The present embodiments relate generally to telematics data and/or insurance policies. More particularly, the present embodiments relate to performing certain actions, and/or adjusting insurance policies, based upon telematics and/or other data indicative of risk or insured behavior.
At times, insurance providers are able to provide helpful information to customers who have recently been in an accident. When a customer calls a claims associate to report an accident and initiate a claim, for example, the associate may be able to offer suggestions with respect to the next steps that the customer should take. Often, however, customers do not call their insurance providers promptly after an accident, and/or it takes a significant amount of time for the associate to locate and relay useful information. Moreover, in an emergency situation (e.g., a serious car accident), a claim associate may be very limited in his or her ability to provide assistance. In such a situation, the customer may be unable to contact a claim associate and, more importantly, may be unable to contact emergency services/responders.
The present embodiments may overcome these and/or other deficiencies.
The present embodiments disclose systems and methods that may relate to the intersection of telematics and insurance. In some embodiments, for example, telematics and/or other data may be collected and used to determine a likely severity of a vehicle accident. The data may be gathered from one or more sources, such as mobile devices (e.g., smart phones, smart glasses, smart watches, smart wearable devices, smart contact lenses, and/or other devices capable of wireless communication); smart vehicles; smart vehicle or smart home mounted sensors; third party sensors or sources of data (e.g., other vehicles, public transportation systems, government entities, and/or the Internet); and/or other sources of information. Based upon the likely severity, a communication related to emergency assistance or an emergency assistance request may be generated. The communication may be sent to a driver involved in the accident (e.g., for approval, rejection or modification prior to being sent to an emergency service provider), and/or sent directly to an emergency service provider, for example.
In one aspect, a computer-implemented method of loss mitigation may include (1) collecting, by one or more remote servers associated with an insurance provider, accident data associated with a vehicle accident involving a driver. The accident data may include vehicle telematics data, and/or the driver may be associated with an insurance policy issued by the insurance provider. The method may also include (2) analyzing, by the one or more remote servers, the accident data; (3) determining, by the one or more remote servers and based upon the analysis of the accident data, a likely severity of the vehicle accident; (4) generating, by the one or more remote servers and based upon the determined likely severity of the vehicle accident, a communication related to emergency assistance or an emergency assistance recommendation; (5) transmitting, via wireless communication, the communication related to the emergency assistance or emergency assistance recommendation from the one or more remote servers to a mobile device associated with the driver; (6) receiving, at the one or more remote servers, a wireless communication from the driver indicating approval or modification of the emergency assistance or emergency assistance recommendation; and/or (7) notifying, via a communication sent from the one or more remote servers, a third party of requested emergency assistance in accordance with the emergency assistance or emergency assistance recommendation as approved or modified by the driver. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
In another aspect, a computer-implemented method of loss mitigation may include (1) collecting, by one or more remote servers associated with an insurance provider, accident data associated with a vehicle accident involving a driver. The accident data may include vehicle telematics data, and/or the driver may be associated with an insurance policy issued by the insurance provider. The method may also include (2) analyzing, by the one or more remote servers, the accident data; (3) determining, by the one or more remote servers and based upon the analysis of the accident data, a likely severity of the vehicle accident; (4) generating, by the one or more remote servers and based upon the determined likely severity of the vehicle accident, a communication related to emergency assistance or an emergency assistance recommendation; and/or (5) transmitting the communication related to the emergency assistance or emergency assistance recommendation from the one or more remote servers to a third party to facilitate a prompt and appropriate emergency responder response to the vehicle accident. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
In another aspect, a system for facilitating loss mitigation may include one or more processors and one or more memories. The one or more memories may store instructions that, when executed by the one or more processors, cause the one or more processors to (1) collect accident data associated with a vehicle accident involving a driver. The accident data may include vehicle telematics data, and/or the driver may be associated with an insurance policy issued by the insurance provider. The instructions may also cause the one or more processors to (2) analyze the accident data; (3) determine, based upon the analysis of the accident data, a likely severity of the vehicle accident; (4) generate, based upon the determined likely severity of the vehicle accident, a communication related to emergency assistance or an emergency assistance recommendation; (5) cause the communication related to the emergency assistance or emergency assistance recommendation to be transmitted, via wireless communication, to a mobile device associated with the driver; (6) receive a wireless communication from the driver indicating approval or modification of the emergency assistance or emergency assistance recommendation; and/or (7) cause a third party to be notified of requested emergency assistance in accordance with the emergency assistance or emergency assistance recommendation as approved or modified by the driver.
Advantages will become more apparent to those skilled in the art from the following description of the preferred embodiments which have been shown and described by way of illustration. As will be realized, the present embodiments may be capable of other and different embodiments, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.
There are shown in the drawings arrangements which are presently discussed. It is understood, however, that the present embodiments are not limited to the precise arrangements and instrumentalities shown.
The present embodiments may relate to, inter alia, collecting data, including telematics and/or other data, and analyzing the data (e.g., by an insurance provider server or processor) to provide insurance-related benefits to insured individuals, and/or to apply the insurance-related benefits to insurance policies or premiums of insured individuals. For example, the insurance-related benefits may include risk or loss mitigation and/or prevention, and/or may include theft protection, mitigation, and/or avoidance. The insurance-related benefits may also, or instead, include other products and/or services, such as intelligent vehicle routing in real-time, for example. The present embodiments may prevent losses/injury/damage to persons and/or property, and/or reward an insured for exhibiting risk-averse behavior (e.g., in the form of lower insurance premiums or rates, or additional insurance discounts, points, and/or rewards).
The front-end components 2 may obtain information regarding a vehicle 8 (e.g., a car, truck, motorcycle, etc.) and/or the surrounding environment. Information regarding the surrounding environment may be obtained by one or more other vehicles 6, public transportation system components 22 (e.g., a train, a bus, a trolley, a ferry, etc.), infrastructure components 26 (e.g., a bridge, a stoplight, a tunnel, a rail crossing, etc.), smart homes 28 having smart home controllers 29, and/or other components communicatively connected to a network 30. Information regarding the vehicle 8 may be obtained by a mobile device 10 (e.g., a smart phone, a tablet computer, a special purpose computing device, etc.) and/or a smart vehicle controller 14 (e.g., an on-board computer, a vehicle diagnostic system, a vehicle control system or sub-system, etc.), which may be communicatively connected to each other and/or the network 30.
In some embodiments, telematics data may be generated by and/or received from sensors 20 associated with the vehicle 8. Such telematics data from the sensors 20 may be received by the mobile device 10 and/or the smart vehicle controller 14, in some embodiments. Other, external sensors 24 (e.g., sensors associated with one or more other vehicles 6, public transportation system components 22, infrastructure components 26, and/or smart homes 28) may provide further data regarding the vehicle 8 and/or its environment, in some embodiments. For example, the external sensors 24 may obtain information pertaining to other transportation components or systems within the environment of the vehicle 8, and/or information pertaining to other aspect so of that environment. The sensors 20 and the external sensors 24 are described further below, according to some embodiments.
In some embodiments, the mobile device 10 and/or the smart vehicle controller 14 may process the sensor data from sensors 20, and/or other of the front-end components 2 may process the sensor data from external sensors 24. The processed data (and/or information derived therefrom) may then be communicated to the back-end components 4 via the network 30. In other embodiments, the front-end components 2 may communicate the raw sensor data from sensors 20 and/or external sensors 24, and/or other telematics data, to the back-end components 4 for processing. In thin-client embodiments, for example, the mobile device 10 and/or the smart vehicle controller 14 may act as a pass-through communication node for communication with the back-end components 4, with minimal or no processing performed by the mobile device 10 and/or the smart vehicle controller 14. In other embodiments, the mobile device 10 and/or the smart vehicle controller 14 may perform substantial processing of received sensor, telematics, or other data. Summary information, processed data, and/or unprocessed data may be communicated to the back-end components 4 via the network 30.
The mobile device 10 may be a general-use personal computer, cellular phone, smart phone, tablet computer, or a dedicated vehicle use monitoring device. In some embodiments, the mobile device 10 may include a wearable device such as a smart watch, smart glasses, wearable smart technology, or a pager. Although only one mobile device 10 is illustrated, it should be understood that a plurality of mobile devices may be used in some embodiments. The smart vehicle controller 14 may be a general-use on-board computer capable of performing many functions relating to vehicle operation, an on-board computer system or sub-system, or a dedicated computer for monitoring vehicle operation and/or generating telematics data. Further, the smart vehicle controller 14 may be installed by the manufacturer of the vehicle 8 or as an aftermarket modification or addition to the vehicle 8. Either or both of the mobile device 10 and the smart vehicle controller 14 may communicate with the network 30 over link 12 and link 18, respectively. Additionally, the mobile device 10 and smart vehicle controller 14 may communicate with one another directly over link 16. In some embodiments, the mobile device 10 and/or the smart vehicle controller 14 may communicate with other of the front-end components 2, such as the vehicles 6, public transit system components 22, infrastructure components 26, and/or smart homes 28, either directly or indirectly (e.g., via the network 30).
The one or more sensors 20 referenced above may be removably or fixedly disposed within (and/or on the exterior of) the vehicle 8, within the mobile device 10, and/or within the smart vehicle controller 14, for example. The sensors 20 may include any one or more of various different sensor types, such as an ignition sensor, an odometer, a system clock, a speedometer, a tachometer, an accelerometer, a gyroscope, a compass, a geolocation unit (e.g., a GPS unit), a camera and/or video camera, a distance sensor (e.g., radar, LIDAR, etc.), and/or any other sensor or component capable of generating or receiving data regarding the vehicle 8 and/or the environment in which the vehicle 8 is located.
Some of the sensors 20 (e.g., radar, LIDAR, ultrasonic, infrared, or camera units) may actively or passively scan the vehicle environment for objects (e.g., other vehicles, buildings, pedestrians, etc.), traffic control elements (e.g., lane markings, signs, signals, etc.), external conditions (e.g., weather conditions, traffic conditions, road conditions, etc.), and/or other physical characteristics of the environment. Other sensors of sensors 20 (e.g., GPS, accelerometer, or tachometer units) may provide operational and/or other data for determining the location and/or movement of the vehicle 8. Still other sensors of sensors 20 may be directed to the interior or passenger compartment of the vehicle 8, such as cameras, microphones, pressure sensors, thermometers, or similar sensors to monitor the vehicle operator and/or passengers within the vehicle 8.
The external sensors 24 may be disposed on or within other devices or components within the vehicle's environment (e.g., other vehicles 6, infrastructure components 26, etc.), and may include any of the types of sensors listed above. For example, the external sensors 24 may include sensors that are the same as or similar to sensors 20, but disposed on or within some of the vehicles 6 rather than the vehicle 8.
To send and receive information, each of the sensors 20 and/or external sensors 24 may include a transmitter and/or a receiver designed to operate according to predetermined specifications, such as the dedicated short-range communication (DSRC) channel, wireless telephony, Wi-Fi, or other existing or later-developed communications protocols. As used herein, the terms “sensor” or “sensors” may refer to the sensors 20 and/or external sensors 24.
The other vehicles 6, public transportation system components 22, infrastructure components 26, and/or smart homes 28 may be referred to herein as “external” data sources. The other vehicles 6 may include any other vehicles, including smart vehicles, vehicles with telematics-capable mobile devices, autonomous vehicles, and/or other vehicles communicatively connected to the network 30 via links 32.
The public transportation system components 22 may include bus, train, ferry, ship, airline, and/or other public transportation system components. Such components may include vehicles, tracks, switches, access points (e.g., turnstiles, entry gates, ticket counters, etc.), and/or payment locations (e.g., ticket windows, fare card vending machines, electronic payment devices operated by conductors or passengers, etc.), for example. The public transportation system components 22 may further be communicatively connected to the network 30 via a link 34, in some embodiments.
The infrastructure components 26 may include smart infrastructure or devices (e.g., sensors, transmitters, etc.) disposed within or communicatively connected to transportation or other infrastructure, such as roads, bridges, viaducts, terminals, stations, fueling stations, traffic control devices (e.g., traffic lights, toll booths, entry ramp traffic regulators, crossing gates, speed radar, cameras, etc.), bicycle docks, footpaths, or other infrastructure system components. In some embodiments, the infrastructure components 26 may be communicatively connected to the network 30 via a link (not shown in
The smart homes 28 may include dwellings or other buildings that generate or collect data regarding their condition, occupancy, proximity to a mobile device 10 or vehicle 8, and/or other information. The smart homes 28 may include smart home controllers 29 that monitor the local environment of the smart home, which may include sensors (e.g., smoke detectors, radon detectors, door sensors, window sensors, motion sensors, cameras, etc.). In some embodiments, the smart home controller 29 may include or be communicatively connected to a security system controller for monitoring access and activity within the environment. The smart home 28 may further be communicatively connected to the network 30 via a link 36, in some embodiments.
The external data sources may collect data regarding the vehicle 8, a vehicle operator, a user of an insurance program, and/or an insured of an insurance policy. Additionally, or alternatively, the other vehicles 6, the public transportation system components 22, the infrastructure components 26, and/or the smart homes 28 may collect such data, and provide that data to the mobile device 10 and/or the smart vehicle controller 14 via links not shown in
In some embodiments, the front-end components 2 communicate with the back-end components 4 via the network 30. The network 30 may include a proprietary network, a secure public internet, a virtual private network and/or one or more other types of networks, such as dedicated access lines, plain ordinary telephone lines, satellite links, cellular data networks, or combinations thereof. In embodiments where the network 30 comprises the Internet, data communications may take place over the network 30 via an Internet communication protocol.
The back-end components 4 may use a remote server 40 to receive data from the front-end components 2, determine characteristics of vehicle use, determine risk levels, modify insurance policies, and/or perform other processing functions in accordance with any of the methods described herein. In some embodiments, the server 40 may be associated with an insurance provider, either directly or indirectly. The server 40 may include one or more computer processors adapted and configured to execute various software applications and components of the telematics system 1.
The server 40 may further include a database 46, which may be adapted to store data related to the operation of the vehicle 8 and/or other information. As used herein, the term “database” may refer to a single database or other structured data storage, or to a collection of two or more different databases or structured data storage components. Additionally, the server 40 may be communicatively coupled via the network 30 to one or more data sources, which may include an accident database 42 and/or a third party database 44. The accident database 42 and/or third party database 44 may be communicatively connected to the network via a communication link 38. The accident database 42 and/or the third party database 44 may be operated or maintained by third parties, such as commercial vendors, governmental entities, industry associations, nonprofit organizations, or others.
The data stored in the database 46 might include, for example, dates and times of vehicle use, duration of vehicle use, speed of the vehicle 8, RPM or other tachometer readings of the vehicle 8, lateral and longitudinal acceleration of the vehicle 8, incidents or near-collisions of the vehicle 8, communications between the vehicle 8 and external sources (e.g., other vehicles 6, public transportation system components 22, infrastructure components 26, smart homes 28, and/or external information sources communicating through the network 30), environmental conditions of vehicle operation (e.g., weather, traffic, road condition, etc.), errors or failures of vehicle features, and/or other data relating to use of the vehicle 8 and/or the vehicle operator. Prior to storage in the database 46, some of the data may have been uploaded to the server 40 via the network 30 from the mobile device 10 and/or the smart vehicle controller 14. Additionally, or alternatively, some of the data may have been obtained from additional or external data sources via the network 30. Additionally, or alternatively, some of the data may have been generated by the server 40. The server 40 may store data in the database 46 and/or may access data stored in the database 46 when executing various functions and tasks associated with the methods described herein.
The server 40 may include a controller 55 that is operatively connected to the database 46 via a link 56. It should be noted that, while not shown in
The server 40 may further include a number of software applications stored in a program memory 60. The various software applications on the server 40 may include specific programs, routines, or scripts for performing processing functions associated with the methods described herein. Additionally, or alternatively, the various software application on the server 40 may include general-purpose software applications for data processing, database management, data analysis, network communication, web server operation, or other functions described herein or typically performed by a server. The various software applications may be executed on the same computer processor or on different computer processors. Additionally, or alternatively, the software applications may interact with various hardware modules that may be installed within or connected to the server 40. Such modules may implement part of all of the various exemplary methods discussed herein or other related embodiments.
In some embodiments, the server 40 may be a remote server associated with or operated by or on behalf of an insurance provider. The server 40 may be configured to receive, collect, and/or analyze telematics and/or other data in accordance with any of the methods described herein. The server 40 may be configured for one-way or two-way wired or wireless communication via the network 30 with a number of telematics and/or other data sources, including the accident database 42, the third party database 44, the database 46 and/or the front-end components 2. For example, the server 40 may be in wireless communication with mobile device 10; insured smart vehicles 8; smart vehicles of other motorists 6; smart homes 28; present or past accident database 42; third party database 44 operated by one or more government entities and/or others; public transportation system components 22 and/or databases associated therewith; smart infrastructure components 26; and/or the Internet. The server 40 may be in wired or wireless communications with other sources of data, including those discussed elsewhere herein.
Although the telematics system 1 is shown in
The sensor 76 may be able to record audio or visual information. If
The memory 78 may include software applications that control the mobile device 10 and/or smart vehicle controller 14, and/or control the display 74 configured for accepting user input. The memory 78 may include instructions for controlling or directing the operation of vehicle equipment that may prevent, detect, and/or mitigate vehicle damage. The memory 78 may further include instructions for controlling a wireless or wired network of a smart vehicle, and/or interacting with mobile device 10 and remote server 40 (e.g., via the network 30).
The power supply 80 may be a battery or dedicated energy generator that powers the mobile device 10 and/or smart vehicle controller 14. The power supply 80 may harvest energy from the vehicle environment and be partially or completely energy self-sufficient, for example.
The transceiver 82 may be configured for wireless communication with sensors 20 located about the vehicle 8, other vehicles 6, other mobile devices similar to mobile device 10, and/or other smart vehicle controllers similar to smart vehicle controller 14. Additionally, or alternatively, the transceiver 82 may be configured for wireless communication with the server 40, which may be remotely located at an insurance provider location.
The clock 84 may be used to time-stamp the date and time that information is gathered or sensed by various sensors. For example, the clock 84 may record the time and date that photographs are taken by the camera 88, video is captured by the camera 88, and/or other data is received by the mobile device 10 and/or smart vehicle controller 14.
