DETAILED ACTION
Introduction
This Office action is responsive to the communications filed June 13, 2024. Claims 1-20 are pending.
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 .
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 an abstract idea without significantly more.
In the instant case, claims 1-10 are directed to method. Claims 11-17 are directed to a system. Claims 19 and 20 are directed to non-transitory computer readable medium. Therefore, these claims fall within the four statutory categories of invention.
For example, claim 1 recites an abstract idea of organizing and manipulating information through mathematical correlations. The claim under its broadest reasonable interpretation recites limitations grouped within the “mathematical concepts” grouping of abstract ideas. Mathematical concepts abstract idea grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. See MPEP § 2106.04(a)(2), subsection I. The claim limitations reciting the abstract idea are grouped within the “mathematical concepts” grouping of abstract ideas as they relate to organizing and manipulating information through mathematical correlations. More specifically, the following the bolded claim elements recite additional elements while the other claim elements recite the abstract idea. according to MPEP 2106.04(a).
1. A computer-implemented method comprising:
receiving, by one or more processors, a plurality of data from one or more sources;
processing, by the one or more processors, the plurality of data to select one or more relevant variables;
training, by the one or more processors, one or more prediction models based on the one or more relevant variables, and a combination of an advanced statistical model and a machine-learning model;
evaluating, by the one or more processors, performance of the one or more trained prediction models based on one or more validation techniques; and
deploying, by the one or more processors, at least one prediction model based on the performance for generating one or more predictions.
Independent claims 11 and 19 recite similar language.
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See MPEP 2106.04(d)), the additional element(s) of the claim(s) such as the processors and machine learning model is merely used as tools to perform an abstract idea and/or generally link the use of a judicial exception to a particular technological environment. Specifically, these additional elements perform the steps or functions of organizing and manipulating information. Viewed as a whole, the use of processors and machine learning model as tools to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer or computer networks performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See MPEP 2106.05), the additional element(s) of the processors and machine learning model to perform the steps amounts to no more than using generic hardware or software to automate and/or implement the abstract idea of organizing and manipulating information through mathematical correlations. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of organizing and manipulating information through mathematical correlations. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05 (f) & (h)). Therefore, the claim is not patent eligible.
The dependent claims further describe the abstract idea such as processing the plurality of data to select the one or more relevant variables comprises: causing, by the one or more processors, a detection and an imputation of missing values in the plurality of data using one or more imputation techniques; normalizing, by the one or more processors, one or more features in the plurality of data using one or more normalization techniques; and selecting, by the one or more processors, the one or more relevant variables using one or more feature selection techniques
The dependent claims do not include additional elements that integrate the abstract idea into a practical application or that provide significantly more than the abstract idea. Therefore, the dependent claims are also not patent eligible.
Claim Rejections - 35 USC § 103
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over “Forecasting Sales Using Various Time-Series and Machine Learning Methods” to Padberg (“Padberg”) in view of U.S. Patent 12,530,614 to Frosch et al. (“Frosch”).
As per claim 1, Padberg discloses receiving, by one or more processors, a plurality of data from one or more sources (p. 12 at section 3.1 – the data comes from different sources);
processing, by the one or more processors, the plurality of data to select one or more relevant variables (p. 21 – feature selection, scaling; section 4.1);
training, by the one or more processors, one or more prediction models based on the one or more relevant variables, and a combination of an advanced statistical model and a machine-learning model (sections 4.1-4.5 and section 5. 2); and
evaluating, by the one or more processors, performance of the one or more trained prediction models based on one or more validation techniques (sections 5.1 and 5.2 – cross validation).
Although it is known that the steps are being performed by a processor. Padberg does not expressly recite a processor. Also, Padberg does not expressly disclose deploying, by the one or more processors, at least one prediction model based on the performance for generating one or more predictions.
Frosch discloses a processor (col. 32, l. 65 -col. 44, l. 45) and deploying, by the one or more processors, at least one prediction model based on the performance for generating one or more predictions (col. 1, ll. 14-29).
