Providing a user interface experience based on inferred vehicle state

Abstract
A mobile device is described herein that provides a user interface experience to a user who is operating the mobile device within a vehicle. The mobile device provides the user interface experience using mode functionality. The mode functionality operates by receiving inference-input information from one or more input sources. At least one input source corresponds to at least one movement-sensing device, provided by the mobile device, that determines movement of the mobile device. The mode functionality then infers a state of the vehicle based on the inference-input information and presents a user interface experience that is appropriate for the vehicle state. In one scenario, the mode functionality can also infer that the vehicle is in a distress condition. In response, the mode functionality can solicit assistance for the user.
Description
BACKGROUND

A user who is driving a vehicle may wish to interact with his or her mobile device. For example, a user may wish to make and receive calls, conduct searches, read Email, and so forth. These activities may distract the user from the primary task of driving the vehicle, and therefore pose a significant risk to the safety of the user (as well as the safety of others). To address this issue, many jurisdictions have enacted laws which prevent users from manually interacting with mobile devices in their vehicles.


One solution to the above concerns is to outright preclude a user from interacting with his or her mobile phone while driving the vehicle. In another solution, a user can use various hands-free interaction devices. For example, a user can use voice recognition technology to initiate a call. The user can then conduct the call using a headset or the like, without holding the mobile device. While these solutions may help the user reduce the risk of using his or her mobile device in certain circumstances, they do not provide a generally satisfactory solution to the myriad distractions that may confront a user while driving.


SUMMARY

A mobile device is described herein that provides a user interface experience to a user who is operating the mobile device within a vehicle. The mobile device performs this task using mode functionality. The mode functionality operates by receiving inference-input information from one or more input sources. At least one input source corresponds to a movement-sensing device provided by the mobile device. The mode functionality device then infers a state of the vehicle (i.e., a “vehicle state”) based on the inference-input information. The mode functionality then presents a user interface experience to the user that is appropriate in view of the vehicle state. More specifically, the mode functionality presents a user interface experience to the user that imposes certain attention-related demands; those attention-related demands are appropriate in view of the vehicle state. For example, the mode functionality may present a user interface experience that provides minimal demands on the attention of the user when the vehicle state indicates that the vehicle is traveling at a high speed.


In one scenario, the mode functionality can also infer, based on the inference-input information, that the vehicle is in a distress condition, e.g., as a result of an accident or other mishap. In response to this assessment, the mode functionality can provide assistance to the user. In one case, the mode functionality can infer that the vehicle is in a distress condition based on evidence, gleaned from the inference-input information, that: (a) the mobile device is located in a vehicle; (b) the vehicle has come to an abrupt stop or otherwise abruptly decelerated; and (c) the mobile device has become dislodged from its mount (or where a subset of these events have occurred).


The above approach can be manifested in various types of systems, components, methods, computer readable media, data structures, articles of manufacture, and so on.


This Summary is provided to introduce a selection of concepts in a simplified form; these concepts are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 shows an illustrative environment in which a user receives a user interface experience that is based on an inferred state of a vehicle (i.e., a vehicle state).



FIG. 2 depicts an interior region of a vehicle. The interior region includes a mobile device secured to a surface of the vehicle using a mount.



FIG. 3 shows one type of representative mount that can be used to secure the mobile device within a vehicle.



FIG. 4 shows one illustrative implementation of a mobile device, for use in the environment of FIG. 1.



FIG. 5 shows illustrative movement-sensing devices that can be used by the mobile device of FIG. 4.



FIG. 6 shows illustrative output functionality that can be used by the mobile device of FIG. 4 to present output information.



FIG. 7 shows illustrative functionality associated with the mount of FIG. 3, and the manner in which this functionality can interact with the mobile device.



FIGS. 8 and 9 depict two respective output modes provided by the mobile device of FIG. 4.



FIGS. 10-12 depict three respective input modes provided by the mobile device of FIG. 4.



FIG. 13 shows further details regarding a representative application and mode functionality, which can be provided by the mobile device of FIG. 4.



FIG. 14 enumerates illustrative options by which the mobile device of FIG. 4 can control a user interface experience, in response to the state of the vehicle.



FIG. 15 shows an illustrative environment in which functionality can infer and respond to a distress condition that may affect the vehicle. For example, a distress condition may befall the vehicle when it is in an accident.



FIG. 16 shows an illustrative distress management module that can be used in the environment of FIG. 15.



FIG. 17 shows an illustrative procedure that explains one manner of operation of the environment of FIG. 1, from the perspective of a user.



FIG. 18 shows an illustrative procedure by which a mobile device can provide a user interface experience based on an inferred state of a vehicle.



FIGS. 19-21 show three different instantiations of the procedure of FIG. 18, corresponding to three different vehicle state scenarios.



FIG. 22 shows an illustrative procedure by which the distress management module of FIG. 16 can infer and respond to a distress condition that may affect the vehicle.



FIG. 23 shows illustrative computing functionality that can be used to implement any aspect of the features shown in the foregoing drawings.





The same numbers are used throughout the disclosure and figures to reference like components and features. Series 100 numbers refer to features originally found in FIG. 1, series 200 numbers refer to features originally found in FIG. 2, series 300 numbers refer to features originally found in FIG. 3, and so on.


DETAILED DESCRIPTION

This disclosure is organized as follows. Section A describes illustrative functionality for providing a user interface experience that depends on an inferred vehicle state. Section B describes illustrative methods which explain the operation of the functionality of Section A. Section C describes illustrative computing functionality that can be used to implement any aspect of the features described in Sections A and B.


As a preliminary matter, some of the figures describe concepts in the context of one or more structural components, variously referred to as functionality, modules, features, elements, etc. The various components shown in the figures can be implemented in any manner by any physical and tangible mechanisms, for instance, by software, hardware (e.g., chip-implemented logic functionality), firmware, etc., and/or any combination thereof. In one case, the illustrated separation of various components in the figures into distinct units may reflect the use of corresponding distinct physical and tangible components in an actual implementation. Alternatively, or in addition, any single component illustrated in the figures may be implemented by plural actual physical components. Alternatively, or in addition, the depiction of any two or more separate components in the figures may reflect different functions performed by a single actual physical component. FIG. 23, to be discussed in turn, provides additional details regarding one illustrative physical implementation of the functions shown in the figures.


Other figures describe the concepts in flowchart form. In this form, certain operations are described as constituting distinct blocks performed in a certain order. Such implementations are illustrative and non-limiting. Certain blocks described herein can be grouped together and performed in a single operation, certain blocks can be broken apart into plural component blocks, and certain blocks can be performed in an order that differs from that which is illustrated herein (including a parallel manner of performing the blocks). The blocks shown in the flowcharts can be implemented in any manner by any physical and tangible mechanisms, for instance, by software, hardware (e.g., chip-implemented logic functionality), firmware, etc., and/or any combination thereof.


As to terminology, the phrase “configured to” encompasses any way that any kind of physical and tangible functionality can be constructed to perform an identified operation. The functionality can be configured to perform an operation using, for instance, software, hardware (e.g., chip-implemented logic functionality), firmware, etc., and/or any combination thereof.


The term “logic” encompasses any physical and tangible functionality for performing a task. For instance, each operation illustrated in the flowcharts corresponds to a logic component for performing that operation. An operation can be performed using, for instance, software, hardware (e.g., chip-implemented logic functionality), firmware, etc., and/or any combination thereof. When implemented by a computing system, a logic component represents an electrical component that is a physical part of the computing system, however implemented.


The phrase “means for” in the claims, if used, is intended to invoke the provisions of 35 U.S.C. §112, sixth paragraph. No other language, other than this specific phrase, is intended to invoke the provisions of that portion of the statute.


The following explanation may identify one or more features as “optional.” This type of statement is not to be interpreted as an exhaustive indication of features that may be considered optional; that is, other features can be considered as optional, although not expressly identified in the text. Finally, the terms “exemplary” or “illustrative” refer to one implementation among potentially many implementations.


A. Illustrative Mobile Device and its Environment of Use



FIG. 1 shows an illustrative environment 100 in which users can operate mobile devices within vehicles. For example, FIG. 1 depicts an illustrative user 102 who operates a mobile device 104 within a vehicle 106, and a user 108 who operates a mobile device 110 within a vehicle 112. However, the environment 100 can accommodate any number of users, mobile devices, and vehicles. To simplify the explanation, this section will set forth the illustrative composition and manner of operation of the mobile device 104 operated by the user 102, treating this mobile device 104 as representative of any mobile device's operation within the environment 100. Moreover, in certain cases, this explanation will state that the mobile device 104 performs certain processing functions. This statement is to be construed liberally. In some cases, the mobile device 104 can perform a function by providing logic which executes this function. Alternatively, or in addition, the mobile device 104 can perform a function by interacting with a remote entity, which performs the function on behalf of the mobile device 104.


More specifically, the mobile device 104 operates in at least two modes. In a handheld mode of operation, the user 102 can interact with the mobile device 104 while holding it in his or her hands. For example, the user 102 can interact with a touch input device of the mobile device 104 and/or a keypad of the mobile device 104 to perform any device function. In a vehicle mode of operation, the user 102 can interact with the mobile device 104 in his or her vehicle 106. In this mode, the mobile device 104 automatically assesses the state of the vehicle 106 (i.e., the “vehicle state” according to the terminology used herein) based on inference-input information. The mobile device 104 then presents a user interface experience based on the vehicle state, as set forth below in greater detail.


By way of overview, the vehicle state of the vehicle state characterizes the manner in which the vehicle 106 is currently being operated by the user 102. Some aspects of the vehicle state may directly pertain to the dynamics of the vehicle's movement. Such direct aspects can include, but are not limited to: the speed at which the vehicle 106 is traveling; the manner in which the vehicle 106 is being accelerated and decelerated; the manner in which the vehicle 106 is being steered; the manner in which the breaks of the vehicle 106 are being applied, and so on. Other aspects of the vehicle state may have a more indirect bearing on the manner in which the vehicle 106 is moving. For example, these aspects of the vehicle state may pertain to the qualifying circumstances in which vehicle 106 movement is taking place. Such indirect aspects can include, but are not limited to: the region in which the vehicle 106 is traveling; the time of day in which the vehicle 106 is traveling; the date at which the vehicle 106 is traveling; the weather through which the vehicle 106 is traveling; the road condition over which the vehicle 106 is traveling, and so forth.


The mobile device 104 can determine the vehicle state based on inference-input information. The inference-input information pertains to any information that can be used to infer the vehicle state. Some of the inference-input information may originate from input sources which are internal to the mobile device 104. Other inference-input information may originate from input sources which are external to the mobile device 104.


Ultimately, the vehicle state correlates to an attention profile. The attention profile characterizes a level of attention and a type of attention which is appropriate for the user 102 to maintain while driving within the vehicle state. For example, assume that the vehicle state indicates that the user 102 is traveling at a high rate of speed in a congested urban area. Based on these considerations, the mobile device 104 may reach the conclusion that it is appropriate for the user 102 to pay close attention to the task of operating the vehicle 106. In contrast, assume that the vehicle state indicates that the user 102 is sitting in his vehicle 106, stopped in a traffic jam. In this circumstance, the mobile device 104 can reach the conclusion that it is permissible for the user 102 to devote greater attention to supplemental non-driving-related tasks (compared to the first scenario).


The mobile device 104 then presents a user interface experience that makes attention-related demands on the user 102 that are commensurate with the vehicle state. In other words, the mobile device 104 engages the user 102 in a manner that is appropriate in view of the attention profile of the vehicle state, e.g., by not demanding a level and type of attention that goes beyond what the user 102 can “afford” to provide while driving the vehicle 106. For example, in the first scenario described above (in which the user 102 is traveling at high speed in a congested area), the mobile device 104 can present a user interface experience which places few if any demands on the attention of the user 102. In the second scenario described above (in which the user 102 is sitting in his or her vehicle 106 without moving), the mobile device 104 can place far greater demands on the attention of the user 102.


The mobile device 104 can provide an appropriate user interface experience in different ways. Generally, a user interface experience refers to the manner in which a user 102 interacts with the mobile device 104, either by providing user-input information to the mobile device 104 or receiving output information from the mobile device 104. More specifically, the manner in which the user 102 provides user-input information to the mobile device 104 is defined by various input modes that a user 102 can use to provide the user-input information to the mobile device 104. Illustrative input modes can include a keypad input mode, a touch screen input mode, a voice-recognition input mode, a gesture-recognition input mode, and so on (to be described in greater detail below). The manner in which the mobile device 104 provides output information to the user is defined by various output modes. Illustrative output modes can include a display output mode, a speech output mode, and so on (to be described in greater detail below). The mobile device 104 can vary the user interface experience by activating and/or deactivating certain input modes and/or output modes. Alternatively, or in addition, the mobile device 104 can vary the user interface experience by changing the manner of operation of any input mode and/or any output mode (again, to be described in greater detail below).


Given the above overview, the description will now advance to a more detailed description of the individual features depicted in FIG. 1. Starting with the mobile device 104 itself, this apparatus can be implemented in any manner and can perform any function or combination of functions. For example, the mobile device 104 can correspond to a mobile telephone device of any type (such as a smart phone device), a book reader device, a personal digital assistant device, a laptop computing device, a tablet-type computing device, a netbook-type computing device, a portable game device, a portable media system interface module device, and so on.


The vehicle 106 can correspond to any mechanism for transporting the user 102. For example, the vehicle 106 may correspond to an automobile of any type, a truck, a bus, a motorcycle, a scooter, a bicycle, an airplane, a boat, and so on. However, to facilitate explanation, it will henceforth be assumed that the vehicle 106 corresponds to a personal automobile operated by the user 102.


