DETAILED ACTION
The following is a FINAL office action upon examination of the application number 18/567762. Claims 1-8 are pending in the application and have been examined on the merits discussed below.
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 .
Response to Amendment
Claims 1, 7, and 8 have been amended.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1) Claims 1-6 are directed to device comprising at least one processor; thus the device comprises a device or set of devices, and therefore, is directed to a machine which is a statutory category of invention. Claim 7 is directed to a method; thus this claims is directed to a process, which is one of the statutory categories of invention. Claim 8 is directed to a non-transitory storage medium, which is a manufacture, and this a statutory category of invention.
(Step 2A) The claims recite an abstract idea instructing how to predict sales based on predicted visits and sales per customer visit, which is described by claim limitations reciting:
acquire factor information regarding a factor influencing a number of sales of a commodity in a store;
predict a number of visits to the store for a prediction target period based on a number-of-visits prediction model used to predict the number of visits to the store for the prediction target period, wherein the number-of visits prediction model is learned … based on factor information and number-of-visits information regarding a past number of visits to the store, and the acquired factor information, wherein … learning uses the number-of-visits as an objective variable and a plurality of explanatory variables to continuously update the prediction model;
predict a ratio of the number of sales of the commodity to the number of visits based on a ratio prediction model used to predict the ratio, and wherein the ratio prediction model is learned … based on the past factor information and the ratio, and the acquired factor information; and
predict the number of sales of the commodity based on the predicted number of visits and the predicted ratio.
The identified limitations in the claims describing predicting sales based on predicted visits and sales per customer visit (i.e., the abstract idea) fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers fundamental economic practices and sales activities or, alternatively, the “Mental Processes” grouping of abstract ideas since the identified limitations can be performed by a human, mentally or with pen and paper. Dependent claims 2, 3, 4, 5, and 6, recite limitations that further narrow/describe the abstract idea (i.e., predicting sales based on predicted visits and sales per customer visit); therefore, these claims are also found to recite an abstract idea.
This judicial exception is not integrated into a practical application because additional elements such as the memory, and at least one processor coupled to the memory, the at least one processor being configured in claim 1; the acquisition unit, visit prediction unit, ratio prediction unit, and sales prediction unit in claim 7; and the non-transitory storage medium storing a program executable by a computer in claim 8, do not add a meaningful limitation to the abstract idea since these elements are only broadly applied to the abstract ideas at a high level of generality; thus, none of recited hardware offers a meaningful limitation beyond generally linking the abstract idea to a particular technological environment, in this case, implementation via a computer/processor.
Additional elements such as using machine learning do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. Accordingly, these additional element do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into a practical application, the hardware additional elements amount to no more than mere instructions to apply the exception using a generic computer component (See Spec. [0013][Fig. 1]). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additional elements such as using machine learning do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. 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 improves the functioning of a computer or improves any other technology.
Claim Rejections - 35 USC § 103
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) 1-3, 5, 7, and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over JP 2002024350 (Kasumi); in view of US 2020/0248920 (Kulkarni).
As per claim 1, Kasumi teaches: a demand prediction device comprising: a memory, and at least one processor coupled to the memory, the at least one processor being configured to: ([0002][0026])
acquire factor information regarding a factor influencing a number of sales of a commodity in a store; ([0040] … causal information such as days of the week, special sales, special events, weather, and temperature. [0045] … The given data includes external data that affects sales volume and internal data that affects sales. External data includes, for example, weather, temperature, day of the week characteristics (events, sports days, competitive store status))
predict a number of visits to the store for a prediction target period based on a number-of-visits prediction model used to predict the number of visits to the store for the prediction target period, wherein the number-of-visits prediction model is learned … based on factor information and number-of-visits information regarding a past number of visits to the store, and the acquired factor information, ([0049] To predict the number of customers, the store manager searches for the number of customers visiting the store (by day, day of the week}, sales results, weather, temperature, special events, etc. for the same month of the previous year, and also refers to the next month's event information, etc. On the 20th of every month, the number of customers visiting the store each day for the next month is simulated and predicted. Here, the predicted daily number of customers visiting the store is predicted by time zone based on past results [0018] … visitor number forecasting unit predicts the number of visitors based on the number of customers visiting the store by day and day of the week in the same month of the previous year, sales results, causal information, and event information for the next month.)
