DETAILED ACTION
This is a Final Office Action in response to claims on 11/20/2025. Claims 1-20 are pending. The effective filing date is 12/12/2023.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/17/2024 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1, 6, 9, 14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0219171 A1 Zhuang et al. (hereinafter Zhuang) in view of US 2022/0092497 A1 Roth et al. (hereinafter Roth) and in further view of US 2024/0161060 A1 Francis (hereinafter Francis).
Regarding claim 1, Zhuang teaches a method comprising, at an online concierge system comprising a processor and a computer-readable medium (Zhuang [0082-0083] computing system with a processor and computer readable storage medium):
obtaining, by the online concierge system, a store plan indicating standard storage locations of items in a physical retailer and (Zhuang [0024-0028] an online concierge system (102) that monitors changes within a retailer (warehouse), and is connected to an order fulfillment engine; [0038] the item characteristic included is the aisle of the warehouse associated with the item, and therefore gives the location of the item within the retailer; [0077]);
obtaining, by the online concierge system, upcoming order information associated with one or more actual or predicted orders for items in an upcoming time window (Zhuang [0005] machine learning model to predict item availability; [0024] receiving actual orders, it also specifies the location the goods are to be delivered, and a time window during which the goods should be delivered);
applying an optimization model to determine, based on the store plan and the one or more actual or predicted orders, one or more items for the upcoming time window (Zhuang [0032] multiple machine learning engines, including availability, and fulfillment, to create models, [0034] functions for picking determined by the models);
generating a first set of instructions for a first picker client device that cause the picker client device to facilitate picking of the items (Zhuang [0034] functions for picking determined by the models, [0042] flowchart to show the predicting model to fulfillment of items; [0044] order fulfillment engine generates instructions to a shopper);
receiving information identifying an order from a customer (Zhuang [0025] transmitting orders from an online concierge system to be fulfilled from a warehouse; [0029] order fulfillment is accomplished by sending warehouse location closest to the item for pickup);
generating a second set of instructions that direct a picker of a second picker client device to procure the order from the physical retailer (Zhuang [0042] online concierge system identifies a warehouse for the picking; Fig. 2, 4);
and
generating instructions that facilitate delivery of the order to the customer (Zhuang [0035] determine instructions delivered to the customer; [0044] generate instructions to a shopper).
Zhuang fails to explicitly disclose information about a rapid fulfillment area of the physical retailer available to stage select items for rapid fulfillment; one or more items for staging in the rapid fulfillment area; picking of the items from the standard storage locations to the rapid fulfillment area; order from a customer that includes at least one item stocked to the rapid fulfillment area; and procurement of the at least one item from the rapid fulfillment area; causing the second picker client device to present an option to accept a replenishment task in exchange for enabling the picker to pick an additional item from the rapid fulfillment area;
in response to receiving an indication from the second picker client device that the picker accepted the replenishment task, re-generating the second set of instructions that direct the picker to procure the order from the physical retailer, including procurement of the at least one item and the additional item from the rapid fulfillment area.
Roth is in the field of customer order picking (Roth Abstract, shoppers select goods from shelves) and teaches information about a rapid fulfillment area of the physical retailer available to stage select items for rapid fulfillment; one or more items for staging in the rapid fulfillment area; picking of the items from the standard storage locations to the rapid fulfillment area; order from a customer that includes at least one item stocked to the rapid fulfillment area; and procurement of the at least one item from the rapid fulfillment area (Roth [0002-0003] the storage area includes a regular storage area, and then the automated device moves some items to a pre-staging area that is proximate to a dispensation area; [0049] the location nest to the dispensation area makes for quick and convenient transfer (rapid); Fig. 2- multiple areas, with pickers moving items between areas). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction model of Zhuang with the rapid fulfillment area taught by Roth. The motivation to combine the two would be to create a more efficient pickup routine, when predictive modeling can move items prior to their actual needs it creates less wait time, and therefore more efficiency (Roth [0021-0023] predicting pickup to preemptively move items from storage to the pre-staging area).
