Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/06/2025 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 are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 708, 710, 726. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a control system for an autonomous or semi-autonomous machine”; “a perception system for an autonomous or semi-autonomous machine”; “a system for performing one or more simulation operations”; “a system for performing one or more digital twin operations”; “a system for performing light transport simulation”; “a system for performing collaborative content creation for 3D assets”; “a system for performing one or more deep learning operations”; “a system implemented using an edge device”; “a system implemented using a robot”; “a system for performing one or more generative AI operations”; “a system for performing operations using one or more large language models (LLMs)”; “a system for performing operations using one or more vision language models (VLMs)”; “a system for performing one or more conversational AI operations”; “a system for generating synthetic data”; and “a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content” in claims 18 and 20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5, 7-12, and 17-20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Somers (US 11110353 B2), and further in view of Khaderi (US 10209773 B2).
Regarding claim 1, Somers teaches a method comprising:
applying, to one or more machine learning models, first image data representative of one or more frames associated with an interactive application (col. 8, lines 23-26: “The simulation engine can output graphical state data (e.g., game state data 114) that is used by presentation engine to generate and render frames within the game application 110.”), the one or more frames including one or more visual characteristics associated with one or more first reaction times (col. 4, line 66 – col. 5, line 4: “System and methods for utilizing a computing system and/or video game console to monitor the player's video game, detect when a particular gameplay situation occurs during the player's video game experience, and collect game state data corresponding to how the player reacts to the particular gameplay situation or an effect of the reaction.”);
generating, based at least on the one or more machine learning models processing the first image data, second image data representative of one or more updated frames, the one or more updated frames including one or more updated visual characteristics (col. 8, lines 34-40: “The presentation engine can use the graphical state data to generate and render frames for output to a display within the game application 110. The presentation engine can to combine the virtual objects, such as virtual characters, animate objects, inanimate objects, background objects, lighting, reflection, and the like, in order to generate a full scene and a new frame for display.”) associated with one or more second reaction times that are less than the one or more first reaction times (col. 9, lines 47-50: “The graphical game state data can include game state data that is generated based on the simulation state data and is used to generate and render frames for output on a display.”; col. 5, lines 15-21: “As an example, a player computing system and/or console can be used to monitor the player's video game, detect when a particular gameplay situation occurs during the player's video game experience, and record game state data corresponding to how the player reacts to the particular gameplay situation and an effect of the reaction (sometimes referred to as the reward).” NOTE: These passages show a connection between player reaction data and the game state data.); and
causing an output of the one or more updated frames represented by the second image data (col. 8, lines 34-40, as above).
Somers fails to teach the one or more updated frames including one or more updated visual characteristics being associated with one or more second reaction times that are less than the one or more first reaction times.
Khaderi teaches a method comprising:
applying, to one or more machine learning models, first image data representative of one or more frames associated with an interactive application (col. 17, lines 44-51: “ A machine learning module 152 within data processing system 114 may communicate with a storage 154 and a real time queue 156 to output data to a data serving system 116, which may include an Application Program Interface (API). In embodiments, the machine learning system may implement one or more known and custom models to process data output from data ingestion system 112.”; col. 17, lines 26-30: “ Data adapters, which are a set of objects used to communicate between a data source and a dataset, may constitute data ingestion system 112. For example, an image data adapter module may extract metadata from images, and may also process image data.”), the one or more frames including one or more visual characteristics associated with one or more first reaction times (col. 51, lines 39-52: “The user may be provided with tools to vary display parameters of the distorted image in order to match it to the original image. In an embodiment, the display parameters may include a combination of one or more of color, sharpness, and size, among other. Once the user confirms completing the task, results may be presented to the user by comparing the user's selections and the correct selections. In an embodiment, greater the proximity of the user's selection to the correct selection, the greater is the vision performance of the user. The Picture Perfect game may not require a fast reaction, although users may be encouraged to work fast (for example, the number of settings made in a fixed period of time may be used to generate a Reaction score).”); and
one or more updated visual characteristics associated with one or more second reaction times that are less than the one or more first reaction times (col. 3, lines 3-7: “Optionally, the quantitative values representative of the patient's visual endurance comprises data representative of an improvement in the patient's reaction time over a duration of presenting the second set of visual and/or auditory stimuli after a rest period.”).
