DETAILED ACTION
This Action is responsive to Claims filed 09/13/2024.
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 statement(s) (IDS) submitted were filed before the mailing date of the first Action on the Merits. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) are being considered by the examiner.
Drawings
Receipt of Drawings filed 09/13/2024 is acknowledged. These Drawings are acceptable.
Status of the Claims
Claims 1-23 are currently pending.
Claim Objections
Claims 10-22 objected to because of the following informalities:
Claims 10-12 claim a different method than that of Claim 1, although they depend on Claim 1. Rewording Claim 10 in independent form would improve clarity.
Claims 13-15 claim a different method than that of Claim 1, although they depend on Claim 1. Rewording Claim 13 in independent form would improve clarity.
Claims 16 claims a different method than that of Claim 1, although it depends on Claim 1. Rewording Claims 16 in independent form would improve clarity.
Claim 17 claims a different method than that of Claim 1, although it depends on Claim 1. Rewording Claims 17 in independent form would improve clarity.
Claims 18-22 claim various apparatuses, although they depend on previous method claims. Rewording these claims in independent forms for consistency between the Claims and their statutory categories would improve clarity.
Appropriate correction is advised.
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 9-10, 13-17, 19, and 21 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 9-10 and 13-17 recite “…providing an entropy…” and how that “…providing…” is performed by performing the steps of Claim 1. It is unclear from the claims or the instant Specification how performing the aforementioned steps of Claim 1 “provide” a model, under the BRI of “providing…”
The Claims dependent on Claims 9-10 and 13-17 do not rectify this ambiguity, and are therefore similarly rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 23 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the recitation of “A neural network…” does not recite sufficient structure or implementation to preclude the claimed neural network from being software per se.
Claims 1-22 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1: All Claims
Claims 1-9 recite a method of operating a neural network, which falls under the statutory category of a process. Claims 10-12 depend on Claim 1, but recite a method of encoding data, which falls under the statutory category of a process. Claims 13-15 depend on Claim 1, but recite a method of decoding encoded data, which falls under the statutory category of a process. Claim 16 depends on Claim 1, but recites a method of encoding at least a portion of an image, which falls under the statutory category of a process. Claim 17 depends on Claim 1, but recites a method of reconstructing at least a portion of an image, which falls under the statutory category of a process. Claim 18 depends on Claim 1, but recites a computer program product comprising a program code stored on a non-transitory computer-readable medium, which falls under the statutory category of a manufacture. Claim 19 depends on Claim 1, but recites an apparatus for encoding data, which falls under the statutory category of a machine. Claim 20 depends on Claim 1, but recites an apparatus for encoding at least a portion of an image, which falls under the statutory category of a machine. Claim 21 depends on Claim 1, but recites an apparatus for decoding data, which falls under the statutory category of a machine. Claim 22 depends on Claim 1, but recites an apparatus for decoding at least a portion of an encoded image, which falls under the statutory category of a machine.
Step 2A – Prong 1: Claim 1
Claim 1 recites an abstract idea, law of nature, or natural phenomenon. The limitations “defining an integer lower threshold value and an integer upper threshold value for values of integer numbers comprised in data entities of input data for the at least one neural network layer;”, “clipping, based on a value of a respective integer number comprised in a data entity of the input data being smaller than the defined integer lower threshold value, the value of the respective integer number comprised in the data entity of the input data to the defined integer lower threshold value,”, and “and/or clipping, based on a value of a respective integer number comprised in a data entity of the input data being larger than the defined integer upper threshold value, the value of the respective integer number comprised in the data entity of the input data to the defined integer upper threshold value such that integer overflow of the accumulator register is avoided.” under the broadest reasonable interpretation, cover a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. These limitations therefore fall within the mental process group.
Generically defining a threshold is practically performed within the human mind or with the aid of pen and paper. Generically clipping data one way or another based on the threshold is practically performed within the human mind or with the aid of pen and paper.
