Patent Grant
10748529
The disclosed implementations relate generally to voice-based digital assistants. More specifically, it relates to a voice activated device for interacting with a voice-based digital assistant.
Recently, voice-based digital assistants have been introduced into the marketplace to handle various tasks such as web searching and navigation. One advantage of such voice-based digital assistants is that users can interact with a device in a hands-free manner without handling or even looking at the device. Hands-free operation can be particularly beneficial when a person cannot or should not physically handle a device, such as when they are cooking. Accordingly, it would be advantageous to provide a voice-activated device for interacting with a voice-based digital assistant (or other speech-based service).
The implementations described below provide voice activated devices for interacting with a voice-based assistant. Interactions with a voice-based digital assistant (or other speech-based services, such as a speech-to-text transcription service) can be performed using only an audio interface on the user device. Thus, it is advantageous to limit the human-machine interface on the device to conserve power, reduce production costs, and/or reduce the size of the device. As described herein, a voice activated device with a human-machine interface consisting essentially of an audio interface can be used to interact with a speech-based service without requiring any physical interaction by the user. For example, a user is enabled to activate a digital assistant on the voice activated device by reciting the phrase “Hey, Assistant.” In response, the voice activated device outputs a beep, sound, or speech output (e.g., “what can I do for you?”) indicating to the user that the listening mode is active. Accordingly, the user can initiate an interaction with the voice-based digital assistant without having to physically touch the voice activated device.
One technique for initiating a speech-based service with a voice trigger is to have the speech-based service continuously listen for a predetermined trigger word, phrase, or sound (any of which may be referred to herein as “the trigger sound”). However, continuously operating the speech-based service (e.g., the voice-based digital assistant) requires substantial audio processing and battery power. In order to reduce the power consumed by providing voice trigger functionality, several techniques are, optionally, employed. In some implementations, the main processor of the voice activated device (e.g., an “application processor”) is kept in a low-power or un-powered state while one or more sound detectors that use less power (e.g., because they do not rely on the application processor) remain active. (When it is in a low-power or un-powered state, an application processor or any other processor, program, or module may be described as being inactive or in a standby mode.) For example, a low-power sound detector is used to monitor an audio channel for a trigger sound even when the application processor is inactive. This sound detector is sometimes referred to herein as a trigger sound detector. In some implementations, the trigger sound detector is configured to detect particular sounds, phonemes, and/or words. The trigger sound detector (including hardware and/or software components) is designed to recognize specific words, sound, or phrases, but is generally not capable of or optimized for providing full speech-to-text functionality, as such tasks require greater computational and power resources. Thus, in some implementations, the trigger sound detector recognizes whether a voice input includes a predefined pattern (e.g., a sonic pattern matching the words “Hey, Assistant”), but is not able to (or is not configured to) convert the voice input into text or recognize a significant amount of other words. Once the trigger sound has been detected, then, the voice-based digital assistant is brought out of a standby mode so that the user can provide a voice command.
In some implementations, the trigger sound detector is configured to detect several different trigger sounds, such as a set of words, phrases, sounds, and/or combinations thereof. The user can then use any of those sounds to initiate the speech-based service. In one example, a voice trigger is preconfigured to respond to the phrases “Hey, Assistant,” “Wake up, Assistant,” “Invoke my digital assistant,” or “Hello, HAL, do you read me, HAL?” In some implementations, the user must select one of the preconfigured trigger sounds as the sole trigger sound. In some implementations, the user selects a subset of the preconfigured trigger sounds, so that the user can initiate the speech-based service with different trigger sounds. In some implementations, all of the preconfigured trigger sounds remain valid trigger sounds.
In some implementations, another sound detector is used so that even the trigger sound detector can be kept in a low-power or no-power mode for much of the time. For example, a different type of sound detector (e.g., one that uses less power than the trigger sound detector) is used to monitor an audio channel to determine whether the sound input corresponds to a certain type of sound. Sounds are categorized as different “types” based on certain identifiable characteristics of the sounds. For example, sounds that are of the type “human voice” have certain spectral content, periodicity, fundamental frequencies, etc. Other types of sounds (e.g., whistles, hand claps, etc.) have different characteristics. Sounds of different types are identified using audio and/or signal processing techniques, as described herein.
