Abstract: The present disclosure relates to a system, a method, and a product for using deep learning models to quantify and/or improve trust in conversations. The system includes a non-transitory memory storing instructions executable to construct a deep-learning network to quantify trust scores; and a processor in communication with the non-transitory memory. The processor executes the instructions to cause the system to: obtain a trust score for each voice sample in a plurality of audio samples, generate a predicated trust score by the deep-learning network based on each voice sample in the plurality of audio samples, wherein the deep-learning network comprises a plurality of branches and an aggregation network configured to aggregate results from the plurality of branches, and train the deep-learning network based on the predicated trust score and the trust score for each voice sample to obtain a training result.

Abstract: The present disclosure relates to a system, a method, and a product for using machine learning models to quantify and/or improve trust in conversations. The system includes a non-transitory memory; and a processor in communication with the non-transitory memory. The processor executes the instructions to cause the system to: obtain a set of vocal features and a set of text features for each sample in audio samples; obtain a trust score for each sample; perform a preprocess to obtain a set of input features for each sample; determine a type of machine-learning algorithm for the machine-learning network; tune a set of hyper parameters for the machine-learning network; generate a predicated trust score by the machine-learning network with the sets of input features for each sample; and train the machine-learning network based on the predicated trust score and the trust score for each sample to obtain the training result.

Abstract: The disclosed system and method focus on applying machine learning to monitor, analyze, and optimize operational procedures. A role-tailored user interaction with a dashboard that enables a user with multiplicity of views, including but not limited to operational data feeds, analytic and visualization feeds, supervisory, policy making, personnel management and other organizational capabilities is disclosed. The multiplicity of dashboard features relates to measurement and assessment of an organization's compliance with operational performance metrics, that are quantified based on real-time, near real-time data feeds, statistical and algorithmic models. The metrics on the dashboard may be presented in the role-tailored fashion with statistical view of the next best action and recommendations when analyzed metrics exceed safe limits. Alert and communication features may be implemented in the dashboard to promote timely response to suggested corrective actions across the organization.

Abstract: Systems and methods for content management are disclosed. A content management system may include a data sourcing and data streaming engine configured to aggregate content data from data sources, a trend detection and monitoring engine configured to match data sources with content management metadata and to provide relevance scoring of the content data, and a trend recommendation and visualization engine configured to present to a user (e.g., content reviewer or subject matter expert), through a graphical user interface, an output comprising a relevance score and relevant trend, topic, and/or data source information, and to receive from the user through the graphical user interface input and/or activity. The data sourcing and data streaming engine, the trend detection and monitoring engine, and/or the trend recommendation and visualization engine may be updated with the input and/or activity for processing subsequent content data.

Abstract: Systems and methods for content management are disclosed. A content management system may include a data sourcing and data streaming engine configured to aggregate content data from data sources, a trend detection and monitoring engine configured to match data sources with content management metadata and to provide relevance scoring of the content data, and a trend recommendation and visualization engine configured to present to a user (e.g., content reviewer or subject matter expert), through a graphical user interface, an output comprising a relevance score and relevant trend, topic, and/or data source information, and to receive from the user through the graphical user interface input and/or activity. The data sourcing and data streaming engine, the trend detection and monitoring engine, and/or the trend recommendation and visualization engine may be updated with the input and/or activity for processing subsequent content data.