DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This Office action is in response to Applicant’s communication filed on January 28, 2026. Amendments to claims 1, 12, and 19 have been entered. Claims 1-6, 8-16, 18 and 19 are pending and have been examined. The statement of reasons for the indication of allowable subject matter (over prior art) was already discussed in the Office action mailed on November 25, 2024 and hence not repeated here. The rejections and response to arguments, are stated below.
Claim Rejections - 35 USC § 101
2. 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.
3. Claims 1-6, 8-16, 18 and 19 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim(s) recite(s) a method for optimizing computational resources during training of a Constrained Linear Regression (CLR) model to prevent coefficient updates from exceeding predefined bounds while maintaining convergence speed through dynamic multiplier adjustment, which is considered a judicial exception because it falls under the categories of “Mathematical Concepts such as Mathematical Relationships an Mathematical Calculations”, and “Certain Methods of organizing human activity” such as commercial or legal interactions as discussed below. This judicial exception is not integrated into a practical application as discussed below. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
Analysis
Step 1: In the instant case, exemplary claim 1 is directed to a method (process).
Step 2A – Prong One: The limitations of “A computer implemented method for optimizing computational resources during training of a Constrained Linear Regression (CLR) model to prevent coefficient updates from exceeding predefined bounds while maintaining convergence speed through dynamic multiplier adjustment, the method comprising:
receiving, by a processor, time series sales data for an SKU, wherein the time series sales data comprises information relating to independent variables;
receiving, by the processor, one or more bounds or constraints for a coefficient of an independent variables, wherein the one or more bounds represent operational constraints related to the independent variable;
fine-tuning sensitivity of the CLR model to variations in input data based on a steepness value which is a hyperparameter distinct from a learning rate that controls a velocity of weight updates for the coefficient by regulating coefficient adjustments when approaching the one or more bounds to prevent boundary violations while maintaining model convergence;
for an initial training phase, determining, by the processor, the learning rate for the CLR model and maintaining the learning rate for at least one training phase to evaluate CLR model performance;
determining, by the processor, an update vector based on a number of the independent variables present in the time series sales data, wherein the update vector comprises binary elements that selectively enable coefficient updates based on coefficient proximity to the one or more bounds, and wherein updating the coefficient comprises skipping an update when a corresponding binary element of the update vector is zero and maintaining updated coefficients within the one or more bounds, thereby preventing boundary violations and reducing computational overhead during training;
dynamically adjusting, by the processor, a stopping criteria for the CLR model optimization process, the stopping criteria comprising at least one of: (i) a specified percentage decrease in a cost function; (ii) an absolute change in the cost function less than a minimum threshold; or (iii) a change in coefficient values between successive iterations less than a predetermined threshold; and, responsive to satisfaction of any one of the stopping criteria, declaring convergence and terminating the optimization of the CLR model;
iteratively optimizing, by the processor, the CLR model until convergence, wherein each iteration comprises:
computing, by the processor, a gradient of the cost function with respect to the coefficient;
automatically adjusting, by the processor, the learning rate to optimize convergence speed of the CLR model based on performance metrics from the initial training phase;
computing, by the processor, a multiplier for the independent variable based on the gradient, wherein the multiplier is dynamically adjusted based on whether the gradient of the cost function is positive or negative, wherein the multiplier is computed to optimize a response of the CLR model to fluctuating market conditions;
updating, by the processor, the coefficient based on the computed gradient, the computed multiplier, the learning rate, and the update vector, wherein the coefficient is updated to improve accuracy of the CLR model in attributing sales outcomes under varying conditions; and
monitoring, by the processor, optimization process of the CLR model for convergence based on whether a change in value of the cost function is below a predefined threshold or a maximum number of iterations is reached;
automatically generating, by the processor, an optimized CLR model having model coefficient defined as the updated coefficients that remain constrained within the one or more bounds throughout the iterative optimization process;
executing, by the processor, the optimized CLR model to generate attributions of sales performance in Revenue Growth Management (RGM) applications;
receiving, by the processor, user inputs to determine an impact of each of one or more potential actions on the sales performance;
generating, by the processor, an actionable report including the attributions of sales performance corresponding to each of the potential actions; and
based on the actionable report, providing, by the processor, a recommendation identifying a potential action for strategic decision-making” as drafted, when considered collectively as an ordered combination without the italicized portions, is a process that, under the broadest reasonable interpretation, covers the categories of “Mathematical Concepts such as Mathematical Relationships and Mathematical Calculations”, and also “Certain Methods of organizing human activity” such as commercial or legal interactions.
