Abstract: There is a need for more effective and efficient predictive data analysis. This need can be addressed by, for example, solutions for performing/executing hybrid input predictive data analysis.

Abstract: Systems and methods are configured for predictive prompt generation for an interaction between a party and an automated prompt system. In various embodiments, metadata is received on the interaction and provided as input to a multi-label predictive model to generate interaction probabilities for a plurality of prompt information data objects. Each probability generated by the predictive model provides a likelihood a particular information data object needs to be provided to the first party during the interaction. Accordingly, one or more of the prompt information data objects are identified based on the probability of each piece found in the one or more prompt information data objects that satisfy a set threshold and one or more notifications are provided so that the automated prompt system provides at least one of the prompt information data objects to the first party during the interaction.

Abstract: An example device is configured to encode first sensed data using a first encoder and to predict a first behavior based on the encoded first sensed data to create a first prediction using a first prediction model. The example device is configured to store the encoded first sensed data in the one or more memory units. The example device is configured to control the communication unit to transmit the encoded first sensed data in a first batch to a computing system. The example device is configured to receive, from the computing system via the communication unit, a second encoder, the second encoder being based at least in part on the encoded first sensed data. The example device is also configured to receive, from the computing system via the communication unit, a second prediction model, the second prediction model being based at least in part on the encoded first sensed data.

Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for contextually optimizing routings for interactions. This may include receiving an interaction, wherein the interaction is selected from the group consisting of a voice-based interaction and a textual-based interaction; receiving an interaction problem statement for the interaction; generating, based at least in part on the interaction problem statement, an interaction problem statement summary, wherein the interaction problem statement comprises the context of the interaction; identifying one or more features for the interaction, wherein the features are input for one or more machine learning models; predicting an optimal route for the interaction, wherein the optimality of each route, hence, the optimal route is determined by the one or more machine learning models; and routing the interaction to the optimal route.

Abstract: There is a need for faster and more accurate predictive data analysis steps/operations. This need can be addressed by, for example, techniques for efficient predictive data analysis steps/operations. In one example, a computer-implemented method for generating a predictive output with respect to a primary audio data embedding data object associated with a primary audio data object, is provided. The method includes generating, using one or more computer processors, by utilizing a similarity determination machine learning model and based at least in part on the primary audio data embedding data object, the predictive output for the primary audio data embedding data object; generating, by the one or more computer processors, a forwarding recommendation prediction based at least in part on the predictive output; and performing, by the one or more computer processors, one or more prediction-based actions based at least in part on the forwarding recommendation prediction.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations for parasomnia episode management. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations for parasomnia episode management using at least one of pre-sleep parasomnia episode likelihood prediction machine learning models, in-sleep parasomnia episode likelihood prediction machine learning models, augmented parasomnia episode likelihood prediction machine learning models that are configured to generate conditional likelihood scores for candidate parasomnia reduction interventions, deep reinforcement learning machine learning models that are configured to generate recommended parasomnia reduction interventions, and dynamically-deployable parasomnia episode likelihood prediction machine learning models.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations for parasomnia episode management. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations for parasomnia episode management using at least one of pre-sleep parasomnia episode likelihood prediction machine learning models, in-sleep parasomnia episode likelihood prediction machine learning models, augmented parasomnia episode likelihood prediction machine learning models that are configured to generate conditional likelihood scores for candidate parasomnia reduction interventions, deep reinforcement learning machine learning models that are configured to generate recommended parasomnia reduction interventions, and dynamically-deployable parasomnia episode likelihood prediction machine learning models.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations for parasomnia episode management. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations for parasomnia episode management using at least one of pre-sleep parasomnia episode likelihood prediction machine learning models, in-sleep parasomnia episode likelihood prediction machine learning models, augmented parasomnia episode likelihood prediction machine learning models that are configured to generate conditional likelihood scores for candidate parasomnia reduction interventions, deep reinforcement learning machine learning models that are configured to generate recommended parasomnia reduction interventions, and dynamically-deployable parasomnia episode likelihood prediction machine learning models.

Abstract: Various embodiments of the present disclosure facilitate recommendation prediction using machine learning. In one example, an embodiment provides for generating embeddings data related to one or more provider entities, predicting a set of provider entities for a patient entity based on a provider machine learning model, ranking provider entities in the set of provider entities to generate a ranked set of provider entities, and performing one or more actions to provide a recommendation for the patient entity based on the ranked set of provider entities.

Abstract: A computing system may train an autoencoder to generate a first set of codes from a first set of thermal video images of activities of a user in an environment. The activities may represent routine behaviors of the user in the environment. The computing system may use an unsupervised machine-learning algorithm to categorize the first set of codes into a set of clusters. The computing system may use the autoencoder to determine a code representative of a second set of thermal video images of an activity in the environment. Based on the code not being associated with any cluster in the set of clusters, the computing system may determine that the code is an anomalous code. The computing system may perform an alert action based on the anomalous code.

