DETAILED ACTION
This Final Office action is in response to Applicant’s Amendment filed on 01/16/2026. Claims 1-7 and 22-34 are pending. The effective filing date of the claimed invention is 09/28/2022.
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 .
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 1-7 and 22-34 are rejected under 35 U.S.C. 101 because the claims are directed to abstract idea without significantly more.
Step 1 – Claims 1-7 and 22-34 relate to statutory category of claim. Step 1 is satisfied.
Step 2A, Prong 1 – Claim 1 recites the following abstract idea limitations:
A method comprising:
receiving a plurality of orders of items at an online system from devices associated with a plurality of users of the online system, each order comprising a request for the online system to fulfill the order during a timeslot, wherein the plurality of orders comprise one or more immediate orders placed during the timeslot and one or more scheduled orders that are scheduled before the timeslot for fulfillment during the timeslot (see MPEP 2106.04(a)(2)(III) receiving data is done in the mind, and can be recorded on pen and paper; see also Sanderling Mngt. Ltd. v. Snap Inc., No. 2021-2173 (Fed. Cir. Apr. 12, 2023)(“As the district court articulated, in a formulation we agree with, the claims are directed to the abstract idea "`of providing information — in this case, a processing function — based on meeting a condition,' e.g., matching a GPS location indication with a geographic location." Appx11. Even though the information being distributed is of a particular variety — here, digital imaging processing based on a distribution rule that determines when a condition is met — distribution of information is an abstract idea. See Intell. Ventures I LLC v. Cap. One Bank, 792 F.3d 1363, 1369 (Fed. Cir. 2015) ("Providing this minimal tailoring — e.g., providing different newspaper inserts based on the location of the individual — is an abstract idea.").”; see also MPEP 2106.04(a)(2)(II)(A-B) fundamental economic practice of receiving orders from customer(s) for time period);
determining, by the online system, a quantity of a resource available in the timeslot to fulfill the plurality of orders of items during the timeslot, the resource1 configured to obtain the items of the plurality of items during the timeslot (see MPEP 2106.04(a)(2)(I)(B); see also MPEP 2106.04(a)(2)(III));
applying, by the online system, the quantity of the resource to a machine learning model (see abstract idea finding in Recentive v. Fox, App. No. 2023-2437 (Fed. Cir. 2025)) to produce a predicted relationship between a value of a fulfillment metric and an allocation of the quantity of the resource reserved for immediate orders during the timeslot, wherein the predicted relationship includes a plurality of allocations of the quantity of the resource and a corresponding fulfillment metric for each allocation from the plurality of allocations (see MPEP 2106.04(a)(2)(I); and see July 2024, Subject Matter Eligibility Examples, Example 47, claim 2 – found to be abstract idea);
determining, by the online system, based on the predicted relationship, an expected optimal allocation of the quantity of the resource that maximizes the fulfillment metric during the timeslot, wherein determining the expected optimal allocation comprises (see e,g, MPEP 2106.04(a)(2)(II)(B) ii. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 485, 203 USPQ 812, 816 (CCPA 1979));
identifying an allocation from the plurality of allocations that corresponds to a highest fulfillment metric (see e.g. MPEP 2106.04(a)(2)(II)(A) The term “fundamental” is not used in the sense of necessarily being “old” or “well-known.” See, e.g., OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1364, 115 U.S.P.Q.2d 1090, 1092 (Fed Cir. 2015) (a new method of price optimization was found to be a fundamental economic concept));
wherein the allocation corresponding to the highest fulfillment metric is the expected optimal allocation of the quantity of the resource during the timeslot (see e.g. MPEP 2106.04(a)(2)(II)(A-B));