The environment 100 also includes a communication conduit 114 for allowing the mobile device 104 to interact with any remote entity (where a “remote entity” means an entity that is remote with respect to the user 102). For example, the communication conduit 114 may allow the user 102 to use the mobile device 104 to interact with another user who is using another mobile device (such as the user 108 who is using the mobile device 110). In addition, the communication conduit 114 may allow the user 102 to interact with any remote services. Generally speaking, the communication conduit 114 can represent a local area network, a wide area network (e.g., the Internet), or any combination thereof. The communication conduit 114 can be governed by any protocol or combination of protocols.


More specifically, the communication conduit 114 can include wireless communication infrastructure 116 as part thereof. The wireless communication infrastructure 116 represents the functionality that enables the mobile device 104 to communicate with remote entities via wireless communication. The wireless communication infrastructure 116 can encompass any of cell towers, base stations, central switching stations, satellite functionality, and so on. The communication conduit 114 can also include hardwired links, routers, gateway functionality, name servers, etc.


The environment 100 also includes one or more remote processing systems 118. The remote processing systems 118 provide any type of services to the users. In one case, each of the remote processing systems 118 can be implemented using one or more servers and associated data stores. For instance, FIG. 1 shows that the remote processing systems 118 can include at least one instance of remote processing functionality 120 and an associated system store 122. The ensuing description will set forth illustrative functions that the remote processing functionality 120 can perform that are germane to the operation of the mobile device 104 within the vehicle 106.


Advancing to FIG. 2, this figure shows a portion of a representative interior region 200 of the vehicle 106. A mount 202 secures the mobile device 104 within the interior region 200. More specifically, the mount 202 secures the mobile device 104 to the top of the vehicle's dashboard, to the left of the user 102, just above the vehicle control panel region 204. A power cord 206 supplies power from any power source provided by the vehicle 106 to the mobile device 104 (either directly or indirectly, as will be described in connection with FIG. 7).


The mobile device 104 can include at least one internal camera device (not shown in FIG. 2) having a field of view that projects out from a face of the mobile device 104, towards the user 102. More specifically, the user 102 can place the mobile device 104 within the interior region 200 in such a manner that the field of view of the camera device encompasses at least a part of the anatomy of the user 102. In one implementation, this placement enables the internal camera device to establish an interaction space. The internal camera device can capture gestures made by the user 102 within that interaction space. In one illustrative implementation, the interaction space may generally correspond to a conic volume that extends approximately 60 cm from the face of the mobile device 104, pointed towards the user 102 who is driving the vehicle 106 (although different end-use environments can adopt interaction spaces having different “sizes” and shapes).


However, the placement of the mobile device 104 shown in FIG. 2 is merely representative, meaning that the user 102 can choose other locations and orientations of the mobile device 104. For example, the user 102 can place the mobile device 104 in a left region with respect to the steering wheel, instead of a right region with respect to the steering wheel (as shown in FIG. 2). This might be appropriate, for example, in countries in which the steering wheel is provided on the right side of the vehicle 106. Alternatively, the user 102 can place the mobile device 104 directly behind the steering wheel or on the steering wheel. Alternatively, the user 102 can secure the mobile device 104 to the windshield of the vehicle 106. These options are mentioned by way of illustration, not limitation; still other placements of the mobile device 104 are possible.



FIG. 3 shows one merely representative mount 302 that can be used to secure the mobile device 104 to some surface of the interior region 200 of the car. (Note that this mount 302 is a different type of mount than the mount 202 shown in FIG. 2). Without limitation, the mount 302 of FIG. 3 includes any type of coupling mechanism 304 for fastening the mount 302 to a surface within the interior region 200. For instance, the coupling mechanism 304 can include a clamp or protruding member (not shown) that attaches to an air movement grill of the vehicle 106. In other cases, the coupling mechanism 304 can include a plate or other type of member which can be fastened to any surface of the vehicle 106 using any type of fastener (e.g., screws, clamps, a Velcro coupling mechanism, a sliding coupling mechanism, a snapping coupling mechanism, a suction cup coupling mechanism, etc.). In still other cases, the mount 302 can merely sit on a generally horizontal surface of the interior region 200, such as on the top of the dashboard, without being fastened to that surface. To reduce the risk of this type of mount sliding on the surface during movement of the vehicle 106, it can include a weighted member, such as a sand-filled malleable base member.


In one merely illustrative implementation, the representative mount 302 shown in FIG. 3 includes a flexible arm 306 which extends from the coupling mechanism 304 and terminates in a cradle 308. The cradle 308 can include an adjustable clamp mechanism 310 for securing the mobile device 104 to the cradle 308. In this particular scenario, the user 102 has attached the mobile device 104 to the cradle 308 so that it can be operated in a portrait mode. But the user 102 can alternatively attach the mobile device 104 so that it can be operated in a landscape mode (as shown in FIG. 2).


As mentioned above, the mobile device 104 includes at least one internal camera device 312 which projects out from a front face 314 of the mobile device 104 (or other face of the mobile device 104). The internal camera device 312 is identified as “internal” insofar as it is typically considered an integral part of the mobile device 104. In addition, the mobile device 104 can receive image information from one or more external camera devices (not shown).


Further, the mount 302 may incorporate any attachment-sensing mechanism 316 for determining when the mobile device 104 has been inserted in the cradle 308 of the mount 302. For example, the attachment-sensing mechanism 316 can comprise a mechanical switch that that is toggled from an OFF to an ON state when the user 102 inserts the mobile device 104 into the cradle 308, and from an ON to OFF state when the mobile device 104 becomes dislodged from the cradle 308. Other implementations of the attachment-sensing device include a light-sensing switch, a pressure-sensing switch, and so on. Alternatively, or in addition, the mobile device 104 can implement an attachment sensing mechanism (not shown). That is, in complementary fashion, a device-implemented attachment sensing mechanism is configured to be activated when the user 102 places the mobile device 104 in the cradle 308. Alternatively, or in addition, the mobile device 104 can infer the fact that it has become dislodged from the cradle 308 based on indirect evidence. In any implementation, as will be described below, the attachment-sensing mechanism 316 plays a role in determining whether the vehicle 106 is in a distress condition.


Further, the mount 302 can include one or more supplemental sensor devices 320 (depicted generically in FIG. 3 by a dashed box). For example, the sensor devices 320 can encompass one or more of the types of movement-sensing devices 430 shown in FIG. 5 (to be described below). In addition, the mount 302 can encompass additional image-sensing mechanisms, such one or more additional camera devices of any type, etc.



FIG. 4 shows various components that can be used to implement the mobile device 104. This figure will be described in a generally top-to-bottom manner. To begin with, the mobile device 104 includes communication functionality 402 for receiving and transmitting information to remote entities via wireless communication. That is, the communication functionality 402 may comprise a transceiver that allows the mobile device 104 to interact with the wireless communication infrastructure 116 of the communication conduit 114.


The mobile device 104 can also include a set of one or more applications 404. The applications 404 represent any type of functionality for performing any respective tasks. In some cases, the applications 404 perform high-level tasks. To cite representative examples, a first application may perform a map navigation task, a second application can perform a media presentation task, a third application can perform an Email interaction task, and so on. In other cases, the applications 404 perform lower-level management or support tasks. The applications 404 can be implemented in any manner, such as by executable code, script content, etc., or any combination thereof. The mobile device 104 can also include at least one device store 406 for storing any application-related information, as well as other information.


In other implementations, at least part of the applications 404 can be implemented by the remote processing systems 118. For example, in certain implementations, some of the applications 404 may represent network-accessible pages and/or other type of functionality.


The mobile device 104 can also include a device operating system 408. The device operating system 408 provides functionality for performing low-level device management tasks. Any application can rely on the device operating system 408 to utilize various resources provided by the mobile device 104.


The mobile device 104 can also include input functionality 410 for receiving and processing input information. Generally, the input functionality 410 includes some functionality for receiving input information from internal input devices (which represent components that are part of the mobile device 104 itself), and some functionality for receiving input information from external input devices. The input functionality 410 can receive input information from external input devices using any coupling technique or combination of coupling techniques, such as hardwired connections, wireless connections (e.g., Bluetooth® connections), and so on.


This explanation refers to the input information that is ultimately used to infer the state of the vehicle 106 as inference-input information. This explanation refers to the input information that is provided by the user 102 as user-input information. These two classes of input information are not necessarily mutually exclusive; that is, some of the information that is input by a user 102 may constitute inference-input information. A generic reference to “input information,” without the qualifier “user” or “inference,” refers to any type of input information.


The input functionality 410 includes an optional gesture recognition module 412 for receiving image information from at least one internal camera device 414, and/or from at least one external camera device 416. (For example, the external camera device 416 can be associated with the mount 302, or by some other unit within the vehicle 106.) Any of these camera devices can provide any type of image information. For example, in one case, a camera device can provide video image information, produced by receiving visible-spectrum radiation, infrared-spectrum radiation, etc., or combination thereof. In another case, a camera device can provide image information that can be further processed to provide depth information. Depth information provides an indication of the distances between different points in a captured scene and a reference point (e.g., corresponding to the location of the camera device). Depth processing functionality can generate depth information using any technique, such as a time-of-flight technique, a structured light technique, a stereoscopic technique, and so on. After receiving the image information, the gesture recognition module 412 can determine whether the image information reveals that the user 102 has made a recognizable gesture.


The input functionality 410 can also receive image information from one or more camera devices that capture a scene that is external to the vehicle 106. For example, an internal or external camera device can capture a scene in front of the vehicle 106, in back of the vehicle 106, to either side of the vehicle 106, etc. These camera devices can also be used in conjunction with any type depth processing functionality described above. The use of depth processing functionality allows the mobile device 104 to assess the distance between the vehicle 106 and other nearby vehicles and obstacles. The input functionality 410 can also receive inference-input information from any other type of distance sensing mechanism, such as a Light Detection And Ranging (LIDAR) sensing device, etc.


The input functionality 410 can also include a supplemental system interface module 418. The supplemental system interface module 418 receives inference-input information from any vehicle system 420, and/or from the mount 302, and/or from any other external system. For example, the supplemental system interface module 418 can receive any type of OBDII information provided by the vehicle's information management system. Such information can describe the operating state of the vehicle 106 at a particular point in time, such as by providing information regarding the vehicle's speed, steering state, breaking state, engine temperature, engine performance, odometer reading, oil level, fuel level, the presence of passengers in the vehicle 106, and so on. To provide this information, the vehicle system 420 can receive sensor information from a plurality of sensing devices provided by the vehicle 106. Alternatively, or in addition, the supplemental system interface module 318 can receive inference-input information collected by one or more sensor devices (such as one or more supplemental accelerometer devices provided by the mount 302).


The input functionality 410 can also include a touch input module 422 for receiving user-input information when a user 102 touches a touch input device 424. Although not depicted in FIG. 4, the input functionality 410 can also include any type of physical keypad input mechanism, any type of joystick control mechanism, any type of mouse device mechanism, and so on. The input functionality 410 can also include a voice recognition module 426 for receiving voice commands from one or more microphone devices 428.


The input functionality 410 can also include one or more movement-sensing devices 430. Generally, the movement-sensing devices 430 determine the manner in which the mobile device 104 is being moved at any given time. That information, in turn, can pertain to either the dynamic movement of the mobile device 104 and/or its position at any given time. Advancing momentarily to FIG. 5, this figure indicates that the movement-sensing devices 430 can include any of an accelerometer device 502, a gyro device 504, a magnetometer device 506, a GPS device 508 (or other satellite-based position-determining mechanism), a dead-reckoning position-determining device (not shown), a cell tower or WiFi triangulation device (not shown), and so on. Further, the movement-sensing device 430 can include any type of vision device described above, e.g., corresponding to one or more camera devices and associated functionality. That is, the images captured by the vision device comprise evidence regarding the movement of the vehicle 106; therefore, the vision device can be considered as a type of movement-sensing device. This set of possible devices is representative, rather than exhaustive. In other cases, some other entity (besides, or in addition to the mobile device 104) can assess the movement of the mobile device 104, such as any functionality provided by the remote processing systems 118.


The mobile device 104 also includes output functionality 432 for conveying information to a user 102 in an output presentation. Advancing momentarily to FIG. 6, this figure indicates that the output functionality 432 can include any of a device screen 602, one or more speaker devices 604, a projector device 606 for projecting output information onto any surface, and so on.


The output functionality 432 also includes a vehicle interface module 608 that enables the mobile device 104 to send output information to any vehicle system 420 associated with the vehicle 106. This allows the user 102 to interact with the mobile device 104 to control the operation of any functionality associated with the vehicle 106 itself. For example, the user 102 can interact with the mobile device 104 to control the playback of media content on a separate vehicle media system. The user 102 may prefer to directly interact with the mobile device 104 rather than the systems of the vehicle 106 because the user 102 is presumably already familiar with the manner in which the mobile device 104 operates. Moreover, the mobile device 104 has access to a remote system store 122 which can provide user-specific information. The mobile device 104 can leverage this information to control any vehicle system 420 in a manner that is customized for a particular user 102.


Finally, the mobile device 104 can optionally include mode functionality 434. The mode functionality 434 performs the core functions summarized above, which include assessing the state of the vehicle 106 at a particular point in time and providing a user interface experience that takes into consideration the vehicle state. Alternatively, at least parts of the mode functionality 434 can be implemented by the remote processing systems 118.