Francis is in the field of order pickups (Francis Abstract, scheduled customer order) and teaches causing the second picker client device to present an option to accept a replenishment task in exchange for enabling the picker to pick an additional item from the rapid fulfillment area (Francis [1222] there may be a request for a replacement or substitute item, which would be an exchange of the original item for a new item);
in response to receiving an indication from the second picker client device that the picker accepted the replenishment task, re-generating the second set of instructions that direct the picker to procure the order from the physical retailer, including procurement of the at least one item and the additional item from the rapid fulfillment area (Francis [1223] the additional elements are then assigned to a picker using the central computing system, which is able to communicate with a plurality of devices).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the picking of Zhuang with the optional exchange as taught by Francis. The motivation for doing so would be to fulfill all customer orders, and in order to complete orders there needs to be a variety of fulfillment procedures, including replacement or substitution of items (Francis [1209] Computer configured to perform multiple types of fulfillment including substitution).
Regarding claim 6, Zhuang teaches the method of claim 1, wherein obtaining the upcoming order information comprises: applying a predictive model to historical data indicative of historical orders in the online concierge system (Zhuang [0050] confidence scores based on statistics).
Regarding claim 9, Zhuang teaches a non-transitory computer-readable storage medium storing instructions that, when executed by an online concierge system comprising at least one processor (Zhuang [0082-0083] computing system with a processor and computer readable storage medium), cause the online concierge system to perform steps including: obtaining, by the online concierge system, a store plan indicating standard storage locations of items in a physical retailer (Zhuang [0024-0028] an online concierge system (102) that monitors changes within a retailer (warehouse), and is connected to an order fulfillment engine; [0038] the item characteristic included is the aisle of the warehouse associated with the item, and therefore gives the location of the item within the retailer; [0077]); obtaining, by the online concierge system, upcoming order information associated with one or more actual or predicted orders for items in an upcoming time window (Zhuang [0005] machine learning model to predict item availability; [0024] receiving actual orders, it also specifies the location the goods are to be delivered, and a time window during which the goods should be delivered); applying an optimization model to determine, based on the store plan and the one or more actual or predicted orders, one or more items for the upcoming time window (Zhuang [0032] multiple machine learning engines, including availability, and fulfillment, to create models, [0034] functions for picking determined by the models); generating a first set of instructions for a first picker client device to facilitate picking of the items from the standard storage locations (Zhuang [0034] functions for picking determined by the models, [0042] flowchart to show the predicting model to fulfillment of items); receiving information identifying an order from a customer that includes at least one item stocked (Zhuang [0025] transmitting orders from an online concierge system to be fulfilled from a warehouse; [0029] order fulfillment is accomplished by immediately sending warehouse location closest to the item for pickup); generating a second set of instructions that direct a picker of a second picker client device to procure the order from the physical retailer (Zhuang [0042] online concierge system identifies a warehouse for the picking; Fig. 2, 4); and
generating instructions that facilitate delivery of the order to the customer (Zhuang [0035] determine instructions delivered to the customer; [0044] generate instructions to a shopper).
Zhuang fails to explicitly disclose information about a rapid fulfillment area of the physical retailer available to stage select items for rapid fulfillment; one or more items for staging in the rapid fulfillment area; picking of the items from the standard storage locations to the rapid fulfillment area; order from a customer that includes at least one item stocked to the rapid fulfillment area; and procurement of the at least one item from the rapid fulfillment area; causing the second picker client device to present an option to accept a replenishment task in exchange for enabling the picker to pick an additional item from the rapid fulfillment area; and in response to receiving an indication from the second picker client device that the picker accepted the replenishment task, re-generating the second set of instructions that direct the picker to procure the order from the physical retailer, including procurement of the at least one item and the additional item from the rapid fulfillment area.
