Abstract: Systems and methods are provided for generating and updating a dependency graph that is used in combination with textual information about incidents to improve incident-linking suggestions. Systems and methods are also provided for generating, training, and using a machine learning model configured to perform incident linking using both graph data and text data. Beneficially, these systems and methods align the graph data and text data in order to more efficiently and accurately leverage information from the multi-modal data.

Abstract: A confidence estimation tool uses a calibrated confidence mapping model to estimate confidence for a model-generated candidate root cause. The tool uses a generative artificial intelligence (“AI”) model to determine, based on a description of a current event, a candidate root cause of the current event. The tool determines a description-based confidence score using the description of the current event and descriptions of a set of relevant historical events in a target domain. The tool also determines a cause-based confidence score using the candidate root cause of the current event and root causes of the set of relevant historical events. Finally, the tool determines a final confidence score using the description-based and cause-based confidence scores. Even if the generative AI model is configured for general-domain applications, by referencing relevant historical events, the tool can accurately estimate confidence for a model-generated candidate root cause within the target domain.

Abstract: Generating and utilizing personalized predictive models are provided. When an electronic input is received, a generic predictive model is used to predict a user response to the input. After a prescribed period of time, an analysis is performed to determine the user's actual response to the input, as well as, the user's actual responses to other inputs of the same type. Training is performed on the generic predictive model to generate a new and personalized predictive model based on the user's actual responses to the analyzed inputs. The personalized predictive model is then utilized for predicting user response to future inputs of the same type. At a prescribed frequency, the generated personalized predictive model is updated by analyzing actual user responses to predictions provided by the personalized predictive model.

Abstract: Generating and utilizing personalized predictive models are provided. When an electronic input is received, a generic predictive model is used to predict a user response to the input. After a prescribed period of time, an analysis is performed to determine the user's actual response to the input, as well as, the user's actual responses to other inputs of the same type. Training is performed on the generic predictive model to generate a new and personalized predictive model based on the user's actual responses to the analyzed inputs. The personalized predictive model is then utilized for predicting user response to future inputs of the same type. At a prescribed frequency, the generated personalized predictive model is updated by analyzing actual user responses to predictions provided by the personalized predictive model.