rejection analysis for claim 1 is also applied to claim 10. Jones in view of Michael teaches the additional limitations “[a] system comprising: a set of one or more processing devices to perform operations …” (Jones, ¶ [0174]) In regards to claim 18 , claim 1 is substantially similar to claim 18, hence the rejection analysis for claim 1 is also applied to claim 18. Jones in view of Michael teaches the additional limitations “[a] processor comprising a set of one or more processing units to: …” (Jones, ¶ [0174]) Regarding claims 2, 11, & 19 , Jones in view of Michael teaches [t]he method of claim 1, wherein the articulation data indicates at least one of a plurality of stationary states or a plurality of dynamic states of the object based on the physical characteristics of the object, wherein at least one of each stationary state of the plurality of stationary states or each dynamic state of the plurality of dynamic states corresponds to a respective physical behavior of the object in an environment corresponding to the virtual scene “In some embodiments, the variation in the distinct views and scenes for different versions of scenarios may be data-driven. Distinct views and scenes may be generated for different versions of scenarios using the logic associated with potential animals, plants, and terrain features in ways that adhere to human expectations. For example, in the generated scenario, animals that should swarm, swarm, and animals that should fly, fly, while animals that should mingle and meander, navigate the terrain as they would in real life” (Jones, ¶ [0035]) where the data used to create a scene is articulated, or used to generate animations corresponding with the logic or human expectations of the identified object . The physical view and appearance of the object is read as the stationary state, and the animation of the associated object is read as the dynamic state as the object behaves in its environment. In regards to claim 11, claim 2 is substantially similar to claim 11, hence the rejection analysis for claim 2 is also applied to claim 11. Jones in view of Michael teaches the additional limitations “[t]he system of claim 10 …” (Jones, ¶ [0174]) In regards to claim 19, claim 2 is substantially similar to claim 19, hence the rejection analysis for claim 2 is also applied to claim 19. Jones in view of Michael teaches the additional limitations “[t]he processor of claim 18 …” (Jones, ¶ [0174]) Regarding claim 5 & 14 , Jones in view of Michael teaches [t]he method of claim 1, further comprising: receiving a request for access to the virtual asset in a rendering of the virtual scene at a client device connected to the 3D graphics platform and “The template 3D mesh may be selected from a set of previously generated and stored, template meshes, and may include both user generated and automatically generated meshes. In some implementations, multiple template meshes may be presented to the user and a template mesh selected based on input received from a user. In some implementations, a plurality of template meshes may be selected for the performance of methods 300” (Jones, ¶ [0099]) where the user may request a previously generated and stored mesh/model ; providing the client device with access to at least a portion of the model file in accordance with the request “Some implementations can have one or more blocks of method 300 performed by one or more other devices (e.g., other client devices or server devices) that can send results or data to the first device.” (Jones, ¶ [0096]) where the user may request for a specific 3D mesh, or virtual asset, and the requesting device will be provided with access from another client device or the server based on the request . In regards to claim 14, claim 5 is substantially similar to claim 14, hence the rejection analysis for claim 5 is also applied to claim 14. Jones in view of Michael teaches the additional limitations “[t]he system of claim 10 …” (Jones, ¶ [0174]) Regarding claim 6 & 15 , Jones in view of Michael teaches [t]he method of claim 5, further comprising: determining one or more simulated physical conditions associated with the rendering of the virtual scene at the client device “Distinct views and scenes may be generated for different versions of scenarios using the logic associated with potential animals, plants, and terrain features in ways that adhere to human expectations” (Jones, ¶ [0035]) ; identifying, from the model file, one or more instructions pertaining to at least one of a target stationary state or a target dynamic state of the virtual asset in the virtual scene based on the determined one or more simulated physical conditions “One general aspect includes a non-transitory computer-readable medium may include instructions that. The non - transitory computer - readable medium also includes providing a two-dimensional (2D) image of the object as input to the trained machine learning model; obtaining a template three-dimensional (3D) mesh; and generating, using the trained machine learning model and based on the 2D image and the template 3D mesh, a 3D mesh for the object, where the 3D mesh for the object is usable to map a texture or to generate a 3D animation of the object” (Jones, ¶ [0010]) where the instructions are the provided model generation request, or 2D image, and the target stationary or dynamic state are the template 3D mesh, which can be made dynamic via an applied animation template. “At block 320, a template three-dimensional (3D) mesh associated with an object is obtained. In some implementations, a category of the object may be determined based on an image analysis of the provided 2D image. As described earlier, objects and/or characters in virtual environments may be implemented as a 3D model and may include a surface representation used to draw the object/character (also known as a skin or mesh) and a hierarchical set of interconnected bones (also known as a skeleton or rig). The rig may be utilized to animate the character and to simulate motion and action by the object” (Jones, ¶ [0098]) and providing an indication of the identified one or more instructions to the client device with the model file for presentation via a user interface (UI) of the client device “One or more methods described herein can be run in a standalone program that can be run on any type of computing device, a program run on a web browser, a mobile application (“app”) run on a mobile computing device (e.g., cell phone, smart phone, tablet computer, wearable device (wristwatch, armband, jewelry, headwear, goggles, glasses, etc.), laptop computer, etc.). In one example, a client/server architecture can be used, e.g., a mobile computing device (as a client device) sends user input data to a server device and receives from the server the final output data for output (e.g., for display)” (Jones, ¶ [0172]) . In regards to claim 15, claim 6 is substantially similar to claim 15, hence the rejection analysis for claim 6 is also applied to claim 15. Jones teaches the additional limitations “[t]he system of claim 10 …” (Jones, ¶ [0174]) Regarding claim 7 & 16 , Jones in view of Michael teaches [t]he method of claim 1, wherein the AI model is a large language model “At block 330, a 3D mesh for the object is generated using the trained machine learning model based on the 2D image and the template 3D mesh” (Jones, ¶ [0100]) it is noted that the learning model may be a different artificial intelligence . In regards to claim 16, claim 7 is substantially similar to claim 16, hence the rejection analysis for claim 7 is also applied to claim 16. Jones in view of Michael teaches the additional limitations “[t]he system of claim 10 …” (Jones, ¶ [0174]) Regarding claim 8 , Jones in view of Michael teaches [t]he method of claim 1, wherein the characteristic data comprises at least one of (Jones, ¶ [0007]) : image data comprising one or more images depicting the one or more physical characteristics of the object, derivative data obtained based on an output of one or more generative AI models. “prior to obtaining the template 3D mesh, the category of the object may be determined based on the 2D image using image matching. In some implementations, image segmentation may be performed to determine a category of the object. In some implementations, the category of the object may be specified based on user input” (Jones, ¶ [0104]) additionally, “one or more textures from the 2D image may be mapped to the 3D mesh of the object, depending on texture information in the 2D image. In some implementations, a semantic segmentation may be performed, and texture information from different portions of the 2D image may be mapped to corresponding portions of the 3D mesh of the object” (Jones, ¶ [0105]) the image data is derived from multiple characteristics, determined through several methods (Jones, ¶ [0102] – [0109]) e.g. texture information. Regarding claim 9 & 17 , Jones in view of Michael teaches [t]he method of claim 1, wherein the 3D graphics platform is associated with a computing system comprised in at least one of (Jones, ¶ [0007]) : a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for three-dimensional (3D) assets; a system for performing deep learning operations; a system for performing operations using a large language model (LLM); a system for performing synthetic data generation; a system for generating synthetic data; a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. “In some implementations, online assessment platform 102 or client device 110 may include the assessment engine 104 or virtual assessment 112. In