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 .
DETAILED ACTION
1. This Office Action is in response to the Amendment filed on September 16, 2025, which paper has been placed of record in the file.
2. Claims 1-4 and 6-10 are pending in this application.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-4 and 6-10 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more.
Regarding independent claim 1, which is analyzing as the following:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites a demand prediction device. Thus, the claim is to a machine, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The claim recites a demand prediction device for predicting demand of a product. The claim recites the steps: acquire based on a time zone of a store and numbers of receipts issued multiple times daily by a POS terminal of the store, a number of customers in the store, determine a quantity of missing products of a target product based on the number of customers in the store, average sales quantities of the target product per a plurality of days of a week, and a final sales time; acquire deviation information indicating a deviation from an actual demand of a target product and a sales quantity, correct the sales quantity based on the deviation information; and generate a demand prediction model based on learning data including the corrected sales quantity, under its broadest reasonable interpretation when read in light of the Specification, falls within “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, business relations. See MPEP 2106.04(a)(2), subsection III.
Moreover, the claim recites the steps: , determine a quantity of missing products of a target product based on the number of customers in the store…; acquire deviation information indicating a deviation from an actual demand of a target product and a sales quantity, correct the sales quantity based on the deviation information; and generate a demand prediction model based on learning data including the corrected sales quantity, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III.
The claim recites “a demand prediction model” which is directed to mathematical formulas or equations, falls within “Mathematical Concepts” grouping of abstract ideas (mathematical relationships, mathematical formulas or equations, mathematical calculations). See MPEP 2106.04(a)(2), subsection III.
Accordingly, the claim recites an abstract idea. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The claim recites the additional elements of “acquire, from a point-of-sale server, via a communication interface (IF) of a network connection.” The claim also recites that the steps of “acquire based on a time zone of a store and numbers of receipts issued multiple times daily by a POS terminal of the store, a number of customers in the store, determine a quantity of missing products of a target product based on the number of customers in the store, average sales quantities of the target product per a plurality of days of a week, and a final sales time; acquire deviation information indicating a deviation from an actual demand of a target product and a sales quantity, correct the sales quantity based on the deviation information; and generate a demand prediction model based on learning data including the corrected sales quantity”, are performed by at least one processor.
The additional elements “acquire, from a point-of-sale server, via a communication interface (IF) of a network connection” are mere data gathering and transmitting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and transmitting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and transmitting. See MPEP 2106.05. Moreover, these additional elements do not provide any improvements to the technology, improvements to the functioning of the computer, improvements to the point-of-sale server/the communication interface/the network, they just merely used as general means for gathering and transmitting data. It is similar to other concepts that have been identified by the courts Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; Collecting information, analyzing it, and displaying certain results of the collection and analysis, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Further, the limitations “acquire based on a time zone of a store and numbers of receipts issued multiple times daily by a POS terminal of the store, a number of customers in the store, determine a quantity of missing products of a target product based on the number of customers in the store, average sales quantities of the target product per a plurality of days of a week, and a final sales time; acquire deviation information indicating a deviation from an actual demand of a target product and a sales quantity, correct the sales quantity based on the deviation information; and generate a demand prediction model based on learning data including the corrected sales quantity” are recited as being performed by the processor. The processor is recited at a high level of generality. In the limitations “acquire based on a time zone of a store and numbers of receipts issued multiple times daily by a POS terminal of the store, a number of customers in the store”, the processor is used as a tool to perform the generic computer function of gathering and transmitting data. See MPEP 2106.05(f). In limitations “determine a quantity of missing products of a target product based on the number of customers in the store, average sales quantities of the target product per a plurality of days of a week, and a final sales time; acquire deviation information indicating a deviation from an actual demand of a target product and a sales quantity, correct the sales quantity based on the deviation information; and generate a demand prediction model based on learning data including the corrected sales quantity”, the processor is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The additional elements recite generic computer components the processor, a memory, and software programming instructions that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception (Step 2A, Prong One: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole, amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The additional elements “acquire, from a point-of-sale server, via a communication interface (IF) of a network connection”, were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data transmitting. However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).
