DETAILED ACTION
This office action is responsive to applicant’s communications filed 03/02/2026.
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 .
Priority
Application claims priority to foreign application with application number CN 202210832558 dated 07/15/2022. Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 and 3, as well as similar claims 11, 13, 16, and 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.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 9, 15, 20, 21, 24, and 26 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The specification does not include support for the limitations:
“based on a difference between the control parameter and the historical control parameter exceeding a threshold, generating the query feature of the virtual object based on the difference between the control parameter and the historical control parameter.” (claims 9, 15, 20), or:
“based on a difference between the control parameter and the historical control parameter not exceeding a threshold, using a historical query feature of the virtual object as the query feature.” (claims 21, 24, 26).
Paragraphs [0054] and [0055] of the specification provide the closest description to the claim limitations, discussing generating query features based on a historical control parameter in a historical motion control signal. However, they do not mention comparing the difference between a control parameter and a historical control parameter to a threshold.
Paragraph [0061] discusses comparing query features and only updating the feature generation network if changes in query features satisfy a condition. However, this is a later step in the process, occurring after query features have already been generated. The claim limitations describe comparing control parameters to generate a query feature, not comparing query features to generate an additional feature.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 5, 11, 16, 22, 25, and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Starke et al. (US 20230310998 A1, hereinafter "Starke") in view of Holden et al. (“A Deep Learning Framework for Character Motion Synthesis and Editing”. ACM Transactions on Graphics (TOG), vol. 35 issue 4 (11 Jul 2016). https://doi.org/10.1145/2897824.2925975; hereinafter “Holden”) and Wang et al. (US 20230410397 A1; hereinafter "Wang").
Regarding claim 1, Starke teaches: A method, comprising:
receiving a plurality of weights for a neural network ([0062] to [0065] describes training the neural network which is later used for animation generation);
receiving, from the first terminal, a motion control signal for a virtual object in a virtual scene ([0073] “While the character control variables 118 may be used to predict motion, as may be appreciated the end user may adjust the in-game character's motion via user input 114. For example, the end user may utilize an electronic game controller to provide user input in the form of interactions with buttons, control sticks, and so on. In the above-described example in which the in-game character is running, the end user may provide user input 114 to maintain the running. For example, the user input 114 may indicate that a certain control stick is being pushed forward. However, the user input 114 may also indicate that the in-game character is to cease running or perform another movement (e.g., run, shift directions, pass, and shoot a ball, and so on).”);
generating, based on the motion control signal and running data of the virtual scene, a query feature of the virtual object ([0068] “The dynamic animation generation system 100 may generate particular information, referred to herein as ‘animation control information’, which may be autoregressively used to generate motion for an in-game character. For example, the animation control information may indicate a character pose (e.g., character poses 102A-102B illustrated in FIG. 1). As illustrated, animation control information 112A generated for a first frame (e.g., frame ‘i’) may be used as an input for a second frame (e.g., frame ‘i+1’). Animation control information 112B for the second frame may be generated by the system 100, and an underlying skeleton or rig of an in-game character may be updated according to an indicated character pose for the second frame.”
