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 .
Acknowledgments
Applicant provided information disclosure statement.
Claims 1-20 are pending.
This Application is a continuation of application 18751900.
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 than the judicial exception itself.
Regarding Step 1 of subject matter eligibility for whether the claims fall within a statutory category (See MPEP 2106.03), claims 1-20 are directed to non-transitory computer-readable medium, system, and method.
Regarding step 2A-1, Claims 1-20 recite a Judicial Exception. Exemplary independent claim 1 and similarly claims 8 and 15 recite the limitations of
storing and transmitting…item data associated with one or more items, …defining… a model based, at least in part, on price changes without estimating cross-price elasticity; defining…groups of items to be included in a particular category; receiving… a linear programming optimization problem and one or more constraints; and calculating…base prices under the one or more constraints.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation cover concepts of storing, transmitting, defining, receiving, and calculating data. The claim limitations fall under the abstract idea grouping of mental process, because the limitations can be performed in the human mind, or by a human using a pen and paper. For example, but for the language of sever, the claim language encompasses simply receiving and transmitting item data, defining a model and groups of items, receiving a linear programming optimization problem with constraints, and calculating base prices with respect to the constraints. These are mere data manipulation steps that do not require a computer. A user does not need a computer to calculate base prices. A user does not need a computer to define a model or to receive a linear optimization problem to solve. Determining prices is not novel and has been done before the technological age.
The claims recite cross price elasticity and base prices. This is with respect to demand planning when it comes to selling items (See para 0003 in Applicant’s specification). Selling items is a common business practice by retailers. This clearly makes the claims fall in the abstract idea grouping of certain methods of organizing human activity (fundamental economic principles or practices (including hedging, insurance, mitigating risk), sales activities or behaviors).
It is clear the limitations recite these abstract idea groupings, but for the recitations of generic computer components. The mere nominal recitations of generic computer components does not take the limitations out of the mental process and certain methods of organizing human activity grouping. The claims are focused on the combination of these abstract idea processes.
Regarding step 2A-2- This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements of system, software, non-transitory computer-readable medium server, pricing module, processor, memory, modeler, categorization module, and solver.
These components are recited at a high level of generality, and merely automate the steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component.
The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer components or software. Accordingly, even in combination, 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.
Further, the claims do not provide for recite any improvements to the functioning of a computer, or to any other technology or technical field; 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; effecting a transformation or reduction of a particular article to a different state or thing; or 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.
The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims. For example, the dependent claims further describe what the item data compromises such as historical prices and seasonality data. In addition, the dependent claims further describe what the categories consist of such as classes and sub-classes.
Regarding step 2B the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim 1 recites
Method, however method is not considered an additional element.
Claim 1 further recites server, processor, memory, modeler, solver, categorization module, and pricing module.
Claim 8 recites system, processor, memory, modeler, solver, categorization module, and pricing module.
Claim 15 recites non-transitory computer-readable medium, software, modeler, solver, categorization module, and pricing module.
When looking at these additional elements individually, the additional elements are purely functional and generic the Applicant specification states a general-purpose computer configurations as seen in para 0022-0024.
When looking at the additional elements in combination, the Applicant’s specification merely states a general-purpose computer configurations as seen in para 0022-0024. The computer components add nothing that is not already present when the steps are considered separately. See MPEP 2106.05
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, recitations of generic computer structure to perform generic computer functions that are used to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1-5, 7-12, and 14-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Delurgio (US20020165760A1) in further view of Solanki (US7996331B1).
