Abstract: Apparatuses, systems, and methods for more accurate remote monitoring of a user's body to stabilize the user during fall events and to thereby prevent the user from falling. In some embodiments, a wearable device comprising a power source, one or more sensors configured to monitor a user's COG (COG), at least one plurality of electrodes, a communications interface and a control device is provided. The wearable device is configured to apply electrical pulses according to defined electrical pulse stimulation protocols via the electrodes to target muscle groups of the user's body, causing those target muscle groups to contract and thereby stabilize the user's body during a fall event.

Abstract: Various embodiments of the present disclosure performing conversation sentiment monitoring for a conversation data object. In various embodiments, a text block that can be resized is identified within a conversation data object and successive regularized sentiment profile generation iterations are performed until a regularized sentiment score of the block exceeds a regularized sentiment score threshold. A current regularized sentiment profile generation iteration involves determining a regularized sentiment score for the block based on an initial sentiment score, a subjectivity probability value, and, optionally, a stage-wise penalty factor. A determination is then made as to whether the score exceeds the threshold. If so, then a regularized sentiment profile of the conversation data object is updated based on the regularized sentiment score. If not, then the text block is resized and a subsequent regularized sentiment profile generation iteration is performed based on the resized block.

Abstract: Apparatuses, systems, and methods for more accurate remote monitoring of a user's body to stabilize the user during fall events and to thereby prevent the user from falling. In some embodiments, a wearable device comprising a power source, one or more sensors configured to monitor a user's COG (COG), at least one plurality of electrodes, a communications interface and a control device is provided. The wearable device is configured to apply electrical pulses according to defined electrical pulse stimulation protocols via the electrodes to target muscle groups of the user's body, causing those target muscle groups to contract and thereby stabilize the user's body during a fall event.

Abstract: Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for performing AR processing using at least one of: (i) an exposure classification machine learning framework comprises a real-time embedding machine learning model, an exposure clustering machine learning model, and a cluster mapping machine learning model, (ii) supervision boundary reliability scores determined based at least in part on covered subsets for immersive AR supervision boundaries, (iii) supervision boundary reachability scores determined based at least in part on response node locations and least reachable locations of immersive AR supervision boundaries, or (iv) performing one or more AR interaction actions based at least in part on an updated immersive AR supervision boundary that is generated by reducing a supervision boundary area of the current immersive AR supervision boundary so that the supervision boundary reliability score satisfies the supervision

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 using a hierarchical intervention recommendation machine learning framework. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations using at least one of the techniques using real-time sensory timeseries data object, techniques using global baseline sensory feature data object, techniques using intermediate intervention operations, techniques using real-time risk scores, techniques using intermediate risk scores, and/or the like.

Abstract: An example method includes obtaining first audio data representing one or more initial utterances during an interactive voice session with an interactive voice system (IVS); generating, based on the first audio data, a prediction regarding whether a subsequent utterance of the user in the interactive voice session will contain sensitive information; obtaining second audio data representing the subsequent utterance; determining, based on the prediction, whether to transmit the second audio data to the IVS via a first communication channel; based on a determination not to transmit the second audio data to the IVS via the first communication channel: transmitting third audio data to the IVS via the first communication channel in place of the second audio data; and transmitting the second audio data to a server via a second communication channel that bypasses the IVS.

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 invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing health-related predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform health-related predictive data analysis by generating an optimal predictor set for a gene regulatory network using a quantum logic circuit that comprises one or more quantum logic subcircuits for each quantum processing unit that is associated with a quantum subcircuit and is configured to perform a conjunctive phase logic operation performed on each ancilla bit of a quantum subcircuit.

Abstract: An automated system and corresponding method is configured to predict a call duration of a customer service interaction between a caller and a customer-service agent of a call center, based at least in part on information provided orally by the caller to the automated system. The automated system transcribes the orally provided information, preprocesses the transcribed data, adds feature enrichment data to supplement the transcribed data, and executes a machine-learning model to predict the caller’s intent. If the predicted caller’s intent does not have an adequate confidence score associated therewith, the system requests additional data from the caller, and supplements the original data with newly provided data, and again determines a predicted call intent. This process may iterate until the confidence score satisfies applicable confidence criteria prior to utilizing two additional machine-learning models to predict a call duration of the interaction between the caller and a customer-service agent.

Abstract: To schedule a future interaction between a caller and a call-center, a management computing system associated with the call center is configured to determine an estimated call time for addressing the caller's issues, based at least in part on a predicted call intent as well as feature enrichment data received from one or more memory storage areas. Upon determining a predicted call duration for the call, the management computing entity accesses the caller's calendar, such as through an internet-based communication between the caller's user computing entity and the management computing entity, or via third-party access permissions provided by the caller. The management computing entity identifies one or more candidate timeslots based at least in part on the predicted call duration, and receives user input selecting a candidate timeslot for scheduling the callback.

