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 .
DETAILED ACTION
This is Non-Final Office Action, in responses to Patent Application filed 03/27/2024, in light of the “Preliminary Amendments and Remarks” filed 04/09/2024; is a Continuation of PCT/CN2022/120497, filed 09/22/2022; claims foreign priority to 202111166585.0, filed 09/30/2021. Claim(s) 1-16 are pending. Claim(s) 1, 7, 9 and 15 is/are independent.
Examiner is acknowledged the “Preliminary Amendments” to the specification (substituted specification) filed 04/09/2024.
In addition, 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 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.
Information Disclosure Statement
A signed and dated copy of applicant’s IDSs, which were filed 07/26/24,12/26/24, 2/10/25 & 5/7/25 is/are attached to this Office Action.
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.
Claim(s) 1-16 fail to recite statutory subject matter, as defined in 35 U.S.C. 101, because: The claimed invention is/are directed to a judicial exception (i.e., abstract idea) without significantly more.
Step 1: YES (Claim(s) is/are process, machine, manufacture or composition of the matter). ... method for a neural network, the method comprising: obtaining first indication information corresponding to a first neural architecture cell, wherein the first indication information indicates a probability AND/OR a quantity of times that each of k to-be-selected neural network modules appears in the first neural architecture cell, and k is a positive integer;
generating the first neural architecture cell based on the first indication information and the k to-be-selected neural network modules, and
generating a first neural network based on the generated first neural architecture cell, wherein the first neural network comprises at least one first neural architecture cell;
obtaining a target score corresponding to the first indication information, wherein the target score indicates performance, of the first neural network corresponding to the first indication information, in processing target data; and
obtaining second indication information from a plurality of pieces of first indication information based on a plurality of target scores corresponding to the plurality of pieces of first indication information, and obtaining a target neural network corresponding to the second indication information... and therefore fall into one of the four categories of patent eligible subject matter (process, machine, manufacture or composition of the matter).
Step 2A : Prong One: ( whether a claim recites a judicial exception ?) the claim(s) recite ... method for a neural network, the method comprising: obtaining first indication information corresponding to a first neural architecture cell, wherein the first indication information indicates a probability AND/OR a quantity of times that each of k to-be-selected neural network modules appears in the first neural architecture cell, and k is a positive integer;
generating the first neural architecture cell based on the first indication information and the k to-be-selected neural network modules, and
generating a first neural network based on the generated first neural architecture cell, wherein the first neural network comprises at least one first neural architecture cell;
obtaining a target score corresponding to the first indication information, wherein the target score indicates performance, of the first neural network corresponding to the first indication information, in processing target data; and
obtaining second indication information from a plurality of pieces of first indication information based on a plurality of target scores corresponding to the plurality of pieces of first indication information, and obtaining a target neural network corresponding to the second indication information …These limitation(s) recite mental processes and mathematical calculation...since calculates the probability that each of k to-be-selected neural network modules appears in the first neural architecture cell, and to generate generating a first neural network based on the generated first neural architecture cell, wherein the first neural network comprises at least one first neural architecture cell based on the obtaining a target score … [is a high level mathematical calculation(s) ... Also, [APPLY IT] “obtaining …information indicates a probability.. for generating a first neural network based … obtaining a target score ..” Thus, these limitation(s) recite mental processes and mathematical calculation(s). [see the current specification USPGPUB 2024/0232475 A1 Para(s) 100-108…especially the Dirichlet distribution space equation (2) and equation (1) in Para(s) 100-101].
--------------Step 2A : Prong Two: (Do the claim(s) recite “additional element(s) that integrate the “Judicial Exception” into “A Practical Application” ? The claim(s) recite additional limitation(s) such as “neural architecture cell”; “apparatus, memory, professor and input device”... for obtaining … probability.. generating a first neural network based on …by obtaining a target score ..and obtaining second indication information … based on the obtaining a target neural network corresponding to the second indication information… it is noted, the improvement in the abstract idea itself ... but do not integrate the judicial exception into a practical application, i.e., “i.e., obtaining a target score corresponding to the first indication information, wherein the target score indicates performance, of the first neural network corresponding to the first indication information, in processing target data; and obtaining second indication information from a plurality of pieces of first indication information based on a plurality of target scores corresponding to the plurality of pieces of first indication information, and obtaining a target neural network corresponding to the second indication information… “ Also, these limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not integrate the judicial exception into a practical application. (MPEP 2106.04(d), 2106.05(f)).
Step 2B: (Whether a Claim Amounts to Significantly More) ? The claim(s) recite additional limitation(s) such as ... “neural architecture cell”; “apparatus, memory, professor and input device”. .. for obtaining …information indicates a probability.. generating a first neural network based on … obtaining a target score ..and obtaining second indication information … based on a … and obtaining a target neural network corresponding to the second indication information .......These limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore do not amount to significantly more than the abstract idea itself (MPEP 2106.05, 2106.04(d) and 2106.05(f)).
