DETAILED ACTION
This Final Office action is in response to Applicant’s Amendments filed on 12/01/2025. Claims 1-20 are pending. The effective filing date of the claimed invention is 09/19/2022.
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 .
Specification
The disclosure is objected to because of the following informalities: [0009] is appearing blank on the originally-filed Specification document. Please remove [0009] and renumber the paragraphs in the Specification.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, (12/01/2025), page 3, line 5 recites “reviewjng”. This renders the claim indefinite as it appears this is supposed to be “reviewing” but it is unclear based on the j in the word. Appropriate correction is required.
Claim 7, line 1 recites “wherein at least one the reallocated. . . .” This renders the claim indefinite due to grammatical mistake. The examiner recommends amending to include, “wherein at least one of the reallocated. . . .” Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1 – Claims 1-7 relate to machine claims; claims 8-14 relate to process claims; and, claims 15-20 relate to non-transitory CRM claims. Step 1 is satisfied.
Step 2A, Prong 1 – Exemplary claim 1 recites the abstract idea of: data processing system for dynamically optimizing inventory of multiple types of goods among multiple distribution warehouses across multiple time periods within a time span, the data processing system comprising:
responsive to first user input (for the responsive to inputs, see e.g. MPEP 2106.04(a)(2)(II)(C) citing Voter Verified, Inc.; see also MPEP 2106.04(a)(2)(II)(A) citing Bancorp, establishing inputs to an equation), receiving an identified configuration of the multiple distribution warehouses including associated incoming shipping lanes, associated outgoing shipping lanes and transfer shipping lanes among the multiple distribution warehouses, and more added information about the shipping lanes (see e.g. MPEP 2106.04(a)(2)(II)(A) & (B) and 2106.04(a)(2)(III); the recitations about the shipping lanes are more abstract idea, fundamental economic practice of organizing shipping lanes and the products/trucks/etc. that come through);
receiving an identified set of good types suitable for receipt by the multiple distribution warehouses on the associated incoming shipping lanes, storage as inventory in the multiple distribution warehouses, transfer among the multiple distribution warehouses on the transfer shipping lanes, and shipment from the multiple distribution warehouses on associated outgoing shipping lanes (see e.g. MPEP 2106.04(a)(2)(II)(A) & (B) and 2106.04(a)(2)(III));
responsive to second user input, receiving an identified set of constraints and capacities applicable to the identified configuration of the multiple distribution warehouse including shipping lanes for incoming, outgoing and transfer shipments on the shipping lanes to, from and among the multiple distribution warehouses and applicable to inventory levels for storing the set of good types in the multiple distribution warehouses (see e.g. MPEP 2106.04(a)(2)(II)(A) & (B) and 2106.04(a)(2)(III));
receiving a set of incentive based weights associated with inventory levels of the set of good types stored in the multiple distribution warehouses and associated with incoming, outgoing and transfer shipments on the shipping lanes to, from and among the multiple distribution warehouses (see e.g. MPEP 2106.04(a)(2)(I)(A));
generating a model of the multiple distribution warehouses including the identified configuration of the multiple distribution warehouses and shipping lanes, the identified set of good types, the identified set of constraints and capacities, and the incentive based weights associated with inventory levels and shipping lanes, the model including incoming shipment destinations and outgoing shipment originations as decision variables and including transfer shipments with good types and quantities thereof as decision variables (see e.g. MPEP 2106.04(a)(2)(I)(A) & (C); MPEP 2106.04(a)(2)(II)(A-B));
receiving a set of current inventory levels of the set of good types (see e.g. MPEP 2106.04(a)(2)(II)(B));
receiving a set of preexisting scheduled incoming, outgoing and transfer shipments over the time span including good types and quantities thereof included in each of the scheduled shipments (see e.g. MPEP 2106.04(a)(2)(I)(A));
determining inventory levels for each of the good types for each distribution warehouse for each of a set of time periods across the time span (see e.g. MPEP 2106.04(a)(2)(II)(A) and (III));
utilizing the model to optimize inventory levels, based on the incentive based weights, for each of the good types in each distribution warehouse across the set of time periods across the multiple distribution warehouses by identifying reallocations of preexisting incoming shipment destinations, reallocations of outgoing shipment originations, and good types and quantities thereof of transfer shipments (see e.g. MPEP 2106.04(a)(2)(II)(A) citing 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); MPEP 2106.04(a)(2)(III) pen and paper);
providing the reallocations and transfer shipments identified by the model for user approval (see e.g. MPEP 2106.04(a)(2)(III)(B) & (C));
responsive to receiving user approval, automatically (see MPEP 2106.05(a)(I) Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality: iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential)) providing notification of the approved shipment reallocations and transfer shipments to the distribution warehouses for implementation (see e.g. MPEP 2106.04(a)(2)(II)(A)); and
implementing the approved shipment reallocations and transfer shipments including receiving incoming shipments at the reallocated destinations, shipping outgoing shipments from the reallocated originations, and shipping and receiving transfer shipments including identified good types and quantities (see e.g. MPEP 2106.04(a)(2)(II)(A) citing Secured Mail Solutions; MPEP 2106.04(a)(2)(III)(C)(2) citing Symantec).
