DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/08/2025 has been entered.
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 .
Disposition of Claims
Claims 1-20 are pending in the instant application. No claims have been cancelled. No claims have been added. Claims 1, 15, and 20 have been amended. The rejection of the pending claims is hereby made non-final.
Response to Remarks
101
Applicant’s arguments and amendments pertaining to the rejection of the pending claims under 35 USC 101 has been considered by the examiner, and is found to be persuasive. The rejection of the pending claims under 35 USC 101 is hereby withdrawn.
103
Applicant’s arguments and amendments pertaining to the rejection of the pending claims under 35 USC 103 in view of the previously applied prior art of record has been considered by the examiner, but is not found to be persuasive. The rejection of the pending claims is hereby maintained in view of the grounds of rejection presented below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ettl et al (US 2015/0317653) in view of Gonzalez Macias et al (US 2022/0342860).
Regarding claim 1, the prior art discloses a system, comprising: a non-transitory memory having instructions stored thereon (see at least paragraph [0008] to Ettl et al); and at least one processor operatively coupled to the non-transitory memory, and configured to read the instructions (see at least paragraph [0008] to Ettl et al) to: obtain raw sale data of an item in a store for a past time period, wherein the item is offered for sale in the store (see at least paragraph [0031] to Ettl et al), detect at least one out-of-stock (OOS) time period within the past time period based on the raw sales data (see at least paragraph [0057] to Ettl et al), wherein sale of the item is impacted by an OOS status of the item in the store in the at least one OOS time period, execute a non-linear model (see at least paragraph [0057] to Ettl et al), lost sales estimate of the item in the store for each of the at least one OOS time period, generate, based on the raw sales data and the lost sales estimate, recommended inventory for the item in the store for a future time period, and transmit the recommended inventory over a network to a computing device associated with the store for inventory arrangement in the future time period (see at least paragraphs [0025]-[0026] to Ettl et al).
The applied prior art reference Ettl et al does not appear to explicitly disclose execut[ing] a detection model including a spectral residual algorithm to detect at least one out-of-stock (OOS) time period based on a machine learning model, to generate a lost sales estimate of the item in the store for each of the at least one OOS time period.
However, Gonzalez Macias et al discloses an anomaly detection system and method in a split timeseries dataset, wherein the model selection system may detect the period using the spectral/residual algorithm (see at least paragraph [0011] to Gonzalez Macias et al), and wherein the anomaly detection model is based on machine learning (see at least paragraph [0040] to Gonzalez Macias et al).
The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). The examiner submits that the combination of the teaching of the system and method of omnichannel demand modeling and price optimization, as disclosed by Ettl et al and the system and method of anomaly detection in a split timeseries dataset as taught by Gonzalez Macias et al, in order to improve accuracy and efficiency of anomaly detection for the data analysis based on smoothed out data sets (see at least paragraph [0002] to Gonzales Macias et al), could have been readily and easily implemented, with a reasonable expectation of success. As such, the aforementioned combination is found to be obvious to try, given the state of the art at the time of filing.
Regarding claim 2, the prior art discloses the system of claim 1, wherein: the item is also offered for sale in at least one other store; the store and the at least one other store include all physical stores associated with a same retailer; and the lost sales estimate is computed based on sales data of the item in the at least one other store (see at least paragraph [0037] to Ettl et al).
Regarding claim 3, the prior art discloses the system of claim 2, wherein: the store is a physical store associated with a retailer; and the at least one other store includes an online store associated with the retailer (see at least paragraph [0020] to Ettl et al).
Regarding claim 4, the prior art discloses the system of claim 1, wherein the at least one OOS time period is detected based on: obtaining an inventory status of the item in the store during the at least one OOS time period; determining, based on the inventory status, whether the item has a first OOS status in the store during the at least one OOS time period based on a first method; obtaining historical sales data of the item in the store; determining, based on the inventory status and the historical sales data, whether the item has a second OOS status in the store during the at least one OOS time period based on a second method; and detecting the at least one OOS time period in accordance with a determination that the item has at least one of the first OOS status or the second OOS status in the at least one OOS time period (see at least paragraph [0032] to Ettl et al).
Regarding claim 5, the prior art discloses the system of claim 4, wherein determining whether the item has the first OOS status based on the first method comprises: dividing the at least one OOS time period into one or more weeks (see at least paragraph [0028] to Ettl et al); for each day in the one or more weeks: determining whether the store has any on-hand inventory of the item at end of the day, determining the day as an OOS day for the item in accordance with a determination that the store has no on-hand inventory of the item at end of the day, in accordance with a determination that the store has on-hand inventory of the item at end of the day (see at least paragraph [0057] to Ettl et al), computing an average one day of supply (DOS) for the item, determining whether the on-hand inventory of the item is less than the average one DOS at end of the day, determining the day as an in-stock (IS) day for the item in accordance with a determination that the on-hand inventory of the item is greater than or equal to the average one DOS at end of the day (see at least paragraph [0025] to Ettl et al), in accordance with a determination that the on-hand inventory of the item is less than the average one DOS at end of the day, determining whether the store has any sale of the item during the day, determining the day as an IS day for the item in accordance with a determination that the store has sale of the item during the day, determining the day as an OOS day for the item in accordance with a determination that the store has no sale of the item during the day (see at least paragraph [0026] to Ettl et al); and for each week of the one or more weeks, determining the week as an OOS week in accordance with a determination that at least one day of the week is determined as an OOS day.
