Lyft, Inc.

Lytt Limited

Ritwik Subir Das

Nadha Nafeeza Gafoor

Kris Richard Efland

Meng Tao

Nastaran Ghadar

Joan Devassy

Wei You

Swarn Avinash Kumar

John Michael Sparks

Kia Nishimine

Angela Lam

Wiebke Poerschke

Hristo Stefanov Stefanov

Aahel Iyer

Examples disclosed herein may involve a computing system configured to (i) identify a stationary element in a real-world environment for which to infer information, (ii) detect a semantic relationship between the stationary element and one or more other stationary elements in the real-world environment, (iii) based on the detected semantic relationship, infer information about the stationary element, (iv) include the inferred information about the stationary element within a set of information that describes the stationary element.

Systems and methods for inferring information about stationary elements based on semantic relationships

This disclosure describes a card-scan system that can update a card-scan machine learning model to improve card-character predictions for payment cards, driver licenses, or other character-bearing cards by using an active-learning technique that learns from card-scan representations indicating corrections by users to predicted card characters. In particular, the disclosed systems can use a client device to capture and analyze a set of card images of a character-bearing card to predict card characters using a card-scan machine learning model. The disclosed systems can further receive card-scan gradients representing one or more corrections to incorrectly predicted card characters. Based on the card-scan gradients, the disclosed systems can generate active-learning metrics and retrain or update the card-scan machine learning model based on such active-learning metrics. By utilizing and updating the card-scan machine learning model, the disclosed systems can improve the accuracy with which card-character-detection systems predict card characters while preserving data security and verifying the presence of a physical character-bearing card.

Automatically updating a card-scan machine learning model based on predicting card characters

Examples disclosed herein may involve a computing system that is operable to (i) identify a source map and a target map for transferring map data, where the source map and the target map have different respective coordinate frames and respective coverage areas that at least partially overlap, (ii) select a real-world element for which to transfer previously-created map data from the source map to the target map, (iii) select a source image associated with the source map in which the selected real-world element appears and has been labeled, (iv) select a target image associated with the target map in which the selected real-world element appears, (v) derive a geometric relationship between the source image and the target image, and (vi) use the derived geometric relationship between the source image and the target image to determine a position of the real-world element within the respective coordinate frame of the target map.

Systems and methods for transferring map data between different maps

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Mountain View, CA, United States of America

Ritwik Subir Das