Abstract: In some embodiments, a method receives data for a user from an application that is used for an intervention. The data is analyzed to determine unstructured data and structured data. The method determines a context from the structured data to transform text of the unstructured data to transformed unstructured data. The transformed unstructured data is analyzed via a first model to generate a first prediction fit score for an intervention modality. The first model is configured to analyze text of the unstructured data to determine a fit with the intervention modality. The structured data is analyzed via a second model to generate a second prediction fit score for the intervention modality. The second model is configured to analyze numerical values for features of the structured data to determine the fit with the intervention modality. The method combines the first prediction fit score and the second prediction fit score.

Abstract: In some embodiments, a method receives a first transcript that includes a first speaker and a second speaker. A boundary of a primary turn between the first speaker and the second speaker is determined in the first transcript. The method compares a time in which the first speaker paused to a threshold. When the threshold is met, speech by the second speaker is determined that should be labeled with a first label as the primary turn. When the threshold is not met, speech by the second speaker is determined that should be labeled with a second label as a secondary turn. The method transforms the first transcript into a second transcript based on whether speech is labeled with the first label or the second label. The second transcript is analyzed to generate an analysis of primary turns between the first speaker and the second speaker.

Abstract: A multi-stage refinement process that utilizes a filtering engine, various subsystems, and artificial intelligence/machine learning (AI/ML) processes is deployed to identify a unique set of candidate-coaches for presentation to a user-member. A coach application may be instantiated on a user computing device that interoperates with a remote intelligent determination system. The determination system receives user data from queries presented to the user and/or remote third-party systems during a retrieval process. Such received data is encoded and ultimately utilized to generate a vector index that is then fed into a filtering engine with a number of subsystems. The subsystems include any one or more of user-defined policies and criteria, NLP (natural language processing), and ML/AI subsystems, among other subsystems, during a candidate generation process. A ranking and ordering process is then utilized to further refine the results before presentation to the user.

Abstract: Technology is provided for causing a computing system to extract conversation features from a multiparty conversation (e.g., between a coach and mentee), apply the conversation features to a machine learning system to generate conversation analysis indicators, and apply a mapping of conversation analysis indicators to actions and inferences to determine actions to take or inferences to make for the multiparty conversation. In various implementations, the actions and inferences can include determining scores for the multiparty conversation such as a score for progress toward a coaching goal, instant scores for various points throughout the conversation, conversation impact score, ownership scores, etc. These scores can be, e.g., surfaced in various user interfaces along with context and benchmark indicators, used to select resources for the coach or mentee, used to update coach/mentee matchings, used to provide real-time alerts to signify how the conversation is going, etc.

Abstract: Technology is provided for identifying synthesized conversation features from recorded conversations. The technology can identify, for each of one or more utterances, data for multiple modalities, such as acoustic data, video data, and text data. The technology can extract features, for each particular utterance of the one or more utterances, from each of the data for the multiple modalities associated with that particular utterance. The technology can also apply a machine learning model that receives the extracted features and/or previously synthesized conversation features and produces one or more additional synthesized conversation features.

Abstract: Technology is provided for generating conversation features for recorded conversations. The technology includes, receiving videos depicting a multiple-user interaction, segmenting the videos into multiple utterances based on identifying utterances from individual users, receiving label data for the utterance segments specifying conversation features, and storing the label data in association with the utterance segments.

Abstract: Technology is provided for conversation analysis. The technology includes, receiving multiple utterance representations, where each utterance representation represents a portion of a conversation performed by at least two users, and each utterance representation is associated with video data, acoustic data, and text data. The technology further includes generating a first utterance output by applying video data, acoustic data, and text data of the first utterance representation to a respective video processing part of the machine learning system to generate video, text, and acoustic-based outputs. A second utterance output is further generated for a second user. Conversation analysis indicators are generated by applying, to a sequential machine learning system the combined speaker features and a previous state of the sequential machine learning system.

Abstract: Technology is provided for conversation analysis. The technology includes, receiving multiple utterance representations, where each utterance representation represents a portion of a conversation performed by at least two users, and each utterance representation is associated with video data, acoustic data, and text data. The technology further includes generating a first utterance output by applying video data, acoustic data, and text data of the first utterance representation to a respective video processing part of the machine learning system to generate video, text, and acoustic-based outputs. A second utterance output is further generated for a second user. Conversation analysis indicators are generated by applying, to a sequential machine learning system the combined speaker features and a previous state of the sequential machine learning system.