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

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

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.)

IMPACT OF INFORMATION TECHNOLOGY ON TOLL COLLECTION AT THE PENNSYLVANIA TURNPIKE

Measuring and understanding Lhe business value of information technology (IT) is a significant and difficult problem facing researchers. We propose that the impact of IT applications can be best understood through an analysis at the task level. We report on a field research conducted to study the impact of IT on the toll collection system of thirty-eight interchanges al the Pennsylvania Turnpike. The advantages of this setting are clear causal links between information technology and the nature of work, access to data of excellent quality and the ability to isolate relevant factors in the production process. Our results indicate that the new IT at the turnpike had a substantial impact on the efficiency of processing complex transactions but no impact on simple transactions. These results can be explained by examining the nature of the toll collection task and the changes on this task produced by the new IT. The toll charged for each vehicle is based on its class (determined by the number of axles and the weight of the vehicle) iuid the distance travelled. The old toll collection system at the turnpike was replaced in July 1987. Unlike the old system, lhe new system is automatic at enlry. The new system also automatically matches entry and exit classifications and calculates the toll, leaving the operator to collect the toll and deal with possible mismatches funds, in classifications. Finally, it is easier to handle exception transactions such as authorized U-turn, insufficient funds, or lost ticket in the new syslem. We define passenger cars as simple transactions because they can be classified by sight (without weighing and counting ax les), thus eliminating the need for matching entry and exit classifications. All other vehicles (complex transactions) require matching the two classifications and resolving any possible discrepancy. We measure labor productivity before and after the introduction of the new IT for toll collection. We summarize and explain the results below: • The new technology reduced indirect labor at the interchange level. We expected indirect labor to decline with the new system due to its improved reliability and flexibility. The new system also makes the scheduling task easier because it does not require entry lanes to be manned. · The new IT reduced direct labor for complex transactions. Complex transactions get the full benefits of the new system. The automation of the vehicle entry process, matching of entry and exit classifications, and calculation of the toll charge significantly reduce worker effort. • The new system did not have an impact 01, simple transactions. The new system improves the processing of simple transactions by automating the vehicle entry process and providing easier methods to deal with exception transactions. However, the advantage of automating the matching of entry and exit classifications does not apply to passenger cars which are not subject to classification problems. Moreover, with the old system, a collector at the exit point could often verbally tell the driver of a passenger car the toll charge because most collectors remembered the toll charge for local interchanges. The new system requires all car(is to be read by the machine, which slows down the processing of passenger cars at exit This effect possibly negates the advantages of the new system for simple transactions. In future work, we will compare the impact of the technology by assessing the relative productivity of the interchanges using Data Envelopmenl Analysis. Our goal is to understand the specific factors that facilitate or hinder the utilization of technology in this setting. We will also analyze how the technological change accentuates or attenuates the importance of these factors in the production process at each interchange

Real-Time Bi-directional Electric Vehicle Charging Control with Distribution Grid Implementation

As electric vehicle (EV) adoption is growing year after year, there is no doubt that EVs will occupy a significant portion of transporting vehicle in the near future. Although EVs have benefits for environment, large amount of un-coordinated EV charging will affect the power grid and degrade power quality. To alleviate negative effects of EV charging load and turn them to opportunities, a decentralized real-time control algorithm is developed in this paper to provide optimal scheduling of EV bi-directional charging. To evaluate the performance of the proposed algorithm, numerical simulation is performed based on real-world EV user data, and power flow analysis is carried out to show how the proposed algorithm improve power grid steady state operation. . The results show that the implementation of proposed algorithm can effectively coordinate bi-directional charging by 30% peak load shaving, more than 2% of voltage drop reduction, and 40% transmission line current decrease

Distributed Optimal Vehicle Grid Integration Strategy with User Behavior Prediction

With the increasing of electric vehicle (EV) adoption in recent years, the impact of EV charging activities to the power grid becomes more and more significant. In this article, an optimal scheduling algorithm which combines smart EV charging and V2G gird service is developed to integrate EVs into power grid as distributed energy resources, with improved system cost performance. Specifically, an optimization problem is formulated and solved at each EV charging station according to control signal from aggregated control center and user charging behavior prediction by mean estimation and linear regression. The control center collects distributed optimization results and updates the control signal, periodically. The iteration continues until it converges to optimal scheduling. Experimental result shows this algorithm helps fill the valley and shave the peak in electric load profiles within a microgrid, while the energy demand of individual driver can be satisfied.Comment: IEEE PES General Meeting 201