Roth teaches information about a rapid fulfillment area of the physical retailer available to stage select items for rapid fulfillment; one or more items for staging in the rapid fulfillment area; picking of the items from the standard storage locations to the rapid fulfillment area; order from a customer that includes at least one item stocked to the rapid fulfillment area; and procurement of the at least one item from the rapid fulfillment area (Roth [0002-0003] the storage area includes a regular storage area, and then the automated device moves some items to a pre-staging area that is proximate to a dispensation area; [0049] the location nest to the dispensation area makes for quick and convenient transfer (rapid); Fig. 2- multiple areas, with pickers moving items between areas). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction model of Zhuang with the rapid fulfillment area taught by Roth. The motivation to combine the two would be to create a more efficient pickup routine, when predictive modeling can move items prior to their actual needs it creates less wait time, and therefore more efficiency (Roth [0021-0023] predicting pickup to preemptively move items from storage to the pre-staging area).
Francis is in the field of order pickups (Francis Abstract, scheduled customer order) and teaches causing the second picker client device to present an option to accept a replenishment task in exchange for enabling the picker to pick an additional item from the rapid fulfillment area (Francis [1222] there may be a request for a replacement or substitute item, which would be an exchange of the original item for a new item);
in response to receiving an indication from the second picker client device that the picker accepted the replenishment task, re-generating the second set of instructions that direct the picker to procure the order from the physical retailer, including procurement of the at least one item and the additional item from the rapid fulfillment area (Francis [1223] the additional elements are then assigned to a picker using the central computing system, which is able to communicate with a plurality of devices).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the picking of Zhuang with the optional exchange as taught by Francis. The motivation for doing so would be to fulfill all customer orders, and in order to complete orders there needs to be a variety of fulfillment procedures, including replacement or substitution of items (Francis [1209] Computer configured to perform multiple types of fulfillment including substitution).
Regarding claim 14, Zhuang teaches the non-transitory computer-readable storage medium of claim 9, wherein obtaining the upcoming order information comprises: applying a predictive model to historical data indicative of historical orders in the online concierge system (Zhuang [0050] confidence scores based on statistics).
Regarding claim 17, Zhuang teaches an online concierge system comprising: one or more processors (Zhuang [0082-0083] computing system with a processor and computer readable storage medium); and a non-transitory computer-readable storage medium storing instructions that, when executed by the one or more processors (Zhuang [0082-0083] computing system with a processor and computer readable storage medium), cause the online concierge system to perform steps including: obtaining, by the online concierge system, a store plan indicating standard storage locations of items in a physical retailer (Zhuang [0024-0028] an online concierge system (102) that monitors changes within a retailer (warehouse), and is connected to an order fulfillment engine; [0038] the item characteristic included is the aisle of the warehouse associated with the item, and therefore gives the location of the item within the retailer; [0077]); obtaining, by the online concierge system, upcoming order information associated with one or more actual or predicted orders for items in an upcoming time window (Zhuang [0005] machine learning model to predict item availability; [0024] receiving actual orders, it also specifies the location the goods are to be delivered, and a time window during which the goods should be delivered); applying an optimization model to determine, based on the store plan and the one or more actual or predicted orders, one or more items for staging during the upcoming time window (Zhuang [0032] multiple machine learning engines, including availability, and fulfillment, to create models, [0034] functions for picking determined by the models); generating a first set of instructions for a first picker client device that cause the picker client device to facilitate picking of the items from the standard storage locations (Zhuang [0034] functions for picking determined by the models, [0042] flowchart to show the predicting model to fulfillment of items); receiving information identifying an order from a customer that includes at least one item stocked (Zhuang [0025] transmitting orders from an online concierge system to be fulfilled from a warehouse; [0029] order fulfillment is accomplished by immediately sending warehouse location closest to the item for pickup); generating a second set of instructions that direct a picker of a second picker client device to procure the order from the physical retailer (Zhuang [0042] online concierge system identifies a warehouse for the picking; Fig. 2, 4); and
generating instructions that facilitate delivery of the order to the customer (Zhuang [0035] determine instructions delivered to the customer; [0044] generate instructions to a shopper).