predict a ratio of the number of sales of the commodity to the number of visits based on a ratio prediction model used to predict the ratio, wherein the ratio prediction model is learned … based on the past factor information and the ratio, and the acquired factor information; and ([0046] … the ratio of the actual number of sales to the number of customers [0040] …Predicted number of sales = Predicted product support rate x Predicted number of customers visiting the store [0017] … The product support rate for annual sales results is stored in a database that is stored in association with the causal information, fencing management information such as sales location and number of faces, and flat/ end layout management information such as number of faces and depth, and conditions are specified… extracts the product support rate that matches the causal information specified by the condition from the database.)
predict the number of sales of the commodity based on the predicted number of visits predicted by the visit prediction unit and the predicted ratio predicted by the ratio prediction unit ([0040] … engine that calculates the predicted number of sales using the following formula. It consists of a sales volume prediction engine. Predicted number of sales = Predicted product support rate x Predicted number of customers visiting the store).
Although not explicitly taught by Kasumi, Kulkarni teaches: …prediction model is learning using machine learning…, wherein machine learning uses the number-of-visits as an objective variable and a plurality of explanatory variables to continuously update the prediction model; …prediction model is learned using machine learning… ([0034] External factors, such as holidays (e.g., national and local holidays) and building events affect foot traffic and are included as external variables for a machine learning model to forecast foot traffic. In one example, the machine learning model uses historical foot traffic data, holidays relevant to the region/location of the air handling unit, and internal building events. It is to be understood that less, more, or other external factors may be used in other examples. In one example, the machine learning algorithm used is the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX), with the foot traffic as the target variable, and the holidays and building events as the exogenous variables. [0035] The machine learning model forecasts the foot traffic for the specified time period (e.g., the next day i+1). The output of the machine learning model is the number of people per hour (or other time interval) for the specified time period. Once the true foot traffic for the next day is known, a fresh model is trained when forecasting foot traffic for day i+2).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Kasumi with the aforementioned teachings of Kulkarni with the motivation of generating a foot traffic forecasts (Kulkarni [0033]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Kulkarni to the system of Kasumi would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the use of machine learning to model a target variable.
As per claim 2, Kasumi teaches: wherein the factor information includes at least one of environment information regarding an external environment around the store or feature information regarding a feature of the commodity ([0045] … The given data includes external data that affects sales volume and internal data that affects sales. External data includes, for example, weather, temperature, day of the week characteristics (events, sports days, competitive store status)).
As per claim 3, Kasumi teaches: wherein the at least one processor acquires the past number-of-visits information, and wherein the at least one processor predicts the number of visits to the store based on the acquired factor information and the acquired number-of-visits information and the number-of-visits prediction model ([0049] To predict the number of customers, the store manager searches for the number of customers visiting the store (by day, day of the week}, sales results, weather, temperature, special events, etc. for the same month of the previous year, and also refers to the next month's event information, etc. On the 20th of every month, the number of customers visiting the store each day for the next month is simulated and predicted. Here, the predicted daily number of customers visiting the store is predicted by time zone based on past results [0018] … visitor number forecasting unit predicts the number of visitors based on the number of customers visiting the store by day and day of the week in the same month of the previous year, sales results, causal information, and event information for the next month.)
As per claim 5, Kasumi teaches: learn the number-of-visits prediction model based on the number-of-visits information and the factor information; and ([0049] To predict the number of customers, the store manager searches for the number of customers visiting the store (by day, day of the week}, sales results, weather, temperature, special events, etc. for the same month of the previous year, and also refers to the next month's event information, etc. On the 20th of every month, the number of customers visiting the store each day for the next month is simulated and predicted. Here, the predicted daily number of customers visiting the store is predicted by time zone based on past results [0018] … visitor number forecasting unit predicts the number of visitors based on the number of customers visiting the store by day and day of the week in the same month of the previous year, sales results, causal information, and event information for the next month.)
calculate the ratio based on the past number-of-visits information and number-of-sales information regarding a past number of sales of the commodity and to learn the ratio prediction model based on the past factor information and the calculated ratio ([0046] … the ratio of the actual number of sales to the number of customers [0017] … The product support rate for annual sales results is stored in a database that is stored in association with the causal information, fencing management information such as sales location and number of faces, and flat/ end layout management information such as number of faces and depth, and conditions are specified… extracts the product support rate that matches the causal information specified by the condition from the database.)
As per claims 7 and 8, these claims recite limitations substantially similar to those addressed by the rejection of claim 1; therefore, the same rejection applies.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over JP 2002024350 (Kasumi); in view of US 2020/0248920 (Kulkarni); in view of US 2020/0273079 (Raviv).