The microphone and speaker 86 may be configured for recognizing voice or audio input and/or commands. The clock 84 may record the time and date that various sounds are collected by the microphone and speaker 86, such as sounds of windows breaking, air bags deploying, tires skidding, conversations or voices of passengers, music within the vehicle 8, rain or wind noise, and/or other sound heard within or outside of the vehicle 8.
The present embodiments may be implemented without changes or extensions to existing communications standards. The smart vehicle controller 14 may also include a relay, node, access point, Wi-Fi AP (Access Point), local node, pico-node, relay node, and/or the mobile device 10 may be capable of RF (Radio Frequency) communication, for example. The mobile device 10 and/or smart vehicle controller 14 may include Wi-Fi, Bluetooth, GSM (Global System for Mobile communications), LTE (Long Term Evolution), CDMA (Code Division Multiple Access), UMTS (Universal Mobile Telecommunications System), and/or other types of components and functionality.
Telematics data, as used herein, may include telematics data, and/or other types of data that have not been conventionally viewed as “telematics data.” The telematics data may be generated by, and/or collected or received from, various sources. For example, the data may include, indicate, and/or relate to vehicle (and/or mobile device) speed; acceleration; braking; deceleration; turning; time; GPS (Global Positioning System) or GPS-derived location, speed, acceleration, or braking information; vehicle and/or vehicle equipment operation; external conditions (e.g., road, weather, traffic, and/or construction conditions); other vehicles or drivers in the vicinity of an accident; vehicle-to-vehicle (V2V) communications; vehicle-to-infrastructure communications; and/or image and/or audio information of the vehicle and/or insured driver before, during, and/or after an accident. The data may include other types of data, including those discussed elsewhere herein. The data may be collected via wired or wireless communication.
The data may be generated by mobile devices (smart phones, cell phones, lap tops, tablets, phablets, PDAs (Personal Digital Assistants), computers, smart watches, pagers, hand-held mobile or portable computing devices, smart glasses, smart electronic devices, wearable devices, smart contact lenses, and/or other computing devices); smart vehicles; dash or vehicle mounted systems or original telematics devices; public transportation systems; smart street signs or traffic lights; smart infrastructure, roads, or highway systems (including smart intersections, exit ramps, and/or toll booths); smart trains, buses, or planes (including those equipped with Wi-Fi or hotspot functionality); smart train or bus stations; internet sites; aerial, drone, or satellite images; third party systems or data; nodes, relays, and/or other devices capable of wireless RF (Radio Frequency) communications; and/or other devices or systems that capture image, audio, or other data and/or are configured for wired or wireless communication.
In some embodiments, the data collected may also derive from police or fire departments, hospitals, and/or emergency responder communications; police reports; municipality information; automated Freedom of Information Act requests; and/or other data collected from government agencies and officials. The data from different sources or feeds may be aggregated.
The data generated may be transmitted, via wired or wireless communication, to a remote server, such as a remote server and/or other processor(s) associated with an insurance provider. The remote server and/or associated processors may build a database of the telematics and/or other data, and/or otherwise store the data collected.
The remote server and/or associated processors may analyze the data collected and then perform certain actions and/or issue tailored communications based upon the data, including the insurance-related actions or communications discussed elsewhere herein. The automatic gathering and collecting of data from several sources by the insurance provider, such as via wired or wireless communication, may lead to expedited insurance-related activity, including the automatic identification of insured events, and/or the automatic or semi-automatic processing or adjusting of insurance claims.
In one embodiment, telematics data may be collected by a mobile device (e.g., smart phone) application. An application that collects telematics data may ask an insured for permission to collect and send data about driver behavior and/or vehicle usage to a remote server associated with an insurance provider. In return, the insurance provider may provide incentives to the insured, such as lower premiums or rates, or discounts. The application for the mobile device may be downloadable off of the internet.
Gathered telematics and/or other data (e.g., any type or types of telematics and/or other data described above in Section I and/or Section II) may facilitate determining the severity of (i) an accident; (ii) damage to a vehicle; and/or (iii) the injuries to the persons involved. The data gathered, such as data gathered after the accident, may facilitate determining what vehicle systems are broken or damaged, and/or are in need of minor or substantial repairs. The data gathered may indicate how much vehicle damage has occurred, and whether or not emergency services may be necessary and/or should be called or otherwise contacted.
The telematics and/or other data may also be used to (a) identify a first notice of loss, which in turn may be used to automatically start or initiate the claim handling process; and/or (b) accident reconstruction. Loss identification and/or accident reconstruction may then be paired individually and/or collectively with insurance policy data to automatically generate an insurance claim for an insured event. External data (e.g., public infrastructure or transportation system data) may also be used to determine the type and/or severity of the insured event, and the insurance claim may be modified accordingly.
A. Accident Identification
An insurance provider remote server (e.g., server 40 of
As an example, in the case of an accident, communications and/or options may be pushed to the insured's mobile device (e.g., mobile device 10 of
In some embodiments, a customer or insured may control whether or not emergency responders (e.g., police, fire fighters, ambulances, tow trucks, or even insurance agents) are deployed to the scene of an accident. As suggested above, for example, a mobile device or smart phone application may ask the insured: “Have you been in an accident”; “Do you need assistance?”; “Is the accident serious?”; and/or other questions. The mobile device application may allow an insured to communicate with an insurance provider, and/or communicate directly to emergency responders, more effectively and efficiently than with conventional techniques, and/or may save time when it is of critical importance for those injured in a vehicle accident. Additionally or alternatively, the mobile device (and/or insurance provider remote server, such as remote server 40 of
B. Post-Accident Services
The mobile device application may (1) include and/or present a list of next steps that the insured should take after a vehicle accident (including instructions on how to submit an insurance claim, or automatically generate an insurance claim, for the insured event); (2) provide post-accident assistance; (3) allow for pre-selecting towing and/or auto repair service providers; and/or (4) call pre-determined persons (e.g., spouse, significant other, loved one, parents, children, friends, etc.). The mobile device application may allow the insured to customize the automatic or semi-automatic services that may be provided and/or presented to the insured when an insured event (e.g, vehicle accident) is detected from analysis of the telematics and/or other data.
The mobile device application (and/or application or functionality of a smart vehicle display or controller, such as smart vehicle controller 14 of
The mobile device and/or smart vehicle application may also present options, such as whether to direct the mobile device and/or smart vehicle to call an insurance agent and/or family members. The options may allow the insured to control the communications, and/or the communications may be pre-set by the insured to automatically occur. For instance, if the telematics and/or other data gathered indicates that the insured is in a serious vehicle accident, the mobile device and/or smart vehicle application may direct the mobile device and/or smart vehicle to automatically notify the insured's spouse of the details of the accident, including severity, accident location, status of the insured or driver, and/or current location of the insured or driver (e.g., in an ambulance or at a hospital).
The mobile device and/or smart vehicle application may automatically generate an insurance claim, and/or attach associated data gathered from various sensors or systems pertinent to the insured event. The application may present the insured an option to automatically submit the automatically generated insurance claim, such as by pressing an icon or button on a user interface or display screen of a mobile device application or smart vehicle control system.
C. Application Customization
The mobile device and/or smart vehicle application may allow the insured to customize the application. The application may allow the insured to select services that are requested when an accident is detected from the data collected. The accident detection may trigger the pre-selected services being requested and/or include contacting police, an ambulance, and/or an insurance agent.
In one embodiment, the insurance provider may keep a user-customized profile of user preferences for an insured. The profile may indicate if a customer call center should proactively call the insured when collected data indicates that an accident has occurred. Also, for a serious accident, the insurance provider remote server may send a text or other message to the responsible insurance agent. The responsible insurance agent may then reach out to the insured promptly to provide individual customer service.
Gathered telematics and/or other data (e.g., any type or types of telematics and/or other data described above in Section I and/or Section II) may facilitate loss mitigation services. If an insured event happens, an insurance provider may be remotely notified via wireless communication and/or may identify such insured events based upon data remotely received from vehicles, mobile devices, and/or other electronic devices or systems.
The telematics and/or other data gathered may lead to triage of an auto accident. The data gathered may facilitate identification of whether the claim is minor and may be a “self-serve” type of claim. Additionally or alternatively, the data gathered may indicate that the claim is major, and may involve a fatality or a total loss claim. An application on a smart phone (e.g., mobile device 10 of
The mobile device and/or smart vehicle application may allow two customers of the same insurance provider to exchange information after an accident. For instance, the applications and/or mobile devices may be equipped for Near Field Communication (NFC). The insurance customers may agree upon the facts of the accident, including who was at fault, and submit a single or joint insurance claim to the insurance provider via their mobile devices. Such submission, especially for minor accidents, may facilitate prompt and efficient handling of the insurance claim(s) by the insurance provider, and alleviate any inconvenience incurred on the part of the insured or group of insurance customers with respect to filing insurance claims and/or other paperwork.
The present embodiments may facilitate generating intelligent routing and/or other recommendations, and transmitting those to an insured. Intelligent routing recommendations may be based upon vehicle location, route, and/or destination information. The intelligent routing may also be based upon historical data and/or real-time data. The historical and/or real-time data may relate to past or current accidents, weather, traffic, traffic patterns, road conditions, and/or road construction. The intelligent routing functionality, and/or usage (or percentage of usage) thereof, may be used to adjust insurance premiums or rates, and/or discounts.
A. Route Guidance
The intelligent routing recommendations may provide (e.g., via wireless communication, from server 40 of
The intelligent routing recommendations may provide real-time warnings or updates to drivers or insurance customers. Moreover, the intelligent routing recommendations may lead to collision or accident avoidance; more efficient or quicker trips; driving through less traffic or construction; better gas mileage; and/or other benefits.
For instance, short-term or minor road construction projects that may occur with little or no notice may be promptly detected by an insured or the insured's smart vehicle. The GPS location of the minor road construction project (which may be temporarily shutting down a main traffic route or otherwise slowing down traffic) may be sent from the smart vehicle of the insured to the insurance provider remote server. The remote server may then estimate routes to divert traffic around the construction project and notify other insurance customers in the area of an alternate recommended route, such as via wireless communication to their smart vehicles (e.g., vehicle 8 or smart vehicle controller 14 of
The telematics and/or other data may be used to generate messages or alerts transmitted to a smart vehicle or mobile device. A message may indicate that the driver is entering a danger zone associated with an above average risk. For instance, the area may have a lot of ongoing construction, and/or be associated with a higher than average number of accidents.
In one embodiment, the intelligent routing may utilize vehicle-to-vehicle (V2V) communication. The V2V communication may reveal that the vehicles ahead of an insured vehicle are all braking, indicating an accident ahead. The V2V communication data may be sent directly from one vehicle to an insured vehicle (e.g., from vehicle 6 to vehicle 8 of
V2V communication may include sending a message to a smart vehicle or mobile device directing the smart vehicle or mobile device to automatically start recording telematics data. For instance, V2V communication may indicate that an accident has occurred or is likely to happen. In such situations, automatically recording telematics and/or other data may facilitate accident reconstruction and/or cause of accident determination.
B. Accident Location Reporting
In one embodiment, an insured may opt-in to a program that allows or facilitates, from telematics and/or other data, automatic vehicle accident location reporting. Reporting accident locations in real-time to an insurance provider remote server may facilitate the remote server determining intelligent routes for a group of other insurance customers presently on the road. Customers currently traveling toward the scene of the accident may be re-directed by the remote server. The intelligent routes may direct each of the other insurance customers away from, or to avoid, the scene of the accident, facilitating more efficient and safer travel.
In other words, if one insured self-reports an accident location (e.g., via automatic wireless communication indicating GPS location information), other insurance customers or drivers may be able to promptly and effectively avoid the accident scene through intelligent routing. The intelligent routing may not only consider avoidance of the accident scene, but also other driving risk conditions, such as current traffic, construction, and/or weather conditions, to determine an overall lowest risk alternate route to each vehicle's respective destination.
C. Other Recommendations
Telematics and/or other data gathered (e.g., any type or types of telematics and/or other data described above in Section I and/or Section II) may reveal certain trends about an insured. The data may indicate that the insured is typically driving in areas associated with an above-average number of accidents and/or high crime neighborhoods. The data may also indicate that the insured is driving over the speed limit too much and/or taking turns while driving above a recommended speed. The high risk accident areas or corners may be highlighted on a road map display, such as a vehicle navigation unit, for ease of viewing.
In response, the insurance provider remote server may push appropriate recommendations to the insured, such as recommendations to take certain corners at a slower speed and/or avoid traveling on roads, or through areas, associated with a high number of past accidents. The insurance provider remote server may also present an insurance-related benefit on a display that may be earned if the insured follows the insurance-related recommendations as a means of incentivizing lower risk driving behavior.
A telematics device may determine that the driver of a vehicle is not the owner or an authorized driver (e.g., not someone covered under the auto insurance policy). The vehicle and/or mobile device may determine that an unknown driver is attempting or may attempt to start an insured vehicle, or is already driving the insured vehicle, by detecting that an unknown or unrecognized mobile device (e.g., smart phone) is in the vehicle.
As an example, allowed/authorized mobile device Bluetooth signatures may be detected from normal mobile device operation. However, if an unrecognized Bluetooth signal is detected, it may be determined that the vehicle has been stolen, especially if GPS information from the insured's mobile device indicates that the insured is not presently in the insured vehicle. The insured, insurance provider, and/or police may all be automatically notified of the theft.
Additionally or alternatively, a current GPS location of the insured vehicle may be displayed on a virtual map of a mobile device application, along with speed and direction information. The mobile device application with “Find My Car” functionality may be used to locate vehicles parked in large parking lots, such as a shopping mall or airport, where the insured may have forgotten where they have parked, and/or may be used to locate stolen vehicles.
The telematics and/or other data may indicate that a home is left unoccupied for a substantial length of time. For instance, it may be determined from the data collected indicates how often an insured visits and/or how much the insured spends at a second or vacation home. If an insured home is left unoccupied for a substantial amount of time, a recommendation may be sent to the insured to monitor the condition or status of the home more closely to alleviate the risk of theft and/or needed repairs being left unattended to. Insurance savings (e.g., a premium discount) may be provided to the insured in return for following the recommendations provided by the insurance provider.
The method 100 may include collecting accident data associated with a vehicle accident involving a driver (block 102). The driver may be associated with an insurance policy issued by the insurance provider (e.g., an owner of the policy, or another individual listed on the policy). The accident data may include telematics data, and possibly other data, collected from one or more sources. For example, the accident data may include data associated with or generated by one or more mobile devices (e.g., mobile device 10 of
The method 100 may also include analyzing any or all of the collected accident data (block 104), and determining a likely severity of the accident based upon the analysis (block 106). For example, it may be determined that an accident is likely severe (e.g., likely involves severe personal injury) if accelerometer data included in the accident data indicates a very large and abrupt change in speed. As another example, it may be determined that an accident is likely severe if the accident data (e.g., from a vehicle-mounted camera) shows that the accident was a head-on accident between two vehicles.
The method 100 may also include automatically communicating with the driver (e.g., the insured) (block 108). For example, a communication related to emergency assistance or an emergency assistance recommendation may be generated based upon the likely severity as determined at block 106, and then transmitted from one or more remote servers implementing the method 100 to a mobile device associated with (e.g., owned and/or carried by) the driver, such as mobile device 10 of
Alternative embodiments and/or scenarios corresponding to block 108 (and/or a process subsequent to block 108) are reflected in blocks 108A through 108C. For example, the driver (e.g., insured) may either accept or reject the emergency assistance indicated in the communication (block 108A), e.g., by making a selection via a user interface of the mobile device, in response to a prompt that appears in connection with the communication. Alternatively, the driver may modify the emergency assistance request or recommendation (block 108B), e.g., by indicating a different type of emergency assistance (ambulance, police, etc.). Again, the modification may be made via a user interface of the mobile device, in response to a prompt that appears in connection with the communication. As yet another alternative, an emergency assistance request may automatically be sent to a third party (e.g., police department, fire department, hospital, etc.) without waiting for any feedback from the driver (block 108C). For example, the communication at block 108 may merely notify the driver that emergency assistance has been requested, and possibly specify the type of assistance requested.
Although not shown in
The method 100 may also include determining (e.g., based upon the analysis at block 104) fault of the driver for the accident. As seen in
The method 100 may also include handling an insurance claim associated with the accident (block 112). For example, the fault of the driver (e.g., insured) determined at block 110 may be used to determine the appropriate payout by the insurance provider, or whether another insurance provider should be responsible for payment, etc.
The method 100 may also include adjusting, generating and/or updating one or more insurance-related items (block 114). The insurance-related item(s) may include, for example, parameters of the insurance policy (e.g., a deductible), a premium, a rate, a discount, and/or a reward. The adjustment, generation and/or update may be based upon the fault determined at block 110, or based upon the driver having the emergency assistance functionality that allows the method 100 to be performed (e.g., a mobile device application that enables the driver to receive the communication sent at block 108 and/or to send the wireless communication received at block 108), for example.
In other embodiments, the method 100 may include additional, fewer, or alternate actions as compared to those shown in
As another example, the method 100 may further include receiving a wireless communication from the driver cancelling emergency assistance that has already been requested from a third party. As yet another example, the method 100 may include (1) generating an estimated insurance claim based upon the likely severity determined at block 106; (2) transmitting the estimated insurance claim to the driver's or insured's mobile device to facilitate presenting all or some of the claim to the driver or insured; (3) receiving a wireless communication from the driver or insured indicating approval, rejection or modification of the claim; and/or (4) handling the claim in accordance with the approval, rejection or modification. In still other example embodiments, the method 100 may omit blocks 110, 112 and/or 114.