Zhuang fails to explicitly disclose information about a rapid fulfillment area of the physical retailer available to stage select items for rapid fulfillment; one or more items for staging in the rapid fulfillment area; picking of the items from the standard storage locations to the rapid fulfillment area; order from a customer that includes at least one item stocked to the rapid fulfillment area; and procurement of the at least one item from the rapid fulfillment area; causing the second picker client device to present an option to accept a replenishment task in exchange for enabling the picker to pick an additional item from the rapid fulfillment area; and in response to receiving an indication from the second picker client device that the picker accepted the replenishment task, re-generating the second set of instructions that direct the picker to procure the order from the physical retailer, including procurement of the at least one item and the additional item from the rapid fulfillment area.
Roth teaches information about a rapid fulfillment area of the physical retailer available to stage select items for rapid fulfillment; one or more items for staging in the rapid fulfillment area; picking of the items from the standard storage locations to the rapid fulfillment area; order from a customer that includes at least one item stocked to the rapid fulfillment area; and procurement of the at least one item from the rapid fulfillment area (Roth [0002-0003] the storage area includes a regular storage area, and then the automated device moves some items to a pre-staging area that is proximate to a dispensation area; [0049] the location nest to the dispensation area makes for quick and convenient transfer (rapid); Fig. 2- multiple areas, with pickers moving items between areas). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction model of Zhuang with the rapid fulfillment area taught by Roth. The motivation to combine the two would be to create a more efficient pickup routine, when predictive modeling can move items prior to their actual needs it creates less wait time, and therefore more efficiency (Roth [0021-0023] predicting pickup to preemptively move items from storage to the pre-staging area).
Francis is in the field of order pickups (Francis Abstract, scheduled customer order) and teaches causing the second picker client device to present an option to accept a replenishment task in exchange for enabling the picker to pick an additional item from the rapid fulfillment area (Francis [1222] there may be a request for a replacement or substitute item, which would be an exchange of the original item for a new item);
in response to receiving an indication from the second picker client device that the picker accepted the replenishment task, re-generating the second set of instructions that direct the picker to procure the order from the physical retailer, including procurement of the at least one item and the additional item from the rapid fulfillment area (Francis [1223] the additional elements are then assigned to a picker using the central computing system, which is able to communicate with a plurality of devices).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the picking of Zhuang with the optional exchange as taught by Francis. The motivation for doing so would be to fulfill all customer orders, and in order to complete orders there needs to be a variety of fulfillment procedures, including replacement or substitution of items (Francis [1209] Computer configured to perform multiple types of fulfillment including substitution).
Claims 2, 3, 10, 11, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhuang in view of Roth, Francis and in further view of US 2022/0292580 A1 Putrevu et al. (hereinafter Putrevu).
Regarding claim 2, Zhuang teaches the method of claim 1. Zhuang fails to explicitly disclose wherein applying the optimization model comprises: determining, for each of a set of candidate items, respective cost metrics characterizing incremental costs associated with the picker picking the candidate items from their respective standard storage locations instead of the rapid fulfillment area; generating a ranking of the set of candidate items based on the respective cost metrics; and selecting a subset of the candidate items for stocking in the rapid fulfillment area based on the ranking.
Roth teaches that items may be in a standard storage area, re-staging area, or dispensation area, and the location of the tote is determined at pickup (Roth Fig. 2, different storage areas). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the online concierge system of Zhuang with the multiple storage areas as taught by Roth. The motivation to combine the two would be to create a more efficient pickup routine, when predictive modeling can move items prior to their actual needs it creates less wait time, and therefore more efficiency (Roth [0021-0023] predicting pickup to preemptively move items from storage to the pre-staging area).
