Flexible automated sorting and transport arrangement (FAST) asset monitor

Patent metadata

Field	Value
Publication number	US-11928608-B2
Application number	US-202218077458-A
Country	US
Kind code	B2
Filing date	Dec 8, 2022
Priority date	Feb 27, 2019
Publication date	Mar 12, 2024
Grant date	Mar 12, 2024

Abstract

A disclosed system for transport asset monitoring, for example monitoring truck trailer unloading progress at large retail locations, includes an artificial intelligence (AI) solution for managing operations at a facility. The AI solution analyzes current load percentage and other data to predict availability for moving the transport asset and ability to accept a new incoming transport asset. Predictions of availability can reduce response times, resulting in higher utilization rates for assets, thereby improving efficiency. An exemplary system includes a sensor configured to sense operation progress parameter data for a transport asset; and logic to receive the operation progress parameter data from the sensor; determine, using the AI solution and based at least on the operation progress parameter data, a predicted milestone parameter; and report the predicted milestone parameter to a remote node.

First claim

What is claimed is:

1. A system for transport asset monitoring, the system comprising:
a first sensor configured to sense a first unloading operation progress parameter data for a first transport asset at an unloading location, wherein the first transport asset is partially unloaded, wherein the first unloading operation progress parameter data comprises identification of items unloaded from the first transport asset;
a processor; and
a non-transitory computer-readable medium storing instructions that are operative when executed by the processor to:
receive the first unloading operation progress parameter data from the first sensor;
determine, based at least on the first unloading operation progress parameter data, a predicted milestone parameter, the milestone parameter comprising a determination of an expected completion time of unloading at least one item remaining on the first transport asset and a determination of an expected departure-ready time when the first transport asset is expected to be ready to depart, based on current data and historical data, the current data comprising at least one of the first unloading operation progress parameter data received from the first sensor or a data set, and the historical data comprising at least one of a record of past contents of the current data or actual operational activity timing;
and
report the predicted milestone parameter to a remote node to integrate activity schedules for the first transport asset and at least one other transport asset, wherein integrating activity schedules further comprises scheduling acceptance of a second incoming transport asset to the unloading location based on the expected completion time for the first transport asset.

2. The system of claim 1 wherein the first transport asset comprises a trailer, and wherein integrating the activity schedules further comprises:
sending a driver to retrieve the first transport asset being unloaded at the expected completion time.

3. The system of claim 1 wherein the first sensor comprises at least one sensor selected from a list consisting of:
an RFID sensor, a barcode scanner, and a computer vision (CV) sensor.

4. The system of claim 1 wherein integrating the activity schedules further comprises:
diverting a third incoming transport asset from the unloading location to a different destination.

5. The system of claim 1 further comprising:
a second sensor configured to sense a second operation progress parameter data for the first transport asset, wherein the second operation progress parameter data comprises an identification of items remaining on the first transport asset; and
wherein the instructions are further operative to:
receive the second operation progress parameter data from the second sensor;
monitor the asset unloading progress at a determined location;
and
wherein determining the predicted milestone parameter comprises determining, based at least on the first unloading operation progress parameter data and the second operation progress parameter data, the predicted milestone parameter.

6. The system of claim 1 wherein the instructions are further operative to:
receive, from a user interface (UI), confirmation or correction of the predicted milestone parameter.

7. The system of claim 1 further comprising:
a machine learning (ML) component that generates an AI solution using at least historical operation progress parameter data.

8. The system of claim 1 further comprising:
a wireless communication module; and
an automated ground vehicle (AGV) in communication with the processor via the wireless communication module.

9. The system of claim 1 wherein the instructions are further operative to:
generate, based at least on the first unloading operation progress parameter data, logistical instructions for the second incoming transport asset to delay the second incoming transport asset to the same unloading location to provide a minimal arrival interval.

10. A method of transport asset monitoring, the method comprising:
receiving a first operation progress parameter data for a first transport asset from a first sensor, wherein receiving the first operation progress parameter data from the first sensor comprises receiving the first operation progress parameter data from at least one sensor selected from a list consisting of: an RFID sensor, a barcode scanner, and a computer vision (CV) sensor;
determining, based at least on the first operation progress parameter data, a predicted milestone parameter, the milestone parameter comprising a determination of an expected completion time of unloading at least one item remaining on the first transport asset and a determination of an expected departure-ready time when the first transport asset is expected to be ready to depart, based on current data and historical data, the current data comprising at least one of the first operation progress parameter data received from the first sensor or a data set, and the historical data comprising at least one of a record of past contents of the current data or actual operational activity timing; and
reporting the predicted milestone parameter to a remote node to integrate activity schedules for the first transport asset and at least one other transport asset.

11. The method of claim 10 wherein receiving the first operation progress parameter data for the first transport asset comprises receiving the first operation progress parameter data for a trailer.

12. The method of claim 10 wherein the first operation progress parameter data comprises an identification of items unloaded from the first transport asset.

13. The method of claim 10 further comprising:
receiving a second operation progress parameter data from a second sensor, wherein the second operation progress parameter data comprises an identification of items remaining on the first transport asset; and
wherein determining the predicted milestone parameter comprises determining, based at least on the first operation progress parameter data and the second operation progress parameter data, the predicted milestone parameter.

14. The method of claim 10 further comprising:
receiving, from a user interface (UI), confirmation or correction of the predicted milestone parameter.

15. The method of claim 10 further comprising:
generating, with a machine learning (ML) component, an AI solution using at least historical operation progress parameter data.

16. The method of claim 10 further comprising:
wirelessly communicating, with an automated ground vehicle (AGV), logistical data related to the AGV.

17. The method of claim 10 further comprising:
generating based at least on the first operation progress parameter data, logistical instructions for a second transport asset.

18. One or more computer storage devices having computer-executable instructions stored thereon for transport asset monitoring, which, on execution by a computer, cause the computer to perform operations comprising:
receiving a first operation progress parameter data for a first transport asset from a first sensor, wherein receiving the first operation progress parameter data for the first transport asset comprises receiving the first operation progress parameter data for a trailer, wherein the first operation progress parameter data comprises an identification of items unloaded from the first transport asset, and wherein receiving the first operation progress parameter data from the first sensor comprises receiving the first operation progress parameter data from at least one sensor sel

Assignees

• Walmart Apollo Llc

Inventors

• Bellar Jason
• Propes William Mark
• Ford Chris
• Alexander Matthew D
• Savaiinaea Amy

Classifications

• G06N5/04 Primary

  Inference or reasoning models · CPC title

• G05D1/0088

  characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours (using knowledge based models G06N5/00) · CPC title

• G06N20/00

  Machine learning · CPC title

• H04L67/12 Primary

  specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks · CPC title

• G05D2201/0213

  Physics · mapped topic