Models for Estimating ETA and Dwell Times for Transportation of Objects

ChangeBridge: Patent Grants - Business Methods (G06Q)

Published April 7th, 2026

Detected April 7th, 2026

Summary

The USPTO granted patent US12596968B2 to CHORUSVIEW, Inc. on April 7, 2026. The patent covers machine learning models configured to estimate time of arrival (ETA), dwell times, and dwell locations for transported objects using tracking tag data. The models process location data and timestamps from reader devices to output predictive shipping and logistics information.

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What changed

The USPTO issued patent US12596968B2 covering models for estimating ETA and dwell times during object transportation. The patent describes training a machine learning model using tracking tag data including location, timestamps, starting points, destinations, and dwell information.

Companies developing logistics tracking, supply chain visibility, or transportation management systems should review this patent for potential licensing implications or design-around considerations. The intellectual property protection covers algorithmic approaches to predicting shipment arrival times and stop durations using RFID or similar tracking technologies.

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Source document (simplified)

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Models for estimating eta and dwell times for transportation of objects

Grant US12596968B2 Kind: B2 Apr 07, 2026

Assignee

CHORUSVIEW, Inc.

Inventors

Geoffrey Anders, Zhipeng Wang

Abstract

Aspects of the disclosure provide for the use and training of a model configured to provide an estimated time of arrival (ETA) for transporting an object. Data may be received from a reader device. The data may identify a tracking tag and a first timestamp. Based on the received data, a location of the tracking tag at the first timestamp may be determined. A starting location and second timestamp for the tracking tag and a destination location and third timestamp for the tracking tag may be identified. A list including at least one dwell time and dwell location for the tracking tag may be identified. The model may be trained to output the ETA, a dwell time, and a dwell location based on the determined location, the starting location, the destination location, the first timestamp, the second timestamp, the third timestamp, and the list.