Accordingly, when viewed alone and in ordered combination, claim 1 is found to recite abstract idea.
Step 2A, Prong 2 – Exemplary claim 1 (and similarly claims 8 and 15) is not found to integrate the abstract idea with practical application. Claim 1 recites the additional limitations of, a processor, a memory, and where the data processing system receives data from a set of computer systems. These additional limitations are recited at a high level of generality and generality act as tools in an apply it manner to implement the recited abstract idea found in Step 2A, Prong 1. Specifically, for the processor, the examiner refers to “apply it” rationale at MPEP 2106.05(f). For the memory, the examiner refers to “apply it” rationale at MPEP 2106.05(f). For the “from a set of computer systems,” the examiner refers to the “apply it” rationale at MPEP 2106.05(f). The examiner does not find any improvement to the underlying computer technology. See MPEP 2106.05(f) As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”). Accordingly, when these additional limitations are viewed alone and in ordered combination, the examiner does not find that the claims integrate the abstract idea into practical application. Claim 1 (8 and 15) are found to be directed to abstract idea.
Step 2B – Exemplary claim 1 (and similarly claims 8 and 15) is not found to include significantly more. The additional limitation analysis of Step 2A, Prong 2 is equally applied to Step 2B. 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 (WURC) activities previously known to the industry. This consideration is only evaluated in Step 2B of the eligibility analysis. 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.
Claim 1 (8 and 15) includes many limitations of receiving data and transmitting data
See MPEP 2106.05(d)(II) finding reciving/transmitting to be WURC 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));
Claim 1 (8 and 15) includes recitations of repetitive calculations, such as by optimizing inventory levels based on weights and providing reallocations, see MPEP 2106.05(d)(II):
ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) (“The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.”);
Claims 1 (8 and 15) includes limitations relating to providing input(s), where iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 was found to be WURC activity. See MPEP 2106.05(d)(II).
Accordingly, when these additional limitations are viewed alone and in ordered combination, the examiner does not find that the claims to recite significantly more.
Dependent Claims – Claims 2, 9, and 16 recite more abstract idea. See e.g. MPEP 2106.04(a)(2)(I). Claims 3, 10, and 17 recite more abstract idea. See Recentive Analytics, Inc. v. Fox Corp., Appeal No. 2023-2437 (Fed. Cir. 04/18/2025). Claims 4, 11, and 18 recite more abstract idea. See MPEP 2106.04(a)(2)(II)(A-C). Claims 5, 12, and 19 recite more abstract idea. See MPEP 2106.04(a)(2)(C). Claims 6, 7, 13, and 20 recite more abstract idea. See Recentive and MPEP 2106.04(a)(2)(I). Accordingly, claims 1-20 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. Pub. No. 2024/0005237 to Muthusamy (“Muthusamy”) in view of U.S. Pat. Pub. No. 2008/0219827 to Lanigan et al. (“Lanigan”).