[0074] “The character control variables 118 and user input 114 may therefore be combined by the dynamic animation generation system 100. In this way, the user input 114 may provide adjustments to motion predicted in frame ‘i’.”), wherein the query feature comprises:
a bone feature, indicating a bone of the virtual object (Starke [0071] “Animation control information 112A may further include character control variables 118. These variables 118 may inform the in-game character's motion. For example, the character control variables 118 may include trajectory information for the in-game character. In this example, the trajectory information may indicate positions of the in-game character (e.g., a current position and optionally one or more prior positions), velocity of the in-game character (current velocity and optionally one or more prior velocities), and so on.”), and
a trajectory feature, indicating a trajectory of the virtual object (Starke [0075] “The animation control information 112A may further include additional information 119. For example, character state information associated with an in-game character may be included. The character state may indicate, for example, the character pose for the in-game character. For example, the character state may represent positions, rotations, and velocities of bones used in an underlying skeleton or rig of the in-game character.”);
generating, using the neural network, animation data for the virtual object ([0111] “At block 504, the system accesses animation control information for the frame.”; [0112] “At block 506, the system uses the current phase state and character state as input for the neural network. The system can autoregressively update the phase state of the character as well as its motion.” [0113] “At block 508, the system predicts the next character state and phase update as the output of the neural network.”);
generating, using the neural network and based on the current frame of the virtual object in the virtual scene, a next frame for the virtual object ([0115] “At block 510, the system produces a pose for the subsequent frame.”); and
implementing the next frame in the virtual scene ([0109] “At block 502, the system executes an electronic game and outputs a frame.”).
Starke may not explicitly teach: using the plurality of weights for the neural network, increasing, using the neural network and based on the query feature of the virtual object, a feature dimension of the virtual object;
generating, using the neural network and based on the feature dimension, animation data for the virtual object; or
generating, using the neural network and based on the feature dimension and current frame of the virtual object in the virtual scene, a next frame for the virtual object.
Holden teaches: using the plurality of weights for the neural network, increasing, using the neural network and based on the query feature of the virtual object, a feature dimension of the virtual object (pg. 4 section 6 “Mapping High Level Parameters to Human Motions” explains mapping from low dimensional input parameters to higher-dimension full body motion, which constitutes an increase in dimension: “We learn a regression between high level parameters and the character motion using a feedforward convolutional neural network. The high level parameters are abstract parameters for describing the motion, such as the root trajectory projected onto the terrain or the trajectories of the end effectors such as the hands and feet. This is a general framework that can be applied for various types of high level parameters and animation outputs. Producing a mapping between the low dimensional, high level parameters to the full body motion is a difficult task due to the huge amount of ambiguity and multi-modality in the output. There can be many different valid motions that could be performed to follow the high level parameters.”, where the “high level parameters” of Holden correspond to the claimed “query feature”, including trajectory features;
Pg. 5 sections 6.1 “Structure of the Feedforward Network” and 6.2 “Training the Feedforward Network” explains how the feature dimension increase is performed by a neural network including weights which are optimized via training); and
generating, using the neural network and based on the feature dimension and current frame of the virtual object in the virtual scene, a next frame for the virtual object (Holden pg. 5 section 6.1 “These filter widths ensure each frame of motion is generated using roughly one second of trajectory information.”).
Holden and Starke are analogous to the claimed invention because they are both in the same field of generating 3D character animation using a neural network trained on motion capture data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Starke with the teachings of Holden to use a neural network to generate additional features from the input data beyond the scope of what is taught in the input data. The motivation would have been to generate realistic human motion based on the limited inputs of a video game controller, mapping a single button or joystick press to a complex animation involving many moving joints.
The combination of Starke in view of Holden does not explicitly teach: receiving, at a first terminal and from a remote server, a plurality of weights for a neural network.
Wang teaches: receiving, at a first terminal and from a remote server, a plurality of weights for a neural network ([0082] “In at least one embodiment, a transmission mechanism such as data streaming can be used to transfer this content from server 620, or content database 634, to client device 602.… In at least one embodiment, locations where at least some of this functionality is performed may be configurable, or may depend upon factors such as a type of client device 602 or availability of a network connection with appropriate bandwidth, among other such factors. In at least one embodiment, one or more neural networks can be used for performing or assisting in this functionality, where those neural networks (or at least network parameters for those networks) can be provided by content server 620 or third party system 660.”).
Wang is analogous to the claimed invention because it is in the same field of generating 3D character animation using a neural network. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Starke in view of Holden with the teachings of Wang to transmit neural network parameters to a client device from a remote server. The motivation would have been to reduce the computational load on the client device by performing tasks such as neural network training on a remote server.
