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 .
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 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-20 recite:
1. A computer-implemented method comprising: training a Graph Neural Network (GNN) using a loss function to obtain a GNN model, the loss function configured to minimize cumulative supply prediction errors and inventory-level prediction errors; accessing data about planned shipments and planned inventory levels for nodes in a supply chain network, each planned shipment having a planned shipping date and a planned unit amount; for each shipment between nodes, calculating, utilizing the GNN model, an outgoing supply prediction comprising a probability distribution of estimated shipment dates and a probability distribution for estimated amount of units shipped; for each node in the supply chain network: aggregating results for incoming and outgoing shipments associated with the node based on the probability distribution of estimated shipment dates and the probability distribution for estimated amount of units shipped; and calculating a predicted inventory level by date at the node based on the aggregated results for incoming and outgoing shipments associated with the node; and causing presentation of updated inventory levels by date in one or more nodes.
2. The method as recited in claim 1, wherein the loss function includes a predicted cumulative daily quantity vector for the nodes in the supply chain network and an actual cumulative daily quantity vector of outgoing supply for the nodes in the supply chain network.
3. The method as recited in claim 1, wherein calculating the outgoing supply prediction comprises calculating a normalized cumulative error over a planning horizon, wherein calculating the normalized cumulative error comprises: calculating a predicted cumulative quantity vector; calculating an actual cumulative daily quantity vector; and normalizing a difference between the predicted cumulative quantity vector and the actual cumulative daily quantity vector based on an actual daily quantity.
4. The method as recited in claim 1, wherein an input to the GNN model includes planned shipment events and demand forecasting over a predefined time horizon.
5. The method as recited in claim 1, wherein the GNN model is a delta model and calculates incremental modifications to the planned shipments and planned inventory levels.
6. The method as recited in claim 1, wherein the GNN model is a horizon model that calculates shipments and inventory levels without considering the data about planned shipments and planned inventory levels.
7. The method as recited in claim 1, wherein the GNN model is a hybrid model that utilizes a delta model for a period within a planning horizon and a hybrid model for times outside the period in the planning horizon.
8. The method as recited in claim 1, wherein aggregating results for the node further comprises: calculating a cumulative predicted supply over by date; calculating a cumulative actual supply by date; and calculating a cumulative supply prediction error based on the cumulative predicted supply and the cumulative actual supply.
9. The method as recited in claim 1, wherein node features for the GNN model comprise demand information, planned shipment information, and edge-level features corresponding to shipments between two nodes.
10. The method as recited in claim 1, wherein an output of the GNN model comprises a shipment event delta probability and a shipped quantity scaler.
11. The method as recited in claim 1, further comprising: creating a directional graph for the nodes in the supply chain network, the directional graph comprising edges between nodes in the supply chain network; and creating a reverse graph of the directional graph with same nodes and features as the directional graph and with directions of edges reversed.
12. A system comprising: a memory comprising instructions; and one or more computer processors, wherein the instructions, when executed by the one or more computer processors, cause the system to perform operations comprising: training a Graph Neural Network (GNN) using a loss function to obtain a GNN model, the loss function configured to minimize cumulative supply prediction errors and inventory-level prediction errors; accessing data about planned shipments and planned inventory levels for nodes in a supply chain network, each planned shipment having a planned shipping date and a planned unit amount; for each shipment between nodes, calculating, utilizing the GNN model, an outgoing supply prediction comprising a probability distribution of estimated shipment dates and a probability distribution for estimated amount of units shipped; for each node in the supply chain network: aggregating results for incoming and outgoing shipments associated with the node based on the probability distribution of estimated shipment dates and the probability distribution for estimated amount of units shipped; and calculating a predicted inventory level by date at the node based on the aggregated results for incoming and outgoing shipments associated with the node; and causing presentation of updated inventory levels by date in one or more nodes.
13. The system as recited in claim 12, wherein the loss function includes a predicted cumulative daily quantity vector for the nodes in the supply chain network and an actual cumulative daily quantity vector of outgoing supply for the nodes in the supply chain network.
