CTNF 19/128,501 CTNF 91532 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement The information disclosure statement (IDS) submitted on 05/08/2025 is in compliance with the provisions of 37 CFR 1.97 and have been entered into the record. Accordingly, the information disclosure statements are being considered by the examiner. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more. Claims 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106) , it is first noted that the method (claims 6) and machine (claims 1-5) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied. With respect to Step 2, and in particular Step 2A Prong One , it is next noted that the claims recite an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “ Mental Process ” group; and by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “ Mathematical concepts ” within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of mathematical concepts or the mental processes grouping. Thus, the claim recites a mental process for performing certain mathematical concepts . A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation. The limitations reciting the abstract idea(s) (Mental process and math), as set forth in exemplary claim 1, are: acquire data regarding the number of past visits to a store, input the data to a trained store visit number prediction model, and predict the number of visits to a store on a prediction target day by using an output from the store visit number prediction model, acquire data regarding the number of past visits to a store, a past sales volume until a designated time of the prediction target day, and a sales time feature of the prediction target day, and predict a sales rate of each product, and predict a sales volume of each product on the prediction target day by using the predicted number of visits to a store and the predicted sales rate of each product . Independent claims 4-6 recite the method and similar processes for performing the process of independent claim 1 without adding significantly more. Thus, the same rationale/analysis is applied. With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application . The additional elements are directed to: a memory, and at least one processor that is connected to the memory, wherein the processor is configured to … (as recited in the claims). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: a memory, and at least one processor that is connected to the memory, wherein the processor is configured to … (as recited in the claims) for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). Even if the acquiring steps are considered as additional elements, these steps at most amount to insignificant extra- solution activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g). In addition, Applicant’s Specification describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo . See, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs ., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)). The dependent claims (2-3) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. Claims 2-3“ wherein the processor is further configured to predict a sales rate of each product in consideration of presence or absence of occurrence of sold-out of each product ; wherein the processor is further figured to correct the predicted sales volume of each product by using stock data of each product ” , without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claims 1- 2 and 5-6 are re jected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20230186331 (hereinafter “Sub”) et al., in view of U.S. PGPub 20190236545 to (hereinafter “Umezu”) et al. As per claim 1, Sub teaches a prediction device comprising: a memory, and at least one processor that is connected to the memory; 0063: “FIG. 4 is a diagram showing components of a system in one embodiment that can automate forecasting of demand and market share using censored data. One or more hardware processors 402 such as a central processing unit (CPU), a graphic process unit (GPU), and/or a Field Programmable Gate Array (FPGA), an application specific integrated circuit (ASIC), and/or another processor, may be coupled with a memory device 404 , and generate a prediction model for predicting future global demand (e.g., market size) and market shares at an individual or local level. A memory device 404 may include random access memory (RAM), read-only memory (ROM) or another memory device, and may store data and/or processor instructions for implementing various functionalities associated with the methods and/or systems described herein. One or more processors 402 may execute computer instructions stored in memory 404 or received from another computer device or medium. A memory device 404 may, for example, store instructions and/or data for functioning of one or more hardware processors 402 , and may include an operating system and other program of instructions and/or data. One or more hardware processors 402 may receive input including at least time series data associated with purchases of at least one product and causal influencer data associated with the purchases, the causal influencer data including at least non-stationary data, where lost shares associated with the product are unobserved. For instance, at least one hardware processor 402 may generate a prediction model that predicts a future global demand associated with a product and individual market shares associated with the product. In an aspect, the prediction model can be a neural network framework, for example, including a plurality of sub-neural networks, which can be trained together or simultaneously using the input data. In another aspect, the prediction model may include an optimization engine, which uses mixed integer programming. In one aspect, the input data may be stored in a storage device 406 or received via a network interface 408 from a remote device, and may be temporarily loaded into a memory device 404 for building or generating the prediction model. The learned prediction model may be stored on a memory device 404 , for example, for running by one or more hardware processors 402 . One or more hardware processors 402 may be coupled with interface devices such as a network interface 408 for communicating with remote systems, for example, via a network, and an input/output interface 410 for communicating with input and/or output devices such as a keyboard, mouse, display, and/or others.” wherein the processor is configured to acquire data regarding the number of past visits to a store, input the data to a trained store visit number prediction model, and predict the number of visits to a store on a prediction target day by using an output from the store visit number prediction model; 0019-0024: “size=chain level temporal arrivals model across all stores (chain level) (e.g., number of customers interested in buying the product), e.g., influenced by product