DETAILED ACTION
Status of the Application
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 .
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 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
This action is a Final Action on the merits in response to the application filed on 02/05/2026.
Claims 1, 4, 5, 9, 12, 13, and 17 have been amended.
Claims 21 and 22 have been added
Claims 3 and 11 have been cancelled.
Claims 1, 2, 4-10, 12-22 remain pending in this application.
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 U.S.C. 101 rejections of claims in the previous office action have been maintained.
The 35 U.S.C. 102 rejections of claims in the previous office action are withdrawn in light of applicant’s amendments, however a new 103 rejections was added.
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, 2, 4-8, 21, 22 are directed towards a system, claims 9, 10, 12-16 are directed towards a method, and claims 17-20 are directed computer-readable medium towards a method, all of which are among the statutory categories of invention.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act, including training optimized models. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 1, 2, 4-10, 12-22, the independent claims (claims 1, 9, and 17) are directed to managing distribution data, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
1. (1. A system, comprising:
receive a demand distribution optimization request for a target network;
generate a heuristic demand for one or more demand nodes in the target network;
generate a distribution channel capacity for a distribution channel connecting each of the one or more demand nodes and at least one distribution node;
these steps fall within and recite an abstract ideas because they are directed to mathematical concepts (including mathematical formulas or equations); a method of organizing human activity which includes commercial interactions (including agreements in the form of contracts; marketing or sales activities or behaviors; business relations) (See MPEP 2106.04(a)(2), subsection II).
If a claim limitation, under its broadest reasonable interpretation math or commercial interaction, then it falls within the ” mathematical concepts”; “method of organizing human activity” grouping of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the MPEP 2106, the analysis should proceed to Prong Two. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of memory, processor, trained optimization model, remote computing device, GUI, (Claim 17 recites computer-readable medium). The claims recite the steps are performed by the memory, processor, trained optimization model, remote computing device, GUI.
The limitations of
a non-transitory memory;
a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to:
generate an optimized fulfillment data structure representative of an optimized demand fulfillment for the one or more demand nodes by a trained optimization model configured to apply a mixed integer linear programming framework incorporating constraints including an inventory constraint, a resource constraint, a planned distribution unit constraint, a distribution unit maximum capacity constraint, and a distribution unit minimum capacity constraint with a slack variable representative of a permitted minimum-capacity breach for a last distribution unit, the trained optimization model including an item to demand node allocation cost, a distribution channel unit prioritization cost, and a less than minimum capacity cost; and
transmit the optimized fulfillment data structure over a network to a remote computing device, the remote computing device rendering data characterizing the optimized fulfillment data structure in a graphical user interface.
are mere data processing and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by memory, processor, trained optimization model, remote computing device, GUI. The memory, processor, trained optimization model, remote computing device, GUI are recited at a high level of generality. In limitation (a), memory, processor, trained optimization model, remote computing device, GUI are used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The memory, processor, trained optimization model, remote computing device, GUI are used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Additionally, claim 1 recites trained optimization model. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the trained optimization model. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data processing and outputting. Then, the machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0099 “a variety of machine learning techniques can be used alone or in combination to generate the optimization model and/or the optimized fulfillment data structure.”]) and does not amount to significantly more than the abstract idea.
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
a non-transitory memory;
a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to:
generate an optimized fulfillment data structure representative of an optimized demand fulfillment for the one or more demand nodes by a trained optimization model configured to apply a mixed integer linear programming framework incorporating constraints including an inventory constraint, a resource constraint, a planned distribution unit constraint, a distribution unit maximum capacity constraint, and a distribution unit minimum capacity constraint with a slack variable representative of a permitted minimum-capacity breach for a last distribution unit, the trained optimization model including an item to demand node allocation cost, a distribution channel unit prioritization cost, and a less than minimum capacity cost; and
transmit the optimized fulfillment data structure over a network to a remote computing device, the remote computing device rendering data characterizing the optimized fulfillment data structure in a graphical user interface.
are recited at a high level of generality. These elements amount to transmitting data and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of a memory, processor, trained optimization model, remote computing device, GUI to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 2, 4-8, 10, 12-16, 18-22, do not contain any new additional elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims, dependent claims 6-8, 14-16, 19, 20 recite distribution units; claim 22 recite computing network. The dependent claims 2, 4-8, 10, 12-16, 18-22 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2, 4-8, 10, 12-16, 18-22recites memory, processor, trained optimization model, remote computing device, GUI which are considered an insignificant extra-solution activities of processing and analyzing data; see MPEP 2106.05(g). Claims 2, 4-8, 10, 12-16, 18-22 recites memory, processor, trained optimization model, remote computing device, GUI, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2, 4-8, 10, 12-16, 18-22 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1, 9, and 17. Therefore claims 2, 4-8, 10, 12-16, 18-22 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
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 of this title, 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, 2, 4-10, 12-20, 22 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Number US 10,922,646, Humair, et al. to hereinafter Humair in view of United States Patent Publication US 20210019694, Dhesi, et al.
