DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Application 19/225,832 was filed on June 2, 2025 and claims priority to U.S. Provisional Patent Application 63/655,581 filed June 3, 2024.
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 a judicial exception (i.e., an abstract idea) without significantly more.
Independent Claims
MPEP 2106 Step 2A- Prong 1:
Independent claims 1 and 11 recite, obtaining packet data including provisioning targets for a plurality of packets;
identifying a location of a primary routing hub for routing the plurality of packets;
determining a location for an additional routing hub based on the packet data and the location of the primary routing hub;
generating, based on the packet data, a plurality of routes of provisioning tasks;
associating each of the plurality of routes of provisioning tasks with the primary routing hub or the additional routing hub; and
routing the plurality of packets to the primary routing hub or the additional routing hub for retrieval by provisioning agents.
The limitations above are processes that under broadest reasonable interpretation cover “certain methods of organizing human activity” (including sales activities or behaviors, or business relations). Specifically, routing packets for delivery by provisioning agents (i.e., routing packages for delivery) is performing sales activities (see MPEP 2106.04(a)(2)(II)). Examiner notes paragraphs [0069] of Applicant’s specification discussing “business-related deliveries” and [0075] discussing “business expansion” and “business objectives”, which illustrates the commercial nature of the routing and deliveries.
Additionally, the limitations include mental processes (including an observation, evaluation, judgment, or opinion) because they can be performed in the human mind, or by a human using pen and paper. Specifically, claims to identify routing hubs, generate routes, associate routes with routing hubs, and route packets to routing hub can all be practically performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2)(III)).
MPEP 2106 Step 2A- Prong 2:
The judicial exceptions are not integrated into a practical application. Claims 1 and 11 as a whole amount to: merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, or “apply it”.
Independent claims 1 and 11 recite the following additional elements to perform the above recited steps: a non-transitory, computer-readable medium (claim 11) and one or more processors (claim 11). These additional elements are generic computer components performing generic computer functions at a high level of generality, and are recited at a high level of generality. As such, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component.
Individually and as a whole, these additional elements do not integrate the judicial exceptions into a practical application because the claims do not: improve the functioning of the computer itself or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; effect a transformation or reduction of a particular article to a different state or thing; add meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment to transform the judicial exception into patent-eligible subject matter; amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer.
MPEP 2106 Step 2B:
Independent claims 1 and 11 do not include additional elements that are sufficient to amount to significantly more (also known as an “inventive concept”) than the judicial exception. As discussed above, the additional elements are generic computer components performing generic computer functions at a high level of generality and/or generally link the use of the judicial exception to a particular technological environment or field of use. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Alone or in combination, the additional elements do not contribute significantly more than the judicial exception and as a result, the claims are ineligible.
Dependent Claims
Dependent claims 2, 3, 5-10, 12, 13, and 15-20, recite additional details that merely narrow the previously recited abstract idea limitations without reciting any additional elements. They are therefore, ineligible for the reasons as discussed above with respect to independent claims 1 and 11. The additional elements in claims 4 and 14 are discussed below.
MPEP 2106 Step 2A- Prong 2:
Dependent claims 4 and 14, recite additional details that merely narrow the previously recited abstract idea. Claims 4 and 14 also recite the additional element of a packet forecast machine-learning model. This additional element is recited at a high level of generality such that when viewed as a whole, the additional element amounts to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)).
MPEP 2106 Step 2B:
With respect to claims 4 and 14, as discussed above with respect to Step 2A Prong Two, the additional element amounts to no more than: a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. The same analysis applies here in Step 2B, i.e., applying the exception using a generic computer component, does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional element of a packet forecast machine-learning model, does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, claims 4 and 14 are also ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless —
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an
application for patent published or deemed published under section 122 (b), in which the
patentor application, as the case maybe, names another inventor and was effectively filed
before the effective filing date of the claimed invention.
Claims 1-3, 5-13, and 16-20 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by U.S. Patent Publication No. 2024/0029011 to Stroh et al. (Stroh).