With regard to claims 1, 8, and 15, Muthusamy discloses the claimed data processing system for dynamically optimizing inventory of multiple types of goods among multiple distribution warehouses across multiple time periods within a time span, the data processing system comprising:
a processor (e.g. [0025]); and
a memory storing program instructions which when processed by the processor perform the operations of (see e.g. [0025]):
responsive to user input, receiving from a set of computer systems an identified configuration of the multiple distribution warehouses including associated incoming shipping lanes, associated outgoing shipping lanes and transfer shipping lanes among the multiple distribution warehouses (see e.g. Lanigan below);
receiving from the set of computer systems an identified set of good types suitable for receipt by the multiple distribution warehouses on the associated incoming shipping lanes, storage as inventory in the multiple distribution warehouses, transfer among the multiple distribution warehouses on the transfer shipping lanes, and shipment from the multiple distribution warehouses on associated outgoing shipping lanes (see e.g. Fig. 1 and [0016]);
responsive to user input, receiving from the set of computer systems an identified set of constraints and capacities applicable to the identified configuration of the multiple distribution warehouse including shipping lanes for incoming, outgoing and transfer shipments on the shipping lanes to, from and among the multiple distribution warehouses and applicable to inventory levels for storing the set of good types in the multiple distribution warehouses (see e.g. [0017] [0023] [0024] [0038]);
receiving from the set of computer systems a set of incentive based weights associated with inventory levels of the set of good types stored in the multiple distribution warehouses and associated with incoming, outgoing and transfer shipments on the shipping lanes to, from and among the multiple distribution warehouses (see e.g. [0061-62]);
generating a model of the multiple distribution warehouses including the identified configuration of the multiple distribution warehouses and shipping lanes, the identified set of good types, the identified set of constraints and capacities, and the incentive based weights associated with inventory levels and shipping lanes, the model including incoming shipment destinations and outgoing shipment originations as decision variables and including transfer shipments with good types and quantities thereof as decision variables (see e.g. [0017] In one embodiment, allocation planner 110 calculates a need quantity and an allocation quantity for one or more short-life-cycle products at one or more stores 308a-308n (FIG. 3) of one or more retailers 150 using a reinforcement learning-trained model to improve the transportation of products from one or more distributions centers 160 and/or the removal of one or more allocation-limiting constraints, as described in further detail below. Although short-life cycle products are described as having a life cycle that finishes in as few as two months and being pushed to stores in a few as two allocations, embodiments contemplate allocation planner 110 predicting an allocation for a product having any length of life cycle (such as, for example, one week, two weeks, one month, two months, four months, eight months, a year, a retail season, or any other time period) and pushing the product to stores in any number of allocations (such as, for example, one allocation, two allocation, five allocations, ten allocations, fifty allocations, one hundred allocations, or any other number of allocations), according to particular needs.; [0024]; [0035] etc.);
receiving from the set of computer systems a set of current inventory levels of the set of good types (see e.g. [0019-20]);
receiving from the set of computer systems a set of scheduled incoming, outgoing and transfer shipments over the time span including good types and quantities thereof included in each of the scheduled shipments (see e.g. [0040] future allocations which includes the incoming and outgoing shipments for each situation (location and product) and [0041] where the set of future allocation quantities and such, now preexisting after being calculated, are received at the GUI and displayed, and this is responsive to receiving the user input(s); as for the recitations about incoming/outgoing and destinations, the examiner refers below to Lanigan which fully teaches these limitations.);
determining inventory levels for each of the good types for each distribution warehouse for each of a set of time periods across the time span (see e.g. [0020]);
utilizing the model to optimize inventory levels, based on the incentive based weights, for each of the good types in each distribution warehouse across the set of time periods across the multiple distribution warehouses by identifying reallocations of incoming shipment destinations, reallocations of outgoing shipment originations, and good types and quantities thereof of transfer shipments (see e.g. [0017]; [0038-9] adjusts the allocations and/or inventory of ….; for the transferring from one shipping lane to another, see Lanigan (below) and e.g. [0072] For example, if containers on a west corridor (tracks) must be transferred to an east corridor (different tracks), or vice versa, two grapplers working in harmony can significantly simplify and reduce the interchange cycle time.);
providing the reallocations and transfer shipments identified by the model to the set of computer systems for user approval (see e.g. Fig. 6, 602 clicking and using the program is the user approving at each step of the program/server);
responsive to receiving user approval, automatically providing notification of the approved shipment reallocations and transfer shipments to the distribution warehouses for implementation (see e.g. Fig. 8 automated); and
implementing the approved shipment reallocations and transfer shipments including receiving incoming shipments at the reallocated destinations, shipping outgoing shipments from the reallocated originations, and shipping and receiving transfer shipments including identified good types and quantities (see e.g. Fig. 8 and associated texts.).