At the time of the invention, it would have been obvious to one of ordinary skill in the art to include the elements of Frosch into Padberg as it the last step of the machine learning pipeline. Hence, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, Padberg discloses wherein processing the plurality of data to select the one or more relevant variables comprises: causing, by the one or more processors, a detection and an imputation of missing values in the plurality of data using one or more imputation techniques; normalizing, by the one or more processors, one or more features in the plurality of data using one or more normalization techniques; and selecting, by the one or more processors, the one or more relevant variables using one or more feature selection techniques (p. 21 and p. 29 – feature selection).
As per claim 3, Padberg discloses wherein training the one or more prediction models comprises: identifying, by the one or more processors, the one or more relevant variables within the plurality of data based on one or more of correlation analysis or statistical analysis, wherein the one or more relevant variables has strong linear relationship with a target variable (pp. 12 and 22); and inputting, by the one or more processors, the one or more relevant variables into the advanced statistical model and the machine-learning model, wherein the advanced statistical model and the machine-learning model analyze patterns between the one or more relevant variables and the target variable (p. 35 – random forest regressor).
As per claim 4, Padberg discloses identifying, by the one or more processors, an optimal hyperparameter of the machine-learning model using a Bayesian optimization; applying, by the one or more processors, cross-validation to assess performance of one or more hyperparameter configurations and prevent overfitting; and integrating, by the one or more processors, hyperparameter tuning into the machine-learning model and parameter optimization into the advanced statistical model (p. 44).
As per claim 5, Padberg discloses wherein the advanced statistical model includes Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average With Exogenous Variables (SARIMAX), or Exponential Smoothing (p. 44).
As per claim 6, Padberg discloses wherein the machine-learning model includes Long Short Term Memory (LSTM), Random Forests, deep learning models, Gradient Boosting Machines, Transformers, ExtraTrees, AdaBoost, XGBoost, or LightGBM (see claim 3; pp. 6 and 7).
As per claim 7, Padberg discloses monitoring, by the one or more processors, the performance of the at least one deployed model using performance metrics (p. 44; p. 53).
Padberg does not expressly disclose re-training, by the one or more processors, the deployed models based on updated data to adapt to changing patterns and trends.
Frosch discloses re-training, by the one or more processors, the deployed models based on updated data to adapt to changing patterns and trends (col. 25, ll. 34-36 – update the deployed machine learning model; claim 8 of Frosch – retains the trained machine learning model).
At the time of the invention, it would have been obvious to one of ordinary skill in the art to include the elements of Frosch into Padberg as it is an conventional process of machine pipeline. Hence, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 8, Padberg discloses wherein the one or more relevant variables include an exogenous feature, and wherein the exogenous feature comprises economic indicators, weather data, or promotional events (p. 21 - economic indicators). Additionally, the features comprising economic indicators, weather data, or promotional events, this is considered non-functioning descriptive material. However, this difference is only found in the non-functional descriptive material and is not functionally involved in the steps recited. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
As per claim 9, Padberg discloses performing, by the one or more processors, one or more simulations to assess significance of one or more variables in the plurality of data; and analyzing, by the one or more processors, one or more results from the one or more simulations to identify variables with high effects on model predictions (pp. 29 and 30 - section 4.1).
As per claim 10, Padberg discloses wherein processing the plurality of data and training the one or more prediction models utilize parallel processing techniques (section 4.3 – random forest involves splitting, each tree is built independently).
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Claims 11-17 are rejected on the same rationale as claims 1-7 above.
Claims 18-20 are rejected on the same rationale as claims 1-3 above.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JALATEE WORJLOH whose telephone number is (571)272-6714. The examiner can normally be reached Monday-Friday 6:00am-2:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Hayes can be reached at (571) 272-6708. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jalatee Worjloh/Primary Examiner, Art Unit 3697
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