This sound detector is sometimes referred to herein as a sound-type detector.” For example, if a predetermined trigger phrase is “Hey, Assistant,” the sound-type detector determines whether the input likely corresponds to human speech. If the trigger sound is a non-voiced sound, such as a whistle, the sound-type detector determines whether a sound input likely corresponds to a whistle. When the appropriate type of sound is detected, the sound-type detector initiates the trigger sound detector to further process and/or analyze the sound. And because the sound-type detector requires less power than the trigger sound detector (e.g., because it uses circuitry with lower power demands and/or more efficient audio processing algorithms than the trigger-sound detector), the voice trigger functionality consumes even less power than with a trigger sound detector alone.
In some implementations, yet another sound detector is used so that both the sound-type detector and the trigger sound detector described above can be kept in a low-power or no-power mode for much of the time. For example, a sound detector that uses less power than the sound-type detector is used to monitor an audio channel to determine whether a sound input satisfies a predetermined condition, such as an amplitude (e.g., volume) threshold. This sound detector may be referred to herein as a noise detector. When the noise detector detects a sound that satisfies the predetermined threshold, the noise detector initiates the sound-type detector to further process and/or analyze the sound. And because the noise detector requires less power than either the sound-type detector or the trigger sound detector (e.g., because it uses circuitry with lower power demands and/or more efficient audio processing algorithms), the voice trigger functionality consumes even less power than the combination of the sound-type detector and the trigger sound detector without the noise detector.
In some implementations, any one or more of the sound detectors described above are operated according to a duty cycle, where they are cycled between “on” and “off” states. This further helps to reduce power consumption of the voice trigger. For example, in some implementations, the noise detector is “on” (i.e., actively monitoring an audio channel) for 10 milliseconds, and “off” for the following 90 milliseconds. This way, the noise detector is “off” 90% of the time, while still providing effectively continuous noise detection functionality. In some implementations, the on and off durations for the sound detectors are selected so that all of the detectors are be activated while the trigger sound is still being input. For example, for a trigger phrase of “Hey, Assistant,” the sound detectors are, optionally, configured so that no matter where in the duty cycle(s) the trigger phrase begins, the trigger sound detector is activated in time to analyze a sufficient amount of the input. For example, the trigger sound detector will be activated in time to receive, process, and analyze the sounds “ay Assistant,” which is enough to determine that the sound matches the trigger phrase. In some implementations, sound inputs are stored in memory as they are received and passed to au upstream detector so that a larger portion of the sound input can be analyzed. Accordingly, even if the trigger sound detector is not initiated until after a trigger phrase has been uttered, it can still analyze the entire recorded trigger phrase.
Some implementations provide a voice activated device for interacting with a voice based digital assistant. The voice activated device includes a housing, one or more processors in the housing, memory in the housing, the memory coupled to the one or more processors and comprising instructions for automatically identifying and connecting to a digital assistant server without a user having to enter information about the server. The voice activated device also includes a power supply at least partially within the housing, a wireless network module at least partially within the housing, the wireless network module coupled to the one or more processors, and a human-machine interface. The human-machine interface consists essentially of at least one speaker at least partially within the housing, at least one microphone at least partially within the housing, an analog to digital converter coupled to the microphone and configured to convert speech into digital signals, a digital to analog converter coupled to the at least one speaker and configured to convert received data into audio signals, including speech, and zero or more additional components, coupled to the one or more processors, selected from the set consisting of: a touch-sensitive surface configured to receive touch inputs; one or more cameras; and one or more LEDs. The voice activated device is configured to act as an interface for speech communications between the user and a digital assistant of the user on the digital assistant server.
In some implementations, the touch-sensitive surface corresponds to the housing. In some implementations, the touch-sensitive surface corresponds to a portion of the housing. In some implementations, the touch-sensitive surface replaces a portion of the housing.
In some implementations, the wireless network module is configured to utilize any known wireless network protocol such as Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP), infrared, or any other suitable communication protocol. In some implementations, the wireless network module is further configured to couple to one or more user devices.
In some implementations, the voice activated device is coupled with one or more additional instances of the voice activated device via a wireless network and the voice activated device shares information with the one or more additional instances of the voice activated device. In some implementations, the voice activated device is further configured to provide intercom communication between the one or more coupled devices.
In some implementations, the LEDs are configured to provide user feedback. In some implementations, the LEDs are configured to indicate the voice activated device's current state.