While likely inherent that each warehouse of Muthusamy has various lanes or designations for where inbound and outbound trucks are to be located, this is not explicitly discussed in Muthusamy. Accordingly, this is turned into an obviousness rejection. Lanigan teaches at e.g. Fig. 9, 10, 16 [0155] [0176] etc. that it would have been obvious to one of ordinary skill in the WMS art to include an inbound concourse with various lanes, and an outbound concourse with various lanes, and where each truck/railcar is assigned a position based on their function and availability of the various spacing. Therefore, it would have been obvious to one of ordinary skill in the WMS art before the effective filing date of the claimed invention to monitor the inbound and outbound spacing, and include such data in such machine learning
PNG
media_image1.png
592
439
media_image1.png
Greyscale
modeling, where this data is valuable and beneficial in that, as shown in Lanigan at [0155], the lane management allows one to optimize space utilization and the available real estate, to load and unload containers in high volume with minimal use of a crane. The amendments on 12/01/2025 relate to where the shipping lanes are coming from and going to. Lanigan teaches at e.g. Fig. 9 that there is an inbound concourse and outbound concourse; where these can be related to means of transportation such as rail, semi-truck as shown in Fig. 10, and the like; there are specific loading/unloading locations in the various concourse for specific vehicles to come in (such as the vehicles of the SOUTH CORRIDOR at 356); the examiner finds that it is inherent that there are multiple incoming destinations associated with e.g. NORTHEAST LOCAL CORRIDOR 344 and outgoing destinations of e.g. SOUTH CORRIDOR 356. For the transfer lane aspect, see e.g. Lanigan [0040] Shortage of rail cars at trackside can now be pre-blocked, trailers or containers can be ready to load at trackside when railroad cars arrive at terminal [0041] Extra rail cars can be shipped or stored under crane [0042] Rail interchange can be transferred from one corridor to another under the crane in a matter of minutes instead of days [0043] No chassis required [0044] Movement of containers or trailers to a remote storage area is not required. See further, Lanigan at [0180-184] where each container is sent to its desired remote destination, which could be another warehouse, a house, a temporary storage location, a warehouse, etc.
Thus, there is less dependence on a crane and a crane operator's time. The containers can be loaded on (or unloaded from) a chassis by an operator, such as a truck driver. Thus, many operations can be accomplished substantially simultaneously, enhancing the efficiency of the terminal.
Muthusamy relates to a system and method of reinforced machine-learning retail allocation. See Title. Muthusamy [0016] describes the described supply chain network that includes e.g. the exemplary systems such as planning and execution systems including inventory system, transportation network, supply chain planner, system . . . N; networked imaging devices, other supply chain entities, allocation planner, archiving system, retailers, distribution centers, personal computer(s), etc., all connected over network 190. See Fig. 1. Muthusamy [0017] describes “allocation planner 110 calculates a need quantity and an allocation quantity for one or more short-life-cycle products at one or more stores 308a-308n (FIG. 3) of one or more retailers 150 using a reinforcement learning-trained model to improve the transportation of products from one or more distributions centers 160 and/or the removal of one or more allocation-limiting constraints, as described in further detail below.” Muthusamy [0019] “For example, one or more planning and execution systems 130a-130n typically perform several distinct and dissimilar processes, including, for example, assortment planning, demand planning, operations planning, production planning, supply planning, distribution planning, execution, forecasting, transportation management, warehouse management, inventory management, fulfilment, procurement, allocation, and the like.” In other words, Muthusamy teaches, as shown in the reference, the supply chain management and reallocations, and management of the data relating to the transportation of the items within the supply chain. See further transportation description at Muthusamy [0021-22]. Muthusamy at [0031] “One or more suppliers may be any suitable entity that offers to sell or otherwise provides one or more items (i.e., materials, components, or products) to one or more manufacturers. The one or more suppliers may comprise automated distribution systems that automatically transport products to the one or more manufacturers based, at least in part, on a constrained or unconstrained allocation quantity generated by allocation planner 110, an inventory policy, target service levels, the number of items currently in stock at an inventory of one or more retailers 150, one or more distribution centers 160, or one or more other supply chain entities 170, the number of items currently in transit in transportation network 130b.” Muthusamy relates to a supply chain data management system, that manages, among other things, products in the supply chain and their transportation allocations throughout the supply chain and to end customer, if desired. Lanigan is brought in as a secondary reference to show that the transportation options can include having inbound concourse/outbound concourse, where the containers can be moved throughout the system from inbound-to-a different inbound transportation corridor/area, or even from inbound to outbound, and are transported according to where they are on/in the concourse, as shown in Figs. 9-10. The items are placed on containers and delivered to destinations as desired by the engineer setting up the transportation scheduling and management, as described in Lanigan. See Lanigan, [0235] All in or outbound traffic can by orchestrated, directed and dispatched, by the gate personnel substantially error free with keycard, wireless and computing technology. Lanigan [0236] Keycards and the self-service nature of the system improve driver and crane operator productivity, and truck, buffer and crane utilization and efficiency. Lanigan [0240] The self service operation can improve load pick up dwell times and load drop off dwell times over conventional terminals, which often require waiting or dead time.
