Cell site repair part prediction machine learning system

US11844134B1 · kind B1 · utility

Assignee

• T-Mobile Innovations LLC · US

Inventors

• Diksha Agarwal · Overland Park, US
• Hui-Lin Chang · Baoshan, TW
• Lance Paul Lukens · Templeton, US
• Daqi Li · Overland Park, US
• Leland John Garris · Austin, US
• Wayne Thompson · Quakertown, US

Key dates

• Filing date: Feb 2, 2020
• Grant date: Dec 12, 2023
• Expiry date: Oct 14, 2042

Classification

• Technology area (CPC H): Electricity
• CPC primary: H04W76/00
• WIPO field: Telecommunications
• WIPO sector: Electrical engineering

Abstract

A cell site maintenance system. The system comprises a plurality of data stores, a processor, a memory coupled to the processor, and a part replacement prediction application stored in memory. When executed by the processor the application extracts features based on data read from the data stores, executes a plurality of part machine learning models, where each part machine learning model analyses some of the extracted features by a multi-label classification model trained for a specific part associated with that part machine learning model and each part machine learning model outputs a probability that the associated part can fix the cell site associated with the trouble ticket, and, based on a probability output by a part machine learning model associated with a first part exceeding a predefined threshold, determining that the first part is to be pulled from a part inventory to make ready for a service truck.