DETAILED ACTION
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 2 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The claim 2 recites that causing, during a second portion of the first period of time that precedes the [first] portion of the first period of time, a presentation a first portion of the plurality of frames, the second portion of the first period of time being associated with an idle state for the character; and
based at least on receiving the second data, causing, during the [first] portion of the first period of time, a presentation a second portion of the plurality of frames, the [first] portion of the period of time being associated with a transition state for the character.
The claim limitation is ambiguous and not clear as to whether the claimed portion refers to first portion or the second portion. Moreover, the claim limitation is ambiguous as to whether the first portion of the first period of time precedes or after the second portion of the first period of time even though the claim recites the second portion of the first period of time precedes the portion of the first period of time. Usually, the second portion should be following the first portion while applicant adopted a contrary definition which made the claim limitation ambiguous and not clear. The claim 2 is further contradictory with the claim 1 with respect to the first period of time and the second period of time. According to the claim 1, the transition state of the character is presented during the second period of time. Clarification is required.
Claim 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The claim 3 recites storing, during the at least the portion of the first period of time, image data representing at least a portion of the one or more third frames in one or more buffers,
wherein the causing the presentation of the one or more third frames uses the image data stored in the one or more buffers and occurs at an elapse of the first period of time
However, the claim 3 is a dependent claim of the claim 1. The claim 1 requires the presentation of the one or more third frames during a second period of time as opposed to the claimed first period time. The claim invention is self-contradictory.
Claim 10 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The claim 10 recites that the one or more processors are further to:
cause, during a first period of time associated with a first state of the character, a presentation of one or more fourth frames of a recorded video, the one or more fourth frames representing the character,
wherein: the presentation of the one or more first frames of the recorded video is caused during a second period of time that is associated with a second state of the character;
the one or more third frames are generated during the second period of time; and
the presentation of the one or more third frames is caused during a third period of time that is associated with a third state of the character.
The claim limitation is ambiguous and not clear as to whether the fourth frames are presented during a fourth period of time wherein the claims 3-5 require presentation of one or more fourth frames during a third period of time and determining that one or more fourth frames subsequent to the one or more second frames. Accordingly, the first period of time must be after the claimed third period of time. However, the first period of time defined in the claim 1 must be prior to the third period of time. Clarification is required.
Claim 15 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The claim 15 recites the claim limitation that the one or more processors are further to: generate, using the one or more neural networks and based at least on third data representing at least one or more poses, image data representing the one or more first frames and the one or more second frames; and store the image data in one or more memories, wherein the presentation of the one or more first frames is caused using at least a portion of the image data stored in the one or more memories.
However, throughout applicant’s specification, the one or more first frames and the one or more second frames are associated with first data and second data while third data following the first data and second data. The one or more first frames and the one or more second frames cannot be based on third data representing at least one or more poses. Moreover, the claim recitation of one or more poses is ambiguously defined to mean either first poses or second poses or first and second poses.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sekar et al. US-PGPUB No. 2024/0331445 (hereinafter Sekar).
Re Claim 1:
Sekar teaches a method comprising:
causing, during a first period of time, a presentation of one or more first frames of a recorded video, the one or more first frames representing a character using one or more first poses (Sekar teaches at FIG. 4A-4B and Paragraph 0144-0147 causing during a rest pose time, a presentation of one or more first frames of a recorded video representing a character using one or more rest poses);
determining that one or more second frames of the recorded video, which are subsequent to the one or more first frames of the recorded video, are associated with one or more second poses for the character (Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0147 determining one or more second frames of the recorded video associated with representative pose 404);
generating, during at least a portion the first period of time and based at least on one or more neural networks processing first data representing the one or more second poses and second data associated with speech for the character, one or more third frames representing the character using the one or more second poses (Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0148 generating during the rest pose time and based at least on a recurrent neural network processing first data (frame data) representing the representative pose for a duration of time and second data (speech data) associated with speech for the character by converting input text given by the speaker to voice-only speech so as to translate into body animation motions and at Paragraph 0153 that new text provided to that neural network may generate body animation corresponding to that text and at Paragraph 0150 that animations for the time spent in the representative pose may also be determined by neural networks trained based on videos and at Paragraph 0146 that the state animation may be compensated by adding rest poses, idle animations. Sekar teaches at FIG. 5 that the rest pose time is included in the animation time in the generation of animated video by the recurrent neural network); and
causing, during a second period of time, a presentation of the one or more third frames (Sekar teaches at FIGS. 4A-4B causing a presentation of the one or more third frames with stitching for the video with animation motions corresponding to the speech converted from the input text).
