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 .
The office action is in response to the application filed on October 31, 2023.
Claims 1-20 are pending and have been examined. Claims 1-20 are rejected.
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, 2, and 17 are rejected under 35 U.S.C. 112(b) 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.
Regarding Claims 1, 2, and 17, the limitations "indicating a respective effectiveness of a plurality of model types for generating predictions" AND “wherein each of the first plurality of statistical routines is based on a first respective algorithm" do not clearly set the metes and bounds of the patent protection desired. The term "respective effectiveness" is a relative term which renders the claim indefinite. The specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention in regards to a plurality of model types as well as the particular algorithm structure in use to perform said statistical routines further rendering the claim indefinite. To expedite prosecution under BRI, the examiner will interpret and liken “respective effectiveness” to the performance measurement of the models and or where it applies. Likewise, the “respective algorithm” will be interpreted as any algorithm by definition and or where it applies.
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 non-statutory subject matter.
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
Step 1: The claim does not fall within one of the four statutory categories of invention (process, machine, manufacture, or composition of matter), or,
Step 2: The claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
Step 2A, Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
MPEP 2106.04(a)(2)(I) states: "The mathematical concepts grouping is defined as
mathematical relationships, mathematical formulas or equations, and mathematical
calculations."
MPEP 2106.04(a)(2)(III) states: "Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental
processes include observations, evaluations, judgements, and opinions.
Further, the MPEP states: "The courts do not distinguish between mental processes that
are performed entirely in the human mind and mental processes that require a human to use a
physical aid (e.g. pen and paper or a slide run) to perform the claim limitation.
Using the two-step inquiry, it is clear that Claims 1-20 are each directed to non-statutory
subject matter as shown below:
Please note the following:
The following groups of claims are expressed in different statutory categories:
Claim 1 is directed to a system comprising of one or more processors configured to carry out a process for drastically reducing the time spent developing time-series forecasting models by building predictive AI to automatically match time-series datasets to the best-suited model types and pre-tuning hyperparameters before formal training begins.
Claims 2-16 are directed to a method for providing a meta-learning system designed to minimize the development time and computational overhead of AI forecasting.
Claims 17-20 are directed to a non-transitory computer-readable mediums storing a plurality of instructions which, when executed by a processor, cause the processor to carry out a process.
With respect to Claims 1, 2, and 17, which are independent claims with identical claim limitations:
Step 1: Claim 1 is directed to a system for an automated AI recommendation system that speeds up time-series forecasting by intelligently matching datasets to the most appropriate machine learning model, corresponding to an article of manufacture, which is one of the four statutory categories of patentable subject matter. Claim 2 is directed to a method, also known as a process, which is one of the four statutory categories of patentable subject matter. Claim 17 is directed to a non-transitory computer-readable mediums on which computer-executable instructions are stored, corresponding to an article of manufacture, which is one of the four statutory categories of patentable subject matter.
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“generating a first embedding based on the first dataset;”; Generating a first embedding based on the first dataset is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“....to determine a first plurality of respective outputs, wherein the first plurality of statistical routines perform a respective first statistical analysis of the first dataset, and wherein each of the first plurality of statistical routines is based on a first respective algorithm;”; Determining a first plurality of outputs where the respective plurality of statistical routines based on a first algorithm, performs a respective first statistical analysis of the first dataset is an abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. See MPEP § 2106.04(a)(2)(I)(A).
“determining a first feature input based on the first embedding and the first plurality of respective outputs,”; Determining a first feature input based on the first embedding and the first plurality of respective outputs is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“selecting, based on the first output, a first untrained model from a first plurality of untrained models for training, wherein the first plurality of untrained models comprise respective algorithms for time-series forecasting, and wherein each of the first plurality of untrained models comprises default hyperparameter tuning;”; Selecting a first untrained model based on the first output from a plurality of untrained models for training is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“and generating for display, on a user interface, a recommendation for using the tuned first model for time-series forecasting.”; Generating a recommendation for display regarding the usage of a tuned first model for time-series forecasting is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III). *Examiner’s Note: Limitation exclusive to Claims 1 and 17.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
“receiving a first dataset, wherein the first dataset comprises one or more categories of data trends;” ; Receiving datasets that are made up of one or more categories of data trends is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g).