reserving2, by the online system, the expected optimal allocation of the quantity of the resource for immediate orders such that the expected optimal allocation of the quantity of the resource is allocated for obtaining the items of the plurality of orders during the timeslot, the reservation including updating one or more data records of the online system to reflect the expected optimal allocation of the quantity of the resource (see e.g. MPEP 2106.04(a)(2)(C) Another example of a claim reciting social activities is Interval Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553 (Fed. Cir. 2018). The social activity at issue was the social activity of “’providing information to a person without interfering with the person’s primary activity.’” 896 F.3d at 1344, 127 USPQ2d 1553 (citing Interval Licensing LLC v. AOL, Inc., 193 F. Supp.3d 1184, 1188 (W.D. 2014)). The patentee claimed an attention manager for acquiring content from an information source, controlling the timing of the display of acquired content, displaying the content, and acquiring an updated version of the previously-acquired content when the information source updates its content. 896 F.3d at 1339-40, 127 USPQ2d at 1555. The Federal Circuit concluded that “[s]tanding alone, the act of providing someone an additional set of information without disrupting the ongoing provision of an initial set of information is an abstract idea,” observing that the district court “pointed to the nontechnical human activity of passing a note to a person who is in the middle of a meeting or conversation as further illustrating the basic, longstanding practice that is the focus of the [patent ineligible] claimed invention.” 896 F.3d at 1344-45, 127 USPQ2d at 1559.);
fulfilling the plurality of orders of items using the reserved expected optimal allocation of the quantity of the resource maintained in the one or more data records of the online system (see e.g. MPEP 2106.04(a)(2)(I)(A) The term “fundamental” is not used in the sense of necessarily being “old” or “well-known.” See, e.g., OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1364, 115 U.S.P.Q.2d 1090, 1092 (Fed Cir. 2015) (a new method of price optimization was found to be a fundamental economic concept); In re Smith, 815 F.3d 816, 818-19, 118 USPQ2d 1245, 1247 (Fed. Cir. 2016) (describing a new set of rules for conducting a wagering game as a “fundamental economic practice”); In re Greenstein, 774 Fed. Appx. 661, 664, 2019 USPQ2d 212400 (Fed Cir. 2019) (non-precedential) (claims to a new method of allocating returns to different investors in an investment fund was a fundamental economic concept)(emphasis added));
wherein the plurality of orders of items are fulfilled by resources from the reserved expected optimal allocation of the quantity of the resource that obtain the items from the plurality of orders of items during the timeslot (this squarely fits in abstract idea shown in MPEP 2106.04(a)(2)(II)(C) managing personal behavior/relationships/interactions between people, as the resources are human beings, and the humans are being managed to determine who is going to fulfill the order)
The examiner has reviewed these abstract idea concepts both individually and as a whole, i.e. in ordered combination, and the examiner finds that claim 1 recites abstract idea.
Step 2A, Prong 2 – Claim 1 does not integrate the abstract idea into practical application. Claim 1 recites the additional element of, an online system that receives data from devices, determines a quantity based on data, applies mathematical calculation, and the rest of the abstract idea. This additional element is recited at a high level of generality and is used as a tool to implement the abstract idea. See MPEP 2106.05(f), “apply it” rationale. Accordingly, claim 1 does not integrate the abstract idea with practical application, and is therefore directed to abstract idea.
Step 2B – Claim 1 does not recite significantly more. Claim 1 recites the additional element of, an online system that receives data from devices, determines a quantity based on data, applies mathematical calculation, and the rest of the abstract idea. This additional element is recited at a high level of generality and is used as a tool to implement the abstract idea. See MPEP 2106.05(f), “apply it” rationale.
Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. This consideration is only evaluated in Step 2B of the eligibility analysis. See MPEP 2106.05(d).
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added));
The current claim 1 receives orders over a network (claim 1, and Applicant’s Spec at [0016] a network 120 that facilitates the transmission and reception of data
iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining “shadow accounts”); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
The current claim 1 recites storing data and reserving human by managing data recordkeeping.
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
The current claim 1 recites updating data records based on the data received.
For each of these limitations, the Federal Circuit has indicated that these types of limitations are found to be well-understood, routine, and conventional.
When viewed alone and in ordered combination, these additional element(s) of claim 1 do not recite significantly more, and claim 1 is found to be directed to abstract idea.