In some implementations, the voice activated device also includes one or more light sensors coupled to the one or more processors. In some implementations, the voice activated device also includes a motion sensor to detect motion in proximity to the voice activated device.
In some implementations, the voice activated device is further configured, while in a security mode, to: detect a change in light level above a predefined threshold with the one or more light sensors; in response to the change in light level, audibly request a passcode; in accordance with a determination that the passcode was received within a predetermined amount of time and that the received passcode matches a preset security code, disable the security mode; and in accordance with a determination that the passcode was not received within a predetermined amount of time or that the received passcode did not match the preset security code, activate an alarm routine.
In some implementations, the voice activated device is further configured, while in a security mode, to: detect movement with the motion sensor; in response to the detected movement, audibly request a passcode; in accordance with a determination that the passcode was received within a predetermined amount of time and that the received passcode matches a preset security code, disable the security mode; and in accordance with a determination that the passcode was not received within a predetermined amount of time or that the received passcode did not match the preset security code, activate an alarm routine.
In some implementations, the voice activated device also includes a fire detection module coupled to the one or more processors. In some implementations, the voice activated device also includes a carbon monoxide detection module coupled to the one or more processors.
In some implementations, the memory stores a personalized configuration for each of a plurality of users, and the voice activated device is further configured, when activated by a respective user, to provide services to the respective user utilizing the respective personalized configuration.
In some implementations, the voice activated device is further configured to play audio files associated with the user. In some implementations, the voice activated device is further configured for home automation. In some implementations, the voice activated device is further configured to function as a timer when prompted by the user. In some implementations, the voice activated device is further configured to access a news source and provide news to the user when prompted by the user. In some implementations, the voice activated device is further configured to store the converted user speech in memory when prompted by the user.
In some implementations, the voice activated device is further configured to provide text-to-speech (TTS) communication between the user and a second party. In some implementations, the voice activated device is coupled to a telephone associated with the user and the voice activated device is further configured to provide telecommunication via the telephone for the user. In some implementations, the voice activated device is further configured to provide notifications received from the one or more coupled devices to the user.
In some implementations, the voice activated device is further configured to access the user's calendar. In some of these implementations, the voice activated device is further configured to provide calendar reminders to the user. In some implementations, the voice activated device is further configured to update the user's calendar.
In some implementations, the voice activated device is further configured to access a set of instructions and output the instruction steps when prompted by the user.
In some implementations, the voice activated device is further configured to store in memory one or more macros provided by the user, where each macro is associated with a respective phrase. In some of these implementations, the voice activated device is further configured to play the macro associated with the respective phrase when the respective phrase is spoken by the user.
Like reference numerals refer to corresponding parts throughout the drawings.
Specifically, once initiated, a digital assistant system is capable of accepting a user request at least partially in the form of a natural language command, request, statement, narrative, and/or inquiry. Typically, the user request seeks either an informational answer or performance of a task by the digital assistant system. A satisfactory response to the user request is generally either provision of the requested informational answer, performance of the requested task, or a combination of the two. For example, a user may ask the digital assistant system a question, such as “Where am I right now?” Based on the user's current location, the digital assistant may answer, “You are in Central Park near the west gate.” The user may also request the performance of a task, for example, by stating “Please invite my friends to my girlfriend's birthday party next week.” In response, the digital assistant may acknowledge the request by generating a voice output, “Yes, right away,” and then send a suitable calendar invite from the user's email address to each of the user' friends listed in the user's electronic address book or contact list. 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
In some implementations, the DA server 106 includes a client-facing I/O interface 112, one or more processing modules 114, data and models 116, an I/O interface to external services 118, a photo and tag database 130, and a photo-tag module 132. The client-facing I/O interface facilitates the client-facing input and output processing for the digital assistant server 106. The one or more processing modules 114 utilize the data and models 116 to determine the user's intent based on natural language input and perform task execution based on the deduced user intent. Photo and tag database 130 stores fingerprints of digital photographs, and, optionally digital photographs themselves, as well as tags associated with the digital photographs. Photo-tag module 132 creates tags, stores tags in association with photographs and/or fingerprints, automatically tags photographs, and links tags to locations within photographs.
In some implementations, the DA server 106 communicates with external services 120 (e.g., navigation service(s) 122-1, messaging service(s) 122-2, information service(s) 122-3, calendar service 122-4, telephony service 122-5, photo service(s) 122-6, etc.) through the network(s) 110 for task completion or information acquisition. The I/O interface to the external services 118 facilitates such communications.