As can be seen from the above discussion, the method 100 may enable a prompt response by the appropriate personnel (e.g., by first responders with an ambulance), and various components (e.g., in the example system 1) may complete their associated tasks relatively quickly and/or efficiently. For instance, the processor 62 of
In one aspect, a computer-implemented method of loss mitigation may be provided. The method may include (1) collecting or receiving telematics and/or other data at or via a remote server associated with an insurance provider, the telematics and/or other data being associated with a vehicle accident involving a specific driver and/or an insured. The insured may own an insurance policy issued by the insurance provider, and the telematics and/or other data may be gathered before, during, and/or after the vehicle accident. The method may include (2) analyzing the telematics and/or other data at and/or via the remote server; (3) determining, at and/or via the remote server, a likely severity of the vehicle accident from the analysis of the telematics and/or other data; (4) generating a communication related to emergency assistance or an emergency assistance recommendation, at the remote server, based upon the likely severity of the vehicle accident that is determined from the analysis of the telematics and/or other data; (5) transmitting, via wireless communication, the communication related to the emergency assistance or emergency assistance recommendation from the remote server to a mobile device or smart vehicle associated with the specific driver and/or insured; (6) receiving, at and/or via the remote server, a wireless communication from the specific driver and/or insured indicating approval, rejection, or modification of the emergency assistance or emergency assistance recommendation; and/or (7) notifying, via communication sent from the remote server, the emergency assistance approved and/or requested by the specific driver to a third party, such as emergency responders (i.e., police or medical personnel). The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
For instance, the method may include adjusting, generating, and/or updating, at and/or via the remote server, an insurance policy, premium, rate, discount, and/or reward for the specific driver and/or the insured based upon having the emergency assistance functionality. The method may further comprise transmitting information related to the adjusted, generated, and/or updated insurance policy, premium, rate, discount, and/or reward from the remote server to a mobile device associated with the specific driver and/or insured to facilitate presenting, on a display of the mobile device, all or a portion of the adjusted, generated, and/or updated insurance policy, premium, rate, discount, and/or reward to the specific driver and/or insured for their review, modification, and/or approval. Also, the telematics and/or other data may include the types of data discussed elsewhere herein.
In another aspect, another computer-implemented method of loss mitigation may be provided. The method may include (1) collecting or receiving telematics and/or other data at or via a remote server associated with an insurance provider, the telematics and/or other data being associated with a vehicle accident involving a specific driver and/or an insured. The insured may own an insurance policy issued by the insurance provider, and the telematics and/or other data may be gathered before, during, and/or after the vehicle accident. The method may include (2) analyzing the telematics and/or other data at and/or via the remote server; (3) determining, at and/or via the remote server, a likely severity of the vehicle accident from the analysis of the telematics and/or other data; (4) generating a communication related to emergency assistance or an emergency assistance recommendation, at and/or via the remote server, based upon the likely severity of the vehicle accident that is determined from the analysis of the telematics and/or other data; and/or (5) transmitting, via wireless communication, the communication related to the emergency assistance or emergency assistance recommendation from the remote server directly to a third party, such as a police department, fire department, and/or hospital to facilitate prompt and appropriate emergency responder response to the vehicle accident.
The method may further comprise notifying the specific driver and/or insured, via wireless communication sent from the remote server, that the emergency assistance from the third party has been requested, and/or receiving from the specific driver and/or insured, at or via the remote server, a wireless communication indicating a cancellation of the emergency assistance from the third party and/or that the emergency assistance is not necessary. The method may include adjusting, generating, and/or updating, via the remote server, an insurance policy, premium, rate, discount, and/or reward for the specific driver and/or the insured based upon having the emergency assistance functionality.
The method may include transmitting information related to the adjusted, generated, and/or updated insurance policy, premium, rate, discount, and/or reward from the remote server to a mobile device associated with the specific driver and/or insured to facilitate presenting, on a display of the mobile device, all or a portion of the adjusted, generated, and/or updated insurance policy, premium, rate, discount, and/or reward to the specific driver and/or insured for their review, modification, and/or approval.
IX. EXEMPLARY ESTIMATED CLAIM GENERATION METHOD
In one aspect, a computer-implemented method of generating an insurance claim for an insured may be provided. The method may include: (1) collecting or receiving telematics and/or other data (e.g., any of the telematics and/or other data described above in Section I and/or Section II) at or via a remote server associated with an insurance provider (e.g., server 40 of
The method 200 may include receiving trip information including a vehicle's destination, planned route, and/or current location. As seen in
The method 200 may also include analyzing the data/information collected at block 202 (block 204). In some embodiments and/or scenarios, the method 200 may include comparing/analyzing the vehicle location, route, and/or destination with real-time traffic, construction, and/or weather conditions (block 206A). The real-time traffic, construction, and/or weather conditions may be telematics data collected from other vehicles (and/or roadside equipment or infrastructure, etc.), for example. In other embodiments and/or scenarios, the method 200 may include comparing/analyzing the vehicle location, route, and/or destination with information in a database of traffic conditions, construction conditions, weather conditions, and/or past accidents (block 206B). The method 200 may include building the database using traffic, construction, weather, and/or accident information gathered from one or more sources (e.g., news feeds, telematics data, etc.), for example.
The method 200 may also include identifying a lower risk route or routes between the vehicle's current location and the destination (block 208). For example, the method 200 may include identifying areas (e.g., roads or road segments) associated with higher risk of vehicle accident using collected vehicle telematics data and/or database (e.g., traffic, construction, weather, accident, etc.) information, and the route(s) may be identified/determined at block 208 such that those high-risk areas are avoided. Alternatively, as seen in
The method 200 may also include communicating at least one of the one or more identified lower risk routes to the driver (e.g., the insured) as a recommended route to the destination (block 210). The route may be communicated via wireless communication to a mobile device and/or a smart vehicle of the driver (e.g., to mobile device 10, and/or a vehicle navigation system of vehicle 8, of
The method 200 may also include determining whether the recommended route was taken by the driver based upon analysis of telematics and/or other data (block 212). For example, GPS data may be received from the driver's mobile device or smart vehicle, and used to determine whether the recommended route was followed or a different route was taken instead.
The method 200 may also include adjusting, updating, and/or generating one or more insurance-related items based upon the determination at block 212 (block 214). The insurance-related item(s) may include, for example, parameters of the insurance policy (e.g., a deductible), a premium, a rate, a discount, and/or a reward. Alternatively, or additionally, the insurance-related item(s) may be adjusted, updated, and/or generated (e.g., insurance discounts may be provided) based upon an amount of usage, by the driver or another individual associated with the same insurance policy, of the intelligent routing functionality (e.g., a number or percentage of recommended routes taken, etc.).
In other embodiments, the method 200 may include additional, fewer, or alternate actions as compared to those shown in
As can be seen from the above discussion, the method 200 may efficiently determine low-risk routes for drivers. For instance, the processor 62 of
In another aspect, a computer-implemented method of intelligent routing may be provided. The method may include (1) collecting telematics and/or other data and/or building a database related to multiple vehicle accidents; (2) identifying, via a processor or remote sever, areas of higher than average vehicle accidents and/or less risky travel routes or roads; (3) receiving, at or via the remote server, a destination, a planned route, and/or a current location of a vehicle, such as from telematics related data; (4) based upon the destination, planned route, or current location of the vehicle, determining, at or via the remote server, a recommended route to the destination that avoids the areas of higher than average vehicle accidents and/or reduces accident associated risk; and/or (5) transmitting the recommended route from the remote server to the insured and/or driver via wireless communication for display on the vehicle navigation system and/or a mobile device associated with the insured and/or driver to facilitate the insured and/or driver traveling via a route associated with lower risk of accident.
The method may include generating insurance discounts based upon an amount that the insured uses the intelligent routing functionality provided by an insurance provider. The telematics and/or other data may include the data indicated elsewhere herein. The method of intelligent routing may include additional, fewer, or alternative actions, including those discussed elsewhere herein.
In another aspect, another method of intelligent routing may be provided. The method may include: (1) building a database associated with road traffic, construction areas, and/or vehicle accidents; (2) receiving, at or via an insurance provider remote server, a vehicle destination and a current vehicle location associated with an insured vehicle from the insured vehicle and/or a mobile device of a driver and/or insured associated with the insured vehicle, such as from telematics related data; (3) analyzing, at or via the insurance provider remote server, the vehicle destination and the current vehicle location associated with the insured vehicle in conjunction with the database associated with the road traffic, construction areas, and/or vehicle accidents to determine a low risk recommended or alternate route to the destination; and/or (4) transmitting from the remote server, the low risk recommended or alternate route to the destination to the vehicle and/or a mobile device associated with the driver and/or insured to facilitate the driver and/or insured taking the low risk recommended or alternate route to the destination.
The method may include generating insurance discounts based upon an amount of usage (by an insured) of the intelligent routing functionality provided by an insurance provider. The telematics and/or other data may include the data indicated elsewhere herein. The method of intelligent routing may include additional, fewer, or alternative actions, including those discussed elsewhere herein.
The method 300 may include collecting driver-related data over time (block 302). The data may be associated with one or more authorized drivers of an insured vehicle (e.g., a policy owner and one or more family members), with the driver(s) and vehicle being covered by an insurance policy issued by an insurance provider (e.g., an insurance provider associated with one or more servers implementing the method 300, in one embodiment). In particular, the collected driver-related data may be associated with identification and/or driving behavior of the driver(s). For example, the driver-related data may include data indicative of driver weights, driver appearances, acceleration, braking and/or cornering behaviors of the drivers, and so on.
The driver-related data may include telematics data, and possibly other data, collected from one or more sources. For example, the driver-related data may include data associated with or generated by one or more mobile devices (e.g., mobile device 10 of
The collected driver-related data may be analyzed (block 304). For example, the data may be analyzed in order to determine an “electronic signature” for the mobile device of each authorized driver. As another example, vehicle operation data such as acceleration, braking and cornering, and/or other data, may be analyzed to determine higher-level behaviors of a driver (e.g., how often the driver brakes suddenly, or how often and/or by how much the driver exceeds the speed limit, etc.). Data may also be analyzed to categorize the data according to driver (e.g., determine, based upon weights or other identifying data, which driving behaviors correspond to which authorized driver, etc.).
While not shown in
The known data (e.g., stored in the database) may be compared to new data to determine that a driver is unauthorized, i.e., not one of the individuals covered by the insurance policy (block 306). While referred to here as an unauthorized “driver,” the individual may be currently driving the insured vehicle, or may merely be attempting to start the vehicle or even just sitting in a seat (e.g., driver's seat) of the vehicle.
While not shown in
The comparison at block 306 may include, for example, comparing a weight of the current driver with the weights of each authorized driver (e.g., based upon data that was generated by a driver's seat weight sensor of the insured vehicle), comparing an appearance of the current driver with the appearance of each authorized driver (e.g., based upon image data captured by a vehicle-mounted camera and using suitable image processing techniques), and/or comparing electronic signatures or signals of mobile devices of the authorized drivers with an unknown electronic signature or signal of an unrecognizable mobile device associated with the unauthorized individual. Additionally or alternatively, the comparison may include comparing acceleration, braking and/or cornering behaviors or patterns of the current driver with like behaviors or patterns of each authorized driver, etc.
After determining that the current driver is unauthorized, the insured vehicle may be disabled (block 308). For example, a remote server implementing the method 300 may send a wireless signal to a vehicle controller within the insured vehicle (e.g., smart vehicle controller 14 of
Disablement of the vehicle may also depend upon other conditions being satisfied. For example, it may first need to be verified that the unauthorized individual is sitting in a driver's seat of the insured vehicle (e.g., rather than simply being a passenger). The verification may be made by triangulation or communication techniques between the unauthorized individual's mobile device and a vehicle-mounted transmitter, and/or using a visual image of the unauthorized individual, for example.
As an alternative to block 308, the method 300 may include tracking the location of the insured vehicle (block 310). Vehicle tracking may be accomplished using GPS coordinates, for example, and may persist until the vehicle is returned to the vehicle owner. The method 300 may also include transmitting a current GPS location of the insured vehicle to a mobile device of one of the authorized drivers (e.g., the policy owner and/or vehicle owner), and/or to a third party remote server (e.g., a server associated with a police department).
In other embodiments, the method 300 may include additional, fewer, or alternate actions as compared to those shown in
As can be seen from the above discussion, the method 300 may efficiently prevent vehicle theft, or efficiently mitigate the losses and/or inconveniences due to such a theft. For instance, the processor 62 of
In one aspect, a computer-implemented method of vehicle theft prevention or mitigation may be provided. The method may include: (1) collecting or receiving telematics and/or other data at or via a remote server associated with an insurance provider (or at or via a vehicle controller) over time, the telematics and/or other data being associated with an insured driver or family member driving an insured vehicle (and/or their identification), the insured vehicle being covered by an insurance policy issued by the insurance provider; (2) building, at or via the remote server (or vehicle controller), a database of insured drivers or family members (i) authorized to drive the insured vehicle, and/or (ii) covered by the insurance policy; (3) collecting or receiving current telematics and/or other data at or via the remote server (or vehicle controller) associated with an individual attempting to start or currently driving the insured vehicle; (4) determining, at or via the remote server (or vehicle controller), that the individual attempting to start or currently driving the insured vehicle is not among the insured drivers or family members (i) authorized to drive the insured vehicle, or (ii) covered by the insurance policy; and/or (5) if so, then directing or controlling, at or via the remote server (or vehicle controller), a disablement of an operation of the insured vehicle (i.e., preventing the vehicle from operating, or safely or orderly slowing the down the vehicle to a halt and/or moving the vehicle off to the side of the road) and/or preventing the individual from starting or otherwise operating the insured vehicle to facilitate preventing or mitigating theft of the insured vehicle.
The determining, at or via the remote server (or vehicle controller), that the individual attempting to start, or currently driving, the insured vehicle is not among the insured drivers or family members (i) authorized to drive the insured vehicle, or (ii) covered by the insurance policy may be performed by comparing electronic signatures or signals of mobile devices of the insured drivers or family members with an unknown electronic signature or signal of a unrecognizable mobile device associated with the individual attempting to start, or currently driving, the insured vehicle, or otherwise sitting in a driver's seat of the insured vehicle. The method may include verifying, before preventing operation of the insured vehicle, that the unknown individual attempting to start, or currently driving, the insured vehicle is sitting in the driver's seat of the insured vehicle, such as via (a) triangulation or communication techniques between the unrecognizable mobile device and vehicle mounted transmitters, and/or (b) using visual images gathered or collected from the telematics and/or other data.
In one embodiment, determining, at or via the remote server (or vehicle controller), that the individual attempting to start, or currently driving, the insured vehicle is not among the insured drivers or family members (i) authorized to drive the insured vehicle, or (ii) covered by the insurance policy is performed by comparing electronic signatures or signals of various mobile devices. In another embodiment, determining, at or via the remote server (or vehicle controller), that the individual attempting to start, or currently driving, the insured vehicle is not among the insured drivers or family members (i) authorized to drive the insured vehicle, or (ii) covered by the insurance policy is performed by comparing (a) visual images (such as gathered by vehicle mounted cameras or mobile devices) or weights (such as determined from seat sensors) of the insured drivers or family members with (b) visual images or a weight of the individual attempting to start, or currently driving, the insured vehicle, respectively.
In one aspect, the telematics and/or other data may include data associated with, or generated by, mobile devices, such as smart phones, smart glasses, and/or smart wearable electronic devices capable of wireless communication. The telematics and/or other data may include data associated with, or generated by, an insured vehicle or a computer system of the insured vehicle. The telematics and/or other data may include data associated with, or generated by, (i) a vehicle other than the insured vehicle; (ii) vehicle-to-vehicle (V2V) communication; and/or (iii) road side equipment or infrastructure.
The method may further include, when it is determined, at or via the remote server (or vehicle controller), that the individual attempting to start, or currently driving, the insured vehicle is not among the insured drivers or family members (i) authorized to drive the insured vehicle, or (ii) covered by the insurance policy, generating a message (or wireless communication) and transmitting the message from the remote server (or vehicle controller) to a mobile device of one of the insured drivers or family members, or to authorities to facilitate vehicle recapture or safety. The method may include tracking the GPS location of the insured vehicle at the remote server (or vehicle controller), and/or transmitting the present GPS location of the insured vehicle to a mobile device of an insured or to a third party remote server, such as a police department. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.
The following additional considerations apply to the foregoing discussion. Throughout this specification, plural instances may implement operations or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.
Unless specifically stated otherwise, discussions herein using words such as “processing,” “computing,” “calculating,” “determining,” “presenting,” “displaying,” or the like may refer to actions or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information.
As used herein any reference to “one embodiment” or “an embodiment” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).
In addition, use of “a” or “an” is employed to describe elements and components of the embodiments herein. This is done merely for convenience and to give a general sense of the invention. This description should be read to include one or at least one and the singular also includes the plural unless it is obvious that it is meant otherwise.
Upon reading this disclosure, those of skill in the art will appreciate still additional alternative structural and functional designs through the principles disclosed herein. Thus, while particular embodiments and applications have been illustrated and described, it is to be understood that the disclosed embodiments are not limited to the precise construction and components disclosed herein. Various modifications, changes and variations, which will be apparent to those skilled in the art, may be made in the arrangement, operation and details of the methods and systems disclosed herein without departing from the spirit and scope defined in the appended claims. Finally, the patent claims at the end of this patent application are not intended to be construed under 35 U.S.C. § 112(f) unless traditional means-plus-function language is expressly recited, such as “means for” or “step for” language being explicitly recited in the claim(s).