Regarding claim 6, the prior art discloses the system of claim 5, wherein determining whether the item has the second OOS status based on the second method comprises: for each week in the one or more weeks: generating, based on the inventory status and the historical sales data, a saliency map of the item using a spectral residual method, computing an in-stock moving average of the saliency map, computing an anomaly score for the week based on the in-stock moving average, determining whether the anomaly score is below a predetermined threshold, determining the week as an OOS week for the item in accordance with a determination that the anomaly score is below the predetermined threshold, determining the week as an IS week for the item in accordance with a determination that the anomaly score is higher than or equal to the predetermined threshold (see at least paragraph [0032] to Ettl et al).
Regarding claim 7, the prior art discloses the system of claim 6, wherein the at least one OOS time period is detected based on: for each week in the one or more weeks, determining the week as an OOS week for the item in accordance with a determination that the week is determined as an OOS week based on at least one of the first method or the second method see at least paragraph [0057] to Ettl et al).
Regarding claim 8, the prior art discloses the system of claim 2, wherein the lost sales estimate of the item is computed based on: excluding, from the raw sales data, sales data of the item in the store in the at least one OOS time period to generate IS-only sales data of the item in the store for the past time period; generating IS-only sales data of the item in the at least one other store for the past time period; and combining the IS-only sales data of the item in the store with the IS-only sales data of the item in the at least one other store to generate combined sale data (see at least paragraph [0020] to Ettl et al).
Regarding claim 9, the prior art discloses the system of claim 8, wherein the lost sales estimate of the item is computed further based on: dividing the past time period into a plurality of consumer weeks, wherein a first day of a calendar month always aligns with a first day of one of the consumer weeks; dividing the future time period into one or more retailer weeks, each of which starts at a fixed day of a calendar week; dividing the combined sales data into daily sales data of the item; and computing an average daily sales of the item for each of the consumer weeks (see at least paragraph [0099] to Ettl et al).
Regarding claim 10, the prior art discloses the system of claim 9, wherein the average daily sales is computed based on: generating, based on the combined sales data, covariates related to sale of the item in the store and the at least one other store for the non-linear model; and performing a model fitting on the non-linear model to generate a fitted model, wherein the covariates are used as input to the non-linear model, and wherein the average daily sales is used as a target response variable of the non-linear model (see at least paragraph [0074] to Ettl et al).
Regarding claim 11, the prior art discloses the system of claim 10, wherein: the non-linear model is a generalized additive model (GAM); and the covariates include data related to: week of month, month of year, year, assistance program payout proportion for each local state, latitude and longitude of each store, and a quantity of weeks to or after a predefined set of national and local events and holidays (see at least paragraph [0028] to Ettl et al).
Regarding claim 12, the prior art discloses the system of claim 10, wherein: the non-linear model is a machine learning model trained based on sales data from all stores (see at least paragraph [0057] to Ettl et al).
Regarding claim 13, the prior art discloses the system of claim 10, wherein the lost sales estimate of the item is computed further based on: imputing sales data of the item in the store for each OOS week in the at least one OOS time period using the fitted model, wherein each OOS week aligns with a retailer week; re-assembling the imputed sales data to be aligned with the one or more retailer weeks; and extracting lost sales estimate of the item for each OOS week in the at least one OOS time period by subtracting actual sales data from the imputed sales data in the OOS week (see at least paragraph [0097] to Ettl et al).
Regarding claim 14, the prior art discloses the system of claim 13, wherein the recommended inventory is generated based on: adding the extracted lost sales estimate for each OOS week in the at least one OOS time period to the raw sales data to generate unconstrained sales data of the item for the past time period; forecasting, based on the unconstrained sales data, an unconstrained demand for the item in the store for the future time period; and generating the recommended inventory based on the unconstrained demand forecast (see at least paragraph [0032] to Ettl et al).
Claims 15-20 each contain recitations substantially similar to those addressed above and, therefore, are likewise rejected.
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The examiner has considered all references listed on the Notice of References Cited, PTO-892.
The examiner has considered all references cited on the Information Disclosure Statement submitted by Applicant, PTO-1449.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TALIA F CRAWLEY whose telephone number is (571)270-5397. The examiner can normally be reached on Monday thru Thursday; 8:30 AM-4:30 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fahd A Obeid can be reached on 571-270-3324. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TALIA F CRAWLEY/Primary Examiner, Art Unit 3627
Prosecution Insights