Multi-command single utterance input method

Information

  • Patent Grant
  • 11670289
  • Patent Number
    11,670,289
  • Date Filed
    Friday, December 18, 2020
    3 years ago
  • Date Issued
    Tuesday, June 6, 2023
    a year ago
Abstract
Systems and processes are disclosed for handling a multi-part voice command for a virtual assistant. Speech input can be received from a user that includes multiple actionable commands within a single utterance. A text string can be generated from the speech input using a speech transcription process. The text string can be parsed into multiple candidate substrings based on domain keywords, imperative verbs, predetermined substring lengths, or the like. For each candidate substring, a probability can be determined indicating whether the candidate substring corresponds to an actionable command. Such probabilities can be determined based on semantic coherence, similarity to user request templates, querying services to determine manageability, or the like. If the probabilities exceed a threshold, the user intent of each substring can be determined, processes associated with the user intents can be executed, and an acknowledgment can be provided to the user.
Description
FIELD

This relates generally to speech processing for a virtual assistant and, more specifically, to processing a single utterance having multiple actionable commands for a virtual assistant.


BACKGROUND

Intelligent automated assistants (or virtual assistants) provide an intuitive interface between users and electronic devices. These assistants can allow users to interact with devices or systems using natural language in spoken and/or text forms. For example, a user can access the services of an electronic device by providing a spoken user input in natural language form to a virtual assistant associated with the electronic device. The virtual assistant can perform natural language processing on the spoken user input to infer the user's intent and operationalize the user's intent into tasks. The tasks can then be performed by executing one or more functions of the electronic device, and a relevant output can be returned to the user in natural language form.


While electronic user devices continue to provide enhanced functionality, however, some users can get overwhelmed with notifications, announcements, messages, reminders, or the like. Moreover, it can be inefficient and time consuming for users to deal with each notification, announcement, message, or reminder individually. For example, using speech to interact with a virtual assistant, a user can typically address only a single item, function, or activity at one time. In addition, users may need to wait for a virtual assistant task to be completed before moving on to another task. Such delays, in addition to limiting efficiency, can also break user concentration, which can cause users to forget other items they may have had in mind.


Accordingly, in some instances, it can be time consuming, inefficient, and frustrating for users to deal with multiple tasks—one at a time—using speech to interact with a virtual assistant.


SUMMARY

Systems and processes are disclosed for processing a multi-part voice command. In one example, speech input can be received from a user that includes a single utterance having one or more actionable commands. A text string can be generated based on the speech input using a speech transcription process. The text string can be parsed into multiple candidate substrings. Probabilities can be determined for each of the candidate substrings indicating whether they are likely to correspond to actionable commands. In response to the probabilities exceeding a threshold, user intents can be determined for each of the candidate substrings. Processes associated with the user intents can then be executed. An acknowledgment can also be provided to the user associated with the various user intents.


In some examples, the text string can be parsed by identifying domain keywords. In other examples, the text string can be parsed by identifying imperative verbs. The probability that a substring corresponds to an actionable command can be determined by determining a semantic coherence of the substring. The probability can also be determined by comparing the substring to user request templates. The probability can also be determined by submitting the substring to a service and receiving a likelihood that the service can resolve an actionable command from the substring.


In addition, in some examples, user intent for a substring can be determined based on words in a previous substring. User intent can also be determined based on displayed information. Displayed information can include a list, and user intent can be determined based on ordinal descriptors associated with items in the list. Displayed information can include notifications and emails. User intent can also be determined by determining potential user requests based on displayed information.


Moreover, in some examples, an acknowledgment can include an audible confirmation or haptic feedback. Providing an acknowledgment can also include providing tasks associated with user intents, including displaying the tasks. Providing an acknowledgment can also include providing a completion indicator, including displaying a completion indicator like a checkmark. Providing an acknowledgment can also include providing a status indicator, including displaying a status indicator like an hourglass or a status bar. In other examples, providing an acknowledgement can include displaying different candidate substrings using different forms of emphasis, such as bold text, italic text, underlined text, circled text, outlined text, colored text, and/or clustered text.





BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the various described embodiments, reference should be made to the Description of Embodiments below, in conjunction with the following drawings in which like reference numerals refer to corresponding parts throughout the figures.



FIG. 1 is a block diagram illustrating a system and environment for implementing a digital assistant according to various examples.



FIG. 2A is a block diagram illustrating a portable multifunction device implementing the client-side portion of a digital assistant in accordance with some embodiments.



FIG. 2B is a block diagram illustrating exemplary components for event handling according to various examples.



FIG. 3 illustrates a portable multifunction device implementing the client-side portion of a digital assistant according to various examples.



FIG. 4 is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface according to various examples.



FIG. 5A illustrates an exemplary user interface for a menu of applications on a portable multifunction device according to various examples.



FIG. 5B illustrates an exemplary user interface for a multifunction device with a touch-sensitive surface that is separate from the display according to various examples.



FIG. 6A illustrates a personal electronic device according to various examples.



FIG. 6B is a block diagram illustrating a personal electronic device according to various examples.



FIG. 7A is a block diagram illustrating a digital assistant system or a server portion thereof according to various examples.



FIG. 7B illustrates the functions of the digital assistant shown in FIG. 7A according to various examples.



FIG. 7C illustrates a portion of an ontology according to various examples.



FIG. 8 illustrates an exemplary process for handling multiple actionable commands in a single user utterance.



FIG. 9 illustrates an exemplary parsed multi-part voice command.



FIG. 10 illustrates an exemplary display with context for interpreting a multi-part voice command.



FIG. 11 illustrates an exemplary display with multiple notifications of various types usable as context for interpreting a multi-part voice command.



FIG. 12 illustrates an exemplary display with an email application usable as context for interpreting a multi-part voice command.



FIG. 13 illustrates an exemplary user interface for conveying the status of a multi-part voice command.



FIG. 14A and FIG. 14B illustrate exemplary user interfaces for conveying recognition of a multi-part voice command.



FIG. 15 illustrates a functional block diagram of an electronic device configured to process a multi-part voice command according to various examples.





DESCRIPTION OF EMBODIMENTS

In the following description of examples, reference is made to the accompanying drawings in which it is 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.


Below, FIGS. 2A-2B, 3, 4, 5A-5B, and 6A-6B provide a description of exemplary devices for performing the techniques for processing multi-part voice commands. FIGS. 10-14B illustrate exemplary user interfaces. The user interfaces in the figures are also used to illustrate the processes described below, including the process 800 in FIG. 8.


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 can both be outputs and, in some cases, can be 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



FIG. 1 illustrates a block diagram of system 100 according to various examples. In some examples, system 100 can implement a digital assistant. The terms “digital assistant,” “virtual assistant,” “intelligent automated assistant,” or “automatic digital assistant” can refer to any information processing system that interprets natural language input in spoken and/or textual form to infer user intent, and performs actions based on the inferred user intent. For example, to act on an inferred user intent, the system can perform one or more of the following: identifying a task flow with steps and parameters designed to accomplish the inferred user intent, inputting specific requirements from the inferred user intent into the task flow; executing the task flow by invoking programs, methods, services, APIs, or the like; and generating output responses to the user in an audible (e.g., speech) and/or visual form.


Specifically, a digital assistant can be 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 can seek either an informational answer or performance of a task by the digital assistant. A satisfactory response to the user request can be a provision of the requested informational answer, a performance of the requested task, or a combination of the two. For example, a user can ask the digital assistant a question, such as “Where am I right now?” Based on the user's current location, the digital assistant can answer, “You are in Central Park near the west gate.” The user can also request 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 can sometimes interact 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 can also provide responses in other visual or audio forms, e.g., as text, alerts, music, videos, animations, etc.


As shown in FIG. 1, in some examples, a digital assistant can be implemented according to a client-server model. The digital assistant can include client-side portion 102 (hereafter “DA client 102”) executed on user device 104 and server-side portion 106 (hereafter “DA server 106”) executed on server system 108. DA client 102 can communicate with DA server 106 through one or more networks 110. DA client 102 can provide client-side functionalities such as user-facing input and output processing and communication with DA server 106. DA server 106 can provide server-side functionalities for any number of DA clients 102 each residing on a respective user device 104.


In some examples, DA server 106 can include 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 can facilitate the client-facing input and output processing for DA server 106. One or more processing modules 114 can 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 can communicate with external services 120 through network(s) 110 for task completion or information acquisition. I/O interface to external services 118 can facilitate such communications.


User device 104 can be any suitable electronic device. For example, user device 104 can be a portable multifunctional device (e.g., device 200, described below with reference to FIG. 2A), a multifunctional device (e.g., device 400, described below with reference to FIG. 4), or a personal electronic device (e.g., device 600, described below with reference to FIG. 6A-B.) A portable multifunctional device can be, for example, a mobile telephone that also contains other functions, such as PDA and/or music player functions. Specific examples of portable multifunction devices can include the iPhone®, iPod Touch®, and iPad® devices from Apple Inc. of Cupertino, Calif. Other examples of portable multifunction devices can include, without limitation, laptop or tablet computers. Further, in some examples, user device 104 can be a non-portable multifunctional device. In particular, user device 104 can be a desktop computer, a game console, a television, or a television set-top box. In some examples, user device 104 can include a touch-sensitive surface (e.g., touch screen displays and/or touchpads). Further, user device 104 can optionally include one or more other physical user-interface devices, such as a physical keyboard, a mouse, and/or a joystick. Various examples of electronic devices, such as multifunctional devices, are described below in greater detail.


Examples of communication network(s) 110 can include local area networks (LAN) and wide area networks (WAN), e.g., the Internet. Communication network(s) 110 can be 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 can be implemented on one or more standalone data processing apparatus or a distributed network of computers. In some examples, server system 108 can also employ 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 can communicate with DA server 106 via second user device 122. Second user device 122 can be similar or identical to user device 104. For example, second user device 122 can be similar to devices 200, 400, or 600 described below with reference to FIGS. 2A, 4, and 6A-B. User device 104 can be configured to communicatively couple to second user device 122 via a direct communication connection, such as Bluetooth, NFC, BTLE, or the like, or via a wired or wireless network, such as a local Wi-Fi network. In some examples, second user device 122 can be configured to act as a proxy between user device 104 and DA server 106. For example, DA client 102 of user device 104 can be configured to transmit information (e.g., a user request received at user device 104) to DA server 106 via second user device 122. DA server 106 can process the information and return relevant data (e.g., data content responsive to the user request) to user device 104 via second user device 122.


In some examples, user device 104 can be 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 can be 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 FIG. 1, it should be appreciated that system 100 can include any number and type of user devices configured in this proxy configuration to communicate with DA server system 106.


Although the digital assistant shown in FIG. 1 can include both a client-side portion (e.g., DA client 102) and a server-side portion (e.g., DA server 106), in some examples, the functions of a digital assistant can be implemented as a standalone application installed on a user device. In addition, the divisions of functionalities between the client and server portions of the digital assistant can vary in different implementations. For instance, in some examples, the DA client can be a thin-client that provides only user-facing input and output processing functions, and delegates all other functionalities of the digital assistant to a backend server.


2. Electronic Devices


Attention is now directed toward embodiments of electronic devices for implementing the client-side portion of a digital assistant. FIG. 2A is a block diagram illustrating portable multifunction device 200 with touch-sensitive display system 212 in accordance with some embodiments. Touch-sensitive display 212 is sometimes called a “touch screen” for convenience and is sometimes known as or called a “touch-sensitive display system.” Device 200 includes memory 202 (which optionally includes one or more computer-readable storage mediums), memory controller 222, one or more processing units (CPUs) 220, peripherals interface 218, RF circuitry 208, audio circuitry 210, speaker 211, microphone 213, input/output (I/O) subsystem 206, other input control devices 216, and external port 224. Device 200 optionally includes one or more optical sensors 264. Device 200 optionally includes one or more contact intensity sensors 265 for detecting intensity of contacts on device 200 (e.g., a touch-sensitive surface such as touch-sensitive display system 212 of device 200). Device 200 optionally includes one or more tactile output generators 267 for generating tactile outputs on device 200 (e.g., generating tactile outputs on a touch-sensitive surface such as touch-sensitive display system 212 of device 200 or touchpad 455 of device 400). These components optionally communicate over one or more communication buses or signal lines 203.


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 FIG. 2A are implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application-specific integrated circuits.


Memory 202 may include one or more computer-readable storage mediums. The computer-readable storage mediums may be tangible and non-transitory. Memory 202 may include high-speed random access memory and may also include 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 may control access to memory 202 by other components of device 200.


In some examples, a non-transitory computer-readable storage medium of memory 202 can be used to store instructions (e.g., for performing aspects of process 800, 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 process 800, described below) can be stored on a non-transitory computer-readable storage medium (not shown) of the server system 108 or can be divided between the non-transitory computer-readable storage medium of memory 202 and the non-transitory computer-readable storage medium of server system 108. In the context of this document, a “non-transitory computer-readable storage medium” can be any medium that can contain or store the program for use by or in connection with the instruction execution system, apparatus, or device.


Peripherals interface 218 can be 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 may be implemented on a single chip, such as chip 204. In some other embodiments, they may be 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 (VoW), 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 may be 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, FIG. 3). The headset jack provides an interface between audio circuitry 210 and removable audio input/output peripherals, such as output-only headphones or a headset with both output (e.g., a headphone for one or both ears) and input (e.g., a microphone).


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, FIG. 3) optionally include an up/down button for volume control of speaker 211 and/or microphone 213. The one or more buttons optionally include a push button (e.g., 306, FIG. 3).


A quick press of the push button may disengage 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) may turn power to device 200 on or off. The user may be 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 may include graphics, text, icons, video, and any combination thereof (collectively termed “graphics”). In some embodiments, some or all of the visual output may 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 may use 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 may 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 may be 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 may be 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 may have a video resolution in excess of 100 dpi. In some embodiments, the touch screen has a video resolution of approximately 160 dpi. The user may make 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 may include 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 may be 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 may include 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 may also include one or more optical sensors 264. FIG. 2A shows an optical sensor coupled to optical sensor controller 258 in I/O subsystem 206. Optical sensor 264 may include charge-coupled device (CCD) or complementary metal-oxide semiconductor (CMOS) phototransistors. Optical sensor 264 receives light from the environment, projected through one or more lenses, and converts the light to data representing an image. In conjunction with imaging module 243 (also called a camera module), optical sensor 264 may capture still images or video. In some embodiments, an optical sensor is located on the back of device 200, opposite touch screen display 212 on the front of the device so that the touch screen display may be used as a viewfinder for still and/or video image acquisition. In some embodiments, an optical sensor is located on the front of the device so that the user's image may be obtained for video conferencing while the user views the other video conference participants on the touch screen display. In some embodiments, the position of optical sensor 264 can be changed by the user (e.g., by rotating the lens and the sensor in the device housing) so that a single optical sensor 264 may be used along with the touch screen display for both video conferencing and still and/or video image acquisition.


Device 200 optionally also includes one or more contact intensity sensors 265. FIG. 2A shows a contact intensity sensor coupled to intensity sensor controller 259 in I/O subsystem 206. Contact intensity sensor 265 optionally includes one or more piezoresistive strain gauges, capacitive force sensors, electric force sensors, piezoelectric force sensors, optical force sensors, capacitive touch-sensitive surfaces, or other intensity sensors (e.g., sensors used to measure the force (or pressure) of a contact on a touch-sensitive surface). Contact intensity sensor 265 receives contact intensity information (e.g., pressure information or a proxy for pressure information) from the environment. In some embodiments, at least one contact intensity sensor is collocated with, or proximate to, a touch-sensitive surface (e.g., touch-sensitive display system 212). In some embodiments, at least one contact intensity sensor is located on the back of device 200, opposite touch screen display 212, which is located on the front of device 200.


Device 200 may also include one or more proximity sensors 266. FIG. 2A shows proximity sensor 266 coupled to peripherals interface 218. Alternately, proximity sensor 266 may be coupled to input controller 260 in I/O subsystem 206. Proximity sensor 266 may perform as described in U.S. patent application Ser. No. 11/241,839, “Proximity Detector In Handheld Device”; Ser. No. 11/240,788, “Proximity Detector In Handheld Device”; Ser. No. 11/620,702, “Using Ambient Light Sensor To Augment Proximity Sensor Output”; Ser. No. 11/586,862, “Automated Response To And Sensing Of User Activity In Portable Devices”; and Ser. No. 11/638,251, “Methods And Systems For Automatic Configuration Of Peripherals,” which are hereby incorporated by reference in their entirety. In some embodiments, the proximity sensor turns off and disables touch screen 212 when the multifunction device is placed near the user's ear (e.g., when the user is making a phone call).


Device 200 optionally also includes one or more tactile output generators 267. FIG. 2A shows a tactile output generator coupled to haptic feedback controller 261 in I/O subsystem 206. Tactile output generator 267 optionally includes one or more electroacoustic devices such as speakers or other audio components and/or electromechanical devices that convert energy into linear motion such as a motor, solenoid, electroactive polymer, piezoelectric actuator, electrostatic actuator, or other tactile output generating component (e.g., a component that converts electrical signals into tactile outputs on the device). Contact intensity sensor 265 receives tactile feedback generation instructions from haptic feedback module 233 and generates tactile outputs on device 200 that are capable of being sensed by a user of device 200. In some embodiments, at least one tactile output generator is collocated with, or proximate to, a touch-sensitive surface (e.g., touch-sensitive display system 212) and, optionally, generates a tactile output by moving the touch-sensitive surface vertically (e.g., in/out of a surface of device 200) or laterally (e.g., back and forth in the same plane as a surface of device 200). In some embodiments, at least one tactile output generator sensor is located on the back of device 200, opposite touch screen display 212, which is located on the front of device 200.


Device 200 may also include one or more accelerometers 268. FIG. 2A shows accelerometer 268 coupled to peripherals interface 218. Alternately, accelerometer 268 may be coupled to an input controller 260 in I/O subsystem 206. Accelerometer 268 may perform as described in U.S. Patent Publication No. 20050190059, “Acceleration-based Theft Detection System for Portable Electronic Devices,” and U.S. Patent Publication No. 20060017692, “Methods And Apparatuses For Operating A Portable Device Based On An Accelerometer,” both of which are incorporated by reference herein in their entirety. In some embodiments, information is displayed on the touch screen display in a portrait view or a landscape view based on an analysis of data received from the one or more accelerometers. Device 200 optionally includes, in addition to accelerometer(s) 268, a magnetometer (not shown) and a GPS (or GLONASS or other global navigation system) receiver (not shown) for obtaining information concerning the location and orientation (e.g., portrait or landscape) of device 200.


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 can store data and models, such as user data and models 231. Furthermore, in some embodiments, memory 202 (FIG. 2A) or 470 (FIG. 4) stores device/global internal state 257, as shown in FIGS. 2A and 4. Device/global internal state 257 includes one or more of: active application state, indicating which applications, if any, are currently active; display state, indicating what applications, views or other information occupy various regions of touch screen display 212; sensor state, including information obtained from the device's various sensors and input control devices 216; and location information concerning the device's location and/or attitude.


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 may be a component of graphics module 232, provides soft keyboards for entering text in various applications (e.g., contacts 237, e mail 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 can include various client-side digital assistant instructions to provide the client-side functionalities of the digital assistant. For example, digital assistant client module 229 can be 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) 264, other input control devices 216, etc.) of portable multifunction device 200. Digital assistant client module 229 can also be 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 can be 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 can communicate with DA server 106 using RF circuitry 208.


User data and models 231 can 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 can includes 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 can utilize 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 can provide 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 can also use the contextual information to determine how to prepare and deliver outputs to the user. Contextual information can be referred to as context data.


In some examples, the contextual information that accompanies the user input can include 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 can be provided to DA server 106 as contextual information associated with a user input.


In some examples, the digital assistant client module 229 can selectively provide 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 can also elicit 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 can pass 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 FIGS. 7A-7C. It should be recognized that digital assistant client module 229 can include any number of the sub-modules of digital assistant module 726 described below.


Applications 236 may include the following modules (or sets of instructions), or a subset or superset thereof:

    • Contacts module 237 (sometimes called an address book or contact list);
    • Telephone module 238;
    • Video conference module 239;
    • E-mail client module 240;
    • Instant messaging (IM) module 241;
    • Workout support module 242;
    • Camera module 243 for still and/or video images;
    • Image management module 244;
    • Video player module;
    • Browser module 247;
    • Calendar module 248;
    • Widget modules 249, which may include one or more of: weather widget 249-1, stocks widget 249-2, calculator widget 249-3, alarm clock widget 249-4, dictionary widget 249-5, and other widgets obtained by the user, as well as user-created widgets 249-6;
    • Widget creator module 250 for making user-created widgets 249-6;
    • Search module 251;
    • Video and music player module 252, which merges video player module and music player module;
    • Notes module 253;
    • Map module 254; and/or
    • Online video module 255.


Examples of other applications 236 that may be 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 may be 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 may be 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 may use 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 may 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 may 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 may be 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 may be 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 may be combined or otherwise rearranged in various embodiments. For example, video player module may be combined with music player module into a single module (e.g., video and music player module 252, FIG. 2A). In some embodiments, memory 202 may store a subset of the modules and data structures identified above. Furthermore, memory 202 may store additional modules and data structures not described above.


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 may be 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.



FIG. 2B is a block diagram illustrating exemplary components for event handling in accordance with some embodiments. In some embodiments, memory 202 (FIG. 2A) or 470 (FIG. 4) includes event sorter 270 (e.g., in operating system 226) and a respective application 236-1 (e.g., any of the aforementioned applications 237-251, 255, 480-490).


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 may 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 may be called the hit view, and the set of events that are recognized as proper inputs may be 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 may utilize or call 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 may 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 may also include 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 may 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.



FIG. 3 illustrates a portable multifunction device 200 having a touch screen 212 in accordance with some embodiments. The touch screen optionally displays one or more graphics within user interface (UI) 300. In this embodiment, as well as others described below, a user is enabled to select one or more of the graphics by making a gesture on the graphics, for example, with one or more fingers 302 (not drawn to scale in the figure) or one or more styluses 303 (not drawn to scale in the figure). In some embodiments, selection of one or more graphics occurs when the user breaks contact with the one or more graphics. In some embodiments, the gesture optionally includes one or more taps, one or more swipes (from left to right, right to left, upward and/or downward), and/or a rolling of a finger (from right to left, left to right, upward and/or downward) that has made contact with device 200. In some implementations or circumstances, inadvertent contact with a graphic does not select the graphic. For example, a swipe gesture that sweeps over an application icon optionally does not select the corresponding application when the gesture corresponding to selection is a tap.


Device 200 may also include one or more physical buttons, such as “home” or menu button 304. As described previously, menu button 304 may be used to navigate to any application 236 in a set of applications that may be 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.



FIG. 4 is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface in accordance with some embodiments. Device 400 need not be portable. In some embodiments, device 400 is a laptop computer, a desktop computer, a tablet computer, a multimedia player device, a navigation device, an educational device (such as a child's learning toy), a gaming system, or a control device (e.g., a home or industrial controller). Device 400 typically includes one or more processing units (CPUs) 410, one or more network or other communications interfaces 460, memory 470, and one or more communication buses 420 for interconnecting these components. Communication buses 420 optionally include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. Device 400 includes input/output (I/O) interface 430 comprising display 440, which is typically a touch screen display. I/O interface 430 also optionally includes a keyboard and/or mouse (or other pointing device) 450 and touchpad 455, tactile output generator 457 for generating tactile outputs on device 400 (e.g., similar to tactile output generator(s) 267 described above with reference to FIG. 2A), sensors 459 (e.g., optical, acceleration, proximity, touch-sensitive, and/or contact intensity sensors similar to contact intensity sensor(s) 265 described above with reference to FIG. 2A). Memory 470 includes high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid state memory devices; and optionally includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory 470 optionally includes one or more storage devices remotely located from CPU(s) 410. In some embodiments, memory 470 stores programs, modules, and data structures analogous to the programs, modules, and data structures stored in memory 202 of portable multifunction device 200 (FIG. 2A), or a subset thereof. Furthermore, memory 470 optionally stores additional programs, modules, and data structures not present in memory 202 of portable multifunction device 200. For example, memory 470 of device 400 optionally stores drawing module 480, presentation module 482, word processing module 484, website creation module 486, disk authoring module 488, and/or spreadsheet module 490, while memory 202 of portable multifunction device 200 (FIG. 2A) optionally does not store these modules.


Each of the above-identified elements in FIG. 4 may be stored in one or more of the previously mentioned memory devices. Each of the above-identified modules corresponds to a set of instructions for performing a function described above. The above-identified modules or programs (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules may be combined or otherwise rearranged in various embodiments. In some embodiments, memory 470 may store a subset of the modules and data structures identified above. Furthermore, memory 470 may store additional modules and data structures not described above.


Attention is now directed towards embodiments of user interfaces that may be implemented on, for example, portable multifunction device 200.



FIG. 5A illustrates an exemplary user interface for a menu of applications on portable multifunction device 200 in accordance with some embodiments. Similar user interfaces may be implemented on device 400. In some embodiments, user interface 500 includes the following elements, or a subset or superset thereof:

    • Signal strength indicator(s) 502 for wireless communication(s), such as cellular and Wi-Fi signals;
    • Time 504;
    • Bluetooth indicator 505;
    • Battery status indicator 506;
    • Tray 508 with icons for frequently used applications, such as:
      • Icon 516 for telephone module 238, labeled “Phone,” which optionally includes an indicator 514 of the number of missed calls or voicemail messages;
      • Icon 518 for e-mail client module 240, labeled “Mail,” which optionally includes an indicator 510 of the number of unread e-mails;
      • Icon 520 for browser module 247, labeled “Browser;” and
      • Icon 522 for video and music player module 252, also referred to as iPod (trademark of Apple Inc.) module 252, labeled “iPod;” and
    • Icons for other applications, such as:
      • Icon 524 for IM module 241, labeled “Messages;”
      • Icon 526 for calendar module 248, labeled “Calendar;”
      • Icon 528 for image management module 244, labeled “Photos;”
      • Icon 530 for camera module 243, labeled “Camera;”
      • Icon 532 for online video module 255, labeled “Online Video;”
      • Icon 534 for stocks widget 249-2, labeled “Stocks;”
      • Icon 536 for map module 254, labeled “Maps;”
      • Icon 538 for weather widget 249-1, labeled “Weather;”
      • Icon 540 for alarm clock widget 249-4, labeled “Clock;”
      • Icon 542 for workout support module 242, labeled “Workout Support;”
      • Icon 544 for notes module 253, labeled “Notes;” and
      • Icon 546 for a settings application or module, labeled “Settings,” which provides access to settings for device 200 and its various applications 236.


It should be noted that the icon labels illustrated in FIG. 5A are merely exemplary. For example, icon 522 for video and music player module 252 may optionally be labeled “Music” or “Music Player.” Other labels are, optionally, used for various application icons. In some embodiments, a label for a respective application icon includes a name of an application corresponding to the respective application icon. In some embodiments, a label for a particular application icon is distinct from a name of an application corresponding to the particular application icon.



FIG. 5B illustrates an exemplary user interface on a device (e.g., device 400, FIG. 4) with a touch-sensitive surface 551 (e.g., a tablet or touchpad 455, FIG. 4) that is separate from the display 550 (e.g., touch screen display 212). Device 400 also, optionally, includes one or more contact intensity sensors (e.g., one or more of sensors 457) for detecting intensity of contacts on touch-sensitive surface 551 and/or one or more tactile output generators 459 for generating tactile outputs for a user of device 400.


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 FIG. 5B. In some embodiments, the touch-sensitive surface (e.g., 551 in FIG. 5B) has a primary axis (e.g., 552 in FIG. 5B) that corresponds to a primary axis (e.g., 553 in FIG. 5B) on the display (e.g., 550). In accordance with these embodiments, the device detects contacts (e.g., 560 and 562 in FIG. 5B) with the touch-sensitive surface 551 at locations that correspond to respective locations on the display (e.g., in FIG. 5B, 560 corresponds to 568 and 562 corresponds to 570). In this way, user inputs (e.g., contacts 560 and 562, and movements thereof) detected by the device on the touch-sensitive surface (e.g., 551 in FIG. 5B) are used by the device to manipulate the user interface on the display (e.g., 550 in FIG. 5B) of the multifunction device when the touch-sensitive surface is separate from the display. It should be understood that similar methods are, optionally, used for other user interfaces described herein.


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.



FIG. 6A illustrates exemplary personal electronic device 600. Device 600 includes body 602. In some embodiments, device 600 can include some or all of the features described with respect to devices 200 and 400 (e.g., FIGS. 2A-4B). In some embodiments, device 600 has touch-sensitive display screen 604, hereafter touch screen 604. Alternatively, or in addition to touch screen 604, device 600 has a display and a touch-sensitive surface. As with devices 200 and 400, in some embodiments, touch screen 604 (or the touch-sensitive surface) may have one or more intensity sensors for detecting intensity of contacts (e.g., touches) being applied. The one or more intensity sensors of touch screen 604 (or the touch-sensitive surface) can provide output data that represents the intensity of touches. The user interface of device 600 can respond to touches based on their intensity, meaning that touches of different intensities can invoke different user interface operations on device 600.


Techniques for detecting and processing touch intensity may be 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, can be 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 may permit device 600 to be worn by a user.



FIG. 6B depicts exemplary personal electronic device 600. In some embodiments, device 600 can include some or all of the components described with respect to FIGS. 2A, 2B, and 4. Device 600 has bus 612 that operatively couples I/O section 614 with one or more computer processors 616 and memory 618. I/O section 614 can be connected to display 604, which can have touch-sensitive component 622 and, optionally, touch-intensity sensitive component 624. In addition, I/O section 614 can be connected with communication unit 630 for receiving application and operating system data, using Wi-Fi, Bluetooth, near field communication (NFC), cellular, and/or other wireless communication techniques. Device 600 can include input mechanisms 606 and/or 608. Input mechanism 606 may be a rotatable input device or a depressible and rotatable input device, for example. Input mechanism 608 may be a button, in some examples.


Input mechanism 608 may be a microphone, in some examples. Personal electronic device 600 can include 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 can be operatively connected to I/O section 614.


Memory 618 of personal electronic device 600 can be a non-transitory computer-readable storage medium, for storing computer-executable instructions, which, when executed by one or more computer processors 616, for example, can cause the computer processors to perform the techniques described below, including process 800 (FIG. 8). The computer-executable instructions can also be 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. For purposes of this document, a “non-transitory computer-readable storage medium” can be any medium that can tangibly contain or store computer-executable instructions for use by or in connection with the instruction execution system, apparatus, or device. The non-transitory computer-readable storage medium can include, but is not limited to, magnetic, optical, and/or semiconductor storages. Examples of such storage include magnetic disks, optical discs based on CD, DVD, or Blu-ray technologies, as well as persistent solid-state memory such as flash, solid-state drives, and the like. Personal electronic device 600 is not limited to the components and configuration of FIG. 6B, but can include other or additional components in multiple configurations.


As used here, the term “affordance” refers to a user-interactive graphical user interface object that may be displayed on the display screen of devices 200, 400, and/or 600 (FIGS. 2, 4, and 6). For example, an image (e.g., icon), a button, and text (e.g., hyperlink) may each constitute an affordance.


