Abstract: An integrated platform system that employ a series of machine learning techniques and prediction and detection units that can process input data and extract and generate meaningful insights and predictions therefrom. The system integrates together multiple different data storage types and applications that generates data of different types, and an associated processing system for processing the different data types, store the data in a common data model to normalize the data, determine the data lineage of the data, and then process the data using different types of techniques. The data can also be processed by a prediction unit for generating meaningful insights and predictions or by an anomaly detection unit for detecting one or more anomalies in the data.

Abstract: In one aspect, the present disclosure relates to a method which, in one example embodiment, can include receiving text data that includes at least unstructured data and wherein the text data is associated with a plurality of messages communicated between a plurality of entities. The method can also include determining relationships between the entities, based on the text data associated with the plurality of messages, and generating, from a knowledge base assembled from at least the text data, a response to a user interaction representing a query for information that corresponds to at least one of the entities and indicates information on one or more of the determined relationships between the entities. The method can also include detecting a deviation in communication between the entities that indicates unauthorized disclosure of information between a first entity and a second entity.

Abstract: An integrated platform system that employ a series of machine learning techniques and prediction and detection units that can process input data and extract and generate meaningful insights and predictions therefrom. The system integrates together multiple different data storage types and applications that generates data of different types, and an associated processing system for processing the different data types, store the data in a common data model to normalize the data, determine the data lineage of the data, and then process the data using different types of techniques. The data can also be processed by a prediction unit for generating meaningful insights and predictions or by an anomaly detection unit for detecting one or more anomalies in the data.

Abstract: The invention relates to computer-implemented systems and methods for analyzing and standardizing various types of input data such as structured data, semi-structured data, unstructured data, and images and voice. Embodiments of the systems and the methods further provide for generating responses to specific questions based on the standardized input data.

Abstract: The invention relates to computer-implemented systems and methods for analyzing data from a variety of data sources. Embodiments of the systems and the methods further provide for generating responses to specific questions based on the analyzed data, wherein the generating includes: retrieving related documents associated with the analyzed data; determining which information should be reported from which of the retrieved related documents; and providing a response based on the determination and a graph schema associated with the related documents.

Abstract: The invention relates to computer-implemented systems and methods for analyzing and standardizing various types of input data such as structured data, semi-structured data, unstructured data, and images and voice. Embodiments of the systems and the methods further provide for generating responses to specific questions based on the standardized input data.

Abstract: In one aspect, the present disclosure relates to a method which, in one example embodiment, can include reading text data corresponding to messages and creating semantic annotations to the text data to generate annotated messages. Creating the semantic annotations can include generating, at least in part by at least one trained statistical language model, predictive labels as annotations corresponding to language patterns associated with the text data. The method further includes aggregating the annotated messages and storing information associated with the aggregated annotated messages in a message store, and performing, based on information from the message store and associated with the messages, global analytics functions.

Abstract: The invention relates to computer-implemented systems and methods for analyzing and standardizing various types of input data such as structured data, semi-structured data, unstructured data, and images and voice. Embodiments of the systems and the methods further provide for generating responses to specific questions based on the standardized input data.

Abstract: The invention relates to computer-implemented systems and methods for analyzing data from a variety of data sources. Embodiments of the systems and the methods further provide for generating responses to specific questions based on the analyzed data, wherein the generating includes: retrieving related documents associated with the analyzed data; determining which information should be reported from which of the retrieved related documents; and providing a response based on the determination and a graph schema associated with the related documents.

Abstract: The invention relates to computer-implemented systems and methods for analyzing and standardizing various types of input data such as structured data, semi-structured data, unstructured data, and images and voice. Embodiments of the systems and the methods further provide for generating responses to specific questions based on the standardized input data.

Abstract: In one aspect, the present disclosure relates to a method which, in one example embodiment, can include reading text data corresponding to messages and creating semantic annotations to the text data to generate annotated messages. Creating the semantic annotations can include generating, at least in part by at least one trained statistical language model, predictive labels as annotations corresponding to language patterns associated with the text data. The method further includes aggregating the annotated messages and storing information associated with the aggregated annotated messages in a message store, and performing, based on information from the message store and associated with the messages, global analytics functions.

Abstract: In one aspect, the present disclosure relates to a method which, in one example embodiment, can include reading text data corresponding to messages, creating semantic annotations to the text data to generate one or more annotated messages, and aggregating the annotated messages and storing information associated with the aggregated annotated messages in a message store. The method can further include performing, based on information from the message store and associated with the one or more messages, one or more global analytics functions that include: identifying an annotation error in the semantic annotations created using the trained statistical language model, updating the respective semantic annotation to correct the annotation error, and back-propagating corrected data corresponding to the updated semantic annotation into training data for further language model training.

Abstract: According to one aspect, a method for machine learning management is provided. In one embodiment, the method includes receiving a first segment of text data, identifying data features corresponding to a sequence of characters in the first segment of text data, and generating predictive annotations to the sequence of characters based at least in part on the identified data features. The method can also include identifying inaccurate annotations generated according to the predictive annotations, correcting the identified inaccurate annotations, generating one or more sets of model training data incorporating the corrected annotations, and monitoring progress of annotations made to a second segment of text data associated with the first segment of text data by a plurality of collaborating users of a plurality of managed computers.

Abstract: According to one aspect, a computer-implemented method for entity resolution is disclosed. In one embodiment, the method includes generating a semantic hash for an entity having an assigned entity identifier (ID) and, upon the occurrence of an entity milestone, changing the entity ID. The method further includes generating a semantic hash for the entity having the changed entity ID, and maintaining history information associated with the entity and corresponding entity IDs and semantic hashes over a period of time that includes a plurality of entity milestones. The method also includes periodically removing at least one set of older entities and retaining entity IDs and semantic hashes associated with the removed entities.

Abstract: According to one aspect, a method for coreference resolution is provided. In one embodiment, the method includes receiving a segment of text that includes mentions corresponding to entities. A first feature vector is generated based on one or more features associated with a first mention, and a second feature vector is generated based on based on one or more features associated with a second mention. A measure of similarity between the first feature vector and second feature vector is computed and, based on the computed measure of similarity, it is determined if the first mention and the second mention both correspond to the same entity.