“inputting the first dataset into a first plurality of statistical routines...”; Inputting the first dataset into a first plurality of statistical routines is considered insignificant extra-solution activity (mere data gathering/transmitting + post-solution activity) - see MPEP 2106.05(g).
“inputting the first feature input into a trained model to generate a first output, wherein the trained model is trained on labeled feature inputs to generate outputs indicating a respective effectiveness of a plurality of model types for generating predictions based on the one or more categories of data trends, and wherein the labeled feature inputs comprise respective dataset embeddings of labeled datasets and corresponding statistical analyses of the labeled datasets;”; Inputting the first feature input into a trained model to generate a first output, wherein the model is trained on labeled data to evaluate the effectiveness of multiple model types for predicting data trends, and wherein the labeled data includes dataset embeddings and their corresponding statistical analyses only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
“based on selecting the first untrained model, tuning a first hyperparameter of the first untrained model using the first dataset to generate a tuned first model;”; Tuning a first hyperparameter of the first untrained model using the first dataset to generate a tuned first model only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Receiving datasets that are made up of one or more categories of data trends and inputting the first dataset into a first plurality of statistical routines constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. - See MPEP 2106.05(d)(II). Inputting the first feature input into a trained model to generate a first output, wherein the model is trained on labeled data to evaluate the effectiveness of multiple model types for predicting data trends, and wherein the labeled data includes dataset embeddings and their corresponding statistical analyses AND tuning a first hyperparameter of the first untrained model using the first dataset to generate a tuned first model amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)). The usage of a trained/training model, data trends, dataset embeddings, and labeled data is generally linked to a particular technological environment or field of use (AI/ML) - see MPEP 2106.05(h).
Therefore, Claims 1, 2, and 17 are directed to non-statutory subject matter and rejected.
With respect to Claims 3 and 18, which have identical claim limitations and are dependent on Claim 2 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
"generating a profile matrix for the first dataset; and populating values of the profile matrix based on a comparison of the first plurality of respective outputs and respective model requirements for the first plurality of untrained models."; Generating a profile matrix for the first dataset and populating values of the profile matrix based on the comparison of the first plurality of respective outputs and respective model requirements is the abstract idea of a mathematical calculation, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 3 and 18 are directed to non-statutory subject matter and rejected.
With respect to Claims 4 and 19, which have identical claim limitations and are dependent on Claim 2 respectively:
Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
“receiving an embedding dimension;”; Receiving an embedding dimension is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g).
“and training the trained model to generate embeddings having the embedding dimension based on the labeled datasets.”; Training the trained model to generate embeddings having the embedding dimension based on the labeled datasets only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
Step 2B: Receiving an embedding dimension constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. - See MPEP 2106.05(d)(II). Training the trained model to generate embeddings having the embedding dimension based on the labeled datasets amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)). The usage of a machine learning model is generally linked to a particular technological environment or field of use (AI/ML) - see MPEP 2106.05(h).
Therefore, Claims 4 and 19 are directed to non-statutory subject matter and rejected.
With respect to Claims 5 and 20, which have identical claim limitations and are dependent on Claim 2 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
"sampling a synthetic distribution of the first dataset; generating a first synthetic dataset based on the synthetic distribution;"; Sampling a synthetic distribution of the first dataset and generating a first synthetic dataset based on synthetic distribution is an abstract idea of a mathematical calculation, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
“and adding the first synthetic dataset to the labeled datasets.”; Adding the first synthetic dataset to the labeled datasets is considered insignificant extra-solution activity (post-solution activity) - see MPEP 2106.05(g).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Adding the first synthetic dataset to the labeled datasets constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. - See MPEP 2106.05(d)(II).
Therefore, Claims 5 and 20 are directed to non-statutory subject matter and rejected.