Regarding claim 5, the combination of Starke in view of Holden and Wang teaches: The method of claim 1, wherein the neural network is trained using actions comprising:
obtaining motion capturing data of a motion body, wherein the motion capturing data comprises a plurality of frames (Starke [0033] “To generate motions for in-game characters, electronic game designers are increasingly leveraging motion capture studios. For example, a motion capture studio may be used to learn the realistic gait of an actor as he/she moves about the motion capture studio.”);
obtaining, based on a first frame and a second frame in the motion capturing data, a first query feature and a second query feature of the motion body (Starke [0033] “Specific portions of the actor, such as joints or bones, may be monitored during this movement. Subsequently, movement of these portions may be extracted from image or video data of the actor. This movement may then be translated onto a skeleton or rig for use as an underlying framework of one or more in-game characters.”; [0062] “In an embodiment, to train the periodic autoencoder 200, the 3D joint velocity trajectories can be used as input to the network, each of them transformed into the root space of the character.”;
The bone positions and joint trajectories may correspond to the claimed “first query feature” and “second query feature”, respectively);
based on the first query feature and second query feature of the motion body and using a second neural network, obtaining an auxiliary vector of the motion body (Starke [0047] “The model can be trained to reconstruct input, and each channel of the latent space can be in the form of a periodic function, which allows the periodic autoencoder 200 to learn a phase variable for each latent channel from a small set of parameters. These local motion phase channels can represent spatial and temporal data of the associated movement.”, where the “local motion phase channels” and their associated variables may be considered to correspond to the claimed “auxiliary vector”; [0048] to [0061] explain further;
Holden pg. 4 section 6 “We learn a regression between high level parameters and the character motion using a feedforward convolutional neural network. The high level parameters are abstract parameters for describing the motion, such as the root trajectory projected onto the terrain or the trajectories of the end effectors such as the hands and feet.”, where the root trajectory and end-effector trajectories may be considered to correspond with the claimed “first query feature” and “second query feature”); and
updating, based on the first query feature, the second query feature, and the auxiliary vector, one or more weights in the plurality of weights for the neural network (Starke [0062] to [0065] describes model training, which includes optimizing neural network weights;
Holden pg. 5 section 6.2 describes optimizing a cost function based on the previously discussed parameters).
Regarding claim 22, the combination of Starke in view of Holden and Wang teaches: The method of claim 1, wherein the running data of the virtual scene is one or more historical frames (Starke [0068], animation data for a current frame is generated based on a previous frame: “The dynamic animation generation system 100 may generate particular information, referred to herein as ‘animation control information’, which may be autoregressively used to generate motion for an in-game character. For example, the animation control information may indicate a character pose (e.g., character poses 102A-102B illustrated in FIG. 1). As illustrated, animation control information 112A generated for a first frame (e.g., frame ‘i’) may be used as an input for a second frame (e.g., frame ‘i+1’). Animation control information 112B for the second frame may be generated by the system 100, and an underlying skeleton or rig of an in-game character may be updated according to an indicated character pose for the second frame.”).
Regarding claims 11 and 25, they are rejected with the same references, rationale, and motivation to combine as claims 1 and 22 respectively because their limitations substantially correspond to the limitations of these claims, with the additional limitations of: An apparatus, comprising: a processor; and memory storing computer-readable instructions (Starke [0015] “One embodiment discloses a system comprising one or more processors and non-transitory computer storage media storing computer-readable instructions that when executed by the one or more processors, cause the one or more processors to perform operations including…”).
Regarding claims 16 and 27, they are rejected with the same references, rationale, and motivation to combine as claims 1 and 22 respectively because their limitations substantially correspond to the limitations of these claims, with the additional limitation of: A non-transitory computer-readable storage medium, configured to store a computer program, the computer program, when executed by an animation data generation device, is configured to perform actions (Starke [0128] “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. The code modules may be stored in any type of non-transitory computer-readable medium or other computer storage device. Some or all the methods may be embodied in specialized computer hardware.”).