As to the dependent claim(s) 2-6, 8, 10-14 and 16 further recite, addition limitation(s) such as, (Dirichlet distribution space, multiplying each first probability value by N, to obtain a target result, obtain a rounded target result, sum of the k second values is N, N neural network modules, target data is any one of the following: an image, speech, text, or sequence data, meet a constraint of the rounded target result, and N is a positive integer., etc.,) These limitation(s) only amounts to mere instructions to implement the abstract idea ...and do not include elements that amount to significantly more than the abstract idea and are also rejected under the same rational.
Accordingly, claims 1-16 fail to recite statutory subject matter, as defined in 35 U.S.C. 101.
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.
Claim(s) 4-5 and 12-13 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph. As drafted, Claim(s) 4-5 and 12-13 recite the limitation(s) said “… multiplying each first probability value by N, to obtain a target result…” . There is insufficient antecedent basis for this limitation in the claim.
Allowable Subject Matter
Claim(s) 1-16 would be allowable if rewritten and/or amending to remedy the 101 and 112 rejection(s).
Reason for Allowance
Under the broadest reasonable interpretation of the claimed limitation which is consistence with the Applicant's Specification, the prior arts of recorded when taken individually or in combination do not expressly teach or render obvious the limitations recited in claim(s) 1, 7, 9 and 15 when taken in the context of the claims as a whole, especially the concept of, “… obtaining first indication information corresponding to a first neural architecture cell, wherein the first indication information indicates a probability and/or a quantity of times that each of k to-be-selected neural network modules appears in the first neural architecture cell, and k is a positive integer; generating the first neural architecture cell based on the first indication information and the k to-be-selected neural network modules, and generating a first neural network based on the generated first neural architecture cell, wherein the first neural network comprises at least one first neural architecture cell; obtaining a target score corresponding to the first indication information, wherein the target score indicates performance, of the first neural network corresponding to the first indication information, in processing target data; and obtaining second indication information from a plurality of pieces of first indication information based on a plurality of target scores corresponding to the plurality of pieces of first indication information, and obtaining a target neural network corresponding to the second indication information...” As claimed and further supports in the specifications PGPUB 220240232575 A1- The Abstract and Para(s) 6, 8-14 and 100-101.
In addition, neither a reference uncovered that would have provided a basis of evidence for asserting a motivation, nor one of ordinary skilled in the art before the effective filing date of the claimed invention, would have combined them to arrive at the present invention as recited in the context of independent claim(s) 1, 7, 9 and 15 as a whole. Thus, claim(s) 1, 7, 9 and 15 is/are allowed over the prior arts of record. Dependent claims 2-6, 8, 10-14 and 16 are also allowable due to its dependency of independent claim(s) 1, 7, 9 and 15.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nakazawa et al. (“US 20250104809 A1” PCT/JP2021/035224 dated 09/27/2021, describing a method of producing a protein for which two or more characteristics are optimized simultaneously. More specifically, the present invention relates to a method of producing a protein for which two or more characteristics are optimized, the method comprising: 1) providing a library comprising mutants from random mutation of a target protein; 2) determining respective characteristic values that indicate the two or more characteristics of some of the mutants in the library, and scoring the two or more characteristic values as one value per mutant by normalizing and integrating the characteristic values; 3) conducting machine learning by using the score values and ranking the library; and 4) selecting a protein for which two or more characteristics are optimized, based on the ranking results, wherein the two or more characteristic values are numerical values based on different measurement data related to respective different characteristics… [The Abstract].
Ozbay et al. NPL (“Estimation of incident clearance times using Bayesian Networks approach” Published 2005 by ELSEVIER 14 pages, describing effective incident management requires a full understanding of various characteristics of incidents to accurately estimate incident durations and to help make more efficient decisions to reduce the impact of non-recurring congestion due to these accidents. Our goal is thus to have a comprehensive and clear description of incident clearance patterns and to represent these patterns with formalisms based on Bayesian Networks (BNs). BNs can be used to create dynamic incident duration estimation trees that can be extracted in the presence of a real incident for which data might only be partially available. This capability will enable traffic operators to create case-specific incident management strategies in the presence of incomplete information. In this paper, we employ a unique database created using incident data collected in Northern Virginia. This database is then used to demonstrate the advantages of employing BNs as a powerful modeling and analysis tool especially due to their ability to consider the stochastic variations of the data and to allow bi-directional induction in decision-making. In addition to the presentation of the basic theory behind BNs in the context of our problem and the validation of our estimation results, the dependency relations among all variables in the estimated BN that can be used for both quantitative and qualitative analysis are also discussed in detail .… [The Abstract].
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/QUOC A TRAN/Primary Examiner, Art Unit 2145
