Patent Grant
10928918
This relates generally to intelligent automated assistants and, more specifically, to initiating a speech based service such as an intelligent automated assistant.
Intelligent automated assistants (or digital assistants) can provide a beneficial interface between human users and electronic devices. Such assistants can allow users to interact with devices or systems using natural language in spoken and/or text forms. For example, a user can provide a speech input containing a user request to a digital assistant operating on an electronic device. The digital assistant can interpret the user's intent from the speech input and operationalize the user's intent into tasks. The tasks can then be performed by executing one or more services of the electronic device, and a relevant output responsive to the user request can be returned to the user.
Operating a digital assistant requires electrical power, which is a limited resource on handheld or portable devices that rely on batteries and on which digital assistants often run. Accordingly, it is desirable to initiate and operate a digital assistant in an energy efficient manner.
Example methods are disclosed herein. An example method includes, at an electronic device with a microphone and a display: detecting input representing motion of the electronic device, and sampling, with the microphone, an audio input. The method further includes determining, based on the audio input and the input representing motion of the electronic device, whether to initiate a virtual assistant session. The method further includes, in accordance with a determination to initiate the virtual assistant session, initiating the virtual assistant session, and in accordance with a determination not to initiate the virtual assistant session, forgoing initiating the virtual assistant session.
Example non-transitory computer-readable media are disclosed herein. An example non-transitory computer-readable storage medium stores one or more programs. The one or more programs comprise instructions, which when executed by one or more processors of an electronic device with a microphone and a display, cause the electronic device to: detect input representing motion of the electronic device, sample, with the microphone, an audio input, determine, based on the audio input and the input representing motion of the electronic device, whether to initiate a virtual assistant session, in accordance with a determination to initiate the virtual assistant session, initiate the virtual assistant session, and in accordance with a determination not to initiate the virtual assistant session, forgo initiating the virtual assistant session.
Example electronic devices are disclosed herein. An example electronic device comprises a microphone, a display, one or more processors, a memory, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: detecting input representing motion of the electronic device, sampling, with the microphone, an audio input, determining, based on the audio input and the input representing motion of the electronic device, whether to initiate a virtual assistant session, in accordance with a determination to initiate the virtual assistant session, initiating the virtual assistant session, and in accordance with a determination not to initiate the virtual assistant session, forgoing initiating the virtual assistant session.
An example electronic device comprises means for: detecting input representing motion of the electronic device, sampling an audio input, determining, based on the audio input and the input representing motion of the electronic device, whether to initiate a virtual assistant session, in accordance with a determination to initiate the virtual assistant session, initiating the virtual assistant session, and in accordance with a determination not to initiate the virtual assistant session, forgoing initiating the virtual assistant session.
Determining, based on audio input and input representing motion of the electronic device (e.g., “motion input”), whether to initiate a virtual assistant session provides a natural and efficient way of initiating and interacting with a virtual assistant. For example, to initiate a virtual assistant and to have a task performed by the virtual assistant based on a spoken request, a user simply raises a device near his or her mouth and utters the request at approximately the same time (e.g., without the request including a spoken trigger such as “Hey Siri”). Facilitating virtual assistant interaction in this manner enables efficient virtual assistant interaction (e.g., no spoken trigger is required before each interaction) and quick virtual assistant response to spoken requests. Additionally, using both audio input and motion input to initiate a virtual assistant session prevents inadvertent virtual assistant activation based on either audio input or motion input alone. This decreases the chance of inadvertently initiating a virtual assistant and saves battery power otherwise consumed due to operating a virtual assistant when not desired. In this manner, the user-device interface is made more efficient (e.g., by reducing the amount of audio input users provide, by decreasing virtual assistant response time, by preventing inadvertent virtual assistant activation), which additionally, reduces power usage and improves battery life of the device by enabling the user to use the device more quickly and efficiently.
In the following description of examples, reference is made to the accompanying drawings in which are shown by way of illustration specific examples that can be practiced. It is to be understood that other examples can be used and structural changes can be made without departing from the scope of the various examples.
As discussed, the present disclosure relates to techniques for initiating a virtual assistant session based on audio input and motion input. For example, to initiate a virtual assistant session on a wrist-wearable device (e.g., a smart watch), a user wearing the device raises the device near the user's mouth and provides audio input (e.g., “What's the weather like in Palo Alto”) when (or shortly before or after) the device is raised near the user's mouth. In some examples, the audio input does not include a spoken trigger (e.g., “Hey Siri”) for initiating a virtual assistant. A virtual assistant session is initiated on the device based on the detected motion input and the sampled audio input, and the initiated virtual assistant session responds to the audio input (e.g., by providing the response “It's 70 degrees and sunny”). Accordingly, an intuitive and efficient way of initiating and interacting with virtual assistant sessions is provided.
Although the following description uses terms “first,” “second,” etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another. For example, a first input could be termed a second input, and, similarly, a second input could be termed a first input, without departing from the scope of the various described examples. The first input and the second input are both inputs and, in some cases, are separate and different inputs.
The terminology used in the description of the various described examples herein is for the purpose of describing particular examples only and is not intended to be limiting. As used in the description of the various described examples and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” may be construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.
1. System and Environment
Specifically, a digital assistant is capable of accepting a user request at least partially in the form of a natural language command, request, statement, narrative, and/or inquiry. Typically, the user request seeks either an informational answer or performance of a task by the digital assistant. A satisfactory response to the user request includes a provision of the requested informational answer, a performance of the requested task, or a combination of the two. For example, a user asks the digital assistant a question, such as “Where am I right now?” Based on the user's current location, the digital assistant answers, “You are in Central Park near the west gate.” The user also requests the performance of a task, for example, “Please invite my friends to my girlfriend's birthday party next week.” In response, the digital assistant can acknowledge the request by saying “Yes, right away,” and then send a suitable calendar invite on behalf of the user to each of the user's friends listed in the user's electronic address book. During performance of a requested task, the digital assistant sometimes interacts with the user in a continuous dialogue involving multiple exchanges of information over an extended period of time. There are numerous other ways of interacting with a digital assistant to request information or performance of various tasks. In addition to providing verbal responses and taking programmed actions, the digital assistant also provides responses in other visual or audio forms, e.g., as text, alerts, music, videos, animations, etc.
As shown in
In some examples, DA server 106 includes client-facing I/O interface 112, one or more processing modules 114, data and models 116, and I/O interface to external services 118. The client-facing I/O interface 112 facilitates the client-facing input and output processing for DA server 106. One or more processing modules 114 utilize data and models 116 to process speech input and determine the user's intent based on natural language input. Further, one or more processing modules 114 perform task execution based on inferred user intent. In some examples, DA server 106 communicates with external services 120 through network(s) 110 for task completion or information acquisition. I/O interface to external services 118 facilitates such communications.
User device 104 can be any suitable electronic device. In some examples, user device 104 is a portable multifunctional device (e.g., device 200, described below with reference to
Examples of communication network(s) 110 include local area networks (LAN) and wide area networks (WAN), e.g., the Internet. Communication network(s) 110 is implemented using any known network protocol, including various wired or wireless protocols, such as, for example, Ethernet, Universal Serial Bus (USB), FIREWIRE, Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP), Wi-MAX, or any other suitable communication protocol.
Server system 108 is implemented on one or more standalone data processing apparatus or a distributed network of computers. In some examples, server system 108 also employs various virtual devices and/or services of third-party service providers (e.g., third-party cloud service providers) to provide the underlying computing resources and/or infrastructure resources of server system 108.
In some examples, user device 104 communicates with DA server 106 via second user device 122. Second user device 122 is similar or identical to user device 104. For example, second user device 122 is similar to devices 200, 400, or 600 described below with reference to
In some examples, user device 104 is configured to communicate abbreviated requests for data to second user device 122 to reduce the amount of information transmitted from user device 104. Second user device 122 is configured to determine supplemental information to add to the abbreviated request to generate a complete request to transmit to DA server 106. This system architecture can advantageously allow user device 104 having limited communication capabilities and/or limited battery power (e.g., a watch or a similar compact electronic device) to access services provided by DA server 106 by using second user device 122, having greater communication capabilities and/or battery power (e.g., a mobile phone, laptop computer, tablet computer, or the like), as a proxy to DA server 106. While only two user devices 104 and 122 are shown in
Although the digital assistant shown in
2. Electronic Devices
Attention is now directed toward embodiments of electronic devices for implementing the client-side portion of a digital assistant.
As used in the specification and claims, the term “intensity” of a contact on a touch-sensitive surface refers to the force or pressure (force per unit area) of a contact (e.g., a finger contact) on the touch-sensitive surface, or to a substitute (proxy) for the force or pressure of a contact on the touch-sensitive surface. The intensity of a contact has a range of values that includes at least four distinct values and more typically includes hundreds of distinct values (e.g., at least 256). Intensity of a contact is, optionally, determined (or measured) using various approaches and various sensors or combinations of sensors. For example, one or more force sensors underneath or adjacent to the touch-sensitive surface are, optionally, used to measure force at various points on the touch-sensitive surface. In some implementations, force measurements from multiple force sensors are combined (e.g., a weighted average) to determine an estimated force of a contact. Similarly, a pressure-sensitive tip of a stylus is, optionally, used to determine a pressure of the stylus on the touch-sensitive surface. Alternatively, the size of the contact area detected on the touch-sensitive surface and/or changes thereto, the capacitance of the touch-sensitive surface proximate to the contact and/or changes thereto, and/or the resistance of the touch-sensitive surface proximate to the contact and/or changes thereto are, optionally, used as a substitute for the force or pressure of the contact on the touch-sensitive surface. In some implementations, the substitute measurements for contact force or pressure are used directly to determine whether an intensity threshold has been exceeded (e.g., the intensity threshold is described in units corresponding to the substitute measurements). In some implementations, the substitute measurements for contact force or pressure are converted to an estimated force or pressure, and the estimated force or pressure is used to determine whether an intensity threshold has been exceeded (e.g., the intensity threshold is a pressure threshold measured in units of pressure). Using the intensity of a contact as an attribute of a user input allows for user access to additional device functionality that may otherwise not be accessible by the user on a reduced-size device with limited real estate for displaying affordances (e.g., on a touch-sensitive display) and/or receiving user input (e.g., via a touch-sensitive display, a touch-sensitive surface, or a physical/mechanical control such as a knob or a button).
As used in the specification and claims, the term “tactile output” refers to physical displacement of a device relative to a previous position of the device, physical displacement of a component (e.g., a touch-sensitive surface) of a device relative to another component (e.g., housing) of the device, or displacement of the component relative to a center of mass of the device that will be detected by a user with the user's sense of touch. For example, in situations where the device or the component of the device is in contact with a surface of a user that is sensitive to touch (e.g., a finger, palm, or other part of a user's hand), the tactile output generated by the physical displacement will be interpreted by the user as a tactile sensation corresponding to a perceived change in physical characteristics of the device or the component of the device. For example, movement of a touch-sensitive surface (e.g., a touch-sensitive display or trackpad) is, optionally, interpreted by the user as a “down click” or “up click” of a physical actuator button. In some cases, a user will feel a tactile sensation such as an “down click” or “up click” even when there is no movement of a physical actuator button associated with the touch-sensitive surface that is physically pressed (e.g., displaced) by the user's movements. As another example, movement of the touch-sensitive surface is, optionally, interpreted or sensed by the user as “roughness” of the touch-sensitive surface, even when there is no change in smoothness of the touch-sensitive surface. While such interpretations of touch by a user will be subject to the individualized sensory perceptions of the user, there are many sensory perceptions of touch that are common to a large majority of users. Thus, when a tactile output is described as corresponding to a particular sensory perception of a user (e.g., an “up click,” a “down click,” “roughness”), unless otherwise stated, the generated tactile output corresponds to physical displacement of the device or a component thereof that will generate the described sensory perception for a typical (or average) user.
It should be appreciated that device 200 is only one example of a portable multifunction device, and that device 200 optionally has more or fewer components than shown, optionally combines two or more components, or optionally has a different configuration or arrangement of the components. The various components shown in
Memory 202 includes one or more computer-readable storage mediums. The computer-readable storage mediums are, for example, tangible and non-transitory. Memory 202 includes high-speed random access memory and also includes non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices. Memory controller 222 controls access to memory 202 by other components of device 200.
In some examples, a non-transitory computer-readable storage medium of memory 202 is used to store instructions (e.g., for performing aspects of processes described below) for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In other examples, the instructions (e.g., for performing aspects of the processes described below) are stored on a non-transitory computer-readable storage medium (not shown) of the server system 108 or are divided between the non-transitory computer-readable storage medium of memory 202 and the non-transitory computer-readable storage medium of server system 108.
Peripherals interface 218 is used to couple input and output peripherals of the device to CPU 220 and memory 202. The one or more processors 220 run or execute various software programs and/or sets of instructions stored in memory 202 to perform various functions for device 200 and to process data. In some embodiments, peripherals interface 218, CPU 220, and memory controller 222 are implemented on a single chip, such as chip 204. In some other embodiments, they are implemented on separate chips.
RF (radio frequency) circuitry 208 receives and sends RF signals, also called electromagnetic signals. RF circuitry 208 converts electrical signals to/from electromagnetic signals and communicates with communications networks and other communications devices via the electromagnetic signals. RF circuitry 208 optionally includes well-known circuitry for performing these functions, including but not limited to an antenna system, an RF transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a CODEC chipset, a subscriber identity module (SIM) card, memory, and so forth. RF circuitry 208 optionally communicates with networks, such as the Internet, also referred to as the World Wide Web (WWW), an intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN), and other devices by wireless communication. The RF circuitry 208 optionally includes well-known circuitry for detecting near field communication (NFC) fields, such as by a short-range communication radio. The wireless communication optionally uses any of a plurality of communications standards, protocols, and technologies, including but not limited to Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), high-speed downlink packet access (HSDPA), high-speed uplink packet access (HSDPA), Evolution, Data-Only (EV-DO), HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), near field communication (NFC), wideband code division multiple access (W-CDMA), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n, and/or IEEE 802.11ac), voice over Internet Protocol (VoIP), Wi-MAX, a protocol for e mail (e.g., Internet message access protocol (IMAP) and/or post office protocol (POP)), instant messaging (e.g., extensible messaging and presence protocol (XMPP), Session Initiation Protocol for Instant Messaging and Presence Leveraging Extensions (SIMPLE), Instant Messaging and Presence Service (IMPS)), and/or Short Message Service (SMS), or any other suitable communication protocol, including communication protocols not yet developed as of the filing date of this document.
Audio circuitry 210, speaker 211, and microphone 213 provide an audio interface between a user and device 200. Audio circuitry 210 receives audio data from peripherals interface 218, converts the audio data to an electrical signal, and transmits the electrical signal to speaker 211. Speaker 211 converts the electrical signal to human-audible sound waves. Audio circuitry 210 also receives electrical signals converted by microphone 213 from sound waves. Audio circuitry 210 converts the electrical signal to audio data and transmits the audio data to peripherals interface 218 for processing. Audio data are retrieved from and/or transmitted to memory 202 and/or RF circuitry 208 by peripherals interface 218. In some embodiments, audio circuitry 210 also includes a headset jack (e.g., 312,
I/O subsystem 206 couples input/output peripherals on device 200, such as touch screen 212 and other input control devices 216, to peripherals interface 218. I/O subsystem 206 optionally includes display controller 256, optical sensor controller 258, intensity sensor controller 259, haptic feedback controller 261, and one or more input controllers 260 for other input or control devices. The one or more input controllers 260 receive/send electrical signals from/to other input control devices 216. The other input control devices 216 optionally include physical buttons (e.g., push buttons, rocker buttons, etc.), dials, slider switches, joysticks, click wheels, and so forth. In some alternate embodiments, input controller(s) 260 are, optionally, coupled to any (or none) of the following: a keyboard, an infrared port, a USB port, and a pointer device such as a mouse. The one or more buttons (e.g., 308,
A quick press of the push button disengages a lock of touch screen 212 or begin a process that uses gestures on the touch screen to unlock the device, as described in U.S. patent application Ser. No. 11/322,549, “Unlocking a Device by Performing Gestures on an Unlock Image,” filed Dec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated by reference in its entirety. A longer press of the push button (e.g., 306) turns power to device 200 on or off. The user is able to customize a functionality of one or more of the buttons. Touch screen 212 is used to implement virtual or soft buttons and one or more soft keyboards.
Touch-sensitive display 212 provides an input interface and an output interface between the device and a user. Display controller 256 receives and/or sends electrical signals from/to touch screen 212. Touch screen 212 displays visual output to the user. The visual output includes graphics, text, icons, video, and any combination thereof (collectively termed “graphics”). In some embodiments, some or all of the visual output correspond to user-interface objects.
Touch screen 212 has a touch-sensitive surface, sensor, or set of sensors that accepts input from the user based on haptic and/or tactile contact. Touch screen 212 and display controller 256 (along with any associated modules and/or sets of instructions in memory 202) detect contact (and any movement or breaking of the contact) on touch screen 212 and convert the detected contact into interaction with user-interface objects (e.g., one or more soft keys, icons, web pages, or images) that are displayed on touch screen 212. In an exemplary embodiment, a point of contact between touch screen 212 and the user corresponds to a finger of the user.
Touch screen 212 uses LCD (liquid crystal display) technology, LPD (light emitting polymer display) technology, or LED (light emitting diode) technology, although other display technologies may be used in other embodiments. Touch screen 212 and display controller 256 detect contact and any movement or breaking thereof using any of a plurality of touch sensing technologies now known or later developed, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touch screen 212. In an exemplary embodiment, projected mutual capacitance sensing technology is used, such as that found in the iPhone® and iPod Touch® from Apple Inc. of Cupertino, Calif.
A touch-sensitive display in some embodiments of touch screen 212 is analogous to the multi-touch sensitive touchpads described in the following U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat. No. 6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932 (Westerman), and/or U.S. Patent Publication 2002/0015024A1, each of which is hereby incorporated by reference in its entirety. However, touch screen 212 displays visual output from device 200, whereas touch-sensitive touchpads do not provide visual output.
A touch-sensitive display in some embodiments of touch screen 212 is as described in the following applications: (1) U.S. patent application Ser. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2, 2006; (2) U.S. patent application Ser. No. 10/840,862, “Multipoint Touchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No. 10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30, 2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures For Touch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patent application Ser. No. 11/038,590, “Mode-Based Graphical User Interfaces For Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patent application Ser. No. 11/228,758, “Virtual Input Device Placement On A Touch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patent application Ser. No. 11/228,700, “Operation Of A Computer With A Touch Screen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser. No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen Virtual Keyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No. 11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. All of these applications are incorporated by reference herein in their entirety.
Touch screen 212 has, for example, a video resolution in excess of 100 dpi. In some embodiments, the touch screen has a video resolution of approximately 160 dpi. The user makes contact with touch screen 212 using any suitable object or appendage, such as a stylus, a finger, and so forth. In some embodiments, the user interface is designed to work primarily with finger-based contacts and gestures, which can be less precise than stylus-based input due to the larger area of contact of a finger on the touch screen. In some embodiments, the device translates the rough finger-based input into a precise pointer/cursor position or command for performing the actions desired by the user.
In some embodiments, in addition to the touch screen, device 200 includes a touchpad (not shown) for activating or deactivating particular functions. In some embodiments, the touchpad is a touch-sensitive area of the device that, unlike the touch screen, does not display visual output. The touchpad is a touch-sensitive surface that is separate from touch screen 212 or an extension of the touch-sensitive surface formed by the touch screen.
Device 200 also includes power system 262 for powering the various components. Power system 262 includes a power management system, one or more power sources (e.g., battery, alternating current (AC)), a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator (e.g., a light-emitting diode (LED)) and any other components associated with the generation, management and distribution of power in portable devices.
Device 200 also includes one or more optical sensors 264.
Device 200 optionally also includes one or more contact intensity sensors 265.
Device 200 also includes one or more proximity sensors 266.
Device 200 optionally also includes one or more tactile output generators 267.
Device 200 also includes one or more accelerometers 268.
In some embodiments, the software components stored in memory 202 include operating system 226, communication module (or set of instructions) 228, contact/motion module (or set of instructions) 230, graphics module (or set of instructions) 232, text input module (or set of instructions) 234, Global Positioning System (GPS) module (or set of instructions) 235, Digital Assistant Client Module 229, and applications (or sets of instructions) 236. Further, memory 202 stores data and models, such as user data and models 231. Furthermore, in some embodiments, memory 202 (
Operating system 226 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communication between various hardware and software components.
Communication module 228 facilitates communication with other devices over one or more external ports 224 and also includes various software components for handling data received by RF circuitry 208 and/or external port 224. External port 224 (e.g., Universal Serial Bus (USB), FIREWIRE, etc.) is adapted for coupling directly to other devices or indirectly over a network (e.g., the Internet, wireless LAN, etc.). In some embodiments, the external port is a multi-pin (e.g., 30-pin) connector that is the same as, or similar to and/or compatible with, the 30-pin connector used on iPod® (trademark of Apple Inc.) devices.
Contact/motion module 230 optionally detects contact with touch screen 212 (in conjunction with display controller 256) and other touch-sensitive devices (e.g., a touchpad or physical click wheel). Contact/motion module 230 includes various software components for performing various operations related to detection of contact, such as determining if contact has occurred (e.g., detecting a finger-down event), determining an intensity of the contact (e.g., the force or pressure of the contact or a substitute for the force or pressure of the contact), determining if there is movement of the contact and tracking the movement across the touch-sensitive surface (e.g., detecting one or more finger-dragging events), and determining if the contact has ceased (e.g., detecting a finger-up event or a break in contact). Contact/motion module 230 receives contact data from the touch-sensitive surface. Determining movement of the point of contact, which is represented by a series of contact data, optionally includes determining speed (magnitude), velocity (magnitude and direction), and/or an acceleration (a change in magnitude and/or direction) of the point of contact. These operations are, optionally, applied to single contacts (e.g., one finger contacts) or to multiple simultaneous contacts (e.g., “multitouch”/multiple finger contacts). In some embodiments, contact/motion module 230 and display controller 256 detect contact on a touchpad.
In some embodiments, contact/motion module 230 uses a set of one or more intensity thresholds to determine whether an operation has been performed by a user (e.g., to determine whether a user has “clicked” on an icon). In some embodiments, at least a subset of the intensity thresholds are determined in accordance with software parameters (e.g., the intensity thresholds are not determined by the activation thresholds of particular physical actuators and can be adjusted without changing the physical hardware of device 200). For example, a mouse “click” threshold of a trackpad or touch screen display can be set to any of a large range of predefined threshold values without changing the trackpad or touch screen display hardware. Additionally, in some implementations, a user of the device is provided with software settings for adjusting one or more of the set of intensity thresholds (e.g., by adjusting individual intensity thresholds and/or by adjusting a plurality of intensity thresholds at once with a system-level click “intensity” parameter).
Contact/motion module 230 optionally detects a gesture input by a user. Different gestures on the touch-sensitive surface have different contact patterns (e.g., different motions, timings, and/or intensities of detected contacts). Thus, a gesture is, optionally, detected by detecting a particular contact pattern. For example, detecting a finger tap gesture includes detecting a finger-down event followed by detecting a finger-up (liftoff) event at the same position (or substantially the same position) as the finger-down event (e.g., at the position of an icon). As another example, detecting a finger swipe gesture on the touch-sensitive surface includes detecting a finger-down event followed by detecting one or more finger-dragging events, and subsequently followed by detecting a finger-up (liftoff) event.
Graphics module 232 includes various known software components for rendering and displaying graphics on touch screen 212 or other display, including components for changing the visual impact (e.g., brightness, transparency, saturation, contrast, or other visual property) of graphics that are displayed. As used herein, the term “graphics” includes any object that can be displayed to a user, including, without limitation, text, web pages, icons (such as user-interface objects including soft keys), digital images, videos, animations, and the like.
In some embodiments, graphics module 232 stores data representing graphics to be used. Each graphic is, optionally, assigned a corresponding code. Graphics module 232 receives, from applications etc., one or more codes specifying graphics to be displayed along with, if necessary, coordinate data and other graphic property data, and then generates screen image data to output to display controller 256.
Haptic feedback module 233 includes various software components for generating instructions used by tactile output generator(s) 267 to produce tactile outputs at one or more locations on device 200 in response to user interactions with device 200.
Text input module 234, which is, in some examples, a component of graphics module 232, provides soft keyboards for entering text in various applications (e.g., contacts 237, email 240, IM 241, browser 247, and any other application that needs text input).
GPS module 235 determines the location of the device and provides this information for use in various applications (e.g., to telephone 238 for use in location-based dialing; to camera 243 as picture/video metadata; and to applications that provide location-based services such as weather widgets, local yellow page widgets, and map/navigation widgets).
Digital assistant client module 229 includes various client-side digital assistant instructions to provide the client-side functionalities of the digital assistant. For example, digital assistant client module 229 is capable of accepting voice input (e.g., speech input), text input, touch input, and/or gestural input through various user interfaces (e.g., microphone 213, accelerometer(s) 268, touch-sensitive display system 212, optical sensor(s) 229, other input control devices 216, etc.) of portable multifunction device 200. Digital assistant client module 229 is also capable of providing output in audio (e.g., speech output), visual, and/or tactile forms through various output interfaces (e.g., speaker 211, touch-sensitive display system 212, tactile output generator(s) 267, etc.) of portable multifunction device 200. For example, output is provided as voice, sound, alerts, text messages, menus, graphics, videos, animations, vibrations, and/or combinations of two or more of the above. During operation, digital assistant client module 229 communicates with DA server 106 using RF circuitry 208.
User data and models 231 include various data associated with the user (e.g., user-specific vocabulary data, user preference data, user-specified name pronunciations, data from the user's electronic address book, to-do lists, shopping lists, etc.) to provide the client-side functionalities of the digital assistant. Further, user data and models 231 include various models (e.g., speech recognition models, statistical language models, natural language processing models, ontology, task flow models, service models, etc.) for processing user input and determining user intent.
In some examples, digital assistant client module 229 utilizes the various sensors, subsystems, and peripheral devices of portable multifunction device 200 to gather additional information from the surrounding environment of the portable multifunction device 200 to establish a context associated with a user, the current user interaction, and/or the current user input. In some examples, digital assistant client module 229 provides the contextual information or a subset thereof with the user input to DA server 106 to help infer the user's intent. In some examples, the digital assistant also uses the contextual information to determine how to prepare and deliver outputs to the user. Contextual information is referred to as context data.
In some examples, the contextual information that accompanies the user input includes sensor information, e.g., lighting, ambient noise, ambient temperature, images or videos of the surrounding environment, etc. In some examples, the contextual information can also include the physical state of the device, e.g., device orientation, device location, device temperature, power level, speed, acceleration, motion patterns, cellular signals strength, etc. In some examples, information related to the software state of DA server 106, e.g., running processes, installed programs, past and present network activities, background services, error logs, resources usage, etc., and of portable multifunction device 200 is provided to DA server 106 as contextual information associated with a user input.
In some examples, the digital assistant client module 229 selectively provides information (e.g., user data 231) stored on the portable multifunction device 200 in response to requests from DA server 106. In some examples, digital assistant client module 229 also elicits additional input from the user via a natural language dialogue or other user interfaces upon request by DA server 106. Digital assistant client module 229 passes the additional input to DA server 106 to help DA server 106 in intent deduction and/or fulfillment of the user's intent expressed in the user request.
A more detailed description of a digital assistant is described below with reference to
Applications 236 include the following modules (or sets of instructions), or a subset or superset thereof:
Examples of other applications 236 that are stored in memory 202 include other word processing applications, other image editing applications, drawing applications, presentation applications, JAVA-enabled applications, encryption, digital rights management, voice recognition, and voice replication.
In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, contacts module 237 are used to manage an address book or contact list (e.g., stored in application internal state 292 of contacts module 237 in memory 202 or memory 470), including: adding name(s) to the address book; deleting name(s) from the address book; associating telephone number(s), e-mail address(es), physical address(es) or other information with a name; associating an image with a name; categorizing and sorting names; providing telephone numbers or e-mail addresses to initiate and/or facilitate communications by telephone 238, video conference module 239, e-mail 240, or IM 241; and so forth.
In conjunction with RF circuitry 208, audio circuitry 210, speaker 211, microphone 213, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, telephone module 238 are used to enter a sequence of characters corresponding to a telephone number, access one or more telephone numbers in contacts module 237, modify a telephone number that has been entered, dial a respective telephone number, conduct a conversation, and disconnect or hang up when the conversation is completed. As noted above, the wireless communication uses any of a plurality of communications standards, protocols, and technologies.
In conjunction with RF circuitry 208, audio circuitry 210, speaker 211, microphone 213, touch screen 212, display controller 256, optical sensor 264, optical sensor controller 258, contact/motion module 230, graphics module 232, text input module 234, contacts module 237, and telephone module 238, video conference module 239 includes executable instructions to initiate, conduct, and terminate a video conference between a user and one or more other participants in accordance with user instructions.
In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, e-mail client module 240 includes executable instructions to create, send, receive, and manage e-mail in response to user instructions. In conjunction with image management module 244, e-mail client module 240 makes it very easy to create and send e-mails with still or video images taken with camera module 243.
In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, the instant messaging module 241 includes executable instructions to enter a sequence of characters corresponding to an instant message, to modify previously entered characters, to transmit a respective instant message (for example, using a Short Message Service (SMS) or Multimedia Message Service (MMS) protocol for telephony-based instant messages or using XMPP, SIMPLE, or IMPS for Internet-based instant messages), to receive instant messages, and to view received instant messages. In some embodiments, transmitted and/or received instant messages include graphics, photos, audio files, video files and/or other attachments as are supported in an MMS and/or an Enhanced Messaging Service (EMS). As used herein, “instant messaging” refers to both telephony-based messages (e.g., messages sent using SMS or MMS) and Internet-based messages (e.g., messages sent using XMPP, SIMPLE, or IMPS).
In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, GPS module 235, map module 254, and music player module, workout support module 242 includes executable instructions to create workouts (e.g., with time, distance, and/or calorie burning goals); communicate with workout sensors (sports devices); receive workout sensor data; calibrate sensors used to monitor a workout; select and play music for a workout; and display, store, and transmit workout data.
In conjunction with touch screen 212, display controller 256, optical sensor(s) 264, optical sensor controller 258, contact/motion module 230, graphics module 232, and image management module 244, camera module 243 includes executable instructions to capture still images or video (including a video stream) and store them into memory 202, modify characteristics of a still image or video, or delete a still image or video from memory 202.
In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, and camera module 243, image management module 244 includes executable instructions to arrange, modify (e.g., edit), or otherwise manipulate, label, delete, present (e.g., in a digital slide show or album), and store still and/or video images.
In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, browser module 247 includes executable instructions to browse the Internet in accordance with user instructions, including searching, linking to, receiving, and displaying web pages or portions thereof, as well as attachments and other files linked to web pages.
In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, e-mail client module 240, and browser module 247, calendar module 248 includes executable instructions to create, display, modify, and store calendars and data associated with calendars (e.g., calendar entries, to-do lists, etc.) in accordance with user instructions.
In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, and browser module 247, widget modules 249 are mini-applications that can be downloaded and used by a user (e.g., weather widget 249-1, stocks widget 249-2, calculator widget 249-3, alarm clock widget 249-4, and dictionary widget 249-5) or created by the user (e.g., user-created widget 249-6). In some embodiments, a widget includes an HTML (Hypertext Markup Language) file, a CSS (Cascading Style Sheets) file, and a JavaScript file. In some embodiments, a widget includes an XML (Extensible Markup Language) file and a JavaScript file (e.g., Yahoo! Widgets).
In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, and browser module 247, the widget creator module 250 are used by a user to create widgets (e.g., turning a user-specified portion of a web page into a widget).
In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, search module 251 includes executable instructions to search for text, music, sound, image, video, and/or other files in memory 202 that match one or more search criteria (e.g., one or more user-specified search terms) in accordance with user instructions.
In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, audio circuitry 210, speaker 211, RF circuitry 208, and browser module 247, video and music player module 252 includes executable instructions that allow the user to download and play back recorded music and other sound files stored in one or more file formats, such as MP3 or AAC files, and executable instructions to display, present, or otherwise play back videos (e.g., on touch screen 212 or on an external, connected display via external port 224). In some embodiments, device 200 optionally includes the functionality of an MP3 player, such as an iPod (trademark of Apple Inc.).
In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, notes module 253 includes executable instructions to create and manage notes, to-do lists, and the like in accordance with user instructions.
In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, GPS module 235, and browser module 247, map module 254 are used to receive, display, modify, and store maps and data associated with maps (e.g., driving directions, data on stores and other points of interest at or near a particular location, and other location-based data) in accordance with user instructions.
In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, audio circuitry 210, speaker 211, RF circuitry 208, text input module 234, e-mail client module 240, and browser module 247, online video module 255 includes instructions that allow the user to access, browse, receive (e.g., by streaming and/or download), play back (e.g., on the touch screen or on an external, connected display via external port 224), send an e-mail with a link to a particular online video, and otherwise manage online videos in one or more file formats, such as H.264. In some embodiments, instant messaging module 241, rather than e-mail client module 240, is used to send a link to a particular online video. Additional description of the online video application can be found in U.S. Provisional Patent Application No. 60/936,562, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Jun. 20, 2007, and U.S. patent application Ser. No. 11/968,067, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Dec. 31, 2007, the contents of which are hereby incorporated by reference in their entirety.
Each of the above-identified modules and applications corresponds to a set of executable instructions for performing one or more functions described above and the methods described in this application (e.g., the computer-implemented methods and other information processing methods described herein). These modules (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules can be combined or otherwise rearranged in various embodiments. For example, video player module can be combined with music player module into a single module (e.g., video and music player module 252,
In some embodiments, device 200 is a device where operation of a predefined set of functions on the device is performed exclusively through a touch screen and/or a touchpad. By using a touch screen and/or a touchpad as the primary input control device for operation of device 200, the number of physical input control devices (such as push buttons, dials, and the like) on device 200 is reduced.
The predefined set of functions that are performed exclusively through a touch screen and/or a touchpad optionally include navigation between user interfaces. In some embodiments, the touchpad, when touched by the user, navigates device 200 to a main, home, or root menu from any user interface that is displayed on device 200. In such embodiments, a “menu button” is implemented using a touchpad. In some other embodiments, the menu button is a physical push button or other physical input control device instead of a touchpad.
Event sorter 270 receives event information and determines the application 236-1 and application view 291 of application 236-1 to which to deliver the event information. Event sorter 270 includes event monitor 271 and event dispatcher module 274. In some embodiments, application 236-1 includes application internal state 292, which indicates the current application view(s) displayed on touch-sensitive display 212 when the application is active or executing. In some embodiments, device/global internal state 257 is used by event sorter 270 to determine which application(s) is (are) currently active, and application internal state 292 is used by event sorter 270 to determine application views 291 to which to deliver event information.
In some embodiments, application internal state 292 includes additional information, such as one or more of: resume information to be used when application 236-1 resumes execution, user interface state information that indicates information being displayed or that is ready for display by application 236-1, a state queue for enabling the user to go back to a prior state or view of application 236-1, and a redo/undo queue of previous actions taken by the user.
Event monitor 271 receives event information from peripherals interface 218. Event information includes information about a sub-event (e.g., a user touch on touch-sensitive display 212, as part of a multi-touch gesture). Peripherals interface 218 transmits information it receives from I/O subsystem 206 or a sensor, such as proximity sensor 266, accelerometer(s) 268, and/or microphone 213 (through audio circuitry 210). Information that peripherals interface 218 receives from I/O subsystem 206 includes information from touch-sensitive display 212 or a touch-sensitive surface.
In some embodiments, event monitor 271 sends requests to the peripherals interface 218 at predetermined intervals. In response, peripherals interface 218 transmits event information. In other embodiments, peripherals interface 218 transmits event information only when there is a significant event (e.g., receiving an input above a predetermined noise threshold and/or for more than a predetermined duration).
In some embodiments, event sorter 270 also includes a hit view determination module 272 and/or an active event recognizer determination module 273.
Hit view determination module 272 provides software procedures for determining where a sub-event has taken place within one or more views when touch-sensitive display 212 displays more than one view. Views are made up of controls and other elements that a user can see on the display.
Another aspect of the user interface associated with an application is a set of views, sometimes herein called application views or user interface windows, in which information is displayed and touch-based gestures occur. The application views (of a respective application) in which a touch is detected correspond to programmatic levels within a programmatic or view hierarchy of the application. For example, the lowest level view in which a touch is detected is called the hit view, and the set of events that are recognized as proper inputs is determined based, at least in part, on the hit view of the initial touch that begins a touch-based gesture.
Hit view determination module 272 receives information related to sub events of a touch-based gesture. When an application has multiple views organized in a hierarchy, hit view determination module 272 identifies a hit view as the lowest view in the hierarchy which should handle the sub-event. In most circumstances, the hit view is the lowest level view in which an initiating sub-event occurs (e.g., the first sub-event in the sequence of sub-events that form an event or potential event). Once the hit view is identified by the hit view determination module 272, the hit view typically receives all sub-events related to the same touch or input source for which it was identified as the hit view.
Active event recognizer determination module 273 determines which view or views within a view hierarchy should receive a particular sequence of sub-events. In some embodiments, active event recognizer determination module 273 determines that only the hit view should receive a particular sequence of sub-events. In other embodiments, active event recognizer determination module 273 determines that all views that include the physical location of a sub-event are actively involved views, and therefore determines that all actively involved views should receive a particular sequence of sub-events. In other embodiments, even if touch sub-events were entirely confined to the area associated with one particular view, views higher in the hierarchy would still remain as actively involved views.
Event dispatcher module 274 dispatches the event information to an event recognizer (e.g., event recognizer 280). In embodiments including active event recognizer determination module 273, event dispatcher module 274 delivers the event information to an event recognizer determined by active event recognizer determination module 273. In some embodiments, event dispatcher module 274 stores in an event queue the event information, which is retrieved by a respective event receiver 282.
In some embodiments, operating system 226 includes event sorter 270. Alternatively, application 236-1 includes event sorter 270. In yet other embodiments, event sorter 270 is a stand-alone module, or a part of another module stored in memory 202, such as contact/motion module 230.
In some embodiments, application 236-1 includes a plurality of event handlers 290 and one or more application views 291, each of which includes instructions for handling touch events that occur within a respective view of the application's user interface. Each application view 291 of the application 236-1 includes one or more event recognizers 280. Typically, a respective application view 291 includes a plurality of event recognizers 280. In other embodiments, one or more of event recognizers 280 are part of a separate module, such as a user interface kit (not shown) or a higher level object from which application 236-1 inherits methods and other properties. In some embodiments, a respective event handler 290 includes one or more of: data updater 276, object updater 277, GUI updater 278, and/or event data 279 received from event sorter 270. Event handler 290 utilizes or calls data updater 276, object updater 277, or GUI updater 278 to update the application internal state 292. Alternatively, one or more of the application views 291 include one or more respective event handlers 290. Also, in some embodiments, one or more of data updater 276, object updater 277, and GUI updater 278 are included in a respective application view 291.
A respective event recognizer 280 receives event information (e.g., event data 279) from event sorter 270 and identifies an event from the event information. Event recognizer 280 includes event receiver 282 and event comparator 284. In some embodiments, event recognizer 280 also includes at least a subset of: metadata 283, and event delivery instructions 288 (which include sub-event delivery instructions).
Event receiver 282 receives event information from event sorter 270. The event information includes information about a sub-event, for example, a touch or a touch movement. Depending on the sub-event, the event information also includes additional information, such as location of the sub-event. When the sub-event concerns motion of a touch, the event information also includes speed and direction of the sub-event. In some embodiments, events include rotation of the device from one orientation to another (e.g., from a portrait orientation to a landscape orientation, or vice versa), and the event information includes corresponding information about the current orientation (also called device attitude) of the device.
Event comparator 284 compares the event information to predefined event or sub-event definitions and, based on the comparison, determines an event or sub event, or determines or updates the state of an event or sub-event. In some embodiments, event comparator 284 includes event definitions 286. Event definitions 286 contain definitions of events (e.g., predefined sequences of sub-events), for example, event 1 (287-1), event 2 (287-2), and others. In some embodiments, sub-events in an event (287) include, for example, touch begin, touch end, touch movement, touch cancellation, and multiple touching. In one example, the definition for event 1 (287-1) is a double tap on a displayed object. The double tap, for example, comprises a first touch (touch begin) on the displayed object for a predetermined phase, a first liftoff (touch end) for a predetermined phase, a second touch (touch begin) on the displayed object for a predetermined phase, and a second liftoff (touch end) for a predetermined phase. In another example, the definition for event 2 (287-2) is a dragging on a displayed object. The dragging, for example, comprises a touch (or contact) on the displayed object for a predetermined phase, a movement of the touch across touch-sensitive display 212, and liftoff of the touch (touch end). In some embodiments, the event also includes information for one or more associated event handlers 290.
In some embodiments, event definition 287 includes a definition of an event for a respective user-interface object. In some embodiments, event comparator 284 performs a hit test to determine which user-interface object is associated with a sub-event. For example, in an application view in which three user-interface objects are displayed on touch-sensitive display 212, when a touch is detected on touch-sensitive display 212, event comparator 284 performs a hit test to determine which of the three user-interface objects is associated with the touch (sub-event). If each displayed object is associated with a respective event handler 290, the event comparator uses the result of the hit test to determine which event handler 290 should be activated. For example, event comparator 284 selects an event handler associated with the sub-event and the object triggering the hit test.
In some embodiments, the definition for a respective event (287) also includes delayed actions that delay delivery of the event information until after it has been determined whether the sequence of sub-events does or does not correspond to the event recognizer's event type.
When a respective event recognizer 280 determines that the series of sub-events do not match any of the events in event definitions 286, the respective event recognizer 280 enters an event impossible, event failed, or event ended state, after which it disregards subsequent sub-events of the touch-based gesture. In this situation, other event recognizers, if any, that remain active for the hit view continue to track and process sub-events of an ongoing touch-based gesture.
In some embodiments, a respective event recognizer 280 includes metadata 283 with configurable properties, flags, and/or lists that indicate how the event delivery system should perform sub-event delivery to actively involved event recognizers. In some embodiments, metadata 283 includes configurable properties, flags, and/or lists that indicate how event recognizers interact, or are enabled to interact, with one another. In some embodiments, metadata 283 includes configurable properties, flags, and/or lists that indicate whether sub-events are delivered to varying levels in the view or programmatic hierarchy.
In some embodiments, a respective event recognizer 280 activates event handler 290 associated with an event when one or more particular sub-events of an event are recognized. In some embodiments, a respective event recognizer 280 delivers event information associated with the event to event handler 290. Activating an event handler 290 is distinct from sending (and deferred sending) sub-events to a respective hit view. In some embodiments, event recognizer 280 throws a flag associated with the recognized event, and event handler 290 associated with the flag catches the flag and performs a predefined process.
In some embodiments, event delivery instructions 288 include sub-event delivery instructions that deliver event information about a sub-event without activating an event handler. Instead, the sub-event delivery instructions deliver event information to event handlers associated with the series of sub-events or to actively involved views. Event handlers associated with the series of sub-events or with actively involved views receive the event information and perform a predetermined process.
In some embodiments, data updater 276 creates and updates data used in application 236-1. For example, data updater 276 updates the telephone number used in contacts module 237, or stores a video file used in video player module. In some embodiments, object updater 277 creates and updates objects used in application 236-1. For example, object updater 277 creates a new user-interface object or updates the position of a user-interface object. GUI updater 278 updates the GUI. For example, GUI updater 278 prepares display information and sends it to graphics module 232 for display on a touch-sensitive display.
In some embodiments, event handler(s) 290 includes or has access to data updater 276, object updater 277, and GUI updater 278. In some embodiments, data updater 276, object updater 277, and GUI updater 278 are included in a single module of a respective application 236-1 or application view 291. In other embodiments, they are included in two or more software modules.
It shall be understood that the foregoing discussion regarding event handling of user touches on touch-sensitive displays also applies to other forms of user inputs to operate multifunction devices 200 with input devices, not all of which are initiated on touch screens. For example, mouse movement and mouse button presses, optionally coordinated with single or multiple keyboard presses or holds; contact movements such as taps, drags, scrolls, etc. on touchpads; pen stylus inputs; movement of the device; oral instructions; detected eye movements; biometric inputs; and/or any combination thereof are optionally utilized as inputs corresponding to sub-events which define an event to be recognized.
Device 200 also includes one or more physical buttons, such as “home” or menu button 304. As described previously, menu button 304 is used to navigate to any application 236 in a set of applications that is executed on device 200. Alternatively, in some embodiments, the menu button is implemented as a soft key in a GUI displayed on touch screen 212.
In one embodiment, device 200 includes touch screen 212, menu button 304, push button 306 for powering the device on/off and locking the device, volume adjustment button(s) 308, subscriber identity module (SIM) card slot 310, headset jack 312, and docking/charging external port 224. Push button 306 is, optionally, used to turn the power on/off on the device by depressing the button and holding the button in the depressed state for a predefined time interval; to lock the device by depressing the button and releasing the button before the predefined time interval has elapsed; and/or to unlock the device or initiate an unlock process. In an alternative embodiment, device 200 also accepts verbal input for activation or deactivation of some functions through microphone 213. Device 200 also, optionally, includes one or more contact intensity sensors 265 for detecting intensity of contacts on touch screen 212 and/or one or more tactile output generators 267 for generating tactile outputs for a user of device 200.
Each of the above-identified elements in
Attention is now directed towards embodiments of user interfaces that can be implemented on, for example, portable multifunction device 200.
Signal strength indicator(s) 502 for wireless communication(s), such as cellular and Wi-Fi signals;
It should be noted that the icon labels illustrated in
Although some of the examples which follow will be given with reference to inputs on touch screen display 212 (where the touch-sensitive surface and the display are combined), in some embodiments, the device detects inputs on a touch-sensitive surface that is separate from the display, as shown in
Additionally, while the following examples are given primarily with reference to finger inputs (e.g., finger contacts, finger tap gestures, finger swipe gestures), it should be understood that, in some embodiments, one or more of the finger inputs are replaced with input from another input device (e.g., a mouse-based input or stylus input). For example, a swipe gesture is, optionally, replaced with a mouse click (e.g., instead of a contact) followed by movement of the cursor along the path of the swipe (e.g., instead of movement of the contact). As another example, a tap gesture is, optionally, replaced with a mouse click while the cursor is located over the location of the tap gesture (e.g., instead of detection of the contact followed by ceasing to detect the contact). Similarly, when multiple user inputs are simultaneously detected, it should be understood that multiple computer mice are, optionally, used simultaneously, or a mouse and finger contacts are, optionally, used simultaneously.