seasonality, nationwide promotions, non-stationary global product trends (e.g., product popularity), and/or other features or factors. Share (i)=store i's market share of the product, e.g., influenced by store attributes, inventory, non-stationary local trends (e.g., location demography), and/or other features or factors… a computer processor may fit a global level temporal model 102 , for example, using a neural network that can factor in different features, including the past history for that global level time series. The global level temporal model 102 predicts the progression at the global level of a global aggregate demand 104 …0044: the global forecast can provide the market size, or total number of customers interested in a product being sold by a store, and can provide a proportion of the global forecast that will not be bought from this store (lost shares).” and predict a sales volume of each product on the prediction target day by using the predicted number of visits to a store and the predicted sales rate of each product; 0038-0059: “The processor can multiply the individual share ( 110 , 112 , 114 ) with the global forecast 104 to get a final prediction for each individual one 116 , 118 , 120 . For example, the prediction of demand for each local series can be the share prediction times the global level… the global demand predicted by the first temporal network can be multiplied with the individual market shares to predict future purchases associated with at least one product at local levels of the individual market shares.” Sub may not explicitly teach the following. However, Umezu teaches: acquire data regarding the number of past visits to a store, a past sales volume until a designated time of the prediction target day, and a sales time feature of the prediction target day, and predict a sales rate of each product; 0029-0035: “ the predicted demand quantity calculation means 11 uses a prediction model which predicts demand quantities, to calculate the respective predicted demand quantities. The prediction model used is, for example, a prediction model that predicts a demand quantity on a product category basis (predicted category-wise demand quantity) by day. In this case, the predicted demand quantity calculation means 11 firstly adds up the latest sales results in a category unit, and calculates a sales composition ratio by hour of day. Then, the predicted demand quantity calculation means 11 may multiply the daily predicted result by the calculated sales composition ratio as an hourly distribution rate, to calculate the predicted category-wise demand quantity by hour…the stock quantity calculation means 12 may calculate the stock quantity at the time point of ordering, from the actual sales quantity and the actual delivery quantity from a certain time point (for example, midnight) the stock quantity at which can be confirmed. Such computation can eliminate the need to actually count the number of pieces in stock… 0087: The predicted demand quantity calculation means may proportionally distribute the predicted category-wise demand quantity on the basis of a past sales composition ratio and an hourly sales composition ratio of each product, to calculate a predicted demand quantity of each single product by hour. ” Sub and Umezu are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Sub with the aforementioned teachings from Umezu with a reasonable expectation of success, by adding steps that allow the software to acquire data with the motivation to more efficiently and accurately organize and analyze information [Umezu 0035]. As per claim 2, Sub and Umezu teaches all the limitations of claim 1. In addition, Sub teaches: wherein the processor is further configured to predict a sales rate of each product in consideration of presence or absence of occurrence of sold-out of each product; 0027-0033: “if one of the inputs to the model indicates local series (e.g., product) 1 is stocked out at the store, then the model can learn that some portion of what it would have predicted for its share should be shifted to other series—2 and 3—and some to the lost share portion… there may be a stockout indicator, which if it equals 1 indicating a stockout then the neural networks would predict a share of 0 for the local series/product and a share of 1 for the lost share. Otherwise, if stockout is not indicated, then the neural networks may predict the opposite (share of 1 for the single series and 0 for lost share). In this case the global model predicts a smooth temporal pattern that is not broken as the actual observed sales would be, for example, interpolating between the cases where there is no censoring.” Claim 5 is rejected under the same art and rationale as claim 1. Although the recited claim language is not functionally identical, claim 5 is the training counterpart to claim 1’s prediction operations - reciting the fitting of the visit prediction model and the creation of the sales time feature that claim 1 uses. Thus, the citations and combination rationale set forth for claim 1 are applied. Claim 6 is the method of performing the same functions as claim 1. Thus, the same art and rationale apply . 07-21-aia AIA Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20230186331 (hereinafter “Sub”) et al., in view of of U.S. PGPub 20020116348 (hereinafter “Phillips”) et al . As per claim 4, Sub teaches a prediction device comprising: a memory, and at least one processor that is connected to the memory, wherein the processor is configured to acquire data regarding a past sales volume and predicts a sales volume of each product on a prediction target day, 0038, 0063: “FIG. 4 is a diagram showing components of a system in one embodiment that can automate forecasting of demand and market share using censored data. One or more hardware processors 402 such as a central processing unit (CPU), a graphic process unit (GPU), and/or a Field Programmable Gate Array (FPGA), an application specific integrated circuit (ASIC), and/or another processor, may be coupled with a memory device 404 , and generate a prediction model for predicting future global demand (e.g., market size) and market shares at an individual or local level. A memory device 404 may include random access memory (RAM), read-only memory (ROM) or another memory device, and may store data and/or processor instructions for implementing various functionalities associated with the methods and/or systems described herein. One or more processors 402 may execute computer instructions stored in memory 404 or received from another computer device or medium. A memory device 404 may, for example, store instructions and/or data for functioning of one or more hardware processors 402 , and may include an operating system and other program of instructions and/or data. One or more hardware processors 402 may receive input including at least time series data associated with purchases of at least one