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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/17/2025 has been entered.
Response to Arguments
The rejection under 35 USC 103 is withdrawn in view of Applicant’s amendment.
Applicant’s arguments filed 03/03/2026 regarding the rejection under 35 USC 101 have been fully considered, but are not persuasive. Applicant argues, see especially pages 10-12, that the proposed amendment reflects an improvement in the art of machine learning arising from a reinforcement learning based approach and identifies [0012] as providing support for the improvement. The specification at [0011-0012] does indeed describe an improvement to training models; however, this improvement arises from using the full reinforcement learning loop shown in Figure 2. This loop is not recited in the independent claims and consequently does not reflect the purported improvement to technology.
However, this loop is reflected in claim 2 and similar claims; consequently, the rejection under 35 USC 101 with respect to claims 2-3, 9-10 and 15-16 is withdrawn.
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 1-4, 6-11, 13-17 and 19-20 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.
Claims 1, 8 and 14 recite “generating/generate a first simulated dataset from, at least in part, of the one or more datasets of the using an artificial neural network”. This phrase is ungrammatical; consequently, a person of ordinary skill in the art would not be reasonably apprised of the scope of the claimed invention. For the purposes of examination, this limitation is being interpreted as “generating/generate a first simulated dataset from, at least in part,
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, 4, 6-8, 11, 13-14, 17 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Each of the claims falls into one of the four statutory categories of process, machine, manufacture or composition of matter.
Step 2
To perform the Step 2 analysis, the claims are shown with the abstract ideas indicated in bold and additional elements indicated as non-bolded.
Regarding claim 1, claim 1 recites a computer-implemented method for optimizing data generation for data simulation based on reinforcement learning, the computer-implemented method comprising:
in a computerized-system comprising a processor, a database and a memory, receiving by the processor, one or more datasets within a distributed data processing where a data simulation process is to be performed on the one or more datasets, the processor is configured to:
generating a first simulated dataset from, at least in part, of the one or more datasets of the using an artificial neural network having a first set of parameters, wherein the artificial neural network is a bank transaction data generator, and wherein the first simulated dataset is based at least in part on a Poisson distribution and the first simulated dataset is comprised of account information, credit scores, account counter data and financial transactions;
automatically identifying a randomized subset of one or more parameters of the first set of parameter using a learning program;
automatically modifying the randomized subset of one or more parameters of the first set of parameters into a second set of parameters based on a hyperparameter value using the learning program;
generating a second simulated dataset from, at least in part, the one or more datasets using the artificial neural network having the second set of parameters, wherein the second simulated data set is based at least in part on a Poisson distribution;
determining data discrepancies between the first simulated data set and a target data set;
determining data discrepancies between the second simulated data set and the target data set;
selecting between the first simulated data set and the second simulated data set, a first data set that corresponds to less data discrepancy relative to the target data set; and
comparing data discrepancies of the selected first data set to a data discrepancy threshold.
Step 2A Prong 1: : Claim 1 recites an abstract idea including concepts relating to organizing or analyzing information in a way that can be performed mentally or is analogous to human mental work, such as concepts related to organizing or analyzing information.
The steps of “generating a first simulated dataset from, at least in part, of the one or more datasets…and the first simulated dataset is comprised of account information, credit scores, account counter data and financial transactions, identifying a randomized subset of one or more parameters of the first set of parameter, modifying the randomized subset of one or more parameters of the first set of parameters into a second set of parameters based on a hyperparameter value, generating a second simulated dataset from, at least in part, the one or more datasets, determining data discrepancies between the first simulated data set and a target data set, determining data discrepancies between the second simulated data set and the target data set, selecting between the first simulated data set and the second simulated data set, a first data set that corresponds to less data discrepancy relative to the target data set and comparing data discrepancies of the selected first data set to a data discrepancy threshold”, are all mental processes involving comparison, analysis, judgement and opinion that may be performed by the human mind through observation and laid out with pen and paper. (MPEP §2106.04(a)(2)).
The amended limitation of ‘ wherein the first simulated dataset is based at least in part on a Poisson distribution’ and ‘wherein the second simulated dataset is based at least in part on a Poisson distribution’ merely specifies the mathematical concept specified by the mathematical relationship of a Poisson distribution, implemented as part of the abstract process of generating, (MPEP2106.04)
Step 2A Prong 2: Claim 1 recites the additional claim limitations of “in a computerized-system comprising a process, a database and a memory…the processor is configured to”, ‘using an artificial neural network’ , ‘automatically’, and ‘using a learning program’. These limitations are nothing more than mere instructions to ‘apply it’ a process to a generic computer, using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). They do not improve the operation of the generic components (MPEP 2106.05(a)). Accordingly, the additional elements do not integrate the judicial exception into a practical application.