Techniques for detecting and processing touch intensity are found, for example, in related applications: International Patent Application Serial No. PCT/US2013/040061, titled “Device, Method, and Graphical User Interface for Displaying User Interface Objects Corresponding to an Application,” filed May 8, 2013, and International Patent Application Serial No. PCT/US2013/069483, titled “Device, Method, and Graphical User Interface for Transitioning Between Touch Input to Display Output Relationships,” filed Nov. 11, 2013, each of which is hereby incorporated by reference in their entirety.
In some embodiments, device 600 has one or more input mechanisms 606 and 608. Input mechanisms 606 and 608, if included, are physical. Examples of physical input mechanisms include push buttons and rotatable mechanisms. In some embodiments, device 600 has one or more attachment mechanisms. Such attachment mechanisms, if included, can permit attachment of device 600 with, for example, hats, eyewear, earrings, necklaces, shirts, jackets, bracelets, watch straps, chains, trousers, belts, shoes, purses, backpacks, and so forth. These attachment mechanisms permit device 600 to be worn by a user.
Input mechanism 608 is a microphone, in some examples. Personal electronic device 600 includes, for example, various sensors, such as GPS sensor 632, accelerometer 634, directional sensor 640 (e.g., compass), gyroscope 636, motion sensor 638, and/or a combination thereof, all of which are operatively connected to I/O section 614.
Memory 618 of personal electronic device 600 is a non-transitory computer-readable storage medium, for storing computer-executable instructions, which, when executed by one or more computer processors 616, for example, cause the computer processors to perform the techniques and processes described below. The computer-executable instructions, for example, are also stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. Personal electronic device 600 is not limited to the components and configuration of
As used here, the term “affordance” refers to a user-interactive graphical user interface object that is, for example, displayed on the display screen of devices 200, 400, 600, and/or 800 (
As used herein, the term “focus selector” refers to an input element that indicates a current part of a user interface with which a user is interacting. In some implementations that include a cursor or other location marker, the cursor acts as a “focus selector” so that when an input (e.g., a press input) is detected on a touch-sensitive surface (e.g., touchpad 455 in
As used in the specification and claims, the term “characteristic intensity” of a contact refers to a characteristic of the contact based on one or more intensities of the contact. In some embodiments, the characteristic intensity is based on multiple intensity samples. The characteristic intensity is, optionally, based on a predefined number of intensity samples, or a set of intensity samples collected during a predetermined time period (e.g., 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10 seconds) relative to a predefined event (e.g., after detecting the contact, prior to detecting liftoff of the contact, before or after detecting a start of movement of the contact, prior to detecting an end of the contact, before or after detecting an increase in intensity of the contact, and/or before or after detecting a decrease in intensity of the contact). A characteristic intensity of a contact is, optionally based on one or more of: a maximum value of the intensities of the contact, a mean value of the intensities of the contact, an average value of the intensities of the contact, a top 10 percentile value of the intensities of the contact, a value at the half maximum of the intensities of the contact, a value at the 90 percent maximum of the intensities of the contact, or the like. In some embodiments, the duration of the contact is used in determining the characteristic intensity (e.g., when the characteristic intensity is an average of the intensity of the contact over time). In some embodiments, the characteristic intensity is compared to a set of one or more intensity thresholds to determine whether an operation has been performed by a user. For example, the set of one or more intensity thresholds includes a first intensity threshold and a second intensity threshold. In this example, a contact with a characteristic intensity that does not exceed the first threshold results in a first operation, a contact with a characteristic intensity that exceeds the first intensity threshold and does not exceed the second intensity threshold results in a second operation, and a contact with a characteristic intensity that exceeds the second threshold results in a third operation. In some embodiments, a comparison between the characteristic intensity and one or more thresholds is used to determine whether or not to perform one or more operations (e.g., whether to perform a respective operation or forgo performing the respective operation) rather than being used to determine whether to perform a first operation or a second operation.
In some embodiments, a portion of a gesture is identified for purposes of determining a characteristic intensity. For example, a touch-sensitive surface receives a continuous swipe contact transitioning from a start location and reaching an end location, at which point the intensity of the contact increases. In this example, the characteristic intensity of the contact at the end location is based on only a portion of the continuous swipe contact, and not the entire swipe contact (e.g., only the portion of the swipe contact at the end location). In some embodiments, a smoothing algorithm is applied to the intensities of the swipe contact prior to determining the characteristic intensity of the contact. For example, the smoothing algorithm optionally includes one or more of: an unweighted sliding-average smoothing algorithm, a triangular smoothing algorithm, a median filter smoothing algorithm, and/or an exponential smoothing algorithm. In some circumstances, these smoothing algorithms eliminate narrow spikes or dips in the intensities of the swipe contact for purposes of determining a characteristic intensity.
The intensity of a contact on the touch-sensitive surface is characterized relative to one or more intensity thresholds, such as a contact-detection intensity threshold, a light press intensity threshold, a deep press intensity threshold, and/or one or more other intensity thresholds. In some embodiments, the light press intensity threshold corresponds to an intensity at which the device will perform operations typically associated with clicking a button of a physical mouse or a trackpad. In some embodiments, the deep press intensity threshold corresponds to an intensity at which the device will perform operations that are different from operations typically associated with clicking a button of a physical mouse or a trackpad. In some embodiments, when a contact is detected with a characteristic intensity below the light press intensity threshold (e.g., and above a nominal contact-detection intensity threshold below which the contact is no longer detected), the device will move a focus selector in accordance with movement of the contact on the touch-sensitive surface without performing an operation associated with the light press intensity threshold or the deep press intensity threshold. Generally, unless otherwise stated, these intensity thresholds are consistent between different sets of user interface figures.
An increase of characteristic intensity of the contact from an intensity below the light press intensity threshold to an intensity between the light press intensity threshold and the deep press intensity threshold is sometimes referred to as a “light press” input. An increase of characteristic intensity of the contact from an intensity below the deep press intensity threshold to an intensity above the deep press intensity threshold is sometimes referred to as a “deep press” input. An increase of characteristic intensity of the contact from an intensity below the contact-detection intensity threshold to an intensity between the contact-detection intensity threshold and the light press intensity threshold is sometimes referred to as detecting the contact on the touch-surface. A decrease of characteristic intensity of the contact from an intensity above the contact-detection intensity threshold to an intensity below the contact-detection intensity threshold is sometimes referred to as detecting liftoff of the contact from the touch-surface. In some embodiments, the contact-detection intensity threshold is zero. In some embodiments, the contact-detection intensity threshold is greater than zero.
In some embodiments described herein, one or more operations are performed in response to detecting a gesture that includes a respective press input or in response to detecting the respective press input performed with a respective contact (or a plurality of contacts), where the respective press input is detected based at least in part on detecting an increase in intensity of the contact (or plurality of contacts) above a press-input intensity threshold. In some embodiments, the respective operation is performed in response to detecting the increase in intensity of the respective contact above the press-input intensity threshold (e.g., a “down stroke” of the respective press input). In some embodiments, the press input includes an increase in intensity of the respective contact above the press-input intensity threshold and a subsequent decrease in intensity of the contact below the press-input intensity threshold, and the respective operation is performed in response to detecting the subsequent decrease in intensity of the respective contact below the press-input threshold (e.g., an “up stroke” of the respective press input).
In some embodiments, the device employs intensity hysteresis to avoid accidental inputs sometimes termed “jitter,” where the device defines or selects a hysteresis intensity threshold with a predefined relationship to the press-input intensity threshold (e.g., the hysteresis intensity threshold is X intensity units lower than the press-input intensity threshold or the hysteresis intensity threshold is 75%, 90%, or some reasonable proportion of the press-input intensity threshold). Thus, in some embodiments, the press input includes an increase in intensity of the respective contact above the press-input intensity threshold and a subsequent decrease in intensity of the contact below the hysteresis intensity threshold that corresponds to the press-input intensity threshold, and the respective operation is performed in response to detecting the subsequent decrease in intensity of the respective contact below the hysteresis intensity threshold (e.g., an “up stroke” of the respective press input). Similarly, in some embodiments, the press input is detected only when the device detects an increase in intensity of the contact from an intensity at or below the hysteresis intensity threshold to an intensity at or above the press-input intensity threshold and, optionally, a subsequent decrease in intensity of the contact to an intensity at or below the hysteresis intensity, and the respective operation is performed in response to detecting the press input (e.g., the increase in intensity of the contact or the decrease in intensity of the contact, depending on the circumstances).
For ease of explanation, the descriptions of operations performed in response to a press input associated with a press-input intensity threshold or in response to a gesture including the press input are, optionally, triggered in response to detecting either: an increase in intensity of a contact above the press-input intensity threshold, an increase in intensity of a contact from an intensity below the hysteresis intensity threshold to an intensity above the press-input intensity threshold, a decrease in intensity of the contact below the press-input intensity threshold, and/or a decrease in intensity of the contact below the hysteresis intensity threshold corresponding to the press-input intensity threshold. Additionally, in examples where an operation is described as being performed in response to detecting a decrease in intensity of a contact below the press-input intensity threshold, the operation is, optionally, performed in response to detecting a decrease in intensity of the contact below a hysteresis intensity threshold corresponding to, and lower than, the press-input intensity threshold.
3. Digital Assistant System
Digital assistant system 700 includes memory 702, one or more processors 704, input/output (I/O) interface 706, and network communications interface 708. These components can communicate with one another over one or more communication buses or signal lines 710.
In some examples, memory 702 includes a non-transitory computer-readable medium, such as high-speed random access memory and/or a non-volatile computer-readable storage medium (e.g., one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices).
In some examples, I/O interface 706 couples input/output devices 716 of digital assistant system 700, such as displays, keyboards, touch screens, and microphones, to user interface module 722. I/O interface 706, in conjunction with user interface module 722, receives user inputs (e.g., voice input, keyboard inputs, touch inputs, etc.) and processes them accordingly. In some examples, e.g., when the digital assistant is implemented on a standalone user device, digital assistant system 700 includes any of the components and I/O communication interfaces described with respect to devices 200, 400, 600, or 800 in
In some examples, the network communications interface 708 includes wired communication port(s) 712 and/or wireless transmission and reception circuitry 714. The wired communication port(s) receives and send communication signals via one or more wired interfaces, e.g., Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wireless circuitry 714 receives and sends RF signals and/or optical signals from/to communications networks and other communications devices. The wireless communications use any of a plurality of communications standards, protocols, and technologies, such as GSM, EDGE, CDMA, TDMA, Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communication protocol. Network communications interface 708 enables communication between digital assistant system 700 with networks, such as the Internet, an intranet, and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN), and/or a metropolitan area network (MAN), and other devices.
In some examples, memory 702, or the computer-readable storage media of memory 702, stores programs, modules, instructions, and data structures including all or a subset of: operating system 718, communications module 720, user interface module 722, one or more applications 724, and digital assistant module 726. In particular, memory 702, or the computer-readable storage media of memory 702, stores instructions for performing the processes described below. One or more processors 704 execute these programs, modules, and instructions, and reads/writes from/to the data structures.
Operating system 718 (e.g., Darwin, RTXC, LINUX, UNIX, iOS, OS X, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communications between various hardware, firmware, and software components.
Communications module 720 facilitates communications between digital assistant system 700 with other devices over network communications interface 708. For example, communications module 720 communicates with RF circuitry 208 of electronic devices such as devices 200, 400, and 600 shown in
User interface module 722 receives commands and/or inputs from a user via I/O interface 706 (e.g., from a keyboard, touch screen, pointing device, controller, and/or microphone), and generate user interface objects on a display. User interface module 722 also prepares and delivers outputs (e.g., speech, sound, animation, text, icons, vibrations, haptic feedback, light, etc.) to the user via the I/O interface 706 (e.g., through displays, audio channels, speakers, touch-pads, etc.).
Applications 724 include programs and/or modules that are configured to be executed by one or more processors 704. For example, if the digital assistant system is implemented on a standalone user device, applications 724 include user applications, such as games, a calendar application, a navigation application, or an email application. If digital assistant system 700 is implemented on a server, applications 724 include resource management applications, diagnostic applications, or scheduling applications, for example.
Memory 702 also stores digital assistant module 726 (or the server portion of a digital assistant). In some examples, digital assistant module 726 includes the following sub-modules, or a subset or superset thereof: input/output processing module 728, speech-to-text (STT) processing module 730, natural language processing module 732, dialogue flow processing module 734, task flow processing module 736, service processing module 738, and speech synthesis processing module 740. Each of these modules has access to one or more of the following systems or data and models of the digital assistant module 726, or a subset or superset thereof: ontology 760, vocabulary index 744, user data 748, task flow models 754, service models 756, and ASR systems 758.
In some examples, using the processing modules, data, and models implemented in digital assistant module 726, the digital assistant can perform at least some of the following: converting speech input into text; identifying a user's intent expressed in a natural language input received from the user; actively eliciting and obtaining information needed to fully infer the user's intent (e.g., by disambiguating words, games, intentions, etc.); determining the task flow for fulfilling the inferred intent; and executing the task flow to fulfill the inferred intent.
In some examples, as shown in
STT processing module 730 includes one or more ASR systems 758. The one or more ASR systems 758 can process the speech input that is received through I/O processing module 728 to produce a recognition result. Each ASR system 758 includes a front-end speech pre-processor. The front-end speech pre-processor extracts representative features from the speech input. For example, the front-end speech pre-processor performs a Fourier transform on the speech input to extract spectral features that characterize the speech input as a sequence of representative multi-dimensional vectors. Further, each ASR system 758 includes one or more speech recognition models (e.g., acoustic models and/or language models) and implements one or more speech recognition engines. Examples of speech recognition models include Hidden Markov Models, Gaussian-Mixture Models, Deep Neural Network Models, n-gram language models, and other statistical models. Examples of speech recognition engines include the dynamic time warping based engines and weighted finite-state transducers (WFST) based engines. The one or more speech recognition models and the one or more speech recognition engines are used to process the extracted representative features of the front-end speech pre-processor to produce intermediate recognitions results (e.g., phonemes, phonemic strings, and sub-words), and ultimately, text recognition results (e.g., words, word strings, or sequence of tokens). In some examples, the speech input is processed at least partially by a third-party service or on the user's device (e.g., device 104, 200, 400, or 600) to produce the recognition result. Once STT processing module 730 produces recognition results containing a text string (e.g., words, or sequence of words, or sequence of tokens), the recognition result is passed to natural language processing module 732 for intent deduction. In some examples, STT processing module 730 produces multiple candidate text representations of the speech input. Each candidate text representation is a sequence of words or tokens corresponding to the speech input. In some examples, each candidate text representation is associated with a speech recognition confidence score. Based on the speech recognition confidence scores, STT processing module 730 ranks the candidate text representations and provides the n-best (e.g., n highest ranked) candidate text representation(s) to natural language processing module 732 for intent deduction, where n is a predetermined integer greater than zero. For example, in one example, only the highest ranked (n=1) candidate text representation is passed to natural language processing module 732 for intent deduction. In another example, the five highest ranked (n=5) candidate text representations are passed to natural language processing module 732 for intent deduction.
More details on the speech-to-text processing are described in U.S. Utility application Ser. No. 13/236,942 for “Consolidating Speech Recognition Results,” filed on Sep. 20, 2011, the entire disclosure of which is incorporated herein by reference.
In some examples, STT processing module 730 includes and/or accesses a vocabulary of recognizable words via phonetic alphabet conversion module 731. Each vocabulary word is associated with one or more candidate pronunciations of the word represented in a speech recognition phonetic alphabet. In particular, the vocabulary of recognizable words includes a word that is associated with a plurality of candidate pronunciations. For example, the vocabulary includes the word “tomato” that is associated with the candidate pronunciations of// and /
/. Further, vocabulary words are associated with custom candidate pronunciations that are based on previous speech inputs from the user. Such custom candidate pronunciations are stored in STT processing module 730 and are associated with a particular user via the user's profile on the device. In some examples, the candidate pronunciations for words are determined based on the spelling of the word and one or more linguistic and/or phonetic rules. In some examples, the candidate pronunciations are manually generated, e.g., based on known canonical pronunciations.
In some examples, the candidate pronunciations are ranked based on the commonness of the candidate pronunciation. For example, the candidate pronunciation // is ranked higher than /
/, because the former is a more commonly used pronunciation (e.g., among all users, for users in a particular geographical region, or for any other appropriate subset of users). In some examples, candidate pronunciations are ranked based on whether the candidate pronunciation is a custom candidate pronunciation associated with the user. For example, custom candidate pronunciations are ranked higher than canonical candidate pronunciations. This can be useful for recognizing proper nouns having a unique pronunciation that deviates from canonical pronunciation. In some examples, candidate pronunciations are associated with one or more speech characteristics, such as geographic origin, nationality, or ethnicity. For example, the candidate pronunciation /
/ is associated with the United States, whereas the candidate pronunciation /
/ is associated with Great Britain. Further, the rank of the candidate pronunciation is based on one or more characteristics (e.g., geographic origin, nationality, ethnicity, etc.) of the user stored in the user's profile on the device. For example, it can be determined from the user's profile that the user is associated with the United States. Based on the user being associated with the United States, the candidate pronunciation /
/ (associated with the United States) is ranked higher than the candidate pronunciation /
/ (associated with Great Britain). In some examples, one of the ranked candidate pronunciations is selected as a predicted pronunciation (e.g., the most likely pronunciation).
When a speech input is received, STT processing module 730 is used to determine the phonemes corresponding to the speech input (e.g., using an acoustic model), and then attempt to determine words that match the phonemes (e.g., using a language model). For example, if STT processing module 730 first identifies the sequence of phonemes // corresponding to a portion of the speech input, it can then determine, based on vocabulary index 744, that this sequence corresponds to the word “tomato.”
In some examples, STT processing module 730 uses approximate matching techniques to determine words in an utterance. Thus, for example, the STT processing module 730 determines that the sequence of phonemes // corresponds to the word “tomato,” even if that particular sequence of phonemes is not one of the candidate sequence of phonemes for that word.
Natural language processing module 732 (“natural language processor”) of the digital assistant takes the n-best candidate text representation(s) (“word sequence(s)” or “token sequence(s)”) generated by STT processing module 730, and attempts to associate each of the candidate text representations with one or more “actionable intents” recognized by the digital assistant. An “actionable intent” (or “user intent”) represents a task that can be performed by the digital assistant, and can have an associated task flow implemented in task flow models 754. The associated task flow is a series of programmed actions and steps that the digital assistant takes in order to perform the task. The scope of a digital assistant's capabilities is dependent on the number and variety of task flows that have been implemented and stored in task flow models 754, or in other words, on the number and variety of “actionable intents” that the digital assistant recognizes. The effectiveness of the digital assistant, however, also dependents on the assistant's ability to infer the correct “actionable intent(s)” from the user request expressed in natural language.
In some examples, in addition to the sequence of words or tokens obtained from STT processing module 730, natural language processing module 732 also receives contextual information associated with the user request, e.g., from I/O processing module 728. The natural language processing module 732 optionally uses the contextual information to clarify, supplement, and/or further define the information contained in the candidate text representations received from STT processing module 730. The contextual information includes, for example, user preferences, hardware, and/or software states of the user device, sensor information collected before, during, or shortly after the user request, prior interactions (e.g., dialogue) between the digital assistant and the user, and the like. As described herein, contextual information is, in some examples, dynamic, and changes with time, location, content of the dialogue, and other factors.
In some examples, the natural language processing is based on, e.g., ontology 760. Ontology 760 is a hierarchical structure containing many nodes, each node representing either an “actionable intent” or a “property” relevant to one or more of the “actionable intents” or other “properties.” As noted above, an “actionable intent” represents a task that the digital assistant is capable of performing, i.e., it is “actionable” or can be acted on. A “property” represents a parameter associated with an actionable intent or a sub-aspect of another property. A linkage between an actionable intent node and a property node in ontology 760 defines how a parameter represented by the property node pertains to the task represented by the actionable intent node.
In some examples, ontology 760 is made up of actionable intent nodes and property nodes. Within ontology 760, each actionable intent node is linked to one or more property nodes either directly or through one or more intermediate property nodes. Similarly, each property node is linked to one or more actionable intent nodes either directly or through one or more intermediate property nodes. For example, as shown in
In addition, property nodes “cuisine,” “price range,” “phone number,” and “location” are sub-nodes of the property node “restaurant,” and are each linked to the “restaurant reservation” node (i.e., the actionable intent node) through the intermediate property node “restaurant.” For another example, as shown in
An actionable intent node, along with its linked property nodes, is described as a “domain.” In the present discussion, each domain is associated with a respective actionable intent, and refers to the group of nodes (and the relationships there between) associated with the particular actionable intent. For example, ontology 760 shown in
While
In some examples, ontology 760 includes all the domains (and hence actionable intents) that the digital assistant is capable of understanding and acting upon. In some examples, ontology 760 is modified, such as by adding or removing entire domains or nodes, or by modifying relationships between the nodes within the ontology 760.
In some examples, nodes associated with multiple related actionable intents are clustered under a “super domain” in ontology 760. For example, a “travel” super-domain includes a cluster of property nodes and actionable intent nodes related to travel. The actionable intent nodes related to travel includes “airline reservation,” “hotel reservation,” “car rental,” “get directions,” “find points of interest,” and so on. The actionable intent nodes under the same super domain (e.g., the “travel” super domain) have many property nodes in common. For example, the actionable intent nodes for “airline reservation,” “hotel reservation,” “car rental,” “get directions,” and “find points of interest” share one or more of the property nodes “start location,” “destination,” “departure date/time,” “arrival date/time,” and “party size.”
In some examples, each node in ontology 760 is associated with a set of words and/or phrases that are relevant to the property or actionable intent represented by the node. The respective set of words and/or phrases associated with each node are the so-called “vocabulary” associated with the node. The respective set of words and/or phrases associated with each node are stored in vocabulary index 744 in association with the property or actionable intent represented by the node. For example, returning to
Natural language processing module 732 receives the candidate text representations (e.g., text string(s) or token sequence(s)) from STT processing module 730, and for each candidate representation, determines what nodes are implicated by the words in the candidate text representation. In some examples, if a word or phrase in the candidate text representation is found to be associated with one or more nodes in ontology 760 (via vocabulary index 744), the word or phrase “triggers” or “activates” those nodes. Based on the quantity and/or relative importance of the activated nodes, natural language processing module 732 selects one of the actionable intents as the task that the user intended the digital assistant to perform. In some examples, the domain that has the most “triggered” nodes is selected. In some examples, the domain having the highest confidence value (e.g., based on the relative importance of its various triggered nodes) is selected. In some examples, the domain is selected based on a combination of the number and the importance of the triggered nodes. In some examples, additional factors are considered in selecting the node as well, such as whether the digital assistant has previously correctly interpreted a similar request from a user.
User data 748 includes user-specific information, such as user-specific vocabulary, user preferences, user address, user's default and secondary languages, user's contact list, and other short-term or long-term information for each user. In some examples, natural language processing module 732 uses the user-specific information to supplement the information contained in the user input to further define the user intent. For example, for a user request “invite my friends to my birthday party,” natural language processing module 732 is able to access user data 748 to determine who the “friends” are and when and where the “birthday party” would be held, rather than requiring the user to provide such information explicitly in his/her request.
It should be recognized that in some examples, natural language processing module 732 is implemented using one or more machine learning mechanisms (e.g., neural networks). In particular, the one or more machine learning mechanisms are configured to receive a candidate text representation and contextual information associated with the candidate text representation. Based on the candidate text representation and the associated contextual information, the one or more machine learning mechanisms are configured to determine intent confidence scores over a set of candidate actionable intents. Natural language processing module 732 can select one or more candidate actionable intents from the set of candidate actionable intents based on the determined intent confidence scores. In some examples, an ontology (e.g., ontology 760) is also used to select the one or more candidate actionable intents from the set of candidate actionable intents.
Other details of searching an ontology based on a token string are described in U.S. Utility application Ser. No. 12/341,743 for “Method and Apparatus for Searching Using An Active Ontology,” filed Dec. 22, 2008, the entire disclosure of which is incorporated herein by reference.
In some examples, once natural language processing module 732 identifies an actionable intent (or domain) based on the user request, natural language processing module 732 generates a structured query to represent the identified actionable intent. In some examples, the structured query includes parameters for one or more nodes within the domain for the actionable intent, and at least some of the parameters are populated with the specific information and requirements specified in the user request. For example, the user says “Make me a dinner reservation at a sushi place at 7.” In this case, natural language processing module 732 is able to correctly identify the actionable intent to be “restaurant reservation” based on the user input. According to the ontology, a structured query for a “restaurant reservation” domain includes parameters such as {Cuisine}, {Time}, {Date}, {Party Size}, and the like. In some examples, based on the speech input and the text derived from the speech input using STT processing module 730, natural language processing module 732 generates a partial structured query for the restaurant reservation domain, where the partial structured query includes the parameters {Cuisine=“Sushi”} and {Time=“7 pm”}. However, in this example, the user's utterance contains insufficient information to complete the structured query associated with the domain. Therefore, other necessary parameters such as {Party Size} and {Date} are not specified in the structured query based on the information currently available. In some examples, natural language processing module 732 populates some parameters of the structured query with received contextual information. For example, in some examples, if the user requested a sushi restaurant “near me,” natural language processing module 732 populates a {location} parameter in the structured query with GPS coordinates from the user device.
In some examples, natural language processing module 732 identifies multiple candidate actionable intents for each candidate text representation received from STT processing module 730. Further, in some examples, a respective structured query (partial or complete) is generated for each identified candidate actionable intent. Natural language processing module 732 determines an intent confidence score for each candidate actionable intent and ranks the candidate actionable intents based on the intent confidence scores. In some examples, natural language processing module 732 passes the generated structured query (or queries), including any completed parameters, to task flow processing module 736 (“task flow processor”). In some examples, the structured query (or queries) for the m-best (e.g., m highest ranked) candidate actionable intents are provided to task flow processing module 736, where m is a predetermined integer greater than zero. In some examples, the structured query (or queries) for the m-best candidate actionable intents are provided to task flow processing module 736 with the corresponding candidate text representation(s).
Other details of inferring a user intent based on multiple candidate actionable intents determined from multiple candidate text representations of a speech input are described in U.S. Utility application Ser. No. 14/298,725 for “System and Method for Inferring User Intent From Speech Inputs,” filed Jun. 6, 2014, the entire disclosure of which is incorporated herein by reference.
Task flow processing module 736 is configured to receive the structured query (or queries) from natural language processing module 732, complete the structured query, if necessary, and perform the actions required to “complete” the user's ultimate request. In some examples, the various procedures necessary to complete these tasks are provided in task flow models 754. In some examples, task flow models 754 include procedures for obtaining additional information from the user and task flows for performing actions associated with the actionable intent.
As described above, in order to complete a structured query, task flow processing module 736 needs to initiate additional dialogue with the user in order to obtain additional information, and/or disambiguate potentially ambiguous utterances. When such interactions are necessary, task flow processing module 736 invokes dialogue flow processing module 734 to engage in a dialogue with the user. In some examples, dialogue flow processing module 734 determines how (and/or when) to ask the user for the additional information and receives and processes the user responses. The questions are provided to and answers are received from the users through I/O processing module 728. In some examples, dialogue flow processing module 734 presents dialogue output to the user via audio and/or visual output, and receives input from the user via spoken or physical (e.g., clicking) responses. Continuing with the example above, when task flow processing module 736 invokes dialogue flow processing module 734 to determine the “party size” and “date” information for the structured query associated with the domain “restaurant reservation,” dialogue flow processing module 734 generates questions such as “For how many people?” and “On which day?” to pass to the user. Once answers are received from the user, dialogue flow processing module 734 then populates the structured query with the missing information, or pass the information to task flow processing module 736 to complete the missing information from the structured query.
Once task flow processing module 736 has completed the structured query for an actionable intent, task flow processing module 736 proceeds to perform the ultimate task associated with the actionable intent. Accordingly, task flow processing module 736 executes the steps and instructions in the task flow model according to the specific parameters contained in the structured query. For example, the task flow model for the actionable intent of “restaurant reservation” includes steps and instructions for contacting a restaurant and actually requesting a reservation for a particular party size at a particular time. For example, using a structured query such as: {restaurant reservation, restaurant=ABC Café, date=3/12/2012, time=7 pm, party size=5}, task flow processing module 736 performs the steps of: (1) logging onto a server of the ABC Café or a restaurant reservation system such as OPENTABLE®, (2) entering the date, time, and party size information in a form on the website, (3) submitting the form, and (4) making a calendar entry for the reservation in the user's calendar.
In some examples, task flow processing module 736 employs the assistance of service processing module 738 (“service processing module”) to complete a task requested in the user input or to provide an informational answer requested in the user input. For example, service processing module 738 acts on behalf of task flow processing module 736 to make a phone call, set a calendar entry, invoke a map search, invoke or interact with other user applications installed on the user device, and invoke or interact with third-party services (e.g., a restaurant reservation portal, a social networking website, a banking portal, etc.). In some examples, the protocols and application programming interfaces (API) required by each service are specified by a respective service model among service models 756. Service processing module 738 accesses the appropriate service model for a service and generates requests for the service in accordance with the protocols and APIs required by the service according to the service model.
For example, if a restaurant has enabled an online reservation service, the restaurant submits a service model specifying the necessary parameters for making a reservation and the APIs for communicating the values of the necessary parameter to the online reservation service. When requested by task flow processing module 736, service processing module 738 establishes a network connection with the online reservation service using the web address stored in the service model, and sends the necessary parameters of the reservation (e.g., time, date, party size) to the online reservation interface in a format according to the API of the online reservation service.
In some examples, natural language processing module 732, dialogue flow processing module 734, and task flow processing module 736 are used collectively and iteratively to infer and define the user's intent, obtain information to further clarify and refine the user intent, and finally generate a response (i.e., an output to the user, or the completion of a task) to fulfill the user's intent. The generated response is a dialogue response to the speech input that at least partially fulfills the user's intent. Further, in some examples, the generated response is output as a speech output. In these examples, the generated response is sent to speech synthesis processing module 740 (e.g., speech synthesizer) where it can be processed to synthesize the dialogue response in speech form. In yet other examples, the generated response is data content relevant to satisfying a user request in the speech input.
In examples where task flow processing module 736 receives multiple structured queries from natural language processing module 732, task flow processing module 736 initially processes the first structured query of the received structured queries to attempt to complete the first structured query and/or execute one or more tasks or actions represented by the first structured query. In some examples, the first structured query corresponds to the highest ranked actionable intent. In other examples, the first structured query is selected from the received structured queries based on a combination of the corresponding speech recognition confidence scores and the corresponding intent confidence scores. In some examples, if task flow processing module 736 encounters an error during processing of the first structured query (e.g., due to an inability to determine a necessary parameter), the task flow processing module 736 can proceed to select and process a second structured query of the received structured queries that corresponds to a lower ranked actionable intent. The second structured query is selected, for example, based on the speech recognition confidence score of the corresponding candidate text representation, the intent confidence score of the corresponding candidate actionable intent, a missing necessary parameter in the first structured query, or any combination thereof.
Speech synthesis processing module 740 is configured to synthesize speech outputs for presentation to the user. Speech synthesis processing module 740 synthesizes speech outputs based on text provided by the digital assistant. For example, the generated dialogue response is in the form of a text string. Speech synthesis processing module 740 converts the text string to an audible speech output. Speech synthesis processing module 740 uses any appropriate speech synthesis technique in order to generate speech outputs from text, including, but not limited, to concatenative synthesis, unit selection synthesis, diphone synthesis, domain-specific synthesis, formant synthesis, articulatory synthesis, hidden Markov model (HMM) based synthesis, and sinewave synthesis. In some examples, speech synthesis processing module 740 is configured to synthesize individual words based on phonemic strings corresponding to the words. For example, a phonemic string is associated with a word in the generated dialogue response. The phonemic string is stored in metadata associated with the word. Speech synthesis processing module 740 is configured to directly process the phonemic string in the metadata to synthesize the word in speech form.
In some examples, instead of (or in addition to) using speech synthesis processing module 740, speech synthesis is performed on a remote device (e.g., the server system 108), and the synthesized speech is sent to the user device for output to the user. For example, this can occur in some implementations where outputs for a digital assistant are generated at a server system. And because server systems generally have more processing power or resources than a user device, it is possible to obtain higher quality speech outputs than would be practical with client-side synthesis.
Additional details on digital assistants can be found in the U.S. Utility application Ser. No. 12/987,982, entitled “Intelligent Automated Assistant,” filed Jan. 10, 2011, and U.S. Utility application Ser. No. 13/251,088, entitled “Generating and Processing Task Items That Represent Tasks to Perform,” filed Sep. 30, 2011, the entire disclosures of which are incorporated herein by reference.
4. Exemplary Techniques for Initiating and Deactivating a Virtual Assistant
Electronic device 800 is in a dropped pose in
A dropped pose is a pose of a plurality of poses (e.g., dropped, raising, raised, and dropping) of an electronic device.
In the present example, because electronic device 800 is on the wrist of user 802 and the wrist of user 802 is lowered as shown, electronic device 800 is in a dropped pose. But as discussed, in some examples, a dropped pose of an electronic device is different from the illustrated dropped pose. For example, if user 802 is wearing device 800 on his wrist and user 802 is typing with his hands on a keyboard, device 800 is in a dropped pose. As another example, if user 802 is wearing device 800 on his wrist and user 802 is lying down with his wrist to the side (e.g., at hip level), device 800 is in a dropped pose. Accordingly, in some examples, device 800 is in a dropped pose if it is not being lifted towards or away from user 802's mouth and/or is positioned away from a user 802's mouth (e.g., positioned more than 0.5, 1, 1.5, or 2 feet away, positioned a length of user 802's arm away, etc.).
In some examples, as shown in
In
In some examples, while motion of a device is provided, the device detects input representing its motion, referred to herein as motion input. In some examples, the motion input is detected by one or more motion sensors of the device. The one or more motion sensors include any sensor(s) capable of detecting motion input, such as one or more accelerometers, one or more gyroscopes, one or more cameras, one or more proximity sensors, one or more ultrasonic sensors, one or more microwave motion sensors, and the like.
In some examples, detecting motion input of a device includes sampling the motion input using a first processor. In some examples, the first processor is a low-power processor of the device that is separate from a main processor of the device. Accordingly, in some examples, the first processor is able to continuously sample motion input (e.g., from one or more gyroscopes and/or accelerometers) without significantly compromising battery life. For example, the first processor continually samples motion input by writing the motion input into a memory buffer (e.g., a ring buffer).
In some examples, an electronic device samples audio input using one or more microphones of the electronic device. In some examples, the one or more microphones continuously sample audio input. In some examples, the one or more microphones sample audio input in accordance with being powered on according to the techniques described herein.
In some examples, audio input is sampled while detecting at least a portion of the motion input. For example, as shown in
In some examples, sampling audio input at a device includes sampling the audio input using the first processor of the device (e.g., the same processor that samples motion input of the device). For example, the first processor is able to continuously sample audio input (e.g., from one or more microphones) by writing audio input into a memory buffer (e.g., a ring buffer).
In some examples, sampling audio input at a device includes sampling the audio input using a second processor. In some examples, the second processor is the main processor (e.g., an application processor) of the device.
In some examples, the second processor is activated based on at least a portion of the motion input. In some examples, the portion of the motion input represents a wrist raise and/or a wrist rotation gesture. For example, as user 802 lifts device 800 from a dropped pose to a raising pose, user 802 performs a wrist raise gesture. The first processor of device 800 detects the wrist raise gesture and activates the second processor which then samples audio input 806 “What's the weather like in Palo Alto?” Exemplary techniques for determining a wrist raise and/or wrist rotation gesture are described in U.S. patent application Ser. No. 14/796,675, titled “Raise Gesture Detection in a Device,” filed Jul. 10, 2015, the content of which is hereby incorporated by reference in its entirety.
In some examples, activating the second processor includes causing the second processor to exit a low-power mode. In some examples, the second processor is in a low-power mode if it is constrained in the number or type of operations it can perform while operating in the low-power mode. In some examples, the second processor is in a low-power mode if it is operating with some of the processor circuitry disabled. In some examples, the second processor is in a low-power mode if it is operating at a reduced clock speed, reduced voltage, or reduced current relative to values when the second processor is operating in a normal mode. In some examples, the second processor must exit the low-power mode to provide certain functionality, such as launching or executing applications, operating a virtual assistant, and/or sampling audio input.
In some examples, as shown in
In some examples, powering on display 804 causes one or more microphones of device 800 to be powered on. Accordingly, in some examples, audio input 806 is sampled in accordance with powering on display 804. For example, after user 802 initiates a wrist raise gesture, device 800 powers on display 804 and one or more microphones and samples audio input 806 “What's the weather like in Palo Alto?” using the one or more microphones. Although powering on display 804 (and thus powering on or more microphones) is described in some examples as being based on motion input, in other examples, powering on display 804 is based on input other than, or in addition to, motion input. For example, powering on display 804 is based on tactile input (e.g., a touch on display 804, a press of a button on device 800) or audio input (e.g., receiving an audio input of sufficient amplitude), in some examples.
As an example of the above described techniques, the first processor of device 800 continuously samples motion input and detects motion input representing a wrist raise and/or wrist rotation gesture of device 800. Responsive to detecting the motion input representing the wrist raise and/or wrist rotation gesture, display 804 and one or more microphones of device 800 are powered on and the first processor activates the second processor. The second processor samples audio input (e.g., “What's the weather like in Palo Alto?”) using the one or more microphones. In this way, display 804, the one or more microphones, and the second processor of device 800 are powered off (or in a low-power mode) when user 802 is unlikely to issue voice commands (e.g., when device 800 is in a dropped pose), thus conserving battery power.
In
In some examples, sampled audio input does not include a spoken trigger. For example, audio input 806 “What's the weather like in Palo Alto?” does not include a spoken trigger for initiating a virtual assistant such as “Hey Siri.” Accordingly, in some examples, a user simply utters a request to a virtual assistant (e.g., while lifting a device, or shortly before or after lifting a device) to cause the virtual assistant to initiate and respond to the user request.
In some examples, determining whether to initiate a virtual assistant session includes determining whether one or more criteria for initiating a virtual assistant session are satisfied. In some examples, determining whether to initiate a virtual assistant session includes determining a probability of initiating a virtual assistant session. For example, if a criterion for initiating a virtual assistant session is satisfied, a probability of initiating a virtual assistant session is increased. If a criterion for initiating a virtual assistant is not satisfied, the probability of initiating a virtual assistant session is decreased. In some examples, a determination is made to initiate a virtual assistant session if the probability of initiating the virtual assistant session is greater than a predetermined threshold and/or if one or more criteria for initiating the virtual assistant session are satisfied.
In some examples, one or more of the above determinations are made by a motion model, by an audio model, and/or by a virtual assistant initiation model (e.g., a raise to speak model). For example, a motion model determines, based on motion input, a probability of initiating a virtual assistant session and makes a determination to initiate the virtual assistant session. An audio model determines, based on audio input, a probability of initiating a virtual assistant session and makes a determination to initiate the virtual assistant session. An exemplary motion model, an exemplary audio model, and exemplary virtual assistant initiation model are discussed with respect to
In some examples, respective probabilities and respective determinations to initiate the virtual assistant session determined by the motion model and the audio model are separate from each other. Accordingly, in some examples, a virtual assistant session is initiated responsive to both the motion model and the audio model determining to initiate the virtual assistant session. In other examples, a virtual assistant session is initiated responsive to one of the motion model or the audio model determining to initiate the virtual assistant session. In this way, two separate determinations (e.g., one based on motion input and one based on audio input) are used to initiate a virtual assistant session, in some examples.
Exemplary criteria for initiating a virtual assistant session are discussed below.
An exemplary criterion includes whether an electronic device transitions between two or more poses. An exemplary transition is from a raising pose to a raised pose, as illustrated in
Another exemplary transition is from a raised to a dropping pose. An exemplary dropping pose of an electronic device is illustrated in
An exemplary criterion includes whether the respective times of transitions between poses are within a predetermined duration. For example, a first time of a transition from a raising pose to a raised pose is determined (e.g., when device 800 first enters a raised pose from a raising pose) and a second time of a transition from a raised pose to a dropping pose is determined (e.g., when device first enters a dropping pose from a raised pose). If the first time is within a predetermined duration (e.g., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.5, 2 seconds) of the second time, a criterion is not satisfied. If the first time is not within the predetermined duration, a criterion is satisfied. In this way, if the duration between a raising to raised pose transition and a raised to dropped pose transition is relatively short, a virtual assistant session is not initiated. This, in some examples, prevents inadvertent virtual assistant initiation due to motions including raising to raised to dropping pose transitions (e.g., running, arm exercises, etc.). It will be appreciated that comparing the respective times of transitions between any poses in this manner is consistent with the teachings herein.
Exemplary criteria also include respective durations of poses. For example, a duration of a raised pose is determined and compared to a predetermined duration (e.g., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.5, 2 seconds). In some examples, if the raised pose duration exceeds the predetermined duration, a criterion is satisfied. In some examples, if the raise posed duration does not exceed the predetermined duration, a criterion is not satisfied. In this way, the stability of the electronic device in a raised pose is considered when determining whether to initiate the virtual assistant session (e.g., when user 802 raises device 800 near his or her mouth and provides a request, the device is likely to remain in the raised pose for greater than some predetermined duration). Although the above discussion focuses on the duration of a raised pose, in some examples, the duration of any pose (e.g., dropped, raising, raised, dropping) is considered in a similar manner.
Exemplary criteria also include respective durations of transitions between poses. For example, a duration of a raising to raised pose transition is determined and compared to a predetermined duration (e.g., 1 second, 2 seconds, 5 seconds). If the duration of a raising to raised pose transition exceeds the predetermined duration, a criterion is not satisfied. If the duration of a raising to raised pose transition does not exceed the predetermined duration, a criterion is satisfied. Comparing the duration of a raising to raised pose transition to a predetermined duration in this manner can ensure that raising to raised pose transitions of an appropriate duration initiate the virtual assistant session. For example, if user 802 takes too long (e.g., 3, 4, 5, 6, 7, 8, 9, 10 seconds) to raise device 800 from the raising to raised pose, the user may be unlikely to be raising device 800 to issue a command, so the virtual assistant session is not initiated. Although the above discussion focuses on the duration of a raising to raised pose transition, in some examples, the duration of the transition between any two poses (e.g., dropped to raising, dropped to raised) is considered in an analogous manner.
An exemplary criterion also includes whether audio input includes human speech. For example, a determination is made as to whether the audio input includes human speech. If the audio input includes human speech, a criterion is satisfied. If the audio input does not include human speech, a criterion is not satisfied. In this way, inadvertent initiation of a virtual assistant session responsive to receiving audio input that does not include human speech (e.g., background noise) may be avoided. For example, if user 802 is in a noisy environment and lifts device 800 from a raising to raised pose (e.g., to check the time, respond to a message, etc.) without issuing a spoken request to device 800, a virtual assistant may not inadvertently initiate.
In some examples, determining whether audio input includes human speech includes performing frequency domain analysis of the audio input. For example, a spectrogram of the audio input is generated (e.g., using a Fourier Transform) and the spectral components of the audio input are analyzed to determine whether at least a portion of the audio input corresponds to human speech. It will be appreciated that, in some examples, any suitable technique for determining whether an audio input includes human speech is employed consistent with the teachings herein.
An exemplary criterion includes whether audio input includes human speech spoken by an authorized user of a device. In some examples, the authorized user is a user that is closely associated with the device sampling the audio input (e.g., the owner or predominant user of the device). In some examples, the authorized user is one of a limited set of enrolled users sharing the user device. A determination whether audio input includes human speech spoken by an authorized user is made. If the audio input includes human speech spoken by an authorized user of the device, a criterion is satisfied. If the audio input does not include human speech spoken by an authorized user of the device, a criterion is not satisfied. In this way, inadvertent activation of a virtual assistant session responsive to receiving audio input that does not include speech of an authorized user may be prevented. For example, if authorized user 802 is in an environment including background speech (e.g., babble noise) and lifts device 800 from a raising to raised pose (e.g., to check the time, respond to a message, etc.) without issuing a spoken request to device 800, a virtual assistant may not inadvertently initiate.
In some examples, determining whether audio input includes human speech spoken by an authorized user includes performing speech authentication. For example, the features of the audio input are compared to those of one or more speech models (e.g., Gaussian Mixture models, Hidden Markov models, neural networks, and the like). In some examples, the one or more speech models include a predetermined universal background model and a predetermined speech model of an authorized user. In some examples, the predetermined speech model of the authorized user is based on speech of the authorized user (e.g., speech of the authorized user received before sampling the audio input). It will be appreciated that, in some examples, any suitable technique for determining whether an audio input includes human speech spoken by an authorized user is employed consistent with the teachings herein.
An exemplary criterion includes a direction associated with audio input. For example, a direction associated with the audio input is determined. If the audio input has a direction towards the device (e.g., towards one or more microphones or towards a screen of the device), a criterion is satisfied. If the audio input has a direction away from the device (e.g., away from one or more microphones or away from a screen of the device), a criterion is not satisfied. In this way, inadvertent virtual assistant activation responsive to receiving audio input not directed towards the device (e.g., from originating another person standing next to user 802) may be prevented. It will be appreciated that, in some examples, any suitable technique for sound localization (e.g., beamforming) are employed consistent with the teachings herein.
An exemplary criterion includes a proximity of audio input from an electronic device. For example, a proximity of the audio input from an electronic device is determined and compared to a threshold proximity (e.g., 2 cm, 5 cm, 10 cm, 15 cm, or 20 cm). If the determined proximity is less than or equal to the threshold proximity, a criterion is satisfied. If the determined proximity is greater than the threshold proximity, a criterion is not satisfied. In this way, audio input originating from a source close to the device is considered as an indication to initiate the virtual assistant session. Audio input originating from a source not close to the device (e.g., from another person not within the threshold proximity of device 800) may not inadvertently initiate a virtual assistant session.
In some examples, determining proximity of the audio input from an electronic device is based on the amplitude of the audio input. In some examples, the amplitude of the audio input within a particular spectral range (e.g., a frequency range characteristic of human speech) is analyzed in determining the proximity of the audio input. In some examples, determining proximity of the audio input is, alternatively or additionally, based on other acoustic features of the audio input. For example, audio inputs originating from further sources often include a greater degree of echoing than audio inputs originating from closer sources. Accordingly, in some examples, audio inputs are analyzed to determine a degree of echoing to determine the proximity of the audio input.
An exemplary criterion includes a duration of audio input. For example, a duration of audio input is determined and compared to a predetermined duration (e.g., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.5, 2 seconds). In some examples, the duration of audio input is the duration during which the audio input is within a predetermined amplitude and/or frequency range (e.g., an amplitude and/or frequency range typical of human speech and sounds). If a duration of audio input is greater than the predetermined duration, a criterion is satisfied. If a duration of audio input is less than the predetermined duration, a criterion is not satisfied. In this way, too short of an audio input (e.g., that is unlikely to be a spoken request) does not initiate the virtual assistant session in some examples. For example, if user 802 lifts device 800 from a raising to raised pose to cover his or her mouth while coughing or sneezing (e.g., a short audio input), the cough or sneeze does not inadvertently initiate a virtual assistant.
