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 INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
“a first portion of a graphics rendering pipeline having multiple processing stages, the first portion to receive” in claim 10.
“a second portion of the graphics rendering pipeline, the second portion to generate” in claim 10.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The structure of the first portion in the claim is server and the structure of the second portion is a client and disclosed in ¶39 of specification as well as a CPU or a GPU as disclosed in ¶43 of the specification.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recites sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1-5, 10-14, 18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Panneer et al. (US Pub. No. 2024/0312113 A1).
Regarding claim 1, Panneer discloses, a method comprising: receiving, by a graphics rendering pipeline having multiple processing stages, scene data representing at least a portion of a frame to be rendered for display; (See Panneer Fig. 32A, which shows the scene data in 3212 - UV Space G-buffer, this data is received by 3216 - Cloud Rendering at the server which has multiple processing stages and then is transported to 3218 – 3218 Cloud Client, which also has multiple processing stages.)
generating, by each of one or more of the multiple processing stages, intermediate data based on the received scene data, wherein for at least one processing stage of the multiple processing stages, generating the intermediate data comprises performing one or more transformations on input data from a previous one of the multiple processing stages by a trained neural network; and rendering the frame for display based at least in part on the intermediate data generated by the trained neural network. (See Panneer ¶402, “can then apply AI techniques, such as upscaling and frame data generation, to G-buffer data 3212 in UV space. … In cloud rendering 3216, the deferred rendering 3211 is performed on one or more GPUs of a cloud gaming server. The cloud gaming server can then encode UV space deltas in the G-buffer data 3212 relative to the previous frame in a manner that is suitable to enable remote rendering of the frame at a cloud client 3218, which is a client device of the cloud gaming server. The deltas may be encoded via data compression or using media encoding CODECs. The encoded deltas are then transported to the cloud client 3218. The cloud client 3218 can decode the encoded UV space deltas, apply AI techniques in UV space, then rasterize the UV space data to screen space 3206 at a target resolution configured by the cloud client 3218.”
Further see Panneer ¶419, “As shown in FIG. 34B, a system 3430 can be configured to perform neural upsampling and/or super resolution dynamically according to a scale factor for each frame. A unified neural network 3435 is used that includes multiple subnetworks that are trained to perform frame generation via extrapolation and interpolation in addition to super resolution operations on rendered and generated frame data.”)
Regarding claim 2, Panneer discloses, the method of claim 1, wherein performing the one or more transformations comprises performing one or more of a group that includes denoising operations, (See Panneer ¶107, “In this distributed approach, the interconnected computing devices may share neural network learning/training data to improve the speed with which the overall system learns to perform denoising for different types of image frames and/or different graphics applications.”)
encoding operations, decoding operations, and upscaling operations. (See Panneer ¶402, “can then apply AI techniques, such as upscaling and frame data generation, to G-buffer data 3212 in UV space. … In cloud rendering 3216, the deferred rendering 3211 is performed on one or more GPUs of a cloud gaming server. The cloud gaming server can then encode UV space deltas in the G-buffer data 3212 relative to the previous frame in a manner that is suitable to enable remote rendering of the frame at a cloud client 3218, which is a client device of the cloud gaming server. The deltas may be encoded via data compression or using media encoding CODECs. The encoded deltas are then transported to the cloud client 3218. The cloud client 3218 can decode the encoded UV space deltas, apply AI techniques in UV space, then rasterize the UV space data to screen space 3206 at a target resolution configured by the cloud client 3218.”)
Regarding claim 3, Panneer discloses, the method of claim 2, further comprising training the neural network based on a training dataset comprising pairs of original frames and corresponding frames that have been transformed in a manner corresponding to the one or more transformations. (See Panneer ¶209, “Supervised learning is a learning method in which training is performed as a mediated operation, such as when the training dataset 1102 includes input paired with the desired output for the input, or where the training dataset includes input having known output and the output of the neural network is manually graded. The network processes the inputs and compares the resulting outputs against a set of expected or desired outputs.”)