Putrevu is in the field of online concierge systems (Putrevu Abstract, online concierge systems) and teaches wherein applying the optimization model comprises:
determining, for each of a set of candidate items, respective cost metrics characterizing incremental costs associated with the picker picking the candidate items from their respective standard storage locations instead of the rapid fulfillment area (Putrevu [0012-0013] the concierge system creates candidate groups, and then gives a cost for a shopper fulfillment; [0034] flexible fulfillment to determine cost for fulfilling orders);
generating a ranking of the set of candidate items based on the respective cost metrics (Putrevu [0034] the grouping of items and accounts gives order in a specific order, and therefore is given a rank); and
selecting a subset of the candidate items for stocking in the rapid fulfillment area based on the ranking (Putrevu [0014] grouping items; [0034] the minimum cost for fulfillment is selected as the first choice, and the other costs are ranked accordingly; Fig. 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimization model of Zhuang with the cost metrics of Putrevu. The motivation for doing so would be to group similar items and increase the range of orders the system is capable of handling (Putrevu [0014] greater opportunities to combine and allow a broader range of orders).
Regarding claim 3, Zhuang teaches the method of claim 2. Zhuang fails to explicitly disclose wherein determining the respective cost metrics comprises: determining, for each of the set of candidate items, respective time differences between a picker procuring the candidate items from their respective standard storage locations and the picker procuring the candidate items from the rapid fulfillment area; and determining the respective cost metrics based at least in part on the respective time differences.
Roth teaches that items may be in a standard storage area, re-staging area, or dispensation area, and the location of the tote is determined at pickup (Roth Fig. 2, different storage areas). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the online concierge system of Zhuang with the multiple storage areas as taught by Roth. The motivation to combine the two would be to create a more efficient pickup routine, when predictive modeling can move items prior to their actual needs it creates less wait time, and therefore more efficiency (Roth [0021-0023] predicting pickup to preemptively move items from storage to the pre-staging area).
Putrevu teaches wherein determining the respective cost metrics comprises:
determining, for each of the set of candidate items, respective time differences between a picker procuring the candidate items from their respective standard storage locations and the picker procuring the candidate items from the rapid fulfillment area (Putrevu [0014] grouping items; [0059]); and
determining the respective cost metrics based at least in part on the respective time differences (Putrevu [0058-0059]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimization model of Zhuang with the cost metrics of Putrevu. The motivation for doing so would be to group similar items and increase the range of orders the system is capable of handling (Putrevu [0014] greater opportunities to combine and allow a broader range of orders).
Regarding claim 10, Zhuang teaches the non-transitory computer-readable storage medium of claim 9. Zhuang fails to explicitly disclose wherein applying the optimization model comprises: determining, for each of a set of candidate items, respective cost metrics characterizing incremental costs associated with the picker picking the candidate items from their respective standard storage locations instead of the rapid fulfillment area; generating a ranking of the set of candidate items based on the respective cost metrics; and selecting a subset of the candidate items for stocking in the rapid fulfillment area based on the ranking.
Roth teaches that items may be in a standard storage area, re-staging area, or dispensation area, and the location of the tote is determined at pickup (Roth Fig. 2, different storage areas). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the online concierge system of Zhuang with the multiple storage areas as taught by Roth. The motivation to combine the two would be to create a more efficient pickup routine, when predictive modeling can move items prior to their actual needs it creates less wait time, and therefore more efficiency (Roth [0021-0023] predicting pickup to preemptively move items from storage to the pre-staging area).
Putrevu teaches wherein applying the optimization model comprises:
determining, for each of a set of candidate items, respective cost metrics characterizing incremental costs associated with the picker picking the candidate items from their respective standard storage locations instead of the rapid fulfillment area (Putrevu [0012-0013] the concierge system creates candidate groups, and then gives a cost for a shopper fulfillment; [0034] flexible fulfillment to determine cost for fulfilling orders);
generating a ranking of the set of candidate items based on the respective cost metrics (Putrevu [0034] the grouping of items and accounts gives order in a specific order, and therefore is given a rank); and
selecting a subset of the candidate items for stocking in the rapid fulfillment area based on the ranking (Putrevu [0014] grouping items; [0034] the minimum cost for fulfillment is selected as the first choice, and the other costs are ranked accordingly; Fig. 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimization model of Zhuang with the cost metrics of Putrevu. The motivation for doing so would be to group similar items and increase the range of orders the system is capable of handling (Putrevu [0014] greater opportunities to combine and allow a broader range of orders).