Khaderi fails to teach generating, based at least on the one or more machine learning models processing the first image data, second image data representative of one or more updated frames; and
causing an output of the one or more updated frames represented by the second image data.
It would have been obvious to one familiar in the art prior to the effective filing date of the claimed invention to include the reaction time tracking of Khaderi in the training data for AI-controlled entities of Somers, as both are in the same field of endeavor of training machine-learning systems for use in video games. Including this information is clearly beneficial to training a machine learning program on how best to interact with a player, as Somers states that studying a player’s reaction is integral to properly training their model (col. 5, lines 15-21, as above).
Regarding claim 2, Somers and Khaderi teach the method of claim 1. Somers further teaches wherein:
the one or more visual characteristics associated with the one or more first reaction times include at least one of one or more first luminance values or one or more first contrast values associated with one or more pixels of the one or more frames (col. 8, lines 28-33: “Each state stream process can generate graphical state data for the presentation engine. For example, the state stream processes can include emitters, lights, models, occluders, terrain, visual environments, and other virtual objects with the game application 110 that affect the state of the game.”); and
the one or more updated visual characteristics associated with the one or more second reaction times include at least one of one or more second luminance values or one or more second contrast values associated with the one or more frames (col. 8, lines 36-40: “The presentation engine can to combine the virtual objects, such as virtual characters, animate objects, inanimate objects, background objects, lighting, reflection, and the like, in order to generate a full scene and a new frame for display.”).
Regarding claim 5, Somers and Khaderi teach the method of claim 1. Somers further teaches:
applying, to the one or more machine learning models, third image data representative of one or more second frames associated with the interactive application, the one or more second frames including one or more second visual characteristics associated with one or more third reaction times (col. 8, lines 34-40: “The presentation engine can use the graphical state data to generate and render frames for output to a display within the game application 110. The presentation engine can to combine the virtual objects, such as virtual characters, animate objects, inanimate objects, background objects, lighting, reflection, and the like, in order to generate a full scene and a new frame for display.”);
generating, based at least on the one or more machine learning models processing the third image data, fourth image data representative of one or more updated second frames, the one or more updated second frames including one or more second updated visual characteristics associated with one or more fourth reaction times that are less than the one or more third reaction times (col. 5, lines 15-21: “As an example, a player computing system and/or console can be used to monitor the player's video game, detect when a particular gameplay situation occurs during the player's video game experience, and record game state data corresponding to how the player reacts to the particular gameplay situation and an effect of the reaction (sometimes referred to as the reward).”); and
causing an output of the one or more second updated frames represented by the fourth image data (col. 8, lines 23-28: “The simulation engine can output graphical state data (e.g., game state data 114) that is used by presentation engine to generate and render frames within the game application 110. In some embodiments, each virtual object can be configured as a state stream process that is handled by the simulation engine.”).
Regarding claim 7, Somers and Khaderi teach the method of claim 1. Somers further teaches wherein the one or more machine learning models are updated, at least, by:
generating, based at least on the one or more machine learning models processing third image data representative of one or more second frames, fourth image data representative of one or more updated second frames (col. 8, lines 23-26, as above in claim 1 rejection);
determining one or more third reaction times associated with the one or more updated second frames (col. 4, line 66 – col. 5, line 4: “System and methods for utilizing a computing system and/or video game console to monitor the player's video game, detect when a particular gameplay situation occurs during the player's video game experience, and collect game state data corresponding to how the player reacts to the particular gameplay situation or an effect of the reaction.”);
determining one or more fourth reaction times associated with one or more target frames corresponding to the one or more second frames (col. 5, lines 9-14: “A system can receive the game state data from many computing systems and/or video game consoles and train a rule set based on the game state data. Advantageously, the system can save computational resources by utilizing the players' video game experience to train the rule set.”);
determining one or more loss values corresponding to one or more loss functions based at least on the one or more third reaction times and the one or more fourth reaction times (col. 18, lines 43-50: “At block 308, the user computing system 102 monitors, tracks, or stores the runtime state of the game application. For example, during runtime of the game application 110, the game application 110 can monitor, track, or store game state data 118, which can include, but is not limited to, a game state, character states, environment states, scene object storage, route information, or information associated with a runtime state of the game application 110.”); and
updating, based at least on the one or more loss values, one or more parameters of the one or more machine learning models (col. 2, lines 20-22: “The interactive computing system can update the exploratory rule set based at least in part on the game state data and the other game state data.”).