As discussed in Step 2A, Prong Two above, the additional elements of “acquire, from a point-of-sale server, via a communication interface (IF) of a network connection” are recited at a high level of generality. These elements amount to gathering and transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely genetic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: Receiving or transmitting data over a network, e.g., using the Internet to gather data, 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).
As discussed in Step 2A, Prong Two above, the recitation of the processor to perform limitations “acquire based on a time zone of a store and numbers of receipts issued multiple times daily by a POS terminal of the store, a number of customers in the store, determine a quantity of missing products of a target product based on the number of customers in the store, average sales quantities of the target product per a plurality of days of a week, and a final sales time; acquire deviation information indicating a deviation from an actual demand of a target product and a sales quantity, correct the sales quantity based on the deviation information; and generate a demand prediction model based on learning data including the corrected sales quantity”, amounts to no more than mere instructions to apply the exception using a generic computer component.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept. Therefore, the claim is not patent eligible. (Step 2B: NO).
Regarding independent claims 7 and 8, Alice Corp. establishes that the same analysis should be used for all categories of claims. Therefore, independent claim 7 directed to a method, independent claim 8 directed to a medium, are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent method claim 1.
Regarding dependent claims 2-4, 6 and 9-10, the dependent claims do not impart patent eligibility to the abstract idea of the independent claim. The dependent claims rather further narrow the abstract idea and the narrower scope does not change the outcome of the two-part Mayo test. Narrowing the scope of the claims is not enough to impart eligibility as it is still interpreted as an abstract idea, a narrower abstract idea.
Regarding to claims 2-4 and 6, Claim 2 recites predict a sales quantity of the target product in a predetermined period using the demand prediction model; Claim 3 recites wherein the deviation information is a quantity of a parting product…; Claim 4 recites wherein the parting product is a product sold at a predetermined discount rate…; Claim 6 recites when there is no stock of the target product…, calculate the quantity of the missing product on a business day; that fall under the category of Organizing Human Activity, Mental Processes, and Mathematical Concepts groupings of abstract ideas as described above in the independent claim 1;
Regarding to claim 9, the claim recites the additional elements wherein generating the demand prediction model comprises performing machine learning using the cored sales quantity as training data, provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The additional elements “generating the demand prediction model comprises performing machine learning using the cored sales quantity as training data” are used to generally apply the abstract idea without placing any limits on how the machine learning model functions. Rather, these limitations only recite the outcome of “generating the demand prediction model” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f).
The additional elements “generating the demand prediction model comprises performing machine learning using the cored sales quantity as training data” also merely indicate a field of use or technological environment in which the judicial exception is performed. Although the additional elements “generating the demand prediction model comprises performing machine learning using the cored sales quantity as training data” limit the identified judicial exceptions “generating the demand prediction model”, this type of limitations merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Regarding to claim 10, the claim recites the additional elements wherein the least least one processor is further confiture to execute the instruction to: support decision making for an order of a quantity of the target product by outputting an order input screen based on the predicted sales quantity, which are mere data gathering and outputting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05 (See claim 1 above).
Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Therefore, none of the dependent claims alone or as an ordered combination add limitations that qualify as significantly more than the abstract idea.
Accordingly, claims 1-4 and 6-10 are not draw to eligible subject matter as they are directed to an abstract idea without significantly more and are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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.
Claim Rejections - 35 USC § 103
5. 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 of this title, 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.
6. Claims 1-4 and 6-10 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (hereinafter Wang, US 2021/0110461) in view of Shinoda et al. (hereinafter Shinoda, US 2002/0215411).
