DETAILED ACTION
1. This Office Action is sent in response to Applicant’s communication received on 12/03/2024 for application number 19/007,203. The Office herby acknowledges receipt of the following and placed of record in file: Specification, Drawings, Abstract, Oath/Declaration, and claims.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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 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.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 01/22/2025, 09/23/2025 and 11/03/2025 is in accordance with provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Preliminary Amendments
4. The preliminary amendments filed 01/02/2025 has been entered and made of record.
Claim Rejections - 35 USC § 112
5. The following is a quotation of 35 U.S.C. 112(b):
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.
6. The term “tensor” in claim 1,5,9-10 and 13 is a relative term which renders the claim indefinite. The term “tensor” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The wording "tensor" is unclear. It is not clear what kind of data is meant by a tensor, i.e., what properties of the picture are represented
and in which form.
7. The term “collocated tiles” in claim 1 is a relative term which renders the claim indefinite. The term “collocated tiles” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The concept of "collocated tiles" is unclear, since it is not clear how to interpret "location" of the tiles in different tensors. There is no relationship defined between the first and the second "tensor" and it is unclear how these
are related to the input tensor.
8. The term “collocation” in claim 5 is a relative term which renders the claim indefinite. The term “collocation” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The concept of collocation is undefined for tiles.
9. The term “predefined condition” in claim 5 is a relative term which renders the claim indefinite. The term “predefined condition” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The wording "predefined condition" is a vague wording without clear meaning and without clear technical limitation.
10. The term “motion” in claim 5 is a relative term which renders the claim indefinite. The term “motion” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Type of motion is unclear.
Claim Rejections - 35 USC § 102
11. 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.
12. Claim(s) 1-7 and 9-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Huang et al., [US Patent No.: 11,875,248 B2].
Re. Claim 1, Huang et al., [US Patent No.: 11,875,248 B2] discloses:
A method for encoding or decoding a tensor [Fig.1 encoding input data 104], the method comprising: processing, by a neural network that includes at least a first subnetwork [Fig.1 layers are equivalent a subnetwork] and a second subnetwork [Fig.1 a different layer is a equivalent to a second subnetwork], an input tensor representing picture data [Fig.1 input data is image data], wherein the processing comprises: applying the first subnetwork to a first tensor [Fig. 1 different layers 1-3], including spatially dividing the first tensor into a first plurality of tiles and processing the first plurality of tiles by the first subnetwork [Figs 6 and 7, Input data Layer0, Layer0 output see also [0107-0108 for 2D image data, a block may be a stripe or a rectangle of data];
and after applying the first subnetwork, applying the second subnetwork to a second tensor, including spatially dividing the second tensor into a second plurality of tiles and processing the second plurality of tiles by the second subnetwork [Figs 6 and 7: Layer0 output Layer 1];
wherein at least two respective collocated tiles of the first plurality of tiles differ in size relative to at least two respective collocated tiles of the second plurality of tiles [Figs 6 & 7 tiles in different layers are different; see also thus tile in the input data is larger again than tile in the layer 0 output |0091].
Re. Claim 2, Huang discloses:
The method according to claim 1, wherein tiles of the first plurality of tiles that are adjacent in at least one spatial dimension partly overlap [adjacent Tiles overlap in a spatial dimension |Figs. 6 & 7];
and/or wherein tiles of the second plurality of tiles that are adjacent in at least one spatial dimension partly overlap [adjacent Tiles overlap in a spatial dimension |Figs. 6 & 7].
Re. Claim 3, Huang discloses:
The method according to claim 1, wherein tiles of the first plurality of tiles are processed independently by the first subnetwork [splitting data into tiles is used to facilitate a parallel multicore implementation of a convolutional neural network Figs. 6 & 7];
and/or wherein tiles of the second plurality of tiles are processed independently by the second subnetwork [splitting data into tiles is used to facilitate a parallel multicore implementation of a convolutional neural network Figs. 6 & 7].
Re. Claim 4, Huang discloses:
The method according to claim 3, wherein at least two tiles of the first plurality of tiles are processed in parallel by the first subnetwork [splitting data into tiles is used to facilitate a parallel multicore implementation of a convolutional neural network Figs. 6 & 7];
and/or wherein at least two tiles of the second plurality of tiles are processed in parallel by the second subnetwork [splitting data into tiles is used to facilitate a parallel multicore implementation of a convolutional neural network Figs. 6 & 7].