Dependent Claims – Claim 2, 23, 30, recites more abstract idea performed by “apply it” rationale. See MPEP 2106.04(a)(2)(III). Claim 3, 24, 31 recites more abstract idea performed by “apply it” rationale. See MPEP 2106.04(a)(2)(III). Claim 4, 25, 32 recites more abstract idea performed in “apply it” rationale. See MPEP 2106.04(a)(2)(III). Claim 5, 26, 33 recites more abstract idea. See Recentive; July 2024, Subject Matter Eligibility Examples, Example 47, claim 2, where the training aspect of the model was found to be abstract idea. Claim 6, 27, 34 recites more abstract idea performed “by the online system” or in an “apply it” manner. See e.g. July 2024, Subject Matter Eligibility Examples, Example 47, claim 2. Claim 7, 28 recites more abstract idea. See MPEP 2106.04(a)(2)(III). Accordingly, claims 1-7, 22-34 are found to be directed to abstract idea.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, 22-34 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. Pub. No. 2019/0236740 to Rao et al. (“Rao”) in view of U.S. Pat. Pub. No. 2022/0327599 to Krishnamoorthy et al. (“Krishnamoorthy”) in further view of U.S. Pat. No. 11,526,838 to Sethuraman et al. (“Sethuraman”).
With regard to claims 1, 22, 29, Rao discloses the claimed method performed by a computing system comprising a non-transitory memory and a processor, the method comprising:
receiving a plurality of orders at an online system from devices associated with a plurality of users of the online system (see e.g. [0015]), each order comprising a request for the online system to fulfill the order during a timeslot (see e.g. [0015] where the order(s) include among other things a time window when the item should be delivered), wherein the plurality of orders comprise one or more immediate orders placed during the timeslot and one or more scheduled orders that are scheduled before the timeslot for fulfillment during the timeslot (see e.g. Rao [0017] where the data is unified, including all order information, which would include storing data consecutively as received, for instance; see abstract, [0001-3] Rao discloses receiving customer orders for items via an online concierge system and directing pickers to fulfill the orders, where orders are received and processed with item-level machine learning predictions for availability; See also Krishnamoorthy, [0037-42] [0053-56], also defines first/second sets of timeslots and capacity within each, where Krishnamoorthy teaches timeslot-based pickup scheduling and presenting/assigning orders into time windows, with capacity limits and worker considerations);
determining, by the online system, a quantity of a resource available in the timeslot to fulfill the plurality of orders during the timeslot, the resource configured to obtain the items of the plurality of orders during the timeslot (see e.g. Rao [0021] The picker management engine 210 then identifies one or more appropriate pickers 108 [i.e. a quantity of resource available] to fulfill the order based on one or more parameters, such as the pickers' proximity to the appropriate warehouse 110 (and/or to the customer 104), his/her familiarity level with that particular warehouse 110, and so on. Additionally, the picker management engine 210 accesses a picker database 212 which stores information describing each picker 108, such as his/her name, gender, rating, previous shopping history, and so on. Methods that can be used to identify a warehouse 110 at which a picker 108 can likely find most or all items in an order are described with respect to FIGS. 4-7. (emphasis added); [0032] [0035] [0045]; Fig. 1, [0001-3]; Rao does not explicitly disclose computing quantity of resource per timeslot – Krishnamoorthy teaches at e.g. [0017], [0038-41, [0040-41] computes capacity constraints per timeslot based on inputs like number of workers, parking spots, etc.; forms a capacity constraint (max orders per timeslot); [0054-56] and details using worker counts to bound timeslot capacity);
applying, by the online system, the quantity of the resource to a machine learning model to produce a predicted relationship between a value of a fulfillment metric and an allocation of the quantity of the resource reserved for immediate orders (see e.g. [0033-35]; see [0021-30], [0024] The machine-learned item availability model 216 can be configured to receive as inputs information about an item, the warehouse for picking the item, and the time for picking the item. The machine-learned item availability model 216 may be adapted to receive any information that the modeling engine 218 identifies as indicators of item availability. Based on this teaching of Rao, the examiner finds that one of ordinary skill in the art would include the quantity of pickers (the one or more appropriate pickers) as part of the inputs to the machine learning as this is an indicator of item availability at the warehouse(s), as there must be at least one appropriate picker to pick the item or it is not available; Rao uses ML models, but to predict item availability, not a metric/allocation trade off across timeslots or immediate vs scheduled. Krishnamoorthy teaches ML models (such as support vector machine model, deep learning model, deep neural network model) feeding a slot-detection optimization that explicitly balances service level and resource utilization (i.e. a fulfillment metric v. resource use across timeslots); Krisnamoorthy [0020] The disclosed system may also leverage historical order data (e.g. history of 1000 customer orders) of the retail store to train machine learning models and then use such trained models to predict pickup timeslots, which may further maximize the service levels and resource utilization; Krisnamoorthy [0059] The objective function information 316E may include initial weights (or relative weights) for objectives, such as, but not limited to, a first objective to maximize a service level for the received customer order above a service level threshold, and a second objective to maximize a resource utilization of the number of human workers within each of the first set of timeslots above a utilization threshold; Krisnamoorthy [0065] The determination of the first timeslot may be such that both the first objective (i.e., the maximization of the service level for the received customer order) and the second objective (i.e., the maximization of the resource utilization of the number of the human workers) are satisfied; see also Krisnamoorthy [0045-47] describes objective-function formulations for timeslot selection with capacity constraints Krisnamoorthy [0040-42]);
determining, by the online system, based on the predicted relationship, an expected optimal allocation of the quantity of the resource that maximizes the fulfillment metric during the timeslot (Rao does not disclose optimization of resource allocation and fulfillment metric; Krishnamoorthy teaches [0040-42], Fig. 4 forms/solves an objective function (e.g. maximize service level, worker utilization) subject to capacity constraints to pick optimal timeslot decisions—i.e. allocations that maximize a fulfillment metric; Krisnamoorthy [0065] The determination of the first timeslot may be such that both the first objective (i.e., the maximization of the service level for the received customer order) and the second objective (i.e., the maximization of the resource utilization of the number of the human workers) are satisfied.);
reserving, by the online system, the expected optimal allocation of the quantity of the resource for immediate orders by updating one or more data records of the online system to reflect the expected optimal allocation of the quantity of the resource (Rao maintains system database/records (transaction, picker, inventory), but does not reserve pickers by timeslot for immediate orders; Sethuraman teaches and repeatedly describes reserving capacity (protection limits per class) and maintaining components/data reflecting current capacity and reservations (e.g. current locker capacity component, capacity reservation component, simulation/records, at e.g. Abstract, Figs. 4-6, col. 3, ln. 58 – col. 4, ln. 21; col. 9 ln. 44-67, col 5, ln. 40-50 etc.; Krishnimoorthy teaches at e.g. Figs. 5-6 the system updating and presenting timeslot sets and pipeline sets when the capacity/timeslot decision is made—i.e., implementing the reservation in the scheduling records user downstream);
fulfilling the plurality of orders by allocating the expected optimal allocation of the quantity of the resource maintained in the one or more data records of the online system, wherein the plurality of orders of items are fulfilled by resources from the reserved expected optimal allocation of the quantity of the resource that obtain the items from the plurality of orders of items during the timeslot (Rao further discloses at e.g. Fig. 6 [0035] picker management engine 210 instructs pickers based on probabilities; Krishnamoorthi, Fig. 5, after selection of timeslot(s) under capacity constraints, system proceeds with fulfillment scheduling for pickup (Fig. 5, Fulfillment Scheduling 512, consistent with using the reserved allocation (see Sethuraman) of workers to pick items during the chosen timeslot).
For the current rejections in claim set 01/16/26 (from prior claim 21),
Rao does not teach, and Setheramn teaches:
wherein the predicted relationship includes a plurality of allocations of the quantity of the resource (see e.g. Abstract, the capacity management system described herein may determine portions of a delivery locker to reserve for packages delivered at one or more delivery speeds. For example, the system may train one or more machine learning models to determine factors associated with a delivery demand, dwell time probability, and optimized capacity reservation of the delivery locker. Utilizing these factors, the system may determine a portion of the delivery locker to reserve for packages delivered at each available delivery speed.) and a corresponding fulfillment metric for each allocation from the plurality of allocations (see Setheramn e.g. col. 9 ln 44 – col. 10 ln 25 throughput put is measured at each allocation, and changes to the current throughput are simulated and determined in this section), and determining the expected optimal allocation comprises:
identifying an allocation from the plurality of allocations that corresponds to a highest fulfillment metric (See Setheramn e.g. Fig 4, col. 15 ln 8-60, Selecting the max reservation vector is exactly “identifying an allocation . . . that corresponds to the highest fulfillment metric” and then using that as the optimal allocation.),
wherein the allocation corresponding to the highest fulfillment metric is the expected optimal allocation of the quantity of the resource during the timeslot (See Fig. 4, col. 15, ln. 8-60. Sethuramn further determines portions to reserve and maximizes throughput, and uses those reservations downstream to accept/decline by class.).