An exemplary criterion is based on the respective times associated with sampling audio input and a transition between poses. For example, an exemplary criterion includes whether the respective times associated with sampling audio input and a transition between poses are within a predetermined duration. For example, the audio input is sampled at a first time (e.g., the time device 800 samples the input 806 “What's the weather like in Palo Alto?”) and the transition of the device from a raising to raised pose has a second time (e.g., the time user 802 lifts device 800 into the pose shown in
An exemplary criterion includes whether respective times of separate determinations to initiate the virtual assistant session are within a predetermined duration. For example, separate first and second determinations to initiate the virtual assistant session are respectively made at first and second times (e.g., respective times at which one or more criteria for initiating a virtual assistant session are satisfied). In some examples, a motion model makes the first determination and the audio model makes the second determination (recall that motion and audio models make respective determinations to initiate the virtual assistant session, in some examples). If the first time is within a predetermined duration (e.g., 0.5 seconds, 1 second, 1.5 seconds, 2 seconds, 5 seconds) of the second time, a criterion is satisfied. If the first time is not within the predetermined duration of the second time, a criterion is not satisfied.
Accordingly, in some examples, initiating a virtual assistant session is based on detecting synchronous or approximately synchronous motion and audio input appropriate for initiating the virtual assistant session. Thus, inadvertent initiation based on motion input alone and/or audio input alone may be prevented, despite that the motion input and/or audio input may each be appropriate for (e.g., respectively satisfy one or more criteria for) initiating the virtual assistant. Providing virtual assistant activation in this manner thus provides an efficient and intuitive way of initiating a virtual assistant session while reducing the chance of false initiation. For example, as shown in
In some examples, determining whether to initiate a virtual assistant session is based on input other than, or in addition to, the above described motion and audio input. For example, it is determined whether these other inputs satisfy one or more criteria for initiating a virtual assistant session. Analogous to the techniques discussed above, in some examples, a probability for initiating a virtual assistant session is determined based on whether the one or more criteria are satisfied. In some examples, a determination to initiate a virtual assistant session is made if the probability exceeds a threshold. In some examples, a model for initiating a virtual assistant session (e.g., a raise to speak model) makes one or more of these determinations. An exemplary model for initiating a virtual assistant session is discussed below with respect to
In some examples, an electronic device includes one or more light sensors (e.g., an ambient light sensor, a camera, and the like) on a front and/or on a back surface of the device. An amount of light detected by the light sensor is compared to a threshold. If the amount of light is greater than a threshold, a criterion for initiating the virtual assistant is not satisfied. If the amount of light is less than a threshold, a criterion for initiating the virtual assistant is satisfied. For example, when user 802 lifts device 800 to a raised pose to speak to it, a front surface (e.g., the surface of display 804) of device 800 is closer to the user's lips. Accordingly, a front facing light sensor of device 800 detects a lesser amount of light (e.g., less than the predetermined threshold) compared to if device 800 were in a dropped pose (e.g.,
In some examples, an electronic device includes one or more color sensors (e.g., a camera, any suitable RGB color sensor) on a front and/or on a back surface of the device. Color(s) detected by the one or more color sensors are compared to predetermined color(s) (e.g., colors of human skin, human lips, and the like). If the detected colors match the predetermined colors (e.g., within a predetermined wavelength), a criterion for initiating a virtual assistant is satisfied. If the detected colors do not match the predetermined colors, a criterion for initiating a virtual assistant session is not satisfied. For example, when user 802 lifts device 800 to a raised pose to speak to it, a front surface of device 800 is close to the user's lips. Accordingly, a front facing color sensor of device 800 detects color(s) that match the predetermined color(s). In this way, detection of colors indicative of human skin and/or lips is considered as an indication to initiate a virtual assistant session.
In some examples, an electronic device includes one or more cameras on a front and/or on a back surface of the device. Facial characteristics (e.g., chin shape, mouth shape, face shape, etc.) detected by the one or more cameras are compared to predetermined facial characteristics (e.g., human facial characteristics, the facial characteristics of an authorized user). If the detected facial characteristics match the predetermined facial characteristics, a criterion for initiating a virtual assistant is satisfied. If the detected facial characteristics do not match the predetermined facial characteristics, a criterion for initiating the virtual assistant is not satisfied. For example, when user 802 lifts device 800 to a raised pose to speak to it, a front surface of device 800 points towards the user's face. Accordingly, a front facing camera of device 800 detects facial characteristics that match predetermined facial characteristics.
In some examples, one or more microphones of an electronic device emit signals (e.g., ultrasonic signals) and the one or more microphones detect signals reflected based on the emitted signals. In some examples, the detected signals are used to determine whether one or more criteria for initiating a virtual assistant are satisfied. For example, if the detected signals indicate an object (e.g., a human face, human lips) within a threshold proximity (e.g., 2 cm, 5 cm, 10 cm, 20 cm) of the device, a criterion is satisfied. If the detected signals indicate an object not within the threshold proximity of the device, a criterion is not satisfied. In this way, detection of proximal objects is considered as an indication to initiate a virtual assistant session.
In some examples, an electronic device includes one or more proximity sensors on a front and/or on a back surface of the device. The one or more proximity sensors include any suitable proximity sensor (e.g., capacitive, photoelectric, inductive, time-of-flight, etc.), for instance. Inputs detected by the one or more proximity sensors are analyzed to determine whether they represent an indication to initiate a virtual assistant session. For example, if input from one or more proximity sensors indicates that the device is approaching an object (e.g., a user's face/mouth), a criterion is satisfied. If input from one or more proximity sensors indicates that the device is moving away from an object, a criterion is not satisfied. As another example, if input from one or more proximity sensors indicates that the device is within a threshold proximity (e.g., 2 cm, 5 cm, 10 cm, 20 cm) of an object, a criterion is satisfied.
In some examples, virtual assistant interaction context (e.g., the context of a user (e.g., sleeping, in a car, etc.) and information about a previous virtual assistant state) is considered in determining whether to initiate a virtual assistant. Virtual assistant interaction context provides additional device and/or user information which may be helpful in determining whether to initiate a virtual assistant session. As an example, if a user is sleeping (e.g., as determined by time of day, motion inputs, user indication, etc.), a virtual assistant session is less readily initiated compared to if the user were awake.
Exemplary virtual assistant interaction contexts are discussed below. In some examples, based on virtual assistant interaction context, a probability of initiating a virtual assistant session is determined. In some examples, any of these determinations are made by a model for initiating a virtual assistant (e.g., a raise to speak model).
An exemplary virtual assistant interaction context includes a time at which the virtual assistant session was previously initiated. In some examples, if the time at which the virtual assistant session was previously initiated is within a threshold duration (e.g., 30 seconds, 1 minute, 5 minutes, etc.) of a current time, a probability of initiating the virtual assistant session is increased. In some examples, if the time at which the virtual assistant session was previously initiated is within the threshold duration of a current time, a probability of initiating the virtual assistant session is decreased.
An exemplary virtual assistant interaction context includes whether previous sensor inputs (e.g., any of the sensor inputs discussed above) represent a near-miss of initiating a virtual assistant session or represent a near-initiation of a virtual assistant session. For example, based on one or more previous sensor inputs, a probability of initiating the virtual assistant session is determined. If the probability exceeds a threshold probability for initiating the virtual assistant session by less than a first amount (e.g., a small amount), the one or more previous sensor inputs represent a near-miss. If the probability is less than the threshold probability for initiating the virtual assistant session by at most a second amount (e.g., a small amount), the one or more previous sensor inputs represent a near-initiation.
An exemplary virtual assistant interaction context includes a time associated with a near-miss of initiating the virtual assistant session. In some examples, a time associated with a near-miss of initiating the virtual assistant session is compared to a current time (e.g., a time at which current input is sampled). The time associated with the near-miss is the time at which the one or more previous sensor inputs (representing the near-miss) were sampled or the time at which the probability associated with the one or more previous sensor inputs was determined, for instance. In some examples, if the time associated with a near-miss of initiating the virtual assistant session is within a predetermined duration (e.g., 5 seconds, 15 seconds, 30 seconds, 1 minute, 5 minutes, etc.) of a current time, a probability for initiating a virtual assistant session is increased. In this way, recent previous input(s) that nearly missed initiating a virtual assistant session indicate that the virtual assistant session should be more readily initiated based on current input, in some examples. In other examples, if the time associated with a near miss of initiating the virtual assistant session is within the predetermined duration of a current time, a probability for initiating a virtual assistant session is decreased. It will be appreciated that comparing the time associated with a near-initiation of the virtual assistant session to a current time in an analogous manner (e.g., to increase or decrease a probability of initiating a virtual assistant) is consistent with the teachings herein.
An exemplary virtual assistant interaction context includes a domain associated with a previous input (e.g., text or spoken input) provided to the virtual assistant. For example, it is determined whether a domain associated with the previous input is a predetermined type of domain. If the domain is the predetermined type of domain, a probability of initiating a virtual assistant session is increased. If the domain is not a predetermined type of domain, a probability of initiating the virtual assistant session is decreased (or unchanged).
In some examples, the predetermined type of domain is a domain that is configured to enable multi-turn virtual assistant interaction. A multi-turn virtual assistant interaction includes a virtual assistant interaction where input is provided to the virtual assistant and the virtual assistant responds to the input by eliciting further input, for instance. For example, the “send a message” domain is configured to enable responding “send a message to who?” (e.g., eliciting further user input) responsive to the provided input “send a message.” Accordingly, if a domain associated with a previous input is configured to enable multi-turn interaction, it may be likely that the user intends to initiate a virtual assistant session to provide further input. Thus, in some examples, if a domain associated with a previous input is configured to enable multi-turn interaction, a probability of initiating a virtual assistant session is increased.
An exemplary virtual assistant interaction context includes a previous state of the virtual assistant. A state of a virtual assistant represents whether the virtual assistant is awaiting further input, for instance. For example, if the virtual assistant was previously awaiting a response to the output “send a message to who?” the previous state is a first state. For example, if the virtual assistant was not previously awaiting a response (e.g., the virtual assistant provided an output such as “It's sunny today”), the previous state is a second state. In some examples, a previous state of the virtual assistant is determined. In some examples, if the previous state is the first state, a probability for initiating the virtual assistant is increased. In some examples, if the previous state is the second state, a probability for initiating the virtual assistant is decreased. In this way, if a previous virtual assistant state indicates that the virtual assistant is awaiting a response, a virtual assistant is more readily initiated, in some examples.
Exemplary virtual assistant interaction context includes contextual information (e.g., any of the contextual information discussed above). In some examples, an activity context of the device and/or user (e.g., whether the user is running, in a car, sleeping, at home, at work, traveling, in a movie theatre, in a meeting, etc.) is determined from the contextual information. Certain activity contexts may indicate that a user is more or less likely to initiate a virtual assistant. Accordingly, in some examples, based on the determined activity context, a probability of initiating a virtual assistant is increased or decreased. For example, if a user is determined to be in a meeting (e.g., based on a current time being within a meeting timeframe in a user's schedule), a probability of initiating a virtual assistant is decreased (e.g., because the user may not wish to be interrupted by an initiated virtual assistant during the meeting).
In some examples, in accordance with a determination to initiate a virtual assistant session, a virtual assistant session is initiated. For example, as shown in
In some examples, initiating a virtual assistant session includes providing audio output (e.g., device 800 provides an audio output to indicate that the virtual assistant is listening). In some examples, initiating a virtual assistant session includes providing haptic output (e.g., a buzz or a vibration). In some examples, initiating a virtual assistant session includes powering on processor circuitry (e.g., circuitry of a main processor) configured to operate a virtual assistant. In some examples, initiating a virtual assistant includes initiating one or more programs or modules (e.g., digital assistant module 726).
In some examples, in accordance with a determination not to initiate a virtual assistant session, a virtual assistant session is not initiated. For example, none of the above processes included in initiating a virtual assistant session is performed and a display screen is powered off and/or a user interface that is not a virtual assistant user interface (e.g., a user interface displaying a current time) is displayed (or remains displayed).
Accordingly, in some examples, in accordance with a determination to initiate a virtual assistant session, a user intent is determined based on audio input, a task is performed based on the user intent, and an output associated with the task is provided. For example, as shown in
In some examples, in accordance with initiating a virtual assistant session at a device, the motion input and the audio input from are provided to train a model for initiating a virtual assistant (e.g., a raise to speak model). In some examples, additional input (e.g., input other than motion input and audio input) and/or virtual assistant interaction context are provided to train the model (e.g., if it was determined to initiate the virtual assistant session based on the additional input and/or context). For example, the model is implemented on the device and/or on another electronic device (e.g., server system 108) and the model is trained using the motion input, the audio input, the additional input, and/or the virtual assistant interaction context as training data. Accordingly, inputs and/or virtual assistant interaction context that successfully initiated a virtual assistant session are used to train the model, increasing the likelihood of successful initiation and decreasing the likelihood of inadvertent initiation. Exemplary techniques for training a model for initiating a virtual assistant are discussed with respect to
In some examples, in accordance with not initiating a virtual assistant session at a device, the inputs and/or the virtual assistant interaction context are not provided to train a model for initiating a virtual assistant. For example, the inputs and/or the virtual assistant interaction context are discarded.
In some examples, providing the inputs and/or the virtual assistant interaction context to train a model for initiating a virtual assistant is performed in accordance with providing an output associated with the task. For example, after device 800 provides the audio output 808 “It's 70 degrees and sunny,” the motion input and audio input 806 are provided to train a model. Providing motion input and audio input in this manner can further ensure that appropriate inputs are used to train the model. For example, if user 802 inadvertently initiates the virtual assistant session, he may deactivate the virtual assistant session before device 800 provides output 808. Accordingly, if output 808 is provided, it is likely that the user intended to initiate the virtual assistant session. Thus, correct inputs for initiating a virtual assistant session are provided to train the model.
In some examples, providing the inputs and/or the virtual assistant interaction context to train a model for initiating a virtual assistant is performed in accordance with determining that the virtual assistant session is initiated for greater than a predetermined duration (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 seconds) (e.g., a virtual assistant user interface is displayed for greater than the predetermined duration). Providing inputs and/or context in this manner can further ensure that appropriate inputs and/or context are used to train the model. For example, if user 802 inadvertently initiates a virtual assistant session, he may deactivate the virtual assistant session before the predetermined duration elapses. Accordingly, if the predetermined duration elapses, it is likely that user 802 intended to initiate the virtual assistant session. Thus, correct inputs and/or context for initiating a virtual assistant session are provided to train the model. Exemplary techniques for deactivating a virtual assistant session are described with respect to
In some examples, the inputs and/or the virtual assistant interaction context that inadvertently initiate a virtual assistant session (e.g., incorrect inputs and/or context) are provided to train a model for initiating a virtual assistant. For example, incorrect inputs and/or context train the model to not inadvertently initiate the virtual assistant session based on future incorrect inputs and/or context. Accordingly in some examples, providing the inputs and/or the virtual assistant interaction context to train a model for initiating a virtual assistant session is performed in accordance with a determination that the inputs and/or context are incorrect for initiating a virtual assistant session.
In some examples, determining whether one or more inputs and/or context are incorrect for initiating a virtual assistant session includes determining whether to deactivate the virtual assistant session based on the one or more inputs and/or context. For example, if it is determined based on the motion and/or audio input to deactivate a virtual assistant session according to the techniques discussed below, a determination is made that the motion input and/or audio input are incorrect for initiating a virtual assistant session.
In some examples, determining whether one or more inputs and/or context are incorrect for initiating a virtual assistant session includes determining (e.g., by digital assistant module 726) whether user feedback indicates dissatisfaction with the initiation of the virtual assistant. In some examples, user feedback indicating dissatisfaction with the virtual assistant session includes verbal and/or textual user input provided to the initiated virtual assistant (e.g., user input such as “Not now,” “Why are you here?,” “Go away,” and the like). In some examples, user feedback indicating dissatisfaction with the initiation of the virtual assistant includes user input deactivating the virtual assistant session provided after the virtual assistant session is initiated (e.g., provided shortly (e.g., 0.5, 1, 2, 3, 4, 5 seconds) after). User input deactivating the virtual assistant session includes a button press, a spoken input, and/or motion input, in some examples.
In some examples, deactivating a virtual assistant session includes ceasing display of a user interface associated with the virtual assistant session. In some examples, deactivating a virtual assistant session incudes forgoing responding to audio input. In some examples, deactivating a virtual assistant session includes powering off a display. In some examples, deactivating a virtual assistant session includes providing audio and/or haptic output. In some examples, deactivating a virtual assistant session includes powering off processor circuitry (e.g., circuitry of a main processor) configured to operate a virtual assistant. In some examples, deactivating a virtual assistant includes deactivating one or more programs or modules (e.g., digital assistant module 726).
In some examples, deactivating a virtual assistant session and/or determining whether to deactivate a virtual assistant session is performed before displaying a user interface associated with the virtual assistant session. In other examples, deactivating a virtual assistant session and/or determining whether to deactivate a virtual assistant session is performed after displaying a user interface associated with the virtual assistant session.
In some examples, determining whether to deactivate a virtual assistant session includes determining whether one or more criteria for deactivating a virtual assistant session are satisfied. In some examples, determining whether to deactivate a virtual assistant session includes determining a probability of deactivating a virtual assistant session. For example, if a criterion for deactivating a virtual assistant session is satisfied, a probability of deactivating a virtual assistant session is increased. If a criterion for deactivating a virtual assistant is not satisfied, the probability of deactivating a virtual assistant session is decreased. In some examples, a determination is made to deactivate a virtual assistant session if the probability of deactivating the virtual assistant session is greater than a predetermined threshold and/or if one or more criteria for deactivating the virtual assistant session are satisfied. In some examples, any one of the above determinations is made by one or more models for deactivating a virtual assistant session (e.g., an audio model, a motion model, a raise to speak model).
Additionally, in some examples, any of the techniques for deactivating a virtual assistant session discussed below are applied in an analogous manner in determining whether to initiate a virtual assistant session. For example, if one or more of the below described criteria for deactivating a virtual assistant session are satisfied (e.g., as determined by a motion model, an audio model, and/or a raise to speak model), a virtual assistant is not initiated and/or a probability of initiating a virtual assistant session is decreased. Similarly, in some examples, any of the techniques discussed above for determining not to initiate a virtual assistant session are applied in an analogous manner in determining whether to deactivate a virtual assistant session. For example, if one or more of the above described criteria for initiating a virtual assistant session are not satisfied, a virtual assistant session is deactivated and/or a probability of deactivating a virtual assistant session is increased.
Exemplary criteria for deactivating a virtual assistant session are now discussed.
An exemplary criterion includes whether the content of audio input indicates that the audio input is directed to a device. Audio inputs unlikely directed to the device should deactivate the virtual assistant session, in some examples. Exemplary audio content analyzed (e.g., by device 800 and/or by server system 108) to determine whether audio input is directed to a device includes linguistic content (e.g., sentence structure, sentence length, whether word(s) and/or keyword(s) are included, sentence type (e.g., question or statement), and the like) and whether the device can perform a task specified in the audio input. For example, based on its linguistic content, the audio input “Tina's vacation is this Saturday and she's going to Hawaii with her husband to see volcanoes and dolphins at the beach . . . ,” is determined not to be likely directed to the device. In accordance with a determination that the audio input is not likely directed to the device, a criterion for deactivating a virtual assistant session is satisfied. Accordingly, in some examples, the virtual assistant session is deactivated (e.g., server system 108 sends a control signal to device 800 instructing device 800 to deactivate a virtual assistant session).
In some examples, in accordance with a determination that the audio input is likely directed to the device, a criterion for deactivating a virtual assistant session is not satisfied. Accordingly, in some examples, a virtual assistant session is not deactivated (e.g., a virtual assistant user interface is displayed, the device samples audio input, and the like).
An exemplary criterion includes whether an intent can be determined (e.g., with sufficient confidence by device 800 and/or by sever system 108) for audio input. For instance, certain audio inputs (e.g., coughs, sneezes, long sentences, short expressions such as “uhm,” yay, “and the like) cannot have respective intents determined or respective confidence scores of the determined respective intents are low (e.g., less than a predetermined threshold). Accordingly, in some examples, these audio inputs should deactivate a virtual assistant session. Thus, in some examples, if an intent for an audio input cannot be determined, a criterion is satisfied. In some examples, if a confidence score of an intent for the audio input is low, a criterion is satisfied. In some examples, if an intent for an audio input is determined, a criterion is not satisfied. In some examples, if a confidence score of an intent for the audio input is high (e.g., greater than a predetermined threshold), a criterion is not satisfied.
An exemplary criterion includes a duration of the audio input. For example, short audio inputs (e.g., a cough, a sneeze, short expressions such as “uhm,” “yay,” and the like) are unlikely to be directed to a device, so such audio inputs should deactivate a virtual assistant session. Accordingly, in some examples, an audio input duration is compared to a predetermined duration (e.g., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 seconds). If the audio input duration is less than the predetermined duration, a criterion is satisfied. If the audio input duration is greater than the predetermined duration, a criterion is not satisfied.
Accordingly, an exemplary criterion for deactivating a virtual assistant session includes whether motion input indicates a transition from a raised pose to a dropping pose. In some examples, if motion input indicates a transition from a raised pose to a dropping pose, a criterion is satisfied. If motion input includes does not indicate a transition from a raised pose to a dropping pose, a criterion is not satisfied. In this way, dropping a device from a raised pose to a dropping pose deactivates an initiated virtual assistant session, in some examples.
An exemplary criterion includes whether a time associated with dropping a device from a raised pose is prior to a time associated with end of speech in audio input. For example, a first time associated with the end of speech in the audio input “There is an important meeting on Saturday” is determined. A second time associated with the transition from a raised pose to a dropping pose is determined. The first time is compared to the second time. In some examples, if the second time is before the first time, a criterion is satisfied. In some examples, if the second time is after the first time, a criterion is not satisfied. In this way, if user 802 drops his wrist before he finishes speaking, the speech is not likely directed to device 800, so a virtual assistant session is deactivated. Exemplary techniques for determining the end of speech (e.g., endpointing) in audio input are described in U.S. patent application Ser. No. 14/846,667, entitled “Context-Based Endpoint Detection,” filed Sep. 4, 2015, and in U.S. patent application Ser. No. 14/701,147, entitled “Robust End-Pointing of Speech Signals Using Speaker Recognition,” filed Apr. 30, 2015, the contents of which are hereby incorporated by reference in their entireties.
In some examples, a transition from a dropped pose to a raising pose is used to determine an endpoint of speech in audio input. In particular, in some examples, the time associated with the device transition from a raised pose to a dropped pose is used to determine an endpoint of speech. In some examples, the endpointed speech input is then processed to determine a user intent and a task is performed based on the determined user intent according to the techniques discussed above with respect to
5. Exemplary Techniques for Transmitting a Communication
In some examples, audio input and motion input are used to invoke device functions other than initiating a virtual assistant. Such functions include, for example, answering a phone call and opening a communication channel in a communication session (e.g., a “walkie-talkie” session) with an external device. For example, if the device is engaged in a communication session with one or more other (e.g., external) devices, a user can raise the device near his or her mouth as described above and provide audio input. It is determined that the motion input and the audio input are appropriate for initiating communication with one or more other devices, and the audio input is provided (e.g., transmitted) to the one or more other devices. In this way, to communicate with other user(s)/device(s) engaged in a communication session with the user's device, the user simply raises the device near his or her mouth and provides audio input that is transmitted to the one or more other devices.
Because in some examples, a same or similar combination of motion input and audio input can invoke multiple device functions (e.g., initiating a virtual assistant, answering a phone call), it may be important to determine which device function to invoke. Otherwise, audio input and motion input a user intends to initiate a virtual assistant session may inadvertently cause another function to be performed (e.g., cause another user's device to output the audio), for instance. Accordingly, in some examples, a context of the device is used to determine which device function audio input and motion input should invoke. In this manner, if the determined context is appropriate for initiating a virtual assistant session, then the audio input and motion input are considered in determining whether to initiate a virtual assistant session. If the determined context is appropriate for answering a phone call, then the audio input and the motion input are considered in determining whether to answer the phone call, for instance.
Accordingly, in some examples, a context of the electronic device is determined. It is determined whether the context is a predetermined type of context representing communication with an external electronic device. In some examples, if the context is the predetermined type of context, then sampled audio input and detected motion input are not used to determine whether to initiate a virtual assistant session, but rather used to determine whether to perform another device function. In some examples, if the context is not the predetermined type of context, then sampled audio input and detected motion input are used to determine whether to initiate a virtual assistant session in accordance with the above described techniques.
The context of a device is the predetermined type of context representing communication with an external electronic device when one or more of the below conditions are satisfied, for instance.
An exemplary condition includes that the device is currently receiving a communication (e.g., a phone call, a text message, an email, a voice-mail, etc.) from an external electronic device. If the device is currently receiving the communication, a condition is satisfied. If the device is not currently receiving the communication, a condition is not satisfied. In this way, if a device is currently receiving a phone call and a user provides motion and audio input, the audio input and the motion input are considered in determining whether to answer the phone call. The audio input and the motion input are not considered in determining whether to initiate a virtual assistant session, for instance. If it is determined to answer the phone call, the audio input is transmitted to an external device (e.g., the device of the caller), for instance. In this manner, a user can simply raise his or her device and provide audio to both answer the phone call and to cause the audio to be transmitted to the caller's device.
An exemplary condition includes that the device has recently received a communication (e.g., a phone call, a text message, and email, a voice-mail, etc.) from an external electronic device. For example, when a device receives a communication, a condition is satisfied for a short duration (e.g., 1, 2, 3, 4, 5, 10, 15, 30 seconds) after the receipt of the communication. After the short duration expires, a condition is not satisfied. Accordingly, in some examples, if a user provides motion input and audio input shortly after receiving a message (e.g., a text or an email), the motion and audio input are considered in determining whether to respond to the message. The motion input and the audio input are not are considered in determining whether to initiate a virtual assistant session. In this way, if a user receives a message (e.g., text or email), the user can respond to the message by simply raising his or her device and providing audio input shortly after receiving the message. In some examples, the audio input is converted to text and the text is transmitted to the message sender's device.
An exemplary condition includes that the device is currently engaged in a communication session (e.g., a “walkie-talkie” session) with an external electronic device. If the device is currently engaged in the communication session, a condition is satisfied. If the device is not currently engaged in the communication session, a condition is not satisfied. Accordingly, if the device is currently engaged in a communication session (e.g., one or more programs configured to provide the communication session are executing) and the user provides motion input and audio input, the motion input and the audio input are considered in determining whether to transmit a communication. In this manner, a user can transmit audio to another device engaged in a communication session with the user's device simply by raising his or her device and speaking the audio.
In some examples, if the context is the predetermined type of context representing communication with an external electronic device, it is determined, based on sampled audio input and detected motion input, whether to transmit a communication associated with the audio input to the external electronic device. In some examples, determining whether to transmit the communication is performed analogously to the techniques discussed above for determining whether to initiate a virtual assistant session (e.g., based on audio input, motion input, and/or other input(s)). That is, if motion input, audio input, and/or other input(s) are appropriate for initiating a virtual assistant session, then the motion input, audio input, and/or other input(s) are similarly appropriate for causing transmission of the communication. In some examples, the audio input, motion input, and/or other input(s) are provided to a model (e.g., raise to speak model 924) and the model determines whether to transmit the communication.
In some examples, if the context is not the predetermined type of context representing communication with an external electronic device, it is determined, based on the audio input and the motion input, whether to initiate a virtual assistant session (e.g., according to the techniques discussed above).
In some examples, in accordance with a determination to transmit the communication associated with the audio input to the external electronic device, the communication associated with the audio input is transmitted to the external electronic device. In some examples, the communication associated with the audio input includes a signal representing the audio content of the audio input (e.g., if the user is answering a phone call or is engaged in a communication session). In some examples, the audio input is converted to text and the communication associated with the audio input includes a signal representing the text (e.g., if the user is responding to a text or email message).
In some examples, in accordance with a determination not to transmit the communication associated with the audio input to the external electronic device, transmitting the communication associated with the audio input to the external electronic device is forgone. For example, the sampled audio input and the detected motion input are discarded and/or no signal to be transmitted to the external electronic device is generated.
In some examples, after it is determined to transmit the communication associated with the audio input to the external electronic device, a user drops the device (e.g., from a raised pose, as shown in
As another example, dropping the device after the user has raised the device to respond to a message causes the message to be sent (e.g., transmitted). In this manner, to respond to a message, a user can raise the device and utter a response to the message. The user's response is converted to text, and dropping the device causes the message to be sent (e.g., to the sender of the message).
As yet another example, dropping the device when the device is engaged in a communication session (e.g., a walkie talkie session) with one or more external devices closes a communication channel with the one or more external devices (e.g., so sampled audio input at the device is not transmitted to the one or more external devices). In this manner, to send audio to another device engaged in a communication session with a user's device, the user raises his or her device and provides the audio. The user can then drop his or her device to stop transmitting audio to the another device.
In some examples, the audio input, the motion input, and/or the determined context are provided to train a model (e.g., raise to speak model 924). For example, audio input, motion input, and/or determined context that caused transmission of a communication are provided to train the model (e.g., to successfully cause transmission of communications based on future correct audio inputs, motion inputs, and/or context). As another example, audio input, motion input, and/or the determined context that did not cause transmission of a communication are provided to train the model (e.g., to prevent inadvertent transmission of communications based on future incorrect audio inputs, motion inputs, and/or context).
In some examples, the model (e.g., model 924) that determines whether to transmit a communication associated with the audio input to an external electronic device is the same model that determines whether to initiate a virtual assistant session. Accordingly, training such model allows the model to correctly determine whether to initiate a virtual assistant session or whether to perform another device function based on provided motion input, provided audio input, and context. As discussed, this may be desirable in examples where a same or similar combination of motion input and audio input can invoke multiple device functions.
In some examples, after it is determined to transmit the communication associated with audio input to an external electronic device, transmission of the communication is prevented. This allows users to prevent the inadvertent transmission of communications (e.g., if a device incorrectly determined to transmit a communication based on audio input and motion input). For, example, after it is determined to transmit the communication, a device outputs an indication (e.g., a visual indication, an audio indication, and/or a haptic indication) for a short duration (e.g., 1 second, 2 second, 3 seconds, etc.) indicating that the device will transmit the communication (e.g., after the short duration expires). During this short duration, if it is determined to prevent transmission of the communication, transmission of the communication is prevented (e.g., the communication is not transmitted and/or is discarded).
In some examples, determining whether to prevent transmission of the communication is performed analogously to the above described techniques for determining whether to deactivate a virtual assistant session. For example, a user drops the device (e.g., as shown in
The respective functions of each of the blocks of
System 900 includes input unit 902 coupled to processing unit 916 and to model unit 922. Input unit includes various input devices, such as one or more microphones 904, one or more accelerometers 906, and optionally, one or more gyroscopes 908, one or more cameras 910, one or more color sensors 912, one or more light sensors 914, and/or one or more proximity sensors 932. Inputs respectively collected by the input devices of input unit 902 are provided to processing unit 916 and to model unit 922.
Processing unit 916 includes one or more processors (e.g., any of processor(s) 220, 616, and 704). For example, processing unit 916 includes main processor 918 and low-power processor 920. In some examples, main processor 918 is an application processor that executes, for example, the operating system and applications, including the digital assistant of the device. In some examples, low-power processor 920 consumes less power than main processor 918 while operating, executes different computer-executable instructions than main processor 918, and/or is physically smaller than main processor 918.
In some examples, low-power processor 920 samples input from input unit 902. In some examples, low-power processor 920 is limited in the types of input it samples from input unit 902. For example, low-power processor 920 only samples motion input from one or more accelerometers 906 and/or from one or more gyroscopes 908. In some examples, low-power processor 920 is not limited in the types of input it samples from input unit 902. In some examples, low-power processor 920 activates main processor 918 based on the input from input unit 902, according to the above described examples.
In some examples, main processor 918 samples input from input unit 902. For example, main processor 918 samples audio input from one or more microphones 904 and/or motion input from one or more accelerometers 906 and/or from one or more gyroscopes 908. In some examples, main processor 918 samples audio input in accordance with being activated by low-power processor 920, according to the above described examples.
Model unit 922 includes raise to speak model 924, model training unit 930, and context unit 934. Raise to speak model 924 optionally includes motion model 926 and audio model 928. One of skill in the art will appreciate that although the functions of model unit 922 are described separately from processing unit 916, in some examples, the functions of model unit 922 are implemented by processing unit 916. For example, the functions of raise to speak model 924 and/or model training unit 930 are implemented as computer-executable instructions executing on main processor 918 and/or on low-power processor 920.
Context unit 934 determines a context of an electronic device. For example, context unit 934 determines whether a context of an electronic device is a predetermined type of context representing communication with an external electronic device according to the techniques discussed above.
Raise to speak model 924 receives input from input unit 902. In some examples, based on the context determined by context unit 934, raise to speak model 924 determines either (1) whether to initiate a virtual assistant session or (2) whether to transmit a communication associated with audio input to an external electronic device.
If the context of the electronic device is not the predetermined type of context, raise to speak model 924 determines whether to initiate or deactivate a virtual assistant session according to the techniques discussed above (e.g., based on input from input unit 902). In some examples, raise to speak model 924 determines and/or receives virtual assistant interaction context (e.g., from digital assistant system 700) and determines whether to initiate or deactivate a virtual assistant session based on the virtual assistant interaction context according to any of the techniques discussed above. Raise to speak model 924 optionally determines whether to initiate or deactivate a virtual assistant session based on respective determinations by motion model 926 and audio model 928 to initiate or deactivate a virtual assistant session. In some examples, raise to speak model 924 initiates and/or deactivates a virtual assistant session.
If the context of the electronic device is the predetermined type of context, raise to speak model 924 determines whether to transmit a communication associated with audio input to an external electronic device according to the techniques discussed above (e.g., based on input from input unit 902). In some examples, raise to speak model 924 causes the communication associated with the audio input to be transmitted to the external electronic device (e.g., using RF circuitry 208). In some examples, in accordance with determining that a device is dropping (or has been dropped), raise to speak model 924 causes one or more device functions to be performed according to the above discussed techniques. In some examples, raise to speak model 924 prevents transmission of the communication according to the above described techniques.
In some examples, raise to speak model 924 includes a neural network (e.g., recurrent neural network (RNN), convolutional neural network (CNN), and the like). In some examples, raise to speak model 924 includes a decision tree (e.g., a gradient boosted decision tree) and/or is implemented using any suitable machine learning technique.
In some examples, motion model 926 receives motion input and determines whether to perform a device function (e.g., initiating a virtual assistant session, transmitting a communication associated with audio input) based on the motion input according to the examples discussed above. For example, motion model 926 determines, using motion input, poses of an electronic device (e.g., dropped, raising, raised, dropping) and/or respective pose probabilities. In some examples, motion model 926 further determines, based on motion input, transitions between poses, the respective probabilities of such transitions, respective durations of poses and pose transitions, and/or respective times associated with transitions between poses.
In some examples, determining any of the above pose characteristics includes analyzing accelerometer and/or gyroscope data (e.g., over time). For example, a dropped pose (e.g.,
In some examples, motion model 926 includes a neural network (e.g., a recurrent neural network (RNN), a convolutional neural network (CNN), and the like). In some examples, the motion model includes decision tree (e.g., a gradient boosted decision tree) and/or is implemented using any suitable machine learning technique.
In some examples, motion model 926 is specific to a user (e.g., an authorized user) of an electronic device. For example, motion model 926 is optionally trained and/or optimized to accurately determine whether to perform a device function (e.g., initiating a virtual assistant session, transmitting a communication associated with audio input) based on motion input from a particular user. For example, if a user has long/short arms and/or has particular way of lifting a device to initiate a virtual assistant, motion model 926 accounts for these user specific characteristics in determining poses, pose transitions, pose durations, and the like. Accordingly, the present disclosure contemplates user enrollment to train and/or optimize motion model 926. For example, during enrollment, a user provides device motion(s) that he or she intends to cause performance of a function, and motion model 926 is trained based on the provided motion input.
In some examples, audio model 928 receives audio input (e.g., from one or more microphones 904) and determines whether to perform a device function (e.g., initiating a virtual assistant session, transmitting a communication associated with audio input) based on audio input according to the examples discussed above. For example, audio model 928 is configured to, inter alia, determine whether audio input includes human speech spoken by an authorized user and determine a proximity of audio input from a device according to the examples discussed above.
In some examples, audio model 928 includes neural network (e.g., a recurrent neural network (RNN), a convolutional neural network (CNN), and the like). In some examples, audio model 928 includes a decision tree (e.g., a gradient boosted decision tree) and/or is implemented using any suitable machine learning technique.
In some examples, audio model 928 is specific to a user (e.g., an authorized user) of an electronic device. For example, audio model 928 is optionally trained and/or optimized to accurately determine whether audio input should cause performance of a device function based on user specific audio characteristics. For example, if a user has a particular way (e.g., intonation, pitch, accent, speaking rate, etc.) of speaking to a device when he or she intends to initiate a virtual assistant session, audio model 928 accounts for these user specific characteristics in determining whether to initiate a virtual assistant session. Accordingly, the present disclosure contemplates user enrollment to train and/or optimize audio model 928. For example, during enrollment, a user provides audio input and audio model 928 is trained and/or optimized based on the audio input.
In some examples, enrollment based on audio input and motion input is performed concurrently, simultaneously, or in an otherwise overlapping manner. For example, a user concurrently provides audio and motion input that, in combination, the user would like to cause performance of a function. In some examples, raise to speak model 924 is trained using the motion and audio input (e.g., motion model 926 and audio model 928 are trained using the motion and audio input, respectively).
In some examples, training raise to speak model 924 (e.g., based on user specific preferences for performing a device function) includes adjusting one or more of the durations and/or one or more of the thresholds discussed above. For example, based on virtual assistant interaction context, device context, motion, audio, and/or other inputs, one or more of the durations and/or one or more of the thresholds discussed above are adjusted. In this manner, raise to speak model 924 is adapted (e.g., over time and/or during enrollment) to cause performance of device functions based on user specific preferences.
Model training unit 930 is configured to train raise to speak model 924 according to the above described examples. For example, model training unit receives input from input unit 902, and trains raise to speak model 924 using the received input as training data. For example, model training unit 930 is configured to train motion model 926 and/or audio model 928, e.g., based on respective motion and audio input as training data.
If raise to speak model 924 includes a neural network, training raise to speak model 924 includes determining a set of optimal weighting values for connections between nodes of the neural network, for instance. If raise to speak model 924 includes a gradient boosted tree model, training raise to speak model 924 includes minimizing a loss function, for instance. Training raise to speak model 924 in this way increases the chance that audio input, motion input, other input(s), device context and/or, virtual assistant interaction context cause the device to perform a correct function and decreases the chance of an incorrect function being performed. For example, in this manner, motion input and audio input intended to cause a transmission of a communication do not inadvertently initiate a virtual assistant session. As another example, in this manner, motion input and audio input intended to initiate a virtual assistant session correctly initiate a virtual assistant session and inappropriate motion and audio inputs do not inadvertently initiate the virtual assistant session.
As described below, process 1000 includes detecting input representing motion of an electronic device (e.g., a raising of the electronic device towards a user's mouth) and sampling, with a microphone of the electronic device, an audio input (e.g., a spoken user request such as “What's the weather like in Palo Alto?”). Process 1000 further includes determining, based on the audio input and the input representing motion of the electronic device, whether to initiate a virtual assistant session. In accordance with a determination to initiate the virtual assistant session, process 1000 includes initiating the virtual assistant session (e.g., a user interface associated with the virtual assistant session is displayed). In accordance with a determination not to initiate the virtual assistant session, process 1000 includes forgoing initiating the virtual assistant session.
Determining, based on audio input and input representing motion of the electronic device (e.g., “motion input”), whether to initiate a virtual assistant session provides a natural and efficient way of initiating and interacting with a virtual assistant. For example, to initiate a virtual assistant and to have a task performed by the virtual assistant based on a spoken request, a user simply raises a device near his or her mouth and utters the request at approximately the same time (e.g., without the request including a spoken trigger such as “Hey Siri”). Facilitating virtual assistant interaction in this manner enables efficient virtual assistant interaction (e.g., no spoken trigger is required before each interaction) and quick virtual assistant response to spoken requests. Additionally, using both audio input and motion input to initiate a virtual assistant session prevents inadvertent virtual assistant activation based on either audio input or motion input alone. This decreases the chance of inadvertently initiating a virtual assistant and saves battery power otherwise consumed due to operating a virtual assistant when not desired. In this manner, the user-device interface is made more efficient (e.g., by reducing the amount of audio input users provide, by decreasing virtual assistant response time, by preventing inadvertent virtual assistant activation), which additionally, reduces power usage and improves battery life of the device by enabling the user to use the device more quickly and efficiently.
At block 1002, at an electronic device with a microphone and a display (e.g., device 800), input representing motion of the electronic device is detected. In some examples, the electronic device is a computer, a speaker, a smart watch, a phone, or a combination thereof.
In some examples, the input representing motion of the electronic device is detected by one or more accelerometers (e.g., 906) of the electronic device. In some examples, the input representing motion of the electronic device is detected by one or more gyroscopes (e.g., 908) of the electronic device. In some examples, detecting the input representing motion of the electronic device includes sampling the input representing motion of the electronic device using a first processor (e.g., 920), as shown in optional block 1004.
In some examples, the motion of the electronic device includes a transition of the electronic device between two or more poses of a plurality of poses. In some examples, the plurality of poses includes a dropped pose (e.g., shown in
At optional block 1006, a display of the electronic device (e.g., 804) is powered on based on a first portion of the input representing motion of the electronic device. In some examples, the first portion of the input representing motion of the electronic device represents a wrist raise gesture and/or a wrist rotation gesture (e.g., shown in
At block 1008, an audio input (e.g., 806) is sampled with the microphone (e.g., 904) of the electronic device. In some examples, sampling the audio input is performed in accordance with powering on the display. In some examples, the audio input is sampled while detecting at least a second portion of the input representing motion of the electronic device (e.g., audio input 806 is sampled while detecting motion input in
In some examples, sampling the audio input includes sampling the audio input using the first processor, as shown in optional block 1010. In some examples, sampling the audio input includes sampling the audio input using a second processor (e.g., 918), as shown in optional block 1012. In some examples, the second processor consumes more power while operating than the first processor. In some examples, the second processor is activated based on at least a third portion of the input representing motion of the electronic device.
In some examples, the audio input (e.g., 806) does not include a spoken trigger (e.g., a spoken trigger for initiating a virtual assistant such as “Hey Siri”). Sampling an audio input not including a spoken trigger and using such audio input to initiate a virtual assistant session enables natural and efficient virtual assistant initiation/interaction. For example, a user may simply utter a request (that does not include “Hey Siri”) to a virtual assistant to initiate the virtual assistant and to have it respond to the request. In this manner, the user-device interface is made more efficient (e.g., by reducing the amount of audio input a user provides), which additionally, reduces power usage and improves battery life of the device by enabling the user to use the device more quickly and efficiently.
At block 1014, it is determined (e.g., by raise to speak model 924), based on the audio input and the input representing motion of the electronic device, whether to initiate a virtual assistant session. As discussed, the exemplary techniques for determining whether to initiate a virtual assistant session discussed below may allow for appropriate motion and/or audio input to initiate a virtual assistant session and prevent inappropriate motion and/or audio input from initiating a virtual assistant session. In this manner, device operability is improved and the man-machine interface is made more efficient (e.g., by accurately and efficiently initiating a virtual assistant session when requested, by preventing a user from being bothered by an inadvertently initiated virtual assistant session, by reducing or eliminating battery power otherwise consumed by an inadvertently initiated virtual assistant, and by reducing or eliminating user input provided to deactivate an inadvertently initiated virtual assistant session).
In some examples, determining whether to initiate the virtual assistant session includes determining whether to initiate the virtual assistant session based on a motion model (e.g., 926), as shown in optional block 1016. In some examples, the motion model includes a first neural network. In some examples, the motion model is specific to a first user of the electronic device. In some examples, it is determined, using the motion model, a probability of the transition of the electronic device between two or more poses of the plurality of poses, as shown in optional block 1018. In some examples, it is determined, using the motion model, a probability of a pose of the plurality of poses, as shown in optional block 1020.
In some examples, determining whether to initiate the virtual assistant session includes determining (e.g., by model 926), based on the input representing motion of the electronic device, whether the motion of the electronic device includes a transition from the raising pose to the raised pose (e.g., as shown in
In some examples, determining whether to initiate the virtual assistant session includes determining (e.g., by model 926) a duration of the transition from the raising pose to the raised pose, as shown in optional block 1024.
In some examples, the audio input is sampled at a first time, the transition from the raising pose to the raised pose is associated with a second time, and determining (e.g., by model 924) whether to initiate the virtual assistant session is based on the first time and the second time, as shown in optional block 1026. In this way, a virtual assistant session may be initiated by detecting approximately synchronous motion and audio input for appropriate for initiating a virtual assistant. Further, inadvertent virtual assistant initiation based on motion input alone or audio input alone may be prevented (e.g., if the motion input and the audio input are not received within some appropriate duration of each other, the inputs are unlikely to be for initiating a virtual assistant).
In some examples, determining whether to initiate the virtual assistant session includes determining (e.g., by model 926), based on the input representing motion of the electronic device, whether a duration of the raised pose (e.g., shown in
In some examples, determining whether to initiate the virtual assistant session includes determining (e.g., by model 926), based on the input representing motion of the electronic device, whether the motion of the electronic device includes a first transition from the raised pose to the dropping pose (e.g., shown in
In some examples, determining whether to initiate the virtual assistant session includes determining whether to initiate the virtual assistant session based on an audio model (e.g., 928), as shown in optional block 1032. In some examples, the audio model includes a second neural network. In some examples, the audio model is specific to a second user of the electronic device.
In some examples, determining whether to initiate the virtual session includes determining (e.g., by model 928) whether the audio input includes human speech as shown in optional block 1034. In some examples, determining whether the audio input includes human speech includes determining whether the audio input includes human speech spoken by an authorized user of the electronic device, as shown in optional block 1036. In this way, inadvertent virtual assistant initiation due to sampling noise (and/or speech from a non-authorized user) may be prevented. Further, accurate virtual assistant initiation based on receiving spoken requests from a human (and/or from an authorized user) may be provided.