Regarding claim 8, Somers and Khaderi teach the method of claim 7. Somers further teaches wherein:
the determining the one or more third reaction times is based at least on one or more second visual characteristics associated with the one or more second frames (col. 25, lines 32-36: “RAM is used and holds data that is generated during the play of the game and portions thereof might also be reserved for frame buffers, game state or other data needed or usable for interpreting user input and generating game displays.”); and
the determining the one or more fourth reaction times is based at least on one or more updated second visual characteristics associated with the one or more updated second frames (col. 25, lines 32-36, as above).
Regarding claim 9, Somers and Khaderi teach the method of claim 1. Somers further teaches wherein the one or more machine learning models are updated, at least, by:
generating, based at least on the one or more machine learning models processing third image data representative of one or more second frames, fourth image data representative of one or more updated second frames (col. 9, lines 47-50: “The graphical game state data can include game state data that is generated based on the simulation state data and is used to generate and render frames for output on a display.”);
determining one or more losses based at least on analyzing the one or more second frames with respect to one or more target frames (col. 23, lines 52-61: “The animation engine can determine new poses for the characters and provide the new poses to a skinning and rendering engine. The skinning and rendering engine, in turn, can provide character images to an object combiner in order to combine animate, inanimate, and background objects into a full scene. The full scene can be conveyed to a renderer, which can generate a new frame for display to the user. The process can be repeated for rendering each frame during execution of the game application.”); and
updating, based at least on the one or more losses, one or more parameters of the one or more machine learning models (col. 17, lines 36-40: “As described herein, the interactive computing system 130 can receive the game state data, as well as game state data from other user computing systems 102, and can update or modify a rule set based at least in part on the game state data.”).
Regarding claim 10, Somers and Khaderi teach the method of claim 1. Somers further teaches wherein the causing the output of the one or more updated frames comprises one or more of:
sending the second image data to one or more client devices for output by the one or more client devices (col. 24, line 66 – col. 25, line 9: “ Display output signals may be produced by the display I/O 26 and can include signals for displaying visual content produced by the computing device 110 on a display device, such as graphics, user interfaces, video, or other visual content. The user computing system 102 may include one or more integrated displays configured to receive display output signals produced by the display I/O 26, which may be output for display to a user. According to some embodiments, display output signals produced by the display I/O 26 may also be output to one or more display devices external to the computing device 110.”); or
displaying, using a client device, the one or more updated frames represented by the second image data (col. 24, line 66 – col. 25, line 9, as above).
Claim 11 is substantially similar to claim 1, except that it teaches a system rather than a method. As such, it is rejected for similar reasons to claim 1.
Claim 12 is substantially similar to claim 2, except that it depends from claim 11, rather than claim 1. As such, it is rejected for similar reasons to claim 2.
Claim 17 is substantially similar to claims 8 and 9, except that it is a single claim which depends from claim 11, rather than two separate claims which ultimately depend from claim 1. As such, it is rejected for similar reasons to claims 8 and 9.