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 FIG. 4 or touch-sensitive surface 551 in FIG. 5B) while the cursor is over a particular user interface element (e.g., a button, window, slider or other user interface element), the particular user interface element is adjusted in accordance with the detected input. In some implementations that include a touch screen display (e.g., touch-sensitive display system 212 in FIG. 2A or touch screen 212 in FIG. 5A) that enables direct interaction with user interface elements on the touch screen display, a detected contact on the touch screen acts as a “focus selector” so that when an input (e.g., a press input by the contact) is detected on the touch screen display at a location of a particular user interface element (e.g., a button, window, slider, or other user interface element), the particular user interface element is adjusted in accordance with the detected input. In some implementations, focus is moved from one region of a user interface to another region of the user interface without corresponding movement of a cursor or movement of a contact on a touch screen display (e.g., by using a tab key or arrow keys to move focus from one button to another button); in these implementations, the focus selector moves in accordance with movement of focus between different regions of the user interface. Without regard to the specific form taken by the focus selector, the focus selector is generally the user interface element (or contact on a touch screen display) that is controlled by the user so as to communicate the user's intended interaction with the user interface (e.g., by indicating, to the device, the element of the user interface with which the user is intending to interact). For example, the location of a focus selector (e.g., a cursor, a contact, or a selection box) over a respective button while a press input is detected on the touch-sensitive surface (e.g., a touchpad or touch screen) will indicate that the user is intending to activate the respective button (as opposed to other user interface elements shown on a display of the device).


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 may include 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 may receive 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 may be 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 may be 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 may be 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



FIG. 7A illustrates a block diagram of digital assistant system 700 in accordance with various examples. In some examples, digital assistant system 700 can be implemented on a standalone computer system. In some examples, digital assistant system 700 can be distributed across multiple computers. In some examples, some of the modules and functions of the digital assistant can be divided into a server portion and a client portion, where the client portion resides on one or more user devices (e.g., devices 104, 122, 200, 400, or 600) and communicates with the server portion (e.g., server system 108) through one or more networks, e.g., as shown in FIG. 1. In some examples, digital assistant system 700 can be an implementation of server system 108 (and/or DA server 106) shown in FIG. 1. It should be noted that digital assistant system 700 is only one example of a digital assistant system, and that digital assistant system 700 can have more or fewer components than shown, may combine two or more components, or may have a different configuration or arrangement of the components. The various components shown in FIG. 7A can be implemented in hardware, software instructions for execution by one or more processors, firmware, including one or more signal processing and/or application specific integrated circuits, or a combination thereof.


Digital assistant system 700 can include 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 can include 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 can couple 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, can receive 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 can include any of the components and I/O communication interfaces described with respect to devices 200, 400, or 600 in FIGS. 2A, 4, 6A-6B, respectively. In some examples, digital assistant system 700 can represent the server portion of a digital assistant implementation, and can interact with the user through a client-side portion residing on a user device (e.g., devices 104, 200, 400, or 600).


In some examples, the network communications interface 708 can include wired communication port(s) 712 and/or wireless transmission and reception circuitry 714. The wired communication port(s) can receive and send communication signals via one or more wired interfaces, e.g., Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wireless circuitry 714 can receive and send RF signals and/or optical signals from/to communications networks and other communications devices. The wireless communications can 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 can enable 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, can store 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, can store instructions for performing process 800, described below. One or more processors 704 can 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) can include 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 can facilitate communications between digital assistant system 700 with other devices over network communications interface 708. For example, communications module 720 can communicate with RF circuitry 208 of electronic devices such as devices 200, 400, and 600 shown in FIG. 2A, 4, 6A-B, respectively. Communications module 720 can also include various components for handling data received by wireless circuitry 714 and/or wired communications port 712.


User interface module 722 can receive 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 can also prepare and deliver 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 can 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 can 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 can include resource management applications, diagnostic applications, or scheduling applications, for example.


Memory 702 can also store digital assistant module 726 (or the server portion of a digital assistant). In some examples, digital assistant module 726 can include 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 module 740. Each of these modules can have 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.


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 FIG. 7B, I/O processing module 728 can interact with the user through I/O devices 716 in FIG. 7A or with a user device (e.g., devices 104, 200, 400, or 600) through network communications interface 708 in FIG. 7A to obtain user input (e.g., a speech input) and to provide responses (e.g., as speech outputs) to the user input. I/O processing module 728 can optionally obtain contextual information associated with the user input from the user device, along with or shortly after the receipt of the user input. The contextual information can include user-specific data, vocabulary, and/or preferences relevant to the user input. In some examples, the contextual information also includes software and hardware states of the user device at the time the user request is received, and/or information related to the surrounding environment of the user at the time that the user request was received. In some examples, I/O processing module 728 can also send follow-up questions to, and receive answers from, the user regarding the user request. When a user request is received by I/O processing module 728 and the user request can include speech input, I/O processing module 728 can forward the speech input to STT processing module 730 (or speech recognizer) for speech-to-text conversions.


STT processing module 730 can include one or more ASR systems. The one or more ASR systems can process the speech input that is received through I/O processing module 728 to produce a recognition result. Each ASR system can include a front-end speech pre-processor. The front-end speech pre-processor can extract representative features from the speech input. For example, the front-end speech pre-processor can perform 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 can include one or more speech recognition models (e.g., acoustic models and/or language models) and can implement one or more speech recognition engines. Examples of speech recognition models can include Hidden Markov Models, Gaussian-Mixture Models, Deep Neural Network Models, n-gram language models, and other statistical models. Examples of speech recognition engines can 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 can be 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 can be 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 can be 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 can include and/or access a vocabulary of recognizable words via phonetic alphabet conversion module 731. Each vocabulary word can be associated with one or more candidate pronunciations of the word represented in a speech recognition phonetic alphabet. In particular, the vocabulary of recognizable words can include a word that is associated with a plurality of candidate pronunciations. For example, the vocabulary may include the word “tomato” that is associated with the candidate pronunciations of /custom character/ and /custom character/. Further, vocabulary words can be associated with custom candidate pronunciations that are based on previous speech inputs from the user. Such custom candidate pronunciations can be stored in STT processing module 730 and can be associated with a particular user via the user's profile on the device. In some examples, the candidate pronunciations for words can be determined based on the spelling of the word and one or more linguistic and/or phonetic rules. In some examples, the candidate pronunciations can be manually generated, e.g., based on known canonical pronunciations.


In some examples, the candidate pronunciations can be ranked based on the commonness of the candidate pronunciation. For example, the candidate pronunciation /custom character/ can be ranked higher than /custom character/, 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 can be ranked based on whether the candidate pronunciation is a custom candidate pronunciation associated with the user. For example, custom candidate pronunciations can be 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 can be associated with one or more speech characteristics, such as geographic origin, nationality, or ethnicity. For example, the candidate pronunciation /custom character/ can be associated with the United States, whereas the candidate pronunciation /custom character/ can be associated with Great Britain. Further, the rank of the candidate pronunciation can be 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 /custom character/ (associated with the United States) can be ranked higher than the candidate pronunciation /custom character/ (associated with Great Britain). In some examples, one of the ranked candidate pronunciations can be selected as a predicted pronunciation (e.g., the most likely pronunciation).


When a speech input is received, STT processing module 730 can be 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 can first identify the sequence of phonemes /custom character/ 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 can use approximate matching techniques to determine words in an utterance. Thus, for example, the STT processing module 730 can determine that the sequence of phonemes /custom character/ 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 can take the sequence of words or tokens (“token sequence”) generated by STT processing module 730, and attempt to associate the token sequence with one or more “actionable intents” recognized by the digital assistant. An “actionable intent” can represent 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 can be 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 can be 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, can also be dependent 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 can also receive contextual information associated with the user request, e.g., from I/O processing module 728. The natural language processing module 732 can optionally use the contextual information to clarify, supplement, and/or further define the information contained in the token sequence received from STT processing module 730. The contextual information can include, 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 can be dynamic, and can change with time, location, content of the dialogue, and other factors.


In some examples, the natural language processing can be based on, e.g., ontology 760. Ontology 760 can be 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” can represent a task that the digital assistant is capable of performing, i.e., it is “actionable” or can be acted on. A “property” can represent 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 can define how a parameter represented by the property node pertains to the task represented by the actionable intent node.


In some examples, ontology 760 can be made up of actionable intent nodes and property nodes. Within ontology 760, each actionable intent node can be linked to one or more property nodes either directly or through one or more intermediate property nodes. Similarly, each property node can be linked to one or more actionable intent nodes either directly or through one or more intermediate property nodes. For example, as shown in FIG. 7C, ontology 760 can include a “restaurant reservation” node (i.e., an actionable intent node). Property nodes “restaurant,” “date/time” (for the reservation), and “party size” can each be directly linked to the actionable intent node (i.e., the “restaurant reservation” node).


In addition, property nodes “cuisine,” “price range,” “phone number,” and “location” can be sub-nodes of the property node “restaurant,” and can each be linked to the “restaurant reservation” node (i.e., the actionable intent node) through the intermediate property node “restaurant.” For another example, as shown in FIG. 7C, ontology 760 can also include a “set reminder” node (i.e., another actionable intent node). Property nodes “date/time” (for setting the reminder) and “subject” (for the reminder) can each be linked to the “set reminder” node. Since the property “date/time” can be relevant to both the task of making a restaurant reservation and the task of setting a reminder, the property node “date/time” can be linked to both the “restaurant reservation” node and the “set reminder” node in ontology 760.


An actionable intent node, along with its linked concept nodes, can be described as a “domain.” In the present discussion, each domain can be 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 FIG. 7C can include an example of restaurant reservation domain 762 and an example of reminder domain 764 within ontology 760. The restaurant reservation domain includes the actionable intent node “restaurant reservation,” property nodes “restaurant,” “date/time,” and “party size,” and sub-property nodes “cuisine,” “price range,” “phone number,” and “location.” Reminder domain 764 can include the actionable intent node “set reminder,” and property nodes “subject” and “date/time.” In some examples, ontology 760 can be made up of many domains. Each domain can share one or more property nodes with one or more other domains. For example, the “date/time” property node can be associated with many different domains (e.g., a scheduling domain, a travel reservation domain, a movie ticket domain, etc.), in addition to restaurant reservation domain 762 and reminder domain 764.


While FIG. 7C illustrates two example domains within ontology 760, other domains can include, for example, “find a movie,” “initiate a phone call,” “find directions,” “schedule a meeting,” “send a message,” and “provide an answer to a question,” “read a list,” “providing navigation instructions,” “provide instructions for a task” and so on. A “send a message” domain can be associated with a “send a message” actionable intent node, and may further include property nodes such as “recipient(s),” “message type,” and “message body.” The property node “recipient” can be further defined, for example, by the sub-property nodes such as “recipient name” and “message address.”


In some examples, ontology 760 can include all the domains (and hence actionable intents) that the digital assistant is capable of understanding and acting upon. In some examples, ontology 760 can be 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 can be clustered under a “super domain” in ontology 760. For example, a “travel” super-domain can include a cluster of property nodes and actionable intent nodes related to travel. The actionable intent nodes related to travel can include “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) can 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” can 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 can be 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 can be the so-called “vocabulary” associated with the node. The respective set of words and/or phrases associated with each node can be stored in vocabulary index 744 in association with the property or actionable intent represented by the node. For example, returning to FIG. 7B, the vocabulary associated with the node for the property of “restaurant” can include words such as “food,” “drinks,” “cuisine,” “hungry,” “eat,” “pizza,” “fast food,” “meal,” and so on. For another example, the vocabulary associated with the node for the actionable intent of “initiate a phone call” can include words and phrases such as “call,” “phone,” “dial,” “ring,” “call this number,” “make a call to,” and so on. The vocabulary index 744 can optionally include words and phrases in different languages.


Natural language processing module 732 can receive the token sequence (e.g., a text string) from STT processing module 730, and determine what nodes are implicated by the words in the token sequence. In some examples, if a word or phrase in the token sequence is found to be associated with one or more nodes in ontology 760 (via vocabulary index 744), the word or phrase can “trigger” or “activate” those nodes. Based on the quantity and/or relative importance of the activated nodes, natural language processing module 732 can select 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 can be selected. In some examples, the domain having the highest confidence value (e.g., based on the relative importance of its various triggered nodes) can be selected. In some examples, the domain can be 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 can include 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 can use 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 can be 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.


Other details of searching an ontology based on a token string is 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 can generate a structured query to represent the identified actionable intent. In some examples, the structured query can include 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 may say “Make me a dinner reservation at a sushi place at 7.” In this case, natural language processing module 732 can be 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 may include 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 can generate 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} may not be specified in the structured query based on the information currently available. In some examples, natural language processing module 732 can populate 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 can populate a {location} parameter in the structured query with GPS coordinates from the user device.


In some examples, natural language processing module 732 can pass the generated structured query (including any completed parameters) to task flow processing module 736 (“task flow processor”). Task flow processing module 736 can be configured to receive the structured query 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 can be provided in task flow models 754. In some examples, task flow models 754 can 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 may need 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 can invoke dialogue flow processing module 734 to engage in a dialogue with the user. In some examples, dialogue flow processing module 734 can determine how (and/or when) to ask the user for the additional information and receives and processes the user responses. The questions can be provided to and answers can be received from the users through I/O processing module 728. In some examples, dialogue flow processing module 734 can present 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 can generate 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 can then populate 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 can proceed to perform the ultimate task associated with the actionable intent. Accordingly, task flow processing module 736 can execute 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” can include 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=Mar. 12, 2012, time=7 pm, party size=5}, task flow processing module 736 can perform 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 can employ 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 can act 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 can be specified by a respective service model among service models 756. Service processing module 738 can access the appropriate service model for a service and generate 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 can submit 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 can establish a network connection with the online reservation service using the web address stored in the service model, and send 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 can be 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 can be a dialogue response to the speech input that at least partially fulfills the user's intent. Further, in some examples, the generated response can be output as a speech output. In these examples, the generated response can be sent to speech synthesis 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 can be data content relevant to satisfying a user request in the speech input.


Speech synthesis module 740 can be configured to synthesize speech outputs for presentation to the user. Speech synthesis module 740 synthesizes speech outputs based on text provided by the digital assistant. For example, the generated dialogue response can be in the form of a text string. Speech synthesis module 740 can convert the text string to an audible speech output. Speech synthesis module 740 can use 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 module 740 can be configured to synthesize individual words based on phonemic strings corresponding to the words. For example, a phonemic string can be associated with a word in the generated dialogue response. The phonemic string can be stored in metadata associated with the word. Speech synthesis model 740 can be 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 module 740, speech synthesis can be performed on a remote device (e.g., the server system 108), and the synthesized speech can be 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 can be 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.



FIG. 8 illustrates exemplary process 800 for handling multiple actionable commands in a single user utterance according to various examples. Process 800 can, for example, be executed on processing modules 114 of server system 108 discussed above with reference to FIG. 1. In other examples, process 800 can be executed on processor 704 of digital assistant system 700 discussed above with reference to FIG. 7. In still other examples, processing modules 114 of server system 108 and processor 704 of digital assistant system 700 can be used together to execute some or all of process 800. At block 802, speech input can be received from a user (e.g., from microphone 213 of FIG. 2). In some examples, the speech input can be directed to a virtual assistant, and can include one actionable command (e.g., “What's the weather going to be today?”) or multiple actionable commands (e.g., “Navigate to Jessica's house and send her a message that I'm on my way.”). An actionable command as used herein can include any task, process, query, action, request, or the like that a virtual assistant can perform, respond to, or otherwise handle. For example, an actionable command can include a query about a stock price, a request to send a message, a command to provide navigation services, a command to initiate a phone call, or the like.


In some examples, a user can control the capture of a multi-part command by signaling with a button, touchscreen interface, or the like. For example, to cause a user's device to continue listening for additional commands, a user can push and hold a button (e.g., of user device 104) while speaking, and can release the button after finishing as many commands as the user desires to speak. In other examples, a user device can continue listening while speech continues, or can include an always-listening function that monitors audio continuously (e.g., at all times or for some extended duration).


A multi-part command within a single utterance can include any number of commands with any number of arguments. For example, a user can request multiple actions associated with the same subject (e.g., email a picture to a friend, save the picture to memory, and set the picture as the wallpaper on a device display). In another example, a user can request multiple actions associated with different subjects (e.g., email a picture to a friend, send a message to a relative saying “I'll be there soon,” dismiss a calendar notification, and remind the user of a particular task in an hour). Some speech input can also mix commands with dictation, as in the example of dictating a message to be sent while requesting other actions in the same utterance. Speech input with a single utterance can thus include any number of commands associated with any number of arguments.


At block 804, a text string can be generated based on the speech input using a speech transcription process. Any of a variety of speech transcription approaches can be used. In addition, in some examples, multiple possible transcriptions can be generated and processed in sequence or simultaneously to identify the best possible match (e.g., the most likely match).


At block 806, the text string (or multiple candidate text strings) can be parsed into at least a first candidate substring and a second candidate substring. In one example, any speech input can be parsed into any number of candidate substrings to test for multi-part commands. In other examples, parsing into multiple candidate substrings can be done based on the length of the speech input, a user indication (e.g., holding a button down while speaking multiple commands), identification of multiple imperative verbs, or the like.



FIG. 9 illustrates an exemplary parsed multi-part voice command. User speech 920 can include the transcription of a single utterance saying, “Navigate to Jessica's house and send her a message that I'm on my way.” In one example, user speech 920 can be parsed into two substrings: first candidate substring 922 and second candidate substring 924. Such parsing can be done in a variety of ways. For example, domain keywords can be identified in the text string, and the string can be parsed based on their location. In the example of FIG. 9, the word “navigate” can correspond to a virtual assistant domain for providing maps, turn-by-turn navigation instructions, or other navigation assistance to a user. The word “send” can correspond to a messaging domain or multiple virtual assistant domains (e.g., email, instant messages, text messages, messaging applications, or the like). The word “message” can likewise correspond to multiple virtual assistant domains. In some instances, the combination of “send” and “message” within a certain number of words of one another can indicate a likely matching virtual assistant domain.


Based on the positions of the identified domain keywords, user speech 920 can be split into first candidate substring 922 beginning with the keyword “Navigate” and ending prior to the conjunction “and,” and second candidate substring 924 beginning with the keyword “send” and ending with the end of the string. In other examples, the conjunction “and” can be included within either candidate substring or both candidate substrings. Various other domain keywords can be used in a similar manner to parse a string. For example, a weather domain might be associated with keywords or key phrases like “weather,” “temperature,” “how cold,” “how hot,” “rain,” etc. Similarly, a phone domain might be associated with keywords “call,” “dial,” etc. A calendar domain might be associated with keywords or key phrases like “meeting,” “set up a meeting,” “meet with,” “new appointment,” “schedule,” etc. A reminder domain might be associated with keywords or key phrases like “remind,” “reminder,” “set a reminder,” “remind me,” etc. It should be understood that various other domains can have a variety of associated keywords or key phrases that can be used to parse a text string.


Instead of or in combination with domain keywords, imperative verbs can be identified in the text string, and the string can be parsed based on their location. In the example of FIG. 9, the word “Navigate” is the first imperative verb in the string. The word “send” is the next imperative verb in the string. In some instances, the word “message” can be interpreted as another imperative verb. In this example, however, a variety of factors can cause “message” to correctly be recognized as a noun: it is preceded with “a,” it follows a few words behind “send,” it is followed by “that,” etc. Two imperative verbs can thus be identified in user speech 920. The positions of the imperative verbs can then be used to parse the string into first candidate substring 922 beginning with the imperative verb “Navigate” and second candidate substring 924 beginning with the imperative verb “send.” Other imperative verb examples include words like “schedule,” “set up,” “call,” “email,” “text,” “post,” “play,” “launch,” “remind,” “note,” “turn on,” “search,” etc. In addition, in some examples, a user utterance can be broken into the various parts of speech to aid in correctly identifying imperative verbs (e.g., as opposed to homonyms that might be nouns).


Instead of or in combination with domain keywords and/or imperative verbs, a single user utterance can be parsed in multiple ways, and the multiple parses can be tested to determine the best parse. In other examples, a string can be parsed based on a predetermined substring length (e.g., a number of characters, a number of words, etc.). In still other examples, a string can be parsed in all possible ways (e.g., ranging from each individual word as a substring to all words together in a single string) or nearly all possible ways (e.g., ranging from a minimum of each pair or triple of words as a substring to all words together in a single string). Given multiple ways of parsing the string, parse results can be analyzed to dismiss various parse approaches based on a variety of factors. For example, if a parse results in a candidate substring without a verb, that parse can be dismissed. In some examples, some or all of the various parse results can be analyzed as described below with reference to block 808 of process 800 to identify the parse with the highest likelihood of accurately reflecting a user's intentions. A text string from a single utterance of user speech can thus be parsed in a variety of ways into at least a first candidate substring and a second candidate substring.


Referring again to process 800 of FIG. 8, at block 808, a first probability that the first candidate substring corresponds to a first actionable command and a second probability that the second candidate substring corresponds to a second actionable command can be determined. In some examples, each candidate substring can be analyzed to determine a probability that it corresponds to a valid, actionable command. As noted above, this can be done, in some examples, to verify the accuracy of a particular parse and/or to compare the likelihoods of different parses resulting in different candidate substrings. In some examples, the probabilities can be used to select the parse and its associated substrings that may be most likely to accurately reflect the user's intention (e.g., accurately split up a single utterance into distinct actionable commands). The probabilities for the various candidate substrings can be determined in a variety of ways, and multiple approaches can be combined to arrive at an integrated probability (e.g., using linear interpolation or other combination mechanisms).


In one example, each candidate substring (from one parse or from multiple different potential parses) can be analyzed for semantic coherence. In some examples, semantic coherence can be determined for each candidate substring based on only the words within the substring (e.g., without considering context or previous or subsequent words). Semantic coherence can include a binary yes/no result or a probability. Semantic coherence can reflect whether a substring can stand alone as a coherent command (or request), having meaning in the target language (e.g., English). For example, as noted above, a substring without a verb could be identified as lacking semantic coherence or as having a low probability of being semantically coherent (e.g., “to Jessica's house,” “progress report meeting,” etc.). In another example, a substring lacking a subject, argument, entity, or the like could be identified as lacking semantic coherence or as having a low probability of being semantically coherent (e.g., “Navigate to,” “Tell John that,” etc.). In yet another example, a substring with an incomplete prepositional phrase or preposition lacking a noun could be identified as lacking semantic coherence or as having a low probability of being semantically coherent (e.g., “Set a reminder to buy milk at,” “Schedule a meeting for,” etc.). In addition, in some examples, a candidate substring can be analyzed according to the parts of speech to verify adherence to grammar rules, and semantic coherence can be determined accordingly. It should be understood that semantic coherence can be determined for a substring in a variety of other ways, and different rules and tests can be applied for different languages, dialects, regions, or the like.


Instead of or in combination with semantic coherence, each candidate substring can be analyzed in view of user request templates of a virtual assistant. For example, a virtual assistant can process user requests in part by matching a spoken request to a template. The template can have associated processes, tasks, algorithms, or the like that a virtual assistant can use to handle a user's request. In addition, a template can have designated variables where entities, arguments, or the like can be expected (e.g., a contact name in the template “Call [contact].”). Each candidate substring can thus be compared to some or all of the various templates of a virtual assistant.


In one example, a similarity score or probability of a match can be determined based on how similar a substring is to a user request template. In some examples, words corresponding to expected entities, arguments, or the like can be ignored in the comparison. In another example, a binary yes/no result can be produced based on whether or not one or more matching (or nearly matching) templates can be found from the comparison.


Referring to first candidate substring 922 of FIG. 9 as an example, the substring “Navigate to Jessica's house” can be compared to some or all templates of a virtual assistant. In one example, only templates associated with navigation can be used given the keyword “Navigate.” In other examples, other or all templates can be used. In a virtual assistant, templates associated with providing maps, guidance, or navigation can include “Navigate to [location],” “Get me directions to [location],” “Show me a map of [location],” or the like. In comparing candidate substring 922 to such navigation-related templates, at least one matching template can be identified: “Navigate to [location],” where the location variable can allow the words “Jessica's house” to be ignored for purposes of comparison. In this example, at least one matching template can be identified, so a yes result or a high similarity score or probability can be produced.


In another example, a substring might be similar to a template without actually matching the template (e.g., having extraneous words, including unexpected conjunctions, etc.). In such examples, a lower similarity score or probability can be produced, or, depending on a particular application, either a yes or a no result can be produced. In other examples, no matching or similar template may be found, and a no result or nil similarity score or probability can be produced. Candidate substrings can thus be compared to user request templates of a virtual assistant to identify matches, likelihoods of a match, similarity scores, or the like. It should be understood that any of a variety of probabilities, scores, or the like can be produced as desired for a particular application based on comparisons with virtual assistant request templates.


Instead of or in combination with semantic coherence and/or template matching, each candidate substring can be analyzed by testing them with various services of a virtual assistant. For example, each candidate substring can be submitted to one or more services of the virtual assistant (e.g., a messaging service, a navigation service, a phone service, etc.). The services can be queried to determine whether they can resolve an actionable command from the candidate substring. In one example, services can deny a request outright or reject a substring (e.g., a zero likelihood of resolving the substring). In another example, services can be configured to produce a likelihood of being able to resolve the substring with more information (e.g., context, user data, etc.). In yet another example, services can be configured to produce a likelihood of the service being the appropriate service to handle a user request (e.g., based on domain keywords, available functions, and the like).


Referring to second candidate substring 924 of FIG. 9 as an example, a messaging service can, in some examples, produce a high likelihood of being able to resolve the substring into an actionable command. For example, in the substring “send her a message that I'm on my way,” the words “send” and “message” can correspond to messaging domain keywords or templates and can lead a messaging service to produce a high likelihood that it will be able to resolve the substring to an actionable command. In addition, the messaging service can identify that additional context may be necessary to resolve to a complete command given that “her” may not be known from the substring alone, but the messaging service can produce a moderate likelihood of being able to resolve the substring given additional contextual information. Moreover, the messaging service can compare the substring to a messaging template to recognize that the substring may likely be intended for the messaging service rather than another service. A messaging service could thus produce a high likelihood of being able to resolve candidate substring 924 into an actionable command. In contrast, a navigation service, phone service, or search service might produce a low or nil likelihood of being able to resolve candidate substring 924 based on the lack of keywords, matching templates, salient entities, and the like. Candidate substrings can thus be submitted to services to determine the likelihood that one of the services can resolve the substring to an actionable command. It should be understood that any of a variety of probabilities, likelihoods, binary results, or the like can be produced as desired for a particular application based on submission to various virtual assistant services.


In some examples, a combination of semantic coherence, template matching, and/or service testing can be used to determine a probability that a candidate substring corresponds to an actionable command. For example, the results of two or more tests can be combined using known methods (e.g., linear interpolation, etc.). In addition, a variety of other tests or approaches can be used instead of or in addition to those discussed above to determine the probability that a candidate substring corresponds to an actionable command. For example, ontologies could be used to attempt to resolve a candidate substring into an actionable command, and a probability can be produced based on the success or failure of the ontology traversal. Various other approaches can also be used to test candidate substrings and verify the accuracy of a parse.


Referring again to process 800 of FIG. 8, at block 810, a determination can be made as to whether the probabilities determined at block 808 exceed a threshold. For example, a minimum threshold can be determined below which a particular parse can be deemed unlikely or unacceptable. In some examples, the probability of each candidate substring corresponding to an actionable command can be compared to a threshold level, and the failure of any candidate substring of a parse to meet the level can be deemed a failure of the parse leading to the “no” branch of block 810. In other examples, the probabilities of each candidate substring of a parse can be combined in a variety of ways (e.g., normalization, linear interpolation, etc.) and then compared to a threshold. The threshold level can be determined empirically, based on virtual assistant thresholds, or the like.


In other examples, however, block 808 (and block 810) can be omitted from process 800. For example, in some instances, a parse can be determined to be sufficiently accurate such that additional probability testing or verification may be unnecessary. Common user requests, common combinations in multi-part commands, high probabilities of semantically meaningful parses, or the like can be used to determine that a parse may be trusted enough to proceed without additional verification or accuracy testing. Moreover, in some examples, the approach used to parse a multi-part command can be sufficiently accurate so as to warrant omitting verification or accuracy testing. The testing described with regard to block 808 can thus be optional.


If the probabilities from block 808 fail to meet or exceed the threshold at block 810 (e.g., the “no” branch), in some examples, process 800 can return to block 806 to attempt a different parse of the text string. For example, given the failure of the parse through the first pass, a new parse can be attempted or an adjustment can be made at block 806 to attempt to identify a more accurate parse on a second or subsequent pass. In other examples, however, most or all possible parses can be tested simultaneously, in which case a failure at block 810 can result in the virtual assistant querying for more information, asking for a repeat of the commands, returning a failure message, or the like. Similarly, should repeated parses fail to yield an accurate result, the virtual assistant can query for more information, ask for a repeat of the commands, return a failure message, or the like. In some examples, if one or more of a set of candidate substrings of a parse exceeds a threshold, those candidate substrings can be processed further as discussed below while the virtual assistant addresses the remaining (failing) substring or substrings through dialogue, error messages, or the like. In this manner, portions of a multi-part voice command can be handled whether or not other portions can be accurately resolved without additional information.


If the probabilities from block 808 meet or exceed the threshold at block 810 (e.g., the “yes” branch), process 800 can continue to block 812. At block 812, a first intent associated with the first candidate substring and a second intent associated with the second candidate substring can be determined. In one example, a virtual assistant can determine a user's intent from speech input by matching the user's speech to a particular domain with tasks, processes, and the like that the virtual assistant can perform or execute. Determining a user's intent can also include resolving ambiguous words, pronouns, and the like using context (e.g., user data, displayed information, previous requests, etc.). Thus, at block 812, an intent can be determined for each candidate substring in preparation for performing according to the user's multi-part command.


In some examples, information from previous blocks of process 800 can be used to determine user intent at block 812. For example, domain keywords, request templates, service matching, and the like from previous blocks discussed above can be used to identify the user intent of a particular candidate substring.


Referring to the example of FIG. 9, an intent can be determined for first candidate substring 922 and second candidate substring 924. In one example, first candidate substring 922 can be matched to the navigation domain based on the command word “Navigate.” A matching template “Navigate to [location]” can also be identified within that domain. The location “Jessica's house” can then be resolved using user data, such as identifying a house address from the user's contact information for a contact named Jessica.


Second candidate substring 924 can be matched to a messaging domain based on the words “send” and “message” and/or a corresponding template. For example, a template “send [recipient] a message that [message]” can be identified. The recipient can be resolved based on the context of the previous request from first candidate substring 922. In particular, the intended recipient “Jessica” (and her contact information from the user's contacts) can be identified to replace “her” based on the previous command in the multi-part command. The text in the message variable can be transcribed directly from the substring (e.g., “I'm on my way.”).


In another example, context from a previous command in a multi-part command can similarly be used to resolve ambiguous words. In the multi-part command “Send Steve a message that I'll be late, and remind me to call him in twenty minutes,” the pronoun “him” can be ambiguous without the context of the prior command. In particular, in one example, a first actionable command can be identified for sending Steve a message saying “I'll be late.” The second actionable command, however, can utilize the context of the previous command for accurate intent interpretation of which person the user would like to be reminded to call in twenty minutes. The intent of the second command can thus be interpreted based on the context of the previous command in the utterance to accurately determine that the user would like to be reminded to call Steve in twenty minutes.


