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 .
Information Disclosure Statement
The information disclosure statement filed on 10/2/2024 has been considered.
Drawings
The drawings filed on 10/2/2024 are accepted.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 2 and 5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an Abstract Idea [MPEP 2106.04(a)(2)(II)(C) – A claim that requires a computer may still recite a mental process] without significantly more. Representative claim 5 recites a radio communication method for a terminal that receives “data” about a trained model and a reference model and judges the received “data”. This judicial exception is not integrated into a practical application because receiving data and judging/thinking about data can be performed entirely in the mind of a human or with the aid of a generic computer. The claims does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the network trained model and reference model is not utilized, modified or transformed in any particular way to result in the creation or modification of data and at this time, the claim is directed to receiving data and considering (judging) the data. With specific note to claim 1, “receiving section” and “control section” are functional components within a generic computer and not considered significantly more. With respect to claim 2, the “data” is further defined but again, does not utilize, modify or transform the data in any particular way that could be considered significantly more.
Note Applicant’s definitions of “judging” can be found in [0325-0328] which has informed the Examiner’s comparison of “judging” as being “thinking”. See “considering”.
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)(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, 2, 5 and 6 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Isaksson et al. (US-11,490,313 hereinafter, Isaksson).
Regarding claim 1, Isaksson teaches a terminal (Fig. 1 [10]) comprising:
a receiving section (Fig. 7 [702 & Communication Interface] and Col. 15 lines 60-65) that receives network (NW)-trained model information related to a model trained by a network (NW-trained model) (Fig. 3 [305] “trained model parameters”) and reference model information related to a reference model that is a base of the NW-trained model; (Col. 3 lines 62 through Col. 4 line 3 and Fig. 3 [306]) and
a control section (Fig. 7 [701]) that judges the NW-trained model, based on the NW-trained model information and the reference model information. (Col. 10 lines 23-29 and Fig. 3 [307])
Regarding claim 2, Isaksson teaches the NW-trained model information indicates a difference between the NW-trained model and the reference model (Col. 7 lines 32-36 and Col. 7 line 61 through Col. 8 line 1), and the reference model information indicates an index related to the reference model. (Col. 8 lines 2-4)
Regarding claim 5, the limitations of claim 5 are rejected as being the same reasons set forth above in claim 1.
Regarding claim 6, Isaksson teaches a base station (Fig. 3 [12]) comprising:
a control section (Fig. 8 [803]) that trains a reference model; (Fig. 3 [302]) and
a transmitting section (Fig. 8 [804]) that transmits information related to the trained model (Fig. 3 [305] “trained model parameter(s)”) and reference model information related to the reference model. (Fig. 3 [305] “Indicator” and Col. 3 lines 62 through Col. 4 line 3)
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 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Isaksson in view of Yao et al. (US-2020/0334537 hereinafter Yao).
Regarding claim 3, Isaksson teaches the limitations of claim 1 above, but differs from the claimed invention by not explicitly reciting the control section further trains the NW-trained model.
In an analogous art, Yao teaches an importance-aware model for pruning and re-training neural networks (Abstract) that includes a terminal (Fig. 1 [10] and Pages 2-3 [0030]) with a control section (Fig. 1 [12]) that trains a model. (Page 1 [0011 & 0013] “The illustrated accuracy enhancer 26 uses the training data 16 to re-train the pruned neural network 24” and Page 2 [0029-0030])
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to be motivated to implement the invention of Isaksson after modifying it to incorporate the ability to utilize a terminal to train a model of Yao since it enables reducing complexity by reducing the required computational resources required and reducing the required power consumption. (Yao Page 1 [0010])
Regarding claim 4, Isaksson in view of Yao teaches wherein the NW-trained model information includes information of a parameter to be frozen for training (Yao Page 1 [0010] “parameters corresponding to less relevant features”), and
the control section further trains the NW-trained model with the parameter being not updated. (Yao Page 1 [0010-0011] “The illustrated accuracy enhancer 26 uses the training data 16 to re-train the pruned neural network 24. In one example, the importance metric generator 20 iteratively conducts the importance measurement, the pruner 22 iteratively sets a subset of the parameters to zero and the accuracy enhancer 26 iteratively re-trains the pruned neural network 24 until an iteration manager 28 detects that the pruned neural network 24 satisfies a sparsity condition. Moreover, the importance metric generator 20, the pruner 22 and the accuracy enhancer 26 may maintain zero values of the subset on successive iterations. When the sparsity condition is satisfied, the illustrated iteration manager 28 generates a final result 30 (e.g., final pruned neural network result).”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-2021/0241169 to Gupta et al. which discloses a performance based switching of a mobile training process
US-12,531,787 to Xu which discloses model management and updating of model in a cellular network
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW C SAMS whose telephone number is (571)272-8099. The examiner can normally be reached M-F 8:30-5 EST.
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/Matthew C Sams/ Primary Examiner, Art Unit 2646
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