Abstract: 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.

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.

Abstract: Systems and methods are disclosed relating to building management systems with building equipment servicing. For example, a system can include at least one machine learning model configured using training data that includes at least one of unstructured data or structured data regarding items of equipment. The system can provide inputs, such as prompts, to the at least one machine learning model regarding an item of equipment, and generate, according to the inputs, responses regarding the item of equipment, such as responses for detecting a cause of an issue of the item of equipment, performing a service operation corresponding to the cause, or guiding a user through the service operation.

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.

Abstract: A building management system (BMS) of a building for controlling a healthcare facility. The BMS including one or more processing circuits comprising one or more memory devices configured to store instructions thereon that, when executed by one or more processors, cause the one or more processors to: receive inputs from building devices; determine a state-of-mind (SoM) score of a patient based on the inputs using a learning model; and control one or more building devices based on the SoM score while maintaining a temperature, a humidity, and a pressure within a compliance standard within the building.

Abstract: 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.

Abstract: A method includes training, by one or more processors, a generative AI model using training data including a plurality of first service requests indicating a plurality of first problems associated with building equipment and a plurality of first actions performed in response to the plurality of first service requests. The method may include receiving, by the one or more processors, a second service request indicating a second problem associated with building equipment. The method may include automatically determining, by the one or more processors using the generative AI model, one or more second actions to perform based on characteristics of the second service request. The method may include automatically initiating, by the one or more processors, the one or more second actions to address the second problem associated with the building equipment.

Abstract: A method includes training, by one or more processors, a generative AI model using a plurality of first service requests handled by technicians for servicing building equipment and outcome data indicating outcomes of the plurality of first service requests. The generative AI model may be trained to identify one or more patterns or trends between characteristics of the plurality of first service requests and the outcomes of the plurality of first service requests. The method may include receiving a second service request for servicing building equipment. The method may include automatically determining, using the generative AI model, one or more responses to the second service request based on characteristics of the second service request and the one or more patterns or trends between the characteristics of the plurality of first service requests and the outcomes of the plurality of first service requests identified using the generative AI model.

Abstract: 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 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. The structured service report may include additional content generated by the generative AI model and not provided within the unstructured service report.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A compliance system for a critical environment includes at least one sensor configured to output sensor measurements relating to at least one environmental condition of the critical environment, and a computing system. The computing system is programmed to record a warning when the sensor measurements are out of compliance relative to a compliance standard for longer than a warning delay time, generate an alert in response to a combination of recording the warning and a non-improving trend of the sensor measurements, and in response a combination of recording the warning and an improving trend of the sensor measurements, suppress the alert until the improving trend continues with the sensor measurements out of compliance for greater than an alert delay time.

Abstract: A critical environment monitoring system including a sensor array structured to provide sensor data indicative of temperature, pressure, and humidity in an interior space, and one or more processing circuits comprising one or more memory devices coupled to one or more processors that provides a graphical user interface displaying a compliance standard temperature received from a standard setting body and a temperature value, a compliance standard pressure received from the standard setting body and a pressure value, and a compliance standard humidity received from the standard setting body and a humidity value. The processing circuit also generates an alert in response to one of the temperature value being outside the compliance standard temperature, the pressure value being outside the compliance standard pressure, or the humidity value being outside the compliance standard humidity, records criticality information, and receives a log of actions taken.

Abstract: A building management system includes one or more processing circuits and one or more non-transitory memory devices configured to store instructions thereon that, when executed by one or more processors, cause the one or more processors to: initialize a remote digital twin; input a remote request into the remote digital twin; conduct a remote digital twin compliance check based on the remote request using the remote digital twin; and provide a remote digital twin result.

Abstract: A building management system for monitoring and controlling HVAC parameters in a building includes one or more processing circuits configured to initialize a predictive model for predicting temperature, pressure, and humidity within a target area and an adjacent area of the building, receive target area data from a target area sensor array configured to measure temperature, pressure, and humidity of the target area, receive adjacent area data from an adjacent area sensor array configured to measure temperature, pressure, and humidity of the adjacent area, execute the predictive model based on the target area data and the adjacent area data to generate a prediction of future temperature, pressure, and humidity within the target area, and control operation of HVAC equipment to maintain the temperature, pressure, and humidity of the target area within a compliance standard.

Abstract: A building management system for monitoring and controlling HVAC parameters in a building includes one or more processing circuits configured to initialize a predictive model for predicting temperature, pressure, and humidity within a target area and an adjacent area of the building, receive target area data from a target area sensor array configured to measure temperature, pressure, and humidity of the target area, receive adjacent area data from an adjacent area sensor array configured to measure temperature, pressure, and humidity of the adjacent area, execute the predictive model based on the target area data and the adjacent area data to generate a prediction of future temperature, pressure, and humidity within the target area, and control operation of HVAC equipment to maintain the temperature, pressure, and humidity of the target area within a compliance standard.

Abstract: A building management system (BMS) of a building for controlling a healthcare facility. The BMS including one or more processing circuits comprising one or more memory devices configured to store instructions thereon that, when executed by one or more processors, cause the one or more processors to: receive a signal indicating a code blue event in a room for a patient; and adjust at least one of a temperature, a pressure, a humidity level, a lighting system, or an air composition in the room automatically in response to the received signal.

Abstract: A critical environment monitoring system including a sensor array structured to provide sensor data indicative of temperature, pressure, and humidity in an interior space, and one or more processing circuits comprising one or more memory devices coupled to one or more processors that provides a graphical user interface displaying a compliance standard temperature received from a standard setting body and a temperature value, a compliance standard pressure received from the standard setting body and a pressure value, and a compliance standard humidity received from the standard setting body and a humidity value. The processing circuit also generates an alert in response to one of the temperature value being outside the compliance standard temperature, the pressure value being outside the compliance standard pressure, or the humidity value being outside the compliance standard humidity, records criticality information, and receives a log of actions taken.