Regarding claim 11, Zhuang teaches the non-transitory computer-readable storage medium of claim 10. Zhuang fails to explicitly disclose wherein determining the respective cost metrics comprises: determining, for each of the set of candidate items, respective time differences between a picker procuring the candidate items from their respective standard storage locations and the picker procuring the candidate items from the rapid fulfillment area; and determining the respective cost metrics based at least in part on the respective time differences.
Roth teaches that items may be in a standard storage area, re-staging area, or dispensation area, and the location of the tote is determined at pickup (Roth Fig. 2, different storage areas). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the online concierge system of Zhuang with the multiple storage areas as taught by Roth. The motivation to combine the two would be to create a more efficient pickup routine, when predictive modeling can move items prior to their actual needs it creates less wait time, and therefore more efficiency (Roth [0021-0023] predicting pickup to preemptively move items from storage to the pre-staging area).
Putrevu teaches wherein determining the respective cost metrics comprises:
determining, for each of the set of candidate items, respective time differences between a picker procuring the candidate items from their respective standard storage locations and the picker procuring the candidate items from the rapid fulfillment area (Putrevu [0014] grouping items; [0059]); and
determining the respective cost metrics based at least in part on the respective time differences (Putrevu [0058-0059]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimization model of Zhuang with the cost metrics of Putrevu. The motivation for doing so would be to group similar items and increase the range of orders the system is capable of handling (Putrevu [0014] greater opportunities to combine and allow a broader range of orders).
Regarding claim 18, Zhuang teaches the online concierge system of claim 17. Zhuang fails to explicitly disclose wherein applying the optimization model comprises: determining, for each of a set of candidate items, respective cost metrics characterizing incremental costs associated with the picker picking the candidate items from their respective standard storage locations instead of the rapid fulfillment area; generating a ranking of the set of candidate items based on the respective cost metrics; and selecting a subset of the candidate items for stocking in the rapid fulfillment area based on the ranking.
Roth teaches that items may be in a standard storage area, re-staging area, or dispensation area, and the location of the tote is determined at pickup (Roth Fig. 2, different storage areas). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the online concierge system of Zhuang with the multiple storage areas as taught by Roth. The motivation to combine the two would be to create a more efficient pickup routine, when predictive modeling can move items prior to their actual needs it creates less wait time, and therefore more efficiency (Roth [0021-0023] predicting pickup to preemptively move items from storage to the pre-staging area).
Putrevu teaches wherein applying the optimization model comprises:
determining, for each of a set of candidate items, respective cost metrics characterizing incremental costs associated with the picker picking the candidate items from their respective standard storage locations instead of the rapid fulfillment area (Putrevu [0012-0013] the concierge system creates candidate groups, and then gives a cost for a shopper fulfillment; [0034] flexible fulfillment to determine cost for fulfilling orders);
generating a ranking of the set of candidate items based on the respective cost metrics (Putrevu [0034] the grouping of items and accounts gives order in a specific order, and therefore is given a rank); and
selecting a subset of the candidate items for stocking in the rapid fulfillment area based on the ranking (Putrevu [0014] grouping items; [0034] the minimum cost for fulfillment is selected as the first choice, and the other costs are ranked accordingly; Fig. 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimization model of Zhuang with the cost metrics of Putrevu. The motivation for doing so would be to group similar items and increase the range of orders the system is capable of handling (Putrevu [0014] greater opportunities to combine and allow a broader range of orders).
Regarding claim 19, Zhuang teaches the online concierge system of claim 18. Zhuang fails to explicitly disclose wherein determining the respective cost metrics comprises: determining, for each of the set of candidate items, respective time differences between a picker procuring the candidate items from their respective standard storage locations and the picker procuring the candidate items from the rapid fulfillment area; and determining the respective cost metrics based at least in part on the respective time differences.