In some examples, determining whether to initiate the virtual assistant session includes determining (e.g., by model 928) a direction associated with the audio input, as shown in optional block 1038. In this way, inadvertent virtual assistant initiation due to sampling audio not directed to the device configured to operate the virtual assistant may be prevented. Further, accurate virtual assistant initiation based on receiving audio input directed to the device may be provided.
In some examples, determining whether to initiate the virtual assistant session includes determining (e.g., by model 928) a proximity of the audio input from the electronic device, as shown in optional block 1040. In this way, inadvertent virtual assistant initiation due to sampling audio originating from a source too far from the device configured to operate the virtual assistant may be prevented (e.g., because in some examples, a user speaks to a device when the device is close to the user's mouth). Further, accurate virtual assistant initiation based on receiving audio input originating from a source close to the device may be provided.
In some examples, determining whether to initiate the virtual assistant session includes determining (e.g., by model 928), at a third time, whether to initiate the virtual assistant session based on the audio input, as shown in optional block 1042. In some examples, determining whether to initiate the virtual assistant session includes determining (e.g., by model 926), at a fourth time, whether to initiate the virtual assistant session based on the input representing motion of the electronic device, as shown in optional block 1044. In some examples, determining whether to initiate the virtual assistant session includes determining (e.g., by model 924) whether the third time and the fourth time are within a second predetermined duration, as shown in optional block 1046. In some examples, initiating the virtual assistant session is performed in accordance with a determination that the third time and the fourth time are within the second predetermined duration. Accordingly, in some examples, initiating a virtual assistant session is based on detecting synchronous or approximately synchronous motion and audio input appropriate for initiating the virtual assistant session. Thus, inadvertent initiation based on motion input alone and/or audio input alone may be prevented, despite that the motion input and/or audio input may each be appropriate for initiating the virtual assistant. Providing virtual assistant activation in this manner thus provides an efficient and intuitive way of initiating a virtual assistant session while reducing the chance of inadvertent initiation.
At block 1048, in accordance with a determination to initiate the virtual assistant session, the virtual assistant session is initiated (e.g., by model 924). In some examples, initiating the virtual assistant session includes displaying a first user interface associated with the virtual assistant (e.g., as shown on the rightmost display 804 in
At optional block 1056, a time associated with a third transition from the raised pose to the dropping pose is determined (e.g., by model 924). At optional block 1058, an endpoint of the audio input is determined based on the time associated with the third transition from the raised pose to the dropping pose (e.g., by model 924). Determining an endpoint of the audio input in this manner provides efficient and accurate way of endpointing audio inputs. For example, a user can simply drop his or her wrist (e.g., as shown in
At block 1060, in accordance with a determination not to initiate the virtual assistant session, initiation of the virtual assistant session is forgone.
At optional block 1062, in accordance with initiating the virtual assistant session, a user intent is determined based on the audio input (e.g., by the digital assistant shown in
At optional block 1068, in accordance with initiating the virtual assistant session and in accordance with not determining a user intent based on the audio input, providing an output responsive to the audio input is forgone.
At optional block 1070, in accordance with initiating the virtual assistant session, the virtual assistant session is deactivated (e.g., by model 924). In some examples, deactivating the virtual assistant session includes ceasing display of a second user interface associated with the digital assistant (e.g., as shown in
Deactivating a virtual assistant session allows cancellation of an inadvertently initiated virtual assistant session. Accordingly, battery and processing power otherwise consumed as a result of operating a virtual assistant when not desired is reduced. Additionally, deactivating a virtual assistant session when not desired improves device usability and operability (e.g., a user can continue using the device for other purposes without being bothered by an initiated virtual assistant). In this manner, the user-device interface is made more efficient (e.g., by preemptively deactivating a virtual assistant session when it is not desired), which additionally, reduces power usage and improves battery life of the device by enabling the user to use the device more quickly and efficiently.
Further, the below described techniques for deactivating a virtual assistant session may provide accurate and efficient determination of whether to deactivate the virtual assistant session and accurate and efficient deactivation of the virtual assistant session. Improving virtual assistant deactivation in the manner improves device usability and efficiency (e.g., by deactivating a virtual assistant session when not desired and by preventing a desired initiated virtual assistant session from being deactivated). Accordingly, the user-device interface is made more efficient (e.g., by accurately determining whether to deactivate the virtual assistant session), which additionally, reduces power usage and improves battery life of the device by enabling the user to use the device more quickly and efficiently.
At optional block 1076, in accordance with initiating the virtual assistant session, it is determined (e.g., by model 924), based on the input representing motion of the electronic device, whether the motion of the electronic device includes a second transition from the raised pose to the dropping pose (e.g., as shown in
At optional block 1078, a time associated with the second transition from the raised pose to the dropping pose is determined (e.g., by model 924).
At optional block 1080, a time associated with an end of speech in the audio input is determined (e.g., by model 924).
At optional block 1082, in accordance with a determination that the motion of the electronic device includes the second transition from the raised pose to the dropping pose, the virtual assistant session is deactivated (e.g., by model 924). In some examples, deactivating the virtual assistant session is further performed in accordance with a determination (e.g., by model 924) that the time associated with the second transition from the raised pose to the dropping pose is prior to the time associated with the end of speech in the audio input, as shown in optional block 1084.
At optional block 1086, in accordance with initiating the virtual assistant session, it is determined (e.g., by model 924) based on the linguistic content of the audio input (e.g., the audio input shown in
At optional block 1092, in accordance with initiating the virtual assistant session, it is determined whether a duration of the audio input is less than a threshold duration (e.g., by model 924). At optional block 1094, in accordance with a determination that the duration of the audio input is less than the threshold duration, the virtual assistant session is deactivated (e.g., by model 924).
The operations described above with reference to
As described below, process 1100 includes detecting input representing motion of an electronic device (e.g., a raising of the electronic device towards a user's mouth) and sampling, with a microphone of the electronic device, an audio input (e.g., “Hey Shawn, how are you?”). Process 1100 further includes determining a context of the electronic device (e.g., that the device is currently receiving a phone call (e.g., from a contact named “Shawn”)). In accordance with a determination that the context is a predetermined type of context representing communication with an external electronic device (e.g., the context of receiving a phone call), process 1110 includes determining, based on the audio input and the input representing motion of the electronic device, whether to transmit a communication associated with the audio input to the external electronic device. E.g., whether to transmit the audio “Hey Shawn, how are you?” to Shawn's device. In accordance with a determination to transmit the communication associated with the audio input to the external electronic device, process 1110 includes transmitting the communication associated with the audio input to the external electronic device. E.g., the device answers the phone call from Shawn and transmits “Hey Shawn, how are you?” to Shawn's device. In accordance with a determination not to transmit the communication associated with the audio input to the external electronic device, process 1100 includes forgoing transmitting the communication associated with the audio input to the external electronic device.
Determining a context of an electronic device allows the electronic device to accurately determine which device function to perform based on audio input and motion input. Specifically, in examples where a same or similar combination of motion input and audio input can cause performance of multiple device functions (e.g., the answering of a phone call, the initiation of a virtual assistant session), it may be important to distinguish (e.g., based on determined device context) which function of the device should be invoked based on audio input and motion input. Otherwise, for example, audio and motion input intended to answer a phone call may inadvertently initiate a virtual assistant session. Accordingly, using context information to determine which device function to perform enables a correct device function to be performed. In this manner, the user-device interface is made more efficient (e.g., by causing correct performance of a device function, by preventing incorrect performance of a device function, by reducing or eliminating user input to cancel an incorrectly performed device function) which additionally, reduces power usage and improves battery life of the device by enabling the user to use the device more quickly and efficiently.
Determining, based on audio input and input representing motion of the electronic device (e.g., “motion input”), whether to transmit a communication associated with the audio input to an external electronic device provides a natural and efficient way of communicating with external electronic devices. For example, to answer a phone call, a user simply raises a device near his or her mouth and provides a response to the phone call at approximately the same time (e.g., without providing tactile input to answer the phone call). Facilitating communication with external electronic devices in this manner enables quick communication with external devices (e.g., a user simply raises his or her device and speaks to cause a communication to be transmitted). Additionally, using both audio input and motion input to transmit a communication may prevent inadvertent communication transmission based on either audio input or motion input alone. This decreases the chance of transmitting communications when not desired. In this manner, the user-device interface is made more efficient (e.g., by enabling quick response to communications from external devices, by preventing inadvertent communication transmission), which additionally, reduces power usage and improves battery life of the device by enabling the user to use the device more quickly and efficiently.
At block 1102, input representing motion of an electronic device is detected (e.g., by input unit 902).
At block 1104, audio input is sampled with a microphone of the electronic device (e.g., by microphone(s) 904).
At block 1106, a context of the electronic device is determined (e.g., by context unit 934).
At block 1108, it is determined whether the context of the electronic device is a predetermined type of context representing communication with an external electronic device (e.g., by context unit 934).
At block 1110, in accordance with a determination that the context is the predetermined type of context representing communication with the external electronic device (block 1108, YES), it is determined (e.g. by model 924) based on the audio input and the input representing motion of the electronic device, whether to transmit a communication associated with the audio input to the external electronic device.
At block 1112, in accordance with a determination to transmit the communication associated with the audio input to the external electronic device, the communication associated with the audio input is transmitted to the external electronic device (e.g., by RF circuitry 208).
At block 1114, in accordance with a determination not to transmit the communication associated with the audio input to the external electronic device, transmitting the communication associated with the audio input to the external electronic device is forgone.
At block 1116, accordance with a determination that the context is not the predetermined type of context representing communication with the external electronic device (block 1108 NO), it is determined, based on the audio input and the input representing motion of the electronic device, whether to initiate a virtual assistant session (e.g., based on the techniques discussed above with respect to
The operations described above with reference to
In accordance with some implementations, a computer-readable storage medium (e.g., a non-transitory computer readable storage medium) is provided, the computer-readable storage medium storing one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for performing any of the methods or processes described herein.
In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises means for performing any of the methods or processes described herein.
In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises a processing unit configured to perform any of the methods or processes described herein.
In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises one or more processors and memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for performing any of the methods or processes described herein.
The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the techniques and their practical applications. Others skilled in the art are thereby enabled to best utilize the techniques and various embodiments with various modifications as are suited to the particular use contemplated.
Although the disclosure and examples have been fully described with reference to the accompanying drawings, it is to be noted that various changes and modifications will become apparent to those skilled in the art. Such changes and modifications are to be understood as being included within the scope of the disclosure and examples as defined by the claims.
As described above, one aspect of the present technology is the gathering and use of data available from various sources to improve a model for initiating/deactivating a virtual assistant. The present disclosure contemplates that in some instances, this gathered data may include personal information data that uniquely identifies or can be used to contact or locate a specific person. Such personal information data can include demographic data, location-based data, telephone numbers, email addresses, twitter IDs, home addresses, data or records relating to a user's health or level of fitness (e.g., vital signs measurements, medication information, exercise information), date of birth, or any other identifying or personal information.
The present disclosure recognizes that the use of such personal information data, in the present technology, can be used to the benefit of users. For example, the personal information data can be used to generate and/or a train a personalized model for initiating a virtual assistant. Accordingly, use of such personalized information data enables improved models for initiating a virtual assistant that are tailored to the way a specific user initiates a virtual assistant. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure. For instance, health and fitness data may be used to provide insights into a user's general wellness, or may be used as positive feedback to individuals using technology to pursue wellness goals.
The present disclosure contemplates that the entities responsible for the collection, analysis, disclosure, transfer, storage, or other use of such personal information data will comply with well-established privacy policies and/or privacy practices. In particular, such entities should implement and consistently use privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining personal information data private and secure. Such policies should be easily accessible by users, and should be updated as the collection and/or use of data changes. Personal information from users should be collected for legitimate and reasonable uses of the entity and not shared or sold outside of those legitimate uses. Further, such collection/sharing should occur after receiving the informed consent of the users. Additionally, such entities should consider taking any needed steps for safeguarding and securing access to such personal information data and ensuring that others with access to the personal information data adhere to their privacy policies and procedures. Further, such entities can subject themselves to evaluation by third parties to certify their adherence to widely accepted privacy policies and practices. In addition, policies and practices should be adapted for the particular types of personal information data being collected and/or accessed and adapted to applicable laws and standards, including jurisdiction-specific considerations. For instance, in the US, collection of or access to certain health data may be governed by federal and/or state laws, such as the Health Insurance Portability and Accountability Act (HIPAA); whereas health data in other countries may be subject to other regulations and policies and should be handled accordingly. Hence different privacy practices should be maintained for different personal data types in each country.
Despite the foregoing, the present disclosure also contemplates embodiments in which users selectively block the use of, or access to, personal information data. That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, in the case of collecting user specific motion inputs and audio inputs for initiating a virtual assistant (e.g., during an enrollment process), the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services or anytime thereafter. In another example, users can select to limit the length of time user specific motion input and/or audio input is maintained or entirely prohibit the collection of user specific motion input and/or audio input. In addition to providing “opt in” and “opt out” options, the present disclosure contemplates providing notifications relating to the access or use of personal information. For instance, a user may be notified upon downloading an app that their personal information data will be accessed and then reminded again just before personal information data is accessed by the app.
Moreover, it is the intent of the present disclosure that personal information data should be managed and handled in a way to minimize risks of unintentional or unauthorized access or use. Risk can be minimized by limiting the collection of data and deleting data once it is no longer needed. In addition, and when applicable, including in certain health related applications, data de-identification can be used to protect a user's privacy. De-identification may be facilitated, when appropriate, by removing specific identifiers (e.g., date of birth, etc.), controlling the amount or specificity of data stored (e.g., collecting location data at city level rather than at an address level), controlling how data is stored (e.g., aggregating data across users), and/or other methods.
Therefore, although the present disclosure broadly covers use of personal information data to implement one or more various disclosed embodiments, the present disclosure also contemplates that the various embodiments can also be implemented without the need for accessing such personal information data. That is, the various embodiments of the present technology are not rendered inoperable due to the lack of all or a portion of such personal information data. For example, a virtual assistant may be initiated using a generic (e.g., not specific to a user and/or to a particular group of users) model for initiating a virtual assistant based on non-personal information data or a bare minimum amount of personal information, such as the content being requested by the device associated with a user, other non-personal information available to the model for initiating a virtual assistant, or publicly available information.
This application claims priority to U.S. Patent Application No. 62/668,154, entitled “RAISE TO SPEAK,” filed on May 7, 2018, and to U.S. Patent Application No. 62/679,798, entitled “RAISE TO SPEAK,” filed on Jun. 2, 2018, the contents of which are hereby incorporated by reference in their entireties
| Number | Name | Date | Kind |
|---|---|---|---|
| 7315809 | Xun | Jan 2008 | B2 |
| 7315818 | Stevens et al. | Jan 2008 | B2 |
| 7318020 | Kim | Jan 2008 | B1 |
| 7319957 | Robinson et al. | Jan 2008 | B2 |
| 7321783 | Kim | Jan 2008 | B2 |
| 7322023 | Shulman et al. | Jan 2008 | B2 |
| 7324833 | White et al. | Jan 2008 | B2 |
| 7324947 | Jordan et al. | Jan 2008 | B2 |
| 7328155 | Endo et al. | Feb 2008 | B2 |
| 7328250 | Wang et al. | Feb 2008 | B2 |
| 7333998 | Heckerman et al. | Feb 2008 | B2 |
| 7337108 | Florencio et al. | Feb 2008 | B2 |
| 7345670 | Armstrong | Mar 2008 | B2 |
| 7345671 | Robbin et al. | Mar 2008 | B2 |
| 7349953 | Lisitsa et al. | Mar 2008 | B2 |
| 7353139 | Burrell et al. | Apr 2008 | B1 |
| 7356748 | Taleb | Apr 2008 | B2 |
| 7359493 | Wang et al. | Apr 2008 | B1 |
| 7359671 | Richenstein et al. | Apr 2008 | B2 |
| 7359851 | Tong et al. | Apr 2008 | B2 |
| 7360158 | Beeman | Apr 2008 | B1 |
| 7362738 | Taube et al. | Apr 2008 | B2 |
| 7363227 | Mapes-Riordan et al. | Apr 2008 | B2 |
| 7363586 | Briggs et al. | Apr 2008 | B1 |
| 7365260 | Kawashima | Apr 2008 | B2 |
| 7366461 | Brown | Apr 2008 | B1 |
| 7369984 | Fairweather | May 2008 | B2 |
| 7369993 | Atal | May 2008 | B1 |
| 7373291 | Garst | May 2008 | B2 |
| 7373612 | Risch et al. | May 2008 | B2 |
| 7376556 | Bennett | May 2008 | B2 |
| 7376632 | Sadek et al. | May 2008 | B1 |
| 7376645 | Bernard | May 2008 | B2 |
| 7378963 | Begault et al. | May 2008 | B1 |
| 7379874 | Schmid et al. | May 2008 | B2 |
| 7380203 | Keely et al. | May 2008 | B2 |
| 7383170 | Mills et al. | Jun 2008 | B2 |
| 7386110 | Petrunka et al. | Jun 2008 | B2 |
| 7386438 | Franz et al. | Jun 2008 | B1 |
| 7386449 | Sun et al. | Jun 2008 | B2 |
| 7386799 | Clanton et al. | Jun 2008 | B1 |
| 7389224 | Elworthy | Jun 2008 | B1 |
| 7389225 | Jensen et al. | Jun 2008 | B1 |
| 7392185 | Bennett | Jun 2008 | B2 |
| 7394947 | Li et al. | Jul 2008 | B2 |
| 7398209 | Kennewick et al. | Jul 2008 | B2 |
| 7401300 | Nurmi | Jul 2008 | B2 |
| 7403938 | Harrison et al. | Jul 2008 | B2 |
| 7403941 | Bedworth et al. | Jul 2008 | B2 |
| 7404143 | Freelander et al. | Jul 2008 | B2 |
| 7409337 | Potter et al. | Aug 2008 | B1 |
| 7409347 | Bellegarda | Aug 2008 | B1 |
| 7412389 | Yang | Aug 2008 | B2 |
| 7412470 | Masuno et al. | Aug 2008 | B2 |
| 7415100 | Cooper et al. | Aug 2008 | B2 |
| 7415469 | Singh et al. | Aug 2008 | B2 |
| 7418382 | Maes | Aug 2008 | B1 |
| 7418389 | Chu et al. | Aug 2008 | B2 |
| 7418392 | Mozer et al. | Aug 2008 | B1 |
| 7426467 | Nashida et al. | Sep 2008 | B2 |
| 7426468 | Coifman et al. | Sep 2008 | B2 |
| 7427024 | Gazdzinski et al. | Sep 2008 | B1 |
| 7428541 | Houle | Sep 2008 | B2 |
| 7430508 | Williamson et al. | Sep 2008 | B2 |
| 7433869 | Gollapudi | Oct 2008 | B2 |
| 7433921 | Ludwig et al. | Oct 2008 | B2 |
| 7436947 | Wadler et al. | Oct 2008 | B2 |
| 7441184 | Frerebeau et al. | Oct 2008 | B2 |
| 7443316 | Lim | Oct 2008 | B2 |
| 7444589 | Zellner | Oct 2008 | B2 |
| 7447360 | Li et al. | Nov 2008 | B2 |
| 7447624 | Fuhrmann et al. | Nov 2008 | B2 |
| 7447635 | Konopka et al. | Nov 2008 | B1 |
| 7447637 | Grant et al. | Nov 2008 | B1 |
| 7451081 | Gajic et al. | Nov 2008 | B1 |
| 7454351 | Jeschke et al. | Nov 2008 | B2 |
| 7460652 | Chang | Dec 2008 | B2 |
| 7461043 | Hess | Dec 2008 | B2 |
| 7467087 | Gillick et al. | Dec 2008 | B1 |
| 7467164 | Marsh | Dec 2008 | B2 |
| 7472061 | Alewine et al. | Dec 2008 | B1 |
| 7472065 | Aaron et al. | Dec 2008 | B2 |
| 7475010 | Chao | Jan 2009 | B2 |
| 7475015 | Epstein et al. | Jan 2009 | B2 |
| 7475063 | Datta et al. | Jan 2009 | B2 |
| 7477238 | Fux et al. | Jan 2009 | B2 |
| 7477240 | Yanagisawa | Jan 2009 | B2 |
| 7478037 | Strong | Jan 2009 | B2 |
| 7478091 | Mojsilovic et al. | Jan 2009 | B2 |
| 7478129 | Chemtob | Jan 2009 | B1 |
| 7479948 | Kim et al. | Jan 2009 | B2 |
| 7479949 | Jobs et al. | Jan 2009 | B2 |
| 7483832 | Tischer | Jan 2009 | B2 |
| 7483894 | Cao | Jan 2009 | B2 |
| 7487089 | Mozer | Feb 2009 | B2 |
| 7487093 | Mutsuno et al. | Feb 2009 | B2 |
| 7490034 | Finnigan et al. | Feb 2009 | B2 |
| 7490039 | Shaffer et al. | Feb 2009 | B1 |
| 7493251 | Gao et al. | Feb 2009 | B2 |
| 7493560 | Kipnes et al. | Feb 2009 | B1 |
| 7496498 | Chu et al. | Feb 2009 | B2 |
| 7496512 | Zhao et al. | Feb 2009 | B2 |
| 7499923 | Kawatani | Mar 2009 | B2 |
| 7502738 | Kennewick et al. | Mar 2009 | B2 |
| 7505795 | Lim et al. | Mar 2009 | B1 |
| 7508324 | Suraqui | Mar 2009 | B2 |
| 7508373 | Lin et al. | Mar 2009 | B2 |
| 7516123 | Betz et al. | Apr 2009 | B2 |
| 7519327 | White | Apr 2009 | B2 |
| 7519398 | Hirose | Apr 2009 | B2 |
| 7522927 | Fitch et al. | Apr 2009 | B2 |
| 7523036 | Akabane et al. | Apr 2009 | B2 |
| 7523108 | Cao | Apr 2009 | B2 |
| 7526466 | Au | Apr 2009 | B2 |
| 7526738 | Ording et al. | Apr 2009 | B2 |
| 7528713 | Singh et al. | May 2009 | B2 |
| 7529671 | Rockenbeck et al. | May 2009 | B2 |
| 7529676 | Koyama | May 2009 | B2 |
| 7535997 | McQuaide, Jr. et al. | May 2009 | B1 |
| 7536029 | Choi et al. | May 2009 | B2 |
| 7536565 | Girish et al. | May 2009 | B2 |
| 7538685 | Cooper et al. | May 2009 | B1 |
| 7539619 | Seligman et al. | May 2009 | B1 |
| 7539656 | Fratkina et al. | May 2009 | B2 |
| 7541940 | Upton | Jun 2009 | B2 |
| 7542967 | Hurst-Hiller et al. | Jun 2009 | B2 |
| 7542971 | Thione et al. | Jun 2009 | B2 |
| 7543232 | Easton, Jr. et al. | Jun 2009 | B2 |
| 7546382 | Healey et al. | Jun 2009 | B2 |
| 7546529 | Reynar et al. | Jun 2009 | B2 |
| 7548895 | Pulsipher | Jun 2009 | B2 |
| 7552045 | Barliga et al. | Jun 2009 | B2 |
| 7552055 | Lecoeuche | Jun 2009 | B2 |
| 7555431 | Bennett | Jun 2009 | B2 |
| 7555496 | Lantrip et al. | Jun 2009 | B1 |
| 7558381 | Ali et al. | Jul 2009 | B1 |
| 7558730 | Davis et al. | Jul 2009 | B2 |
| 7559026 | Girish et al. | Jul 2009 | B2 |
| 7561069 | Horstemeyer | Jul 2009 | B2 |
| 7562007 | Hwang | Jul 2009 | B2 |
| 7562032 | Abbosh et al. | Jul 2009 | B2 |
| 7565104 | Brown et al. | Jul 2009 | B1 |
| 7565380 | Venkatachary | Jul 2009 | B1 |
| 7568151 | Bargeron et al. | Jul 2009 | B2 |
| 7571092 | Nieh | Aug 2009 | B1 |
| 7571106 | Cao et al. | Aug 2009 | B2 |
| 7577522 | Rosenberg | Aug 2009 | B2 |
| 7580551 | Srihari et al. | Aug 2009 | B1 |
| 7580576 | Wang et al. | Aug 2009 | B2 |
| 7580839 | Tamura et al. | Aug 2009 | B2 |
| 7584092 | Brockett et al. | Sep 2009 | B2 |
| 7584093 | Potter et al. | Sep 2009 | B2 |
| 7584278 | Rajarajan et al. | Sep 2009 | B2 |
| 7584429 | Fabritius | Sep 2009 | B2 |
| 7593868 | Margiloff et al. | Sep 2009 | B2 |
| 7596269 | King et al. | Sep 2009 | B2 |
| 7596499 | Anguera et al. | Sep 2009 | B2 |
| 7596606 | Codignotto | Sep 2009 | B2 |
| 7596765 | Almas | Sep 2009 | B2 |
| 7599918 | Shen et al. | Oct 2009 | B2 |
| 7603349 | Kraft et al. | Oct 2009 | B1 |
| 7603381 | Burke et al. | Oct 2009 | B2 |
| 7606444 | Erol et al. | Oct 2009 | B1 |
| 7609179 | Diaz-Gutierrez et al. | Oct 2009 | B2 |
| 7610258 | Yuknewicz et al. | Oct 2009 | B2 |
| 7613264 | Wells et al. | Nov 2009 | B2 |
| 7614008 | Ording | Nov 2009 | B2 |
| 7617094 | Aoki et al. | Nov 2009 | B2 |
| 7620407 | Donald et al. | Nov 2009 | B1 |
| 7620549 | Di Cristo et al. | Nov 2009 | B2 |
| 7620894 | Kahn | Nov 2009 | B1 |
| 7623119 | Autio et al. | Nov 2009 | B2 |
| 7624007 | Bennett | Nov 2009 | B2 |
| 7627481 | Kuo et al. | Dec 2009 | B1 |
| 7630900 | Strom | Dec 2009 | B1 |
| 7630901 | Omi | Dec 2009 | B2 |
| 7633076 | Huppi et al. | Dec 2009 | B2 |
| 7634409 | Kennewick et al. | Dec 2009 | B2 |
| 7634413 | Kuo et al. | Dec 2009 | B1 |
| 7634718 | Nakajima | Dec 2009 | B2 |
| 7634732 | Blagsvedt et al. | Dec 2009 | B1 |
| 7636657 | Ju et al. | Dec 2009 | B2 |
| 7640158 | Detlef et al. | Dec 2009 | B2 |
| 7640160 | Di Cristo et al. | Dec 2009 | B2 |
| 7643990 | Bellegarda | Jan 2010 | B1 |
| 7647225 | Bennett et al. | Jan 2010 | B2 |
| 7649454 | Singh et al. | Jan 2010 | B2 |
| 7649877 | Vieri et al. | Jan 2010 | B2 |
| 7653883 | Hotelling et al. | Jan 2010 | B2 |
| 7656393 | King et al. | Feb 2010 | B2 |
| 7657424 | Bennett | Feb 2010 | B2 |
| 7657828 | Lucas et al. | Feb 2010 | B2 |
| 7657844 | Gibson et al. | Feb 2010 | B2 |
| 7657849 | Chaudhri et al. | Feb 2010 | B2 |
| 7660715 | Thambiratnam | Feb 2010 | B1 |
| 7663607 | Hotelling et al. | Feb 2010 | B2 |
| 7664558 | Lindahl et al. | Feb 2010 | B2 |
| 7664638 | Cooper et al. | Feb 2010 | B2 |
| 7668710 | Doyle | Feb 2010 | B2 |
| 7669134 | Christie et al. | Feb 2010 | B1 |
| 7672841 | Bennett | Mar 2010 | B2 |
| 7672952 | Isaacson et al. | Mar 2010 | B2 |
| 7673238 | Girish et al. | Mar 2010 | B2 |
| 7673251 | Wibisono | Mar 2010 | B1 |
| 7673340 | Cohen et al. | Mar 2010 | B1 |
| 7676026 | Baxter, Jr. | Mar 2010 | B1 |
| 7676365 | Hwang et al. | Mar 2010 | B2 |
| 7676463 | Thompson et al. | Mar 2010 | B2 |
| 7679534 | Kay et al. | Mar 2010 | B2 |
| 7680649 | Park | Mar 2010 | B2 |
| 7681126 | Roose | Mar 2010 | B2 |
| 7683886 | Willey | Mar 2010 | B2 |
| 7683893 | Kim | Mar 2010 | B2 |
| 7684985 | Dominach et al. | Mar 2010 | B2 |
| 7684990 | Caskey et al. | Mar 2010 | B2 |
| 7684991 | Stohr et al. | Mar 2010 | B2 |
| 7689245 | Cox et al. | Mar 2010 | B2 |
| 7689408 | Chen et al. | Mar 2010 | B2 |
| 7689409 | Heinecke | Mar 2010 | B2 |
| 7689412 | Wu et al. | Mar 2010 | B2 |
| 7689421 | Li et al. | Mar 2010 | B2 |
| 7693715 | Hwang et al. | Apr 2010 | B2 |
| 7693717 | Kahn et al. | Apr 2010 | B2 |
| 7693719 | Chu et al. | Apr 2010 | B2 |
| 7693720 | Kennewick et al. | Apr 2010 | B2 |
| 7698131 | Bennett | Apr 2010 | B2 |
| 7702500 | Blaedow | Apr 2010 | B2 |
| 7702508 | Bennett | Apr 2010 | B2 |
| 7703091 | Martin et al. | Apr 2010 | B1 |
| 7706510 | Ng | Apr 2010 | B2 |
| 7707026 | Liu | Apr 2010 | B2 |
| 7707027 | Balchandran et al. | Apr 2010 | B2 |
| 7707032 | Wang et al. | Apr 2010 | B2 |
| 7707221 | Dunning et al. | Apr 2010 | B1 |
| 7707226 | Tonse | Apr 2010 | B1 |
| 7707267 | Lisitsa et al. | Apr 2010 | B2 |
| 7710262 | Ruha | May 2010 | B2 |
| 7711129 | Lindahl et al. | May 2010 | B2 |
| 7711550 | Feinberg et al. | May 2010 | B1 |
| 7711565 | Gazdzinski | May 2010 | B1 |
| 7711672 | Au | May 2010 | B2 |
| 7712053 | Bradford et al. | May 2010 | B2 |
| 7716056 | Weng et al. | May 2010 | B2 |
| 7716216 | Harik et al. | May 2010 | B1 |
| 7720674 | Kaiser et al. | May 2010 | B2 |
| 7720683 | Vermeulen et al. | May 2010 | B1 |
| 7721226 | Barabe et al. | May 2010 | B2 |
| 7721301 | Wong et al. | May 2010 | B2 |
| 7724242 | Hillis et al. | May 2010 | B2 |
| 7724696 | Parekh | May 2010 | B1 |
| 7725307 | Bennett | May 2010 | B2 |
| 7725318 | Gavalda et al. | May 2010 | B2 |
| 7725320 | Bennett | May 2010 | B2 |
| 7725321 | Bennett | May 2010 | B2 |
| 7725838 | Williams | May 2010 | B2 |
| 7729904 | Bennett | Jun 2010 | B2 |
| 7729916 | Coffman et al. | Jun 2010 | B2 |
| 7734461 | Kwak et al. | Jun 2010 | B2 |
| 7735012 | Naik | Jun 2010 | B2 |
| 7739588 | Reynar et al. | Jun 2010 | B2 |
| 7742953 | King et al. | Jun 2010 | B2 |
| 7743188 | Haitani et al. | Jun 2010 | B2 |
| 7747616 | Yamada et al. | Jun 2010 | B2 |
| 7752152 | Paek et al. | Jul 2010 | B2 |
| 7756708 | Cohen et al. | Jul 2010 | B2 |
| 7756868 | Lee | Jul 2010 | B2 |
| 7756871 | Yacoub et al. | Jul 2010 | B2 |
| 7757173 | Beaman | Jul 2010 | B2 |
| 7757182 | Elliott et al. | Jul 2010 | B2 |
| 7761296 | Bakis et al. | Jul 2010 | B1 |
| 7763842 | Hsu et al. | Jul 2010 | B2 |
| 7774202 | Spengler et al. | Aug 2010 | B2 |
| 7774204 | Mozer et al. | Aug 2010 | B2 |
| 7774388 | Runchey | Aug 2010 | B1 |
| 7777717 | Fux et al. | Aug 2010 | B2 |
| 7778432 | Larsen | Aug 2010 | B2 |
| 7778595 | White et al. | Aug 2010 | B2 |
| 7778632 | Kurlander et al. | Aug 2010 | B2 |
| 7778830 | Davis et al. | Aug 2010 | B2 |
| 7779353 | Grigoriu et al. | Aug 2010 | B2 |
| 7779356 | Griesmer | Aug 2010 | B2 |
| 7779357 | Naik | Aug 2010 | B2 |
| 7783283 | Kuusinen et al. | Aug 2010 | B2 |
| 7783486 | Rosser et al. | Aug 2010 | B2 |
| 7788590 | Taboada et al. | Aug 2010 | B2 |
| 7788663 | Illowsky et al. | Aug 2010 | B2 |
| 7796980 | McKinney et al. | Sep 2010 | B1 |
| 7797265 | Brinker et al. | Sep 2010 | B2 |
| 7797269 | Rieman et al. | Sep 2010 | B2 |
| 7797331 | Theimer et al. | Sep 2010 | B2 |
| 7797629 | Fux et al. | Sep 2010 | B2 |
| 7801721 | Rosart et al. | Sep 2010 | B2 |
| 7801728 | Ben-David et al. | Sep 2010 | B2 |
| 7801729 | Mozer | Sep 2010 | B2 |
| 7805299 | Coifman | Sep 2010 | B2 |
| 7809550 | Barrows | Oct 2010 | B1 |
| 7809565 | Coifman | Oct 2010 | B2 |
| 7809569 | Attwater et al. | Oct 2010 | B2 |
| 7809570 | Kennewick et al. | Oct 2010 | B2 |
| 7809610 | Cao | Oct 2010 | B2 |
| 7809744 | Nevidomski et al. | Oct 2010 | B2 |
| 7818165 | Carlgren et al. | Oct 2010 | B2 |
| 7818176 | Freeman et al. | Oct 2010 | B2 |
| 7818215 | King et al. | Oct 2010 | B2 |
| 7818291 | Ferguson et al. | Oct 2010 | B2 |