some implementations, assessment engine 104 may be used for the development or execution of assessments 105. For example, assessment engine 104 may include a rendering engine (“renderer”) for 2D, 3D, VR, or AR graphics, a physics engine, a collision detection engine (and collision response), sound engine, scripting functionality, haptics engine, artificial intelligence engine, networking functionality, streaming functionality, memory management functionality, threading functionality, scene graph functionality, or video support for cinematics, among other features” (Jones, ¶ [0056]) it is also noted that this may be implemented in other methods/engines In regards to claim 17, claim 9 is substantially similar to claim 17, hence the rejection analysis for claim 9 is also applied to claim 17. Jones in view of Michael teaches the additional limitations “[t]he system of claim 10 …” (Jones, ¶ [0174]) 07-21-aia AIA Claim s 3-4, 12-13, & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jones in view of Michael and Daniel Shapiro Et. Al. (U.S. Pat. Pub. No. US-20240176321-A1, herein after “Shapiro”) . Regarding claim 3, 12, & 20 , Jones in view of Michael teaches [t]he method of claim 2, wherein updating the model file associated with the virtual asset comprises “The 3D model may be represented as a data structure, and one or more parameters of the data structure may be modified to change various properties of the character” (Jones, ¶ [0086) : obtaining, for the at least one of the plurality of stationary states or the plurality of dynamic states, a set of instructions associated with rendering the virtual asset in the virtual scene according to at least one of a respective stationary state or a respective dynamic state “The components of the assessment engine 104 may generate commands that help compute and render the assessment (e.g., rendering commands, collision commands, physics commands, etc.)” (Jones, ¶ [0056]) where the commands generated by the assessment engine to render the model are instructions associated with rendering the virtual asset based on the user selection (Jones, ¶ [0058]) ; Jones in view of Michael does not explicitly teach updating the model file to include the set of instructions for each of the at least one of the plurality of stationary states or the plurality of dynamic states. Shapiro teaches updating the model file to include the set of instructions for each of the at least one of the plurality of stationary states or the plurality of dynamic states “In some embodiments, the CNC machine executes an implementation file to create a rendered fabrication result, where the implementation file includes the machine-generated rendering instructions” (Shapiro, ¶ [0056]), additionally, “Transforming the SVG image into rendering instructions, and in some instances, additionally packaging the rendering instructions into an implementation file can be performed by the same AI model, a different AI model, or perhaps via other computing methods” (Shapiro, ¶ [0120]). In regards to claim 12, claim 3 is substantially similar to claim 12, hence the rejection analysis for claim 3 is also applied to claim 12. Jones in view of Michael teaches the additional limitations “[t]he system of claim 10 …” (Jones, ¶ [0174]) In regards to claim 20, claim 3 is substantially similar to claim 20, hence the rejection analysis for claim 3 is also applied to claim 20. Jones in view of Michael teaches the additional limitations “[t]he processor of claim 18 …” (Jones, ¶ [0174]) It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of virtual asset generation using AI, and storing it to later be rendered by Jones with the method of including the rendering instructions taught by Shapiro to include all relevant data in one file. The suggestion/motivation to do so would have been to improve the time required to load a 3D asset. Regarding claim 4 & 13 , Jones in view of Michael teaches in view of Shapiro teach [t]he method of claim 3, wherein the set of instructions for each of the at least one of the plurality of stationary states or the plurality of dynamic states is included in the one or more outputs of the AI model, and wherein obtaining the set of instructions for each of the at least one of the plurality of stationary states or the plurality of dynamic states comprises “Transforming the SVG image into rendering instructions, and in some instances, additionally packaging the rendering instructions into an implementation file can be performed by the same AI model, a different AI model, or perhaps via other computing methods” (Shapiro, ¶ [0120]). : extracting the set of instructions for each of the at least one of the plurality of stationary states or the plurality of dynamic states from the one or more outputs of the AI model “… a client/server