Roth teaches that items may be in a standard storage area, re-staging area, or dispensation area, and the location of the tote is determined at pickup (Roth Fig. 2, different storage areas). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the online concierge system of Zhuang with the multiple storage areas as taught by Roth. The motivation to combine the two would be to create a more efficient pickup routine, when predictive modeling can move items prior to their actual needs it creates less wait time, and therefore more efficiency (Roth [0021-0023] predicting pickup to preemptively move items from storage to the pre-staging area).
Putrevu teaches wherein determining the respective cost metrics comprises:
determining, for each of the set of candidate items, respective time differences between a picker procuring the candidate items from their respective standard storage locations and the picker procuring the candidate items from the rapid fulfillment area (Putrevu [0014] grouping items; [0059]); and
determining the respective cost metrics based at least in part on the respective time differences (Putrevu [0058-0059]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimization model of Zhuang with the cost metrics of Putrevu. The motivation for doing so would be to group similar items and increase the range of orders the system is capable of handling (Putrevu [0014] greater opportunities to combine and allow a broader range of orders).
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Zhuang in view of Roth, Putrevu, Francis and in further view of US 2017/0083862 A1 Loubriel (hereinafter Loubriel).
Regarding claim 5, Zhuang teaches the method of claim 2. Zhuang fails to explicitly disclose wherein determining the respective cost metrics comprises: determining size information for each of the set of candidate items; and determining the respective cost metrics based at least in part on the size information relative to available space in the rapid fulfillment area.
Roth teaches that items may be in a standard storage area, re-staging area, or dispensation area, and the location of the tote is determined at pickup (Roth Fig. 2, different storage areas). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the online concierge system of Zhuang with the multiple storage areas as taught by Roth. The motivation to combine the two would be to create a more efficient pickup routine, when predictive modeling can move items prior to their actual needs it creates less wait time, and therefore more efficiency (Roth [0021-0023] predicting pickup to preemptively move items from storage to the pre-staging area).
Loubriel is in the field of inventory management (Loubriel Abstract, product inventory is maintained) and teaches wherein determining the respective cost metrics comprises:
determining size information for each of the set of candidate items (Loubriel [0054-0056] defined sized to determine the logical spacing of item for picking); and
determining the respective cost metrics based at least in part on the size information relative to available space in the rapid fulfillment area (Loubriel [0089-0091] cost is determined based on available space; Fig. 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimization of Zhuang with the space estimate of Loubriel. The motivation for doing so would be to make efficient picking of items, filling packages to their fullest potential for the least amount of money (Loubriel [0090]).
Regarding claim 13, Zhuang teaches the non-transitory computer-readable storage medium of claim 10. Zhuang fails to explicitly disclose wherein determining the respective cost metrics comprises: determining size information for each of the set of candidate items; and determining the respective cost metrics based at least in part on the size information relative to available space in the rapid fulfillment area.
Roth teaches that items may be in a standard storage area, re-staging area, or dispensation area, and the location of the tote is determined at pickup (Roth Fig. 2, different storage areas). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the online concierge system of Zhuang with the multiple storage areas as taught by Roth. The motivation to combine the two would be to create a more efficient pickup routine, when predictive modeling can move items prior to their actual needs it creates less wait time, and therefore more efficiency (Roth [0021-0023] predicting pickup to preemptively move items from storage to the pre-staging area).
Loubriel teaches wherein determining the respective cost metrics comprises:
determining size information for each of the set of candidate items (Loubriel [0054-0056] defined sized to determine the logical spacing of item for picking); and
determining the respective cost metrics based at least in part on the size information relative to available space in the rapid fulfillment area (Loubriel [0089-0091] cost is determined based on available space; Fig. 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optimization of Zhuang with the space estimate of Loubriel. The motivation for doing so would be to make efficient picking of items, filling packages to their fullest potential for the least amount of money (Loubriel [0090]).
