IP Library Granted Patent US 9,760,827
Granted Patent B1
US 9,760,827 · App. 15/397,715 · Granted Sep 12, 2017

Neural network applications in resource constrained environments

Inventors: Rocky Chau-Hsiung Lin (Cupertino, CA); Thomas Yamasaki (Anaheim Hills, CA); Koichiro Kanda (San Jose, CA); Diego Rodriguez Risco (Campbell, CA); Alexander Joseph Ryan (Mountain View, CA)
Assignee: Alpine Electronics of Silicon Valley, Inc.
G06N3/08G06N3/04
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Quick Facts
Patent No.
US 9,760,827
App. No.
15/397,715
Granted
Sep 12, 2017
Kind
B1
Abstract

Systems and methods are disclosed for applying neural networks in resource-constrained environments. A system may include a sensor located in a resource-constrained environment configured to generate sensor data of the resource-constrained environment. The system may also include a first computing device not located in the resource-constrained environment configured to produce a neural network structure based on the sensor data. The system may further include a second computing device located in the resource-constrained environment configured to provide the sensor data as input to the neural network structure. The second computing device may be further configured to determine a state of the resource-constrained environment based on the input of the sensor data to the neural network structure.

Claims (16)

1. A system comprising:

a sensor located in an automobile, wherein the sensor is configured to produce first sensor data related to an interior of the automobile and second sensor data related to the interior of the automobile, wherein the first sensor data and the second sensor data are images of the interior of the automobile;

a local transceiver device located in the automobile, wherein the local transceiver device is configured to receive the first sensor data from the sensor;

a remote computing device located remote from the automobile, wherein the remote computing device is configured to apply a neural network to the first sensor data in order to produce neural network configuration parameters, wherein the local transceiver device is configured to transmit the first sensor data over a communication link to the remote computing device, and wherein the local transceiver device is configured to receive the neural network configuration parameters over the communication link from the remote computing device;

a local computing device located in the automobile, wherein the local computing device is configured to receive the neural network configuration parameters from the local transceiver device, wherein the local computing device is configured to receive the second sensor data from the sensor, and wherein the local computing device is configured to determine whether a driver of the automobile is in a distracted state based on the neural network configuration parameters and the second sensor data;

an automobile controller located in the automobile, wherein the automobile controller is configured to control a transition of the automobile between a self-driving mode and a non-self-driving mode, wherein the automobile controller is configured to receive an output of the distracted state determination from the local computing device, and wherein the automobile controller is configured to restrict the transition of the automobile between the self-driving mode and the non-self-driving mode based on the output of the distracted state determination; and

a second sensor located in the automobile, wherein the second sensor is configured to produce third sensor data related to an interior of the automobile and fourth sensor data related to the interior of the automobile, wherein the third sensor data and the fourth sensor data are images of the interior of the automobile,

wherein the remote computing device is configured to apply the neural network to the first sensor data and the third sensor data in order to produce the neural network configuration parameters,

wherein the local computing device is configured to receive the fourth sensor data from the second sensor, and wherein the local computing device is configured to determine whether the driver of the automobile is in a distracted state based on the neural network configuration parameters, the second sensor data, and the fourth sensor data,

wherein the remote computing device is configured to combine the first sensor data and the third sensor data into a stereovision image prior to producing the neural network configuration parameters,

wherein the sensor is a camera located adjacent to a rearview mirror in the automobile, and

wherein the second sensor is a camera located adjacent to an A-pillar in the automobile.

2. The system of claim 1 , further comprising:

a third sensor located in the automobile, wherein the third sensor is configured to produce fifth sensor data that comprises non-image data related to the automobile and sixth sensor data that comprises non-image data related to the automobile,

wherein the remote computing device is configured to apply the neural network to the first sensor data, the third sensor data, and the fifth sensor data in order to produce the neural network configuration parameters, and

wherein the local computing device is configured to receive the sixth sensor data from the third sensor, and wherein the local computing device is configured to determine whether the driver of the automobile is in a distracted state based on the neural network configuration parameters, the second sensor data, the fourth sensor data, and the sixth sensor data.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 28, 2024
From: ALPINE ELECTRONICS OF SILICON VALLEY, INC.
To: REVEAL INNOVATIONS, LLC
Reel/Frame 069042/0477 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 5, 2017
From: LIN, ROCKY CHAU-HSIUNG; YAMASAKI, THOMAS; KANDA, KOICHIRO; RISCO, DIEGO RODRIGUEZ; RYAN, ALEXANDER JOSEPH
To: ALPINE ELECTRONICS OF SILICON VALLEY, INC.
Reel/Frame 040866/0008 →
Continuity (1)
Provisional Application 62365629 · Jul 22, 2016