product and causal influencer data associated with the purchases, the causal influencer data including at least non-stationary data, where lost shares associated with the product are unobserved. For instance, at least one hardware processor 402 may generate a prediction model that predicts a future global demand associated with a product and individual market shares associated with the product. In an aspect, the prediction model can be a neural network framework, for example, including a plurality of sub-neural networks, which can be trained together or simultaneously using the input data. In another aspect, the prediction model may include an optimization engine, which uses mixed integer programming. In one aspect, the input data may be stored in a storage device 406 or received via a network interface 408 from a remote device, and may be temporarily loaded into a memory device 404 for building or generating the prediction model. The learned prediction model may be stored on a memory device 404 , for example, for running by one or more hardware processors 402 . One or more hardware processors 402 may be coupled with interface devices such as a network interface 408 for communicating with remote systems, for example, via a network, and an input/output interface 410 for communicating with input and/or output devices such as a keyboard, mouse, display, and/or others.” 0038-0059: “The processor can multiply the individual share ( 110 , 112 , 114 ) with the global forecast 104 to get a final prediction for each individual one 116 , 118 , 120 . For example, the prediction of demand for each local series can be the share prediction times the global level… the global demand predicted by the first temporal network can be multiplied with the individual market shares to predict future purchases associated with at least one product at local levels of the individual market shares.” Sub may not explicitly teach the following. However, Phillips teaches: and correct the predicted sales volume of each product by using stock data of each product; 0049-0050: “the system 100 further considers inventory levels. In particular, a basic premise of the dynamic system 100 is that future sales cannot exceed future inventory levels. Accordingly, the dynamic pricing system 100 caps sales forecasts at the forecasted inventory levels. In the dynamic pricing system 100, a Supply Forecaster (SUF) 190 forms an estimate of the future inventory in each channel segment. The SUF 190 may form an inventory forecast using any known accounting techniques and typically looks to current inventory levels and expected future changes to the inventory levels, such as sales and restocking. Where the seller may purchase unlimited additional inventory, the system can operate without the SUF 190 since any level of sales may be accomplished. The SUF 190 may also be replaced with a corresponding third party system to provide the same supply inputs. If forecast horizon ends before a restocking date, then all of current inventory may not be available for use to satisfy the demand through the forecasting horizon. In this case, the SUF 190 determines how much of the current inventory is available to satisfy a future demand through the forecast horizon. One simple approach uses a linear approximation in which an amount of new inventory is added constantly, rather than using a step function having large, sudden changes in the inventory levels. For example, available inventory may be approximated as the current inventory multiplied by the ratio of the forecast horizon divided by the time until the next restocking.” Sub and Phillips are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Sub with the aforementioned teachings from Phillips with a reasonable expectation of success, by adding steps that allow the software to acquire data with the motivation to more efficiently and accurately organize and analyze information [Umezu 0035] . 07-21-aia AIA Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20230186331 (hereinafter “Sub”) et al., in view of U.S. PGPub 20190236545 to (hereinafter “Umezu”) et al., in further view of U.S. PGPub 20020116348 (hereinafter “Phillips”) et al . As per claim 3, Sub and Umezu teaches all the limitations of claim 1. Sub and Umezu may not explicitly teach the following. However, Phillips teaches: wherein the processor is further figured to correct the predicted sales volume of each product by using stock data of each product; 0049-0050: “ In one embodiment, the system 100 further considers inventory levels. In particular, a basic premise of the dynamic system 100 is that future sales cannot exceed future inventory levels. Accordingly, the dynamic pricing system 100 caps sales forecasts at the forecasted inventory levels… the SUF 190 determines how much of the current inventory is available to satisfy a future demand through the forecast horizon.” Sub, Umezu, and Phillips are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Sub and Umezu with the aforementioned teachings from Phillips with a reasonable expectation of success, by adding steps that allow the software to acquire data with the motivation to more efficiently and accurately organize and analyze information [Umezu 0035] . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : CHOI; HoonMin. METHOD OF FORECASTING STORE DEMAND BASED ON ARTIFICIAL INTELLIGENCE AND SYSTEM THEREFOR, .U.S. PGPub 20220398610 The present disclosure relates to a method of forecasting store demand, and more particularly, to a method of forecasting store demand based on store visit history and sales history of existing customers using an artificial neural network and a system therefor. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”)./Arif Ullah/Primary Examiner, Art Unit 3625 Application/Control Number: 19/128,501 Page 2 Art Unit: 3625 Application/Control Number: 19/128,501 Page 3 Art Unit: 3625 Application/Control Number: 19/128,501 Page 4 Art Unit: 3625 Application/Control Number: 19/128,501 Page 5 Art Unit: 3625 Application/Control Number: 19/128,501 Page 6 Art Unit: 3625 Application/Control Number: 19/128,501 Page 7 Art Unit: 3625 Application/Control Number: 19/128,501 Page 8 Art Unit: 3625 Application/Control Number: 19/128,501 Page 9 Art Unit: 3625 Application/Control Number: 19/128,501 Page 10 Art Unit: 3625 Application/Control Number: 19/128,501 Page 11 Art Unit: 3625 Application/Control Number: 19/128,501 Page 12 Art Unit: 3625 Application/Control Number: 19/128,501 Page 13 Art Unit: 3625 Application/Control Number: 19/128,501 Page 14 Art Unit: 3625 Application/Control Number: 19/128,501 Page 15 Art Unit: 3625 Application/Control Number: 19/128,501 Page 16 Art Unit: 3625 Application/Control Number: 19/128,501 Page 17 Art Unit: 3625 Application/Control Number: 19/128,501 Page 18 Art Unit: 3625 Application/Control Number: 19/128,501 Page 19 Art Unit: 3625
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