The steps of “dynamically adjusting, by the processor, a stopping criteria for the CLR model optimization process, the stopping criteria comprising at least one of: (i) a specified percentage decrease in a cost function; (ii) an absolute change in the cost function less than a minimum threshold; or (iii) a change in coefficient values between successive iterations less than a predetermined threshold; and, responsive to satisfaction of any one of the stopping criteria, declaring convergence and terminating the optimization of the CLR model;
iteratively optimizing, by the processor, the CLR model until convergence, wherein each iteration comprises:
computing, by the processor, a gradient of the cost function with respect to the coefficient;
automatically adjusting, by the processor, the learning rate to optimize convergence speed of the CLR model based on performance metrics from the initial training phase;
computing, by the processor, a multiplier for the independent variable based on the gradient, wherein the multiplier is dynamically adjusted based on whether the gradient of the cost function is positive or negative, wherein the multiplier is computed to optimize a response of the CLR model to fluctuating market conditions;
updating, by the processor, the coefficient based on the computed gradient, the computed multiplier, the learning rate, and the update vector, wherein the coefficient is updated to improve accuracy of the CLR model in attributing sales outcomes under varying conditions; and
monitoring, by the processor, optimization process of the CLR model for convergence based on whether a change in value of the cost function is below a predefined threshold or a maximum number of iterations is reached;
automatically generating, by the processor, an optimized CLR model having model coefficient defined as the updated coefficients that remain constrained within the one or more bounds throughout the iterative optimization process; and
executing, by the processor, the optimized CLR model to generate attributions of sales performance in Revenue Growth Management (RGM) applications” considered collectively as an ordered combination without the italicized portions, under the broadest reasonable interpretation, covers the category of “Mathematical Concepts such as Mathematical Relationships and Mathematical Calculations”. The CLR model is broadly interpreted to correspond to a statistical (mathematical) model.
The steps of “receiving, by the processor, user inputs to determine an impact of each of one or more potential actions on the sales performance;
generating, by the processor, an actionable report including the attributions of sales performance corresponding to each of the potential actions; and
based on the actionable report, providing, by the processor, a recommendation identifying a potential action for strategic decision-making” considered collectively as an ordered combination without the italicized portions, under the broadest reasonable interpretation, is a form of commercial interactions such as advertising, marketing or sales activities or behaviors, and business relations. The CLR model is broadly interpreted to correspond to a statistical (mathematical) model. Hence, the steps of the claim, considered collectively as an ordered combination without the italicized portions, covers the abstract categories of “Mathematical Concepts” and also “Certain Methods of organizing human activity” such as advertising, marketing or sales activities or behaviors, and business relations.
That is, other than, a processor, nothing in the claim precludes the steps from being performed as Mathematical concept including Mathematical Relationships and Mathematical Calculations and also Methods of organizing human activity. If the claim limitations, under the broadest reasonable interpretation, covers methods of organizing human activity but for the recitation of generic computer components, then it falls within the “Mathematical concepts” and “Certain methods of organizing human activity” groupings of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application. In particular, the claim (Claim 1) only recites the additional elements of a processor to perform all the steps. A plain reading of Figures 1 and 4 and descriptions in at least paragraphs [0007] – [0013], [0037] and [0122] reveals that processor may be a generic processor suitably programmed to execute the claimed steps. The modules recited in Claim 12 are broadly interpreted to include suitably programmed generic computer component to perform the associated functions. As discussed earlier, the CLR model is broadly interpreted to correspond to a statistical (mathematical) model. Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. The additional elements in all the steps are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Hence, claims 1, 12 and 19 are directed to an abstract idea.
Step 2B: The claim (Claim 1) does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements (identified above) to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer component. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, independent claim 1 is not patent eligible. Independent claims 12 and 19 are also not patent eligible based on similar reasoning and rationale.