Re. Claim 5, Huang discloses:
The method according to claim 1, wherein: dividing the first tensor includes determining sizes of tiles in the first plurality of tiles based on a first predefined condition [determining tile size based on a condition such as available hardware resources |0120];
and/or dividing the second tensor includes determining sizes of tiles in the second plurality of tiles based on a second predefined condition [If the size is greater than the threshold, splitting the input data for the first layer group may comprise splitting it into the first tile, the second tile and a third tile. |Col 4 Lines 15-20];
and wherein the first predefined condition and/or the second predefined condition is based on available decoder hardware resources and/or motion present in the picture data [the input data may be split into tiles in the Y dimension. Alternatively, the input data may be stored column-first (ordered according to the Y dimension first), and it may be split into tiles along the X dimension. |Col 5 lines 65-66 and Col 6 Line 1-7].
Re. Claim 6, Huang discloses:
The method according to claim 1, wherein the first subnetwork performs processing by one or more layers including at least one convolutional layer and at least one pooling layer [a layer group may comprise any one or any two or more of: a single convolutional layer, a single pooling layer, a single activation layer, a single normalization layer, and a single layer of element-wise operations. |Col 2 Lines 34-49];
and/or wherein the second subnetwork performs processing by one or more layers including at least one convolutional layer and at least one pooling layer [a layer group may comprise any one or any two or more of: a single convolutional layer, a single pooling layer, a single activation layer, a single normalization layer, and a single layer of element-wise operations. |Col 2 Lines 34-49].
Re. Claim 7, Huang discloses:
The method according to claim 1, wherein the first subnetwork [layer 1] and the second subnetwork [layer 2] perform respective processing that is a part of picture or moving picture compression [Figs 6 and 7 subnetworks comprise layers].
Re. Claim 9, Huang discloses:
The method according to claim 1, further comprising: generating a bitstream by including into the bitstream an output of the processing by the neural network [the splitting may be planned before a training phase or inference phase of the neural network begins. | Col 2 Lines 64-
67 & Col 3 Lines 1-5];
and including into the bitstream an indication of size of the tiles in the first plurality of tiles and/or an indication of size of the tiles of the second plurality of tiles [The splitting may be planned in advance based on the parameters of the layers in the various layer groups, and the tensor size at the input and output of each layer. Col 2 Lines 64- 67 & Col 3 Lines 1-5].
Re. Claim 10, Huang discloses:
The method according to claim 1, further comprising: extracting the input tensor from a bitstream for the processing by the neural network [Fig.1 Each core may comprise a plurality of processing elements configured to process input data to evaluate a layer of the neural network. The cores may be configured to operate in parallel].
Re. Claim 11, Huang discloses:
The method according to claim 10, wherein the second subnetwork performs picture post-filtering [Fig. 3 Each accumulator 304 receives the output of one convolution engine 302 and adds the output to the previous convolution engine output that relates to the same filter. Since the convolution engine may not generate or produce outputs that relate to the same filter in consecutive cycles the partial results of one or more filters may be stored in an accumulation buffer 306 and then the appropriate partial result may be provided to the accumulator each cycle by the accumulation buffer 306.];
and wherein for at least two tiles of the second plurality of tiles one or more parameters of post-filtering differ and are extracted from the bitstream [Figs 6 & 7 tiles in different layers are different; see also thus tile in the input data is larger again than tile in the layer 0 output |0091].
Re. Claim 12. Huang discloses:
The method according to claim 10, further comprising: parsing from the bitstream an indication of tile size of the first plurality of tiles and/or an indication of tile size of the second plurality of tiles [The tile sizes may be selected so that the size of the leading tile in the final layer group is smaller than some or all of the other tiles in the final layer group. |Col 3 Lines 25-41].
Re. Claim 13, This claim is interpreted and rejected for the same reason set forth in claim 1, including a non-transitory computer-readable medium having processor-executable instructions for encoding or decoding a tensor [There is also provided a non-transitory computer readable storage medium having stored there on a computer readable description of an artificial intelligence accelerator system as claimed in claim 18 that, when processed in an integrated circuit manufacturing system, causes the integrated circuit manufacturing system to manufacture an integrated circuit embodying the artificial intelligence accelerator system. |Col 8 Lines 40-59], wherein the processor-executable instructions…etc. [Processor |Col 29 51-60].
Re. Claim 14, Huang discloses:
The non-transitory computer-readable medium according to claim 13, wherein tiles of the first plurality of tiles that are adjacent in at least one spatial dimension partly overlap [adjacent Tiles overlap in a spatial development |Figs. 6 & 7];
and/or wherein tiles of the second plurality of tiles that are adjacent in at least one spatial dimension partly overlap [adjacent Tiles overlap in a spatial dimension |Figs. 6 & 7].
