Abstract: An error documentation system including tools to collect and analyze application error data for individual development teams and tools to share documented defects and solutions across development teams during any stage of development cycle. The system may receive and analyze event logs for error events triggered by applications on end-user devices. The system may automatically generate defect tickets and/or ticket entries for defects identified in event logs. The system may train one or more machine learning (ML) models to correlate input with identified defects from a defects database. In response to identifying correlated identified defects, the system may generate ticket entries indicating the correlated identified defects and associated solutions for the defects. The system may provide an interface for users to query the data stored in the database.

Abstract: An error documentation system including tools to collect and analyze application error data for individual development teams and tools to share documented defects and solutions across development teams during any stage of development cycle. The system may receive and analyze event logs for error events triggered by applications on end-user devices. The system may automatically generate defect tickets and/or ticket entries for defects identified in event logs. The system may train one or more machine learning (ML) models to correlate input with identified defects from a defects database. In response to identifying correlated identified defects, the system may generate ticket entries indicating the correlated identified defects and associated solutions for the defects. The system may provide an interface for users to query the data stored in the database.

Abstract: An error documentation system including tools to collect and analyze application error data for individual development teams and tools to share documented defects and solutions across development teams during any stage of development cycle. The system may receive and analyze event logs for error events triggered by applications on end-user devices. The system may automatically generate defect tickets and/or ticket entries for defects identified in event logs. The system may train one or more machine learning (ML) models to correlate input with identified defects from a defects database. In response to identifying correlated identified defects, the system may generate ticket entries indicating the correlated identified defects and associated solutions for the defects. The system may provide an interface for users to query the data stored in the database.

Abstract: Disclosed are various embodiments for determining an expected number of times that content may be served in conjunction with a user interface. A forecasting model for determining an expected bid requests inventory is generated, wherein a bid request indicates an opportunity to submit a bid to present content in conjunction with a user interface. A bid success rate is determined. An expected impressions inventory is determined based on the expected bid requests inventory and the bid success rate.

Abstract: A power controller capable of controling electric loads accurately and stably is described, which is to regulate the power supply of the electric load and to reform it to be rectangular electric pressure pulse and is characterized by having current mirror circuit, comparators, condensor and voltage follower.

Abstract: An outdoor lighting controller including a light sensing element for automatically activating and de-activating an outdoor light source. A wiper is provided for removing accumulated dust from the light sensing element. The wiper is initiated when the light source is activated.