公开(公告)号：US20210348930A1

公开(公告)日：2021-11-11

申请号：US17273220

申请日：2020-03-10

Applicant: Google LLC

Inventor： Joseph Johnson, Jr. ,  Shiblee Hasan ,  Chris Hluchan ,  David Lee

IPC: G01C21/34 ,  G06F16/901 ,  G01C21/36

Abstract: The present disclosure is directed towards systems and methods for receiving environmental data from device sensors. A computing system stores environmental data in an environmental feature database at the computing system for a plurality of geographic locations. The computing system receives, from one or more remote systems, data indicating one or more environmental features for a particular geographic location. The computing system accesses stored environmental data for the particular geographic location to determine whether the environmental features are included in the environmental feature database. In response to determining that the environmental features are included in the environmental feature database, the operations further comprise, updates a confidence value associated with the environmental features. In response to determining that the one or more environmental features are not included in the environmental feature database, the computing system adds the environmental feature to the environmental feature database in associated a geographic location.

公开(公告)号：US11532958B2

公开(公告)日：2022-12-20

申请号：US17249065

申请日：2021-02-18

Applicant: Google LLC

Inventor： Shiblee Hasan ,  Joseph Johnson, Jr.

IPC: H02J50/90 ,  H02J50/10 ,  G02B27/01

Abstract: Techniques of wireless charging involve a charging base configured to predict when the user is about to charge a device based on a state of the device. For example, the state of the device may be defined by inertial measurement units (IMUs). The charging base then activates in response to predicting that the user intends to charge the device. In some implementations, the prediction is based on a machine learning (ML) engine trained to classify a movement of a device by a user as being about to charge or not being about to charge. If a movement of a device is classified as being about to charge, the charging base activates before the device is detected by the charging base.

公开(公告)号：US20250069270A1

公开(公告)日：2025-02-27

申请号：US18724026

申请日：2022-01-24

Applicant: Google LLC

Inventor： Joseph Johnson, Jr. ,  Shiblee Hasan ,  Emmanouil Koukoumidis ,  Dustin Abramson

IPC: G06T9/00 ,  G06T7/11 ,  G06V10/74

Abstract: A method includes obtaining image data, identifying a machine learning-compressible (ML-compressible) portion of the image data, and determining a location of the ML-compressible portion within the image data. The method also includes selecting, from a plurality of ML compression models, an ML compression model for the ML-compressible portion based on an image content thereof, and generating, based on the ML-compressible portion and by the ML compression model, an ML-compressed representation of the ML-compressible portion. The method further includes generating a compressed image data file that includes the ML-compressed representation and the location of the ML-compressible portion, and outputting the compressed image data file. The compressed image data file is configured to cause an ML decompression model corresponding to the ML compression model to generate a reconstruction of the ML-compressible portion of the image data based on the ML-compressed representation.

公开(公告)号：US11448516B2

公开(公告)日：2022-09-20

申请号：US16707408

申请日：2019-12-09

Applicant: GOOGLE LLC

Inventor： Joseph Johnson, Jr. ,  Chris Hluchan ,  Shiblee Hasan ,  David Lee

IPC: G01C21/34 ,  G06N20/00

Abstract: To provide navigation directions for a scenic route, a machine learning model is trained using (i) characteristics of road segments that have been assigned a scenic metric and (ii) the scenic metrics for the road segments. In response to a request for navigation directions, a set of candidate routes for navigating from the starting location to the destination location is identified. Then for each road segment within each candidate route, characteristics of the road segment are applied to the machine learning model to generate the scenic metric for the road segment. A route is then selected from the set of candidate routes based at least in part on the scenic metrics of the road segments within the route. A set of navigation directions is provided for presentation on a client device for navigating from the starting location to the destination location via the selected route.