Regarding to claim 1, Wang discloses a demand prediction device comprising:
a memory storing instructions (para [0006], The system may comprise a memory storing instructions); and
at least one processor configured to execute the instructions to (para [0006], at least one processor configured to execute the instructions):
acquire, from a point-of-sale server, via a communication interface (I/F) of a network connection, and based on numbers of receipts issued multiple times daily by a POS terminal of the store, a number of customers in the store (para [0079], When generating POs, PO generator 326 may issue a paper PO to be mailed or faxed to the supplier or an electronic PO to be transmitted to the same; para [0042], Shipment and order tracking system 111, in some embodiments, may be implemented as a computer system that receives, stores, and forwards information regarding the location of packages containing products ordered by customers (e.g., by a user using devices 102A-102B). In some embodiments, shipment and order tracking system 111 may request or store information from web servers (not pictured) operated by shipping companies that deliver packages containing products ordered by customers);
determine a quantity of missing products of a target product based on average sales quantities of the target product per a plurality of days of a week, and a final sales time (para [0074], The order information may include sales statistics such as a number of items sold over time, a number of items sold during promotion periods, and a number of items sold during regular periods. Data science module 321 may train the forecast model based on parameters such as the sales statistics, glance view, season, day of the week, upcoming holidays, and the like.; para [0103], the total quantity, however, may not be an accurate estimate of products associated with the receiving day because a subset of products delivered by suppliers may be non-saleable (e.g., damaged, missing, defective, etc.). IPS 324 thus may apply a fulfillment ratio to the total quantity in order to obtain a more realistic estimate of the quantity. As used herein, a fulfillment ratio may be a parameter determined from data science module 321 as part of the supplier statistics data. In some embodiments, the fulfillment ratio may be based on a percentage of products that are received in a saleable condition compared to an ordered quantity. For example, a fulfillment ratio of 60% for a particular product supplied by a particular supplier indicates that, on average, only 60% of the products delivered by the supplier arrive in saleable condition);
acquire, as the quantity of missing product, deviation information indicating a deviation from an actual demand of the target product and a sales quantity (para [0074], data science module 321 may retrieve order information from FO system 311 and glance view (i.e., number of webpage views for the product) from external front end system 313 to train the forecast model and anticipate a level of future demand. The order information may include sales statistics such as a number of items sold over time, a number of items sold during promotion periods, and a number of items sold during regular periods);
correct the sales quantity based on the deviation information (para [0090], TIP 323, in some embodiments, may adjust the preliminary order quantities using a set of rules configured to fine tune the preliminary order quantities based on data such as sales statistics, the current product inventory levels and the currently ordered quantities); and
generate a demand prediction model based on learning data including the corrected sales quantity (para [0075], Demand forecast generator 322, in some embodiments, may include one or more computing devices configured to forecast a level of demand for a particular product using the forecast model developed by data science module 321. More specifically, the forecast model may output a demand forecast quantity for each product, where the demand forecast quantity is a specific quantity of the product expected to be sold to one or more customers in a given period (e.g., a day). In some embodiments, demand forecast generator 322 may output demand forecast quantities for each given period over a predetermined period (e.g., a demand forecast quantity for each day over a 5-week period). Each demand forecast quantity may also comprise a standard deviation quantity (e.g., ±5) or a range (e.g., maximum of 30 and minimum of 25) to provide more flexibility in optimizing product inventory levels).
Wang does not disclose, however, Shinoda discloses:
acquire, from a point-of-sale server, via a communication interface (I/F) of a network connection, and based on a time zone of a store, a number of customers in the store (para [0047], As the objective variable, a sales amount log for a corresponding period registered in a point of sales (POS) system present at a corresponding store is acquired through the demand prediction device 1 or manually, correct answer data is calculated by automatically performing a collection process for the acquired data using the demand prediction device 1, a relative position is calculated for the past sales result value distribution using the state prediction model generating unit 14; para [0040], The unusual state demand prediction model generating unit 12 may generate an unusual state demand prediction model for predicting a demand in an unusual state by performing learning mainly using the number of in-zone persons 30 minutes before, the amount of rainfall, and the air volume, which are short-term variation components of components varying in a short term, in the learning data for a demand prediction model);
determine a quantity of missing products of a target product based on the number of customers in the store, average sales quantities of the target product per a plurality of days of a week, and a final sales time (para [0038], The normal state demand prediction model generating unit 11 generates a normal state demand prediction model by performing learning using learning data (past data) for demand prediction models including the normal state demand prediction model and the unusual state demand prediction model and causes the information storing unit 10 to store the generated normal state demand prediction model. FIG. 3 is a diagram illustrating an example of a table of learning data for a demand prediction model. As illustrated in FIG. 3, in the learning data for a demand prediction model, a store (a store name used for identifying the store), a period, the number of in-zone persons (real-time population information) in the vicinity of the store 30 minutes before the corresponding period, the amount of rainfall in the vicinity of the store for the period, an air volume in the vicinity of the store for the period, an average sales amount of the same week and the same day one year before the period (a one-year-before same-week same-day average sales amount) at the store, a same-week same-day average sales amount three months before the period (a three-months-before same-week same-day average sales amount) at the store, and a result value of the sales amount of the period at the store are associated with each other).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the Wang’s to incorporate the features taught by Shinoda above, for the purpose of providing more effectiveness and accuracy in generating prediction demand by determining the number of customers in the store. Since Wang discloses generating prediction demand, Shinoda discloses generating prediction demand based on the number of customers in the store, as described above, therefore, one of ordinary skill in the art would have recognized that the combination of Wang and Shinoda would have yield predictable results in generating prediction demand for a product.