| 7818672 | Mccormack et al. | Oct 2010 | B2 |
| 7822608 | Cross, Jr. et al. | Oct 2010 | B2 |
| 7823123 | Sabbouh | Oct 2010 | B2 |
| 7826945 | Zhang et al. | Nov 2010 | B2 |
| 7827047 | Anderson et al. | Nov 2010 | B2 |
| 7831246 | Smith et al. | Nov 2010 | B1 |
| 7831423 | Schubert | Nov 2010 | B2 |
| 7831426 | Bennett | Nov 2010 | B2 |
| 7831432 | Bodin et al. | Nov 2010 | B2 |
| 7835504 | Donald et al. | Nov 2010 | B1 |
| 7836437 | Kacmarcik et al. | Nov 2010 | B2 |
| 7840348 | Kim et al. | Nov 2010 | B2 |
| 7840400 | Lavi et al. | Nov 2010 | B2 |
| 7840447 | Kleinrock et al. | Nov 2010 | B2 |
| 7840581 | Ross et al. | Nov 2010 | B2 |
| 7840912 | Elias et al. | Nov 2010 | B2 |
| 7844394 | Kim | Nov 2010 | B2 |
| 7848924 | Nurminen et al. | Dec 2010 | B2 |
| 7848926 | Goto et al. | Dec 2010 | B2 |
| 7853444 | Wang et al. | Dec 2010 | B2 |
| 7853445 | Bachenko et al. | Dec 2010 | B2 |
| 7853574 | Kraenzel et al. | Dec 2010 | B2 |
| 7853577 | Sundaresan et al. | Dec 2010 | B2 |
| 7853664 | Wang et al. | Dec 2010 | B1 |
| 7853900 | Nguyen et al. | Dec 2010 | B2 |
| 7865817 | Ryan et al. | Jan 2011 | B2 |
| 7869998 | Di Fabbrizio et al. | Jan 2011 | B1 |
| 7869999 | Amato et al. | Jan 2011 | B2 |
| 7870118 | Jiang et al. | Jan 2011 | B2 |
| 7870133 | Krishnamoorthy et al. | Jan 2011 | B2 |
| 7873149 | Schultz et al. | Jan 2011 | B2 |
| 7873519 | Bennett | Jan 2011 | B2 |
| 7873654 | Bernard | Jan 2011 | B2 |
| 7877705 | Chambers et al. | Jan 2011 | B2 |
| 7880730 | Robinson et al. | Feb 2011 | B2 |
| 7881283 | Cormier et al. | Feb 2011 | B2 |
| 7881936 | Longe et al. | Feb 2011 | B2 |
| 7885390 | Chaudhuri et al. | Feb 2011 | B2 |
| 7885844 | Cohen et al. | Feb 2011 | B1 |
| 7886233 | Rainisto et al. | Feb 2011 | B2 |
| 7889101 | Yokota | Feb 2011 | B2 |
| 7889184 | Blumenberg et al. | Feb 2011 | B2 |
| 7889185 | Blumenberg et al. | Feb 2011 | B2 |
| 7890330 | Ozkaragoz et al. | Feb 2011 | B2 |
| 7890652 | Bull et al. | Feb 2011 | B2 |
| 7895039 | Braho et al. | Feb 2011 | B2 |
| 7895531 | Radtke et al. | Feb 2011 | B2 |
| 7899666 | Varone | Mar 2011 | B2 |
| 7904297 | Mirkovic et al. | Mar 2011 | B2 |
| 7908287 | Katragadda | Mar 2011 | B1 |
| 7912289 | Kansal et al. | Mar 2011 | B2 |
| 7912699 | Saraclar et al. | Mar 2011 | B1 |
| 7912702 | Bennett | Mar 2011 | B2 |
| 7912720 | Hakkani-Tur et al. | Mar 2011 | B1 |
| 7912828 | Bonnet et al. | Mar 2011 | B2 |
| 7913185 | Benson et al. | Mar 2011 | B1 |
| 7916979 | Simmons | Mar 2011 | B2 |
| 7917367 | Di Cristo et al. | Mar 2011 | B2 |
| 7917497 | Harrison et al. | Mar 2011 | B2 |
| 7920678 | Cooper et al. | Apr 2011 | B2 |
| 7920682 | Byrne et al. | Apr 2011 | B2 |
| 7920857 | Lau et al. | Apr 2011 | B2 |
| 7925525 | Chin | Apr 2011 | B2 |
| 7925610 | Elbaz et al. | Apr 2011 | B2 |
| 7929805 | Wang et al. | Apr 2011 | B2 |
| 7930168 | Weng et al. | Apr 2011 | B2 |
| 7930183 | Odell et al. | Apr 2011 | B2 |
| 7930197 | Ozzie et al. | Apr 2011 | B2 |
| 7936339 | Marggraff et al. | May 2011 | B2 |
| 7936861 | Martin et al. | May 2011 | B2 |
| 7936863 | John et al. | May 2011 | B2 |
| 7937075 | Zellner | May 2011 | B2 |
| 7941009 | Li et al. | May 2011 | B2 |
| 7945294 | Zhang et al. | May 2011 | B2 |
| 7945470 | Cohen et al. | May 2011 | B1 |
| 7949529 | Weider et al. | May 2011 | B2 |
| 7949534 | Davis et al. | May 2011 | B2 |
| 7949752 | Lange et al. | May 2011 | B2 |
| 7953679 | Chidlovskii et al. | May 2011 | B2 |
| 7957975 | Burns et al. | Jun 2011 | B2 |
| 7958136 | Curtis et al. | Jun 2011 | B1 |
| 7962179 | Huang | Jun 2011 | B2 |
| 7974835 | Balchandran et al. | Jul 2011 | B2 |
| 7974844 | Sumita | Jul 2011 | B2 |
| 7974972 | Cao | Jul 2011 | B2 |
| 7975216 | Woolf et al. | Jul 2011 | B2 |
| 7983478 | Liu et al. | Jul 2011 | B2 |
| 7983915 | Knight et al. | Jul 2011 | B2 |
| 7983917 | Kennewick et al. | Jul 2011 | B2 |
| 7983919 | Conkie | Jul 2011 | B2 |
| 7983997 | Allen et al. | Jul 2011 | B2 |
| 7984062 | Dunning et al. | Jul 2011 | B2 |
| 7986431 | Emori et al. | Jul 2011 | B2 |
| 7987151 | Schott et al. | Jul 2011 | B2 |
| 7987244 | Lewis et al. | Jul 2011 | B1 |
| 7991614 | Washio et al. | Aug 2011 | B2 |
| 7992085 | Wang-Aryattanwanich et al. | Aug 2011 | B2 |
| 7996228 | Miller et al. | Aug 2011 | B2 |
| 7996589 | Schultz et al. | Aug 2011 | B2 |
| 7996769 | Fux et al. | Aug 2011 | B2 |
| 7996792 | Anzures et al. | Aug 2011 | B2 |
| 7999669 | Singh et al. | Aug 2011 | B2 |
| 8000453 | Cooper et al. | Aug 2011 | B2 |
| 8005664 | Hanumanthappa | Aug 2011 | B2 |
| 8005679 | Jordan et al. | Aug 2011 | B2 |
| 8006180 | Tunning et al. | Aug 2011 | B2 |
| 8014308 | Gates et al. | Sep 2011 | B2 |
| 8015006 | Kennewick et al. | Sep 2011 | B2 |
| 8015011 | Nagano et al. | Sep 2011 | B2 |
| 8015144 | Zheng et al. | Sep 2011 | B2 |
| 8018431 | Zehr et al. | Sep 2011 | B1 |
| 8019271 | Izdepski | Sep 2011 | B1 |
| 8019604 | Ma | Sep 2011 | B2 |
| 8020104 | Robarts et al. | Sep 2011 | B2 |
| 8024195 | Mozer et al. | Sep 2011 | B2 |
| 8024415 | Horvitz et al. | Sep 2011 | B2 |
| 8027836 | Baker et al. | Sep 2011 | B2 |
| 8031943 | Chen et al. | Oct 2011 | B2 |
| 8032383 | Bhardwaj et al. | Oct 2011 | B1 |
| 8036901 | Mozer | Oct 2011 | B2 |
| 8037034 | Plachta et al. | Oct 2011 | B2 |
| 8041557 | Liu | Oct 2011 | B2 |
| 8041570 | Mirkovic et al. | Oct 2011 | B2 |
| 8041611 | Kleinrock et al. | Oct 2011 | B2 |
| 8042053 | Darwish et al. | Oct 2011 | B2 |
| 8046363 | Cha et al. | Oct 2011 | B2 |
| 8046374 | Bromwich et al. | Oct 2011 | B1 |
| 8050500 | Batty et al. | Nov 2011 | B1 |
| 8054180 | Scofield et al. | Nov 2011 | B1 |
| 8055502 | Clark et al. | Nov 2011 | B2 |
| 8055708 | Chitsaz et al. | Nov 2011 | B2 |
| 8056070 | Goller et al. | Nov 2011 | B2 |
| 8060824 | Brownrigg, Jr. et al. | Nov 2011 | B2 |
| 8064753 | Freeman | Nov 2011 | B2 |
| 8065143 | Yanagihara | Nov 2011 | B2 |
| 8065155 | Gazdzinski | Nov 2011 | B1 |
| 8065156 | Gazdzinski | Nov 2011 | B2 |
| 8068604 | Leeds et al. | Nov 2011 | B2 |
| 8069046 | Kennewick et al. | Nov 2011 | B2 |
| 8069422 | Sheshagiri et al. | Nov 2011 | B2 |
| 8073681 | Baldwin et al. | Dec 2011 | B2 |
| 8073695 | Hendricks et al. | Dec 2011 | B1 |
| 8077153 | Benko et al. | Dec 2011 | B2 |
| 8078473 | Gazdzinski | Dec 2011 | B1 |
| 8082153 | Coffman et al. | Dec 2011 | B2 |
| 8082498 | Salamon et al. | Dec 2011 | B2 |
| 8090571 | Elshishiny et al. | Jan 2012 | B2 |
| 8095364 | Longe et al. | Jan 2012 | B2 |
| 8099289 | Mozer et al. | Jan 2012 | B2 |
| 8099395 | Pabla et al. | Jan 2012 | B2 |
| 8099418 | Inoue et al. | Jan 2012 | B2 |
| 8103510 | Sato | Jan 2012 | B2 |
| 8107401 | John et al. | Jan 2012 | B2 |
| 8112275 | Kennewick et al. | Feb 2012 | B2 |
| 8112280 | Lu | Feb 2012 | B2 |
| 8117037 | Gazdzinski | Feb 2012 | B2 |
| 8117542 | Radtke et al. | Feb 2012 | B2 |
| 8121413 | Hwang et al. | Feb 2012 | B2 |
| 8121837 | Agapi et al. | Feb 2012 | B2 |
| 8122094 | Kotab | Feb 2012 | B1 |
| 8122353 | Bouta | Feb 2012 | B2 |
| 8130929 | Wilkes et al. | Mar 2012 | B2 |
| 8131557 | Davis et al. | Mar 2012 | B2 |
| 8135115 | Hogg, Jr. et al. | Mar 2012 | B1 |
| 8138912 | Singh et al. | Mar 2012 | B2 |
| 8140335 | Kennewick et al. | Mar 2012 | B2 |
| 8140567 | Padovitz et al. | Mar 2012 | B2 |
| 8145489 | Freeman et al. | Mar 2012 | B2 |
| 8150694 | Kennewick et al. | Apr 2012 | B2 |
| 8150700 | Shin et al. | Apr 2012 | B2 |
| 8155956 | Cho et al. | Apr 2012 | B2 |
| 8156005 | Vieri | Apr 2012 | B2 |
| 8160877 | Nucci et al. | Apr 2012 | B1 |
| 8160883 | Lecoeuche | Apr 2012 | B2 |
| 8165321 | Paquier et al. | Apr 2012 | B2 |
| 8165886 | Gagnon et al. | Apr 2012 | B1 |
| 8166019 | Lee et al. | Apr 2012 | B1 |
| 8166032 | Sommer et al. | Apr 2012 | B2 |
| 8170790 | Lee et al. | May 2012 | B2 |
| 8175872 | Kristjansson et al. | May 2012 | B2 |
| 8179370 | Yamasani et al. | May 2012 | B1 |
| 8188856 | Singh et al. | May 2012 | B2 |
| 8190359 | Bourne | May 2012 | B2 |
| 8190596 | Nambiar et al. | May 2012 | B2 |
| 8195467 | Mozer et al. | Jun 2012 | B2 |
| 8195468 | Kennewick et al. | Jun 2012 | B2 |
| 8200489 | Baggenstoss | Jun 2012 | B1 |
| 8200495 | Braho et al. | Jun 2012 | B2 |
| 8201109 | Van Os et al. | Jun 2012 | B2 |
| 8204238 | Mozer | Jun 2012 | B2 |
| 8205788 | Gazdzinski et al. | Jun 2012 | B1 |
| 8209183 | Patel et al. | Jun 2012 | B1 |
| 8213911 | Williams et al. | Jul 2012 | B2 |
| 8219115 | Nelissen | Jul 2012 | B1 |
| 8219406 | Yu et al. | Jul 2012 | B2 |
| 8219407 | Roy et al. | Jul 2012 | B1 |
| 8219608 | alSafadi et al. | Jul 2012 | B2 |
| 8224649 | Chaudhari et al. | Jul 2012 | B2 |
| 8228299 | Maloney et al. | Jul 2012 | B1 |
| 8233919 | Haag et al. | Jul 2012 | B2 |
| 8234111 | Lloyd et al. | Jul 2012 | B2 |
| 8239206 | LeBeau et al. | Aug 2012 | B1 |
| 8239207 | Seligman et al. | Aug 2012 | B2 |
| 8244712 | Serlet et al. | Aug 2012 | B2 |
| 8250071 | Killalea et al. | Aug 2012 | B1 |
| 8254829 | Kindred et al. | Aug 2012 | B1 |
| 8255216 | White | Aug 2012 | B2 |
| 8255217 | Stent et al. | Aug 2012 | B2 |
| 8260247 | Lazaridis et al. | Sep 2012 | B2 |
| 8260617 | Dhanakshirur et al. | Sep 2012 | B2 |
| 8270933 | Riemer et al. | Sep 2012 | B2 |
| 8271287 | Kermani | Sep 2012 | B1 |
| 8275621 | Alewine et al. | Sep 2012 | B2 |
| 8279171 | Hirai et al. | Oct 2012 | B2 |
| 8280438 | Barbera | Oct 2012 | B2 |
| 8285546 | Reich | Oct 2012 | B2 |
| 8285551 | Gazdzinski | Oct 2012 | B2 |
| 8285553 | Gazdzinski | Oct 2012 | B2 |
| 8290777 | Nguyen et al. | Oct 2012 | B1 |
| 8290778 | Gazdzinski | Oct 2012 | B2 |
| 8290781 | Gazdzinski | Oct 2012 | B2 |
| 8296124 | Holsztynska et al. | Oct 2012 | B1 |
| 8296145 | Clark et al. | Oct 2012 | B2 |
| 8296146 | Gazdzinski | Oct 2012 | B2 |
| 8296153 | Gazdzinski | Oct 2012 | B2 |
| 8296380 | Kelly et al. | Oct 2012 | B1 |
| 8296383 | Lindahl | Oct 2012 | B2 |
| 8300801 | Sweeney et al. | Oct 2012 | B2 |
| 8301456 | Gazdzinski | Oct 2012 | B2 |
| 8311189 | Champlin et al. | Nov 2012 | B2 |
| 8311834 | Gazdzinski | Nov 2012 | B1 |
| 8311835 | Lecoeuche | Nov 2012 | B2 |
| 8311838 | Lindahl et al. | Nov 2012 | B2 |
| 8312017 | Martin et al. | Nov 2012 | B2 |
| 8321786 | Lunati et al. | Nov 2012 | B2 |
| 8326627 | Kennewick et al. | Dec 2012 | B2 |
| 8332205 | Krishnan et al. | Dec 2012 | B2 |
| 8332218 | Cross et al. | Dec 2012 | B2 |
| 8332224 | Di Cristo et al. | Dec 2012 | B2 |
| 8332748 | Karam | Dec 2012 | B1 |
| 8335689 | Wittenstein et al. | Dec 2012 | B2 |
| 8340975 | Rosenberger | Dec 2012 | B1 |
| 8345665 | Vieri et al. | Jan 2013 | B2 |
| 8346563 | Hjelm et al. | Jan 2013 | B1 |
| 8352183 | Thota et al. | Jan 2013 | B2 |
| 8352268 | Naik et al. | Jan 2013 | B2 |
| 8352272 | Rogers et al. | Jan 2013 | B2 |
| 8355919 | Silverman et al. | Jan 2013 | B2 |
| 8359234 | Vieri | Jan 2013 | B2 |
| 8370145 | Endo et al. | Feb 2013 | B2 |
| 8370158 | Gazdzinski | Feb 2013 | B2 |
| 8371503 | Gazdzinski | Feb 2013 | B2 |
| 8374871 | Ehsani et al. | Feb 2013 | B2 |
| 8375320 | Kotler et al. | Feb 2013 | B2 |
| 8380504 | Peden et al. | Feb 2013 | B1 |
| 8380507 | Herman et al. | Feb 2013 | B2 |
| 8381107 | Rottler et al. | Feb 2013 | B2 |
| 8381135 | Hotelling et al. | Feb 2013 | B2 |
| 8386485 | Kerschberg et al. | Feb 2013 | B2 |
| 8386926 | Matsuoka | Feb 2013 | B1 |
| 8391844 | Lamiraux et al. | Mar 2013 | B2 |
| 8396714 | Rogers et al. | Mar 2013 | B2 |
| 8401163 | Kirchhoff et al. | Mar 2013 | B1 |
| 8406745 | Upadhyay et al. | Mar 2013 | B1 |
| 8423288 | Stahl et al. | Apr 2013 | B2 |
| 8428758 | Naik et al. | Apr 2013 | B2 |
| 8433778 | Shreesha et al. | Apr 2013 | B1 |
| 8442821 | Vanhoucke | May 2013 | B1 |
| 8447612 | Gazdzinski | May 2013 | B2 |
| 8452597 | Bringert et al. | May 2013 | B2 |
| 8457959 | Kaiser | Jun 2013 | B2 |
| 8458115 | Cai et al. | Jun 2013 | B2 |
| 8458278 | Christie et al. | Jun 2013 | B2 |
| 8464150 | Davidson et al. | Jun 2013 | B2 |
| 8473289 | Jitkoff et al. | Jun 2013 | B2 |
| 8479122 | Hotelling et al. | Jul 2013 | B2 |
| 8484027 | Murphy | Jul 2013 | B1 |
| 8489599 | Bellotti | Jul 2013 | B2 |
| 8498857 | Kopparapu et al. | Jul 2013 | B2 |
| 8514197 | Shahraray et al. | Aug 2013 | B2 |
| 8515750 | Lei et al. | Aug 2013 | B1 |
| 8521513 | Millett et al. | Aug 2013 | B2 |
| 8521531 | Kim | Aug 2013 | B1 |
| 8527276 | Senior et al. | Sep 2013 | B1 |
| 8537033 | Gueziec | Sep 2013 | B2 |
| 8543398 | Strope et al. | Sep 2013 | B1 |
| 8560229 | Park et al. | Oct 2013 | B1 |
| 8571851 | Tickner et al. | Oct 2013 | B1 |
| 8583416 | Huang et al. | Nov 2013 | B2 |
| 8583511 | Hendrickson | Nov 2013 | B2 |
| 8589869 | Wolfram | Nov 2013 | B2 |
| 8589911 | Sharkey et al. | Nov 2013 | B1 |
| 8595004 | Koshinaka | Nov 2013 | B2 |
| 8600743 | Lindahl et al. | Dec 2013 | B2 |
| 8600930 | Sata et al. | Dec 2013 | B2 |
| 8606568 | Tickner et al. | Dec 2013 | B1 |
| 8620659 | Di Cristo et al. | Dec 2013 | B2 |
| 8620662 | Bellegarda | Dec 2013 | B2 |
| 8626681 | Jurca et al. | Jan 2014 | B1 |
| 8638363 | King et al. | Jan 2014 | B2 |
| 8639516 | Lindahl et al. | Jan 2014 | B2 |
| 8645137 | Bellegarda et al. | Feb 2014 | B2 |
| 8645138 | Weinstein et al. | Feb 2014 | B1 |
| 8654936 | Tofighbakhsh et al. | Feb 2014 | B1 |
| 8655646 | Lee et al. | Feb 2014 | B2 |
| 8655901 | Li et al. | Feb 2014 | B1 |
| 8660843 | Falcon et al. | Feb 2014 | B2 |
| 8660849 | Gruber et al. | Feb 2014 | B2 |
| 8660970 | Fiedorowicz | Feb 2014 | B1 |
| 8661112 | Creamer et al. | Feb 2014 | B2 |
| 8661340 | Goldsmith et al. | Feb 2014 | B2 |
| 8670979 | Gruber et al. | Mar 2014 | B2 |
| 8675084 | Bolton et al. | Mar 2014 | B2 |
| 8676904 | Lindahl et al. | Mar 2014 | B2 |
| 8677377 | Cheyer et al. | Mar 2014 | B2 |
| 8681950 | Vlack et al. | Mar 2014 | B2 |
| 8682667 | Haughay et al. | Mar 2014 | B2 |
| 8687777 | Lavian et al. | Apr 2014 | B1 |
| 8688446 | Yanagihara et al. | Apr 2014 | B2 |
| 8688453 | Joshi et al. | Apr 2014 | B1 |
| 8695074 | Saraf et al. | Apr 2014 | B2 |
| 8696364 | Cohen | Apr 2014 | B2 |
| 8706472 | Ramerth et al. | Apr 2014 | B2 |
| 8706474 | Blume et al. | Apr 2014 | B2 |
| 8706503 | Cheyer et al. | Apr 2014 | B2 |
| 8713119 | Lindahl et al. | Apr 2014 | B2 |
| 8713418 | King et al. | Apr 2014 | B2 |
| 8719006 | Bellegarda et al. | May 2014 | B2 |
| 8719014 | Wagner et al. | May 2014 | B2 |
| 8731610 | Appaji | May 2014 | B2 |
| 8731912 | Tickner et al. | May 2014 | B1 |
| 8731942 | Cheyer et al. | May 2014 | B2 |
| 8739208 | Rodriguez et al. | May 2014 | B2 |
| 8744852 | Seymour et al. | Jun 2014 | B1 |
| 8760537 | Johnson et al. | Jun 2014 | B2 |
| 8762145 | Ouchi et al. | Jun 2014 | B2 |
| 8762156 | Chen et al. | Jun 2014 | B2 |
| 8762469 | Lindahl et al. | Jun 2014 | B2 |
| 8768693 | Lempel et al. | Jul 2014 | B2 |
| 8768702 | Boettcher et al. | Jul 2014 | B2 |
| 8775154 | Clinchant et al. | Jul 2014 | B2 |
| 8775931 | Fux et al. | Jul 2014 | B2 |
| 8781456 | Prociw | Jul 2014 | B2 |
| 8781841 | Wang | Jul 2014 | B1 |
| 8798255 | Lubowich et al. | Aug 2014 | B2 |
| 8798995 | Edara et al. | Aug 2014 | B1 |
| 8799000 | Guzzoni et al. | Aug 2014 | B2 |
| 8805690 | LeBeau et al. | Aug 2014 | B1 |
| 8812302 | Xiao et al. | Aug 2014 | B2 |
| 8838457 | Cerra et al. | Sep 2014 | B2 |
| 8855915 | Furuhata et al. | Oct 2014 | B2 |
| 8861925 | Ohme | Oct 2014 | B1 |
| 8862252 | Rottler et al. | Oct 2014 | B2 |
| 8868409 | Mengibar et al. | Oct 2014 | B1 |
| 8880405 | Cerra et al. | Nov 2014 | B2 |
| 8886534 | Nakano et al. | Nov 2014 | B2 |
| 8886540 | Cerra et al. | Nov 2014 | B2 |
| 8886541 | Friedlander | Nov 2014 | B2 |
| 8892446 | Cheyer et al. | Nov 2014 | B2 |
| 8893023 | Perry et al. | Nov 2014 | B2 |
| 8898568 | Bull et al. | Nov 2014 | B2 |
| 8903716 | Chen et al. | Dec 2014 | B2 |
| 8909693 | Frissora et al. | Dec 2014 | B2 |
| 8922485 | Lloyd | Dec 2014 | B1 |
| 8930176 | Li et al. | Jan 2015 | B2 |
| 8930191 | Gruber et al. | Jan 2015 | B2 |
| 8938394 | Faaborg et al. | Jan 2015 | B1 |
| 8938688 | Bradford et al. | Jan 2015 | B2 |
| 8942986 | Cheyer et al. | Jan 2015 | B2 |
| 8943423 | Merrill et al. | Jan 2015 | B2 |
| 8972240 | Brockett et al. | Mar 2015 | B2 |
| 8972432 | Shaw et al. | Mar 2015 | B2 |
| 8972878 | Mohler et al. | Mar 2015 | B2 |
| 8983383 | Haskin | Mar 2015 | B1 |
| 8989713 | Doulton | Mar 2015 | B2 |
| 8990235 | King et al. | Mar 2015 | B2 |
| 8994660 | Neels et al. | Mar 2015 | B2 |
| 8996350 | Dub et al. | Mar 2015 | B1 |
| 8996376 | Fleizach et al. | Mar 2015 | B2 |
| 8996381 | Mozer et al. | Mar 2015 | B2 |
| 8996639 | Faaborg et al. | Mar 2015 | B1 |
| 9009046 | Stewart | Apr 2015 | B1 |
| 9020804 | Barbaiani et al. | Apr 2015 | B2 |
| 9026425 | Nikoulina et al. | May 2015 | B2 |
| 9031834 | Coorman et al. | May 2015 | B2 |
| 9037967 | Al-Jefri et al. | May 2015 | B1 |
| 9043208 | Koch et al. | May 2015 | B2 |
| 9049255 | MacFarlane et al. | Jun 2015 | B2 |
| 9049295 | Cooper et al. | Jun 2015 | B1 |
| 9053706 | Jitkoff et al. | Jun 2015 | B2 |
| 9058811 | Wang et al. | Jun 2015 | B2 |
| 9063979 | Chiu et al. | Jun 2015 | B2 |
| 9070247 | Kuhn et al. | Jun 2015 | B2 |
| 9070366 | Mathias et al. | Jun 2015 | B1 |
| 9071701 | Donaldson et al. | Jun 2015 | B2 |
| 9076448 | Bennett et al. | Jul 2015 | B2 |
| 9076450 | Sadek et al. | Jul 2015 | B1 |
| 9081411 | Kalns et al. | Jul 2015 | B2 |
| 9081482 | Zhai et al. | Jul 2015 | B1 |
| 9082402 | Yadgar et al. | Jul 2015 | B2 |
| 9098467 | Blanksteen et al. | Aug 2015 | B1 |
| 9101279 | Ritchey et al. | Aug 2015 | B2 |
| 9112984 | Sejnoha et al. | Aug 2015 | B2 |
| 9117447 | Gruber et al. | Aug 2015 | B2 |
| 9123338 | Sanders et al. | Sep 2015 | B1 |
| 9171541 | Kennewick et al. | Oct 2015 | B2 |
| 9171546 | Pike | Oct 2015 | B1 |
| 9190062 | Haughay | Nov 2015 | B2 |
| 9208153 | Zaveri et al. | Dec 2015 | B1 |
| 9218809 | Bellegarda | Dec 2015 | B2 |
| 9218819 | Stekkelpak et al. | Dec 2015 | B1 |
| 9223537 | Brown et al. | Dec 2015 | B2 |
| 9255812 | Maeoka et al. | Feb 2016 | B2 |
| 9258604 | Bilobrov et al. | Feb 2016 | B1 |
| 9262612 | Cheyer | Feb 2016 | B2 |
| 9286910 | Li et al. | Mar 2016 | B1 |
| 9292487 | Weber | Mar 2016 | B1 |
| 9292489 | Sak et al. | Mar 2016 | B1 |
| 9299344 | Braho et al. | Mar 2016 | B2 |
| 9300718 | Khanna | Mar 2016 | B2 |
| 9305543 | Fleizach et al. | Apr 2016 | B2 |
| 9305548 | Kennewick et al. | Apr 2016 | B2 |
| 9311912 | Swietlinski et al. | Apr 2016 | B1 |
| 9313317 | LeBeau et al. | Apr 2016 | B1 |
| 9318108 | Gruber et al. | Apr 2016 | B2 |
| 9325809 | Barros et al. | Apr 2016 | B1 |
| 9330659 | Ju et al. | May 2016 | B2 |
| 9330720 | Lee | May 2016 | B2 |
| 9338493 | Van Os et al. | May 2016 | B2 |
| 9349368 | LeBeau et al. | May 2016 | B1 |
| 9361084 | Costa | Jun 2016 | B1 |
| 9367541 | Servan et al. | Jun 2016 | B1 |
| 9377871 | Waddell et al. | Jun 2016 | B2 |
| 9378740 | Rosen et al. | Jun 2016 | B1 |
| 9380155 | Reding et al. | Jun 2016 | B1 |
| 9390726 | Smus et al. | Jul 2016 | B1 |
| 9401147 | Jitkoff et al. | Jul 2016 | B2 |
| 9406224 | Sanders et al. | Aug 2016 | B1 |
| 9412392 | Lindahl | Aug 2016 | B2 |
| 9423266 | Clark et al. | Aug 2016 | B2 |
| 9436918 | Pantel et al. | Sep 2016 | B2 |
| 9437186 | Liu et al. | Sep 2016 | B1 |
| 9437189 | Epstein et al. | Sep 2016 | B2 |
| 9454957 | Mathias et al. | Sep 2016 | B1 |
| 9484021 | Mairesse et al. | Nov 2016 | B1 |
| 9495129 | Fleizach et al. | Nov 2016 | B2 |
| 9501741 | Cheyer et al. | Nov 2016 | B2 |
| 9502025 | Kennewick et al. | Nov 2016 | B2 |
| 9508028 | Bannister et al. | Nov 2016 | B2 |
| 9510044 | Pereira et al. | Nov 2016 | B1 |
| 9535906 | Lee et al. | Jan 2017 | B2 |
| 9536527 | Carlson | Jan 2017 | B1 |
| 9547647 | Badaskar | Jan 2017 | B2 |
| 9548050 | Gruber et al. | Jan 2017 | B2 |
| 9569549 | Jenkins et al. | Feb 2017 | B1 |
| 9575964 | Yadgar et al. | Feb 2017 | B2 |
| 9578173 | Sanghavi et al. | Feb 2017 | B2 |
| 9607612 | Deleeuw | Mar 2017 | B2 |
| 9620113 | Kennewick et al. | Apr 2017 | B2 |
| 9620126 | Chiba | Apr 2017 | B2 |
| 9626955 | Fleizach et al. | Apr 2017 | B2 |
| 9633004 | Giuli et al. | Apr 2017 | B2 |
| 9633660 | Haughay | Apr 2017 | B2 |
| 9652453 | Mathur et al. | May 2017 | B2 |
| 9658746 | Cohn et al. | May 2017 | B2 |
| 9668121 | Naik et al. | May 2017 | B2 |
| 9672725 | Dotan-Cohen et al. | Jun 2017 | B2 |
| 9691378 | Meyers et al. | Jun 2017 | B1 |
| 9697827 | Lilly et al. | Jul 2017 | B1 |
| 9720907 | Bangalore et al. | Aug 2017 | B2 |
| 9734839 | Adams | Aug 2017 | B1 |
| 9741343 | Miles et al. | Aug 2017 | B1 |
| 20080001785 | Elizarov et al. | Jan 2008 | A1 |
| 20080010050 | Fux et al. | Jan 2008 | A1 |
| 20080010355 | Vieri et al. | Jan 2008 | A1 |
| 20080010605 | Frank et al. | Jan 2008 | A1 |
| 20080012950 | Lee et al. | Jan 2008 | A1 |
| 20080013751 | Hiselius | Jan 2008 | A1 |
| 20080015863 | Agapi et al. | Jan 2008 | A1 |
| 20080015864 | Ross et al. | Jan 2008 | A1 |
| 20080016575 | Vincent et al. | Jan 2008 | A1 |
| 20080021708 | Bennett et al. | Jan 2008 | A1 |
| 20080021886 | Wang-Aryattanwanich et al. | Jan 2008 | A1 |
| 20080022208 | Morse | Jan 2008 | A1 |
| 20080027711 | Rajendran et al. | Jan 2008 | A1 |
| 20080027726 | Hansen et al. | Jan 2008 | A1 |
| 20080031475 | Goldstein | Feb 2008 | A1 |
| 20080033719 | Hall et al. | Feb 2008 | A1 |
| 20080033723 | Jang et al. | Feb 2008 | A1 |
| 20080034032 | Healey et al. | Feb 2008 | A1 |
| 20080034044 | Bhakta et al. | Feb 2008 | A1 |
| 20080034081 | Marshall et al. | Feb 2008 | A1 |
| 20080036743 | Westerman et al. | Feb 2008 | A1 |
| 20080040339 | Zhou et al. | Feb 2008 | A1 |
| 20080042970 | Liang et al. | Feb 2008 | A1 |
| 20080043936 | Liebermann | Feb 2008 | A1 |
| 20080043943 | Sipher et al. | Feb 2008 | A1 |
| 20080046239 | Boo | Feb 2008 | A1 |
| 20080046250 | Agapi et al. | Feb 2008 | A1 |
| 20080046422 | Lee et al. | Feb 2008 | A1 |
| 20080046820 | Lee et al. | Feb 2008 | A1 |
| 20080046948 | Verosub | Feb 2008 | A1 |
| 20080048908 | Sato | Feb 2008 | A1 |
| 20080050027 | Bashyam et al. | Feb 2008 | A1 |
| 20080052063 | Bennett et al. | Feb 2008 | A1 |
| 20080052073 | Goto et al. | Feb 2008 | A1 |
| 20080052077 | Bennett et al. | Feb 2008 | A1 |
| 20080052080 | Narayanan et al. | Feb 2008 | A1 |
| 20080052262 | Kosinov et al. | Feb 2008 | A1 |
| 20080055194 | Baudino et al. | Mar 2008 | A1 |
| 20080056459 | Vallier et al. | Mar 2008 | A1 |
| 20080056579 | Guha | Mar 2008 | A1 |
| 20080057922 | Kokes et al. | Mar 2008 | A1 |
| 20080059190 | Chu et al. | Mar 2008 | A1 |
| 20080059200 | Puli | Mar 2008 | A1 |
| 20080059876 | Hantler et al. | Mar 2008 | A1 |
| 20080062141 | Chandhri | Mar 2008 | A1 |
| 20080065382 | Gerl et al. | Mar 2008 | A1 |
| 20080065387 | Cross et al. | Mar 2008 | A1 |
| 20080071529 | Silverman et al. | Mar 2008 | A1 |
| 20080071544 | Beaufays et al. | Mar 2008 | A1 |
| 20080071742 | Yang et al. | Mar 2008 | A1 |
| 20080072143 | Assadollahi | Mar 2008 | A1 |
| 20080075296 | Lindahl et al. | Mar 2008 | A1 |
| 20080076972 | Dorogusker et al. | Mar 2008 | A1 |
| 20080077310 | Murlidar et al. | Mar 2008 | A1 |
| 20080077384 | Agapi et al. | Mar 2008 | A1 |
| 20080077386 | Gao et al. | Mar 2008 | A1 |
| 20080077391 | Chino et al. | Mar 2008 | A1 |
| 20080077393 | Gao et al. | Mar 2008 | A1 |
| 20080077406 | Ganong, III | Mar 2008 | A1 |
| 20080077859 | Schabes et al. | Mar 2008 | A1 |
| 20080079566 | Singh et al. | Apr 2008 | A1 |
| 20080080411 | Cole | Apr 2008 | A1 |
| 20080082332 | Mallett et al. | Apr 2008 | A1 |
| 20080082338 | O″Neil et al. | Apr 2008 | A1 |
| 20080082390 | Hawkins et al. | Apr 2008 | A1 |
| 20080082576 | Bodin et al. | Apr 2008 | A1 |
| 20080082651 | Singh et al. | Apr 2008 | A1 |
| 20080084974 | Dhanakshirur | Apr 2008 | A1 |
| 20080085689 | Zellner | Apr 2008 | A1 |
| 20080091406 | Baldwin et al. | Apr 2008 | A1 |
| 20080091426 | Rempel et al. | Apr 2008 | A1 |
| 20080091428 | Bellegarda | Apr 2008 | A1 |
| 20080091443 | Strope et al. | Apr 2008 | A1 |
| 20080096531 | Mcquaide et al. | Apr 2008 | A1 |
| 20080096533 | Manfredi et al. | Apr 2008 | A1 |
| 20080096726 | Riley et al. | Apr 2008 | A1 |
| 20080097937 | Hadjarian | Apr 2008 | A1 |
| 20080098302 | Roose | Apr 2008 | A1 |
| 20080098480 | Henry et al. | Apr 2008 | A1 |
| 20080100579 | Robinson et al. | May 2008 | A1 |
| 20080101584 | Gray et al. | May 2008 | A1 |
| 20080103774 | White | May 2008 | A1 |
| 20080109222 | Liu | May 2008 | A1 |
| 20080109402 | Wang et al. | May 2008 | A1 |
| 20080114480 | Harb | May 2008 | A1 |
| 20080114598 | Prieto et al. | May 2008 | A1 |
| 20080114604 | Wei et al. | May 2008 | A1 |
| 20080114841 | Lambert | May 2008 | A1 |
| 20080115084 | Scott et al. | May 2008 | A1 |
| 20080118143 | Gordon et al. | May 2008 | A1 |
| 20080119953 | Reed | May 2008 | A1 |
| 20080120102 | Rao | May 2008 | A1 |
| 20080120112 | Jordan et al. | May 2008 | A1 |
| 20080120196 | Reed et al. | May 2008 | A1 |
| 20080120311 | Reed | May 2008 | A1 |
| 20080120312 | Reed | May 2008 | A1 |
| 20080120330 | Reed | May 2008 | A1 |
| 20080120342 | Reed et al. | May 2008 | A1 |
| 20080122796 | Jobs et al. | May 2008 | A1 |
| 20080124695 | Myers et al. | May 2008 | A1 |
| 20080126075 | Thorn et al. | May 2008 | A1 |
| 20080126077 | Thorn | May 2008 | A1 |
| 20080126091 | Clark et al. | May 2008 | A1 |
| 20080126093 | Sivadas | May 2008 | A1 |
| 20080126100 | Grost et al. | May 2008 | A1 |
| 20080126491 | Portele et al. | May 2008 | A1 |
| 20080129520 | Lee | Jun 2008 | A1 |
| 20080130867 | Bowen | Jun 2008 | A1 |
| 20080131006 | Oliver | Jun 2008 | A1 |
| 20080132221 | Willey et al. | Jun 2008 | A1 |
| 20080132295 | Horowitz | Jun 2008 | A1 |
| 20080133215 | Sarukkai | Jun 2008 | A1 |
| 20080133228 | Rao | Jun 2008 | A1 |
| 20080133230 | Herforth et al. | Jun 2008 | A1 |
| 20080133241 | Baker et al. | Jun 2008 | A1 |
| 20080133245 | Proulx et al. | Jun 2008 | A1 |
| 20080133479 | Zelevinsky et al. | Jun 2008 | A1 |
| 20080133956 | Fadell | Jun 2008 | A1 |
| 20080140413 | Millman et al. | Jun 2008 | A1 |
| 20080140415 | Shostak | Jun 2008 | A1 |
| 20080140416 | Shostak | Jun 2008 | A1 |
| 20080140652 | Millman et al. | Jun 2008 | A1 |
| 20080140657 | Azvine et al. | Jun 2008 | A1 |
| 20080140702 | Reed et al. | Jun 2008 | A1 |
| 20080141125 | Ghassabian et al. | Jun 2008 | A1 |
| 20080141180 | Reed et al. | Jun 2008 | A1 |
| 20080141182 | Barsness et al. | Jun 2008 | A1 |
| 20080146245 | Appaji | Jun 2008 | A1 |
| 20080146290 | Sreeram et al. | Jun 2008 | A1 |
| 20080147408 | Da Palma et al. | Jun 2008 | A1 |
| 20080147411 | Dames et al. | Jun 2008 | A1 |
| 20080147874 | Yoneda et al. | Jun 2008 | A1 |
| 20080150900 | Han | Jun 2008 | A1 |
| 20080154577 | Kim et al. | Jun 2008 | A1 |
| 20080154599 | Muschett et al. | Jun 2008 | A1 |
| 20080154600 | Tian et al. | Jun 2008 | A1 |
| 20080154603 | Oddo | Jun 2008 | A1 |
| 20080154612 | Evermann et al. | Jun 2008 | A1 |
| 20080154828 | Antebi et al. | Jun 2008 | A1 |
| 20080157867 | Krah | Jul 2008 | A1 |
| 20080161113 | Hansen et al. | Jul 2008 | A1 |
| 20080162120 | MacTavish et al. | Jul 2008 | A1 |
| 20080162137 | Saitoh et al. | Jul 2008 | A1 |
| 20080162471 | Bernard | Jul 2008 | A1 |
| 20080163119 | Kim et al. | Jul 2008 | A1 |
| 20080163131 | Hirai et al. | Jul 2008 | A1 |
| 20080165144 | Forstall et al. | Jul 2008 | A1 |
| 20080165980 | Pavlovic et al. | Jul 2008 | A1 |
| 20080165994 | Caren et al. | Jul 2008 | A1 |
| 20080167013 | Novick et al. | Jul 2008 | A1 |
| 20080167858 | Christie et al. | Jul 2008 | A1 |
| 20080167876 | Bakis et al. | Jul 2008 | A1 |
| 20080168052 | Ott et al. | Jul 2008 | A1 |
| 20080168144 | Lee | Jul 2008 | A1 |
| 20080168366 | Kocienda et al. | Jul 2008 | A1 |
| 20080172698 | Berger et al. | Jul 2008 | A1 |
| 20080183473 | Nagano et al. | Jul 2008 | A1 |
| 20080186960 | Kocheisen et al. | Aug 2008 | A1 |
| 20080189099 | Friedman et al. | Aug 2008 | A1 |
| 20080189106 | Low et al. | Aug 2008 | A1 |
| 20080189110 | Freeman et al. | Aug 2008 | A1 |
| 20080189114 | Fail et al. | Aug 2008 | A1 |
| 20080189360 | Kiley et al. | Aug 2008 | A1 |
| 20080189606 | Rybak | Aug 2008 | A1 |
| 20080195312 | Aaron et al. | Aug 2008 | A1 |
| 20080195388 | Bower et al. | Aug 2008 | A1 |
| 20080195391 | Marple et al. | Aug 2008 | A1 |
| 20080195601 | Ntoulas et al. | Aug 2008 | A1 |
| 20080195630 | Exartier et al. | Aug 2008 | A1 |
| 20080195940 | Gail et al. | Aug 2008 | A1 |
| 20080200142 | Abdel-Kader et al. | Aug 2008 | A1 |
| 20080201000 | Heikkila et al. | Aug 2008 | A1 |
| 20080201306 | Cooper et al. | Aug 2008 | A1 |
| 20080201375 | Khedouri et al. | Aug 2008 | A1 |
| 20080201434 | Holmes et al. | Aug 2008 | A1 |
| 20080204379 | Perez-Noguera | Aug 2008 | A1 |
| 20080207176 | Brackbill et al. | Aug 2008 | A1 |
| 20080208585 | Ativanichayaphong et al. | Aug 2008 | A1 |
| 20080208587 | Ben-David et al. | Aug 2008 | A1 |
| 20080208864 | Cucerzan et al. | Aug 2008 | A1 |
| 20080212796 | Denda | Sep 2008 | A1 |
| 20080219641 | Sandrew et al. | Sep 2008 | A1 |
| 20080221866 | Katragadda et al. | Sep 2008 | A1 |
| 20080221879 | Cerra et al. | Sep 2008 | A1 |
| 20080221880 | Cerra et al. | Sep 2008 | A1 |
| 20080221887 | Rose et al. | Sep 2008 | A1 |
| 20080221889 | Cerra et al. | Sep 2008 | A1 |
| 20080221903 | Kanevsky et al. | Sep 2008 | A1 |
| 20080222118 | Scian et al. | Sep 2008 | A1 |
| 20080226130 | Kansal et al. | Sep 2008 | A1 |
| 20080228463 | Mori et al. | Sep 2008 | A1 |
| 20080228485 | Owen | Sep 2008 | A1 |
| 20080228490 | Fischer et al. | Sep 2008 | A1 |
| 20080228495 | Cross et al. | Sep 2008 | A1 |
| 20080228496 | Yu et al. | Sep 2008 | A1 |
| 20080228928 | Donelli et al. | Sep 2008 | A1 |
| 20080229185 | Lynch | Sep 2008 | A1 |
| 20080229218 | Maeng | Sep 2008 | A1 |
| 20080235017 | Satomura et al. | Sep 2008 | A1 |
| 20080235023 | Kennewick et al. | Sep 2008 | A1 |
| 20080235024 | Goldberg et al. | Sep 2008 | A1 |
| 20080235027 | Cross | Sep 2008 | A1 |
| 20080240569 | Tonouchi | Oct 2008 | A1 |
| 20080242280 | Shapiro et al. | Oct 2008 | A1 |
| 20080242322 | Scott et al. | Oct 2008 | A1 |
| 20080242363 | Onda et al. | Oct 2008 | A1 |
| 20080243501 | Hafsteinsson et al. | Oct 2008 | A1 |
| 20080243834 | Rieman et al. | Oct 2008 | A1 |
| 20080244390 | Fux et al. | Oct 2008 | A1 |
| 20080244446 | Lefevre et al. | Oct 2008 | A1 |
| 20080247519 | Abella et al. | Oct 2008 | A1 |
| 20080247529 | Barton et al. | Oct 2008 | A1 |
| 20080248797 | Freeman et al. | Oct 2008 | A1 |
| 20080249770 | Kim et al. | Oct 2008 | A1 |
| 20080249778 | Barton et al. | Oct 2008 | A1 |
| 20080253577 | Eppolito | Oct 2008 | A1 |
| 20080254419 | Cohen | Oct 2008 | A1 |
| 20080254425 | Cohen et al. | Oct 2008 | A1 |
| 20080255837 | Kahn et al. | Oct 2008 | A1 |
| 20080255842 | Simhi et al. | Oct 2008 | A1 |
| 20080255845 | Bennett | Oct 2008 | A1 |
| 20080256613 | Grover | Oct 2008 | A1 |
| 20080259022 | Mansfield et al. | Oct 2008 | A1 |
| 20080261572 | Tsui et al. | Oct 2008 | A1 |
| 20080262828 | Och et al. | Oct 2008 | A1 |
| 20080262838 | Nurminen et al. | Oct 2008 | A1 |
| 20080262846 | Burns et al. | Oct 2008 | A1 |
| 20080263139 | Martin | Oct 2008 | A1 |
| 20080267416 | Goldstein et al. | Oct 2008 | A1 |
| 20080270118 | Kuo et al. | Oct 2008 | A1 |
| 20080270138 | Knight et al. | Oct 2008 | A1 |
| 20080270139 | Shi et al. | Oct 2008 | A1 |
| 20080270140 | Hertz et al. | Oct 2008 | A1 |
| 20080270151 | Mahoney et al. | Oct 2008 | A1 |
| 20080270344 | Yurick et al. | Oct 2008 | A1 |
| 20080273672 | Didcock et al. | Nov 2008 | A1 |
| 20080277473 | Kotlarsky et al. | Nov 2008 | A1 |
| 20080281510 | Shahine | Nov 2008 | A1 |
| 20080281582 | Hsu et al. | Nov 2008 | A1 |
| 20080288259 | Chambers et al. | Nov 2008 | A1 |
| 20080288460 | Poniatowski et al. | Nov 2008 | A1 |
| 20080292112 | Valenzuela et al. | Nov 2008 | A1 |
| 20080294418 | Cleary et al. | Nov 2008 | A1 |
| 20080294517 | Hill | Nov 2008 | A1 |
| 20080294651 | Masuyama et al. | Nov 2008 | A1 |
| 20080294981 | Balzano et al. | Nov 2008 | A1 |
| 20080298563 | Rondeau et al. | Dec 2008 | A1 |
| 20080298766 | Wen et al. | Dec 2008 | A1 |
| 20080299523 | Chai et al. | Dec 2008 | A1 |
| 20080300857 | Barbaiani et al. | Dec 2008 | A1 |
| 20080300871 | Gilbert | Dec 2008 | A1 |
| 20080300877 | Gilbert et al. | Dec 2008 | A1 |
| 20080300878 | Bennett | Dec 2008 | A1 |
| 20080301567 | Martin et al. | Dec 2008 | A1 |
| 20080303645 | Seymour et al. | Dec 2008 | A1 |
| 20080306727 | Thurmair et al. | Dec 2008 | A1 |
| 20080312909 | Hermansen et al. | Dec 2008 | A1 |
| 20080312928 | Goebel et al. | Dec 2008 | A1 |
| 20080313335 | Jung et al. | Dec 2008 | A1 |
| 20080316183 | Westerman et al. | Dec 2008 | A1 |
| 20080319735 | Kambhatla et al. | Dec 2008 | A1 |
| 20080319738 | Liu et al. | Dec 2008 | A1 |
| 20080319753 | Hancock | Dec 2008 | A1 |
| 20080319763 | Di Fabbrizio et al. | Dec 2008 | A1 |
| 20080319783 | Yao et al. | Dec 2008 | A1 |
| 20090003115 | Lindahl et al. | Jan 2009 | A1 |
| 20090005012 | Van Heugten | Jan 2009 | A1 |
| 20090005891 | Batson et al. | Jan 2009 | A1 |
| 20090006096 | Li et al. | Jan 2009 | A1 |
| 20090006097 | Etezadi et al. | Jan 2009 | A1 |
| 20090006099 | Sharpe et al. | Jan 2009 | A1 |
| 20090006100 | Badger et al. | Jan 2009 | A1 |
| 20090006343 | Platt et al. | Jan 2009 | A1 |
| 20090006345 | Platt et al. | Jan 2009 | A1 |
| 20090006488 | Lindahl et al. | Jan 2009 | A1 |
| 20090006671 | Batson et al. | Jan 2009 | A1 |
| 20090007001 | Morin et al. | Jan 2009 | A1 |
| 20090011709 | Akasaka et al. | Jan 2009 | A1 |
| 20090012748 | Beish et al. | Jan 2009 | A1 |
| 20090012775 | El Hady et al. | Jan 2009 | A1 |
| 20090018828 | Nakadai et al. | Jan 2009 | A1 |
| 20090018829 | Kuperstein | Jan 2009 | A1 |
| 20090018834 | Cooper et al. | Jan 2009 | A1 |
| 20090018835 | Cooper et al. | Jan 2009 | A1 |
| 20090018839 | Cooper et al. | Jan 2009 | A1 |
| 20090018840 | Lutz et al. | Jan 2009 | A1 |
| 20090022329 | Mahowald | Jan 2009 | A1 |
| 20090024595 | Chen | Jan 2009 | A1 |
| 20090028435 | Wu et al. | Jan 2009 | A1 |
| 20090030685 | Cerra et al. | Jan 2009 | A1 |
| 20090030800 | Grois | Jan 2009 | A1 |
| 20090030978 | Johnson et al. | Jan 2009 | A1 |
| 20090043580 | Mozer et al. | Feb 2009 | A1 |
| 20090043583 | Agapi et al. | Feb 2009 | A1 |
| 20090043763 | Peng | Feb 2009 | A1 |
| 20090044094 | Rapp et al. | Feb 2009 | A1 |
| 20090048821 | Yam et al. | Feb 2009 | A1 |
| 20090048841 | Pollet et al. | Feb 2009 | A1 |
| 20090048845 | Burckart et al. | Feb 2009 | A1 |
| 20090049067 | Murray | Feb 2009 | A1 |
| 20090055168 | Wu et al. | Feb 2009 | A1 |
| 20090055175 | Terrell et al. | Feb 2009 | A1 |
| 20090055179 | Cho et al. | Feb 2009 | A1 |
| 20090055186 | Lance et al. | Feb 2009 | A1 |
| 20090055380 | Peng et al. | Feb 2009 | A1 |
| 20090055381 | Wu et al. | Feb 2009 | A1 |
| 20090055648 | Kim et al. | Feb 2009 | A1 |
