Utilizing In-Vehicle Computing Devices to Exchange Information During a Traffic Stop

This publication describes techniques directed at utilizing in-vehicle computing devices to facilitate an electronic traffic stop process that enables the electronic exchange of information between a police officer and a vehicle operator. This electronic exchange of information facilitates safer and less-stressful interactions during traffic stops. The information exchanged through this process includes, but is not limited to, copies of the operator’s driver’s license, vehicle registration, and proof of insurance. By exchanging the information electronically, a police officer can perform their initial investigation without approaching the vehicle on foot

COLLECTIVE CONSUMER INCENTIVE SYSTEM

A system and process for creating a network of businesses to collectively grant and manage consumer incentives are disclosed. The system allows collaboration and management of joint promotion campaigns between business entities through the Web and mobile technology platforms. The system manages incentives for businesses, their networks and customers. It allows customers to search, find businesses and network with other customers. The customers can view and use incentives that are available to them. The system combines a social network concept for business with customer deals and incentives. It has the advantage of increased customer outreach through cross traffic and referrals between businesses in a network and increased efficiency with lowered cost in launching and managing a promotional campaign

PANIC WORD

A computing device (e.g., a mobile phone, camera, tablet computer, etc.) may include an integrated display device (e.g., a presence-sensitive screen) at which a user interface is presented. Additionally, a computing device may include a microphone that generates audio data and the capability to process the audio data. For instance, the computing device may process the audio data to identify one or more commands or requests spoken by a user of the computing device, and perform various actions associated with those commands or requests. In some situations, a user may desire for the computing device to perform one or more actions that would typically be performed via interacting with the displayed user interface without having to touch the phone. For instance, in an emergency situation, it may be desirable for the user to cause the computing device to contact emergency services (e.g., call 911 or a local variant of such number) without having to physically interact with the device. The user may utter a specific word or group of words which the computing device recognizes to cause the computing device to contact emergency services

Crowdsourced Categorization of Environmental Noise Data by Wireless-Communication Devices

Exposure to noise pollution has been shown to contribute to cardiovascular effects in humans, can cause permanent hearing loss, and can interfere with the enjoyment of indoor and outdoor activities. As a result, there is a great need to aggregate and categorize environmental noise data to help the world both understand noise pollution as well as how to alleviate it. This publication describes techniques for wireless-communication devices, such as smartphones, to categorize measured environmental noise data to generate location noise level information. For example, a wireless-communication device can categorize measured environmental noise data into a number of different categories utilizing an on-device machine-learned model to generate location noise level information. The location noise level information can then be utilized to build location area maps of the noise level which can, in turn, be utilized to aid in the global reduction of exposure to noise pollution

PASSIVE SLEEP DETECTION

A system is described that enables a computing system (e.g., a mobile phone, a smartwatch, a tablet computer, etc.) to passively detect a user’s sleep duration. That is, without a user configuring the computing system into a sleep mode or otherwise inputting a sleep duration, the computing system may, after receiving explicit permission from the user, monitor various contextual signals to automatically determine the user’s sleep duration. The computing system may passively capture various data using sensors (e.g., accelerometers, ambient light sensors, microphones, etc.) in the computing system and analyze the captured data to estimate a user’s sleep duration. For example, the computing system may analyze accelerometer data to determine when a user is moving and how much the user is moving, analyze audio data captured by a microphone to determine if the audio captured is indicative of sleep, and/or analyze ambient light data to determine ambient light conditions. Such sensor data may be periodically generated and analyzed to generate sleep information for s series of time intervals. Based on the analysis of such sensor data, the computing system may determine whether the user was asleep when the computing system generated the sensor data. In some examples, the computing device may further classify sleep stages (e.g., rapid eye movement (REM) stage, light sleep stage, deep sleep stage, etc.) using the generated sensor data (e.g., classify sleep stages using user’s breathing rate, heart rates, or movement, etc.)