Claim(s) 3, 13, 18, and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Starke (US 20230310998 A1) in view of Holden (“A Deep Learning Framework for Character Motion Synthesis and Editing”) and Wang (US 20230410397 A1) as applied to claims 1, 11, and 16 above, and further in view of Krishnan Gorumkonda et al. (US 20210097744 A1; hereinafter "Krishnan Gorumkonda").
Regarding claim 3, the combination of Starke in view of Holden and Wang teaches: The method of claim 1, but does not explicitly teach: wherein increasing the feature dimension of the virtual object is further based on a maximum velocity of the virtual object.
Krishnan Gorumkonda teaches: wherein increasing the feature dimension of the virtual object is further based on a maximum velocity of the virtual object ([0101] In some embodiments, tools for filtering motion states or selecting groups of motion states can be present… For motion patterns that are not strictly defined, but simply create random motion within a range (e.g. motion pattern 620), limits can be provided for the motions within the range, such as number of momentum changes per unit time, maximum velocity, or other such characteristics of the motion.”; where “creating random motion” corresponds to “increasing the feature dimension” because motion parameters are being generated which were not present in the original input).
Krishnan Gorumkonda is analogous to the claimed invention because it is in the same field of generating 3D character animation using a neural network. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Starke in view of Holden and Wang with the teachings of Krishnan Gorumkonda to add a maximum velocity constraint to the generated motion parameters. The motivation would have been to prevent a character from moving in an unrealistic way.
Regarding claims 13 and 18, they are rejected with the same references, rationale, and motivation to combine as claim 3 because their limitations substantially correspond to the limitations of claim 3.
Regarding claim 23, the combination of Starke in view of Holden and Wang and further in view of Krishnan Gorumkonda teaches: The method of claim 3, wherein:
increasing the feature dimension is further based on a period of time; and the period of time is based on a frame rate of the virtual scene (Holden section 6.3 “Disambiguation for Locomotion” teaches generating parameters for periodic motion such as walking, which specifically takes into account the number of frames of the footstep cycle).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Starke in view of Holden and Wang and further in view of Krishnan Gorumkonda with the additional teachings of Holden to take the duration of motions into account. The motivation would have been to solve the issue discussed by Holden in section 6: “For example, when the character is instructed to walk along a line, the timing of the motion is completely invariant: a character may walk with different step sizes or walk out of sync with another motion performed on the same trajectory. Naively mixing these out of-sync motions results in an averaging of the output, making the character appear to float along the path.”
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Starke (US 20230310998 A1) in view of Holden (“A Deep Learning Framework for Character Motion Synthesis and Editing”) and Wang (US 20230410397 A1) as applied to claim 5 above, and further in view of Maeda et al. (“Data Augmentation for Human Motion Prediction”. MVA 2021 17th International Conference on Machine Vision and Applications (25-27 July 2021). https://doi.org/10.23919/MVA51890.2021.9511368; hereinafter “Maeda”).
Regarding claim 6, the combination of Starke in view of Holden and Wang teaches: The method of claim 5, wherein obtaining motion capturing data further comprises:
performing motion capturing on the motion body, wherein the motion body moves based on a preset motion capturing route and performs a preset motion, to obtain initial motion capturing data (Starke [0009] “As an example, a real-life wrestler may be used as an actor. In this example, motion capture video of the wrestler may be recorded which depicts the soccer player dribbling a ball, interacting with other players, performing different moves, and so on. Local motion phase data can be determined based on this motion capture data, such that the data can be used during the motion matching processes to reproduce highly stylized, and personal, movement of the player.”; [0033] “To generate motions for in-game characters, electronic game designers are increasingly leveraging motion capture studios. For example, a motion capture studio may be used to learn the realistic gait of an actor as he/she moves about the motion capture studio.”; [0034] “For example, and with respect to a sports electronic game, a real-life basketball player may be used as an actor to perform common basketball motions.”).