Regarding claim 4, Panneer discloses, the method of claim 1, wherein the multiple processing stages comprise a neural network trained to generate one or more additional frames of a frame sequence. (See Panneer ¶419, “As shown in FIG. 34B, a system 3430 can be configured to perform neural upsampling and/or super resolution dynamically according to a scale factor for each frame. A unified neural network 3435 is used that includes multiple subnetworks that are trained to perform frame generation via extrapolation and interpolation in addition to super resolution operations on rendered and generated frame data.”)
Regarding claim 5, Panneer discloses, the method of claim 1, wherein a first portion of the multiple processing stages is performed at a server, wherein a second portion of the multiple processing stages is performed at a client device located remotely from the server, and wherein the first portion of the multiple processing stages comprises encoding frame data for transmission from the server to the client device. (See Panneer ¶402, “In cloud rendering 3216, the deferred rendering 3211 is performed on one or more GPUs of a cloud gaming server. The cloud gaming server can then encode UV space deltas in the G-buffer data 3212 relative to the previous frame in a manner that is suitable to enable remote rendering of the frame at a cloud client 3218, which is a client device of the cloud gaming server. The deltas may be encoded via data compression or using media encoding CODECs. The encoded deltas are then transported to the cloud client 3218. The cloud client 3218 can decode the encoded UV space deltas, apply AI techniques in UV space, then rasterize the UV space data to screen space 3206 at a target resolution configured by the cloud client 3218.”)
Regarding claim 10, Panneer discloses, a system, comprising: a first portion of a graphics rendering pipeline having multiple processing stages, the first portion to receive scene data representing at least a portion of a frame to be rendered for display; (See Panneer ¶402, “can then apply AI techniques, such as upscaling and frame data generation, to G-buffer data 3212 in UV space. … In cloud rendering 3216, the deferred rendering 3211 is performed on one or more GPUs of a cloud gaming server. The cloud gaming server can then encode UV space deltas in the G-buffer data 3212 relative to the previous frame in a manner that is suitable to enable remote rendering of the frame at a cloud client 3218, which is a client device of the cloud gaming server. The deltas may be encoded via data compression or using media encoding CODECs. The encoded deltas are then transported to the cloud client 3218.”)
and a second portion of the graphics rendering pipeline, the second portion to generate one or more output frames for display based at least in part on the received scene data; (See Panneer ¶402, “The cloud client 3218 can decode the encoded UV space deltas, apply AI techniques in UV space, then rasterize the UV space data to screen space 3206 at a target resolution configured by the cloud client 3218.”
Further see Panneer ¶419, “As shown in FIG. 34B, a system 3430 can be configured to perform neural upsampling and/or super resolution dynamically according to a scale factor for each frame. A unified neural network 3435 is used that includes multiple subnetworks that are trained to perform frame generation via extrapolation and interpolation in addition to super resolution operations on rendered and generated frame data.”)
wherein at least one processing stage of the multiple processing stages comprises a neural network trained to perform one or more transformations on input data from a previous processing stage of the multiple processing stages. (See Panneer ¶419, “As shown in FIG. 34B, a system 3430 can be configured to perform neural upsampling and/or super resolution dynamically according to a scale factor for each frame. A unified neural network 3435 is used that includes multiple subnetworks that are trained to perform frame generation via extrapolation and interpolation in addition to super resolution operations on rendered and generated frame data.”)
Regarding claim 11, Panneer discloses, the system of claim 10, wherein the one or more transformations comprises one or more of a group that includes denoising operations, encoding operations, decoding operations, and upscaling operations. (See the rejection of claim 2 as it is equally applicable for claim 11 as well.)
Regarding claim 12, Panneer discloses, the system of claim 11, wherein the neural network is trained based on a training dataset comprising pairs of original frames and corresponding frames that have been transformed in a manner corresponding to the one or more transformations. (See the rejection of claim 3 as it is equally applicable for claim 12 as well.)