As per claim 4, Kasumi teaches: wherein the at least one processor …predicts the ratio based on the acquired factor information, the predicted popularity, and the ratio prediction model ([0046] … the ratio of the actual number of sales to the number of customers [0040] …Predicted number of sales = Predicted product support rate x Predicted number of customers visiting the store [0017] … The product support rate for annual sales results is stored in a database that is stored in association with the causal information, fencing management information such as sales location and number of faces, and flat/ end layout management information such as number of faces and depth, and conditions are specified… extracts the product support rate that matches the causal information specified by the condition from the database.)
Although not explicitly taught by Kasumi, Raviv teaches: wherein the at least one processor predicts popularity indicating a relative magnitude of the ratio based on the acquired factor information and a popularity prediction model used to predict the popularity and learned based on the past factor information and the popularity ([0099] … For example, a number of occurrences (e.g., number of purchases) of an item (determined across merchants) can be used as a popularity score associated with the item. For example, an item's popularity score can correspond to the number of purchases of the item, in a given time period, determined from the data obtained from electronic messages sent/received in the time period. The popularity score for a given item can be determined for a given demographic segment (e.g., age, race, gender, ethnicity, education, family size, religion, etc.), across demographic segments, etc. [0104] … for a given item, the data store can store a set of popularity measures for the item, each measure in the set corresponding to the item, a time period and/or a demographic segment. [0107] … analyzing observed data (e.g., time series data, current or past) and/or for predicting (or forecasting) future data points).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Kasumi with the aforementioned teachings of Raviv with the motivation of detecting trends in user consumption (Raviv [0007]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Raviv to the system of Kasumi would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the prediction of the popularity of an item.
Response to Arguments
Applicant's arguments filed 9/4/2025 have been fully considered but they are not persuasive.
With respect to the rejection under 35 USC 101, Applicant argues that the claims do not recite a judicial exception.
Examiner respectfully disagrees. Examiner maintains that the identified limitations in step 2A (i.e., acquire factor information regarding a factor influencing a number of sales of a commodity in a store; predict a number of visits to the store for a prediction target period based on a number-of-visits prediction model used to predict the number of visits to the store for the prediction target period, wherein the number-of visits prediction model is learned … based on factor information and number-of-visits information regarding a past number of visits to the store, and the acquired factor information, wherein … learning uses the number-of-visits as an objective variable and a plurality of explanatory variables to continuously update the prediction model; predict a ratio of the number of sales of the commodity to the number of visits based on a ratio prediction model used to predict the ratio, and wherein the ratio prediction model is learned … based on the past factor information and the ratio, and the acquired factor information; and predict the number of sales of the commodity based on the predicted number of visits and the predicted ratio) can be performed mentally or with the help of pen and paper. Examiner acknowledges that the claims recite limitations such as machine learning which fall outside the abstract idea; these additional elements are evaluated in prong 2.
Additionally, the identified limitations in the claims describing predicting sales based on predicted visits and sales per customer visit (i.e., the abstract idea) fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers fundamental economic practices and sales activities.
With respect to the rejection under 35 USC 101, Applicant argues that the claims are integrated into a practical application.
Examiner respectfully disagrees. The use of a machine learning model to model number of visits based on variables does not improve the functioning of the computer or a technology.
If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. See MPEP 2106.05(a). The present specification does not provide any details of an unconventional technical solution providing an improvement. The machine learning in the claims is limited to high level use of machine learning to model a target variable based on explanatory variables.
With respect to the rejection under 35 USC 101, Applicant argues that the claims recite an inventive concept.
Examiner respectfully disagrees. Additional elements such as using machine learning do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. 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 improves the functioning of a computer or improves any other technology.
A general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (noting that Alappat’s rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court’s Bilski and Alice Corp. decisions). Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014). See In re Alappat, 33 F.3d 1526, 1545, 31 USPQ2d 1545, 1558 (Fed. Cir. 1994); In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008)
With respect to the rejection under 35 USC 102/103, Applicant argues that the art of record does not disclose the claimed features.
Examiner respectfully disagrees. The Applicant’s arguments are directed to newly amended features; additional search has been conducted and the rejection has been updated to address said amendments. See updated Claim Rejections - 35 USC § 103 above.
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/ALAN TORRICO-LOPEZ/Primary Examiner, Art Unit 3625