Referring to Claim 1, Humair teaches a system, comprising:
a non-transitory memory (
Humair: Col. 10 Ln. 14-25, The memory 470 generally includes RAM, ROM, and/or other persistent, non-transitory computer readable media. );
a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to:
receive a demand distribution optimization request for a target network (
Humair: Col. 15 Ln. 15-40, other detectable parameter of the fulfillment center. Given that this planning horizon may be short (e.g., a few days), it may be assumed that the optimal inventory target at FC n will remain constant over this period. Any left-over inventory at the end of the planning horizon may be associated with a salvage value equal to the full procurement cost perceived by FC n. Under such models, targets for the destination nodes may be identified that are large fractiles of the overall demand distribution over this planning horizon.);
generate a heuristic demand for one or more demand nodes in the target network (
Humair: Col. 13 Ln. 60- Col. 14 Ln. 30, For a serial supply chain with common review periods at each stage, backordered demand, linear holding costs, and a specific replenishment policy (e.g., that ships the minimum of on-hand stock and demanded quantity from a stage), the main result of such models is structural: that an echelon stock policy is optimal for this problem…Heuristics that can approximate the optimal solution may be included to address these issues and reveal relationships between key model parameters and the optimal solution. These heuristics however are limited to providing solutions only for serial networks, not dynamic fulfillment networks with multiple and/or alternative sources of an item that may be represented with complex interactions such as a hierarchical tree structure. Furthermore, the heuristics do not consider resource constraints within the fulfillment network such as fulfillment center capacity for stocking heterogeneous assortments of items.);
generate a distribution channel capacity for a distribution channel connecting each of the one or more demand nodes and at least one distribution node (
Humair: Col. 3 Ln. 35-60, New distribution channels (e.g., same day distribution, direct retail distribution, subscription service, etc.) may increase the variety of demand streams. Further changes in supply chain logistics may also provide a geographically diverse distribution network. In view of such dynamic and potentially complex distribution networks, a multi-echelon solution may be desirable such that adequate inventory targets are maintained for transferring inventory to some FCs while also maintaining adequate supply at feeder FCs. For example, a distribution network may include forward deploy FCs (FDFCs) that can only be supplied from other FCs. If this distribution network expands and FC sizes (e.g., capacity) become smaller, the new FCs may require replenishment from other FCs within the distribution network.);
generate an optimized fulfillment data structure representative of an optimized demand fulfillment for the one or more demand nodes by applying a mixed integer linear programming framework (
Humair: Col. 13 Ln. 5-40, At block 514, a current quantity estimate for the item for each fulfillment center on the path identified at block 512 is generated. Within a serial supply chain, planning accurate inventory positions for each node is a complex operation due to the variety of possible assumptions, e.g. in cost structures (fixed or linearly varying), ordering policies (one-for-one, batch, state-dependent), end-user behavior (backorder or lost sales), inter-facility replenishment policies, transshipment policies etc. leading to a large set of possible decisions.
Humair: Col. 16 Ln. 55- Col. 17 Ln. 15, Similar to the quantity estimates generated for intermediate FCs, to generate a quantity estimate for a receiving node, the serial network from the receiving node to downstream nodes may be collapsed into a single FC. In the collapsed form, different candidate echelon targets may be generated at this node based on different specification of the costs. The different scenarios may be combined (e.g., averaged) to generate the final echelon target. One difference between an intermediate node estimate and receiving node estimate is that, for a single item estimate with assumed infinite capacity, estimating quantities for a receiving node may explicitly use the distribution of the random planning horizon. The estimation may depend on a salvage value function r. The salvage function may be a non-linear function such as a net present value function. Otherwise, the receiving node inventory estimate may be generated using a similar process as applied for the intermediate node quantities.);
store the optimized fulfillment data structure in a data storage mechanism (
Humair: Col. 3 Ln. 45-60, storage-capacity constraints. Included are features for an inventory target setting mechanism for individual items, assuming no capacity constraints. Features are also described to address complexities raised by limited and dynamic storage capacities and address the associated problem of planning for multiple items.
Humair: Col. 8 Ln. 60- Col. 9 Ln. 30, The fulfillment network topology server 180 may generate a distribution network a based at least in part on a set of available fulfillment centers identified from a fulfillment center data store 165. For example, the fulfillment center data store 165 may include four different fulfillment centers. Each fulfillment center may be associated with a demand stream. Some may be capable of providing the item to a downstream fulfillment center. Because these FCs may be organized in a variety of ways to ensure satisfaction of all demand scenarios, it may be desirable to identify an “optimal” network. The optimal network may be identified based on the transfer cost for a network (e.g., configuration that provides the lowest overall or average transfer costs).
