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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/05/2026 has been entered.
Response to Arguments
Applicant's arguments filed on 01/05/2026 have been fully considered but they are not persuasive.
“Applicant submits that the cited references fail to disclose or suggest each and every feature of the pending claims. Claim 1, as amended …”
Examiner notes that the amended claim languages is addressed in the updated reasons for rejection below. Examiner suggests clarifying and elaborating on the claim language particular to the definition of different layers as structures and functions performed by the claims, and with respect to selection functions that are performed by the claims. See reasons for rejection and claim construction below.
Applicant explains: “As such, in the claimed approach, "such selection in certain layer does not cover areas of the original feature map covered by other layers," which provides the non-obvious advantage of being "particularly efficient in terms of coding overhead" (present application, para. [0024]). Redundant signaling of different-resolution information for the same picture region is explicitly avoided, …”
Examiner notes that this feature and benefit is not made clear by the claim language. It is also not clear that prior art requires redundant coding of the feature maps in the bitstream and that coding each feature map only once was not a known solution in the art. Please clarify and update the claim language based on the comments in the reasons for rejection below.
Applicant argues: “By contrast, in Golinski, different convolution kernels are applied at different layers to the entire image.”
Examiner notes that the claim appear to imply that signals are processed by multiple layers regardless of any selection taking place for purposes of encoding. Also, redundant processing does not imply redundant signaling, which is seen as undesirable by both the Specification and the cited prior art. Examiner suggests clarifying the intended process in the claims.
Information Disclosure Statement
Given the large number of references amounting to thousands of pages submitted with the Information Disclosure Statements in this case, without citation to relevant portions or explanation of relevance to the present claims, only a cursory consideration has been afforded to this disclosure. Consideration by the examiner of the information submitted in an IDS means nothing more than considering the documents in the same manner as other documents in Office search files are considered by the examiner while conducting a search of the prior art in a proper field of search. The initials of the examiner placed adjacent to the citations on the PTO/SB/08 or its equivalent mean that the information has been considered by the examiner to the extent noted above. See MPEP 609.
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 1, 3-4, 8-9, 12, 17, 23, 25-26, and 28 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.
Claims 1, 3-4, 8-9, 12, 17, 23, 25-26, and 28 recites the limitation “processing the data includes processing by each layer j … obtaining as layer input a feature map processed by the (j-1)-th layer … excluding, from selection in feature maps processed by layers k, … wherein k is an integer equal to or larger than 1 and k<j;” There is insufficient antecedent basis for this limitation in the claim. The claim provides antecedent basis for processing by each layer j. However, “(j-1) [and] k is an integer equal to or larger than 1 and k<j” are not layer j, and references to including or excluding the results of these processes lack antecedent basis in the claims. It is not clear as to what processes are included in the claim and what processes are excluded, and how they are related to information inserted into the bitstream.
Examiner suggests clarifying these features in the claims
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.
Claims 1, 3-4, 8-9, 12, 17, 23, 25-26, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210281867 to Golinski (“Golinski”) in view of US 20150256828 to Doug (“Doug”), and in view of US 20210365716 to Li (“Li”).
Regarding Claim 1: “A method for encoding data for image or video processing into a bitstream, the method comprising:
obtaining data to be encoded; (For example, “obtaining, by an encoder portion of a neural network system, an input video frame … the DCN 200 may be presented with an image” Golinki, Paragraphs 33, 82.)
processing the data, wherein processing the data comprises, in a plurality of cascaded layers, generating feature maps, (“The second set of feature maps 220 may be further convolved via one or more subsequent convolutional layers [cascading layers] (not shown) to generate one or more subsequent sets of feature maps (not shown).” Golinski, Paragraph 83 and Fig. 2D.)
each feature map comprising a respective resolution, wherein the resolutions of at least two of the feature maps differ from each other; (“The first set of feature maps 218 may be subsampled [lower resolution] by a max pooling layer (not shown) to generate a second set of feature maps 220. The max pooling layer reduces the size of the first set of feature maps 218.” Golinski, Paragraph 83 and Fig. 2D.)