With respect to Claim 6, which is dependent on Claim 2 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
"determining a first time period for a first model of the first plurality of statistical routines;"; Determining a first time period for a first model of the first plurality of statistical routines is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“determining a first statistical variation for the first model over the first time period;”; Determining a first statistical variation for the first model over the first time period is an abstract idea of a mathematical calculation, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP § 2106.04(a)(2)(I)(C).
“and determining a respective output, of the first plurality of respective outputs, for the first model based on the first statistical variation.”; Determining a respective output, of the first plurality of respective outputs, for the first model based on the first statistical variation is an abstract idea of a mathematical calculation, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 6 is directed to non-statutory subject matter and rejected.
With respect to Claim 7, which is dependent on Claim 2 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
"comparing a first respective output of the first plurality of respective outputs to a threshold value;"; Comparing a first respective output of the first plurality of respective outputs to a threshold value is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“and determining a difference between the first respective output and the threshold value, wherein selecting the first untrained model is based on the difference.”; Determining a difference between the first respective output and the threshold value, wherein selecting the first untrained model is based on the difference is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 7 is directed to non-statutory subject matter and rejected.
With respect to Claim 8, which is dependent on Claim 2 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
"filtering the first plurality of untrained models to generate a filtered subset of the first plurality of untrained models;"; Filtering the first plurality of untrained models to generate a filtered subset of the first plurality of untrained models is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“and selecting the first untrained model from the filtered subset.”; Selecting the first untrained model from the filtered subset is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 8 is directed to non-statutory subject matter and rejected.
With respect to Claim 9, which is dependent on Claim 2 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
"filtering the first plurality of untrained models based on an age of the first dataset to generate a filtered subset of the first plurality of untrained models;"; Filtering the first plurality of untrained models based on an age of the first dataset to generate a filtered subset of the first plurality of untrained models is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“and selecting the first untrained model from the filtered subset.”; Selecting the first untrained model from the filtered subset is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 9 is directed to non-statutory subject matter and rejected.
With respect to Claim 10, which is dependent on Claim 2 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
"filtering the first plurality of untrained models based on a reliability of the first dataset to generate a filtered subset of the first plurality of untrained models;"; Filtering the first plurality of untrained models based on a reliability of the first dataset to generate a filtered subset of the first plurality of untrained models is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“and selecting the first untrained model from the filtered subset.”; Selecting the first untrained model from the filtered subset is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 10 is directed to non-statutory subject matter and rejected.
With respect to Claim 11, which is dependent on Claim 2 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
"ranking the first plurality of untrained models to generate a ranked order of the first plurality of untrained models;"; Ranking the first plurality of untrained models to generate a ranked order of the first plurality of untrained models is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“and selecting the first untrained model based on the ranked order.”; Selecting the first untrained model based on the ranked order is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 11 is directed to non-statutory subject matter and rejected.
With respect to Claim 12, which is dependent on Claim 2 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
"determining respective training time predictions for each of the first plurality of untrained models;"; Determining respective training time predictions for each of the first plurality of untrained models is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“and selecting the first untrained model based on the respective training time predictions.”; Selecting the first untrained model based on the respective training time predictions is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 12 is directed to non-statutory subject matter and rejected.
With respect to Claim 13, which is dependent on Claim 2 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
"determining respective performance predictions for each of the first plurality of untrained models;"; Determining respective performance predictions for each of the first plurality of untrained models is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“and selecting the first untrained model based on the respective performance predictions.”; Selecting the first untrained model based on the respective performance predictions is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 13 is directed to non-statutory subject matter and rejected.
With respect to Claim 14, which is dependent on Claim 2 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
"determining respective predictions for a number of hyperparameters requiring training for each of the first plurality of untrained models;"; Determining respective predictions for a number of hyperparameters requiring training for each of the first plurality of untrained models is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“and selecting the first untrained model based on the respective predictions for the number of hyperparameters requiring training.”; Selecting the first untrained model based on the respective predictions for the number of hyperparameters requiring training is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 14 is directed to non-statutory subject matter and rejected.