Regarding claim 18, Somers teaches the system of claim 11, wherein the system is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine (col. 25, lines 61-64: “All of the processes described herein may be embodied in, and fully automated via, software code modules executed by a computing system that includes one or more computers or processors.”);
a perception system for an autonomous or semi-autonomous machine;
a system for performing one or more simulation operations (col. 17, lines 5-9: “During runtime of the game application 110, the user computing system 102 executes the game logic, controls execution of the simulation of gameplay, and controls rendering within the game application 110.”);
a system for performing one or more digital twin operations;
a system for performing light transport simulation (col. 8, lines 28-33: “Each state stream process can generate graphical state data for the presentation engine. For example, the state stream processes can include emitters, lights, models, occluders, terrain, visual environments, and other virtual objects with the game application 110 that affect the state of the game.”);
a system for performing collaborative content creation for 3D assets;
a system for performing one or more deep learning operations (col. 15, lines 36-40: “In some implementations, the rule set update system 150 creates or develops the rule sets, or updates thereto. For example, the rule set update system 150 can utilize artificial intelligence or machine learning principals, such as reinforcement learning, to “train” the rule sets.”);
a system implemented using an edge device;
a system implemented using a robot;
a system for performing one or more generative AI operations (col. 15, lines 36-40, as above);
a system for performing operations using one or more large language models (LLMs);
a system for performing operations using one or more vision language models (VLMs);
a system for performing one or more conversational AI operations;
a system for generating synthetic data;
a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content (col. 5, line 62 – col. 6, line 7: “To simplify discussion, the present disclosure is primarily described with respect to a video game. However, the present disclosure is not limited as such may be applied to other types of applications. Further, the present disclosure is not limited with respect to the type of video game. The use of the term “video game” herein includes all types of games, including, but not limited to web-based games, console games, personal computer (PC) games, computer games, games for mobile devices (for example, smartphones, portable consoles, gaming machines, or wearable devices, such as virtual reality glasses, augmented reality glasses, or smart watches), or virtual reality games, as well as other types of games.”);
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center (col. 7, lines 5-8: “The user computing system 102 can execute a game application 110 based on software code stored at least in part in the application data store 106.”); or
a system implemented at least partially using cloud computing resources.
Claim 19 is substantially similar to claim 1, except that it teaches a number of processers configured to perform the method of claim 1 rather than the method itself. As such, it is rejected for similar reasons to claim 1.
Claim 20 is substantially similar to claim 18, except that it depends from claim 19, rather than claim 11. As such, it is rejected for similar reasons to claim 18.
Claim(s) 3-4, 6, and 13-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Somers (US 11110353 B2) and Khaderi (US 10209773 B2) as applied to claims 1 and 11 above, and further in view of Li (US 20240073452 A1).
Regarding claim 3, Somers and Khaderi teach the method of claim 1, but fail to teach the elements of claim 3.
Li teaches wherein the generating the second image data comprises:
determining, based at least on the one or more machine learning models processing the first image data, one or more weights associated with one or more first color lookup tables (par. 0005: “In some embodiments, each of the weights of the trained machine learning model and the stored color table are used, e.g., by a decoding device, in recovering the color for a voxel of a particular frame.”); and
determining a second color lookup table based at least on the one or more weights and the one or more first color lookup tables (par. 0032: “FIG. 2A shows an illustrative data structure generated for at least one frame of 3D media content, based on color attributes information of the at least one frame, in accordance with some embodiments of this disclosure. In some embodiments, the codec application may be configured to generate data structure 200, which may be, for example, a color table.”); and
generating the second image data by applying one or more values of the second color lookup table to one or more pixels of the one or more frames represented by the first image data (par. 0032: “The codec application may generate data structure 200 for one or more frames of 3D media content using one or more of any suitable computer-implemented technique. In generating data structure 200, the codec application may leverage the observation that frames of content generally utilize a limited number of colors from among a total number of possible available colors.”).
It would have been obvious to one familiar in the art prior to the effective filing date of the claimed invention to incorporate the color tables of Li into the AI training method of Somers, as both are in the same field of endeavor of graphical frame editing. Li’s invention is not directed to video games in particular, but rather three-dimensional media in general; if adapted for video games, as Li anticipates elsewhere in their invention (par. 0064: “Memory may be an electronic storage device provided as storage 508 that is part of control circuitry 504. As referred to herein, the phrase “electronic storage device” or “storage device” should be understood to mean any device for storing electronic data, computer software, or firmware, such as… …gaming consoles, gaming media, or any other suitable fixed or removable storage devices, and/or any combination of the same.”), it would be obvious to incorporate it into a method like that of Somers.
Regarding claim 4, Somers and Khaderi teach the method of claim 1, but fail to teach the elements of claim 4.
Li teaches wherein the generating the second image data comprises:
determining, based at least on the one or more machine learning models processing the first image data, one or more weights associated with one or more lookup tables (par. 0005, as above in claim 3 rejection); and
generating the second image data by applying, based at least on the one or more weights, the one or more lookup tables to one or more pixels of the one or more frames represented by the first image data (par. 0032, as above in claim 3 rejection).
