Abstract: Systems and methods for summarizing a real-time event are disclosed herein. A system obtains a set of multimedia data feeds from image capturing devices, wherein the set of multimedia data feeds correspond to a real-time event. The system processes the obtained set of multimedia data feeds using model hyperparameters and sequences the processed set of multimedia data feeds in a predetermined order. The system also obtains one or more input prompts corresponding to the set of multimedia data feeds. The system generates an output representation of the real-time event by encoding the sequenced set of multimedia data feeds and the obtained one or more input prompts using a trained vision encoder model, wherein the generated output representation corresponds to a multi-resolution summary image of the real-time event at a time instance. The system also predicts one or more actions performed in the generated output representation using an action prediction model.

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: Method, application server, and non-transitory computer-readable medium for question-and-answer generation using a fortune analytics language model (FALM) are disclosed. In an aspect, a pre-trained large language model (LLM) is generated using information associated with a particular practice area. Further, fine tuning of the pre-trained LLM is performed for a plurality of different aspects to generate the FALM. A user query is then received from a client device. A plurality of new queries are then regenerated based upon the user query. Furthermore, the new queries are executed using the FALM to receive a plurality of answers, each answer of the plurality of answers corresponds with a new query of the new queries. Each answer is then ranked. Also, one or more answers are presented on a display of the client device, the one or more answers are displayed according to a predefined criterion and based upon the ranking.

Abstract: Implementations of the present disclosure provide receiving, by an intelligent router of the switchboard platform, a first request from an application, the first request including at least a portion of a prompt and a set of policy parameters, selecting, by the intelligent router, a foundation model of a sub-set of foundation models at least partially based on at least one policy parameter in the set of policy parameters, determining, from a model registry of the switchboard platform, connection data for the foundation model, transmitting, by the intelligent router and through a model connector of the switchboard platform, a second request for processing by the foundation model, the second request being transmitted using the connection data and including at least a portion of the prompt, receiving, by the intelligent router, a response from the foundation model, and transmitting the response to the application.

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 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: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support generative artificial intelligence (AI)-assisted analytics of structured data sets. For example, a system may receive a prompt that includes information associated with a structured data set which includes at least some numerical data. The system may provide the prompt as input to an agent orchestrator to select one or more generative AI agents to perform analytics tasks corresponding to the information. The agent orchestrator includes a trained AI classifier configured to select the one or more generative AI agents from a plurality of generative AI agents. The system may execute an ensemble model to generate a response to the prompt based on the structured data set. The ensemble model includes the one or more generative AI agents. The system may output a graphical user interface (GUI) that includes one or more elements based on the response.

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: 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: 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: 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 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.