Humair: Col. 19 Ln. 35-67, The method 700 for generating multi-item estimates uses implicitly, for each item and demand stream, a single-item target setting method, such as the method 500 shown in FIG. 5. In part for scalability reasons, the method 700 may consider the FCs one at a time, and generate target quantity estimates for each item and demand stream at a FC while meeting its storage capacity constraint. Then, with those targets fixed, the method 700 may generate target quantity estimates for the next FC in the distribution network..).
Humair does not explicitly teach generate an optimized fulfillment data structure representative of an optimized demand fulfillment for the one or more demand nodes by a trained optimization model configured to apply a mixed integer linear programming framework incorporating constraints including an inventory constraint, a resource constraint, a planned distribution unit constraint, a distribution unit maximum capacity constraint, and a distribution unit minimum capacity constraint with a slack variable representative of a permitted minimum-capacity breach for a last distribution unit the trained optimization model including an item to demand node allocation cost, a distribution channel unit prioritization cost, and a less than minimum capacity cost; transmit the optimized fulfillment data structure over a network to a remote computing device, the remote computing device rendering data characterizing the optimized fulfillment data structure in a graphical user interface
However, Dhesi teaches these limitations
generate an optimized fulfillment data structure representative of an optimized demand fulfillment for the one or more demand nodes by a trained optimization model configured to apply a mixed integer linear programming framework incorporating constraints (
Dhesi: Sec. 0021, “In some embodiments, the fulfillment engine includes an optimization function that optimizes for one or more of profit margins, customer satisfaction, vendor satisfaction, order quality, and fungibility of order.”
Dhesi: Sec. 0022, “In one embodiment, the fulfillment engine optimizes a merchant-client relation through the application of a linear objective function applying different weights to different factors, such as profit, customer restrictions, and/or merchant restrictions.”
Dhesi: Sec. 0084-0086, “In Step 510, the optimizer converts client order information to linear constraints. … In step 520, merchant information is converted to linear constraints. … In Step 530, a linear objective function is loaded. In one exemplary embodiment, the following equation may be used 0.5×Profit 0.3×ClientSatisfaction 0.2×MerchantSatisfaction Equation 3.”
Dhesi: Sec. 0070, “At a high level, optimizer 210 takes in information such as client constraints 212, merchant constraints 214, merchant-client pair weights 216, profit information 218, analyzes this information in an optimizer function 211 which may be, in some embodiments, a linear objective function 211, and may output a merchant client allocation 220.”);
Dhesi teaches that the fulfillment engine includes an optimization function that uses a linear objective function with constraints.
Dhesi: Sec. 0029, “In one embodiment, the output of the optimization function is an optimized or best relationship of a single merchant and a single client. In an alternate embodiment, the output of the optimization function is a set of one or more ordered or ranked tuples, each representing a merchant client relationship, where higher ranked or scored relationships are considered more optimal.”
Dhesi: Sec. 0093, Fig. 5“At step 560, the results are output to the fulfillment engine 20.”
Dhesi teaches the output as an allocation result usable for fulfillment.
Dhesi: Sec. 0084, “In the example of FIG. 5, the optimizer solves the allocation problems of assigning client orders to merchants 30 by use of one or more linear programming methods.” (Paragraph 0084)
Dhesi teaches using linear programming methods to solve the allocation problem.
including an inventory constraint, a resource constraint, a planned distribution unit constraint, a distribution unit maximum capacity constraint, and a distribution unit minimum capacity constraint with a slack variable representative of a permitted minimum-capacity breach for a last distribution unit (
Dhesi: Sec. 0084, “For example, client constraints may include any or all of a number or percentage of products subject to hard restrictions … and restrictions arising from information relating to client feedback …”
Dhesi: Sec. 0085, “For example, merchant constraints may include any or all of a product or menu availability, delivery times, fulfillment capacity minimum maximum values, hours of operation/fulfillment hours, current inventory, restrictions set by merchant hours or availability, staffing limitations, circumstantial limitations such as weather, power, supply that may limit merchant production, or any other appropriate value.”
Dhesi teaches client and merchant constraints which embody inventory and capacity/resource limitations.
Dhesi: 0097–0099, FIG. 6-7B, “Can the order be broken up into smaller portions so other merchants can be used to fill the order? … Add options to insert a slight penalty if multiple merchants are necessary to fill the order to compensate for use of multiple couriers.”
Dhesi teaches penalties and order splitting (analogous to slack/less-than-capacity penalties),
the trained optimization model including an item to demand node allocation cost, a distribution channel unit prioritization cost, and a less than minimum capacity cost (
Dhesi: Sec. 0087, “The objective function is a composite computation that makes a tradeoff between factors such as client satisfaction, profit margin, restaurant satisfaction, and assigns weights thereto.”