Re Claim 2:
The claim 2 encompasses the same scope of invention as that of the claim 1 except additional claim limitation that the one or more first frames include a plurality of frames of the recorded video and the causing the presentation of the plurality of frames of the recorded video comprises:
causing, during a second portion of the first period of time that precedes the [first] portion of the first period of time, a presentation a first portion of the plurality of frames, the second portion of the first period of time being associated with an idle state for the character; and
based at least on receiving the second data, causing, during the [second] portion of the first period of time, a presentation a second portion of the plurality of frames, the [second] portion of the period of time being associated with a transition state for the character.
However, Sekar further teaches the claim limitation that claim limitation that the one or more first frames include a plurality of frames of the recorded video and the causing the presentation of the plurality of frames of the recorded video comprises:
causing, during a second portion of the first period of time that precedes the [first] portion of the first period of time, a presentation a first portion of the plurality of frames, the second portion of the first period of time being associated with an idle state for the character; and
based at least on receiving the second data, causing, during the [first] portion of the first period of time, a presentation a second portion of the plurality of frames, the [first] portion of the period of time being associated with a transition state for the character (Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0147 causing during the second portion of the time period for the presentation of the state A of the character, a presentation of the first portion of the frames for the idle animation (rest pose) because idle animation can be inserted before or after the one or more frames for the representative pose. Sekar teaches at FIG. 4A-4B causing during the first portion of the time period for the presentation of the state A of the character, presentation of the second portion of the frames associated with the transition state of the character).
Re Claim 3:
The claim 3 encompasses the same scope of inventio as that of the claim 1 except additional claim limitation that storing, during the at least the portion of the first period of time, image data representing at least a portion of the one or more third frames in one or more buffers,
wherein the causing the presentation of the one or more third frames uses the image data stored in the one or more buffers and occurs at an elapse of the first period of time.
However, Sekar further teaches the claim limitation that claim limitation storing, during the at least the portion of the first period of time, image data representing at least a portion of the one or more third frames in one or more buffers,
wherein the causing the presentation of the one or more third frames uses the image data stored in the one or more buffers and occurs at an elapse of the first period of time (
Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0147 causing during the second portion of the time period for the presentation of the state A of the character, a presentation of the first portion of the frames for the idle animation (rest pose) because idle animation can be inserted before or after the one or more frames for the representative pose. Sekar teaches at FIG. 4A-4B causing during the first portion of the time period for the presentation of the state A of the character, presentation of the second portion of the frames associated with the transition state of the character.
It is noted that the one or more third frames are stored in the display buffer in order to be presented for display as Sekar teaches at Paragraph 0296 that at least one animated avatar is provided via a display of the computer system and at Paragraph 0153 that a library of video 1201 of a certain speaker may be compiled and at Paragraph 0205 rendering video after finishing creating slide 3812 and its narration and this process may be known as pre-caching and rendering for previous slides known as intermediate cached copies may be partially or completely finished already, allowing the six slide rendering to be shown in as little as five minutes. Pre-caching means that the one or more third frames are stored in one or more buffers before presentation of the one or more third frames).
Re Claim 4:
The claim 4 encompasses the same scope of invention as that of the claim 1 except additional claim limitation causing, during a third period of time, a presentation of one or more fourth frames of the recorded video, the one or more fourth frames being subsequent to the one or more second frames in the recorded video.
Sekar further teaches the claim limitation causing, during a third period of time, a presentation of one or more fourth frames of the recorded video, the one or more fourth frames being subsequent to the one or more second frames in the recorded video (Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0147 causing during a time period for the representative pose for state B a presentation of one or more fourth frames of the recorded video associated with the representative pose for state B).