Examples of the user device 104 include, but are not limited to, a handheld computer, a personal digital assistant (PDA), a tablet computer, a laptop computer, a desktop computer, a cellular telephone, a smartphone, an enhanced general packet radio service (EGPRS) mobile phone, a media player, a navigation device, a game console, a television, a remote control, or a combination of any two or more of these data processing devices or any other suitable data processing devices.
More details on a voice activated device are provided in reference to an exemplary voice activated device 102 shown in
Examples of the communication network(s) 110 include local area networks (LAN) and wide area networks (WAN), e.g., the Internet. The communication network(s) 110 may be implemented using any known network protocol, including various wired or wireless protocols, such as 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.
The server system 108 can be implemented on at least one data processing apparatus and/or a distributed network of computers. In some implementations, the server system 108 also employs various virtual devices and/or services of third party service providers (e.g., third-party cloud service providers) to provide the underlying computing resources and/or infrastructure resources of the server system 108.
Although the digital assistant system shown in
In some implementations, human-machine interface 212 includes audio subsystem 214 coupled to one or more speakers 216 and one or more microphones 218. In some implementations, human-machine interface 212 includes camera subsystem 222 connected to one or more cameras 223. In some of these implementations, camera subsystem 222 facilitates security and communications functionalities (e.g., such as taking photographs and recording video clips). In some implementations, human-machine interface 212 includes LED subsystem 224 connected to one or more LEDs 225. In some of these implementations, LED subsystem 224 facilitates user feedback functionalities. In some implementations, human-machine interface 212 includes touch subsystem 226 connected to a touch-sensitive surface 227. In some of these implementations, touch subsystem 226 facilitates user input functionalities.
Wireless network module 210 optionally includes radio frequency receivers and transmitters and/or optical (e.g., infrared) receivers and transmitters. When voice activated device 102 includes touch subsystem 226 and the touch-sensitive surface 227, touch subsystem 226 and the touch-sensitive surface 227 are typically configured to, for example, detect contact and movement or break thereof using any of a plurality of touch sensitivity technologies, such as capacitive, resistive, infrared, surface acoustic wave technologies, proximity sensor arrays, and the like.
In some implementations, audio subsystem 214 is coupled to one or more speakers 216 and one or more microphones 218 to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording, and telephony functions. In some implementations, audio subsystem 214 includes voice trigger system 220. In some implementations, voice trigger system 220 and/or the audio subsystem 214 includes low-power audio circuitry and/or programs (e.g., including hardware and/or software) for receiving and/or analyzing sound inputs, including, for example, one or more analog-to-digital converters, digital signal processors (DSPs), sound detectors, memory buffers, codecs, and the like. In some implementations, the low-power audio circuitry (alone or in addition to other components of voice activated device 102) provides voice (or sound) trigger functionality for one or more aspects of voice activated device 102, such as a voice-based digital assistant or other speech-based service. In some implementations, the low-power audio circuitry provides voice trigger functionality even when other components of voice activated device 102 are shut down and/or in a standby mode, such as the processor(s) 204, memory 206, and the like. Voice trigger system 220 is described in further detail with respect to
In some implementations, memory 206 includes a non-transitory computer readable storage medium, such as high-speed random access memory and/or non-volatile memory (e.g., one or more magnetic disk storage devices, one or more flash memory devices, one or more optical storage devices, and/or other non-volatile solid-state memory devices).
In some implementations, memory 206 stores an operating system, a communications module, a sensor processing module, and applications, and a subset or superset thereof. The operating system includes instructions for handling basic system services and for performing hardware dependent tasks. The communications module facilitates communicating with one or more additional devices, one or more computers and/or one or more servers. The sensor processing module facilitates sensor-related processing and functions (e.g., processing voice input received with the one or more microphones 218). The application module facilitates various functionalities of user applications, such as electronic-messaging, web browsing, media processing, and/or other processes and functions. In some implementations, voice activated device 102 stores in memory 206 one or more software applications each associated with at least one of the external service providers.
As described above, in some implementations, memory 206 also stores client-side digital assistant instructions (e.g., in a digital assistant client module 240) and various user data (e.g., user-specific vocabulary data, preference data, and/or other data such as the user's electronic address book or contact list, to-do lists, shopping lists, etc.) to provide the client-side functionalities of the digital assistant.