Step 2A – Prong 2:
The additional elements of claim 1 do not integrate the abstract idea into a judicial exception. The claim recites the additional elements “A method”, “register”, and “summation results” are recognized as a generic computer components recited at a high level of generality. Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
The additional elements “a neural network” and “a neural network layer” are recognized as non-generic computing components, but are recited at a level of generality so as to merely link the claims to a particular technological area or field of use (See MPEP 2106.05(h)).
The additional element “buffering summation results” is recognized as mere pre- or post-extra solution data gathering or transmittal (See MPEP 2106.05(g)).
Step 2B:
The only limitation on the performance of the described method is limitations reciting “A method”, “register”, and “summation results”. These elements are insufficient to transform a judicial exception to a patentable invention because the recited element is considered insignificant extra-solution activity (generic computer system, processing resources, links the judicial exception to a particular, respective, technological environment). The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components; mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)).
The additional elements “a neural network” and “a neural network layer” are recognized as non-generic computing components, but are recited at a level of generality so as to merely link the claims to a particular technological area or field of use (See MPEP 2106.05(h)).
The additional elements “buffering summation results” are found to be generally data transmittal and are acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(I)(ii)).
Taken alone or in ordered combination, these additional elements do not amount to significantly more than the above-identified abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Dependent Claims:
Claim 2 recites an abstract idea mental process step “scaling data entities of the input data by a first scaling factor to obtain scaled values of the data entities of the input data.”
Claim 3 recites an abstract idea mental process step “rounding the scaled values of the data entities of the input data to respective closest integer values to obtain values of the integer numbers comprised in the data entities of the input data.”
Claim 4 recites abstract idea mental process steps “dividing the output data entities by a third scaling factor,”, “dividing the outputs of the activation function by a third scaling factor,”, “factorizing a third scaling factor into a first part and a second part, dividing the output data entities by the first part of the factorized third scaling factor to obtain partially un-scaled output data entities,”, and “dividing the outputs of the activation functions by the second part of the factorized third scaling factor.”
The additional elements “processing the input data by the at least one neural network layer to obtain output data comprising output data entities…” is found to mere instructions to apply the abstract idea mental process steps of Claim 1 and/or 2 (See MPEP 2106.05(f)).
Claim 5 recites refinements to the threshold.
Claim 6 recites refinements to the threshold.
Claim 7 recites refinements to the neural network additional element.
Claim 8 recites refinements to the neural network additional element.
Claim 9 recites abstract idea mental process steps “providing real valued weights;”, “scaling the real valued weights by second scaling factors to obtain scaled weights;” and “and rounding the scaled weights to respective closest integer values to obtain the integer valued weights of the at least one neural network, wherein the second scaling factors are given by 2sj wherein sj denotes the number of bits representing the fractional parts of real numbers comprised in the real valued weights.”
Claim 10 recites instructions to apply the abstract idea mental process steps of Claim 1 “providing an entropy model via a neural network and entropy encoding the data based on the provided entropy model,” (See MPEP 2106.05(f)).
Claim 11 recites additional elements generally linking the abstract ideas to a particular technology or field of use (See MPEP 2106.05(h)).
Claim 12 recites refinements to the abstract idea(s).
Claim 13 recites instructions to apply the abstract idea mental process steps of Claim 1 “providing an entropy model via a neural network and entropy decoding the data based on the provided entropy model,” (See MPEP 2106.05(f)).
Claim 14 recites additional elements generally linking the abstract ideas to a particular technology or field of use (See MPEP 2106.05(h)).
Claim 15 recites refinements to the abstract idea(s).
Claim 16 recites abstract idea mental process step “transforming a tensor representing a component of the image into a latent tensor;” and instructions to apply “providing an entropy model; and processing the latent tensor via a neural network based on the provided entropy model to generate a bitstream; wherein the providing the entropy model comprises performing the method according to claim 1.” (See MPEP 2106.05(f)).