Re Claim 5:
The claim 5 encompasses the same scope of invention as that of the claim 1 except additional claim limitation that: determining that one or more fourth frames of the recorded video, which are subsequent to the one or more second frames of the recorded video, are associated with one or more third poses for the character;
generating, during the second period of time and based at least on the one or more neural networks processing third data representing one or more third poses and fourth data associated with second speech for the character, one or more fifth frames representing the character using the one or more third poses; and
causing, during a third period of time, a presentation of the one or more fifth frames.
Sekar further teaches the claim limitation that determining that one or more fourth frames of the recorded video, which are subsequent to the one or more second frames of the recorded video, are associated with one or more third poses for the character;
generating, during the second period of time and based at least on the one or more neural networks processing third data representing one or more third poses and fourth data associated with second speech for the character, one or more fifth frames representing the character using the one or more third poses; and
causing, during a third period of time, a presentation of the one or more fifth frames (Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0147 causing during a time period for the representative pose for state B a presentation of one or more fourth frames of the recorded video associated with the raised arm gesture and generating during the time duration for the representative pose for state B the video frame associated with speech of the character).
Re Claim 6:
The claim 6 encompasses the same scope of invention as that of the claim 1 except additional claim limitation that: generating, during the second period of time and based at least on the one or more neural networks processing third data representing the one or more first poses and fourth data associated with second speech for the character, one or more fourth frames representing the character using the one or more first poses; and
causing, during a third period of time, a presentation of the one or more fourth frames.
Sekar further teaches the claim limitation that generating, during the second period of time and based at least on the one or more neural networks processing third data representing the one or more first poses and fourth data associated with second speech for the character, one or more fourth frames representing the character using the one or more first poses; and
causing, during a third period of time, a presentation of the one or more fourth frames (Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0147 causing during a time period for the representative pose for state B a presentation of one or more fourth frames of the recorded video associated with the raised arm gesture and generating during the time duration for the representative pose for state B the video frame associated with speech of the character).
Re Claim 7:
The claim 7 encompasses the same scope of invention as that of the claim 1 except additional claim limitation that: generating, based at least on the one or more neural networks processing third data representing at least the one or more first poses and the one or more second poses associated with the character, image data representing the recorded video; and
storing the image data in one or more memories.
However, Sekar further teaches the claim limitation that claim limitation generating, based at least on the one or more neural networks processing third data representing at least the one or more first poses and the one or more second poses associated with the character, image data representing the recorded video; and
storing the image data in one or more memories (
Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0147 causing during the second portion of the time period for the presentation of the state A of the character, a presentation of the first portion of the frames for the idle animation (rest pose) because idle animation can be inserted before or after the one or more frames for the representative pose. Sekar teaches at FIG. 4A-4B causing during the first portion of the time period for the presentation of the state A of the character, presentation of the second portion of the frames associated with the transition state of the character.
It is noted that the one or more third frames are stored in the display buffer in order to be presented for display as Sekar teaches at Paragraph 0296 that at least one animated avatar is provided via a display of the computer system and at Paragraph 0153 that a library of video 1201 of a certain speaker may be compiled and at Paragraph 0205 rendering video after finishing creating slide 3812 and its narration and this process may be known as pre-caching and rendering for previous slides known as intermediate cached copies may be partially or completely finished already, allowing the six slide rendering to be shown in as little as five minutes. Pre-caching means that the one or more third frames are stored in one or more buffers before presentation of the one or more third frames).
