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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on November 26 and December 19 of 2024 were filed after the mailing date of the application on November 26, 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities: Applicant’s disclosure on p. 1, [0001] recites “…U.S. Patent Application No. 18/351,531, filed July 13, 2023, now pending; which is a continuation…” where it should instead recite “…U.S. Patent Application No. 18/351,531, filed July 13, 2023, now U.S. Patent No. 12,182,966, issued December 31, 2024; which is a continuation…”
Appropriate correction is required.
The disclosure is objected to because of the following informalities: According to MPEP 608.01(m), the present Office practice is to insist that each claim must be the object of a sentence starting with “I (or we) claim,” “The invention is” (or the equivalent). Thus, the heading simply stating “CLAIMS” is not sufficient.
Appropriate correction is required.
Claim Objections
Claims 11-12 are objected to because of the following informalities: Claims 11-12 each recite “…(c2) generating, based on one of the plurality of separate output channels, a second image that is at a resolution that is lower than the resolution of the of the one…” where they should instead recite “…(c2) generating, based on one of the plurality of separate output channels, a second image that is at a resolution that is lower than the resolution of the one…” Appropriate correction is required.
Claim 13 is objected to because of the following informalities: Claim 13 recites “…plurality of different trained neural network…based data included the corresponding request…” where it should instead recite “…plurality of different trained neural networks…based on data included in the corresponding request…” Appropriate correction is required.
Claims 14-15 are objected to because of the following informalities: Claims 14-15 each recite “…data included the corresponding request…” where it should instead recite “…data included in the corresponding request…” Appropriate correction is required.
Claim 17 is objected to because of the following informalities: Claim 17 recites “The computer system of claim 16…” However, Claim 16 is directed to the non-transitory computer readable storage medium, and not to the computer system. Thus, Applicant is assumed to have meant “The non-transitory computer readable storage medium of claim 16…” Appropriate correction is required.
Claim 18 is objected to because of the following informalities: Claim 18 recites “The computer system of claim 17…”. However, Claim 17 is supposed to be directed to the non-transitory computer readable storage medium, and not to the computer system, as discussed above. Thus, Applicant is assumed to have meant “The non-transitory computer readable storage medium of claim 17…” Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 2, 4, 5, 8, 9, 12, 16-19, and 21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 15-21 of U.S. Patent No. 11,494,875, as shown in the tables below. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application claims are broader in every aspect than the patent claims and are therefore an obvious variant thereof.
As per Claim 10, patent Claim 15 recites splitting the plurality of context blocks into a plurality of separate input channels, and patent Claim 16 recites each of the plurality of separate channels is composed of a plurality of sub-channels. It would have been obvious to one of ordinary skill in the art that the number of separate input channels can be any number desired by the user, and thus it could be four separate input channels.
Claim 3 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 15 of U.S. Patent No. 11,494,875 in view of Park (US 20200219233A1).
Patent Claim 15 is relied upon for the teachings relative to Claim 2.
However, the patent claims do not recite wherein the second resolution is less than the first resolution. However, Park teaches wherein the second resolution is less than the first resolution [0184, 0075].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the patent claims so that the second resolution is less than the first resolution because Park suggests that this way, the image is downscaled so that the image can be transmitted more quickly [0184].
Claim 6 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 15 of U.S. Patent No. 11,494,875 in view of Kalchbrenner (US 20210027425A1).
Patent Claim 15 is relied upon for the teachings relative to Claim 2.
However, the patent claims do not expressly recite wherein the plurality of separate input channels is four separate channels, and one of the four channels is used to generate the second image. However, Kalchbrenner teaches wherein the plurality of separate input channels is four separate channels, and one of the four channels is used to generate the second image [0043].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the patent claims so that the plurality of separate input channels is four separate channels, and one of the four channels is used to generate the second image as suggested by Kalchbrenner. It is well-known in the art to have four separate color channels, such as cyan, magenta, yellow, and black.
Claims 13 and 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 15 of U.S. Patent No. 11,494,875 in view of Cox (US 20210092462A1).
As per Claim 13, patent Claim 15 is relied upon for the teachings relative to Claim 2.
