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 Objections
Claims 2, 5, 10-15, 17-20 and 22 are objected to because of the following informalities:
For claim 2, Examiner believes this claim should be amended in the following manner:
The one or more processors of claim 1, wherein the one or more circuits are to:
determine a configuration for the windowed attention regions that, for each of the at least one patch, identifies tokens of [[the]] that patch and one or more adjacent patches, and
wherein, to execute the one or more attention operations, the one or more circuits are to,
for each of the at least one patch, execute the one or more attention operations based at least on the tokens.
For claim 5, Examiner believes this claim should be amended in the following manner:
The one or more processors of claim 2, wherein the one or more circuits are to:
determine [[an]] at least one offset to [[the]] at least one windowed attention region for each of the at least one patch, the at least one offset indicating a vertical offset or a horizontal offset of the at least one windowed attention region relative to each of the at least one patch, and
wherein, to execute the one or more attention operations, the one or more circuits are to:
apply the at least one offset to the at least one windowed attention region for each of the at least one patch, and
execute the one or more attention operations based at least on applying the at least one offset.
For claim 10, Examiner believes this claim should be amended in the following manner:
The one or more processors of claim 8, wherein, to execute the first transformer along the contraction path and the second transformer along the contraction path, the one or more circuits are to:
execute the first transformer along the contraction path based at least on a first windowed attention region and a first offset, and
execute the second transformer along the contraction path based at least on a second windowed attention region and a second offset.
For claim 11, Examiner believes this claim should be amended in the following manner:
The one or more processors of claim 8, wherein, to execute the first transformer along the expansion path and the second transformer along the expansion path, the one or more circuits are to:
execute the first transformer along the expansion path based at least on a first windowed attention region and a first offset, and
execute the second transformer along the expansion path based at least on a second windowed attention region and a second offset.
For claim 12, Examiner believes this claim should be amended in the following manner:
The one or more processors of claim 1, wherein 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 implemented using a robot;
an aerial system;
a medical system;
a boating system;
a smart area monitoring system;
a system for performing deep learning operations;
a system for performing simulation operations;
a system for generating or presenting virtual reality (VR) content, augmented reality (AR) content, or mixed reality (MR) content;
a system for performing digital twin operations;
a system implemented using an edge device;
a system incorporating one or more virtual machines (VMs);
a system for generating synthetic data;
a system implemented at least partially in a data center;
a system for performing conversational artificial intelligence (AI) operations;
a system for performing generative AI operations;
a system implementing language models;
a system for performing generative AI operations;
a system for implementing vision language models (VLMs);
a system for implementing large language models (LLMs);
a system for implementing small language models (SLMs);
a system for hosting one or more real-time streaming applications;
a system for performing light transport simulation;
a system for performing collaborative content creation for three-dimensional (3D) assets;
a system using or deploying one or more inference microservices;
a system that incorporates one or more machine learning models deployed in a service or microservice along with an operating system (OS)-level virtualization package (e.g., a container); or
a system implemented at least partially using cloud computing resources.
For claim 13, Examiner believes this claim should be amended in the following manner:
A system comprising:
one or more processors and memory for storing instructions for execution by the one or more processors to perform operations comprising:
obtaining image data for a current image being generated, in accordance with one or more light transport simulation operations;
executing one or more attention operations to determine an update to the current image based at least on a plurality of patches of the current image and windowed attention regions corresponding to at least one patch of the plurality of patches, the windowed attention regions extending at least in part beyond boundaries of each of the at least one patch; and
providing updated image data according to the update, to generate the current image.
For claim 14, Examiner believes this claim should be amended in the following manner:
The system of claim 13, wherein the one or more processors are to perform [[the]] an operation of:
determining a configuration for the windowed attention regions that, for each of the at least one patch, identifies tokens of [[the]] that patch and one or more adjacent patches, and
wherein, to execute the one or more attention operations, the one or more processors are to,
for each of the at least one patch, execute the one or more attention operations based at least on the tokens.
For claim 15, Examiner believes this claim should be amended in the following manner:
The system of claim 14, wherein the one or more processors that perform the operation of determining the configuration for the windowed attention regions are to perform [[the]] an operation of:
determining sub-regions for the windowed attention regions that extend across at least one horizontally adjacent patch or at least one vertically adjacent patch.