wherein the processing the data includes processing by each layer j of the plurality of cascaded layers including: (“the output of a first layer of artificial neurons becomes an input to a second layer of artificial neurons, the output of a second layer of artificial neurons becomes an input to a third layer of artificial neurons, and so on,” thus forming cascaded layers. See, Golinski, Paragraphs 69, 82, 91.)
obtaining as layer input the data to be encoded if j=1, and otherwise obtaining as layer input a feature map processed by the (j-1)-th layer; (The alternative of this element is not clear since input feature map is also data to be encoded. Under the broadest reasonable interpretation consistent with the specification and ordinary skill in the art, the input data to be encoded can be a feature map or an image. See Specification, Paragraph 9. Prior art teaches: “The first set of feature maps [i.e. j=1] 218 may be subsampled [lower resolution] by a max pooling layer (not shown) to generate a second set of feature maps 220. The max pooling layer reduces the size of the first set of feature maps 218.” Golinski, Paragraph 83 and Fig. 2D. See a similar application in Li, Paragraphs 46, 91.)
processing the obtained layer input by performing downsampling to obtain a downsampled feature map; and … outputting the downsampled feature map for the layer j; (The results of the convolution filters, “The first set of feature maps 218 may be subsampled [lower resolution] by a max pooling layer (not shown) to generate a second set of feature maps 220. The max pooling layer reduces the size of the first set of feature maps 218.” Golinski, Paragraph 83 and Fig. 2D.
Cumulatively, note that the convolution filters may also be used to simultaneously downsample the feature map as noted in Li, Paragraph 46 and consistent with Golinski, Paragraph 79. Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to supplement the teachings of Golinski and Doug to downsample the feature map using a convolution filter as taught in Li, in order to “to reduce computational load and parameters” Li, Paragraph 47 and Golinski, Paragraph 83.)
selecting information for inserting into the bitstream, … the information relating to a first area in a feature map processed by a layer j> 1, wherein the first area corresponds to an area in the feature map or initial data to be encoded in a layer smaller than j that includes a plurality of elements; (For example, the selecting information can be “video data according to a video coding Standard to generate an encoded video bitstream. … For each CU [selected area], a prediction mode may be signaled inside the bitstream using syntax data. … partitioning information, and any other suitable data, such as other syntax data. … The information from a previous time step t-1 … and/or previous recurrent state(s) from the neural network 438” which are all related to a first area in a feature map by one mechanism or another. Golinski, Paragraphs 100, 103, 109, and 128 and similarly in Doug, Paragraph 24.)
excluding, from selection in feature maps processed by layers k, areas that correspond to the first area from being selected, wherein k is an integer equal to or larger than 1 and k<j; and (First, this element does not clearly limit the claim to performing particular steps. On one hand, this element suggests including for selection feature maps processed by layers j>k and excluding feature maps from layers between 1 and k. However, the claim does not define these layers, does not require these layers to perform any particular processing or function, does not uses the information from these layers to make the bitstream, and does not actually perform the selection of feature maps. So first, this element is rejected for reasons stated above, because it does not limit the claims to performing a particular function.
Cumulatively, this element is rejected because prior art teaches: “The results of the simple operations performed on the input data are selectively passed on to other neurons,” thus prior art allow for selecting which layers pass the data on to other layers and which do not. Golinski, Paragraphs 67-69.)
generating the bitstream by inserting into the bitstream information related to the selected information.” (Under the broadest reasonable interpretation consistent with the specification and ordinary skill in the art, the inserted information may be the selected information, or it may be related information such as information relating to a first area, information that defines the first area, a flag related to the coding of the first area, and so on. As one example of such information, prior art teaches: “an encoding device encodes video data according to a video coding Standard to generate an encoded video bitstream. … For each CU [selected area], a prediction mode may be signaled inside the bitstream using syntax data.” Golinski, Paragraphs 100, 103.
