DETAILED ACTION
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 . Claims 1-20 have been reviewed and are under consideration by this office action.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 04/23/2025, 04/29/2025, and 10/01/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claim(s) is/are directed to statutory categories.
Step 2A, Prong One – The claims are found to recite limitations that set forth the abstract idea(s), namely in independent claims recite a series of steps for the abstract idea recited below.
Regarding independent claims, (additional elements bolded)
Regarding Claims 1 and 10, A method of determining demand transference for an item assortment of a retailer, the method comprising:/ A non-transitory computer readable medium having instructions stored thereon that, when executed by one or more processors, cause the processors to determine demand transference for an item assortment of a retailer, the determining demand transference comprising:
receiving historical sales data for a category of items corresponding to the retailer;
receiving hierarchy data for the category of items corresponding to the retailer;
based on the historical sales data and the hierarchy data, estimating first variables of a multinomial logit (MNL) model (recited at a high level of generality); and
based on the historical sales data and the hierarchy data, estimating second variables of a log linear retail sales model.
Regarding Claim 19, A sales forecast system for determining demand transference for an item assortment of a retailer, the system comprising:
a first database storing historical sales data for a category of items corresponding to the retailer;
a second database storing hierarchy data for the category of items corresponding to the retailer;
a multinomial logit (MNL) model;
a log linear retail sales model;
one or more processors configured to: based on the historical sales data and the hierarchy data, estimate first variables of the multinomial logit (MNL) model; and
based on the historical sales data and the hierarchy data, estimate second variables of the log linear retail sales model.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea groupings of “Mental processes—concepts performed in the human mind” (observation, evaluation, judgment, opinion) as the claims are directed towards receiving historic sales data, receiving hierarchy data, and estimating variables all of which are concepts capable of being performed in the human mind (i.e. via pen and paper).
Further the claims are directed towards the abstract idea grouping of “Certain methods of organizing human activity” — commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as the claims are directed towards determining a demand transference of an item assortment (See Specification, [09]).
Step 2A, Prong Two - This judicial exception is not integrated into a practical application. The independent claims utilize at least the additional elements bolded above The additional elements are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Step 2B - The claim 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, the additional elements are just “apply it” on a computer. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Regarding Claims 2-3, 6-9, 11-12, 15-18, and 20, the claim further narrows the abstract idea or recite additional elements previously addressed in the independent claims.
Regarding Claims 4-5 and 13-14, the claim further recite the additional element(s) of a Scan*Pro model. This elements is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B.
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-4, 9-10, 12-13, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Subramanian et al. (US 20120130726 A1) (herein after referred to as “Subra”) in view of Harsha et al. (US 20160155137 A1).
Regarding Claims 1 and 10, Subra teaches: A method of determining demand transference for an item assortment of a retailer, the method comprising:/ A non-transitory computer readable medium having instructions stored thereon that, when executed by one or more processors, cause the processors to determine demand transference for an item assortment of a retailer, the determining demand transference comprising: (Subra, [19]; The DNFP at (1a) above aims to determine the market share for each substitutive item in the category, while optimizing multiple objectives such as gross margin, sales, and revenue, and also considering the dynamic response of category-level sales to price changes and pricing rules. More specifically, it determines the price points in the price ladder for each item i that maximize the gross margin (1a) for the category and Subra, [claim 10]; A computer implemented method of determine a product price, the method comprising and Subra, [26]; the functionality of the flow diagram of FIG. 2 is implemented by software stored in memory or other computer readable or tangible medium, and executed by a processor. In other embodiments, the functionality may be performed by hardware (e.g., through the use of an application specific integrated circuit ("ASIC").
receiving… data for a category of items corresponding to the retailer; (Subra, [28]; calibration parameters are used to set up the MNL model. If a higher (category level) elasticity model is used, as described below, a single additional elasticity parameter is received. The MNL model or the MNL-elasticity model combination describes how the sales of the items vary with the prices. The other input parameters include data such as item costs, current prices, business rules, and goals etc. These are added to provide directions on what constitutes a feasible (acceptable) pricing answer and also what the prices (and hence the sales levels) should be set to maximize the desired business goals (e.g., gross margin for the category, or total sales $ for the category, etc).