Sethuramn col. 1, ln. 60—col. 2, ln. 31; col. 4, ln. 20-30; col. 6, ln. 60—col. 7, ln. 15, discloses optimizing reservation amounts to maximize the operational metric (throughput). Selecting the max reservation vector is exactly “identifying an allocation . . . that corresponds to the highest fulfillment metric” and then using that as the optimal allocation. See Fig. 4, col. 15, ln. 8-60. Sethuramn further determines portions to reserve and maximizes throughput, and uses those reservations downstream to accept/decline by class. Krishnamoorthi likewise frames objective-function based selection for timeslot decisions (maximize service level utilization subject to human capacity)—i.e., pick the option with the highest objective value.
Therefore, it would have been obvious to one of ordinary skill in the retail fulfillment/pickup logistics art before the effective filing date to modify Rao’s picker-based fulfillment and ML pipeline infrastructure, with Krishnimoorthi’s established timeslots with capacity constraints tied to the number of workers (the same resource as the claimed pickers) and determines/controls which orders can be accepted and when—i.e. reservation and system recorded slot allocation, and Setheraman’s ML driven capacity reservation across order classes to optimize a performance metric (e.g. throughput/SLA).
The motivation to combine Krishnamoorthi with Rao comes from Krishnimoorthi at e.g. [0017] “to maximize a service level for the received customer order above a service level threshold, and a second objective to maximize a resource utilization of the number of human workers within each of the first set of timeslots above a utilization threshold”; [0074] [0076]. The motivation to combine Sethuraman with Rao/Krishnimoorthi is shown in Sethuraman at e.g. col. 2 ln 50 – col. 3 ln. 5 “The capacity management techniques described herein help improve capacity reservation predictions and prevent unjustified rejections of packages,” improve accuracy and the like.
All three references, Rao, Krishnimoorthi, and Sethuraman, are squarely in online retail fulfillment/pickup logistics with human resources (pickers, workers) and system-controlled scheduling and constraints; a POSITA would naturally look to slot-capacity and reservation techniques to improve Rao’s order-fulfillment system. Krishnamoorthi explicitly optimizes service level/worker utilization under timeslot capacity; Sethuraman shows that reserving capacity, informed by ML prediction, improves throughput/metrics—a predictable improvement when grafted onto Rao’s picker-based fulfillment flow.
With regard to claims 2, 23, 30, Rao further discloses: receiving, by the online system, a potential scheduled order for fulfillment during the timeslot; and determining, by the online system, whether a portion of the quantity of the resource, besides the reserved expected optimal allocation of the quantity of the resource, is available to fulfill the potential scheduled order (see e.g. [0003] Based on the availability predictions from the machine-learned model, instructions are generated to a picker who fulfills a delivery order.).
With regard to claims 3, 24, 31, Rao further discloses: determining, by the online system, that there is not the portion of the quantity of the resource available to fulfill the potential scheduled order; and declining, by the online system, the potential scheduled order (see e.g. Fig. 7, 708, 712, 714).
With regard to claims 4, 25, 32, Rao further discloses: determining, by the online system, that there is the portion of the quantity of the resource available to fulfill the potential scheduled order; and confirming, by the online system, the potential scheduled order (see e.g. Fig. 7, 710 and fulfillment above).
With regard to claims 5, 26, 33, Rao further discloses where the machine learning model is trained on experimentally gathered data comprising a plurality of experimental timeslots and, for each experimental timeslot in the plurality of experimental timeslots, one or more of: a quantity of the resource available during the experimental timeslot, a quantity of immediate orders during the experimental timeslot, a quantity of scheduled orders during the experimental timeslot, an experimental allocation of the quantity of the resource reserved for immediate orders during the experimental timeslot, and a resulting value of the fulfillment metric for the experimental timeslot (see e.g. [0003], [0004] Fig. 5, [0018], [0023], [0025], [0027-30] [0036] etc.).
With regard to claims 6, 27, 34, Rao further discloses where determining, by the online system, the quantity of the resource available in the timeslot to fulfill orders during the timeslot, comprises: applying, by the online system, a second machine learning model to the timeslot to produce an estimate of the quantity of the resource available in the timeslot to fulfill orders during the timeslot (see e.g. [0025] The set of functions of the item availability model 216 may be updated and adapted following retraining with new training datasets 220.).