Cumulatively, this information can also be “partitioning information, and any other suitable data, such as other syntax data. … The information from a previous time step t-1 … and/or previous recurrent state(s) from the neural network 438” Golinski, Paragraphs 103, 109, and 128.
Doug broadly teaches that such data and syntax are ordinarily included in the bitstream in the context of using the scalable coding features of the HEVC video standard: “The encoded indices and the encoded filter coefficients may be transmitted together in a video bitstream … the parameters that may be used in the processing ( e.g., any upsampling filters that may be used),” Doug, Paragraph 24.
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to supplement the teachings of Golinski to insert the above claimed information into the bitstream as taught in Doug, in order “to form a scalable bitstream” that includes the reference “recurrent state(s) from the neural network.” See Doug, Paragraph 24 and Golinski, Paragraph 128.)
Finally, in reviewing the present application, there does not seem to be objective evidence that the claim limitations are particularly directed to: addressing a particular problem which was recognized but unsolved in the art, producing unexpected results at the level of the ordinary skill in the art, or any other objective indicators of non-obviousness.)
Regarding Claim 3: “The method according to claim 2, wherein downsampling comprises average pooling or max pooling.” (“The first set of feature maps 218 may be subsampled by a max pooling layer (not shown) to generate a second set of feature maps 220.” Golinski, Paragraph 83.)
Regarding Claim 4: “The method according to claim 3, wherein downsampling for a given layer j comprises
downsampling an input feature map using a first filter to obtain a first feature map, and downsampling the input feature map using a second filter to obtain a second feature map, (“In the present example, because four different feature maps are generated in the first set of feature maps 218, four different convolutional kernels were applied to the image 226 at the convolutional layer 232. The convolutional kernels may also be referred to as filters or convolutional filters.” Golinski, Paragraph 82.)
the method further comprising: determining a third cost resulting from reconstructing a portion of a reconstructed picture using the first feature map; and (For example, “The first loss function [representing cost] determines a loss between one or more input video frames and one or more warped reconstructed video frames. For instance, the first loss function can include any of the loss functions Loss1 , Loss2, Loss3, and/or Loss4 described above.” Golinski, Paragraph 212. This corresponds to the example in the original Claim 15.)
determining a fourth cost resulting from reconstructing the portion of the reconstructed picture using the second feature map; (For example, “The first loss function [representing cost] determines a loss between one or more input video frames and one or more warped reconstructed video frames. For instance, the first loss function can include any of the loss functions Loss1 , Loss2, Loss3, and/or Loss4 described above.” Golinski, Paragraph 212. This corresponds to the example in the original Claim 15.)
wherein selecting the information comprises selecting the first feature map based on the third cost being [smaller] than the fourth cost.” (“trained to reduce (e.g., minimize) a difference [cost or loss] between the input data 440 and the representation 446 of the input data 440 over a training set ( e.g., a training set of input images and output images). In some implementations, the first state data 450 matches the second state data 452 (e.g., the first state data 450 is the same as the second state data 452). In other implementations the first state data 450 can differ from the second state data 452.” Golinski, Paragraph 127. . “In some cases, the loss term Loss1 can be used on the encoder side rather than the Loss2. … The same concept can be used on the decoder side using Loss3 or Loss4 to train the decoder …” Golinski, Paragraphs 180-181.)
Golinski does not explicitly discuss comparison of costs “the third cost being smaller than the fourth cost” when selecting the feature map and the coding modes, however this is a fundamental operation of video coding to select the best coding mode (with the lowest cost function) for video compression.
Doug describes this function in the context of selecting the best layer and filter for video coding under the industry standards: “Similar to picture/slice-level adaptation, at the block level, the one or more (e.g., the best) M upsampling filter candidates may be determined by different means. For example, the up sampling filter that minimizes the [has a smaller] rate-distortion cost for the block may be selected. The distortion between the upsampled base layer block and the corresponding enhancement layer original block may be considered when choosing the best block level filter. Considering distortion and not the bit cost may be faster.” Doug, Paragraph 47.