Dhesi: Sec. 0090, “User-merchant pairing information, which indicate among other things a rating information, service dates, and service frequency information, can stored in the memory 205. The ratings can take the form of positive or negative ratings, a star system … This paring information can be used … in the calculation of a customer satisfaction score for the optimizer.”);
Dhesi teaches an objective function combining profit, client satisfaction, and merchant satisfaction, each based on factors including merchant costs and client ratings. Dhesi collectively show a cost-based allocation framework where each merchant-client pair has a weight (cost/benefit) and the linear objective function optimizes over those weights and constraints.
transmit the optimized fulfillment data structure over a network to a remote computing device, the remote computing device rendering data characterizing the optimized fulfillment data structure in a graphical user interface (
Dhesi: Sec. 0047, “Fulfillment engine 20 transmits and or receives order data, profile preference data, fulfillment data, and or feedback data, etc. to from one or more of merchant devices 30 and customer devices 10 … These types of data transmission are performed over a communication network not specifically shown.”
Dhesi: Sec. 0040, “In some embodiments, fulfillment engine 20 may present a user interface (e.g., a graphical user interface) to a client device 10 through which the client may interact with the fulfillment engine. Such a user interface may be a dedicated application app or website …”
Dhesi: Sec. 0013, 0015, 0017, “FIG. 7A is a client user interface display for placing or modifying of client orders … FIG. 8A is a merchant user interface display for the accepting or rejecting of client orders. … FIG. 9A is a courier user interface display for accepting or rejecting of courier transportation orders.”).
Dhesi teaches that the optimized results are transmitted and rendered via GUIs.
Humair and Dhesi are both directed to the analysis of fulfillment and capacities (See Humair at Col. 2 Ln. 20-50; Dhesi at 0054-0059). Humair discloses that additional elements, such as the business inventory management can be considered (See Humair at Col. 19 Ln. 20-35). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Humair, which teaches detecting and repairing inventory management problems in view of Dhesi, to efficiently apply analysis of fulfillment and capacities by including the use of optimizing order fulfillment from multiple sources. (See Dhesi at 0020, 0036, 0096).
Referring to Claim 2, Humair teaches the system of claim 1, wherein the heuristic demand comprises replenishment forecast for each of a plurality of consumable resources associated with the demand node (
Humair: Col. 3 Ln. 20-50, To provide accurate inventory quantity estimations, it may be desirable to explicitly model the multi-echelon nature of the distribution network. For example, the inventory required at a target FC need only be sufficient to cover demand over the relevant replenishment time from its feeder FC (e.g., quantity on hand until next shipment received from a feeder FC). The quantity should ensure ample stock in the feeder FC to cover demand over a period that includes an item review period (e.g., time to inspect and in-take items from a vendor), vendor lead time (e.g., time to order and receive items), and additional transfer time towards the target FC (e.g., shipping time, facility availability, etc.)).
Humair describes the replenishing of fulfillment centers of items, in which the Examiner is interpreting items as consumables
Referring to Claim 4, Humair teaches the system of claim 1, wherein the demand node allocation cost comprises allocation of an ith item at a jth demand node at time t, a quantification factor for the ith item at the jth demand node at time t, and a weight vector (
Humair describes determining demand allocation cost via Equation (1)- Equation (2) - Equation (3) - Equation (4)-Equation (5)- Equation (6) - Equation (7) - Equation (8), which disclose the various viables.
Humair: Col. 5 Ln. 15-45, a certain percentage of the training data may be provided to train a model to assign weights to various item attributes, and then the remaining training data may be used to test the model's prediction capability for demand for the item under the provided forecast input values. The training data results (e.g., estimated demand for the provided forecast input values) may have been initially determined by a retailer, a manufacturer, a distributor, consumer surveys, and/or by other methods.
Humair: Col. 15 Ln. 15-40, The spillover demand may be demand for another fulfillment center or demand for a group of end users (e.g., demand within a geographic region). Equation (1) provides one expression of such an expectation problem.
Humair: Col. 27, Some of the expressions and equations may include notations which are summarized in Table 2).
Referring to Claim 5, Humair teaches the system of claim 1, wherein a weighting factor is applied to each of the item to demand node allocation cost, the distribution channel unit prioritization cost, and the less than minimum capacity cost (
Humair describes determining demand allocation cost via Equation (1)- Equation (2) - Equation (3) - Equation (4)-Equation (5)- Equation (6) - Equation (7) - Equation (8), which disclose the various viables.
Humair: Col. 5 Ln. 15-45, a certain percentage of the training data may be provided to train a model to assign weights to various item attributes, and then the remaining training data may be used to test the model's prediction capability for demand for the item under the provided forecast input values. The training data results (e.g., estimated demand for the provided forecast input values) may have been initially determined by a retailer, a manufacturer, a distributor, consumer surveys, and/or by other methods.
Humair: Col. 27, Some of the expressions and equations may include notations which are summarized in Table 2.