FIG. 7 illustrates one manner in which the functionality provided by the mount 302 (of FIG. 3) can interact with the mobile device 104. The mount 302 can include the attachment sensing mechanism 316 (described above) which provides an attachment signal to the input functionality 410 of the mobile device 104. The attachment signal indicates whether or not the mobile device 104 is presently coupled to the mount 302. The mount 302 can also include any of the type of the movement-sensing devices 430 shown in FIG. 5 for providing inference-input information to the input functionality 410 of the mobile device 104. The mount 302 can also include any other optional devices 702 for providing inference-input information to the input functionality 410 of the mobile device 104. Alternatively, or in addition, the devices 702 can perform various processing functions, and can then send the results of such processing to the mobile device 104.


The mount 302 can also include a power source 704 which feeds power to the mobile device 104, e.g., via an external power interface module 706 provided by the mobile device 104. The power source 704 may, in turn, receive power from any external source, such as a power source (not shown) associated with the vehicle 106. In this implementation, the power source 704 powers both the components of the mount 302 and the mobile device 104. Alternatively, each of the mobile device 104 and the mount 302 can be supplied with separate sources of power.


Finally, FIG. 7 shows interfaces (708, 710) that allow the input functionality 410 of the mobile device 104 to communicate with the components of the mount 302.



FIGS. 8 and 9 pictorially summarize two output modes. That is, in FIG. 8, the mobile device 104 presents visual content 802 on the display screen 602 of the mobile device 104. In FIG. 9 the mobile device 104 presents audio content 902 that supplements or replaces the visual content 802.



FIGS. 10-12 pictorially summarize three input modes. That is, in FIG. 10, the touch input module 422 accepts user-input information when the user 102 uses a hand 1002 to touch an icon 1004 or other object presented on a touch input screen of the mobile device 104. In FIG. 11, the gesture recognition module 412 receives user-input information when the user 102 makes a gesture that is captured by the internal camera device 414 of the mobile device 104, without touching the mobile device 104. The gesture recognition module 412 can recognize this gesture by comparing the captured image information with candidate gesture information associated with each of a set of possible candidate gestures. In FIG. 12, the voice recognition module 426 receives user-input information when the user 102 annunciates a voice command.



FIG. 13 shows additional information regarding a subset of the components of the mobile device 104, introduced above in the context of FIGS. 4-7. The components include a representative application 1302 and the mode functionality 434. As the name suggests, the “representative application” 1302 represents one of the set of applications 404 that may run on the mobile device 104 (and/or may run on remote processing functionality).


More specifically, FIG. 13 depicts the representative application 1302 and the mode functionality 434 as separate entities that perform respective functions. However, any aspect of the mode functionality 434 can be alternatively, or in addition, be performed by the representative application 1302. Similarly, any aspect of the representative application 1302 can alternatively, or in addition, be performed by the mode functionality 434. Further, the components shown in FIG. 13 are described herein as being performed by the mobile device 104. However, alternatively, or in addition, at least some of the functions of the representative application 1302 and the mode functionality 434 can be performed by any functionality of the remote processing systems 118 and/or the mount 302.


The representative application 1302 can be conceptualized as comprising a set of resources 1304. The application resources 1304 represent image content, text content, audio content, programmatic content, control settings, etc. that the representative application 1302 may use to provide its services. Moreover, in some cases, a developer can provide multiple collections of resources for invocation in different vehicle states. For example, assume that there are two principal vehicle states: moving and not moving. The developer can provide a first collection of interface icons and prompting messages that the mobile device 104 can present in the moving state, and a second collection of interface icons and prompting messages that the mobile device 104 can present in a non-moving state. The moving-state collection can differ from the non-moving-state collection. For example, the moving-state collection can use larger size icons and fonts compared to the non-moving-state collection. During execution of the application, the mode functionality 434 can determine the vehicle state at a particular time. In response, the mode functionality 434 can invoke the moving-collection collection to provide a user interface experience in the moving state and the non-moving-collection to provide a user interface experience in the non-moving state. (As will be described below, the mode functionality 434 can make other changes to produce an appropriate user interface experience.)


The two-collection example is merely illustrative; other applications can provide more than two classes of resource collections corresponding to different respective ways in which the vehicle 106 is being driven. For example, a developer can create a resource collection for use for a nighttime driving vehicle state and a resource collection for a daytime driving vehicle state (as well as a resource collection for a non-moving state).


In the above type of development environment, the developer can consult an appropriate software development kit (SDK) to assist him or her in creating the different sets of resources. The SDK describes various requirements and recommendations regarding the characteristics of resources to be used in different vehicle states. For example, the SDK can require or recommend that the developer use fonts no smaller than a certain size for certain vehicle states.


Advancing now to a description of the mode functionality 434, this component is shown as comprising three sub-modules: an interface module 1306, a state detection module 1308, and an experience presentation module 1310. To facilitate description, it will be assumed that all of the logic for implementing these three functions is indeed encapsulated in a unit being referred to as the mode functionality 434. But as stated above, any aspect of the mode functionality 434 can be alternatively, or in addition, performed by the representative application 1302 and/or some other entity (such as the remote processing systems 118).


The interface module 1306 receives various forms of inference-input information. A subset 1312 of the instances of inference-input information originates from input sources that are associated with the mobile device 104 itself Another subset 1314 of the instances of inference-input information originates from input sources that are external to the mobile device 104 (e.g., from the vehicle system 420, the mount 302, etc.).


For example, the subset 1312 of internal instances of inference-input information can originate from any of the movement-sensing devices 430 enumerated in FIG. 5. The subset 1312 can also include image information received from one or more internal camera devices which capture a scene or scenes inside the vehicle 106 and/or outside the vehicle 106. The subset 1312 can also include audio information captured by one or more microphone devices.


The subset 1314 of instances of external inference-input information can originate from any sensor devices which feed sensor information into any vehicle system 420, e.g., as expressed by OBDII information or the like. The subset 1314 can also include image information received from one or more external camera devices which capture a scene or scenes inside the vehicle 106 and/or outside the vehicle 106. For example, image information captured by an outward-pointing camera device can be used to reveal the presence of pedestrians and nearby vehicles, the presence of stop lights, and so on. The subset 1314 can also include audio information captured by one or more microphone devices.


This subset 1314 can also encompass any information that is extracted from a remote source (e.g., from any of the remote processing systems 118). Such information can include map information, traffic information, road condition information, hazard information, weather information, region population information, point of interest information, legal information regarding driving-related rules pertinent to a particular jurisdiction, and so on. Moreover, the map information can provide information regarding a region in any level of granularity. For example, the map information can identify the location of traffic lights, complex intersections, school zones, etc. in a region.


The information maintained by the remote processing systems 118 can be collected in various ways. In one approach, the remote processing systems 118 can collect the information based on in-field sensing devices, such as roadway camera devices, aerial and satellite camera devices, temperature sensing devices, precipitation-sensing devices, and so forth. In addition, or alternatively, the remote processing systems 118 can collect the information from human observers who manually report the information. In addition, or alternatively, the remote processing systems 118 can collect the information by crowd-sourcing it from a plurality of mobile devices provided in respective vehicles.


The above-identified forms of inference-input information are cited by way of illustration, not limitation; other implementations can provide other forms of inference-input information, and/or can omit one or more forms of inference-input information described above.


The state detection module 1308 infers the state of the vehicle 106 based on any combination of the forms of inference-input information enumerated above (and/or other forms of inference-input information). The state detection module 1308 can perform this task in different ways. In one implementation, the state detection module 1308 can maintain a lookup table which maps different permutations of input conditions (defined by the inference-input information) to corresponding vehicle state information. That is, the state detection module 1308 can indicate that, if input conditions L, M, N, and P are present, the vehicle state is in state X. In another case, the state detection module 1308 can use a statistical model to map a feature vector associated with a set of input conditions into an identified vehicle state. That statistical model can be produced in a machine-learning process. In another case, the state detection module 1308 can use a rules-based engine of any type or a neural network to map the input conditions into an identified vehicle state, and so on. These implementations are cited by way of example, not limitation. Section B will describe the illustrative behavior of the state detection module 1308 in greater detail, in the context of representative scenarios.


In addition, the state detection module 1308 can consult a route prediction module to determine the route that the user 102 is likely to take to reach a specified or predicted destination. The route information helps the state detection module 1308 operate in a more proactive manner by predicting difficult driving conditions that the user 102 is likely to confront as the trip progresses, before those conditions are actually encountered. The state detection module 1308 can also mine any other user resources in order to generate the vehicle state, such as calendar information, purchase history information, prior travel route information, and so on.


The experience presentation module 1310 receives information regarding the inferred vehicle state from the state detection module 1308. In response, the experience presentation module 1310 maps the vehicle state into a user interface experience. In general, as described above, the mode functionality 434 attempts to provide a user interface experience which consumes the attention of the user 102 in a way that is commensurate with the vehicle state. This means that that the user interface experience is such that it does not demand a level and type of attention from the user 102 that the user 102 cannot safely give in view of the vehicle state. This behavior, in turn, ultimately reduces the risk associated with the use of the mobile device 104 within the vehicle 106. At the same time, the mode functionality 434 provides a user experience that is not unduly restrictive, e.g., by unnecessarily precluding certain interactions that do not pose a significant risk to the user 102.


The experience presentation module 1310 can also consult functionality provided in the remote processing systems 118 (and its associated system store 122) to choose the user interface experience that it presents to the user 102. For example, the experience presentation module 1310 can determine the preferences and habits of the user 102, and then use this information to influence the selection of the user interface experience. The preferences may indicate the configurations of the user interface experience which the user prefers to receive in different driving circumstances. The experience presentation module 1310 may attempt to satisfy a preference of the user for a particular driving circumstance, providing that such a choice is not contradicted by other considerations. The habits can indicate the manner in which the user has driven the vehicle 106 (on past occasions) when confronted with various driving circumstances in conjunction with different user interface experiences. If the user performed poorly for a particular combination of a driving circumstance and a user interface experience, the experience presentation module 1310 can negatively weight this combination to disfavor its use on a future occasion.


In addition to providing a user interface experience, the experience presentation module 1310 can present warnings to the user. For example, a warning may alert the user to the fact that he or she is approaching a school zone. The warning may encourage the driver to be watchful for the presence of children. In addition, or alternatively, the warning may alert the user that he or she is driving too fast for the circumstances.



FIG. 14 enumerates some of the different ways that the experience presentation module 1310 can produce a desired user interface experience. (Section B will describe yet more examples of the operation of the experience presentation module 1310.) As one general category, the experience presentation module 1310 can adjust some aspect of the output functionality 432. As another general category, the experience presentation module 1310 can adjust some aspect of the input functionality 410. The experience presentation module 1310 can also modify any other aspect of the environment 100 shown in FIG. 1.


First consider changes made to the output functionality 432. As a first change, the experience presentation module 1310 can enable or disable certain output modes in response to the vehicle state (or at least partially enable or restrict one or more parts of certain output modes). To cite one example, the experience presentation module 1310 can disable a display output mode when the vehicle 106 is moving. In lieu of that manner of output, the experience presentation module 1310 can provide output information via the speech output mode, or produce no output information at all so long as the moving condition prevails.


Alternatively, or in addition, the experience presentation module 1310 can change the content that it presents in response to the vehicle state. For example, as noted above, an application can include two or more collections of resources for use in providing an output presentation. The experience presentation module 1310 can present an output presentation using an appropriate collection of resources based on the vehicle state. For example, the experience presentation module 1310 can display large-sized icons when the speed of the vehicle 106 exceeds a prescribed threshold.


Alternatively, or in addition, the experience presentation module 1310 can change any property or properties of the output presentation itself in response to vehicle state. This type of change is similar to the one described immediately above. But here, instead of choosing an entirely new collection of resources, the experience presentation module 1310 can modify one or more variable attributes of the output presentation. This category encompasses a wide range of options. For example, for visual output presentations, the experience presentation module 1310 can adjust any of the size, contrast, color, transparency, etc. of the content that is displayed, the length of time that the content is displayed, the spatial organization between different parts of the content that is displayed, and so on. For audio output presentations, the experience presentation module 1310 can adjust the volume of the audio content that is presented, the rate of speaking provided by the audible content, and so on.


Alternatively, or in addition, the experience presentation module 1310 can send output information to different destinations based on the vehicle state. For example, for some vehicle states, the mobile device 104 may route the output information to an output device associated with the mobile device 104 itself. For other vehicle states, the mobile device 104 may route the output information to any vehicle system 420, such as a media system associated with the vehicle 106.


The experience presentation module 1310 can use yet other strategies for modifying any output presentation based on vehicle state.


Next consider the input functionality 410. As a first change, the experience presentation module 1310 can enable or disable certain input modes (or at least partially enable or restrict one or more parts of certain input modes). To cite one example, the experience presentation module 1310 can disable the touch screen input mode and the keypad input mode when the vehicle 106 is moving at a high speed. In lieu of that manner of input, the experience presentation module 1310 can provide input via the voice-recognition input mode and/or the gesture-recognition input mode.


Alternatively, or in addition, the experience presentation module 1310 can change the type of user-input information that is obtained based on vehicle state. For example, the experience presentation module 1310 can accept a fewer number of voice commands while the vehicle 106 is traveling at high speeds, compared to when the vehicle 106 is moving at slower speeds. This change can help reduces the complexity of the voice-recognition input mode at higher speeds, and hence the distraction that this mode may impose on the user 102.


Alternatively, or in addition, the experience presentation module 1310 can change the manner in which any input mode collects user-input information. For example, at certain junctures, an input mode may present a query to the user 102, requiring a response; after a certain amount of time without receiving an answer, the input mode can deactivate the query. At higher speeds, the input mode can extend the length of time for which it solicits a response from the user 102, as the user 102 may be distracted and unable to provide a quick answer.


The experience presentation module 1310 can use yet other strategies for modifying any input mode based on vehicle state.