The combination of Starke in view of Holden and Wang does not explicitly teach: obtaining, by processing the initial motion capturing data, the motion capturing data, wherein processing the initial motion capturing data comprises using noise reduction, data extension, or generation of data in a coordinate system of an animation engine.
Maeda teaches: obtaining, by processing the initial motion capturing data, the motion capturing data, wherein processing the initial motion capturing data comprises using noise reduction (Section 1 “Introduction”: “This is because the acquisition of motion data requires a vast amount of costs such as the motion capture equipment and post-processing such as denoising.”), data extension (the entire paper is about the extension/augmentation of human motion datasets of a limited size; Abstract “Human motion prediction is seldom deployed to real-world tasks due to difficulty in collecting a huge amount of motion data. We propose two motion data augmentation approaches using Variational AutoEncoder (VAE) and Inverse Kinematics (IK). Our VAEbased generative model with adversarial training and sampling near samples generates various motions even with insufficient original motion data.”), or generation of data in a coordinate system of an animation engine.
Maeda is analogous to the claimed invention because it pertains to the same issue of obtaining 3D motion capture datasets. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Starke in view of Holden and Wang with the teachings of Maeda to augment the size of the input motion capture dataset, and to improve its quality via noise reduction. The motivation would have been to improve neural network performance by increasing the quantity and quality of the training data.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Starke (US 20230310998 A1) in view of Holden (“A Deep Learning Framework for Character Motion Synthesis and Editing”) and Wang (US 20230410397 A1) and further in view of Maeda ("Data Augmentation for Human Motion Prediction") as applied to claim 6 above, and further in view of Starke et al. (US 11830121 B1, hereinafter "Starke 2").
Regarding claim 7, the combination of Starke in view of Holden and Wang and further in view of Maeda teaches: The method of claim 6, further comprising:
performing the data extension on the initial motion capturing data in a timeline zooming manner (Maeda section 3 “Proposed Motion Augmentation”: “We also temporally expand and contract motion sequences in the range from 10% longer to 10% shorter as temporal data augmentation.”).
The combination of Starke in view of Holden and Wang and further in view of Maeda does not explicitly teach: extracting the motion capturing data from the initial motion capturing data through a mirroring method.
Starke 2 teaches: extracting the motion capturing data from the initial motion capturing data through a mirroring method (col. 14 lines 22-23 “The complete dataset is doubled by mirroring”).
Starke 2 is analogous to the claimed invention because it is in the same field of generating 3D character animation using a neural network trained on motion capture data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Starke in view of Holden and Wang and further in view of Maeda with the teachings of Starke 2 to increase the size of the input motion capture dataset by mirroring each recorded motion. The motivation would have been to improve neural network performance by increasing the quantity of training data.
Claim(s) 9, 15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Starke (US 20230310998 A1) in view of Holden (“A Deep Learning Framework for Character Motion Synthesis and Editing”) and Wang (US 20230410397 A1) as applied to claims 1, 11, and 16 above, and further in view of Sung (US 20130069939 A1).
Regarding claim 9, the combination of Starke in view of Holden and Wang teaches: The method of claim 1, wherein generating the query feature of the virtual object further comprises:
comparing a control parameter in the motion control signal to a historical control parameter in a historical motion control signal (Starke [0102] “The user input may also be aggregated over a threshold number of prior frames and used to update the character control variables every threshold number of frames (e.g., every 5 frames)”;
[0104] “For example, the character control variables may include information associated with a window of time. In this example, the variables may store information for a prior threshold number of frames along with a current frame. Thus, the information for earlier of the frames may be weighted less than the information for later of the frames.”).
The combination of Starke in view of Holden and Wang does not explicitly teach: based on a difference between the control parameter and the historical control parameter exceeding a threshold, generating the query feature of the virtual object based on the difference between the control parameter and the historical control parameter.