Humair: Col. 2 Ln. 25-58, Some fulfillment centers may distribute an item to end users. Still other fulfillment centers may distribute an item to both other fulfillment centers and to end users. As the source of an item can vary within a distribution network, the modeling features described provide a way to account for inventory from intermediate fulfillment centers that can be used to satisfy demand at different locations. The systems and methods described may generate an optimal inventory quantity of each item at each distribution node to maximize a benefit or minimize a cost. At an item level, the features account for transfer costs between fulfillment centers, time to transfer between centers, resource constraints (such as capacity) at each fulfillment center and potential costs to shipping items to end-users under a dynamic fulfillment policy.
Humair: Col. 9 Ln. 18-50, FIG. 3 is a tree diagram depicting an illustrative distribution network for several item demand streams. FIG. 3 illustrates one network that may be generated by the fulfillment network topology server. Fulfillment center information may be generated and transmitted by the fulfillment network topology server in a structured format such at the tree structure shown in FIG. 3. Each path may be associated with a respective cost (e.g., time, transfer cost, etc.). The network 300 may be the organization of nodes that represents the lowest cost(s). Each node in FIG. 3 may represent a fulfillment center, a supplier, or a demand stream.).
Humair describes less than minimum capacity cost, when disclosing the analyzing of cost with the fulfillment distribution network
Referring to Claim 6, Humair teaches the system of claim 1, wherein the optimized fulfillment data structure is generated by an iterative batched submodular process to assign distribution units of the distribution channel to each of the one or more demand nodes (
Humair: Col. 3 Ln. 1-40, Some entities may have multiple, different demand streams for a given item. The fulfillment centers preferably form a distribution network that can satisfy each demand stream so that FCs that transfer inventory from feeder FCs (e.g., FC that distributes to other FCs) can replenish other FCs adequately. In addition, it may be desirable to ensure adequate quantities are obtained from a vendor or supplier of the item to maintain sufficient inventory in feeder FCs for replenishing other FCs.
Humair: Col. 3 Ln. 35-67, New distribution channels (e.g., same day distribution, direct retail distribution, subscription service, etc.) may increase the variety of demand streams. Further changes in supply chain logistics may also provide a geographically diverse distribution network. In view of such dynamic and potentially complex distribution networks, a multi-echelon solution may be desirable such that adequate inventory targets are maintained for transferring inventory to some FCs while also maintaining adequate supply at feeder FCs. For example, a distribution network may include forward deploy FCs (FDFCs) that can only be supplied from other FCs. If this distribution network expands and FC sizes (e.g., capacity) become smaller, the new FCs may require replenishment from other FCs within the distribution network.
Humair: Col. 5 Ln. 1-35, The temporal information may be generated based on the fulfillment center information. For example, it may be desirable to provide demand estimates for a period of time that it would take to resupply a node within the distribution network. This time may be generated based on review time for each node along a path to the node being resupplied, transit time along a path to the node being resupplied, or other temporal factor imposed by one or more fulfillment centers in the distribution network.).
Humair describes fulfillment data being structured based on distribution and demand nodes
Referring to Claim 7, Humair teaches the system of claim 6, wherein the iterative batched submodular process assigns distribution units based on an incremental gain generated by assigning a distribution unit to one of the one or more demand nodes (
Humair: Col. 3 Ln. 35-67, New distribution channels (e.g., same day distribution, direct retail distribution, subscription service, etc.) may increase the variety of demand streams. Further changes in supply chain logistics may also provide a geographically diverse distribution network. In view of such dynamic and potentially complex distribution networks, a multi-echelon solution may be desirable such that adequate inventory targets are maintained for transferring inventory to some FCs while also maintaining adequate supply at feeder FCs. For example, a distribution network may include forward deploy FCs (FDFCs) that can only be supplied from other FCs. If this distribution network expands and FC sizes (e.g., capacity) become smaller, the new FCs may require replenishment from other FCs within the distribution network.
Humair: Col. 24 Ln. 15-40, The multi-echelon plan distributes more items throughout the network to ensure sufficient supply is on hand for the expected demand. The multi-echelon plan also increases the overall benefit due to the availability of inventory to satisfy the expected demand in a timely fashion. The multi-echelon plan also affords more control over the overall distribution network performance by allowing upstream fulfillment centers to hold more inventory for redistribution to downstream fulfillment centers when needed. This allows the upstream fulfillment centers to dynamically adjust where the inventory is redistributed to achieve the highest benefit. This avoids asymmetries between supply within the fulfillment network and how heterogeneous demand streams may be satisfied.).
