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 .
Response to Amendment and Arguments
Applicant' s amendment filed on May 29, 2026 has been entered and made of record. Claims 1-20 are pending and are being examined in this application.
In light of Applicant' s amendments to the claims, the 101 rejection is withdrawn.
Applicant' s arguments with respect to the Jiang reference have been fully considered, and are persuasive. As such, the current rejection is a 2nd Non-Final Rejection with new grounds of rejection provided below.
Allowable Subject Matter
Claims 3 and 6-9 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 5, 11, 14, and 20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Li et al. (US Pub. 20210232890).
Referring to claim 1, Li discloses A computer-implemented method for reducing machine learning models for target hardware, the method comprising:
providing a model, a set of training data, and a training threshold ([0044-0052] model, training data, predetermined sparsities);
determining a search space for reducing the model with a pruning function and a pruning factor, wherein the pruning function increases compression along a depth of the model, and the compression increases are based on the pruning factor ([0052-0064] compression by pruning for compressed weight pattern), by:
bounding the pruning function with two or more constraints ([0067-0078] constraints, numbers of weights);
determining, based on the two or more constraints, boundaries for the pruning factor, the determined boundaries defining at least in part the search space ([0067-0078] boundaries of weight factor to define to determine weight pattern);
training the model to learn a reduced model by iteratively ([0059-0061] iterative training of model):
updating model parameters based on the pruning function and the pruning factor and within the search space ([0059-0061] output weight pattern);
evaluating the updated model based on the set of training data and the training threshold ([0059-0061] comparison to input weight pattern); and
providing the reduced model to a target hardware ([0062] compressed output weight pattern transmitted to client device).
Referring to claim 5, Li discloses The method of claim 1, wherein the pruning function is a linear or exponential function ([0067-0078] discloses methods of pruning, wherein there is a resulting output weight pattern and associated pruning thereof).
Referring to claim 11, see at least the rejection for claim 1. Li further discloses A computer readable medium comprising computer executable instructions for reducing machine learning models for target hardware, the instructions for performing the claimed steps [fig. 6, CPU(s) 601, GPU(s) 619, system memor(ies) 602].
Referring to claim 14, see the rejection for claim 5.
Referring to claim 20, see at least the rejection for claim 1. Li further discloses A device comprising a processor and memory, the memory comprising computer executable instructions for reducing machine learning models for target hardware, the instructions causing the processor to perform the claimed steps [fig. 6, CPU(s) 601, GPU(s) 619, system memor(ies) 602].
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 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Bai et al. (US Pub. 20200210113).
Referring to claim 2, Li does not appear to explicitly disclose The method of claim 1, the method comprising determining a granularity of the search space based on a number of searching steps.
However, Bai discloses The method of claim 1, the method comprising determining a granularity of the search space based on a number of searching steps [pars. 44 and 51; in an iterative process, steps within a search space are increased to more extensively explore the search space in a finer granularity].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural network model taught by Li so that the number of searching steps in the re-training can be increased as taught by Bai, with a reasonable expectation of success. The motivation for doing so would have been to optimize the search (e.g., explore more if the cost/ target loss is still acceptable) [Bai, par. 44].
Referring to claim 12, see the rejection for claim 2.
Claims 4, 10, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Rao et al. (US Pub. 20230091667).
Referring to claim 4, Li does not appear to explicitly disclose The method of claim 1, wherein the training threshold is a target accuracy.
However, Rao discloses The method of claim 1, wherein the training threshold is a target accuracy [pars. 82-84; one or more performance indicators are used to evaluate training of a candidate neural network; examples of the performance indicators include model inference accuracy].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural network model taught by Li so that the re-training is evaluated based on one or more performance indicators such as model inference accuracy as taught by Rao, with a reasonable expectation of success. The motivation for doing so would have been to ensure that the neural network model satisfies performance constraints [Rao, par. 85].
Referring to claim 10, Li does not appear to explicitly disclose The method of claim 1, the method comprising: employing knowledge distillation to train the model for the target hardware for classification tasks.
However, Rao discloses The method of claim 1, the method comprising: employing knowledge distillation to train the model for the target hardware for classification tasks [pars. 12, 40, and 87; a knowledge distillation operation is executed on a second (i.e., smaller) neural network for a specific computer vision task to be performed on an electronic device].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural network model taught by Li so that a knowledge distillation operation is executed on the neural network model as taught by Rao, with a reasonable expectation of success. The motivation for doing so would have been to improve the accuracy of the neural network model in performing a specific computer vision task [Rao, par. 87].
Referring to claim 19, see the rejection for claim 10.
Conclusion
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/Grace Park/Primary Examiner, Art Unit 2144
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