Regarding claim 13, Panneer discloses, the system of claim 10, wherein the multiple processing stages comprise a neural network trained to generate one or more intermediate frames for a frame sequence comprising the one or more output frames. (See the rejection of claim 4 as it is equally applicable for claim 13 as well.)
Regarding claim 14, Panneer discloses, the system of claim 10, wherein the first portion of the graphics rendering pipeline comprises one or more processing stages at a server, wherein the second portion of the graphics rendering pipeline comprises one or more processing stages at a client device located remotely from the server, and wherein the first portion of the graphics rendering pipeline encodes frame data for transmission from the server to the client device. (See the rejection of claim 5 as it is equally applicable for claim 14 as well.)
Regarding claim 18, Panneer discloses, the system of claim 10, wherein the second portion of the graphics rendering pipeline comprises an interpolation neural network trained to generate one or more additional frames of a frame sequence, and wherein the one or more transformations include one or more of a group that comprises frame interpolation operations and frame extrapolation operations. (See Panneer ¶402, “can then apply AI techniques, such as upscaling and frame data generation, to G-buffer data 3212 in UV space. … The encoded deltas are then transported to the cloud client 3218. The cloud client 3218 can decode the encoded UV space deltas, apply AI techniques in UV space, then rasterize the UV space data to screen space 3206 at a target resolution configured by the cloud client 3218.”
Further see Panneer ¶419, “As shown in FIG. 34B, a system 3430 can be configured to perform neural upsampling and/or super resolution dynamically according to a scale factor for each frame. A unified neural network 3435 is used that includes multiple subnetworks that are trained to perform frame generation via extrapolation and interpolation in addition to super resolution operations on rendered and generated frame data.”)
Regarding claim 20, Panneer discloses, a non-transitory computer-readable medium storing a set of executable instructions that, when executed by one or more processors, manipulate the one or more processors to: (See Panneer ¶344, “One or more aspects may be implemented by representative code stored on a machine-readable medium which represents and/or defines logic within an integrated circuit such as a processor. For example, the machine-readable medium may include instructions which represent various logic within the processor.”)
receive, by a graphics rendering pipeline having multiple processing stages, scene data representing at least a portion of a frame to be rendered for display; (See Panneer Fig. 32A, which shows the scene data in 3212 - UV Space G-buffer, this data is received by 3216 - Cloud Rendering at the server which has multiple processing stages and then is transported to 3218 – 3218 Cloud Client, which also has multiple processing stages.)
generate, by each of one or more of the multiple processing stages, intermediate data based on the received scene data, wherein at least one processing stage of the multiple processing stages generates the intermediate data by performing, by a trained neural network, one or more transformations on input data from a previous processing stage of the multiple processing stages; and render the frame for display based at least in part on the intermediate data generated by the trained neural network. (See Panneer ¶402, “can then apply AI techniques, such as upscaling and frame data generation, to G-buffer data 3212 in UV space. … In cloud rendering 3216, the deferred rendering 3211 is performed on one or more GPUs of a cloud gaming server. The cloud gaming server can then encode UV space deltas in the G-buffer data 3212 relative to the previous frame in a manner that is suitable to enable remote rendering of the frame at a cloud client 3218, which is a client device of the cloud gaming server. The deltas may be encoded via data compression or using media encoding CODECs. The encoded deltas are then transported to the cloud client 3218. The cloud client 3218 can decode the encoded UV space deltas, apply AI techniques in UV space, then rasterize the UV space data to screen space 3206 at a target resolution configured by the cloud client 3218.”
Further see Panneer ¶419, “As shown in FIG. 34B, a system 3430 can be configured to perform neural upsampling and/or super resolution dynamically according to a scale factor for each frame. A unified neural network 3435 is used that includes multiple subnetworks that are trained to perform frame generation via extrapolation and interpolation in addition to super resolution operations on rendered and generated frame data.”)