For claim 17, Examiner believes this claim should be amended in the following manner:
The system of claim 14, wherein the one or more processors are to perform [[the]] an operation of:
determining [[an]] at least one offset to [[the]] at least one windowed attention region for each of the at least one patch, the at least one offset indicating a vertical offset or a horizontal offset of the at least one windowed attention region relative to each of the at least one patch, and
wherein the one or more processors that perform the operation of executing the one or more attention operations are to perform [[the]] an operation of:
applying the at least one offset to the at least one windowed attention region for each of the at least one patch, and
executing the one or more attention operations based at least on applying the at least one offset.
For claim 18, Examiner believes this claim should be amended in the following manner:
The system of claim 13, wherein the plurality of patches of the current image comprises a first plurality of patches, and
wherein the one or more processors that perform the operation of executing the one or more attention operations are to perform [[the]] an operation of:
executing a first transformer along a contraction path of an attention-based model based at least on first patch embeddings for the first plurality of patches to generate updated first patch embeddings,
merging the updated first patch embeddings to form second patch embeddings; and
executing a second transformer along the contraction path based at least on the second patch embeddings.
For claim 19, Examiner believes this claim should be amended in the following manner:
The system of claim 18, wherein the one or more processors that perform the operation of executing the one or more attention operations are to perform [[the]] an operation of:
executing a first transformer along an expansion path of the attention-based model based at least on the second patch embeddings to generate updated second patch embeddings;
expanding the updated second patch embeddings to form third patch embeddings; and
executing a second transformer along the expansion path based at least on the third patch embeddings.
For claim 20, Examiner believes this claim should be amended in the following manner:
The system of claim 13, wherein 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 simulation operations;
a system for performing digital twin operations;
a system for performing light transport simulation;
a system for performing collaborative content creation for three-dimensional (3D) assets;
a system for performing deep learning operations;
a system for performing remote operations;
a system for performing real-time streaming;
a system for generating or presenting one or more of augmented reality content, virtual reality content, or mixed reality content;
a system implemented using an edge device;
a system implemented using a robot;
a system for performing conversational artificial intelligence (AI) operations;
a system implementing one or more multi-model language models;
a system implementing one or more large language models (LLMs);
a system implementing one or more vision language models (VLMs);
a system implementing one or more small language models (SLMs);
a system for generating synthetic data;
a system for generating synthetic data using AI;
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center;
a system using or deploying one or more inference microservices;
a system that incorporates one or more machine learning models deployed in a service or microservice along with an operating system (OS)-level virtualization package (e.g., a container); or
a system implemented at least partially using cloud computing resources.
For claim 22, Examiner believes this claim should be amended in the following manner:
The method of claim 21, wherein the method is performed by 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 simulation operations;
a system for performing digital twin operations;
a system for performing light transport simulation;
a system for performing collaborative content creation for three-dimensional (3D) assets;
a system for performing deep learning operations;
a system for performing remote operations;
a system for performing real-time streaming;
a system for generating or presenting one or more of augmented reality content, virtual reality content, or mixed reality content;
a system implemented using an edge device;
a system implemented using a robot;
a system for performing conversational artificial intelligence (AI) operations;
a system implementing one or more multi-model language models;
a system implementing one or more large language models (LLMs);
a system implementing one or more small language models (SLMs);
a system implementing one or more vision language models (VLMs);
a system for generating synthetic data;
a system for generating synthetic data using AI;
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center;
a system using or deploying one or more inference microservices;
a system that incorporates one or more machine learning models deployed in a service or microservice along with an operating system (OS)-level virtualization package (e.g., a container); or
a system implemented at least partially using cloud computing resources.
Appropriate correction is required.
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.
Claims 5, 10, 11 and 17 are 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.
For dependent claim 5, parent claim 1 establishes a first “one or more attention operations” and claim 5 establishes a second “one or more attention operations”. Claim 5 goes on to recite the phrase “the one or more attention operations” and it is unclear and ambiguous to which of the previously establishes first “one or more attention operations” or second “one or more attention operations” is being referenced by the phrase “the one or more attention operations”. Claim 5 further establishes “an offset”, “a vertical offset” and “a horizontal offset”. Claim 5 goes on to recite the phrase “the offset” and it is unclear and ambiguous to which of the previously “offset”, “vertical offset” and “horizontal offset” is being referenced by the phrase “the offset”. Examiner has suggested amendments in the claim objections discussed above to resolve the ambiguities.