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhuang in view of Roth, Francis and in further view of US 2023/0049669 A1 Yuan et al. (hereinafter Yuan).
Regarding claim 7, Zhuang teaches the method of claim 1. Zhuang fails to explicitly disclose wherein obtaining the upcoming order information comprises:
determining respective likelihoods associated with the items being ordered within the upcoming time window.
Yuan is in the field of online concierge systems (Yuan Abstract, online concierge system) and teaches wherein obtaining the upcoming order information comprises:
determining respective likelihoods associated with the items being ordered within the upcoming time window (Yuan [0006-0008] the system is able to calculate the probability that an item will be ordered within a specific time interval).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the online concierge system of Zhuang with the prediction taught by Yuan. The motivation for doing so would be to improve service quality by more closely predicting future purchases (Yuan [0001]).
Regarding claim 15, Zhuang teaches the non-transitory computer-readable storage medium of claim 9. Zhuang fails to explicitly disclose wherein obtaining the upcoming order information comprises: determining respective likelihoods associated with the items being ordered within the upcoming time window.
Yuan teaches wherein obtaining the upcoming order information comprises:
determining respective likelihoods associated with the items being ordered within the upcoming time window (Yuan [0006-0008] the system is able to calculate the probability that an item will be ordered within a specific time interval).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the online concierge system of Zhuang with the prediction taught by Yuan. The motivation for doing so would be to improve service quality by more closely predicting future purchases (Yuan [0001]).
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zhuang in view Roth, Francis and WO 2022/115679 A1 Francis (hereinafter Francis II).
Regarding claim 8, Zhuang teaches the method of claim 1. Zhuang fails to explicitly disclose wherein obtaining the upcoming order information comprises:
determining for each of the items, respective predicted amounts of time until the items will be picked.
Francis II teaches wherein obtaining the upcoming order information comprises:
determining for each of the items, respective predicted amounts of time until the items will be picked (Francis II [0712-0714] predicting the order of picking, and knowing the distance between the item, they can therefore predict the amount of time between items).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the online concierge system with the time prediction taught by Francis. The motivation for doing so would be to utilize prior history to predict future items for better efficiency (Francis II [0713]).
Regarding claim 16, Zhuang teaches the non-transitory computer-readable storage medium of claim 9. Zhuang fails to explicitly disclose wherein obtaining the upcoming order information comprises: determining for each of the items, respective predicted amounts of time until the items will be picked.
Francis II teaches wherein obtaining the upcoming order information comprises:
determining for each of the items, respective predicted amounts of time until the items will be picked (Francis II [0712-0714] predicting the order of picking, and knowing the distance between the item, they can therefore predict the amount of time between items).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the online concierge system with the time prediction taught by Francis. The motivation for doing so would be to utilize prior history to predict future items for better efficiency (Francis II [0713]).
Reasons to Indicate Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter:
Regarding Claims 4, 12 and 20. The independent claims have added new limitations, which are found in prior art, however, the combination with the dependent claims presents a specific way in which the elements interact that no longer are taught by the combination of prior art. Therefore, claims 4, 12 and 20 indicate allowable subject matter but depend from rejected independent claims.
Claims 4, 12 and 20 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments filed 11/20/2025 have been fully considered but they are not persuasive.
Regarding 101: The proposed amendment includes the necessary action of the second picker client device to select an option. This interaction between a user, and its necessary action to move the method forward, showcases integration into a practical application. The selection by the client device then changes how procurement in the physical retailer occurs, and therefore move beyond just tools to implement the abstract idea.
Regarding 103: The claim amendments were not found in the prior art, therefore Examiner searched and found new reference to Francis. Francis is able to teach the substitution selection of an original item for a new item, and therefore it able to teach the new limitations in the claim.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2018/0247257 A1 Lert et al. teaches a system for tracking item location (Abstract).
Conclusion
THIS ACTION IS MADE FINAL. 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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/JESSICA E SULLIVAN/Examiner, Art Unit 3627
/AARON TUTOR/Primary Examiner, Art Unit 3627