Humair describes increases, in which the Examiner is interpreting as incremental gain
Referring to Claim 8, Humair teaches the system of claim 7, wherein an updated incremental gain is generated for a corresponding one of the one or more demand nodes when the distribution unit is assigned to the corresponding one of the one or more demand nodes (
Humair: Col. 3 Ln. 35-67, New distribution channels (e.g., same day distribution, direct retail distribution, subscription service, etc.) may increase the variety of demand streams. Further changes in supply chain logistics may also provide a geographically diverse distribution network. In view of such dynamic and potentially complex distribution networks, a multi-echelon solution may be desirable such that adequate inventory targets are maintained for transferring inventory to some FCs while also maintaining adequate supply at feeder FCs. For example, a distribution network may include forward deploy FCs (FDFCs) that can only be supplied from other FCs. If this distribution network expands and FC sizes (e.g., capacity) become smaller, the new FCs may require replenishment from other FCs within the distribution network.
Humair: Col. 12 Ln. 11-40, The demand for coats in each scenario would likely be different to account for increased demand due to the severe weather in the first scenario as compared to the more mild weather in the second scenario. The scenarios or selections of parameters for identifying specific scenarios may be identified in the message received at block 504. At block 510, the network inventory planning server or the fulfillment network topology server may generate an optimal distribution network for the item based at least in part on the fulfillment centers identified at block 506 and the first and second forecasted demand streams received at block 508. The optimal network may be the configuration of nodes that minimizes total cost for satisfying a demand stream. For example, the cost may be measured as transportation cost between nodes or time to transfer between nodes.
Humair: Col. 24 Ln. 15-40, The multi-echelon plan distributes more items throughout the network to ensure sufficient supply is on hand for the expected demand. The multi-echelon plan also increases the overall benefit due to the availability of inventory to satisfy the expected demand in a timely fashion. The multi-echelon plan also affords more control over the overall distribution network performance by allowing upstream fulfillment centers to hold more inventory for redistribution to downstream fulfillment centers when needed. This allows the upstream fulfillment centers to dynamically adjust where the inventory is redistributed to achieve the highest benefit. This avoids asymmetries between supply within the fulfillment network and how heterogeneous demand streams may be satisfied.).
Humair describes increases, in which the Examiner is interpreting as incremental gain, as the increase cause changes the distribution network.
Claims 9, 10, 12-16 recite limitations that stand rejected via the art citations and rationale applied to claims 1, 2, 4-8.
Claims 17-20 recite limitations that stand rejected via the art citations and rationale applied to claims 1 and 4-8. Regarding, a non-transitory computer-readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor (
Humair: Col. 9 Ln. 62-Col. 10 Ln. 15, The network inventory planning server 120 can be a server or other computing device, and can comprise a processing unit 440, a network interface 445, a computer readable medium drive 450, an input/output device interface 455, and a memory 470. The network interface 455 can provide connectivity to one or more networks or computing systems such as the network 104 shown in FIG. 2. The processing unit 440 can receive information and instructions from other computing systems or services via the network interface 445.
Humair: Col. 10 Ln. 14-40, The memory 470 generally includes RAM, ROM, and/or other persistent, non-transitory computer readable media. The memory 470 can store an operating system 474 that provides computer program instructions for use by the processing unit 440 or other elements included in the computing device in the general administration and operation of the network inventory planning server 120. The memory 470 can further include computer program instructions and other information for implementing aspects of the present disclosure.),
Referring to Claim 22, Humair teaches the system of claim 1, wherein generating the distribution channel capacity comprises determining a data transfer capacity representative of a data transfer rate or quantity for a computing network (
Humair: Col. 2 Ln. 40-58, In a packet network, the service level may represent transfer bit rate or network used to move an item through the packet network. One example of a demand stream would be one-day delivery service demand for an item. Table 1 provides examples of demand streams for physical items.
Humair: Col. 2 Ln. 48-57, When generating or regenerating the downstream estimates, it may be desirable to include a cap on the capacity for the downstream FCs. For example, expected fill rate in single FC approximation for k may be assigned to match a fill rate in single FC approximation for i under yi,j. For example, if a fulfillment center has only one upstream fulfillment center that can provide inventory, if the upstream fulfillment center has an inventory capacity of x for an item, the fulfillment center should have a capacity that is no greater than x.).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Number US 10,922,646, Humair, et al. to hereinafter Humair in view of United States Patent Publication US 20210019694, Dhesi, et al. to hereinafter Dhesi in view of United States Patent Publication US 11,551,179, Jonte, et al.
Referring to Claim 21, the system of claim 1, Humair does explicitly teach wherein generating the distribution channel capacity comprises determining a trailer capacity representative of an available freight shipping capacity for a scheduled trailer between a distribution node and a demand node.
However, Dhesi teaches teach wherein generating the distribution channel capacity comprises determining a trailer capacity representative of an available freight shipping capacity for a scheduled trailer (See ) between a distribution node and a demand node (
Dhesi: Sec. 0054, “Merchant 32 may use device 30 to send or otherwise inform the fulfillment engine 20 … of information such as the merchant’s catalog or product offerings, per unit price, location, and capacity schedule (e.g., capacity min max or other restrictions, among other things)”
Dhesi: Sec. 0055, The capacity schedule sent in transmission 202-1 provides information that allows the fulfillment engine to know how much product the merchant is able to provide on various days.” ).
