# US 2025/0069029 A1 · Predicting package distribution network attributes

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- Number: US 2025/0069029 A1
- Document ID: US20250069029A1
- Status: Published application
- Kind: A1
- Application number: 18/453,562
- Filed: 2023-08-22
- Published: 2025-02-27
- Earliest claimed: 2023-08-22
- Assignees: Inxeption Corporation
- Applicants: Inxeption Corporation
- Inventors: Mark Moore; Dat Doan; Marc Verbeek; Jacob Throgmorton; John Spenlau
- Web page: https://patentgrep.com/patent/US20250069029A1

## CPC

- G06Q 10/0838: {Historical data}
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

Techniques are described herein for predicting package distribution network attributes. A model of a package distribution network may be initialized in a digital computing system. The model may be a graph with nodes representing intermediate waypoints and edges representing distribution channels between intermediate waypoints. Some edges may include initial values of attribute(s) of the respective distribution channels of the package distribution network. Using the model, traversal of package(s) may be simulated through candidate pathways across the package distribution network. This may include applying data indicative of nodes and edges (including the initial values) of the graph as input across machine learning model(s) to generate predicted values of attribute(s) of the package distribution network. Metric(s) associated with each of the candidate pathways across the package distribution network may be determined and used to select candidate pathway(s) across the package distribution network for use in one or more downstream computing processes.

## Claims

### Claim 1

A method implemented by a digital computing system, comprising:
initializing a model of a package distribution network in the digital computing system, the model comprising a graph with nodes representing intermediate waypoints of the package distribution network and edges representing distribution channels between the intermediate waypoints, wherein at least some of the edges include initial values of one or more attributes of the respective distribution channels of the package distribution network;
using the model, simulating traversal of one or more packages through a plurality of candidate pathways across the package distribution network, wherein the simulating comprises applying data indicative of nodes and edges of the graph as input across one or more machine learning models to generate predicted values of one or more attributes of the distribution channels of the package distribution network, wherein the data indicative of nodes and edges includes the initial values;
based on the simulating, determining at least one metric associated with each of the candidate pathways across the package distribution network; and
based on the metrics, selecting one or more of the candidate pathways across the package distribution network for use in one or more downstream computing processes.

### Claim 2 (depends on 1)

The method of claim 1, wherein the one or more downstream computing processes includes causing output indicative of the one or more selected candidate pathways to be rendered at one or more output devices.

### Claim 3 (depends on 2)

The method of claim 2, further comprising:
determining, based on user input received at the digital computing system or another digital computing system, that a given candidate pathway has been selected;
identifying a distribution entity that facilitates usage of the given candidate pathway; and
automatically generating and transmitting, to the identified distribution entity, a digital message comprising a directive to distribute the one or more packages through at least a portion of the given candidate pathway.

### Claim 4 (depends on 1)

The method of claim 1, further comprising ranking the plurality of candidate pathways based on the metrics.

### Claim 5 (depends on 1)

The method of claim 1, wherein the predicted values of one or more attributes of the distribution channels include a capacity of one of the distribution channels.

### Claim 6 (depends on 1)

The method of claim 1, wherein the predicted values of one or more attributes of the distribution channels include a risk of payload loss or damage associated with one of the distribution channels.

### Claim 7 (depends on 1)

The method of claim 1, wherein the initial values include a capacity of one of the distribution channels of the package distribution network.

### Claim 8 (depends on 1)

The method of claim 1, wherein one or more of the machine learning models comprises a transformer model, and the method further comprises, prior to the applying, linearizing at least a portion of the graph into a sequence of tokens representing nodes and edges of the graphs.

### Claim 9 (depends on 1)

The method of claim 1, wherein one or more of the machine learning models comprises a graph neural network.

### Claim 10 (depends on 1)

The method of claim 1, wherein the metric associated with each candidate pathway comprises a predicted time interval to traverse the one or more packages through the candidate pathway.

### Claim 11 (depends on 1)

The method of claim 1, wherein the metric associated with each candidate pathway comprises a predicted risk of loss of the one or more packages in transit.

### Claim 12 (depends on 1)

The method of claim 1, wherein one or more of the machine learning models has been trained based on historical distribution data to generate output indicative of predicted distribution channel attributes.

### Claim 13

A method implemented using a digital computing system, comprising”
initializing a model of a package distribution network in the digital computing system, the model comprising a graph with nodes connected by edges, wherein at least some of the nodes or edges include initial values of one or more attributes of distribution channels of the package distribution network;
using the model, identifying a plurality of candidate pathways for sending one or more payloads across the package distribution network;
encoding the graph, including the initial values, into a reduced-dimensionality graph embedding using one or more first machine learning models;
applying the reduced-dimensionality graph embedding as input across one or more second machine learning models to generate a probability distribution over the plurality of candidate pathways; and
based on the probability distribution, selecting one or more of the candidate pathways across the package distribution network for use in one or more downstream computing processes.

### Claim 14 (depends on 13)

The method of claim 13, wherein each probability of the probability distribution represents a probability that traversal of one or more of the payloads across a respective distribution channel will satisfy a constraint.

### Claim 15 (depends on 14)

The method of claim 14, wherein the constraint comprises a temporal constraint.

### Claim 16 (depends on 14)

The method of claim 14, wherein the constraint comprises a risk of payload loss constraint.

### Claim 17 (depends on 13)

The method of claim 13, wherein the one or more downstream computing processes comprises a computing process for selecting one or more distribution channels between an origin and a destination.

### Claim 18 (depends on 13)

The method of claim 13, wherein one or more of the first machine learning models comprises a graph neural network.

### Claim 19 (depends on 13)

The method of claim 13, wherein the nodes represent waypoints of the package distribution network and edges represent distribution channels between the waypoints.

### Claim 20 (depends on 13)

The method of claim 13, wherein the edges represent waypoints of the package distribution network and the nodes represent distribution channels between the waypoints.