This application is a continuation of U.S. application Ser. No. 16/178,838 (filed Nov. 2, 2018), which is a continuation of U.S. application Ser. No. 15/676,470 (filed Aug. 14, 2017), which is a continuation of U.S. application Ser. No. 14/798,757 (filed Jul. 14, 2015), which claims the benefit of U.S. Provisional Application No. 62/027,021 (filed Jul. 21, 2014); U.S. Provisional Application No. 62/040,735 (filed Aug. 22, 2014); U.S. Provisional Application No. 62/145,022 (filed Apr. 9, 2015); U.S. Provisional Application No. 62/145,024 (filed Apr. 9, 2015); U.S. Provisional Application No. 62/145,027 (filed Apr. 9, 2015); U.S. Provisional Application No. 62/145,028 (filed Apr. 9, 2015); U.S. Provisional Application No. 62/145,029 (filed Apr. 9, 2015); U.S. Provisional Application No. 62/145,032 (filed Apr. 9, 2015); U.S. Provisional Application No. 62/145,033 (filed Apr. 9, 2015); U.S. Provisional Application No. 62/145,145 (filed Apr. 9, 2015); U.S. Provisional Application No. 62/145,228 (filed Apr. 9, 2015); U.S. Provisional Application No. 62/145,232 (filed Apr. 9, 2015); and U.S. Provisional Application No. 62/145,234 (filed Apr. 9, 2015). The entirety of each of the foregoing applications is incorporated by reference herein. Additionally, the present application is related to co-pending U.S. patent application Ser. No. 14/798,741 (filed Jul. 14, 2015); co-pending U.S. patent application Ser. No. 14/798,750 (filed Jul. 14, 2015); co-pending U.S. patent application Ser. No. 14/798,745 (filed Jul. 14, 2015); co-pending U.S. patent application Ser. No. 14/798,763 (filed Jul. 14, 2015); co-pending U.S. patent application Ser. No. 14/798,609 (filed Jul. 14, 2015); co-pending U.S. patent application Ser. No. 14/798,615 (filed Jul. 14, 2015); co-pending U.S. patent application Ser. No. 14/798,626 (filed Jul. 14, 2015); co-pending U.S. patent application Ser. No. 14/798,633 (filed Jul. 14, 2015); co-pending U.S. patent application Ser. No. 14/798,769 (filed Jul. 14, 2015); and co-pending U.S. patent application Ser. No. 14/798,770 (filed Jul. 14, 2015).
Number | Name | Date | Kind |
---|---|---|---|
4218763 | Kelley et al. | Aug 1980 | A |
4386376 | Takimoto et al. | May 1983 | A |
4565997 | Seko et al. | Jan 1986 | A |
4833469 | David | May 1989 | A |
5214582 | Gray | May 1993 | A |
5220919 | Phillips et al. | Jun 1993 | A |
5363298 | Survanshi et al. | Nov 1994 | A |
5367456 | Summerville et al. | Nov 1994 | A |
5368484 | Copperman et al. | Nov 1994 | A |
5436839 | Dausch et al. | Jul 1995 | A |
5453939 | Hoffman et al. | Sep 1995 | A |
5488353 | Kawakami et al. | Jan 1996 | A |
5499182 | Ousborne | Mar 1996 | A |
5515026 | Ewert | May 1996 | A |
5574641 | Kawakami et al. | Nov 1996 | A |
5626362 | Mottola | May 1997 | A |
5689241 | Clarke, Sr. et al. | Nov 1997 | A |
5797134 | McMillan et al. | Aug 1998 | A |
5825283 | Camhi | Oct 1998 | A |
5835008 | Colemere, Jr. | Nov 1998 | A |
5978720 | Hieronymus et al. | Nov 1999 | A |
5983161 | Lemelson et al. | Nov 1999 | A |
6031354 | Wiley et al. | Feb 2000 | A |
6064970 | McMillan et al. | May 2000 | A |
6067488 | Tano | May 2000 | A |
6141611 | Mackey et al. | Oct 2000 | A |
6151539 | Bergholz et al. | Nov 2000 | A |
6215200 | Genzel | Apr 2001 | B1 |
6246933 | Bague | Jun 2001 | B1 |
6253129 | Jenkins et al. | Jun 2001 | B1 |
6271745 | Anzai et al. | Aug 2001 | B1 |
6285931 | Hattori et al. | Sep 2001 | B1 |
6298290 | Abe et al. | Oct 2001 | B1 |
6313749 | Horne et al. | Nov 2001 | B1 |
6323761 | Son | Nov 2001 | B1 |
6353396 | Atlas | Mar 2002 | B1 |
6400835 | Lemelson et al. | Jun 2002 | B1 |
6473000 | Secreet et al. | Oct 2002 | B1 |
6477117 | Narayanaswami et al. | Nov 2002 | B1 |
6553354 | Hausner et al. | Apr 2003 | B1 |
6556905 | Mittelsteadt et al. | Apr 2003 | B1 |
6570609 | Heien | May 2003 | B1 |
6579233 | Hursh | Jun 2003 | B2 |
6661345 | Bevan et al. | Dec 2003 | B1 |
6701234 | Vogelsang | Mar 2004 | B1 |
6704434 | Sakoh et al. | Mar 2004 | B1 |
6727800 | Dutu | Apr 2004 | B1 |
6765495 | Dunning et al. | Jul 2004 | B1 |
6795759 | Doyle | Sep 2004 | B2 |
6832141 | Skeen et al. | Dec 2004 | B2 |
6889137 | Rychlak | May 2005 | B1 |
6909407 | Schradi et al. | Jun 2005 | B1 |
6909947 | Douros et al. | Jun 2005 | B2 |
6925425 | Remboski et al. | Aug 2005 | B2 |
6931309 | Phelan et al. | Aug 2005 | B2 |
6934365 | Suganuma et al. | Aug 2005 | B2 |
6944536 | Singleton | Sep 2005 | B2 |
6956470 | Heise et al. | Oct 2005 | B1 |
6974414 | Victor | Dec 2005 | B2 |
6983313 | Korkea-Aho | Jan 2006 | B1 |
6989737 | Yasui | Jan 2006 | B2 |
7027621 | Prokoski | Apr 2006 | B1 |
7054723 | Seto et al. | May 2006 | B2 |
7056265 | Shea | Jun 2006 | B1 |
7102496 | Ernst, Jr. et al. | Sep 2006 | B1 |
7138922 | Strumolo et al. | Nov 2006 | B2 |
7149533 | Laird et al. | Dec 2006 | B2 |
7200207 | Meer et al. | Apr 2007 | B2 |
7253724 | Prakah-Asante et al. | Aug 2007 | B2 |
7254482 | Kawasaki et al. | Aug 2007 | B2 |
7266532 | Sutton et al. | Sep 2007 | B2 |
7290275 | Baudoin et al. | Oct 2007 | B2 |
7302344 | Olney et al. | Nov 2007 | B2 |
7315233 | Yuhara | Jan 2008 | B2 |
7330124 | Ota | Feb 2008 | B2 |
7348882 | Adamczyk et al. | Mar 2008 | B2 |
7349860 | Wallach et al. | Mar 2008 | B1 |
7356392 | Hubbard et al. | Apr 2008 | B2 |
7386376 | Basir et al. | Jun 2008 | B2 |
7391784 | Renkel | Jun 2008 | B1 |
7423540 | Kisacanin | Sep 2008 | B2 |
7424414 | Craft | Sep 2008 | B2 |
7480501 | Petite | Jan 2009 | B2 |
7499774 | Barrett et al. | Mar 2009 | B2 |
7565230 | Gardner et al. | Jul 2009 | B2 |
7596242 | Breed et al. | Sep 2009 | B2 |
7609150 | Wheatley et al. | Oct 2009 | B2 |
7639148 | Victor | Dec 2009 | B2 |
7676062 | Breed et al. | Mar 2010 | B2 |
7692552 | Harrington et al. | Apr 2010 | B2 |
7719431 | Bolourchi | May 2010 | B2 |
7783426 | Kato et al. | Aug 2010 | B2 |
7783505 | Roschelle et al. | Aug 2010 | B2 |
7791503 | Breed et al. | Sep 2010 | B2 |
7792328 | Albertson et al. | Sep 2010 | B2 |
7797107 | Shiller | Sep 2010 | B2 |
7812712 | White et al. | Oct 2010 | B2 |
7813888 | Vian et al. | Oct 2010 | B2 |
7835834 | Smith et al. | Nov 2010 | B2 |
7865378 | Gay | Jan 2011 | B2 |
7870010 | Joao | Jan 2011 | B2 |
7877275 | Ball | Jan 2011 | B2 |
7881914 | Trotta et al. | Feb 2011 | B2 |
7881951 | Roschelle et al. | Feb 2011 | B2 |
7890355 | Gay et al. | Feb 2011 | B2 |
7904219 | Lowrey et al. | Mar 2011 | B1 |
7912740 | Vahidi et al. | Mar 2011 | B2 |
7973674 | Bell et al. | Jul 2011 | B2 |
7979172 | Breed | Jul 2011 | B2 |
7979173 | Breed | Jul 2011 | B2 |
7983802 | Breed | Jul 2011 | B2 |
7987103 | Gay et al. | Jul 2011 | B2 |
7991629 | Gay et al. | Aug 2011 | B2 |
8005467 | Gerlach et al. | Aug 2011 | B2 |
8009051 | Omi | Aug 2011 | B2 |
8010283 | Yoshida et al. | Aug 2011 | B2 |
8016595 | Aoki et al. | Sep 2011 | B2 |
8027853 | Kazenas | Sep 2011 | B1 |
8035508 | Breed | Oct 2011 | B2 |
8036160 | Oakes, III | Oct 2011 | B1 |
8040247 | Gunaratne | Oct 2011 | B2 |
8068983 | Vian et al. | Nov 2011 | B2 |
8078334 | Goodrich | Dec 2011 | B2 |
8090598 | Bauer et al. | Jan 2012 | B2 |
8095394 | Nowak et al. | Jan 2012 | B2 |
8102901 | Aissi et al. | Jan 2012 | B2 |
8106769 | Maroney et al. | Jan 2012 | B1 |
8108655 | Abernathy et al. | Jan 2012 | B2 |
8117049 | Berkobin et al. | Feb 2012 | B2 |
8123686 | Fennell et al. | Feb 2012 | B2 |
8139109 | Schmiedel et al. | Mar 2012 | B2 |
8140249 | Hessling et al. | Mar 2012 | B2 |
8140358 | Ling et al. | Mar 2012 | B1 |
8140359 | Daniel | Mar 2012 | B2 |
8164432 | Broggi et al. | Apr 2012 | B2 |
8180522 | Tuff | May 2012 | B2 |
8180655 | Hopkins, III | May 2012 | B1 |
8185380 | Kameyama | May 2012 | B2 |
8188887 | Catten et al. | May 2012 | B2 |
8190323 | Maeda et al. | May 2012 | B2 |
8204766 | Bush | Jun 2012 | B2 |
8255144 | Breed et al. | Aug 2012 | B2 |
8255243 | Raines et al. | Aug 2012 | B2 |
8255244 | Raines et al. | Aug 2012 | B2 |
8260489 | Nielsen et al. | Sep 2012 | B2 |
8260639 | Medina, III et al. | Sep 2012 | B1 |
8265861 | Ikeda et al. | Sep 2012 | B2 |
8275417 | Flynn | Sep 2012 | B2 |
8280752 | Cripe et al. | Oct 2012 | B1 |
8311858 | Everett et al. | Nov 2012 | B2 |
8314708 | Gunderson et al. | Nov 2012 | B2 |
8332242 | Medina, III | Dec 2012 | B1 |
8340893 | Yamaguchi et al. | Dec 2012 | B2 |
8340902 | Chiang | Dec 2012 | B1 |
8344849 | Larsson et al. | Jan 2013 | B2 |
8352118 | Mittelsteadt et al. | Jan 2013 | B1 |
8355837 | Avery et al. | Jan 2013 | B2 |
8364391 | Nagase et al. | Jan 2013 | B2 |
8384534 | James et al. | Feb 2013 | B2 |
8386168 | Hao | Feb 2013 | B2 |
8423239 | Blumer et al. | Apr 2013 | B2 |
8437966 | Connolly et al. | May 2013 | B2 |
8447231 | Bai et al. | May 2013 | B2 |
8451105 | McNay | May 2013 | B2 |
8457880 | Malalur et al. | Jun 2013 | B1 |
8473143 | Stark et al. | Jun 2013 | B2 |
8487775 | Victor et al. | Jul 2013 | B2 |
8510196 | Brandmaier et al. | Aug 2013 | B1 |
8520695 | Rubin et al. | Aug 2013 | B1 |
8554468 | Bullock | Oct 2013 | B1 |
8554587 | Nowak et al. | Oct 2013 | B1 |
8566126 | Hopkins, III | Oct 2013 | B1 |
8571895 | Medina, III | Oct 2013 | B1 |
8595034 | Bauer et al. | Nov 2013 | B2 |
8595037 | Hyde et al. | Nov 2013 | B1 |
8605947 | Zhang et al. | Dec 2013 | B2 |
8606512 | Bogovich et al. | Dec 2013 | B1 |
8618922 | Debouk et al. | Dec 2013 | B2 |
8634980 | Urmson et al. | Jan 2014 | B1 |
8645014 | Kozlowski et al. | Feb 2014 | B1 |
8645029 | Kim et al. | Feb 2014 | B2 |
8660734 | Zhu et al. | Feb 2014 | B2 |
8698639 | Fung et al. | Apr 2014 | B2 |
8700251 | Zhu et al. | Apr 2014 | B1 |
8712893 | Brandmaier | Apr 2014 | B1 |
8725311 | Breed | May 2014 | B1 |
8725472 | Hagelin et al. | May 2014 | B2 |
8731977 | Hardin et al. | May 2014 | B1 |
8738523 | Sanchez et al. | May 2014 | B1 |
8742936 | Galley et al. | Jun 2014 | B2 |
8781442 | Link, II | Jul 2014 | B1 |
8781669 | Teller et al. | Jul 2014 | B1 |
8788299 | Medina, III | Jul 2014 | B1 |
8799034 | Brandmaier | Aug 2014 | B1 |
8816836 | Lee et al. | Aug 2014 | B2 |
8818608 | Cullinane et al. | Aug 2014 | B2 |
8825258 | Cullinane et al. | Sep 2014 | B2 |
8849558 | Morotomi et al. | Sep 2014 | B2 |
8868288 | Plante et al. | Oct 2014 | B2 |
8874301 | Rao et al. | Oct 2014 | B1 |
8874305 | Dolgov et al. | Oct 2014 | B2 |
8876535 | Fields et al. | Nov 2014 | B2 |
8880291 | Hampiholi | Nov 2014 | B2 |
8892271 | Breed | Nov 2014 | B2 |
8902054 | Morris | Dec 2014 | B2 |
8909428 | Lombrozo | Dec 2014 | B1 |
8917182 | Chang et al. | Dec 2014 | B2 |
8928495 | Hassib et al. | Jan 2015 | B2 |
8935036 | Christensen et al. | Jan 2015 | B1 |
8954205 | Sagar et al. | Feb 2015 | B2 |
8954217 | Montemerlo et al. | Feb 2015 | B1 |
8954226 | Binion et al. | Feb 2015 | B1 |
8954340 | Sanchez et al. | Feb 2015 | B2 |
8965677 | Breed et al. | Feb 2015 | B2 |
8972100 | Mullen et al. | Mar 2015 | B2 |
8981942 | He et al. | Mar 2015 | B2 |
8989959 | Plante et al. | Mar 2015 | B2 |
8996228 | Ferguson et al. | Mar 2015 | B1 |
8996240 | Plante | Mar 2015 | B2 |
9008952 | Caskey et al. | Apr 2015 | B2 |
9019092 | Brandmaier | Apr 2015 | B1 |
9020876 | Rakshit | Apr 2015 | B2 |
9026266 | Aaron et al. | May 2015 | B2 |
9049584 | Hatton | Jun 2015 | B2 |
9053588 | Briggs | Jun 2015 | B1 |
9055407 | Riemer et al. | Jun 2015 | B1 |
9056395 | Ferguson et al. | Jun 2015 | B1 |
9056616 | Fields et al. | Jun 2015 | B1 |
9063543 | An et al. | Jun 2015 | B2 |
9070243 | Kozlowski et al. | Jun 2015 | B1 |
9075413 | Cullinane et al. | Jul 2015 | B2 |
9079587 | Rupp et al. | Jul 2015 | B1 |
9081650 | Brinkmann et al. | Jul 2015 | B1 |
9098080 | Norris et al. | Aug 2015 | B2 |
9123250 | Duncan et al. | Sep 2015 | B2 |
9135803 | Fields et al. | Sep 2015 | B1 |
9141582 | Brinkmann et al. | Sep 2015 | B1 |
9141995 | Brinkmann et al. | Sep 2015 | B1 |
9141996 | Christensen et al. | Sep 2015 | B2 |
9144389 | Srinivasan et al. | Sep 2015 | B2 |
9147219 | Binion et al. | Sep 2015 | B2 |
9147353 | Slusar | Sep 2015 | B1 |
9151692 | Breed | Oct 2015 | B2 |
9164507 | Cheatham, III et al. | Oct 2015 | B2 |
9177475 | Sellschopp | Nov 2015 | B2 |
9180888 | Fields et al. | Nov 2015 | B1 |
9182942 | Kelly et al. | Nov 2015 | B2 |
9188985 | Hobbs et al. | Nov 2015 | B1 |
9194168 | Lu et al. | Nov 2015 | B1 |
9205805 | Cudak et al. | Dec 2015 | B2 |
9205842 | Fields et al. | Dec 2015 | B1 |
9221396 | Zhu et al. | Dec 2015 | B1 |
9224293 | Taylor | Dec 2015 | B2 |
9229905 | Penilla et al. | Jan 2016 | B1 |
9230441 | Sung et al. | Jan 2016 | B2 |
9235211 | Davidsson et al. | Jan 2016 | B2 |
9262787 | Binion et al. | Feb 2016 | B2 |
9274525 | Ferguson et al. | Mar 2016 | B1 |
9275417 | Binion et al. | Mar 2016 | B2 |
9275552 | Fields et al. | Mar 2016 | B1 |
9279697 | Fields et al. | Mar 2016 | B1 |
9280252 | Brandmaier | Mar 2016 | B1 |
9282430 | Brandmaier et al. | Mar 2016 | B1 |
9282447 | Gianakis | Mar 2016 | B2 |
9283847 | Riley, Sr. et al. | Mar 2016 | B2 |
9299108 | Diana et al. | Mar 2016 | B2 |
9308891 | Cudak et al. | Apr 2016 | B2 |
9311271 | Wright | Apr 2016 | B2 |
9317983 | Ricci | Apr 2016 | B2 |
9325807 | Meoli | Apr 2016 | B1 |
9342074 | Dolgov et al. | May 2016 | B2 |
9342993 | Fields et al. | May 2016 | B1 |
9352709 | Brenneis et al. | May 2016 | B2 |
9352752 | Cullinane et al. | May 2016 | B2 |
9355423 | Slusar | May 2016 | B1 |
9361599 | Biemer et al. | Jun 2016 | B1 |
9361650 | Binion et al. | Jun 2016 | B2 |
