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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Starke et al. (Pub. No.: US 2023/0186543).
Regarding claim 1, Starke discloses a method comprising: obtaining first data corresponding to a first scene model (Fig. 1B, “Environment Labels 116”), a first character (Fig. 1B, “Character control variables 118), and a first location of the first character in the first scene model (Fig. 1A, 102A and para # [0032], “ the character pose 102A depicts an in-game character running up a dirt hill out of a trench. This character pose 102A may therefore represent a specific pose used as part of the running motion on a dirt hill surface”), wherein a terrain of the first scene model is a three-dimensional terrain (para # [0060], “the surface deformation may be estimated or determined using image processing to detect changes in the environment (e.g., changes in ground surface height or terrain height) relating to motion by the individual”); and generating N first images based on the first data (para #[0031, “dynamic animation generation system 100 generating a subsequent character pose 102B based on a current character pose 102A within a particular environment (e.g., a dirt or muddy field)”) , wherein the N first images are in one-to-one correspondence with N second locations (e.g. the character 102A is in first frame and 102B is in second frame with a second location since the character is in motion), wherein an nth first image of the N first images presents a first pose of the first character at an nth second location of the N second locations (e.g. character 102B is in second location), and wherein the first pose corresponds to a terrain feature of the first scene model at the nth second location (para #[0033], “the dynamic animation generation system 100 can generate the subsequent character pose 102B based on the character pose 102A. Further, the dynamic animation generation system 100 can generate the subsequent character pose 102B based on the surface or material properties of the environment in which the character is interacting or is located”).
Regarding claim 2, Starke discloses the method of claim 1, wherein the first data further third location is a randomly-generated location or a user- specified location (see para #[0043], “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., jump, shift directions, stop and throw a grenade, and so on”), and wherein generating the N first images comprises: determining the N second locations based on the first location and the third location, wherein the N second locations are on a first path, and wherein the first path is an actual motion path of the first character from the first location to the third location; and generating the N first images based on the N second locations and terrain features corresponding to the N second locations (see para #[0051], “the pose generation engine 130 may output a character pose for frame ‘i+1’. Similar to the above, the animation control information 112B may then be provided as input to the dynamic animation generation system 100, which may continue autoregressively generating motion for the in-game character. In some cases, the animation control information 112B may include a change in environment labels 116 compared to the animation control information 112A. The change in environment labels 116 may occur because, for example, as the character moves, the environment may change. For example, the character may run from snowy ground to frozen dirt”).
Regarding claim 3, Starke discloses the method claim 1, wherein the first data further correspond to a second image, wherein the second image presents a second pose of the first character at the first location, wherein the N first images are frames between the second image and a third image in a first video, wherein the third image presents a third pose of the first character at a third location, wherein the N second locations are on a first path, and wherein the first path is an actual motion path of the first character from the first location to the third location (see para #[0051], “the pose generation engine 130 may output a character pose for frame ‘i+1’. Similar to the above, the animation control information 112B may then be provided as input to the dynamic animation generation system 100, which may continue autoregressively generating motion for the in-game character. In some cases, the animation control information 112B may include a change in environment labels 116 compared to the animation control information 112A. The change in environment labels 116 may occur because, for example, as the character moves, the environment may change. For example, the character may run from snowy ground to frozen dirt”).
Regarding claim 4, Starke discloses the method of claim 2, wherein a similarity between the first path and a second path is within a preset range, and wherein the second path is a navigation path of the first character from the first location to the third location (see para #[0051], “the pose generation engine 130 may output a character pose for frame ‘i+1’. Similar to the above, the animation control information 112B may then be provided as input to the dynamic animation generation system 100, which may continue autoregressively generating motion for the in-game character. In some cases, the animation control information 112B may include a change in environment labels 116 compared to the animation control information 112A. The change in environment labels 116 may occur because, for example, as the character moves, the environment may change. For example, the character may run from snowy ground to frozen dirt”).
Regarding claim 5, Starke discloses the method of claim 1, further comprising obtaining, based on an image preceding the nth first image, the nth first image through prediction using a neural networ
Regarding claim 6, Starke discloses the method of claim 1, further comprising: identifying the first as an abnormal pose when the first character is in a fallen state or stuck; and using one or more of the N first images for presenting a process that transitions the first character from the abnormal pose to a normal pose that the pose of the first character is abnormal comprises: the first character is in a fallen state, and the first character is stuck (see paras # [0018] and [0090]).