It would have been obvious to one familiar in the art prior to the effective filing date of the claimed invention to incorporate the color tables of Li into the AI training method of Somers, as both are in the same field of endeavor of graphical frame editing. Li’s invention is not directed to video games in particular, but rather three-dimensional media in general; if adapted for video games, as Li anticipates elsewhere in their invention (par. 0064: “Memory may be an electronic storage device provided as storage 508 that is part of control circuitry 504. As referred to herein, the phrase “electronic storage device” or “storage device” should be understood to mean any device for storing electronic data, computer software, or firmware, such as… …gaming consoles, gaming media, or any other suitable fixed or removable storage devices, and/or any combination of the same.”), it would be obvious to incorporate it into a method like that of Somers.
Regarding claim 6, Somers and Khaderi teach the method of claim 5, but fail to teach the elements of claim 6.
Li teaches wherein the generating the second image data comprises:
determining, based at least on the one or more machine learning models processing the first image data, one or more first weights associated with one or more color lookup tables (par. 0005, as above in claim 3 rejection);
determining, based at least on the one or more machine learning models processing the third image data, one or more second weights associated with the one or more color lookup tables (par. 0032, as above in claim 3 rejection);
determining one or more third weights based at least on the one or more first weights, the one or more second weights, and the color value (par. 0042: “Such particular color may be compared to a real color value (e.g., as defined by a full voxel table that stores voxel location and color attributes information, such as, for example, vector quantized color attributes information, and/or other color attributes information, for a particular voxel coordinate of a particular frame of 3D media content). The codec application may then adjust weights or other parameters of machine learning model 300 based on how closely the output color attributes information (e.g., determined based on the particular element of data structure 200 output by model 300) matches the real color value.”); and
generating the fourth image data based at least on the third image data, the one or more third weights, and the one or more lookup tables (par. 0049: “The encoded data transmitted to the decoding device may comprise an indication of the coordinates of the voxel of a particular frame associated with the 3D media content, data structure 200 (e.g., a color table) associated with the particular frame, stored weights for layers of machine learning model 300 having been learned by way of training model 300, and/or any other suitable data.”).
It would have been obvious to one familiar in the art prior to the effective filing date of the claimed invention to incorporate the color tables of Li into the AI training method of Somers, as both are in the same field of endeavor of graphical frame editing. Li’s invention is not directed to video games in particular, but rather three-dimensional media in general; if adapted for video games, as Li anticipates elsewhere in their invention (par. 0064, as above in claim 3 rejection), it would be obvious to incorporate it into a method like that of Somers.
Claim 13 is substantially similar to claim 3, except that it depends from claim 11, rather than claim 1. As such, it is rejected for similar reasons to claim 3.
Claim 14 is substantially similar to claim 4, except that it depends from claim 11, rather than claim 1. As such, it is rejected for similar reasons to claim 4.
Regarding claim 15, Somers, Khaderi and Li teach the system of claim 14. Li further teaches wherein the one or more processors are further to:
determine, based at least on the one or more visual characteristics and the one or more second visual characteristics, a color value associated with the one or more second frames (par. 0004: “The codec application may generate a data structure (e.g., a color table) for the first frame based on color attributes information (e.g., RGB values) of the first frame, wherein each element of the data structure may encode a single color.”),
wherein the generation of the fourth image data is further based at least on the color value (par. 0029: “In one approach, the codec application may encode 3D frame 102 based at least in part on (e.g., by storing) each portion of voxel data individually, such as, for example, as a table of horizontal, vertical and depth coordinates (x, y, z, coordinates) associated with color data (e.g., 3-value red, green, blue color data). At render time, the codec application may perform live rendering based on voxel data (e.g., using ray tracing) to render the voxel data from a certain angle. Such voxel data may be used instead of vector-based graphics, e.g., when 3D data is generated using live motion capture.”).
Claim 16 is substantially similar to claim 6, except that it depends from claim 11, rather than claim 1. As such, it is rejected for similar reasons to claim 6.
Conclusion
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xiao Wu, can be reached at (571) 272-7761. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RYAN ALLEN BARHAM/Examiner, Art Unit 2613
/XIAO M WU/Supervisory Patent Examiner, Art Unit 2613
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