Abstract: A process flow for model-based applications, including: receiving data from one or more data sources; applying at least one first transformation on at least a portion of the data to generate transformed input data encoded according to a predefined format; providing the transformed input data to an executed instance of a model facilitating a prediction associated with the data; and exposing access to application data based on an output associated with the model.

Abstract: A method includes obtaining battery characterization data based on sensor data from one or more sensors onboard a vehicle. The method includes providing the battery characterization data as input to a trained model to generate a model output. The methos further includes altering a vehicle operating parameter based on the model output.

Abstract: A method includes obtaining driver characterization data based on sensor data from one or more sensors onboard a vehicle. The sensor data is captured during a time period that includes multiple discharging operations and multiple recharging operations of a battery of the vehicle. The method also includes providing the driver characterization data as input to a trained model to generate a model output. The model output includes a classification that associates the driver characterization data with a driver type profile. The method also includes generating an alert responsive to a charge of the battery deviating from an expected charge of the battery. The expected charge is based on the classification.

Abstract: Standardized model packaging and deployment, including: generating a model package comprising: model definition data for a model; function code facilitating execution of the model; and at least one interface for at least one operating system.

Abstract: Mechanisms are provided for implementing a virtual corpus engine that receives an inquiry to be processed and analyzes the inquiry to extract one or more features of the inquiry. The virtual corpus engine selects a weight matrix associated with a virtual corpus based on the extracted one or more features of the inquiry. The virtual corpus comprises a plurality of actual corpora of information. The weight matrix comprises a separate weight value for each actual corpus in the plurality of actual corpora. The virtual corpus engine processes the inquiry using a set of selected actual corpora selected from the plurality of actual corpora based on the weight values in the weight matrix and receives results of the processing of the inquiry using the set of selected actual corpora. The virtual corpus engine outputs the results of the processing of the inquiry.

Abstract: A method includes receiving, at a mobile hub device, communications including location-specific risk data and a task assignment. The method also includes generating an output indicating dispatch coordinates. The dispatch coordinates identifying a dispatch location from which to dispatch, from the mobile hub device, one or more unmanned vehicles to perform a task of the task assignment.

Abstract: A method of controlling an electronically controllable valve of an engine includes receiving, from one or more operation sensors, operation data including sensor data corresponding to a condition of the engine, control inputs indicative of operation of equipment that includes the engine, or a combination thereof. The method includes determining, using a trained valve control model, an operating characteristic of the valve at least partially based on the operation data, and generating a control signal to effect operation of the valve in accordance with the operating characteristic.

Abstract: A method includes obtaining driver characterization data based on sensor data from one or more sensors onboard a vehicle. The sensor data is captured during a time period that includes multiple discharging operations and multiple recharging operations of a battery of the vehicle. The method also includes providing the driver characterization data as input to a trained model to generate a model output. The model output includes a classification that associates the driver characterization data with a driver type profile. The method also includes generating an alert responsive to a charge of the battery deviating from an expected charge of the battery. The expected charge is based on the classification.

Abstract: A method includes obtaining driver characterization data based on sensor data from one or more sensors onboard a vehicle. The sensor data is captured during a time period that includes multiple discharging operations and multiple recharging operations of a battery pack of the vehicle. The method also includes providing the driver characterization data as input to a trained model to generate a model output and estimating a battery failure timeline for the battery pack based on the model output.

Abstract: A method includes obtaining driver characterization data based on sensor data from one or more sensors onboard a vehicle. The sensor data is captured during a time period that includes multiple discharging operations and multiple recharging operations of a battery pack of the vehicle. The method also includes providing the driver characterization data as input to a trained model to generate a model output and estimating a battery failure timeline for the battery pack based on the model output.

Abstract: Calibration of offline combustion engines using simulations, including: simulating, based on one or more simulator models operating on simulator input, operation of an engine being simulated; training, based on simulating the operation of the engine being simulated, one or more trained models; and generating, for offline engine calibration and based at least on the one or more trained models, calibration data corresponding to one or more electronically controllable components of an engine being calibrated.

Abstract: Calibrating combustion engines using simulation learning, including: receiving simulator input for one or more simulator models corresponding to one or more aspects of an engine; simulating, based at least on one or more simulator models operating on the simulator input, operation of the engine; and generating, based at least on simulator output from simulating operation of the engine, calibration data corresponding to one or more electronically controllable components of the engine.

Abstract: Calibration of online combustion engines using simulations, including: simulating, on a processor coupled to an engine and based on operation data generated during operation of the engine, operation of the engine; training, based on simulating the operation of the engine, one or more trained models; and generating, based at least on the one or more trained models, calibration data corresponding to one or more electronically controllable components of the engine.

Abstract: A first seed concept term may be identified. The first seed concept term may be to train a cognitive computing system. The cognitive computing system may analyze the first seed concept term to generate a first set one or more concept terms that are candidates for being conceptually related to the first seed concept term. A first plurality of individual characters and the first seed concept term may be provided. A first user of a client computing device may be prompted to generate a second set of one or more concept terms that are conceptually related to the first seed concept term using one or more of the first plurality of individual characters.

Abstract: A method of controlling an electronically controllable valve of an engine includes receiving, from one or more operation sensors, operation data including sensor data corresponding to a condition of the engine, control inputs indicative of operation of equipment that includes the engine, or a combination thereof. The method includes determining, using a trained valve control model, an operating characteristic of the valve at least partially based on the operation data, and generating a control signal to effect operation of the valve in accordance with the operating characteristic.

Abstract: A method includes providing multiple datasets as input to a plurality of data reduction models to generate digest data and performing clustering to group the digest data into a plurality of clusters, where each cluster is associated with a subset of the digest data. The method also includes providing a subset of the digest data as input to event classifiers to generate event classification and evaluation data. The method also includes generating output based on the event classification data.

Abstract: Standardized model packaging and deployment, including: generating a model package comprising: model definition data for a model; function code facilitating execution of the model; and at least one interface for at least one operating system.

Abstract: Distributed model execution, including: identifying, for each model of a plurality of models, based on one or more execution constraints for the plurality of models, a corresponding node of a plurality of nodes, wherein the plurality of nodes each comprise one or more computing devices or one or more virtual machines; deploying each model of the plurality of models to the identified corresponding node of the plurality of nodes; and wherein the plurality of models are configured to generate, based on data input to at least one model of the plurality of models, a prediction associated with the data.

Abstract: A process flow for model-based applications, including: receiving data from one or more data sources; applying at least one first transformation on at least a portion of the data to generate transformed input data encoded according to a predefined format; providing the transformed input data to an executed instance of a model facilitating a prediction associated with the data; and exposing access to application data based on an output associated with the model.

Abstract: An engagement classifier for a group chatbot is trained by leveraging the implicit dataset generated by humans engaging in both direct messages as well as group conversations. Human-to-human direct messages are used as an approximate representation of the domain knowledge and expertise of each user. The decision to engage in a group conversation is assumed to be based on that domain knowledge. The knowledge representations and instances of engagements in group conversations yields an effective set of features and labels which can be used to model the engagement decision. The same transfer learning technique is used to generate a knowledge representation for the group chatbot. Given this representation of the domain knowledge of the chatbot, the classifier can predict whether it should engage in any particular group conversation.