Uber Technologies, Inc.

David Purdy

Michael Truong

Rami Mawas

Shan He

Franziska Bell

Laszlo Korsos

Theodore Russell Sumers

Li Chen

Jason Byron Yosinski

Thibault Doutre

Benjamin Kolin

Cascaded, boosted predictive models trained using distinct sets of exogenous and endogenous features are configured to predict component of performance ratings of entities. From the distinct predicted components, the second entity's rating factor can be determined. A second entity's rating factor represents the specific contribution a second entity makes to his average performance rating, as distinct from the rating that an arbitrary or hypothetical second entity would obtain.

Cascaded boosted predictive models

An image quality scorer machine accesses a candidate image to be analyzed for visual quality. The image quality scorer machine generates a visual quality score of the candidate image by first generating a prediction of a similarity score for the candidate image. The predicted similarly score of the candidate image may be generated by a process including inputting the candidate image into a neural network that has been trained to detect a set of image features in the candidate image and then to generate a corresponding predicted similarity score based on degrees to which the image features in the set are present in the candidate image. The image quality scorer machine derives the visual quality score based on the predicted similarity score outputted by the neural network. Accordingly, the image quality score machine may provide or store the generated visual quality score of candidate image for subsequent usage.

Image quality scorer machine

An event analysis system receives events in a time-series from a set of monitored systems and identifies a set of alert threshold values for each of the types of events to identify outliers in the time-series at an evaluated time. Portions of historic event data is selected to identify windows of event data near the evaluated time at a set of seasonally-adjusted times to predict the value of the event type. The alert threshold value may also account for a prediction based on recent, higher-frequency events. Using the alert threshold values for a plurality of event types, the event data is compared with the alert threshold values to determine an alert level for the data. The event data types are also clustered and displayed with the alert levels to provide a visualization of the event data and identify outliers when the new event data is received.

System event analyzer and outlier visualization

An anomaly detection system is provided in connection with a transport service. The anomaly detection system can monitor progress of the transport service using received location data from driver and/or user mobile devices. During the progress of the transport service, the anomaly detection system can determine a probable anomaly by determining that a geographic position of the driver's vehicle has remained static for a period of time exceeding a threshold period of time, and in response to determining the probable anomaly, transmit a notification message to at least one of the driver and user mobile devices.

Trip anomaly detection system

Trip is analyzed from a population of drivers in order to determine one or more indicators of one or more driving styles. The trip data may include sensor information obtained from one or more sensor devices which are present in a vehicle of each driver of the population. A driving style is determined for the driver during a monitored trip by analyzing sensor information obtained from one or more sensor devices of the driver during the trip for at least one of the indicators of the one or more driving styles.

Augmenting transport services using driver profiling

An anomaly detection system is provided in connection with a transport service. The anomaly detection system can construct routine route profiles for individual users of the transport service using historical route data. The anomaly detection system can monitor a current route traveled by a user. The anomaly detection system can further identify a matching routine route profile of the respective user. The anomaly detection system can utilize the matching routine route profile to identify a probable anomaly in the current route. In response to detecting the probable anomaly, the anomaly detection system can enable a safety protocol to perform a number of actions.

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Millbrae, CA, United States of America

David Purdy