Regarding claims 1, 8, and 15 Delurgio teaches
A computer-implemented method for managing demand (See abstract-An apparatus and method are provided for an interface enabling a user to determine optimum prices of products for sale) This shows a method. The art also deals with managing demand a seen here (See para 0064- the optimization engine 234 directs the demand engine 236 to extract data from the customer data set 239 that applies to the optimization scenario that is being executed. The demand engine 236 predicts sales and market share of products as a function of price according to rules and constraints of the optimization scenario and the activity based cost engine 235 calculates variable and fixed costs for products at specific store locations according to parameters of the optimization scenario)
A system for managing demand …a system architecture comprising a pricing module, a modeler, a categorization module, a solver and at least one server; the at least one server, each comprising a processor and memory (See fig. 2) This shows a system. The pricing module, a modeler, a categorization module, a solver and at least one server all correspond to item 230. Item 230 includes a webserver. Servers include processor and memory. The art also deals with managing demand a seen here (See para 0064- the optimization engine 234 directs the demand engine 236 to extract data from the customer data set 239 that applies to the optimization scenario that is being executed. The demand engine 236 predicts sales and market share of products as a function of price according to rules and constraints of the optimization scenario and the activity based cost engine 235 calculates variable and fixed costs for products at specific store locations according to parameters of the optimization scenario)
A non-transitory computer-readable medium embodied with software for…the software when executed configured for managing inventory by (See fig. 2) Figure 2 includes a webserver and client computers which include a memory. The art also teaches managing inventory as seen here (See para 0071- The logistics tool 310 is employed to determine an optimum inventory strategy within stores for products of a product category comprising a plurality of demand groups. )
storing and transmitting, by a pricing module of at least one server, item data associated with one or more items (See figure 2 item 238 and 230). Item 230 corresponds to the pricing module and it contains item 238 which is a data base that stores and transmits item data such as POS data. (See para 0062- In operation, each of the customers maintains a protected data set 239 within the customer data base 238. Point of sale data is uploaded over the data network 220 from files on the customer computers 210 into corresponding data sets 239 within the data base. ) (See para 0080- In one embodiment, primary point of sale data is uploaded into a client data base according to the present invention and any additional data required for the optimization is provided interactively by the user.)
wherein the at least one server each comprises a processor and memory; (See figure 2) This includes a webserver which includes a processor and memory.
defining, by a modeler of the at least one server, a model based, at least in part, on price changes without estimating cross-price elasticity The scenario wizard of item 230 (i.e. NOC) corresponds to the modeler since this will make a scenario model made by the user. The purpose of this scenario model is for price optimization (i.e. based on price changes). The user can choose to execute this model based on only one group category so there is no cross elasticity calculated. For example, from fig. 10, a user would only want to calculate the optimized prices for bar soaps and not liquid soaps so the user selects only bar soaps and no cross elasticity is calculated in comparison to liquid soaps.
defining, by a categorization module of the at least one server, groups of items to be included in a particular category The scenario wizard of the NOC also corresponds to the categorization module since categorizes/defines products/items at region level as seen in fig. 12. This shows different regions such as midtown or north Atlanta.
receiving, by a solver of the at least one server, a…optimization problem and one or more constraints The user puts in the constraints such as in figs 14 and 15. The solver corresponds to the optimization engine of item 230 (i.e. NOC) since it receives the scenario pricing problem with user defined constraints (See para 0068- The optimization engine 234 executes the optimization scenario that clients configure using the scenario/results processor 233. Using estimated sales and market share data provided by the demand engine 236, along with fixed and variable activity based costs calculated by the activity based cost engine 235, in a price optimization embodiment, the optimization engine 234 determines optimum prices for selected products within one or more demand groups across a product category as constrained by rules and constraints provided by clients)
and calculating, by the solver of the at least one server,…prices under the one or more constraints. (See para 0068- The optimization engine 234 executes the optimization scenario that clients configure using the scenario/results processor 233. Using estimated sales and market share data provided by the demand engine 236, along with fixed and variable activity based costs calculated by the activity based cost engine 235, in a price optimization embodiment, the optimization engine 234 determines optimum prices for selected products within one or more demand groups across a product category as constrained by rules and constraints provided by clients) The optimization engine determines the optimal prices. (See fig. 19).
It is not clear that the optimal prices determined by the optimization engine are the base prices but another section of Delurgio teaches base prices (See para 0067- imputed variables (such as baseline sales and baseline prices) and data from the demand engine 236 (via the optimization engine 234) to calculate fixed and variable costs for the sale of each product.) This teaches base prices.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the optimization engine of Delurgio also calculate the base prices alongside the optimal prices. The base price can have its own column as seen in fig. 19. In another interpretation, the optimal price calculated by the optimization engine can be the base price and the user would be able to further manipulate this price based on other factors such as seasonality. This would make the optimization engine of Delurgio more sophisticated.
In addition, Delurgio doesn’t teach linear programming, however Solanki teaches linear programming (See col 3. Each sub-problem then may be solved through use of either a non-linear optimization program 44 or a linear optimization program 46) This shows a linear optimization programming.