Re Claim 8:
Sekar teaches a system comprising:
one or more processors to (e.g., the system executes via least one processor of the computer image, image processing of the speaker video at Paragraph 0289):
cause a presentation of one or more first frames representing a character; determine that one or more second frames, which are subsequent to the one or more first frames, are associated with one or more poses of the character (Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0147 causing a presentation of one or more first frames representing the rest pose and determining that one or more second frame representing a representative pose);
generate, using one or more neural networks and based at least on first data representing the one or more poses and second data associated with speech for the character, one or more third frames representing the character using the one or more poses (
Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0148 generating during the rest pose time and based at least on a recurrent neural network processing first data (frame data) representing the representative pose for a duration of time and second data (speech data) associated with speech for the character by converting input text given by the speaker to voice-only speech so as to translate into body animation motions and at Paragraph 0153 that new text provided to that neural network may generate body animation corresponding to that text and at Paragraph 0150 that animations for the time spent in the representative pose may also be determined by neural networks trained based on videos and at Paragraph 0146 that the state animation may be compensated by adding rest poses, idle animations. Sekar teaches at FIG. 5 that the rest pose time is included in the animation time in the generation of animated video by the recurrent neural network); and
cause a presentation of the one or more third frames (Sekar teaches at FIGS. 4A-4B causing a presentation of the one or more third frames with stitching for the video with animation motions corresponding to the speech converted from the input text).
Re Claim 9:
The claim 9 encompasses the same scope of invention as that of the claim 8 except additional claim limitation that: the one or more first frames are associated with a recorded video; the one or more processors are further to identify the one or more second frames as being subsequent to the one or more first frames in the recorded video; and
the one or more third frames are generated during the presentation of the one or more first frames.
Sekar further teaches the claim limitation that the one or more first frames are associated with a recorded video; the one or more processors are further to identify the one or more second frames as being subsequent to the one or more first frames in the recorded video; and
the one or more third frames are generated during the presentation of the one or more first frames (
Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0148 generating during the rest pose time and based at least on a recurrent neural network processing first data (frame data) representing the representative pose for a duration of time and second data (speech data) associated with speech for the character by converting input text given by the speaker to voice-only speech so as to translate into body animation motions and at Paragraph 0153 that new text provided to that neural network may generate body animation corresponding to that text and at Paragraph 0150 that animations for the time spent in the representative pose may also be determined by neural networks trained based on videos and at Paragraph 0146 that the state animation may be compensated by adding rest poses, idle animations. Sekar teaches at FIG. 5 that the rest pose time is included in the animation time in the generation of animated video by the recurrent neural network.
Sekar teaches at FIGS. 4A-4B causing a presentation of the one or more third frames with stitching for the video with animation motions corresponding to the speech converted from the input text).
Re Claim 10:
The claim 10 encompasses the same scope of invention as that of the claim 8 except additional claim limitation that the one or more processors are further to:
cause, during a first period of time associated with a first state of the character, a presentation of one or more fourth frames of a recorded video, the one or more fourth frames representing the character,
wherein: the presentation of the one or more first frames of the recorded video is caused during a second period of time that is associated with a second state of the character;
the one or more third frames are generated during the second period of time; and
the presentation of the one or more third frames is caused during a third period of time that is associated with a third state of the character.
Sekar further teaches the claim limitation that the one or more processors are further to:
cause, during a first period of time associated with a first state of the character, a presentation of one or more fourth frames of a recorded video, the one or more fourth frames representing the character,
wherein: the presentation of the one or more first frames of the recorded video is caused during a second period of time that is associated with a second state of the character;
the one or more third frames are generated during the second period of time; and
the presentation of the one or more third frames is caused during a third period of time that is associated with a third state of the character (Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0147 causing during a first period of time for the state A of the character, a presentation of one or more fourth frames associated with the rest pose following the representative pose, and the one or more first frames for the rest pose in state B is caused for presentation during a second period of time and the one or more third frames for the representative pose in state A are generated during the first period of time for state A).
Re Claim 11:
The claim 11 encompasses the same scope of invention as that of the claim 8 except additional claim limitation that: obtain the second data associated with the speech,
wherein the determination that the one or more second poses are associated with the one or more poses for the character is based at least on the second data associated with the speech for the character being obtained.