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 6-7 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Panneer et al. (US Pub. No. 2024/0312113 A1) in view of Le et al. (US Pub. No. 2024/0013441 A1).
Regarding claim 6, Panneer discloses, the method of claim 5, but he fails to disclose, wherein the first portion of the multiple processing stages comprises a neural network that is trained to perform one or more of a group that comprises denoising operations and encoding operations.
However, Le discloses, wherein the first portion of the multiple processing stages comprises a neural network that is trained to perform one or more of a group that comprises denoising operations and encoding operations. (As alternatively required by the claim Le disclose a neural network for encoding at a server, see Le ¶111, “The frames of cloud gaming video data can be encoded using a neural video encoder included in the cloud gaming server. As illustrated in FIG. 6, the neural video encoder can obtain the frames of cloud gaming video data (e.g., generated by the game engine in response to the user input commands) from the G-buffer, and encodes each frame into a bitstream.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the encoding using a neural network at server for cloud gaming data as suggested by Le to Panneer’s encoding of gaming server data. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is better compression efficiency. Neural networks adapt to game content. They compress rapid camera movements and fine textures better than standard codecs like H.264 or HEVC.
Regarding claim 7, Panneer discloses, the method of claim 5, wherein the second portion of the multiple processing stages comprises one or more neural networks, the one or more neural networks being trained to perform one or more of a group that comprises denoising operations, upscaling operations, frame interpolation, and frame extrapolation. (See Panneer ¶402, “can then apply AI techniques, such as upscaling and frame data generation, to G-buffer data 3212 in UV space. … The encoded deltas are then transported to the cloud client 3218. The cloud client 3218 can decode the encoded UV space deltas, apply AI techniques in UV space, then rasterize the UV space data to screen space 3206 at a target resolution configured by the cloud client 3218.”
Further see Panneer ¶419, “As shown in FIG. 34B, a system 3430 can be configured to perform neural upsampling and/or super resolution dynamically according to a scale factor for each frame. A unified neural network 3435 is used that includes multiple subnetworks that are trained to perform frame generation via extrapolation and interpolation in addition to super resolution operations on rendered and generated frame data.”)
Panneer discloses the above limitations but he fails to disclose, decoding operations.
However, Le discloses, decoding operations, (See Le ¶112, “The bitstream associated with the encoded frames of cloud gaming video data can be transmitted to the client or user device, where the bitstream is decoded by a neural video decoder.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the decoding using a neural network at a client for cloud gaming data as suggested by Le to Panneer’s decoding for gaming client data. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is better picture quality, whereby AI models make blurry text and game details look clear, and low-resolution video streams scale up to 4K smoothly.
Regarding claim 15, Panneer and Le disclose, the system of claim 14, wherein the first portion of the graphics rendering pipeline comprises a neural network that is trained to perform one or more of a group that comprises denoising operations and encoding operations. (See the rejection of claim 6 as it is equally applicable for claim 15 as well.)
Regarding claim 16, Panneer and Le disclose, the system of claim 14, wherein the second portion of the graphics rendering pipeline comprises one or more neural networks trained to perform one or more of a group that comprises decoding operations, denoising operations, upscaling operations, frame interpolation, and frame extrapolation. (See the rejection of claim 7 as it is equally applicable for claim 16 as well.)
Claims 8-9, 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Panneer et al. (US Pub. No. 2024/0312113 A1) in view of Le et al. (US Pub. No. 2024/0013441 A1) and in further view of Grzesiak et al. (US Pub. No. 2023/0171444 A1).
Regarding claim 8, Panneer discloses, the method of claim 6, but he fails to disclose, further comprising providing local user input at the client device to the one or more trained neural networks of the second portion of the multiple processing stages.
