DETAILED ACTION
Claims 1 – 5, 7-20 and 22 are currently pending.
Claims 20 and 22 are currently amended.
Claims 6, 21 and 23 – 25 are cancelled.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/27/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 – 5, 7 – 20 and 22 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.
Regarding claim 1, the claim recites “the near data processing apparatus updates the parameters based on the quantized training result”. There is nothing in claim 1 and describes what the parameter is or how the quantized training result is used to update the parameter. Claim 2 appears to cure these deficiencies.
Claim 1 further recites “image data infers the deep neural network based on the updated parameters”. The subsequent claims to not make it clear how the image data infers the deep neural network. No subsequent claims describe using the image data, or what happens after the image data infers the deep neural network. The specification does not go into further details about the image data, the use of image data or how the image data infers the deep neural network. It is not clear how the image data changes the functionality of the system or what significance should be put on the image data as it is laid out in the claims. Dependent claims 2 -5, 7 -20 and 22 are rejected for at least their inability to cure the deficiencies within claim 1 as set forth above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Sakai et al (US 2023/0123756) teaches the use of processors and quantizing a plurality of elements included in a tensor in the training of a neural network. Baum et al (US 11,615,297) teaches the use of quantizing weights within an artificial neural network. Sakai (EP 3848858) teaches the quantizing of intermediate data and training results within a deep neural network. Kaul et al (US 2018/0315399) generally teaches the use of neural networks, quantizing intermediate output data and the use of acceleration logic.
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/ZACHARY K HUSON/Primary Examiner, Art Unit 2181
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