receiving hierarchy data for the category of items corresponding to the retailer; (Subra, [15]; Items can represent stock-keeping units ("SKU"s), product subclasses, or product classes within the category, depending on the level of aggregation in the merchandise hierarchy at which the analysis is performed by the category manager. For simplicity, it is assumed that prices of SKUs are optimized at the store-level of the location hierarchy and Subra, [25]; customer data sets are encountered in practice that consist of several isolated demand subgroups (sub-categories), each of which contains items that are substitutable only by items within that sub-category. Allowing each such sub-category to be governed by its own statistically calibrated item-level MNL model, as well as its own sub-category-level demand model, helps improve the empirical performance of the overall predictive modeling framework).
based on the… data and the hierarchy data, estimating first variables of a multinomial logit (MNL) model; and (Subra, [05]; system receives product pricing constraints and multinomial logit ("MNL") calibration parameters. The system then generates a calibrated MNL model using the calibration parameters and encodes the MNL model and the product pricing constraints into a mixed-integer program ("MIP") and Subra, [15]; the models can be readily extended to manage higher levels of aggregation (e.g., at the zonal level). Further, the models can address more general situations faced by category managers such as the need to jointly optimize multiple categories that are inter-linked by pricing constraints and/or objectives, or manage several distinct subsets of substitutable items within the same category and Subra, [28]; MNL calibration parameters are received. The calibration parameters are used to set up the MNL model. If a higher (category level) elasticity model is used, as described below, a single additional elasticity parameter is received. The MNL model or the MNL-elasticity model combination describes how the sales of the items vary with the prices. The other input parameters include data such as item costs, current prices, business rules, and goals etc.).
based on the… data and the hierarchy data, estimating second variables of a…retail sales model. (Subra, [18]; category pricing problem (i.e., MNL model) can then be recast as a discrete nonlinear fractional programming problem ("DNFP")and Subra, [50]; instead of just the MNL model, a bi-level empirical predictive framework is used that includes a high-level price elasticity-based demand model, as well as a MNL-based market-share prediction model at the lower level. This is embedded within an optimization framework to formulate the discrete, nonlinear fractional program ("DNFP"), which is subsequently transformed/encoded into an equivalent MIP having a relatively tight underlying LP relaxation. In one embodiment, the high-level price elasticity-based demand model is the "SCAN*PRO" model).
While Subra teaches determining demand transference using retail data, categorizing items, a hierarchy of categories of items, plurality of models and retail sales models Subra does not appear to explicitly teach the use of historical sales data or log linear models. However, Subra in view of the analogous art of Harsha(i.e. demand modeling) does teach historical sales data. (Harsha, [22]; Retrieving a historical dataset and corresponding sales attributes dataset 210 may include activating the data retrieval module 110 to retrieve datasets. The retrieved datasets may include a historical sales dataset 122 and a corresponding sales attributes dataset 124 for one or more sellable commodities and Harsha, [23]; The demand forecasting method allows for modeling a variety of functional forms, but for executing a specific instance the market size and the market share must each be set to have a particular form. For example, in one embodiment the market size can be a log-linear model and the market share can be a multinomial logit model and Harsha, [103]; it may be able to identify linear or log-linear relationships between various types of data. One such relationship is a demand curve, which explores the relationship between rates and unit sold. More examples of specific types of analysis are provided in the discussion below).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Subra including determining demand transference using retail data, categorizing items, a hierarchy of categories of items, and retail sales models with the teachings of Harsha including historical sales data and log-linear models in order to determine lost market rate, lost sales, etc. and provide a separate model for market size/share (Harsha, [02]; process the historical sales dataset to provide a lost market rate probability dataset, a lost-sales forecasting module configured to process the lost market rate probability dataset to provide a lost-sales dataset as a function of the lost market rate probability, a market size forecasting module configured to process the lost market rate probability dataset to provide a market size dataset as a function of the lost market rate probability, and a demand forecasting module configured to process the lost-sales dataset and the historical sales dataset to provide a demand dataset and a market share dataset as functions of the lost-sales dataset and the historical sales dataset and Harsha, [23]; he demand forecasting method allows for modeling a variety of functional forms, but for executing a specific instance the market size and the market share must each be set to have a particular form. For example, in one embodiment the market size can be a log-linear model and the market share can be a multinomial logit model.