A variety of other contextual information can similarly be used to determine user intent for a particular candidate substring. For example, FIG. 10 illustrates exemplary user device 104 with display 1030 having context for interpreting a multi-part voice command. It should be understood that FIG. 10 illustrates one example of a user device 104 according to various examples herein, but many other examples are possible (including devices without displays, devices with different proportions, and the like). Likewise, it should be appreciated that although display 1030 is illustrated as being incorporated with user device 104 (e.g., as a touchscreen), in other examples, display 1030 can be separate from user device 104 (e.g., as in a television, computer monitor, separate user device, or the like).


In one example, when viewing the content shown in FIG. 10, a user can utter a multi-part command, such as “Save that picture, send it to my dad, and set it as my home screen wallpaper.” Such a multi-part command could be parsed into three candidate substrings: “Save that picture,” “send it to my dad,” and “set it as my home screen wallpaper.” For the first substring, the subject “that picture” can be ambiguous without additional contextual information. Here, the content displayed on display 1030, including links 1034 and picture 1032, can be used to disambiguate (or resolve) the first substring. In particular, given the context of the appearance of picture 1032 on display 1030, the subject “that picture” can be resolved to picture 1032. The user's intent for the first substring can thus be determined to be saving picture 1032 (e.g., to memory on the user's device). Other content elements can similarly be used as context, including displayed text, emails, notifications, reminders, albums, songs, lists, calendar entries, or the like.


The second and third substrings can be similarly ambiguous with the pronoun “it” in “send it to my dad” and “set it as my home screen wallpaper.” To resolve the ambiguity, context from the resolved first substring can be used. In particular, the resolution of the subject “that picture” to picture 1032 on display 1030 can be used to identify the intended subject of the second and third substrings. The user intent of the second substring can thus be determined to be sending picture 1032 to a user's father (e.g., as identified in a contact list or the like), and the user intent of the third substring can be determined to be setting picture 1032 as a home screen wallpaper of a user's device. Displayed content can thus be used as context for interpreting user intent from user speech, and commands can be disambiguated based on previously issued commands within the same utterance (or from a previous utterance).



FIG. 11 illustrates exemplary user device 104 with display 1030 having multiple notifications of various types that can be used as context for interpreting multi-part voice commands. In particular, display 1030 shows notifications 1040, including emails 1142, messages 1144 (e.g., instant messages, text messages, etc.), reminders 1146, and meeting request 1148. A multi-part voice command as discussed herein can be an efficient way for a user to manage multiple notifications at one time. For example, a user might utter “Reply to those emails with my out of office reply, remind me to call mom in twenty minutes, tell Joe sure, snooze those reminders an hour, and accept that meeting request.” In one example, such a lengthy multi-part command can be uttered with pauses while a user thinks about next actions, and a virtual assistant can continue to await further commands without interrupting the user (e.g., based on a button being held down, a user preference or setting, or the like). In addition, a virtual assistant can begin to take action immediately upon interpreting user intent for one command without waiting to resolve subsequent commands in a multi-part command. For example, a virtual assistant might begin sending out of office emails to Jane Doe and Jennifer Smith while the user continues with commands related to messages 1144, reminders 1146, and meeting request 1148. In other examples, a virtual assistant can wait for a user to finish all commands before beginning to execute processes associated with them.


A multi-part command associated with the content shown in FIG. 11 can be disambiguated (as needed) using the types of notifications displayed, names associated with those notifications, details about those notifications, or the like. For example, for the substring “Reply to those emails with my out of office reply,” the subject “those emails” can be ambiguous, but the content displayed can be used to determine that “those emails” correspond to emails 1142 shown on display 1030. In particular, the email notification or content type can be used to accurately associate “those emails” with the displayed email-related notifications. Similarly, the substring “tell Joe sure” can be ambiguous (e.g., which Joe), but the content displayed can be used to determine that “Joe” corresponds to the sender of one of messages 1144. In particular, the name “Joe” can be checked against names shown on display 1030 (as well as against user contacts and the like) to accurately identify which Joe the user intended.


The substring “snooze those reminders an hour” can similarly be ambiguous, but the content displayed can be used to determine that “those reminders” correspond to reminders 1146 shown on display 1030. In particular, the reminder notification type can be used to accurately associate “those reminders” with reminders 1146. Finally, the substring “accept that meeting request” can similarly be ambiguous, but the content displayed can be used to determine that “that meeting request” corresponds to meeting request 1148 shown on display 1030. In particular, the meeting request notification type can be used to accurately associate “that meeting request” with meeting request 1148.


In other examples, a user can reference notifications 1040 in other ways, and the displayed content can similarly be used to disambiguate a user's request. For example, a user can reference notifications by type (e.g., mail, reminder, etc.), names (e.g., Jane, Jennifer, Mom, etc.), subject matter (e.g., dinner invitation, report on progress, milk, progress report meeting, etc.), position reference or ordinal descriptor (e.g., the top two, the first email, the second reminder, the last three, etc.), or the like.



FIG. 12 illustrates exemplary user device 104 with display 1030 having multiple emails 1142 shown in email application 1250 that can be used as context for interpreting a multi-part voice command. A multi-part voice command as discussed herein can be an efficient way for a user to manage lists of items, including the list of emails 1142 illustrated in FIG. 12. It should be understood that email application 1250 is provided as an example of how emails might be displayed in a list, but many other configurations are possible (e.g., including a preview pane or the like), and any type of list can be used as context for interpreting multi-part voice commands.


In one example, a user can refer to listed emails 1142 by ordinal descriptors in issuing commands. For example, a user might utter “Send my out of office reply to the first three, add the sender of the last one to the blocked contact list, and move the last one to spam.” The ordinal position of emails 1142 within the list can then be used to disambiguate the user's multi-part command. The descriptor “first three” in the first substring can be ambiguous, but the content displayed can be used to determine that “the first three” corresponds to the first three emails 1142 shown on display 1030 (e.g., emails from Jane, Jennifer, and Alan). In particular, the ordinal position of the first three emails can be used to accurately associate “the first three” with the first, second, and third emails 1142 in the list to determine the user's intent. Similarly, the descriptor “the last one” in the second and third substrings can be ambiguous, but the content displayed can be used to determine that “the last one” corresponds to the last email 1142 shown on display 1030 (e.g., the email from Investor Tip Co.). In particular, the ordinal position of the fourth and final email in the list can be used to accurately associate “the last one” with the fourth email 1142 in the list to determine the user's intent. In other examples, a user can refer to listed items in other ways that can be similarly disambiguated (e.g., second email, third reminder, second to last, penultimate, final, middle one, last two entries, top three items, bottom four, etc.).


In other examples, a user can refer to an entire list of content in issuing commands. For example, a user might utter “Mark all of those as read, and move them all to trash.” The appearance of a list of emails 1142 on display 1030 can be used to disambiguate the user's multi-part command. The descriptors “all of those” and “them all” can be ambiguous, but the content displayed can be used to determine that “all of those” and “them all” correspond to the complete list of four emails 1142 shown in FIG. 12. In still other examples, various other ambiguous reference terms can be employed and similarly disambiguated based on list content.


In addition to using displayed content to disambiguate terms in a multi-part command, various other methods can be employed for determining user intent for candidate substrings. In one example, potential (or likely) user requests can be determined based on displayed information, and user intent can be determined based on the potential user requests. Referring again to FIG. 12 as an example, a user may be likely to issue a certain set of commands related to email management when viewing emails 1142 in email application 1250. For example, potential user requests related to email management can include replying, deleting, moving, filing, forwarding, marking as read, marking as unread, marking as spam, archiving, blocking a sender, and the like. When determining user intent for substrings, these potential user requests can be given additional weight or priority, or can be used as a comparison (e.g., comparing templates) to accurately interpret user commands. In particular, based on emails 1142 appearing on display 1030, potential user requests associated with email management can be used in interpreting user commands based on at least some likelihood that a user will issue commands associated with what is displayed.


In other examples, potential user requests can be determined based on a variety of other content to provide accurate intent interpretation. For example, referring again to FIG. 11, potential user requests can be identified for handling notifications 1140, and these potential user requests can be used in determining user intent. Associated commands can include dismissing an item, snoozing a reminder, replying to a message, accepting a meeting request, proposing a new time for a meeting, or the like. In another example, referring again to FIG. 10, potential user requests can be identified for interacting with the displayed content, including links 1034 and picture 1032, and these potential user requests can be used in determining user intent. Associated commands can include saving a picture, sharing a picture, sharing a link, printing a page, selecting a displayed item, or the like. Potential user requests can thus be identified based on displayed content, and the identified potential user requests can be used to determine user intent for substrings in a multi-part command.


Referring again to process 800 of FIG. 8, at block 814, a first process associated with the first intent and a second process associated with the second intent can be executed. With user intents determined for each candidate substring (or some candidate substrings), the processes associated with the user intents can be executed. For example, messages can be composed and sent, emails can be deleted, notifications can be dismissed, or the like. In some examples, multiple tasks or processes can be associated with individual user intents, and the various tasks or processes can be executed at block 814. In other examples, the virtual assistant can engage the user in a dialogue to acquire additional information as necessary for completing task flows. As mentioned above, in some examples, if only a subset of the substrings of a multi-part command can be interpreted into user intents, the processes associated with those user intents can be executed, and the virtual assistant can handle the remaining substrings in a variety of ways (e.g., request more information, return an error, etc.).


Referring again to process 800 of FIG. 8, at block 816, an acknowledgment associated with the first intent and the second intent can be provided to the user. For example, an acknowledgment can be provided to the user to indicate acceptance of one or more commands, status of executing various commands, interpretations of various commands, errors associated with particular commands, information being needed for some commands, or the like. In one example, such an acknowledgment can include an audible confirmation (e.g., a tone, spoken words, or the like). For example, a virtual assistant can repeat back to the user a list of commands—as interpreted—as confirmation (or to allow the user the opportunity to correct, interject, cancel, etc.). In another example, a confirmatory tone can be played to indicate valid interpretation of a user's commands. Various other audible forms of feedback can likewise be used.


In another example, acknowledgment of a multi-part command can include haptic feedback. For example, a user device can be vibrated to either confirm valid interpretation of a user's commands or to indicate an error or lack of information. Haptic feedback can also be performed in patterns to indicate certain information, such as vibrating a set number of pulses to indicate the number of identified commands.


In yet another example, acknowledgment of a multi-part command can include providing tasks to the user by, for example, speaking the tasks and/or displaying them on a display. In particular, user intents (e.g., the first intent and the second intent of blocks 812, 814, and 816 of process 800) can be associated with particular virtual assistant tasks, and the virtual assistant can paraphrase those tasks and provide them to the user by, for example, speaking and/or displaying them.



FIG. 13 illustrates exemplary user device 104 with display 1030 showing paraphrased tasks 1360 associated with a multi-part command. It should be understood that tasks 1360 can be spoken instead of or in addition to being displayed. As illustrated, three tasks are displayed as an acknowledgment to the user of a multi-part command. For example, such a command might have been uttered with reference to FIG. 12, and could include a multi-part command such as “Put the last email into spam, and add its sender to the blocked list, and reply to the first three saying I'm on vacation in Bali.” Three substrings may have been identified that could be associated with the paraphrased tasks shown in FIG. 13. In particular, a first paraphrased task can include “Put ‘Great New Opportunity’ email into spam folder,” a second paraphrased task can include “Add ‘Investor Tip Co.’ to blocked sender list,” and a third paraphrased task can include “Send email to Jane, Jennifer, and Alan: ‘I'm on vacation in Bali.’” Acknowledgment of a multi-part user command can thus include providing a paraphrased task to the user for each interpreted command by speaking and/or displaying them.


In some examples, paraphrased tasks can be displayed within a dialogue interface of a virtual assistant (e.g., in a conversation format), with or without a transcription of a user's speech. In other examples, paraphrased tasks can be displayed in a pop-up window, notification area, or the like. In still other examples, displayed tasks 1360 (or a transcription of a user's speech) can be selectable for editing, canceling, prioritizing, or the like. For example, a user can select one of tasks 1360, and a menu of actions can be provided for editing the task parameters, canceling the task, prioritizing the task, delaying the task, or the like. In another example, a default action can be associated with selecting a displayed task, such as editing the task, pausing the task, canceling the task, or the like.


Moreover, in some examples, completion indicators and/or status indicators can be provided to the user by, for example, playing an associated indicator tone, speaking an associated status indicator, or displaying associated indicators. FIG. 13 illustrates exemplary indicators associated with tasks 1360. In one example, upon completion of a process or task, a completion indicator can be provided to the user, such as displaying checkmark 1362 by the first task 1360 to indicate completion. In another example, a tone could be played or words could be spoken to indicate completion (e.g., “first task completed,” or the like).


In another example, while a process or task is still being executed (before completion), a processing status indicator can be provided to the user. Status could be provided by sounding a tone or speaking the status (e.g., “processing the second task,” or the like). Status could also be provided by displaying a processing status indicator such as hourglass 1364 and/or status bar 1366 (or an empty box where checkmark 1362 can be placed upon completion of the task). In one example, hourglass 1364 can indicate that the task is still being executed. In other examples, however, hourglass 1364 can indicate that more information is needed, that the request is still being interpreted, that a search is being conducted, or the like. Status bar 1366 can also be used to indicate task execution status, and can reflect the percentage of completion of a particular task. In other examples, processing status indicators can include animations, graphs, font changes (e.g., text color, size, etc.), or the like.


Various other acknowledgments can also be provided to a user to convey information about a multi-part command (e.g., illuminating indicator lights, animating emails being trashed, animating messages being composed and sent, etc.). In addition, any combination of acknowledgments can be provided, such as displaying a list of tasks, speaking the tasks aloud, and displaying status and completion indicators.


Furthermore, in some examples, acknowledgments can be provided while a user is still uttering commands. For example, confirmatory tones, haptic feedback, speech, or the like can be provided to a user upon, for example, detecting a complete actionable command. In particular, a virtual assistant can briefly speak “okay,” play a tone, vibrate a user device, display user speech, or the like as commands are being spoken. This can provide confirmation to a user that a command is understood, thereby allowing the user to continue issuing commands with confidence that the virtual assistant is handling the commands along the way.


In other examples, the virtual assistant can provide visual confirmation to the user that it is recognizing multiple commands in a single stream of user speech. FIG. 14A and FIG. 14B illustrate exemplary virtual assistant interfaces 1470 for conveying recognition of a multi-part voice command on display 1030 of user device 104. In one example, different commands recognized within a stream of user speech can be emphasized differently than other commands to indicate that the virtual assistant has correctly recognized that a multi-part command is being issued. In one example, such emphasis can be added after the user finishes speaking. In other examples, however, the user's speech can be transcribed as the user speaks, and the candidate substrings can be dynamically emphasized differently while the transcribed text is streaming on a display of a user device. This dynamic feedback while the user is speaking can confirm to the user that the virtual assistant is understanding that a multi-part command is being issued, which can allow the user to confidently continue to issue commands as desired.



FIG. 14A illustrates one example of emphasizing different candidate substrings differently in a virtual assistant interface 1470. As illustrated, interface 1470 can include a conversational dialogue interface with assistant greeting 1472 prompting a user to make a request. Transcribed user speech 1474 illustrates one example of differently emphasizing candidate substrings in user speech as it is transcribed. In particular, the first candidate substring is shown in bold, italic, and underlined text. The second candidate substring is shown in bold, underlined text. The third candidate substring is shown in italic, underlined text. In this manner, different candidate substrings or commands can be emphasized so as to convey that the virtual assistant recognizes that a multi-part command is being issued, and that the virtual assistant is correctly recognizing the different commands individually.


In the example of FIG. 14A, transcribed user speech 1474 is shown as incomplete as the user may still be issuing a command. In particular, the third command begins with “reply to the first three saying,” but the text of the associated message has not yet been spoken. In some examples, auto-complete suggestions 1476 can be displayed, and the user can select one of the auto-complete suggestions to complete a command that is currently being issued. For example, the user can tap on the auto-complete suggestion “I'm out of the office” to complete the third command and provide the message text of the reply. In other examples, auto-complete suggestions 1476 can provide example commands related to previous commands in an utterance, commands related to objects shown on a display, commands frequently issued by the user, or the like. Moreover, in some examples, auto-complete suggestions 1476 can be positioned next to, in-line with, or in another position relative to the current command being issued. For example, auto-complete suggestions 1476 can be positioned near an object or partial command and connected to it with a line or other graphical association (e.g., dots).



FIG. 14B illustrates another example of emphasizing different candidate substrings differently in a virtual assistant interface 1470. In particular, transcribed user speech 1478 illustrates emphasizing different candidate substrings using tag-cloud-like clustering. In one examples, the various commands can be spatially separated with the words of each command clustered together. In some examples, words that can be edited, keywords, command words, subject words, or otherwise important words can be emphasized with bold text, larger font size, or the like. In other examples, words can be emphasized differently based on importance. For example, the primary command word (e.g., send) can be shown in the largest, boldest font, subject words (e.g., recipient names) can be shown in a medium font, and other words can be shown in a small font. The words can be clustered together in variety of ways, including out of order and in different orientations. In some examples, different commands can be sized differently based on interpretation confidence associated with a command (e.g., how confident the virtual assistant is that the interpretation is accurate based on the user's speech). Command clusters can be separated and displayed as a user is speaking commands or after a user finishes speaking a multi-part command. Various other cluster-type displays can also be used to emphasize multi-part command distinctions.


Different commands or candidate substrings in a multi-part command can also be emphasized in a variety of other ways. For example, individual commands can be circled, outlined, or otherwise separated by drawing graphical markers (e.g., drawing a shape around a command, drawing separating lines or graphics between commands, placing different commands in different speech bubbles, or the like). In another example, different commands can be colored differently (e.g., red text for a first command, green text for a second command, blue text for a third command, etc.). In yet another example, various combinations of the above approaches can be used to differently emphasize different candidate substrings, commands, tasks, or the like (e.g., using differently colored words within a clustered command that is spatially separated from another clustered command). Accordingly, using any of these various visual display approaches, the virtual assistant can show that multiple commands are being recognized in a single stream. Moreover, in other examples, these visual display approaches can be combined with other forms of feedback (e.g., audible, haptic, etc.) discussed herein.


Process 800 of FIG. 8 can thus be used to handle multi-part commands. As noted above, and as will be understood by one of ordinary skill in the art, various modifications can be made, including removing blocks of process 800, modifying the order of operations, expanding the quantities of various operations, or the like.


In addition, in any of the various examples discussed herein, various aspects can be personalized for a particular user. As discussed above, user data including contacts, preferences, location, and the like can be used to interpret voice commands. The various processes discussed herein can also be modified in various other ways according to user preferences, contacts, text, usage history, profile data, demographics, or the like. In addition, such preferences and settings can be updated over time based on user interactions (e.g., frequently uttered commands, frequently selected applications, etc.). Gathering and use of user data that is available from various sources can be used to improve the delivery to users of invitational content or any other content that may be of interest to them. The present disclosure contemplates that in some instances, this gathered data can 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, home addresses, or any other identifying 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 deliver targeted content that is of greater interest to the user. Accordingly, use of such personal information data enables calculated control of the delivered content. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure.


The present disclosure further 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 as private and secure. For example, 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 should occur only after receiving the informed consent of the users. Additionally, such entities would take 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.


Despite the foregoing, the present disclosure also contemplates examples 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 advertisement delivery services, 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. In another example, users can select not to provide location information for targeted content delivery services. In yet another example, users can select not to provide precise location information, but permit the transfer of location zone information.


Therefore, although the present disclosure broadly covers use of personal information data to implement one or more various disclosed examples, the present disclosure also contemplates that the various examples can also be implemented without the need for accessing such personal information data. That is, the various examples of the present technology are not rendered inoperable due to the lack of all or a portion of such personal information data. For example, content can be selected and delivered to users by inferring preferences 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 content delivery services, or publicly available information.


In accordance with some examples, FIG. 15 shows a functional block diagram of an electronic device 1500 configured in accordance with the principles of the various described examples. The functional blocks of the device can be implemented by hardware, software, or a combination of hardware and software to carry out the principles of the various described examples. It is understood by persons of skill in the art that the functional blocks described in FIG. 15 can be combined or separated into sub-blocks to implement the principles of the various described examples. Therefore, the description herein optionally supports any possible combination or separation or further definition of the functional blocks described herein.


As shown in FIG. 15, electronic device 1500 can include an input unit 1502 configured to receive information (e.g., a microphone for capturing user speech, a device for receiving user speech through a network, etc.). Electronic device 1500 can further include an output unit 1504 configured to output information (e.g., a speaker for playing sounds, a display for displaying information, a device for transmitting information through a network, etc.). Electronic device 1500 can further include processing unit 1506 coupled to input unit 1502 and output unit 1504. In some examples, processing unit 1506 can include a speech input receiving unit 1508, text string generating unit 1510, text string parsing unit 1512, probability determining unit 1514, intent determining unit 1516, process execution unit 1518, and acknowledgment providing unit 1520.


Processing unit 1506 can be configured to receive speech input from a user (e.g., through input unit 1502 using speech input receiving unit 1508), wherein the speech input comprises a single utterance having one or more actionable commands. Processing unit 1506 can be further configured to generate a text string (e.g., using text string generating unit 1510) based on the speech input using a speech transcription process. Processing unit 1506 can be further configured to parse the text string (e.g., using text string parsing unit 1512) into at least a first candidate substring and a second candidate substring. Processing unit 1506 can be further configured to determine (e.g., using probability determining unit 1514) a first probability that the first candidate substring corresponds to a first actionable command and a second probability that the second candidate substring corresponds to a second actionable command. Processing unit 1506 can be further configured to, in response to the first probability and the second probability exceeding a threshold, determine (e.g., using intent determining unit 1516) a first intent associated with the first candidate substring and a second intent associated with the second candidate substring. Processing unit 1506 can be further configured to execute (e.g., using process execution unit 1518) a first process associated with the first intent and a second process associated with the second intent. Processing unit 1506 can be further configured to provide to the user (e.g., through output unit 1504 using acknowledgment providing unit 1520) an acknowledgment associated with the first intent and the second intent.


In some examples, parsing the text string (e.g., using text string parsing unit 1512) into at least the first candidate substring and the second candidate substring comprises identifying a first keyword in the text string that corresponds to a first domain to determine the first candidate substring, and identifying a second keyword in the text string that corresponds to a second domain to determine the second candidate substring. In other examples, parsing the text string (e.g., using text string parsing unit 1512) into at least the first candidate substring and the second candidate substring comprises identifying a first imperative verb in the text string to determine the first candidate substring, and identifying a second imperative verb in the text string to determine the second candidate substring.


In some examples, determining (e.g., using probability determining unit 1514) the first probability that the first candidate substring corresponds to the first actionable command and the second probability that the second candidate substring corresponds to the second actionable command comprises determining a first semantic coherence of the first candidate substring and a second semantic coherence of the second candidate substring, and determining the first probability and the second probability based on the first semantic coherence and the second semantic coherence. In other examples, determining (e.g., using probability determining unit 1514) the first probability that the first candidate substring corresponds to the first actionable command and the second probability that the second candidate substring corresponds to the second actionable command comprises comparing the first candidate substring and the second candidate substring to one or more user request templates, and determining the first probability and the second probability based on the comparison. In still other examples, determining (e.g., using probability determining unit 1514) the first probability that the first candidate substring corresponds to the first actionable command and the second probability that the second candidate substring corresponds to the second actionable command comprises submitting the first candidate substring and the second candidate substring to at least a first service and a second service, receiving a first likelihood that the first service can resolve the first actionable command and a second likelihood that the second service can resolve the second actionable command, and determining the first probability and the second probability based on the first likelihood and the second likelihood.


In some examples, determining (e.g., using intent determining unit 1516) the first intent associated with the first candidate substring and the second intent associated with the second candidate substring comprises determining the second intent based on at least one word in the first candidate substring. In other examples, determining (e.g., using intent determining unit 1516) the first intent associated with the first candidate substring and the second intent associated with the second candidate substring comprises determining the first intent or the second intent based on information displayed on a display associated with the electronic device. In some examples, the information comprises a list, and determining (e.g., using intent determining unit 1516) the first intent associated with the first candidate substring and the second intent associated with the second candidate substring comprises determining the first intent or the second intent based on an ordinal descriptor in the first candidate substring or the second candidate substring, wherein the ordinal descriptor is associated with one or more items in the list. In other examples, the information comprises one or more notifications. In still other examples, the information comprises one or more emails. In some examples, determining (e.g., using intent determining unit 1516) the first intent associated with the first candidate substring and the second intent associated with the second candidate substring comprises determining one or more potential user requests based on the information displayed on the display, and determining the first intent or the second intent based on the one or more potential user requests.


In some examples, the acknowledgment (e.g., from acknowledgment providing unit 1520) comprises an audible confirmation. In other examples, the acknowledgment comprises haptic feedback. In some examples, providing to the user (e.g., through output unit 1504 using acknowledgment providing unit 1520) the acknowledgment associated with the first intent and the second intent comprises providing a first task associated with the first intent and a second task associated with the second intent. In one example, providing the first task associated with the first intent and the second task associated with the second intent comprises displaying the first task and the second task.


In some examples, processing unit 1506 can be further configured to, in response to completing the first process, provide (e.g., using acknowledgment providing unit 1520) a first indicator associated with the first task, and in response to completing the second process, provide a second indicator associated with the second task. In one example, providing the first indicator associated with the first task comprises displaying the first indicator, and providing the second indicator associated with the second task comprises displaying the second indicator. In other examples, processing unit 1506 can be further configured to, before completing the first process, provide (e.g., using acknowledgment providing unit 1520) a first processing status indicator associated with the first task, and before completing the second process, provide a second processing status indicator associated with the second task. In one example, providing the first processing status indicator associated with the first task comprises displaying the first processing status indicator, and providing the second processing status indicator associated with the second task comprises displaying the second processing status indicator. In some examples, the first indicator and the second indicator comprise a checkmark. In other examples, the first processing status indicator and the second processing status indicator comprise one or more of an hourglass, an animation, or a status bar.


In other examples, providing to the user (e.g., through output unit 1504 using acknowledgment providing unit 1520) the acknowledgment associated with the first intent and the second intent comprises displaying the first candidate substring using a first emphasis and displaying the second candidate substring using a second emphasis that is different than the first emphasis. In some examples, the first emphasis and the second emphasis comprise one or more of bold text, italic text, underlined text, circled text, outlined text, colored text, and clustered text.


The operations described above with reference to FIG. 8 are, optionally, implemented by components depicted in FIGS. 1A-1B or FIG. 15. For example, receiving operation 802, generating operation 804, parsing operation 806, determining operations 808-812, executing operation 814, and providing operation 816 are, optionally, implemented by event sorter 170, event recognizer 180, and event handler 190. Event monitor 171 in event sorter 170 detects a contact on touch-sensitive display 112, and event dispatcher module 174 delivers the event information to application 136-1. A respective event recognizer 180 of application 136-1 compares the event information to respective event definitions 186, and determines whether a first contact at a first location on the touch-sensitive surface (or whether rotation of the device) corresponds to a predefined event or sub-event, such as selection of an object on a user interface, or rotation of the device from one orientation to another. When a respective predefined event or sub-event is detected, event recognizer 180 activates an event handler 190 associated with the detection of the event or sub-event. Event handler 190 optionally uses or calls data updater 176 or object updater 177 to update the application internal state 192. In some embodiments, event handler 190 accesses a respective GUI updater 178 to update what is displayed by the application. Similarly, it would be clear to a person having ordinary skill in the art how other processes can be implemented based on the components depicted in FIGS. 1A-1B.


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.

