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 .
Continuity/reexam data
Parent data
18889814 filed 09/19/2024 Claims Priority from Provisional Application 63539542 , filed 09/20/2023
Child data
None
Last updated by opsgusr on 09/19/2024 13:49:09
Foreign data
No foreign data information
(*) - Request to retrieve electronic copy of foreign priority from participating receiving offices.
1. Claims presented for examination: 1-20
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.
2. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1 (See MPEP 2106)
Claims 1-20 are directed to a method, a system and non-transitory computer readable medium which belongs to a statutory class.
Step 2A, Prong One:
Claims 1, 8 and 15 recites “determining information about weights associated with the machine learning model; determining an objective function using the information about the weights associated with the machine learning model; determining a solution vector to the objective function; and determining a pruning mask from the solution vector” which are processes that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two:
Claims 1, 8 and 15, recite memory, processor and instructions to process the process. These are generic computer components and program which use to perform abstract ideas.
The limitation:
“Receive receiving an input dataset” is process using the information for retrieval/analysis.
“Updating the machine learning model using the pruning mask and the solution vector” is the process of provide new data to be used for the computer analysis.
The limitation is thus insignificant extra-solution activity. Limitations that the courts have found not to be enough to qualify as "significantly more” when recited in a claim with a judicial exception include: i. Adding the words "apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)). 2106.05(g)--Insignificant Extra-Solution Activity.
Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
As to claims 2, 9 and 16, the limitation:
“The information about weights associated with the machine learning model is determined during the training process” is further defined what the information is and insignificantly more.
As to claims 3, 10 and 17, the limitation:
“The information about weights includes a strength of each weight (W) and a change in strength between iterations (W)” is further defined what the information is and insignificantly to amount significantly more.
As to claims 4, 11 and 18, the limitation:
“The training process of the machine learning model will automatically stop after a predetermined number of iterations while the pruning mask is determined” is mental process.
As to claims 5, 12 and 19, the limitation:
“The objective function will maximize a strength of the weights and minimize correlations between the weights” is the mathematical algorithm.
As to claims 6, 13 and 20, the limitation:
“Pruning is implemented globally on the weights” is the process of eliminating record which has meet a criteria.
As to claims 7 and 14, the limitation:
“Pruning is implemented layer-wise on the weights” is the process of eliminating record which has meet a criteria.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
3. Claim(s) 1-4, 6-11, 13-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (Pub. No. US 2023/0084203 A1) in view of Li (Pub. No. US 2023/0072199 A1).
As to claim 1, Li discloses a method comprising:
receiving an input dataset (data to be processed) (paragraph 0156);
upon providing the input dataset to a training process of a machine learning model (…machine learning…) (paragraph 0027), determining information about weights associated with the machine learning model (… generate weights for all candidate pruned architecture…) (paragraph 0028);
determining an objective function (transformation function) (paragraph 0114) using the information about the weights associated with the machine learning model (…machine learning…) (paragraph 0027);
determining a solution vector (score vectors) (paragraph 0050) to the objective function (transformation function) (paragraph 0114);
determining a pruning mask (masks 235) (paragraph 0050) from the solution vector (vector) (paragraph 0050); and
Li et al. does not disclose updating the machine learning model using the pruning mask and the solution vector.
However, Li updating the machine learning model using the pruning mask and the solution vector (during the fifth iteration of the disclosed techniques, the solution selection and validation module 108 determines that none of the pruned decision branches 154 form the firth version of the machine learning mode 150 (mode 150E that was trained based on updated training dataset 140D from the fourth iteration…) (paragraph 0029).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include updating the machine learning model using the pruning mask and the solution vector as disclosed by Li in order to provide better result after the training.
As to claim 2, Li discloses the method of claim 1, wherein the information about weights associated with the machine learning model is determined during the training process (hyperframe network may be defined as a network to generate weights for another network. It has been applied various applications, e.g., neural architecture search…) (paragraph 0044).
As to claim 3, Li the method of claim 1, wherein the information about weights includes a strength of each weight (W) and a change in strength between iterations (W) (…when pre-trained CNN weights are known, node embeddings may be generated from the known CNN channel/layer weights…) (paragraph 0060).
As claim 4, LI discloses the method of claim 1, wherein the training process of the machine learning model will automatically stop after a predetermined number of iterations while the pruning mask is determined (responsive to a stop condition satisfied…) (paragraph 0114).
As to claim 6, Li discloses the method of claim 1, wherein pruning is implemented globally on the weights (… generate weights for all candidate pruned…) (paragraph 0028).
As to claim 7, Li discloses the method of claim 1, wherein pruning is implemented layer-wise on the weights (layer-wise cosine similarity between a pruned network…) (paragraph 0015).
Claim 8 is rejected under the same reason as to claim 1, Li discloses a pruning system comprising: a memory (storage medium) (paragraph 0156); and a processor (CPUs 701) (paragraph 0155) that is configured to execute machine readable instructions (programs or instructions for operating system) (paragraph 0156) stored in the memory(storage medium) (paragraph 0156) for causing the processor (CPUs 701) (paragraph 0155).
Claim 9 is rejected under the same reason as to claim 2.
Claim 10 is rejected under the same reason as to claim 3.
Claim 11 is rejected under the same reason as to claim 4.
Claim 13 is rejected under the same reason as to claim 6.
Claim 14 is rejected under the same reason as to claim 7
Claim 15 is rejected under the same reason as to claim 1, Li discloses a non-transitory computer-readable storage medium (storage medium) (paragraph 0156) storing a plurality of instructions (programs or instructions for operating system) (paragraph 0156) executable by a processor (CPUs 701) (paragraph 0155), the plurality of instructions when executed by the processor cause the processor to perform operations (programs or instructions for operating system) (paragraph 0156).
Claim 16 is rejected under the same reason as to claim 2.
Claim 17 is rejected under the same reason as to claim 3.
Claim 18 is rejected under the same reason as to claim 4.
Claim 20 is rejected under the same reason as to claim 6.
4. Claim(s) 1-4, 6-11, 13-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (Pub. No. US 2023/0084203 A1) and further in view of Cantrell et al. (Pub. No. US 2025/0123015 A1).
As to claim 5, Li discloses the method of claim 1 excepting for wherein the objective function will maximize a strength of the weights and minimize correlations between the weights. Cantrell discloses the objective function will maximize a strength of the weights and minimize correlations between the weights (…. At block 406, normal behavior module 114 determines the strength of the local minima and maxima within each window and, at block 408…) (paragraph 0041). Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to
Claim 12 is rejected under the same reason as to claim 5.
Claim 19 is rejected under the same reason as to claim 5.
Conclusion
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BAOQUOC N. TO
Examiner
Art Unit 2154
/BAOQUOC N TO/Primary Examiner, Art Unit 2154
