Tyco Fire & Security GmbH (20240345560). BUILDING MANAGEMENT SYSTEM WITH GENERATIVE AI-BASED COUPLING OF UNSTRUCTURED SERVICE DATA TO OTHER INPUT / OUTPUT DATA SOURCES AND ANALYTICS simplified abstract

BUILDING MANAGEMENT SYSTEM WITH GENERATIVE AI-BASED COUPLING OF UNSTRUCTURED SERVICE DATA TO OTHER INPUT / OUTPUT DATA SOURCES AND ANALYTICS

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 COUPLING OF UNSTRUCTURED SERVICE DATA TO OTHER INPUT / OUTPUT DATA SOURCES AND ANALYTICS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240345560 titled 'BUILDING MANAGEMENT SYSTEM WITH GENERATIVE AI-BASED COUPLING OF UNSTRUCTURED SERVICE DATA TO OTHER INPUT / OUTPUT DATA SOURCES AND ANALYTICS

The method described in the abstract involves processing unstructured service data related to service requests for building equipment maintenance by technicians. The method includes identifying building equipment, building spaces, or customers from the unstructured data, retrieving additional data from separate sources based on this identification, and training a generative AI model using the collected data.

• Identification of building equipment, building spaces, or customers from unstructured service data
• Retrieval of additional data from separate sources based on the identification
• Training a generative AI model using the collected data

Potential Applications: - Streamlining building equipment maintenance processes - Enhancing customer service by providing technicians with relevant information - Improving efficiency and accuracy in servicing building equipment

Problems Solved: - Difficulty in extracting meaningful information from unstructured service data - Inefficient data retrieval for building equipment maintenance - Lack of personalized service for customers in building maintenance

Benefits: - Increased efficiency in handling service requests - Enhanced customer satisfaction through personalized service - Improved maintenance of building equipment

Commercial Applications: Title: AI-Driven Building Equipment Maintenance Optimization This technology can be applied in facility management companies, building maintenance services, and property management firms to optimize maintenance processes, improve customer service, and increase operational efficiency.

Questions about AI-Driven Building Equipment Maintenance Optimization: 1. How does this technology improve the accuracy of identifying building equipment and spaces? 2. What are the potential cost-saving benefits for companies implementing this AI-driven maintenance optimization system?

Frequently Updated Research: Stay updated on advancements in AI models for building equipment maintenance optimization, data integration techniques for unstructured service data, and customer relationship management strategies in facility management.

Original Abstract Submitted

a method includes receiving, by one or more processors, unstructured service data corresponding to one or more service requests handled by technicians for servicing building equipment of a building. the method may include detecting, by the one or more processors, an identifier of the building equipment, a space of the building, or a customer associated with the building using the unstructured service data. the method may include retrieving, by the one or more processors based on the identifier of the building equipment, the space, or the customer, additional data associated with the building equipment, the space, or the customer from one or more additional data sources separate from the unstructured service data. the method may include training, by the one or more processors, a generative ai model using training data including the unstructured service data and the additional data.