As to claim 1, Stroh teaches, obtaining packet data including provisioning targets for a plurality of packets (“… In some examples, customer data database 314 may include, send, and/or receive data regarding customers [i.e., provisioning targets for a plurality of packets], such as addresses and postal codes of delivery locations for customers, customer preferences, customer orders, or customer payment and personal information” [0055-0057]);
identifying a location of a primary routing hub for routing the plurality of packets (“FIG. 1 illustrates an example system 100 for fulfilling deliveries of an enterprise from a warehouse [i.e., a primary routing hub] to a customer, according to an example …” and “… In some examples, supply chain system 316 may include, send, and/or receive data such as warehouse locations, warehouse types, warehouse throughput and/or storage capacity, warehouse defined geographical areas, courier/carrier types and their associated vehicles, vehicle capacities, maps, roads, route information, distance data, or other supply chain information” [0032-0033 and 0058]);
determining a location for an additional routing hub based on the packet data and the location of the primary routing hub (“In some examples, the subject matter of this disclosure relates to utilizing a model to determine appropriate locations (herein referred to as “Transload” locations [i.e., a location for an additional routing hub]) for consolidated relocation and redistribution to last mile delivery services, from intermediate sortation or warehousing facilities, as well as determining which customer delivery locations may be served by Transload points …” and “FIG. 3 illustrates an example system for facilitating a determination of transload locations for fulfilling deliveries, according to an example …” [0025-0028 and 0050-0062]);
generating, based on the packet data, a plurality of routes of provisioning tasks (“… In such cases, a last-mile delivery cost may be reduced by introducing a Transload point 118, and a defined transit route between the warehouse 106 and the Transload point 118 …” and “… In some examples, outputs may include descriptions and data associated with selected Transload locations and/or shipping routes to the selected Transload locations from the warehouse and shipping routes from the selected Transload locations to efficiently deliver items to each delivery location” [0044-0045 and 0092]);
associating each of the plurality of routes of provisioning tasks with the primary routing hub or the additional routing hub (“In some examples, the model may group customer delivery locations of a geographical area into clusters based on minimizing an overall shipping cost to each customer delivery location. In some examples, each customer delivery location may be assigned to a cluster which may receive items delivered directly from a warehouse or through a Transload point …” and “In some examples, at step 512, each delivery location may be assigned to either the warehouse or to one of the selected Transload locations …” [0028 and 0089-0092] Examiner notes that, under the broadest reasonable interpretation, assigning a location to a routing hub is “associating” a route with a routing hub because the location is part of a route);
and routing the plurality of packets to the primary routing hub or the additional routing hub for retrieval by provisioning agents (“In some examples, items destined for customer delivery location 110 may be loaded onto last-mile delivery vehicle 108 and transported directly from warehouse 106 to customer delivery location 110 … In some examples, items destined for customer delivery location 122 may be loaded onto large container truck 116 and transported from warehouse 106 to Transload point 118 …” and “In some examples, items may be picked up from warehouse 202 by delivery vehicle 204, which may deliver the items directly to customer delivery locations 206, 208 … In some examples, items may be picked up by delivery vehicle 220 from Transload point 216 for delivery to customer delivery locations 230, 232 …” [0034-0036 and 0047-0049]).
Regarding claim 11, this claim is essentially coextensive with claim 1 other than it recites a non-transitory, computer-readable medium instead of a method. Because Stroh teaches a non-transitory, computer-readable medium as well as a method (see at least Stroh [0119]), claim 11 can be rejected with the same rationale as claim 1, relying on Stroh to render the claim obvious. Similar dependent claims below will be treated together for the sake of brevity.
As to claims 2 and 12, Stroh teaches all of the limitations of claim 1 as discussed above. Stroh further teaches, wherein the plurality of routes of provisioning tasks are generated based on the packet data and the locations of the primary routing hub and the additional routing hub (“… In some examples, warehouse 106 may sort items for customer delivery locations into groups of items which may be grouped by customer, delivery location, or delivery route (for example, delivery route of a last-mile delivery vehicle where the delivery route includes delivery of items to multiple customers/delivery locations)” and “… In such cases, a last-mile delivery cost may be reduced by introducing a Transload point 118, and a defined transit route between the warehouse 106 and the Transload point 118 …” [0033 and 0044-0045]).
As to claims 3 and 13, Stroh teaches all of the limitations of claim 1 as discussed above. Stroh further teaches, wherein generating the plurality of routes of provisioning tasks includes associating each of the plurality of routes of provisioning tasks with the primary hub or the additional routing hub (“In some examples, the model may group customer delivery locations of a geographical area into clusters based on minimizing an overall shipping cost to each customer delivery location. In some examples, each customer delivery location may be assigned to a cluster which may receive items delivered directly from a warehouse or through a Transload point …” and “In some examples, at step 512, each delivery location may be assigned to either the warehouse or to one of the selected Transload locations …” [0028 and 0089-0092]).
As to claims 5 and 15, Stroh teaches all of the limitations of claim 1 as discussed above. Stroh further teaches, further comprising determining execution data based on the provisioning targets for the plurality of packets and path data, wherein the execution data comprises at least one of execution distance and execution time (“… In some examples, a cost may be associated with a distance [i.e., execution distance] or throughput capacity” and “In some examples, the direct delivery cost may include distance traveled, time duration of travel [i.e., execution time], delivery vehicle operational costs, costs associated with shipment vehicle capacity, personnel costs, or other relevant costs …” [0047-0049 and 0096]).
As to claims 6 and 16, Stroh teaches all of the limitations of claim 5 as discussed above. Stroh further teaches, wherein the plurality of routes of provisioning tasks are generated based on the packet data and the execution data (“… In some examples, customer delivery locations 206, 208 may correspond to a lower shipping cost and/or a shorter distance directly from warehouse 202 than customer delivery locations 224, 226, 228, 230, 232. In some examples, a cost may be associated with a distance or throughput capacity …” and “… In some examples, at step 604, a shipping cost for shipping an item from a warehouse to a delivery location may be minimized by selecting either: a minimized direct delivery cost of an item from the warehouse to each delivery location; or a minimized Transload delivery cost that may include at least: a minimized intermediate delivery cost of shipping an item from the warehouse to the Transload locations; and a minimized final delivery cost of an item from the Transload locations to each delivery location …” [0047-0049 and 0094-0098]).
