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 .
DETAILED ACTION
The instant application having Application No. 18399715 has a total of 20 claims pending in the application, of which claims 7-11 and 18-20 have been withdrawn as non-elected claims, and claims 1 and 12 have elected image data and excluded Audio and text data as non-elected in the response to restriction filed 8/24/26.
I. ACKNOWLEDGEMENT OF REFERENCES CITED BY APPLICANT
Information Disclosure Statement
As required by M.P.E.P 609(c), the applicant’s submissions of the Information Disclosure Statements dated 1/7/2025 and 1/2/2025 are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C(2), a copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
II. REJECTIONS NOT BASED ON PRIOR ART
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 1-6 and 12-17 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.
As per claims 1 and 12, these claims call for “an activation function of the target neural network model.” However, the claim has “a target neural network quantization model” and “a quantized target neural network model.” It is unclear just which one of these is being referred to by “the target neural network model” and this causes the claim to be unclear, and therefore rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
As per claims 2-6 and 13-17, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
As per claims 4 and 15, these claims call for “quantizing a weight parameter of a second network layer in the target neural network model” and “the second network layer is a network layer that is in the target neural network model.” However, parent claim 1 (and 12) have “a target neural network quantization model” and “a quantized target neural network model.” It is unclear just which one of these is being referred to by “the target neural network model” and this causes the claim to be unclear, and therefore rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
As per claims 5-6 and 16-17, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
III. REJECTIONS BASED ON PRIOR ART
Examiners Note: Some rejections will be followed by an ‘EN’ that will denote an examiners note. This will be placed to further explain a rejection.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3 and 12-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Babu et al (”SpinAPS: A High -Performance Spintronic Accelerator for Probabilistic Spiking Neural Networks”)
As per claims 1 and 12, Babu discloses, “A data processing method” (Abstract; EN: this denotes the system being used to process data).
“Obtaining to-be-processed data, wherein the data comprises image data, audio data, or text data” (Pg.7, particularly section 3.2; EN: this denotes processing Images, audio data and text data have been restricted out).
“Processing the to-be processed data by using a target neural network” (Pg.7, particularly section 3.2; EN: this denotes using a neural network to process the data). “Quantization model” (pg.10, particularly section 4.2; EN: This denotes the neural network being quantized). “Wherein the target neural network quantization model comprises a plurality of groups of fusion parameters” (Pg.5, particularly section 3; EN: this denotes the neural network being made up of neurons with weights. The examiner is interpreting this to be “fusion parameters” since they take in data and use the weights to interact with (i.e. Fuse) with the inputs to provide results for that neuron). “The target neural network quantization model is a quantized target neural network model” (pg.10, particularly section 4.2; EN: This denotes the neural network being quantized).”An activation function of the target neural network model comprises a piecewise linear function (PWL)” (pg.9, particularly section 4.1; EN: this denotes the activation function being a piecewise linear approximation function). “the PWL comprises a plurality of intervals” (Pg.15-16, particularly section 6.1.2; EN: this denotes breaking the pieces up into 8 bit numbers). “and there is a correspondence between the plurality of groups of fusion parameters and the plurality of intervals” (Pg.5, particularly section 3; EN: this denotes the neural network being made up of neurons with weights and using the input with the activation function to provide results for the neuron. Here the “correspondence” is the intervals of the input using the weights of the neuron along with the activation function to provide results for the neuron).
As per claims 2 and 13, Babu discloses, “determining target indication information based on the to-be-processed data” (pg.7, particularly section 3.2; EN: this denotes the target information being identifying digits in the images).
“Obtaining, from the plurality of groups of fusion parameters based on the target indication information, a target fusion parameter corresponding to the target indication information, and processing the to-be processed data based on the target fusion parameter” (Pg.7-8, particularly section 3.2; EN: this denotes training and testing the neural network with the training dataset. Here the training is where the fusion parameters are created based upon the target indication information, and the processing is the classification of the data).
