DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2/13/2026 has been entered.
Claims 1, 2, 3, 6, 8, 9, 10, 11, 13, 15-18, and 20 have been amended.
Claims 1-20 are pending and have been rejected as follows.
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.
Step 1: The claims 1-7 are a method, claims 8-14 are a computer readable medium, and claims 15-20 are a system. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: The independent claims (1, 8 and 15, taking claim 8 as a representative claim) recite:
receiving, via a client device, a user selection of a first item to purchase online and pick up via an in-store pickup;
clustering, by a computing system, a plurality of items into item clusters based on selection patterns using a clustering model;
determining an item categorization of the first item corresponding to one or more of the item clusters;
accessing physical store traffic modeling for a store;
determining, based on the item categorization and the physical store traffic modeling, an item selection metric comprising a predicted rate of user selection relative to predicted user encounters during the in-store pickup;
generating an analysis of historical return of items, wherein the analysis distinguishes between online purchase return rates and in-store purchase return rates;
utilizing a delayed in-situ collaborative filter engine to apply a time-series model fitted to the item categorization to determine a second item to view in the store based on weighting the item selection metric, the analysis of historical return of items, and inventory data for the second item;
and generating, for presentation via the client device, a graphical user interface output identifying the second item for in-store viewing and comprising a selectable option to reserve the second item, based on the determination of the second item by the delayed in-situ collaborative filter engine.
These limitations, except for the italicized portions, under their broadest reasonable interpretations, recite certain methods of organizing human activity for managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as well as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The claimed invention recites steps for determining an item to recommend to a user to view in the store based on item data, store traffic data, and return data. The steps under its broadest reasonable interpretation specifically fall under sales activities. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
Prong 2: This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of
A computer-implemented method comprising: (claim 1)
A non-transitory computer-readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising: (claim 8)
A system comprising: one or more memory devices comprising a client device and a delayed in-situ collaborative filter recommendation engine; and one or more processors configured to cause the system to: (Claim 15)
receiving, via a client device, a user selection of a first item to purchase online and pick up via an in-store pickup;
clustering, by a computing system, a plurality of items into item clusters based on selection patterns using a clustering model;
determining an item categorization of the first item corresponding to one or more of the item clusters;
accessing physical store traffic modeling for a store;
determining, based on the item categorization and the physical store traffic modeling, an item selection metric comprising a predicted rate of user selection relative to predicted user encounters during the in-store pickup;
generating an analysis of historical return of items, wherein the analysis distinguishes between online purchase return rates and in-store purchase return rates;
utilizing a delayed in-situ collaborative filter engine to apply a time-series model fitted to the item categorization to determine a second item to view in the store based on weighting the item selection metric, the analysis of historical return of items, and inventory data for the second item;
and generating, for presentation via the client device, a graphical user interface output identifying the second item for in-store viewing and comprising a selectable option to reserve the second item, based on the determination of the second item by the delayed in-situ collaborative filter engine.
The additional elements of emphasized above are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. These limitations) do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application – MPEP 2106.05(f).
Accordingly, these additional elements when considered individually or as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The independent claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong two, the additional elements in the claims amount to no more than mere instructions to apply the judicial exception using a generic computer component.
Dependent claims 2-7, 9-14, and 16-20 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitations fail to establish that the claims are not directed to the same abstract idea of Independent Claims 1, 8 and 15 without significantly more.
Claim 9 recites wherein the operations further comprise receiving user interaction with the selectable option and providing an indication to an administrator device to retrieve the second item. While the claim recites the additional element of an administrator device, the additional element does not integrate the judicial exception into a practical application.
Claim 10 recites wherein clustering the plurality of items into the item clusters based on similar historical selection patterns; selecting a cluster corresponding to the first item; and utilizing the delayed in-situ collaborative filter engine to fit a time-series model to the cluster. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 11 recites wherein the delayed in-situ collaborative filter engine determines the second item to view in the store based on similarities between items at the store and similarities between users associated with the items at the store. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 12 recites wherein accessing the physical store traffic modeling comprises generating an item selection metric for items at the store based on a rate of user selection of the items at the store relative to a predicted number of user encounters for the items at the store. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 13 recites wherein the operations further comprise: determining a traffic prediction model for the second item as a function of a general traffic prediction model for the store; and modulating the traffic prediction model for the second item by a time-series traffic forecasting model derived as a function of the item clusters. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 14 recites wherein generating the analysis of historical returns comprises determining an overall return rate and an online purchase return rate for the second item. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claims 2-7 and 16-20 recite parallel claim language and therefore are also rejected for the reasons set forth for claims 9-14. Therefore claims 1-20 are rejected under 35 USC 101.
Subject Matter Free of Prior Art
Claims 1, 8 and 15 are determined to have overcome the prior art of rejection and are free of prior art, however the claims remain rejected under 35 USC 101, as set forth above.
The claims as amended are found to overcome the prior art rejection for the reasons set forth below.