Abstract: A method for managing sleep of a user comprises obtaining, by a computing system, sleep data and environmental data for the user; determining, by the computing system, a sleep state of the user based on the sleep data; determining, by the computing system, one or more awakening actions based on the sleep state of the user and the environmental data; and causing one or more devices in an environment of the user to perform the one or more awakening actions to awaken the user.

Abstract: There is a need to accurately and dynamically predicting a probability for an event and a likely cause for the event prior to the event occurring using collected data from disparate data sources. This need can be addressed, for example, by generating an event prediction data object by utilizing an event prediction machine learning model, wherein the event prediction data object describes an event likelihood prediction and in an instance where the event likelihood prediction is an affirmative likelihood prediction, one or more fall cause predictions; and performing one or more prediction-based actions based at least in part on the event likelihood prediction.

Abstract: There is a need to accurately and dynamically predicting a probability for an event and a likely cause for the event prior to the event occurring using collected data from disparate data sources. This need can be addressed, for example, by generating an event prediction data object by utilizing an event prediction machine learning model, wherein the event prediction data object describes an event likelihood prediction and in an instance where the event likelihood prediction is an affirmative likelihood prediction, one or more fall cause predictions; and performing one or more prediction-based actions based at least in part on the event likelihood prediction.

Abstract: Apparatuses, systems, and methods for more accurate remote monitoring of a user's body to stabilize the user during fall events and to thereby prevent the user from falling. In some embodiments, a wearable device comprising a power source, one or more sensors configured to monitor a user's COG (COG), at least one plurality of electrodes, a communications interface and a control device is provided. The wearable device is configured to apply electrical pulses according to defined electrical pulse stimulation protocols via the electrodes to target muscle groups of the user's body, causing those target muscle groups to contract and thereby stabilize the user's body during a fall event.

Abstract: Apparatuses, systems, and methods for more accurate remote monitoring of a user's body to stabilize the user during fall events and to thereby prevent the user from falling. In some embodiments, a wearable device comprising a power source, one or more sensors configured to monitor a user's COG (COG), at least one plurality of electrodes, a communications interface and a control device is provided. The wearable device is configured to apply electrical pulses according to defined electrical pulse stimulation protocols via the electrodes to target muscle groups of the user's body, causing those target muscle groups to contract and thereby stabilize the user's body during a fall event.

Abstract: Various embodiments of the present disclosure performing conversation sentiment monitoring for a conversation data object. In various embodiments, a text block that can be resized is identified within a conversation data object and successive regularized sentiment profile generation iterations are performed until a regularized sentiment score of the block exceeds a regularized sentiment score threshold. A current regularized sentiment profile generation iteration involves determining a regularized sentiment score for the block based on an initial sentiment score, a subjectivity probability value, and, optionally, a stage-wise penalty factor. A determination is then made as to whether the score exceeds the threshold. If so, then a regularized sentiment profile of the conversation data object is updated based on the regularized sentiment score. If not, then the text block is resized and a subsequent regularized sentiment profile generation iteration is performed based on the resized block.

Abstract: Various embodiments of the present disclosure performing conversation sentiment monitoring for a conversation data object. In various embodiments, a text block that can be resized is identified within a conversation data object and successive regularized sentiment profile generation iterations are performed until a regularized sentiment score of the block exceeds a regularized sentiment score threshold. A current regularized sentiment profile generation iteration involves determining a regularized sentiment score for the block based on an initial sentiment score, a subjectivity probability value, and, optionally, a stage-wise penalty factor. A determination is then made as to whether the score exceeds the threshold. If so, then a regularized sentiment profile of the conversation data object is updated based on the regularized sentiment score. If not, then the text block is resized and a subsequent regularized sentiment profile generation iteration is performed based on the resized block.

Abstract: Various embodiments of the present disclosure performing conversation sentiment monitoring for a conversation data object. In various embodiments, a text block that can be resized is identified within a conversation data object and successive regularized sentiment profile generation iterations are performed until a regularized sentiment score of the block exceeds a regularized sentiment score threshold. A current regularized sentiment profile generation iteration involves determining a regularized sentiment score for the block based on an initial sentiment score, a subjectivity probability value, and, optionally, a stage-wise penalty factor. A determination is then made as to whether the score exceeds the threshold. If so, then a regularized sentiment profile of the conversation data object is updated based on the regularized sentiment score. If not, then the text block is resized and a subsequent regularized sentiment profile generation iteration is performed based on the resized block.