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 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.
Claim(s) 1 – 4 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (US 2020/0226476, hereafter Wang) in view of Yin et al (US 2022/0180202, hereafter Yin).
As per claim 1, Wang discloses a computer-implemented method to generate a second machine learning model based on a first machine learning model, wherein the second machine learning model is structured for more efficient computation, comprising:
processing, by a computing system comprising one or more processor devices, an input with a hidden layer of a student machine-learned model to obtain an intermediate output; providing, by the computing system, an encoded message descriptive of the input and the intermediate output for processing with a teacher machine-learned model (¶ 111);
responsive to providing the encoded message, obtaining, by the computing system, a second encoded message descriptive of a second intermediate output of one or more hidden layers of the teacher machine-learned model (¶ 111); and
performing, by the computing system, a knowledge distillation training process to train the student machine-learned model based on a difference between the intermediate output and the second intermediate output (¶ 111).
However, Wang does not explicitly teach an encoded message descriptive.
In the same field of endeavor, Yin teaches an encoded message descriptive (¶ 162, 230, and 255).
Therefore, it would have been obvious for one of ordinary skill in the art at the time the invention was effectively filed to modify the invention of Wang in view of Yin. The advantage is optimizing knowledge distillation.
As per claim 2, Wang discloses the computer-implemented method of claim 1, wherein the encoded message further comprises information descriptive of the input, and wherein providing the encoded message comprises.
However, Wang does not explicitly teach processing, by the computing system, the input and the intermediate output with a machine-learned message encoding model to obtain the encoded message.
In the same field of endeavor, Yin teaches teach processing, by the computing system, the input and the intermediate output with a machine-learned message encoding model to obtain the encoded message (¶ 162, 230, and 255).
Therefore, it would have been obvious for one of ordinary skill in the art at the time the invention was effectively filed to modify the invention of Wang in view of Yin. The advantage is optimizing knowledge distillation.
As per claim 3, Wang discloses the computer-implemented method of claim 2, wherein the obtaining the second encoded message further comprises:
decoding, by the computing system, the second encoded message with a machine-learned message decoding model to obtain:
(a) information descriptive of a second input to the one or more hidden layers of the teacher machine-learned model; and (b) information descriptive of the second intermediate output of the one or more hidden layers of the teacher machine-learned model (¶ 111).
As per claim 4, Wang discloses the computer-implemented method of claim 3, wherein performing the knowledge distillation training process comprises performing, by the computing system, a knowledge distillation training process to train the student machine-learned model based on:
(a) a difference between the intermediate output and the second intermediate output; and (b) a difference between the input and the second input (¶ 111).
Regarding claim 20, arguments analogous to those presented for claim 1 are applicable for claim 20.
Allowable Subject Matter
Claim(s) 5 - 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(s) 10 - 19 are allowed.
Conclusion
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/CHIKAODILI E ANYIKIRE/Primary Examiner, Art Unit 2487
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