However, the patent claims do not recite wherein the operations further comprise: a transceiver configured to receive, from a plurality of different computing devices, a plurality of different requests for neural networks usable by video games, receiving a plurality of different requests for neural networks usable by video games; for each corresponding request, selecting, from among a plurality of different trained neural network, at least one of the trained neural network(s) based data included the corresponding request; and communicating, via the transceiver, the selected at least one of the trained neural networks to a requesting computing device. However, Cox teaches wherein the operations further comprise: a transceiver configured to receive, from a plurality of different computing devices (112a, 112b, 112c, Fig. 1), a plurality of different requests [0050, 0032] for neural networks [0044, 0150] usable by video games [0038, 0061], receiving a plurality of different requests [0050, 0032] for neural networks [0044, 0150] usable by video games [0038, 0061]. Cox teaches the model library 120 includes machine learning models, associated with content provided to the file server 108, which is provided to the viewer 112 to allow the viewer 112 to provide resolution upscaling (with a hardware-accelerated super-resolution CNN) to improve the visual quality of reduced-resolution video at the viewer 112. The model library 120 includes ML models generated on similar content to that provided to the viewer 112. The provided model from the model library 120 allows the viewer 112 to compensate for lower resolution content [0038]. Thus, it would have been obvious to one of ordinary skill in the art that for each corresponding request, selecting, from among a plurality of different trained neural network (ML models), at least one of the trained neural network(s) (ML models) based data included the corresponding request; and communicating, via the transceiver, the selected at least one of the trained neural networks to a requesting computing device [0050, 0038].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the patent claims so that the operations further comprise: a transceiver configured to receive, from a plurality of different computing devices, a plurality of different requests for neural networks usable by video games, receiving a plurality of different requests for neural networks usable by video games; for each corresponding request, selecting, from among a plurality of different trained neural network, at least one of the trained neural network(s) based data included the corresponding request; and communicating, via the transceiver, the selected at least one of the trained neural networks to a requesting computing device because Cox suggests that for game streaming services [0001], generating multiple video qualities in real-time is computationally intensive and requires specialized hardware or dedicated hardware transcoders to keep streaming at low latency, and thus, having multiple streams received by a server for provision to viewers, and the viewers can employ a machine learning co-processor that can improve the inbound content, if that content is provided at a lower resolution, will reduce the computational intensity and will not require specialized hardware or dedicated hardware transcoders to keep streaming at low latency [0002, 0003].
As per Claim 15, the patent claims do not recite wherein data included the corresponding request indicates a target resolution. However, Cox teaches wherein data included the corresponding request indicates a target resolution [0070].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the patent claims so that data included the corresponding request indicates a target resolution because Cox suggests that this way, the user will retrieve the stream at the target resolution that is desired by the user [0070].
18,959,747
Claim 2
3
4
5
6
8
9
12
13
15
16
17
11,494,875
Claim 15
15
16
17
15
15
15
18
15
15
19
20
18/959,747
18
19
21
11,494,875
21
15
15
18/959,747 (Claim 2)
11,494,875 (Claim 15)
A non-transitory computer readable storage medium storing computer executable instructions for use with a computing system, the stored instructions configured to perform operations comprising: accessing a plurality of images that include a first image in the first resolution;
A computer system for training a neural network to transform images from a first resolution into a second resolution, the computer system comprising: non-transitory computer readable storage configured to store a plurality of target images, the plurality of images including a first image in the first resolution; and a processing system that includes at least one hardware processor, the processing system configured to:
dividing the first image into a first plurality of pixel blocks;
divide the first image into a first plurality of pixel blocks,
splitting each one of the first plurality of pixel blocks into a plurality of separate output channels to form target output data;
split each one of the first plurality of pixel blocks into a plurality of separate output channels to form target output data,
generating, based on one of the plurality of separate output channels, data for a second image that is of the second resolution;
generate, based on one of the plurality of separate output channels, second image that is of the second resolution,
generating, from the data for the second image, a plurality of context blocks;
generate, from the second image, a plurality of context blocks based on selection of a second plurality of pixels blocks from the generated second image, each one of the second plurality of pixel blocks including data for multiple pixels from the generated second image, wherein each one of the plurality of context blocks is based on a corresponding one of the second plurality of pixel blocks, wherein context data is added to each one of the second plurality of pixel blocks to create a corresponding one of the plurality of context blocks,
splitting the plurality of context blocks into a plurality of separate input channels to form input data; and
split the plurality of context blocks into a plurality of separate input channels, and
training a neural network by using the input data until convergence of the neural network to the target output data.
train a neural network by using the plurality of separate input channels until convergence of the neural network to the target output data.
Claim Rejections - 35 USC § 102
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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 2, 4, 6, 8, 19, and 21 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kalchbrenner (US 20210027425A1).