With respect to claim 1, claim 1 now recites the additional claimed features of:
accessing physical store traffic modeling for a store;
determining, based on the item categorization and the physical store traffic modeling, an item selection metric comprising a predicted rate of user selection relative to predicted user encounters during the in-store pickup;
generating an analysis of historical return of items;
utilizing a delayed in-situ collaborative filter recommendation engine to determine a recommendation of a second item to view in the store based weighting the item selection metric, the analysis of historical return of items, and inventory data for the second item; and
With respect to claims 8 and 15, the claims now recite the additional claimed features of:
access physical store traffic modeling for a store;
determine an item selection metric comprising a predicted rate of user selection relative to predicted user encounters during the in-store pickup utilizing a time series model and based on the item categorization and the physical store traffic modeling;
generate an analysis of historical return of items;
utilize the delayed in-situ collaborative filter recommendation engine to determine a recommendation of a second item to view in the store based on an optimization equation comprising weighting the item selection metric, the analysis of historical return of items, and inventory data for the second item;
The closest prior art was found to be as follows:
Mamgain (US 20160275592) discloses a recommendation system identify items based on the category of items a user wishes to purchase [0029]. The system may access inventory information to determine if the requested item is in stock [0038]. Then the e-commerce system 42 at 261 may obtain recommended items 25 from the recommendation system 49 and present the recommended items 25 to the customer via customer computing device 30. To this end, the e-commerce system 42 may send the recommendation request to the recommendation system 49 in order to identify which items are being requested by the customer as well as which customer is requesting the items 24 [0051] and an additional item may be reserved [Abstract]. The reference does not disclose access physical store traffic modeling for a store; determine an item selection metric comprising a predicted rate of user selection relative to predicted user encounters during the in-store pickup utilizing a time series model and based on the item categorization and the physical store traffic modeling; generate an analysis of historical return of items; utilize the delayed in-situ collaborative filter recommendation engine to determine a recommendation of a second item to view in the store based on an optimization equation comprising weighting the item selection metric, the analysis of historical return of items.”
Shipman (US 20170262815) discloses enabling a user to buy parts and pick them up at a store location [0128], the prioritization of parts may also be informed by historical rates of part returns (e.g., a part that is frequently returned may be ranked lower than a part that is very rarely returned [0124], and the ranking may be based on identifying orders of priority of the required parts and the recommended parts, wherein causing presentation of the interface facilitating purchase of the required parts, the required tools, and the recommended parts includes causing presentation of the required parts and the recommended parts in the corresponding orders of priority. In some such embodiments, the orders of priority of the required parts and the recommended parts are based on one or more of: price, inventory levels, vendor-negotiated deals, geography, or historical rates of part returns. [0010]. However while the reference teaches the recommendation of a second item and ranking the items based on inventory and rate of return, the reference does not disclose “access physical store traffic modeling for the a store; determine an item selection metric comprising a predicted rate of user selection relative to predicted user encounters during the in-store pickup utilizing a time series model and based on the item categorization and the physical store traffic modeling; utilize the delayed in-situ collaborative filter recommendation engine to determine a recommendation of a second item to view in the store based on an optimization equation comprising weighting the item selection metric, the analysis of historical return of items, and inventory data for the second item.”
Paolella (US20200394697) discloses determining a predicted rate of consumption of a product based on historical inventory levels, historical traffic levels, and a current traffic level. The historical inventory levels may be determined from the inventory system 110 for the retail location. The historical traffic level and the current traffic level may be determined by the retail traffic system 114 for the current location. The machine-learning model 130 may determine the predicted rate of consumption of the product, which may be specific to the retail location [0034]. While the reference reviews foot traffic of a store to determine consumption rate of the product for a specific location, the reference does not disclose “generating an analysis of historical return of items; utilize the delayed in-situ collaborative filter recommendation engine to determine a recommendation of a second item to view in the store based on an optimization equation comprising weighting the item selection metric, the analysis of historical return of items, and inventory data for the second item”.
Ouimet (US 20050273377) discloses [Abstract] A computer system models customer response using observable data. The observable data includes transaction, product, price, and promotion. The computer system receives data observable from customer responses. A set of factors including customer traffic within a store, selecting a product, and quantity of selected product is defined as expected values, each in terms of a set of parameters related to customer buying decision. A likelihood function is defined for each of the set of factors. The parameters are solved using the observable data and associated likelihood function. The customer response model is time series of unit sales defined by a product combination of the expected value of customer traffic and the expected value of selecting a product and the expected value of quantity of selected product. A linear relationship is given between different products which includes a constant of proportionality that determines affinity and cannibalization relationships between the products. While the reference discloses using foot traffic data to determined the expectancy of the user to purchase a product and then determine an appropriate promotion for identified products, the reference does not disclose “utilize the delayed in-situ collaborative filter recommendation engine to determine a recommendation of a second item to view in the store based on an optimization equation comprising weighting the item selection metric, the analysis of historical return of items, and inventory data for the second item;.”