| 20090058823 | Kocienda | Mar 2009 | A1 |
| 20090058860 | Fong et al. | Mar 2009 | A1 |
| 20090060351 | Li et al. | Mar 2009 | A1 |
| 20090060472 | Bull et al. | Mar 2009 | A1 |
| 20090063974 | Bull et al. | Mar 2009 | A1 |
| 20090064031 | Bull et al. | Mar 2009 | A1 |
| 20090070097 | Wu et al. | Mar 2009 | A1 |
| 20090070102 | Maegawa | Mar 2009 | A1 |
| 20090070109 | Didcock et al. | Mar 2009 | A1 |
| 20090070114 | Staszak | Mar 2009 | A1 |
| 20090074214 | Bradford et al. | Mar 2009 | A1 |
| 20090076792 | Lawson-Tancred | Mar 2009 | A1 |
| 20090076796 | Daraselia | Mar 2009 | A1 |
| 20090076798 | Oh et al. | Mar 2009 | A1 |
| 20090076819 | Wouters et al. | Mar 2009 | A1 |
| 20090076821 | Brenner et al. | Mar 2009 | A1 |
| 20090076825 | Bradford et al. | Mar 2009 | A1 |
| 20090077165 | Rhodes et al. | Mar 2009 | A1 |
| 20090077464 | Goldsmith et al. | Mar 2009 | A1 |
| 20090079622 | Seshadri et al. | Mar 2009 | A1 |
| 20090083034 | Hernandez et al. | Mar 2009 | A1 |
| 20090083035 | Huang et al. | Mar 2009 | A1 |
| 20090083036 | Zhao et al. | Mar 2009 | A1 |
| 20090083037 | Gleason et al. | Mar 2009 | A1 |
| 20090083047 | Lindahl et al. | Mar 2009 | A1 |
| 20090089058 | Bellegarda | Apr 2009 | A1 |
| 20090091537 | Huang et al. | Apr 2009 | A1 |
| 20090092239 | Macwan | Apr 2009 | A1 |
| 20090092260 | Powers | Apr 2009 | A1 |
| 20090092261 | Bard | Apr 2009 | A1 |
| 20090092262 | Costa et al. | Apr 2009 | A1 |
| 20090094029 | Koch et al. | Apr 2009 | A1 |
| 20090094033 | Mozer et al. | Apr 2009 | A1 |
| 20090097634 | Nambiar et al. | Apr 2009 | A1 |
| 20090097637 | Boscher et al. | Apr 2009 | A1 |
| 20090098903 | Donaldson et al. | Apr 2009 | A1 |
| 20090100049 | Cao | Apr 2009 | A1 |
| 20090100454 | Weber | Apr 2009 | A1 |
| 20090104898 | Harris | Apr 2009 | A1 |
| 20090106026 | Ferrieux | Apr 2009 | A1 |
| 20090106376 | Tom et al. | Apr 2009 | A1 |
| 20090106397 | O'Keefe | Apr 2009 | A1 |
| 20090112572 | Thorn | Apr 2009 | A1 |
| 20090112576 | Jackson et al. | Apr 2009 | A1 |
| 20090112592 | Candelore et al. | Apr 2009 | A1 |
| 20090112677 | Rhett | Apr 2009 | A1 |
| 20090112892 | Cardie et al. | Apr 2009 | A1 |
| 20090119587 | Allen et al. | May 2009 | A1 |
| 20090123021 | Jung et al. | May 2009 | A1 |
| 20090123071 | Iwasaki | May 2009 | A1 |
| 20090125477 | Lu et al. | May 2009 | A1 |
| 20090125602 | Bhatia et al. | May 2009 | A1 |
| 20090125947 | Ibaraki | May 2009 | A1 |
| 20090128505 | Partridge et al. | May 2009 | A1 |
| 20090132253 | Bellegarda | May 2009 | A1 |
| 20090132255 | Lu | May 2009 | A1 |
| 20090137286 | Luke et al. | May 2009 | A1 |
| 20090138263 | Shozakai et al. | May 2009 | A1 |
| 20090138430 | Nambiar et al. | May 2009 | A1 |
| 20090138736 | Chin | May 2009 | A1 |
| 20090138828 | Schultz et al. | May 2009 | A1 |
| 20090144036 | Jorgensen et al. | Jun 2009 | A1 |
| 20090144049 | Haddad et al. | Jun 2009 | A1 |
| 20090144428 | Bowater et al. | Jun 2009 | A1 |
| 20090144609 | Liang et al. | Jun 2009 | A1 |
| 20090146848 | Ghassabian | Jun 2009 | A1 |
| 20090150147 | Jacoby et al. | Jun 2009 | A1 |
| 20090150156 | Kennewick et al. | Jun 2009 | A1 |
| 20090152349 | Bonev et al. | Jun 2009 | A1 |
| 20090153288 | Hope et al. | Jun 2009 | A1 |
| 20090154669 | Wood et al. | Jun 2009 | A1 |
| 20090157382 | Bar | Jun 2009 | A1 |
| 20090157384 | Toutanova et al. | Jun 2009 | A1 |
| 20090157401 | Bennett | Jun 2009 | A1 |
| 20090158200 | Palahnuk et al. | Jun 2009 | A1 |
| 20090158323 | Bober et al. | Jun 2009 | A1 |
| 20090158423 | Orlassino et al. | Jun 2009 | A1 |
| 20090160803 | Hashimoto | Jun 2009 | A1 |
| 20090163243 | Barbera | Jun 2009 | A1 |
| 20090164219 | Yeung et al. | Jun 2009 | A1 |
| 20090164301 | O'Sullivan et al. | Jun 2009 | A1 |
| 20090164441 | Cheyer | Jun 2009 | A1 |
| 20090164655 | Pettersson et al. | Jun 2009 | A1 |
| 20090164937 | Alviar et al. | Jun 2009 | A1 |
| 20090167508 | Fadell et al. | Jul 2009 | A1 |
| 20090167509 | Fadell et al. | Jul 2009 | A1 |
| 20090171578 | Kim et al. | Jul 2009 | A1 |
| 20090171662 | Huang et al. | Jul 2009 | A1 |
| 20090171664 | Kennewick et al. | Jul 2009 | A1 |
| 20090172108 | Singh | Jul 2009 | A1 |
| 20090172542 | Girish et al. | Jul 2009 | A1 |
| 20090174667 | Kocienda et al. | Jul 2009 | A1 |
| 20090174677 | Gehani et al. | Jul 2009 | A1 |
| 20090177300 | Lee | Jul 2009 | A1 |
| 20090177461 | Ehsani et al. | Jul 2009 | A1 |
| 20090177966 | Chaudhri | Jul 2009 | A1 |
| 20090182445 | Girish et al. | Jul 2009 | A1 |
| 20090182549 | Anisimovich et al. | Jul 2009 | A1 |
| 20090182702 | Miller | Jul 2009 | A1 |
| 20090183070 | Robbins | Jul 2009 | A1 |
| 20090187402 | Scholl | Jul 2009 | A1 |
| 20090187577 | Reznik et al. | Jul 2009 | A1 |
| 20090187950 | Nicas et al. | Jul 2009 | A1 |
| 20090190774 | Wang et al. | Jul 2009 | A1 |
| 20090191895 | Singh et al. | Jul 2009 | A1 |
| 20090192782 | Drewes | Jul 2009 | A1 |
| 20090192787 | Roon | Jul 2009 | A1 |
| 20090192798 | Basson et al. | Jul 2009 | A1 |
| 20090198497 | Kwon | Aug 2009 | A1 |
| 20090204402 | Marhawa et al. | Aug 2009 | A1 |
| 20090204409 | Mozer et al. | Aug 2009 | A1 |
| 20090204478 | Kaib et al. | Aug 2009 | A1 |
| 20090204596 | Brun et al. | Aug 2009 | A1 |
| 20090204601 | Grasset | Aug 2009 | A1 |
| 20090204620 | Thione et al. | Aug 2009 | A1 |
| 20090210230 | Schwarz et al. | Aug 2009 | A1 |
| 20090210232 | Sanchez et al. | Aug 2009 | A1 |
| 20090213134 | Stephanick et al. | Aug 2009 | A1 |
| 20090215466 | Ahl et al. | Aug 2009 | A1 |
| 20090215503 | Zhang et al. | Aug 2009 | A1 |
| 20090216396 | Yamagata | Aug 2009 | A1 |
| 20090216528 | Gemello et al. | Aug 2009 | A1 |
| 20090216540 | Tessel et al. | Aug 2009 | A1 |
| 20090216704 | Zheng et al. | Aug 2009 | A1 |
| 20090219166 | MacFarlane et al. | Sep 2009 | A1 |
| 20090221274 | Venkatakrishnan et al. | Sep 2009 | A1 |
| 20090222257 | Sumita et al. | Sep 2009 | A1 |
| 20090222270 | Likens et al. | Sep 2009 | A2 |
| 20090222488 | Boerries et al. | Sep 2009 | A1 |
| 20090228126 | Spielberg et al. | Sep 2009 | A1 |
| 20090228273 | Wang et al. | Sep 2009 | A1 |
| 20090228277 | Bonforte et al. | Sep 2009 | A1 |
| 20090228281 | Singleton et al. | Sep 2009 | A1 |
| 20090228439 | Manolescu et al. | Sep 2009 | A1 |
| 20090228792 | Van Os et al. | Sep 2009 | A1 |
| 20090228842 | Westerman et al. | Sep 2009 | A1 |
| 20090233264 | Rogers et al. | Sep 2009 | A1 |
| 20090234638 | Ranjan et al. | Sep 2009 | A1 |
| 20090234651 | Basir et al. | Sep 2009 | A1 |
| 20090234655 | Kwon | Sep 2009 | A1 |
| 20090235280 | Tannier et al. | Sep 2009 | A1 |
| 20090239202 | Stone | Sep 2009 | A1 |
| 20090239552 | Churchill et al. | Sep 2009 | A1 |
| 20090240485 | Dalal et al. | Sep 2009 | A1 |
| 20090241054 | Hendricks | Sep 2009 | A1 |
| 20090241760 | Georges | Oct 2009 | A1 |
| 20090247237 | Mittleman et al. | Oct 2009 | A1 |
| 20090248182 | Logan et al. | Oct 2009 | A1 |
| 20090248395 | Alewine et al. | Oct 2009 | A1 |
| 20090248402 | Ito et al. | Oct 2009 | A1 |
| 20090248420 | Basir et al. | Oct 2009 | A1 |
| 20090248422 | Li et al. | Oct 2009 | A1 |
| 20090248456 | Fahmy et al. | Oct 2009 | A1 |
| 20090249198 | Davis et al. | Oct 2009 | A1 |
| 20090249247 | Tseng et al. | Oct 2009 | A1 |
| 20090252350 | Seguin | Oct 2009 | A1 |
| 20090253457 | Seguin | Oct 2009 | A1 |
| 20090253463 | Shin et al. | Oct 2009 | A1 |
| 20090254339 | Seguin | Oct 2009 | A1 |
| 20090254345 | Fleizach et al. | Oct 2009 | A1 |
| 20090254819 | Song et al. | Oct 2009 | A1 |
| 20090254823 | Barrett | Oct 2009 | A1 |
| 20090259475 | Yamagami et al. | Oct 2009 | A1 |
| 20090259969 | Pallakoff | Oct 2009 | A1 |
| 20090265171 | Davis | Oct 2009 | A1 |
| 20090265368 | Crider et al. | Oct 2009 | A1 |
| 20090271109 | Lee et al. | Oct 2009 | A1 |
| 20090271175 | Bodin et al. | Oct 2009 | A1 |
| 20090271176 | Bodin et al. | Oct 2009 | A1 |
| 20090271178 | Bodin et al. | Oct 2009 | A1 |
| 20090271188 | Agapi et al. | Oct 2009 | A1 |
| 20090271189 | Agapi et al. | Oct 2009 | A1 |
| 20090274315 | Carnes et al. | Nov 2009 | A1 |
| 20090278804 | Rubanovich et al. | Nov 2009 | A1 |
| 20090281789 | Waibel et al. | Nov 2009 | A1 |
| 20090284482 | Chin | Nov 2009 | A1 |
| 20090286514 | Lichorowic et al. | Nov 2009 | A1 |
| 20090287583 | Holmes | Nov 2009 | A1 |
| 20090290718 | Kahn et al. | Nov 2009 | A1 |
| 20090292987 | Sorenson | Nov 2009 | A1 |
| 20090296552 | Hicks et al. | Dec 2009 | A1 |
| 20090298474 | George | Dec 2009 | A1 |
| 20090298529 | Mahajan | Dec 2009 | A1 |
| 20090299745 | Kennewick et al. | Dec 2009 | A1 |
| 20090299849 | Cao et al. | Dec 2009 | A1 |
| 20090300391 | Jessup et al. | Dec 2009 | A1 |
| 20090300488 | Salamon et al. | Dec 2009 | A1 |
| 20090304198 | Herre et al. | Dec 2009 | A1 |
| 20090305203 | Okumura et al. | Dec 2009 | A1 |
| 20090306967 | Nicolov et al. | Dec 2009 | A1 |
| 20090306969 | Goud et al. | Dec 2009 | A1 |
| 20090306979 | Jaiswal et al. | Dec 2009 | A1 |
| 20090306980 | Shin | Dec 2009 | A1 |
| 20090306981 | Cromack et al. | Dec 2009 | A1 |
| 20090306985 | Roberts et al. | Dec 2009 | A1 |
| 20090306988 | Chen et al. | Dec 2009 | A1 |
| 20090306989 | Kaji | Dec 2009 | A1 |
| 20090307162 | Bui et al. | Dec 2009 | A1 |
| 20090307201 | Dunning et al. | Dec 2009 | A1 |
| 20090307584 | Davidson et al. | Dec 2009 | A1 |
| 20090307594 | Kosonen et al. | Dec 2009 | A1 |
| 20090309352 | Walker et al. | Dec 2009 | A1 |
| 20090313014 | Shin et al. | Dec 2009 | A1 |
| 20090313020 | Koivunen | Dec 2009 | A1 |
| 20090313023 | Jones | Dec 2009 | A1 |
| 20090313026 | Coffman et al. | Dec 2009 | A1 |
| 20090313544 | Wood et al. | Dec 2009 | A1 |
| 20090313564 | Rottler et al. | Dec 2009 | A1 |
| 20090316943 | Frigola Munoz et al. | Dec 2009 | A1 |
| 20090318119 | Basir et al. | Dec 2009 | A1 |
| 20090318198 | Carroll | Dec 2009 | A1 |
| 20090319257 | Blume et al. | Dec 2009 | A1 |
| 20090319266 | Brown et al. | Dec 2009 | A1 |
| 20090319342 | Shilman et al. | Dec 2009 | A1 |
| 20090326923 | Yan et al. | Dec 2009 | A1 |
| 20090326936 | Nagashima | Dec 2009 | A1 |
| 20090326938 | Marila et al. | Dec 2009 | A1 |
| 20090326949 | Douthitt et al. | Dec 2009 | A1 |
| 20090327977 | Bachfischer et al. | Dec 2009 | A1 |
| 20100004918 | Lee et al. | Jan 2010 | A1 |
| 20100004930 | Strope et al. | Jan 2010 | A1 |
| 20100004931 | Ma et al. | Jan 2010 | A1 |
| 20100005081 | Bennett | Jan 2010 | A1 |
| 20100010803 | Ishikawa et al. | Jan 2010 | A1 |
| 20100010814 | Patel | Jan 2010 | A1 |
| 20100013760 | Hirai et al. | Jan 2010 | A1 |
| 20100013796 | Abileah et al. | Jan 2010 | A1 |
| 20100017212 | Attwater et al. | Jan 2010 | A1 |
| 20100017382 | Katragadda et al. | Jan 2010 | A1 |
| 20100019834 | Zerbe et al. | Jan 2010 | A1 |
| 20100023318 | Lemoine | Jan 2010 | A1 |
| 20100023320 | Di Cristo et al. | Jan 2010 | A1 |
| 20100023331 | Duta et al. | Jan 2010 | A1 |
| 20100026526 | Yokota | Feb 2010 | A1 |
| 20100030549 | Lee et al. | Feb 2010 | A1 |
| 20100030928 | Conroy et al. | Feb 2010 | A1 |
| 20100031143 | Rao et al. | Feb 2010 | A1 |
| 20100036653 | Kim et al. | Feb 2010 | A1 |
| 20100036655 | Cecil et al. | Feb 2010 | A1 |
| 20100036660 | Bennett | Feb 2010 | A1 |
| 20100036928 | Granito et al. | Feb 2010 | A1 |
| 20100037183 | Miyashita et al. | Feb 2010 | A1 |
| 20100042400 | Block et al. | Feb 2010 | A1 |
| 20100042576 | Roettger et al. | Feb 2010 | A1 |
| 20100046842 | Conwell et al. | Feb 2010 | A1 |
| 20100049498 | Cao et al. | Feb 2010 | A1 |
| 20100049514 | Kennewick et al. | Feb 2010 | A1 |
| 20100050064 | Liu et al. | Feb 2010 | A1 |
| 20100054512 | Solum | Mar 2010 | A1 |
| 20100057435 | Kent et al. | Mar 2010 | A1 |
| 20100057443 | Di Cristo et al. | Mar 2010 | A1 |
| 20100057457 | Ogata et al. | Mar 2010 | A1 |
| 20100057461 | Neubacher et al. | Mar 2010 | A1 |
| 20100057643 | Yang | Mar 2010 | A1 |
| 20100058200 | Jablokov et al. | Mar 2010 | A1 |
| 20100060646 | Unsal et al. | Mar 2010 | A1 |
| 20100063804 | Sato et al. | Mar 2010 | A1 |
| 20100063825 | Williams et al. | Mar 2010 | A1 |
| 20100063961 | Guiheneuf et al. | Mar 2010 | A1 |
| 20100064113 | Lindahl et al. | Mar 2010 | A1 |
| 20100064218 | Bull et al. | Mar 2010 | A1 |
| 20100064226 | Stefaniak et al. | Mar 2010 | A1 |
| 20100066546 | Aaron | Mar 2010 | A1 |
| 20100066684 | Shahraray et al. | Mar 2010 | A1 |
| 20100067723 | Bergmann et al. | Mar 2010 | A1 |
| 20100067867 | Lin et al. | Mar 2010 | A1 |
| 20100070281 | Conkie et al. | Mar 2010 | A1 |
| 20100070521 | Clinchant et al. | Mar 2010 | A1 |
| 20100070899 | Hunt et al. | Mar 2010 | A1 |
| 20100071003 | Bychkov et al. | Mar 2010 | A1 |
| 20100076760 | Kraenzel et al. | Mar 2010 | A1 |
| 20100076993 | Klawitter et al. | Mar 2010 | A1 |
| 20100077350 | Lim et al. | Mar 2010 | A1 |
| 20100079501 | Ikeda et al. | Apr 2010 | A1 |
| 20100080398 | Waldmann | Apr 2010 | A1 |
| 20100080470 | Deluca et al. | Apr 2010 | A1 |
| 20100081456 | Singh et al. | Apr 2010 | A1 |
| 20100081487 | Chen et al. | Apr 2010 | A1 |
| 20100082286 | Leung | Apr 2010 | A1 |
| 20100082327 | Rogers et al. | Apr 2010 | A1 |
| 20100082328 | Rogers et al. | Apr 2010 | A1 |
| 20100082329 | Silverman et al. | Apr 2010 | A1 |
| 20100082333 | Al-Shammari | Apr 2010 | A1 |
| 20100082346 | Rogers et al. | Apr 2010 | A1 |
| 20100082347 | Rogers et al. | Apr 2010 | A1 |
| 20100082348 | Silverman et al. | Apr 2010 | A1 |
| 20100082349 | Bellegarda et al. | Apr 2010 | A1 |
| 20100082567 | Rosenblatt et al. | Apr 2010 | A1 |
| 20100082970 | Lindahl et al. | Apr 2010 | A1 |
| 20100086152 | Rank et al. | Apr 2010 | A1 |
| 20100086153 | Hagen et al. | Apr 2010 | A1 |
| 20100086156 | Rank et al. | Apr 2010 | A1 |
| 20100088020 | Sano et al. | Apr 2010 | A1 |
| 20100088093 | Lee et al. | Apr 2010 | A1 |
| 20100088100 | Lindahl | Apr 2010 | A1 |
| 20100094632 | Davis et al. | Apr 2010 | A1 |
| 20100098231 | Wohlert et al. | Apr 2010 | A1 |
| 20100100212 | Lindahl et al. | Apr 2010 | A1 |
| 20100100384 | Ju et al. | Apr 2010 | A1 |
| 20100100385 | Davis et al. | Apr 2010 | A1 |
| 20100100816 | Mccloskey et al. | Apr 2010 | A1 |
| 20100103776 | Chan | Apr 2010 | A1 |
| 20100106486 | Hua et al. | Apr 2010 | A1 |
| 20100106498 | Morrison et al. | Apr 2010 | A1 |
| 20100106500 | McKee et al. | Apr 2010 | A1 |
| 20100106503 | Farrell et al. | Apr 2010 | A1 |
| 20100114856 | Kuboyama | May 2010 | A1 |
| 20100114887 | Conway et al. | May 2010 | A1 |
| 20100121637 | Roy et al. | May 2010 | A1 |
| 20100125456 | Weng et al. | May 2010 | A1 |
| 20100125458 | Franco et al. | May 2010 | A1 |
| 20100125460 | Mellott et al. | May 2010 | A1 |
| 20100125811 | Moore et al. | May 2010 | A1 |
| 20100131269 | Park et al. | May 2010 | A1 |
| 20100131273 | Aley-Raz et al. | May 2010 | A1 |
| 20100131498 | Linthicum et al. | May 2010 | A1 |
| 20100131899 | Hubert | May 2010 | A1 |
| 20100138215 | Williams | Jun 2010 | A1 |
| 20100138224 | Bedingfield, Sr. | Jun 2010 | A1 |
| 20100138416 | Bellotti | Jun 2010 | A1 |
| 20100138680 | Brisebois et al. | Jun 2010 | A1 |
| 20100138759 | Roy | Jun 2010 | A1 |
| 20100138798 | Wilson et al. | Jun 2010 | A1 |
| 20100142740 | Roerup | Jun 2010 | A1 |
| 20100145694 | Ju et al. | Jun 2010 | A1 |
| 20100145700 | Kennewick et al. | Jun 2010 | A1 |
| 20100145707 | Ljolje et al. | Jun 2010 | A1 |
| 20100146442 | Nagasaka et al. | Jun 2010 | A1 |
| 20100150321 | Harris et al. | Jun 2010 | A1 |
| 20100153114 | Shih et al. | Jun 2010 | A1 |
| 20100153115 | Klee et al. | Jun 2010 | A1 |
| 20100153448 | Harpur et al. | Jun 2010 | A1 |
| 20100161311 | Massuh | Jun 2010 | A1 |
| 20100161313 | Karttunen | Jun 2010 | A1 |
| 20100161337 | Pulz et al. | Jun 2010 | A1 |
| 20100161554 | Datuashvili et al. | Jun 2010 | A1 |
| 20100164897 | Morin et al. | Jul 2010 | A1 |
| 20100169075 | Raffa et al. | Jul 2010 | A1 |
| 20100169093 | Washio | Jul 2010 | A1 |
| 20100169097 | Nachman et al. | Jul 2010 | A1 |
| 20100169098 | Patch | Jul 2010 | A1 |
| 20100171713 | Kwok et al. | Jul 2010 | A1 |
| 20100174544 | Heifets | Jul 2010 | A1 |
| 20100175066 | Paik | Jul 2010 | A1 |
| 20100179932 | Yoon et al. | Jul 2010 | A1 |
| 20100179991 | Lorch et al. | Jul 2010 | A1 |
| 20100180218 | Boston et al. | Jul 2010 | A1 |
| 20100185448 | Meisel | Jul 2010 | A1 |
| 20100185949 | Jaeger | Jul 2010 | A1 |
| 20100191520 | Gruhn et al. | Jul 2010 | A1 |
| 20100197359 | Harris | Aug 2010 | A1 |
| 20100199180 | Brichter et al. | Aug 2010 | A1 |
| 20100199215 | Seymour et al. | Aug 2010 | A1 |
| 20100204986 | Kennewick et al. | Aug 2010 | A1 |
| 20100211199 | Naik et al. | Aug 2010 | A1 |
| 20100211379 | Gorman et al. | Aug 2010 | A1 |
| 20100211644 | Lavoie et al. | Aug 2010 | A1 |
| 20100216509 | Riemer et al. | Aug 2010 | A1 |
| 20100217604 | Baldwin et al. | Aug 2010 | A1 |
| 20100222033 | Scott et al. | Sep 2010 | A1 |
| 20100222098 | Garg | Sep 2010 | A1 |
| 20100223055 | Mclean | Sep 2010 | A1 |
| 20100223056 | Kadirkamanathan et al. | Sep 2010 | A1 |
| 20100223131 | Scott et al. | Sep 2010 | A1 |
| 20100225599 | Danielsson et al. | Sep 2010 | A1 |
| 20100225809 | Connors et al. | Sep 2010 | A1 |
| 20100227642 | Kim et al. | Sep 2010 | A1 |
| 20100228540 | Bennett | Sep 2010 | A1 |
| 20100228549 | Herman et al. | Sep 2010 | A1 |
| 20100228691 | Yang et al. | Sep 2010 | A1 |
| 20100229082 | Karmarkar et al. | Sep 2010 | A1 |
| 20100229100 | Miller et al. | Sep 2010 | A1 |
| 20100231474 | Yamagajo et al. | Sep 2010 | A1 |
| 20100235167 | Bourdon | Sep 2010 | A1 |
| 20100235341 | Bennett | Sep 2010 | A1 |
| 20100235729 | Kocienda et al. | Sep 2010 | A1 |
| 20100235732 | Bergman | Sep 2010 | A1 |
| 20100235770 | Ording et al. | Sep 2010 | A1 |
| 20100235780 | Westerman et al. | Sep 2010 | A1 |
| 20100241418 | Maeda et al. | Sep 2010 | A1 |
| 20100250542 | Fujimaki | Sep 2010 | A1 |
| 20100250599 | Schmidt et al. | Sep 2010 | A1 |
| 20100255858 | Juhasz | Oct 2010 | A1 |
| 20100257160 | Cao | Oct 2010 | A1 |
| 20100257478 | Longe et al. | Oct 2010 | A1 |
| 20100262599 | Nitz | Oct 2010 | A1 |
| 20100268537 | Al-Telmissani | Oct 2010 | A1 |
| 20100268539 | Xu et al. | Oct 2010 | A1 |
| 20100269040 | Lee | Oct 2010 | A1 |
| 20100274753 | Liberty et al. | Oct 2010 | A1 |
| 20100277579 | Cho et al. | Nov 2010 | A1 |
| 20100278320 | Arsenault et al. | Nov 2010 | A1 |
| 20100278453 | King | Nov 2010 | A1 |
| 20100280983 | Cho et al. | Nov 2010 | A1 |
| 20100281034 | Petrou et al. | Nov 2010 | A1 |
| 20100286984 | Wandinger et al. | Nov 2010 | A1 |
| 20100286985 | Kennewick et al. | Nov 2010 | A1 |
| 20100287514 | Cragun et al. | Nov 2010 | A1 |
| 20100290632 | Lin | Nov 2010 | A1 |
| 20100293460 | Budelli | Nov 2010 | A1 |
| 20100295645 | Falldin et al. | Nov 2010 | A1 |
| 20100299133 | Kopparapu et al. | Nov 2010 | A1 |
| 20100299138 | Kim | Nov 2010 | A1 |
| 20100299142 | Freeman et al. | Nov 2010 | A1 |
| 20100302056 | Dutton et al. | Dec 2010 | A1 |
| 20100304342 | Zilber | Dec 2010 | A1 |
| 20100304705 | Hursey et al. | Dec 2010 | A1 |
| 20100305807 | Basir et al. | Dec 2010 | A1 |
| 20100305947 | Schwarz et al. | Dec 2010 | A1 |
| 20100312547 | Van Os et al. | Dec 2010 | A1 |
| 20100312566 | Odinak et al. | Dec 2010 | A1 |
| 20100318366 | Sullivan et al. | Dec 2010 | A1 |
| 20100318576 | Kim | Dec 2010 | A1 |
| 20100322438 | Siotis | Dec 2010 | A1 |
| 20100324709 | Starmen | Dec 2010 | A1 |
| 20100324895 | Kurzweil et al. | Dec 2010 | A1 |
| 20100324896 | Attwater et al. | Dec 2010 | A1 |
| 20100324905 | Kurzweil et al. | Dec 2010 | A1 |
| 20100325131 | Dumais et al. | Dec 2010 | A1 |
| 20100325158 | Oral et al. | Dec 2010 | A1 |
| 20100325573 | Estrada et al. | Dec 2010 | A1 |
| 20100325588 | Reddy et al. | Dec 2010 | A1 |
| 20100330908 | Maddern et al. | Dec 2010 | A1 |
| 20100332220 | Hursey et al. | Dec 2010 | A1 |
| 20100332224 | Mäkelä et al. | Dec 2010 | A1 |
| 20100332235 | David | Dec 2010 | A1 |
| 20100332236 | Tan | Dec 2010 | A1 |
| 20100332280 | Bradley et al. | Dec 2010 | A1 |
| 20100332348 | Cao | Dec 2010 | A1 |
| 20100332428 | Mchenry et al. | Dec 2010 | A1 |
| 20100332976 | Fux et al. | Dec 2010 | A1 |
| 20100333030 | Johns | Dec 2010 | A1 |
| 20100333163 | Daly | Dec 2010 | A1 |
| 20110002487 | Panther et al. | Jan 2011 | A1 |
| 20110004475 | Bellegarda | Jan 2011 | A1 |
| 20110006876 | Moberg et al. | Jan 2011 | A1 |
| 20110009107 | Guba et al. | Jan 2011 | A1 |
| 20110010178 | Lee et al. | Jan 2011 | A1 |
| 20110010644 | Merrill et al. | Jan 2011 | A1 |
| 20110015928 | Odell et al. | Jan 2011 | A1 |
| 20110016150 | Engstrom et al. | Jan 2011 | A1 |
| 20110018695 | Bells et al. | Jan 2011 | A1 |
| 20110021211 | Ohki | Jan 2011 | A1 |
| 20110021213 | Carr | Jan 2011 | A1 |
| 20110022292 | Shen et al. | Jan 2011 | A1 |
| 20110022388 | Wu et al. | Jan 2011 | A1 |
| 20110022393 | Waller et al. | Jan 2011 | A1 |
| 20110022394 | Wide et al. | Jan 2011 | A1 |
| 20110022472 | Zon et al. | Jan 2011 | A1 |
| 20110022952 | Wu et al. | Jan 2011 | A1 |
| 20110029616 | Wang et al. | Feb 2011 | A1 |
| 20110030067 | Wilson | Feb 2011 | A1 |
| 20110033064 | Johnson et al. | Feb 2011 | A1 |
| 20110034183 | Haag et al. | Feb 2011 | A1 |
| 20110035144 | Okamoto et al. | Feb 2011 | A1 |
| 20110035434 | Lockwood | Feb 2011 | A1 |
| 20110038489 | Visser et al. | Feb 2011 | A1 |
| 20110040707 | Theisen et al. | Feb 2011 | A1 |
| 20110045841 | Kuhlke et al. | Feb 2011 | A1 |
| 20110047072 | Ciurea | Feb 2011 | A1 |
| 20110047149 | Vaananen | Feb 2011 | A1 |
| 20110047161 | Myaeng et al. | Feb 2011 | A1 |
| 20110047266 | Yu et al. | Feb 2011 | A1 |
| 20110047605 | Sontag et al. | Feb 2011 | A1 |
| 20110050591 | Kim et al. | Mar 2011 | A1 |
| 20110050592 | Kim et al. | Mar 2011 | A1 |
| 20110054647 | Chipchase | Mar 2011 | A1 |
| 20110054894 | Phillips et al. | Mar 2011 | A1 |
| 20110054901 | Qin et al. | Mar 2011 | A1 |
| 20110055256 | Phillips et al. | Mar 2011 | A1 |
| 20110060584 | Ferrucci et al. | Mar 2011 | A1 |
| 20110060587 | Phillips et al. | Mar 2011 | A1 |
| 20110060589 | Weinberg et al. | Mar 2011 | A1 |
| 20110060807 | Martin et al. | Mar 2011 | A1 |
| 20110064387 | Mendeloff et al. | Mar 2011 | A1 |
| 20110065456 | Brennan et al. | Mar 2011 | A1 |
| 20110066366 | Ellanti et al. | Mar 2011 | A1 |
| 20110066468 | Huang et al. | Mar 2011 | A1 |
| 20110066634 | Phillips et al. | Mar 2011 | A1 |
| 20110072492 | Mohler et al. | Mar 2011 | A1 |
| 20110076994 | Kim et al. | Mar 2011 | A1 |
| 20110077943 | Miki et al. | Mar 2011 | A1 |
| 20110080260 | Wang et al. | Apr 2011 | A1 |
| 20110081889 | Gao et al. | Apr 2011 | A1 |
| 20110082688 | Kim et al. | Apr 2011 | A1 |
| 20110083079 | Farrell et al. | Apr 2011 | A1 |
| 20110087491 | Wittenstein et al. | Apr 2011 | A1 |
| 20110087685 | Lin et al. | Apr 2011 | A1 |
| 20110090078 | Kim et al. | Apr 2011 | A1 |
| 20110092187 | Miller | Apr 2011 | A1 |
| 20110093261 | Angott | Apr 2011 | A1 |
| 20110093265 | Stent et al. | Apr 2011 | A1 |
| 20110093271 | Bernard et al. | Apr 2011 | A1 |
| 20110099000 | Rai et al. | Apr 2011 | A1 |
| 20110103682 | Chidlovskii et al. | May 2011 | A1 |
| 20110105097 | Tadayon et al. | May 2011 | A1 |
| 20110106736 | Aharonson et al. | May 2011 | A1 |
| 20110106892 | Nelson et al. | May 2011 | A1 |
| 20110110502 | Daye et al. | May 2011 | A1 |
| 20110111724 | Baptiste | May 2011 | A1 |
| 20110112827 | Kennewick et al. | May 2011 | A1 |
| 20110112837 | Kurki-Suonio et al. | May 2011 | A1 |
| 20110112838 | Adibi | May 2011 | A1 |
| 20110112921 | Kennewick et al. | May 2011 | A1 |
| 20110116610 | Shaw et al. | May 2011 | A1 |
| 20110119049 | Ylonen | May 2011 | A1 |
| 20110119051 | Li et al. | May 2011 | A1 |
| 20110119623 | Kim | May 2011 | A1 |
| 20110119715 | Chang et al. | May 2011 | A1 |
| 20110123004 | Chang et al. | May 2011 | A1 |
| 20110125498 | Pickering et al. | May 2011 | A1 |
| 20110125540 | Jang et al. | May 2011 | A1 |
| 20110125701 | Nair et al. | May 2011 | A1 |
| 20110130958 | Stahl et al. | Jun 2011 | A1 |
| 20110131036 | DiCristo et al. | Jun 2011 | A1 |
| 20110131038 | Oyaizu et al. | Jun 2011 | A1 |
| 20110131045 | Cristo et al. | Jun 2011 | A1 |
| 20110137636 | Srihari et al. | Jun 2011 | A1 |
| 20110141141 | Kankainen | Jun 2011 | A1 |
| 20110143726 | de Silva | Jun 2011 | A1 |
| 20110143811 | Rodriguez | Jun 2011 | A1 |
| 20110144857 | Wingrove et al. | Jun 2011 | A1 |
| 20110144901 | Wang | Jun 2011 | A1 |
| 20110144973 | Bocchieri et al. | Jun 2011 | A1 |
| 20110144999 | Jang et al. | Jun 2011 | A1 |
| 20110145718 | Ketola et al. | Jun 2011 | A1 |
| 20110151830 | Blanda et al. | Jun 2011 | A1 |
| 20110153209 | Geelen | Jun 2011 | A1 |
| 20110153322 | Kwak et al. | Jun 2011 | A1 |
| 20110153324 | Ballinger et al. | Jun 2011 | A1 |
| 20110153329 | Moorer | Jun 2011 | A1 |
| 20110153330 | Yazdani et al. | Jun 2011 | A1 |
| 20110153373 | Dantzig et al. | Jun 2011 | A1 |
| 20110154193 | Creutz et al. | Jun 2011 | A1 |
| 20110157029 | Tseng | Jun 2011 | A1 |
| 20110161072 | Terao et al. | Jun 2011 | A1 |
| 20110161076 | Davis et al. | Jun 2011 | A1 |
| 20110161079 | Gruhn et al. | Jun 2011 | A1 |
| 20110161309 | Lung et al. | Jun 2011 | A1 |
| 20110161852 | Vainio et al. | Jun 2011 | A1 |
| 20110166851 | LeBeau et al. | Jul 2011 | A1 |
| 20110167350 | Hoellwarth | Jul 2011 | A1 |
| 20110175810 | Markovic et al. | Jul 2011 | A1 |
| 20110179002 | Dumitru et al. | Jul 2011 | A1 |
| 20110179372 | Moore et al. | Jul 2011 | A1 |
| 20110183650 | Mckee et al. | Jul 2011 | A1 |
| 20110184721 | Subramanian et al. | Jul 2011 | A1 |
| 20110184730 | LeBeau et al. | Jul 2011 | A1 |
| 20110184736 | Slotznick | Jul 2011 | A1 |
| 20110184737 | Nakano et al. | Jul 2011 | A1 |
| 20110184768 | Norton et al. | Jul 2011 | A1 |
| 20110185288 | Gupta et al. | Jul 2011 | A1 |
| 20110191108 | Friedlander | Aug 2011 | A1 |
| 20110191271 | Baker et al. | Aug 2011 | A1 |
| 20110191344 | Jin et al. | Aug 2011 | A1 |
| 20110195758 | Damale et al. | Aug 2011 | A1 |
| 20110196670 | Dang et al. | Aug 2011 | A1 |
| 20110197128 | Assadollahi et al. | Aug 2011 | A1 |
| 20110199312 | Okuta | Aug 2011 | A1 |
| 20110201385 | Higginbotham et al. | Aug 2011 | A1 |
| 20110201387 | Paek et al. | Aug 2011 | A1 |
| 20110202526 | Lee et al. | Aug 2011 | A1 |
| 20110205149 | Tom et al. | Aug 2011 | A1 |
| 20110208511 | Sikstrom et al. | Aug 2011 | A1 |
| 20110208524 | Haughay | Aug 2011 | A1 |
| 20110209088 | Hinckley et al. | Aug 2011 | A1 |
| 20110212717 | Rhoads et al. | Sep 2011 | A1 |
| 20110216093 | Griffin | Sep 2011 | A1 |
| 20110218806 | Alewine et al. | Sep 2011 | A1 |
| 20110218855 | Cao et al. | Sep 2011 | A1 |
| 20110219018 | Bailey et al. | Sep 2011 | A1 |
| 20110223893 | Lau et al. | Sep 2011 | A1 |
| 20110224972 | Millett et al. | Sep 2011 | A1 |
| 20110228913 | Cochinwala et al. | Sep 2011 | A1 |
| 20110231182 | Weider et al. | Sep 2011 | A1 |
| 20110231184 | Kerr | Sep 2011 | A1 |
| 20110231188 | Kennewick et al. | Sep 2011 | A1 |
| 20110231432 | Sata et al. | Sep 2011 | A1 |
| 20110231474 | Locker et al. | Sep 2011 | A1 |
| 20110238191 | Kristjansson et al. | Sep 2011 | A1 |
| 20110238407 | Kent | Sep 2011 | A1 |
| 20110238408 | Larcheveque et al. | Sep 2011 | A1 |
| 20110238676 | Liu et al. | Sep 2011 | A1 |
| 20110239111 | Grover | Sep 2011 | A1 |
| 20110242007 | Gray et al. | Oct 2011 | A1 |
| 20110244888 | Ohki | Oct 2011 | A1 |
| 20110246471 | Rakib et al. | Oct 2011 | A1 |
| 20110249144 | Chang | Oct 2011 | A1 |
| 20110250570 | Mack et al. | Oct 2011 | A1 |
| 20110257966 | Rychlik | Oct 2011 | A1 |
| 20110258188 | Abdalmageed et al. | Oct 2011 | A1 |
| 20110260829 | Lee | Oct 2011 | A1 |
| 20110260861 | Singh et al. | Oct 2011 | A1 |
| 20110264643 | Cao | Oct 2011 | A1 |
| 20110264999 | Bells et al. | Oct 2011 | A1 |
| 20110274303 | Filson et al. | Nov 2011 | A1 |
| 20110276595 | Kirkland et al. | Nov 2011 | A1 |
| 20110276598 | Kozempel | Nov 2011 | A1 |
| 20110276944 | Bergman et al. | Nov 2011 | A1 |
| 20110279368 | Klein et al. | Nov 2011 | A1 |
| 20110282663 | Talwar et al. | Nov 2011 | A1 |
| 20110282888 | Koperski et al. | Nov 2011 | A1 |
| 20110282906 | Wong | Nov 2011 | A1 |
| 20110283189 | McCarty | Nov 2011 | A1 |
| 20110288852 | Dymetman et al. | Nov 2011 | A1 |
| 20110288855 | Roy | Nov 2011 | A1 |
| 20110288861 | Kurzweil et al. | Nov 2011 | A1 |
| 20110288863 | Rasmussen | Nov 2011 | A1 |
| 20110288866 | Rasmussen | Nov 2011 | A1 |
| 20110298585 | Barry | Dec 2011 | A1 |
| 20110301943 | Patch | Dec 2011 | A1 |
| 20110302162 | Xiao et al. | Dec 2011 | A1 |
| 20110306426 | Novak et al. | Dec 2011 | A1 |
| 20110307241 | Waibel et al. | Dec 2011 | A1 |
| 20110307491 | Fisk et al. | Dec 2011 | A1 |
| 20110307810 | Hilerio et al. | Dec 2011 | A1 |
| 20110313775 | Laligand et al. | Dec 2011 | A1 |
| 20110314003 | Ju et al. | Dec 2011 | A1 |
| 20110314032 | Bennett et al. | Dec 2011 | A1 |
| 20110314404 | Kotler et al. | Dec 2011 | A1 |
| 20110320187 | Motik et al. | Dec 2011 | A1 |
| 20120002820 | Leichter | Jan 2012 | A1 |
| 20120005602 | Anttila et al. | Jan 2012 | A1 |
| 20120008754 | Mukherjee et al. | Jan 2012 | A1 |
| 20120010886 | Razavilar | Jan 2012 | A1 |
| 20120011138 | Dunning et al. | Jan 2012 | A1 |
| 20120013609 | Reponen et al. | Jan 2012 | A1 |
| 20120015629 | Olsen et al. | Jan 2012 | A1 |
| 20120016658 | Wu et al. | Jan 2012 | A1 |
| 20120016678 | Gruber et al. | Jan 2012 | A1 |
| 20120019400 | Patel et al. | Jan 2012 | A1 |
| 20120020490 | Leichter | Jan 2012 | A1 |
| 20120022787 | LeBeau et al. | Jan 2012 | A1 |
| 20120022857 | Baldwin et al. | Jan 2012 | A1 |
| 20120022860 | Lloyd et al. | Jan 2012 | A1 |
| 20120022868 | LeBeau et al. | Jan 2012 | A1 |
| 20120022869 | Lloyd et al. | Jan 2012 | A1 |
| 20120022870 | Kristjansson et al. | Jan 2012 | A1 |
| 20120022872 | Gruber et al. | Jan 2012 | A1 |
| 20120022874 | Lloyd et al. | Jan 2012 | A1 |
| 20120022876 | LeBeau et al. | Jan 2012 | A1 |
| 20120022967 | Bachman et al. | Jan 2012 | A1 |
| 20120023088 | Cheng et al. | Jan 2012 | A1 |
| 20120023095 | Wadycki et al. | Jan 2012 | A1 |
| 20120023462 | Rosing et al. | Jan 2012 | A1 |
| 20120029661 | Jones et al. | Feb 2012 | A1 |
| 20120029910 | Medlock et al. | Feb 2012 | A1 |
| 20120034904 | LeBeau et al. | Feb 2012 | A1 |
| 20120035907 | Lebeau et al. | Feb 2012 | A1 |
| 20120035908 | Lebeau et al. | Feb 2012 | A1 |
| 20120035924 | Jitkoff et al. | Feb 2012 | A1 |
| 20120035925 | Friend et al. | Feb 2012 | A1 |
| 20120035926 | Ambler | Feb 2012 | A1 |
| 20120035931 | LeBeau et al. | Feb 2012 | A1 |
| 20120035932 | Jitkoff et al. | Feb 2012 | A1 |
| 20120035935 | Park et al. | Feb 2012 | A1 |
| 20120036556 | LeBeau et al. | Feb 2012 | A1 |
| 20120039539 | Boiman et al. | Feb 2012 | A1 |
| 20120041752 | Wang et al. | Feb 2012 | A1 |
| 20120042014 | Desai et al. | Feb 2012 | A1 |
| 20120042343 | Laligand et al. | Feb 2012 | A1 |
| 20120053815 | Montanari et al. | Mar 2012 | A1 |
| 20120053829 | Agarwal et al. | Mar 2012 | A1 |
| 20120053945 | Gupta et al. | Mar 2012 | A1 |
| 20120056815 | Mehra | Mar 2012 | A1 |
| 20120059655 | Cartales | Mar 2012 | A1 |
| 20120059813 | Sejnoha et al. | Mar 2012 | A1 |
| 20120062473 | Xiao et al. | Mar 2012 | A1 |
| 20120066212 | Jennings | Mar 2012 | A1 |
| 20120066581 | Spalink | Mar 2012 | A1 |
| 20120075054 | Ge et al. | Mar 2012 | A1 |
| 20120077479 | Sabotta et al. | Mar 2012 | A1 |
| 20120078611 | Soltani et al. | Mar 2012 | A1 |
| 20120078624 | Yook et al. | Mar 2012 | A1 |
| 20120078627 | Wagner | Mar 2012 | A1 |
| 20120078635 | Rothkopf et al. | Mar 2012 | A1 |
| 20120078747 | Chakrabarti et al. | Mar 2012 | A1 |
| 20120082317 | Pance et al. | Apr 2012 | A1 |
| 20120083286 | Kim et al. | Apr 2012 | A1 |
| 20120084086 | Gilbert et al. | Apr 2012 | A1 |
| 20120084634 | Wong et al. | Apr 2012 | A1 |
| 20120088219 | Briscoe et al. | Apr 2012 | A1 |
| 20120089331 | Schmidt et al. | Apr 2012 | A1 |
| 20120101823 | Weng et al. | Apr 2012 | A1 |
| 20120108166 | Hymel | May 2012 | A1 |
| 20120108221 | Thomas et al. | May 2012 | A1 |
| 20120116770 | Chen et al. | May 2012 | A1 |
| 20120117499 | Mori et al. | May 2012 | A1 |
| 20120124126 | Alcazar et al. | May 2012 | A1 |
| 20120128322 | Shaffer et al. | May 2012 | A1 |
| 20120130709 | Bocchieri et al. | May 2012 | A1 |
| 20120136572 | Norton | May 2012 | A1 |
| 20120136855 | Ni et al. | May 2012 | A1 |
| 20120136985 | Popescu et al. | May 2012 | A1 |
| 20120137367 | Dupont et al. | May 2012 | A1 |
| 20120149342 | Cohen et al. | Jun 2012 | A1 |
| 20120149394 | Singh et al. | Jun 2012 | A1 |
| 20120150544 | McLoughlin et al. | Jun 2012 | A1 |
| 20120150580 | Norton | Jun 2012 | A1 |
| 20120158293 | Burnham | Jun 2012 | A1 |
| 20120158399 | Tremblay et al. | Jun 2012 | A1 |
| 20120158422 | Burnham et al. | Jun 2012 | A1 |
| 20120159380 | Kocienda et al. | Jun 2012 | A1 |
| 20120163710 | Skaff et al. | Jun 2012 | A1 |
| 20120166196 | Ju et al. | Jun 2012 | A1 |
| 20120173222 | Wang et al. | Jul 2012 | A1 |
| 20120173244 | Kwak et al. | Jul 2012 | A1 |
| 20120173464 | Tur et al. | Jul 2012 | A1 |
| 20120174121 | Treat et al. | Jul 2012 | A1 |
| 20120179457 | Newman et al. | Jul 2012 | A1 |
| 20120179467 | Williams | Jul 2012 | A1 |
| 20120185237 | Gajic et al. | Jul 2012 | A1 |
| 20120185480 | Ni et al. | Jul 2012 | A1 |
| 20120185781 | Guzman et al. | Jul 2012 | A1 |
| 20120191461 | Lin et al. | Jul 2012 | A1 |
| 20120192096 | Bowman et al. | Jul 2012 | A1 |
| 20120197743 | Grigg et al. | Aug 2012 | A1 |
| 20120197995 | Caruso | Aug 2012 | A1 |
| 20120197998 | Kessel et al. | Aug 2012 | A1 |
| 20120201362 | Crossan et al. | Aug 2012 | A1 |
| 20120209853 | Desai et al. | Aug 2012 | A1 |
| 20120209874 | Wong et al. | Aug 2012 | A1 |
| 20120210266 | Jiang et al. | Aug 2012 | A1 |
| 20120214141 | Raya et al. | Aug 2012 | A1 |
| 20120214517 | Singh et al. | Aug 2012 | A1 |
| 20120215762 | Hall et al. | Aug 2012 | A1 |
| 20120221339 | Wang et al. | Aug 2012 | A1 |
| 20120221552 | Reponen et al. | Aug 2012 | A1 |
| 20120223889 | Medlock et al. | Sep 2012 | A1 |
| 20120223936 | Aughey et al. | Sep 2012 | A1 |
| 20120232885 | Barbosa et al. | Sep 2012 | A1 |
| 20120232886 | Capuozzo et al. | Sep 2012 | A1 |
| 20120232906 | Lindahl et al. | Sep 2012 | A1 |
| 20120233266 | Hassan et al. | Sep 2012 | A1 |
| 20120239661 | Giblin | Sep 2012 | A1 |
| 20120239761 | Linner et al. | Sep 2012 | A1 |
| 20120242482 | Elumalai et al. | Sep 2012 | A1 |
| 20120245719 | Story, Jr. et al. | Sep 2012 | A1 |
| 20120245939 | Braho et al. | Sep 2012 | A1 |
| 20120245941 | Cheyer | Sep 2012 | A1 |
| 20120245944 | Gruber et al. | Sep 2012 | A1 |
| 20120246064 | Balkow | Sep 2012 | A1 |
| 20120250858 | Iqbal et al. | Oct 2012 | A1 |
| 20120252367 | Gaglio et al. | Oct 2012 | A1 |
| 20120252540 | Kirigaya | Oct 2012 | A1 |
| 20120253785 | Hamid et al. | Oct 2012 | A1 |