Regarding to claim 2, Wang discloses the demand prediction device according to claim 1, wherein the at least one processor is further configured to execute the instructions to:
predict a sales quantity of the target product in a predetermined period using the demand prediction model (para [0075], Demand forecast generator 322, in some embodiments, may include one or more computing devices configured to forecast a level of demand for a particular product using the forecast model developed by data science module 321); and
output the predicted sales quantity (para [0075], More specifically, the forecast model may output a demand forecast quantity for each product, where the demand forecast quantity is a specific quantity of the product expected to be sold to one or more customers in a given period (e.g., a day)).
Regarding to claim 3, Wang discloses the demand prediction device according to claim 1, wherein the deviation information is a quantity of a parting product or a missing product of the target product (para [0074], the order information may include sales statistics such as a number of items sold over time, a number of items sold during promotion periods, and a number of items sold during regular periods).
Regarding to claim 4, Wang discloses the demand prediction device according to claim 3, wherein the parting product is a product sold at a predetermined discount rate or more from a normal price (para [0074], the order information may include sales statistics such as a number of items sold over time, a number of items sold during promotion periods, and a number of items sold during regular periods).
Regarding to claim 6, Wang discloses the demand prediction device according to claim 5, wherein the at least one processor is further configured to execute the instructions to: when there is no stock of the target product at a predetermined time point, calculate the quantity of the missing product on a business day (para [0083], At step 401, TIP 323 may receive a demand forecast quantity for each product from demand forecast generator 322. In some embodiments, the demand forecast quantities may be in the form of a table of numerical values organized by SKU in one dimension and number of units forecasted to be sold for a given day in the other dimension).
Claim 7 is written in method and contains the same limitations found in claim 1 above, therefore is rejected by the same rationale.
Claim 8 is written in computer-readable recording medium and contains the same limitations found in claim 1 above, therefore is rejected by the same rationale.
Regarding to claim 9, Wang discloses the demand prediction device according to claim 1, wherein generating demand prediction model comprises performing machine learning using the corrected sales quantity as training data (para [0122], For example, IPS 324 may sort the recommended order quantities by product based on the urgency scores, make further adjustments to the quantities based on corresponding current inventory levels, and order the products in sequence from top-priority products to low-priority products. In some embodiments, the urgency scores may be determined through a machine learning model, where the machine learning model is trained with data from data science module 321 and the urgency scores are logit values of the machine learning model. Logit values refer to unnormalized or raw predictions or probability values of a model as known in the art).
Regarding to claim 10, Wang discloses the demand prediction device according to claim 2, wherein the at least one processor is further configured to execute the instructions to prepare at least part of an order of a quantity of the target products based on the predicted sales quantity (para [0079], PO generator 326, in some embodiments, may include one or more computing devices configured to generate POs to one or more suppliers based on the recommended order quantities or results of the distribution by IPS 324. SCM 320, by this point, would have determined a recommended order quantity for each product that requires additional inventory and for each FC 200, where each product has one or more suppliers that procure or manufacture the particular product and ship it to one or more FCs. A particular supplier may supply one or more products, and a particular product may be supplied by one or more suppliers. When generating POs, PO generator 326 may issue a paper PO to be mailed or faxed to the supplier or an electronic PO to be transmitted to the same).