With respect to Claim 15, which is dependent on Claim 2 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
"determining respective sample size requirements for training for each of the first plurality of untrained models;"; Determining respective sample size requirements for training for each of the first plurality of untrained models is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“and selecting the first untrained model based on the respective sample size requirements for training.”; Selecting the first untrained model based on the respective sample size requirements for training is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 15 is directed to non-statutory subject matter and rejected.
With respect to Claim 16, which is dependent on Claim 2 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
"determining respective processing power requirements for training for each of the first plurality of untrained models;"; Determining respective processing power requirements for training for each of the first plurality of untrained models is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“and selecting the first untrained model based on the respective processing power requirements for training.”; Selecting the first untrained model based on the respective processing power requirements for training is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 16 is directed to non-statutory subject matter and rejected.
Allowable Subject Matter
Claims 1-20 are subject to potential allowance. Subject matter ineligibility as a judicial exception under 35 U.S.C. $ 101 still stands.
The following is a statement of reasons for the indication of allowable subject matter: The prior art of record fails to teach or suggest, (a trained model that receives as input the specific conjunctive combination of lower-dimensional dataset embeddings together with corresponding statistical analyses as labeled feature inputs that generate statistical routine-based outputs indicating an overall respective effectiveness across a plurality of model types for generating predictions, selecting a first untrained model from a plurality of untrained models to perform default hyperparameter tuning, generating a profile matrix, sampling a synthetic distribution of a first dataset, multi-criteria filtering and ranking of untrained models, generating an optimized tuned first model all with the goal of significantly reducing the time and expertise required to develop effective AI models for time-series forecasting applications), as a whole.
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Bledsoe et. Al (US20180300737A1, filed April 17, 2017) Methods and Apparatus for Self-Adaptive Time Series Forecasting Engine: An apparatus has a memory with processor-executable instructions and a processor operatively coupled to the memory. The apparatus receives datasets including time series data points that are descriptive of a feature of a given entity. The processor determines a time series characteristic based on the data content, and selects, based on the determined characteristic, a set of entrant forecasting models from a pool of forecasting models stored in the memory. Next, the processor trains each entrant forecasting model with the time series data points to produce a set of trained entrant forecasting models. The processor executes each trained entrant forecasting model to generate a set of forecasted values indicating estimations of the feature of the given entity. Thereafter the processor selects at least one forecasting model from the set of trained entrant forecasting models based on computed accuracy evaluations performed over the set of forecasted values.
Bledsoe either alone or in-combination fails to disclose the claimed subject matter as a whole.
Srivastava et. Al (US20230185540A1, filed December 08, 2022) System and Method for Cross Domain Generalization for Industrial Artificial Intelligence Applications: A cross-domain generalization system for industrial artificial intelligence (AI) applications is disclosed. A target encoder subsystem obtains target data from a target machine product and generates lower dimensional data for obtained target data using a target artificial intelligence (AI) model. The generated lower dimensional data are corresponding to a plurality of target embeddings data. The target encoder subsystem further applies the plurality of target embeddings data into a source classifier AI model. A source classifier subsystem predicts a quality of the target machine product by generating class labels for each of the plurality of target embeddings data based on a result of the classifier AI model. The goal of the present invention is to learn features or representations such that the correlation with a label space is similar both in source and target domains while being invariant of data distributions.
Srivastava either alone or in-combination fails to disclose the claimed subject matter as a whole.
Ghanta et. Al (US20230161843A1, filed November 21, 2022) Detecting Suitability of Machine Learning Models for Datasets: Apparatuses, systems, program products, and method are disclosed for detecting suitability of machine learning models for datasets. An apparatus includes a training evaluation module configured to calculate a first statistical data signature for a training data set of a machine learning system using one or more predefined statistical algorithms. An apparatus includes an inference evaluation module configured to calculate a second statistical data signature for an inference data set of a machine learning system using one or more predefined statistical algorithms. An apparatus includes a score module configured to calculate a suitability score describing the suitability of a training data set to an inference data set as a function of a first and a second statistical data signature. An apparatus includes an action module configured to perform an action related to a machine learning system in response to a suitability score satisfying an unsuitability threshold.