In various implementations, digital assistant client module 240 is capable of accepting voice input, touch input, and/or visual input through human-machine interface 212 of voice activated device 102. Digital assistant client module 240 is also capable of providing output in audio and visual forms. For example, output can be provided as voice, sound, alerts, and/or combinations of two or more of the above. During operation, digital assistant client module 240 optionally communicates with the digital assistant server (e.g., the digital assistant server 106,
In some implementations, the digital assistant client module 240 utilizes various sensors and subsystems to gather additional information from the surrounding environment of voice activated device 102 to establish a context associated with a user input. In some implementations, the digital assistant client module 240 provides the context information or a subset thereof with the user input to the digital assistant server (e.g., the digital assistant server 106,
In some implementations, the context information that can accompany the user input includes sensor information, e.g., lighting, ambient noise, ambient temperature, images or videos of the surrounding environment, etc. In some implementations, the context information also includes the physical state of the voice activated device, e.g., device orientation, device location, device temperature, power level, speed, wireless signals strength, etc. In some implementations, information related to the software state of voice activated device 102, e.g., running processes, installed programs, past and present network activities, background services, error logs, resources usage, etc., of voice activated device 102 is also provided to the digital assistant server (e.g., the digital assistant server 106,
In some implementations, the DA client module 240 selectively provides information (e.g., at least a portion of the user data) stored on voice activated device 102 in response to requests from the digital assistant server. In some implementations, the digital assistant client module 240 also elicits additional input from the user via a natural language dialogue upon request by the digital assistant server 106 (
In some implementations, memory 206 includes additional instructions or fewer instructions. Furthermore, various functions of the voice activated device 102 are, optionally, implemented in hardware and/or in firmware, including in one or more signal processing and/or application specific integrated circuits. Thus, in some implementations, the voice activated device 102 includes fewer modules and/or applications than those illustrated in
In some embodiments, voice activated device 102 does not include any physical ports (e.g., all communication between voice activated device 102 and external networks occurs via wireless communications, including communications related to software updates, communications related to performing voice commands received from a user, communications to and from other user devices, and the like). In embodiments, where the voice activated device does not include any physical ports, the lack of physical ports improves the appearance and functionality of the voice activated device, by making the voice activated device less susceptible to physical damage and allowing for a more symmetrical and visually pleasing appearance. Additionally, having fewer components reduces the manufacturing cost and the price of voice activated device 102.
Some embodiments provide a voice activated device (e.g., device 102) for interacting with a voice based digital assistant. The voice activated device includes a housing (e.g., housing 202), one or more processors (e.g., processor(s) 204) in the housing, memory (e.g., memory 206) in the housing, the memory coupled to the one or more processors and comprising instructions for automatically identifying and connecting to a digital assistant server without a user having to enter information about (e.g., an internet address for) the server.
The voice activated device also includes a power supply (e.g., a transformer or a battery such as power supply 208) at least partially within the housing (e.g., with a fold-out plug), a wireless network module (e.g., wireless network module 210) at least partially within the housing, the wireless network module coupled to the one or more processors, and a human-machine interface (e.g., human machine interface 212). In some implementations, the wireless network module is configured to utilize any known wireless network protocol such as Bluetooth, WiFi, voice over Internet Protocol (VoIP), infrared, or any other suitable communication protocol.
The human-machine interface consists essentially of at least one speaker (e.g., speaker 216) at least partially within the housing, at least one microphone (e.g., microphone 218) at least partially within the housing, an analog to digital converter (e.g., ADC 410 in audio subsystem 214, as described below with reference to
In some embodiments, the voice activated device is configured to act as an interface for speech communications between the user and a digital assistant of the user on the digital assistant server.
In some embodiments, the voice activated device is coupled with one or more additional instances of the voice activated device via a wireless network and the voice activated device shares information with the one or more additional instances of the voice activated device. For example, voice activated devices in different rooms of a house are coupled together and function as a single voice activated device when interacting with a user (e.g., the voice activated devices collaborate on assisting the user).
In some embodiments, the voice activated device is further configured to provide intercom communication between the one or more coupled devices. For example, when prompted by a user, a voice activated device in a kitchen of a house acts as an intercom with a voice activated device in a bedroom of the house.