Claim 1 recites the additional limitation “receiving by the processor, one or more datasets within a distributed data processing where a data simulated process is to be performed on the one or more datasets”. This is insignificant extra-solution activity. See MPEP 2106.05(g).
Claim 1 recites the additional limitation of “wherein the artificial neural network is a bank transaction generator”. This limitation simply generally links the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h), and therefore does not integrate the judicial exception into a practical application.
Step 2B: Claim 1 recites the additional claim limitations of “in a computerized-system comprising a process, a database and a memory…the processor is configured to”, ‘using an artificial neural network’ , ‘automatically’, and ‘using a learning program’. These limitations are nothing more than mere instructions to ‘apply it’ a process to a generic computer, using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). They do not improve the operation of the generic components (MPEP 2106.05(a)). Accordingly, the additional elements do not amount to significantly more than the judicial exception.
Claim 1 recites the additional limitation “receiving by the processor, one or more datasets within a distributed data processing where a data simulated process is to be performed on the one or more datasets”. This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, sending or receiving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data.
Claim 1 recites the additional limitation of “wherein the artificial neural network is a bank transaction generator”. This limitation simply generally links the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h), and therefore does not amount to significantly more than the judicial exception.
Regarding claim 4, claim 4 recites the method of claim 1, further comprising:
in response to determining that the selected first data set does meet the data discrepancy threshold, storing the parameter set that corresponds to the selected first data set.
Step 2A Prong 1: Determining that the selected first data set does not meet the discrepancy threshold is a mental processes involving comparison, analysis, judgement and opinion that may be performed by the human mind through observation and laid out with pen and paper. (MPEP §2106.04(a)(2)).
Step 2A Prong 2: Claim 4 recites the additional claim limitations of ‘storing the parameter set that corresponds to the selected first data set.’ This limitation is simply adding insignificant extra-solution activity to the judicial exception, for example, such as mere data gathering, as discussed in MPEP § 2106.05(g), and therefore does not integrate the judicial exception into a practical application.
Step 2B: The additional claim limitations of ‘storing the parameter set that corresponds to the selected first data set’ is directed to the well understood and routine activity of storing and retrieving information in memory, and does not amount to significantly more than the judicial exception. MPEP 2106(05)(d)(iv).
Regarding claim 6, claim 6 recites the method of claim 1, wherein determining data discrepancies between the first simulated data set and the target data set further comprises:
determining distance between respective data of the first simulated data set and the target data set.
Step 2A Prong 1: Determining data discrepancies between the first simulated data set and the target data set and determining distance between respective data are mental processes involving comparison, analysis, judgement and opinion that may be performed by the human mind through observation and laid out with pen and paper. (MPEP §2106.04(a)(2)).
Step 2A Prong 2/ Step 2B: Claim 6 includes no additional elements that would integrate the claimed judicial exceptions into a practical application, or amount to significantly more than the judicial exception.
Regarding claim 7, claim 7 recites the method of claim 1, further comprising: determining a data discrepancy score for the first simulated data set based on the data discrepancies between the first simulated data set and the target data set; and
determining a data discrepancy score for the second simulated data set based on the data discrepancies between the second simulated data set and the target data set.
Step 2A Prong 1: Determining discrepancy scores are mental processes involving comparison, analysis, judgement and opinion that may be performed by the human mind through observation and laid out with pen and paper. (MPEP §2106.04(a)(2)).
Step 2A Prong 2/ Step 2B: Claim 7 includes no additional elements that would integrate the claimed judicial exceptions into a practical application, or amount to significantly more than the judicial exception.
Claim 8 recites substantially similar subject matter to claim 1 including substantially the same abstract idea.
Claim 8 recites the following additional elements which, considered individually and as an ordered combination with the additional elements discussed above with respect to claim 1, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
A computer program product for optimizing data generation for data simulation based on reinforcement learning, the computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising: (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 8 does not reflect an improvement to computer technology or any other technology.
Claims 11 and 13 recite substantially similar subject matter to claims 4 and 6, respectively, and are rejected with the same rationale, mutatis mutandis.
Claim 14 recites substantially similar subject matter to claim 1 including substantially the same abstract idea.
Claim 14 recites the following additional elements which, considered individually and as an ordered combination with the additional elements discussed above with respect to claim 1, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
A computer system for optimizing data generation for data simulation based on reinforcement learning, the computer system comprising: one or more processors; one or more computer readable storage media; and program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising: (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 14 does not reflect an improvement to computer technology or any other technology.
Claims 17 and 19-20 recite substantially similar subject matter to claims 4 and 6-7, respectively, and are rejected with the same rationale, mutatis mutandis.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Markus A Vasquez whose telephone number is (303)297-4432. The examiner can normally be reached Monday to Friday 10AM to 2PM PT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARKUS A. VASQUEZ/Primary Examiner, Art Unit 2121
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