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
2. This office action is in response to the original filing of 11/2/2023. Claims 1-20 are pending and have been considered below.
Claim Rejections - 35 USC § 101
3. 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 abstract ideas without significantly more.
Claim 1:
Step 1: The claim is directed to a system, falling under one of the four statutory categories of invention.
Step 2A Prong 1: The claim recites following abstract ideas:
The limitations “a computation component that employs weighted model evaluation to compute stability of time series pipelines over respective holdout datasets”; and “a determination component that, based on the computed pipeline stabilities, selects a most stable time series pipeline” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
2A – Prong 2: This judicial exception is not integrated into a practical application. Claim 1 recites the additional elements:
“a processor and memory” merely uses a computer as a tool to perform an abstract idea, MPEP 2106.05(f)). These computer components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of state transition probability calculation) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
“a processor and memory” merely uses a computer as a tool to perform an abstract idea, MPEP 2106.05(f)). These computer components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of state transition probability calculation) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Claim 10:
Step 1: The claim is directed to a method, falling under one of the four statutory categories of invention.
Step 2A Prong 1: The claim recites following abstract ideas:
The limitations “employing, by the system, weighted model evaluation to compute stability of time series pipelines over respective holdout datasets; and selecting, by the system, a most stable time series pipeline based on the computed pipeline stabilities.” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
2A – Prong 2: This judicial exception is not integrated into a practical application. Claim 10 does not recite additional elements that are sufficient to amount to significantly more than the judicial exception
2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 19:
Step 1: The claim is directed to a system, falling under one of the four statutory categories of invention.
Step 2A Prong 1: The claim recites following abstract ideas:
The limitations “engage a computation component that employs weighted model evaluation to compute stability of time series pipelines over respective holdout datasets; and engage a determination component that, based on the computed pipeline stabilities, selects a most stable time series pipeline.” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
2A – Prong 2: This judicial exception is not integrated into a practical application. Claim 1 recites the additional elements:
“a processor and memory” merely uses a computer as a tool to perform an abstract idea, MPEP 2106.05(f)). These computer components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of state transition probability calculation) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
“a processor and memory” merely uses a computer as a tool to perform an abstract idea, MPEP 2106.05(f)). These computer components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of state transition probability calculation) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Claim 2 recites wherein the system further comprises an assignment component that assigns weights to time points of holdout data based on importance of near future over far future amount to insignificant extra solution activity like mere data gathering, MPEP 2106.05(g)).
Claim 3 recites wherein each pipeline is re-trained on a multiplicity of training data sets and wherein the computation component computes corresponding accuracies of validation datasets” constitute mathematical calculations and an evaluation/judgment. Mathematical relationships and mathematical calculations, See MPEP §2106.04(a)(2).
Claim 4 recites wherein the determination component utilizes average of computed accuracies of the validation data for final back testing pipeline evaluation” constitute mathematical calculations and an evaluation/judgment. Mathematical relationships and mathematical calculations, See MPEP §2106.04(a)(2).
Claim 5 recites wherein the computation component employs a linear regressive weight function on evaluation data to predict curve and evaluation metrics of the weighted model evaluation” constitute mathematical calculations and an evaluation/judgment. Mathematical relationships and mathematical calculations, See MPEP §2106.04(a)(2).
Claim 6 recites wherein the computation component employs a set of predefined regressive functions to represent relation between weight of prediction and weight of forecast of the weighted model evaluation” constitute mathematical calculations and an evaluation/judgment. Mathematical relationships and mathematical calculations, See MPEP §2106.04(a)(2)..
Claim 7 recites wherein the computation component employs evaluation data to build a linear regression model to determine trend of evaluation with different testing datasets” constitute mathematical calculations and an evaluation/judgment. Mathematical relationships and mathematical calculations, See MPEP §2106.04(a)(2)..
Claim 8 recites wherein the computation component computes variance of weighted mean absolute error to indicate similarity of back testing models and reflect stability in pipeline refreshment after deployment constitute mathematical calculations and an evaluation/judgment. Mathematical relationships and mathematical calculations, See MPEP §2106.04(a)(2)..
Claim 9 recites wherein the determination component selects a pipeline based on a determination that the pipeline does not have a positive slope and has smallest variance for back testing constitute mathematical calculations and an evaluation/judgment. Mathematical relationships and mathematical calculations, See MPEP §2106.04(a)(2).
Claim 11 recites engaging an assignment component that assigns weights to time points of holdout data based on importance of near future over far future amount to insignificant extra solution activity like mere data gathering, MPEP 2106.05(g)).
Claims 12-18 are similar in scope as claims 3-9, respectively; therefore, they are rejected under the same rationale.
Claim 20 recites wherein the program instructions are further executable to cause the processor to: engage an assignment component that assigns weights to time points of holdout data based on importance of near future over far future amount to insignificant extra solution activity like mere data gathering, MPEP 2106.05(g)).
Claim Rejections - 35 USC § 103
4. 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.
