Systems and methods thereof, of generating a unique identification code for an industrial commodity. The method includes receiving a user query indicative of at least one constructional and operational characteristic of the commodity, inspecting the user query to determine whether the user query is complete for identification of the commodity, updating the user query based on the inspection, identifying at least one attribute of the commodity from the updated user query, based on a list of predefined attributes of the commodity, mapping the at least one attribute to at least one of predefined attribute types, predefined regional standards, predefined commodity rules, and predefined commodity types, and generating the unique identification code for the commodity, based on the mapping. The predefined attribute types may be a predefined commodity group and a predefined commodity part.

Techniques are disclosed for generating a database schema using trained machine learning models that, in some embodiments, may include graph neural networks (GNN). A GNN may identify source to target database schema mappings using, among other features of the graph, context data associated with each node in a graph. Context data describes relationships between a particular node and some (or all) of the other nodes in the graph. The system may use this context data (and other graph data) in combination with a trained GNN model to identify a mapping between one or more source database entities to corresponding target database entities.

Mechanisms are provided for performing artificial intelligence-based video question answering. A video parser parses an input video data sequence to generate situation data structure(s), each situation data structure comprising data elements corresponding to entities, and first relationships between entities, identified by the video parser as present in images of the input video data sequence. First machine learning computer model(s) operate on the situation data structure(s) to predict second relationship(s) between the situation data structure(s). Second machine learning computer model(s) execute on a received input question to predict an executable program to execute to answer the received question. The program is executed on the situation data structure(s) and predicted second relationship(s). An answer to the question is output based on results of executing the program.

Example techniques for identification of diagnostic messages corresponding to exceptions are described. A determination model may determine whether a set of diagnostic messages generated based on analysis of a source code includes a diagnostic message that likely corresponds to an exception. The determination may be used to identify a set of diagnostic messages including the diagnostic message that likely corresponds to an exception.

A method for receiving a historical incident data set with the historical incident data set including a plurality of data records, for each given data record of the plurality of data records, applying a causal analysis algorithm to determine a set of causal factor(s) for the historical instance of an incident corresponding to the given data record to obtain a problems and solutions data set, and automatically, and by machine logic, generating a chatbot based, at least in part, on the problems and solutions data set.

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to accurately and concisely generate one or more action item logs of one or more document data objects. For example, certain embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to generate an action item log of a document data object comprising one or more semantically complete or incomplete units of text data, by generating content segmentation units, determining action item presence predictions, generating action item sets from each content segmentation unit within a candidate action item subset, aggregating the action item sets to create an action item log, and storing the action item log.

A system and method for providing real-time distributed micro-grid optimization using price signals to the electrical grid system by allowing bi-directional electricity usage from a distributed network of energy storage stations to form a large, distributed resource for the grid. A machine learning optimization module ingests various forms of data-from grid telemetry to traffic data to trip-to-trip data and more-in order to make informed spatiotemporal decisions about optimal pricing signals as well as strategically placing and balancing energy stores across various regions to support optimum energy usage, risk mitigation, grid fortification, and revenue generation. Energy stores are then sent updated price signals and updated parameters as to the amount of energy to hold or release.

Automated real-time characterization of resistance spot welds using ultrasound-based nondestructive evaluation requires a computational process and system to accurately and rapidly interpret the ultrasonic data in real time. Such a process can be automatically learned using artificial intelligence, from a dataset of exemplary ultrasonic data from nondestructive evaluation of resistance spot welds for which a corresponding ideal evaluation of each weld is provided. The process can then be implemented into a system to automatically interpret data from non-destructive evaluation in real-time. The ideal evaluation of each weld requires identification a large set of features that are observable in the ultrasonic signature and comprehensively characterize the corresponding weld process.

Automated real-time characterization of resistance spot welds using ultrasound-based nondestructive evaluation requires a computational process and system to accurately and rapidly interpret the ultrasonic data in real time. Such a process can be automatically learned using artificial intelligence, from a dataset of exemplary ultrasonic data from nondestructive evaluation of resistance spot welds for which a corresponding ideal evaluation of each weld is provided. The process can then be implemented into a system to automatically interpret data from non-destructive evaluation in real-time. The ideal evaluation of each weld requires identification a large set of features that are observable in the ultrasonic signature and comprehensively characterize the corresponding weld process.

Techniques for segmenting a percutaneous medical procedure based on one or more determinable phases. The techniques may include obtaining a first set of features over a first time period. The first set of features may be derived from instrument telemetry data corresponding to an endoluminal scope instrument. The technique may also include obtaining a second set of features over the first time period. The second set of features may be derived from instrument telemetry data corresponding to a percutaneous needle instrument. Based on the first set of features and the second set of features, the techniques may classify at least a portion of the first time period as a first phase of the percutaneous medical procedure.