As per Claim 2, Kalchbrenner teaches a non-transitory computer readable storage medium storing computer executable instructions for use with a computing system, the stored instructions configured to perform operations (embodiments of the subject matter described in this specification can be implemented as computer program instructions encoded on a non-transitory storage medium for execution by data processing apparatus, [0078]) comprising:
Accessing a plurality of images that include a first image in the first resolution (sample the initial low-resolution image 108 from a set of low-resolution images, [0028]); dividing the first image into a first plurality of pixel blocks; splitting each one of the first plurality of pixel blocks into a plurality of separate output channels to form target output data; generating, based on one of the plurality of separate output channels, data for a second image that is of the second resolution (image is divided into disjoint group of pixels, [0041], to upscale an image from a given KxK resolution to 2Kx2K resolution, processes the current version having the given resolution using a first CNN and a set of pixel groups specific to the given resolution, the first CNN is configured to generate a first output image, which corresponds to a new pixel group, based on previous pixel groups included in the current image, generates an intermediate version (Kx2K version of the output image) by merging the current version and the first output image according to the predefined grouping, processes the intermediate version using a second CNN to generate a second output image, generates a 2Kx2K version by merging the intermediate version and the second output image according to the predefined grouping, [0042]); generating, from the data for the second image, a plurality of context blocks; splitting the plurality of context blocks into a plurality of separate input channels to form input data; and training a neural network by using the input data until convergence of the neural network to the target output data (during training, the system 100 trains the autoregressive model 102 on a training dataset by adjusting the values of parameters ϴ of the autoregressive model 102 to maximize log P (x; ϴ), since the joint distribution factorizes over pixel groups and scales, the training can be effectively parallelized, i.e., processing of the convolutional neural networks in the autoregressive model 102 can be parallelized during training, therefore, the convolutional neural networks in the model 102 can be trained in a resource and time-efficient manner, [0039], once trained, the autoregressive model 102 upscales the low-resolution image 108, for example by iteratively performing the following operations: obtaining a current version of the output image having a current KxK resolution, i.e., the version of the image from the previous iteration, and processing the current version of the output image using a set of CNNs and a predefined grouping and ordering rule that are specific to the current resolution to generate an updated version of the output image having a 2Kx2K resolution, the above operations are repeatedly performed until a desirable resolution (e.g., NxN) is obtained, [0040], [0060-0062]).
As per Claim 4, Kalchbrenner teaches wherein each of the plurality of separate input channels and the plurality of separate output channels is composed of a plurality of sub-channels (each pixel in a higher resolution image generated by the CNNs, has a respective color value for each channel in a set of multiple color channels, the set of color channels may include cyan, magenta, yellow, black, the color channels in the set are ordered according to a channel order, CMYK order, the first and second convolutional networks take into account the channel order when generating the first output image and the second output image, the process for generating color values for color channels for pixels in the first output image and second output image, [0043]).
As per Claim 6, Kalchbrenner teaches wherein the plurality of separate input channels is four separate channels, and one of the four channels is used to generate the second image [0043].
As per Claim 8, Kalchbrenner teaches wherein the operations further comprise selecting a second plurality of pixel blocks from the data for the second image, each one of the plurality of pixel blocks including data for multiple pixels, wherein each one of the plurality of context blocks is based on a corresponding one of the second plurality of pixel blocks [0039, 0040, 0060-0062].
As per Claims 19 and 21, these claims are each similar in scope to Claim 2, and therefore are rejected under the same rationale.
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 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) 3, 5, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kalchbrenner (US 20210027425A1) in view of Park (US 20200219233A1).
As per Claim 3, Kalchbrenner is relied upon for the teachings as discussed above relative to Claim 2.
However, Kalchbrenner does not teach wherein the second resolution is less than the first resolution. However, Park teaches wherein the second resolution is less than the first resolution (AI down-scaler 612 may divide the frames included in the original image 105 into a certain number of groups, and independently determine the down-scaling target for each group, [0184], AI down-scale denotes a process of decreasing resolution of an image based on AI, [0075]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kalchbrenner so that the second resolution is less than the first resolution because Park suggests that this way, the image is downscaled so that the image can be transmitted more quickly [0184].
As per Claim 5, Kalchbrenner teaches wherein each one of the plurality of sub-channels corresponds to a different color value [0043].
As per Claim 20, Claim 20 is similar in scope to Claim 3, and therefore is rejected under the same rationale.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kalchbrenner (US 20210027425A1).
Kalchbrenner teaches dividing into G groups of T pixels each [0037]. It would have been obvious to one of ordinary skill in the art that the number of G groups can be any number desired by the user, and thus can be four. Thus, Kalchbrenner teaches wherein each content block is divided into four separate input channels [0037] and each one of the plurality of separate input channels includes multiple sub-channels [0043].
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kalchbrenner (US 20210027425A1) in view of Dinerstein (US 20200356827A1).