5. Claims 1-2, 4-11, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over SGLAVO et al. (US 2019/0394083 in view of Marti et al. US 2021/0090101
Claim 1. SGLAVO discloses a system, comprising:
a processor that executes computer-executable components stored in a non-transitory computer-readable memory ([0132]), the computer-executable components comprising:
a computation component that employs weighted model evaluation to compute time series pipelines over respective holdout datasets (abstract, [0149]-[0151])..( Each of the model strategies is further configured to determine error values associated with the champion forecasts, each of the error values indicating the accuracy of a respective champion forecast among the champion forecasts…) ([0006]); and
a determination component that, based on the computed pipeline, selects a time series pipeline ([0037]-[0038]).
SGLAVO does not explicitly disclose stability. However, Marti discloses (weights are assigned to forecasting ability, model longevity and model stability) ([0092]-[0093]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of SGLAVO to include the stability of Marti to apply known model-evaluation and statistical-selection techniques to their established purpose of improving selection of forecasting pipelines, with predictable results.
Claim 2. SGLAVO and Marti disclose the system of claim 1, Marti further discloses wherein the system further comprises an assignment component that assigns weights to time points of holdout data based on importance of near future over far future (assigning different weights to model metrics and expressly includes target-horizon penalties among the metrics used for model scoring) ([0090]-[0093])…( further states that model target horizon is received during model management and that weights are applied to selected metrics) ([0087]-[0090])..( weights are assigned to forecasting ability, model longevity and model stability) ([0092]-[0093]).. One would have been motivated to recognize that forecast errors closer to deployment are often more immediately relevant and would have assigned larger weights to nearer forecast periods.
Claim 4. SGLAVO and Marti disclose the system of claim 3, Marti further discloses wherein the determination component utilizes average of computed accuracies of the validation data for final back testing pipeline evaluation (..averaging error over multiple historical regression periods and calculating average single-period and aggregate error) ([0103]). One would have been motivated to improve selection of forecasting pipelines, with predictable results.
Claim 5. SGLAVO and Marti disclose the system of claim 1, Marti further discloses wherein the computation component employs a linear regressive weight function on evaluation data to predict curve and evaluation metrics of the weighted model evaluation (applies a primary regression to model metrics and multiplies the regression results by corresponding weights) ([0094])..( regression models consume forecast-model metrics to predict forecast-model error) ([0104])…( metrics associated with residual trends being multiplied by weights to generate score components) ([0096])…( expressly states that certain penalty values may correlate in a linear fashion with p-values and VIF metrics) ([0100]). One would have been motivated to improve selection of forecasting pipelines, with predictable results.
Claim 6. SGLAVO and Marti disclose the system of claim 1, Marti further discloses wherein the computation component employs a set of predefined regressive functions to represent relation between weight of prediction and weight of forecast of the weighted model evaluation (select model metrics, assigns weights, performs primary regression, and multiplies regression results against corresponding weights) ([0090]-[0094])...( specific weight ranges and weighting rules)([0092]–[0093]) [using predefined regression/weighting functions to represent forecast-performance relationships would have been an obvious implementation of the disclosed weighted regression scoring system.]. One would have been motivated to improve selection of forecasting pipelines, with predictable results.
Claim 7. SGLAVO and Marti disclose the system of claim 1, Marti further discloses wherein the computation component employs evaluation data to build a linear regression model to determine trend of evaluation with different testing datasets (regression models that consume forecast-model metrics to predict model error)([0104])...(evaluate residual trends and uses them as weighted model-score components)([0096]). One would have been motivated to improve selection of forecasting pipelines, with predictable results.
Claim 8. SGLAVO and Marti disclose the system of claim 1, SGLAVO further discloses wherein the computation component computes variance of weighted mean absolute error to indicate similarity of back testing models ([0151],[0173]) and Marti further discloses reflect stability in pipeline refreshment after deployment ([0090]-[0105], [0113]). One would have been motivated to improve selection of forecasting pipelines, with predictable results.
Claim 9. SGLAVO and Marti disclose the system of claim 1, SGLAVO further discloses wherein the determination component selects a pipeline based on a determination ([0037]-[0038]) and Marti further discloses that the pipeline does not have a positive slope and has smallest variance for back testing ([0016],[0065],[0104]). One would have been motivated to improve selection of forecasting pipelines, with predictable results.
Claims 10-20 are similar in scope as claims 1-2 and 4-9, respectively; therefore, they are
rejected under the same rationale.
6. Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over SGLAVO et al. (US 2019/0394083 in view of Marti et al. US 2021/0090101 and further in view of Schwiep et al. (US 2022/0292308).
Claim 3. SGLAVO and Marti disclose the system of claim 1 but fail to explicitly disclose wherein each pipeline is re-trained on a multiplicity of training data sets and wherein the computation component computes corresponding accuracies of validation datasets.
However, Schwiep discloses wherein each pipeline is re-trained on a multiplicity of training data sets and wherein the computation component computes corresponding accuracies of validation datasets ([0099], [0102]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of SGLAVO to include the re-training of Schwiep to improve selection of forecasting pipelines with predictable results.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Phenuel S. Salomon whose telephone number is (571) 270-1699. The examiner can normally be reached on Mon-Fri 7:00 A.M. to 4:00 P.M. (Alternate Friday Off) EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Usmaan Saeed can be reached on (571) 272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-3800.
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/PHENUEL S SALOMON/Primary Examiner, Art Unit 2146
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