Response to Arguments/Amendment
7. Applicant's arguments with respect to claims 1-4 and 6-10 have been fully considered but are not persuasive.
I. Claim Rejections - 35 USC § 101
Claims 1-4 and 6-10 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more (see more details above).
In response to the Applicant’s arguments that the pending claims do not represent such “sales activities”, the Examiner respectfully disagrees and submits that the claims recite the pending claims recite a demand prediction device and method for predicting demand of a product. The Specification described in pare 1 that “A method of learning a prediction model based on past product sales results and performing future demand prediction based on the prediction model is widely known.” Thus, the pending claimed invention directed to sales activities, behavior and business relations. The pending claims recite the steps: acquire based on a time zone of a store and numbers of receipts issued multiple times daily by a POS terminal of the store, a number of customers in the store, determine a quantity of missing products of a target product based on the number of customers in the store, average sales quantities of the target product per a plurality of days of a week, and a final sales time; acquire deviation information indicating a deviation from an actual demand of a target product and a sales quantity, correct the sales quantity based on the deviation information; and generate a demand prediction model based on learning data including the corrected sales quantity, under its broadest reasonable interpretation when read in light of the Specification, falls within “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, business relations. See MPEP 2106.04(a)(2), subsection III.
Moreover, the claim recites the steps: , determine a quantity of missing products of a target product based on the number of customers in the store…; acquire deviation information indicating a deviation from an actual demand of a target product and a sales quantity, correct the sales quantity based on the deviation information; and generate a demand prediction model based on learning data including the corrected sales quantity, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III.
Therefore, the claims recite an abstract idea.
In response to the Applicant’s arguments that the claimed invention provides improvement the technology, the Examiner respectfully disagrees and submits that while the Specification paragraphs [0003], [0015], [0041], and other related description indicate that although background technology may have attempted to consider “an increase in the number of products sold due to a bargain price” and “generating and using a demand prediction model” [0003], that background technology was deficient and may be technically improved upon by involving “a deviation from an actual demand of a target product and a sales quantity, as a correction used in generating a demand prediction model by which accuracy of the demand prediction and be improved” [0015], there is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of generating a prediction model, rather than to any technology. See MPEP 2106.05(a). Thus, even when considering the elements in combination, the claims as a whole does not integrate the recited exception into a practical application.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Therefore, the claims are not patent eligible.
Accordingly, the 101 rejection is maintained.
II. Claim Rejections - 35 USC § 102
Applicant’s arguments with respect to claims 1-4 and 6-10 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The new ground of 103 rejection described above.
In response to the Applicant’s argument that Wang does not disclose “quantity of the missing product”, the Examiner respectfully disagrees and submits that Wang discloses in para [0074] that “The order information may include sales statistics such as a number of items sold over time, a number of items sold during promotion periods, and a number of items sold during regular periods. Data science module 321 may train the forecast model based on parameters such as the sales statistics, glance view, season, day of the week, upcoming holidays, and the like.” The Specification defines “the missing product” as “the missing product is a product that is considered to be sold when there is stock during the operation of the store.” Thus, Wang’s “number of items sold” is equivalent to “quantity of the missing product”. Therefore, Wang does teach “quantity of the missing product” as claimed.
Conclusion
8. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
9. Claims 1-4 and 6-10 are rejected.
10. The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure:
Sharma et al. (US 2008/0140511) disclose predicting future sales of a product includes determining a first, historical sales rate curve of historical sales of the product and determining a second sales rate curve for a time t from sales of the product occurring up to a time t-1.
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner NGA B NGUYEN whose telephone number is (571) 272-6796. The examiner can normally be reached on Monday-Friday 7AM-5PM.
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/NGA B NGUYEN/Primary Examiner, Art Unit 3625 January 5, 2026