Dhesi teaches generating capacities (capacity schedule, fulfillment capacity minimum maximum values, current inventory, etc.) for merchants and using such capacity constraints in the optimization.
Humair and Dhesi are both directed to the analysis of fulfillment and capacities (See Humair at Col. 2 Ln. 20-50; Dhesi at 0054-0059). Humair discloses that additional elements, such as the business inventory management can be considered (See Humair at Col. 19 Ln. 20-35). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Humair, which teaches detecting and repairing inventory management problems in view of Dhesi, to efficiently apply analysis of fulfillment and capacities by including the use of optimizing order fulfillment from multiple sources. (See Dhesi at 0020, 0036, 0096).
Humair in view of Dhesi does explicitly teach scheduled trailer.
However, Jonte teaches scheduled trailer (
Jonte: Col. 8 Ln. 10-35, Fig. 4 “determines, estimates, and identifies freight capacity.., a tractor-trailer, a tractor without a trailer, a truck, or another type of vehicle. “
Jonte: Col. 7 Ln. 1-35, “if the commodity(s) being shipped contains a hazardous material in a quantity or amount that requires (by federal regulation) that the truck or trailer be placarded as hazardous, then a hazardous materials certified vehicle and driver are required and therefore only a HazMat certified motor carrier and driver are viable sources of capacity for this type of shipment. As another example, if at least a portion of the shipment is perishable then an insulated and temperature controlled vehicle may be required. As a result, only straight trucks or trailers that are insulated and that can maintain a specific temperature setting (or range) may be viable for this type of shipment. As another example, if the requested delivery time is not attainable because a single driver is unable to arrive in time, a driver team (e.g., two drivers) may be required. As a result, only trucks or tractor-trailers with two person team drivers are considered viable for this type of shipment. As another example, if the shipment's dimensions exceed federal highway legal limits for length, height, width, or weight then only specialized deck trailers with the required special permits are to be considered as sources of viable capacity for this type of shipment. As another example, if the delivery destination does not have a standard height receiving dock, then a lift gate or ramp is required. As a result, only trucks or trailers with a ramp or lift gate are considered as viable sources of capacity for this type of shipment. As another example, the cargo may be “heavy” but not heavy enough to require overweight permits and a specialized multi-axle trailer. In that case, only trucks or tractor trailers that can scale the heavy shipment weight are considered to be viable capacity for this type of shipment and therefore listed by the system as a capacity solution for a specific shipment.“
Jonte teaches distribution channel capacity is defined as a trailer freight capacity for a scheduled trailer between nodes. Jonte further teaches that certain shipments require specific trailer characteristics (e.g., hazardous-material placarding, refrigerated trailers, specialized deck trailers for oversized loads) and that only vehicles with appropriate trailers are viable capacity sources. (Column 7, lines 1–32). The shipment offer example in FIG. 4 explicitly describes a scheduled freight shipment with a particular trailer equipment type (53-foot dry van), equipment size (Standard 102), load type (FTL), total net/gross weight, and required dock space in feet, reflecting a concrete trailer freight shipping capacity between an origin and destination location.)
Humair, Dhesi and Jonte are all directed to the analysis of fulfillment and capacities (See Humair at Col. 2 Ln. 20-50; Dhesi at 0054-0059; Jonte at Col. 6 Ln. 1-33, Col. 8 Ln. 1-20). Humair discloses that additional elements, such as the business inventory management can be considered (See Humair at Col. 19 Ln. 20-35). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Humair in view of Dhesi, which teaches detecting and repairing inventory management problems in view of Jonte, to efficiently apply analysis of fulfillment and capacities to improving logistics by including vehicles in the determining optimization. (See Jonte at Col. 11, Col. 12).
Response to Arguments
Applicant’s arguments filed 02/05/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 02/05/2026.
Regarding the 35 U.S.C. 101 rejection, at pg. 7-10 Applicant argues with respect to claims at issue are not directed to an abstract idea
In response to the 35 USC § 101 claim rejection argument, the Examiner respectfully disagrees. The Examiner did consider each claim and every limitation both individually and as a whole, since the grounds of rejection clearly indicates that an abstract idea has been identified from elements recited in the claims. Using the two-part analysis, the Office has determined there are no elements, in the claim sufficient enough to ensure that the claims amounts to significantly more than the abstract idea itself. As recited, the claims are directed towards:
a non-transitory memory;
a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to:
receive a demand distribution optimization request for a target network;
generate a heuristic demand for one or more demand nodes in the target network;
generate a distribution channel capacity for a distribution channel connecting each of the one or more demand nodes and at least one distribution node;
generate an optimized fulfillment data structure representative of an optimized demand fulfillment for the one or more demand nodes by a trained optimization model configured to apply a mixed integer linear programming framework incorporating constraints including an inventory constraint, a resource constraint, a planned distribution unit constraint, a distribution unit maximum capacity constraint, and a distribution unit minimum capacity constraint with a slack variable representative of a permitted minimum-capacity breach for a last distribution unit, the trained optimization model including an item to demand node allocation cost, a distribution channel unit prioritization cost, and a less than minimum capacity cost; and
transmit the optimized fulfillment data structure over a network to a remote computing device, the remote computing device rendering data characterizing the optimized fulfillment data structure in a graphical user interface.
The claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer as recited is a generic computer component that performs functions.
Examiner finds the claim recite concepts which are now described in the 2019 PEG as certain methods of organizing human activity. In particular the claims recites limitations for managing distribution data, which constitutes methods related to mathematical concepts; commercial or legal interactions relating behaviors and business relations which are still considered an abstract idea under the 2019 PEG. The computing device, GUI are comprised of generic computer elements to perform an existing business process. Examiner finds the claims recite mere instructions to implement the abstract idea on a computer and uses the computer as a tool to perform the abstract idea without reciting any improvements to a technology, technological process or computer-related technology.
Regarding, the steps at pg. 8, 9 that Applicant points to as practical application are merely narrowing the abstract idea to a particular technological environment, which has been found to be ineffective to render an abstract idea eligible. Furthermore, the Examiner respectfully disagrees because the steps of:
pg. 9 “recite a trained optimization model configured to apply a mixed integer linear programming (MILP) framework incorporating specific, enumerated constraints and objective-function cost terms, and further requires transmission of the optimized fulfillment data structure over a network to a remote computing device that renders the optimized fulfillment in a graphical user interface. These additional elements integrate any alleged abstract idea into a concrete, technology-focused practical application.”
pg. 10 “The application describes concrete performance improvements and the inability for
humans to practically perform this type of optimization at scale, underscoring that the approach is not routine/conventional extra-solution activity but a specific, trained computational technique that yields improved operation of a networked resource allocation system.”
These arguments at pg. 9, 10 seems to describe a “particular way” of managing distribution data of the abstract idea. “ The Applicant is basically relying on the system elements as integrating the abstract idea into a practical application but those system elements aren't really utilized in any particular manner, and the specification indicates that at 0021 " an optimization computing device 4, a web server 6, a cloud-based engine 8 including one or more processing devices 10, workstation(s) 12, a database 14, and/or one or more user computing devices 16, 18, 20 operatively coupled over the network 22. The optimization computing device 4, the web server 6, the processing device(s) 10, the workstation(s) 12, and the user computing devices 16, 18, 20 may each be any suitable computing device that includes any hardware or hardware and software combination for processing and handling information ", which indicates the lack of particularity in the application to the technological environment.
Furthermore, at pg. 8 the Applicant recites that “ The optimization is not a disembodied equation; it is implemented by a trained optimization model configured to apply a MILP framework that accounts for specific, real-world distribution resources and channel capacities across a target network. The claims recite concrete, applied controls over networked resources and physical/logistical capacity-not mere "data gathering" or "displaying results."”; pg. 9 “This is a technology-focused improvement to the functioning of a computerized resource allocation system operating across networked distribution and communications infrastructure.” the Examiner wants to point out that claims do not show or break down how this is actually executed, at this point it’s just an aspirational statement. There is no technological problem or solution here, as the current claims are just using typically known actions/steps of a machine learning model and no improvements.
Then at pg. 9, 10 the Applicant admission that the application is directed to improving the user’s experience and not the computer itself
(at pg.9 “optimized fulfillment data structure that is rendered to a user via GUTI for operational network control”; “pg. 10 “The claim's new network transmission and GUI rendering limitation further ties the optimized fulfillment data structure to real-world system use in an interactive interface for deployment and simulation,”)
these arguments the Applicant is admitting that the application is directed to improving the user’s experience and not the system software or any type of computer or structure.
Additionally, the Examiner would like to point the Applicant to the 2019 PEG, in which managing distribution data will fall under. The 2019 PEG which states:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Regarding the 35 U.S.C. 103 rejection, Applicant’s arguments with respect to claims has been considered but are moot in view of the new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Field-Darragh et al., C.A. Pub. 2846273, (discussing the management of inventory and order fulfillment).
Filani et al., Artificial Intelligence in Demand Forecasting and Inventory Optimization, https://www.multiresearchjournal.com/admin/uploads/archives/archive-1753782345.pdf, International Journal of Advanced Multidisciplinary Research and Studies, 2023 (discussing the use of AI for managing and determining forecasting for inventory).
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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/UCHE BYRD/Examiner, Art Unit 3624
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