FIG. 15 shows another environment 1500 in which the user 102 can operate his or her mobile device 104 within the vehicle 106. In this context, the environment 1500 determines when the vehicle 106 appears to be in a distress condition. A distress condition corresponds to any traumatic event that befalls the vehicle 106, and by extension, the user 102 who is driving the vehicle 106. For example, a distress condition may correspond to an accident that has occurred that involves the vehicle 106. When a distress condition has occurred, the environment 1500 solicits help from a diver assistance system 1502. The driver assistance system 1502 can help the user 102 in various ways, such as by: (a) contacting the user 102 by telephone, text messaging, or other communication mechanism; (b) contacting an emergency response service (or services); (c) contacting the user's family members or other designated points of contact; (d) providing information regarding service stations and/or other assistance centers, and so on. Whenever the driver assistance system 1502 notifies a party of the occurrence of the distress condition, it can identify the location of the vehicle 106 and any qualifying circumstances surrounding the distress condition. The driver assistance system 1502 may be staffed by human agents who assist the user 102 in the event of a distress condition. In addition, or alternatively, the driver assistance system 1502 can include automated functionality for assisting the user 102.



FIG. 16 provides additional information regarding a distress management module 1602 that can detect and respond to a distress condition within the environment 1500 of FIG. 15. In one case, the mobile device 104 implements the distress management module 1602. Alternatively, or in addition, the remote processing systems 118 and/or mount 302 can implement at least part of the distress management module 1602.


The distress management module 1602 includes an interface module 1604 that receives a subset 1606 of instances of inference-input information from one or more internal input sources and/or a subset 1608 of instances of inference-input information from one or more external input sources. In other words, the interface module 1604 functions in the same manner as the interface module 1306 of FIG. 13.


A distress condition detection module 1610 analyzes the input information to determine whether a distress condition has occurred. Different environments can make this judgment in different ways. Generally, the distress condition detection module 1610 forms a signature from the various instances of inference-input information that have been received, and then determines whether this signature matches the telltale signature of a distress condition. In one case, the distress condition detection module 1610 determines that a distress condition has occurred if: (1) the mobile device 104 is present in the vehicle 106; and (2) the vehicle 106 came to an abrupt stop or otherwise abruptly decelerated (or accelerated); and/or (3) the mobile device 104 became dislodged from the mount 302 at about the same time as the occurrence of the abrupt deceleration (or acceleration). Informally, this means that an accident may have occurred which jolted the mobile device 104 out of its cradle 308. Or the mobile device 104 may otherwise experience a dramatic (e.g., a jarring) deceleration or acceleration without necessarily becoming dislodged from the cradle 308. A jolting deceleration may indicate that the moving vehicle 106 has collided with an object in its path. A jolting acceleration may indicate that the vehicle 106 has been hit by another moving object, including while the vehicle 106 is originally at rest.


The distress condition detection module 1610 can presume that the mobile device 104 is located in the vehicle 106 if, just prior to the abrupt deceleration, the attachment-sensing mechanism 316 indicates that the mobile device 104 is inserted in the cradle 308 of the mount 302. Likewise, the distress condition detection module 1610 can determine that the mobile device 104 has broken free of the mount 302 based on the output of the attachment-sensing mechanism 316. The distress condition detection module 1610 can determine that the mobile device 104 has come to an abrupt stop or otherwise abruptly decelerated (or accelerated) based on the output of the accelerometer device 502, for example.


In other cases, the distress condition detection module 1610 can indicate the occurrence of a distress condition without the occurrence of events (2) and/or (3). For example, the distress condition detection module 1610 take into consideration any of the following events in assessing the occurrence of a distress condition: (a) a dramatic application of the breaks; (b) erratic steering; (c) traversal of significantly uneven surfaces (as when the vehicle 106 veers off a roadway); (d) the vehicle 106 turning on its side or completely overturning, etc. In addition, or alternatively, the distress condition detection module 1610 can base its analysis on image information captured by one or more camera devices and/or audio information captured by one or more microphone devices. These events are cited by way of illustration, not limitation.


An action module 1612 can notify the driver assistance system 1502 when the distress condition detection module 1610 informs it that a distress condition has occurred. An assistance center interaction module 1614 allows the user 102 to subsequently communicate with the driver assistance system 1502 to receive manual and/or automated help from that entity.


As a closing point, the above-described explanation has set forth the use of the mode functionality 434 within vehicles. But the user 102 can use the mode functionality 434 to interact with the mobile device 104 in any environment. Generally stated, the mode functionality 434 provides a particularly useful service in those environments in which the user 102 may interact with the mobile device 104 in different use scenarios, and further where the user 102 has different respective capabilities of interacting with the mobile device 104 in these different scenarios. To cite merely one example, the mobile device 104 can determine whether the user 102 is interacting with the mobile device 104 while walking or running; if so, the mobile device 104 can present a user interface experience to the user 102 which takes into consideration various constraints to which the user 102 may be subject while walking or running (as opposed to interacting with the mobile device 104 while at a single location).


B. Illustrative Processes



FIGS. 17-22 show procedures that explain one manner of operation of the environment 100 of FIG. 1. Since the principles underlying the operation of the environment 100 have already been described in Section A, certain operations will be addressed in summary fashion in this section.


Starting with FIG. 17, this figure shows an illustrative procedure 1700 that sets forth one manner of operation of the environment 100 of FIG. 1, from the perspective of the user 102. In block 1702, the user 102 may use his or her mobile device 104 in a conventional mode of operation, e.g., by using his or her hands to interact with the mobile device 104 using the touch input device 424. In block 1704, the user 102 enters the vehicle 106 and places the mobile device 104 in any type of mount, such as the mount 302. In block 1706, the user 102 instructs the mobile device 104 to operate in the vehicle mode. In block 1708, the user 102 begins navigation using the vehicle 106. In block 1708, the user 102 receives a user interface experience that is tailored to a current vehicle state. The vehicle state, in turn, is based on input information supplied by various input sources. In block 1712, after completion of the user's trip, the user 102 may remove the mobile device 104 from the mount 302. The user 102 may then resume using the mobile device 104 in a normal handheld mode of operation.



FIG. 18 shows an illustrative procedure 1800 which explains one manner of operation of the mode functionality 434, from the perspective of the mode functionality 434. In block 1802, the mode functionality 434 receives inference-input information from one or more input sources, including one or more internal input sources (e.g., corresponding to the movement-sensing devices 430), and/or one or more external input sources (e.g., corresponding to sensor information provided by a vehicle system 420). In block 1804, the mode functionality 434 infers the vehicle state based on the inference-input information. In block 1806, the mode functionality 434 presents a user interface experience based on the inferred driving state.



FIGS. 19-21 show three instantiations of the procedure 1700 of FIG. 17. For example, FIG. 19 presents a scenario that hinges on whether the vehicle 106 is moving or not. In block 1902, the mode functionality 434 receives inference-input information. In block 1904, the mode functionality 434 determines whether the vehicle 106 is moving or not. In block 1906, the mode functionality 434 can make any combination of the changes summarized in FIG. 8.


For example, in one scenario, the mode functionality 434 can use the inference-input information provided by any of the movement-sensing devices 430 and/or external sensor devices to determine that the vehicle 106 is in motion. In response, the mode functionality 434 can terminate the use of the display input mode, or use the display input mode to present simplified content (compared to the content it would present if the vehicle 106 was stationary). In lieu of the display input mode, the mode functionality 434 can optionally interact with the user 102 using the voice-recognition mode and/or the gesture-recognition input mode. Alternatively, the mode functionality 434 can preclude the presentation of certain types of content, such as video content, while the vehicle 106 is in motion.



FIG. 20 presents a scenario that depends on the manner in which the user 102 is driving the vehicle 106. In block 2002, the mode functionality 434 receives inference-input information. In block 2004, the mode functionality 434 classifies the manner in which the mobile device 104 is moving based on the inference-input information. In block 2006, the mode functionality 434 can make any combination of the changes summarized in FIG. 8.


For example, the mode functionality 434 can use any combination of inference-input information to compile a movement signature that characterizes the manner in which the device is moving. The mode functionality 434 can then compare this movement signature to telltale movement signatures associated with different classes of movement; a matching telltale signature indicates the type of movement that the vehicle 106 is currently undergoing. Such classes of movement can include (but are not limited to): (a) traveling at speeds over a prescribed threshold; (b) traveling at dramatically varying speeds; (c) traveling over a winding roadway; (d) traveling over a roadway with marked elevation changes; (e) traveling over an uneven surface; (f) making frequent lane changes while traveling; (g) frequently applying the breaks of the vehicle 106 while traveling; (h) frequently shifting gears while traveling (i) drifting over the roadway while traveling or traveling in an otherwise erratic manner, and so on. The mode functionality 434 can then apply a user interface experience which correlates to the matching telltale movement signature. As a general principle, if the collected evidence indicates that the task of driving is (or should be) an arduous or complex task at the current time, then the mode functionality 434 will seek to reduce the attention-related demands that it imposes on the user 102. Alternatively, or in addition, if the collected evidence indicates that the user 102 is already distracted (as evidenced by poor driving), then the mode functionality 434 will seek to lessen the attention-related burden on the user 102.



FIG. 21 presents a scenario that depends on an assessed location of the vehicle 106. In block 2102, the mode functionality 434 receives inference-input information. The inference-input information can include any evidence pertaining to the location of the vehicle 106. Such evidence can include position information, such as GPS information, WiFi or cell tower triangulation information, dead reckoning information, and so on. In addition, or alternatively, the mode functionality 434 can directly monitor the environment in which the user 102 is traveling based on image information captured by one or more camera devices and/or audio information captured by one or more microphone devices.


In block 2104, the mode functionality 434 identifies the region in which the vehicle 106 is located based on the inference-input information. This can comprise position-related analysis of position information received by any position-determining device. For example, this operation may involve determining a street location of the vehicle 106 by consulting map information that is provided by the mobile device 104 and/or the remote processing systems 118. The determination of the region can also involve analysis of image information received from camera devices and/or analysis of audio information received from microphone devices. For example, the mode functionality 434 can rely on image analysis to determine that the roadway on which the user 102 is traveling is congested with pedestrians and/or other vehicles.


As another part of this block 2104, the mode functionality 434 can ascertain the driving-related implications of the region in which the vehicle 106 is located. In one implementation, the mode functionality 434 can make this assessment by consulting the remote processing systems 118 (and the associated system store 122). The remote processing systems 118 can determine whether there are any attention-related considerations that have a bearing on the amount and type of attention that a user 102 is expected to maintain while in the identified region. Based on this information, in block 2106, the mode functionality 434 can make any combination of the changes summarized in FIG. 8.


For example, the mode functionality 434 can ascertain whether the user 102 is within any one or the following representative regions to which a particular attention profile may apply: (a) a school zone; (b) a construction zone; (c) an area in proximity to emergency services; (d) a hazard zone, and so on. More generally, the mode functionality can also use any position-related evidence to determine the driving rules which are applicable to the vehicle 106 at a particular point in time. The mode functionality 434 can then apply a user interface experience which it appropriate for the identified region.


Alternatively, the mode functionality 434 can make its determination of the vehicle state based on the manner in which the user 102 is driving his or her vehicle 106 (as ascertained in scenario A or scenario B), combined with insight regard the present location of the vehicle 106 (as ascertained in scenario C). For example, the mode functionality 434 can selectively disable a display input mode output presentation when the user 102 is driving more than 20 MPH on a street that borders a park.



FIG. 22 shows a procedure 2200 which summarizes one manner of operation of the distress management module 1602 shown in FIG. 16. In block 2202, the distress management module 1602 receives inference-input information. In block 2204, the distress management module 1602 determines whether the vehicle 106 is in a distress condition at the present time, based on the inference-input information. In block 2206, the distress management module 1602 presents assistance to the user 102 (presuming that the vehicle 106 is in a distress condition). This assistance can include contacting the remote driver assistance system 1502.


C. Representative Computing functionality



FIG. 23 sets forth illustrative computing functionality 2300 that can be used to implement any aspect of the functions described above. For example, the computing functionality 2300 can be used to implement any aspect of the mobile device 104. In addition, the type of computing functionality 2300 shown in FIG. 23 can be used to implement any aspect of the remote processing systems 118. In one case, the computing functionality 2300 may correspond to any type of computing device that includes one or more processing devices. In all cases, the computing functionality 2300 represents one or more physical and tangible processing mechanisms.


The computing functionality 2300 can include volatile and non-volatile memory, such as RAM 2302 and ROM 2304, as well as one or more processing devices 2306 (e.g., one or more CPUs, and/or one or more GPUs, etc.). The computing functionality 2300 also optionally includes various media devices 2308, such as a hard disk module, an optical disk module, and so forth. The computing functionality 2300 can perform various operations identified above when the processing device(s) 2306 executes instructions that are maintained by memory (e.g., RAM 2302, ROM 2304, or elsewhere).


More generally, instructions and other information can be stored on any computer readable medium 2310, including, but not limited to, static memory storage devices, magnetic storage devices, optical storage devices, and so on. The term computer readable medium also encompasses plural storage devices. In all cases, the computer readable medium 2310 represents some form of physical and tangible entity.


The computing functionality 2300 also includes an input/output module 2312 for receiving various inputs (via input modules 2314), and for providing various outputs (via output modules). One particular output mechanism may include a presentation module 2316 and an associated graphical user interface (GUI) 2318. The computing functionality 2300 can also include one or more network interfaces 2320 for exchanging data with other devices via one or more communication conduits 2322. One or more communication buses 2324 communicatively couple the above-described components together.


The communication conduit(s) 2322 can be implemented in any manner, e.g., by a local area network, a wide area network (e.g., the Internet), etc., or any combination thereof. The communication conduit(s) 2322 can include any combination of hardwired links, wireless links, routers, gateway functionality, name servers, etc., governed by any protocol or combination of protocols.


