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 .
This action is responding to application papers dated 3/14/2024.
Claims 1-20 are pending in the application.
The information disclosure statement filed on 5/28/2024 has been considered.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required: “computer readable media” is not described in the specification. It is recommended to use the term, “computer readable storage media” recited in the specification or add “computer readable media” into the specification as the term is originally recited in the claims.
Claim Objections
Claim 15 is objected to because of the following informalities: it appears that “memory” on line 2 needs to be “a memory.” Appropriate correction is required.
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 1-20 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 15 recite the limitation "the first set of performance metrics,” and “the second set of performance metrics.” There is insufficient antecedent basis for these limitations in the claims. Interpretation: the first set of performance metric values; the second set of performance metric values, respectively.
Per claims 2-7, 9-14, and 16-20, these claims are rejected because they depend from claims 1, 8 and 15 respectively and do not cure the deficiency in claims 1, 8 and 15.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Specifically, claims 1-20 are directed to an abstract idea.
Per claim 1, the claim is directed to an idea of itself, mental processes that can be performed in the human mind, or by a human using a pen and paper. The steps of modifying a first version, determining a performance, generating a first distribution, generating a second distribution, determining an improvement value based on the first and second distributions, determining that the improvement value meets one or more implantation criteria, and replacing the first application and/or generating a third application can be pure mental processes because a developer can perform the steps of modifying code, determining, and generating or replacing an application manually using a pen and paper through observation, evaluation, judgment, opinion, Under Prong 1.
Under Prong 2, the additional limitations, the computer readable media recited at the preamble and the application-execution environments are generic computing components and the steps of obtaining a first and second sets of performance metric values are mere data gathering for the mental steps which are insignificant extra solution activity, using a generic learning algorithm or computer component described at a high level of generality for applying or performing the abstract idea and do not indicate any integration of the abstract idea into a practical application as the mental steps are merely applied with a generic computing component(s). See MPEP see MPEP 2106.05(f) /2106.05(h). Therefore, the additional limitations do not integrate the abstract idea into a practical application. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components or insignificant extra solution activities (e.g. processors, devices, program instructions), then it falls within the "Mental Processes" grouping of abstract ideas (2019 PEG step 2A, Prong 1: Abstract idea grouping? Yes, Mental Process). At most, the steps of obtaining are not found to include anything more than what is well-understood, routine, conventional activity in the field. In this case, it is noted that the claimed extra-solution of data gathering is acknowledged to be a well-understood, routine, conventional activity court recognized as WURC examples in MPEP 2106.05(d)(ll), for example, data gathering and retrieving, storing data, updating, transmitting, and displaying a result - Symantec, Versata Dev, Content extraction, Electric Power Group). Insignificant extra solution activities or mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Viewing the limitations individually and as a combination, the additional elements merely perform data gathering and perform the mental steps using generic computing components as tools without integrating the abstract idea into a practical application. For at least these reasons, claim 1 is not patent eligible.
Per claims 2-7, these claims are directed to the same idea itself as in claim 1, reciting details of data and the mental steps without adding any other additional element that is significantly more. Therefore, the claims are rejected for the same reasons as in claim 1.
Per claims 8-14, these claims are directed to the same idea itself as claims 1-7, reciting the same mental steps and the additional limitations including at least one device including a hardware processor that is a generic computer to apply the mental steps without adding any other additional element that is significantly more. Therefore, the claims are rejected for the same reasons as in claims 1-7.
Per claims 15-20, these claims are directed to the same idea itself as in claims 1-7, reciting the same mental steps and the additional limitations including one or more processors and memory that are generic computing components to apply the mental steps without adding any other additional element that is significantly more. Therefore, the claims are rejected for the same reasons as in claims 1-7.
Allowable Subject Matter
Claims 1-20 are allowable over the prior arts.
While the closest reference Szeto et al. (US20170124487) teaches generating predictive engine variants from a base engine and evaluating the performance metrics of the engine variants to compare different algorithms as well as different engine variants, sort or rank the performances of such multiple engine variants, provide statistical features to summarize the prediction performance, deploy the new predictive engine variant into a production environment to replace a prior version of the predictive engine variant, and roll back the new predictive engine variant from the production environment to a specified version; Scott et al. (cited) teaches the Bayesian approaches to multiple testing; US 20200301672 teaches determines that the model is not well trained, continuously monitors the fitness of the model allowing for timely warnings when a prediction performance metric deviates out of a tolerance region or a specified threshold set a confidence threshold using z-score; Chen et al. (MANDOLINE: Model Evaluation under Distribution Shift, 2021) teach an evaluation framework for distribution shifts and a density ratio estimation framework for the slices and show how its estimation error scales with slice quality and dataset size; US 20230162040 teaches custom source code generation models generation by fine-tuning a pre-trained neural transformer model with a particular strategy for updating, selecting parameters of the pre-trained neural transformer model by using algorithms and statistical models to analyze and draw inferences from patterns in data; US 12321876 B2 teaches statistical distributions of AI/ML model; US 12045610 teaches a model deployment criterion indicating a difference in a value against which a proxy model measured to determine when the proxy model should be deployed to replace an existing rule engine based on comparisons of performances between models;
Proksch et al. (Intelligent Code Completion with Bayesian Networks) teaches Bayesian Networks evaluation methodologies to prediction quality, model size and inference speed, the prior arts of record, taken along or in combination, do not teach the combinations that includes … wherein generating the second distribution includes fitting the second set of performance metrics to a statistical model including an influence factor that corresponds to a shift of the center of the second distribution towards a clustering of the second set of performance metric values; and based on the first distribution and the second distribution, determining an improvement value representing an improvement in performance between the first version of the application-generating code and the second version of the application- generating code; based on determining that the improvement value meets one or more implementation criteria, performing at least one of: replacing, in an application-execution environment, the first application with the second application; and generating, in the application-execution environment, a third application using the second version of the application-generating code.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20230205665 teaches determining a leading version in versions based on corresponding performance measures relative to reports of the versions;
CN 112148347 teaches selecting the optimal model to be used for the model online service by comparing the models of different versions trained by the model training sub-process with optimal model performance evaluation index above a certain threshold from the current multiple versions.
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/INSUN KANG/ Primary Examiner, Art Unit 2193
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