Regarding Claims 3 and 12, Subra/Harsha teaches: The method of claim 1, wherein the estimating second variables of the log linear retail sales model comprises determining geometric means. (Subra, [19]; The price effect on sales is captured using an empirical predictive model (1b) that attempts to explain the overall category sales as some nonlinear function of the geometric mean of the scaled prices of the items in the category based on an estimated value for the price elasticity parameter (.psi.) for category-level sales).
Regarding Claims 4 and 13, Subra/Harsha teaches: The method of claim 1, wherein the log linear retail sales model is derived from a Scan*Pro model. (Subra, [50]; In one embodiment, the high-level price elasticity-based demand model is the "SCAN*PRO" model).
Regarding Claim 9, 18, and 20, Subra/Harsha teaches: The method of claim 1, further comprising: generating a sales forecast for the category of items using the estimated first variables of the MNL model and the estimated second variables of the log linear retail sales model. (Subra, [16]; Whereas MNL models can be used to predict market shares based on relative utilities or attractions, the overall sales of the demand group (or category) itself does not change and Subra, [52]; An example business use case would be to adjust prices to: maximize total predicted margin across the category such that: (a) total predicted volume (sales units) across category does not drop by more than 1%; (b) all recommended prices are within 10% of current price; (c) store brand items are always priced less than the corresponding national brand items (per ounce) (d) prices should always ends in .x9 cents, (e) . . . etc., where the predicted level of sales of any item depends on its own price as well as the prices of other items as specified by a calibrated MNL (+high-level elasticity) model).
Regarding Claim 19, Subra teaches: A sales forecast system for determining demand transference for an item assortment of a retailer, the system comprising: (Subra, [19]; The DNFP at (1a) above aims to determine the market share for each substitutive item in the category, while optimizing multiple objectives such as gross margin, sales, and revenue, and also considering the dynamic response of category-level sales to price changes and pricing rules. More specifically, it determines the price points in the price ladder for each item i that maximize the gross margin (1a) for the category and Subra, [claim 10]; A computer implemented method of determine a product price, the method comprising and Subra, [26]; the functionality of the flow diagram of FIG. 2 is implemented by software stored in memory or other computer readable or tangible medium, and executed by a processor. In other embodiments, the functionality may be performed by hardware (e.g., through the use of an application specific integrated circuit ("ASIC").
a first database…data for a category of items corresponding to the retailer; (Subra, [28]; calibration parameters are used to set up the MNL model. If a higher (category level) elasticity model is used, as described below, a single additional elasticity parameter is received. The MNL model or the MNL-elasticity model combination describes how the sales of the items vary with the prices. The other input parameters include data such as item costs, current prices, business rules, and goals etc. These are added to provide directions on what constitutes a feasible (acceptable) pricing answer and also what the prices (and hence the sales levels) should be set to maximize the desired business goals (e.g., gross margin for the category, or total sales $ for the category, etc and Subra, [10]; System 10 further includes a memory 14 for storing information and instructions to be executed by processor 22. Memory 14 can be comprised of any combination of random access memory ("RAM"), read only memory ("ROM"), static storage such as a magnetic or optical disk, or any other type of computer readable media. System 10 further includes a communication device 20, such as a network interface card, to provide access to a network and Subra, [13]; A database 17 is coupled to bus 12 to provide centralized storage for modules 16 and 18 and store pricing information, inventory information, etc.).
a second database storing hierarchy data for the category of items corresponding to the retailer; (Subra, [15]; Items can represent stock-keeping units ("SKU"s), product subclasses, or product classes within the category, depending on the level of aggregation in the merchandise hierarchy at which the analysis is performed by the category manager. For simplicity, it is assumed that prices of SKUs are optimized at the store-level of the location hierarchy and Subra, [25]; customer data sets are encountered in practice that consist of several isolated demand subgroups (sub-categories), each of which contains items that are substitutable only by items within that sub-category. Allowing each such sub-category to be governed by its own statistically calibrated item-level MNL model, as well as its own sub-category-level demand model, helps improve the empirical performance of the overall predictive modeling framework).
