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: 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: 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: According to one aspect of the present disclosure, a method of delivering a self-directed user experience includes encoding access to an intermediate menu in at least one matrix bar code, developing an intermediate menu that facilitates a self-directed user experience, and presenting the at least one matrix bar code to a user via placement on a product. Further in accordance with this aspect, the method may include directing the user to the intermediate menu via the at least one matrix bar code such that the self-directed user experience is responsive to user interaction with intermediate menu.

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: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a historically dynamic explanation data object for a dental image data object. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform generating a historically dynamic explanation data object for a dental image data object using an encoder-decoder architecture, where the encoder machine learning framework of the encoder-decoder architecture comprises a current diagnosis identification machine learning model, a historical diagnosis identification machine learning model, a convolutional embedding machine learning model, a new diagnosis code inference machine learning model, and a feature vector combination machine learning model.

Abstract: Various embodiments provide methods, apparatus, systems, computing entities, and/or the like, identifying object transformations in images and determining a sufficiency measure for an image set in capturing object transformations for one or more objects. In an embodiment, an example method comprises receiving a transformation record data entity and an image set comprising a plurality of images, the transformation record data entity comprising one or more object identifiers. The method further comprises identifying each object depicted by each image using an object identification machine learning and determining a sufficiency measure for the image set based at least in part on determining a transformation state for each object associated with one of the one or more object identifiers using one or more transformation classification machine learning models.

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: 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: According to one aspect of the present disclosure, a method of delivering a self-directed user experience includes encoding access to an intermediate menu in at least one matrix bar code, developing an intermediate menu that facilitates a self-directed user experience, and presenting the at least one matrix bar code to a user via placement on a product. Further in accordance with this aspect, the method may include directing the user to the intermediate menu via the at least one matrix bar code such that the self-directed user experience is responsive to user interaction with intermediate menu.