However, Grzesiak discloses, further comprising providing local user input at the client device to the one or more trained neural networks of the second portion of the multiple processing stages. (See Grzesiak ¶51, “Based on the delay time and a current user input, the electronic device 150 determines, from the received streaming data, whether to display M prediction frames included in a prediction frame set corresponding to the current user input among the K prediction frame sets corresponding to a plurality of possible user inputs at operation 170. The current user input may be a click on a screen, an input of a keyboard, an input of a mouse, an input of a controller, an input of a steering wheel, and the like. When it is determined to display the M prediction frames, the electronic device 150 displays the M prediction frames at operation 175. The prediction frames may be generated by inputting, by the electronic device 150, streaming data transmitted from the server 100, to an artificial intelligence model trained to generate prediction frames.”
Further see Grzesiak ¶37, “In addition, the prediction frame may be generated using an artificial intelligence model trained to generate a prediction frame after frames, for example a DNN, according to types of the frames and user inputs.”)
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the generating predicted frames based on user input to a neural network at a gaming client as suggested by Grzesiak to Panneer’s generating of predicted frames using interpolation and extrapolation. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is because predicting frames using a neural network cuts down on network lag, and makes fast games run smoothly on weak client devices like cellphones.
Regarding claim 9, Panneer discloses, the method of claim 1, but he fails to disclose, wherein the multiple processing stages comprise a neural network trained to generate one or more additional frames of a frame sequence, and wherein the method further comprises providing to the trained neural network one or more predicted user inputs for use in generating the one or more additional frames.
However, Grzesiak discloses, wherein the multiple processing stages comprise a neural network trained to generate one or more additional frames of a frame sequence, and wherein the method further comprises providing to the trained neural network one or more predicted user inputs for use in generating the one or more additional frames. (See Grzesiak ¶51, “Based on the delay time and a current user input, the electronic device 150 determines, from the received streaming data, whether to display M prediction frames included in a prediction frame set corresponding to the current user input among the K prediction frame sets corresponding to a plurality of possible user inputs at operation 170. The current user input may be a click on a screen, an input of a keyboard, an input of a mouse, an input of a controller, an input of a steering wheel, and the like. When it is determined to display the M prediction frames, the electronic device 150 displays the M prediction frames at operation 175. The prediction frames may be generated by inputting, by the electronic device 150, streaming data transmitted from the server 100, to an artificial intelligence model trained to generate prediction frames.”
Further see Grzesiak ¶37, “In addition, the prediction frame may be generated using an artificial intelligence model trained to generate a prediction frame after frames, for example a DNN, according to types of the frames and user inputs.”)
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the generating predicted frames based on user input to a neural network at a gaming client as suggested by Grzesiak to Panneer’s generating of predicted frames using interpolation and extrapolation. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is because predicting frames using a neural network cuts down on network lag, and makes fast games run smoothly on weak client devices like cellphones.
Regarding claim 17, Panneer and Grzesiak disclose, the system of claim 16, further comprising a lag adjustment processing stage to provide local user input at the client device to the one or more trained neural networks of the second portion of the graphics rendering pipeline. (See the rejection of claim 8 as it is equally applicable for claim 17 as well.)
Regarding claim 19, Panneer and Grzesiak disclose, the system of claim 18, further comprising an input prediction processing stage to provide one or more predicted user inputs to the interpolation neural network for use in generating the one or more additional frames. (See the rejection of claim 9 as it is equally applicable for claim 19 as well.)
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
Osman et al. (US Pub. No. 2024/0374998 A1) Methods and system for providing streaming content of a video game at a client device includes receiving frames of streaming content from a game server. The frames represent a current game state. The frames are analyzed to generate predicted frames that are likely to occur following the current frames. The predicted frames are stored in a prediction frame buffer and used to fill any gaps in subsequent frames representing subsequent game state received from the game server.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID PERLMAN whose telephone number is (571) 270-1417.
The examiner can normally be reached on Monday - Friday; 10:00am -6:30pm.
Examiner interviews are available via telephone 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, Chineyere Wills-Burns can be reached at (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is
(571) 273-8300.
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/DAVID PERLMAN/Primary Examiner, Art Unit 2673
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