a multinomial logit (MNL) model; (Subra, [05]; One embodiment is a product pricing system that determines a product price. The system receives product pricing constraints and multinomial logit ("MNL") calibration parameters. The system then generates a calibrated MNL model using the calibration parameters and encodes the MNL model and the product pricing constraints into a mixed-integer program ("MIP") and Subra, [15]; the models can be readily extended to manage higher levels of aggregation (e.g., at the zonal level). Further, the models can address more general situations faced by category managers such as the need to jointly optimize multiple categories that are inter-linked by pricing constraints and/or objectives, or manage several distinct subsets of substitutable items within the same category.
one or more processors configured to: based on the historical sales data and the hierarchy data, estimate first variables of the multinomial logit (MNL) model; and (Subra, [05]; system receives product pricing constraints and multinomial logit ("MNL") calibration parameters. The system then generates a calibrated MNL model using the calibration parameters and encodes the MNL model and the product pricing constraints into a mixed-integer program ("MIP") and Subra, [28]; MNL calibration parameters are received. The calibration parameters are used to set up the MNL model. If a higher (category level) elasticity model is used, as described below, a single additional elasticity parameter is received. The MNL model or the MNL-elasticity model combination describes how the sales of the items vary with the prices. The other input parameters include data such as item costs, current prices, business rules, and goals etc.).
based on the… and the hierarchy data, estimate second variables of the… retail sales model. (Subra, [18]; category pricing problem (i.e., MNL model) can then be recast as a discrete nonlinear fractional programming problem ("DNFP")and Subra, [50]; instead of just the MNL model, a bi-level empirical predictive framework is used that includes a high-level price elasticity-based demand model, as well as a MNL-based market-share prediction model at the lower level. This is embedded within an optimization framework to formulate the discrete, nonlinear fractional program ("DNFP"), which is subsequently transformed/encoded into an equivalent MIP having a relatively tight underlying LP relaxation. In one embodiment, the high-level price elasticity-based demand model is the "SCAN*PRO" model).
While Subra teaches determining demand transference using retail data, categorizing items, a hierarchy of categories of items, and retail sales models Subra does not appear to explicitly teach the use of historical sales data or log linear models. However, Subra in view of the analogous art of Harsha(i.e. demand modeling) does teach historical sales data. (Harsha, [22]; Retrieving a historical dataset and corresponding sales attributes dataset 210 may include activating the data retrieval module 110 to retrieve datasets. The retrieved datasets may include a historical sales dataset 122 and a corresponding sales attributes dataset 124 for one or more sellable commodities and Harsha, [23]; The demand forecasting method allows for modeling a variety of functional forms, but for executing a specific instance the market size and the market share must each be set to have a particular form. For example, in one embodiment the market size can be a log-linear model and the market share can be a multinomial logit model and Harsha, [103]; it may be able to identify linear or log-linear relationships between various types of data. One such relationship is a demand curve, which explores the relationship between rates and unit sold. More examples of specific types of analysis are provided in the discussion below).
a second database… (Harsha, [40]; The operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Subra including determining demand transference using retail data, categorizing items, a hierarchy of categories of items, and retail sales models with the teachings of Harsha including historical sales data and log-linear models in order to determine lost market rate, lost sales, etc. and provide a separate model for market size/share (Harsha, [02]; process the historical sales dataset to provide a lost market rate probability dataset, a lost-sales forecasting module configured to process the lost market rate probability dataset to provide a lost-sales dataset as a function of the lost market rate probability, a market size forecasting module configured to process the lost market rate probability dataset to provide a market size dataset as a function of the lost market rate probability, and a demand forecasting module configured to process the lost-sales dataset and the historical sales dataset to provide a demand dataset and a market share dataset as functions of the lost-sales dataset and the historical sales dataset and Harsha, [23]; he demand forecasting method allows for modeling a variety of functional forms, but for executing a specific instance the market size and the market share must each be set to have a particular form. For example, in one embodiment the market size can be a log-linear model and the market share can be a multinomial logit model.
Claims 2, 5, 11, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Subramanian et al. (US 20120130726 A1) (herein after referred to as “Subra”) in view of Harsha et al. (US 20160155137 A1), and Bateni et al. (US 20090177520 A1).