Dependent claims 2-6, 8-11, 13-16, and 18, when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations only refine the abstract idea further.
For instance, in claims 2 and 13, the step “wherein the independent variables include one or more of pricing information, promotional data, distribution metrics, competitor activity data, holiday impact, marketing spend, advertisement spend, economic indicators, demographic factors, weather, and seasonality” under the broadest reasonable interpretation, further define the methods of organizing human activity because this step describes the independent variables used in the intermediate steps of the underlying process.
In claims 3-4 and 14-15, the steps “wherein the one or more bounds comprises at least one of a lower limit and an upper limit for the coefficient associated with the independent variable”, and “wherein the coefficient within the CLR model is initialized based on at least one of a predefined criteria that include statistical analysis of historical data sets and heuristic methods to ensure initial conditions are optimized for convergence” under the broadest reasonable interpretation, further define the methods of organizing human activity because these steps further describe the rules/criteria used in the intermediate steps of the underlying process.
In claim 5, the step “wherein the RGM applications comprise at least one of elasticity analysis, sales attribution, pricing simulation and recommendation, and promotional simulation and recommendation” under the broadest reasonable interpretation, further defines the methods of organizing human activity because this step describes the features of the RGM applications used in the intermediate steps of the underlying process.
In claim 6, the steps “wherein the learning rate is automatically adjusted by monitoring performance metrics of the CLR model, and wherein the performance metrics comprise at least one of: a change in the cost function, a convergence rate threshold, or oscillations in coefficient values” under the broadest reasonable interpretation, further define the methods of organizing human activity because these steps further describe the intermediate/final steps of the underlying process.
In claim 16, the steps “further comprise dynamically selecting a learning rate for determining a size of steps taken in a direction of the gradient during the optimization process of the CLR model” under the broadest reasonable interpretation, further define the methods of organizing human activity because these steps further describe the intermediate/final steps of the underlying process.
In claims 8-9, the steps “wherein the update vector is utilized to manage the bounds of the coefficients to prevent updates beyond a predefined thresholds”, and “wherein updating the coefficient further comprises validating the updated coefficient within the received bounds for the coefficient of the independent variable” under the broadest reasonable interpretation, further define the methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claims 10 and 18, the step “wherein updating the coefficient further comprises validating the updated coefficient within the received bounds for the coefficient of the independent variable” under the broadest reasonable interpretation, further defines the methods of organizing human activity because this step further describes the intermediate steps of the underlying process.
In claim 11, the steps “further comprises: generating a user interface on a display device for visualizing the optimized CLR model and enabling user interactions; and providing recommendations based on the optimized CLR model to facilitate data-driven decision-making across a diverse array of business scenarios” under the broadest reasonable interpretation, further define the methods of organizing human activity because these steps further describe the intermediate and/or final steps of the underlying process. The additional element of a user interface on a display device are broadly interpreted to correspond to generic computer components that perform their generic functions recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components.
In all the dependent claims, the judicial exception is not integrated into a practical application because the limitations are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. Also, the claims do not affect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer system itself; the claims do not affect a transformation or reduction of a particular article to a different state or thing; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment. In addition, the dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. The claims as a whole, do not amount to significantly more than the abstract idea itself. For these reasons, the dependent claims also are not patent eligible.
Response to Arguments
4. In response to Applicants arguments on pages 10-15 of the Applicant’s remarks that the claims are patent-eligible under 35 USC 101 when considered under MPEP 2106, the Examiner respectfully disagrees.
The fact that the claims are Patent-Ineligible when considered under the MPEP 2106 has already been addressed in the rejection and hence not all the details of the rejection are repeated here.
Response to Applicants’ arguments regarding Step 2A – Prong one:
The claim(s) recite(s) a method for optimizing computational resources during training of a Constrained Linear Regression (CLR) model to prevent coefficient updates from exceeding predefined bounds while maintaining convergence speed through dynamic multiplier adjustment, which is considered a judicial exception because it falls under the categories of “Mathematical Concepts such as Mathematical Relationships an Mathematical Calculations”, and “Certain Methods of organizing human activity” such as commercial or legal interactions as discussed, which is considered a judicial exception because it falls under the categories of “Mathematical Concepts such as Mathematical Relationships an Mathematical Calculations”, and “Certain Methods of organizing human activity” such as commercial or legal interactions as discussed in the rejection. Contrary to Applicant’s assertion that the Office action oversimplifies the claims and fails to account for the specific, computer-implemented mechanisms recited in amended Claim 1, the Examiner would like to point out that all limitations of all claims have been fully considered (in the rejections). Abstract ideas can be characterized at different levels of abstraction. (See Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1240-41 (Fed. Cir. 2016) (“An abstract idea can generally be described at different levels of abstraction.”)).
