Abstract: A computing system initializes a score for each chatbot profile of a plurality of chatbot profiles. The chatbot profiles correspond to different personas. For each chatbot profile, the computing system collects biometric response data for a user while the user has an interaction session with the chatbot profile. The computing system updates the score for the chatbot profile based on the biometric response data for the user collected while the user has the interaction session with the chatbot profile. The computing system ranks the chatbot profiles based on the scores and selects a chatbot profile from the plurality of chatbot profiles for a subsequent interaction session with the user based on the ranking of the chatbot profiles.

Abstract: There is a need for more effective and efficient anomalous text detection.

Abstract: There is a need for more effective and efficient detection of cross-data-column relationships. This need can be addressed by, for example, techniques for detecting cross-data-column data relationships that utilize at least one of feature-based similarity models and deep-learning-based similarity models. The cross-data-column data relationships may be displayed to an end-user using a cross-column relationship detection user interface.

Abstract: There is a need for more accurate and more efficient 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 method includes mapping a primary event having a primary event code to a related subset of a plurality of candidate secondary events by at least processing one or more lifecycle-related attributes for the primary event code using a lifecycle inference machine learning model to detect an inferred lifecycle for the primary event.

Abstract: There is a need for more effective and efficient text categorization. This need can be addressed by, for example, techniques for semantic text categorization. In one example, a method includes determining an input vector-based representation of an input document; processing the input vector-based representation using a trained supervised machine learning model to generate the categorization based at least in part on the input vector-based representation, wherein: (i) the trained supervised machine learning model has been trained using automatically-generated training data, and (ii) the automatically generated training data is generated by determining an inferred semantic label for each unlabeled training document of one or more unlabeled training documents; and performing one or more categorization-based actions based at least in part on the categorization, and (iii) the labels are described by one or more short documents/short texts.

Abstract: Systems and methods are configured to generate a set of related document objects for a predictive entity and/or to generate an optimal document sequence for a set of related document objects. In one embodiment, a set of related document objects for a predictive entity is generated by processing entity metadata features associated with the predictive entity using an entity-document correlation machine learning model, and an optimal document sequence is generated for the set of related document objects by processing the set of related document objects using a document sequence optimization machine learning model.

Abstract: There is a need for more effective and efficient predictive natural language topic detection. This need can be addressed by, for example, solutions for performing sequential topic detection. In one example, a method includes determining a sequential topic distribution data object for the current document sequence, determining a current term-context correlation data object for the current document sequence, determining a current context-topic correlation data object for the current document sequence, determining an updated term-topic correlation data object based at least in part on the current context-topic correlation data object, determining topic modeling predictions based at least in part on the sequential topic distribution data object and the updated term-topic correlation data object, and performing prediction-based actions based at least in part on the topic modeling predictions.

Abstract: Various embodiments provide for decentralized crowd sourced generation of recommendation data objects. An example apparatus receives, originating from an external computing device, a recommendation data object request, the recommendation data object request comprising a user identifier and one or more user attributes. The example apparatus may further retrieve, based on a predictive recommendation model, one or more therapy identifiers associated with a therapy efficacy score exceeding a therapy efficacy score threshold for attributes of a first attributes set associated with a first cluster identifier, the first attributes set comprising one or more of the one or more user attributes.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive structural analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive structural analysis using at least one of table column classification machine learning models, table column clustering machine learning models, structural variance generation machine learning models, and emergence report generation machine learning models.

Abstract: Systems, methods, and computer program products may be configured to generate a set of related document objects for a predictive entity and/or to generate an optimal document sequence for a set of related document objects. In one embodiment, for example, a set of related document objects for a predictive entity is generated by processing entity metadata features associated with the predictive entity using an entity-document correlation machine learning model, and an optimal document sequence is generated for the set of related document objects by processing the set of related document objects using a document sequence optimization machine learning model.

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: There is a need for more effective and efficient natural language processing (NLP) solutions. This need can be addressed by, for example, solutions for performing NLP-based document prioritization by utilizing joint sentiment-topic (JST) modeling.

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: Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a protocol recommendation for an application data object. In accordance with one embodiment, a method is provided comprising: extracting application codes from the application data object representing application codes; generating a graph representation data object that includes code nodes representing the application codes, and edges connecting code nodes to indicate an association between the code nodes; identifying a replaceable code node; extracting substitute code nodes from the graph representation data object; identifying a replacement configuration of the graph representation data object with the replaceable code node with one of the substitute code nodes; and generating the protocol recommendation based at least in part on the replacement configuration.

Abstract: In general, embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for performing mixed reality processing using at least one of depth-based partitioning of a point cloud capture data object, object-based partitioning of a point cloud capture data object, mapping a partitioned point cloud capture data object to detected objects of a three-dimensional scan data object, performing noise filtering on point cloud capture data objects based at least in part on geometric inferences from three-dimensional scan data objects, and performing geometrically-aware object detection using point cloud capture data objects based at least in part on geometric inferences from three-dimensional scan data objects.

Abstract: There is a need for more effective and efficient predictive natural language summarization. This need can be addressed by, for example, solutions for performing predictive natural language summarization using a constrained optimization model. In one example, a method includes identifying one or more per-party utterance subsets in a multi-party call transcript; generating a plurality of eligible extractive summaries that comply with one or more optimization constraints; for each eligible extractive summary of the plurality of eligible extractive summaries, determining an overall summary utility measure; generating the optimal extractive summary based at least in part on each overall summary utility measure for an eligible extractive summary of the plurality of eligible extractive summaries; and performing one or more summary-based actions based at least in part on the optimal extractive summary.

Abstract: There is a need for more effective and efficient cross-dataset field integration.

Abstract: There is a need for more effective and efficient predictive data analysis solutions. This need can be addressed by, for example, obtaining prediction input objects each associated with a predictive entity; performing iterations of an iterative cohort generation routine until a qualified predictive model is identified, wherein each iteration comprises determining one or more predictive cohorts for predictive entities based on the prediction input objects, generating a predictive model based on the predictive cohorts, performing a predictive inference based on the predictive model to generate a current iteration prediction, generating a predictive score based on the current iteration prediction, and determining whether the predictive model is the qualified predictive model based on whether the predictive score exceeds a predictive score threshold; and performing cohort-based predictive data analysis based on the qualified predictive model to generate a respective final prediction for each predictive entity.

Abstract: There is a need for more effective and efficient detection of cross-data-column relationships. This need can be addressed by, for example, techniques for detecting cross-data-column data relationships that utilize at least one of feature-based similarity models and deep-learning-based similarity models. The cross-data-column data relationships may be displayed to an end-user using a cross-column relationship detection user interface.