Regarding Claims 2 and 11, While Subra/Harsha teaches estimating second variables and log linear models, Subra does not appear to teach determining ratios of pairs. However, Subra/Harsha in view of the analogous art of Bateni (i.e. demand modeling) does teach: The method of claim 1, wherein the estimating second variables of the log linear retail sales model comprises determining ratio of pairs. (Bateni, [330, 332]; So for any give pair of variables we need to test the ratio of the corresponding elements. If all the ratios are equal the variables are dependent, otherwise they are independent… Calculate the ratio rij=vix/vjx for each pair of variables).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Subra including estimating second variables, with the teachings of Bateni including determining ratios of pairs in order to better determine causal variables for calculations (Bateni, [337, 344-345]; The ratio rij cannot be calculated when both of the corresponding elements are zero. In this case the calculation is skipped (the statistic S need not to be updated). If only one of the elements is zero, however, the two variables are independent (S=large number)… The following ratio must be met:… If the ratio is not met, causal variables must be removed from the right hand side of the active list (least significant) until the ratio is satisfied).
Regarding Claims 5 and 14, While Subra teaches estimating second variables, Subra does not appear to teach determining ratios of pairs. However, Subra/Harsha in view of Bateni does teach The method of claim 1, wherein the log linear retail sales model comprises a Scan*Pro model, and the estimating second variables of the log linear retail sales model comprises using a determining ratio of pairs estimation or a determining geometric means estimation. (Bateni, [50]; cited above regarding Scan*Pro and [Bateni, 330, 332]; So for any give pair of variables we need to test the ratio of the corresponding elements. If all the ratios are equal the variables are dependent, otherwise they are independent… Calculate the ratio rij=vix/vjx for each pair of variables).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Subra/Harsha including estimating second variables, with the teachings of Bateni including determining ratios of pairs in order to better determine causal variables for calculations (Bateni, [337, 344-345]; The ratio rij cannot be calculated when both of the corresponding elements are zero. In this case the calculation is skipped (the statistic S need not to be updated). If only one of the elements is zero, however, the two variables are independent (S=large number)… The following ratio must be met:… If the ratio is not met, causal variables must be removed from the right hand side of the active list (least significant) until the ratio is satisfied).
Claims 6 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Subramanian et al. (US 20120130726 A1) (herein after referred to as “Subra”) in view of Harsha et al. (US 20160155137 A1), and Li et al. (US 20190325463 A1).
Regarding Claims 6 and 15, While Subra teach the use of an MNL model, Subra does not appear to explicitly describe a non-nested model. However, Subra/Harsha in view of the analogous art of Li, (i.e. product modeling) does teach: The method of claim 1, wherein the MNL model is non-nested. (Li, [02]; The present disclosure generally relates to extrinsic pricing solutions, and in particular to systems and methods for product-line pricing under discrete mixed multinomial logit demand).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Subra including the use of an MNL model, with the teachings of Li including a non-nested model in order to allow for a model that allows emphasis on particular parameters such as feature or pricing (Li, [23]; a discrete mixed multinomial logit (MMNL) demand model was considered, which aligns with the setting of a market that can be decomposed into a finite number of market segments, each with its own set of product utility parameters reflecting the unique emphasis that this segment of customers place on the features and price. This model is referred to as the MMNL model and the MNL model without customer-specific consideration is referred to as the basic MNL model or simply the MNL model in the remainder of the present disclosure.
Claims 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Subramanian et al. (US 20120130726 A1) (herein after referred to as “Subra”) in view of Harsha et al. (US 20160155137 A1), and Malov et al. (US 20120254092 A1).
Regarding Claims 7 and 16, Subra/Harsha teaches: The method of claim 1, wherein the estimated first variables of the MNL model comprise M, γTj, and BTj,…, γTj is a price elasticity as it relates the price of an item to its sales, and BTj is a base utility of an item. (Subra, [17]; In one embodiment, an MNL nonlinear optimization model is used to represent the category pricing problem. The following notation is used in the model to represent MNL calibration parameters:… obtained by fixing prices of items at the initial prices p.sub.i.sup.0, .A-inverted.i=1, . . . , n. .psi.=price-elasticity parameter for category-level demand. U.sub.i(p.sub.i)=deterministic component of the utility of item i (function of the variables p.sub.i). .mu..sub.i, .lamda..sub.i=coefficients used in the utility expression for item I and Subra, [25]; The underlying MNL model in (1) assumes that all the items in the demand group are substitutes. However, in some embodiments/implementations, customer data sets (i.e. potential customers) are encountered in practice that consist of several isolated demand subgroups (sub-categories), each of which contains items that are substitutable only by items within that sub-category). Examiner notes the set of customers would be a discrete number of potential customers.