Regarding claim 7, Starke discloses the method of claim 1, wherein the first scene model is a three-dimensional scene model, wherein the first character is a non-player character (NPC) and comprises a plurality of joint points, and wherein each of the joint points can be driven (see para #[0020]).
Regarding claim 8. (Currently Amended) A computer program product comprising computer-executable instructions that are stored on a non-transitory computer-readable storage medium and that,102A depicts an in-game character running up a dirt hill out of a trench. This character pose 102A may therefore represent a specific pose used as part of the running motion on a dirt hill surface”), wherein a terrain of the first scene model is a three-dimensional terrain (para # [0060], “the surface deformation may be estimated or determined using image processing to detect changes in the environment (e.g., changes in ground surface height or terrain height) relating to motion by the individual”); and generate N first images based on the first data (para #[0031, “dynamic animation generation system 100 generating a subsequent character pose 102B based on a current character pose 102A within a particular environment (e.g., a dirt or muddy field)”) , wherein the N first images are in one-to-one correspondence with N second locations (e.g. the character 102A is in first frame and 102B is in second frame with a second location since the character is in motion), wherein an nth first image of the N first images presents a first pose of the first character at an nth second location of the N second locations (e.g. character 102B is in second location), and wherein the first pose corresponds to a terrain feature of the first scene model at the nth second location (para #[0033], “the dynamic animation generation system 100 can generate the subsequent character pose 102B based on the character pose 102A. Further, the dynamic animation generation system 100 can generate the subsequent character pose 102B based on the surface or material properties of the environment in which the character is interacting or is located”).
Regarding claim 9, Starke disclose a chip (see para [0094]) configured to: obtain first data corresponding to a first scene model (Fig. 1B, “Environment Labels 116”), a first character (Fig. 1B, “Character control variables 118), and a first location of the first character in the first scene model (Fig. 1A, 102A and para # [0032], “ the character pose 102A depicts an in-game character running up a dirt hill out of a trench. This character pose 102A may therefore represent a specific pose used as part of the running motion on a dirt hill surface”), wherein a terrain of the first scene model is a three-dimensional terrain (para # [0060], “the surface deformation may be estimated or determined using image processing to detect changes in the environment (e.g., changes in ground surface height or terrain height) relating to motion by the individual”); and generate N first images based on the first data (para #[0031, “dynamic animation generation system 100 generating a subsequent character pose 102B based on a current character pose 102A within a particular environment (e.g., a dirt or muddy field)”) , wherein the N first images are in one-to-one correspondence with N second locations (e.g. the character 102A is in first frame and 102B is in second frame with a second location since the character is in motion), wherein an nth first image of the N first images presents a first pose of the first character at an nth second location of the N second locations (e.g. character 102B is in second location), and wherein the first pose corresponds to a terrain feature of the first scene model at the nth second location (para #[0033], “the dynamic animation generation system 100 can generate the subsequent character pose 102B based on the character pose 102A. Further, the dynamic animation generation system 100 can generate the subsequent character pose 102B based on the surface or material properties of the environment in which the character is interacting or is located”).
Regarding claim 10, it has the similar claimed scope of claim 2. Thus, claim 10 is rejected for the same reason as claim 2 above
Regarding claims 11 and 17, they haves similar claimed scope of claim 3. Thus, claims 11 and 17 are rejected for the same reason as claim 3 above.
Regarding claims 12 and 16, they have the similar claimed scope of claim 4. Thus, claim 12 is rejected for the same reason as claim 4 above.
Regarding claims 13 and 18. They have the similar claimed scope of claim 5. Thus, claims 13 and 18 are rejected for the same reason as claim 5 above.
Regarding claims 14 and 19, they have the similar claimed scope of claim 6. Thus, claim 14 and 19 are rejected for the same reason as claim 6 above.
Regarding claims 15 and 20, they have the similar claimed scope of claim 7. Thus claim 15 and 20 are rejected for the same reason as claim 7 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rozantsev et al. (Pub. No.: US 2022/0230376 is cited to teach Animation can be generated with a high perceptive quality by utilizing a trained neural network that takes as input a current state of a virtual character to be animated and predict how this character would appear in one or more subsequent frames.
Starke et al. (Patent No.: US 11,830,121) is cited to teach a dynamic animation generation system can provide a deep learning framework to produce a large variety of martial arts movements in a controllable manner from unstructured motion capture data. The system can imitate animation layering using neural networks with the aim to overcome challenges when mixing, blending and editing movements from unaligned motion sources.
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/XIAO M WU/Supervisory Patent Examiner, Art Unit 2613
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