Sung teaches: comparing a control parameter in the motion control signal to a historical control parameter in a historical motion control signal; and
based on a difference between the control parameter and the historical control parameter exceeding a threshold, generating the query feature of the virtual object based on the difference between the control parameter and the historical control parameter ([0075] “In order to keep consistency between postures decided for the individual character image frames, if the hip joint angle θh, knee joint angle θk, and ankle joint angle θa of a character of a constraint frame are compared to the hip joint angle θh, knee joint angle θk, and ankle joint angle θa in a character image frame received just before the constraint frame, respectively, and at least one of change values obtained from the comparison exceeds a predetermined threshold change angle set for the corresponding body part, the smoothing unit 246 (see FIG. 2) may perform smoothing on the constraint frame using the previous character image frame.”).
Sung is analogous to the claimed invention because it is in the same field of generating 3D character animation. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the invention of Starke in view of Holden and Wang with the teachings of Sung to blend animation features corresponding to parameters of a current frame and a previous frame if a comparison between the parameters shows that they are too different. The motivation would have been to generate a smoother looking animation, as taught by Sung.
Regarding claims 15 and 20, they are rejected with the same references, rationale, and motivation to combine as claim 9 because their limitations substantially correspond to the limitations of claim 9.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Starke (US 20230310998 A1) in view of Holden (“A Deep Learning Framework for Character Motion Synthesis and Editing”) and Wang (US 20230410397 A1) as applied to claim 1 above, and further in view of Starke 3 (US 12138543 B1).
Regarding claim 10, the combination of Starke in view of Holden and Wang teaches: The method of claim 1, wherein:
the trajectory feature further comprises a trajectory velocity and a trajectory direction (Starke [0071] “Animation control information 112A may further include character control variables 118. These variables 118 may inform the in-game character's motion. For example, the character control variables 118 may include trajectory information for the in-game character. In this example, the trajectory information may indicate positions of the in-game character (e.g., a current position and optionally one or more prior positions), velocity of the in-game character (current velocity and optionally one or more prior velocities), and so on.”), wherein the trajectory feature is based on a projection (Holden pg. 4 section 6 “Mapping High Level Parameters to Human Motions”: “The high level parameters are abstract parameters for describing the motion, such as the root trajectory projected onto the terrain or the trajectories of the end effectors such as the hands and feet.”; one of ordinary skill may infer that the root joint is more centrally located on the body in comparison to the “end effectors such as the hands and feet”); and
the bone feature further comprises bone position information and bone rotation information for a foot bone (Starke [0075] “The animation control information 112A may further include additional information 119. For example, character state information associated with an in-game character may be included. The character state may indicate, for example, the character pose for the in-game character. For example, the character state may represent positions, rotations, and velocities of bones used in an underlying skeleton or rig of the in-game character.”).
The combination of Starke in view of Holden and Wang does not explicitly teach: wherein the trajectory feature is based on a projection of a hip bone.
Starke 3 teaches: wherein the trajectory feature is based on a projection of a hip bone (col. 12 lines 55-59 “Character root trajectory 208 may be determined by the dynamic animation generation system 100. These trajectory variables may include position information of the actor for the frame (e.g., horizontal path), directional information, and velocity information.”; col. 13 line 3 “An example root may represent a hip bone of the actor.”).
Starke 3 is analogous to the claimed invention because it is in the same field of generating 3D character animation using a neural network trained on motion capture data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Starke in view of Holden and Wang with the teachings of Starke 3 to establish a hip bone as the root, since it is more centrally located on a human and generally will not experience as much relative motion as extremities such as hands or feet.
Conclusion
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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/BENJAMIN TOM STATZ/ Examiner, Art Unit 2611
/TAMMY PAIGE GODDARD/ Supervisory Patent Examiner, Art Unit 2611
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