Ghost Autonomy Inc.

Oracle International Corporation

Applied Intuition, Inc.

Ghost Locomotion Inc.

Akash J Sagar

John Martin Hayes

Volkmar Uhlig

Feng Tian

Nima Soltani

Christopher R Lumb

Justin Erickson

Shaun M Pacheco

James S Cross

Riaz Ali

Anush Kumar

Daniel D Schmidt

David Stephenson

Jeff Hagen

Daniel J Fillingham

Timothy Cerexhe

Luke Liu

Faissal Sleiman

Reducing throughput of paired cameras, including: selecting, by a first camera of an autonomous vehicle, a first area of focus for a first frame generated by the first camera; selecting, by a second camera of the autonomous vehicle, a second area of focus for a second frame generated by the second camera by matching the first area of focus for the first frame; generating, by the first camera from the first frame, a first downsampled frame and a first cropped frame, wherein the first cropped frame is based on the first area of focus; and generating, by the second camera from the second frame, a second downsampled frame and a second cropped frame, wherein the second cropped frame is based on the second area of focus.

Reducing throughput of paired cameras

Selective training of neural networks using motion estimation, including: selecting, from a plurality of pixels in video data from a vehicle, based on motion relative to the vehicle, one or more pixels; and training a neural network based on the video data by zeroing out an error function applied to the selected one or more pixels.

Selective training of neural networks using motion estimation

Indexing a data corpus to a set of multidimensional points, including: generating a set of points in a multidimensional space; identifying, for each sample in a plurality of samples in a data corpus, a nearest point in the set of points; and generating an index mapping each sample with the nearest point in the set of points.

Indexing a data corpus to a set of multidimensional points

Distributed data sampling, including: receiving a sampling target; generating, based on one or more sensors, sampled data; determining, based on the sampling target, a value for the sampled data; and determining, based on the value for the sampled data, whether to provide the sampled data to a remotely disposed computing device.

Distributed data sampling

Throughput reduction in autonomous vehicle camera sensors, including: generating, by a camera sensor, a frame; selecting an area of focus for the frame; and generating, by the camera sensor from the frame, a downsampled frame and a cropped frame, wherein the cropped frame is based on the area of focus.

Throughput reduction in autonomous vehicle camera sensors

Determining control operations for an autonomous vehicle may include receiving, by an operational model, an operational model input based on camera data from one or more cameras of an automated vehicle; determining, by the operational model, based on the input, a current environmental state and a predicted environmental state; providing, to a rules module, a differential between the current environmental state and a previously predicted environmental state; and determining, based on the rules module, one or more control operations for the automated vehicle.

Determining control operations for an autonomous vehicle

Camera data normalization for an autonomous vehicle are described herein, including: receiving, from one or more cameras of the autonomous vehicle, camera data; applying a color normalization to the camera data; applying a spherical reprojection to the camera data; and applying, based on a registration point, a stabilization to the camera data.

Camera data normalization for an autonomous vehicle

Detecting out-of-model scenarios for an autonomous vehicle including: determining, based on first sensor data from one or more sensors, an environmental state relative to the autonomous vehicle, wherein operational commands for the autonomous vehicle are based on a selected machine learning model, wherein the selected machine learning model comprises a first machine learning model; comparing the environmental state to a predicted environmental state relative to the autonomous vehicle; and determining, based on a differential between the environmental state and the predicted environmental state, whether to select a second machine learning model as the selected machine learning model.

Detecting out-of-model scenarios for an autonomous vehicle

Scene filtering using motion estimation, including identifying, in camera data from an autonomous vehicle, based on motion relative to the autonomous vehicle, one or more pixels; filtering, from the camera data, the one or more pixels; and training, based on the filtered camera data, a neural network.

Scene filtering using motion estimation

Controlling an automated vehicle using visual anchors, including receiving, from one or more cameras of an autonomous vehicle, first video data; identifying one or more visual anchors in the first video data; determining one or more differentials between the one or more visual anchors and one or more predicted visual anchors; and determining, based on the one or more differentials, one or more control operations for the autonomous vehicle to reduce the one or more differentials.

Controlling an automated vehicle using visual anchors

Provided are methods, systems, and computer-program products for server collaboration. For example, a method can include receiving, by a first bot server from a messaging application, a hypertext transfer protocol (HTTP) post call message associated with a session. The first bot server sends a transfer request to transfer the session to a second bot server. A timer can be set for completing the operations associated with the session by the second bot server. If the timer expires and the first bot server determines the operations have yet to be performed, the first bot server transmits another transfer request to transfer the session from the second bot server to the first bot server, whereby the first bot server then performs the operations.

Internet cloud-hosted natural language interactive messaging system server collaboration

Provided are methods, systems, and computer-program products for providing a bot server to communicate with users using messaging applications. In some implementations, a method, system, and computer-program product for associating a message received by a bot server using a messaging application is provided. For example, a method can include receiving a hypertext transfer protocol (HTTP) post call message from a messaging application server. The method can further include parsing content of the message to identify a keyword. The method can further include identifying an existing session from a plurality of sessions based on a context of the message and the keyword. The method can further include associating information from the HTTP post call message with the existing session. The method can further include generating and sending a response to the HTTP post call message based on the existing session.

Internet cloud-hosted natural language interactive messaging system sessionizer

Provided are methods, systems, and computer-program products for associating a plurality of messages with a user. For example, a method can include receiving two hypertext transfer protocol (HTTP) post call messages. The method can further include parsing content of the post call messages to identify keywords, identifying existing users from a plurality of users based on context of the post call messages and the keywords, and associating information from the post call messages with the existing users. In some examples, the existing users can be the same between the messages. The method can further include responding to a second HTTP post call message based on information from at least one or more of a first HTTP post call message, the second HTTP post call message, and an existing user.

Internet cloud-hosted natural language interactive messaging system user resolver

Provided are methods, systems, and computer-program products for server collaboration. For example, a method can include receiving a hypertext transfer protocol (HTTP) post call message. The HTTP post call message can be received by a first bot server. In some implementations, the first bot server can include a first uniform resource identifier (URI). The HTTP post call message can be associated with a user and be directed to the URI from a first messaging application server. Information from the HTTP post call message can be associated with a session. The method can further include determining to transfer the session to a second bot server based on the HTTP post message. The method can further include sending a transfer request by the first bot server. The method can further include receiving a return request by the first bot server.

Techniques are described for implementing a cloud computer system (e.g., 'a cloud service') for facilitating the synchronization of enterprise data between mobile computing devices and enterprise computing systems (e.g., human resource management systems, business management systems, or the like). The cloud service may convert enterprise data received from different enterprise computing systems to a format that can be managed and updated easily by mobile computing devices. The converted format may enable distinct information to be easily identified. The enterprise data may be associated with policy information and version information to manage synchronization of the enterprise data. In some embodiments, the cloud service may manage synchronization with mobile computing devices based on classification of a communication connection the mobile computing devices have to a wireless network. The communication connection may be used to determine a manner for synchronizing enterprise data between the mobile computing devices and the cloud computer system.

Growing community of inventors

Redwood City, CA, United States of America

Akash J Sagar