Sekar further teaches the claim limitation that obtain the second data associated with the speech,
wherein the determination that the one or more second poses are associated with the one or more poses for the character is based at least on the second data associated with the speech for the character being obtained (
Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0148 generating during the rest pose time and based at least on a recurrent neural network processing first data (frame data) representing the representative pose for a duration of time and second data (speech data) associated with speech for the character by converting input text given by the speaker to voice-only speech so as to translate into body animation motions and at Paragraph 0153 that new text provided to that neural network may generate body animation corresponding to that text and at Paragraph 0150 that animations for the time spent in the representative pose may also be determined by neural networks trained based on videos and at Paragraph 0146 that the state animation may be compensated by adding rest poses, idle animations. Sekar teaches at FIG. 5 that the rest pose time is included in the animation time in the generation of animated video by the recurrent neural network.
Sekar teaches at FIGS. 4A-4B causing a presentation of the one or more third frames with stitching for the video with animation motions corresponding to the speech converted from the input text).
Re Claim 12:
The claim 12 encompasses the same scope of invention as that of the claim 8 except additional claim limitation that the one or more second frames are subsequent to the one or more first frames in a recorded video; and
the one or more processors are further to cause, after the presentation of the one or more third frames, a presentation of one or more fourth frames of the recorded video that are subsequent to the one or more second frames, the one or more fourth frames representing the character.
Sekar further teaches the claim limitation that the one or more second frames are subsequent to the one or more first frames in a recorded video; and
the one or more processors are further to cause, after the presentation of the one or more third frames, a presentation of one or more fourth frames of the recorded video that are subsequent to the one or more second frames, the one or more fourth frames representing the character (Sekar teaches at Paragraph 0144-0147 and FIG. 4A-4B the one or more frames representing the representative pose are subsequent to the one or more first frames representing the rest pose and the presentation of the one or more third frames for a second state are presented subsequent to the one or more second frames of the representative pose in first state and one or more fourth frames are processed by the neural network as replacement of the frames of the representative pose).
Re Claim 13:
The claim 13 encompasses the same scope of invention as that of the claim 8 except additional claim limitation that the one or more processors are further to: determine that one or more fourth frames, which are subsequent to the one or more second frames, are associated with one or more second poses;
during the presentation of the one or more third frames, generate, using the one or more neural networks and based at least on third data representing the one or more second poses and fourth data associated with second speech for the character, one or more fifth frames representing the character in the one or more second poses; and
cause a presentation of the one or more fifth frames.
Sekar further teaches the claim limitation that the one or more processors are further to: determine that one or more fourth frames, which are subsequent to the one or more second frames, are associated with one or more second poses;
during the presentation of the one or more third frames, generate, using the one or more neural networks and based at least on third data representing the one or more second poses and fourth data associated with second speech for the character, one or more fifth frames representing the character in the one or more second poses; and
cause a presentation of the one or more fifth frames (Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0147 determining one or more fourth frames for the state B associated with the first representative pose and during the presentation of the third frames, generating fourth data for the fourth frames with the second speech for state B, one or more fifth frames representing in the second representative pose for state B and causing a presentation of the fifth frames as Sekar teaches at Paragraph 0144 that a state for multiple poses for that state are placed one after another).
Re Claim 14:
The claim 14 encompasses the same scope of invention as that of the claim 8 except additional claim limitation that the one or more processors are further to: determine that the one or more first frames are associated with one or more second poses;
during the presentation of the one or more third frames, generate, using the one or more neural networks and based at least on third data representing the one or more second poses and fourth data associated with second speech for the character, one or more fourth frames representing the character in the one or more second poses; and
cause a presentation of the one or more fifth frames.
Sekar further teaches the claim limitation that the one or more processors are further to: determine that the one or more first frames are associated with one or more second poses;
during the presentation of the one or more third frames, generate, using the one or more neural networks and based at least on third data representing the one or more second poses and fourth data associated with second speech for the character, one or more fourth frames representing the character in the one or more second poses; and
cause a presentation of the one or more fifth frames (
Sekar teaches determining that the one or more first frames are associated with the second representative pose for state A. Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0147 determining one or more fourth frames for the state B associated with the first representative pose for state B and during the presentation of the third frames, generating fourth data for the fourth frames with the second speech for state B, one or more fifth frames representing in the second representative pose for state B and causing a presentation of the fifth frames as Sekar teaches at Paragraph 0144 that a state for multiple poses for that state are placed one after another).
