Abstract: A document processing system processes documents including typewritten and/or handwritten data by converting them to document images for entity extraction. A received document is initially processed to generate a deep document data structured and for classification as one of a structured or an unstructured document. If the document is classified as a structured document, it is processed for entity extraction based on a matching template and image alignment of the document image with the matching template. If the document is classified as an unstructured document, entities are extracted by obtaining nodes and providing the nodes to a self-supervised masked visual language model.

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: 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: The proposed systems and methods describe an autonomous asset detection system that leverages artificial intelligence (AI) models for three-dimensional asset identification and damage detection, asset damage classification, automatic in-field asset tag readings, and real-time asset management. In some embodiments, a deep learning-based system receives a set of aerial images of one or more assets and automatically identifies each asset in the image(s) using rotational coordinates. In some embodiments, an image annotation tool labels the images either manually or automatically. The system then detects whether the asset is damaged and, if so, determine the type of damage, and further captures and stores asset tag information for the target asset. The collected and processed data is then provided to end-users via a comprehensive user interface platform for managing the assets in real-time.

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: A attribute-based data matching system determines top matches for a first element from a plurality of second elements. The data matching system extracts attributes for the first element from first dataset and attributes for the plurality of second elements from a plurality of second datasets. Pairs of attributes are generated wherein each attribute pair includes an attribute of the first element and an attribute of one of the plurality of second elements. Pairwise rankings of the plurality of second elements corresponding to the attributes of the first element are generated based on weights of the attribute pairs. The pairwise rankings of the attribute pairs are aggregated to determine a ranked list that orders the plurality of second elements based on the extent of their match with the first element. User feedback to the ranked list can be collected and used to adjust the data matching system.

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: 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 document processing system processes documents including typewritten and/or handwritten data by converting them to document images for entity extraction. A received document is initially processed to generate a deep document data structured and for classification as one of a structured or an unstructured document. If the document is classified as a structured document, it is processed for entity extraction based on a matching template and image alignment of the document image with the matching template. If the document is classified as an unstructured document, entities are extracted by obtaining nodes and providing the nodes to a self-supervised masked visual language model.

Abstract: A model optimization system monitors a model deployed to an external system to determine the performance of the model and to replace the model with one of a plurality of models stored to a model repository if degradation of model performance is detected or if one of the models in the plurality of models is evaluated as having better performance than the model deploy the external system. A model evaluation trigger can be generated based on dates or data criteria. Various metrics are used in the model evaluation to calculate values of a model optimization function for each of the plurality of models. If a model that is better optimized than the deployed model is identified from the model evaluation, then the deployed model is replaced with the identified model and the external system continues to use the deployed model.

Abstract: The proposed systems and methods describe an autonomous asset detection system that leverages artificial intelligence (AI) models for three-dimensional asset identification and damage detection, asset damage classification, automatic in-field asset tag readings, and real-time asset management. In some embodiments, a deep learning-based system receives a set of aerial images of one or more assets and automatically identifies each asset in the image(s) using rotational coordinates. In some embodiments, an image annotation tool labels the images either manually or automatically. The system then detects whether the asset is damaged and, if so, determine the type of damage, and further captures and stores asset tag information for the target asset. The collected and processed data is then provided to end-users via a comprehensive user interface platform for managing the assets in real-time.

Abstract: A attribute-based data matching system determines top matches for a first element from a plurality of second elements. The data matching system extracts attributes for the first element from first dataset and attributes for the plurality of second elements from a plurality of second datasets. Pairs of attributes are generated wherein each attribute pair includes an attribute of the first element and an attribute of one of the plurality of second elements. Pairwise rankings of the plurality of second elements corresponding to the attributes of the first element are generated based on weights of the attribute pairs. The pairwise rankings of the attribute pairs are aggregated to determine a ranked list that orders the plurality of second elements based on the extent of their match with the first element. User feedback to the ranked list can be collected and used to adjust the data matching system.

Abstract: A data extraction and expansion system receives documents with data to be processed, extracts a set of a specific type of entities from the received documents, expands the set of entities by retrieving additional entities of the specific type from an ontology and other external data sources to improve the match between the received documents. The ontology includes data regarding entities and relationships between entities. The ontology is built by extracting the entity and relationship information from external data sources and can be constantly updated. If the additional entities to expand the set of entities cannot be retrieved from the ontology then a real-time search of the external data sources is executed to retrieve the additional entities from the external data sources.