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 .
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (hereinafter “Li”), US Pub. 2025/0323663, in view of Tan, US Patent No. 11,604,993.
Regarding claim 1, Li teaches a method (AI-enhanced optimization system for machine learning models executing across computing hardware) comprising: determining, for a machine learning (ML) model comprising a set of layers, current benchmark information for each layer ([0361]) of the set of layers and a set of predefined operating thresholds corresponding to hardware on which the ML model is executing ([0317, 0322, 0364]); obtaining context information regarding the hardware on which the ML model is executing (obtaining hardware information such as available hardware resources, processor capability, computational resource availability, etc., [0196]).
Li teaches identifying layers to prevent performance degradation of the ML model based on the current benchmark information for each layer of the set of layers, the set of predefined operating thresholds and the context information (see [0322]) but fails to explicitly teach identifying one or more layers of the set of layers that must be modified and modifying, by the automation controller, each of the one or more layers.
However, in the same field of endeavor, Tan teaches determining a selected convolution layer, comparing it against a threshold, and determining whether it should be replaced or removed (see [0074-0075]) and replacing or removing the layer ([0074-0076]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Li to include the layer modification techniques of Tan. As such, a person having ordinary skill in the art would appreciate the motivation for doing so would have been to improve computational efficiency.
Regarding claim 2, Li teaches wherein the context information comprises: processing tasks currently assigned to the ML model; a current state of the hardware; and an ideal status of the hardware for optimal execution of the ML model (task allocation, [0196]).
Regarding claim 3, Tan teaches wherein modifying a layer of the one or more layers comprises one or more of: expanding or reducing a size of the layer ([0074-0076]; and tuning a weighting of the layer ([0076-0078]).
Regarding claim 4, Li teaches wherein the automation controller comprises a set of rules associated with the hardware on which the ML model is executing, and wherein modifying each of the one or more layers comprises: modifying the layer based on the set of rules so that the layer does not exceed any of the predefined operating thresholds ([0299, 0317, 0322, 0364]).
Regarding claim 5, Tan teaches removing the layer from the ML model; and deploying the layer back to the ML model after the layer has been modified ([0074-0076]).
Regarding claim 6, Li teaches modifying a bit precision of data operated on by the layer ([0354, 0361, 0362, 0364]).
Regarding claim 7, Li teaches wherein the current benchmark information for each layer of the set of layers comprises: a maximum layer size; and a maximum usage of the processing device ([0322]).
Regarding claim 8, it is a system of claim 1 and is rejected on the same grounds presented above.
Regarding claims 9-14, they have similar limitations to those of claims 2-7 and are rejected on the same grounds presented above.
Regarding claim 15, it is a non-transitory computer-readable medium of claim 1 and is rejected on the same grounds presented above.
Regarding claims 16-20, they have similar limitations to those of claims 2-6 and are rejected on the same grounds presented above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Edrenkin (US Pub. No. 2020/0380355) teaches a method for optimizing throughput of classification models.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNETH B LEE JR whose telephone number is (571)270-3147. The examiner can normally be reached Mon - Fri 9am-5pm.
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/KENNETH B LEE JR/Primary Examiner, Art Unit 2625
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