Re Claim 15:
The claim 15 encompasses the same scope of invention as that of the claim 8 except additional claim limitation that the one or more processors are further to: generate, using the one or more neural networks and based at least on third data representing at least one or more poses, image data representing the one or more first frames and the one or more second frames; and
store the image data in one or more memories,
wherein the presentation of the one or more first frames is caused using at least a portion of the image data stored in the one or more memories.
Sekar further teaches the claim limitation that the one or more processors are further to: generate, using the one or more neural networks and based at least on third data representing at least one or more poses, image data representing the one or more first frames and the one or more second frames; and
store the image data in one or more memories (
Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0147 that using the recurrent neural networks, and based on data representing at least one or more rest poses and representative poses, image data representing the one or more first frames and the one or more second frames and storing the image data in the display buffers or cache memories.
It is noted that the one or more third frames are stored in the display buffer in order to be presented for display as Sekar teaches at Paragraph 0296 that at least one animated avatar is provided via a display of the computer system and at Paragraph 0153 that a library of video 1201 of a certain speaker may be compiled and at Paragraph 0205 rendering video after finishing creating slide 3812 and its narration and this process may be known as pre-caching and rendering for previous slides known as intermediate cached copies may be partially or completely finished already, allowing the six slide rendering to be shown in as little as five minutes. Pre-caching means that the one or more third frames are stored in one or more buffers before presentation of the one or more third frames).
Re Claim 16:
The claim 16 encompasses the same scope of invention as that of the claim 8 except additional claim limitation that: the one or more first frames represent the character as refraining from interacting; the one or more second frames represent the character as refraining from interacting; and
the one or more third frames representing the character interacting based at least on the speech.
Sekar further teaches the claim limitation that the one or more first frames represent the character as refraining from interacting; the one or more second frames represent the character as refraining from interacting; and
the one or more third frames representing the character interacting based at least on the speech (Sekar teaches at Paragraph 0144 that a state for multiple poses for that state are placed one after another and therefore multiple representative poses are placed one after another or multiple rest poses are placed one after another.
Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0147 that the one or more first frames for the rest poses or idle animation as refraining from interaction and the one or more second frames for the rest poses or idle animation as refraining from interaction and the one or more third frames for the representative pose represent the character interacting based on the speech).
Re Claim 17:
The claim 17 encompasses the same scope of invention as that of the claim 9 except additional claim limitation that the system is comprised in at least one of: 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 that provides one or more cloud gaming applications;
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 operations using one or more multi-modal language models;
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;
systems using or deploying one or more inference microservices;
systems that incorporate one or more machine learning models deployed in a service or microservice along with an OS-level virtualization package (e.g., a container);
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources.
Sekar further teaches the claim limitation that the system is comprised in at least one of: 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 that provides one or more cloud gaming applications;
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 operations using one or more multi-modal language models;
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;
systems using or deploying one or more inference microservices;
systems that incorporate one or more machine learning models deployed in a service or microservice along with an OS-level virtualization package (e.g., a container);
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources (Sekar teaches at Paragraph 0152 that the number of iterations of training the recurrent neural network 1006 may be adjusted until an acceptable training loss is reached in step 1008. The model may be validated using validation dataset in step 1010, if validation losses are high, model complexity may be changed and trained again. This cycle may be repeated till decent results are obtained in step 1012. The recurrent neural network model may then be ready for predictions. Sekar teaches at Paragraph 0327-0331 this may be done by training a machine learning model or a large language model based on lots of examples and an avatar 9114 is animated and the bullets points may be obtained using a machine learning model or a large language model and keywords for the image 9108 may be generated based on the portion of text 9002 as well using a machine learning model or large language model and at Paragraph 0352-0354 that multiple sentences using the word pleasure may be predicted using a large language model and text-to-speech versions of those may be generated and these generated sentence audios may then be converted into text using speech-to-text systems and different parts of the sentences are given facial animation based on audio intensity and pitch).
