Abstract: Methods and apparatuses are described for automated predictive analysis of user interactions to determine a modification based upon competing classification models. A server computing device receives first encoded text for prior user interactions and trains a plurality of classification models using the first text. The server determines a prediction cost for each of the models based upon the training. The server receives second encoded text for a current user interaction and executes the trained models using the second text to generate a prediction vector for each model that maximizes user engagement. The server selects one of the models based upon the prediction vectors, identifies a communication feature of the model, generates a user interaction modification, and transmits the user interaction modification to a client computing device.

Abstract: Methods and apparatuses are described for generation of a data model for identifying and classifying training needs of individuals. A computer data store stores unstructured text. A server computing device generates a vector for search queries in the unstructured text, and generates a training course classification data model that comprises a multi-layered neural network. The server computing device executes the training course classification model using the vectors as input to generate a training course recommendation output vector. The server computing device updates the training course classification data model based upon a rating value for a training course.

Abstract: Methods and apparatuses are described for automated predictive analysis of user interactions to determine a modification based upon competing classification models. A server computing device receives first encoded text for prior user interactions and trains a plurality of classification models using the first text. The server determines a prediction cost for each of the models based upon the training. The server receives second encoded text for a current user interaction and executes the trained models using the second text to generate a prediction vector for each model that maximizes user engagement. The server selects one of the models based upon the prediction vectors, identifies a communication feature of the model, generates a user interaction modification, and transmits the user interaction modification to a client computing device.

Abstract: Described herein are methods and systems for secure communication of private audio data in a zero user interface computing environment. A server receives text generated from a first digital audio bitstream, the digital audio bitstream corresponding to speech captured by a zero user interface computing device from a user. The server analyzes the text to extract a set of keywords from the text. The server determines whether information responsive to the keywords comprises private data related to the user. If the information responsive to the set of keywords comprises private data: the server generates a text response to the set of keywords that includes the private data relating to the user, determines a personal audio playback device associated with the user, and transmits the generated text response to the personal audio playback device for playback as a second digital audio bitstream.

Abstract: Described herein are methods and systems for secure communication of private audio data in a zero user interface computing environment. A server receives text generated from a first digital audio bitstream, the digital audio bitstream corresponding to speech captured by a zero user interface computing device from a user. The server analyzes the text to extract a set of keywords from the text. The server determines whether information responsive to the keywords comprises private data related to the user. If the information responsive to the set of keywords comprises private data: the server generates a text response to the set of keywords that includes the private data relating to the user, determines a personal audio playback device associated with the user, and transmits the generated text response to the personal audio playback device for playback as a second digital audio bitstream.

Abstract: Methods and apparatuses are described for generation of a data model for identifying and classifying training needs of individuals. A computer data store stores unstructured text. A server computing device generates a vector for search queries in the unstructured text, and generates a training course classification data model that comprises a multi-layered neural network. The server computing device executes the training course classification model using the vectors as input to generate a training course recommendation output vector. The server computing device updates the training course classification data model based upon a rating value for a training course.