Ghanta either alone or in-combination fails to disclose the claimed subject matter as a whole.
Moghadam et. Al (US20200334569A1, filed April 18, 2019) Using Hyperparameter Predictors to Improve Accuracy of Automatic Machine Learning Model Selection: Techniques are provided for selection of machine learning algorithms based on performance predictions by using hyperparameter predictors. In an embodiment, for each mini-machine learning model (MML model) of a plurality of MML models, a respective hyperparameter predictor set that predicts a respective set of hyperparameter settings for a first data set is trained. Each MML model represents a respective reference machine learning model (RML model) of a plurality of RML models. A first plurality of data set samples is generated from the first data set. A first plurality of first meta-feature sets is generated, each first meta-feature set describing a respective first data set sample of said first plurality. A respective target set of hyperparameter settings are generated for said each MML model using a hyper tuning algorithm. The first plurality of first meta-feature sets and the respective target set of hyperparameter settings are used to train the respective hyperparameter predictor set. Each hyperparameter predictor set is used during training and inference to improve the accuracy of automatically selecting a RML model per data set.
Moghadam either alone or in-combination fails to disclose the claimed subject matter as a whole.
Vu et. Al (US20220327058A1, filed October 13, 2022) Automated Time Series Forecasting Pipeline Generation: To automate time series forecasting machine learning pipeline generation, a data allocation size of time series data may be determined based on one or more characteristics of a time series data set. The time series data may be allocated for use by candidate machine learning pipelines based on the data allocation size. Features for the time series data may be determined and cached by the candidate machine learning pipelines. Predictions of each of the candidate machine learning pipelines using at least the one or more features may be evaluated. A ranked list of machine learning pipe lines may be automatically generated from the candidate machine learning pipelines for time series forecasting based upon evaluating predictions of each of the one or more candidate machine learning pipelines.
Vu either alone or in-combination fails to disclose the claimed subject matter as a whole.
Yakovlev et. Al (US20200327448A1, filed April 15, 2019) Predicting Machine Learning or Deep Learning Model Training Time: Herein are techniques for exploring hyperparameters of a machine learning model (MLM) and to train a regressor to predict a time needed to train the MLM based on a hyperparameter configuration and a dataset. In an embodiment that is deployed in production inferencing mode, for each landmark configuration, each containing values for hyperparameters of a MLM, a computer configures the MLM based on the landmark configuration and measures time spent training the MLM on a dataset. An already trained regressor predicts time needed to train the MLM based on a proposed configuration of the MLM, dataset meta-feature values, and training durations and hyperparameter values of landmark configurations of the MLM. When instead in training mode, a regressor in training ingests a training corpus of MLM performance history to learn, by reinforcement, to predict a training time for the MLM for new datasets and/or new hyperparameter configurations.
Yakovlev either alone or in-combination fails to disclose the claimed subject matter as a whole.
Clark et. Al (US20230060886A1, filed October 26, 2022) Training Neural Networks to Generate Structured Embeddings: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model to generate embeddings of inputs to the machine learning model, the machine learning model having an encoder that generates the embeddings from the inputs and a decoder that generates outputs from the generated embeddings, wherein the embedding is partitioned into a sequence of embedding partitions that each includes one or more dimensions of the embedding, the operations comprising: for a first embedding partition in the sequence of embedding partitions: performing initial training to train the encoder and a decoder replica corresponding to the first embedding partition; for each particular embedding partition that is after the first embedding partition in the sequence of embedding partitions: performing incremental training to train the encoder and a decoder replica corresponding to the particular partition.
Clark either alone or in-combination fails to disclose the claimed subject matter as a whole.
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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Conclusion
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/N.F.C./Examiner, Art Unit 2142 /Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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