Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing text similarity determination. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform text similarity determination by using at least one of Word Mover's Similarity measures, Relaxed Word Mover's Similarity measures, and Related Relaxed Word Mover's Similarity measures.

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: An example method includes obtaining, by a computing system, first audio data representing one or more initial utterances during an interactive voice session with an interactive voice system; generating, by the computing system, based on the first audio data, a prediction regarding whether a subsequent utterance of a user in the interactive voice session will contain sensitive information, wherein the subsequent utterance follows the one or more initial utterances in time; obtaining, by the computing system, second audio data representing the subsequent utterance; determining, by the computing system, based on the prediction and based on a risk profile of the interactive voice system, whether to transmit the second audio data to the interactive voice system; and based on the determination to transmit the second audio data to the interactive voice system, transmitting the second audio data to the interactive voice system.

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: A method comprises obtaining, by a computing system, first audio data representing one or more initial utterances during an interactive voice session with an interactive voice system; generating, by the computing system, based on the first audio data, a prediction regarding whether a subsequent utterance of a user during the interactive voice session will contain sensitive information, the subsequent utterance following the one or more initial utterances in time; obtaining, by the computing system, second audio data representing the subsequent utterance; determining, by the computing system, based on the prediction, whether to transmit the second audio data; and based on a determination not to transmit the second audio data: replacing, by the computing system, the second audio data with third audio data that is based on a voice of the user; and transmitting, by the computing system, the third audio data.

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: There is a need for more effective and efficient prediction data analysis. This need can be addressed by, for example, solutions for performing first-occurrence multi-disease prediction. In one example, a method includes determining a per-event-type loss value for each event type of a group of event types; determining a cross-event-type loss value based at least in part on each per-event-type loss value; training a multi-event-type prediction model based at least in part on the cross-event type loss value; generating a first-occurrence prediction based at least in part on the multi-event-type prediction model, wherein the first occurrence-prediction comprises a first-occurrence prediction item for each event type of the group of event types; and performing one or more prediction-based actions based at least in part on the first-occurrence prediction.

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: Solutions for more efficient and effective optical character recognition with respect to an input text segment are disclosed. In one example, a method includes processing an input text image using a deep character overlap detection machine learning model in order to generate a character map for the input text image, an overlap map for the input text image, and an affinity map for the input text image; generating an overlap-aware word boundary recognition output based at least in part on the character map, the overlap map, and the affinity map, wherein the overlap-aware word boundary recognition output describes one or more inferred word regions of the input text image; and performing one or more prediction-based actions based at least in part on the overlap-aware word boundary recognition output.

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.

Abstract: There is a need for more effective and efficient document summarization. This need can be addressed by, for example, techniques for contextual summarization using semantic intelligence. In one example, a method includes identifying a plurality of senses associated with a plurality of words in a document; for each word-sense pair, determining a word-sense probability score; determining, based at least in part on each word-sense probability score for a word-sense pair, one or more context buckets for the document and one or more sense buckets for the document; determining, based at least in part on the one or more context buckets for the document and the one or more sense buckets for the document, the contextual summarization of the document; and performing one or more document processing actions based at least in part on the contextual summarization.

Abstract: Systems and methods are configured to enable guided interaction with query assistant software using brainwave data. In various embodiments, a client device presents a query assistant user interface to a monitored end-user that describes a query associated with a plurality of response options and an intended physiological action for each response option. Accordingly, one or more user monitoring data objects associated with the monitored end-user are received that include user brainwave monitoring data objects. These user monitoring data objects are processed using one or more response designation machine learning models to generate response designators based on the user monitoring data objects that includes a physiological response designator describing a selected intended physiological action that is deemed to be related to the user monitoring data objects. Accordingly, a user response is then determined based on the response designators and the user interface may be updated based on the user response.

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.