Claims
  • 1. An electronic device, comprising: one or more processors;a memory; andone or more programs, wherein 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: receiving speech input from a user;in response to receiving the speech input: obtaining, from the speech input, a first candidate substring and a second candidate substring;determining a first probability that the first candidate substring corresponds to a first actionable command and a second probability that the second candidate substring corresponds to a second actionable command;combining the first probability and the second probability to obtain a combined probability;in response to the combined probability exceeding a threshold, determining, based on the first probability and the second probability, a first intent associated with the first candidate substring and a second intent associated with the second candidate sub string;providing to the user an acknowledgement associated with the first intent and the second intent, wherein providing the acknowledgement includes displaying a first task associated with the first intent and a second task associated with the second intent; andinitiating a first process identified by the first intent and a second process identified by the second intent.
  • 2. The electronic device of claim 1, wherein obtaining the first candidate substring and the second candidate substring comprises: generating a first text string based on the speech input;identifying a first keyword in the first text string that corresponds to a first domain to determine the first candidate substring; andidentifying a second keyword in the first text string that corresponds to a second domain to determine the second candidate substring.
  • 3. The electronic device of claim 1, wherein obtaining the first candidate substring and the second candidate substring comprises: generating a second text string based on the speech input;identifying a first imperative verb in the second text string to determine the first candidate substring; andidentifying a second imperative verb in the second text string to determine the second candidate substring.
  • 4. The electronic device of claim 1, wherein determining the first probability that the first candidate substring corresponds to the first actionable command and the second probability that the second candidate substring corresponds to the second actionable command comprises: determining a first semantic coherence of the first candidate substring and a second semantic coherence of the second candidate substring; anddetermining the first probability and the second probability based on the first semantic coherence and the second semantic coherence.
  • 5. The electronic device of claim 1, wherein determining the first probability that the first candidate substring corresponds to the first actionable command and the second probability that the second candidate substring corresponds to the second actionable command comprises: comparing the first candidate substring and the second candidate substring to one or more user request templates; anddetermining the first probability and the second probability based on the comparison.
  • 6. The electronic device of claim 1, wherein determining the first probability that the first candidate substring corresponds to the first actionable command and the second probability that the second candidate substring corresponds to the second actionable command comprises: submitting the first candidate substring and the second candidate substring to at least a first service and a second service;receiving a first likelihood that the first service can resolve the first actionable command and a second likelihood that the second service can resolve the second actionable command; anddetermining the first probability and the second probability based on the first likelihood and the second likelihood.
  • 7. The electronic device of claim 1, wherein determining the first intent associated with the first candidate substring and the second intent associated with the second candidate substring comprises: determining the second intent based on at least one word in the first candidate substring.
  • 8. The electronic device of claim 1, wherein determining the first intent associated with the first candidate substring and the second intent associated with the second candidate substring comprises: determining at least one of the first intent and the second intent based on information displayed on a display associated with the electronic device.
  • 9. The electronic device of claim 8, wherein the information comprises a list; and wherein determining the first intent associated with the first candidate substring and the second intent associated with the second candidate substring comprises: determining the first intent or the second intent based on an ordinal descriptor in the first candidate substring or the second candidate substring, wherein the ordinal descriptor is associated with one or more items in the list.
  • 10. The electronic device of claim 8, wherein determining the first intent associated with the first candidate substring and the second intent associated with the second candidate substring comprises: determining one or more potential user requests based on the information displayed on the display; anddetermining the first intent or the second intent based on the one or more potential user requests.
  • 11. The electronic device of claim 1, wherein the acknowledgment comprises an audible confirmation.
  • 12. The electronic device of claim 1, wherein the acknowledgment comprises haptic feedback.
  • 13. The electronic device of claim 1, the one or more programs further including instructions for: in response to completing the first process, providing a first indicator associated with the first task; andin response to completing the second process, providing a second indicator associated with the second task.
  • 14. The electronic device of claim 1, the one or more programs further including instructions for: before completing the first process, providing a first processing status indicator associated with the first task; andbefore completing the second process, providing a second processing status indicator associated with the second task.
  • 15. The electronic device of claim 14, wherein providing the first processing status indicator associated with the first task comprises displaying the first processing status indicator; and wherein providing the second processing status indicator associated with the second task comprises displaying the second processing status indicator.
  • 16. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to: receive speech input from a user;in response to receiving the speech input: obtain, from the speech input, a first candidate substring and a second candidate sub string;determine a first probability that the first candidate substring corresponds to a first actionable command and a second probability that the second candidate substring corresponds to a second actionable command;combine the first probability and the second probability to obtain a combined probability;in response to the combined probability exceeding a threshold, determine, based on the first probability and the second probability, a first intent associated with the first candidate substring and a second intent associated with the second candidate substring;provide to the user an acknowledgement associated with the first intent and the second intent, wherein providing the acknowledgement includes displaying a first task associated with the first intent and a second task associated with the second intent; andinitiate a first process identified by the first intent and a second process identified by the second intent.
  • 17. A method, comprising: at an electronic device: receiving speech input from a user;in response to receiving the speech input: obtaining, from the speech input, a first candidate substring and a second candidate substring;determining a first probability that the first candidate substring corresponds to a first actionable command and a second probability that the second candidate substring corresponds to a second actionable command;combining the first probability and the second probability to obtain a combined probability;in response to the combined probability exceeding a threshold, determining, based on the first probability and the second probability, a first intent associated with the first candidate substring and a second intent associated with the second candidate sub string;providing to the user an acknowledgement associated with the first intent and the second intent, wherein providing the acknowledgement includes displaying a first task associated with the first intent and a second task associated with the second intent; andinitiating a first process identified by the first intent and a second process identified by the second intent.
  • 18. The non-transitory computer readable storage medium of claim 16, wherein obtaining the first candidate substring and the second candidate substring comprises: generating a first text string based on the speech input;identifying a first keyword in the first text string that corresponds to a first domain to determine the first candidate substring; andidentifying a second keyword in the first text string that corresponds to a second domain to determine the second candidate substring.
  • 19. The non-transitory computer readable storage medium of claim 16, wherein obtaining the first candidate substring and the second candidate substring comprises: generating a second text string based on the speech input;identifying a first imperative verb in the second text string to determine the first candidate substring; andidentifying a second imperative verb in the second text string to determine the second candidate substring.
  • 20. The non-transitory computer readable storage medium of claim 16, wherein determining the first probability that the first candidate substring corresponds to the first actionable command and the second probability that the second candidate substring corresponds to the second actionable command comprises: determining a first semantic coherence of the first candidate substring and a second semantic coherence of the second candidate substring; anddetermining the first probability and the second probability based on the first semantic coherence and the second semantic coherence.
  • 21. The non-transitory computer readable storage medium of claim 16, wherein determining the first probability that the first candidate substring corresponds to the first actionable command and the second probability that the second candidate substring corresponds to the second actionable command comprises: comparing the first candidate substring and the second candidate substring to one or more user request templates; anddetermining the first probability and the second probability based on the comparison.
  • 22. The non-transitory computer readable storage medium of claim 16, wherein determining the first probability that the first candidate substring corresponds to the first actionable command and the second probability that the second candidate substring corresponds to the second actionable command comprises: submitting the first candidate substring and the second candidate substring to at least a first service and a second service;receiving a first likelihood that the first service can resolve the first actionable command and a second likelihood that the second service can resolve the second actionable command; anddetermining the first probability and the second probability based on the first likelihood and the second likelihood.
  • 23. The non-transitory computer readable storage medium of claim 16, wherein determining the first intent associated with the first candidate substring and the second intent associated with the second candidate substring comprises: determining the second intent based on at least one word in the first candidate substring.
  • 24. The non-transitory computer readable storage medium of claim 16, wherein determining the first intent associated with the first candidate substring and the second intent associated with the second candidate substring comprises: determining at least one of the first intent and the second intent based on information displayed on a display associated with the electronic device.
  • 25. The non-transitory computer readable storage medium of claim 24, wherein the information comprises a list; and wherein determining the first intent associated with the first candidate substring and the second intent associated with the second candidate substring comprises: determining the first intent or the second intent based on an ordinal descriptor in the first candidate substring or the second candidate substring, wherein the ordinal descriptor is associated with one or more items in the list.
  • 26. The non-transitory computer readable storage medium of claim 24, wherein determining the first intent associated with the first candidate substring and the second intent associated with the second candidate substring comprises: determining one or more potential user requests based on the information displayed on the display; anddetermining the first intent or the second intent based on the one or more potential user requests.
  • 27. The non-transitory computer readable storage medium of claim 16, wherein the acknowledgment comprises an audible confirmation.
  • 28. The non-transitory computer readable storage medium of claim 16, wherein the acknowledgment comprises haptic feedback.
  • 29. The non-transitory computer readable storage medium of claim 16, the one or more programs further comprising instructions, which when executed by the one or more processors of the electronic device, cause the electronic device to: in response to completing the first process, provide a first indicator associated with the first task; andin response to completing the second process, provide a second indicator associated with the second task.
  • 30. The non-transitory computer readable storage medium of claim 16, the one or more programs further comprising instructions, which when executed by the one or more processors of the electronic device, cause the electronic device to: before completing the first process, provide a first processing status indicator associated with the first task; andbefore completing the second process, provide a second processing status indicator associated with the second task.
  • 31. The non-transitory computer readable storage medium of claim 30, wherein providing the first processing status indicator associated with the first task comprises displaying the first processing status indicator; and wherein providing the second processing status indicator associated with the second task comprises displaying the second processing status indicator.
  • 32. The method of claim 17, wherein obtaining the first candidate substring and the second candidate substring comprises: generating a first text string based on the speech input;identifying a first keyword in the first text string that corresponds to a first domain to determine the first candidate substring; andidentifying a second keyword in the first text string that corresponds to a second domain to determine the second candidate substring.
  • 33. The method of claim 17, wherein obtaining the first candidate substring and the second candidate substring comprises: generating a second text string based on the speech input;identifying a first imperative verb in the second text string to determine the first candidate substring; andidentifying a second imperative verb in the second text string to determine the second candidate substring.
  • 34. The method of claim 17, wherein determining the first probability that the first candidate substring corresponds to the first actionable command and the second probability that the second candidate substring corresponds to the second actionable command comprises: determining a first semantic coherence of the first candidate substring and a second semantic coherence of the second candidate substring; anddetermining the first probability and the second probability based on the first semantic coherence and the second semantic coherence.
  • 35. The method of claim 17, wherein determining the first probability that the first candidate substring corresponds to the first actionable command and the second probability that the second candidate substring corresponds to the second actionable command comprises: comparing the first candidate substring and the second candidate substring to one or more user request templates; anddetermining the first probability and the second probability based on the comparison.
  • 36. The method of claim 17, wherein determining the first probability that the first candidate substring corresponds to the first actionable command and the second probability that the second candidate substring corresponds to the second actionable command comprises: submitting the first candidate substring and the second candidate substring to at least a first service and a second service;receiving a first likelihood that the first service can resolve the first actionable command and a second likelihood that the second service can resolve the second actionable command; anddetermining the first probability and the second probability based on the first likelihood and the second likelihood.
  • 37. The method of claim 17, wherein determining the first intent associated with the first candidate substring and the second intent associated with the second candidate substring comprises: determining the second intent based on at least one word in the first candidate substring.
  • 38. The method of claim 17, wherein determining the first intent associated with the first candidate substring and the second intent associated with the second candidate substring comprises: determining at least one of the first intent and the second intent based on information displayed on a display associated with the electronic device.
  • 39. The method of claim 38, wherein the information comprises a list; and wherein determining the first intent associated with the first candidate substring and the second intent associated with the second candidate substring comprises: determining the first intent or the second intent based on an ordinal descriptor in the first candidate substring or the second candidate substring, wherein the ordinal descriptor is associated with one or more items in the list.
  • 40. The method of claim 38, wherein determining the first intent associated with the first candidate substring and the second intent associated with the second candidate substring comprises: determining one or more potential user requests based on the information displayed on the display; anddetermining the first intent or the second intent based on the one or more potential user requests.
  • 41. The method of claim 17, wherein the acknowledgment comprises an audible confirmation.
  • 42. The method of claim 17, wherein the acknowledgment comprises haptic feedback.
  • 43. The method of claim 17, further comprising: in response to completing the first process, providing a first indicator associated with the first task; andin response to completing the second process, providing a second indicator associated with the second task.
  • 44. The method of claim 17, further comprising: before completing the first process, providing a first processing status indicator associated with the first task; andbefore completing the second process, providing a second processing status indicator associated with the second task.
  • 45. The method of claim 44, wherein providing the first processing status indicator associated with the first task comprises displaying the first processing status indicator; and wherein providing the second processing status indicator associated with the second task comprises displaying the second processing status indicator.
CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No. 16/600,950, entitled “MULTI-COMMAND SINGLE UTTERANCE INPUT METHOD,” filed Oct. 14, 2019, which is a continuation of U.S. patent application Ser. No. 15/971,787, entitled “MULTI-COMMAND SINGLE UTTERANCE INPUT METHOD,” filed May 4, 2018, which is a continuation of U.S. patent application Ser. No. 14/724,623, entitled “MULTI-COMMAND SINGLE UTTERANCE INPUT METHOD,” filed May 28, 2015, which claims the benefit of priority of U.S. Provisional Patent Application No. 62/005,556, entitled “MULTI-COMMAND SINGLE UTTERANCE INPUT METHOD,” filed May 30, 2014; and U.S. Provisional Patent Application No. 62/129,851, entitled “MULTI-COMMAND SINGLE UTTERANCE INPUT METHOD,” filed Mar. 8, 2015. The content of these applications is hereby incorporated by reference in its entirety.