In some embodiments, the voice activated device also includes one or more light sensors coupled to the one or more processors. In some implementations, the light sensors are configured to assist the voice activated device in detecting nearby activity. In some implementations, the voice activated device is further configured to utilize the light sensors when determining the appropriate operating mode (e.g., when to enter sleep mode).
In some embodiments, the voice activated device also includes a motion sensor to detect motion in proximity to the voice activated device.
In some embodiments, the voice activated device also includes a fire detection module (e.g., a smoke detector) coupled to the one or more processors. In some embodiments, the voice activated device also includes a carbon monoxide detection module coupled to the one or more processors.
In some embodiments, the memory stores a personalized configuration (e.g., user specific settings files) for each of a plurality of users, and the voice activated device is further configured, when activated by a respective user, to provide services to the respective user utilizing the respective personalized configuration. In some implementations, activating the voice activated device includes logging into the voice activated device. In some implementations, activating the voice activated device includes identifying the respective user by using a voice recognition model corresponding to the respective user.
In some embodiments, the voice activated device is further configured to play audio files (e.g., music files) associated with the user. In some implementations, the audio files are stored in memory on the voice activated device. In some implementations, the audio files are stored in memory on a server (e.g., digital assistant server 106, a remote media server, or a network attached storage system), distinct from the voice activated device. In some implementations, the audio files are streamed from a remote server (e.g., a radio station).
In some embodiments, the voice activated device is further configured for home automation. For example, in some implementations, the voice activated device is configured to control room lights, house lights, room temperature, various electronics (e.g., a television) and various kitchen appliances (e.g., a coffee maker).
In some embodiments, the voice activated device is further configured to function as a timer when prompted by the user. For example, in some implementations, the voice activated device is used as a cooking timer and/or an exercise timer.
In some embodiments, the voice activated device is further configured to access (e.g., via the wireless network module) a news source (e.g., the associated press or user preferred news site) and provide news to the user when prompted by the user. For example, in some implementations, the voice activated device provides weather information, stock information, sports scores, and/or headlines.
In some embodiments, the voice activated device is further configured to store the converted user speech in memory when prompted by the user. For example, in some implementations, take notes while the user dictates information (e.g., a speech or a paper).
In some embodiments, the wireless network module is further configured to couple to one or more user devices. For example, user devices include cellular phones, tablet computers, laptop computers, desktop computers, televisions, cable boxes, and the like.
In some embodiments, the voice activated device is further configured to provide text-to-speech (TTS) communication between the user and a second party. For example, in some implementations, the voice activated device receives a text message via a wireless network, converts the text message to a corresponding audio signal, and outputs (e.g., via speaker 216 in
In some embodiments, the voice activated device is further configured to provide speech-to-text communication between the user and a second party. For example, in some implementations, the voice activated device receives user speech (e.g., via microphone 218 in
In some embodiments, the voice activated device is coupled to a telephone associated with the user and the voice activated device is further configured to provide telecommunication via the telephone for the user. For example, in some implementations, the voice activated device is configured to function similar to a wireless headset (e.g., receiving voice inputs via microphone 218 and providing audible outputs via speaker 216).
In some embodiments, the voice activated device is further configured to provide notifications received from the one or more coupled devices to the user. In some implementations, the voice activated device receives a low battery warning from a coupled device (e.g., a cellphone or a laptop computer). In some of these implementations, the voice activated device audibly provides the warning to the user. In some implementations, the voice activated device receives an event notification (e.g., a voice message, text message, and/or calendar event) from a coupled device. In some of these implementations, the voice activated device audibly provides the event notification to the user.
In some embodiments, the voice activated device is further configured to access the user's calendar. In some implementations, the user's calendar is stored in memory on the voice activated device. In some implementations, the user's calendar is stored on a device (e.g., digital assistant server 106, a remote media server, a network attached storage system, or a user's phone or personal computer) that is remote from the voice activated device and is accessed via the wireless network module. In some implementations, the user's calendar is stored on the digital assistant server.
In some embodiments, the voice activated device is further configured to provide calendar reminders to the user. In some implementations, the voice activated device provides reminders when prompted by the user. In some implementations, the voice activated device provides reminders at a preset time before (e.g., a reminder 30 minutes for a meeting) and/or after the event occurs.
In some embodiments, the voice activated device is further configured to update the user's calendar. In some implementations, the voice activated device adds a calendar event after making an appointment (e.g., a doctor's appointment, a restaurant reservation, and the like) for the user. In some implementations, the voice activated device adds a calendar event when prompted by the user.