With regard to claims 7, 28, Rao further discloses where determining, by the online system, the quantity of the resource available in the timeslot to fulfill orders during the timeslot, comprises: determining a number of shoppers (see e.g. [0002] In current delivery systems, shoppers, or “pickers,” fulfill orders at a physical warehouse, such as a retailer, on behalf of customers as part of an online shopping concierge service. In current delivery systems, the pickers may be sent to various warehouse locations with instructions to fulfill orders for items, and the pickers then find the items included in the customer order in the warehouse) available to fulfill orders, wherein the fulfillment metric is a conversion rate based on a number of users placing orders before and end of the timeslot divided by a number of users that visit a page of a customer mobile application (see e.g. [0021] [0027] [0032] etc.).
Response to Arguments
Applicant's arguments filed 01/16/2026 have been fully considered but they are not persuasive.
The examiner has withdrawn the previously made 112 rejection.
The examiner has reviewed Applicant’s arguments under 101. The examiner respectfully disagrees. The examiner has modified the 101 rejection above to address the arguments, and amendments, from the claimed subject matter. The examiner recommends adding a technical solution to a technical problem. The examiner has not been able to find that in the claimed subject matter and therefore maintains the 101 rejection.
As for the 103 rejection, the examiner respectfully disagrees. Applicant appears to argue that Setheuraman is non analogous art as it relates to managing delivery lockers capacity, as opposed to managing pickers. In order for a reference to be proper for use in an obviousness rejection under 35 U.S.C. 103, the reference must be analogous art to the claimed invention. In re Bigio, 381 F.3d 1320, 1325, 72 USPQ2d 1209, 1212 (Fed. Cir. 2004). A reference is analogous art to the claimed invention if: (1) the reference is from the same field of endeavor as the claimed invention (even if it addresses a different problem); or (2) the reference is reasonably pertinent to the problem faced by the inventor (even if it is not in the same field of endeavor as the claimed invention).
1) Same field of endeavor?
Lockers and pickers are not strictly the same physical artifact, but both disclosures sit in the e-commerce fulfillment/scheduling domain and address how an online system decides who/what capacity to accept, reserve, and use in a time-bounded resource to serve customer orders. Sethuraman expressly frames an online capacity management system that (a) predicts demand and dwell with ML, and (b) reserves capacity slices across demand classes (e.g., ship speeds) to optimize an operational KPI (throughput). That’s the same type of systems problem the claims frame for pickers: how much capacity to hold back for “immediate” orders vs. scheduled orders in a given timeslot to maximize a fulfillment metric.
2) Reasonably pertinent to the problem faced?
Yes—Sethuraman tackles the same problem structure the claims do:
Finite, time-windowed capacity: a locker bank has limited compartments over time; a store has limited picker-minutes per timeslot. Both are scarce, time-indexed resources.
Heterogeneous demand classes: expedited/next-day vs. standard (lockers) mirrors immediate vs. scheduled (pickers). Each class competes for the same finite capacity in the same window.
Reservation decision variable: both decide what portion of capacity to reserve for the priority/“now” class before arrivals fully realize, to avoid starving either class.
ML-driven tradeoff + KPI optimization: both use predictive models to estimate demand/occupancy and then optimize reservations to maximize a fulfillment KPI (throughput, service level, utilization). That is exactly the “predicted relationship … across a plurality of allocations” and “choose the allocation with the highest metric.”
Operational enforcement in records: both persist the chosen reservation so the system can accept/decline (lockers) or schedule/fulfill (pickers) consistent with the reservation.
Applicant also argues that the locker can only have one product at a time. This is not true, The locker system is divided into portions, where part of the locker is designated for a specific product, and another portion of the locker is designated to a different product. See e.g. Setheramn at e.g. col. 3, ln. 45-70 and throughout. The other arguments are not persuasive.
Conclusion
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/PETER LUDWIG/ Primary Examiner, Art Unit 3627
1 See Applicant’s originally-filed Specification at e.g. [0050], where the resource could be a human being employed as a “shopper.”
2 See Applicant’s Specification at [0064] “to determine what fraction of a quantity of a resource available during that timeslot to reserve (i.e., allocate) exclusively to immediate orders placed during the timeslot.” In other words, the limitation relating to reserving, can also be worded as allocating.