| 20120254143 | Varma et al. | Oct 2012 | A1 |
| 20120254152 | Park et al. | Oct 2012 | A1 |
| 20120254290 | Naaman | Oct 2012 | A1 |
| 20120259615 | Morin et al. | Oct 2012 | A1 |
| 20120265528 | Gruber et al. | Oct 2012 | A1 |
| 20120265535 | Bryant-Rich et al. | Oct 2012 | A1 |
| 20120265806 | Blanchflower et al. | Oct 2012 | A1 |
| 20120271625 | Bernard | Oct 2012 | A1 |
| 20120271634 | Lenke | Oct 2012 | A1 |
| 20120271635 | Ljolje | Oct 2012 | A1 |
| 20120271640 | Basir | Oct 2012 | A1 |
| 20120271676 | Aravamudan et al. | Oct 2012 | A1 |
| 20120275377 | Lehane et al. | Nov 2012 | A1 |
| 20120284015 | Drewes | Nov 2012 | A1 |
| 20120284027 | Mallett et al. | Nov 2012 | A1 |
| 20120290291 | Shelley et al. | Nov 2012 | A1 |
| 20120290300 | Lee et al. | Nov 2012 | A1 |
| 20120295708 | Hernandez-Abrego et al. | Nov 2012 | A1 |
| 20120296649 | Bansal et al. | Nov 2012 | A1 |
| 20120296654 | Hendrickson et al. | Nov 2012 | A1 |
| 20120296891 | Rangan | Nov 2012 | A1 |
| 20120297348 | Santoro | Nov 2012 | A1 |
| 20120303369 | Brush et al. | Nov 2012 | A1 |
| 20120303371 | Labsky et al. | Nov 2012 | A1 |
| 20120304124 | Chen et al. | Nov 2012 | A1 |
| 20120309363 | Gruber et al. | Dec 2012 | A1 |
| 20120310642 | Cao et al. | Dec 2012 | A1 |
| 20120310649 | Cannistraro et al. | Dec 2012 | A1 |
| 20120310652 | O'Sullivan | Dec 2012 | A1 |
| 20120310922 | Johnson et al. | Dec 2012 | A1 |
| 20120311478 | Van Os et al. | Dec 2012 | A1 |
| 20120311583 | Gruber et al. | Dec 2012 | A1 |
| 20120311584 | Gruber et al. | Dec 2012 | A1 |
| 20120311585 | Gruber et al. | Dec 2012 | A1 |
| 20120316862 | Sultan et al. | Dec 2012 | A1 |
| 20120316875 | Nyquist et al. | Dec 2012 | A1 |
| 20120316878 | Singleton et al. | Dec 2012 | A1 |
| 20120317194 | Tian | Dec 2012 | A1 |
| 20120317498 | Logan et al. | Dec 2012 | A1 |
| 20120321112 | Schubert et al. | Dec 2012 | A1 |
| 20120324391 | Tocci et al. | Dec 2012 | A1 |
| 20120327009 | Fleizach | Dec 2012 | A1 |
| 20120329529 | van der Raadt | Dec 2012 | A1 |
| 20120330660 | Jaiswal | Dec 2012 | A1 |
| 20120330661 | Lindahl | Dec 2012 | A1 |
| 20120330990 | Chen et al. | Dec 2012 | A1 |
| 20130002716 | Walker et al. | Jan 2013 | A1 |
| 20130005405 | Prociw | Jan 2013 | A1 |
| 20130006633 | Grokop et al. | Jan 2013 | A1 |
| 20130006637 | Kanevsky et al. | Jan 2013 | A1 |
| 20130006638 | Lindahl | Jan 2013 | A1 |
| 20130007648 | Gamon et al. | Jan 2013 | A1 |
| 20130010575 | He et al. | Jan 2013 | A1 |
| 20130013313 | Shechtman et al. | Jan 2013 | A1 |
| 20130013319 | Grant et al. | Jan 2013 | A1 |
| 20130018659 | Chi | Jan 2013 | A1 |
| 20130024576 | Dishneau et al. | Jan 2013 | A1 |
| 20130027875 | Zhu et al. | Jan 2013 | A1 |
| 20130030787 | Cancedda et al. | Jan 2013 | A1 |
| 20130030789 | Dalce | Jan 2013 | A1 |
| 20130030804 | Zavaliagko et al. | Jan 2013 | A1 |
| 20130030815 | Madhvanath et al. | Jan 2013 | A1 |
| 20130030955 | David | Jan 2013 | A1 |
| 20130031162 | Willis et al. | Jan 2013 | A1 |
| 20130031476 | Coin et al. | Jan 2013 | A1 |
| 20130033643 | Kim et al. | Feb 2013 | A1 |
| 20130035086 | Chardon et al. | Feb 2013 | A1 |
| 20130035942 | Kim et al. | Feb 2013 | A1 |
| 20130035961 | Yegnanarayanan | Feb 2013 | A1 |
| 20130041647 | Ramerth et al. | Feb 2013 | A1 |
| 20130041654 | Walker et al. | Feb 2013 | A1 |
| 20130041661 | Lee et al. | Feb 2013 | A1 |
| 20130041665 | Jang et al. | Feb 2013 | A1 |
| 20130041667 | Longe et al. | Feb 2013 | A1 |
| 20130041968 | Cohen et al. | Feb 2013 | A1 |
| 20130046544 | Kay et al. | Feb 2013 | A1 |
| 20130050089 | Neels et al. | Feb 2013 | A1 |
| 20130054550 | Bolohan | Feb 2013 | A1 |
| 20130054609 | Rajput et al. | Feb 2013 | A1 |
| 20130054613 | Bishop | Feb 2013 | A1 |
| 20130054675 | Jenkins et al. | Feb 2013 | A1 |
| 20130054706 | Graham et al. | Feb 2013 | A1 |
| 20130055099 | Yao et al. | Feb 2013 | A1 |
| 20130055147 | Vasudev et al. | Feb 2013 | A1 |
| 20130063611 | Papakipos et al. | Mar 2013 | A1 |
| 20130066832 | Sheehan et al. | Mar 2013 | A1 |
| 20130067307 | Tian et al. | Mar 2013 | A1 |
| 20130073286 | Bastea-Forte et al. | Mar 2013 | A1 |
| 20130073346 | Chun et al. | Mar 2013 | A1 |
| 20130080152 | Brun et al. | Mar 2013 | A1 |
| 20130080162 | Chang et al. | Mar 2013 | A1 |
| 20130080167 | Mozer | Mar 2013 | A1 |
| 20130080177 | Chen | Mar 2013 | A1 |
| 20130080251 | Dempski | Mar 2013 | A1 |
| 20130082967 | Hillis et al. | Apr 2013 | A1 |
| 20130085755 | Bringert et al. | Apr 2013 | A1 |
| 20130085761 | Bringert et al. | Apr 2013 | A1 |
| 20130090921 | Liu et al. | Apr 2013 | A1 |
| 20130091090 | Spivack et al. | Apr 2013 | A1 |
| 20130095805 | Lebeau et al. | Apr 2013 | A1 |
| 20130096909 | Brun et al. | Apr 2013 | A1 |
| 20130096917 | Edgar et al. | Apr 2013 | A1 |
| 20130097566 | Berglund | Apr 2013 | A1 |
| 20130097682 | Zeljkovic et al. | Apr 2013 | A1 |
| 20130100268 | Mihailidis et al. | Apr 2013 | A1 |
| 20130103391 | Millmore et al. | Apr 2013 | A1 |
| 20130103405 | Namba et al. | Apr 2013 | A1 |
| 20130106742 | Lee et al. | May 2013 | A1 |
| 20130110505 | Gruber et al. | May 2013 | A1 |
| 20130110515 | Guzzoni et al. | May 2013 | A1 |
| 20130110518 | Gruber et al. | May 2013 | A1 |
| 20130110519 | Cheyer et al. | May 2013 | A1 |
| 20130110520 | Cheyer et al. | May 2013 | A1 |
| 20130110943 | Menon et al. | May 2013 | A1 |
| 20130111330 | Staikos et al. | May 2013 | A1 |
| 20130111348 | Gruber et al. | May 2013 | A1 |
| 20130111487 | Cheyer et al. | May 2013 | A1 |
| 20130111581 | Griffin et al. | May 2013 | A1 |
| 20130115927 | Gruber et al. | May 2013 | A1 |
| 20130117022 | Chen et al. | May 2013 | A1 |
| 20130124189 | Baldwin et al. | May 2013 | A1 |
| 20130132084 | Stonehocker et al. | May 2013 | A1 |
| 20130132089 | Fanty et al. | May 2013 | A1 |
| 20130132871 | Zeng et al. | May 2013 | A1 |
| 20130141551 | Kim | Jun 2013 | A1 |
| 20130142317 | Reynolds | Jun 2013 | A1 |
| 20130142345 | Waldmann | Jun 2013 | A1 |
| 20130144594 | Bangalore et al. | Jun 2013 | A1 |
| 20130144616 | Bangalore et al. | Jun 2013 | A1 |
| 20130151339 | Kim et al. | Jun 2013 | A1 |
| 20130152092 | Yadgar et al. | Jun 2013 | A1 |
| 20130154811 | Ferren et al. | Jun 2013 | A1 |
| 20130157629 | Lee et al. | Jun 2013 | A1 |
| 20130158977 | Senior | Jun 2013 | A1 |
| 20130165232 | Nelson et al. | Jun 2013 | A1 |
| 20130166303 | Chang et al. | Jun 2013 | A1 |
| 20130166442 | Nakajima et al. | Jun 2013 | A1 |
| 20130170738 | Capuozzo et al. | Jul 2013 | A1 |
| 20130172022 | Seymour et al. | Jul 2013 | A1 |
| 20130176244 | Yamamoto et al. | Jul 2013 | A1 |
| 20130176592 | Sasaki | Jul 2013 | A1 |
| 20130179440 | Gordon | Jul 2013 | A1 |
| 20130183944 | Mozer et al. | Jul 2013 | A1 |
| 20130185059 | Riccardi et al. | Jul 2013 | A1 |
| 20130185074 | Gruber et al. | Jul 2013 | A1 |
| 20130185081 | Cheyer et al. | Jul 2013 | A1 |
| 20130185336 | Singh et al. | Jul 2013 | A1 |
| 20130187850 | Schulz et al. | Jul 2013 | A1 |
| 20130191117 | Atti et al. | Jul 2013 | A1 |
| 20130197911 | Wei et al. | Aug 2013 | A1 |
| 20130204813 | Master et al. | Aug 2013 | A1 |
| 20130204897 | McDougall | Aug 2013 | A1 |
| 20130207898 | Sullivan et al. | Aug 2013 | A1 |
| 20130218553 | Fujii et al. | Aug 2013 | A1 |
| 20130218560 | Hsiao et al. | Aug 2013 | A1 |
| 20130222249 | Pasquero et al. | Aug 2013 | A1 |
| 20130225128 | Gomar | Aug 2013 | A1 |
| 20130231917 | Naik | Sep 2013 | A1 |
| 20130234947 | Kristensson et al. | Sep 2013 | A1 |
| 20130235987 | Arroniz-Escobar et al. | Sep 2013 | A1 |
| 20130238647 | Thompson | Sep 2013 | A1 |
| 20130244615 | Miller et al. | Sep 2013 | A1 |
| 20130246048 | Nagase et al. | Sep 2013 | A1 |
| 20130246050 | Yu et al. | Sep 2013 | A1 |
| 20130246329 | Pasquero et al. | Sep 2013 | A1 |
| 20130253911 | Petri et al. | Sep 2013 | A1 |
| 20130253912 | Medlock et al. | Sep 2013 | A1 |
| 20130275117 | Winer | Oct 2013 | A1 |
| 20130275138 | Gruber et al. | Oct 2013 | A1 |
| 20130275164 | Gruber et al. | Oct 2013 | A1 |
| 20130275199 | Proctor, Jr. et al. | Oct 2013 | A1 |
| 20130275625 | Taivalsaari et al. | Oct 2013 | A1 |
| 20130275875 | Gruber et al. | Oct 2013 | A1 |
| 20130275899 | Schubert et al. | Oct 2013 | A1 |
| 20130283168 | Brown et al. | Oct 2013 | A1 |
| 20130289991 | Eshwar et al. | Oct 2013 | A1 |
| 20130289993 | Rao et al. | Oct 2013 | A1 |
| 20130289994 | Newman et al. | Oct 2013 | A1 |
| 20130291015 | Pan | Oct 2013 | A1 |
| 20130297317 | Lee et al. | Nov 2013 | A1 |
| 20130297319 | Kim | Nov 2013 | A1 |
| 20130297348 | Cardoza et al. | Nov 2013 | A1 |
| 20130300645 | Fedorov | Nov 2013 | A1 |
| 20130303106 | Martin | Nov 2013 | A1 |
| 20130304479 | Teller et al. | Nov 2013 | A1 |
| 20130304758 | Gruber et al. | Nov 2013 | A1 |
| 20130304815 | Puente et al. | Nov 2013 | A1 |
| 20130305119 | Kern et al. | Nov 2013 | A1 |
| 20130307855 | Lamb et al. | Nov 2013 | A1 |
| 20130307997 | O'Keefe et al. | Nov 2013 | A1 |
| 20130308922 | Sano et al. | Nov 2013 | A1 |
| 20130311997 | Gruber et al. | Nov 2013 | A1 |
| 20130316746 | Miller et al. | Nov 2013 | A1 |
| 20130322634 | Bennett et al. | Dec 2013 | A1 |
| 20130325436 | Wang et al. | Dec 2013 | A1 |
| 20130325443 | Begeja et al. | Dec 2013 | A1 |
| 20130325447 | Levien et al. | Dec 2013 | A1 |
| 20130325448 | Levien et al. | Dec 2013 | A1 |
| 20130325481 | Van Os et al. | Dec 2013 | A1 |
| 20130325484 | Chakladar et al. | Dec 2013 | A1 |
| 20130325967 | Parks et al. | Dec 2013 | A1 |
| 20130325979 | Mansfield et al. | Dec 2013 | A1 |
| 20130329023 | Suplee, III et al. | Dec 2013 | A1 |
| 20130331127 | Sabatelli et al. | Dec 2013 | A1 |
| 20130332159 | Federighi et al. | Dec 2013 | A1 |
| 20130332162 | Keen | Dec 2013 | A1 |
| 20130332164 | Nalk | Dec 2013 | A1 |
| 20130332168 | Kim et al. | Dec 2013 | A1 |
| 20130332400 | González | Dec 2013 | A1 |
| 20130339256 | Shroff | Dec 2013 | A1 |
| 20130346068 | Solem et al. | Dec 2013 | A1 |
| 20130346347 | Patterson et al. | Dec 2013 | A1 |
| 20140006012 | Zhou et al. | Jan 2014 | A1 |
| 20140006025 | Krishnan et al. | Jan 2014 | A1 |
| 20140006027 | Kim et al. | Jan 2014 | A1 |
| 20140006030 | Fleizach et al. | Jan 2014 | A1 |
| 20140006153 | Thangam et al. | Jan 2014 | A1 |
| 20140012574 | Pasupalak et al. | Jan 2014 | A1 |
| 20140012580 | Ganong et al. | Jan 2014 | A1 |
| 20140012586 | Rubin et al. | Jan 2014 | A1 |
| 20140019116 | Lundberg et al. | Jan 2014 | A1 |
| 20140019133 | Bao et al. | Jan 2014 | A1 |
| 20140028735 | Williams et al. | Jan 2014 | A1 |
| 20140032453 | Eustice et al. | Jan 2014 | A1 |
| 20140033071 | Gruber et al. | Jan 2014 | A1 |
| 20140035823 | Khoe et al. | Feb 2014 | A1 |
| 20140039894 | Shostak | Feb 2014 | A1 |
| 20140040274 | Aravamudan et al. | Feb 2014 | A1 |
| 20140040748 | Lemay et al. | Feb 2014 | A1 |
| 20140040801 | Patel et al. | Feb 2014 | A1 |
| 20140040918 | Li et al. | Feb 2014 | A1 |
| 20140046934 | Zhou et al. | Feb 2014 | A1 |
| 20140047001 | Phillips et al. | Feb 2014 | A1 |
| 20140052680 | Nitz et al. | Feb 2014 | A1 |
| 20140052791 | Chakra et al. | Feb 2014 | A1 |
| 20140053082 | Park et al. | Feb 2014 | A1 |
| 20140053210 | Cheong et al. | Feb 2014 | A1 |
| 20140057610 | Olincy et al. | Feb 2014 | A1 |
| 20140059030 | Hakkani-Tur et al. | Feb 2014 | A1 |
| 20140067361 | Nikoulina et al. | Mar 2014 | A1 |
| 20140067371 | Liensberger | Mar 2014 | A1 |
| 20140067402 | Kim | Mar 2014 | A1 |
| 20140068751 | Last et al. | Mar 2014 | A1 |
| 20140074466 | Sharifi et al. | Mar 2014 | A1 |
| 20140074470 | Jansche et al. | Mar 2014 | A1 |
| 20140074472 | Lin et al. | Mar 2014 | A1 |
| 20140074815 | Plimton | Mar 2014 | A1 |
| 20140078065 | Akkok et al. | Mar 2014 | A1 |
| 20140080428 | Rhoads et al. | Mar 2014 | A1 |
| 20140081633 | Badaskar et al. | Mar 2014 | A1 |
| 20140082501 | Bae et al. | Mar 2014 | A1 |
| 20140086458 | Rogers et al. | Mar 2014 | A1 |
| 20140087711 | Geyer et al. | Mar 2014 | A1 |
| 20140088961 | Woodward et al. | Mar 2014 | A1 |
| 20140095171 | Lynch et al. | Apr 2014 | A1 |
| 20140095172 | Cabaco et al. | Apr 2014 | A1 |
| 20140095173 | Lynch et al. | Apr 2014 | A1 |
| 20140096209 | Saraf et al. | Apr 2014 | A1 |
| 20140098247 | Rao et al. | Apr 2014 | A1 |
| 20140108017 | Mason et al. | Apr 2014 | A1 |
| 20140114554 | Lagassey | Apr 2014 | A1 |
| 20140118155 | Bowers et al. | May 2014 | A1 |
| 20140122059 | Patel et al. | May 2014 | A1 |
| 20140122086 | Kapur et al. | May 2014 | A1 |
| 20140122136 | Jayanthi | May 2014 | A1 |
| 20140122153 | Truitt | May 2014 | A1 |
| 20140134983 | Jung et al. | May 2014 | A1 |
| 20140135036 | Bonanni et al. | May 2014 | A1 |
| 20140136187 | Wolverton et al. | May 2014 | A1 |
| 20140136195 | Abdossalami et al. | May 2014 | A1 |
| 20140136212 | Kwon et al. | May 2014 | A1 |
| 20140136946 | Matas | May 2014 | A1 |
| 20140142923 | Jones et al. | May 2014 | A1 |
| 20140142935 | Lindahl et al. | May 2014 | A1 |
| 20140143550 | Ganong, III et al. | May 2014 | A1 |
| 20140143721 | Suzuki et al. | May 2014 | A1 |
| 20140146200 | Scott et al. | May 2014 | A1 |
| 20140152577 | Yuen et al. | Jun 2014 | A1 |
| 20140155031 | Lee et al. | Jun 2014 | A1 |
| 20140157422 | Livshits et al. | Jun 2014 | A1 |
| 20140163951 | Nikoulina et al. | Jun 2014 | A1 |
| 20140163953 | Parikh | Jun 2014 | A1 |
| 20140163954 | Joshi et al. | Jun 2014 | A1 |
| 20140163977 | Hoffmeister et al. | Jun 2014 | A1 |
| 20140163981 | Cook et al. | Jun 2014 | A1 |
| 20140163995 | Burns et al. | Jun 2014 | A1 |
| 20140164476 | Thomson | Jun 2014 | A1 |
| 20140164532 | Lynch et al. | Jun 2014 | A1 |
| 20140164533 | Lynch et al. | Jun 2014 | A1 |
| 20140169795 | Clough | Jun 2014 | A1 |
| 20140173460 | Kim | Jun 2014 | A1 |
| 20140180499 | Cooper et al. | Jun 2014 | A1 |
| 20140180689 | Kim et al. | Jun 2014 | A1 |
| 20140180697 | Torok et al. | Jun 2014 | A1 |
| 20140181865 | Koganei | Jun 2014 | A1 |
| 20140188477 | Zhang | Jul 2014 | A1 |
| 20140195230 | Han et al. | Jul 2014 | A1 |
| 20140195233 | Bapat | Jul 2014 | A1 |
| 20140195244 | Cha et al. | Jul 2014 | A1 |
| 20140195251 | Zeinstra et al. | Jul 2014 | A1 |
| 20140195252 | Gruber et al. | Jul 2014 | A1 |
| 20140198048 | Unruh et al. | Jul 2014 | A1 |
| 20140203939 | Harrington et al. | Jul 2014 | A1 |
| 20140207439 | Venkatapathy et al. | Jul 2014 | A1 |
| 20140207446 | Klein et al. | Jul 2014 | A1 |
| 20140207468 | Bartnik | Jul 2014 | A1 |
| 20140207582 | Flinn et al. | Jul 2014 | A1 |
| 20140214429 | Pantel | Jul 2014 | A1 |
| 20140214537 | Yoo et al. | Jul 2014 | A1 |
| 20140218372 | Missig et al. | Aug 2014 | A1 |
| 20140222436 | Binder et al. | Aug 2014 | A1 |
| 20140222678 | Sheets et al. | Aug 2014 | A1 |
| 20140223377 | Shaw et al. | Aug 2014 | A1 |
| 20140223481 | Fundament | Aug 2014 | A1 |
| 20140230055 | Boehl | Aug 2014 | A1 |
| 20140232656 | Pasquero et al. | Aug 2014 | A1 |
| 20140236595 | Gray | Aug 2014 | A1 |
| 20140236986 | Guzman | Aug 2014 | A1 |
| 20140237042 | Ahmed et al. | Aug 2014 | A1 |
| 20140244248 | Arisoy et al. | Aug 2014 | A1 |
| 20140244254 | Ju et al. | Aug 2014 | A1 |
| 20140244257 | Colibro et al. | Aug 2014 | A1 |
| 20140244258 | Song et al. | Aug 2014 | A1 |
| 20140244263 | Pontual et al. | Aug 2014 | A1 |
| 20140244268 | Abdelsamie et al. | Aug 2014 | A1 |
| 20140244271 | Lindahl | Aug 2014 | A1 |
| 20140244712 | Walters et al. | Aug 2014 | A1 |
| 20140245140 | Brown et al. | Aug 2014 | A1 |
| 20140247383 | Dave et al. | Sep 2014 | A1 |
| 20140247926 | Gainsboro et al. | Sep 2014 | A1 |
| 20140249817 | Hart et al. | Sep 2014 | A1 |
| 20140249820 | Hsu et al. | Sep 2014 | A1 |
| 20140249821 | Kennewick et al. | Sep 2014 | A1 |
| 20140250046 | Winn et al. | Sep 2014 | A1 |
| 20140257815 | Zhao et al. | Sep 2014 | A1 |
| 20140258857 | Dykstra-Erickson et al. | Sep 2014 | A1 |
| 20140267022 | Kim | Sep 2014 | A1 |
| 20140267599 | Drouin et al. | Sep 2014 | A1 |
| 20140272821 | Pitschel et al. | Sep 2014 | A1 |
| 20140274203 | Ganong et al. | Sep 2014 | A1 |
| 20140274211 | Sejnoha et al. | Sep 2014 | A1 |
| 20140278343 | Tran | Sep 2014 | A1 |
| 20140278349 | Grieves et al. | Sep 2014 | A1 |
| 20140278379 | Coccaro et al. | Sep 2014 | A1 |
| 20140278390 | Kingsbury et al. | Sep 2014 | A1 |
| 20140278391 | Braho et al. | Sep 2014 | A1 |
| 20140278394 | Bastyr et al. | Sep 2014 | A1 |
| 20140278406 | Tsumura et al. | Sep 2014 | A1 |
| 20140278413 | Pitschel et al. | Sep 2014 | A1 |
| 20140278429 | Ganong, III | Sep 2014 | A1 |
| 20140278435 | Ganong et al. | Sep 2014 | A1 |
| 20140278443 | Gunn et al. | Sep 2014 | A1 |
| 20140278513 | Prakash et al. | Sep 2014 | A1 |
| 20140280138 | Li et al. | Sep 2014 | A1 |
| 20140280292 | Skinder | Sep 2014 | A1 |
| 20140280353 | Delaney et al. | Sep 2014 | A1 |
| 20140280450 | Luna | Sep 2014 | A1 |
| 20140281983 | Xian et al. | Sep 2014 | A1 |
| 20140282003 | Gruber et al. | Sep 2014 | A1 |
| 20140282007 | Fleizach | Sep 2014 | A1 |
| 20140282045 | Ayanam et al. | Sep 2014 | A1 |
| 20140282201 | Pasquero et al. | Sep 2014 | A1 |
| 20140282586 | Shear et al. | Sep 2014 | A1 |
| 20140282743 | Howard et al. | Sep 2014 | A1 |
| 20140288990 | Moore et al. | Sep 2014 | A1 |
| 20140289508 | Wang | Sep 2014 | A1 |
| 20140297267 | Spencer et al. | Oct 2014 | A1 |
| 20140297281 | Togawa et al. | Oct 2014 | A1 |
| 20140297284 | Gruber et al. | Oct 2014 | A1 |
| 20140297288 | Yu et al. | Oct 2014 | A1 |
| 20140304605 | Ohmura et al. | Oct 2014 | A1 |
| 20140310001 | Kalns et al. | Oct 2014 | A1 |
| 20140310002 | Nitz et al. | Oct 2014 | A1 |
| 20140316585 | Boesveld et al. | Oct 2014 | A1 |
| 20140317030 | Shen et al. | Oct 2014 | A1 |
| 20140317502 | Brown et al. | Oct 2014 | A1 |
| 20140324884 | Lindahl et al. | Oct 2014 | A1 |
| 20140337048 | Brown et al. | Nov 2014 | A1 |
| 20140337266 | Wolverton et al. | Nov 2014 | A1 |
| 20140337371 | Li | Nov 2014 | A1 |
| 20140337438 | Govande et al. | Nov 2014 | A1 |
| 20140337621 | Nakhimov | Nov 2014 | A1 |
| 20140337751 | Lim et al. | Nov 2014 | A1 |
| 20140337814 | Kalns et al. | Nov 2014 | A1 |
| 20140342762 | Hajdu et al. | Nov 2014 | A1 |
| 20140344627 | Schaub et al. | Nov 2014 | A1 |
| 20140344687 | Durham et al. | Nov 2014 | A1 |
| 20140350924 | Zurek et al. | Nov 2014 | A1 |
| 20140350933 | Bak et al. | Nov 2014 | A1 |
| 20140351741 | Medlock et al. | Nov 2014 | A1 |
| 20140351760 | Skory et al. | Nov 2014 | A1 |
| 20140358519 | Mirkin et al. | Dec 2014 | A1 |
| 20140358523 | Sheth et al. | Dec 2014 | A1 |
| 20140361973 | Raux et al. | Dec 2014 | A1 |
| 20140365209 | Evermann | Dec 2014 | A1 |
| 20140365214 | Bayley | Dec 2014 | A1 |
| 20140365216 | Gruber et al. | Dec 2014 | A1 |
| 20140365226 | Sinha | Dec 2014 | A1 |
| 20140365227 | Cash et al. | Dec 2014 | A1 |
| 20140365407 | Brown et al. | Dec 2014 | A1 |
| 20140365880 | Bellegarda | Dec 2014 | A1 |
| 20140365885 | Carson et al. | Dec 2014 | A1 |
| 20140365895 | Paulson et al. | Dec 2014 | A1 |
| 20140370817 | Luna | Dec 2014 | A1 |
| 20140370841 | Roberts et al. | Dec 2014 | A1 |
| 20140372112 | Xue et al. | Dec 2014 | A1 |
| 20140372356 | Bilal et al. | Dec 2014 | A1 |
| 20140372931 | Zhai et al. | Dec 2014 | A1 |
| 20140379334 | Fry | Dec 2014 | A1 |
| 20140379341 | Seo et al. | Dec 2014 | A1 |
| 20150003797 | Schmidt | Jan 2015 | A1 |
| 20150006148 | Goldszmit et al. | Jan 2015 | A1 |
| 20150006157 | Andrade Silva et al. | Jan 2015 | A1 |
| 20150006176 | Pogue et al. | Jan 2015 | A1 |
| 20150006178 | Peng et al. | Jan 2015 | A1 |
| 20150006184 | Marti et al. | Jan 2015 | A1 |
| 20150006199 | Snider et al. | Jan 2015 | A1 |
| 20150012271 | Peng et al. | Jan 2015 | A1 |
| 20150019219 | Tzirkel-hancock et al. | Jan 2015 | A1 |
| 20150019221 | Lee et al. | Jan 2015 | A1 |
| 20150031416 | Wells et al. | Jan 2015 | A1 |
| 20150033219 | Breiner et al. | Jan 2015 | A1 |
| 20150039292 | Suleman et al. | Feb 2015 | A1 |
| 20150039299 | Weinstein et al. | Feb 2015 | A1 |
| 20150039305 | Huang | Feb 2015 | A1 |
| 20150040012 | Faaborg et al. | Feb 2015 | A1 |
| 20150045003 | Vora et al. | Feb 2015 | A1 |
| 20150045068 | Soifer et al. | Feb 2015 | A1 |
| 20150046537 | Rakib | Feb 2015 | A1 |
| 20150050633 | Christmas et al. | Feb 2015 | A1 |
| 20150058013 | Pakhomov et al. | Feb 2015 | A1 |
| 20150058018 | Georges et al. | Feb 2015 | A1 |
| 20150058785 | Ookawara | Feb 2015 | A1 |
| 20150065200 | Namgung et al. | Mar 2015 | A1 |
| 20150066494 | Salvador et al. | Mar 2015 | A1 |
| 20150066496 | Deoras et al. | Mar 2015 | A1 |
| 20150066506 | Romano et al. | Mar 2015 | A1 |
| 20150066516 | Nishikawa et al. | Mar 2015 | A1 |
| 20150067485 | Kim et al. | Mar 2015 | A1 |
| 20150067822 | Randall | Mar 2015 | A1 |
| 20150073788 | Allauzen et al. | Mar 2015 | A1 |
| 20150073804 | Senior et al. | Mar 2015 | A1 |
| 20150074524 | Nicholson et al. | Mar 2015 | A1 |
| 20150082229 | Ouyang et al. | Mar 2015 | A1 |
| 20150088511 | Bharadwaj et al. | Mar 2015 | A1 |
| 20150088514 | Typrin | Mar 2015 | A1 |
| 20150088522 | Hendrickson et al. | Mar 2015 | A1 |
| 20150088523 | Schuster | Mar 2015 | A1 |
| 20150092520 | Robison | Apr 2015 | A1 |
| 20150095031 | Conkie et al. | Apr 2015 | A1 |
| 20150095278 | Flinn et al. | Apr 2015 | A1 |
| 20150100316 | Williams et al. | Apr 2015 | A1 |
| 20150100537 | Grieves et al. | Apr 2015 | A1 |
| 20150100983 | Pan | Apr 2015 | A1 |
| 20150106093 | Weeks et al. | Apr 2015 | A1 |
| 20150113407 | Hoffert et al. | Apr 2015 | A1 |
| 20150120723 | Deshmukh et al. | Apr 2015 | A1 |
| 20150121216 | Brown et al. | Apr 2015 | A1 |
| 20150123898 | Kim et al. | May 2015 | A1 |
| 20150127350 | Agiomyrgiannakis | May 2015 | A1 |
| 20150133109 | Freeman et al. | May 2015 | A1 |
| 20150134334 | Sachidanandam et al. | May 2015 | A1 |
| 20150135085 | Shoham et al. | May 2015 | A1 |
| 20150135123 | Carr et al. | May 2015 | A1 |
| 20150140934 | Abdurrahman et al. | May 2015 | A1 |
| 20150142420 | Sarikaya et al. | May 2015 | A1 |
| 20150142438 | Dai et al. | May 2015 | A1 |
| 20150142447 | Kennewick et al. | May 2015 | A1 |
| 20150142851 | Gupta et al. | May 2015 | A1 |
| 20150148013 | Baldwin et al. | May 2015 | A1 |
| 20150149177 | Kalns et al. | May 2015 | A1 |
| 20150149182 | Kalns et al. | May 2015 | A1 |
| 20150149354 | Mccoy | May 2015 | A1 |
| 20150149469 | Xu et al. | May 2015 | A1 |
| 20150154185 | Waibel | Jun 2015 | A1 |
| 20150161370 | North et al. | Jun 2015 | A1 |
| 20150161989 | Hsu et al. | Jun 2015 | A1 |
| 20150162001 | Kar et al. | Jun 2015 | A1 |
| 20150163558 | Wheatley | Jun 2015 | A1 |
| 20150169284 | Quast et al. | Jun 2015 | A1 |
| 20150169336 | Harper et al. | Jun 2015 | A1 |
| 20150170664 | Doherty et al. | Jun 2015 | A1 |
| 20150172463 | Quast et al. | Jun 2015 | A1 |
| 20150178388 | Winnemoeller et al. | Jun 2015 | A1 |
| 20150179176 | Ryu et al. | Jun 2015 | A1 |
| 20150185964 | Stout | Jul 2015 | A1 |
| 20150186012 | Coleman et al. | Jul 2015 | A1 |
| 20150186110 | Kannan | Jul 2015 | A1 |
| 20150186155 | Brown et al. | Jul 2015 | A1 |
| 20150186156 | Brown et al. | Jul 2015 | A1 |
| 20150186351 | Hicks et al. | Jul 2015 | A1 |
| 20150187355 | Parkinson et al. | Jul 2015 | A1 |
| 20150187369 | Dadu et al. | Jul 2015 | A1 |
| 20150189362 | Lee et al. | Jul 2015 | A1 |
| 20150193379 | Mehta | Jul 2015 | A1 |
| 20150193391 | Khvostichenko et al. | Jul 2015 | A1 |
| 20150193392 | Greenblatt et al. | Jul 2015 | A1 |
| 20150194152 | Katuri et al. | Jul 2015 | A1 |
| 20150195379 | Zhang et al. | Jul 2015 | A1 |
| 20150195606 | McDevitt | Jul 2015 | A1 |
| 20150199077 | Zuger et al. | Jul 2015 | A1 |
| 20150199960 | Huo et al. | Jul 2015 | A1 |
| 20150199965 | Leak et al. | Jul 2015 | A1 |
| 20150199967 | Reddy et al. | Jul 2015 | A1 |
| 20150201064 | Bells et al. | Jul 2015 | A1 |
| 20150205858 | Xie et al. | Jul 2015 | A1 |
| 20150208226 | Kuusilinna et al. | Jul 2015 | A1 |
| 20150212791 | Kumar et al. | Jul 2015 | A1 |
| 20150213796 | Waltermann et al. | Jul 2015 | A1 |
| 20150220507 | Mohajer et al. | Aug 2015 | A1 |
| 20150221304 | Stewart | Aug 2015 | A1 |
| 20150221307 | Shah et al. | Aug 2015 | A1 |
| 20150227633 | Shapira | Aug 2015 | A1 |
| 20150228281 | Raniere | Aug 2015 | A1 |
| 20150234636 | Barnes, Jr. | Aug 2015 | A1 |
| 20150234800 | Patrick et al. | Aug 2015 | A1 |
| 20150242091 | Lu et al. | Aug 2015 | A1 |
| 20150243278 | Kibre et al. | Aug 2015 | A1 |
| 20150243283 | Halash et al. | Aug 2015 | A1 |
| 20150245154 | Dadu et al. | Aug 2015 | A1 |
| 20150248651 | Akutagawa et al. | Sep 2015 | A1 |
| 20150248886 | Sarikaya et al. | Sep 2015 | A1 |
| 20150254057 | Klein et al. | Sep 2015 | A1 |
| 20150254058 | Klein et al. | Sep 2015 | A1 |
| 20150254333 | Fife et al. | Sep 2015 | A1 |
| 20150255071 | Chiba | Sep 2015 | A1 |
| 20150256873 | Klein et al. | Sep 2015 | A1 |
| 20150261496 | Faaborg et al. | Sep 2015 | A1 |
| 20150269139 | McAteer et al. | Sep 2015 | A1 |
| 20150277574 | Jain et al. | Oct 2015 | A1 |
| 20150278370 | Stratvert et al. | Oct 2015 | A1 |
| 20150279358 | Kingsbury et al. | Oct 2015 | A1 |
| 20150279360 | Mengibar et al. | Oct 2015 | A1 |
| 20150281380 | Wang et al. | Oct 2015 | A1 |
| 20150286627 | Chang et al. | Oct 2015 | A1 |
| 20150287401 | Lee et al. | Oct 2015 | A1 |
| 20150287409 | Jang | Oct 2015 | A1 |
| 20150288629 | Choi et al. | Oct 2015 | A1 |
| 20150294086 | Kare et al. | Oct 2015 | A1 |
| 20150294516 | Chiang | Oct 2015 | A1 |
| 20150295915 | Xiu | Oct 2015 | A1 |
| 20150302855 | Kim et al. | Oct 2015 | A1 |
| 20150302856 | Kim et al. | Oct 2015 | A1 |
| 20150302857 | Yamada | Oct 2015 | A1 |
| 20150302870 | Burke et al. | Oct 2015 | A1 |
| 20150309997 | Lee et al. | Oct 2015 | A1 |
| 20150310858 | Li et al. | Oct 2015 | A1 |
| 20150310862 | Dauphin et al. | Oct 2015 | A1 |
| 20150310879 | Buchanan et al. | Oct 2015 | A1 |
| 20150312182 | Langholz | Oct 2015 | A1 |
| 20150317069 | Clements et al. | Nov 2015 | A1 |
| 20150317310 | Eiche et al. | Nov 2015 | A1 |
| 20150324041 | Varley et al. | Nov 2015 | A1 |
| 20150324334 | Lee et al. | Nov 2015 | A1 |
| 20150331664 | Osawa et al. | Nov 2015 | A1 |
| 20150331711 | Huang et al. | Nov 2015 | A1 |
| 20150332667 | Mason | Nov 2015 | A1 |
| 20150339049 | Kasemset et al. | Nov 2015 | A1 |
| 20150339391 | Kang et al. | Nov 2015 | A1 |
| 20150340040 | Mun et al. | Nov 2015 | A1 |
| 20150340042 | Sejnoha et al. | Nov 2015 | A1 |
| 20150341717 | Song et al. | Nov 2015 | A1 |
| 20150347086 | Liedholm et al. | Dec 2015 | A1 |
| 20150347381 | Bellegarda | Dec 2015 | A1 |
| 20150347382 | Dolfing et al. | Dec 2015 | A1 |
| 20150347385 | Flor et al. | Dec 2015 | A1 |
| 20150347393 | Futrell et al. | Dec 2015 | A1 |
| 20150347733 | Tsou et al. | Dec 2015 | A1 |
| 20150347985 | Gross et al. | Dec 2015 | A1 |
| 20150348547 | Paulik et al. | Dec 2015 | A1 |
| 20150348548 | Piernot | Dec 2015 | A1 |
| 20150348549 | Giuli et al. | Dec 2015 | A1 |
| 20150348551 | Gruber et al. | Dec 2015 | A1 |
| 20150348554 | Orr et al. | Dec 2015 | A1 |
| 20150350031 | Burks et al. | Dec 2015 | A1 |
| 20150352999 | Bando et al. | Dec 2015 | A1 |
| 20150355879 | Beckhardt et al. | Dec 2015 | A1 |
| 20150364140 | Thorn | Dec 2015 | A1 |
| 20150370531 | Faaborg | Dec 2015 | A1 |
| 20150370780 | Wang et al. | Dec 2015 | A1 |
| 20150371639 | Foerster et al. | Dec 2015 | A1 |
| 20150371665 | Naik et al. | Dec 2015 | A1 |
| 20150373183 | Woolsey et al. | Dec 2015 | A1 |
| 20150382047 | Napolitano et al. | Dec 2015 | A1 |
| 20150382079 | Lister et al. | Dec 2015 | A1 |
| 20160004690 | Bangalore et al. | Jan 2016 | A1 |
| 20160014476 | Caliendo, Jr. et al. | Jan 2016 | A1 |
| 20160018872 | Tu et al. | Jan 2016 | A1 |
| 20160018900 | Tu et al. | Jan 2016 | A1 |
| 20160019886 | Hong | Jan 2016 | A1 |
| 20160026258 | Ou et al. | Jan 2016 | A1 |
| 20160027431 | Kurzweil et al. | Jan 2016 | A1 |
| 20160028666 | Li | Jan 2016 | A1 |
| 20160029316 | Mohan et al. | Jan 2016 | A1 |
| 20160034811 | Paulik et al. | Feb 2016 | A1 |
| 20160042735 | Vibbert et al. | Feb 2016 | A1 |
| 20160042748 | Jain et al. | Feb 2016 | A1 |
| 20160048666 | Dey et al. | Feb 2016 | A1 |
| 20160055422 | Li | Feb 2016 | A1 |
| 20160062605 | Agarwal et al. | Mar 2016 | A1 |
| 20160063998 | Krishnamoorthy et al. | Mar 2016 | A1 |
| 20160070581 | Soon-Shiong | Mar 2016 | A1 |
| 20160071516 | Lee et al. | Mar 2016 | A1 |
| 20160071521 | Haughay | Mar 2016 | A1 |
| 20160077794 | Kim et al. | Mar 2016 | A1 |
| 20160078860 | Paulik et al. | Mar 2016 | A1 |
| 20160080165 | Ehsani et al. | Mar 2016 | A1 |
| 20160086116 | Rao et al. | Mar 2016 | A1 |
| 20160091967 | Prokofieva et al. | Mar 2016 | A1 |
| 20160092447 | Venkataraman et al. | Mar 2016 | A1 |
| 20160093291 | Kim | Mar 2016 | A1 |
| 20160093298 | Naik et al. | Mar 2016 | A1 |
| 20160093301 | Bellegarda et al. | Mar 2016 | A1 |
| 20160093304 | Kim et al. | Mar 2016 | A1 |
| 20160094979 | Naik et al. | Mar 2016 | A1 |
| 20160111091 | Bakish | Apr 2016 | A1 |
| 20160117386 | Ajmera et al. | Apr 2016 | A1 |
| 20160119338 | Cheyer | Apr 2016 | A1 |
| 20160125048 | Hamada | May 2016 | A1 |
| 20160125071 | Gabbai | May 2016 | A1 |
| 20160132484 | Nauze et al. | May 2016 | A1 |
| 20160139662 | Dabhade | May 2016 | A1 |
| 20160147725 | Patten et al. | May 2016 | A1 |
| 20160148610 | Kennewick, Jr. et al. | May 2016 | A1 |
| 20160154624 | Son et al. | Jun 2016 | A1 |
| 20160155442 | Kannan et al. | Jun 2016 | A1 |
| 20160155443 | Khan et al. | Jun 2016 | A1 |
| 20160162456 | Munro et al. | Jun 2016 | A1 |
| 20160163311 | Crook et al. | Jun 2016 | A1 |
| 20160163312 | Naik et al. | Jun 2016 | A1 |
| 20160170966 | Kolo | Jun 2016 | A1 |
| 20160173578 | Sharma et al. | Jun 2016 | A1 |
| 20160173960 | Snibbe et al. | Jun 2016 | A1 |
| 20160179462 | Bjorkengren | Jun 2016 | A1 |
| 20160180844 | Vanblon et al. | Jun 2016 | A1 |
| 20160182410 | Janakiraman et al. | Jun 2016 | A1 |
| 20160188181 | Smith | Jun 2016 | A1 |
| 20160188738 | Gruber et al. | Jun 2016 | A1 |
| 20160189717 | Kannan et al. | Jun 2016 | A1 |
| 20160210981 | Lee | Jul 2016 | A1 |
| 20160212488 | Os et al. | Jul 2016 | A1 |
| 20160217784 | Gelfenbeyn et al. | Jul 2016 | A1 |
| 20160224540 | Stewart et al. | Aug 2016 | A1 |
| 20160224774 | Pender | Aug 2016 | A1 |
| 20160225372 | Cheung et al. | Aug 2016 | A1 |
| 20160240187 | Fleizach et al. | Aug 2016 | A1 |
| 20160247061 | Trask et al. | Aug 2016 | A1 |
| 20160253312 | Rhodes | Sep 2016 | A1 |
| 20160259623 | Sumner et al. | Sep 2016 | A1 |
| 20160259656 | Sumner et al. | Sep 2016 | A1 |
| 20160260431 | Newendorp | Sep 2016 | A1 |
| 20160260433 | Sumner et al. | Sep 2016 | A1 |
| 20160260434 | Gelfenbeyn et al. | Sep 2016 | A1 |
| 20160260436 | Lemay et al. | Sep 2016 | A1 |
| 20160266871 | Schmid et al. | Sep 2016 | A1 |
| 20160267904 | Biadsy et al. | Sep 2016 | A1 |
| 20160275941 | Bellegarda et al. | Sep 2016 | A1 |
| 20160275947 | Li et al. | Sep 2016 | A1 |
| 20160282956 | Ouyang et al. | Sep 2016 | A1 |
| 20160284199 | Dotan-Cohen et al. | Sep 2016 | A1 |
| 20160286045 | Shaltiel et al. | Sep 2016 | A1 |
| 20160299685 | Zhai et al. | Oct 2016 | A1 |
| 20160299882 | Hegerty et al. | Oct 2016 | A1 |
| 20160299883 | Zhu et al. | Oct 2016 | A1 |
| 20160307566 | Bellegarda | Oct 2016 | A1 |
| 20160314788 | Jitkoff et al. | Oct 2016 | A1 |
| 20160314792 | Alvarez et al. | Oct 2016 | A1 |
| 20160321261 | Spasojevic et al. | Nov 2016 | A1 |
| 20160322045 | Hatfeild et al. | Nov 2016 | A1 |
| 20160322050 | Wang et al. | Nov 2016 | A1 |
| 20160328893 | Cordova et al. | Nov 2016 | A1 |
| 20160336007 | Hanazawa | Nov 2016 | A1 |
| 20160336010 | Lindahl | Nov 2016 | A1 |
| 20160336024 | Choi et al. | Nov 2016 | A1 |
| 20160337299 | Lane et al. | Nov 2016 | A1 |
| 20160337301 | Rollins et al. | Nov 2016 | A1 |
| 20160342685 | Basu et al. | Nov 2016 | A1 |
| 20160351190 | Binder et al. | Dec 2016 | A1 |
| 20160357304 | Hatori et al. | Dec 2016 | A1 |
| 20160357728 | Bellegarda et al. | Dec 2016 | A1 |
| 20160357861 | Carlhian et al. | Dec 2016 | A1 |
| 20160357870 | Hentschel et al. | Dec 2016 | A1 |
| 20160358598 | Williams et al. | Dec 2016 | A1 |
| 20160358600 | Nallasamy et al. | Dec 2016 | A1 |
| 20160358619 | Ramprashad et al. | Dec 2016 | A1 |
| 20160359771 | Sridhar | Dec 2016 | A1 |
| 20160360039 | Sanghavi et al. | Dec 2016 | A1 |
| 20160364378 | Futrell et al. | Dec 2016 | A1 |
| 20160371250 | Rhodes | Dec 2016 | A1 |
| 20160378747 | Orr et al. | Dec 2016 | A1 |
| 20160379626 | Deisher et al. | Dec 2016 | A1 |
| 20160379641 | Liu et al. | Dec 2016 | A1 |
| 20170004824 | Yoo et al. | Jan 2017 | A1 |
| 20170019987 | Dragone et al. | Jan 2017 | A1 |
| 20170031576 | Saoji et al. | Feb 2017 | A1 |
| 20170040002 | Basson et al. | Feb 2017 | A1 |
| 20170055895 | Des Jardins et al. | Mar 2017 | A1 |
| 20170060853 | Lee et al. | Mar 2017 | A1 |
| 20170068423 | Napolitano et al. | Mar 2017 | A1 |
| 20170068513 | Stasior et al. | Mar 2017 | A1 |
| 20170068550 | Zeitlin | Mar 2017 | A1 |
| 20170068670 | Orr et al. | Mar 2017 | A1 |
| 20170083179 | Gruber et al. | Mar 2017 | A1 |
| 20170091168 | Bellegarda et al. | Mar 2017 | A1 |
| 20170092270 | Newendorp et al. | Mar 2017 | A1 |
| 20170092278 | Evermann et al. | Mar 2017 | A1 |
| 20170102915 | Kuscher et al. | Apr 2017 | A1 |
| 20170105190 | Logan et al. | Apr 2017 | A1 |
| 20170116989 | Yadgar et al. | Apr 2017 | A1 |
| 20170127124 | Wilson et al. | May 2017 | A9 |
| 20170132199 | Vescovi et al. | May 2017 | A1 |
| 20170140644 | Hwang et al. | May 2017 | A1 |
| 20170161018 | Lemay et al. | Jun 2017 | A1 |
| 20170169818 | VanBlon et al. | Jun 2017 | A1 |
| 20170169819 | Mese et al. | Jun 2017 | A1 |
| 20170178619 | Naik et al. | Jun 2017 | A1 |
| 20170178626 | Gruber et al. | Jun 2017 | A1 |
| 20170180499 | Gelfenbeyn et al. | Jun 2017 | A1 |
| 20170185375 | Martel et al. | Jun 2017 | A1 |
| 20170186429 | Giuli et al. | Jun 2017 | A1 |
| 20170193083 | Bhatt et al. | Jul 2017 | A1 |
| 20170199874 | Patel et al. | Jul 2017 | A1 |
| 20170227935 | Su et al. | Aug 2017 | A1 |
| 20170230709 | Van Os et al. | Aug 2017 | A1 |
| 20170242653 | Lang et al. | Aug 2017 | A1 |
| 20170243468 | Dotan-Cohen et al. | Aug 2017 | A1 |
| 20170256256 | Wang et al. | Sep 2017 | A1 |
| 20170263248 | Gruber et al. | Sep 2017 | A1 |
| 20170285915 | Napolitano et al. | Oct 2017 | A1 |
| 20170316775 | Le et al. | Nov 2017 | A1 |
| 20170316782 | Haughay et al. | Nov 2017 | A1 |
| 20170323637 | Naik | Nov 2017 | A1 |
| 20170345411 | Raitio et al. | Nov 2017 | A1 |
| 20170346949 | Sanghavi et al. | Nov 2017 | A1 |
| 20170352346 | Paulik et al. | Dec 2017 | A1 |
| 20170352350 | Booker et al. | Dec 2017 | A1 |
| 20170357478 | Piersol et al. | Dec 2017 | A1 |
| 20170357632 | Pagallo et al. | Dec 2017 | A1 |
| 20170357633 | Wang et al. | Dec 2017 | A1 |
| 20170357637 | Nell et al. | Dec 2017 | A1 |