Delurgio and Solanki are analogous art because they are from the same problem-solving area of pricing analysis and both belong to G06Q30. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Delurgio’s invention by incorporating the method of Solanki because the optimization engine of Delurgio would also be able to solve linear programming problems which makes the engine more sophisticated. Solving the price optimization problem with linear programming techniques would ensure a more efficient result based on all the data and constraints the user inputs.
Regarding claims 2, 9, and 16 Delurgio and Solanki teach the limitations of claims 1, 8, and 15, however Delurgio further teaches
wherein the item data comprises one or more of: attribute information, ingredients, brand, price, promotion, allergy information, inventory availability, identifiers, dimensions, product images, three dimensional product representations, substitutable products, expiration date, shipping information and lead time. (See fig. 11) (See para 0062- In operation, each of the customers maintains a protected data set 239 within the customer data base 238. Point of sale data is uploaded over the data network 220 from files on the customer computers 210 into corresponding data sets 239 within the data base. ) Examiner interprets the point-of-sale data includes attribute information as seen in fig. 11 such as categories of bar soap and liquid soap and further attributes in these categories such as brand.
Regarding claims 3, 10, and 17 Delurgio and Solanki teach the limitations of claims 1, 8, and 15, however Delurgio further teaches
wherein the particular category comprises categorization of the one or more items at a store level, a city level, a state level, a region level, or a geographic division. The scenario wizard of the NOC also corresponds to the categorization module since categorizes/defines products/items at region level as seen in fig. 12. This shows different regions such as midtown or north Atlanta.
Regarding claims 4, 11, and 18 Delurgio and Solanki teach the limitations of claims 1, 8, and 15, Delurgio already teaches base prices as taught above, however Delurgio further teaches
wherein the calculated base prices incorporate one or more of: product interactions, direct effect factors, cross-effect factors, pricing, margin, size, brand, and competitor constraints. (See fig. 11) This teaches different brands such as Irish Spring and Private Label.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the optimization engine of Delurgio also calculate the base prices alongside the optimal prices. The base price can have its own column as seen in fig. 19. In another interpretation, the optimal price calculated by the optimization engine can be the base price and the user would be able to further manipulate this price based on other factors such as seasonality and brand. This would make the optimization engine of Delurgio more sophisticated.
Regarding claims 5, 12, and 19 Delurgio and Solanki teach the limitations of claims 1, 8, and 15, however Delurgio further teaches
wherein the item data comprises one or more of: historical prices, price changes and seasonality data. (See fig. 19) This shows item data as well which includes initial price of $2.19. Examiner interprets the initial price as historical price. In another interpretation the pos data would also have historical prices of what people bought before. (See para 0080- At block 506, the system acquires data that is required to perform an optimization according to the selection provided in block 504. In one embodiment, primary point of sale data is uploaded into a client data base according to the present invention and any additional data required for the optimization is provided interactively by the user.)
Regarding claims 7 and 14 Delurgio and Solanki teach the limitations of claims 1 and 8, however Delurgio further teaches
wherein the particular category is defined in a hierarchy of classes and sub-classes. (See fig. 12) This shows categories have classes such as regions and further subclass such as the particular stores in those regions.
Claim(s) 6, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Delurgio (US20020165760A1) in further view of Solanki (US7996331B1) in further view of Sarin (US20170323318A1).
Regarding claims 6, 13, and 20, Delurgio and Solanki teach the limitations of claims 1, 8, and 15, however even though Delurgio teaches elasticity it is not clear that a value is seen in the scenario wizard, however Sarin teaches
wherein the item data comprises one or more of: item elasticity, category elasticity and other elasticity data. (See abstract- An elasticity estimation module receives a data request for an item associated with an individual entity, and identifies a value response curve for the item associated with the individual entity. The elasticity estimation module determines an elasticity measure for the item associated with the individual entity) This shows a price elasticity is calculated for an item.
Delurgio and Sarin are analogous art because they are from the same problem-solving area of pricing analysis and both belong to G06Q30. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Delurgio’s invention by incorporating the method of Sarin because the art of Delurgio could also calculate a price elasticity and provide it in the results section as seen in figure 19. This would easily show an elasticity calculation for the various items. This would make the system of Delurgio more sophisticated since it would provide more information about the items to the user.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Pradip (20180268429) Discloses a system for generating an optimum price for a plurality of commodities is provided. The system includes an insight module configured to enumerate a plurality of components that contribute to the pricing model.
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/MUSTAFA IQBAL/Primary Examiner, Art Unit 3625
