Tyco Fire & Security GmbH (20240346060). BUILDING MANAGEMENT SYSTEM WITH GENERATIVE AI-BASED UNSTRUCTURED SERVICE DATA INGESTION simplified abstract

BUILDING MANAGEMENT SYSTEM WITH GENERATIVE AI-BASED UNSTRUCTURED SERVICE DATA INGESTION

Organization Name

Tyco Fire & Security GmbH

Inventor(s)

Julie J. Brown of Yardley PA (US)

Young M. Lee of Old Westbury NY (US)

Rajiv Ramanasankaran of San Jose CA (US)

Sastry KM Malladi of Fremont CA (US)

Michael Tenbrock of Dachsen (CH)

Levent Tinaz of Tampa Bay FL (US)

Samuel A. Girard of Kenosha WI (US)

David S. Elario of Hartland WI (US)

Juliet A. Pagliaro Herman of Waukesha WI (US)

Miguel Galvez of Westford MA (US)

Trent M. Swanson of Wellington FL (US)

John F. Kuchler of Muskego WI (US)

Deepak Budhiraja of Ashburn VA (US)

Daniela M. Natali of Kensington MD (US)

Josip Lazarevski of Zurich (CH)

Scott Deering of Milwaukee WI (US)

Gary W. Gavin of Franklin WI (US)

Kristen Sheppard-guzelaydin of West Chester PA (US)

James Young of Cork (IE)

Prashanthi Sudhakar of San Francisco CA (US)

Kaleb Luedtke of West Bend WI (US)

Karl F. Reichenberger of Mequon WI (US)

Wenwen Zhao of Santa Clara CA (US)

Adam R. Grabowski of Brookfield WI (US)

Lauren C. Dern of Fox Point WI (US)

Nicole A. Madison of Milwaukee WI (US)

Dana S. Petersen of Milwaukee WI (US)

Nevin L. Forry of York PA (US)

Pedriant Pena of Groveland MA (US)

Ghassan R. Hamoudeh of San Marcos CA (US)

Ryan G. Danielson of Castle Rock CO (US)

BUILDING MANAGEMENT SYSTEM WITH GENERATIVE AI-BASED UNSTRUCTURED SERVICE DATA INGESTION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240346060 titled 'BUILDING MANAGEMENT SYSTEM WITH GENERATIVE AI-BASED UNSTRUCTURED SERVICE DATA INGESTION

The method described in the abstract involves receiving a variety of unstructured service reports related to service requests for building equipment, training a generative AI model using these reports, and then using the trained model to perform actions on subsequent service requests.

• The method receives unstructured service reports for building equipment service requests.
• It trains a generative AI model with the received unstructured data.
• The trained model is then used to perform actions on future service requests.
• The AI model helps in processing and analyzing unstructured data efficiently.
• This method streamlines the handling of service requests for building equipment.

Potential Applications: This technology can be applied in various industries where service requests need to be processed efficiently, such as facilities management, maintenance services, and equipment servicing companies.

Problems Solved: This technology addresses the challenge of handling unstructured data from service reports and automates the processing of service requests for building equipment.

Benefits: The benefits of this technology include improved efficiency in handling service requests, faster response times, and better utilization of resources.

Commercial Applications: This technology can be commercially used by facilities management companies, maintenance service providers, and equipment servicing firms to streamline their operations and improve customer satisfaction.

Questions about the technology: 1. How does the generative AI model help in processing unstructured service reports? 2. What are the potential cost savings associated with using this technology in building equipment servicing operations?

Original Abstract Submitted

a method includes receiving, by one or more processors, a plurality of first unstructured service reports corresponding to a plurality of first service requests handled by technicians for servicing building equipment. the plurality of first unstructured service reports may include unstructured data not conforming to a predetermined format or conforming to a plurality of different predetermined formats. the method may include training, by the one or more processors, a generative ai model using the plurality of first unstructured service reports. the method may include performing, by the one or more processors using the trained generative ai model, one or more actions with respect a second service request subsequent to training the generative ai model.