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to supplement the teachings of Golinski to use the coding option that provides the lower coding cost as taught in Doug, in order to “to compress video data into a form that uses a lower bit rate, while avoiding or minimizing degradations to video quality.” See Golinski, Paragraph 4 and Doug, Paragraphs 4-5.
Regarding Claim 5: “The method according to claim 4, wherein a shape of the first filter and a shape of the second filter may be any of a square, horizontal oriented rectangular, or vertical oriented rectangular.” (“As an example, the convolutional kernel for the convolutional layer 232 may be a 5x5 [square] kernel that generates 28x28 feature maps.” Golinski, Paragraphs 82, 74.)
Regarding Claim 6. “The method according to claim 4, further comprising:
obtaining a mask comprised of flags, wherein the mask represents an arbitrary filter shape; (“As shown in FIG. 12, the one or more postrecurrent layers 573 can output an element wise [flag] mask m, … The mask m, can be a per-pixel mask having a value for each” Golinski, Paragraph 190.)
wherein one of the first filter or the second filter has the arbitrary filter shape.” (“The mask m, is used to mask ( or weight) the contributions of the warped previous reconstructed frame f,(x,_ 1 )” which acts as a filter of the contributions of the previously reconstructed frame. See Golinski, Paragraph 190 and Fig. 12.)
Regarding Claim 8: “The method according to claim 1, wherein the information includes an element of the feature map processed by the layer j>1.” (For example, “The outputs of the convolutional connections may be considered to form a feature map in the subsequent layer, with each element of the feature map ( e.g., 220) receiving input from a range of neurons in the previous layer (e.g., feature maps 218) and from each of the multiple channels.” Golinski, Paragraph 91.)
Regarding Claim 9: “The method according to claim 8, wherein the information includes: … information indicating from which layer the element of the feature map processed by the layer j>1 was selected; and/or from which part of the feature map processed by the layer j>1, the element was selected.” (For example, “The outputs of the convolutional connections may be considered to form a feature map in the subsequent layer, with each element of the feature map ( e.g., 220) receiving input from a range of neurons in the previous layer (e.g., feature maps 218) and from each of the multiple channels.” Golinski, Paragraph 91.)
Regarding Claim 12: “The method according to claim 1, wherein the processing by a layer j of the plurality of cascaded layers comprises:
determining a first cost resulting from reconstructing a portion of a reconstructed picture using a feature map element output by the j-th layer, (For example, “the process 1800 includes training, for one or more training iterations, the neural network system using a first loss function. The first loss function [representing cost] determines a loss between one or more input video frames and one or more warped reconstructed video frames. For instance, the first loss function can include any of the loss functions Loss1 , Loss2, Loss3, and/or Loss4 described above.” Golinski, Paragraph 212. This corresponds to the example in the original Claim 15.
In some examples, the process 1800 includes processing, by a first layer [j-th layer]of the encoder portion of the neural network system, input data. In such examples, the process 1800 includes determining, by the first layer of the encoder portion, a plurality of weight values for the first layer of the encoder portion. In such examples, the process 1800 includes setting weights of a second layer [(j-1)-th layer] of the encoder portion of the neural network system to the plurality of weight values, and processing, by the second layer of the encoder portion,” Golinski, Paragraph 217.)
determining a second cost resulting from reconstructing the portion of the picture using feature map elements output by the (j-1)-th layer; (For example, “the process 1800 includes training, for one or more training iterations, the neural network system using a first loss function. The first loss function [representing cost] determines a loss between one or more input video frames and one or more warped reconstructed video frames. For instance, the first loss function can include any of the loss functions Loss1 , Loss2, Loss3, and/or Loss4 described above.” Golinski, Paragraph 212. This corresponds to the example in the original Claim 15.