Alternatively, or in addition, any of the functions described in Sections A and B can be performed, at least in part, by one or more hardware logic components. For example, without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.


In closing, functionality described herein can employ various mechanisms to ensure the privacy of user data maintained by the functionality. For example, the functionality can allow a user to expressly opt in to (and then expressly opt out of) the provisions of the functionality. The functionality can also provide suitable security mechanisms to ensure the privacy of the user data (such as data-sanitizing mechanisms, encryption mechanisms, password-protection mechanisms, etc.).


Further, the description may have described various concepts in the context of illustrative challenges or problems. This manner of explanation does not constitute an admission that others have appreciated and/or articulated the challenges or problems in the manner specified herein.


Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims
  • 1. A computer implemented method, comprising: determining that a mobile device is in a vehicle based upon at least one of mobile device-provided input information provided by the mobile device or vehicle-provided input information provided by the vehicle;automatically switching the mobile device from a handheld mode to a vehicle mode based upon the at least one of the mobile device-provided input information or the vehicle-provided input information;evaluating the at least one of the mobile device-provided input information or the vehicle-provided input information to select a current vehicle state of the vehicle having a corresponding level of driving complexity, the current vehicle state being selected from a plurality of predetermined vehicle states associated with different levels of driving complexity; and,generating a user interface experience for a user who is operating the vehicle based at least in part on the corresponding level of driving complexity of the current vehicle state, wherein the user interface experience imposes attention-related demands on the user and wherein the user interface experience replaces at least some input commands with other input commands that are associated with the corresponding level of driving complexity of the current vehicle state.
  • 2. The method of claim 1, further comprising: obtaining the mobile device-provided input information from one or more of:an accelerometer device of the mobile device;a gyro device of the mobile device;a vision device of the mobile device;a magnetometer device of the mobile device;or a position-determining device of the mobile device,wherein the mobile device-provided input information is evaluated to select the current vehicle state.
  • 3. The method of claim 2, wherein the mobile device-provided input information is obtained from the position-determining device and the position-determining device comprises a GPS device.
  • 4. The method of claim 1, further comprising: obtaining the vehicle-provided input information, wherein the vehicle- provided input information indicates whether passengers are present in the vehicle and the vehicle-provided input information is evaluated to select the current vehicle state.
  • 5. The method of claim 1, further comprising: obtaining and evaluating both the vehicle-provided input information and the mobile device-provided input information to select the current vehicle state.
  • 6. The method of claim 1, wherein the evaluating comprises: compiling a movement signature from the at least one of the mobile device-provided input information or the vehicle-provided input information.
  • 7. The method of claim 6, wherein the evaluating comprises comparing the movement signature to a plurality of different movement signatures associated with different classes of movement to determine the current vehicle state.
  • 8. The method of claim 7, wherein the different classes of movement correspond to different variations in speed of the vehicle and the current vehicle state characterizes an extent to which the speed of the vehicle varies.
  • 9. The method of claim 7, wherein the different classes of movement correspond to different amounts of braking of the vehicle and the current vehicle state characterizes an extent to which the brakes of the vehicle are being applied.
  • 10. The method of claim 1, wherein, in the vehicle mode, the generating comprises sending the generated user interface experience to the vehicle for presentation by the vehicle.
  • 11. The method of claim 1, wherein the at least some input commands that are replaced are touch input commands and the other input commands are voice-based or gesture-based commands.
  • 12. The method of claim 1, wherein the generating comprises presenting the user interface experience on the mobile device.
  • 13. A mobile device, comprising: a display;a processor; anda computer-readable medium storing computer readable instructions which, when executed by the processor, cause the processor to:allow a user to specify that the mobile device operate in either a handheld mode or a vehicle mode; in the vehicle mode: obtain sensor information from one or more sensors, anduse the sensor information to predict a route that a vehicle is likely to take to reach a specified or predicted destination; based at least on the predicted route, determine a predicted future vehicle state of the vehicle, the predicted future vehicle state having an attention profile that characterizes a level of attention and a type of attention which is appropriate for the user to maintain while operating the vehicle; andconfigure a user interface of the mobile device to comply with the attention profile for the predicted future vehicle state.
  • 14. The mobile device of claim 13, wherein: an application with which the mobile device interacts includes a plurality of resources, the plurality of resources including at least a first group of resources adapted for application in a first vehicle state, and a second group of resources adapted for application in a second vehicle state, andthe computer readable instructions, when executed by the processor, cause the processor to use either the first group of resources or the second group of resources based at least on the predicted future vehicle state.
  • 15. The mobile device of claim 13, further comprising the one or more sensors.
  • 16. The mobile device of claim 15, wherein: the one or more sensors indicate a location of the mobile device, andthe computer readable instructions, when executed by the processor, cause the processor to consult prior routes traveled by the mobile device to predict the route.
  • 17. The mobile device of claim 13, wherein the computer readable instructions, when executed by the processor, cause the processor to: determine predicted driving conditions associated with the predicted route; anddetermine the predicted future vehicle state based at least on the predicted driving conditions.
  • 18. The mobile device of claim 13, wherein: the one or more sensors are provided by the vehicle, and the computer readable instructions, when executed by the processor,cause the processor to obtain the sensor information from the vehicle.
  • 19. A method comprising: operating a mobile device having a handheld mode and a vehicle mode in the vehicle mode;while in the vehicle mode: obtaining sensor information from one or more sensors, andusing the sensor information to predict a route that a vehicle is likely to take to reach a specified or predicted destination;based at least on the predicted route, determining a predicted future vehicle state of the vehicle, the predicted future vehicle state having an attention profile that characterizes a level of attention and a type of attention which is appropriate for a user to maintain while operating the vehicle; andconfiguring a user interface of the mobile device in accordance with the attention profile for the predicted future vehicle state.
  • 20. The method of claim 19, performed entirely by the mobile device.
US Referenced Citations (427)
Number Name Date Kind
4627620 Yang Dec 1986 A
4630910 Ross Dec 1986 A
4645458 Williams Feb 1987 A
4695953 Blair Sep 1987 A
4702475 Elstein Oct 1987 A
4711543 Blair Dec 1987 A
4731860 Wahl Mar 1988 A
4751642 Silva Jun 1988 A
4751643 Lorensen Jun 1988 A
4796997 Svetkoff Jan 1989 A
4809065 Harris Feb 1989 A
4817950 Goo Apr 1989 A
4843568 Krueger Jun 1989 A
4893183 Nayar Jan 1990 A
4901362 Terzian Feb 1990 A
4925189 Braeunig May 1990 A
5101444 Wilson Mar 1992 A
5109537 Toki Apr 1992 A
5139261 Openiano Aug 1992 A
5148154 MacKay Sep 1992 A
5156243 Aoki et al. Oct 1992 A
5184295 Mann Feb 1993 A
5229754 Aoki Jul 1993 A
5229756 Kosugi Jul 1993 A
5239463 Blair Aug 1993 A
5239464 Blair Aug 1993 A
5288078 Capper et al. Feb 1994 A
5295491 Gevins Mar 1994 A
5320538 Baum Jun 1994 A
5347306 Nitta Sep 1994 A
5385519 Hsu Jan 1995 A
5405152 Katanics Apr 1995 A
5414643 Blackman et al. May 1995 A
5417210 Funda May 1995 A
5423554 Davis Jun 1995 A
5454043 Freeman Sep 1995 A
5469740 French Nov 1995 A
5495576 Ritchey Feb 1996 A
5516105 Eisenbrey May 1996 A
5524637 Erickson Jun 1996 A
5525901 Clymer et al. Jun 1996 A
5528263 Platzker et al. Jun 1996 A
5534917 MacDougall Jul 1996 A
5563988 Maes Oct 1996 A
5577981 Jarvik Nov 1996 A
5580249 Jacobsen Dec 1996 A
5594469 Freeman Jan 1997 A
5597309 Riess Jan 1997 A
5611731 Bouton et al. Mar 1997 A
5615132 Horton et al. Mar 1997 A
5616078 Oh Apr 1997 A
5617312 Iura Apr 1997 A
5638300 Johnson Jun 1997 A
5641288 Zaenglein Jun 1997 A
5682196 Freeman Oct 1997 A
5682229 Wangler Oct 1997 A
5690582 Ulrich Nov 1997 A
5703367 Hashimoto Dec 1997 A
5704837 Iwasaki Jan 1998 A
5715834 Bergamasco Feb 1998 A
5732227 Kuzunuki et al. Mar 1998 A
5757360 Nitta et al. May 1998 A
5801704 Oohara et al. Sep 1998 A
5828779 Maggioni Oct 1998 A
5864808 Ando et al. Jan 1999 A
5875108 Hoffberg Feb 1999 A
5877803 Wee Mar 1999 A
5909189 Blackman et al. Jun 1999 A
5913727 Ahdoot Jun 1999 A
5933125 Fernie Aug 1999 A
5959574 Poore, Jr. Sep 1999 A
5971583 Ohnishi Oct 1999 A
5980256 Carmein Nov 1999 A
5989157 Walton Nov 1999 A
5995649 Marugame Nov 1999 A
6002808 Freeman Dec 1999 A
6005548 Latypov Dec 1999 A
6009210 Kang Dec 1999 A
6016487 Rioux Jan 2000 A
6054991 Crane Apr 2000 A
6066075 Poulton May 2000 A
6067077 Martin et al. May 2000 A
6072467 Walker Jun 2000 A
6072494 Nguyen Jun 2000 A
6073489 French Jun 2000 A
6077201 Cheng Jun 2000 A
6098458 French Aug 2000 A
6100896 Strohecker Aug 2000 A
6101289 Kellner Aug 2000 A
6111580 Kazama et al. Aug 2000 A
6128003 Smith Oct 2000 A
6130677 Kunz Oct 2000 A
6141463 Covell Oct 2000 A
6147678 Kumar et al. Nov 2000 A
6151009 Kanade Nov 2000 A
6152856 Studor Nov 2000 A
6159100 Smith Dec 2000 A
6173066 Peurach Jan 2001 B1
6173070 Michael Jan 2001 B1
6181343 Lyons Jan 2001 B1
6188777 Darrell Feb 2001 B1
6191773 Maruno et al. Feb 2001 B1
6195104 Lyons Feb 2001 B1
6205231 Isadore-Barreca Mar 2001 B1
6215890 Matsuo et al. Apr 2001 B1
6215898 Woodfill Apr 2001 B1
6222465 Kumar et al. Apr 2001 B1
6226388 Qian et al. May 2001 B1
6226396 Marugame May 2001 B1
6229913 Nayar May 2001 B1
6256033 Nguyen Jul 2001 B1
6256400 Takata Jul 2001 B1
6269172 Regh et al. Jul 2001 B1
6283860 Lyons Sep 2001 B1
6289112 Jain Sep 2001 B1
6299308 Voronka Oct 2001 B1
6301370 Steffens et al. Oct 2001 B1
6308565 French Oct 2001 B1
6311159 Van Tichelen et al. Oct 2001 B1
