Target. has filed a patent for a method to automatically implement changes to item listing data in online retail. The technology involves using machine learning models to identify changes, suggest remediations, and auto-remediate data to ensure accuracy. GlobalData’s report on Target gives a 360-degree view of the company including its patenting strategy. Buy the report here.
According to GlobalData’s company profile on Target, Treatment progress monitoring was a key innovation area identified from patents. Target's grant share as of January 2024 was 86%. Grant share is based on the ratio of number of grants to total number of patents.
Automated item listing data remediation in online retail
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The patent application (Publication Number: US20240037497A1) describes a method for implementing remediations to item listing data in an online retail environment using machine learning techniques. The method involves receiving a topic for a change in item listing data, retrieving trained models to identify changes and suggest remediations, inputting the data, receiving suggestions, and auto-remediating the data if it meets certain criteria. The models include electronic service plan, package dimensions, item type, profanity, dimensional drawings, and image labeling models, each trained to address specific aspects of the item listing data.
Furthermore, the method includes determining confidence metrics for suggested remediations, flagging data that does not meet auto-remediation criteria, allowing user-defined remediations, and training the models based on user input. The models are trained to identify changes in accuracy, completeness, timeliness, uniqueness, validity, and consistency of item listing data, enabling accurate and efficient remediation processes. Additionally, the models are designed to address specific issues such as warranty indications, package dimensions criteria, item categorization, license associations, profanity detection, image dimensions, text compliance, and image viewpoints, providing a comprehensive approach to improving item listing data quality in an online retail environment.
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