For dependent claim 10, parent claims 6 and 8 establish “a first transformer along a contraction path” and “a first transformer along an expansion path”. Claim 10 goes on to recite the phrase “the first transformer” and it is unclear and ambiguous to which of the previously established “first transformer along a contraction path” and “first transformer along an expansion path” is being referenced by the phrase “the first transformer”. Parent claims 6 and 8 further establish “a second transformer along a contraction path” and “a second transformer along an expansion path”. Claim 10 goes on to recite the phrase “the second transformer” and it is unclear and ambiguous to which of the previously established “second transformer along a contraction path” and “second transformer along an expansion path” is being referenced by the phrase “the second transformer”. Examiner has suggested amendments in the claim objections discussed above to resolve the ambiguities.
For dependent claim 11, parent claims 6 and 8 establish “a first transformer along a contraction path” and “a first transformer along an expansion path”. Claim 11 goes on to recite the phrase “the first transformer” and it is unclear and ambiguous to which of the previously established “first transformer along a contraction path” and “first transformer along an expansion path” is being referenced by the phrase “the first transformer”. Parent claims 6 and 8 further establish “a second transformer along a contraction path” and “a second transformer along an expansion path”. Claim 11 goes on to recite the phrase “the second transformer” and it is unclear and ambiguous to which of the previously established “second transformer along a contraction path” and “second transformer along an expansion path” is being referenced by the phrase “the second transformer”. Examiner has suggested amendments in the claim objections discussed above to resolve the ambiguities.
For claim 17, parent claim 13 establishes a first “one or more attention operations” and claim 17 establishes a second “one or more attention operations”. Claim 17 goes on to recite the phrase “the one or more attention operations” and it is unclear and ambiguous to which of the previously establishes first “one or more attention operations” or second “one or more attention operations” is being referenced by the phrase “the one or more attention operations”. Claim 17 further establishes “an offset”, “a vertical offset” and “a horizontal offset”. Claim 17 goes on to recite the phrase “the offset” and it is unclear and ambiguous to which of the previously “offset”, “vertical offset” and “horizontal offset” is being referenced by the phrase “the offset”. Examiner has suggested amendments in the claim objections discussed above to resolve the ambiguities.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 6-16 and 18-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al., Monte Carlo Denoising via Auxiliary Feature Guided Self-Attention, ACM Transactions on Graphics, Vol. 40, Issue 6, Article No. 273, December 2021 (hereinafter “Yu”) in view of Papas et al. (U.S. Patent Application Publication 2026/0099902 A1, hereinafter “Papas”).
For claim 1, Yu discloses one or more processors (disclosing a graphics processing unit (GPU) (page 8)) comprising: obtain image data for a current image being generated, in accordance with one or more light transport simulation operations (disclosing input of a noisy image as image data for a current image being generated in accordance with Monte Carlo path tracing as light transport simulation operations (pages 3-4/Fig. 2)); execute one or more attention operations to determine an update to the current image based at least on a plurality of patches of the current image and windowed attention regions corresponding to at least one patch of the plurality of patches, the windowed attention regions extending at least in part beyond boundaries of each of the at least one patch (disclosing execution of attention operations to determine denoising as an update to the current image based on patches of the current image and local windows as windowed attention regions corresponding to at least one patch of the patches where each local window may extend beyond boundaries of each of the at least one patch (page 4/Fig. 2, page 6/Fig. 4, and pages 10-11/Table 4 and Fig. 10)); and provide updated image data according to the update, to generate the current image (disclosing the denoising is applied to the input noisy image to provide a denoised image to generate the current image as a high-quality noise free image (pages 3-4/Fig. 2, and page 11/Fig. 10)).
Yu does not specifically disclose one or more circuits.
However, these limitations are well-known in the art as disclosed in Papas.
Papas similarly discloses a system and method for generating and rendering images by simulating light transportation through Monte Carlo path tracing (par. 4 and 51). Papas explains its system may be implemented as processors comprising circuits (par. 257 and 267). It follows Yu may be accordingly modified with the teachings of Papas to implements its one or more processors with one or more circuits.
A person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention would find it obvious to modify Yu with the teachings of Papas. Papas is analogous art in dealing with a system and method for generating and rendering images by simulating light transportation through Monte Carlo path tracing (par. 4 and 51). Papas discloses its use of circuits is advantageous in implementing processors to appropriately perform the functions of a computer system (par. 257 and 267). Consequently, a PHOSITA would incorporate the teachings of Papas into Yu for implementing processors to appropriately perform the functions of a computer system. Therefore, claim 1 is rendered obvious to a PHOSITA before the effective filing date of the claimed invention.