| 20170357640 | Bellegarda et al. | Dec 2017 | A1 |
| 20170357716 | Bellegarda et al. | Dec 2017 | A1 |
| 20170358300 | Laurens et al. | Dec 2017 | A1 |
| 20170358301 | Raitio et al. | Dec 2017 | A1 |
| 20170358304 | Castillo et al. | Dec 2017 | A1 |
| 20170358305 | Kudurshian et al. | Dec 2017 | A1 |
| 20180007538 | Naik et al. | Jan 2018 | A1 |
| 20180012596 | Piernot et al. | Jan 2018 | A1 |
| 20180033431 | Newendorp et al. | Feb 2018 | A1 |
| 20180090143 | Saddler et al. | Mar 2018 | A1 |
| 20180108346 | Paulik et al. | Apr 2018 | A1 |
| Number | Date | Country |
|---|---|---|
| 2015203483 | Jul 2015 | AU |
| 2694314 | Aug 2010 | CA |
| 2792412 | Jul 2011 | CA |
| 2666438 | Jun 2013 | CA |
| 101162153 | Apr 2008 | CN |
| 101174366 | May 2008 | CN |
| 101179754 | May 2008 | CN |
| 101183525 | May 2008 | CN |
| 101188644 | May 2008 | CN |
| 101228503 | Jul 2008 | CN |
| 101233741 | Jul 2008 | CN |
| 101246020 | Aug 2008 | CN |
| 101271689 | Sep 2008 | CN |
| 101277501 | Oct 2008 | CN |
| 101281745 | Oct 2008 | CN |
| 101292282 | Oct 2008 | CN |
| 101297541 | Oct 2008 | CN |
| 101325756 | Dec 2008 | CN |
| 101416471 | Apr 2009 | CN |
| 101427244 | May 2009 | CN |
| 101448340 | Jun 2009 | CN |
| 101453498 | Jun 2009 | CN |
| 101499156 | Aug 2009 | CN |
| 101500041 | Aug 2009 | CN |
| 101535983 | Sep 2009 | CN |
| 101547396 | Sep 2009 | CN |
| 101557432 | Oct 2009 | CN |
| 101604521 | Dec 2009 | CN |
| 101632316 | Jan 2010 | CN |
| 101636736 | Jan 2010 | CN |
| 101673544 | Mar 2010 | CN |
| 101751387 | Jun 2010 | CN |
| 101847405 | Sep 2010 | CN |
| 101894547 | Nov 2010 | CN |
| 101939740 | Jan 2011 | CN |
| 101951553 | Jan 2011 | CN |
| 102137193 | Jul 2011 | CN |
| 102160043 | Aug 2011 | CN |
| 102246136 | Nov 2011 | CN |
| 202035047 | Nov 2011 | CN |
| 102282609 | Dec 2011 | CN |
| 202092650 | Dec 2011 | CN |
| 102368256 | Mar 2012 | CN |
| 102498457 | Jun 2012 | CN |
| 102629246 | Aug 2012 | CN |
| 102682771 | Sep 2012 | CN |
| 102685295 | Sep 2012 | CN |
| 102693725 | Sep 2012 | CN |
| 102792320 | Nov 2012 | CN |
| 102870065 | Jan 2013 | CN |
| 102917004 | Feb 2013 | CN |
| 103035240 | Apr 2013 | CN |
| 103038728 | Apr 2013 | CN |
| 104284257 | Jan 2015 | CN |
| 104423625 | Mar 2015 | CN |
| 104516522 | Apr 2015 | CN |
| 105278681 | Jan 2016 | CN |
| 105320726 | Feb 2016 | CN |
| 102008024258 | Nov 2009 | DE |
| 1892700 | Feb 2008 | EP |
| 1912205 | Apr 2008 | EP |
| 1939860 | Jul 2008 | EP |
| 1944997 | Jul 2008 | EP |
| 651543 | Sep 2008 | EP |
| 1909263 | Jan 2009 | EP |
| 1335620 | Mar 2009 | EP |
| 2069895 | Jun 2009 | EP |
| 2081185 | Jul 2009 | EP |
| 2094032 | Aug 2009 | EP |
| 2096840 | Sep 2009 | EP |
| 2107553 | Oct 2009 | EP |
| 2109295 | Oct 2009 | EP |
| 1720375 | Jul 2010 | EP |
| 2205010 | Jul 2010 | EP |
| 2309491 | Apr 2011 | EP |
| 2329348 | Jun 2011 | EP |
| 2400373 | Dec 2011 | EP |
| 2431842 | Mar 2012 | EP |
| 2523188 | Nov 2012 | EP |
| 2551784 | Jan 2013 | EP |
| 2555536 | Feb 2013 | EP |
| 2575128 | Apr 2013 | EP |
| 2733598 | May 2014 | EP |
| 2801890 | Nov 2014 | EP |
| 2801972 | Nov 2014 | EP |
| 2881939 | Jun 2015 | EP |
| 2930715 | Oct 2015 | EP |
| 2938022 | Oct 2015 | EP |
| 2940556 | Nov 2015 | EP |
| 2947859 | Nov 2015 | EP |
| 2950307 | Dec 2015 | EP |
| 2957986 | Dec 2015 | EP |
| 3246916 | Nov 2017 | EP |
| 2983065 | Aug 2018 | EP |
| 2911201 | Jul 2008 | FR |
| 2445436 | Jul 2008 | GB |
| 2445667 | Jul 2008 | GB |
| 2008-009120 | Jan 2008 | JP |
| 2008-21002 | Jan 2008 | JP |
| 2008-26381 | Feb 2008 | JP |
| 2008-39928 | Feb 2008 | JP |
| 2008-58813 | Mar 2008 | JP |
| 2008-064687 | Mar 2008 | JP |
| 2008-90545 | Apr 2008 | JP |
| 2008-97003 | Apr 2008 | JP |
| 2008-134949 | Jun 2008 | JP |
| 2008-158510 | Jul 2008 | JP |
| 2008-526101 | Jul 2008 | JP |
| 2008-185693 | Aug 2008 | JP |
| 2008-198022 | Aug 2008 | JP |
| 2008-217468 | Sep 2008 | JP |
| 2008-228129 | Sep 2008 | JP |
| 2008-233678 | Oct 2008 | JP |
| 2008-236448 | Oct 2008 | JP |
| 2008-252161 | Oct 2008 | JP |
| 2008-268684 | Nov 2008 | JP |
| 2008-269480 | Nov 2008 | JP |
| 2008-271481 | Nov 2008 | JP |
| 2008-275731 | Nov 2008 | JP |
| 2008-299221 | Dec 2008 | JP |
| 2009-2850 | Jan 2009 | JP |
| 2009-503623 | Jan 2009 | JP |
| 2009-36999 | Feb 2009 | JP |
| 2009-505142 | Feb 2009 | JP |
| 2009-47920 | Mar 2009 | JP |
| 2009-069062 | Apr 2009 | JP |
| 2009-98490 | May 2009 | JP |
| 2009-110300 | May 2009 | JP |
| 2009-134409 | Jun 2009 | JP |
| 2009-140444 | Jun 2009 | JP |
| 2009-186989 | Aug 2009 | JP |
| 2009-193448 | Aug 2009 | JP |
| 2009-193457 | Aug 2009 | JP |
| 2009-193532 | Aug 2009 | JP |
| 2009-205367 | Sep 2009 | JP |
| 2009-223840 | Oct 2009 | JP |
| 2009-294913 | Dec 2009 | JP |
| 2009-294946 | Dec 2009 | JP |
| 2010-66519 | Mar 2010 | JP |
| 2010-78979 | Apr 2010 | JP |
| 2010-108378 | May 2010 | JP |
| 2010-518475 | May 2010 | JP |
| 2010-518526 | May 2010 | JP |
| 2010-157207 | Jul 2010 | JP |
| 2010-224236 | Oct 2010 | JP |
| 4563106 | Oct 2010 | JP |
| 2010-535377 | Nov 2010 | JP |
| 2010-287063 | Dec 2010 | JP |
| 2011-33874 | Feb 2011 | JP |
| 2011-41026 | Feb 2011 | JP |
| 2011-45005 | Mar 2011 | JP |
| 2011-59659 | Mar 2011 | JP |
| 2011-81541 | Apr 2011 | JP |
| 2011-525045 | Sep 2011 | JP |
| 2011-238022 | Nov 2011 | JP |
| 2011-250027 | Dec 2011 | JP |
| 2012-014394 | Jan 2012 | JP |
| 2012-508530 | Apr 2012 | JP |
| 2012-089020 | May 2012 | JP |
| 2012-116442 | Jun 2012 | JP |
| 2012-147063 | Aug 2012 | JP |
| 2012-518847 | Aug 2012 | JP |
| 2013-37688 | Feb 2013 | JP |
| 2013-511214 | Mar 2013 | JP |
| 2013-65284 | Apr 2013 | JP |
| 2013-73240 | Apr 2013 | JP |
| 2013-513315 | Apr 2013 | JP |
| 2013-080476 | May 2013 | JP |
| 2013-517566 | May 2013 | JP |
| 2013-134430 | Jul 2013 | JP |
| 2013-527947 | Jul 2013 | JP |
| 2013-528012 | Jul 2013 | JP |
| 2013-156349 | Aug 2013 | JP |
| 2013-200423 | Oct 2013 | JP |
| 2013-205999 | Oct 2013 | JP |
| 2013-238936 | Nov 2013 | JP |
| 2014-10688 | Jan 2014 | JP |
| 2014-026629 | Feb 2014 | JP |
| 2014-60600 | Apr 2014 | JP |
| 2014-72586 | Apr 2014 | JP |
| 2014-077969 | May 2014 | JP |
| 2014-124332 | Jul 2014 | JP |
| 2014-145842 | Aug 2014 | JP |
| 2014-150323 | Aug 2014 | JP |
| 2014-222514 | Nov 2014 | JP |
| 2015-501022 | Jan 2015 | JP |
| 2015-41845 | Mar 2015 | JP |
| 2015-94848 | May 2015 | JP |
| 2015-519675 | Jul 2015 | JP |
| 2015-528140 | Sep 2015 | JP |
| 2015-528918 | Oct 2015 | JP |
| 2016-119615 | Jun 2016 | JP |
| 10-0801227 | Feb 2008 | KR |
| 10-0810500 | Mar 2008 | KR |
| 10-2008-0033070 | Apr 2008 | KR |
| 10-0819928 | Apr 2008 | KR |
| 10-2008-0049647 | Jun 2008 | KR |
| 10-2008-0059332 | Jun 2008 | KR |
| 10-2008-0109322 | Dec 2008 | KR |
| 10-2009-0001716 | Jan 2009 | KR |
| 10-2009-0028464 | Mar 2009 | KR |
| 10-2009-0030117 | Mar 2009 | KR |
| 10-2009-0086805 | Aug 2009 | KR |
| 10-0920267 | Oct 2009 | KR |
| 10-2009-0122944 | Dec 2009 | KR |
| 10-2009-0127961 | Dec 2009 | KR |
| 10-2009-0129192 | Dec 2009 | KR |
| 10-2010-0015958 | Feb 2010 | KR |
| 10-2010-0048571 | May 2010 | KR |
| 10-2010-0053149 | May 2010 | KR |
| 10-2010-0119519 | Nov 2010 | KR |
| 10-2011-0043644 | Apr 2011 | KR |
| 10-1032792 | May 2011 | KR |
| 10-2011-0068490 | Jun 2011 | KR |
| 10-2011-0072847 | Jun 2011 | KR |
| 10-2011-0086492 | Jul 2011 | KR |
| 10-2011-0100620 | Sep 2011 | KR |
| 10-2011-0113414 | Oct 2011 | KR |
| 10-2011-0115134 | Oct 2011 | KR |
| 10-2012-0020164 | Mar 2012 | KR |
| 10-2012-0031722 | Apr 2012 | KR |
| 10-1178310 | Aug 2012 | KR |
| 10-2012-0120316 | Nov 2012 | KR |
| 10-2012-0137435 | Dec 2012 | KR |
| 10-2012-0137440 | Dec 2012 | KR |
| 10-2012-0138826 | Dec 2012 | KR |
| 10-2012-0139827 | Dec 2012 | KR |
| 10-1193668 | Dec 2012 | KR |
| 10-2013-0035983 | Apr 2013 | KR |
| 10-1334342 | Nov 2013 | KR |
| 10-2013-0131252 | Dec 2013 | KR |
| 10-2013-0133629 | Dec 2013 | KR |
| 10-2014-0147557 | Dec 2014 | KR |
| 10-2015-0043512 | Apr 2015 | KR |
| 10-2016-0010523 | Jan 2016 | KR |
| 10-2016-0065503 | Jun 2016 | KR |
| 10-2017-0107058 | Sep 2017 | KR |
| 2349970 | Mar 2009 | RU |
| 2353068 | Apr 2009 | RU |
| 2364917 | Aug 2009 | RU |
| 200801988 | Jan 2008 | TW |
| I301373 | Sep 2008 | TW |
| M348993 | Jan 2009 | TW |
| 200943903 | Oct 2009 | TW |
| 201018258 | May 2010 | TW |
| 201027515 | Jul 2010 | TW |
| 201028996 | Aug 2010 | TW |
| 201110108 | Mar 2011 | TW |
| 2011-42823 | Dec 2011 | TW |
| 201227715 | Jul 2012 | TW |
| 201245989 | Nov 2012 | TW |
| 201312548 | Mar 2013 | TW |
| 2008030970 | Mar 2008 | WO |
| 2008071231 | Jun 2008 | WO |
| 2008085742 | Jul 2008 | WO |
| 2008098900 | Aug 2008 | WO |
| 2008109835 | Aug 2008 | WO |
| 2008120036 | Oct 2008 | WO |
| 2008130095 | Oct 2008 | WO |
| 2008140236 | Nov 2008 | WO |
| 2008142472 | Nov 2008 | WO |
| 2008153639 | Dec 2008 | WO |
| 2009009240 | Jan 2009 | WO |
| 2009016631 | Feb 2009 | WO |
| 2009017280 | Feb 2009 | WO |
| 2009075912 | Jun 2009 | WO |
| 2009104126 | Aug 2009 | WO |
| 2009156438 | Dec 2009 | WO |
| 2009156978 | Dec 2009 | WO |
| 2010054373 | May 2010 | WO |
| 2010075623 | Jul 2010 | WO |
| 2010100937 | Sep 2010 | WO |
| 2010141802 | Dec 2010 | WO |
| 2011057346 | May 2011 | WO |
| 2011060106 | May 2011 | WO |
| 2011088053 | Jul 2011 | WO |
| 2011093025 | Aug 2011 | WO |
| 2011116309 | Sep 2011 | WO |
| 2011133543 | Oct 2011 | WO |
| 2011150730 | Dec 2011 | WO |
| 2011163350 | Dec 2011 | WO |
| 2011088053 | Jan 2012 | WO |
| 2012019637 | Feb 2012 | WO |
| 2012129231 | Sep 2012 | WO |
| 2012135157 | Oct 2012 | WO |
| 2012154317 | Nov 2012 | WO |
| 2012155079 | Nov 2012 | WO |
| 2012167168 | Dec 2012 | WO |
| 2013009578 | Jan 2013 | WO |
| 2013022135 | Feb 2013 | WO |
| 2013022223 | Feb 2013 | WO |
| 2013048880 | Apr 2013 | WO |
| 2013049358 | Apr 2013 | WO |
| 2013163113 | Oct 2013 | WO |
| 2013169842 | Nov 2013 | WO |
| 2013173504 | Nov 2013 | WO |
| 2013173511 | Nov 2013 | WO |
| 2013184953 | Dec 2013 | WO |
| 2013184990 | Dec 2013 | WO |
| 2014003138 | Jan 2014 | WO |
| 2014022148 | Feb 2014 | WO |
| 2014028797 | Feb 2014 | WO |
| 2014031505 | Feb 2014 | WO |
| 2014066352 | May 2014 | WO |
| 2014078965 | May 2014 | WO |
| 2014096506 | Jun 2014 | WO |
| 2014124332 | Aug 2014 | WO |
| 2014137074 | Sep 2014 | WO |
| 2014138604 | Sep 2014 | WO |
| 2014143959 | Sep 2014 | WO |
| 2014144579 | Sep 2014 | WO |
| 2014159581 | Oct 2014 | WO |
| 2014197336 | Dec 2014 | WO |
| 2014200728 | Dec 2014 | WO |
| 2014204659 | Dec 2014 | WO |
| 2015018440 | Feb 2015 | WO |
| 2015030796 | Mar 2015 | WO |
| 2015041892 | Mar 2015 | WO |
| 2015084659 | Jun 2015 | WO |
| 2015092943 | Jun 2015 | WO |
| 2015094169 | Jun 2015 | WO |
| 2015094369 | Jun 2015 | WO |
| 2015099939 | Jul 2015 | WO |
| 2015116151 | Aug 2015 | WO |
| 2015151133 | Oct 2015 | WO |
| 2015157013 | Oct 2015 | WO |
| 2015183401 | Dec 2015 | WO |
| 2015184186 | Dec 2015 | WO |
| 2015200207 | Dec 2015 | WO |
| 2016028946 | Feb 2016 | WO |
| 2016033257 | Mar 2016 | WO |
| 2016057268 | Apr 2016 | WO |
| 2016075081 | May 2016 | WO |
| 2016144982 | Sep 2016 | WO |
| 2016144983 | Sep 2016 | WO |
| 2016209444 | Dec 2016 | WO |
| 2017044260 | Mar 2017 | WO |
| 2017044629 | Mar 2017 | WO |
| 2017053311 | Mar 2017 | WO |
| Entry |
|---|
| Office Action received for Danish Patent Application No. PA201870384, dated Oct. 8, 2018, 3 pages. |
| Search Report received for Danish Patent Application No. PA201870384, dated Sep. 10, 2018, 11 pages. |
| “Alexa, Turn Up the Heat!”, Smartthings Samsung [online], Available online at https://web.archive.org/web/20160329142041/https://blog.smartthings.com/news/smartthingsupdates/alexa-turn-up-the-heat/, Mar. 3, 2016, 3 pages. |
| “Ask Alexa—Things That Are Smart Wiki”, Available online at <URL:http://thingsthataresmart.wiki/index.php?title=Ask_Alexa&oldid=4283>, [retrieved from internet on Aug. 2, 2017], Jun. 8, 2016, pp. 1-31. |
| “DIRECTV™ Voice”, Now Part of the DIRECTTV Mobile App for Phones, Sep. 18, 2013, 5 pages. |
| “Headset Button Controller v7.3 APK Full APP Download for Android”, Blackberry, iPhone, Jan. 27, 2014, 11 pages. |
| “Hear voice from Google Translate”, Available on URL:https://www.youtube.com/watch?v=18AvMhFqD28, Jan. 28, 2011, 1 page. |
| “Interactive Voice”, available at <http://www.helloivee.com/company/>, retrieved on Feb. 10, 2014, 2 pages. |
| “Meet Ivee, Your Wi-Fi Voice Activated Assistant”, available at <http://www.helloivee.com/>, retrieved on Feb. 10, 2014, 8 pages. |
| “Mel Scale”, Wikipedia the Free Encyclopedia, Last modified on Oct. 13, 2009 and retrieved on Jul. 28, 2010, available at <http://en.wikipedia.org/wiki/Mel_scale>, 2 pages. |
| “Minimum Phase”, Wikipedia the free Encyclopedia, Last modified on Jan. 12, 2010 and retrieved on Jul. 28, 2010, available at <http://en.wikipedia.org/wiki/Minimum_phase>, 8 pages. |
| Mobile Speech Solutions, Mobile Accessibility, SVOX AG Product Information Sheet, available at <http://www.svox.com/site/bra840604/con782768/mob965831936.aSQ?osLang=1>, Sep. 27, 2012, 1 page. |
| “SmartThings +Amazon Echo”, Smartthings Samsung [online], Available online at <https://web.archive.org/web/20160509231428/https://blog.smartthings.com/featured/alexa-turn-on-my-smartthings/>, Aug. 21, 2015, 3 pages. |
| “Speaker Recognition”, Wkipedia, The Free Enclyclopedia, Nov. 2, 2010, 4 pages. |
| “The world of Virtual Assistants—more SemTech . . . ”, End of Business as Usual—Glenn's External blog, Online Available at <https://web.archive.org/web/20091101840940/http://glennas.wordpress.com/2009/10/17/the-world-of-virtual-assistants-more-semtech/>, Oct. 17, 2009, 5 pages. |
| Adium, “AboutAdium—Adium X—Trac”, available at <http://web.archive.org/web/20070819113247/http://trac.adiumx.com/wiki/AboutAdium>, retrieved on Nov. 25, 2011, 2 pages. |
| Alfred App, “Alfred”, available at <http://www.alfredapp.com/>, retrieved on Feb. 8, 2012, 5 pages. |
| Anania, Peter, “Amazon Echo with Home Automation (Smartthings)”, Available online at https://www.youtube.com/watch?v=LMW6aXmsWNE, Dec. 20, 2015, 1 page. |
| API.AI, “Android App Review—Speaktoit Assistant”, Available at <https://www.youtube.com/watch?v=myE498nyfGw>, Mar. 30, 2011, 3 pages. |
| Apple, “VoiceOver”, available at <http://www.apple.com/accessibility/voiceover/>, May 19, 2014, 3 pages. |
| Apple Computer, “Knowledge Navigator”, published by Apple Computer no later than 2008, as depicted in Exemplary Screenshots from video entitled ‘Knowledge Navigator’, 2008, 7 pages. |
| Asakura et al., “What LG thinks; How the TV should be in the Living Room”, HiVi, vol. 31, No. 7 (Jul. 2013), Stereo Sound Publishing, Inc., Jun. 17, 2013, pp. 68-71 (Official Copy Only). (See Communication under 37 CFR § 1.98(a) (3)). |
| Berry et al., “PTIME: Personalized Assistance for Calendaring”, ACM Transactions on Intelligent Systems and Technology, vol. 2, No. 4, Article 40, Jul. 2011, pp. 1-22. |
| Bertulucci, Jeff, “Google Adds Voice Search to Chrome Browser”, PC World, Jun. 14, 2011, 5 pages. |
| Bocchieri et al., “Use of Geographical Meta-Data in ASR Language and Acoustic Models”, IEEE International Conference on Acoustics Speech and Signal Processing, 2010, pp. 5118-5121. |
| Butcher, Mike, “EVI Arrives in Town to go Toe-to-Toe with Siri”, TechCrunch, Jan. 23, 2012, 2 pages. |
| Cambria et al., “Jumping NLP Curves: A Review of Natural Language Processing Research”, IEEE Computational Intelligence Magazine, 2014, vol. 9, May 2014, pp. 48-57. |
| Caraballo et al., “Language Identification Based on a Discriminative Text Categorization Technique”, Iberspeech 2012—Vii Jornadas En Tecnologia Del Habla and Iii Iberiansl Tech Workshop, Nov. 21, 2012, pp. 1-10. |
| Castleos, “Whole House Voice Control Demonstration”, available online at : https://www.youtube.com/watch?v=9SRCoxrZ_W4, Jun. 2, 2012, 26 pages. |
| Chamberlain, Kim, “Quick Start Guide Natural Reader”, available online at <http://atrc.colostate.edu/files/quickstarts/Natural_Reader_Quick_Start_Guide.>, Apr. 2008, 5 pages. |
| Chen, Yi, “Multimedia Siri Finds and Plays Whatever You Ask for”, PSFK Report, Feb. 9, 2012, 9 pages. |
| Cheyer, Adam, “About Adam Cheyer”, available at <http://www.adam.cheyer.com/about.html>, retrieved on Sep. 17, 2012, 2 pages. |
| Choi et al., “Acoustic and Visual Signal based Context Awareness System for Mobile Application”, IEEE Transactions on Consumer Electronics, vol. 57, No. 2, May 2011, pp. 738-746. |
| Colt, Sam, “Here's One Way Apple's Smartwatch Could Be Better Than Anything Else”, Business Insider, Aug. 21, 2014, pp. 1-4. |
| Deedeevuu, “Amazon Echo Alarm Feature”, Available online at https://www.youtube.com/watch?v=fdjU8eRLk7c, Feb. 16, 2015, 1 page. |
| Elliott et al., “Annotation Suggestion and Search for Personal Multimedia Objects on the Web”, CIVR, Jul. 7-9, 2008, pp. 75-84. |
| Erol et al., “Multimedia Clip Generation From Documents for Browsing on Mobile Devices”, IEEE Transactions on Multimedia, vol. 10, No. 5, Aug. 2008, 13 pages. |
| Evi, “Meet Evi: The One Mobile Application that Provides Solutions for your Everyday Problems”, Feb. 2012, 3 pages. |
| Exhibit 1, “Natural Language Interface Using Constrained Intermediate Dictionary of Results”, List of Publications Manually Reviewed for the Search of U.S. Pat. No. 7,177,798, Mar. 22, 2013, 1 page. |
| Filipowicz, Luke, “How to use the Quick Type Keyboard in iOS 8”, available online at <https://www.imore.com/comment/568232>, Oct. 11, 2014, pp. 1-17. |
| Findlater et al., “Beyond QWERTY: Augmenting Touch-Screen Keyboards with Multi-Touch Gestures for Non-Alphanumeric Input”, CHI '12, Austin, Texas, USA, May 5-10, 2012, 4 pages. |
| Finkel et al., “Joint Parsing and Named Entity Recognition”, Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the ACL, Jun. 2009, pp. 326-334. |
| Gannes, Liz, “Alfred App Gives Personalized Restaurant Recommendations”, AllThingsD, Jul. 18, 2011, pp. 1-3. |
| Gomez et al., “Mouth Gesture and Voice Command Based Robot Command Interface”, IEEE International Conference on Robotics and Automation, May 12-17, 2009, pp. 333-338. |
| Gruber, Tom, “Big Think Small Screen: How Semantic Computing in the Cloud will Revolutionize the Consumer Experience on the Phone”, Keynote Presentation at Web 3.0 Conference, Jan. 2010, 41 pages. |
| Gruber, Tom, “Despite Our Best Efforts, Ontologies are not the Problem”, AAAI Spring Symposium, Available online at <http://tomgruber.org/writing/aaai-ss08.htm>, Mar. 2008, pp. 1-40. |
| Gruber, Tom, “Intelligence at the Interface: Semantic Technology and the Consumer Internet Experience”, Presentation at Semantic Technologies Conference, Available online at <http://tomgruber.org/writing/semtech08.htm>, May 20, 2008, pp. 1-40. |
| Gruber, Tom, “Siri, A Virtual Personal Assistant—Bringing Intelligence to the Interface”, Semantic Technologies Conference, Jun. 16, 2009, 21 pages. |
| Gruber, Thomas R., et al., U.S. Appl. No. 61/186,414, filed Jun. 12, 2009 titled “System and Method for Semantic Auto-Completion” 13 pages. |
| Guay, Matthew, “Location-Driven Productivity with Task Ave”, available at <http://iphone.appstorm.net/reviews/productivity/location-driven-productivity-with-task-ave/>, Feb. 19, 2011, 7 pages. |
| Guim, Mark, “How to Set a Person-Based Reminder with Cortana”, available at <http://www.wpcentral.com/how-to-person-based-reminder-cortana>, Apr. 26, 2014, 15 pages. |
| Hardwar, Devindra, “Driving App Waze Builds its own Siri for Hands-Free Voice Control”, Available online at <http://venturebeat.com/2012/02/09/driving-app-waze-builds-its-own-siri-for-hands-free-voice-control/>, retrieved on Feb. 9, 2012, 4 pages. |
| Hashimoto, Yoshiyuki, “Simple Guide for iPhone Siri, Which Can Be Operated with Your Voice”, Shuwa System Co., Ltd., vol. 1, Jul. 5, 2012, pp. 8, 130, 131. |
| id3.org, “id3v2.4.0—Frames”, available at <http://id3.org/id3v2.4.0-frames?action=print>, retrieved on Jan. 22, 2015, 41 pages. |
| Iowegian International, “FIR Filter Properties, DSPGuru, Digital Signal Processing Central”, available at <http://www.dspguru.com/dsp/faq/fir/properties> retrieved on Jul. 28, 2010, 6 pages. |
| Jawaid et al., “Machine Translation with Significant Word Reordering and Rich Target-Side Morphology”, WDS'11 Proceedings of Contributed Papers, Part I, 2011, pp. 161-166. |
| Jiang et al., “A Syllable-based Name Transliteration System”, Proc. of the 2009 Named Entities Workshop, Aug. 7, 2009, pp. 96-99. |
| Jonsson et al, “Proximity-based Reminders Using Bluetooth”, 2014 IEEE International Conference on Pervasive Computing and Communications Demonstrations, 2014, pp. 151-153. |
| Jouvet et al., “Evaluating Grapheme-to-phoneme Converters in Automatic Speech Recognition Context”, IEEE, 2012, pp. 4821-4824. |
| Kane et al., “Slide Rule: Making Mobile Touch Screens Accessible to Blind People Using Multi-Touch Interaction Techniques”, ASSETS, Oct. 13-15, 2008, pp. 73-80. |
| Karn, Ujjwal, “An Intuitive Explanation of Convolutional Neural Networks”, The Data Science Blog, Aug. 11, 2016, 23 pages. |
| Kazmucha, Allyson, “How to Send Map Locations Using iMessage”, iMore.com, Available at <http://www.imore.com/how-use-imessage-share-your-location-your-iphone>, Aug. 2, 2012, 6 pages. |
| Kickstarter, “Ivee Sleek: Wi-Fi Voice-Activated Assistant”, available at <https://www.kickstarter.com/projects/ivee/ivee-sleek-wi-fi-voice-activated-assistant>, retrieved on Feb. 10, 2014, 13 pages. |
| Knownav, “Knowledge Navigator”, YouTube Video available at <http://www.youtube.com/watch?v=QRH8eimU_20>, Apr. 29, 2008, 1 page. |
| Lewis, Cameron, “Task Ave for iPhone Review”, Mac Life, Available at <http://www.maclife.com/article/reviews/task_ave_iphone_review>, Mar. 3, 2011, 5 pages. |
| Mactech, “KeyStrokes 3.5 for Mac OS X Boosts Word Prediction”, available at <http://www.mactech.com/news/?p=1007129>, retrieved on Jan. 7, 2008, 3 pages. |
| Majerus, Wesley, “Cell Phone Accessibility for your Blind Child”, Retrieved from the Internet <URL:https://web.archive.org/web/20100210001100/https://nfb.org/images/nfb/publications/fr/fr28/3/fr280314.htm>, 2010, pp. 1-5. |
| Martins et al., “Extracting and Exploring the Geo-Temporal Semantics of Textual Resources”, Semantic Computing, IEEE International Conference, 2008, pp. 1-9. |
| Mhatre et al., “Donna Interactive Chat-bot acting as a Personal Assistant”, International Journal of Computer Applications (0975-8887), vol. 140, No. 10, Apr. 2016, 6 pages. |
| Microsoft, “Turn On and Use Magnifier”, available at <http://www.microsoft.com/windowsxp/using/accessibility/magnifierturnon.mspx>, retrieved on Jun. 6, 2009. |
| Miller, Chance, “Google Keyboard Updated with New Personalized Suggestions Feature”, available at <http://9to5google.com/2014/03/19/google-keyboard-updated-with-new-personalized-suggestions-feature/>, Mar. 19, 2014, 4 pages. |
| Morrison, Jonathan, “iPhone 5 Siri Demo”, Online Available at <https://www.youtube.com/watch?v=_wHWwG5lhWc>, Sep. 21, 2012, 3 pages. |
| Morton, Philip, “Checking If an Element Is Hidden”, StackOverflow, Available at <http://stackoverflow.com/questions/178325/checking-if-an-element-is-hidden>, Oct. 7, 2008, 12 pages. |
| My Cool Aids, “What's New”, available at <http://www.mycoolaids.com/>, 2012, 1 page. |
| Myers, Brad A., “Shortcutter for Palm”, available at <http://www.cs.cmu.edu/˜pebbles/v5/shortcutter/palm/index.html>, retrieved on Jun. 18, 2014, 10 pages. |
| Nakazawa et al., “Detection and Labeling of Significant Scenes from TV program based on Twitter Analysis”, Proceedings of the 3rd Forum on Data Engineering and Information Management (DEIM 2011 proceedings), IEICE Data Engineering Technical Group. Available online at: http://db-event.jpn.org/deim2011/proceedings/pdf/f5-6.pdf, Feb. 28, 2011, 10 pages (Official Copy Only). (See Communication under 37 CFR § 1.98(a) (3)). |
| Naone, Erica, “TR10: Intelligent Software Assistant”, Technology Review, Mar.-Apr. 2009, 2 pages. |
| Navigli, Roberto, “Word Sense Disambiguation: A Survey”, ACM Computing Surveys, vol. 41, No. 2, Feb. 2009, 69 pages. |
| NDTV, “Sony SmartWatch 2 Launched in India for Rs. 14,990”, available at <http://gadgets.ndtv.com/others/news/sony-smartwatch-2-launched-in-india-for-rs-14990-420319>, Sep. 18, 2013, 4 pages. |
| Ng, Simon, “Google's Task List Now Comes to Iphone”, SimonBlog, Available at <http://www.simonblog.com/2009/02/04/googles-task-list-now-comes-to-iphone/>, Feb. 4, 2009, 3 pages. |
| Nozawa, Naoki et al., “iPhone 4S Perfect Manual”, vol. 1, First Edition, Nov. 11, 2011, 5 pages. |
| Osxdaily, “Get a List of Siri Commands Directly from Siri”, Available at <http://osxdaily.com/2013/02/05/list-siri-commands/>, Feb. 5, 2013, 15 pages. |
| Pan et al., “Natural Language Aided Visual Query Building for Complex Data Access”, In proceeding of: Proceedings of the Twenty-Second Conference on Innovative Applications of Artificial Intelligence, XP055114607, Jul. 11, 2010, pp. 1821-1826. |
| Pathak et al., “Privacy-preserving Speech Processing: Cryptographic and String-matching Frameworks Show Promise”, In: IEEE signal processing magazine, retrieved from <http://www.merl.com/publications/docs/TR2013-063.pdf>, Feb. 13, 2013, 16 pages. |
| Patra et al., “A Kernel-Based Approach for Biomedical Named Entity Recognition”, Scientific World Journal, vol. 2013, 2013, pp. 1-7. |
| Phoenix Solutions, Inc., “Declaration of Christopher Schmandt Regarding the MIT Galaxy System”, West Interactive Corp., A Delaware Corporation, Document 40, Jul. 2, 2010, 162 pages. |
| Powell, Josh, “Now You See Me . . . Show/Hide Performance”, available at http://www.learningjquery.com/2010/05/now-you-see-me-showhide-performance, May 4, 2010, 3 pages. |
| Rios, Mafe, “New bar search for Facebook”, Youtube, available at “https://www.youtube.com/watch?v=vwgN1WbvCas”, 1 page. |
| Routines, “SmartThings Support”, Available online at <https://web.archive.org/web/20151207165701/https://support.smartthings.com/hc/en-us/articles/205380034-Routines>, 2015, 2 pages. |
| Sarawagi, Sunita, “CRF Package Page”, available at <http://crf.sourceforge.net/>, retrieved on Apr. 6, 2011, 2 pages. |
| Selfrifge et al., “Interact: Tightly-coupling Multimodal Dialog with an Interactive Virtual Assistant”, International Conference on Multimodal Interaction, ACM, Nov. 9, 2015, pp. 381-382. |
| Simonite, Tom, “One Easy Way to Make Siri Smarter”, Technology Review, Oct. 18, 2011, 2 pages. |
| Spivack, Nova, “Sneak Preview of Siri—Part Two—Technical Foundations—Interview with Tom Gruber, CTO of Siri”, Online Available at <https://web.archive.org/web/20100114234454/http://www.twine.com/item/12vhy39k4-22m/interview-with-tom-gruber-of-siri>, Jan. 14, 2010, 5 pages. |
| SRI, “SRI Speech: Products: Software Development Kits: EduSpeak”, available at <http://web.archive.org/web/20090828084033/http://www.speechatsri.com/products/eduspeak>shtml, retrieved on Jun. 20, 2013, 2 pages. |
| Stent et al., “Geo-Centric Language Models for Local Business Voice Search”, AT&T Labs—Research, 2009, pp. 389-396. |
| Sullivan, Danny, “How Google Instant's Autocomplete Suggestions Work”, available at <http://searchengineland.com/how-google-instant-autocomplete-suggestions-work-62592>, Apr. 6, 2011, 12 pages. |
| Sundaram et al., “Latent Perceptual Mapping with Data-Driven Variable-Length Acoustic Units for Template-Based Speech Recognition”, ICASSP 2012, Mar. 2012, pp. 4125-4128. |
| Sundermeyer et al., “From Feedforward to Recurrent LSTM Neural Networks for Language Modeling”, IEEE Transactions to Audio, Speech, and Language Processing, 2015, vol. 23, Mar. 2015, pp. 517-529. |
| Tanaka, Tatsuo, “Next Generation IT Channel Strategy Through “Experience Technology””, Intellectual Resource Creation, Japan, Nomura Research Institute Ltd. vol. 19, No. 1, Dec. 20, 2010, 17 pages. (Official Copy only) (See Communication under 37 CFR § 1.98(a) (3)). |
| TextnDrive, “Text'nDrive App Demo—Listen and Reply to your Messages by Voice while Driving!”, YouTube Video available at <http://www.youtube.com/watch?v=WaGfzoHsAMw>, Apr. 27, 2010, 1 page. |
| Tofel, Kevin C., “SpeakToIt: A Personal Assistant for Older iPhones, iPads”, Apple News, Tips and Reviews, Feb. 9, 2012, 7 pages. |
| Tucker, Joshua, “Too Lazy to Grab Your TV Remote? Use Siri Instead”, Engadget, Nov. 30, 2011, 8 pages. |
| Tur et al., “The CALO Meeting Assistant System”, IEEE Transactions on Audio, Speech and Language Processing, vol. 18, No. 6, Aug. 2010, pp. 1601-1611. |
| Tur et al., “The CALO Meeting Speech Recognition and Understanding System”, Proc. IEEE Spoken Language Technology Workshop, 2008, 4 pages. |
| Vlingo InCar, “Distracted Driving Solution with Vlingo InCar”, YouTube Video, Available online at <http://www.youtube.com/watch?v=Vqs8XfXxgz4>, Oct. 2010, 2 pages. |
| Vlingo, “Vlingo Launches Voice Enablement Application on Apple App Store”, Press Release, Dec. 3, 2008, 2 pages. |
| Vodafone Deutschland, “Samsung Galaxy S3 Tastatur Spracheingabe”, Available online at—“https://www.youtube.com/watch?v=6kOd6Gr8uFE”, Aug. 22, 2012, 1 page. |
| Voiceassist, “Send Text, Listen to and Send E-Mail by Voice”, YouTube Video, Available online at <http://www.youtube.com/watch?v=0tEU61nHHA4>, Jul. 30, 2009, 1 page. |
| VoiceontheGo, “Voice on the Go (BlackBerry)”, YouTube Video, available online at <http://www.youtube.com/watch?v=pJqpWgQS98w>, Jul. 27, 2009, 1 page. |
| Wikipedia, “Acoustic Model”, available at <http://en.wikipedia.org/wiki/AcousticModel>, retrieved on Sep. 14, 2011, 2 pages. |
| Wikipedia, “Language Model”, available at <http://en.wikipedia.org/wiki/Language_model>, retrieved on Sep. 14, 2011, 4 pages. |
| Wikipedia, “Speech Recognition”, available at <http://en.wikipedia.org/wiki/Speech_recognition>, retrieved on Sep. 14, 2011, 12 pages. |
| Wilson, Mark, “New iPod Shuffle Moves Buttons to Headphones, Adds Text to Speech”, available at <http://gizmodo.com/5167946/new-ipod-shuffle-moves-buttons-to-headphones-adds-text-to-speech>, Mar. 11, 2009, 12 pages. |
| X.Ai, “How it Works”, May 2016, 6 pages. |
| Xiang et al., “Correcting Phoneme Recognition Errors in Learning Word Pronunciation through Speech Interaction”, Speech Communication, vol. 55, No. 1, Jan. 1, 2013, pp. 190-203. |
| Xu, Yuhong, “Policy optimization of dialogue management in spoken dialogue system for out-of-domain utterances”, 2016 International Conference on Asian Language Processing (IALP), IEEE, Nov. 21, 2016, pp. 10-13. |
| Xu et al., “Speech-Based Interactive Games for Language Learning: Reading, Translation, and Question-Answering”, Computational Linguistics and Chinese Language Processing, vol. 14, No. 2, Jun. 2009, pp. 133-160. |
| Yan et al., “A Scalable Approach to Using DNN-Derived Features in GMM-HMM Based Acoustic Modeling for LVCSR”, InInterspeech, 2013, pp. 104-108. |
| Young et al., “The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management”, Computer Speech & Language, vol. 24, Issue 2, 2010, pp. 150-174. |
| Zainab, “Google Input Tools Shows Onscreen Keyboard in Multiple Languages [Chrome]”, available at <http://www.addictivetips.com/internet-tips/google-input-tools-shows-multiple-language-onscreen-keyboards-chrome/>, Jan. 3, 2012, 3 pages. |
| Zangerle et al., “Recommending #-Tag in Twitter”, Proceedings of the Workshop on Semantic Adaptive Socail Web, 2011, pp. 1-12. |
| Zhang et al., “Research of Text Classification Model Based on Latent Semantic Analysis and Improved HS-SVM”, Intelligent Systems and Applications (ISA), 2010 2nd International Workshop, May 22-23, 2010, 5 pages. |
| Zhong et al., “JustSpeak: Enabling Universal Voice Control on Android”, W4A'14, Proceedings of the 11th Web for All Conference, No. 36, Apr. 7-9, 2014, 8 pages. |
| Notice of Acceptance received for Australian Patent Application No. 2019203209, dated Jun. 5, 2019, 3 pages. |
| Partial European Search Report received for European Patent Application No. 19171414.6, dated Jun. 19, 2019, 14 pages. |
| Intention to Grant received for Danish Patent Application No. PA201870384, dated Mar. 27, 2019, 2 pages. |
| Office Action received for European Patent Application No. 19171414.6, dated Sep. 18, 2019, 12 pages. |
| Office Action received for Korean Patent Application No. 10-2019-0051414, dated Sep. 16, 2019, 16 pages (7 pages of English Translation and 9 pages of Official Copy). |
| Search Report and Opinion received for Danish Patent Application No. PA201970332, dated Oct. 23, 2019, 12 pages. |
| Invitation to Pay Addition Fees and Partial International Search Report received for PCT Patent Application No. PCT/US2019/023337, dated Jun. 27, 2019, 14 pages. |
| European Search Report received for European Patent Application No. 19171414.6, dated Sep. 5, 2019, 6 pages. |
| International Search Report and Written Opinion received for PCT Patent Application No. PCT/US2019/023337, dated Aug. 22, 2019, 20 pages. |
| Decision to Grant received for Danish Patent Application No. PA201870384, dated Jun. 26, 2019, 2 pages. |
| Office Action received for Danish Patent Application No. PA201970332, dated Jan. 17, 2020, 5 pages. |
| Office Action received for Chinese Patent Application No. 201910373311.5, dated Feb. 25, 2020, 22 pages (12 pages of English Translation and 10 pages of Official Copy). |
| Office Action received for Australian Patent Application No. 2019232936, dated May 6, 2020, 3 pages. |
| Notice of Acceptance received for Australian Patent Application No. 2019232936, dated Jul. 10, 2020, 3 pages. |
| Office Action received for Chinese Patent Application No. 201910373311.5, dated Jun. 22, 2020, 17 pages (10 pages of English Translation and 7 pages of Official Copy). |
| Office Action received for Korean Patent Application No. 10-2019-0051414, dated May 25, 2020, 7 pages (3 pages of English Translation and 4 pages of Official Copy). |
| Office Action received for German Patent Application No. 112019000018.7, dated Feb. 27, 2020, 12 pages (6 pages of English Translation and 6 pages of Official Copy). |
| Office Action received for Danish Patent Application No. PA201970332, dated Sep. 4, 2020, 9 pages. |
| Office Action received for Korean Patent Application No. 10-2019-0051414, dated Aug. 25, 2020, 6 pages (3 pages of English Translation and 3 pages of Official Copy). |
| Office Action received for European Patent Application No. 19171414.6, dated Sep. 18, 2020, 6 pages. |
| Office Action received for Indian Patent Application No. 201914014951, dated Oct. 7, 2020, 6 pages. |
| Office Action received for Chinese Patent Application No. 201910373311.5, dated Sep. 10, 2020, 16 pages (10 pages of English Translation and 6 pages of Official Copy). |
| Number | Date | Country | |
|---|---|---|---|
| 20190339784 A1 | Nov 2019 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62679798 | Jun 2018 | US | |
| 62668154 | May 2018 | US |