US Referenced Citations (2821)
Number Name Date Kind
5799279 Gould et al. Aug 1998 A
5878386 Coughlin Mar 1999 A
6292778 Sukkar Sep 2001 B1
6453292 Ramaswamy et al. Sep 2002 B2
6631346 Karaorman et al. Oct 2003 B1
6952675 Tahara Oct 2005 B1
7865817 Ryan et al. Jan 2011 B2
7869998 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
7873523 Potter et al. 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
7890329 Wu 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
7917364 Yacoub 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
7933399 Knott et al. Apr 2011 B2
7936339 Marggraff et al. May 2011 B2
7936861 Knott 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 White 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
7987176 Latzina 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
8001125 Magdalin et al. Aug 2011 B1
8005664 Hanumanthappa Aug 2011 B2
8005679 Jordan et al. Aug 2011 B2
8006180 Tunning et al. Aug 2011 B2
8010367 Muschett et al. Aug 2011 B2
8010614 Musat et al. Aug 2011 B1
8014308 Gates, III 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
8032409 Mikurak 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
8046231 Hirota et al. Oct 2011 B2
8046363 Cha et al. Oct 2011 B2
8046374 Bromwich Oct 2011 B1
8050500 Batty et al. Nov 2011 B1
8050919 Das Nov 2011 B2
8054180 Scofield et al. Nov 2011 B1
8055296 Persson 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
8078978 Perry et al. Dec 2011 B2
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
8103947 Lunt et al. Jan 2012 B2
8107401 John et al. Jan 2012 B2
8112275 Kennewick et al. Feb 2012 B2
8112280 Lu Feb 2012 B2
8117026 Lee et al. 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
8140330 Cevik et al. Mar 2012 B2
8140335 Kennewick et al. Mar 2012 B2
8140368 Eggenberger 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
8170966 Musat et al. May 2012 B1
8171137 Parks et al. May 2012 B1
8175872 Kristjansson et al. May 2012 B2
8175876 Bou-ghazale 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
8194827 Jaiswal et al. Jun 2012 B2
8195460 Degani et al. Jun 2012 B2
8195467 Mozer et al. Jun 2012 B2
8195468 Weider 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
8204827 Gupta et al. Jun 2012 B1
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
8219555 Mianji Jul 2012 B1
8219608 alSafadi et al. Jul 2012 B2
8224649 Chaudhari et al. Jul 2012 B2
8224757 Bohle 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
8244545 Paek 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
8260117 Xu et al. Sep 2012 B1
8260247 Lazaridis et al. Sep 2012 B2
8260617 Dhanakshirur et al. Sep 2012 B2
8260619 Bansal et al. Sep 2012 B1
8270933 Riemer et al. Sep 2012 B2
8271287 Kermani Sep 2012 B1
8275621 Alewine et al. Sep 2012 B2
8275736 Guo 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
8285737 Lynn et al. Oct 2012 B1
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
8300776 Davies et al. 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 Nov 2012 B2
8326627 Kennewick et al. Dec 2012 B2
8332205 Krishnan et al. Dec 2012 B2
8332218 Cross, Jr. 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
8346757 Lamping 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 et al. Feb 2013 B1
8391844 Novick et al. Mar 2013 B2
8396714 Rogers et al. Mar 2013 B2
8396715 Odell et al. Mar 2013 B2
8401163 Kirchhoff et al. Mar 2013 B1
8406745 Upadhyay et al. Mar 2013 B1
8407239 Dean et al. Mar 2013 B2
8423288 Stahl et al. Apr 2013 B2
8428758 Naik et al. Apr 2013 B2
8433572 Caskey et al. Apr 2013 B2
8433778 Shreesha et al. Apr 2013 B1
8434133 Kulkarni et al. Apr 2013 B2
8442821 Vanhoucke May 2013 B1
8447612 Gazdzinski May 2013 B2
8452597 Bringert et al. May 2013 B2
8452602 Bringert et al. May 2013 B1
8453058 Coccaro et al. May 2013 B1
8457959 Kaiser Jun 2013 B2
8458115 Cai et al. Jun 2013 B2
8458278 Christie et al. Jun 2013 B2
8463592 Lu et al. Jun 2013 B2
8464150 Davidson et al. Jun 2013 B2
8473289 Jitkoff et al. Jun 2013 B2
8477323 Low et al. Jul 2013 B2
8478816 Parks et al. Jul 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
8515736 Duta Aug 2013 B1
8515750 Lei et al. Aug 2013 B1
8521513 Millett et al. Aug 2013 B2
8521526 Lloyd et al. Aug 2013 B1
8521531 Kim Aug 2013 B1
8527276 Senior et al. Sep 2013 B1
8533266 Koulomzin et al. Sep 2013 B2
8537033 Gueziec Sep 2013 B2
8539342 Lewis Sep 2013 B1
8543375 Hong Sep 2013 B2
8543397 Nguyen Sep 2013 B1
8543398 Strope et al. Sep 2013 B1
8560229 Park et al. Oct 2013 B1
8560366 Mikurak Oct 2013 B2
8571528 Channakeshava Oct 2013 B1
8571851 Tickner et al. Oct 2013 B1
8577683 Dewitt Nov 2013 B2
8583416 Huang et al. Nov 2013 B2
8583511 Hendrickson Nov 2013 B2
8583638 Donelli Nov 2013 B2
8589156 Burke et al. Nov 2013 B2
8589161 Kennewick et al. Nov 2013 B2
8589374 Chaudhari Nov 2013 B2
8589869 Wolfram Nov 2013 B2
8589911 Sharkey et al. Nov 2013 B1
8595004 Koshinaka Nov 2013 B2
8595642 Lagassey Nov 2013 B1
8600743 Lindahl et al. Dec 2013 B2
8600746 Lei et al. Dec 2013 B1
8600930 Sata et al. Dec 2013 B2
8606090 Eyer Dec 2013 B2
8606568 Tickner et al. Dec 2013 B1
8606576 Barr et al. Dec 2013 B1
8606577 Stewart et al. Dec 2013 B1
8615221 Cosenza 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
8630841 Van Caldwell et al. Jan 2014 B2
8635073 Chang Jan 2014 B2
8638363 King et al. Jan 2014 B2
8639516 Lindahl et al. Jan 2014 B2
8645128 Agiomyrgiannakis Feb 2014 B1
8645137 Bellegarda et al. Feb 2014 B2
8645138 Weinstein et al. Feb 2014 B1
8654936 Eslambolchi 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
8660924 Hoch 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 Mar 2014 B2
8677377 Cheyer et al. Mar 2014 B2
8681950 Vlack et al. Mar 2014 B2
8682667 Haughay Mar 2014 B2
8687777 Lavian et al. Apr 2014 B1
8688446 Yanagihara Apr 2014 B2
8688453 Joshi et al. Apr 2014 B1
8689135 Portele et al. Apr 2014 B2
8694322 Snitkovskiy et al. Apr 2014 B2
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
8707195 Fleizach et al. Apr 2014 B2
8712778 Thenthiruperai Apr 2014 B1
8713119 Lindahl et al. Apr 2014 B2
8713418 King et al. Apr 2014 B2
8719006 Bellegarda May 2014 B2
8719014 Wagner May 2014 B2
8719039 Sharifi May 2014 B1
8731610 Appaji May 2014 B2
8731912 Tickner et al. May 2014 B1
8731942 Cheyer et al. May 2014 B2
8739208 Davis et al. May 2014 B2
8744852 Seymour et al. Jun 2014 B1
8751971 Fleizach et al. Jun 2014 B2
8760537 Johnson et al. Jun 2014 B2
8762145 Ouchi et al. Jun 2014 B2
8762156 Chen Jun 2014 B2
8762469 Lindahl Jun 2014 B2
8768693 Somekh et al. Jul 2014 B2
8768702 Mason et al. Jul 2014 B2
8775154 Clinchant et al. Jul 2014 B2
8775177 Heigold et al. Jul 2014 B1
8775931 Fux et al. Jul 2014 B2
8781456 Prociw Jul 2014 B2
8781841 Wang Jul 2014 B1
8793301 Wegenkittl et al. Jul 2014 B2
8798255 Lubowich et al. Aug 2014 B2
8798995 Edara Aug 2014 B1
8799000 Guzzoni et al. Aug 2014 B2
8805690 Lebeau et al. Aug 2014 B1
8812299 Su Aug 2014 B1
8812302 Xiao et al. Aug 2014 B2
8812321 Gilbert et al. Aug 2014 B2
8823507 Touloumtzis Sep 2014 B1
8831947 Wasserblat et al. Sep 2014 B2
8831949 Smith et al. Sep 2014 B1
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
8868111 Kahn et al. Oct 2014 B1
8868409 Mengibar et al. Oct 2014 B1
8868469 Xu et al. Oct 2014 B2
8868529 Lerenc Oct 2014 B2
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
8897822 Martin Nov 2014 B2
8898064 Thomas et al. Nov 2014 B1
8898568 Bull et al. Nov 2014 B2
8903716 Chen et al. Dec 2014 B2
8909693 Frissora et al. Dec 2014 B2
8918321 Czahor 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
8938450 Spivack et al. Jan 2015 B2
8938688 Bradford et al. Jan 2015 B2
8942986 Cheyer et al. Jan 2015 B2
8943423 Merrill et al. Jan 2015 B2
8964947 Noolu et al. Feb 2015 B1
8972240 Brockett et al. Mar 2015 B2
8972432 Shaw et al. Mar 2015 B2
8972878 Mohler et al. Mar 2015 B2
8976063 Hawkins et al. Mar 2015 B1
8976108 Hawkins et al. Mar 2015 B2
8977255 Freeman 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
8995972 Cronin Mar 2015 B1
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
9002714 Kim et al. Apr 2015 B2
9009046 Stewart Apr 2015 B1
9015036 Karov Zangvil et al. Apr 2015 B2
9020804 Barbaiani et al. Apr 2015 B2
9026425 Nikoulina et al. May 2015 B2
9026426 Wu et al. May 2015 B2
9031834 Coorman et al. May 2015 B2
9031970 Das et al. May 2015 B1
9037967 Al-jefri et al. May 2015 B1
9043208 Koch et al. May 2015 B2
9043211 Haiut et al. May 2015 B2
9046932 Medlock et al. Jun 2015 B2
9049255 Macfarlane et al. Jun 2015 B2
9049295 Cooper et al. Jun 2015 B1
9053706 Jitkoff et al. Jun 2015 B2
9058105 Drory et al. Jun 2015 B2
9058332 Darby et al. Jun 2015 B1
9058811 Wang et al. Jun 2015 B2
9063979 Chiu et al. Jun 2015 B2
9064495 Torok et al. Jun 2015 B1
9065660 Ellis 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
9075435 Noble et al. Jul 2015 B1
9076448 Bennett et al. Jul 2015 B2
9076450 Sadek et al. Jul 2015 B1
9081411 Kains et al. Jul 2015 B2
9081482 Zhai et al. Jul 2015 B1
9082402 Yadgar et al. Jul 2015 B2
9083581 Addepalli et al. Jul 2015 B1
9094636 Sanders et al. Jul 2015 B1
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
9143907 Caldwell et al. Sep 2015 B1
9159319 Hoffmeister Oct 2015 B1
9164983 Liu et al. Oct 2015 B2
9171541 Kennewick et al. Oct 2015 B2
9171546 Pike Oct 2015 B1
9183845 Gopalakrishnan et al. Nov 2015 B1
9190062 Haughay Nov 2015 B2
9208153 Zaveri et al. Dec 2015 B1
9213754 Zhan et al. Dec 2015 B1
9218122 Thoma et al. Dec 2015 B2
9218809 Bellegard et al. Dec 2015 B2
9218819 Stekkelpa et al. Dec 2015 B1
9223537 Brown et al. Dec 2015 B2
9236047 Rasmussen Jan 2016 B2
9241073 Rensburg et al. Jan 2016 B1
9251713 Giovanniello et al. Feb 2016 B1
9255812 Maeoka et al. Feb 2016 B2
9258604 Bilobrov et al. Feb 2016 B1
9262412 Yang et al. Feb 2016 B2
9262612 Cheyer Feb 2016 B2
9263058 Huang et al. Feb 2016 B2
9280535 Varma et al. Mar 2016 B2
9282211 Osawa Mar 2016 B2
9286910 Li et al. Mar 2016 B1
9292487 Weber Mar 2016 B1
9292489 Sak et al. Mar 2016 B1
9292492 Sarikaya et al. Mar 2016 B2
9299344 Braho et al. Mar 2016 B2
9300718 Khanna Mar 2016 B2
9301256 Mohan et al. Mar 2016 B2
9305543 Fleizach et al. Apr 2016 B2
9305548 Kennewick et al. Apr 2016 B2
9311308 Sankarasubramaniam 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
9325842 Siddiqi et al. Apr 2016 B1
9330659 Ju et al. May 2016 B2
9330668 Nanavati et al. May 2016 B2
9330720 Lee May 2016 B2
9335983 Breiner et al. May 2016 B2
9338493 Van Os et al. May 2016 B2
9349368 Lebeau et al. May 2016 B1
9355472 Kocienda et al. May 2016 B2
9361084 Costa Jun 2016 B1
9367541 Servan et al. Jun 2016 B1
9368114 Larson et al. Jun 2016 B2
9377871 Waddell et al. Jun 2016 B2
9378456 White et al. Jun 2016 B2
9378740 Rosen et al. Jun 2016 B1
9380155 Reding et al. Jun 2016 B1
9383827 Faaborg et al. Jul 2016 B1
9384185 Medlock et al. Jul 2016 B2
9390726 Smus et al. Jul 2016 B1
9396722 Chung et al. Jul 2016 B2
9401147 Jitkoff et al. Jul 2016 B2
9406224 Sanders et al. Aug 2016 B1
9406299 Gollan et al. Aug 2016 B2
9408182 Hurley et al. Aug 2016 B1
9412392 Lindahl Aug 2016 B2
9418650 Bharadwaj et al. Aug 2016 B2
9423266 Clark et al. Aug 2016 B2
9424246 Spencer et al. Aug 2016 B2
9424840 Hart et al. Aug 2016 B1
9431021 Scalise et al. Aug 2016 B1
9432499 Hajdu 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
9442687 Park et al. Sep 2016 B2
9443527 Watanabe et al. Sep 2016 B1
9454599 Golden et al. Sep 2016 B2
9454957 Mathias et al. Sep 2016 B1
9465798 Lin Oct 2016 B2
9465833 Aravamudan et al. Oct 2016 B2
9465864 Hu et al. Oct 2016 B2
9466027 Byrne et al. Oct 2016 B2
9466294 Tunstall-Pedoe et al. Oct 2016 B1
9471566 Zhang et al. Oct 2016 B1
9472196 Wang et al. Oct 2016 B1
9483388 Sankaranarasimhan et al. Nov 2016 B2
9483461 Fleizach et al. Nov 2016 B2
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
9514470 Topatan et al. Dec 2016 B2
9516014 Zafiroglu et al. Dec 2016 B2
9519453 Perkuhn et al. Dec 2016 B2
9524355 Forbes et al. Dec 2016 B2
9531862 Vadodaria Dec 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
9548979 Johnson et al. Jan 2017 B1
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
9619200 Chakladar et al. Apr 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
9633191 Fleizach et al. Apr 2017 B2
9633660 Haughay Apr 2017 B2
9652453 Mathur et al. May 2017 B2
9658746 Cohn et al. May 2017 B2
9659002 Medlock et al. May 2017 B2
9659298 Lynch et al. May 2017 B2
9665567 Li et al. May 2017 B2
9665662 Gautam et al. May 2017 B1
9668121 Naik et al. May 2017 B2
9672725 Dotan-Cohen et al. Jun 2017 B2
9691378 Meyers et al. Jun 2017 B1
9697822 Naik et al. Jul 2017 B1
9697827 Lilly et al. Jul 2017 B1
9698999 Mutagi Jul 2017 B2
9720907 Bangalore et al. Aug 2017 B2
9721566 Newendorp et al. Aug 2017 B2
9721570 Beal et al. Aug 2017 B1
9723130 Rand Aug 2017 B2
9734817 Putrycz Aug 2017 B1
9734839 Adams Aug 2017 B1
9741343 Miles et al. Aug 2017 B1
9747083 Roman et al. Aug 2017 B1
9747093 Latino et al. Aug 2017 B2
9755605 Li et al. Sep 2017 B1
9760566 Heck et al. Sep 2017 B2
9767710 Lee et al. Sep 2017 B2
9786271 Combs et al. Oct 2017 B1
9792907 Bocklet et al. Oct 2017 B2
9812128 Mixter et al. Nov 2017 B2
9813882 Masterman Nov 2017 B1
9818400 Paulik et al. Nov 2017 B2
9823811 Brown et al. Nov 2017 B2
9823828 Zambetti et al. Nov 2017 B2
9830044 Brown et al. Nov 2017 B2
9830449 Wagner Nov 2017 B1
9842584 Hart et al. Dec 2017 B1
9846685 Li Dec 2017 B2
9858925 Gruber et al. Jan 2018 B2
9858927 Williams et al. Jan 2018 B2
9886953 Lemay et al. Feb 2018 B2
9887949 Shepherd et al. Feb 2018 B2
9916839 Scalise et al. Mar 2018 B1
9922642 Pitschel et al. Mar 2018 B2
9934777 Joseph et al. Apr 2018 B1
9934785 Hulaud Apr 2018 B1
9946862 Yun et al. Apr 2018 B2
9948728 Linn et al. Apr 2018 B2
9959129 Kannan et al. May 2018 B2
9966065 Gruber et al. May 2018 B2
9966068 Cash et al. May 2018 B2
9967381 Kashimba et al. May 2018 B1
9971495 Shetty et al. May 2018 B2
9984686 Mutagi et al. May 2018 B1
9986419 Naik et al. May 2018 B2
9990129 Yang et al. Jun 2018 B2
9990176 Gray Jun 2018 B1
9998552 Ledet Jun 2018 B1
10001817 Zambetti et al. Jun 2018 B2
10013416 Bhardwaj et al. Jul 2018 B1
10013654 Levy et al. Jul 2018 B1
10013979 Roma et al. Jul 2018 B1
10019436 Huang Jul 2018 B2
10032451 Mamkina et al. Jul 2018 B1
10032455 Newman et al. Jul 2018 B2
10037758 Jing et al. Jul 2018 B2
10043516 Saddler et al. Aug 2018 B2
10049161 Kaneko Aug 2018 B2
10049663 Orr et al. Aug 2018 B2
10049668 Huang et al. Aug 2018 B2
10055681 Brown et al. Aug 2018 B2
10074360 Kim Sep 2018 B2
10074371 Wang et al. Sep 2018 B1
10083213 Podgorny et al. Sep 2018 B1
10083690 Giuli et al. Sep 2018 B2
10088972 Brown et al. Oct 2018 B2
10089072 Piersol et al. Oct 2018 B2
10096319 Jin et al. Oct 2018 B1
10101887 Bernstein et al. Oct 2018 B2
10102359 Cheyer Oct 2018 B2
10127901 Zhao et al. Nov 2018 B2
10127908 Deller et al. Nov 2018 B1
10134425 Johnson, Jr. Nov 2018 B1
10169329 Futrell et al. Jan 2019 B2
10170123 Orr et al. Jan 2019 B2
10170135 Pearce et al. Jan 2019 B1
10175879 Missig et al. Jan 2019 B2
10176167 Evermann Jan 2019 B2
10176802 Ladhak et al. Jan 2019 B1
10185542 Carson et al. Jan 2019 B2
10186254 Williams et al. Jan 2019 B2
10186266 Devaraj et al. Jan 2019 B1
10191627 Cieplinski et al. Jan 2019 B2
10191646 Zambetti et al. Jan 2019 B2
10191718 Rhee et al. Jan 2019 B2
10192546 Piersol et al. Jan 2019 B1
10192552 Raitio et al. Jan 2019 B2
10192557 Lee et al. Jan 2019 B2
10199051 Binder et al. Feb 2019 B2
10200824 Gross et al. Feb 2019 B2
10216351 Yang Feb 2019 B2
10216832 Bangalore Feb 2019 B2
10223066 Martel et al. Mar 2019 B2
10225711 Parks et al. Mar 2019 B2
10229356 Liu et al. Mar 2019 B1
10237711 Linn et al. Mar 2019 B2
10248308 Karunamuni et al. Apr 2019 B2
10255922 Sharifi et al. Apr 2019 B1
10269345 Castillo Sanchez et al. Apr 2019 B2
10296160 Shah et al. May 2019 B2
10297253 Walker, II et al. May 2019 B2
10303772 Hosn et al. May 2019 B2
10304463 Mixter et al. May 2019 B2
10311482 Baldwin Jun 2019 B2
10311871 Newendorp et al. Jun 2019 B2
10325598 Basye et al. Jun 2019 B2
10332513 D'souza et al. Jun 2019 B1
10332518 Garg et al. Jun 2019 B2
10346753 Soon-Shiong et al. Jul 2019 B2
10353975 Oh et al. Jul 2019 B2
10354677 Mohamed et al. Jul 2019 B2
10356243 Sanghavi et al. Jul 2019 B2
10366692 Adams et al. Jul 2019 B1
10372814 Gliozzo et al. Aug 2019 B2
10389876 Engelke et al. Aug 2019 B2
10402066 Kawana Sep 2019 B2
10403283 Schramm et al. Sep 2019 B1
10409454 Kagan et al. Sep 2019 B2
10410637 Paulik et al. Sep 2019 B2
10417037 Gruber et al. Sep 2019 B2
10417554 Scheffler Sep 2019 B2
10446142 Lim et al. Oct 2019 B2
10469665 Bell et al. Nov 2019 B1
10474961 Brigham et al. Nov 2019 B2
10496705 Irani et al. Dec 2019 B1
10497365 Gruber et al. Dec 2019 B2
10504518 Irani et al. Dec 2019 B1
10521946 Roche et al. Dec 2019 B1
10528386 Yu Jan 2020 B2
10568032 Freeman et al. Feb 2020 B2
10630795 Aoki et al. Apr 2020 B2
10659851 Lister et al. May 2020 B2
10755032 Douglas et al. Aug 2020 B2
10757499 Vautrin et al. Aug 2020 B1
10811013 Seeker-Walker et al. Oct 2020 B1
20010041980 Howard et al. Nov 2001 A1
20010056344 Ramaswamy Dec 2001 A1
20020133341 Gillick et al. Sep 2002 A1
20020143540 Malayath Oct 2002 A1
20020188454 Sauber Dec 2002 A1
20030014260 Coffman et al. Jan 2003 A1
20030038786 Nguyen et al. Feb 2003 A1
20050283363 Weng et al. Dec 2005 A1
20050289458 Kylmanen Dec 2005 A1
20070233487 Cohen et al. Oct 2007 A1
20090018829 Kuperstein Jan 2009 A1
20090055175 Terrell, II et al. Feb 2009 A1
20090171662 Huang et al. Jul 2009 A1
20090234655 Kwon Sep 2009 A1
20090249247 Tseng et al. Oct 2009 A1
20100010803 Ishikawa et al. Jan 2010 A1
20100235732 Bergman Sep 2010 A1
20100318536 Bandholz et al. 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
20110016421 Krupka 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 Wäller et al. Jan 2011 A1
20110022394 Wide Jan 2011 A1
20110022472 Zon Jan 2011 A1
20110022952 Wu et al. Jan 2011 A1
20110028083 Soitis Feb 2011 A1
20110029616 Wang et al. Feb 2011 A1
20110029637 Morse 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
20110039584 Merrett 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
20110047246 Frissora 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
20110055244 Donelli 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 Mar 2011 A1
20110060807 Martin et al. Mar 2011 A1
20110060812 Middleton Mar 2011 A1
20110064378 Gharaat 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
20110066436 Bezar Mar 2011 A1
20110066468 Huang et al. Mar 2011 A1
20110066602 Studer et al. Mar 2011 A1
20110066634 Phillips et al. Mar 2011 A1
20110072033 White et al. Mar 2011 A1
20110072114 Hoffert et al. Mar 2011 A1
20110072492 Mohler et al. Mar 2011 A1
20110075818 Vance 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 Apr 2011 A1
20110093272 Isobe et al. Apr 2011 A1
20110099000 Rai et al. Apr 2011 A1
20110099157 LeBeau et al. Apr 2011 A1
20110102161 Heubel et al. May 2011 A1
20110103682 Chidlovskii et al. May 2011 A1
20110105097 Tadayon et al. May 2011 A1
20110106534 Lebeau et al. May 2011 A1
20110106536 Klappert May 2011 A1
20110106736 Aharonson et al. May 2011 A1
20110106878 Cho et al. May 2011 A1
20110106892 Nelson et al. May 2011 A1
20110110502 Daye et al. May 2011 A1
20110111724 Baptiste May 2011 A1
20110112825 Bellegarda 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
20110116480 Li 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
20110119713 Chang et al. 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
20110137664 Kho et al. Jun 2011 A1
20110141141 Kankainen Jun 2011 A1
20110143718 Engelhart, Sr. 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
20110151415 Darling et al. Jun 2011 A1
20110151830 Blanda, Jr. et al. Jun 2011 A1
20110153209 Geelen Jun 2011 A1
20110153322 Kwak et al. Jun 2011 A1
20110153324 Ballinger et al. Jun 2011 A1
20110153325 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
20110166855 Vermeulen et al. Jul 2011 A1
20110166862 Eshed et al. Jul 2011 A1
20110167350 Hoellwarth Jul 2011 A1
20110173003 Levanon et al. Jul 2011 A1
20110173537 Hemphill Jul 2011 A1
20110175810 Markovic et al. Jul 2011 A1
20110178804 Inoue et al. Jul 2011 A1
20110179002 Dumitru et al. Jul 2011 A1
20110179372 Moore et al. Jul 2011 A1
20110183627 Ueda et al. Jul 2011 A1
20110183650 McKee 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
20110184789 Kirsch 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 Aug 2011 A1
20110199312 Okuta Aug 2011 A1
20110201385 Higginbotham Aug 2011 A1
20110201387 Paek et al. Aug 2011 A1
20110202526 Lee et al. Aug 2011 A1
20110202594 Ricci Aug 2011 A1
20110202874 Ramer et al. Aug 2011 A1
20110205149 Tom 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
20110231189 Anastasiadis et al. Sep 2011 A1
20110231218 Tovar 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 Oct 2011 A1
20110249144 Chang Oct 2011 A1
20110250570 Mack Oct 2011 A1
20110252108 Morris 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
20110264530 Santangelo et al. Oct 2011 A1
20110264643 Cao Oct 2011 A1
20110264999 Bells et al. Oct 2011 A1
20110270604 Qi et al. Nov 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
20110280143 Li et al. Nov 2011 A1
20110282663 Talwar et al. Nov 2011 A1
20110282888 Koperski et al. Nov 2011 A1
20110282903 Zhang Nov 2011 A1
20110282906 Wong Nov 2011 A1
20110283189 McCarty Nov 2011 A1
20110283190 Poltorak Nov 2011 A1
20110288852 Dymetman et al. Nov 2011 A1
20110288855 Roy Nov 2011 A1
20110288861 Kurzwei et al. Nov 2011 A1
20110288863 Rasmussen Nov 2011 A1
20110288866 Rasmussen Nov 2011 A1
20110288917 Wanek et al. Nov 2011 A1
20110289530 Dureau et al. Nov 2011 A1
20110298585 Barry Dec 2011 A1
20110301943 Patch Dec 2011 A1
20110302162 Xiao et al. Dec 2011 A1
20110302645 Headley Dec 2011 A1
20110306426 Novak et al. Dec 2011 A1
20110307241 Waibel et al. Dec 2011 A1
20110307254 Hunt 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
20110313803 Friend 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
20110314539 Horton 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
20120020503 Endo et al. 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
20120026395 Jin et al. Feb 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
20120039578 Issa et al. Feb 2012 A1
20120041752 Wang et al. Feb 2012 A1
20120041756 Hanazawa et al. Feb 2012 A1
20120041759 Barker et al. Feb 2012 A1
20120042014 Desai et al. Feb 2012 A1
20120042343 Laligand et al. Feb 2012 A1
20120052945 Miyamoto et al. Mar 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
20120060052 White et al. Mar 2012 A1
20120062473 Xiao et al. Mar 2012 A1
20120064975 Gault et al. Mar 2012 A1
20120066212 Jennings Mar 2012 A1
20120066581 Spalink Mar 2012 A1
20120075054 Ge et al. Mar 2012 A1
20120075184 Madhvanath 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
20120084087 Yang et al. Apr 2012 A1
20120084089 Lloyd 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
20120089659 Halevi et al. Apr 2012 A1
20120094645 Jeffrey Apr 2012 A1
20120101823 Weng et al. Apr 2012 A1
20120105257 Murillo et al. May 2012 A1
20120108166 Hymel May 2012 A1
20120108221 Thomas et al. May 2012 A1
20120109632 Sugiura et al. May 2012 A1
20120109753 Kennewick et al. May 2012 A1
20120109997 Sparks et al. May 2012 A1
20120110456 Larco et al. May 2012 A1
20120114108 Katis et al. May 2012 A1
20120116770 Chen et al. May 2012 A1
20120117499 Mori et al. May 2012 A1
20120117590 Agnihotri et al. May 2012 A1
20120124126 Alcazar et al. May 2012 A1
20120124177 Sparks May 2012 A1
20120124178 Sparks May 2012 A1
20120128322 Shaffer et al. May 2012 A1
20120130709 Bocchieri et al. May 2012 A1
20120130995 Risvik et al. May 2012 A1
20120135714 King, II May 2012 A1
20120136529 Curtis et al. May 2012 A1
20120136572 Norton May 2012 A1
20120136649 Freising et al. 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
20120150532 Mirowski 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
20120166177 Beld et al. Jun 2012 A1
20120166196 Ju et al. Jun 2012 A1
20120166429 Moore et al. Jun 2012 A1
20120166942 Ramerth et al. Jun 2012 A1
20120166959 Hilerio et al. Jun 2012 A1
20120166998 Cotterill 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
20120176255 Choi et al. Jul 2012 A1
20120179457 Newman et al. Jul 2012 A1
20120179467 Williams et al. Jul 2012 A1
20120179471 Newman et al. 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
20120203767 Williams et al. Aug 2012 A1
20120209454 Miller et al. Aug 2012 A1
20120209654 Romagnino 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
20120210378 Mccoy et al. Aug 2012 A1
20120214141 Raya et al. Aug 2012 A1
20120214517 Singh et al. Aug 2012 A1
20120215640 Ramer 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 Sep 2012 A1
20120233207 Mohajer Sep 2012 A1
20120233266 Hassan et al. Sep 2012 A1
20120233280 Ebara Sep 2012 A1
20120239403 Cano 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
20120253791 Heck 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
20120262296 Bezar Oct 2012 A1
20120265482 Grokop et al. Oct 2012 A1
20120265528 Gruber et al. Oct 2012 A1
20120265535 Bryant-Rich et al. Oct 2012 A1
20120265787 Hsu 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
20120278744 Kozitsyn et al. Nov 2012 A1
20120278812 Wang 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
20120290657 Parks et al. Nov 2012 A1
20120290680 Hwang Nov 2012 A1
20120295708 Hernandez-Abrego et al. Nov 2012 A1
20120296638 Patwa Nov 2012 A1
20120296649 Bansal et al. Nov 2012 A1
20120296654 Hendrickson et al. Nov 2012 A1
20120296891 Rangan Nov 2012 A1
20120297341 Glazer et al. 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
20120304239 Shahraray 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
20120316774 Yariv 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
20120316955 Panguluri Dec 2012 A1
20120317194 Tian Dec 2012 A1
20120317498 Logan et al. Dec 2012 A1
20120321112 Schubert et al. Dec 2012 A1
20120323560 Perez Cortes et al. Dec 2012 A1
20120324391 Tocci 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
20130007240 Qiu et al. Jan 2013 A1
20130007648 Gamon et al. Jan 2013 A1
20130009858 Lacey Jan 2013 A1
20130010575 He et al. Jan 2013 A1
20130013313 Shechtman et al. Jan 2013 A1
20130013319 Grant et al. Jan 2013 A1
20130014026 Beringer et al. Jan 2013 A1
20130018659 Chi Jan 2013 A1
20130018863 Regan et al. Jan 2013 A1
20130024277 Tuchman et al. Jan 2013 A1
20130024576 Dishneau et al. Jan 2013 A1
20130027875 Zhu et al. Jan 2013 A1
20130028404 Omalley et al. Jan 2013 A1
20130030787 Cancedda et al. Jan 2013 A1
20130030789 Dalce Jan 2013 A1
20130030804 Zavaliagkos et al. Jan 2013 A1
20130030815 Madhvanath et al. Jan 2013 A1
20130030904 Aidasani et al. Jan 2013 A1
20130030913 Zhu 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
20130047178 Moon 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
20130054631 Govani et al. 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
20130060571 Soemo et al. Mar 2013 A1
20130061139 Mahkovec et al. Mar 2013 A1
20130063611 Papakipos et al. Mar 2013 A1
20130066832 Sheehan et al. Mar 2013 A1
20130067307 Tian et al. Mar 2013 A1
20130067312 Rose Mar 2013 A1
20130067421 Osman et al. Mar 2013 A1
20130069769 Pennington et al. Mar 2013 A1
20130073286 Bastea-Forte et al. Mar 2013 A1
20130073293 Jang et al. Mar 2013 A1
20130073346 Chun et al. Mar 2013 A1
20130073580 Mehanna et al. Mar 2013 A1
20130073676 Cockcroft Mar 2013 A1
20130078930 Chen 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
20130080178 Kang et al. 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
20130086609 Levy 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
20130096911 Beaufort et al. Apr 2013 A1
20130096917 Edgar et al. Apr 2013 A1
20130097566 Berglund Apr 2013 A1
20130097682 Zeljkovic et al. Apr 2013 A1
20130100017 Papakipos 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
20130107053 Ozaki 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
20130111365 Chen 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
20130124672 Pan May 2013 A1
20130125168 Agnihotri et al. May 2013 A1
20130132081 Ryu 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
20130138440 Strope 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 Jun 2013 A1
20130151258 Chandrasekar et al. Jun 2013 A1
20130151339 Kim et al. Jun 2013 A1
20130152092 Yadgar Jun 2013 A1
20130154811 Ferren et al. Jun 2013 A1
20130155948 Pinheiro et al. Jun 2013 A1
20130156198 Kim et al. Jun 2013 A1
20130157629 Lee et al. Jun 2013 A1
20130158977 Senior Jun 2013 A1
20130159847 Banke et al. Jun 2013 A1
20130159861 Rottier et al. Jun 2013 A1
20130165232 Nelson et al. Jun 2013 A1
20130166278 James et al. Jun 2013 A1
20130166303 Chang et al. Jun 2013 A1
20130166332 Hammad Jun 2013 A1
20130166442 Nakajima et al. Jun 2013 A1
20130167242 Paliwal Jun 2013 A1