9371072 | Sisbot | Jun 2016 | B1 |
9373203 | Fields et al. | Jun 2016 | B1 |
9376090 | Gennermann | Jun 2016 | B2 |
9377315 | Grover et al. | Jun 2016 | B2 |
9381916 | Zhu et al. | Jul 2016 | B1 |
9384491 | Briggs et al. | Jul 2016 | B1 |
9384674 | Nepomuceno | Jul 2016 | B2 |
9390451 | Slusar | Jul 2016 | B1 |
9390452 | Biemer et al. | Jul 2016 | B1 |
9390567 | Kim et al. | Jul 2016 | B2 |
9398421 | Guba et al. | Jul 2016 | B2 |
9399445 | Abou Mahmoud et al. | Jul 2016 | B2 |
9406177 | Attard et al. | Aug 2016 | B2 |
9421972 | Davidsson et al. | Aug 2016 | B2 |
9424607 | Bowers et al. | Aug 2016 | B2 |
9426293 | Oakes, III | Aug 2016 | B1 |
9429943 | Wilson et al. | Aug 2016 | B2 |
9430944 | Grimm et al. | Aug 2016 | B2 |
9440657 | Fields et al. | Sep 2016 | B1 |
9443152 | Atsmon et al. | Sep 2016 | B2 |
9443436 | Scheidt | Sep 2016 | B2 |
9454786 | Srey et al. | Sep 2016 | B1 |
9457754 | Christensen et al. | Oct 2016 | B1 |
9466214 | Fuehrer | Oct 2016 | B2 |
9475496 | Attard et al. | Oct 2016 | B2 |
9477990 | Binion et al. | Oct 2016 | B1 |
9478150 | Fields et al. | Oct 2016 | B1 |
9489635 | Zhu | Nov 2016 | B1 |
9505494 | Marlow et al. | Nov 2016 | B1 |
9511765 | Obradovich | Dec 2016 | B2 |
9511767 | Okumura et al. | Dec 2016 | B1 |
9511779 | Cullinane et al. | Dec 2016 | B2 |
9517771 | Attard et al. | Dec 2016 | B2 |
9524648 | Gopalakrishnan et al. | Dec 2016 | B1 |
9529361 | You et al. | Dec 2016 | B2 |
9530333 | Fields et al. | Dec 2016 | B1 |
9542846 | Zeng et al. | Jan 2017 | B2 |
9558667 | Bowers et al. | Jan 2017 | B2 |
9566959 | Breuer et al. | Feb 2017 | B2 |
9567007 | Cudak et al. | Feb 2017 | B2 |
9583017 | Nepomuceno | Feb 2017 | B2 |
9586591 | Fields et al. | Mar 2017 | B1 |
9587952 | Slusar | Mar 2017 | B1 |
9594373 | Solyom et al. | Mar 2017 | B2 |
9601027 | Nepomuceno | Mar 2017 | B2 |
9604652 | Strauss | Mar 2017 | B2 |
9632502 | Levinson et al. | Apr 2017 | B1 |
9633318 | Plante | Apr 2017 | B2 |
9646428 | Konrardy et al. | May 2017 | B1 |
9646433 | Sanchez et al. | May 2017 | B1 |
9650051 | Hoye et al. | May 2017 | B2 |
9656606 | Vose et al. | May 2017 | B1 |
9663112 | Abou-Nasr et al. | May 2017 | B2 |
9665101 | Templeton | May 2017 | B1 |
9679487 | Hayward | Jun 2017 | B1 |
9697733 | Penilla et al. | Jul 2017 | B1 |
9707942 | Cheatham, III et al. | Jul 2017 | B2 |
9712549 | Almurayh | Jul 2017 | B2 |
9715711 | Konrardy et al. | Jul 2017 | B1 |
9720419 | O'Neill et al. | Aug 2017 | B2 |
9725036 | Tarte | Aug 2017 | B1 |
9727920 | Healy et al. | Aug 2017 | B1 |
9734685 | Fields et al. | Aug 2017 | B2 |
9753390 | Kabai | Sep 2017 | B2 |
9754325 | Konrardy et al. | Sep 2017 | B1 |
9754424 | Ling et al. | Sep 2017 | B2 |
9754490 | Kentley et al. | Sep 2017 | B2 |
9761139 | Acker, Jr. et al. | Sep 2017 | B2 |
9766625 | Boroditsky et al. | Sep 2017 | B2 |
9767516 | Konrardy et al. | Sep 2017 | B1 |
9773281 | Hanson | Sep 2017 | B1 |
9783159 | Potter et al. | Oct 2017 | B1 |
9786154 | Potter | Oct 2017 | B1 |
9792656 | Konrardy et al. | Oct 2017 | B1 |
9797881 | Biondo et al. | Oct 2017 | B2 |
9805423 | Konrardy et al. | Oct 2017 | B1 |
9805601 | Fields et al. | Oct 2017 | B1 |
9816827 | Slusar | Nov 2017 | B1 |
9847033 | Carmack et al. | Dec 2017 | B1 |
9852475 | Konrardy et al. | Dec 2017 | B1 |
9858621 | Konrardy et al. | Jan 2018 | B1 |
9868394 | Fields et al. | Jan 2018 | B1 |
9870649 | Fields et al. | Jan 2018 | B1 |
9878617 | Mochizuki | Jan 2018 | B2 |
9884611 | Abou Mahmoud et al. | Feb 2018 | B2 |
9892567 | Binion et al. | Feb 2018 | B2 |
9896062 | Potter et al. | Feb 2018 | B1 |
9904928 | Leise | Feb 2018 | B1 |
9908530 | Fields et al. | Mar 2018 | B1 |
9934667 | Fields et al. | Apr 2018 | B1 |
9939279 | Pan et al. | Apr 2018 | B2 |
9940676 | Biemer | Apr 2018 | B1 |
9940834 | Konrardy et al. | Apr 2018 | B1 |
9944282 | Fields et al. | Apr 2018 | B1 |
9946531 | Fields et al. | Apr 2018 | B1 |
9948477 | Marten | Apr 2018 | B2 |
9972054 | Konrardy et al. | May 2018 | B1 |
9986404 | Mehta et al. | May 2018 | B2 |
10007263 | Fields et al. | Jun 2018 | B1 |
10013697 | Cote et al. | Jul 2018 | B1 |
10017153 | Potter et al. | Jul 2018 | B1 |
10019901 | Fields et al. | Jul 2018 | B1 |
10026130 | Konrardy et al. | Jul 2018 | B1 |
10026237 | Fields et al. | Jul 2018 | B1 |
10032360 | Kelsh | Jul 2018 | B1 |
10042359 | Konrardy et al. | Aug 2018 | B1 |
10043323 | Konrardy et al. | Aug 2018 | B1 |
10055794 | Konrardy et al. | Aug 2018 | B1 |
10065517 | Konrardy et al. | Sep 2018 | B1 |
10086782 | Konrardy et al. | Oct 2018 | B1 |
10089693 | Konrardy et al. | Oct 2018 | B1 |
10102587 | Potter et al. | Oct 2018 | B1 |
10102590 | Farnsworth et al. | Oct 2018 | B1 |
10106083 | Fields et al. | Oct 2018 | B1 |
10121204 | Brandmaier | Nov 2018 | B1 |
10134278 | Konrardy et al. | Nov 2018 | B1 |
10156848 | Konrardy et al. | Dec 2018 | B1 |
10157423 | Fields et al. | Dec 2018 | B1 |
10163327 | Potter | Dec 2018 | B1 |
10163350 | Fields et al. | Dec 2018 | B1 |
10166994 | Fields et al. | Jan 2019 | B1 |
10168703 | Konrardy et al. | Jan 2019 | B1 |
10181161 | Konrardy et al. | Jan 2019 | B1 |
10185997 | Konrardy et al. | Jan 2019 | B1 |
10185998 | Konrardy et al. | Jan 2019 | B1 |
10185999 | Konrardy et al. | Jan 2019 | B1 |
10351097 | Potter et al. | Jul 2019 | B1 |
10387962 | Potter | Aug 2019 | B1 |
10475127 | Potter et al. | Nov 2019 | B1 |
10540723 | Potter et al. | Jan 2020 | B1 |
20010005217 | Hamilton et al. | Jun 2001 | A1 |
20020016655 | Joao | Feb 2002 | A1 |
20020049535 | Rigo et al. | Apr 2002 | A1 |
20020091483 | Douet | Jul 2002 | A1 |
20020103622 | Burge | Aug 2002 | A1 |
20020103678 | Burkhalter et al. | Aug 2002 | A1 |
20020111725 | Burge | Aug 2002 | A1 |
20020115423 | Hatae | Aug 2002 | A1 |
20020116228 | Bauer et al. | Aug 2002 | A1 |
20020128751 | Engstrom et al. | Sep 2002 | A1 |
20020128882 | Nakagawa et al. | Sep 2002 | A1 |
20020135618 | Maes et al. | Sep 2002 | A1 |
20020146667 | Dowdell et al. | Oct 2002 | A1 |
20030028298 | Macky et al. | Feb 2003 | A1 |
20030046003 | Smith et al. | Mar 2003 | A1 |
20030061160 | Asahina | Mar 2003 | A1 |
20030095039 | Shimomura et al. | May 2003 | A1 |
20030112133 | Webb et al. | Jun 2003 | A1 |
20030120576 | Duckworth | Jun 2003 | A1 |
20030139948 | Strech | Jul 2003 | A1 |
20030146850 | Fallenstein | Aug 2003 | A1 |
20030182042 | Watson et al. | Sep 2003 | A1 |
20030182183 | Pribe | Sep 2003 | A1 |
20030200123 | Burge et al. | Oct 2003 | A1 |
20030233261 | Kawahara | Dec 2003 | A1 |
20040005927 | Bonilla et al. | Jan 2004 | A1 |
20040017106 | Aizawa et al. | Jan 2004 | A1 |
20040019539 | Raman et al. | Jan 2004 | A1 |
20040039503 | Doyle | Feb 2004 | A1 |
20040054452 | Bjorkman | Mar 2004 | A1 |
20040077285 | Bonilla et al. | Apr 2004 | A1 |
20040085198 | Saito et al. | May 2004 | A1 |
20040085211 | Gotfried | May 2004 | A1 |
20040090334 | Zhang et al. | May 2004 | A1 |
20040111301 | Wahlbin et al. | Jun 2004 | A1 |
20040122639 | Qiu | Jun 2004 | A1 |
20040139034 | Farmer | Jul 2004 | A1 |
20040153362 | Bauer et al. | Aug 2004 | A1 |
20040158476 | Blessinger et al. | Aug 2004 | A1 |
20040169034 | Park | Sep 2004 | A1 |
20040185842 | Spaur et al. | Sep 2004 | A1 |
20040198441 | Cooper et al. | Oct 2004 | A1 |
20040204837 | Singleton | Oct 2004 | A1 |
20040226043 | Mettu et al. | Nov 2004 | A1 |
20040252027 | Torkkola et al. | Dec 2004 | A1 |
20040260579 | Tremiti | Dec 2004 | A1 |
20050007438 | Busch et al. | Jan 2005 | A1 |
20050046584 | Breed | Mar 2005 | A1 |
20050055249 | Helitzer et al. | Mar 2005 | A1 |
20050059151 | Bosch | Mar 2005 | A1 |
20050065678 | Smith et al. | Mar 2005 | A1 |
20050071052 | Coletrane et al. | Mar 2005 | A1 |
20050071202 | Kendrick | Mar 2005 | A1 |
20050073438 | Rodgers et al. | Apr 2005 | A1 |
20050075782 | Torgunrud | Apr 2005 | A1 |
20050080519 | Oesterling et al. | Apr 2005 | A1 |
20050088291 | Blanco et al. | Apr 2005 | A1 |
20050088521 | Blanco et al. | Apr 2005 | A1 |
20050093684 | Cunnien | May 2005 | A1 |
20050107673 | Ball | May 2005 | A1 |
20050108065 | Dorfstatter | May 2005 | A1 |
20050108910 | Esparza et al. | May 2005 | A1 |
20050131597 | Raz et al. | Jun 2005 | A1 |
20050134443 | Hottebart et al. | Jun 2005 | A1 |
20050154513 | Matsunaga et al. | Jul 2005 | A1 |
20050216136 | Lengning et al. | Sep 2005 | A1 |
20050227712 | Estevez et al. | Oct 2005 | A1 |
20050228763 | Lewis et al. | Oct 2005 | A1 |
20050237784 | Kang | Oct 2005 | A1 |
20050246256 | Gastineau et al. | Nov 2005 | A1 |
20050259151 | Hamilton et al. | Nov 2005 | A1 |
20050267784 | Slen et al. | Dec 2005 | A1 |
20060010665 | Watzl | Jan 2006 | A1 |
20060031103 | Henry | Feb 2006 | A1 |
20060052909 | Cherouny | Mar 2006 | A1 |
20060052929 | Bastian et al. | Mar 2006 | A1 |
20060053038 | Warren et al. | Mar 2006 | A1 |
20060055565 | Kawamata et al. | Mar 2006 | A1 |
20060079280 | LaPerch | Apr 2006 | A1 |
20060089763 | Barrett et al. | Apr 2006 | A1 |
20060089766 | Allard et al. | Apr 2006 | A1 |
20060092043 | Lagassey | May 2006 | A1 |
20060095302 | Vahidi et al. | May 2006 | A1 |
20060106650 | Bush | May 2006 | A1 |
20060122748 | Nou | Jun 2006 | A1 |
20060136291 | Morita et al. | Jun 2006 | A1 |
20060149461 | Rowley et al. | Jul 2006 | A1 |
20060155616 | Moore et al. | Jul 2006 | A1 |
20060184295 | Hawkins et al. | Aug 2006 | A1 |
20060212195 | Veith et al. | Sep 2006 | A1 |
20060220905 | Hovestadt | Oct 2006 | A1 |
20060229777 | Hudson et al. | Oct 2006 | A1 |
20060232430 | Takaoka et al. | Oct 2006 | A1 |
20060244746 | England et al. | Nov 2006 | A1 |
20060294514 | Bauchot et al. | Dec 2006 | A1 |
20070001831 | Raz et al. | Jan 2007 | A1 |
20070027726 | Warren et al. | Feb 2007 | A1 |
20070048707 | Caamano et al. | Mar 2007 | A1 |
20070055422 | Anzai et al. | Mar 2007 | A1 |
20070066276 | Kuz | Mar 2007 | A1 |
20070080816 | Haque et al. | Apr 2007 | A1 |
20070088469 | Schmiedel et al. | Apr 2007 | A1 |
20070093947 | Gould et al. | Apr 2007 | A1 |
20070096886 | Lich | May 2007 | A1 |
20070122771 | Maeda et al. | May 2007 | A1 |
20070124599 | Morita et al. | May 2007 | A1 |
20070132773 | Plante | Jun 2007 | A1 |
20070149208 | Syrbe et al. | Jun 2007 | A1 |
20070159309 | Ito | Jul 2007 | A1 |
20070159344 | Kisacanin | Jul 2007 | A1 |
20070159354 | Rosenberg | Jul 2007 | A1 |
20070171854 | Chen | Jul 2007 | A1 |
20070208498 | Barker et al. | Sep 2007 | A1 |
20070219720 | Trepagnier et al. | Sep 2007 | A1 |
20070249372 | Gao et al. | Oct 2007 | A1 |
20070263628 | Axelsson et al. | Nov 2007 | A1 |
20070265540 | Fuwamoto et al. | Nov 2007 | A1 |
20070282489 | Boss et al. | Dec 2007 | A1 |
20070282638 | Surovy | Dec 2007 | A1 |
20070291130 | Broggi et al. | Dec 2007 | A1 |
20070299700 | Gay et al. | Dec 2007 | A1 |
20080007451 | De Maagt et al. | Jan 2008 | A1 |
20080027761 | Bracha | Jan 2008 | A1 |
20080028974 | Bianco | Feb 2008 | A1 |
20080033684 | Vian et al. | Feb 2008 | A1 |
20080052134 | Nowak et al. | Feb 2008 | A1 |
20080061953 | Bhogal et al. | Mar 2008 | A1 |
20080064014 | Wojtczak et al. | Mar 2008 | A1 |
20080065427 | Helitzer et al. | Mar 2008 | A1 |
20080077383 | Hagelin et al. | Mar 2008 | A1 |
20080082372 | Burch | Apr 2008 | A1 |
20080084473 | Romanowich | Apr 2008 | A1 |
20080106390 | White | May 2008 | A1 |
20080111666 | Plante et al. | May 2008 | A1 |
20080114502 | Breed et al. | May 2008 | A1 |
20080114530 | Petrisor et al. | May 2008 | A1 |
20080126137 | Kidd et al. | May 2008 | A1 |
20080143497 | Wasson et al. | Jun 2008 | A1 |
20080147265 | Breed | Jun 2008 | A1 |
20080147266 | Plante et al. | Jun 2008 | A1 |
20080147267 | Plante et al. | Jun 2008 | A1 |
20080161989 | Breed | Jul 2008 | A1 |
20080167821 | Breed | Jul 2008 | A1 |
20080180237 | Fayyad et al. | Jul 2008 | A1 |
20080189142 | Brown et al. | Aug 2008 | A1 |
20080195457 | Sherman et al. | Aug 2008 | A1 |
20080204256 | Omi | Aug 2008 | A1 |
20080243558 | Gupte | Oct 2008 | A1 |
20080255887 | Gruter | Oct 2008 | A1 |
20080255888 | Berkobin et al. | Oct 2008 | A1 |
20080258885 | Akhan | Oct 2008 | A1 |
20080258890 | Follmer et al. | Oct 2008 | A1 |
20080291008 | Jeon | Nov 2008 | A1 |
20080294690 | McClellan et al. | Nov 2008 | A1 |
20080297488 | Operowsky et al. | Dec 2008 | A1 |
20080300733 | Rasshofer et al. | Dec 2008 | A1 |
20080306996 | McClellan | Dec 2008 | A1 |
20080313007 | Callahan et al. | Dec 2008 | A1 |
20080319665 | Berkobin et al. | Dec 2008 | A1 |
20090005979 | Nakao et al. | Jan 2009 | A1 |
20090015684 | Ooga et al. | Jan 2009 | A1 |
20090027188 | Saban | Jan 2009 | A1 |
20090040060 | Anbuhl et al. | Feb 2009 | A1 |
20090063030 | Howarter et al. | Mar 2009 | A1 |
20090063174 | Fricke | Mar 2009 | A1 |
20090069953 | Hale et al. | Mar 2009 | A1 |
20090079839 | Fischer et al. | Mar 2009 | A1 |
20090081923 | Dooley et al. | Mar 2009 | A1 |
20090085770 | Mergen | Apr 2009 | A1 |
20090106135 | Steiger | Apr 2009 | A1 |
20090115638 | Shankwitz et al. | May 2009 | A1 |
20090132294 | Haines | May 2009 | A1 |
20090140887 | Breed et al. | Jun 2009 | A1 |
20090174573 | Smith | Jul 2009 | A1 |
20090207005 | Habetha et al. | Aug 2009 | A1 |
20090210257 | Chalfant et al. | Aug 2009 | A1 |
20090233572 | Basir | Sep 2009 | A1 |
20090247113 | Sennett et al. | Oct 2009 | A1 |
20090254240 | Olsen, III et al. | Oct 2009 | A1 |
20090267801 | Kawai et al. | Oct 2009 | A1 |
20090300065 | Birchall | Dec 2009 | A1 |