Abstract: Disclosed in some examples are methods, systems, and machine readable mediums which automatically generate standardized interfaces to sensor data consumers, provide sensor data search functionality, automatically determine data quality, and cache previously used sensor data to minimize the burden on application developers and minimize API call costs.

Abstract: There is a need for more accurate and more efficient hybrid-input prediction steps/operations. This need can be addressed by, for example, techniques for efficient joint processing of data objects. In one example, a method includes: processing an audio data object using an audio processing machine learning model to generate an audio-based feature data object, processing an acceleration data object using an acceleration processing machine learning model to generate an acceleration-based feature data object, processing the audio-based feature data object and the acceleration-based feature data object using an feature synthesis machine learning model in order to generate a hybrid-input prediction data object; and performing one or more prediction-based actions based at least in part on the hybrid-input prediction data object.

Abstract: Systems and methods are configured for predictive prompt generation for an interaction between a party and an automated prompt system. In various embodiments, metadata is received on the interaction and provided as input to a multi-label predictive model to generate interaction probabilities for a plurality of prompt information data objects. Each probability generated by the predictive model provides a likelihood a particular information data object needs to be provided to the first party during the interaction. Accordingly, one or more of the prompt information data objects are identified based on the probability of each piece found in the one or more prompt information data objects that satisfy a set threshold and one or more notifications are provided so that the automated prompt system provides at least one of the prompt information data objects to the first party during the interaction.

Abstract: Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, and/or computing entities for processing a call drop likelihood prediction for an interactive call data object.

Abstract: Various embodiments provide methods, apparatus, systems, computing entities, and/or the like, for identifying deviated behavior of an individual indicated by outlying activity counts and outlying activity timings classified by a prediction interval profile generated from historical behavior data. In an embodiment, an example method comprises accessing sensor data describing behavioral activities of an individual during historical time periods and generating a prediction interval profile for the behavioral activities comprising a predicted count interval within a prediction time period for at least some behavioral activities. The method further includes receiving sensor data for the prediction time period and extracting an observed activity count and an observed activity timing for each behavioral activity.

Abstract: There is a need for faster and more accurate predictive data analysis steps/operations. This need can be addressed by, for example, techniques for efficient predictive data analysis steps/operations. In one example, a computer-implemented method for generating a predictive output with respect to a primary audio data embedding data object associated with a primary audio data object, is provided. The method includes generating, using one or more computer processors, by utilizing a similarity determination machine learning model and based at least in part on the primary audio data embedding data object, the predictive output for the primary audio data embedding data object; generating, by the one or more computer processors, a forwarding recommendation prediction based at least in part on the predictive output; and performing, by the one or more computer processors, one or more prediction-based actions based at least in part on the forwarding recommendation prediction.

Abstract: An enteral feeding apparatus comprises a pod having an expansile pouch which defines a reservoir for enteral fluid and a gas impermeable barrier surrounding the pouch. The pod has an inlet port for delivery of enteral fluid into the pouch and an outlet port having a seal which is pierceable to release enteral fluid from the pouch for delivery to a PEG via a feeding line. The expansile pouch provides the sole force by which enteral fluid is delivered from the pouch through a regulator. The system can accommodate a range of enteral fluids with a wide range of viscosities.

Abstract: There is a need for more accurate and more efficient hybrid-input prediction steps/operations. This need can be addressed by, for example, techniques for efficient joint processing of data objects. In one example, a method includes: processing an audio data object using an audio processing machine learning model to generate an audio-based feature data object, processing an acceleration data object using an acceleration processing machine learning model to generate an acceleration-based feature data object, processing the audio-based feature data object and the acceleration-based feature data object using an feature synthesis machine learning model in order to generate a hybrid-input prediction data object; and performing one or more prediction-based actions based at least in part on the hybrid-input prediction data object.

Abstract: An enteral feeding apparatus includes a pod having an expansile pouch which defines a reservoir for enteral fluid and a gas impermeable barrier surrounding the pouch. The pod has an inlet port for delivery of enteral fluid into the pouch and an outlet port having a seal which is pierceable to release enteral fluid from the pouch for delivery to a PEG via a feeding line. The expansile pouch provides the sole force by which enteral fluid is delivered from the pouch through a regulator. The system can accommodate a range of enteral fluids with a wide range of viscosities.

Abstract: There is a need for more accurate and more efficient hybrid-input prediction steps/operations. This need can be addressed by, for example, techniques for efficient joint processing of data objects. In one example, a method includes: processing an audio data object using an audio processing machine learning model to generate an audio-based feature data object, processing an acceleration data object using an acceleration processing machine learning model to generate an acceleration-based feature data object, processing the audio-based feature data object and the acceleration-based feature data object using an feature synthesis machine learning model in order to generate a hybrid-input prediction data object; and performing one or more prediction-based actions based at least in part on the hybrid-input prediction data object.