architecture can be used, e.g., a mobile computing device (as a client device) sends user input data to a server device and receives from the server the final output data for output (e.g., for display)” (Jones, ¶ [0172]) where a client device receives or extracts the data or image information from a server . In regards to claim 13, claim 4 is substantially similar to claim 13, hence the rejection analysis for claim 4 is also applied to claim 13. Jones in view of Michael teaches the additional limitations “[t]he system of claim 10 …” (Jones, ¶ [0174]) Response to Arguments 07-38-01 AIA Applicant’s arguments, see Remarks, Page 11 , filed April 30, 2026 , with respect to the specification objection have been fully considered and are persuasive. The objection of the specification has been withdrawn. The typographical error which originally used “ IA ” has been corrected to “ AI ”, the abstract has no further objections. 07-38-01 AIA Applicant’s arguments, see Remarks, Page 11 , filed April 30, 2026 , with respect to the 35 U.S.C. § 112(b) rejection of claims 2-4, 11-13, & 19-20 have been fully considered and are persuasive. The rejection of claims 2-4, 11-13, & 19-20 has been withdrawn. The amendments to the rejected claims provide clarity that overcomes the rejection. The original indefinite claim language stated “ each of the at least one of the plurality of stationary states or the plurality of dynamic states ” which was unclear as referencing to the plurality of states, or the states themselves. The replaced language “ at least one of each stationary state of the plurality of stationary states or each dynamic state of the plurality of dynamic states ” is no longer unclear. Applicant’s arguments with respect to claims 1, 10, & 18 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Applicant’s arguments, see Remarks, Pages 11-14, filed April 30, 2026, with respect to the rejections of claims 1-2, 5-11, & 14-19 under 35 U.S.C. § 102(a)(1) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Michael. The amendments to the rejected claims 1, 10, & 18 provided clarity regarding the object components and their interaction that overcomes the previous rejection. Applicant argues that Jones is silent regarding the use of articulation data indicating at least one of a stationary state or a dynamic state of at least one component relative to another component of the object, the stationary state and the dynamic state reflecting how the at least one component and the other component interact based on the physical characteristics of the object. The Examiner is persuaded by this argument and withdraws the 35 U.S.C. § 102 rejection, however, a new grounds of rejection is made under 35 U.S.C. § 103(a)(1). Michael teaches the use of points of articulation which can be read as articulation data that indicates or emphasizes a positional or motional relationship between components of an object. The articulation segments may include joints or vertices that couple objects, the same way an elbow connects arm objects. This interpretation of the prior art reads upon the amended claims as the articulation data is used to indicate a stationary and dynamic state of the object, and how the components interact based on their physical capabilities. 07-38-02 Applicant’s arguments, see Remarks, Pages 14, filed April 30, 2026, with respect to the rejections of claims 3-4, 12-13, & 20 under 35 U.S.C. § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Michael. The aforementioned claims directly or indirectly depend on amended claims 1, 10, & 18 The new ground of rejection of the amended claims cures the argued deficiencies. 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. 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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. /C.A.U./Examiner, Art Unit 2611 /TAMMY GODDARD/Supervisory Patent Examiner, Art Unit 2611 Application/Control Number: 18/731,055 Page 2 Art Unit: 2611 Application/Control Number: 18/731,055 Page 3 Art Unit: 2611 Application/Control Number: 18/731,055 Page 4 Art Unit: 2611 Application/Control Number: 18/731,055 Page 5 Art Unit: 2611 Application/Control Number: 18/731,055 Page 6 Art Unit: 2611 Application/Control Number: 18/731,055 Page 7 Art Unit: 2611 Application/Control Number: 18/731,055 Page 8 Art Unit: 2611 Application/Control Number: 18/731,055 Page 9 Art Unit: 2611 Application/Control Number: 18/731,055 Page 10 Art Unit: 2611 Application/Control Number: 18/731,055 Page 11 Art Unit: 2611 Application/Control Number: 18/731,055 Page 12 Art Unit: 2611 Application/Control Number: 18/731,055 Page 13 Art Unit: 2611 Application/Control Number: 18/731,055 Page 14 Art Unit: 2611 Application/Control Number: 18/731,055 Page 15 Art Unit: 2611
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