As per claims 3 and 14, Babu discloses, “processing an input feature of a first network layer based on a weight parameter of the first network layer in the target neural network quantization model, wherein the input feature of the first network layer is determined based on the to be processed data” (Pg.7-8, particularly section 3.2; EN: this denotes training and testing the neural network with the training dataset, this is input to the first layer of the neural network).
“Determining the target indication information based on a processing result of the first network layer” (Pg.7-8, particularly section 3.2; EN: this denotes training and testing the neural network with the training dataset, this is input to the first layer of the neural network. The output of the neural network is based on the results of the first layer, along with other layers).
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.
Claims 4-6 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Babu et al (”SpinAPS: A High -Performance Spintronic Accelerator for Probabilistic Spiking Neural Networks”) and further in view of Dong et al (“HAWQ: Hessian Aware Quantization of Neural Networks with Mixed Precision”).
As per claims 4 and 15, Babu fails to explicitly disclose, “wherein the weight parameter of the first network layer is obtained by quantizing a weight parameter of a second network layer in the target neural network model, and the second neural network layer is a network layer this is the target neural network mode and that corresponds to the first network layer.”
Dong discloses, “wherein the weight parameter of the first network layer is obtained by quantizing a weight parameter of a second network layer in the target neural network model, and the second neural network layer is a network layer this is the target neural network mode and that corresponds to the first network layer” (Pg.2-3, particularly section III; EN: this denotes quantizing blocks of learnable parameters from neural networks, with multiple layers in each block, with the quantization based on the values of all the weights in that block. Since this is multiple layers, weights in one layer will be affected by the quantization of other layers in the block).
Babu and Dong are analogous art because both involve neural network quantization.
Before the effective filing date it would have been obvious to one skilled in the art of neural network quantization to combine the work of Babu and Dong in order to allow quantization in one layer to be affected by quantization in another layer.
The motivation for doing so would be to “achieve similar/better accuracy with 8x activation compression ratio on resnet20 as compared to DNAS and up to 1% higher accuracy with up to 14% smaller models on Resnet50 and Inception V3” (Dong, Abstract) or in the case of Babu, allow the system to quantize efficiently to improve accuracy while reducing model size.
Therefore before the effective filing date it would have been obvious to one skilled in the art of neural network quantization to combine the work of Babu and Dong in order to allow quantization in one layer to be affected by quantization in another layer.
As per claims 5 and 16, Babu discloses, “Processing the processing result of the first network layer based on a fusion bias parameter to obtain a first result” (Pg.13, particularly section 5.2; EN: this denotes the use of bias parameters).
“Obtaining the target indication information through calculation based on the first result” (Pg.7-8, particularly section 3.2; EN: this denotes training and testing the neural network with the training dataset, this is input to the first layer of the neural network. The output of the neural network is based on the results of the first layer, along with other layers). “and an equivalent length of an intermediate interval in a plurality of intervals of a PWL corresponding to the second network layer” (Pg.9-10, particularly section 4.1; EN: This denotes processing via the PWL breakpoints (i.e. segments). “Wherein the equivalent length of the intermediate interval is determined based on a length of the intermediate interval and a quantization parameter of the second network layer” (Pg.7-8, particularly section 3.2; EN: this denotes training and testing the neural network with the training dataset, this is input to the first layer of the neural network. The output of the neural network is based on the results of the first layer, along with other layers. The “equivalent length” is the length of the piecewise data when manipulated by the quantized weights of the neural network).
As per claims 6 and 17, Babu discloses, “Wherein the equivalent length of the intermediate interval is an integer power of 2” (Pg.9, particularly section 4.1; EN: this denotes the breakpoints being set up to be expressed as powers of 2).
Conclusion
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEN M RIFKIN whose telephone number is (571)272-9768. The examiner can normally be reached Monday-Friday 9 am - 5 pm.
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, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BEN M RIFKIN/Primary Examiner, Art Unit 2123
