Abstract: As described herein, a system, method, and computer program are provided for intelligent self-healing optimization for fallout reduction. A set of self-healing rules are stored that are configured to provide resolutions to failures detected in a computer process. Data associated with use of the self-healing rules is collected. The data is processed using a machine learning model to generate one or more recommendations for optimizing the set of self-healing rules. The one or more recommendations are output.

Abstract: As described herein, a system, method, and computer program are provided for intelligent self-healing optimization for fallout reduction. A set of self-healing rules are stored that are configured to provide resolutions to failures detected in a computer process. Data associated with use of the self-healing rules is collected. The data is processed using a machine learning model to generate one or more recommendations for optimizing the set of self-healing rules. The one or more recommendations are output.

Abstract: As described herein, a machine learning system, method, and computer program provide marketing segmentation of residential spaces. In use, network usage data is collected from each residential network router of a plurality of residential network routers operating in a different residential space of a plurality of residential spaces. Additionally, the network usage data is processed by a machine learning algorithm to segment the plurality of residential spaces into a plurality of segments. Further, the plurality of segments are output.

Abstract: As described herein, a machine learning system, method, and computer program are provided for inferring user presence in a residential space. In use, network usage data is collected from a residential network router operating in a residential space. Additionally, the network usage data is processed by a machine learning algorithm to infer whether a user is present in the residential space. Further, the inference is output for performing one or more related actions.

Abstract: As described herein, a system, method, and computer program are provided for generating insights from home network router data. In use, network usage/connection data is collected from a home network router operating in a residential space. Additionally, the network usage/connection data is processed to generate insights for the network usage/connection data. Further, the insights are output for display to one or more users.

Abstract: As described herein, a machine learning system, method, and computer program are provided for inferring user presence in a residential space. In use, network usage data is collected from a residential network router operating in a residential space. Additionally, the network usage data is processed by a machine learning algorithm to infer whether a user is present in the residential space. Further, the inference is output for performing one or more related actions.

Abstract: As described herein, a machine learning system, method, and computer program are provided for managing guest network access in a residential space. In use, network usage data is collected from a residential network router operating in a residential space. Additionally, a machine learning algorithm processes the network usage data to classify a user device connected to the residential network router as being operated by a guest of the residential space. Further, the classification is output for performing one or more related actions.

Abstract: A system, method, and computer program product are provided for automatic root cause analysis. In operation, a root cause analysis system identifies at least one event associated with one or more records for which to perform a root cause analysis. The root cause analysis system performs a root cause analysis of the event by automatically generating a decision tree based on all records in the current time-window such that each leaf in the decision tree represents the probabilities for class labels of a target variable and each branch in the decision tree represents a feature that leads to a corresponding class label probability. The root cause analysis system automatically generates the decision tree by automatically selecting at each step the feature that maximizes information gain based on a current subset of data.