Claim 17 recites instructions to apply the abstract ideas “providing an entropy model; processing a bitstream via a neural network based on the provided entropy model to obtain a latent tensor representing a component of the image; and processing the latent tensor to obtain a tensor representing the component of the image; wherein at least one of the providing of the entropy model and the processing of the latent tensor comprises performing the method according to claim 1.” (See MPEP 2106.05(f)).
Claim 18 recites generic computer components performing the abstract ideas of Claim 1
Claim 19 recites generic computer components performing the abstract ideas of Claim 1
Claim 20 recites instructions to apply the abstract idea mental process steps of Claim 1.
Claim 21 recites generic computer components performing the abstract ideas of Claim 1
Claim 22 recites instructions to apply the abstract idea mental process steps of Claim 1.
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.
Claim(s) 1-10, 18-19, and 23 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dai et al. (VS-QUANT: PER-VECTOR SCALED QUANTIZATION FOR ACCURATE LOW-PRECISION NEURAL NETWORK INFERENCE, 2021), hereinafter Dai.
In regards to Claim 1: The present invention claims: “A method of operating a neural network comprising at least one neural network layer comprising or connected with an accumulator register for buffering summation results, the method comprising:” Dai Figure 2 (Page 8) illustrates hardware implementation for connecting layers to an accumulation collector.
“defining an integer lower threshold value and an integer upper threshold value for values of integer numbers comprised in data entities of input data for the at least one neural network layer;” Dai defines a threshold for rounding scaled values to the nearest integer (Page 3: Quantization Fundamentals and Equation 2).
“clipping, based on a value of a respective integer number comprised in a data entity of the input data being smaller than the defined integer lower threshold value, the value of the respective integer number comprised in the data entity of the input data to the defined integer lower threshold value, and/or clipping, based on a value of a respective integer number comprised in a data entity of the input data being larger than the defined integer upper threshold value, the value of the respective integer number comprised in the data entity of the input data to the defined integer upper threshold value…” Dai teaches “First, we must choose a range of real values to be represented so that any value out-of-range will be clipped. We may not necessarily want to choose the full range of presented real values, but rather clip outliers to improve the precision of quantized values within the range where most values reside.” (Page 3, right column) and shows the clip operation with the lower and upper thresholds in Equation 2.
“…such that integer overflow of the accumulator register is avoided.” Dai teaches “For improved energy efficiency, the vector MAC unit can optionally round the product of the scale factors to fewer than 2M bits before multiplying with the dot-product result. Finally, the accumulation collectors are designed with appropriate widths to avoid overflow.” (Page 7, right column).
In regards to Claim 2: The present invention claims: “scaling data entities of the input data by a first scaling factor to obtain scaled values of the data entities of the input data.” See Dai Equation 2 and “For a convolutional layer shown in Figure 1, per-vector scaling subdivides the input channel (C) dimension of the weight or activation tensor into dC=V e vectors each with V elements.” (Page 4, right column).
In regards to Claim 3: The present invention claims: “rounding the scaled values of the data entities of the input data to respective closest integer values to obtain values of the integer numbers comprised in the data entities of the input data.” See Dai Equation 2 (Page 3) and “…rounding the scaled value to the nearest integer.” (Page 3, right column)
In regards to Claim 4: The present invention claims: “processing the input data by the at least one neural network layer to obtain output data comprising output data entities and dividing the output data entities by a third scaling factor,
or processing the input data by the at least one neural network layer to obtain output data comprising output data entities, processing the output data entities by activation functions to obtain outputs of the activation functions, and dividing the outputs of the activation function by a third scaling factor,
or processing the input data by the at least one neural network layer to obtain output data comprising output data entities, factorizing a third scaling factor into a first part and a second part, dividing the output data entities by the first part of the factorized third scaling factor to obtain partially un-scaled output data entities, processing the partially un-scaled output data entities by activation functions to obtain outputs of the activation functions, and dividing the outputs of the activation functions by the second part of the factorized third scaling factor.” Dai teaches “For a convolutional layer shown in Figure 1, per-vector scaling subdivides the input channel (C) dimension of the weight or activation tensor into dC=V e vectors each with V elements. The number of vectors contained within a tensor depends on its shape and the designated vector size V.” (Page 4) and “Figure 2(c) shows the block diagram of the VS-Quant PPU that performs the calibrate-and-quantize operations. As a post-processing step following the completion of a layer of computation, we leverage a vector max unit to implement Equation 7a to compute the absolute maximum of each vector of elements.” (Page 7, right column). See Figure 2(c) (Page 8) and Equation 2, if the values are scaled on a per-vector basis between each layer, this would necessarily introduce additional scaling factor(s), which reads on the first limitation by the claim’s exclusive “…or…” listing of alternatives.