For claim 2, depending on claim 1, Yu as modified by Papas discloses wherein the one or more circuits are to: determine a configuration for the windowed attention regions that, for each of the at least one patch, identifies tokens of the patch and one or more adjacent patches, and wherein, to execute the one or more attention operations, the one or more circuits are to, for each of the at least one patch, execute the one or more attention operations based at least on the tokens (Yu discloses determining a configuration for its local windows to identify a patch and adjacent patches for executing attention operations (page 6/Fig. 4); Papas similarly discloses a system and method for generating and rendering images by simulating light transportation through Monte Carlo path tracing (par. 4 and 51); Papas explains its system identifies tokens for a local region to perform attention operations (par. 97 and 109); and it follows Yu may be accordingly modified with the teachings of Papas to identify tokens of its patch and adjacent patches to execute its one or more attention operations).
For claim 3, depending on claim 2, Yu as modified by Papas discloses wherein, to determine the configuration for the windowed attention regions, the one or more circuits are to: determine sub-regions for the windowed attention regions that extend across at least one horizontally adjacent patch or at least one vertically adjacent patch (Yu discloses determining sub-regions for its local windows that extend across horizontally adjacent patches or vertically adjacent patches (page 6/Fig. 4)).
For claim 4, depending on claim 3, Yu as modified by Papas discloses wherein, to determine the configuration for the windowed attention regions, the one or more circuits are to: forgo determining sub-regions for the windowed attention regions that extend across at least one diagonally adjacent patch (Yu discloses it is known to forego determining sub-regions for its local windows that extend across diagonally adjacent patches (page 6/Fig. 4(c))).
For claim 6, depending on claim 1, Yu as modified by Papas discloses wherein the plurality of patches of the current image comprises a first plurality of patches (Yu discloses its patches comprise a first plurality of patches page 6/Fig. 4, and pages 10-11/Fig. 10)), and wherein, to execute the one or more attention operations, the one or more circuits are to: execute a first transformer along a contraction path of an attention-based model based at least on first patch embeddings for the first plurality of patches to generate updated first patch embeddings, merge the updated first patch embeddings to form second patch embeddings; and execute a second transformer along the contraction path based at least on the second patch embeddings (Papas similarly discloses a system and method for generating and rendering images by simulating light transportation through Monte Carlo path tracing (par. 4 and 51); Papas discloses it is known to execute a first transformer along a downscaling path as a contraction path of an attention-based model based on first embeddings of bins corresponding to cells or patches of an image to generate updated first embeddings (Fig. 14; par. 7, 179 and 185); Papas discloses embeddings may be merged along a path in the attention-based model to form additional embeddings (par. 177); Papas explains it is further known to execute a second transformer along the downscaling path based on the merged embeddings as second embeddings (Fig. 14; par. 179); and it follows Yu may be accordingly modified with the teachings of Papas to execute a first transformer along a contraction path of an attention-based model based on first embeddings for its first plurality of patches to generate updated first patch embeddings, merge the updated first patch embeddings to form second patch embeddings, and execute a second transformer along the contraction path based on the second patch embeddings).
For claim 7, depending on claim 6, Yu as modified by Papas discloses wherein, to execute the first transformer along the contraction path, the one or more circuits are to: determine at least one first threshold value associated with the execution of the one or more attention operations; and generate the updated first patch embeddings based at least on the at least one first threshold value (Papas similarly discloses a system and method for generating and rendering images by simulating light transportation through Monte Carlo path tracing (par. 4 and 51); Papas discloses it is known to execute a first transformer along a downscaling path as a contraction path of an attention-based model based on first embeddings of bins corresponding to cells or patches of an image to generate updated first embeddings (Fig. 14; par. 7, 179 and 185); Papas further explains it is known to implement the operations of a machine learning model with various parameters such as thresholds (par. 21); and it follows Yu may be accordingly modified with the teachings of Yu to determine a first threshold value associated with its execution of its one or more attention operations and generate the update first patch embeddings based on the first threshold value).