20130170738 Capuozzo et al. Jul 2013 A1
20130172022 Seymour et al. Jul 2013 A1
20130173258 Liu et al. Jul 2013 A1
20130173268 Weng et al. Jul 2013 A1
20130173513 Chu et al. Jul 2013 A1
20130174034 Brown et al. Jul 2013 A1
20130176147 Anderson et al. Jul 2013 A1
20130176244 Yamamoto et al. Jul 2013 A1
20130176592 Sasaki Jul 2013 A1
20130179168 Bae et al. Jul 2013 A1
20130179172 Nakamura et al. Jul 2013 A1
20130179440 Gordon Jul 2013 A1
20130183942 Novick et al. Jul 2013 A1
20130183944 Mozer et al. Jul 2013 A1
20130185059 Riccardi Jul 2013 A1
20130185066 Tzirkel-hancock 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
20130187857 Griffin et al. Jul 2013 A1
20130190021 Vieri et al. Jul 2013 A1
20130191117 Atti et al. Jul 2013 A1
20130191408 Volkert Jul 2013 A1
20130197911 Wei et al. Aug 2013 A1
20130197914 Yelvington et al. Aug 2013 A1
20130198159 Hendry Aug 2013 A1
20130198841 Poulson Aug 2013 A1
20130204813 Master et al. Aug 2013 A1
20130204897 McDougall Aug 2013 A1
20130204967 Seo et al. Aug 2013 A1
20130207898 Sullivan et al. Aug 2013 A1
20130210410 Xu Aug 2013 A1
20130210492 You et al. Aug 2013 A1
20130218553 Fujii et al. Aug 2013 A1
20130218560 Hsiao et al. Aug 2013 A1
20130218574 Falcon et al. Aug 2013 A1
20130218899 Raghavan et al. Aug 2013 A1
20130219333 Palwe et al. Aug 2013 A1
20130222249 Pasquero et al. Aug 2013 A1
20130225128 Gomar Aug 2013 A1
20130226935 Bai et al. Aug 2013 A1
20130231917 Naik Sep 2013 A1
20130234947 Kristensson et al. Sep 2013 A1
20130235987 Arroniz-Escobar Sep 2013 A1
20130238326 Kim et al. Sep 2013 A1
20130238647 Thompson Sep 2013 A1
20130238729 Holzman et al. Sep 2013 A1
20130244615 Miller 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
20130262168 Makanawala et al. Oct 2013 A1
20130268263 Park et al. Oct 2013 A1
20130268956 Recco Oct 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
20130279724 Stafford et al. Oct 2013 A1
20130282709 Zhu et al. Oct 2013 A1
20130283168 Brown et al. Oct 2013 A1
20130283199 Selig et al. Oct 2013 A1
20130283283 Wang et al. Oct 2013 A1
20130285913 Griffin et al. Oct 2013 A1
20130289991 Eshwar et al. Oct 2013 A1
20130289993 Rao Oct 2013 A1
20130289994 Newman et al. Oct 2013 A1
20130291015 Pan Oct 2013 A1
20130297078 Kolavennu Nov 2013 A1
20130297198 Velde et al. Nov 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
20130300648 Kim et al. 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
20130311179 Wagner Nov 2013 A1
20130311184 Badavne et al. Nov 2013 A1
20130311487 Moore et al. Nov 2013 A1
20130311997 Gruber et al. Nov 2013 A1
20130315038 Ferren et al. Nov 2013 A1
20130316679 Miller et al. Nov 2013 A1
20130316746 Miller et al. Nov 2013 A1
20130317921 Havas Nov 2013 A1
20130318478 Ogura Nov 2013 A1
20130321267 Bhatti et al. Dec 2013 A1
20130322634 Bennett et al. Dec 2013 A1
20130322665 Bennett et al. Dec 2013 A1
20130325340 Forstall 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
20130325480 Lee 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
20130325970 Roberts et al. Dec 2013 A1
20130325979 Mansfield et al. Dec 2013 A1
20130328809 Smith 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
20130332172 Prakash et al. Dec 2013 A1
20130332400 González Dec 2013 A1
20130332538 Clark et al. Dec 2013 A1
20130339256 Shroff Dec 2013 A1
20130339454 Walker et al. Dec 2013 A1
20130339991 Ricci Dec 2013 A1
20130342672 Gray et al. Dec 2013 A1
20130343584 Bennett et al. Dec 2013 A1
20130343721 Abecassis Dec 2013 A1
20130346065 Davidson et al. Dec 2013 A1
20130346068 Solem et al. Dec 2013 A1
20130346347 Patterson et al. Dec 2013 A1
20130347018 Limp et al. Dec 2013 A1
20130347029 Tang et al. Dec 2013 A1
20130347102 Shi Dec 2013 A1
20130347117 Parks et al. Dec 2013 A1
20140001255 Anthoine Jan 2014 A1
20140002338 Raffa et al. Jan 2014 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
20140006483 Garmark et al. Jan 2014 A1
20140006496 Dearman et al. Jan 2014 A1
20140006562 Handa et al. Jan 2014 A1
20140006947 Garmark et al. Jan 2014 A1
20140006955 Greenzeiger et al. Jan 2014 A1
20140008163 Mikonaho et al. Jan 2014 A1
20140012574 Pasupalak et al. Jan 2014 A1
20140012580 Ganong, III et al. Jan 2014 A1
20140012586 Rubin et al. Jan 2014 A1
20140012587 Park Jan 2014 A1
20140019116 Lundberg et al. Jan 2014 A1
20140019133 Bao et al. Jan 2014 A1
20140019460 Sambrani et al. Jan 2014 A1
20140028029 Jochman Jan 2014 A1
20140028477 Michalske 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
20140037075 Bouzid et al. Feb 2014 A1
20140039888 Taubman et al. Feb 2014 A1
20140039893 Weiner et al. Feb 2014 A1
20140039894 Shostak Feb 2014 A1
20140040274 Aravamudan et al. Feb 2014 A1
20140040748 Lemay et al. Feb 2014 A1
20140040754 Donelli Feb 2014 A1
20140040801 Patel et al. Feb 2014 A1
20140040918 Li Feb 2014 A1
20140040961 Green et al. Feb 2014 A1
20140046934 Zhou et al. Feb 2014 A1
20140047001 Phillips et al. Feb 2014 A1
20140052451 Cheong et al. Feb 2014 A1
20140052680 Nitz et al. Feb 2014 A1
20140052791 Chakra et al. Feb 2014 A1
20140053082 Park Feb 2014 A1
20140053101 Buehler 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
20140067738 Kingsbury Mar 2014 A1
20140068751 Last Mar 2014 A1
20140074454 Brown 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
20140074483 Van Os Mar 2014 A1
20140074589 Nielsen et al. Mar 2014 A1
20140074815 Plimton Mar 2014 A1
20140075453 Bellessort et al. Mar 2014 A1
20140078065 Akkok Mar 2014 A1
20140079195 Srivastava et al. Mar 2014 A1
20140080410 Jung et al. Mar 2014 A1
20140080428 Rhoads et al. Mar 2014 A1
20140081619 Solntseva et al. Mar 2014 A1
20140081633 Badaskar Mar 2014 A1
20140081635 Yanagihara Mar 2014 A1
20140081829 Milne Mar 2014 A1
20140081941 Bai et al. Mar 2014 A1
20140082500 Wilensky et al. Mar 2014 A1
20140082501 Bae et al. Mar 2014 A1
20140082545 Zhai et al. Mar 2014 A1
20140082715 Grajek et al. Mar 2014 A1
20140086458 Rogers Mar 2014 A1
20140087711 Geyer et al. Mar 2014 A1
20140088952 Fife et al. Mar 2014 A1
20140088961 Woodward et al. Mar 2014 A1
20140088964 Bellegarda Mar 2014 A1
20140088970 Kang Mar 2014 A1
20140095171 Lynch et al. Apr 2014 A1
20140095172 Cabaco et al. Apr 2014 A1
20140095173 Lynch et al. Apr 2014 A1
20140095601 Abuelsaad et al. Apr 2014 A1
20140095965 Li Apr 2014 A1
20140096209 Saraf et al. Apr 2014 A1
20140098247 Rao et al. Apr 2014 A1
20140100847 Ishii et al. Apr 2014 A1
20140101127 Simhon et al. Apr 2014 A1
20140104175 Ouyang et al. Apr 2014 A1
20140108017 Mason et al. Apr 2014 A1
20140108391 Volkert Apr 2014 A1
20140112556 Kalinli-akbacak Apr 2014 A1
20140114554 Lagassey Apr 2014 A1
20140115062 Liu et al. Apr 2014 A1
20140115114 Garmark et al. Apr 2014 A1
20140118155 Bowers et al. May 2014 A1
20140118624 Jang et al. May 2014 A1
20140122059 Patel et al. May 2014 A1
20140122085 Piety et al. May 2014 A1
20140122086 Kapur et al. May 2014 A1
20140122136 Jayanthi May 2014 A1
20140122153 Truitt May 2014 A1
20140123022 Lee et al. May 2014 A1
20140129226 Lee et al. May 2014 A1
20140132935 Kim et al. May 2014 A1
20140134983 Jung et al. May 2014 A1
20140135036 Bonanni et al. May 2014 A1
20140136013 Wolverton 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
20140136987 Rodriguez May 2014 A1
20140142922 Liang et al. May 2014 A1
20140142923 Jones et al. May 2014 A1
20140142935 Lindahl et al. May 2014 A1
20140142953 Kim et al. May 2014 A1
20140143550 Ganong, III et al. May 2014 A1
20140143721 Suzuki et al. May 2014 A1
20140143784 Mistry et al. May 2014 A1
20140146200 Scott et al. May 2014 A1
20140149118 Lee et al. May 2014 A1
20140152577 Yuen et al. Jun 2014 A1
20140153709 Byrd et al. Jun 2014 A1
20140155031 Lee et al. Jun 2014 A1
20140156262 Yuen et al. Jun 2014 A1
20140156279 Okamoto et al. Jun 2014 A1
20140157319 Kimura 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
20140163962 Castelli et al. Jun 2014 A1
20140163976 Park 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
20140164305 Lynch et al. Jun 2014 A1
20140164312 Lynch et al. Jun 2014 A1
20140164476 Thomson Jun 2014 A1
20140164508 Lynch et al. Jun 2014 A1
20140164532 Lynch et al. Jun 2014 A1
20140164533 Lynch et al. Jun 2014 A1
20140164953 Lynch et al. Jun 2014 A1
20140169795 Clough Jun 2014 A1
20140171064 Das Jun 2014 A1
20140172878 Clark et al. Jun 2014 A1
20140173460 Kim Jun 2014 A1
20140176814 Ahn Jun 2014 A1
20140179295 Luebbers et al. Jun 2014 A1
20140180499 Cooper et al. Jun 2014 A1
20140180689 Kim Jun 2014 A1
20140180697 Torok et al. Jun 2014 A1
20140181865 Koganei Jun 2014 A1
20140188460 Ouyang et al. Jul 2014 A1
20140188477 Zhang Jul 2014 A1
20140188478 Zhang Jul 2014 A1
20140188485 Kim et al. Jul 2014 A1
20140188835 Zhang et al. Jul 2014 A1
20140195226 Yun et al. Jul 2014 A1
20140195230 Han et al. Jul 2014 A1
20140195233 Bapat et al. 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
20140205076 Kumar et al. Jul 2014 A1
20140207439 Venkatapathy et al. Jul 2014 A1
20140207446 Klein et al. Jul 2014 A1
20140207447 Jiang et al. Jul 2014 A1
20140207466 Smadi Jul 2014 A1
20140207468 Bartnik Jul 2014 A1
20140207582 Flinn et al. Jul 2014 A1
20140211944 Hayward et al. Jul 2014 A1
20140214429 Pantel Jul 2014 A1
20140214537 Yoo et al. Jul 2014 A1
20140215367 Kim et al. Jul 2014 A1
20140215513 Ramer et al. Jul 2014 A1
20140218372 Missig et al. Aug 2014 A1
20140222435 Li et al. Aug 2014 A1
20140222436 Binder et al. Aug 2014 A1
20140222678 Sheets et al. Aug 2014 A1
20140222967 Harrang et al. Aug 2014 A1
20140223377 Shaw et al. Aug 2014 A1
20140223481 Fundament Aug 2014 A1
20140226503 Cooper et al. Aug 2014 A1
20140229158 Zweig et al. Aug 2014 A1
20140229184 Shires Aug 2014 A1
20140230055 Boehl Aug 2014 A1
20140232570 Skinder et al. 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
20140237366 Poulos et al. Aug 2014 A1
20140244248 Arisoy et al. Aug 2014 A1
20140244249 Mohamed 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
20140244266 Brown et al. Aug 2014 A1
20140244268 Abdelsamie et al. Aug 2014 A1
20140244270 Han 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
20140249812 Bou-Ghazale et al. Sep 2014 A1
20140249816 Pickering 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
20140257809 Goel et al. Sep 2014 A1
20140257815 Zhao et al. Sep 2014 A1
20140257902 Moore et al. Sep 2014 A1
20140258324 Mauro et al. Sep 2014 A1
20140258357 Singh et al. Sep 2014 A1
20140258857 Dykstra-Erickson et al. Sep 2014 A1
20140258905 Lee et al. Sep 2014 A1
20140267022 Kim Sep 2014 A1
20140267599 Drouin et al. Sep 2014 A1
20140267933 Young Sep 2014 A1
20140272821 Pitschel et al. Sep 2014 A1
20140273979 Van Os et al. Sep 2014 A1
20140274005 Luna et al. Sep 2014 A1
20140274203 Ganong, III et al. Sep 2014 A1
20140274211 Sejnoha et al. Sep 2014 A1
20140278051 Mcgavran 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
20140278426 Jost et al. Sep 2014 A1
20140278429 Ganong, III Sep 2014 A1
20140278435 Ganong, III et al. Sep 2014 A1
20140278436 Khanna et al. Sep 2014 A1
20140278438 Hart et al. Sep 2014 A1
20140278443 Gunn et al. Sep 2014 A1
20140278444 Larson et al. Sep 2014 A1
20140278513 Prakash et al. Sep 2014 A1
20140279622 Lamoureux et al. Sep 2014 A1
20140279739 Elkington et al. Sep 2014 A1
20140279787 Cheng et al. Sep 2014 A1
20140280072 Coleman Sep 2014 A1
20140280107 Heymans 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
20140281944 Winer Sep 2014 A1
20140281983 Xian et al. Sep 2014 A1
20140281997 Fleizach et al. Sep 2014 A1
20140282003 Gruber et al. Sep 2014 A1
20140282007 Fleizach Sep 2014 A1
20140282045 Ayanam et al. Sep 2014 A1
20140282178 Borzello et al. Sep 2014 A1
20140282201 Pasquero et al. Sep 2014 A1
20140282203 Pasquero et al. Sep 2014 A1
20140282559 Verduzco 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
20140298395 Yang et al. Oct 2014 A1
20140304086 Dasdan et al. Oct 2014 A1
20140304605 Ohmura et al. Oct 2014 A1
20140309990 Gandrabur et al. Oct 2014 A1
20140309996 Zhang Oct 2014 A1
20140310001 Kains et al. Oct 2014 A1
20140310002 Nitz et al. Oct 2014 A1
20140310348 Keskitalo et al. Oct 2014 A1
20140310365 Sample et al. Oct 2014 A1
20140310595 Acharya et al. Oct 2014 A1
20140313007 Harding Oct 2014 A1
20140315492 Woods Oct 2014 A1
20140316585 Boesveld et al. Oct 2014 A1
20140317030 Shen et al. Oct 2014 A1
20140317502 Brown et al. Oct 2014 A1
20140324429 Weilhammer et al. Oct 2014 A1
20140324884 Lindahl et al. Oct 2014 A1
20140330569 Kolavennu et al. Nov 2014 A1
20140330951 Sukoff et al. Nov 2014 A1
20140335823 Heredia et al. Nov 2014 A1
20140337037 Chi Nov 2014 A1
20140337048 Brown et al. Nov 2014 A1
20140337266 Wolverton et al. Nov 2014 A1
20140337370 Aravamudan 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
20140343834 Demerchant et al. Nov 2014 A1
20140343943 Al-telmissani Nov 2014 A1
20140343946 Torok et al. Nov 2014 A1
20140344205 Luna et al. Nov 2014 A1
20140344627 Schaub et al. Nov 2014 A1
20140344687 Durham et al. Nov 2014 A1
20140347181 Luna et al. Nov 2014 A1
20140350847 Ichinokawa 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
20140358549 O'connor et al. Dec 2014 A1
20140359637 Yan Dec 2014 A1
20140359709 Nassar et al. Dec 2014 A1
20140361973 Raux et al. Dec 2014 A1
20140363074 Dolfing et al. Dec 2014 A1
20140364149 Marti 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
20140365505 Clark et al. Dec 2014 A1
20140365880 Bellegarda Dec 2014 A1
20140365885 Carson et al. Dec 2014 A1
20140365895 Magahern et al. Dec 2014 A1
20140365922 Yang Dec 2014 A1
20140365945 Karunamuni 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
20140372468 Collins et al. Dec 2014 A1
20140372931 Zhai et al. Dec 2014 A1
20140379334 Fry Dec 2014 A1
20140379341 Seo et al. Dec 2014 A1
20140379798 Bunner et al. Dec 2014 A1
20140380285 Gabel et al. Dec 2014 A1
20150003797 Schmidt Jan 2015 A1
20150004958 Wang et al. Jan 2015 A1
20150006148 Goldszmit et al. Jan 2015 A1
20150006157 Silva et al. Jan 2015 A1
20150006167 Kato 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
20150019944 Kalgi Jan 2015 A1
20150019974 Doi et al. Jan 2015 A1
20150025405 Vairavan et al. Jan 2015 A1
20150025890 Jagatheesan et al. Jan 2015 A1
20150026620 Kwon et al. Jan 2015 A1
20150027178 Scalisi Jan 2015 A1
20150031416 Labowicz et al. Jan 2015 A1
20150032443 Karov et al. Jan 2015 A1
20150033219 Breiner et al. Jan 2015 A1
20150033275 Natani et al. Jan 2015 A1
20150034855 Shen Feb 2015 A1
20150038161 Jakobson et al. Feb 2015 A1
20150039292 Suleman et al. Feb 2015 A1
20150039295 Soschen Feb 2015 A1
20150039299 Weinstein et al. Feb 2015 A1
20150039305 Huang Feb 2015 A1
20150039606 Salaka et al. Feb 2015 A1
20150040012 Faaborg et al. Feb 2015 A1
20150045003 Vora et al. Feb 2015 A1
20150045007 Cash Feb 2015 A1
20150045068 Softer et al. Feb 2015 A1
20150046434 Lim et al. Feb 2015 A1
20150046537 Rakib Feb 2015 A1
20150046828 Desai et al. Feb 2015 A1
20150050633 Christmas et al. Feb 2015 A1
20150050923 Tu et al. Feb 2015 A1
20150051754 Kwon et al. Feb 2015 A1
20150053779 Adamek et al. Feb 2015 A1
20150053781 Nelson et al. Feb 2015 A1
20150055879 Yang Feb 2015 A1
20150058013 Pakhomov et al. Feb 2015 A1
20150058018 Georges et al. Feb 2015 A1
20150058720 Smadja et al. Feb 2015 A1
20150058785 Ookawara Feb 2015 A1
20150065149 Russell et al. Mar 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
20150066817 Slayton et al. Mar 2015 A1
20150067485 Kim et al. Mar 2015 A1
20150067822 Randall Mar 2015 A1
20150071121 Patil et al. Mar 2015 A1
20150073788 Sak et al. Mar 2015 A1
20150073804 Senior et al. Mar 2015 A1
20150074524 Nicholson et al. Mar 2015 A1
20150074615 Han et al. Mar 2015 A1
20150081295 Yun et al. Mar 2015 A1
20150082229 Ouyang et al. Mar 2015 A1
20150086174 Abecassis et al. Mar 2015 A1
20150088511 Bharadwaj et al. Mar 2015 A1
20150088514 Typrin Mar 2015 A1
20150088518 Kim et al. Mar 2015 A1
20150088522 Hendrickson et al. Mar 2015 A1
20150088523 Schuster Mar 2015 A1
20150088998 Isensee et al. Mar 2015 A1
20150092520 Robison et al. Apr 2015 A1
20150094834 Vega et al. Apr 2015 A1
20150095031 Conkie et al. Apr 2015 A1
20150095268 Greenzeiger et al. Apr 2015 A1
20150095278 Flinn et al. Apr 2015 A1
20150100144 Lee et al. Apr 2015 A1
20150100313 Sharma 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
20150106737 Montoy-Wilson et al. Apr 2015 A1
20150113407 Hoffert et al. Apr 2015 A1
20150113435 Phillips Apr 2015 A1
20150120296 Stern et al. Apr 2015 A1
20150120641 Soon-shiong 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
20150127337 Heigold et al. May 2015 A1
20150127348 Follis May 2015 A1
20150127350 Agiomyrgiannakis May 2015 A1
20150133049 Lee et al. May 2015 A1
20150133109 Freeman et al. May 2015 A1
20150134318 Cuthbert et al. May 2015 A1
20150134322 Cuthbert 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
20150141150 Zha 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
20150143419 Bhagwat et al. May 2015 A1
20150148013 Baldwin et al. May 2015 A1
20150149177 Kains et al. May 2015 A1
20150149182 Kains et al. May 2015 A1
20150149354 Mccoy May 2015 A1
20150149469 Xu et al. May 2015 A1
20150149899 Bernstein et al. May 2015 A1
20150149964 Bernstein et al. May 2015 A1
20150154001 Knox et al. Jun 2015 A1
20150154185 Waibel Jun 2015 A1
20150154976 Mutagi Jun 2015 A1
20150160855 Bi Jun 2015 A1
20150161291 Gur et al. Jun 2015 A1
20150161370 North et al. Jun 2015 A1
20150161521 Shah et al. Jun 2015 A1
20150161989 Hsu et al. Jun 2015 A1
20150162001 Kar et al. Jun 2015 A1
20150162006 Kummer Jun 2015 A1
20150163558 Wheatley Jun 2015 A1
20150169081 Neels et al. Jun 2015 A1
20150169284 Quast et al. Jun 2015 A1
20150169336 Harper et al. Jun 2015 A1
20150169696 Krishnappa et al. Jun 2015 A1
20150170073 Baker Jun 2015 A1
20150170664 Doherty et al. Jun 2015 A1
20150172262 Ortiz, Jr. et al. Jun 2015 A1
20150172463 Quast et al. Jun 2015 A1
20150178388 Winnemoeller et al. Jun 2015 A1
20150178785 Salonen Jun 2015 A1
20150179176 Ryu et al. Jun 2015 A1
20150181285 Zhang et al. Jun 2015 A1
20150185964 Stout Jul 2015 A1
20150185996 Brown et al. Jul 2015 A1
20150186012 Coleman et al. Jul 2015 A1
20150186110 Kannan Jul 2015 A1
20150186154 Brown et al. Jul 2015 A1
20150186155 Brown et al. Jul 2015 A1
20150186156 Brown et al. Jul 2015 A1
20150186351 Hicks et al. Jul 2015 A1
20150186538 Yan et al. Jul 2015 A1
20150186783 Byrne 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
20150194165 Faaborg 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
20150201077 Konig et al. Jul 2015 A1
20150205425 Kuscher et al. Jul 2015 A1
20150205568 Matsuoka Jul 2015 A1
20150205858 Xie et al. Jul 2015 A1
20150206529 Kwon et al. Jul 2015 A1
20150208226 Kuusilinna et al. Jul 2015 A1
20150212791 Kumar et al. Jul 2015 A1
20150213140 Volkert Jul 2015 A1
20150213796 Waltermann et al. Jul 2015 A1
20150215258 Nowakowski et al. Jul 2015 A1
20150215350 Slayton et al. Jul 2015 A1
20150220264 Lewis et al. Aug 2015 A1
20150220507 Mohajer et al. Aug 2015 A1
20150220715 Kim et al. Aug 2015 A1
20150220972 Subramanya et al. Aug 2015 A1
20150221304 Stewart Aug 2015 A1
20150221307 Shah et al. Aug 2015 A1
20150222586 Ebersman et al. Aug 2015 A1
20150227505 Morimoto Aug 2015 A1
20150227633 Shapira Aug 2015 A1
20150228274 Leppanen et al. Aug 2015 A1
20150228275 Watanabe et al. Aug 2015 A1
20150228281 Raniere Aug 2015 A1
20150228283 Ehsani et al. Aug 2015 A1
20150228292 Goldstein et al. Aug 2015 A1
20150230095 Smith et al. Aug 2015 A1
20150234636 Barnes, Jr. Aug 2015 A1
20150234800 Patrick et al. Aug 2015 A1
20150237301 Shi et al. Aug 2015 A1
20150242091 Lu et al. Aug 2015 A1
20150242385 Bao et al. Aug 2015 A1
20150243278 Kibre et al. Aug 2015 A1
20150243279 Morse et al. Aug 2015 A1
20150243283 Halash et al. Aug 2015 A1
20150244665 Choi 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
20150253146 Annapureddy et al. Sep 2015 A1
20150253885 Kagan 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
20150261298 Li Sep 2015 A1
20150261496 Faaborg et al. Sep 2015 A1
20150261850 Mittal Sep 2015 A1
20150269139 McAteer et al. Sep 2015 A1
20150269617 Mikurak Sep 2015 A1
20150269677 Milne Sep 2015 A1
20150269943 VanBlon et al. Sep 2015 A1
20150277574 Jain et al. Oct 2015 A1
20150278348 Paruchuri et al. Oct 2015 A1
20150278370 Stratvert et al. Oct 2015 A1
20150278737 Chen Huebscher et al. Oct 2015 A1
20150279358 Kingsbury et al. Oct 2015 A1
20150279360 Mengibar et al. Oct 2015 A1
20150279366 Krestnikov et al. Oct 2015 A1
20150281380 Wang et al. Oct 2015 A1
20150281401 Le et al. Oct 2015 A1
20150286627 Chang et al. Oct 2015 A1
20150286716 Snibbe et al. Oct 2015 A1
20150286937 Hildebrand Oct 2015 A1
20150287401 Lee et al. Oct 2015 A1
20150287409 Jang Oct 2015 A1
20150287411 Kojima et al. Oct 2015 A1
20150288629 Choi et al. Oct 2015 A1
20150294086 Kare et al. Oct 2015 A1
20150294377 Chow Oct 2015 A1
20150294516 Chiang Oct 2015 A1
20150294670 Roblek et al. Oct 2015 A1
20150295915 Xiu Oct 2015 A1
20150301796 Visser et al. 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
20150310114 Ryger 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
20150310888 Chen Oct 2015 A1
20150312182 Langholz Oct 2015 A1
20150312409 Czarnecki et al. Oct 2015 A1
20150314454 Breazeal et al. Nov 2015 A1
20150317069 Clements et al. Nov 2015 A1
20150317310 Eiche et al. Nov 2015 A1
20150319411 Kasmir 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
20150334346 Cheatham, III et al. Nov 2015 A1
20150339049 Kasemset et al. Nov 2015 A1
20150339391 Kang et al. Nov 2015 A1
20150340033 Di Fabbrizio 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
20150346845 Di Censo et al. Dec 2015 A1
20150347086 Liedholm et al. Dec 2015 A1
20150347381 Bellegarda Dec 2015 A1
20150347382 Dolfing et al. Dec 2015 A1
20150347383 Willmore et al. Dec 2015 A1
20150347385 Flor et al. Dec 2015 A1
20150347393 Futrell et al. Dec 2015 A1
20150347552 Habouzit et al. Dec 2015 A1
20150347733 Tsou et al. Dec 2015 A1
20150347985 Gross et al. Dec 2015 A1
20150348533 Saddler et al. Dec 2015 A1
20150348547 Paulik et al. Dec 2015 A1
20150348548 Piernot et al. Dec 2015 A1
20150348549 Giuli et al. Dec 2015 A1
20150348551 Gruber et al. Dec 2015 A1
20150348554 Orr et al. Dec 2015 A1
20150348555 Sugita Dec 2015 A1
20150348565 Rhoten et al. Dec 2015 A1
20150349934 Pollack et al. Dec 2015 A1
20150350031 Burks et al. Dec 2015 A1
20150350342 Thorpe et al. Dec 2015 A1
20150350594 Mate et al. Dec 2015 A1
20150352999 Bando et al. Dec 2015 A1
20150355879 Beckhardt et al. Dec 2015 A1
20150356410 Faith et al. Dec 2015 A1
20150363587 Ahn et al. Dec 2015 A1
20150364128 Zhao et al. Dec 2015 A1
20150364140 Thörn Dec 2015 A1
20150370531 Faaborg Dec 2015 A1
20150370780 Wang et al. Dec 2015 A1
20150370787 Akbacak et al. Dec 2015 A1
20150370884 Hurley et al. Dec 2015 A1
20150371215 Zhou et al. Dec 2015 A1
20150371529 Dolecki Dec 2015 A1
20150371639 Foerster et al. Dec 2015 A1
20150371663 Gustafson et al. Dec 2015 A1
20150371665 Naik et al. Dec 2015 A1
20150373183 Woolsey et al. Dec 2015 A1
20150379118 Wickenkamp et al. Dec 2015 A1
20150379414 Yeh et al. Dec 2015 A1
20150379993 Subhojit et al. Dec 2015 A1
20150381923 Wickenkamp et al. Dec 2015 A1
20150382047 Van Os et al. Dec 2015 A1
20150382079 Lister et al. Dec 2015 A1
20150382147 Clark et al. Dec 2015 A1
20160004690 Bangalore et al. Jan 2016 A1
20160005320 deCharms et al. Jan 2016 A1
20160012038 Edwards 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
20160018959 Yamashita et al. Jan 2016 A1
20160019886 Hong Jan 2016 A1
20160021414 Padi et al. 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
20160034042 Joo Feb 2016 A1
20160034811 Paulik et al. Feb 2016 A1
20160036953 Lee et al. Feb 2016 A1
20160041809 Clayton et al. Feb 2016 A1
20160042735 Vibbert et al. Feb 2016 A1
20160042748 Jain et al. Feb 2016 A1
20160043905 Fiedler Feb 2016 A1
20160048666 Dey et al. Feb 2016 A1
20160050254 Rao et al. Feb 2016 A1
20160055422 Li Feb 2016 A1
20160062605 Agarwal et al. Mar 2016 A1
20160063094 Udupa 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
20160071517 Beaver et al. Mar 2016 A1
20160071521 Haughay Mar 2016 A1
20160072940 Cronin Mar 2016 A1
20160077794 Kim et al. Mar 2016 A1
20160078860 Paulik et al. Mar 2016 A1
20160080165 Ehsani et al. Mar 2016 A1
20160080475 Singh et al. Mar 2016 A1
20160085295 Shimy et al. Mar 2016 A1
20160085827 Chadha et al. Mar 2016 A1
20160086116 Rao et al. Mar 2016 A1
20160086599 Kurata et al. Mar 2016 A1
20160088335 Zucchetta Mar 2016 A1
20160091967 Prokofieva et al. Mar 2016 A1
20160092434 Bellegarda Mar 2016 A1
20160092447 Pathurudeen et al. Mar 2016 A1
20160092766 Sainath 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
20160094700 Lee et al. Mar 2016 A1
20160094889 Venkataraman et al. Mar 2016 A1
20160094979 Naik et al. Mar 2016 A1
20160098991 Luo et al. Apr 2016 A1
20160098992 Renard et al. Apr 2016 A1
20160099892 Palakovich et al. Apr 2016 A1
20160099984 Karagiannis et al. Apr 2016 A1
20160104480 Sharifi Apr 2016 A1
20160104486 Penilla et al. Apr 2016 A1
20160111091 Bakish Apr 2016 A1
20160112746 Zhang et al. Apr 2016 A1
20160117386 Ajmera et al. Apr 2016 A1
20160118048 Heide Apr 2016 A1
20160119338 Cheyer Apr 2016 A1
20160125048 Hamada May 2016 A1
20160125071 Gabbai May 2016 A1
20160132046 Beoughter et al. May 2016 A1
20160132484 Nauze et al. May 2016 A1
20160132488 Clark et al. May 2016 A1
20160133254 Vogel et al. May 2016 A1
20160139662 Dabhade May 2016 A1
20160140951 Agiomyrgiannakis et al. May 2016 A1
20160140962 Sharifi May 2016 A1
20160147725 Patten et al. May 2016 A1
20160148610 Kennewick, Jr. et al. May 2016 A1
20160150020 Farmer et al. May 2016 A1
20160154624 Son et al. Jun 2016 A1
20160154880 Hoarty Jun 2016 A1
20160155442 Kannan et al. Jun 2016 A1
20160155443 Khan et al. Jun 2016 A1
20160156574 Hum 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
20160173617 Allinson Jun 2016 A1
20160173960 Snibbe et al. Jun 2016 A1
20160179462 Bjorkengren Jun 2016 A1
20160179464 Reddy et al. Jun 2016 A1
20160179787 Deleeuw Jun 2016 A1
20160180840 Siddiq et al. Jun 2016 A1
20160180844 Vanblon et al. Jun 2016 A1
20160182410 Janakiraman et al. Jun 2016 A1
20160182709 Kim et al. Jun 2016 A1
20160188181 Smith Jun 2016 A1
20160188738 Gruber et al. Jun 2016 A1
20160189717 Kannan et al. Jun 2016 A1
20160196110 Yehoshua et al. Jul 2016 A1
20160198319 Huang et al. Jul 2016 A1
20160203002 Kannan et al. Jul 2016 A1
20160210551 Lee et al. Jul 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
20160227107 Beaumont Aug 2016 A1
20160232500 Wang et al. Aug 2016 A1
20160239645 Heo et al. Aug 2016 A1
20160240187 Fleizach et al. Aug 2016 A1
20160240189 Lee et al. Aug 2016 A1
20160240192 Raghuvir Aug 2016 A1
20160247061 Trask et al. Aug 2016 A1
20160249319 Dotan-Cohen et al. Aug 2016 A1
20160253312 Rhodes Sep 2016 A1
20160253528 Gao et al. Sep 2016 A1
20160259623 Sumner et al. Sep 2016 A1
20160259656 Sumner et al. Sep 2016 A1
20160259779 Labský et al. Sep 2016 A1
20160260431 Newendorp et al. 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
20160274938 Strinati et al. Sep 2016 A1
20160275941 Bellegarda et al. Sep 2016 A1
20160275947 Li et al. Sep 2016 A1
20160282824 Smallwood et al. Sep 2016 A1
20160282956 Ouyang et al. Sep 2016 A1
20160283185 Mclaren et al. Sep 2016 A1
20160284005 Daniel et al. Sep 2016 A1
20160284199 Dotan-Cohen et al. Sep 2016 A1
20160285808 Franklin et al. Sep 2016 A1
20160286045 Shaltiel et al. Sep 2016 A1
20160293157 Chen et al. Oct 2016 A1
20160293168 Chen Oct 2016 A1
20160294755 Prabhu Oct 2016 A1
20160299685 Zhai et al. Oct 2016 A1
20160299882 Hegerty et al. Oct 2016 A1
20160299883 Zhu et al. Oct 2016 A1
20160299977 Hreha Oct 2016 A1
20160300571 Foerster et al. Oct 2016 A1
20160301639 Liu et al. Oct 2016 A1
20160307566 Bellegarda Oct 2016 A1
20160308799 Schubert et al. Oct 2016 A1
20160313906 Kilchenko et al. Oct 2016 A1
20160314788 Jitkoff et al. Oct 2016 A1
20160314789 Marcheret et al. Oct 2016 A1
20160314792 Alvarez et al. Oct 2016 A1
20160315996 Ha et al. Oct 2016 A1
20160317924 Tanaka et al. Nov 2016 A1
20160321239 Iso-Sipilä et al. Nov 2016 A1
20160321261 Spasojevic et al. Nov 2016 A1
20160321358 Kanani et al. Nov 2016 A1
20160322043 Bellegarda Nov 2016 A1
20160322044 Jung et al. Nov 2016 A1
20160322045 Hatfield et al. Nov 2016 A1
20160322048 Amano et al. Nov 2016 A1
20160322050 Wang et al. Nov 2016 A1
20160328147 Zhang et al. Nov 2016 A1
20160328205 Agrawal et al. Nov 2016 A1
20160328893 Cordova et al. Nov 2016 A1
20160329060 Ito et al. Nov 2016 A1
20160334973 Reckhow et al. Nov 2016 A1
20160335532 Sanghavi et al. Nov 2016 A1
20160336007 Hanazawa et al. Nov 2016 A1
20160336010 Lindahl Nov 2016 A1
20160336011 Koll et al. Nov 2016 A1
20160336024 Choi et al. Nov 2016 A1
20160337299 Lane et al. Nov 2016 A1
20160337301 Rollins et al. Nov 2016 A1
20160342317 Lim et al. Nov 2016 A1
20160342685 Basu et al. Nov 2016 A1
20160342781 Jeon Nov 2016 A1
