Abstract: Systems and methods are provided for the classification of identified security vulnerabilities in software applications, and their triage based on automated decision-tree triage and/or machine learning. The disclosed system may generate a report listing detected potential vulnerability issues, and automatically determine whether the potential vulnerability issues are exploitable using automated triage policies containing decision trees or by extracting vulnerability features from the report and processing the extracted vulnerability features using machine learning models.

Abstract: Systems and methods are provided for the classification of identified security vulnerabilities in software applications, and their triage based on automated decision-tree triage and/or machine learning. The disclosed system may generate a report listing detected potential vulnerability issues, and automatically determine whether the potential vulnerability issues are exploitable using automated triage policies containing decision trees or by extracting vulnerability features from the report and processing the extracted vulnerability features using machine learning models.

Abstract: Systems and methods for predictively responding to real-time sensor data from an computer-augmented environment are provided. A system may receive sensor data from the computer-augmented environment. The system may obtain a feature model, a prediction model, and a filter criteria. The system may derive, based on the feature model and the sensor data, a first event. The system may determine the first event is associated with an authorized event identifier in the filter criteria. The system may forecast, based on the authorized event identifier and the prediction model that a second event occurs after the first event. The system may transmit an action message to the computer-augmented environment, the action message indicative of the second event. Updates to the feature model, the prediction model, and/or the filter criteria may occur in batch using machine learning and statistical analytics while the real-time platform predictively responds to the sensor data.

Abstract: Systems and methods for predictively responding to real-time sensor data from an computer-augmented environment are provided. A system may receive sensor data from the computer-augmented environment. The system may obtain a feature model, a prediction model, and a filter criteria. The system may derive, based on the feature model and the sensor data, a first event. The system may determine the first event is associated with an authorized event identifier in the filter criteria. The system may forecast, based on the authorized event identifier and the prediction model that a second event occurs after the first event. The system may transmit an action message to the computer-augmented environment, the action message indicative of the second event. Updates to the feature model, the prediction model, and/or the filter criteria may occur in batch using machine learning and statistical analytics while the real-time platform predictively responds to the sensor data.

Abstract: A device may obtain information included in a corpus of documents relating to an organization. The device may identify a set of values indicating personal information for one or more individuals by using a set of natural language processing (NLP) techniques to analyze the information included in the corpus. The device may determine a set of relationships between one or more values, of the set of values indicating the personal information using one or more additional NLP techniques and/or one or more rules. The device may generate a set of user profiles for the one or more individuals based on the set of relationships between the one or more values indicating the personal information. The device may perform one or more actions associated with using the set of user profiles to service a request for information.

Abstract: A device may obtain information included in a corpus of documents relating to an organization. The device may identify a set of values indicating personal information for one or more individuals by using a set of natural language processing (NLP) techniques to analyze the information included in the corpus. The device may determine a set of relationships between one or more values, of the set of values indicating the personal information using one or more additional NLP techniques and/or one or more rules. The device may generate a set of user profiles for the one or more individuals based on the set of relationships between the one or more values indicating the personal information. The device may perform one or more actions associated with using the set of user profiles to service a request for information.

Abstract: Implementations are directed to receiving source data comprising data representative of medical events, processing the source data using natural language processing (NLP) techniques to provide a plurality of feature sets, providing event-specific predictive models based on the plurality of feature sets, each event-specific predictive model being specific to a particular medical event, receiving real-time data from data sources, the real-time data being representative of occurring healthcare conditions, processing the real-time data using at least one event-specific predictive model associated with a medical event to provide a predictive output that indicates a likelihood of occurrence of the medical event, and selectively broadcasting electronic messages to remote devices at least partially based on the predictive output, at least one electronic message including data associated with the medical event, and data indicative of information and sources of information for mitigating exposure to the medical event.