Abstract: Systems and methods for training and executing machine-learning models for building data conversion are disclosed. A system can identify a plurality of unstructured object names each associated with a respective object tag. The plurality of unstructured object names can correspond to a plurality of building devices of a building. The system can determine a plurality of embeddings based on the plurality of unstructured object names. The plurality of embeddings can include a position embedding. The system can train a machine-learning model based on (i) the plurality of embeddings including the position embedding, and (ii) the respective object tag of the plurality of unstructured object names. The machine-learning model can be trained to generate corrected object tags for the plurality of building devices.

Abstract: A computer system configured to identify errors in a session launch initiated by a client application is provided. The computer system includes a memory and at least one processor coupled to the memory. The at least one processor is configured to receive one or more events from one or more applications or devices involved in the session launch, wherein an event of the one or more events comprises information from an application or device call (e.g., an application programming interface (API) call) communicated during the session launch, the information comprising destination information; build a primary Directed Acyclic Graph (DAG) based on the information from the API call; determine an error identifier based on the primary DAG; retrieve a troubleshooting recommendation from a library based on the error identifier; and send the troubleshooting recommendation to the client application.

Abstract: A computer system configured to identify errors in a session launch initiated by a client application is provided. The computer system includes a memory and at least one processor coupled to the memory. The at least one processor is configured to receive one or more events from one or more applications or devices involved in the session launch, wherein an event of the one or more events comprises information from an application or device call (e.g., an application programming interface (API) call) communicated during the session launch, the information comprising destination information; build a primary Directed Acyclic Graph (DAG) based on the information from the API call; determine an error identifier based on the primary DAG; retrieve a troubleshooting recommendation from a library based on the error identifier; and send the troubleshooting recommendation to the client application.