The features recited on pages 10-12 of the remarks such as “
Dynamic multiplier adjustment and learning rate control, which enables the model to adaptively respond to varying gradients during training; and maintains high convergence speed while preventing coefficient violations, reducing unnecessary iterations, and optimizing processor and memory usage.
o Hyperparameter-based steepness adjustment, which allows for fine-tuning sensitivity of coefficient updates to prevent overshooting bounds.
o Adaptive stopping criteria, which allows for terminating training when further
iterations provide negligible improvement, thereby preventing overfitting and reducing computational resource usage by avoiding unnecessary computation.
o Iterative gradient-based coefficient updates with update vectors ensures coefficients are updated in a controlled, constrained manner according to operational bounds, which in turn maximizes attribution accuracy and model stability while respecting constraints” may at best be characterized as improvements in the mathematical concepts and relationships. Such improvements belong in the realm of abstract ideas. The additional elements are used as tools in their ordinary capacity to apply the abstract idea. The alleged advantages such as “reducing unnecessary iterations, and optimizing processor and memory usage…. provides improved computational efficiency and robustness, ensuring stable model training even under variable or noisy input data …. helps maintain coefficient stability while preserving responsiveness to input variations to enhance model accuracy and reliability, preventing instability or computational errors. By preventing overshooting while preserving responsiveness to input variations, the steepness factor maintains coefficient stability and improves model accuracy and reliability. This controlled update behavior minimizes unnecessary recalculations, reduces computational overhead, and leads to faster model training and prediction times, thereby optimizing processor usage, memory consumption, and overall system performance, particularly when processing large datasets …. preventing overfitting and reducing computational resource usage by avoiding unnecessary computation. …. reducing unnecessary recalculations, conserves processor cycles and memory resources, and shortens training time, while ensuring parameter stability and high-quality attribution accuracy in the resulting CLR model …. ensures coefficients are updated in a controlled, constrained manner according to operational bounds, which in turn maximizes attribution accuracy and model stability while respecting constraints. …. maintains the integrity and real-world applicability of the CLR model while improving stability and attribution accuracy” are due to improvements in the abstract idea of a method for optimizing computational resources during training of a Constrained Linear Regression (CLR) model to prevent coefficient updates from exceeding predefined bounds while maintaining convergence speed through dynamic multiplier adjustment. It does not involve any improvements to another technology, technical field, or improvements to the functioning of the computer itself. The Examiner does not see the parallel between the Applicant’s claims and those in Ex parte Desjardins and Ex parte Carmody. Therefore, the Applicant’s arguments are not persuasive.
Response to Applicants’ arguments regarding Step 2A – Prong two:
According to MPEP 2106, limitations that are indicative of integration into a practical application include:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e).
In the instant case, the judicial exception is not integrated into a practical application, because none of the above criteria is met. The claim (Claim 1) only recites the additional elements of a processor to perform all the steps. A plain reading of Figures 1 and 4 and descriptions in at least paragraphs [0007] – [0013], [0037] and [0122] reveals that processor may be a generic processor suitably programmed to execute the claimed steps. The modules recited in Claim 12 are broadly interpreted to include suitably programmed generic computer component to perform the associated functions. As discussed earlier, the CLR model is broadly interpreted to correspond to a statistical (mathematical) model. Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. The additional elements in all the steps are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The Applicants are merely using the additional elements, as tools in their ordinary capacity, to apply the abstract idea. The claimed features and those recited on pages 13-14 of the remarks such as “dynamic learning rate and multiplier adjustment, a steepness hyperparameter controlling the velocity of coefficient updates, update vectors that selectively enable or restrict
coefficient updates, and adaptive stopping criteria” may at best be considered an improvement in the mathematical concepts and relationships. Such improvements belong in the realm of abstract ideas. An improvement in abstract idea is still abstract. An improvement in abstract idea is still abstract (SAP America v. Investpic *2-3 (“We may assume that the techniques claimed are “groundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Association for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); accord buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89–90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“A claim for a new abstract idea is still an abstract idea). The additional elements (identified in the claim) are generic computer components used to apply the abstract idea. The alleged advantages such as “enhances the functioning of the computing system itself by reducing processor cycles, memory usage, and training time while generating a more robust and reliable optimized CLR model” are due to improvements in the abstract idea. It does not involve any improvements to another technology, technical field, or improvements to the functioning of the computer itself. Therefore, the Applicants’ arguments are not persuasive.