20090303026 | Broggi et al. | Dec 2009 | A1 |
20090313566 | Vian et al. | Dec 2009 | A1 |
20100004995 | Hickman | Jan 2010 | A1 |
20100005649 | Kim et al. | Jan 2010 | A1 |
20100013130 | Ramirez et al. | Jan 2010 | A1 |
20100014570 | Dupis et al. | Jan 2010 | A1 |
20100015706 | Quay et al. | Jan 2010 | A1 |
20100030540 | Choi et al. | Feb 2010 | A1 |
20100030586 | Taylor et al. | Feb 2010 | A1 |
20100042318 | Kaplan et al. | Feb 2010 | A1 |
20100043524 | Takata | Feb 2010 | A1 |
20100055649 | Takahashi et al. | Mar 2010 | A1 |
20100076646 | Basir et al. | Mar 2010 | A1 |
20100082244 | Yamaguchi et al. | Apr 2010 | A1 |
20100085171 | Do | Apr 2010 | A1 |
20100106346 | Badli et al. | Apr 2010 | A1 |
20100106356 | Trepagnier et al. | Apr 2010 | A1 |
20100128127 | Ciolli | May 2010 | A1 |
20100131300 | Collopy et al. | May 2010 | A1 |
20100131302 | Collopy et al. | May 2010 | A1 |
20100131304 | Collopy et al. | May 2010 | A1 |
20100131307 | Collopy et al. | May 2010 | A1 |
20100142477 | Yokota | Jun 2010 | A1 |
20100143872 | Lankteee | Jun 2010 | A1 |
20100157061 | Katsman et al. | Jun 2010 | A1 |
20100157255 | Togino | Jun 2010 | A1 |
20100164737 | Lu et al. | Jul 2010 | A1 |
20100174564 | Stender | Jul 2010 | A1 |
20100198491 | Mays | Aug 2010 | A1 |
20100205012 | McClellan | Aug 2010 | A1 |
20100214087 | Nakagoshi et al. | Aug 2010 | A1 |
20100219944 | McCormick et al. | Sep 2010 | A1 |
20100253541 | Seder et al. | Oct 2010 | A1 |
20100256836 | Mudalige | Oct 2010 | A1 |
20100286845 | Rekow et al. | Nov 2010 | A1 |
20100293033 | Hall et al. | Nov 2010 | A1 |
20100299021 | Jalili | Nov 2010 | A1 |
20100332131 | Horvitz et al. | Dec 2010 | A1 |
20110009093 | Self et al. | Jan 2011 | A1 |
20110010042 | Boulet et al. | Jan 2011 | A1 |
20110043350 | Ben David | Feb 2011 | A1 |
20110043377 | McGrath et al. | Feb 2011 | A1 |
20110054767 | Schafer et al. | Mar 2011 | A1 |
20110060496 | Nielsen et al. | Mar 2011 | A1 |
20110066310 | Sakai et al. | Mar 2011 | A1 |
20110077809 | Leary | Mar 2011 | A1 |
20110087505 | Terlep | Apr 2011 | A1 |
20110090075 | Armitage et al. | Apr 2011 | A1 |
20110090093 | Grimm et al. | Apr 2011 | A1 |
20110093134 | Emanuel et al. | Apr 2011 | A1 |
20110093350 | Laumeyer et al. | Apr 2011 | A1 |
20110106370 | Duddle et al. | May 2011 | A1 |
20110109462 | Deng et al. | May 2011 | A1 |
20110118907 | Elkins | May 2011 | A1 |
20110128161 | Bae et al. | Jun 2011 | A1 |
20110133954 | Ooshima et al. | Jun 2011 | A1 |
20110137684 | Peak et al. | Jun 2011 | A1 |
20110140919 | Hara et al. | Jun 2011 | A1 |
20110140968 | Bai et al. | Jun 2011 | A1 |
20110144854 | Cramer et al. | Jun 2011 | A1 |
20110153118 | Lim | Jun 2011 | A1 |
20110153367 | Amigo et al. | Jun 2011 | A1 |
20110161116 | Peak | Jun 2011 | A1 |
20110161119 | Collins | Jun 2011 | A1 |
20110169625 | James et al. | Jul 2011 | A1 |
20110184605 | Neff | Jul 2011 | A1 |
20110187559 | Applebaum | Aug 2011 | A1 |
20110190972 | Timmons et al. | Aug 2011 | A1 |
20110196571 | Foladare et al. | Aug 2011 | A1 |
20110202305 | Willis et al. | Aug 2011 | A1 |
20110238997 | Bellur et al. | Sep 2011 | A1 |
20110241862 | Debouk et al. | Oct 2011 | A1 |
20110246244 | O'Rourke | Oct 2011 | A1 |
20110251751 | Knight | Oct 2011 | A1 |
20110279263 | Rodkey et al. | Nov 2011 | A1 |
20110288770 | Greasby | Nov 2011 | A1 |
20110295446 | Basir et al. | Dec 2011 | A1 |
20110295546 | Khazanov | Dec 2011 | A1 |
20110301839 | Pudar et al. | Dec 2011 | A1 |
20110304465 | Boult et al. | Dec 2011 | A1 |
20110307188 | Peng et al. | Dec 2011 | A1 |
20110307336 | Smirnov et al. | Dec 2011 | A1 |
20120004933 | Foladare et al. | Jan 2012 | A1 |
20120007224 | Hasebe et al. | Jan 2012 | A1 |
20120010185 | Stenkamp et al. | Jan 2012 | A1 |
20120010906 | Foladare et al. | Jan 2012 | A1 |
20120013582 | Inoue et al. | Jan 2012 | A1 |
20120019001 | Hede et al. | Jan 2012 | A1 |
20120025969 | Dozza | Feb 2012 | A1 |
20120028680 | Breed | Feb 2012 | A1 |
20120047045 | Gopikrishna | Feb 2012 | A1 |
20120053824 | Nam et al. | Mar 2012 | A1 |
20120056758 | Kuhlman et al. | Mar 2012 | A1 |
20120059227 | Friedlander et al. | Mar 2012 | A1 |
20120066007 | Ferrick et al. | Mar 2012 | A1 |
20120071151 | Abramson et al. | Mar 2012 | A1 |
20120072214 | Cox et al. | Mar 2012 | A1 |
20120072243 | Collins et al. | Mar 2012 | A1 |
20120072244 | Collins et al. | Mar 2012 | A1 |
20120081221 | Doerr et al. | Apr 2012 | A1 |
20120083668 | Pradeep et al. | Apr 2012 | A1 |
20120083959 | Dolgov et al. | Apr 2012 | A1 |
20120083960 | Zhu et al. | Apr 2012 | A1 |
20120083964 | Montemerlo et al. | Apr 2012 | A1 |
20120083974 | Sandblom | Apr 2012 | A1 |
20120092157 | Tran | Apr 2012 | A1 |
20120101855 | Collins et al. | Apr 2012 | A1 |
20120108909 | Slobounov et al. | May 2012 | A1 |
20120109407 | Yousefi et al. | May 2012 | A1 |
20120109692 | Collins et al. | May 2012 | A1 |
20120116548 | Goree et al. | May 2012 | A1 |
20120123806 | Schumann, Jr. | May 2012 | A1 |
20120129545 | Hodis et al. | May 2012 | A1 |
20120135382 | Winston et al. | May 2012 | A1 |
20120143391 | Gee | Jun 2012 | A1 |
20120143630 | Hertenstein | Jun 2012 | A1 |
20120146766 | Geisler et al. | Jun 2012 | A1 |
20120172055 | Edge | Jul 2012 | A1 |
20120185204 | Jallon et al. | Jul 2012 | A1 |
20120188100 | Min et al. | Jul 2012 | A1 |
20120190001 | Knight et al. | Jul 2012 | A1 |
20120191343 | Haleem | Jul 2012 | A1 |
20120191373 | Soles et al. | Jul 2012 | A1 |
20120197669 | Kote et al. | Aug 2012 | A1 |
20120200427 | Kamata | Aug 2012 | A1 |
20120203418 | Braennstroem et al. | Aug 2012 | A1 |
20120209634 | Ling et al. | Aug 2012 | A1 |
20120209692 | Bennett et al. | Aug 2012 | A1 |
20120215375 | Chang | Aug 2012 | A1 |
20120221168 | Zeng et al. | Aug 2012 | A1 |
20120235865 | Nath et al. | Sep 2012 | A1 |
20120239242 | Uehara | Sep 2012 | A1 |
20120239281 | Hinz | Sep 2012 | A1 |
20120239471 | Grimm et al. | Sep 2012 | A1 |
20120239822 | Poulson et al. | Sep 2012 | A1 |
20120246733 | Schafer et al. | Sep 2012 | A1 |
20120256769 | Satpathy | Oct 2012 | A1 |
20120258702 | Matsuyama | Oct 2012 | A1 |
20120271500 | Tsimhoni et al. | Oct 2012 | A1 |
20120277950 | Plante et al. | Nov 2012 | A1 |
20120284747 | Joao | Nov 2012 | A1 |
20120286974 | Claussen et al. | Nov 2012 | A1 |
20120289819 | Snow | Nov 2012 | A1 |
20120303177 | Jauch et al. | Nov 2012 | A1 |
20120303222 | Cooprider et al. | Nov 2012 | A1 |
20120303392 | Depura et al. | Nov 2012 | A1 |
20120306663 | Mudalige | Dec 2012 | A1 |
20120315848 | Smith et al. | Dec 2012 | A1 |
20120316406 | Rahman et al. | Dec 2012 | A1 |
20130006674 | Bowne et al. | Jan 2013 | A1 |
20130006675 | Bowne et al. | Jan 2013 | A1 |
20130017846 | Schoppe | Jan 2013 | A1 |
20130018677 | Chevrette | Jan 2013 | A1 |
20130030275 | Seymour et al. | Jan 2013 | A1 |
20130030606 | Mudalige et al. | Jan 2013 | A1 |
20130030642 | Bradley et al. | Jan 2013 | A1 |
20130038437 | Talati et al. | Feb 2013 | A1 |
20130044008 | Gafford et al. | Feb 2013 | A1 |
20130046562 | Taylor et al. | Feb 2013 | A1 |
20130057671 | Levin et al. | Mar 2013 | A1 |
20130066751 | Glazer et al. | Mar 2013 | A1 |
20130073115 | Levin et al. | Mar 2013 | A1 |
20130073318 | Feldman et al. | Mar 2013 | A1 |
20130073321 | Hofmann et al. | Mar 2013 | A1 |
20130093886 | Rothschild | Apr 2013 | A1 |
20130097128 | Suzuki et al. | Apr 2013 | A1 |
20130116855 | Nielsen et al. | May 2013 | A1 |
20130131907 | Green et al. | May 2013 | A1 |
20130144459 | Ricci | Jun 2013 | A1 |
20130144657 | Ricci | Jun 2013 | A1 |
20130151027 | Petrucci et al. | Jun 2013 | A1 |
20130151202 | Denny et al. | Jun 2013 | A1 |
20130164715 | Hunt et al. | Jun 2013 | A1 |
20130179198 | Bowne et al. | Jul 2013 | A1 |
20130189649 | Mannino | Jul 2013 | A1 |
20130190966 | Collins et al. | Jul 2013 | A1 |
20130209968 | Miller et al. | Aug 2013 | A1 |
20130218603 | Hagelstein et al. | Aug 2013 | A1 |
20130218604 | Hagelstein et al. | Aug 2013 | A1 |
20130226369 | Yorio | Aug 2013 | A1 |
20130226391 | Nordbruch et al. | Aug 2013 | A1 |
20130227409 | Das et al. | Aug 2013 | A1 |
20130231824 | Wilson et al. | Sep 2013 | A1 |
20130237194 | Davis | Sep 2013 | A1 |
20130245857 | Gariepy et al. | Sep 2013 | A1 |
20130245881 | Scarbrough | Sep 2013 | A1 |
20130246135 | Wang | Sep 2013 | A1 |
20130257626 | Masli et al. | Oct 2013 | A1 |
20130267194 | Breed | Oct 2013 | A1 |
20130278442 | Rubin et al. | Oct 2013 | A1 |
20130289819 | Hassib et al. | Oct 2013 | A1 |
20130290037 | Hu et al. | Oct 2013 | A1 |
20130295872 | Guday | Nov 2013 | A1 |
20130297418 | Collopy et al. | Nov 2013 | A1 |
20130302758 | Wright | Nov 2013 | A1 |
20130304513 | Hyde et al. | Nov 2013 | A1 |
20130304514 | Hyde et al. | Nov 2013 | A1 |
20130307786 | Heubel | Nov 2013 | A1 |
20130317665 | Fernandes et al. | Nov 2013 | A1 |
20130317693 | Jefferies et al. | Nov 2013 | A1 |
20130317711 | Plante | Nov 2013 | A1 |
20130317786 | Kuhn | Nov 2013 | A1 |
20130317865 | Tofte et al. | Nov 2013 | A1 |
20130332402 | Rakshit | Dec 2013 | A1 |
20130339062 | Brewer et al. | Dec 2013 | A1 |
20140002651 | Plante | Jan 2014 | A1 |
20140004734 | Hoang | Jan 2014 | A1 |
20140006660 | Frei et al. | Jan 2014 | A1 |
20140009307 | Bowers et al. | Jan 2014 | A1 |
20140011647 | Lalaoua | Jan 2014 | A1 |
20140012492 | Bowers et al. | Jan 2014 | A1 |
20140013965 | Perez | Jan 2014 | A1 |
20140019170 | Coleman et al. | Jan 2014 | A1 |
20140027790 | Lin et al. | Jan 2014 | A1 |
20140030073 | Lacy et al. | Jan 2014 | A1 |
20140039934 | Rivera | Feb 2014 | A1 |
20140047347 | Mohn et al. | Feb 2014 | A1 |
20140047371 | Palmer et al. | Feb 2014 | A1 |
20140052323 | Reichel et al. | Feb 2014 | A1 |
20140052336 | Moshchuk et al. | Feb 2014 | A1 |
20140052479 | Kawamura | Feb 2014 | A1 |
20140058761 | Freiberger et al. | Feb 2014 | A1 |
20140059066 | Koloskov | Feb 2014 | A1 |
20140063064 | Seo et al. | Mar 2014 | A1 |
20140070980 | Park | Mar 2014 | A1 |
20140080100 | Phelan et al. | Mar 2014 | A1 |
20140081675 | Ives | Mar 2014 | A1 |
20140095009 | Oshima et al. | Apr 2014 | A1 |
20140095214 | Mathe et al. | Apr 2014 | A1 |
20140099607 | Armitage et al. | Apr 2014 | A1 |
20140100892 | Collopy et al. | Apr 2014 | A1 |
20140104405 | Weidl et al. | Apr 2014 | A1 |
20140106782 | Chitre et al. | Apr 2014 | A1 |
20140108198 | Jariyasunant et al. | Apr 2014 | A1 |
20140111332 | Przybylko et al. | Apr 2014 | A1 |
20140111647 | Atsmon et al. | Apr 2014 | A1 |
20140114691 | Pearce | Apr 2014 | A1 |
20140114692 | Pearce | Apr 2014 | A1 |
20140125474 | Gunaratne | May 2014 | A1 |
20140129053 | Kleve et al. | May 2014 | A1 |
20140129139 | Ellison et al. | May 2014 | A1 |
20140129301 | Van Wiemeersch et al. | May 2014 | A1 |
20140130035 | Desai et al. | May 2014 | A1 |
20140135598 | Weidl et al. | May 2014 | A1 |
20140148988 | Lathrop et al. | May 2014 | A1 |
20140149148 | Luciani | May 2014 | A1 |
20140152422 | Breed | Jun 2014 | A1 |
20140156133 | Cullinane et al. | Jun 2014 | A1 |
20140156134 | Cullinane et al. | Jun 2014 | A1 |
20140156176 | Caskey et al. | Jun 2014 | A1 |
20140167967 | He et al. | Jun 2014 | A1 |
20140168399 | Plummer et al. | Jun 2014 | A1 |
20140172467 | He et al. | Jun 2014 | A1 |
20140172727 | Abhyanker et al. | Jun 2014 | A1 |
20140180727 | Freiberger et al. | Jun 2014 | A1 |
20140188322 | Oh et al. | Jul 2014 | A1 |
20140191858 | Morgan et al. | Jul 2014 | A1 |
20140199962 | Mohammed | Jul 2014 | A1 |
20140207707 | Na et al. | Jul 2014 | A1 |
20140218187 | Chun et al. | Aug 2014 | A1 |
20140218520 | Teich et al. | Aug 2014 | A1 |
20140221781 | Schrauf et al. | Aug 2014 | A1 |
20140227991 | Breton | Aug 2014 | A1 |
20140236638 | Pallesen et al. | Aug 2014 | A1 |
20140240132 | Bychkov | Aug 2014 | A1 |
20140244096 | An et al. | Aug 2014 | A1 |
20140253376 | Large et al. | Sep 2014 | A1 |
20140257866 | Gay et al. | Sep 2014 | A1 |
20140257869 | Binion et al. | Sep 2014 | A1 |
20140266655 | Palan | Sep 2014 | A1 |
20140272810 | Fields et al. | Sep 2014 | A1 |
20140272811 | Palan | Sep 2014 | A1 |
20140277916 | Mullen et al. | Sep 2014 | A1 |
20140278569 | Sanchez et al. | Sep 2014 | A1 |
20140278571 | Mullen et al. | Sep 2014 | A1 |
20140278586 | Sanchez et al. | Sep 2014 | A1 |
20140278840 | Scofield et al. | Sep 2014 | A1 |
20140279707 | Joshua | Sep 2014 | A1 |
20140301218 | Luo et al. | Oct 2014 | A1 |
20140303827 | Dolgov et al. | Oct 2014 | A1 |
20140306799 | Ricci | Oct 2014 | A1 |
20140306814 | Ricci | Oct 2014 | A1 |
20140309864 | Ricci | Oct 2014 | A1 |
20140309870 | Ricci et al. | Oct 2014 | A1 |
20140310186 | Ricci | Oct 2014 | A1 |
20140330478 | Cullinane et al. | Nov 2014 | A1 |
20140335902 | Guba et al. | Nov 2014 | A1 |
20140337930 | Hoyos et al. | Nov 2014 | A1 |
20140343972 | Fernandes et al. | Nov 2014 | A1 |
20140350970 | Schumann, Jr. et al. | Nov 2014 | A1 |
20140358324 | Sagar et al. | Dec 2014 | A1 |
20140358592 | Wedig et al. | Dec 2014 | A1 |
20140376410 | Ros et al. | Dec 2014 | A1 |
20140378082 | Ros et al. | Dec 2014 | A1 |
20140379385 | Duncan et al. | Dec 2014 | A1 |
20140380264 | Misra et al. | Dec 2014 | A1 |
20150006278 | Di Censo et al. | Jan 2015 | A1 |
20150019266 | Stempora | Jan 2015 | A1 |
20150024705 | Rashidi | Jan 2015 | A1 |
20150025917 | Stempora | Jan 2015 | A1 |
20150032581 | Blackhurst et al. | Jan 2015 | A1 |
20150035685 | Strickland et al. | Feb 2015 | A1 |
