Tyco Fire + Security Gmbh

Johnson Controls Tyco IP Holdings Llp

Comcast Cable Communications, LLC

Rajiv Ramanasankaran

Young M Lee

Miguel Galvez

Trent Mark Swanson

Ambuj Shatdal

Karl F Reichenberger

Brian Scott Otto

Adam R Grabowski

Gary W Gavin

John F Kuchler

Julie J Brown

James Young

Juliet A Pagliaro Herman

Nicole A Madison

Dana S Petersen

Ryan G Danielson

Daniela M Natali

Scott Deering

Deepak Budhiraja

Levent Tinaz

Michael Tenbrock

Prashanthi Sudhakar

Kaleb Luedtke

Pedriant Pena

Ghassan R Hamoudeh

Lauren C Dern

Kristen Sheppard-Guzelaydin

Nevin L Forry

Samuel A Girard

David S Elario

Josip Lazarevski

Wenwen Zhao

Young Min Lee

Yaron Sheba

Dominick James O'Dierno

Michael Alexander Yurochko

David Frank Russell Weir

David Kenneth Evans

Sastry Km Malladi

Kristopher Charles Linquist

Hitesh Kalra

Justin J Ploegert

Michael James Risbeck

Chenlu Zhang

Santle Camilus Kulandai Samy

Yohai Falik

Deepak Chembakassery Rajendran

Prantik Sen

Shalin Harshadkumar Patel

Michael J Wenzel

Mohammad N ElBsat

Jonathan D Douglas

Sastry K M Malladi

Rajkiran Kumar Gottumukkal

Krishnamurthy Selvaraj

Dominick James O′Dierno

The present disclosure relates to configuring and operating Internet of things (IoT) elements connected by a network. A computing device receives an interface component corresponding to an IoT element. The computing device retrieves a description of the interface component at least describing a set of restrictions of an operation of the IoT element. The computing device deploys the interface component in the computing device to at least translate events and commands specific to the IoT element to common events and commands for processing in the computing device. The computing device sends at least a subset of the description of the interface component to a user device to cause the user device to generate a user interface for configuring the operation of the IoT element.

Unified description scheme for controlling and operating network connected devices

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 building system of a building operates to store a digital twin of the building in the one or more storage devices, wherein the digital twin further includes an artificial intelligence configured to generate a plurality of inference values of a data point for a plurality of future times, the data point related to the building by the interrelationship of the digital twin. The building system operates to generate a recommendation based on the plurality of inference values for the data point for the plurality of future times, the recommendation recommending making one or more updates to the building, and cause a graphic representation of the building to display an indication of the recommendation.

Building data platform with digital twin based predictive recommendation visualization

A building system including one or more storage devices storing instructions thereon that, when executed cause the one or more processors to store a data structure of a digital twin of an entity of a building, execute a learning algorithm to construct a trigger rule and an action rule as an input to the learning algorithm, store the trigger rule and the action rule in the digital twin, determine that the trigger rule of the digital twin is triggered by comparing at least some of the information of the data structure against one or more conditions of the trigger rule by querying the data structure for data values and comparing the data values against the one or more conditions of the trigger rule, determine an action of the action rule of the digital twin responsive to determining that the trigger rule is triggered by executing the action rule, and cause one or more devices to operate based on the action determined by the action rule.

Building data platform with digital twin triggers and actions

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 receiving, by one or more processing circuits, building data, generating, by the one or more processing circuits, a first digital twin based on the building data, wherein a first system stores the first digital twin and a second system stores a second digital twin generated based on the building data, where the first digital twin includes a relationship that forms a connection between the first digital twin and the second digital twin by linking a first entity of the first entities of the first digital twin and a second entity of the second entities of the second digital twin, and performing, by the one or more processing circuits, one or more operations based on at least one of the first digital twin, the second digital twin, or the relationship that forms the connection between the first digital twin and the second digital twin.

Building data platform with a distributed digital twin

A building system operates to store a digital twin comprising a building graph, the building graph comprising a plurality of nodes representing a plurality of entities of a building and a plurality of edges between the plurality of nodes representing relationships between the plurality of entities. The instructions cause the one or more processors to determine a value for a functionality indicator for a piece of building equipment based on data received from the piece of building equipment, identify a first node of the plurality of nodes representing the functionality indicator by identifying an edge of the plurality of edges relating a second node of the plurality of nodes representing the piece of building equipment to the first node, and cause the first node to store the value for the functionality indicator, or a link to the value for the functionality indicator.

Building data platform with digital twin functionality indicators

A method includes providing a net consumption trajectory comprising net consumption targets for one or more subperiods of a time period. Each net consumption target indicates a target difference from a beginning of a time period to an end of the subperiod between total consumption and total production or offset. The method also includes generating, for a subperiod of the plurality of subperiods, a set of curtailment actions predicted to achieve the net consumption target for the subperiod and implementing the set of curtailment actions.

Generating set of curtailment actions predicted to achieve net consumption target for a subperiod

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 building system including one or more memory devices having instructions stored thereon, that, when executed by one or more processors, cause the one or more processors to receive an event from an event source, the event comprising data and a timestamp. The building system operates to identify first contextual data describing the event in a digital twin, the digital twin comprising a virtual representation of a building, enrich the event with the first contextual data, and provide the enriched event to a consuming system, the consuming system generating an output event based on the enriched event. The building system operates to identify second contextual data describing the output event in the digital twin, enrich the output event with the second contextual data, and provide the enriched output event to the consuming system or another consuming system.

Building data platform with an enrichment loop

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 are disclosed relating to building management systems with language model-based data structure generation. For example, a method can include receiving a query to select, from a plurality of data sources of a building management system, a selected one or more data sources according to a characteristic indicated by the query in at least one of a natural language representation or a semantic representation. The method can further include applying the query as input to a machine learning model to cause the machine learning model to generate an output indicating the selected one or more data sources, the machine learning model configured using training data comprising sample data and metadata from the plurality of data sources. The method can further include presenting, using at least one of a display device or an audio output device, the output.

Building management system with natural language model-based data structure generation

Disclosed herein is an object detection system, including apparatuses and methods for object detection. An implementation may include receiving a first image frame from an ROI detection model that generated a first ROI boundary around a first object detected in the first image frame and subsequently receiving a second image frame. The implementation further includes predicting, using an ROI tracking model, that the first ROI boundary will be present in the second image frame and then detecting whether the first ROI boundary is in fact present in the second image frame. The implementation includes determining that the second image frame should be added to a training dataset for the ROI detection model when detecting that the ROI detection model did not generate the first ROI boundary in the second image frame as predicted and re-training the ROI detection model using the training dataset.

Object detection systems and methods including an object detection model using a tailored training dataset

A building system including one or more storage devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to receive an indication to execute an artificial intelligence (AI) agent of a digital twin, the digital twin including the AI agent and a graph data structure, the graph data structure including nodes representing entities of a building and edges between the nodes representing relationships between the entities of the building. The instructions cause the one or more processors to execute the AI agent based on data of the building to generate an inference that is a prediction of a future data value of a data point of the building for a future time and store at least one of the inference, or a link to the inference, in the graph data structure.

Building data platform with digital twin inferences

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San Jose, CA, United States of America

Rajiv Ramanasankaran