Re. Claim 15, Huang discloses:
The non-transitory computer-readable medium according to claim 13, wherein tiles of the first plurality of tiles are processed independently by the first subnetwork [splitting data into tiles is used to facilitate a parallel multicore implementation of a convolutional neural network Figs. 6 & 7];
and/or wherein tiles of the second plurality of tiles are processed independently by the second subnetwork [splitting data into tiles is used to facilitate a parallel multicore implementation of a convolutional neural network Figs. 6 & 7].
Re. Claim 16, Huang discloses:
The non-transitory computer-readable medium according to claim 15, wherein at least two tiles of the first plurality of tiles are processed in parallel by the first subnetwork [splitting data into tiles is used to facilitate a parallel multicore implementation of a convolutional neural network Figs. 6 & 7]; and/or wherein at least two tiles of the second plurality of tiles are processed in parallel by the second subnetwork [splitting data into tiles is used to facilitate a parallel multicore implementation of a convolutional neural network Figs. 6 & 7].
Re. Claim 17, This claim is interpreted and rejected for the same reason set forth claim 1 and 13.
Re. Claim 18, Huang discloses:
The processing apparatus according to claim 17, wherein tiles of the first plurality of tiles that are adjacent in at least one spatial dimension partly overlap [adjacent Tiles overlap in a spatial dimension |Figs. 6 & 7];
and/or wherein tiles of the second plurality of tiles that are adjacent in at least one spatial dimension partly overlap [adjacent Tiles overlap in a spatial dimension |Figs. 6 & 7].
Re. Claim, 19, Huang discloses:
The processing apparatus according to claim 17, wherein tiles of the first plurality of tiles are processed independently by the first subnetwork [splitting data into tiles is used to facilitate a parallel multicore implementation of a convolutional neural network Figs. 6 & 7];
and/or wherein tiles of the second plurality of tiles are processed independently by the second subnetwork [splitting data into tiles is used to facilitate a parallel multicore implementation of a convolutional neural network Figs. 6 & 7].
.
Re. Claim 20, Huang discloses:
The processing apparatus according to claim 19, wherein at least two tiles of the first plurality of tiles are processed in parallel by the first subnetwork [splitting data into tiles is used to facilitate a parallel multicore implementation of a convolutional neural network Figs. 6 & 7];
and/or wherein at least two tiles of the second plurality of tiles are processed in parallel by the second subnetwork [splitting data into tiles is used to facilitate a parallel multicore implementation of a convolutional neural network Figs. 6 & 7].
Claim Rejections - 35 USC § 103
13. 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.
14. Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang in view of Kianfar et al., [US Pub. No.: 2021/0329267 A1].
Re. Claim 8, Huang discloses:
The method according to claim 7, wherein the first subnetwork and/or the second subnetwork perform one of: picture encoding by a convolutional subnetwork [A convolution layer is configured to convolve the input data using weights associated with that layer. |Col 10 Lines 59-64];
or picture filtering [each convolution layer is associated with a plurality of weights w.sub.1 . . . w.sub.i, which may also be referred to as filter weights or coefficients |Col 10 Lines 59-64].
In the same field endeavor Kianfar [US Pub. No.: 2021/0329267 A1] discloses:
rate distortion optimization quantization (RDOQ) [ a computing system (which may include video encoder 200) may train neural network 211. As part of training neural network 211, the computing system may gather training labels using a heuristic search. The computing system may use the training labels as training targets in a training process for the neural network. In some examples, neural network 211 is trained to imitate a rate-distortion optimization algorithm that performs a brute force search over all possible quantization levels of the transform coefficients of the block.| Figs 6A and B, 0015, 0016, 0186];
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine Huang with Kianfar to train one or more classes of neural networks (e.g., a fully neural convolutional network and an autoregressive model) and may evaluate each as a post-quantization step designed to improve upon quantization schemes such as scalar quantization (SQ) [0070].
15. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20250150589 A1
US 20250173557 A1
US 20240283942 A1
US 20230106778 A1
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HOWARD D BROWN JR whose telephone number is (571)272-4371. The examiner can normally be reached Monday - Friday 7:30AM - 5:00PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sathyanarayanan Perungavoor can be reached at 5712727455. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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HOWARD D. BROWN JR
Primary Examiner
Art Unit 2488
/HOWARD D BROWN JR/Examiner, Art Unit 2488
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