6316934 Amorai-Moriya Nov 2001 B1
6347998 Yoshitomi et al. Feb 2002 B1
6353679 Cham Mar 2002 B1
6363160 Bradski Mar 2002 B1
6375572 Masuyama et al. Apr 2002 B1
6377296 Zlatsin et al. Apr 2002 B1
6384819 Hunter May 2002 B1
6411744 Edwards Jun 2002 B1
6421453 Kanevsky et al. Jul 2002 B1
6430997 French Aug 2002 B1
6476834 Doval Nov 2002 B1
6496598 Harman Dec 2002 B1
6503195 Keller Jan 2003 B1
6509889 Kamper et al. Jan 2003 B2
6539107 Michael Mar 2003 B1
6539931 Trajkovic Apr 2003 B2
6542621 Brill et al. Apr 2003 B1
6545661 Goschy et al. Apr 2003 B1
6570555 Prevost May 2003 B1
6591236 Lewis et al. Jul 2003 B2
6594616 Zhang et al. Jul 2003 B2
6600475 Gutta et al. Jul 2003 B2
6603488 Humpleman et al. Aug 2003 B2
6633294 Rosenthal Oct 2003 B1
6640202 Dietz Oct 2003 B1
6642955 Midgley Nov 2003 B1
6661918 Gordon Dec 2003 B1
6674877 Jojic Jan 2004 B1
6681031 Cohen Jan 2004 B2
6714665 Hanna Mar 2004 B1
6720949 Pryor et al. Apr 2004 B1
6731799 Sun May 2004 B1
6738066 Nguyen May 2004 B1
6744420 Mohri Jun 2004 B2
6750848 Pryor Jun 2004 B1
6753879 Deleeuw Jun 2004 B1
6757571 Toyama Jun 2004 B1
6765726 French Jul 2004 B2
6771818 Krumm Aug 2004 B1
6788809 Grzeszczuk Sep 2004 B1
6795567 Cham et al. Sep 2004 B1
6801637 Voronka Oct 2004 B2
6804396 Higaki et al. Oct 2004 B2
6868383 Bangalore et al. Mar 2005 B1
6873723 Aucsmith Mar 2005 B1
6876496 French Apr 2005 B2
6888960 Penev et al. May 2005 B2
6928344 McWalter Aug 2005 B2
6937742 Roberts Aug 2005 B2
6940538 Rafey Sep 2005 B2
6950534 Cohen Sep 2005 B2
6980312 Czyszczewski et al. Dec 2005 B1
6982697 Wilson et al. Jan 2006 B2
6990639 Wilson Jan 2006 B2
6999084 Mochizuki Feb 2006 B2
7003134 Covell Feb 2006 B1
7007236 Dempski et al. Feb 2006 B2
7036094 Cohen et al. Apr 2006 B1
7038661 Wilson May 2006 B2
7038855 French May 2006 B2
7039676 Day May 2006 B1
7042440 Pryor May 2006 B2
7050606 Paul May 2006 B2
7058204 Hildreth Jun 2006 B2
7060957 Lange Jun 2006 B2
7068842 Liang Jun 2006 B2
7070500 Nomi et al. Jul 2006 B1
7094147 Nakata et al. Aug 2006 B2
7095401 Liu et al. Aug 2006 B2
7113918 Ahmad Sep 2006 B1
7121946 Paul Oct 2006 B2
7148913 Keaton et al. Dec 2006 B2
7170492 Bell Jan 2007 B2
7184048 Hunter Feb 2007 B2
7202898 Braun Apr 2007 B1
7206435 Fujimura et al. Apr 2007 B2
7222078 Abelow May 2007 B2
7225414 Sharma et al. May 2007 B1
7227526 Hildreth Jun 2007 B2
7227893 Srinivasa Jun 2007 B1
7250936 Wilson et al. Jul 2007 B2
7259747 Bell Aug 2007 B2
7274800 Nefian et al. Sep 2007 B2
7307617 Wilson et al. Dec 2007 B2
7308112 Fujimura Dec 2007 B2
7317836 Fujimura Jan 2008 B2
7321854 Sharma et al. Jan 2008 B2
7324671 Li Jan 2008 B2
7340077 Gokturk et al. Mar 2008 B2
7348963 Bell Mar 2008 B2
7359121 French Apr 2008 B2
7367887 Watabe May 2008 B2
7372977 Fujimura May 2008 B2
7379563 Shamaie May 2008 B2
7379566 Hildreth May 2008 B2
7389591 Jaiswal Jun 2008 B2
7394346 Bodin Jul 2008 B2
7412077 Li Aug 2008 B2
7421093 Hildreth Sep 2008 B2
7430312 Gu Sep 2008 B2
7436496 Kawahito Oct 2008 B2
7450736 Yang Nov 2008 B2
7452275 Kuraishi Nov 2008 B2
7460690 Cohen Dec 2008 B2
7489812 Fox Feb 2009 B2
7492367 Mahajan et al. Feb 2009 B2
7519223 Dehlin et al. Apr 2009 B2
7526101 Avidan Apr 2009 B2
7536032 Bell May 2009 B2
7552403 Wilson Jun 2009 B2
7555142 Hildreth Jun 2009 B2
7560701 Oggier Jul 2009 B2
7570805 Gu Aug 2009 B2
7574020 Shamaie Aug 2009 B2
7576727 Bell Aug 2009 B2
7590262 Fujimura Sep 2009 B2
7593552 Higaki Sep 2009 B2
7593593 Wilson Sep 2009 B2
7596767 Wilson Sep 2009 B2
7598942 Underkoffler Oct 2009 B2
7607509 Schmiz Oct 2009 B2
7613358 Wilson Nov 2009 B2
7620202 Fujimura Nov 2009 B2
7639148 Victor Dec 2009 B2
7665041 Wilson Feb 2010 B2
7668340 Cohen Feb 2010 B2
7680298 Roberts Mar 2010 B2
7683883 Touma et al. Mar 2010 B2
7683954 Ichikawa Mar 2010 B2
7684592 Paul Mar 2010 B2
7701439 Hillis Apr 2010 B2
7702130 Im Apr 2010 B2
7704135 Harrison, Jr. Apr 2010 B2
7710391 Bell May 2010 B2
7721231 Wilson May 2010 B2
7725129 Grunhold May 2010 B2
7729530 Antonov Jun 2010 B2
7746345 Hunter Jun 2010 B2
7760182 Ahmad Jul 2010 B2
7809167 Bell Oct 2010 B2
7823089 Wilson Oct 2010 B2
7834846 Bell Nov 2010 B1
7852262 Namineni Dec 2010 B2
7890199 Inagaki Feb 2011 B2
RE42256 Edwards Mar 2011 E
7898522 Hildreth Mar 2011 B2
7907117 Wilson Mar 2011 B2
7927216 Ikeda et al. Apr 2011 B2
7974443 Kipman Jul 2011 B2
7988558 Sato Aug 2011 B2
8009871 Rafii Aug 2011 B2
8035612 Bell Oct 2011 B2
8035614 Bell Oct 2011 B2
8035624 Bell Oct 2011 B2
8049719 Wilson Nov 2011 B2
8072470 Marks Dec 2011 B2
8115732 Wilson Feb 2012 B2
8132126 Wilson Mar 2012 B2
8165422 Wilson Apr 2012 B2
8175374 Pinault May 2012 B2
8187096 Ohta et al. May 2012 B2
8213962 Carr Jul 2012 B2
8246458 Nakajima et al. Aug 2012 B2
8246460 Kitahara Aug 2012 B2
8249334 Berliner Aug 2012 B2
8251820 Marks et al. Aug 2012 B2
8282487 Wilson et al. Oct 2012 B2
8287373 Marks et al. Oct 2012 B2
8295546 Craig Oct 2012 B2
8303411 Marks et al. Nov 2012 B2
8308563 Ikeda et al. Nov 2012 B2
8308564 Yoshida et al. Nov 2012 B2
8428340 Marais Apr 2013 B2
8456419 Wilson Jun 2013 B2
8552976 Wilson Oct 2013 B2
8553094 Lin Oct 2013 B2
8560972 Wilson Oct 2013 B2
8599173 Soo et al. Dec 2013 B2
8612884 Capela et al. Dec 2013 B2
8670632 Wilson Mar 2014 B2
8707216 Wilson Apr 2014 B2
8745541 Wilson et al. Jun 2014 B2
8747224 Miyazaki et al. Jun 2014 B2
8814688 Barney et al. Aug 2014 B2
8834271 Ikeda Sep 2014 B2
8858336 Sawano et al. Oct 2014 B2
9171454 Wilson et al. Oct 2015 B2
20010013890 Narayanaswami Aug 2001 A1
20020004422 Tosaki et al. Jan 2002 A1
20020019258 Kim et al. Feb 2002 A1
20020041327 Hildreth et al. Apr 2002 A1
20020055383 Onda et al. May 2002 A1
20020072418 Masuyama et al. Jun 2002 A1
20030040350 Nakata et al. Feb 2003 A1
20030043270 Rafey Mar 2003 A1
20030095140 Keaton et al. May 2003 A1
20030156756 Gokturk et al. Aug 2003 A1
20030216176 Shimizu et al. Nov 2003 A1
20030216179 Suzuki et al. Nov 2003 A1
20040001113 Zipperer et al. Jan 2004 A1
20040005083 Fujimura et al. Jan 2004 A1
20040056907 Sharma et al. Mar 2004 A1
20040113933 Guler Jun 2004 A1
20040155902 Dempski et al. Aug 2004 A1
20040155962 Marks Aug 2004 A1
20040194129 Carlbom et al. Sep 2004 A1
20040204240 Barney Oct 2004 A1
20040239670 Marks Dec 2004 A1
20040252027 Torkkola et al. Dec 2004 A1
20050030184 Victor Feb 2005 A1
20050037730 Montague Feb 2005 A1
20050076161 Albanna et al. Apr 2005 A1
20050085298 Woolston Apr 2005 A1
20050151850 Ahn et al. Jul 2005 A1
20050212753 Marvit et al. Sep 2005 A1
20050238201 Shamaie Oct 2005 A1
20050239548 Ueshima et al. Oct 2005 A1
20050255434 Lok et al. Nov 2005 A1
20050266893 Lejman et al. Dec 2005 A1
20060007142 Wilson Jan 2006 A1
20060033713 Pryor Feb 2006 A1
20060036944 Wilson Feb 2006 A1
20060092267 Dempski et al. May 2006 A1
20060098873 Hildreth et al. May 2006 A1
20060109245 Wilson May 2006 A1
20060178212 Penzias Aug 2006 A1
20060205394 Vesterinen Sep 2006 A1
20060239558 Rafii Oct 2006 A1
20060250226 Vogel et al. Nov 2006 A1
20060252541 Zalewski Nov 2006 A1
20060274947 Fujimura Dec 2006 A1
20060274957 Suzuki Dec 2006 A1
20070060383 Dohta Mar 2007 A1
20070066393 Paul Mar 2007 A1
20070143333 Laird-McConnell Jun 2007 A1
20070192038 Kameyama Aug 2007 A1
20070195997 Paul Aug 2007 A1
20070243931 Ohta et al. Oct 2007 A1
20070252898 Delean Nov 2007 A1
20070254738 Sato Nov 2007 A1
20080026838 Dunstan Jan 2008 A1
20080036732 Wilson et al. Feb 2008 A1
20080037829 Givon Feb 2008 A1
20080094351 Nogami et al. Apr 2008 A1
20080108329 Cho et al. May 2008 A1
20080122786 Pryor et al. May 2008 A1
20080152191 Fujimura Jun 2008 A1
20080181499 Yang Jul 2008 A1
20080193043 Wilson Aug 2008 A1
20080204410 Wilson Aug 2008 A1
20080204411 Wilson Aug 2008 A1
20080259055 Wilson Oct 2008 A1
20090121894 Wilson et al. May 2009 A1
20090141933 Wagg Jun 2009 A1
20090172606 Dunn et al. Jul 2009 A1
20090207136 Farag Aug 2009 A1
20090215534 Wilson et al. Aug 2009 A1
20090221368 Yen et al. Sep 2009 A1
20090252423 Zhu et al. Oct 2009 A1
20090268945 Wilson Oct 2009 A1
20100004838 Georgis et al. Jan 2010 A1
20100027843 Wilson Feb 2010 A1
20100063813 Richter Mar 2010 A1
20100103169 Zhang Apr 2010 A1
20100113153 Yen et al. May 2010 A1
20100121526 Pham May 2010 A1
20100123605 Wilson May 2010 A1
20100138798 Wilson et al. Jun 2010 A1
20100146455 Wilson Jun 2010 A1
20100146464 Wilson Jun 2010 A1
20100151946 Wilson et al. Jun 2010 A1
20100183192 Fritsch Jul 2010 A1
20100195867 Kipman Aug 2010 A1
20100197390 Craig Aug 2010 A1
20100210359 Krzeslo Aug 2010 A1
20100238160 Yea Sep 2010 A1
20100253624 Wilson Oct 2010 A1
20110001696 Wilson Jan 2011 A1
20110004329 Wilson Jan 2011 A1
20110041100 Boillot Feb 2011 A1
20110081044 Peeper Apr 2011 A1
20110081969 Ikeda et al. Apr 2011 A1
20110093778 Kim et al. Apr 2011 A1
20110105097 Tadayon et al. May 2011 A1
20110106375 Gurusamy Sundaram May 2011 A1
20110124410 Mao et al. May 2011 A1
20110172015 Ikeda et al. Jul 2011 A1
20110195699 Tadayon et al. Aug 2011 A1
20110211073 Foster Sep 2011 A1
20110275321 Zhou et al. Nov 2011 A1
20110286676 El Dokor Nov 2011 A1
20110291926 Gokturk Dec 2011 A1
20110304444 Zhang et al. Dec 2011 A1
20120157207 Craig Jun 2012 A1
20120214472 Tadayon et al. Aug 2012 A1
20120265716 Hunzinger Oct 2012 A1
20120302349 Marks et al. Nov 2012 A1
20130069931 Wilson et al. Mar 2013 A1
20130134730 Ricci May 2013 A1
20130155237 Paek et al. Jun 2013 A1
20130157607 Paek et al. Jun 2013 A1
20130190089 Wilson et al. Jul 2013 A1
20130294016 Wilson et al. Nov 2013 A1
20130297246 Wilson et al. Nov 2013 A1
20130324248 Wilson et al. Dec 2013 A1
20140142729 Lobb et al. May 2014 A1
20140292654 Wilson Oct 2014 A1
20160116995 Wilson Apr 2016 A1
Foreign Referenced Citations (29)
Number Date Country
101141136 Mar 2008 CN
101254344 Mar 2008 CN
201166702 Dec 2008 CN
101364814 Feb 2009 CN
101795504 Aug 2010 CN
201548210 Aug 2010 CN
102204350 Sep 2011 CN
102204650 Oct 2011 CN
0 583 061 Feb 1994 EP
0739491 Apr 2006 EP
2113431 Nov 2009 EP
08-044490 Feb 1996 JP
H1084405 Mar 1998 JP
H10308802 Nov 1998 JP
2004312752 Nov 2004 JP
2005173702 Jun 2005 JP
2005-287830 Oct 2005 JP
2009257832 Nov 2009 JP
2010072833 Apr 2010 JP
20100262400 Nov 2010 JP
10-2010-0116387 Nov 2010 KR
9310708 Jun 1993 WO
9717598 May 1997 WO
9944698 Sep 1999 WO
01-00463 Jan 2001 WO
0207839 Jan 2002 WO
2009059065 Jul 2009 WO
2010051455 Jun 2010 WO
2011-149709 Dec 2011 WO
Non-Patent Literature Citations (181)
Entry
Zhu et al., “Controlled Human Pose Estimation From Depth Image Streams,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Jun. 23, 2008.
Yang et al., “Reconstruction of 3D Human Body Pose From Stereo Image Sequences Based on Top Down Learning,” Proceedings of the 18th International Conference on Pattern Recognition, vol. 40, Issue 11, Nov. 1, 2007, pp. 3120-3131.
“First Office Action and Search Report,” From Chinese Patent Application No. 201210548467.0, Mailed Feb. 28, 2015, 9 Pages.
Badler, “Face Modeling and Editing with Statistical Local Feature Control Models”, International Journal of Imaging Systems and Technology, Apr. 2008.
De Campos, “Articulated Object Tracking and Pose Estimation: A Literature Survey”, Excerpt from Doctorate Thesis, Saint Annes College, University of Oxford, 2006.
Kipman, “Virtual Target Tracking Using Model Fitting and Exemplar”, U.S. Appl. No. 61/148,892, filed Jan. 30, 2009.
Lee, “Body Part Detection for Human Pose Estimation and Tracking”, IEEE Workshop on Motion and Video Computing, Dec. 23-24, 2007.
Mikic, “Human Body Model Acquisition and Motion Capture Using Voxel Data”, Proceedings of the 2nd Interational Workshop on Articulated Motion and Deformable Objects, Nov. 21-23, 2002.