In some examples, the process 1800 includes processing, by a first layer [j-th layer]of the encoder portion of the neural network system, input data. In such examples, the process 1800 includes determining, by the first layer of the encoder portion, a plurality of weight values for the first layer of the encoder portion. In such examples, the process 1800 includes setting weights of a second layer [(j-1)-th layer] of the encoder portion of the neural network system to the plurality of weight values, and processing, by the second layer of the encoder portion,” Golinski, Paragraph 217.)
based on the first cost being higher than the second cost, selecting the (j-1)-th layer and selecting information relating to said portion in the (j-1)-th layer.” (“trained to reduce (e.g., minimize) a difference [cost or loss] between the input data 440 and the representation 446 of the input data 440 over a training set ( e.g., a training set of input images and output images). In some implementations, the first state data 450 matches the second state data 452 (e.g., the first state data 450 is the same as the second state data 452). In other implementations the first state data 450 can differ from the second state data 452.” Golinski, Paragraph 127. . “In some cases, the loss term Loss1 can be used on the encoder side rather than the Loss2. … The same concept can be used on the decoder side using Loss3 or Loss4 to train the decoder …” Golinski, Paragraphs 180-181.
See treatment of obviousness of selecting the coding option that provides the lowest cost in Claim 4.)
Regarding Claim 13: “The method according to claim 12, wherein the first cost and the second cost include an amount of data and/or distortion.” (Note that a loss function is based on distortion in Golinski, Paragraph 179. Also see optimization of the amount of data embodied in storage space or bandwidth in Golinski, Paragraph 128. Also note that, the reduction of the loss / distortion above (Golinski, Paragraphs 179-181, 212, 217) is directly related to reduction of the amount of required data to be transmitted. See Golinski, Paragraph 128.)
Regarding Claim 14: “The method according to claim 13, wherein the amount of data includes the amount of data required to transmit the data related to the selected layer.” (“As a result, encoded data can be stored using a reduced amount of storage space, transmitted using a reduced amount of network bandwidth” Golinski, Paragraph 128.)
Regarding Claim 15: “The method according to claim 13, wherein the distortion is calculated by comparing the reconstructed picture with a target picture.” (For example, “the process 1800 includes training, for one or more training iterations, the neural network system using a first loss function. The first loss function [representing cost] determines a loss between one or more input video frames [target pictures] and one or more warped reconstructed video frames [reconstructed pictures]. For instance, the first loss function can include any of the loss functions Loss1 , Loss2, Loss3, and/or Loss4 described above.” Golinski, Paragraph 212.)
Regarding Claim 16: “The method according to claim 1, wherein the data comprises image information and/or prediction residual information and/or prediction information.” (“The information from a previous time step t-1 that can be provided from the decoder portion 436 to the encoder at the next time step t can include one or more of previously reconstructed frame(s) (denoted as x,), previously reconstructed motion estimation(s), previously reconstructed residual(s), and/or previous recurrent state(s) from the neural network 438 of the decoder portion 436.” Golinski, Paragraph 128.)
Regarding Claim 17: “The method according to claim 1, wherein the information for inserting into the bitstream includes prediction information.” (“The information from a previous time step t-1 [prediction information] that can be provided from the decoder portion 436 to the encoder at the next time step t can include one or more of previously reconstructed frame(s) (denoted as x,), previously reconstructed motion estimation(s), previously reconstructed residual(s), and/or previous recurrent state(s) from the neural network 438 of the decoder portion 436.” Golinski, Paragraph 128.)
Regarding Claim 18: “The method according to claim 17, wherein the prediction information includes a reference index and/or a prediction mode.” (“For each CU, a prediction mode may be signaled inside the bitstream using syntax data. A prediction mode may include intra-prediction (or intra-picture prediction) or inter-prediction (or inter-picture prediction). … prediction information ( e.g., prediction modes, motion vectors [motion estimations], block vectors, or the like),” Golinski, Paragraphs 103, 109, and information used in 128.