Re Claim 18:
Sekar teaches one or more processors comprising: processing circuitry to (e.g., the system executes via least one processor of the computer image, image processing of the speaker video at Paragraph 0289):
cause a presentation of one or more first frames representing a character; determine that one or more second frames, which are subsequent to the one or more first frames, are associated with information corresponding to the character (Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0147 causing a presentation of one or more first frames representing the rest pose and determining that one or more second frame representing a representative pose);
generate, using one or more neural networks and based at least on first data representing the information and second data associated with speech for the character, one or more third frames representing the character; and
Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0148 generating during the rest pose time and based at least on a recurrent neural network processing first data (frame data) representing the representative pose for a duration of time and second data (speech data) associated with speech for the character by converting input text given by the speaker to voice-only speech so as to translate into body animation motions and at Paragraph 0153 that new text provided to that neural network may generate body animation corresponding to that text and at Paragraph 0150 that animations for the time spent in the representative pose may also be determined by neural networks trained based on videos and at Paragraph 0146 that the state animation may be compensated by adding rest poses, idle animations. Sekar teaches at FIG. 5 that the rest pose time is included in the animation time in the generation of animated video by the recurrent neural network); and
cause a presentation of the one or more third frames (Sekar teaches at FIGS. 4A-4B causing a presentation of the one or more third frames with stitching for the video with animation motions corresponding to the speech converted from the input text).
Re Claim 19:
The claim 19 encompasses the same scope of invention as that of the claim 18 except additional claim limitation that the one or more processors are further to:
determine a time period associated with the presentation of the one or more first frames; and
determining the one or more second frames based at least on the period of time.
Sekar further teaches the claim limitation that the one or more processors are further to:
determine a time period associated with the presentation of the one or more first frames; and
determining the one or more second frames based at least on the period of time (Sekar teaches at FIGS. 4A-4B and Paragraph 0144-0147 that determining a rest pose time and determining the one or more second frames based on the rest pose time and at Paragraph 0144 one of multiple poses, allowing for a less robotic, more natural feel. In addition, in some cases the order can vary, such that multiple representative poses 404, or multiple rest poses 402, follow one another. For example, a state may have multiple representative poses for that state placed one after another).
Re Claim 20:
The claim 20 encompasses the same scope of invention as that of the claim 18 except additional claim limitation that the one or more processors are comprised in at least one of:
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 that provides one or more cloud gaming applications;
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 operations using one or more multi-modal language models;
a system for performing one or more conversational Al 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;
systems using or deploying onc or more inference microservices;
systems that incorporate one or more machine learning models deployed in a service or microservice along with an OS-level virtualization package (e.g., a container);
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources.
Sekar further teaches the claim limitation that the one or more processors are comprised in at least one of:
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 that provides one or more cloud gaming applications;
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 operations using one or more multi-modal language models;
a system for performing one or more conversational Al 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;
systems using or deploying onc or more inference microservices;
systems that incorporate one or more machine learning models deployed in a service or microservice along with an OS-level virtualization package (e.g., a container);
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources.
(Sekar teaches at Paragraph 0152 that the number of iterations of training the recurrent neural network 1006 may be adjusted until an acceptable training loss is reached in step 1008. The model may be validated using validation dataset in step 1010, if validation losses are high, model complexity may be changed and trained again. This cycle may be repeated till decent results are obtained in step 1012. The recurrent neural network model may then be ready for predictions. Sekar teaches at Paragraph 0327-0331 this may be done by training a machine learning model or a large language model based on lots of examples and an avatar 9114 is animated and the bullets points may be obtained using a machine learning model or a large language model and keywords for the image 9108 may be generated based on the portion of text 9002 as well using a machine learning model or large language model and at Paragraph 0352-0354 that multiple sentences using the word pleasure may be predicted using a large language model and text-to-speech versions of those may be generated and these generated sentence audios may then be converted into text using speech-to-text systems and different parts of the sentences are given facial animation based on audio intensity and pitch).
Conclusion
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/JIN CHENG WANG/Primary Examiner, Art Unit 2617