20150039348 | Miller et al. | Feb 2015 | A1 |
20150039350 | Martin et al. | Feb 2015 | A1 |
20150039397 | Fuchs | Feb 2015 | A1 |
20150045983 | Fraser et al. | Feb 2015 | A1 |
20150051752 | Paszkowicz | Feb 2015 | A1 |
20150051787 | Doughty et al. | Feb 2015 | A1 |
20150058046 | Huynh et al. | Feb 2015 | A1 |
20150066284 | Yopp | Mar 2015 | A1 |
20150070160 | Davidsson et al. | Mar 2015 | A1 |
20150070265 | Cruz-Hernandez et al. | Mar 2015 | A1 |
20150073645 | Davidsson et al. | Mar 2015 | A1 |
20150088334 | Bowers et al. | Mar 2015 | A1 |
20150088358 | Yopp | Mar 2015 | A1 |
20150088360 | Bonnet et al. | Mar 2015 | A1 |
20150088373 | Wilkins | Mar 2015 | A1 |
20150088550 | Bowers et al. | Mar 2015 | A1 |
20150095132 | Van Heerden et al. | Apr 2015 | A1 |
20150100189 | Tellis et al. | Apr 2015 | A1 |
20150100190 | Yopp | Apr 2015 | A1 |
20150100191 | Yopp | Apr 2015 | A1 |
20150100353 | Hughes et al. | Apr 2015 | A1 |
20150109450 | Walker | Apr 2015 | A1 |
20150112504 | Binion et al. | Apr 2015 | A1 |
20150112543 | Binion et al. | Apr 2015 | A1 |
20150112545 | Binion et al. | Apr 2015 | A1 |
20150112730 | Binion et al. | Apr 2015 | A1 |
20150112731 | Binion et al. | Apr 2015 | A1 |
20150112800 | Binion et al. | Apr 2015 | A1 |
20150113521 | Suzuki et al. | Apr 2015 | A1 |
20150120331 | Russo et al. | Apr 2015 | A1 |
20150127570 | Doughty | May 2015 | A1 |
20150128123 | Eling | May 2015 | A1 |
20150142244 | You et al. | May 2015 | A1 |
20150142262 | Lee | May 2015 | A1 |
20150149017 | Attard et al. | May 2015 | A1 |
20150149018 | Attard et al. | May 2015 | A1 |
20150149023 | Attard et al. | May 2015 | A1 |
20150149265 | Huntzicker et al. | May 2015 | A1 |
20150153733 | Ohmura et al. | Jun 2015 | A1 |
20150154711 | Christopulos et al. | Jun 2015 | A1 |
20150158469 | Cheatham, III et al. | Jun 2015 | A1 |
20150158495 | Duncan et al. | Jun 2015 | A1 |
20150160653 | Cheatham, III et al. | Jun 2015 | A1 |
20150161738 | Stempora | Jun 2015 | A1 |
20150161893 | Duncan et al. | Jun 2015 | A1 |
20150161894 | Duncan et al. | Jun 2015 | A1 |
20150166069 | Engelman et al. | Jun 2015 | A1 |
20150169311 | Dickerson et al. | Jun 2015 | A1 |
20150170287 | Tirone et al. | Jun 2015 | A1 |
20150170290 | Bowne et al. | Jun 2015 | A1 |
20150170522 | Noh | Jun 2015 | A1 |
20150178997 | Ohsaki | Jun 2015 | A1 |
20150178998 | Attard et al. | Jun 2015 | A1 |
20150185034 | Abhyanker | Jul 2015 | A1 |
20150187013 | Adams et al. | Jul 2015 | A1 |
20150187015 | Adams et al. | Jul 2015 | A1 |
20150187016 | Adams et al. | Jul 2015 | A1 |
20150187019 | Fernandes et al. | Jul 2015 | A1 |
20150187194 | Hypolite et al. | Jul 2015 | A1 |
20150189241 | Kim et al. | Jul 2015 | A1 |
20150193219 | Pandya et al. | Jul 2015 | A1 |
20150193220 | Rork et al. | Jul 2015 | A1 |
20150203107 | Lippman | Jul 2015 | A1 |
20150203113 | Duncan et al. | Jul 2015 | A1 |
20150221142 | Kim et al. | Aug 2015 | A1 |
20150229885 | Offenhaeuser | Aug 2015 | A1 |
20150232064 | Cudak et al. | Aug 2015 | A1 |
20150233719 | Cudak et al. | Aug 2015 | A1 |
20150235323 | Oldham | Aug 2015 | A1 |
20150235557 | Engelman et al. | Aug 2015 | A1 |
20150239436 | Kanai et al. | Aug 2015 | A1 |
20150241241 | Cudak et al. | Aug 2015 | A1 |
20150241853 | Vechart et al. | Aug 2015 | A1 |
20150242953 | Suiter | Aug 2015 | A1 |
20150246672 | Pilutti et al. | Sep 2015 | A1 |
20150253772 | Solyom et al. | Sep 2015 | A1 |
20150254955 | Fields et al. | Sep 2015 | A1 |
20150266489 | Solyom et al. | Sep 2015 | A1 |
20150266490 | Coelingh et al. | Sep 2015 | A1 |
20150271201 | Ruvio et al. | Sep 2015 | A1 |
20150274072 | Croteau et al. | Oct 2015 | A1 |
20150284009 | Cullinane et al. | Oct 2015 | A1 |
20150293534 | Takamatsu | Oct 2015 | A1 |
20150294422 | Carver et al. | Oct 2015 | A1 |
20150302719 | Mroszczak et al. | Oct 2015 | A1 |
20150307048 | Santora | Oct 2015 | A1 |
20150307110 | Grewe et al. | Oct 2015 | A1 |
20150310742 | Albornoz | Oct 2015 | A1 |
20150310758 | Daddona et al. | Oct 2015 | A1 |
20150321641 | Abou Mahmoud et al. | Nov 2015 | A1 |
20150332407 | Wilson, II et al. | Nov 2015 | A1 |
20150334545 | Maier et al. | Nov 2015 | A1 |
20150336502 | Hillis et al. | Nov 2015 | A1 |
20150338852 | Ramanujam | Nov 2015 | A1 |
20150339777 | Zhalov | Nov 2015 | A1 |
20150339928 | Ramanujam | Nov 2015 | A1 |
20150343947 | Bernico et al. | Dec 2015 | A1 |
20150346727 | Ramanujam | Dec 2015 | A1 |
20150348335 | Ramanujam | Dec 2015 | A1 |
20150348337 | Choi | Dec 2015 | A1 |
20150356797 | McBride et al. | Dec 2015 | A1 |
20150382085 | Lawrie-Fussey et al. | Dec 2015 | A1 |
20160014252 | Biderman et al. | Jan 2016 | A1 |
20160019790 | Tobolski et al. | Jan 2016 | A1 |
20160026182 | Boroditsky et al. | Jan 2016 | A1 |
20160027276 | Freeck et al. | Jan 2016 | A1 |
20160036899 | Moody et al. | Feb 2016 | A1 |
20160042463 | Gillespie | Feb 2016 | A1 |
20160042644 | Velusamy | Feb 2016 | A1 |
20160042650 | Stenneth | Feb 2016 | A1 |
20160055750 | Linder et al. | Feb 2016 | A1 |
20160068103 | McNew et al. | Mar 2016 | A1 |
20160071418 | Oshida et al. | Mar 2016 | A1 |
20160073324 | Guba et al. | Mar 2016 | A1 |
20160083285 | De Ridder et al. | Mar 2016 | A1 |
20160086285 | Jordan Peters et al. | Mar 2016 | A1 |
20160086393 | Collins et al. | Mar 2016 | A1 |
20160092962 | Wasserman et al. | Mar 2016 | A1 |
20160093212 | Barfield, Jr. et al. | Mar 2016 | A1 |
20160096499 | Nanao | Apr 2016 | A1 |
20160101783 | Abou-Nasr et al. | Apr 2016 | A1 |
20160104250 | Allen et al. | Apr 2016 | A1 |
20160105365 | Droste et al. | Apr 2016 | A1 |
20160116293 | Grover et al. | Apr 2016 | A1 |
20160116913 | Niles | Apr 2016 | A1 |
20160117871 | McClellan et al. | Apr 2016 | A1 |
20160117928 | Hodges et al. | Apr 2016 | A1 |
20160125735 | Tuukkanen | May 2016 | A1 |
20160129917 | Gariepy et al. | May 2016 | A1 |
20160140783 | Catt et al. | May 2016 | A1 |
20160140784 | Akanuma et al. | May 2016 | A1 |
20160147226 | Akselrod et al. | May 2016 | A1 |
20160163217 | Harkness | Jun 2016 | A1 |
20160167652 | Slusar | Jun 2016 | A1 |
20160171521 | Ramirez et al. | Jun 2016 | A1 |
20160187127 | Purohit et al. | Jun 2016 | A1 |
20160187368 | Modi et al. | Jun 2016 | A1 |
20160189303 | Fuchs | Jun 2016 | A1 |
20160189306 | Bogovich et al. | Jun 2016 | A1 |
20160189544 | Ricci | Jun 2016 | A1 |
20160200326 | Cullinane et al. | Jul 2016 | A1 |
20160203560 | Parameshwaran | Jul 2016 | A1 |
20160221575 | Posch et al. | Aug 2016 | A1 |
20160229376 | Abou Mahmoud et al. | Aug 2016 | A1 |
20160231746 | Hazelton et al. | Aug 2016 | A1 |
20160248598 | Lin et al. | Aug 2016 | A1 |
20160255154 | Kim et al. | Sep 2016 | A1 |
20160264132 | Paul et al. | Sep 2016 | A1 |
20160272219 | Ketfi-Cherif et al. | Sep 2016 | A1 |
20160275790 | Kang et al. | Sep 2016 | A1 |
20160277911 | Kang et al. | Sep 2016 | A1 |
20160282874 | Kurata et al. | Sep 2016 | A1 |
20160288833 | Heimberger et al. | Oct 2016 | A1 |
20160291153 | Mossau et al. | Oct 2016 | A1 |
20160292679 | Kolin et al. | Oct 2016 | A1 |
20160301698 | Katara et al. | Oct 2016 | A1 |
20160303969 | Akula | Oct 2016 | A1 |
20160304027 | Di Censo et al. | Oct 2016 | A1 |
20160304038 | Chen et al. | Oct 2016 | A1 |
20160304091 | Remes | Oct 2016 | A1 |
20160313132 | Larroy | Oct 2016 | A1 |
20160314224 | Wei et al. | Oct 2016 | A1 |
20160323233 | Song et al. | Nov 2016 | A1 |
20160327949 | Wilson et al. | Nov 2016 | A1 |
20160343249 | Gao et al. | Nov 2016 | A1 |
20160347329 | Zelman et al. | Dec 2016 | A1 |
20160370194 | Colijn et al. | Dec 2016 | A1 |
20170001146 | Van Baak et al. | Jan 2017 | A1 |
20170015263 | Makled et al. | Jan 2017 | A1 |
20170017734 | Groh et al. | Jan 2017 | A1 |
20170017842 | Ma et al. | Jan 2017 | A1 |
20170023945 | Cavalcanti et al. | Jan 2017 | A1 |
20170024938 | Lindsay | Jan 2017 | A1 |
20170036678 | Takamatsu | Feb 2017 | A1 |
20170038773 | Gordon et al. | Feb 2017 | A1 |
20170067764 | Skupin et al. | Mar 2017 | A1 |
20170072967 | Fendt et al. | Mar 2017 | A1 |
20170076606 | Gupta et al. | Mar 2017 | A1 |
20170078948 | Guba et al. | Mar 2017 | A1 |
20170080900 | Huennekens et al. | Mar 2017 | A1 |
20170084175 | Sedlik et al. | Mar 2017 | A1 |
20170086028 | Hwang et al. | Mar 2017 | A1 |
20170106876 | Gordon et al. | Apr 2017 | A1 |
20170116794 | Gortsas | Apr 2017 | A1 |
20170120761 | Kapadia et al. | May 2017 | A1 |
20170123421 | Kentley et al. | May 2017 | A1 |
20170123428 | Levinson et al. | May 2017 | A1 |
20170136902 | Ricci | May 2017 | A1 |
20170147722 | Greenwood | May 2017 | A1 |
20170148324 | High et al. | May 2017 | A1 |
20170154479 | Kim | Jun 2017 | A1 |
20170168493 | Miller et al. | Jun 2017 | A1 |
20170169627 | Kim et al. | Jun 2017 | A1 |
20170176641 | Zhu et al. | Jun 2017 | A1 |
20170178422 | Wright | Jun 2017 | A1 |
20170178423 | Wright | Jun 2017 | A1 |
20170178424 | Wright | Jun 2017 | A1 |
20170192428 | Vogt et al. | Jul 2017 | A1 |
20170200367 | Mielenz | Jul 2017 | A1 |
20170212511 | Paiva Ferreira et al. | Jul 2017 | A1 |
20170234689 | Gibson et al. | Aug 2017 | A1 |
20170236210 | Kumar et al. | Aug 2017 | A1 |
20170249844 | Perkins et al. | Aug 2017 | A1 |
20170270617 | Fernandes et al. | Sep 2017 | A1 |
20170274897 | Rink et al. | Sep 2017 | A1 |
20170308082 | Ullrich et al. | Oct 2017 | A1 |
20170309092 | Rosenbaum | Oct 2017 | A1 |
20170330448 | Moore et al. | Nov 2017 | A1 |
20180004223 | Baldwin | Jan 2018 | A1 |
20180013831 | Dey et al. | Jan 2018 | A1 |
20180046198 | Nordbruch et al. | Feb 2018 | A1 |
20180053411 | Wieskamp et al. | Feb 2018 | A1 |
20180075538 | Konrardy et al. | Mar 2018 | A1 |
20180080995 | Heinen | Mar 2018 | A1 |
20180099678 | Absmeier et al. | Apr 2018 | A1 |
20180194343 | Lorenz | Jul 2018 | A1 |
20180231979 | Miller et al. | Aug 2018 | A1 |
20180307250 | Harvey | Oct 2018 | A1 |
Number | Date | Country |
---|---|---|
2494727 | Mar 2013 | GB |
2002-259708 | Sep 2002 | JP |
WO-2005083605 | Sep 2005 | WO |
WO-2010034909 | Apr 2010 | WO |
WO-2014139821 | Sep 2014 | WO |
WO-2014148976 | Sep 2014 | WO |
WO-2016067610 | May 2016 | WO |
WO-2016156236 | Oct 2016 | WO |
Entry |
---|
“Biofeedback mobile app”, Kurzweill Accelerating Intelligence, downloaded from the Internet at: ,http://www.kurzweilai.net/biofeedback-mobile-app> (Feb. 12, 2013). |
“Driverless Cars...The Future is Already Here”, AutoInsurance Center, downloaded from the Internet at: <http://www.autoinsurancecenter.com/driverless-cars...the-future-is-already-here.htm> (2010; downloaded on Mar. 27, 2014). |
“Integrated Vehicle-Based Safety Systems (IVBSS)”, Research and Innovative Technology Administration (RITA), http://www.its.dot.gov/ivbss/, retrieved from the internet on Nov. 4, 2013, 3 pages. |
“Intel Capital to Invest in Future of Automotive Technology”, News Release, Intel Corp. (Feb. 29, 2012). |
“Linking Driving Behavior to Automobile Accidents and Insurance Rates: An Analysis of Five Billion Miles Driven”, Progressive Insurance brochure (Jul. 2012). |
“MIT Spin-off Affectiva Raises $5.7 Million to Commercialize Emotion Technology”, Business Wire (Jul. 19, 2011). |
“Private Ownership Costs”, RACQ, Wayback Machine, http://www.racq.com.au:80/-/media/pdf/racqpdfs/cardsanddriving/cars/0714_vehicle_running_costs.ashx/ (Oct. 6, 2014). |
“Self-Driving Cars: The Next Revolution”, KPMG, Center for Automotive Research (2012). |
The Influence of Telematics on Customer Experience: Case Study of Progressive's Snapshot Program, J.D. Power Insights, McGraw Hill Financial (2013). |
Al-Shihabi et al., A framework for modeling human-like driving behaviors for autonomous vehicles in driving simulators, Agents'01, pp. 286-291 (May 2001). |
Alberi et al., A proposed standardized testing procedure for autonomous ground vehicles, Virginia Polytechnic Institute and State University, 63 pages (Apr. 29, 2008). |
Beard et al., Autonomous vehicle technologies for small fixed-wing UAVs, J. Aerospace Computing Info. Commun. (Jan. 2005). |
Birch, ‘Mercedes-Benz’ world class driving simulator complex enhances moose safety, SAE International, Automotive Engineering (Nov. 13, 2010). |
Bondarev, Design of an Emotion Management System for a Home Reboot, Koninklijke Philips Electronics NV, 63 pp. (2002). |
Bosker, Affectiva's Emotion Recognition Tech: When Machines Know What You're Feeling, www.HuffPost.com (Dec. 24, 2012). |
Broggi et al., Extensive Tests of Autonomous Driving Technologies, IEEE Trans on Intelligent Transportation Systems, 14(3):1403-15 (May 30, 2013). |
Campbell et al., Autonomous Driving in Urban Environments: Approaches, Lessons, and Challenges, Phil. Trans. R. Soc. A, 368:4649-72 (2010). |
Carroll et al. “Where Innovation is Sorely Needed”, http://www.technologyreview.com/news/422568/where-innovation-is-sorely-needed/?nlid, retrieved from the internet on Nov. 4, 2013, 3 pages. |
Chan et al., The emotional side of cognitive distraction: implications for road safety, Accident Analysis and Prevention, 50:147-54 (2013). |
Cutler, Using the IPhone's Front-Facing Camera, Cardiio Measures Your Heartrate, downloaded from the Internet at: <https://techcrunch.com/2012/08/09/cardiio/> (Aug. 9, 2012). |
Davies, Avoiding Squirrels and Other Things Google's Robot Car Can't Do, downloaded from the Internet at: <http://www.wired.com/2014/05/google-self-driving-car-can-cant/ (downloaded on May 28, 2014). |