Regarding Claim 20: “The method according to claim 1, wherein positions of selected and nonselected feature map elements are indicated by a plurality of binary flags based on positions of the plurality of binary flags in the bitstream.” (“As shown in FIG. 12, the one or more postrecurrent layers 573 can output an element wise [flag] mask m, … The mask m, can be a per-pixel mask having a value for each” thus marking the selected or masked feature map elements. Golinski, Paragraph 190. Note similarly: “filters may be signaled using one or more flags or indicators” in Doug, Paragraph 49 and statement of motivation in Claim 1.)
Regarding Claim 21: “The method according to claim 1, wherein the data is a motion vector field.” (“Another type of motion estimation that can performed is an optical flow motion estimation technique 604. … Each optical flow map can include a 2D vector field,” Golinski, Paragraphs 166-167, 172.)
Regarding Claim 22: “The method according to claim 1, wherein processing the data comprises additional convolutional layers between the plurality of cascaded layers with different resolutions.” (“In some implementations, an additional recurrent layer can be provided before the warping engine 576. The additional recurrent layer can be used to reconstruct the motion estimation parameters f,+1 (e.g., the optical flow)” Golinski, Paragraphs 189, 69, 74, and Figs. 5, 7, 10, 15.)
Regarding Claim 23: “The method according to claim 1, wherein processing the data comprises: processing, in different layers of the plurality of cascaded layers, data relating to a same picture segmented into blocks with different block sizes and shapes; and wherein the selecting the information for inserting into the bitstream comprises: selecting the selected layer based on a cost calculated for a predetermined set of coding modes.” (See reasons for rejection in Claim 4. Further it is a fundamental principle of video coding standards that “a mode decision block 180 in the encoder may choose a prediction mode, e.g., the best prediction mode, for example, based on a rate-distortion optimization example.” Doug, Paragraphs 20-21 and statement of motivation in Claim 4.)
Regarding Claim 24: “The method according to claim 23, wherein processing the data further comprises: for at least one layer of the plurality of cascaded layers, determining the cost for different sets of coding modes and selecting one coding mode of the set of coding modes based on the determined cost.” (See reasons for rejection in Claim 4. Further it is a fundamental principle of video coding standards that “In High Efficiency Video Coding (HEVC), … a mode decision block 180 in the encoder may choose a prediction mode, e.g., the best prediction mode, for example, based on a rate-distortion [cost] optimization example. … depending on the one or more intra prediction modes” Doug, Paragraphs 20-21, 28, and statement of motivation in Claim 4.)
Regarding Claim 25: “The method according to claim 24, wherein the information for inserting into the bitstream includes the selected one coding mode.” (See reasons for rejection in Claim 4. Further it is a fundamental principle of video coding standards that “a mode decision block 180 in the encoder may choose a prediction mode, e.g., the best prediction mode, for example, based on a rate-distortion optimization example.” Doug, Paragraphs 20-21 and statement of motivation in Claim 4.)
Regarding Claim 26: “A non-transitory computer-readable medium storing instructions that, when executed on one or more processors, causes a computing device to perform the method of claim 1.” (See reasons for rejection for Claim 1. Further, prior art teaches: “the processes described herein (including process 1800, process 1900, and/or other process described herein) may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) executing collectively on one or more processors, by hardware, or combinations thereof. As noted above, the code may be stored on a computer-readable or machine-readable storage medium,” Golinski, Paragraph 232.)
Claim 28: “A device for encoding data for image or video processing into a bitstream, the device comprising: a processor or processing circuitry configured to: …” is rejected for reasons stated for Claim 1, and because prior art teaches: “the processes described herein (including process 1800, process 1900, and/or other process described herein) may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) executing collectively on one or more processors, by hardware, or combinations thereof. As noted above, the code may be stored on a computer-readable or machine-readable storage medium,” Golinski, Paragraph 232.)
Conclusion
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/MIKHAIL ITSKOVICH/Primary Examiner, Art Unit 2483