Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support intelligent re-use of knowledge (e.g., across an organization) using a natural text-based querying framework. A knowledge representation of prior work performed for the organization may be generated based on organizational knowledge (e.g., historical work record data that identifies a plurality of work items across an organization). The knowledge representation may include individual work-record entities for each respective work item and individual knowledge graphs corresponding to the individual work-record entities. For each individual knowledge graph, operations may be performed to identity and store project name, subgraph, sentence embedding, and word embedding information.

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: Implementations are directed to receiving a set of tuples, each tuple including an entity and a product from a set of products, for each tuple: generating, by an embedding module, a total latent vector as input to a recommender network, the total latent vector generated based on a structural vector, a textual vector, and a categorical vector, each generated based on a product profile of a respective product and an entity profile of the entity, generating, by a context integration module, a latent context vector based on a context vector representative of a context of the entity, and inputting the total latent vector and the latent context vector to the recommender network, the recommender network being trained by few-shot learning using a multi-task loss function, and generating, by the recommender network, a prediction including a set of recommendations specific to the entity.

Abstract: A system and method for lead conversion using conversational virtual avatar is disclosed. System comprising processor causes Conversation Virtual Avatar Platform (CVAP) to receive, for first entity, from lead prioritization engine, leads applicable to first entity via lead repository based on scores associated with respective leads. Processor causes CVAP to receive, through conversation management engine (CME) configured in CVAP, from leads, responses to questions pertaining to product attributes and information pertaining to lead.

Abstract: An artificial intelligence (AI) technique to process and query data pertaining to an enterprise. A user raises a request which is processed to predict a knowledge context area based on a predetermined structure of the enterprise. The knowledge context area is predicted from multiple knowledge context areas, on the basis of the received user request and a conversation history of the user in past. Further, a knowledge database is selected from multiple knowledge databases based on the user request and the predicted knowledge context. The knowledge databases include preprocessed data from multiple data sources. The knowledge database is queried on the basis of the user request related to the knowledge context to obtain a result and the result is then displayed as an output.

Abstract: The present disclosure relates to systems, methods, and products for using machine-learning networks to generate trustworthy audio and face mesh. A system, serving as a digital avatar, generates a trust audio and trust face mesh corresponding to an input text. A method includes generating a set of trust embedding vectors based on a reference audio; generate a text embedding vector based on the input text; generate a conditioned vector based on the set of trust embedding vectors and the text embedding vector; synthesize an audio representation based on the conditioned vector; generate the trust audio based on the synthesized audio representation; obtain a speech feature representation based on the trust audio; obtain an abstract feature vector based on the speech feature representation; and generate positions of vertices based on the abstract feature vector, the positions of vertices being used for generating the trust face mesh.

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: Implementations of the present disclosure include receiving a goal, providing a problem-specific knowledge graph that is responsive to at least a portion of the goal, determining a set of events from the problem-specific knowledge graph, processing data representative of events in the set of events through a first machine learning (ML) model to provide a set of event scores, each event score in the set of event scores being associated with a respective event in the set of events, determining a sub-set of events based on the set of event scores, for each event in the sub-set of events, determining at least one action by processing a sequence of actions through a second ML model, and outputting the sub-set of events and a set of actions for execution of at least one action in the set of actions.

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: Implementations are directed to receiving a product profile comprising an image of a product and a text description of the product; encoding the image and the text description of the product to obtain an image vector and a textual vector in a latent space; wherein the encoding comprises encoding the image and the text description using one or more encoders, each encoder corresponding to a respective data type; concatenating the image vector and the textual vector to provide a total latent vector; processing the total latent vector through a neural recommendation model to generate a score for each feature included in a plurality of features, wherein the score for a feature indicates a likelihood of the feature being included as a feature of the product for product development; and generating a recommendation comprising a set of candidate features for the product based on the score of each feature.

Abstract: A system and method for lead conversion using conversational virtual avatar is disclosed. System comprising processor causes Conversation Virtual Avatar Platform (CVAP) to receive, for first entity, from lead prioritization engine, leads applicable to first entity via lead repository based on scores associated with respective leads. Processor causes CVAP to receive, through conversation management engine (CME) configured in CVAP, from leads, responses to questions pertaining to product attributes and information pertaining to lead.

Abstract: Implementations are directed to receiving a set of tuples, each tuple including an entity and a product from a set of products, for each tuple: generating, by an embedding module, a total latent vector as input to a recommender network, the total latent vector generated based on a structural vector, a textual vector, and a categorical vector, each generated based on a product profile of a respective product and an entity profile of the entity, generating, by a context integration module, a latent context vector based on a context vector representative of a context of the entity, and inputting the total latent vector and the latent context vector to the recommender network, the recommender network being trained by few-shot learning using a multi-task loss function, and generating, by the recommender network, a prediction including a set of recommendations specific to the entity.

Abstract: Implementations of the present disclosure include receiving a goal, providing a problem-specific knowledge graph that is responsive to at least a portion of the goal, determining a set of events from the problem-specific knowledge graph, processing data representative of events in the set of events through a first machine learning (ML) model to provide a set of event scores, each event score in the set of event scores being associated with a respective event in the set of events, determining a sub-set of events based on the set of event scores, for each event in the sub-set of events, determining at least one action by processing a sequence of actions through a second ML model, and outputting the sub-set of events and a set of actions for execution of at least one action in the set of actions.

Abstract: A digital content communication system for account management and predictive analytics may be provided. The system may include an analytics system that communicates with one or more servers and one or more data stores to provide digital content management in a network.

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: A digital content communication system for account management and predictive analytics may be provided. The system may include an analytics system that communicates with one or more servers and one or more data stores to provide digital content management in a network.

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: A system implements a method of migrating users to target services including segmenting the users of the target services into macrosegments and micro segments within each macro segment. For each micro segment, rankings of the target services for the users in the micro segment are determined. The rankings are based on the combinations of the target services owned by the users in the micro segment product association, a sequence for acquiring the target services owned by the users in the micro segment, propensities to acquire the target services, and eligibility of the target services for the users in the micro segment. The target services are selected for migration based on the rankings.

Abstract: A customized content selection and delivery system is operable to create a customized content channel package for a subscriber and customized content channel package to a customer premises over a content delivery network. A multi-level analysis based on measured content consumption metrics is performed to select content provider channels for the customized content channel package.