Tyco Fire & Security Gmbh

Johnson Controls Technology Company

Gary W Gavin

Miguel Galvez

Trent Mark Swanson

Karl F Reichenberger

Rajiv Ramanasankaran

Young M Lee

Adam R Grabowski

John F Kuchler

Julie J Brown

Sastry Km Malladi

James Young

Dana S Petersen

Juliet A Pagliaro Herman

Nicole A Madison

Josip Lazarevski

David S Elario

Samuel A Girard

Ryan G Danielson

Nevin L Forry

Kristen Sheppard-Guzelaydin

Ghassan R Hamoudeh

Lauren C Dern

Pedriant Pena

Kaleb Luedtke

Prashanthi Sudhakar

Michael Tenbrock

Levent Tinaz

Deepak Budhiraja

Daniela M Natali

Scott Deering

Wenwen Zhao

Timothy C Gamroth

Abu Bakr Khan

Brandon A Sloat

Thomas M Seneczko

Sastry K M Malladi

Mark G Freund

Troy A Goldschmidt

Gregory A Makowski

Shawn D Schubert

Gladys Belen Ramirez-Garcia

Wesley J Irek

A method including training, by one or more processors, a generative AI model using 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 include unstructured data not conforming to a predetermined format or conforming to a plurality of different predetermined formats. The method includes receiving, by the processors, a second service request for servicing building equipment. The method includes generating, by the processors using the generative AI model, a user interface prompting a user to provide information about a problem leading to the second service request as unstructured data not conforming to the predetermined format or conforming to the plurality of different predetermined formats. The method includes automatically initiating, by the processors, one or more actions to address the problem based on the information provided via the user interface.

Building management system with generative AI-based interactive service tool

A method includes receiving, by one or more processors, an unstructured service report corresponding to a service request handled by one or more technicians for servicing building equipment. The unstructured service report may include unstructured data not conforming to a predetermined format or conforming to a plurality of different predetermined formats. The method may include extracting, by the one or more processors, a set of standards from one or more technical documents associated with the building equipment. The method may include automatically generating, by the one or more processors using a generative AI model, a structured service report in the predetermined format for delivery to a customer associated with the building equipment. Generating the structured service report may include standardizing the structured service report to comply with the set of standards extracted from the one or more technical documents associated with the building equipment.

Building management system with generative AI-based automated flexible customer report generation and standardization

A method including training, by one or more processors, a generative AI model using first operating data from building equipment and a plurality of first service reports indicating a plurality of first problems associated with the building equipment. The method may include predicting, by the one or more processors using the generative AI model, one or more future problems likely to occur with the building equipment based on second operating data from the building equipment. The method may include automatically initiating, by the one or more processors, one or more actions to prevent the one or more future problems from occurring or mitigate an effect of the one or more future problems.

Building management system with generative AI-based predictive maintenance

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.

Building management system with generative AI-based root cause prediction

Systems and methods to integrate equipment models are described herein. Integration of equipment models can occur responsive to detection of new physical devices.

Automatic generation of data models

Systems and methods to generate new equipment models and/or connectors to integrate the new equipment models are described herein. Generation of new equipment models can include retrieving information from one or more publicly accessible data sources.

A system for connecting a building device to a server includes a building network device including a processing circuit configured to receive one or more point identifier values from the building device via a building network. Each of the one or more point identifier values identifies a data point of the building device. The processing circuit is configured to receive one or more rules for generating a communication model for the building device from one or more memory devices. The processing circuit is configured to generate the communication model for the building device based on the received one or more point identifier values and the received one or more rules, receive, via the building network, data for the data points from the building device, and send, via the second network, the received data for the data points to corresponding data points on the server based on the communication model.

Building network device for generating communication models for connecting building devices to a network

A heating, ventilation, or air conditioning (HVAC) system with automated flow direction detection is provided. The HVAC system includes one or more hoses configured to provide airflow from HVAC equipment, a bidirectional pressure sensor coupled to the hoses, and a controller coupled to the bidirectional pressure sensor. The controller is configured to receive a signal from the bidirectional pressure sensor, determine a direction of the airflow relative to the bidirectional pressure sensor based on the signal, correct the signal for a reversed hose polarity relative to the bidirectional sensor based on the direction of the airflow, and perform a control activity using the corrected signal.

HVAC system with automated flow direction detection

Computerized methods and systems for determining flow direction relative to a bidirectional pressure sensor are provided. The method includes receiving pressure information from the bidirectional pressure sensor. The method includes using the pressure information to evaluate, at a processing circuit, pressure at the bidirectional pressure sensor over time, The method includes assigning a flow direction to a current pressure of the bidirectional pressure sensor by comparing the current pressure to at least one past pressure.

Methods and systems for determining flow direction using a bidirectional pressure sensor

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Franklin, WI, United States of America

Gary W Gavin