20160350650 Leeman-Munk et al. Dec 2016 A1
20160351190 Piernot et al. Dec 2016 A1
20160352567 Robbins et al. Dec 2016 A1
20160357304 Hatori et al. Dec 2016 A1
20160357728 Bellegarda et al. Dec 2016 A1
20160357790 Elkington 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
20160360336 Gross et al. Dec 2016 A1
20160360382 Gross et al. Dec 2016 A1
20160364378 Futrell et al. Dec 2016 A1
20160365101 Foy et al. Dec 2016 A1
20160371250 Rhodes Dec 2016 A1
20160372112 Miller et al. Dec 2016 A1
20160372119 Sak et al. Dec 2016 A1
20160378747 Orr et al. Dec 2016 A1
20160379091 Lin et al. Dec 2016 A1
20160379626 Deisher et al. Dec 2016 A1
20160379632 Hoffmeister et al. Dec 2016 A1
20160379633 Lehman et al. Dec 2016 A1
20160379639 Weinstein et al. Dec 2016 A1
20160379641 Liu et al. Dec 2016 A1
20170003931 Dvortsov et al. Jan 2017 A1
20170004824 Yoo et al. Jan 2017 A1
20170005818 Gould Jan 2017 A1
20170011091 Chehreghani Jan 2017 A1
20170011303 Annapureddy et al. Jan 2017 A1
20170011742 Jing et al. Jan 2017 A1
20170013124 Havelka et al. Jan 2017 A1
20170013331 Watanabe et al. Jan 2017 A1
20170018271 Khan et al. Jan 2017 A1
20170019987 Dragone et al. Jan 2017 A1
20170023963 Davis Jan 2017 A1
20170025124 Mixter et al. Jan 2017 A1
20170026318 Daniel et al. Jan 2017 A1
20170026509 Rand Jan 2017 A1
20170031576 Saoji et al. Feb 2017 A1
20170032783 Lord et al. Feb 2017 A1
20170032787 Dayal Feb 2017 A1
20170032791 Elson et al. Feb 2017 A1
20170039283 Bennett et al. Feb 2017 A1
20170039475 Cheyer et al. Feb 2017 A1
20170040002 Basson et al. Feb 2017 A1
20170047063 Ohmura et al. Feb 2017 A1
20170053652 Choi et al. Feb 2017 A1
20170055895 Jardins et al. Mar 2017 A1
20170060853 Lee et al. Mar 2017 A1
20170061423 Bryant 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
20170069308 Aleksic et al. Mar 2017 A1
20170075653 Dawidowsky et al. Mar 2017 A1
20170076720 Gopalan et al. Mar 2017 A1
20170076721 Bargetzi et al. Mar 2017 A1
20170078490 Kaminsky et al. Mar 2017 A1
20170083179 Gruber et al. Mar 2017 A1
20170083285 Meyers et al. Mar 2017 A1
20170083504 Huang Mar 2017 A1
20170084277 Sharifi Mar 2017 A1
20170085547 De Aguiar et al. Mar 2017 A1
20170090569 Levesque Mar 2017 A1
20170091168 Bellegarda et al. Mar 2017 A1
20170091169 Bellegarda et al. Mar 2017 A1
20170091612 Gruber et al. Mar 2017 A1
20170092259 Jeon Mar 2017 A1
20170092270 Newendorp et al. Mar 2017 A1
20170092278 Evermann et al. Mar 2017 A1
20170093356 Cudak et al. Mar 2017 A1
20170102837 Toumpelis Apr 2017 A1
20170102915 Kuscher et al. Apr 2017 A1
20170103749 Zhao et al. Apr 2017 A1
20170105190 Logan et al. Apr 2017 A1
20170110117 Chakladar et al. Apr 2017 A1
20170116177 Walia Apr 2017 A1
20170116982 Gelfenbeyn et al. Apr 2017 A1
20170116989 Yadgar et al. Apr 2017 A1
20170124190 Wang et al. May 2017 A1
20170125016 Wang May 2017 A1
20170127124 Wilson et al. May 2017 A9
20170131778 Iyer May 2017 A1
20170132019 Karashchuk et al. May 2017 A1
20170132199 Vescovi et al. May 2017 A1
20170133007 Drewes May 2017 A1
20170140041 Dotan-Cohen May 2017 A1
20170140644 Hwang et al. May 2017 A1
20170140760 Sachdev May 2017 A1
20170147841 Stagg et al. May 2017 A1
20170148044 Fukuda et al. May 2017 A1
20170154033 Lee Jun 2017 A1
20170154055 Dimson et al. Jun 2017 A1
20170155940 Jin et al. Jun 2017 A1
20170161018 Lemay et al. Jun 2017 A1
20170161268 Badaskar Jun 2017 A1
20170161293 Ionescu et al. Jun 2017 A1
20170161393 Oh et al. Jun 2017 A1
20170162191 Grost et al. Jun 2017 A1
20170162203 Huang et al. Jun 2017 A1
20170169818 Vanblon et al. Jun 2017 A1
20170169819 Mese et al. Jun 2017 A1
20170177547 Ciereszko et al. Jun 2017 A1
20170178619 Naik et al. Jun 2017 A1
20170178620 Fleizach 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
20170185581 Bojja et al. Jun 2017 A1
20170186429 Giuli et al. Jun 2017 A1
20170187711 Joo et al. Jun 2017 A1
20170193083 Bhatt et al. Jul 2017 A1
20170195493 Sudarsan et al. Jul 2017 A1
20170195636 Child et al. Jul 2017 A1
20170199870 Zheng et al. Jul 2017 A1
20170199874 Patel et al. Jul 2017 A1
20170200066 Wang et al. Jul 2017 A1
20170201609 Salmenkaita et al. Jul 2017 A1
20170201613 Engelke et al. Jul 2017 A1
20170206899 Bryant et al. Jul 2017 A1
20170215052 Koum et al. Jul 2017 A1
20170221486 Kurata et al. Aug 2017 A1
20170223189 Meredith et al. Aug 2017 A1
20170227935 Su et al. Aug 2017 A1
20170228367 Pasupalak et al. Aug 2017 A1
20170228382 Haviv et al. Aug 2017 A1
20170230429 Garmark et al. Aug 2017 A1
20170230497 Kim et al. Aug 2017 A1
20170230709 Van Os et al. Aug 2017 A1
20170235361 Rigazio et al. Aug 2017 A1
20170235618 Lin et al. Aug 2017 A1
20170235721 Almosallam et al. Aug 2017 A1
20170236512 Williams et al. Aug 2017 A1
20170236514 Nelson Aug 2017 A1
20170238039 Sabattini Aug 2017 A1
20170242653 Lang et al. Aug 2017 A1
20170242657 Jarvis et al. Aug 2017 A1
20170243468 Dotan-Cohen et al. Aug 2017 A1
20170243576 Millington et al. Aug 2017 A1
20170243586 Civelli et al. Aug 2017 A1
20170256256 Wang et al. Sep 2017 A1
20170263247 Kang et al. Sep 2017 A1
20170263248 Gruber et al. Sep 2017 A1
20170263249 Akbacak et al. Sep 2017 A1
20170264451 Yu et al. Sep 2017 A1
20170264711 Natarajan et al. Sep 2017 A1
20170270912 Levit et al. Sep 2017 A1
20170278514 Mathias et al. Sep 2017 A1
20170285915 Napolitano et al. Oct 2017 A1
20170286397 Gonzalez Oct 2017 A1
20170287472 Ogawa et al. Oct 2017 A1
20170289305 Liensberger et al. Oct 2017 A1
20170295446 Shivappa Oct 2017 A1
20170308609 Berkhin et al. Oct 2017 A1
20170311005 Lin Oct 2017 A1
20170316775 Le et al. Nov 2017 A1
20170316782 Haughay Nov 2017 A1
20170319123 Voss et al. Nov 2017 A1
20170323637 Naik Nov 2017 A1
20170329466 Krenkler Nov 2017 A1
20170329490 Esinovskaya et al. Nov 2017 A1
20170329572 Shah et al. Nov 2017 A1
20170329630 Jann et al. Nov 2017 A1
20170330567 Van Wissen et al. Nov 2017 A1
20170337035 Choudhary et al. Nov 2017 A1
20170337478 Sarikaya et al. Nov 2017 A1
20170345411 Raitio et al. Nov 2017 A1
20170345420 Barnett, Jr. Nov 2017 A1
20170345429 Hardee et al. Nov 2017 A1
20170346949 Sanghavi et al. Nov 2017 A1
20170351487 Avilés-Casco et al. Dec 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
20170358302 Orr et al. Dec 2017 A1
20170358303 Walker, II et al. Dec 2017 A1
20170358304 Castillo et al. Dec 2017 A1
20170358305 Kudurshian et al. Dec 2017 A1
20170358317 James Dec 2017 A1
20170365251 Park et al. Dec 2017 A1
20170371509 Jung et al. Dec 2017 A1
20170371885 Aggarwal et al. Dec 2017 A1
20170374093 Dhar et al. Dec 2017 A1
20170374176 Agrawal et al. Dec 2017 A1
20180005112 Iso-Sipila et al. Jan 2018 A1
20180007060 Leblang et al. Jan 2018 A1
20180007096 Levin et al. Jan 2018 A1
20180007538 Naik et al. Jan 2018 A1
20180012596 Piernot et al. Jan 2018 A1
20180018248 Bhargava et al. Jan 2018 A1
20180024985 Asano Jan 2018 A1
20180033431 Newendorp et al. Feb 2018 A1
20180033436 Zhou Feb 2018 A1
20180047201 Filev et al. Feb 2018 A1
20180047406 Park Feb 2018 A1
20180052909 Sharifi et al. Feb 2018 A1
20180054505 Hart et al. Feb 2018 A1
20180060032 Boesen Mar 2018 A1
20180060301 Li et al. Mar 2018 A1
20180060312 Won Mar 2018 A1
20180061400 Carbune et al. Mar 2018 A1
20180061401 Sarikaya et al. Mar 2018 A1
20180062691 Barnett, Jr. Mar 2018 A1
20180063308 Crystal et al. Mar 2018 A1
20180063324 Van Meter, II Mar 2018 A1
20180063624 Boesen Mar 2018 A1
20180067904 Li Mar 2018 A1
20180067914 Chen et al. Mar 2018 A1
20180067918 Bellegarda et al. Mar 2018 A1
20180069743 Bakken et al. Mar 2018 A1
20180075847 Lee et al. Mar 2018 A1
20180088969 Vanblon et al. Mar 2018 A1
20180089166 Meyer et al. Mar 2018 A1
20180089588 Ravi et al. Mar 2018 A1
20180090143 Saddler et al. Mar 2018 A1
20180091847 Wu et al. Mar 2018 A1
20180096683 James et al. Apr 2018 A1
20180096690 Mixter et al. Apr 2018 A1
20180102914 Kawachi et al. Apr 2018 A1
20180107917 Hewavitharana et al. Apr 2018 A1
20180107945 Gao et al. Apr 2018 A1
20180108346 Paulik et al. Apr 2018 A1
20180113673 Sheynblat Apr 2018 A1
20180121432 Parson et al. May 2018 A1
20180122376 Kojima May 2018 A1
20180122378 Mixter et al. May 2018 A1
20180129967 Herreshoff May 2018 A1
20180130470 Lemay et al. May 2018 A1
20180130471 Trufinescu et al. May 2018 A1
20180137856 Gilbert May 2018 A1
20180137857 Zhou et al. May 2018 A1
20180137865 Ling May 2018 A1
20180143967 Anbazhagan et al. May 2018 A1
20180144615 Kinney et al. May 2018 A1
20180144746 Mishra et al. May 2018 A1
20180144748 Leong May 2018 A1
20180146089 Rauenbuehler et al. May 2018 A1
20180150744 Orr et al. May 2018 A1
20180157372 Kurabayashi Jun 2018 A1
20180157992 Susskind et al. Jun 2018 A1
20180158548 Taheri et al. Jun 2018 A1
20180166076 Higuchi et al. Jun 2018 A1
20180167884 Dawid et al. Jun 2018 A1
20180173403 Carbune et al. Jun 2018 A1
20180173542 Chan et al. Jun 2018 A1
20180174406 Arashi et al. Jun 2018 A1
20180174576 Soltau et al. Jun 2018 A1
20180174597 Lee et al. Jun 2018 A1
20180182376 Gysel et al. Jun 2018 A1
20180188840 Tamura et al. Jul 2018 A1
20180190273 Karimli et al. Jul 2018 A1
20180190279 Anderson et al. Jul 2018 A1
20180191670 Suyama Jul 2018 A1
20180196683 Radebaugh et al. Jul 2018 A1
20180210874 Fuxman et al. Jul 2018 A1
20180213448 Segal et al. Jul 2018 A1
20180218735 Hunt et al. Aug 2018 A1
20180225274 Tommy et al. Aug 2018 A1
20180232203 Gelfenbeyn et al. Aug 2018 A1
20180233140 Da et al. Aug 2018 A1
20180247065 Rhee et al. Aug 2018 A1
20180253209 Jaygarl et al. Sep 2018 A1
20180253652 Palzer et al. Sep 2018 A1
20180260680 Finkelstein et al. Sep 2018 A1
20180268106 Velaga Sep 2018 A1
20180270343 Rout et al. Sep 2018 A1
20180275839 Kocienda et al. Sep 2018 A1
20180276197 Nell et al. Sep 2018 A1
20180277113 Hartung et al. Sep 2018 A1
20180278740 Choi et al. Sep 2018 A1
20180285056 Cutler et al. Oct 2018 A1
20180293984 Lindahl Oct 2018 A1
20180293988 Huang et al. Oct 2018 A1
20180308477 Nagasaka Oct 2018 A1
20180308480 Jang et al. Oct 2018 A1
20180308485 Kudurshian et al. Oct 2018 A1
20180308486 Saddler et al. Oct 2018 A1
20180314552 Kim et al. Nov 2018 A1
20180315416 Berthelsen et al. Nov 2018 A1
20180322112 Bellegarda et al. Nov 2018 A1
20180322881 Min et al. Nov 2018 A1
20180329677 Gruber et al. Nov 2018 A1
20180329957 Frazzingaro et al. Nov 2018 A1
20180329982 Patel et al. Nov 2018 A1
20180329998 Thomson et al. Nov 2018 A1
20180330714 Paulik et al. Nov 2018 A1
20180330721 Thomson et al. Nov 2018 A1
20180330722 Newendorp et al. Nov 2018 A1
20180330723 Acero et al. Nov 2018 A1
20180330729 Golipour et al. Nov 2018 A1
20180330730 Garg et al. Nov 2018 A1
20180330731 Zeitlin et al. Nov 2018 A1
20180330733 Orr et al. Nov 2018 A1
20180330737 Paulik et al. Nov 2018 A1
20180332118 Phipps et al. Nov 2018 A1
20180336184 Bellegarda et al. Nov 2018 A1
20180336197 Skilling et al. Nov 2018 A1
20180336275 Graham et al. Nov 2018 A1
20180336439 Kliger et al. Nov 2018 A1
20180336449 Adan et al. Nov 2018 A1
20180336892 Kim et al. Nov 2018 A1
20180336894 Graham et al. Nov 2018 A1
20180336904 Piercy et al. Nov 2018 A1
20180336905 Kim et al. Nov 2018 A1
20180336920 Bastian et al. Nov 2018 A1
20180341643 Alders et al. Nov 2018 A1
20180343557 Naik et al. Nov 2018 A1
20180349084 Nagasaka et al. Dec 2018 A1
20180349346 Hatori et al. Dec 2018 A1
20180349349 Bellegarda et al. Dec 2018 A1
20180349447 Maccartney et al. Dec 2018 A1
20180349472 Kohlschuetter et al. Dec 2018 A1
20180350345 Naik Dec 2018 A1
20180350353 Gruber et al. Dec 2018 A1
20180357073 Johnson et al. Dec 2018 A1
20180357308 Cheyer Dec 2018 A1
20180358015 Cash et al. Dec 2018 A1
20180358019 Mont-Reynaud Dec 2018 A1
20180365653 Cleaver et al. Dec 2018 A1
20180366105 Kim Dec 2018 A1
20180373487 Gruber et al. Dec 2018 A1
20180374484 Huang et al. Dec 2018 A1
20190012141 Piersol et al. Jan 2019 A1
20190012449 Cheyer Jan 2019 A1
20190013018 Rekstad Jan 2019 A1
20190013025 Alcorn et al. Jan 2019 A1
20190014450 Gruber et al. Jan 2019 A1
20190019077 Griffin et al. Jan 2019 A1
20190027152 Huang et al. Jan 2019 A1
20190034040 Shah et al. Jan 2019 A1
20190034826 Ahmad et al. Jan 2019 A1
20190035405 Haughay Jan 2019 A1
20190042059 Baer Feb 2019 A1
20190042627 Osotio et al. Feb 2019 A1
20190043507 Huang et al. Feb 2019 A1
20190045040 Lee et al. Feb 2019 A1
20190051309 Kim et al. Feb 2019 A1
20190057697 Giuli et al. Feb 2019 A1
20190065144 Sumner et al. Feb 2019 A1
20190065993 Srinivasan et al. Feb 2019 A1
20190066674 Jaygarl et al. Feb 2019 A1
20190068810 Okamoto et al. Feb 2019 A1
20190073998 Leblang et al. Mar 2019 A1
20190074009 Kim et al. Mar 2019 A1
20190074015 Orr et al. Mar 2019 A1
20190074016 Orr et al. Mar 2019 A1
20190079476 Funes Mar 2019 A1
20190080685 Johnson, Jr. Mar 2019 A1
20190080698 Miller Mar 2019 A1
20190087412 Seyed Ibrahim et al. Mar 2019 A1
20190087455 He et al. Mar 2019 A1
20190095050 Gruber et al. Mar 2019 A1
20190095171 Carson et al. Mar 2019 A1
20190102378 Piernot et al. Apr 2019 A1
20190102381 Futrell et al. Apr 2019 A1
20190103103 Ni et al. Apr 2019 A1
20190103112 Walker et al. Apr 2019 A1
20190116264 Sanghavi et al. Apr 2019 A1
20190122666 Raitio et al. Apr 2019 A1
20190122692 Binder et al. Apr 2019 A1
20190124019 Leon et al. Apr 2019 A1
20190129615 Sundar et al. May 2019 A1
20190132694 Hanes et al. May 2019 A1
20190139541 Andersen et al. May 2019 A1
20190141494 Gross et al. May 2019 A1
20190147880 Booker et al. May 2019 A1
20190149972 Parks et al. May 2019 A1
20190156830 Devaraj et al. May 2019 A1
20190158994 Gross et al. May 2019 A1
20190164546 Piernot et al. May 2019 A1
20190172467 Kim et al. Jun 2019 A1
20190179607 Thangarathnam et al. Jun 2019 A1
20190179890 Evermann Jun 2019 A1
20190180770 Kothari et al. Jun 2019 A1
20190182176 Niewczas Jun 2019 A1
20190187787 White et al. Jun 2019 A1
20190188326 Daianu et al. Jun 2019 A1
20190188328 Oyenan Jun 2019 A1
20190189118 Piernot et al. Jun 2019 A1
20190189125 Van Os et al. Jun 2019 A1
20190197053 Graham et al. Jun 2019 A1
20190213999 Grupen et al. Jul 2019 A1
20190214024 Gruber et al. Jul 2019 A1
20190220245 Martel et al. Jul 2019 A1
20190220246 Orr et al. Jul 2019 A1
20190220247 Lemay et al. Jul 2019 A1
20190236130 Li et al. Aug 2019 A1
20190236459 Cheyer et al. Aug 2019 A1
20190244618 Newendorp et al. Aug 2019 A1
20190251339 Hawker Aug 2019 A1
20190251960 Maker et al. Aug 2019 A1
20190259386 Kudurshian et al. Aug 2019 A1
20190272825 O'Malley et al. Sep 2019 A1
20190272831 Kajarekar Sep 2019 A1
20190273963 Jobanputra et al. Sep 2019 A1
20190278841 Pusateri et al. Sep 2019 A1
20190287522 Lambourne et al. Sep 2019 A1
20190295544 Garcia et al. Sep 2019 A1
20190303442 Peitz et al. Oct 2019 A1
20190310765 Napolitano et al. Oct 2019 A1
20190318739 Garg et al. Oct 2019 A1
20190339784 Lemay et al. Nov 2019 A1
20190341027 Vescovi et al. Nov 2019 A1
20190341056 Paulik et al. Nov 2019 A1
20190347063 Liu et al. Nov 2019 A1
20190348022 Park et al. Nov 2019 A1
20190354548 Orr et al. Nov 2019 A1
20190355346 Bellegarda Nov 2019 A1
20190361729 Gruber et al. Nov 2019 A1
20190369748 Hindi et al. Dec 2019 A1
20190369842 Dolbakian et al. Dec 2019 A1
20190370292 Irani et al. Dec 2019 A1
20190370323 Davidson et al. Dec 2019 A1
20190371315 Newendorp et al. Dec 2019 A1
20190371316 Weinstein et al. Dec 2019 A1
20190371317 Irani et al. Dec 2019 A1
20190371331 Schramm et al. Dec 2019 A1
20190372902 Piersol Dec 2019 A1
20190373102 Weinstein et al. Dec 2019 A1
20200019609 Yu et al. Jan 2020 A1
20200042334 Radebaugh et al. Feb 2020 A1
20200043482 Gruber et al. Feb 2020 A1
20200043489 Bradley et al. Feb 2020 A1
20200044485 Smith et al. Feb 2020 A1
20200053218 Gray Feb 2020 A1
20200058299 Lee et al. Feb 2020 A1
20200075018 Chen Mar 2020 A1
20200091958 Curtis et al. Mar 2020 A1
20200092625 Raffle Mar 2020 A1
20200098362 Piernot et al. Mar 2020 A1
20200098368 Lemay Mar 2020 A1
20200104357 Bellegarda et al. Apr 2020 A1
20200104362 Yang et al. Apr 2020 A1
20200104369 Bellegarda Apr 2020 A1
20200104668 Sanghavi et al. Apr 2020 A1
20200105260 Piernot et al. Apr 2020 A1
20200118568 Kudurshian et al. Apr 2020 A1
20200125820 Kim et al. Apr 2020 A1
20200127988 Bradley et al. Apr 2020 A1
20200135209 Delfarah et al. Apr 2020 A1
20200137230 Spohrer Apr 2020 A1
20200143812 Walker, II et al. May 2020 A1
20200159579 Shear et al. May 2020 A1
20200160179 Chien et al. May 2020 A1
20200169637 Sanghavi et al. May 2020 A1
20200175566 Bender et al. Jun 2020 A1
20200184964 Myers et al. Jun 2020 A1
20200193997 Piernot et al. Jun 2020 A1
20200221155 Hansen et al. Jul 2020 A1
20200227034 Summa et al. Jul 2020 A1
20200227044 Lindahl Jul 2020 A1
20200249985 Zeitlin Aug 2020 A1
20200252508 Gray Aug 2020 A1
20200267222 Phipps et al. Aug 2020 A1
20200272485 Karashchuk et al. Aug 2020 A1
20200279556 Gruber et al. Sep 2020 A1
20200279576 Binder et al. Sep 2020 A1
20200279627 Nida et al. Sep 2020 A1
20200285327 Hindi et al. Sep 2020 A1
20200286472 Newendorp et al. Sep 2020 A1
20200286493 Orr et al. Sep 2020 A1
20200302356 Gruber et al. Sep 2020 A1
20200302919 Greborio et al. Sep 2020 A1
20200302925 Shah et al. Sep 2020 A1
20200302932 Schramm et al. Sep 2020 A1
20200304955 Gross et al. Sep 2020 A1
20200304972 Gross et al. Sep 2020 A1
20200305084 Freeman et al. Sep 2020 A1
20200312317 Kothari et al. Oct 2020 A1
20200314191 Madhavan et al. Oct 2020 A1
20200319850 Stasior et al. Oct 2020 A1
20200327895 Gruber et al. Oct 2020 A1
20200356243 Meyer et al. Nov 2020 A1
20200357391 Ghoshal et al. Nov 2020 A1
20200357406 York et al. Nov 2020 A1
20200357409 Sun et al. Nov 2020 A1
20200364411 Evermann Nov 2020 A1
20200365155 Milden Nov 2020 A1
20200372904 Vescovi et al. Nov 2020 A1
20200374243 Jina et al. Nov 2020 A1
20200379610 Ford et al. Dec 2020 A1
20200379640 Bellegarda et al. Dec 2020 A1
20200379726 Blatz et al. Dec 2020 A1
20200379727 Blatz et al. Dec 2020 A1
20200379728 Gada et al. Dec 2020 A1
20200380389 Eldeeb et al. Dec 2020 A1
20200380956 Rossi et al. Dec 2020 A1
20200380963 Chappidi et al. Dec 2020 A1
20200380966 Acero et al. Dec 2020 A1
20200380973 Novitchenko et al. Dec 2020 A1
20200380980 Shum et al. Dec 2020 A1
20200380985 Gada et al. Dec 2020 A1
20200382616 Vaishampayan et al. Dec 2020 A1
20200382635 Vora et al. Dec 2020 A1
20210006943 Gross et al. Jan 2021 A1
20210011557 Lemay et al. Jan 2021 A1
20210012776 Peterson et al. Jan 2021 A1
Foreign Referenced Citations (551)
Number Date Country
2014100581 Sep 2014 AU
2015203483 Jul 2015 AU
2015101171 Oct 2015 AU
2018100187 Mar 2018 AU
2017222436 Oct 2018 AU
2792412 Jul 2011 CA
2666438 Jun 2013 CA
1227657 Sep 1999 CN
1125436 Oct 2003 CN
1633679 Jun 2005 CN
1776583 May 2006 CN
101847405 Sep 2010 CN
101923565 Dec 2010 CN
101939740 Jan 2011 CN
101951553 Jan 2011 CN
101958958 Jan 2011 CN
101971250 Feb 2011 CN
101992779 Mar 2011 CN
102056026 May 2011 CN
102122506 Jul 2011 CN
102124515 Jul 2011 CN
102137085 Jul 2011 CN
102137193 Jul 2011 CN
102160043 Aug 2011 CN
102201235 Sep 2011 CN
102214187 Oct 2011 CN
102237088 Nov 2011 CN
102246136 Nov 2011 CN
202035047 Nov 2011 CN
102282609 Dec 2011 CN
202092650 Dec 2011 CN
102340590 Feb 2012 CN
102346557 Feb 2012 CN
102368256 Mar 2012 CN
102402985 Apr 2012 CN
102405463 Apr 2012 CN
102498457 Jun 2012 CN
102510426 Jun 2012 CN
102629246 Aug 2012 CN
102651217 Aug 2012 CN
102681896 Sep 2012 CN
102682769 Sep 2012 CN
102682771 Sep 2012 CN
102685295 Sep 2012 CN
102693725 Sep 2012 CN
102694909 Sep 2012 CN
202453859 Sep 2012 CN
102722478 Oct 2012 CN
102737104 Oct 2012 CN
102750087 Oct 2012 CN
102792320 Nov 2012 CN
102801853 Nov 2012 CN
102820033 Dec 2012 CN
102844738 Dec 2012 CN
102866828 Jan 2013 CN
102870065 Jan 2013 CN
102882752 Jan 2013 CN
102917004 Feb 2013 CN
102917271 Feb 2013 CN
102918493 Feb 2013 CN
102955652 Mar 2013 CN
103035240 Apr 2013 CN
103035251 Apr 2013 CN
103038728 Apr 2013 CN
103093334 May 2013 CN
103135916 Jun 2013 CN
103198831 Jul 2013 CN
103209369 Jul 2013 CN
103226949 Jul 2013 CN
103236260 Aug 2013 CN
103246638 Aug 2013 CN
103268315 Aug 2013 CN
103277974 Sep 2013 CN
103280218 Sep 2013 CN
103292437 Sep 2013 CN
103327063 Sep 2013 CN
103365279 Oct 2013 CN
103366741 Oct 2013 CN
103390016 Nov 2013 CN
103412789 Nov 2013 CN
103426428 Dec 2013 CN
103455234 Dec 2013 CN
103456306 Dec 2013 CN
103533143 Jan 2014 CN
103533154 Jan 2014 CN
103543902 Jan 2014 CN
103562863 Feb 2014 CN
103608859 Feb 2014 CN
103645876 Mar 2014 CN
103716454 Apr 2014 CN
103727948 Apr 2014 CN
103744761 Apr 2014 CN
103760984 Apr 2014 CN
103765385 Apr 2014 CN
103792985 May 2014 CN
103794212 May 2014 CN
103795850 May 2014 CN
103841268 Jun 2014 CN
103902373 Jul 2014 CN
103930945 Jul 2014 CN
103959751 Jul 2014 CN
203721183 Jul 2014 CN
103971680 Aug 2014 CN
104007832 Aug 2014 CN
104038621 Sep 2014 CN
104090652 Oct 2014 CN
104113471 Oct 2014 CN
104125322 Oct 2014 CN
104144377 Nov 2014 CN
104169837 Nov 2014 CN
104180815 Dec 2014 CN
104243699 Dec 2014 CN
104281259 Jan 2015 CN
104284257 Jan 2015 CN
104335207 Feb 2015 CN
104335234 Feb 2015 CN
104374399 Feb 2015 CN
104423625 Mar 2015 CN
104427104 Mar 2015 CN
104463552 Mar 2015 CN
104487929 Apr 2015 CN
104516522 Apr 2015 CN
104573472 Apr 2015 CN
104575501 Apr 2015 CN
104584010 Apr 2015 CN
104604274 May 2015 CN
104679472 Jun 2015 CN
104769584 Jul 2015 CN
104854583 Aug 2015 CN
104867492 Aug 2015 CN
104869342 Aug 2015 CN
104951077 Sep 2015 CN
104967748 Oct 2015 CN
104969289 Oct 2015 CN
104978963 Oct 2015 CN
105025051 Nov 2015 CN
105027197 Nov 2015 CN
105093526 Nov 2015 CN
105100356 Nov 2015 CN
105190607 Dec 2015 CN
105247511 Jan 2016 CN
105264524 Jan 2016 CN
105278681 Jan 2016 CN
105320251 Feb 2016 CN
105320726 Feb 2016 CN
105379234 Mar 2016 CN
105430186 Mar 2016 CN
105471705 Apr 2016 CN
105472587 Apr 2016 CN
105556592 May 2016 CN
105808200 Jul 2016 CN
105830048 Aug 2016 CN
105869641 Aug 2016 CN
106030699 Oct 2016 CN
106062734 Oct 2016 CN
106415412 Feb 2017 CN
106462383 Feb 2017 CN
106463114 Feb 2017 CN
106465074 Feb 2017 CN
106534469 Mar 2017 CN
106776581 May 2017 CN
107450800 Dec 2017 CN
107480161 Dec 2017 CN
107491468 Dec 2017 CN
107545262 Jan 2018 CN
107608998 Jan 2018 CN
107615378 Jan 2018 CN
107871500 Apr 2018 CN
107919123 Apr 2018 CN
107924313 Apr 2018 CN
107978313 May 2018 CN
108647681 Oct 2018 CN
109447234 Mar 2019 CN
109657629 Apr 2019 CN
110135411 Aug 2019 CN
110531860 Dec 2019 CN
110598671 Dec 2019 CN
110647274 Jan 2020 CN
110825469 Feb 2020 CN
202016008226 May 2017 DE
2309491 Apr 2011 EP
2329348 Jun 2011 EP
2339576 Jun 2011 EP
2355093 Aug 2011 EP
2393056 Dec 2011 EP
2400373 Dec 2011 EP
2431842 Mar 2012 EP
2523109 Nov 2012 EP
2523188 Nov 2012 EP
2551784 Jan 2013 EP
2555536 Feb 2013 EP
2575128 Apr 2013 EP
2632129 Aug 2013 EP
2639792 Sep 2013 EP
2669889 Dec 2013 EP
2672229 Dec 2013 EP
2672231 Dec 2013 EP
2675147 Dec 2013 EP
2680257 Jan 2014 EP
2683147 Jan 2014 EP
2683175 Jan 2014 EP
2672231 Apr 2014 EP
2717259 Apr 2014 EP
2725577 Apr 2014 EP
2733598 May 2014 EP
2733896 May 2014 EP
2743846 Jun 2014 EP
2760015 Jul 2014 EP
2781883 Sep 2014 EP
2787683 Oct 2014 EP
2801890 Nov 2014 EP
2801972 Nov 2014 EP
2801974 Nov 2014 EP
2824564 Jan 2015 EP
2849177 Mar 2015 EP
2879402 Jun 2015 EP
2881939 Jun 2015 EP
2891049 Jul 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
2985984 Feb 2016 EP
2891049 Mar 2016 EP
3032532 Jun 2016 EP
3035329 Jun 2016 EP
3038333 Jun 2016 EP
3115905 Jan 2017 EP
3125097 Feb 2017 EP
3224708 Oct 2017 EP
3246916 Nov 2017 EP
3300074 Mar 2018 EP
2983065 Aug 2018 EP
3392876 Oct 2018 EP
3401773 Nov 2018 EP
3506151 Jul 2019 EP
2343285 May 2000 GB
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
2011-237621 Nov 2011 JP
2011-238022 Nov 2011 JP
2011-250027 Dec 2011 JP
2012-14394 Jan 2012 JP
2012-502377 Jan 2012 JP
2012-22478 Feb 2012 JP
2012-33997 Feb 2012 JP
2012-37619 Feb 2012 JP
2012-63536 Mar 2012 JP
2012-508530 Apr 2012 JP
2012-89020 May 2012 JP
2012-116442 Jun 2012 JP
2012-142744 Jul 2012 JP
2012-147063 Aug 2012 JP
2012-150804 Aug 2012 JP
2012-518847 Aug 2012 JP
2012-211932 Nov 2012 JP
2013-37688 Feb 2013 JP
2013-46171 Mar 2013 JP
2013-511214 Mar 2013 JP
2013-65284 Apr 2013 JP
2013-73240 Apr 2013 JP
2013-513315 Apr 2013 JP
2013-80476 May 2013 JP
2013-517566 May 2013 JP
2013-134430 Jul 2013 JP
2013-134729 Jul 2013 JP
2013-140520 Jul 2013 JP
2013-527947 Jul 2013 JP
2013-528012 Jul 2013 JP
2013-148419 Aug 2013 JP
2013-156349 Aug 2013 JP
2013-200423 Oct 2013 JP
2013-205999 Oct 2013 JP
2013-238936 Nov 2013 JP
2013-258600 Dec 2013 JP
2014-2586 Jan 2014 JP
2014-10688 Jan 2014 JP
20145-2445 Jan 2014 JP
2014-26629 Feb 2014 JP
2014-45449 Mar 2014 JP
2014-507903 Mar 2014 JP
2014-60600 Apr 2014 JP
2014-72586 Apr 2014 JP
2014-77969 May 2014 JP
2014-89711 May 2014 JP
2014-109889 Jun 2014 JP
2014-124332 Jul 2014 JP
2014-126600 Jul 2014 JP
2014-140121 Jul 2014 JP
2014-518409 Jul 2014 JP
2014-142566 Aug 2014 JP
2014-145842 Aug 2014 JP
2014-146940 Aug 2014 JP
2014-150323 Aug 2014 JP
2014-519648 Aug 2014 JP
2014-191272 Oct 2014 JP
2014-219614 Nov 2014 JP
2014-222514 Nov 2014 JP
2015-4928 Jan 2015 JP
2015-8001 Jan 2015 JP
2015-12301 Jan 2015 JP
2015-18365 Jan 2015 JP
2015-501022 Jan 2015 JP
2015-504619 Feb 2015 JP
2015-41845 Mar 2015 JP
2015-52500 Mar 2015 JP
2015-60423 Mar 2015 JP
2015-81971 Apr 2015 JP
2015-83938 Apr 2015 JP
2015-94848 May 2015 JP
2015-514254 May 2015 JP
2015-519675 Jul 2015 JP
2015-524974 Aug 2015 JP
2015-526776 Sep 2015 JP
2015-527683 Sep 2015 JP
2015-528140 Sep 2015 JP
2015-528918 Oct 2015 JP
2015-531909 Nov 2015 JP
2016-504651 Feb 2016 JP
2016-508007 Mar 2016 JP
2016-71247 May 2016 JP
2016-119615 Jun 2016 JP
2016-151928 Aug 2016 JP
2016-524193 Aug 2016 JP
2016-536648 Nov 2016 JP
2017-19331 Jan 2017 JP
2017-516153 Jun 2017 JP
2017-537361 Dec 2017 JP
6291147 Feb 2018 JP
2018-525950 Sep 2018 JP
10-2011-0005937 Jan 2011 KR
10-2011-0013625 Feb 2011 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
10-2012-0066523 Jun 2012 KR
10-2012-0082371 Jul 2012 KR
10-2012-0084472 Jul 2012 KR
10-1178310 Aug 2012 KR
10-2012-0120316 Nov 2012 KR
10-2012-0137424 Dec 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
10-2013-0090947 Aug 2013 KR
10-2013-0108563 Oct 2013 KR
10-1334342 Nov 2013 KR
10-2013-0131252 Dec 2013 KR
10-2013-0133629 Dec 2013 KR
10-2014-0024271 Feb 2014 KR
10-2014-0031283 Mar 2014 KR
10-2014-0033574 Mar 2014 KR
10-2014-0042994 Apr 2014 KR
10-2014-0055204 May 2014 KR
10-2014-0068752 Jun 2014 KR
10-2014-0088449 Jul 2014 KR
10-2014-0106715 Sep 2014 KR
10-2014-0147557 Dec 2014 KR
10-2015-0013631 Feb 2015 KR
10-1506510 Mar 2015 KR
10-2015-0038375 Apr 2015 KR
10-2015-0039380 Apr 2015 KR
10-2015-0041974 Apr 2015 KR
10-2015-0043512 Apr 2015 KR
10-2015-0095624 Aug 2015 KR
10-1555742 Sep 2015 KR
10-2015-0113127 Oct 2015 KR
10-2015-0138109 Dec 2015 KR
10-2016-0004351 Jan 2016 KR
10-2016-0010523 Jan 2016 KR
10-2016-0040279 Apr 2016 KR
10-2016-0055839 May 2016 KR
10-2016-0065503 Jun 2016 KR
10-2016-0101198 Aug 2016 KR
10-2016-0105847 Sep 2016 KR
10-2016-0121585 Oct 2016 KR
10-2016-0140694 Dec 2016 KR
10-2017-0036805 Apr 2017 KR
10-2017-0107058 Sep 2017 KR
10-2018-0032632 Mar 2018 KR
10-2018-0034637 Apr 2018 KR
10-1959328 Mar 2019 KR
201110108 Mar 2011 TW
201142823 Dec 2011 TW
201227715 Jul 2012 TW
201245989 Nov 2012 TW
201312548 Mar 2013 TW
2003058604 Jul 2003 WO
2010054373 May 2010 WO
2011028842 Mar 2011 WO
2011057346 May 2011 WO
2011060106 May 2011 WO
2011082521 Jul 2011 WO
2011088053 Jul 2011 WO
2011093025 Aug 2011 WO
2011100142 Aug 2011 WO
2011116309 Sep 2011 WO
2011123122 Oct 2011 WO
2011133543 Oct 2011 WO
2011133573 Oct 2011 WO
2011097309 Dec 2011 WO
2011150730 Dec 2011 WO
2011163350 Dec 2011 WO
2011088053 Jan 2012 WO
2012008434 Jan 2012 WO
2012019020 Feb 2012 WO
2012019637 Feb 2012 WO
2012063260 May 2012 WO
2012092562 Jul 2012 WO
2012112331 Aug 2012 WO
2012129231 Sep 2012 WO
2012063260 Oct 2012 WO
2012135157 Oct 2012 WO
2012154317 Nov 2012 WO
2012154748 Nov 2012 WO
2012155079 Nov 2012 WO
2012167168 Dec 2012 WO
2012173902 Dec 2012 WO
2013009578 Jan 2013 WO
2013022135 Feb 2013 WO
2013022223 Feb 2013 WO
2013048880 Apr 2013 WO
2013049358 Apr 2013 WO
2013057153 Apr 2013 WO
2013118988 Aug 2013 WO
2013122310 Aug 2013 WO
2013133533 Sep 2013 WO
2013137660 Sep 2013 WO
2013163113 Oct 2013 WO
2013163857 Nov 2013 WO
2013169842 Nov 2013 WO
2013173504 Nov 2013 WO
2013173511 Nov 2013 WO
2013176847 Nov 2013 WO
2013184953 Dec 2013 WO
2013184990 Dec 2013 WO
2014003138 Jan 2014 WO
2014004544 Jan 2014 WO
2014021967 Feb 2014 WO
2014022148 Feb 2014 WO
2014028735 Feb 2014 WO
2014028797 Feb 2014 WO
2014031505 Feb 2014 WO
2014032461 Mar 2014 WO
2014047047 Mar 2014 WO
2014066352 May 2014 WO
2014070872 May 2014 WO
2014078965 May 2014 WO
2014093339 Jun 2014 WO
2014096506 Jun 2014 WO
2014124332 Aug 2014 WO
2014137074 Sep 2014 WO
2014138604 Sep 2014 WO
2014143959 Sep 2014 WO
2014144395 Sep 2014 WO
2014144579 Sep 2014 WO
2014144949 Sep 2014 WO
2014151153 Sep 2014 WO
2014124332 Oct 2014 WO
2014159578 Oct 2014 WO
2014159581 Oct 2014 WO
2014162570 Oct 2014 WO
2014169269 Oct 2014 WO
2014173189 Oct 2014 WO
2013173504 Dec 2014 WO
