Uber Technologies, Inc.

Uber Technology, Inc.

Ankit Tandon

Daniel Wolf

Kaivalya Bachubhai Parikh

Satyendra Kumar Nainwal

Vasudev Parameswaran

Mojtaba Solgi

Systems and methods of providing a user interface in which map features associated with places are selectively highlighted are disclosed herein. In some example embodiments, a computer system receives a request for a transportation service associated with a place, retrieves an entrance geographic location for the place from a database, with the entrance geographic location being stored in association with the place in the database and representing an entrance for accessing the place, generating route information based on the retrieved entrance geographic location, with the route information indicating a route from an origin geographic location of a computing device of a user to the entrance geographic location of the place, and causing the generated route information to be displayed within a user interface on a computing device of the user.

Selectively highlighting map features associated with places

A map labeling system trains a machine-learned model using a set of training data and generates a set of test predictions for a set of test properties by applying the machine-learned model to a set of testing data. Each prediction in the set of test predictions comprises a confidence score representing the machine-learned model's confidence in the prediction. The map labeling system determines a correctness of each prediction in the set of predictions and determines a relationship between the confidence scores and the correctness of the test predictions. The map labeling system establishes a confidence threshold for the machine-learned model based on the determined relationship and labels a production property by applying the machine-learned model to production data.

Confidence threshold determination for machine-learned labeling

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Sunnyvale, CA, United States of America

Ankit Tandon