14. The system as recited in claim 12, wherein calculating the outgoing supply prediction comprises calculating a normalized cumulative error over a planning horizon, wherein calculating the normalized cumulative error comprises: calculating a predicted cumulative quantity vector; calculating an actual cumulative daily quantity vector; and normalizing a difference between the predicted cumulative quantity vector and the actual cumulative daily quantity vector based on an actual daily quantity.
15. The system as recited in claim 12, wherein an input to the GNN model includes planned shipment events and demand forecasting over a predefined time horizon.
16. The system as recited in claim 12, wherein the GNN model is a delta model and calculates incremental modifications to the planned shipments and planned inventory levels.
17. The system as recited in claim 12, wherein the GNN model is a horizon model that calculates shipments and inventory levels without considering the data about planned shipments and planned inventory levels.
18. A non-transitory machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising: training a Graph Neural Network (GNN) using a loss function to obtain a GNN model, the loss function configured to minimize cumulative supply prediction errors and inventory-level prediction errors; accessing data about planned shipments and planned inventory levels for nodes in a supply chain network, each planned shipment having a planned shipping date and a planned unit amount; for each shipment between nodes, calculating, utilizing the GNN model, an outgoing supply prediction comprising a probability distribution of estimated shipment dates and a probability distribution for estimated amount of units shipped; for each node in the supply chain network: aggregating results for incoming and outgoing shipments associated with the node based on the probability distribution of estimated shipment dates and the probability distribution for estimated amount of units shipped; and calculating a predicted inventory level by date at the node based on the aggregated results for incoming and outgoing shipments associated with the node; and causing presentation of updated inventory levels by date in one or more nodes.
19. The non-transitory machine-readable storage medium as recited in claim 18, wherein the loss function includes a predicted cumulative daily quantity vector for the nodes in the supply chain network and an actual cumulative daily quantity vector of outgoing supply for the nodes in the supply chain network.
20. The non-transitory machine-readable storage medium as recited in claim 18, wherein calculating the outgoing supply prediction comprises calculating a normalized cumulative error over a planning horizon, wherein calculating the normalized cumulative error comprises: calculating a predicted cumulative quantity vector; calculating an actual cumulative daily quantity vector; and normalizing a difference between the predicted cumulative quantity vector and the actual cumulative daily quantity vector based on an actual daily quantity.
All claims recite subject matter falling within one of the four categories of invention (Step 1).
Claims 1-20 but for the recitation of the underlined additional elements recite mathematical concepts in the form of mathematical calculations and mathematical relationships. Per MPEP 2106.04(a)(2)(I), mathematical concepts are an abstract idea.
Thus claims 1-20 recite an abstract idea (Step 2A_1).
The computer-related additional elements (computer, processor(s), memory, instructions, system) are recited at a high level of generality such that they amount to mere instructions to implement an abstract idea, which per MPEP 2106.05(f) means they do not provide a practical application or significantly more.
Regarding causing presentation of an output, this amounts to data output, which per MPEP 2106.05(g) is extra-solution activity which does not provide a practical application or significantly more. Data output is also regarded as not significantly more because it is well understood and conventional activity. See MPEP 2106.05(d) citing OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93.
Thus claims 1-20 are considered to be directed to an abstract idea without a practical application or significantly more (Step 2A_2 and Step 2B) and are ineligible.
Prior Art Status
Claims 1-20 are considered to distinguish over the cited art. Melancon (US 20250225474 A1) discloses a system that makes predictions about inventory levels (paragraph 15). Boffo (US 20240095667 A1) discloses a system that analyzes inventory levels, incoming/outgoing shipments to make future inventory predictions (paragraph 55). Borjian (US 20230297948 A1) discloses a system for determining inventory replenishment strategies using machine learning (abstract). Miao (CN 116341752 A) discloses making supply chain predictions using a graph neural network (abstract). Cote (US 20210374632 A1) discloses making supply chain predictions using machine learning. Ohlsson (US 20200143313 A1) discloses using machine learning to make predictions regarding inventory. Leidner (US 10262283 B2) discloses using graphs to represent a supply chain. None of the cited art discloses all of the claimed subject matter as specified in claims 1+, 12+ and 18+.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATHAN A MITCHELL whose telephone number is (571)270-3117. The examiner can normally be reached M-F 9-5.
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/NATHAN A MITCHELL/ Primary Examiner, Art Unit 3627