Claim 11 is similar in scope to Claims 2-3, except that it has the additional limitation wherein the plurality of images includes multiple images of different target resolutions. However, Kalchbrenner does not teach wherein the plurality of images includes multiple images of different target resolutions. However, Dinerstein teaches wherein the plurality of images includes multiple images of different target resolutions (CNN receives the pair of images in a plurality of resolution levels, the CNN generates a pair of feature maps that correspond to each image of the pair of images at each level of resolution, concatenates the pair of feature maps at each level of resolution into a single feature map and processes the single feature map to produce a new feature map with downscaled spatial resolution, [0008]). Thus, Claim 11 is rejected under the same rationale as Claims 2-3 along with this additional teaching from Dinerstein.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kalchbrenner so that the plurality of images includes multiple images of different target resolutions because Dinerstein suggests that this is useful because a device can have multiple images of different resolutions, and thus this process can be performed on multiple images of different resolutions [0008].
Claim(s) 13 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kalchbrenner (US 20210027425A1) in view of Cox (US 20210092462A1).
As per Claim 13, Kalchbrenner is relied upon for the teachings as discussed above relative to Claim 2.
However, Kalchbrenner does not teach wherein the operations further comprise: a transceiver configured to receive, from a plurality of different computing devices, a plurality of different requests for neural networks usable by video games, receiving a plurality of different requests for neural networks usable by video games; for each corresponding request, selecting, from among a plurality of different trained neural network, at least one of the trained neural network(s) based data included the corresponding request; and communicating, via the transceiver, the selected at least one of the trained neural networks to a requesting computing device. However, Cox teaches wherein the operations further comprise: a transceiver configured to receive, from a plurality of different computing devices (112a, 112b, 112c, Fig. 1), a plurality of different requests (viewer/broadcaster interface 240 can receive a request for content received from the viewer 112, [0050], server 108 may be used to represent all of the types of cloud computing systems that provide a service to assist in the delivery of content to viewers 112a, 112b, 112c, etc., [0032]) for neural networks (machine learning (ML) transformer 220 is operable to obtain or retrieve a ML model from the file server 108, the ML transformer 220 may then change the video or other content being provided to the viewer 112 into a higher resolution, [0044], the ML model is applied in a super-resolution convolutional neural network to conduct hardware acceleration on the content to improve the resolution of the content, [0150]) usable by video games (model library 120 can include information or machine learning models, associated with content provided to the filer server 108, which may be provided to the viewer 112 to allow the viewer 112 to provide resolution upscaling (with a hardware-accelerated super-resolution CNN) to improve the visual quality of reduced-resolution video at the viewer 112, model library 120 may store information about the models, the information can include the configuration of the application (game being played, character being played in game, level of game, version of game, etc.) associated with the content, [0038], if a video is the be sent, the type of content 416 can include the type of game being recorded, levels being played in the game, etc., in this way, the file server 108 can send a model to the viewer 112 if the viewer 112 is unable to receive a higher resolution bit-stream, [0061]), receiving a plurality of different requests [0050, 0032] for neural networks [0044, 0150] usable by video games [0038, 0061]. Cox teaches the model library 120 includes machine learning models, associated with content provided to the file server 108, which is provided to the viewer 112 to allow the viewer 112 to provide resolution upscaling (with a hardware-accelerated super-resolution CNN) to improve the visual quality of reduced-resolution video at the viewer 112. The model library 120 includes ML models generated on similar content to that provided to the viewer 112. The provided model from the model library 120 allows the viewer 112 to compensate for lower resolution content [0038]. Thus, it would have been obvious to one of ordinary skill in the art that for each corresponding request, selecting, from among a plurality of different trained neural network (ML models), at least one of the trained neural network(s) (ML models) based data included the corresponding request; and communicating, via the transceiver, the selected at least one of the trained neural networks to a requesting computing device [0050, 0038].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kalchbrenner so that the operations further comprise: a transceiver configured to receive, from a plurality of different computing devices, a plurality of different requests for neural networks usable by video games, receiving a plurality of different requests for neural networks usable by video games; for each corresponding request, selecting, from among a plurality of different trained neural network, at least one of the trained neural network(s) based data included the corresponding request; and communicating, via the transceiver, the selected at least one of the trained neural networks to a requesting computing device because Cox suggests that for game streaming services [0001], generating multiple video qualities in real-time is computationally intensive and requires specialized hardware or dedicated hardware transcoders to keep streaming at low latency, and thus, having multiple streams received by a server for provision to viewers, and the viewers can employ a machine learning co-processor that can improve the inbound content, if that content is provided at a lower resolution, will reduce the computational intensity and will not require specialized hardware or dedicated hardware transcoders to keep streaming at low latency [0002, 0003].