Therefore, it would have been obvious to one of ordinary skill in the supply chain management art before the effective filing date of the claimed invention to modify Muthusamy’s supply chain management system to simply include the inbound/outbound transportation structure, as described in Lanigan, with various lanes for different destination locations such as a lane for NORTHEAST LOCAL CORRIDOR 344, Lanigan Fig. 9. The advantages and motivation to utilize the lanes described in Lanigan in Muthusamy’s transportation network management 130b as this can improve load pick up dwell times and load drop off dwell times over conventional terminals, which often require waiting or dead time. Lanigan [0240]. Lanigan [0236] Keycards and the self-service nature of the system improve driver and crane operator productivity, and truck, buffer and crane utilization and efficiency.
With regard to claims 2, 9, and 16, Muthusamy further discloses where the model is a linear programming based model for optimizing inventory levels across multiple distribution warehouses (see e.g. Fig. 4 and associated text).
With regard to claims 3, 10, and 17, Muthusamy further discloses where the model is an artificial based model trained utilizing historical data and utilizing machine learning for optimizing inventory levels across multiple distribution warehouses (see e.g. [0002] [0017] In one embodiment, allocation planner 110 calculates a need quantity and an allocation quantity for one or more short-life-cycle products at one or more stores 308a-308n (FIG. 3) of one or more retailers 150 using a reinforcement learning-trained model to improve the transportation of products from one or more distributions centers 160 and/or the removal of one or more allocation-limiting constraints, as described in further detail below.).
With regard to claims 4, 11, and 18, Muthusamy further discloses responsive to user input, categorizing selected good types into newer inventory and older inventory (see e.g. [0110] where expiration date is known and if expired the product is flagged to be removed); wherein constraints regarding the good type categorizations are identified (see [0110] brand); wherein incentive based weights regarding the good type categorizations are identified (see e.g. Fig. 9 and associated texts); and wherein the constraints and incentive based weights regarding the good type categorizations are included in the generated model to optimize inventory levels (see Fig. 9 and associated texts).
With regard to claims 5, 12, and 19, Muthusamy further discloses responsive to user input, grouping selected good types into common classifications; wherein constraints, capacities and incentive based weights associated with a good type common classification apply to each good type grouped in that common classification (see e.g. [0040]).
With regard to claims 6, 13, Muthusamy further discloses comprising utilizing historical data to update the constraints, capacities and incentive based weights to update the generated model, wherein AI is used for identifying the constraints, capacities and incentive based weights to update the generated model (see e.g. [0040] [0048] [0053] [0058]; see e.g. [0111] for example, machine learning-based product, customer, channel, or store clusters, according to particular needs. etc.).
With regard to claims 7 and 20, Muthusamy does not disclose, and Lanigan does teach, the reallocation from one shipping lane to another shipping lane inbound and/or outbound at e.g. (see e.g. Lanigan, [0072] For example, if containers on a west corridor (tracks) must be transferred to an east corridor (different tracks), or vice versa, two grapplers working in harmony can significantly simplify and reduce the interchange cycle time.). The motivation to combine is the same as found with regard to claim 1, above.
Response to Arguments
Applicant's arguments filed 12/01/2025 have been fully considered but they are not persuasive.
The examiner has withdrawn the previously-made rejections under 35 USC 112.
Applicant argues that the references teach away from the claimed invention. The examiner respectfully disagrees. The examiner refers to the above rejection which has been amended to show cover the amendments.
Applicant argues that the claims are eligible under 35 USC 101. The examiner respectfully disagrees, and has amended the 101 rejection above to satisfy the claim amendments. The examiner maintains that the claims recite abstract idea, and are directed to abstract idea, for the reasons stated above. The examiner has added the WURC analysis under MPEP 2106.05(d)(II). See above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Peter Ludwig whose telephone number is (571)270-5599. The examiner can normally be reached Mon-Fri 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fahd Obeid can be reached at 571-270-3324. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PETER LUDWIG/Primary Examiner, Art Unit 3627