Building management system with generative AI-based root cause prediction
US12242937B1 · kind B1 · utility
Assignee
Inventors
- Julie J. Brown
- Young-Min Lee
- Rajiv Ramanasankaran
- Sastry K. Malladi
- Michael Tenbrock
- Levent Tinaz
- Samuel A. Girard
- David S. Elario
- Juliet A. Pagliaro Herman
- Miguel Galvez
- Trent M. Swanson
- John F. Kuchler
- Deepak Budhiraja
- Daniela M. Natali
- Josip Lazarevski
- Scott Deering
- Gary W. Gavin
- Kristen Sheppard-Guzelaydin
- James Young
- Prashanthi Sudhakar
- Kaleb Luedtke
- Karl F. Reichenberger
- Wenwen Zhao
- Adam R. Grabowski
- Lauren C. Dern
- Nicole A. Madison
- Dana S. Petersen
- Nevin L. Forry
- Pedriant Pena
- Ghassan R. Hamoudeh
Key dates
| Filing date | Jan 22, 2024 |
| Grant date | Mar 4, 2025 |
| Priority date | — |
| Expiry date | Jan 22, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0475
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method including training, by one or more processors, a generative AI model using a plurality of first service requests handled by technicians for servicing building equipment. The generative AI model may be trained to predict root causes of a plurality of first problems corresponding to the plurality of first service requests. The method may include receiving, by the one or more processors, a second service request for servicing building equipment. The method may include predicting, by the one or more processors using the generative AI model, a root cause of a second problem corresponding to the second service request based on characteristics of the second service request and one or more patterns or trends identified from the plurality of first service requests using the generative AI model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.