As per Claim 15, Kalchbrenner does not teach wherein data included the corresponding request indicates a target resolution. However, Cox teaches wherein data included the corresponding request indicates a target resolution (the user to select which content and which resolution the user desires to receive, the viewer 112 can then retrieve the selected stream, [0070]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kalchbrenner so that data included the corresponding request indicates a target resolution because Cox suggests that this way, the user will retrieve the stream at the target resolution that is desired by the user [0070].
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kalchbrenner (US 20210027425A1) and Cox (US 20210092462A1) in view of Boswell (US 20140274384A1).
Kalchbrenner and Cox are relied upon for the teachings as discussed above relative to Claim 13.
However, Kalchbrenner and Cox do not teach wherein data included the corresponding request is an identifier for a specific video game. However, Boswell teaches wherein data included the corresponding request is an identifier for a specific video game (transmit, to server 112, a message 220 comprising an identifier of the interactive video game selected by the user, [0042]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kalchbrenner and Cox so that data included the corresponding request is an identifier for a specific video game because Boswell suggests that this is an efficient way to retrieve data corresponding to the specific video game desired by the user [0042].
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kalchbrenner (US 20210027425A1) in view of Hasler (see citation below).
Kalchbrenner is relied upon for the teachings as discussed above relative to Claim 2.
However, Kalchbrenner does not teach wherein the trained neural network includes a plurality of separable block transform (SBT) terms over a plurality of layers of the trained neural network. However, Hasler teaches wherein the trained neural network includes a plurality of separable block transform (SBT) terms (p. 28, 2nd paragraph) over a plurality of layers of the trained neural network (Figure 15, p. 16).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kalchbrenner so that the trained neural network includes a plurality of separable block transform (SBT) terms over a plurality of layers of the trained neural network because Hasler suggests that a separable approach significantly reduces the computation as well as resulting communication (Figure 23, p. 23).
Allowable Subject Matter
Claim 7 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 9, 12, 17, and 18 are rejected under double patenting, but would be allowable if a terminal disclaimer is filed and the claims are rewritten in independent form including all of the limitations of the base claim and any intervening claims, and Claims 12, 17, and 18 are rewritten to overcome the objections discussed above.
The following is a statement of reasons for the indication of allowable subject matter:
The prior art taken singly or in combination do not teach or suggest the combination of all the limitations of Claim 7 and base Claim 2, and in particular, do not teach wherein the size of each of the plurality of pixel blocks is the same as the size of each one of the plurality of context blocks.
The prior art taken singly or in combination do not teach or suggest the combination of all the limitations of Claim 9 and base Claim 2, and in particular, do not teach wherein context data is added to each pixel block to create a corresponding context block.
The prior art taken singly or in combination do not teach or suggest the combination of all the limitations of Claim 12 and base Claim 2, and in particular, do not teach wherein the plurality of images includes multiple images that are generated by different game engines of different video games, each of the multiple images having a resolution, wherein the operations further comprise: (a2) dividing one of the multiple images into a first plurality of pixel blocks; (b2) splitting each one of the first plurality of pixel blocks into a plurality of separate output channels to form target output data; (c2) generating, based on one of the plurality of separate output channels, a second image that is at a resolution that is lower than the resolution of the of the one of the multiple images; (d2) generating, from the second image, a plurality of context blocks; (e2) splitting the plurality of context blocks into a plurality of separate input channels; and (f2) training another neural network by using the plurality of separate input channels until convergence of the neural network to the target output data.
The prior art taken singly or in combination do not teach or suggest the combination of all the limitations of Claim 17 and base Claim 2 and intervening Claim 16, and in particular, do not teach pruning the trained neural network by removing at least on SBT term of the plurality of SBT terms, wherein the pruned trained neural network is communicated to a plurality of computing devices for use thereon. Claim 18 depends from Claim 17, and therefore also contains allowable subject matter.
Prior Art of Record
Hasler, Jennifer; Analog Architecture Complexity Theory Empowering Ultra-Low Power Configurable Analog and Mixed Mode SoC Systems; January 2019; Journal of Low Power Electronics and Applications; 9, 4; p. 1, 16, 23, 28; https://doi.org/10.3390/jlpea9010004
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONI HSU whose telephone number is (571)272-7785. The examiner can normally be reached M-F 10am-6:30pm.
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, Kee Tung can be reached at (571)272-7794. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JH
/JONI HSU/Primary Examiner, Art Unit 2611
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