Kanade, “A Stereo Machine for Video-Rate Dense Depth Mapping and Its New Applications”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Jun. 18-20, 1996.
Miyagawa, “CCD-Based Range-Finding Sensor”, IEEE Transactions on Electron Devices, Oct. 1997.
Rosenhann, “Automatic Human Model Generation”, Computer Analysis of Images and Patterns, Sep. 5-8, 2005.
Aggarwal, “Human Motion Analysis: A Review”, IEEE Nonrigid and Articulated Motion Workshop, Jun. 16, 1997.
Shao, “An Open System Architecture for a Multimedia and Multimodel User Interface”, Aug. 24, 1998.
Kohler, “Special Topics of Gesture Recognition Applied in Intelligent Home Environments”, In Proceedings of the Gesture Workshop, Sep. 17-19, 1997.
Kohler, “Technical details and Ergonomical Aspects of Gesture Recognition Applied in Intelligent Home Environments”, 1997.
Hasegawa, “Human-scale Haptic Interaction with a Reactive Virtual Human in a Real-time Physics Simulator”, ACM Computers in Entertainment, Jul. 2008.
Qian, “A Gesture-driven Multimodal Interactive Dance System”, IEEE International Conference on Multimedia and Expo, Jun. 27-30, 2004.
Zhao, “Dressed Human Modeling, Detection, and Parts Localization”, Jul. 26, 2001.
He, “Generation of Human Body Models”, Apr. 2005.
Isard, “Condensation—Conditional Density Propagation for Visual Tracking”, International Journal of Computer Vision, Aug. 1998.
Livingston, “Vision-based Tracking with Dynamic Structured Light for Visual Tracking”, International Journal Computer Vision, Aug. 1998.
Wren, “Pfinder: Real Time Tracking of the Human Body”, IEEE Transactions of Pattern Analysis and Machine Intelligence, Jul. 1997.
Breen, “Interactive Occlusion and Collision of Real and Virtual Objects in Augmented Reality”, European Computer-Industry Research Centre GmbH, 1995.
Freeman, “Television Control by Hand Gestures”, Proceedings of the IEEE International Workshop on Automatic Face and Gesture Recognition. Jun. 1995.
Hongo, “Focus of Attention for Face and Hand Gesture Recognition Using Multiple Cameras”, 4th IEEE International Conference on Automatic Face and Hand Gesture Recognition, Mar. 26-30, 2000.
Pavlovic, “Visual Interpretaion of Hand Gestures for Human-Computer Interaction: A Review”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Jul. 1997.
Azarbayejani et al., “Visually Controlled Graphics”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Jun. 1993.
Granieri, “Simulating Humans in VR”, The British Computer Society, Oct. 12, 1994.
Brogan, “Dynamically Simulated Charachters in Virtual Envirnments”, IEEE Computer Graphics and Applications, Sep. 1998.
Fisher, “Virtual Environment Display System”, ACM Workshop on Interactive 3D Graphics, Oct. 23-24, 1986.
“Virtual High Anxiety”, Tech Update, Aug. 1995.
Sheridan, “Virtual Reality Check”, Technology Review, Oct. 1993.
Stevens, “Flights into Virtual Reality Treating Real World Disorders”, The Washington Post, Mar. 27, 1995.
“Simulation and Training”, Division Incorporated, 1994.
Park, “Segmentation and Tracking of Interacting Human Body Parts under Occlusion and Shadowing”, Proceedings of the IEEE Workshop on Motion and Video Computing, Dec. 2002.
Moeslund, “A Survey of Advances in Vision-based Human Motion Capture and Analysis”, Computer Vision and Image Understanding, Oct. 2, 2005.
Kehl, “Full Body Tracking from Multiple Views Using Stochastic Sampling”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Jun. 20-25, 2005.
Craig, “Pose Tracking Pipeline”, U.S. Appl. No. 13/871,974, filed Apr. 26, 2013.
Dekker, “Building Symbolic Information for 3D Human Body Modeling from Range Data”, Proceedings of the 2nd International Conference on 3-D Digital Imaging and Modeling, Oct. 4-8, 1999.
Sappa, “Monocular 3D Human Body Reconstruction Towards Depth Augmentation of Television Sequences”, Proceedings of the IEEE International Conference on Image Processing, Sep. 1, 2003.
Trivedi, “Occupant Posture Analysis with Stereo and Thermal Infrared Video: Algorithms and Experimental Evaluation”, Proceedings of the IEEE Transactions on Vehicular Technology, Nov. 2004.
Zhang, “Image-Based Modeling of Objects and Human Faces”, Proceedings of the SPIE Conference on Videometncs and Optical Methods for 3D Shape Measurement, Jan. 21-26, 2001.
Navaratnam, “Heirarchical Part-Based Human Body Pose Estimation”, Proceedings of the British Machine Vision Conference, Sep. 2005.
PCT International Search Report and Written Opinion for Application No. PCT/US2010/020793, Aug. 16, 2010.
Pekelny, “Articulated Object Reconstruction and Markerless Motion Capture from Depth Video”, Eurographics, Apr. 14-18, 2008.
Rogez, “A Spatio-Temporal 2D-Models Framework for Human Pose Recovery in Monocular Sequences”, Pattern Recognition, Sep. 2008.
Shaknarovich, “Fast Pose Estimation with Parameter Sensitive Hashing”, Proceedings of the 9th Intenational Conference on Computer Vision, Oct. 14-17, 2003.
Zhou, “A Survey—Human Movement Tracking and Stroke Rehabilitation”, University of Essex, Dec. 8, 2004.
Althoff, “Robust Multimodal Hand and Head Gesture Recognition for Controlling Automotive Infotainment Systems”, Nov. 22-23, 2005.
Kern, “Design Space for Driver-Based Automotive User Interfaces”, Proceedings of the First International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Sep. 21-22, 2009.
Muller, “Multimodal Input in the Car, Today and Tomorrow”, Industry and Standards, IEEE Multimedia, Jan. 2011.
PCT International Search Report and Written Opinion for Application No. PCT/US2012/069968, Reference 333981-03, Apr. 30, 2013.
Reissner, “Gestures and Speech in Cars”, Proceedings of Joint Advanced Student School, Mar. 2007.
Alt, et al. “Enabling Micro-Entertainment in Vehicles Based on Context Information”, Proceedings of the Second International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Nov. 2010, pp. 177-124.
Graf, et al., “In-car Interaction using Search-Based User Interfaces”, Proceedings of the 26th Annual SIGCHi Conference on Human Factors in Computing Systems, Apr. 2008, pp. 1685-1688.
Knies, Rob, “Managing Audio in Wheel Time”, Microsoft Corporation, Redmond, WA, Feb. 25, 2006, 2 pages.
Lindqvist, et al., “Undistracted Driving: A Mobile Phone that Doesn't Distract” HotMobile 2011: 12th Workshop on Mobile Computing Systems and Applications, Mar. 2011, 6 pages.
“OnStar” Wikipedia Online Encyclopedia Entry. http://en.wikipedia.org/wiki/onstar, Dec. 15, 2011, 6 pages.
“EyeSight's Hand Gesture Recognition Technology Allows People to Interact with Devices Using Simple Hand Gestures”, eyeSight, Herzliya, Israel, Dec. 14, 2011, 2 pages.
Paek et al. U.S. Appl. No. 13/327,787, “Interacting with a Mobile Device within a Vehicle Using Gestures”, filed Dec. 16, 2011, 67 pages.
“International Search Report”, Mailed Date: Mar. 20, 2013, Application No. PCT/US2012/068325, Filed Date: Dec. 7, 2012, 8 pages.
Riener, “Natural DVI Based on Intuitive Hand Gestures”, Workshop on User Experience in Cars, Sep. 2011.
Wilson, “GWindows: Robust Stereo Vision for Gesture-Based Control of Windows”, Proceedings of the 5th International Conference on Multimodal Interfaces, Nov. 5-7, 2003.
Wobbrock, “User-Defined Gestures for Surface Computing”, Proceedings of the ACM CHI Conference on Human Factors in Computing Systems, Apr. 4-9, 2009.
Zobl, “A Usability Study on Hand Gesture Controlled Operation of In-Car Devices”, Proceedings of the 9th International Conference on HCI, Aug. 5-10, 2001.
Supplementary Search Report mailed Aug. 4, 2015 from European Patent Application No. 12857653.5, 5 pages.
Examination mailed Aug. 14, 2015 from European Patent Application No. 12857653.5, 6 Pages.
First Office Action and Search Report, Mailed Date: Mar. 31, 2015, From China Patent Application No. 201210545421.3, 14 Pages.
European Search Report, Mailed Apr. 17, 2015, from European Patent Application No. 12857653.5, 6 pages.
Non-Final Office Action mailed Dec. 19, 2012 from U.S. Appl. No. 13/327,786, 27 pages.
Response filed Apr. 29, 2013 to Non-Final Office Action mailed Dec. 19, 2012 from U.S. Appl. No. 13/327,786, 12 pages.
Notice of Allowance and Examiner's Amendment mailed May 24, 2013 from U.S. Appl. No. 13/327,786, 13 pages.
Notice of Allowance mailed Apr. 14, 2014 from U.S. Appl. No. 13/327,786, 8 pages.
International Preliminary Report on Patentability mailed Jun. 17, 2014 from PCT Patent Application No. PCT/US2012/068235, 5 pages.
Response filed Aug. 5, 2015 from China Patent Application No. 201210545421.3, 8 pages.
Third Party Submission under 37 C.F.R. 1.290 filed Sep. 20, 2013 from U.S. Appl. No. 13/327,787, 12 pages.
Non-Final Office Action mailed Jan. 7, 2014 from U.S. Appl. No. 13/327,787, 11 pages.
Response filed Jun. 6, 2014 to Non-Final Office Action mailed Jan. 7, 2014 from U.S. Appl. No. 13/327,787, 12 pages.
Non-Final Office Action mailed Sep. 19, 2014 from U.S. Appl. No. 13/327,787, 15 pages.
Response filed Jan. 20, 2015 to Non-Final Office Action mailed Sep. 19, 2014 from U.S. Appl. No. 13/327,787, 13 pages.
Non-Final Office Action mailed May 26, 2015 from U.S. Appl. No. 13/327,787, 13 pages.
Response filed Aug. 12, 2015 to Non-Final Office Action mailed May 26, 2015 from U.S. Appl. No. 13/327,787, 13 pages.
Response filed Jun. 18, 2015 from China Patent Application No. 201210548467.0, 4 pages.
Second Office Action mailed Oct. 10, 2015 from China Patent Application No. 201210548467.0, 9 pages.
International Preliminary Report on Patentability mailed Jun. 17, 2014 from PCT Patent Application No. PCT/US2012/069968, 6 pages.
Final Office Action mailed Nov. 16, 2015 from U.S. Appl. No. 13/327,787, 16 pages.
Office Action mailed Nov. 30, 2015 from China Patent Application No. 201210545421.3, 15 pages.
Response filed Dec. 9, 2015 from European Patent Application No. 12857653.5, 19 pages.
Request for Examination and Amendment filed Nov. 24, 2015 from Japan Patent Application No. 2014-547303, 7 pages.
Guiard, Yves, “Asymmetric Division of Labor in Human Skilled Bimanual Action: The Kinematic Chain as a Model”, Journal of Motor Behavior, 1987, vol. 19, No. 4, 24 pages.
Horvitz, Eric, “Principles of Mixed-Initiative User Interfaces”, Proceedings of CHI, 1999, 8 pages.
Jojic et al., “Dectection and Estimation of Pointing Gestures in Dense Disparity Maps”, Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, 2000, France, 8 pages.
Kabbash, Paul and William Buxton, “The “Prince” Technique: Fitts' Law and Selection Using Area Cursors”, Proceedings of CHI'95, 1995, 11 pages.
Kanade, Takeo, “Development of a Video-Rate Stereo Machine”, Proceedings of 94 ARPA Image Understanding Workshop, Nov. 14-16, 1994, 4 pages.
Kettebekov, Sanshzar and Rajeev Sharma, “Toward Natural Gesture/Speech Control of a Large Display”, Proceedings of the 8th IFIP International Conference on Engineering for Human-Computer Interaction, Technical Report CVMT 03-01, ISSN 1601-3646, 2001, 13 pages.
Kjeldsen, Frederik C. M., “Visual Interpretation of Hand Gestures as a Practical Interface Modality”, Ph.D. Dissertation, 1997, Columbia University Department of Computer Science, 178 pages.
Kohler, Markus, “Vision Based Remote Control in Intelligent Home Environments”, University of Dortmund, Germany, 1996, 8 pages.
Krahnstoever et al., “Mutimodal Human-Computer Interaction for Crisis Management Systems”, Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision, 2002, 5 pages.
Krum et al., “Speech and Gesture Multimodal Control of a Whole Earth 3D Visualization Environment”, Proceedings of Eurographics-IEEE Visualization Symposium, 2002, 8 pages.