For claim 8, depending on claim 6, Yu as modified by Papas discloses wherein, to execute the one or more attention operations, the one or more circuits are to: execute a first transformer along an expansion path of the attention-based model based at least on the second patch embeddings to generate updated second patch embeddings; expand the updated second patch embeddings to form third patch embeddings; and execute a second transformer along the expansion path based at least on the third patch embeddings (Papas similarly discloses a system and method for generating and rendering images by simulating light transportation through Monte Carlo path tracing (par. 4 and 51); Papas discloses it is known to execute a first transformer along an upscaling path as an expansion path of the attention-based model following the downscaling path as the contraction path based on second embeddings of bins corresponding to cells or patches of an image to generate updated second embeddings (Fig. 14; par. 7, 179 and 185); Papas discloses the updated second embeddings may be expanded along the upscaling path to form third embeddings (Fig. 14; par. 179); Papas explains it is further known to execute a second transformer along the upscaling path based on the third embeddings (Fig. 14; par. 179); and it follows Yu may be accordingly modified with the teachings of Papas to execute a first transformer along an expansion path of its attention-based model based on second embeddings for its first plurality of patches to generate updated second patch embeddings, expand the updated second patch embeddings to form third patch embeddings, and execute a second transformer along the expansion path based on the third patch embeddings).
For claim 9, depending on claim 8, Yu as modified by Papas discloses wherein, to execute the first transformer along the expansion path, the one or more circuits are to: determine at least one second threshold value associated with the execution of the one or more attention operations; and generate the updated first patch embeddings based at least on the at least one second threshold value (Papas similarly discloses a system and method for generating and rendering images by simulating light transportation through Monte Carlo path tracing (par. 4 and 51); Papas discloses it is known to execute a first transformer along a downscaling path as a contraction path of an attention-based model based on first embeddings of bins corresponding to cells or patches of an image to generate updated first embeddings (Fig. 14; par. 7, 179 and 185); Papas further explains it is known to implement the operations of a machine learning model with various parameters such as thresholds (par. 21); and it follows Yu may be accordingly modified with the teachings of Yu to determine a second threshold value associated with its execution of its one or more attention operations and generate the update first patch embeddings based on the second threshold value).
For claim 10, depending on claim 8, Yu as modified by Papas discloses wherein, to execute the first transformer and the second transformer along the contraction path, the one or more circuits are to: execute the first transformer based at least on a first windowed attention region and a first offset, and execute the second transformer based at least on a second windowed attention region and a second offset (Yu discloses execution of attention operations based local windows as windowed attention regions (page 4/Fig. 2, page 6/Fig. 4, and pages 10-11/Table 4 and Fig. 10); Papas similarly discloses a system and method for generating and rendering images by simulating light transportation through Monte Carlo path tracing (par. 4 and 51); Papas discloses it is known to execute a first transformer along a downscaling path as a contraction path and a second transformer along the downscaling path (Fig. 14; par. 179) where the transformers perform attention operations based on respective offsets (par. 183); and it follows Yu may be accordingly modified with the teachings of Papas to execute the first transformer along the contraction path based on a first windowed attention region and a first offset and execute the second transformer along the contraction path based on a second windowed attention region and a second offset).
For claim 11, depending on claim 8, Yu as modified by Papas discloses wherein, to execute the first transformer and the second transformer along the expansion path, the one or more circuits are to: execute the first transformer based at least on a first windowed attention region and a first offset, and execute the second transformer based at least on a second windowed attention region and a second offset (Yu discloses execution of attention operations based local windows as windowed attention regions (page 4/Fig. 2, page 6/Fig. 4, and pages 10-11/Table 4 and Fig. 10); Papas similarly discloses a system and method for generating and rendering images by simulating light transportation through Monte Carlo path tracing (par. 4 and 51); Papas discloses it is known to execute a first transformer along an upscaling path as an expansion path and a second transformer along the upscaling path (Fig. 14; par. 179) where the transformers perform attention operations based on respective offsets (par. 183); and it follows Yu may be accordingly modified with the teachings of Papas to execute the first transformer along the expansion path based on a first windowed attention region and a first offset and execute the second transformer along the expansion path based on a second windowed attention region and a second offset).