Response to Applicants’ arguments regarding Step 2B:
As discussed in the rejection, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements (identified in the rejection) to perform the claimed steps, amount to no more than mere instructions to apply the exception using a generic computer component. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the Applicant’s claims are not patent eligible.
The claimed features including those recited on pages 14-15 such as “dynamic interaction between learning rate control, multiplier adjustment, steepness regulation, update vectors, and adaptive stopping criteria is not routine or conventional, and these elements work together to achieve technical advancements in convergence behavior, stability, and resource utilization …. a specific optimization architecture that constrains and controls coefficient updates to enhance accuracy and computational efficiency under real-world data variability and operational bounds. The automated generation and execution of the optimized CLR model further reflect an inventive concept rooted in enhanced model training and execution …. receiving user input, generating actionable reports, and providing recommendations, enable use of the model's technically enhanced outputs within a computing environment” may at best be characterized as an improvement in the field of mathematical concepts and relationships. Such improvements belong in the realm of abstract ideas. An improvement in abstract idea is still abstract.
In reference to the additional points listed on pages 14-15 of the remarks including the alleged benefits of the Applicant’s invention such as “convergence behavior, stability, and resource utilization ….. enhance accuracy and computational efficiency under real-world data variability and operational bounds …. receiving user input, generating actionable reports, and providing recommendations, enable use of the model's technically enhanced outputs within a computing environment, thereby demonstrating real-world application of the technological improvement while preserving the underlying technical contribution” are due to an improvement in the abstract idea, using the additional elements as tools in their normal capacity. The Examiner does not see the parallel between the Applicant’s claims and those in Ex parte Desjardins and Ex parte Carmody. Therefore, the Applicant’s arguments are not persuasive.
For these reasons and those discussed in the rejection, the rejections under 35 USC § 101 are maintained.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(a) Jordan; Brian C. (US Pub. 2024/0320699 A1) discloses systems and methods for forecasting commercial real estate lease rent rates. Exemplary embodiments receive a series of independent variables that represent attributes of a specific commercial real estate property, scale and normalize the series of independent variables, and assemble the scaled and normalized series of independent variables. Weightings may be applied to the assembled scaled and normalized series of independent variables to predict a rental rate for the specific commercial real estate property.
(b) Zhang; Sai et al. (US Patent 11,532,000 B2) discloses systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of calculating a halo weight for at least one item of a set of items, where the halo weight can comprise an effect sales of the at least one item has on sales of the set of items; calculating an item-wise elasticity for the at least one item; calculating a line-wise elasticity for the at least one item of the set of items; calculating an aggregate elasticity for the at least one item using the item-wise elasticity and the line-wise elasticity; calculating a demand forecast for the at least one item of the set of items; optimizing an objective function comprising the halo weight, the aggregate elasticity, the demand forecast, and at least one external constraint; adjusting at least one price of the at least one item based on the objective function, as optimized; and displaying the at least one price, as adjusted, on a display device. Other embodiments are disclosed herein.
6. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Narayanswamy Subramanian whose telephone number is (571) 272-6751. The examiner can normally be reached Monday-Friday from 9:00 AM to 5:00 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Abhishek Vyas can be reached at (571) 270-1836. The fax number for Formal or Official faxes and Draft to the Patent Office is (571) 273-8300.
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/Narayanswamy Subramanian/
Primary Examiner
Art Unit 3691
March 3, 2026