While Subra/Harsha teaches variable in an MNL model, neither appears to teach: where the set of all potential customers who might consider purchasing an item by M, where M=|M| is a size of the set. However, Subra/Bateni in view of the analogous art of Malov (i.e. demand modeling) does teach the entirety of the limitation: Malov, [68]; Modeling engines, such as demand modeling engines, can be used to identify and quantify relationships between KPIs (such as number of customers, originations) volumes, utilization, retention, etc.) and external econometric valuables (e.g. rates, margins, competitors, incentives, and various marketing indices). Demand modeling may be understood as a core component in pricing analytics. Demand may be understood as the result of individual buying decisions on the part of potential customers. Each potential customer is subjected to the current economy and exposed to a multitude of variables (e.g. pricing variables) in the process of making a decision regarding whether to buy a company's product and Malov, [72]; Modeling may utilize a number of functional forms including a utility function, a response function, a multinomial logit function, and unobserved component model (UCM) techniques).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Subra/Bateni including variables in an MNL model, with the teachings of Malov including a set of potential customers in order to model customer behavior in view of current economy or various other factors (Malov, [68]; Demand may be understood as the result of individual buying decisions on the part of potential customers. Each potential customer is subjected to the current economy and exposed to a multitude of variables (e.g. pricing variables) in the process of making a decision regarding whether to buy a company's product. Those who do not choose the company's product may purchase from the competition, or they might not buy anything).
Claims 8 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Subramanian et al. (US 20120130726 A1) (herein after referred to as “Subra”) in view of Harsha et al. (US 20160155137 A1), and Rangarajan et al. (US 20090063251 A1).
Regarding Claims 8 and 17, While Subra/Harsha teaches estimating a second variable of a log linear model, neither appear to explicitly consider base demand, cross-elasticity, or assortment affect in modeling. However, Subra/Harsha in view of the analogous art of Rangarajan (i.e. demand modeling) does teach: The method of claim 1, wherein the estimated second variables of the log linear retail sales model comprise Bi, γij, and αij, where Bi is a base demand of item i, γij,i≠j is a cross-price elasticity between item i and item j, and αij, is a assortment effect of item j on item i. (Rangarajan, [31, 36]; The nature of the function is non-linear, stochastic, and correlated. The demand model is based on a baseline demand of the item in quantity terms, price elasticity of the item, cross effects from other products, and cross effects from other marketing instruments…. More specifically, the cross elasticity of a pool consisting of a 5 oz. Pepsi, a 10 oz. Pepsi, and a 12. oz Pepsi is used as the Beta coefficient, rather than that of the individual item (5 oz. Pepsi). Of course the Beta coefficients of the individual items may be used instead of the pool if that data is readily available. Computing the Beta values using the pool coefficients allows the cross elasticity to be represented with a reasonable amount of data, reducing the computational challenge of the demand model and Rangarajan, [130]; is constraint ensures that xp.sub.i.sup.j and z.sub.i,k.sup.j,l take only binary values (i.e. 0 or 1). (As outlined in Appendix A, the `z` variables are introduced to linearize the bilinear terms in the constraints and objective arising from the cross-product effects).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Subra/Bateni teaches estimating a second variable of a log linear model with the teachings of Rangarajan including base demand, cross-elasticity, or assortment affect in modeling in order to better provide optimal pricing for a variety of items (Rangarajan, [05]; maximize the outcome of product related decisions, many of these domains have used statistical modeling and strategic planning to optimize the decision making process for each of the product decisions. The objective for price optimization has been pricing the product for existing inventory reduction. These solutions find optimal prices for everyday/dynamic pricing, pricing for promotions, and pricing for markdowns (e.g. discontinued products). Current price optimization solutions deal with only finished goods and the depletion of an existing picture of inventory of the finished goods).
Conclusion
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/JEREMY L GUNN/ Examiner, Art Unit 3624
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