As to claims 7 and 17, Stroh teaches all of the limitations of claim 1 as discussed above. Stroh further teaches, wherein determining the location for the additional routing hub comprises selecting the location from a set of predetermined candidate locations (“In some examples, at step 506, a set of candidate transload locations within the geographical area may be received …” and “FIG. 6 illustrates an example method for selecting Transload locations for fulfilling deliveries based on minimizing a shipping cost, according to an example …” [0086-0088 and 0093-0094]).
As to claims 8 and 18, Stroh teaches all of the limitations of claim 1 as discussed above. Stroh further teaches, further comprising determining a location for a second additional routing hub (“FIG. 7 illustrates an example display depicting a comparison of potential customer delivery points with no Transload locations with potential customer delivery points with two Transload locations, according to an example …” and “FIG. 9 illustrates an example display depicting a comparison of potential customer delivery points with two Transload locations with potential customer delivery points with one transload location and a dummy cluster, according to an example …” [0102-0107 and 0112-0116]).
As to claims 9 and 19, Stroh teaches all of the limitations of claim 1 as discussed above. Stroh further teaches, further comprising receiving user input indicating a target number of routing hubs (“In some examples, at step 508, a number of candidate Transload locations to be selected may be received. For example, it may be specified that one, two, three, four, five, or a higher number of Transload locations may be selected …” and “In some examples, display 902 may enable user 324 to determine where and when to implement Transload locations, the number of Transload locations, and/or determine which customer delivery points should not be eligible for last-mile delivery services …” [0087 and 0116]).
As to claims 10 and 20, Stroh teaches all of the limitations of claim 1 as discussed above. Stroh further teaches, further comprising determining a number of routing hubs based on a cost of additional routing hubs and execution data comprising at least one of execution distance and execution time (“… In such cases, a last-mile delivery cost may be reduced by introducing a Transload point 118, and a defined transit route between the warehouse 106 and the Transload point 118 …” and “In some examples, items may be picked up from warehouse 202 by delivery vehicle 204, which may deliver the items directly to customer delivery locations 206, 208. In some examples, customer delivery locations 206, 208 may correspond to a lower shipping cost and/or a shorter distance directly from warehouse 202 than customer delivery locations 224, 226, 228, 230, 232 …” [0044-0045 and 0047-0049]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103, which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2024/0029011 to Stroh et al. (Stroh) in view of U.S. Patent Publication No. 2024/0311750 to Goeters et al. (Goeters).
As to claims 4 and 14, Stroh teaches all of the limitations of claim 1 as discussed above. Stroh further teaches, wherein the packet data comprises predicted packet data (“… In some examples, this ship-to-home transaction data may be historical data or projected data relating items of an orders which were or are predicted to be delivered to a customer delivery location (for example, delivered to a customer home, and in some examples, same-day delivery to the customer's home)” and “… In some examples, the ship-to-home transaction data may be either historical ship-to-home transaction data or projected ship-to-home transaction data …” [0056 and 0084]).
Stroh does not teach, the method further comprising executing a packet forecast machine-learning model using as input historical packet data to generate the predicted packet data. However, Goeters teaches, the method further comprising executing a packet forecast machine-learning model using as input historical packet data to generate the predicted packet data (“… The shipping data can be looked up, predicted using a trained machine learning model (e.g., neural network model, classifier, etc.), inferred, and/or otherwise determined … In a specific example, models can be trained using historical shipping data (e.g., as a training target)” and “… For example, outputs from the model can include: an estimated delivery time (e.g., date and time), a confidence parameter for the estimated delivery time, and/or any other suitable outputs. Each model is preferably trained using historical shipping data corresponding to the associated shipping data (e.g., same shipping carrier, same delivery zip code, etc.), but can alternatively be trained using any other training data …” [0051-0054 and 0108-0112]).
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to include, the method further comprising executing a packet forecast machine-learning model using as input historical packet data to generate the predicted packet data, as taught by Goeters with the packet routing of Stroh. Motivation to do so comes from the teachings of Goeters that doing so would provide an optimized shipment route despite low accuracy of individual transit predictions at the time of shipment and reduce the computational resources needed to determine an optimal shipping route [0023].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Patent Publication No. 2025/0086576 to Singh et al. (Singh)
U.S. Patent Publication No. 2023/0351314 to Yusa (Yusa)
U.S. Patent Publication No. 2019/0333130 to Jha et al. (Jha)
U.S. Patent Publication No. 2016/0189102 to Schreiber (Schreiber)
U.S. Patent No. 11,501,246 to Futch et al. (Futch)
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/S.S.W./Examiner, Art Unit 3628
/RUPANGINI SINGH/Primary Examiner, Art Unit 3628
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