In regards to Claim 5: The present invention claims: “wherein the integer lower threshold value is less than or equal to 0 and the integer upper threshold value is greater than or equal to 0.” See Dai Page 3, right column for the thresholds being less than and greater than 0.
In regards to Claim 6: The present invention claims: “wherein the lower threshold value is given by -2k-1 and the upper threshold value is given by 2k-1-1, wherein k denotes a pre-defined bitdepth of the input data.” See Dai Page 3, right column and Equation 2 for the thresholds being equivalent to these values.
In regards to Claim 7: The present invention claims: “wherein the at least one neural network layer is or comprises one of a fully connected neural network layer and a convolutional neural network layer.” See Dai Figure 1 (Page 2) for convolutional layers and Figure 2 (Page 8) for overall architecture.
In regards to Claim 8: The present invention claims: “wherein the at least one neural network layer comprises integer valued weights comprising integer numbers.” Dai teaches “Integer quantization maps high-precision floating-point weights and activations in a DNN to low-precision integer representations, typically with 8 or fewer bits.” (Page 3, right column).
In regards to Claim 9: The present invention claims: “providing real valued weights; scaling the real valued weights by second scaling factors to obtain scaled weights; and rounding the scaled weights to respective closest integer values to obtain the integer valued weights of the at least one neural network, wherein the second scaling factors are given by 2sj wherein sj denotes the number of bits representing the fractional parts of real numbers comprised in the real valued weights.” See above where Dai teaches the steps above, similar to Claim 9. See also Dai Section 4.3 (Page 5) for “While it is orthogonal to per-vector scaling, calibration is still needed to determine the range of real values to be represented, which is parameterized by _. As with conventional scaling techniques, weight scale factors sw(j) can be determined statically based on the trained model. Activation scale factors sa(j) can be computed statically with representative input samples or dynamically during inference.”
In regards to Claim 10: The present invention claims: “providing an entropy model via a neural network and entropy encoding the data based on the provided entropy model, wherein the providing the entropy model via the neural network comprises performing the method according to claim 1.” See above where Dai teaches the steps above. Dai teaches “Likewise, calibration methods including maximum absolute value, percentile, and entropy can still be applied.” (Page 5, right column).
In regards to Claim 18: The present invention claims: “A computer program product comprising a program code stored on a non-transitory computer-readable medium, wherein the program code, when executed by one or more processors, causes the one or more processors to perform the method according to claim 1.” See the above Rejection of Claims 1-10 for the steps of Claim 1. See Dai Figure 2 (Page 8), for the general architecture.
In regards to Claim 19: The present invention claims: “An apparatus for encoding data, the apparatus comprising processing circuitry configured to perform the method according to claim 10.” See the above rejections of Claims 1-10 and Dai Figure 2 (Page 8).
In regards to Claim 23: The present invention claims: “A neural network, comprising: a neural network layer configured to process input data to obtain output data; an activation function configured to process the output data to obtain activation function output data; one or more input processing layers configured to scale, round, and clip the input data to be processed by the neural network layer; and one or more output processing layers configured to descale the output data obtained by the neural network layer and/or the activation function output data.” See the above rejection of Claims 1-10 for Dai teaching the aforementioned clipping and scaling steps. See Dai Figure 2 (Page 8) for the general network architecture.