2014197336 Dec 2014 WO
2014197635 Dec 2014 WO
2014197730 Dec 2014 WO
2014200728 Dec 2014 WO
2014204659 Dec 2014 WO
2014210392 Dec 2014 WO
2015018440 Feb 2015 WO
2015020942 Feb 2015 WO
2015029379 Mar 2015 WO
2015030796 Mar 2015 WO
2015041882 Mar 2015 WO
2015041892 Mar 2015 WO
2015047932 Apr 2015 WO
2015053485 Apr 2015 WO
2015084659 Jun 2015 WO
2015092943 Jun 2015 WO
2015094169 Jun 2015 WO
2015094369 Jun 2015 WO
2015098306 Jul 2015 WO
2015099939 Jul 2015 WO
2015116151 Aug 2015 WO
2015151133 Oct 2015 WO
2015153310 Oct 2015 WO
2015157013 Oct 2015 WO
2015183401 Dec 2015 WO
2015183699 Dec 2015 WO
2015184186 Dec 2015 WO
2015184387 Dec 2015 WO
2015200207 Dec 2015 WO
2016027933 Feb 2016 WO
2016028946 Feb 2016 WO
2016033257 Mar 2016 WO
2016039992 Mar 2016 WO
2016052164 Apr 2016 WO
2016054230 Apr 2016 WO
2016057268 Apr 2016 WO
2016075081 May 2016 WO
2016085775 Jun 2016 WO
2016085776 Jun 2016 WO
2016100139 Jun 2016 WO
2016111881 Jul 2016 WO
2016144840 Sep 2016 WO
2016144982 Sep 2016 WO
2016144983 Sep 2016 WO
2016175354 Nov 2016 WO
2016187149 Nov 2016 WO
2016190950 Dec 2016 WO
2016209444 Dec 2016 WO
2016209924 Dec 2016 WO
2017044160 Mar 2017 WO
2017044257 Mar 2017 WO
2017044260 Mar 2017 WO
2017044629 Mar 2017 WO
2017053311 Mar 2017 WO
2017058293 Apr 2017 WO
2017059388 Apr 2017 WO
2017071420 May 2017 WO
2017142116 Aug 2017 WO
2017160487 Sep 2017 WO
2017213682 Dec 2017 WO
2017218194 Dec 2017 WO
2018009397 Jan 2018 WO
2018213401 Nov 2018 WO
2018213415 Nov 2018 WO
2019067930 Apr 2019 WO
2019078576 Apr 2019 WO
2019079017 Apr 2019 WO
2019147429 Aug 2019 WO
2019236217 Dec 2019 WO
2020010530 Jan 2020 WO
Non-Patent Literature Citations (195)
Entry
Aaaaplay, “Sony Media Remote for iOS and Android”, Online available at: <https://www.youtube.com/watch?v=W8QoeQhlGok>, Feb. 4, 2012, 3 pages.
Android Authority, “How to use Tasker: A Beginner's Guide”, Online available at: —<https://youtube.com/watch?v= rDpdS_YWzFc>, May 1, 2013, 1 page.
Automate Your Life, “How to Setup Google Home Routines—A Google Home Routines Walkthrough”, Online Available at: <https://www.youtube.com/watch?v=pXokZHP9kZg>, Aug. 12, 2018, 1 page.
Bell, Jason, “Machine Learning Hands-On for Developers and Technical Professionals”, Wiley, 2014, 82 pages.
Chen et al., “A Convolutional Neural Network with Dynamic Correlation Pooling”, 13th International Conference on Computational Intelligence and Security, IEEE, 2017, pp. 496-499.
Conneau et al., “Supervised Learning of Universal Sentence Representations from Natural Language Inference Data”, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, Sep. 7-11, 2017, pp. 670-680.
Coulouris et al., “Distributed Systems: Concepts and Design (Fifth Edition)”, Addison-Wesley, 2012, 391 pages.
Derrick, Amanda, “How to Set Up Google Home for Multiple Users”, Lifewire, Online available at:—<https://www.lifewire.com/set-up-google-home-multiple-users-4685691>, Jun. 8, 2020, 9 pages.
Dihelson, “How Can I Use Voice or Phrases as Triggers to Macrodroid?”, Macrodroid Forums, Online Available at:—<https://www.tapatalk.com/groups/macrodroid/how-can-i-use-voice-or-phrases-as-triggers-to-macr-t4845.html>, May 9, 2018, 5 pages.
Eder et al., “At the Lower End of Language—Exploring the Vulgar and Obscene Side of German”, Proceedings of the Third Workshop on Abusive Language Online, Florence, Italy, Aug. 1, 2019, pp. 119-128.
Edim, et al., “A Multi-Agent Based Virtual Personal Assistant for E-Health Service”, Journal of Information Engineering and Applications, vol. 3, No. 11, 2013, 9 pages.
Gadget Hacks, “Tasker Too Complicated? Give MacroDroid a Try [How-To]”, Online available at: <https://www.youtube.com/watch?v=8YL9cWCykKc>, May 27, 2016, 1 page.
Ghauth et al., “Text Censoring System for Filtering Malicious Content Using Approximate String Matching and Bayesian Filtering”, Proc. 4th INNS Symposia Series on Computational Intelligence in Information Systems, Bandar Seri Begawan, Brunei, 2015, pp. 149-158.
Gupta, Naresh, “Inside Bluetooth Low Energy”, Artech House, 2013, 274 pages.
Hutsko et al., “iPhone All-in-One For Dummies”, 3rd Edition, 2013, 98 pages.
“IPhone 6 Smart Guide Full Version for SoftBank”, Gijutsu-Hyohron Co., Ltd., vol. 1, Dec. 1, 2014, 4 pages (Official Copy Only). {See communication under 37 CFR § 1.98(a) (3)}.
Kastrenakes, Jacob, “Siri's creators will unveil their new AI bot on Monday”, The Verge, Online available at:—<https://web.archive.org/web/20160505090418/https://www.theverge.com/2016/5/4/11593564/viv-labs-unveiling-monday-new-ai-from-siri-creators>, May 4, 2016, 3 pages.
“Link Your Voice to Your Devices with Voice Match, Google Assistant Help”, Online available at:—<https://support.google.com/assistant/answer/9071681?co=GENIE.Platform%3DAndroid&hl=en>, Retrieved on Jul. 1, 2020, 2 pages.
Loukides et al., “What Is the Internet of Things?”, O'Reilly Media, Inc., Online Available at: <https://www.oreilly.com/library/view/what-is-the/9781491975633/>, 2015, 31 pages.
Lucente et al., “Visualization Space: A Testbed for Deviceless Multimodal User Interface”, Intelligent Environments Symposium. vol. 98, Online Available at: http://www.aaai.org/Papers/Symposia/Spring/1998/SS-98-02/SS98-02-013.pdf, 1998, pp. 87-92.
Modern Techies,“Braina-Artificial Personal Assistant for PC(like Cortana,Siri)!!!!”, Online available at: <https://www.youtube.com/watch?v=_Coo2P8ilqQ>, Feb. 24, 2017, 3 pages.
Nakamura et al., “Study of Information Clouding Methods to Prevent Spoilers of Sports Match”, Proceedings of the International Working Conference on Advanced Visual Interfaces (AVI' 12), ISBN: 978- 1- 4503- 1287- 5, May 2012, pp. 661-664.
Nakamura et al., “Study of Methods to Diminish Spoilers of Sports Match: Potential of a Novel Concept “Information Clouding””, vol. 54, No. 4, ISSN: 1882-7764. Online available at: <https://ipsj.ixsq.nii.ac.jp/ej/index.php?active_action=repository_view_main_item_detail&page_id=13&block_id=8&item_id=91589&item_no=1>, Apr. 2013, pp. 1402-1412 (Official Copy Only). {See communication under 37 CFR § 1.98(a) (3)}.
Pak, Gamerz, “Braina: Artificially Intelligent Assistant Software for Windows PC in (urdu / hindhi)”, Online available at: <https://www.youtube.com/watch?v=JH_rMjw8lqc>, Jul. 24, 2018, 3 pages.
PC Mag, “How to Voice Train Your Google Home Smart Speaker”, Online available at: <https://in.pcmag.com/google-home/126520/how-to-voice-train-your-google-home-smart-speaker>, Oct. 25, 2018, 12 pages.
pocketables.com,“AutoRemote example profile”, Online available at: https://www.youtube.com/watch?v=kC_zhUnNZj8, Jun. 25, 2013, 1 page.
Rasch, Katharina, “Smart Assistants for Smart Homes”, Doctoral Thesis in Electronic and Computer Systems, 2013, 150 pages.
Ritchie, Rene, “QuickType keyboard in iOS 8: Explained”, Online Available at:—<https://www.imore.com/quicktype-keyboards-ios-8-explained>, Jun. 21, 2014, pp. 1-19.
Rowland et al., “Designing Connected Products: UX for the Consumer Internet of Things”, O'Reilly, May 2015, 452 pages.
Santos et al., “Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer”, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (vol. 2: Short Papers), May 20, 2018, 6 pages.
Seroter et al., “SOA Patterns with BizTalk Server 2013 and Microsoft Azure”, Packt Publishing, Jun. 2015, 454 pages.
Shen et al., “Style Transfer from Non-Parallel Text by Cross-Alignment”, 31st Conference on Neural Information Processing Systems (NIPS 2017), 2017, 12 pages.
Simonite, Tom, “Confronting Siri: Microsoft Launches Digital Assistant Cortana”, 2014, 2 pages (Official Copy). {See communication under 37 CFR § 1.98(a) (3)}.
Siou, Serge, “How To Control Apple TV 3rd Generation Using Remote app”, Online available at: <https://www.youtube.com/watch?v=PhyKftZ0S9M>, May 12, 2014, 3 pages.
Summons to Attend Oral Proceedings received for European Patent Application No. 18210587.4, mailed on Dec. 8, 2020, 11 pages.
Yang Astor, “Control Android TV via Mobile Phone APP RKRemoteControl”, Online Available at : <https://www.youtube.com/watch?v=zpmUeOX_xro>, Mar. 31, 2015, 4 pages.
Ye et al., “iPhone 4S Native Secret”, Jun. 30, 2012, 1 page (Official Copy Only). {See communication under 37 CFR § 1.98(a) (3)}.
Yousef, Zulfikara., “Braina (A.I) Artificial Intelligence Virtual Personal Assistant”, Online available at:—<https://www.youtube.com/watch?v=2h6xpB8bPSA>, Feb. 7, 2017, 3 pages.
Zhan et al., “Play with Android Phones”, Feb. 29, 2012, 1 page (Official Copy Only). {See communication under 37 CFR § 1.98(a) (3)}.
Adium, “AboutAdium—Adium X—Trac”, Online available at:—<http://web.archive.org/web/20070819113247/http://trac.adiumx.com/wiki/AboutAdium>, retrieved on Nov. 25, 2011, 2 pages.
“Alexa, Turn Up the Heat!, Smartthings Samsung [online]”, Online available at:—<https://web.archive.org/web/20160329142041/https://blog.smartthings.com/news/smartthingsupdates/alexa-turn-up-the-heat/>, Mar. 3, 2016, 3 pages.
Alfred App, “Alfred”, Online available at:—<http://www.alfredapp.com/>, retrieved on Feb. 8, 2012, 5 pages.
Anania Peter, “Amazon Echo with Home Automation (Smartthings)”, Online available at:—<https://www.youtube.com/watch?v=LMW6aXmsWNE>, Dec. 20, 2015, 1 page.
api.ai, “Android App Review—Speaktoit Assistant”, Online available at:—<https://www.youtube.com/watch?v=myE498nyfGw>, Mar. 30, 2011, 3 pages.
Apple, “VoiceOver for OS X”, Online available at:—<http://www.apple.com/accessibility/voiceover/>, May 19, 2014, pp. 1-3.
Asakura et al., “What LG thinks; How the TV should be in the Living Room”, HiVi, vol. 31, No. 7, Stereo Sound Publishing, Inc., Jun. 17, 2013, pp. 68-71.
Ashingtondctech & Gaming, “SwipeStatusBar—Reveal the Status Bar in a Fullscreen App”, Online Available at: <https://www.youtube.com/watch?v=wA_tT9IAreQ>, Jul. 1, 2013, 3 pages.
“Ask Alexa—Things That Are Smart Wiki”, Online available at:—<http://thingsthataresmart.wiki/index.php?title=Ask_Alexa&oldid=4283>, Jun. 8, 2016, pp. 1-31.
Bellegarda, Jeromer, “Chapter 1: Spoken Language Understanding for Natural Interaction: The Siri Experience”, Natural Interaction with Robots, Knowbots and Smartphones, 2014, pp. 3-14.
Bellegarda, Jeromer, “Spoken Language Understanding for Natural Interaction: The Siri Experience”, Slideshow retrieved from : <https://www.uni-ulm.de/fileadmin/website_uni_ulm/iui.iwsds2012/files/Bellegarda.pdf>, International Workshop on Spoken Dialog Systems (IWSDS), May 2012, pp. 1-43.
beointegration.com, “BeoLink Gateway—Programming Example”, Online Available at: <https:/ /www.youtube.com/watch?v=TXDaJFm5UH4>, Mar. 4, 2015, 3 pages.
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.
Bertolucci, Jeff, “Google Adds Voice Search to Chrome Browser”, PC World, Jun. 14, 2011, 5 pages.
Burgess, Brian, “Amazon Echo Tip: Enable the Wake Up Sound”, Online available at :—<https://www.groovypost.com/howto/amazon-echo-tip-enable-wake-up-sound/>, Jun. 30, 2015, 4 pages.
Butcher, Mike, “EVI Arrives in Town to go Toe-to-Toe with Siri”, TechCrunch, Jan. 23, 2012, pp. 1-2.
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 Iberian Sltech Workshop, Nov. 21, 2012, pp. 1-10.
Castleos, “Whole House Voice Control Demonstration”, Online available at:—<https://www.youtube.com/watch?v=9SRCoxrZ_W4>, Jun. 2, 2012, 1 page.
Chang et al., “Monaural Multi-Talker Speech Recognition with Attention Mechanism and Gated Convolutional Networks”, Interspeech 2018, Sep. 2-6, 2018, pp. 1586-1590.
Chen et al., “Progressive Joint Modeling in Unsupervised Single-Channel Overlapped Speech Recognition”, IEEE/ACM Transactions On Audio, Speech, And Language Processing, vol. 26, No. 1, Jan. 2018, pp. 184-196.
Chen, Yi, “Multimedia Siri Finds and Plays Whatever You Ask For”, PSFK Report, Feb. 9, 2012, pp. 1-9.
Cheyer, Adam, “Adam Cheyer—About”, Online available at:—<http://www.adam.cheyer.com/about.html>, retrieved on Sep. 17, 2012, pp. 1-2.
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.
Corrected Notice of Allowance received for U.S. Appl. No. 15/971,787, dated Sep. 27, 2019, 2 pages.
Corrected Notice of Allowance received for U.S. Appl. No. 16/600,950, dated Dec. 7, 2020, 2 pages.
Czech Lucas, “A System for Recognizing Natural Spelling of English Words”, Diploma Thesis, Karlsruhe Institute of Technology, May 7, 2014, 107 pages.
Decision to Grant received for European Patent Application No. 15727277.4, dated Dec. 20, 2018, 2 pages.
Deedeevuu, “Amazon Echo Alarm Feature”, Online available at:—<https://www.youtube.com/watch?v=fdjU8eRLk7c>, Feb. 16, 2015, 1 page.
Delcroix et al., “Context Adaptive Deep Neural Networks For Fast Acoustic Model Adaptation”, ICASSP, 2015, pp. 4535-4539.
Delcroix et al., “Context Adaptive Neural Network for Rapid Adaptation of Deep CNN Based Acoustic Models”, Interspeech 2016, Sep. 8-12, 2016, pp. 1573-1577.
“DIRECTV™ Voice”, Now Part of the DIRECTTV Mobile App for Phones, Sep. 18, 2013, 5 pages.
Earthling1984, “Samsung Galaxy Smart Stay Feature Explained”, Online available at :—<https://www.youtube.com/watch?v=RpjBNtSjupl>, May 29, 2013, 1 page.
Evi, “Meet Evi: The One Mobile Application that Provides Solutions for your Everyday Problems”, Feb. 2012, 3 pages.
Extended European Search Report received for European Patent Application No. 18210587.4, dated Apr. 4, 2019, 9 pages.
Filipowicz, Luke, “Howto use the QuickType keyboard in iOS 8”, Online available at:—<https://www.imore.com/comment/568232>, Oct. 11, 2014, pp. 1-17.
Final Office Action received for U.S. Appl. No. 14/724,623, dated Apr. 13, 2017, 43 pages.
Findlater et al., “Beyond QWERTY: Augmenting Touch-Screen Keyboards with Multi-Touch Gestures for Non-Alphanumeric Input”, CHI '12, May 5-10, 2012, 4 pages.
“Galaxy S7: How to Adjust Screen Timeout & Lock Screen Timeout”, Online available at :—<https://www.youtube.com/watch?v=n6e1WKUS2ww>, Jun. 9, 2016, 1 page.
Gannes, Liz, “Alfred App Gives Personalized Restaurant Recommendations”, AllThingsD, Jul. 18, 2011, pp. 1-3.
Gasic et al., “Effective Handling of Dialogue State in the Hidden Information State POMDP-based Dialogue Manager”, ACM Transactions on Speech and Language Processing, May 2011, pp. 1-25.
Google Developers,“Voice search in your app”, Online available at :—<https://www.youtube.com/watch?v=PS1FbB5qWEI>, Nov. 12, 2014, 1 page.
Guay, Matthew, “Location-Driven Productivity with Task Ave”, Online 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”, Online available at:—<http://www.wpcentral.com/how-to-person-based-reminder-cortana>, Apr. 26, 2014, 15 pages.
Gupta et al., “I-vector-based Speaker Adaptation Of Deep Neural Networks For French Broadcast Audio Transcription”, ICASSP, 2014, 2014, pp. 6334-6338.
Gurevych et al., “Semantic Coherence Scoring Using an Ontology”, North American Chapter of the Association for Computational Linguistics Archive, Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, May 27, 2003, pp. 9-16.
Hardawar, Devindra, “Driving App Waze Builds its own Siri for Hands-Free Voice Control”, Online available 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.
“Headset Button Controller v7.3 APK Full APP Download for Andriod, Blackberry, iPhone”, Online available at:—<http://fullappdownload.com/headset-button-controller-v7-3-apk/>, Jan. 27, 2014, 11 pages.
“Hear Voice from Google Translate”, Online available at:—<https://www.youtube.com/watch?v=18AvMhFqD28>, Jan. 28, 2011, 1 page.
Hershey et al., “Deep Clustering: Discriminative Embeddings For Segmentation And Separation”, Proc. ICASSP, Mar. 2016, 6 pages.
“Hey Google: How to Create a Shopping List with Your Google Assistant”, Online available at :—<https://www.youtube.com/watch?v=w9NCsElax1Y>, May 25, 2018, 1 page.
“How To Enable Google Assistant on Galaxy S7 and Other Android Phones (No Root)”, Online available at :—<https://www.youtube.com/watch?v=HeklQbWyksE>, Mar. 20, 2017, 1 page.
“How to Use Ok Google Assistant Even Phone is Locked”, Online available at :—<https://www.youtube.com/watch?v=9B_gP4j_SP8>, Mar. 12, 2018, 1 page.
id3.org, “id3v2.4.0-Frames”, Online available at:—<http://id3.org/id3v2.4.0-frames?action=print>, retrieved on Jan. 22, 2015, pp. 1-41.
Ikeda, Masaru, “beGLOBAL SEOUL 2015 Startup Battle: Talkey”, YouTube Publisher, Online Available at:—<https://www.youtube.com/watch?v=4Wkp7sAAIdg>, May 14, 2015, 1 page.
Inews and Tech,“How To Use The QuickType Keyboard In IOS 8”, Online available at :—<http://www.inewsandtech.com/how-to-use-the-quicktype-keyboard-in-ios-8/>, Sep. 17, 2014, 6 pages.
Intention to Grant received for European Patent Application No. 15727277.4, dated Aug. 6, 2018, 6 pages.
“Interactive Voice”, Online available at:—<http://www.helloivee.com/company/>, retrieved on Feb. 10, 2014, 2 pages.
International Preliminary Report on Patentability received for PCT Patent Application No. PCT/US2015/033051, dated Dec. 15, 2016, 10 pages.
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US2015/033051, dated Aug. 5, 2015, 14 pages.
Internet Services and Social Net, “How to Search for Similar Websites”, Online available at:—<https://www.youtube.com/watch?v=nLf2uirpt5s>, see from 0:17 to 1:06, Jul. 4, 2013, 1 page.
Isik et al., “Single-Channel Multi-Speaker Separation using Deep Clustering”, Interspeech 2016, Sep. 8-12, 2016, pp. 545-549.
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.
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.
Karn, Ujjwal, “An Intuitive Explanation of Convolutional Neural Networks”, The Data Science Blog, Aug. 11, 2016, 23 pages.
Kazmucha Allyson, “Howto Send Map Locations Using iMessage”, iMore.com, Online 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”, Online available at:—<https://www.kickstarter.com/projects/ivee/ivee-sleek-wi-fi-voice-activated-assistant>, retrieved on Feb. 10, 2014, pp. 1-13.
King et al., “Robust Speech Recognition Via Anchor Word Representations”, Interspeech 2017, Aug. 20-24, 2017, pp. 2471-2475.
Lee, Sungjin, “Structured Discriminative Model For Dialog State Tracking”, Proceedings of the SIGDIAL 2013 Conference, Aug. 22-24, 2013, pp. 442-451.
Lewis Cameron, “Task Ave for iPhone Review”, Mac Life, Online available at:—<http://www.maclife.com/article/reviews/task_ave_iphone_review>, Mar. 3, 2011, 5 pages.
Liou et al., “Autoencoder for Words”, Neurocomputing, vol. 139, Sep. 2014, pp. 84-96.
Liu et al., “Accurate Endpointing with Expected Pause Duration”, Sep. 6-10, 2015, pp. 2912-2916.
Luo et al., “Speaker-Independent Speech Separation With Deep Attractor Network”, IEEE/ACM Transactions On Audio, Speech, And Language Processing, vol. 26, No. 4, Apr. 2018, pp. 787-796.
Marketing Land,“Amazon Echo: Play music”, Online Available at:—<https://www.youtube.com/watch?v=A7V5NPbsXi4>, Apr. 27, 2015, 3 pages.
“Meet Ivee, Your Wi-Fi Voice Activated Assistant”, Availale Online at:—<http://www.helloivee.com/>, retrieved on Feb. 10, 2014, 8 pages.
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.
Mikolov et al., “Linguistic Regularities in Continuous Space Word Representations”, Proceedings of NAACL-HLT, Jun. 9-14, 2013, pp. 746-751.
Miller Chance, “Google Keyboard Updated with New Personalized Suggestions Feature”, Online available at:—<http://9to5google.com/2014/03/19/google-keyboard-updated-with-new-personalized-suggestions-feature/>, Mar. 19, 2014, 4 pages.
“Mobile Speech Solutions, Mobile Accessibility”, SVOX AG Product Information Sheet, Online available at:—<http://www.svox.com/site/bra840604/con782768/mob965831936.aSQ?osLang=1>, Sep. 27, 2012, 1 page.
Morrison Jonathan, “iPhone 5 Siri Demo”, Online Available at:—<https://www.youtube.com/watch?v=_wHWwG5lhWc>, Sep. 21, 2012, 3 pages.
My Cool Aids, “What's New”, Online available at :—<http://www.mycoolaids.com/>, 2012, 1 page.
Myers, Brad A., “Shortcutter for Palm”, Online 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, Feb. 28, 2011, 11 pages.
“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.
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.
Non-Final Office Action received for U.S. Appl. No. 14/724,623, dated Jan. 3, 2017, 50 pages.
Non-Final Office Action received for U.S. Appl. No. 14/724,623, dated Jul. 1, 2016, 49 pages.
Non-Final Office Action received for U.S. Appl. No. 15/971,787, dated Dec. 11, 2018, 35 pages.
Notice of Acceptance received for Australian Patent Application No. 2015266863, dated Feb. 28, 2018, 3 pages.
Notice of Allowance received for Chinese Patent Application No. 201580028468.4, dated Aug. 5, 2019, 2 pages.
Notice of Allowance received for Taiwanese Patent Application No. 104117244, dated Sep. 30, 2016, 2 pages.
Notice of Allowance received for U.S. Appl. No. 14/724,623, dated Jan. 4, 2018, 9 pages.
Notice of Allowance received for U.S. Appl. No. 14/724,623, dated Sep. 29, 2017, 9 pages.
Notice of Allowance received for U.S. Appl. No. 15/971,787, dated Aug. 30, 2019, 15 pages.
Notice of Allowance received for U.S. Appl. No. 16/600,950, dated Sep. 9, 2020, 12 pages.
Nozawa et al., “iPhone 4S Perfect Manual”, vol. 1, First Edition, Nov. 11, 2011, 4 pages.
Office Action received for Australian Patent Application No. 2015266863, dated Mar. 24, 2017, 3 pages.
Office Action received for Chinese Patent Application No. 201580028468.4, dated Mar. 22, 2019, 12 pages.
Office Action received for European Patent Application No. 15727277.4, dated Oct. 31, 2017, 5 pages.
Office Action received for European Patent Application No. 18210587.4, dated Jan. 20, 2020, 6 pages.
Office Action received for Tiwanese Patent Application No. 104117244, dated Mar. 10, 2016, 22 pages.
Osxdaily, “Get a List of Siri Commands Directly from Siri”, Online available at:—<http://osxdaily.com/2013/02/05/list-siri-commands/>, Feb. 5, 2013, 15 pages.
Pathak et al., “Privacy-preserving Speech Processing: Cryptographic and String-matching Frameworks Show Promise”, In: IEEE signal processing magazine, Online available at: - <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.
Pennington et al., “GloVe: Global Vectors for Word Representation”, Proceedings of the Conference on Empirical Methods Natural Language Processing (EMNLP), Doha, Qatar, Oct. 25-29, 2014, pp. 1532-1543.
Perlow, Jason, “Alexa Loop Mode with Playlist for Sleep Noise”, Online Available at: <https://www.youtube.com/watch?v=nSkSuXziJSg>, Apr. 11, 2016, 3 pages.
Qian et al., “Single-channel Multi-talker Speech Recognition With Permutation Invariant Training”, Speech Communication, Issue 104, 2018, pp. 1-11.
“Quick Type Keyboard on iOS 8 Makes Typing Easier”, Online available at:—<https://www.youtube.com/watch?v=0CldLR4fhVU>, Jun. 3, 2014, 3 pages.
Result of Consultation received for European Patent Application No. 18210587.4, dated Apr. 1, 2020, 3 pages.
Rios Mafe, “New Bar Search for Facebook”, YouTube, available at:—<https://www.youtube.com/watch?v=vwgN1WbvCas>, Jul. 19, 2013, 2 pages.
Routines, “SmartThings Support”, Online available at:—<https://web.archive.org/web/20151207165701/https://support.smartthings.com/hc/en-us/articles/205380034-Routines>, 2015, 3 pages.
Samsung Support, “Create a Quick Command in Bixby to Launch Custom Settings by at Your Command”, Online Available at:—<https://www.facebook.com/samsungsupport/videos/10154746303151213>, Nov. 13, 2017, 1 page.
Sarawagi Sunita, “CRF Package Page”, Online available at:—<http://crf.sourceforge.net/>, retrieved on Apr. 6, 2011, 2 pages.
Seehafer Brent, “Activate Google Assistant on Galaxy S7 with Screen off”, Online available at :—<https://productforums.google.com/forum/#!topic/websearch/lp3qlGBHLVI>, Mar. 8, 2017, 4 pages.
Selfridge et al., “Interact: Tightly-coupling Multimodal Dialog with an Interactive Virtual Assistant”, International Conference on Multimodal Interaction, ACM, Nov. 9, 2015, pp. 381-382.
Senior et al., “Improving DNN Speaker Independence With I-Vector Inputs”, ICASSP, 2014, pp. 225-229.
Settle et al., “End-to-End Multi-Speaker Speech Recognition”, Proc. ICASSP, Apr. 2018, 6 pages.
Simonite, Tom, “One Easy Way to Make Siri Smarter”, Technology Review, Oct. 18, 2011, 2 pages.
“Skilled at Playing my iPhone 5”, Beijing Hope Electronic Press, Jan. 2013, 6 pages.
“SmartThings +Amazon Echo”, Smartthings Samsung [online], Online available at:—<https://web.archive.org/web/20160509231428/https://blog.smartthings.com/featured/alexa-turn-on-my-smartthings/>, Aug. 21, 2015, 3 pages.
Smith, Jake, “Amazon Alexa Calling: How to Set it up and Use it on Your Echo”, iGeneration, May 30, 2017, 5 pages.
SRI, “SRI Speech: Products: Software Development Kits: EduSpeak”, Online available at:—<http://web.archive.org/web/20090828084033/http://www.speechatsri.com/products/eduspeak>shtml, retrieved on Jun. 20, 2013, pp. 1-2.
Sullivan Danny, “How Google Instant's Autocomplete Suggestions Work”, Online 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, vol. 23, No. 3, Mar. 2015, pp. 517-529.
Sundermeyer et al., “LSTM Neural Networks for Language Modeling”, Interspeech 2012, Sep. 9-13, 2012, pp. 194-197.
Tan et al., “Knowledge Transfer In Permutation Invariant Training For Single-channel Multi-talker Speech Recognition”, ICASSP 2018, 2018, pp. 5714-5718.
Tofel et al., “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, pp. 1-8.
Vaswani et al., “Attention Is All You Need”, 31st Conference on Neural Information Processing Systems (NIPS 2017), 2017, pp. 1-11.
Villemure et al., “The Dragon Drive Innovation Showcase: Advancing the State-of-the-art in Automotive Assistants”, 2018, 7 pages.
Vodafone Deutschland, “Samsung Galaxy S3 Tastatur Spracheingabe”, Online available at—<https://www.youtube.com/watch?v=6kOd6Gr8uFE>, Aug. 22, 2012, 1 page.
Wang et al., “End-to-end Anchored Speech Recognition”, Proc. ICASSP2019, May 12-17, 2019, 5 pages.
Weng et al., “Deep Neural Networks for Single-Channel Multi-Talker Speech Recognition”, IEEE/ACM Transactions On Audio, Speech, And Language Processing, vol. 23, No. 10, Oct. 2015, pp. 1670-1679.
Wikipedia, “Acoustic Model”, Online available at:—<http://en.wikipedia.org/wiki/AcousticModel>, retrieved on Sep. 14, 2011, pp. 1-2.
Wikipedia, “Home Automation”, Online Available at:—<https://en.wikipedia.org/w/index.php?title=Home_automation&oldid=686569068>, Oct. 19, 2015, 9 pages.
Wikipedia, “Language Model”, Online available at:—<http://en.wikipedia.org/wiki/Language_model>, retrieved on Sep. 14, 2011, 4 pages.
Wikipedia, “Siri”, Online Available at:—<https://en.wikipedia.org/w/index.php?title=Siri&oldid=689697795>, Nov. 8, 2015, 13 pages.
Wikipedia, “Speech Recognition”, Online available at:—<http://en.wikipedia.org/wiki/Speech_recognition>, retrieved on Sep. 14, 2011, 12 pages.
Wikipedia, “Virtual Assistant”, Wikipedia, Online Available at:—<https://en.wikipedia.org/w/index.php?title=Virtual_assistant&oldid=679330666>, Sep. 3, 2015, 4 pages.
X.AI, “How it Works”, Online available at:—<https://web.archive.org/web/20160531201426/https://x.ai/how-it-works/>, May 31, 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 et al., “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.
Yan et al., “A Scalable Approach to Using DNN-derived Features in GMM-HMM Based Acoustic Modeling for LVCSR”, 14th Annual Conference of the International Speech Communication Association, InterSpeech 2013, Aug. 2013, pp. 104-108.
Yates Michaelc., “How Can I Exit Google Assistant After I'm Finished with it”, Online available at:—<https://productforums.google.com/forum/#!msg/phone-by-google/faECnR2RJwA/gKNtOkQgAQAJ>, Jan. 11, 2016, 2 pages.
Yeh Jui-Feng, “Speech Act Identification Using Semantic Dependency Graphs With Probabilistic Context-free Grammars”, ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 15, No. 1, Dec. 2015, p. 5.1-5.28.
Yu et al., “Permutation Invariant Training Of Deep Models For Speaker-Independent Multi-talker Speech Separation”, Proc. ICASSP, 2017, 5 pages.
Yu et al., “Recognizing Multi-talker Speech with Permutation Invariant Training”, Interspeech 2017, Aug. 20-24, 2017, pp. 2456-2460.
Zainab, “Google Input Tools Shows Onscreen Keyboard in Multiple Languages [Chrome]”, Online 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 #-Tags in Twitter”, proceedings of the Workshop on Semantic Adaptive Social Web, 2011, pp. 1-12.
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.
Zmolikova et al., “Speaker-Aware Neural Network Based Beamformer For Speaker Extraction In Speech Mixtures”, Interspeech 2017, Aug. 20-24, 2017, pp. 2655-2659.
Office Action received for Chinese Patent Application No. 201910910058.2, dated Feb. 17, 2023, 12 pages (6 pages of English Translation and 6 pages of Official Copy).
Related Publications (1)
Number Date Country
20210151041 A1 May 2021 US
Provisional Applications (2)
Number Date Country
62129851 Mar 2015 US
62005556 May 2014 US
Continuations (3)
Number Date Country
Parent 16600950 Oct 2019 US
Child 17127394 US
Parent 15971787 May 2018 US
Child 16600950 US
Parent 14724623 May 2015 US
Child 15971787 US