For claim 12, depending on claim 1, Yu as modified by Papas discloses wherein 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 implemented using a robot; an aerial system; a medical system; a boating system; a smart area monitoring system; a system for performing deep learning operations; a system for performing simulation operations; a system for generating or presenting virtual reality (VR) content, augmented reality (AR) content, or mixed reality (MR) content; a system for performing digital twin operations; a system implemented using an edge device; a system incorporating one or more virtual machines (VMs); a system for generating synthetic data; a system implemented at least partially in a data center; a system for performing conversational artificial intelligence (AI) operations; a system for performing generative AI operations; a system implementing language models; a system for performing generative AI operations; a system for implementing vision language models (VLMs); a system for implementing large language models (LLMs); a system for implementing small language models (SLMs); a system for hosting one or more real-time streaming applications; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system using or deploying one or more inference microservices; a system that incorporates one or more machine learning models deployed in a service or microservice along with an OS-level virtualization package (e.g., a container); or a system implemented at least partially using cloud computing resources (Yu discloses a system for performing Monte Carlo path tracing as light transport simulation operations (pages 3-4/Fig. 2); Papas similarly discloses a system and method for generating and rendering images by simulating light transportation through Monte Carlo path tracing (par. 4 and 51)).
For claim 13, Yu as modified by Papas discloses a system comprising: one or more processors to perform operations (Yu discloses a system implemented with a graphics processing unit (GPU) to perform operations (page 8)) corresponding to the one or more processors of claim 1 (see above as to claim 1).
For claim 14, depending on claim 13, this claim is a combination of the limitations of claim 13 and claim 2. It follows claim 14 is rejected for the same reasons as to claim 13 and claim 2.
For claim 15, depending on claim 14, this claim is a combination of the limitations of claim 14 and claim 3. It follows claim 15 is rejected for the same reasons as to claim 14 and claim 3.
For claim 16, depending on claim 15, this claim is a combination of the limitations of claim 15 and claim 4. It follows claim 16 is rejected for the same reasons as to claim 15 and claim 4.
For claim 18, depending on claim 13, this claim is a combination of the limitations of claim 13 and claim 6. It follows claim 18 is rejected for the same reasons as to claim 13 and claim 6.
For claim 19, depending on claim 18, this claim is a combination of the limitations of claim 18 and claim 8. It follows claim 19 is rejected for the same reasons as to claim 18 and claim 8.
For claim 20, depending on claim 13, Yu as modified by Papas discloses wherein 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 simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system for performing remote operations; a system for performing real-time streaming; a system for generating or presenting one or more of augmented reality content, virtual reality content, or mixed reality content; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system implementing one or more multi-model language models; a system implementing one or more large language models (LLMs); a system implementing one or more vision language models (VLMs); a system implementing one or more small language models (SLMs); a system for generating synthetic data; a system for generating synthetic data using AI; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; a system using or deploying one or more inference microservices; a system that incorporates one or more machine learning models deployed in a service or microservice along with an OS-level virtualization package (e.g., a container); or a system implemented at least partially using cloud computing resources (Yu discloses a system for performing Monte Carlo path tracing as light transport simulation operations (pages 3-4/Fig. 2); Papas similarly discloses a system and method for generating and rendering images by simulating light transportation through Monte Carlo path tracing (par. 4 and 51)).
For claim 21, Yu as modified by Papas discloses a method comprising steps corresponding to the operations performed by the one or more processors of claim 1 (see above as to claim 1).
For claim 22, depending on claim 21, Yu as modified by Papas discloses wherein the method is performed by 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 simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system for performing remote operations; a system for performing real-time streaming; a system for generating or presenting one or more of augmented reality content, virtual reality content, or mixed reality content; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system implementing one or more multi-model language models; a system implementing one or more large language models (LLMs); a system implementing one or more small language models (SLMs); a system implementing one or more vision language models (VLMs); a system for generating synthetic data; a system for generating synthetic data using AI; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; a system using or deploying one or more inference microservices; a system that incorporates one or more machine learning models deployed in a service or microservice along with an OS-level virtualization package (e.g., a container); or a system implemented at least partially using cloud computing resources (Yu discloses a system for performing Monte Carlo path tracing as light transport simulation operations (pages 3-4/Fig. 2); Papas similarly discloses a system and method for generating and rendering images by simulating light transportation through Monte Carlo path tracing (par. 4 and 51)).
Allowable Subject Matter
Claims 5 and 17 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims, and to address any claim objections raised above in the Detailed Action.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES TSENG whose telephone number is (571)270-3857. The examiner can normally be reached 8-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xiao Wu can be reached at (571) 272-7761. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHARLES TSENG/Primary Examiner, Art Unit 2613
Prosecution Insights