Abstract: Techniques performed by a data processing system for diagnosing problems with a communications platform include obtaining query parameters including an aggregation operator for invoking a machine learning algorithm configured to analyze performance data for the communications platform, automatically executing the query on the performance data to obtain query results by invoking the machine learning algorithm on the performance data to automatically identify a plurality of clusters of data indicative of a performance problem, and presenting a visualization of the query results. The visualization includes indicators identifying cluster properties for which the query results are further refinable and one or more second indicators identifying the second subset of the second cluster properties which are not relevant for further refining the first query results. The indicators are actuatable to automatically update and re-execute the first query based on the respective indicator that is actuated.

Abstract: An Augmented Reality (AR) and Artificial Intelligence (AI) based interactive virtual try-on solution that facilitates trying on, fitting, and modularizing a virtual apparel in real-time—as if a consumer were wearing the apparel. A user with a mobile device defines retail adjustment operations on the virtual apparel using an AR-based visual interface. The user can interact with the virtual apparel for identifying, defining, and changing the look, fit, and design of the apparel on the user's body. The real-time interaction is with the same virtual apparel. The system defines operations based on user's features, sartorial measurements, intent, gestures, position, pressure values received from a controller operated by the user, and the sensed motion of the user to translate into a set of machine learning inference models that predict a series of states that visually generate the outcome the user anticipates based on the user's interaction with the virtual clothing.

Abstract: Techniques performed by a data processing system for diagnosing problems with a communications platform include obtaining query parameters including an aggregation operator for invoking a machine learning algorithm configured to analyze performance data for the communications platform, automatically executing the query on the performance data to obtain query results by invoking the machine learning algorithm on the performance data to automatically identify a plurality of clusters of data indicative of a performance problem, and presenting a visualization of the query results. The visualization includes indicators identifying cluster properties for which the query results are further refinable and one or more second indicators identifying the second subset of the second cluster properties which are not relevant for further refining the first query results. The indicators are actuatable to automatically update and re-execute the first query based on the respective indicator that is actuated.

Abstract: Technologies for assigning a user-generated issue report to an appropriate team for investigation are disclosed. A log file associated with an issue report is parsed to create an object model. The object model is a hierarchy of objects containing data emitted over time by a computer application associated with the issue report. Rules analyze the content of the object model, and when a rule is satisfied, the issue report is assigned to a team associated with the rule for investigation. Rules may access objects in the object model on a per-component level, thereby allowing issues to be assigned to teams at the level of individual components. Rules may also perform a root cause analysis of the issue based on the object model.

Abstract: Technologies for assigning a user-generated issue report to an appropriate team for investigation are disclosed. A log file associated with an issue report is parsed to create an object model. The object model is a hierarchy of objects containing data emitted over time by a computer application associated with the issue report. Rules analyze the content of the object model, and when a rule is satisfied, the issue report is assigned to a team associated with the rule for investigation. Rules may access objects in the object model on a per-component level, thereby allowing issues to be assigned to teams at the level of individual components. Rules may also perform a root cause analysis of the issue based on the object model.