Long, Jr., et al., “Implications for a Gesture Design Tool”, Proceedings of CHI'99, 1999, University of California at Berkeley, last accessed Jul. 8, 2005, 8 pages.
Maes et al., “The ALIVE System: Wireless, Full-body Interaction with Autonomous Agents”, ACM Multimedia Systems, Special Issue on Multimedia and Multisensory Virtual Worlds, 1995, 17 pages.
Moeslund, Thomas B. and Erik Granum, “A Survey of Computer Vision-Based Human Motion Capture,” Computer Vision and Image Understanding: CVIU, vol. 81, No. 3, 2001, 38 pages.
Moyle, Michael and Andy Cockburn, “Gesture Navigation: An Alternative ‘Back’ for the Future”, Proceedings of CHI'02, 2002, University of Canterbury, Christchurch, New Zealand, 2 pages.
Nielsen et al., “A Procedure for Developing Intuitive and Erogonomic Gesture Interfaces or Man-Machine Interaction,” Technical Report CVMT 03-01, ISSN 1601-3646, CVMT, Aalborg University, Mar. 2003, 12 pages.
Oh et al. “Evaluating Look-to-Talk: A Gaze-Aware Interface in a Collaborative Environment”, CHI'02, 2002, Cambridge. MA, USA, last accessed Jul. 8, 2005, 3 pages.
Oviatt, Sharon, “Ten Myths of Multimodal Interaction,” Communications of the ACM, vol. 42, No. 11, Nov. 1999, 8 pages.
Rigoll et al., “High Performance Real-Time Gesture Recognition Using Hidden Markov Models,” Gesture and Sign Language in Human-Comptuer Interaction, vol. LNAI 1371, Germany, 1997, 12 pages.
Schmidt, Greg and Donald House, “Towards Model-Based Gesture Recognition”, Texas A&M, 2000, 6 pages.
Sharma et al., “Speech-Gesture Driven Multiomodal Interfaces for Crisis Management,” Proceedings of IEEE Special Issue on Multimodal Human-Computer Interface, 48 pages.
Walker et al., “Age-Related Differences in Movement Control: Adjusting Submovement Structure to Optimize Performance”, Journals of Gerontology, Jan. 1997, vol. 52B, No. 1, 14 pages.
Welford, Alan T., “Signal, Noise, Performance, and Age”, Human Factors, 1981, pp. 97-109, vol. 23, Issue 1, 13 pages.
Wilson, Andrew D. and Aaron F. Bobick, “Hidden Markov Models for Modeling and Recognizing Gesture Under Variation”, World Scientific, 2001, 36 pages.
Worden et al., “Making Computers Easier for Older Adults to Use: Area Cursors and Sticky Icons,” CHI 97, 1997, Atlanta, Georgia, USA, 6 pages.
Yoda, Ikushi and Katsuhiko Sakaue, “Utilization of Stereo Disparity and Optical Flow information for Human Interaction”, Proceedings of the Sixth International Conference on Computer Vision, IEEE Computer Society, Washington, D.C., USA, 1998, 6 pages.
Zhai et al., “The “Silk Cursor”: Investigating Transparency for 3D Target Acquisition”, CHI 94, Apr. 24-28, 1994, Boston, MA, 7 pages.
Zhang, Zhengyou, “A Flexible New Technique for Camera Calibration”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Nov. 2000, vol. 22, No. 11, 5 pages.
Zhang, Zhengyou, “Flexible Camera Calibration by Viewing a Plane from Unknown Orientations”, Microsoft Research 1999, IEEEm 8 pages.
Aviles-Arriaga et al., “Dynamic Bayesian networks for visual recognition of dynamic gestures”, Dec. 2002, Journal of Intelligent & Fuzzy Systems, 10 pages.
Azarbayejani, Ali and Alex Pentland, “Real-Time Self-Calibrating Stereo Person Tracking Using 3-D Shape Estimation from Blob Features”, Proceedings of ICPR'96, Aug. 1996, Vienna, Austria, 6 pages.
Azoz et al., “Reliable Tracking of Human Arm Dynamics by Multiple Cue Integration and Constraint Fusion”, IEEE conference on Computer Vision and Pattern Recognition, 1998, 6 pages.
Baudel, Thomas and Michel Beaudouin-Lafon, “Charade: Remote Control of Objects using Free-Hand Gestures”, Communications of the ACM, vol. 36, No. 7, Jul. 1993, 10 pages.
Berard, Francois, “The Perceptual Window: Head Motion as a new Input Steam”, Proceedings of the Seventh IFIP conference of Human-Computer Interaction, 1999, 8 pages.
Buxton, William and Brad A. Meyers, “A Study in Two-Handed Input”, Proceedings of CHI'86, Apr. 1986, 6 pages.
Cedras, Claudette and Mubarak Shah, “Motion-based recognition: a survey”, IEEE Proceedings, Image and Vision computing, vol. 13, No. 2, Mar. 1995, 27 pages.
Darrell et al., “Integrated person tracking using stereo, color, and pattern detection.”, 1998, IEEE, 10 pages.
Horvitz, Erik and Tim Paek, “A Computational Architecture for Conversation”, Proceedings of the Seventh International Conference on User Modeling, 1999, 10 pages.
Fitzgerald, Will and R. James Firby, “Multimodal Event Parsing for Intelligent User Interfaces”, IUI Conference, Jan. 12-15, 2003, Miami, Florida, 8 pages.
Mignot et al., “An Experimental Study of Future ‘Natural’ Multimodal Human-Computer Interaction”, Proceedings of INTERCHI93, 1993, 2 pages.
“GWindows: Light-weight Stereo Vision for Interaction,” retreived from <<http://research.com/˜nuria/gwindows/gwindows.htm>> on Jul. 7, 2005, 2 pages.
Wilson, Andrew and Nuria Oliver, Gwindows: Towards Robust Perception-Based UI, Microsoft Research, 2003, 8 pages.
Filing transaction history from U.S. Appl. No. 10/396,653, filed Mar. 25, 2003, 527 pages.
Filing transaction history from U.S. Appl. No. 10/724,950, filed Dec. 1, 2003, 1476 pages.
Non-Final Office Action mailed May 14, 2013 from U.S. Appl. No. 12/289,099, 23 pages.
Filing transaction history from U.S. Appl. No. 12/494,303, filed Jun. 30, 2009, 850 pages.
Filing transaction history from U.S. Appl. No. 12/705,014, filed Feb. 12, 2010, 846 pages.
Filing transaction history from U.S. Appl. No. 12/705,113, filed Feb. 12, 2010, 797 pages.
Filing transaction history from U.S. Appl. No. 12/457,656, filed Jun. 17, 2009, 310 pages.
Filing transaction history from U.S. Appl. No. 12/495,105, filed Jun. 30, 2009, 355 pages.
Non-Final Office Action mailed Jan. 20, 2015 from U.S. Appl. No. 13/919,995, filed Jun. 17, 2013, 18 pages.
Response filed Feb. 4, 2016 to the Second Office Action mailed Nov. 30, 2015 from China Patent Application No. 201210545421.3, 11 pages.
Response filed Dec. 25, 2015 to the Second Office Action mailed Oct. 10, 2015 from China Patent Application No. 201210548467.0, 11 pages.
Non-Final Office Action mailed Mar. 28, 2016 from U.S. Appl. No. 12/705,014, 20 pages.
Response filed Mar. 28, 2016 to the Final Office Action mailed Nov. 16, 2015 from U.S. Appl. No. 13/327,787, 14 pages.
Examination Report mailed Apr. 7, 2016 from European Patent Application No. 128576535, 5 pages.
Non-Final Office Action mailed May 3, 2016 from U.S. Appl. No. 12/494,303, 13 pages.
Final Office Action mailed Apr. 25, 2016 from U.S. Appl. No. 12/705,113, 11 pages.
Preliminary Amendment mailed Jul. 20, 2015 from U.S. Appl. No. 14/803,949, 48 pages.
Notice to File Corrected Application Papers mailed Aug. 5, 2015 from U.S. Appl. No. 14/803,949, 2 pages.
Applicant Response to Pre-Exam Formalities Notice filed Jan. 5, 2016 to Notice to File Corrected Application Papers mailed Aug. 5, 2015 from U.S. Appl. No. 14/803,949, 140 pages.
U.S. Appl. No. 12/230,440 titled “System and Method for Determining 3D Orientation of a Pointing Device,” filed Aug. 28, 2008 by inventor Andrew Wilson, 101 pages.
U.S. Appl. No. 12/076,224 titled “System for Displaying and Controlling Electronic Objects,” filed Mar. 14, 2008 by inventor Andrew Wilson, 93 pages.
Response filed May 17, 2016 to the Examination Report mailed Apr. 7, 2016 from European Patent Application No. 12857653.5, 13 pages.
Davies, Chris, “Qualcomm Buys GestureTek Gesture-Recognition Tech for Snapdragon”, published on Jul. 25, 2011, captured by the Internet archive at <<https://web.archive.org/web/20111010005912/http://www.slashgear.com/qualcomm-buys-gesturetek-gesture-recognition-tech-for-snapdragon-25167335/>> on Oct. 10, 2011, 6 pages.
Woyke, Elizabeth, “Texas Instruments Sees Big Market for Smartphone Gesture Recognition”, published on Oct. 31, 2011, captured by the Internet archive at <<https://web.archive.org/web/20111101203247/http://www.forbes.com/sites/elizabethwoyke/2011/10/31/texas-instruments-sees-big-market-for-smartphone-gesture-recognition/>> on Nov. 1, 2011, 4 pages.
Notice of Allowance mailed May 4, 2016 from China Patent Application No. 201210548467.0, 4 pages.
Third Office Action mailed Jun. 3, 2016 from China Patent Application No. 20120545421.3, 6 pages.
Response filed Jun. 28, 2016 to the Non-Final Office Action mailed Mar. 28, 2016 from U.S. Appl. No. 12/705,014, 8 pages.
Response filed Jul. 25, 2016 to the Final Office Action mailed Apr. 25, 2016 from U.S. Appl. No. 12/705,113, 7 pages.
“BMW Innovation Days Highlight Future BMW Technologies”, retrieved from <<http://f30.bimmerpost.com/forums/showthread.php?t=594473>>, Oct. 5, 2011, 50 pages.
Akyol et al., “Gesture Control for use in Automobiles”, Proceedings of MVA2000, IAPR Workshop on Machine Vision Applications, Nov. 28-30, 2000, 4 pages.
Bose et al., “Terminal Mode—Transforming Mobile Devices into Automotive Application Platforms”, Proceedings of the Second International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Nov. 11-12, 2010, 8 pages.
Diewald et al., “Mobile Device Integration and Interaction in the Automotive Domain”, Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Nov. 29-Dec. 2, 2011, 4 pages.
Whitfield, Kermit, “Gesture Interfaces for Automotive Control: (Beyond Digital Expletives)”, 7th Journal for Automotive Design and Production, Jul. 15, 2003, 5 pages.
Response filed Aug. 3, 2016 to the Non-Final Office Action mailed May 3, 2016 from U.S. Appl. No. 12/494,303, 11 pages.
Response filed Aug. 4, 2016 to the Third Office Action mailed Jun. 3, 2016 from China Patent Application No. Response 55 pages.
Final Office Action mailed Aug. 26, 2016 from U.S. Appl. No. 12/705,014, 13 pages.
Final Office Action mailed Sep. 26, 2016 from U.S. Appl. No. 12/494,303, 14 pages.
Summons to Attend Oral Proceedings pursuant to Rule 115(1) mailed Sep. 16, 2016 from European Patent Application No. 12857653.5, 5 pages.
Office Action mailed Nov. 1, 2016 from Japanese Patent Application No. 2014-547303, 4 pages.
Notification on Grant of Patent Right for Invention mailed Nov. 2, 2016 from Chinese Patent Application No. 201210545421.3, 3 pages.
Response filed Oct. 26, 2016 to the Final Office Action mailed Aug. 26, 2016 from U.S. Appl. No. 12/705,014, 10 pages.
Advisory Action and After Final Consideration Program Decision mailed Nov. 4, 2016 from U.S. Appl. No. 12/705,014, 4 pages.
Requirement for Restriction/Election mailed Sep. 26, 2016 from U.S. Appl. No. 14/803,949, 11 pages.
Applicant Initiated Interview Summary mailed Dec. 7, 2016 from U.S. Appl. No. 12/705,014, 3 pages.
Non-Final Office Action mailed Dec. 7, 2016 from U.S. Appl. No. 13/327,787, 12 pages.
Advisory Action, Applicant-Initiated Interview Summary, and After Final Consideration Pilot Program Decision mailed Dec. 22, 2016 from U.S. Appl. No. 12/494,303, 6 pages.
Response filed Dec. 23, 2016 to the Final Office Action mailed Sep. 26, 2016 from U.S. Appl. No. 12/494,303, 9 pages.
Amended Response filed Dec. 12, 2016 to the Final Office Action mailed Aug. 26, 2016 from U.S. Appl. No. 12/705,014, 10 pages.
Notice of Allowance mailed Jan. 23, 2017 from U.S. Appl. No. 12/705,113, 10 pages.
Response filed Jan. 26, 2017 to the Requirement for Restriction/Election mailed Sep. 26, 2016 from U.S. Appl. No. 14/803,949, 3 pages.
Applicant-Initiated Interview Summary mailed Jan. 26, 2017 from U.S. Appl. No. 13/327,787, 3 pages.
Related Publications (1)
Number Date Country
20140329487 A1 Nov 2014 US
Continuations (1)
Number Date Country
Parent 13327786 Dec 2011 US
Child 14332235 US