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.
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.
Claim(s) 11-17 and 20-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai et al. (VS-QUANT: PER-VECTOR SCALED QUANTIZATION FOR ACCURATE LOW-PRECISION NEURAL NETWORK INFERENCE, 2021), hereinafter Dai, Lu et al. (Transformer-based Image Compression, 2021), hereinafter Lu.
In regards to Claim 11: While Dai reads on the Claims above, Dai fails to explicitly teach “wherein the entropy model is provided via one of: a hyperprior of a variational autoencoder, an autoregressive prior of a variational autoencoder, or a combination of hyperprior and autoregressive prior of a variational autoencoder.” However, Lu teaches “A Transformer-based Image Compression (TIC) approach is developed which reuses the canonical variational autoencoder (VAE) architecture with paired main and hyper encoder-decoders. Both main and hyper encoders are comprised of a sequence of neural transformation units (NTUs) to analyse and aggregate important information for more compact representation of input image, while the decoders mirror the encoder-side operations to generate pixel-domain image reconstruction from the compressed bitstream.” (Abstract). See Figure 1 (Page 4) as well for typical architecture(s) known at the time of Lu’s writing.
Lu highlights typical architectures that would have been known to a person of ordinary skill in the art at the time of the Applicant’s filing. It would have been obvious to such a person to leverage the known methods of these architectures to realize known benefits in a system such as Dai’s.
In regards to Claim 12: The present invention claims: “wherein the entropy encoding comprises entropy encoding via an arithmetic encoder.” Lu teaches “(a) The common paradigm of end-to-end learning-based image compression. Q, AE and AD represent the quantization, arithmetic encoding and decoding respectively.” (Figure 1, Page 4)
In regards to Claim 13: The present invention claims: “A method of decoding encoded data, the method comprising: providing an entropy model via a neural network and entropy decoding the encoded data based on the provided entropy model, wherein the entropy decoding the encoded data comprises performing the method according to claim 1.” See above how Dai reads on the limitations of Claim 1. Figure 1 of Lu (Page 4) depicts a decoding structure.
In regards to Claims 14-15: Claims 14-15 recite similar limitations to Claims 11-12, with the exception of recitation of a decoder, which is read on by Lu Figure 1 (Page 4); therefore, both sets of Claims are similarly rejected.
In regards to Claim 16: The present invention claims: “A method of encoding at least a portion of an image, the method comprising: transforming a tensor representing a component of the image into a latent tensor; providing an entropy model; and processing the latent tensor via a neural network based on the provided entropy model to generate a bitstream; wherein the providing the entropy model comprises performing the method according to claim 1.” Dai teaches the limitations of Claim 1 as well as “VS-Quant applies a scale factor for each vector of elements within a single dimension of a tensor. For a convolutional layer shown in Figure 1, per-vector scaling subdivides the input channel (C) dimension of the weight or activation tensor into [C/V].” (Page 4, right column). See above how the combination of Dai and Lu would teach the claimed structure.
In regards to Claim 17: Claim 17 recites similar limitations Claim 16, with the exception of recitation of operation on a bitstream, which Lu reads on in “while the decoders mirror the encoder-side operations to generate pixel-domain image reconstruction from the compressed bitstream.” (Abstract); therefore; both Claims are similarly rejected.
In regards to Claims 20-22: Claims 20-22 recite similar limitations to Claims 16, merely processing circuitry to perform the steps of Claim 13, and an apparatus to perform similar limitations to Claims 16-17, respectively; therefore, both sets of Claims are similarly rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wei et al. (FPGA-Based Hybrid-Type Implementation of Quantized Neural Networks for Remote Sensing Applications, 2019), hereinafter Wei.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRIFFIN T BEAN whose telephone number is (703)756-1473. The examiner can normally be reached M - F 7:30 - 4:30.
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, Li Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GRIFFIN TANNER BEAN/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121