Davies, Here's How Mercedes-Benz Tests its New Self-Driving Car, Business Insider (Nov. 20, 2012). |
Duffy et al., Sit, Stay, Drive: The Future of Autonomous Car Liability, SMU Science & Technology Law Review, vol. 16, pp. 101-123 (Winter 2013). |
Figueiredo et al., An Approach to Simulate Autonomous Vehicles in Urban Traffic Scenarios, University of Porto, 7 pages (Nov. 2009). |
Filev et al., Future Mobility: Integrating Vehicle Control with Cloud Computing, Mechanical Engineering, 135.3:S18-S24, American Society of Mechanical Engineers (Mar. 2013). |
Foo et al., Three-dimensional path planning of unmanned aerial vehicles using particle swarm optimization, Sep. 2006, AIAA. |
Franke et al., Autonomous Driving Goes Downtown, IEEE Intelligent Systems, (Nov. 1998). |
Funkhouser, Paving the Road Ahead: Autonomous vehicles, products liability, and the need for a new approach, Utah Law Review, vol. 437, Issue 1 (2013). |
Garza, “Look Ma, No Hands!” Wrinkles and Wrecks in the Age of Autonomous Vehicles, New England Law Review, vol. 46, pp. 581-616 (2012). |
Gechter et al., Towards a Hybrid Real/Virtual Simulation of Autonomous Vehicles for Critical Scenarios, International Academy Research and Industry Association (IARIA), 4 pages (2014). |
Gerdes et al., Implementable ethics for autonomous vehicles, Chapter 5, In: Maurer et al. (eds.), Autonomes Fahren, Springer Vieweg, Berlin (2015). |
Gleeson, “How much is a monitored alarm insurance deduction?”, Demand Media (Oct. 30, 2014). |
Goldmark, MIT is making a road frustration index to measure stresses of driving, Fast Company (Jul. 23, 2013). |
Graham-Rowe, “A Smart Phone that Knows You're Angry”, MIT Technology Review (Jan. 9, 2012). |
Gray et al., A unified approach to threat assessment and control for automotive active safety, IEEE, 14(3):1490-9 (Sep. 2013). |
Grifantini, Sensor detects emotions through the skin, MIT Technology Review (Oct. 26, 2010). |
Gurney, Sue my car not me: Products liability and accidents involving autonomous vehicles, Journal of Law, Technology & Policy (2013). |
Hancock et al., “The Impact of Emotions and Predominant Emotion Regulation Technique on Driving Performance,” Work, 41 Suppl 1:3608-11 (Feb. 2012). |
Hars, Autonomous Cars: The Next Revolution Looms, Inventivio GmbH, 4 pages (Jan. 2010). |
Healy, Detecting Stress during Real-world Driving Tasks Using Physiological Sensors, IEEE Trans Intelligent Transportation Systems 6.2:156-66 (2005). |
Kluckner et al., Image based building classification and 3D modeling with super-pixels, ISPRS Technical Commission II Symposium, PCV 2010, vol. XXXVIII, part 3A, pp. 233-238 (Sep. 3, 2010). |
Kus, Implementation of 3D optical scanning technology for automotive applications, Sensors, 9:1967-79 (2009). |
Laine et al., Behavioral triggers of skin conductance responses and their neural correlates in the primate amygdala, J. Neurophysiol., 101:1749-54 (2009). |
Lattner et al., Knowledge-based risk assessment for intelligent vehicles, pp. 191-196, IEEE KIMAS, Apr. 18-21, 2005, Waltham, Massachusetts (Apr. 2005). |
Lee et al., Autonomous Vehicle Simulation Project, Int. J. Software Eng. and Its Applications, 7(5):393-402 (2013). |
Lee et al., What is stressful on the road? Analysis on aggression-inducing traffic situations through self-report, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 57(1):1500-1503 (Sep. 2013). |
Levendusky, Advancements in automotive technology and their effect on personal auto insurance, downloaded from the Internet at: <http://www.verisk.com/visualize/advancements-in-automotive-technology-and-their-effect> (2013). |
Lewis, The History of Driverless Cars, downloaded from the Internet at: <www.thefactsite.com/2017/06/driverless-cars-history.html> (Jun. 2017). |
Lomas, Can an algorithm be empathetic? UK startup EI technologies is building software that's sensitive to tone of voice, downloaded from the Internet at: https://techcrunch.com/2013/08/04/empathy/ (Aug. 4, 2013). |
Marchant et al., The coming collision between autonomous vehicles and the liability system, Santa Clara Law Review, 52(4): Article 6 (2012). |
McCraty et al., “The Effects of Different Types of Music on Mood, Tension, and Mental Clarity.” Alternative Therapies in Health and Medicine 4.1 (1998): 75-84. NCBI PubMed. Web. Jul. 11, 2013. |
Mercedes-Benz, Press Information: Networked With All Sense, Mercedes-Benz Driving Simulator (Nov. 2012). |
Merz et al., Beyond Visual Range Obstacle Avoidance and Infrastructure Inspection by an Autonomous Helicopter, Sep. 2011, IEEE. |
Miller, A simulation and regression testing framework for autonomous workers, Case Western Reserve University, 12 pages (Aug. 2007). |
Mui, Will auto insurers survive their collision with driverless cars? (Part 6), downloaded from the Internet at: <http://www.forbes.com/sites/chunkamui/2013/03/28/will-auto-insurers-survive-their-collision> (Mar. 28, 2013). |
Murph, Affectiva's Q Sensor Wristband Monitors and Logs Stress Levels, Might Bring Back the Snap Bracelet, Engadget.com (Nov. 2, 2010). |
Nasoz et al., Emotion recognition from physiological signals using wireless sensors for presence technologies, Cogn. Tech. Work, 6:4-14 (2004). |
Nass et al., Improving automotive safety by pairing driver emotion and car voice emotion. CHI 2005 Late Breaking Results: Short Papers, Portland, Oregon (Apr. 2-7, 2005). |
Pereira, An Integrated Architecture for Autonomous Vehicle Simulation, University of Porto., 114 pages (Jun. 2011). |
Peterson, New technology—old law: autonomous vehicles and California's insurance framework, Santa Clara Law Review, 52(4):Article 7 (Dec. 2012). |
Philipson, Want to drive safely? Listen to Elton John, Aerosmith or S Club 7, The Telegraph (Jan. 8, 2013). |
Pohanka et al., Sensors simulation environment for sensor data fusion, 14th International Conference on Information Fusion, Chicago, IL, pp. 1-8 (2011). |
Quinlan et al., Bringing Simulation to Life: A Mixed Reality Autonomous Intersection, Proc. IROS 2010—IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei Taiwan, 6 pages (Oct. 2010). |
Read, Autonomous cars & the death of auto insurance, downloaded from the Internet at: <http://www.thecarconnection.com/news/1083266_autonomous-cars-the-death-of-auto-insurance> (Apr. 1, 2013). |
Reddy, The New Auto Insurance Ecosystem: Telematics, Mobility and the Connected Car, Cognizant (Aug. 2012). |
Reifel et al., “Telematics: The Game Changer—Reinventing Auto Insurance”, A.T. Kearney (2010). |
Roberts, “What is Telematics Insurance?”, MoneySupermarket (Jun. 20, 2012). |
Ryan Hurlbert, “Can Having Safety Features Reduce Your Insurance Premiums?”, Dec. 15, 2010, 1 page. |
Ryan, Can having safety features reduce your insurance premiums? (Dec. 15, 2010). |
Saberi et al., An approach for functional safety improvement of an existing automotive system, IEEE (2015). |
Sepulcre et al., Cooperative vehicle-to-vehicle active safety testing under challenging conditions, Transportation Research Part C, 26:233-55 (2013). |
Sharma, Driving the future: the legal implications of autonomous vehicles conference recap, downloaded from the Internet at: <http://law.scu.edu/hightech/autonomousvehicleconfrecap2012> (Aug. 2012). |
Shaya, “For Some, Driving Is More Stressful than Skydiving.” AutomotiveNews.com. Automotive News, Jun. 12, 2013. |
Sorrel, App Measures Vital Signs Using IPad Camera, wired.com (Nov. 18, 2011). |
Stavens, Learning to Drive: Perception for Autonomous Cars, Stanford University, 104 pages (May 2011). |
Stienstra, Autonomous Vehicles & the Insurance Industry, 2013 CAS Annual Meeting—Minneapolis, MN (Nov. 2013). |
Talbot, “Wrist Sensor Tells You How Stressed Out You Are”, MIT Technology Review (Dec. 20, 2012). |
Tiberkak et al., An architecture for policy-based home automation system (PBHAS), 2010 IEEE Green Technologies Conference (Apr. 15-16, 2010). |
Toor, Valve looks to sweat levels and eye controls for future game design, downloaded from the Internet at: https://www.theverge.com/2013/5/7/4307750/valve-biometric-eye-tracking-sweat-left-4-dead-portal-2 (May 7, 2013). |
U.S. Appl. No. 14/798,609, Final Office Action, dated Mar. 22, 2019. |
U.S. Appl. No. 14/798,609, Nonfinal Office Action, dated Aug. 23, 2018. |
U.S. Appl. No. 14/798,615, Final Office Action, dated Aug. 3, 2018. |
U.S. Appl. No. 14/798,615, Final Office Action, dated Jun. 25, 2019. |
U.S. Appl. No. 14/798,615, Nonfinal Office Action, dated Feb. 7, 2018. |
U.S. Appl. No. 14/798,615, Nonfinal Office Action, dated Jan. 25, 2019. |
U.S. Appl. No. 14/798,626, Final Office Action, dated Jul. 19, 2018. |
U.S. Appl. No. 14/798,626, Final Office Action, dated Jun. 3, 2019. |
U.S. Appl. No. 14/798,626, Nonfinal Office Action, dated Jan. 23, 2019. |
U.S. Appl. No. 14/798,626, Nonfinal Office Action, dated Jan. 30, 2018. |
U.S. Appl. No. 14/798,633, Final Office Action, dated Sep. 19, 2018. |
U.S. Appl. No. 14/798,633, Nonfinal Office Action, dated Apr. 27, 2018. |
U.S. Appl. No. 14/798,633, Nonfinal Office Action, dated Mar. 4, 2019. |
U.S. Appl. No. 14/798,633, Notice of Allowance, dated Jul. 3, 2019. |
U.S. Appl. No. 14/798,741, Final Office Action, dated Apr. 17, 2019. |
U.S. Appl. No. 14/798,741, Final Office Action, dated Jul. 17, 2018. |
U.S. Appl. No. 14/798,741, Nonfinal Office Action, dated Jan. 29, 2018. |
U.S. Appl. No. 14/798,741, Nonfinal Office Action, dated Nov. 9, 2018. |
U.S. Appl. No. 14/798,745, Final Office Action, dated Aug. 30, 2018. |
U.S. Appl. No. 14/798,745, Nonfinal Office Action, dated Apr. 17, 2018. |
U.S. Appl. No. 14/798,745, Notice of Allowance, dated Apr. 29, 2019. |
U.S. Appl. No. 14/798,750, Final Office Action, dated Aug. 20, 2019. |
U.S. Appl. No. 14/798,750, Final Office Action, dated Aug. 29, 2018. |
U.S. Appl. No. 14/798,750, Nonfinal Office Action, dated Apr. 12, 2019. |
U.S. Appl. No. 14/798,750, Nonfinal Office Action, dated Mar. 5, 2018. |
U.S. Appl. No. 14/798,757, Nonfinal Office Action, dated Jan. 17, 2017. |
U.S. Appl. No. 14/798,757, Notice of Allowance, dated Jul. 12, 2017. |
U.S. Appl. No. 14/798,763, Final Office Action, dated Apr. 11, 2019. |
U.S. Appl. No. 14/798,763, Final Office Action, dated Jul. 12, 2018. |
U.S. Appl. No. 14/798,763, Nonfinal Office Action, dated Aug. 16, 2019. |
U.S. Appl. No. 14/798,763, Nonfinal Office Action, dated Feb. 5, 2018. |
U.S. Appl. No. 14/798,763, Nonfinal Office Action, dated Oct. 25, 2018. |
U.S. Appl. No. 14/798,769, Final Office Action, dated Mar. 14, 2017. |
U.S. Appl. No. 14/798,769, Nonfinal Office Action, dated Oct. 6, 2016. |
U.S. Appl. No. 14/798,769, Notice of Allowance, dated Jun. 27, 2017. |
U.S. Appl. No. 14/798,770, Nonfinal Office Action, dated Nov. 2, 2017. |
U.S. Appl. No. 14/798,770, Notice of Allowance, dated Jun. 25, 2018. |
U.S. Appl. No. 15/676,460, Notice of Allowance, dated Oct. 5, 2017. |
U.S. Appl. No. 15/676,470, Nonfinal Office Action, dated Apr. 24, 2018. |
U.S. Appl. No. 15/676,470, Notice of Allowance, dated Sep. 17, 2018. |
U.S. Appl. No. 15/859,854, Notice of Allowance, dated Mar. 28, 2018. |
U.S. Appl. No. 15/964,971, Nonfinal Office Action, dated Jun. 5, 2018. |
U.S. Appl. No. 16/685,470, Potter et al., “Methods of Facilitating Emergency Assistance”, filed Nov. 15, 2019. |
UTC Spotlight: Superstorm Sandy LiDAR Damage Assessment to Change Disaster Recovery, Feb. 2013. |
Vasudevan et al., Safe semi-autonomous control with enhanced driver modeling, 2012 American Control Conference, Fairmont Queen Elizabeth, Montreal, Canada (Jun. 27-29, 2012). |
Villasenor, Products liability and driverless cars: Issues and guiding principles for legislation, Brookings Center for Technology Innovation, 25 pages (Apr. 2014). |
Wang et al., Shader-based sensor simulation for autonomous car testing, 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, Alaska, pp. 224-229 (Sep. 2012). |
Wardzinski, Dynamic risk assessment in autonomous vehicles motion planning, Proceedings of the 2008 1st International Conference on Information Technology, IT 2008, Gdansk, Poland (May 19-21, 2008). |
Wiesenthal et al., “The Influence of Music on Driver Stress,” J. Applied Social Psychology, 30(8):1709-19 (Aug. 2000). |
Woodbeck et al., “Visual cortex on the GPU: Biologically inspired classifier and feature descriptor for rapid recognition”, Jun. 28, 2008, IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops 2008, p. 1-8. |
Young et al., “Cooperative Collision Warning Based Highway Vehicle Accident Reconstruction”, Eighth International Conference on Intelligent Systems Design and Applications, Nov. 26-28, 2008, pp. 561-565. |
Zhou et al., A Simulation Model to Evaluate and Verify Functions of Autonomous Vehicle Based on Simulink, Tongji University, 12 pages (2009). |
Number | Date | Country | |
---|---|---|---|
62145022 | Apr 2015 | US | |
62145234 | Apr 2015 | US | |
62145027 | Apr 2015 | US | |
62145228 | Apr 2015 | US | |
62145029 | Apr 2015 | US | |
62145232 | Apr 2015 | US | |
62145032 | Apr 2015 | US | |
62145033 | Apr 2015 | US | |
62145024 | Apr 2015 | US | |
62145028 | Apr 2015 | US | |
62145145 | Apr 2015 | US | |
62040735 | Aug 2014 | US | |
62027021 | Jul 2014 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 16178838 | Nov 2018 | US |
Child | 16685392 | US | |
Parent | 15676470 | Aug 2017 | US |
Child | 16178838 | US | |
Parent | 14798757 | Jul 2015 | US |
Child | 15676470 | US |