Maan (US 11017238) discloses [Col. 19 lines 55-Col. 20 lines 6] For example, based on the captured video/images, the server 316 (or some other device connected thereto) may be configured to store and/or present information regarding the number of customers located within the retail location to the user to provide information about customer traffic and compared to sales data for a corresponding time period to provide information about customer-to-sales conversion rates. A customer traffic heat map image correlated to time of day over the course of a period of time may be presented to the merchant. In another embodiment, the server 316 may be configured to count customer traffic, binary events, such as objects moving in camera frame, to discern customer traffic indirectly and optionally correlating the binary events to sales data to customer purchase data over the same time period to determine a specific conversion rate or to determine an ambiguous spectrum, such as “high conversion rate” and “low conversion rate.” While the reference reviews foot traffic of a store to determine conversion rates for periods of time, the reference does not disclose “utilize the delayed in-situ collaborative filter recommendation engine to determine a recommendation of a second item to view in the store based on an optimization equation comprising weighting the item selection metric, the analysis of historical return of items, and inventory data for the second item”.
As claims 1, 8, and 15 are free of prior art, dependent claims 2-7, 9-14, and 16-20 are also free of prior art by virtue of dependency.
Response to Arguments
Applicant’s arguments, 10/2/2025, with respect to 35 USC 103 have been fully considered and are persuasive. The rejection has been withdrawn for the reasons set forth above in “Subject Matter Free of Prior Art”.
Applicant's arguments filed 2/13/2026 have been fully considered but they are not persuasive.
With respect to the remarks directed to “The Currently Amended Claims Are Not Directed to an Abstract Idea”, the examiner maintains the claims still recite an abstract idea as shown in the updated rejection above. The amended limitation of "clustering, by a computing system, a plurality of items into item clusters based on historical selection patterns using a clustering model, “accessing physical store traffic modeling for a store, “determining, based on the item categorization and the physical store traffic modeling, an item selection metric comprising a predicted rate of user selection relative to predicted user encounters during the in-store pickup, “generating an analysis of historical return of items, wherein the analysis distinguishes between online purchase return rates and in-store purchase return rates, “utilizing a delayed in-situ collaborative filter engine to apply a time-series model fitted to the item categorization," and "generating, for presentation via the client device, a graphical user interface output identifying the second item for in-store viewing and comprising a selectable option to reserve the second item.", except for the recitation of the computing system and the a delayed in-situ collaborative filter engine , are part of the abstract idea. These limitations merely set forth the steps characterized as a method of organizing human activities as it relates to sales and marketing. The steps are evaluating products withing a store, traffic within a store, and categorizing the items with relation to the traffic within the store. Further, the relationship is used to predict customer actions and interactions with store versus online purchases. The additional element of the a delayed in-situ collaborative filter engine is merely a computer tool to analyze the data relationships set forth above, but is recited at a high level of generality and not in a manner that integrates the judicial exception into a practical application.
With respect to the remarks directed to “ The Currently Amended Claims are Patent-Eligible Because the Currently Amended Claims Integrate Any Asserted Abstract Idea Into a Practical Application Under Alice Prong Two of Step 2A”
The examiner first asserts that the fact pattern of the instant claimed invention to that of DDR Holdings differ. While DDR Holdings provided a technical solution to a technical problem rooted in the computer technology, the instant application provides a solution rooted in the abstract idea. DDR Holdings solved a technical problem relating to webpage interfaces, and not merely improving the data presented on those interfaces. In the instant application, the alleged improvement is recited in the abstract idea. The instant claims are using additional elements (computer technology) such as the delayed in-situ collaborative filter engine to carry out the abstract idea. As recited in the specification, the application is seeking to improve the recommendations outputs over conventional systems, that is the recommendations (the abstract idea) are improved.
With respect to the remarks directed to “The Currently Amended Claims Recite Additional Elements Amounting to Significantly More Than Any Asserted Abstract Idea Under Alice Step 2B”
The examiner asserts with respect to step 2B, the claims remain rejected for the same reasons set forth under prong 2. When viewed as a whole, the claims still remain rejected. In comparison to Core Wireless, the instant claims are again reciting the alleged improvement in the abstract idea and not reflective of a technical solution to a technical problem as set forth in Core Wireless. Improvement to the information (the recommendation) provided to the user on the interface is merely an improved in the abstract idea. This is in contrast to the fact pattern of Core Wireless which improved the operation of the interface technology itself.
For at least these reasons the claims remain rejected under 35 USC 101.
Relevant Art Not Cited
NPL: “Ecommerce Return Rates: Statistics and Tips to Improve Yours” which discloses the analysis of varying return rates for online purchases, in store purchases, and the return rate variance across industries.
NPL: “Pricing and Return Strategy: Whether to Adopt a Cross-Channel Return Option”? which discloses “that the return rate for online channels is roughly over 30% on average, which is much higher than that of physical channels at 8.89%.” on page 1.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VICTORIA E. FRUNZI whose telephone number is (571)270-1031. The examiner can normally be reached Monday- Friday 7-4 (EST).
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VICTORIA E. FRUNZI
Primary Examiner
Art Unit TC 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 3/12/2026