A method for providing automated predictive analysis for a case management system is disclosed and includes providing, by a server, a case management system comprising configuration information defining a plurality of case attributes and performance criteria comprising at least one performance criterion, and comprising case management data associated with the plurality of case attributes and a plurality of cases, and automatically determining a projected outcome associated with a performance criterion included in the configuration information. The method also includes identifying a correlation between a case attribute and the projected outcome based on case management data associated with the case attribute and/or case management data associated with the performance criterion, generating a prediction model based on the correlation and providing a prediction relating to the projected outcome based on the correlation.

A system, method, and computer program product are provided for parental controls and recommendations based on artificial intelligence. In use, using an artificial intelligence (AI) server, explicit input is received associated with a first presentation of media. Additionally, the AI server is trained based on the explicit input, and using the AI server, implicit filters are created based on the training. Further, using the AI server, the implicit filters are applied to a second presentation of the media.

The present disclosure generally relates to user interfaces for managing exposure notifications. In some embodiments, after enabling exposure notifications, an exposure notification is received; upon interacting with the notification: if an associated application is installed, a user interface of that application is displayed; if the associated application is not installed, a user interface object associated with a process to install the application is displayed. In some embodiments, an exposure notification management interface is displayed that includes a plurality of entities that have been authorized to provide notifications, along with a user interface object to disable transmission of data to those entities and to disable notifications from those entities.

A method for determining a set of output configuration values characterizing a specific configuration of a complex product, includes receiving a set of input configuration parameters, providing at least a part of the input configuration parameters as an input to a solver, using the solver to calculate at least one output value from the provided input configuration parameters, and determining the set of output configuration values from at least the output value calculated by the solver. The solver is configured for solving a first order logic function encoding an algorithm of a trained deep neural network or DNN, wherein the algorithm of the DNN has been trained for modeling a function of an external configuration tool or ECT that is required for determining the specific configuration of the complex product. A system for determining the set of output configuration values, and a training system, are also provided.

An industrial device supports device-level data modeling that pre-models data stored in the device with known relationships, correlations, key variable identifiers, and other such metadata to assist higher-level analytic systems to more quickly and accurately converge to actionable insights relative to a defined business or analytic objective. Data at the device level can be modeled according to modeling templates stored on the device that define relationships between items of device data for respective analytic goals (e.g., improvement of product quality, maximizing product throughput, optimizing energy consumption, etc.). This device-level modeling data can be provided to higher level systems together with their corresponding data tag values to high level analytic systems, which discovers insights into an industrial process or machine based on analysis of the data and its modeling data.

A method for configuring an asset monitoring system is provided. The method can include receiving a configuration including a configuration property of a measurement determined by an asset monitoring system configured to monitor an asset. The method can also include generating a graphical user interface including an identifier of the measurement and one or more configuration properties corresponding to the measurement. The method can also include validating the received configuration and receiving a selection of a measurement within the first window of the GUI. The method can further include updating the GUI to include a validation error corresponding to the selected measurement within a second window. The method can also include correcting the validation error and updating the GUI to include a third window listing at least one correction corresponding to the selected validation error. Related systems and non-transitory medium related to the method are also provided.

The present invention provides a glove/logging system capable of accurately determining periods for glove airtightness inspection and replacement and improving the safety of a work glove in use and the work efficiency by recording and storing the status of use such as the operator name, the frequency of use, the period of use, and the details of work for an individual pair of work gloves attached to an equipment such as an isolator, and retroactively checking improper work and equipment abnormalities by tracing glove operations upon occurrence of operational abnormalities.

The glove/logging system is configured to record and store information on the status of use for a work glove, and include an operator tag, a glove tag, a reader for reading information from the tags, and an information device for recording and storing information. The reader is configured to receive information from the operator tag that passes through the reception region or stops therein or the glove tag via the antenna to communicate the information with the information device.

A safety interlock recommendation system includes at least one process data source, an edge device, wherein the process data source is configured for providing IOS device stream data to the edge device; wherein the edge device comprises an operational technology edge application unit, OT edge application unit, and a stream analysis unit; wherein the OT edge application unit is configured for providing operation technology stream data, OT stream data; wherein the stream analysis unit comprises an online machine learning model, being configured for determining online analysis data using the provided process stream data and the provided OT stream data; wherein the OT edge application unit is configured for determining a short-term recommendation using the online analysis data.

Systems and methods for characterization of retrofit opportunities are described. Some embodiments are directed to methods for determining the air leakage rate of a building, and accordingly, for determining suitability of sealing of air leaks to improve the energy efficiency of a building. The methods may comprise computing, using at least one computing device disposed remote from a building and based at least in part on heating, ventilation and air conditioning (HVAC) runtime data associated with the building, one or more thermal characteristics of the building. The HVAC runtime data may be computed based on data received from a thermostat or a meter, such as an electric or a gas meter. To isolate the impact of air leakage, subsets of the HVAC runtime data at time intervals selected to have substantially the same conditions, but different wind speeds, may be computed.

Methods, systems, and non-transitory computer readable media for analyzing type curve regions in a subsurface volume of interest are disclosed. Exemplary implementations may include: obtaining initial clusters of type curve regions in the subsurface volume of interest; obtaining production values as a function of position; generating an autocorrelation correction factor; attributing the autocorrelation correction factor to the production values as a function of position; generating type curve mean values; generating range distribution values; generating a type curve cluster probability value for each of the type curve regions; generating a first representation of the type curve regions as a function of position; clustering the type curve regions in updated clusters; generating a second representation of the type curve regions as a function of position; and displaying one or more of the first representation and the second representation.