In some embodiments, the voice activated device is further configured to access a set of instructions and output the instruction steps when prompted by the user. For example, the voice activated device accesses a cooking recipe and recites the recipe steps as requested by the user. In some implementations, the voice activated device accesses the instructions from memory in the voice activated device. In some implementations, the voice activated device accesses the instructions from a coupled device (e.g., a user's phone, personal computer or network attached storage). In some implementations, the voice activated device accesses the instructions from the digital assistant server. In some implementations the voice activated device outputs all of the instruction steps when prompted. In some implementations the voice activated device outputs one instruction step when prompted and outputs each instruction step in sequence over successive prompts.
In some embodiments, the voice activated device is further configured to store in memory one or more macros provided by the user, where each macro is associated with a respective phrase (e.g., a trigger word or “Magic Word”). In some of these embodiments, the voice activated device is further configured to play the macro associated with the respective phrase when the respective phrase is spoken by the user. In some implementations, the memory is memory within the voice activated device (e.g., memory 206 in
The digital assistant system 300 includes memory 302, one or more processors 304, an input/output (I/O) interface 306, and a network communications interface 308. These components communicate with one another over one or more communication buses or signal lines 310.
In some implementations, memory 302 includes a non-transitory computer readable medium, such as high-speed random access memory and/or a non-volatile computer readable storage medium (e.g., one or more magnetic disk storage devices, one or more flash memory devices, one or more optical storage devices, and/or other non-volatile solid-state memory devices).
The I/O interface 306 couples input/output devices 316 of the digital assistant system 300, such as displays, a keyboards, touch screens, and microphones, to the user interface module 322. The I/O interface 306, in conjunction with the user interface module 322, receives user inputs (e.g., voice input, keyboard inputs, touch inputs, etc.) and process them accordingly. In some implementations, when the digital assistant is implemented on a standalone user device, the digital assistant system 300 includes any of the components and I/O and communication interfaces described with respect to the voice activated device 102 in
In some implementations, the network communications interface 308 eludes wired communication port(s) 312 and/or wireless transmission and reception circuitry 314. The wired communication port(s) receive and send communication signals via one or more wired interfaces, e.g., Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wireless circuitry 314 typically receives and sends RF signals and/or optical signals from/to communications networks and other communications devices. The wireless communications may 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. The network communications interface 308 enables communication between the digital assistant system 300 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 implementations, the non-transitory computer readable storage medium of memory 302 stores programs, modules, instructions, and data structures including all or a subset of: an operating system 318, a communications module 320, a user interface module 322, one or more applications 324, and a digital assistant module 326. The one or more processors 304 execute these programs, modules, and instructions, and reads/writes from/to the data structures.
The operating system 318 (e.g., Darwin, RTXC, LINUX, UNIX, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communications between various hardware, firmware, and software components.
The communications module 320 facilitates communications between the digital assistant system 300 with other devices over the network communications interface 308. For example, the communication module 320 may communicate with the communications module of the voice activated device 102 shown in
In some implementations, the user interface module 322 receives commands and/or inputs from a user via the I/O interface 306 (e.g., from a keyboard, touch screen, and/or microphone), and provides user interface objects on a display.
The applications 324 include programs and/or modules that are configured to be executed by the one or more processors 304. For example, if the digital assistant system is implemented on a standalone user device, the applications 324 may include user applications, such as games, a calendar application, a navigation application, or an email application. If the digital assistant system 300 is implemented on a server farm, the applications 324 may include resource management applications, diagnostic applications, or scheduling applications, for example.
Memory 302 also stores the digital assistant module (or the server portion of a digital assistant) 326. In some implementations, the digital assistant module 326 includes the following sub-modules, or a subset or superset thereof: an input/output processing module 328, a speech-to-text (STT) processing module 330, a natural language processing module 332, a dialogue flow processing module 334, a task flow processing module 336, a service processing module 338, and a photo module 132. Each of these processing modules has access to one or more of the following data and models of the digital assistant 326, or a subset or superset thereof: ontology 360, vocabulary index 344, user data 348, categorization module 349, disambiguation module 350, task flow models 354, service models 356, photo tagging module 358, search module 360, and local tag/photo storage 362.
In some implementations, using the processing modules (e.g., the input/output processing module 328, the STT processing module 330, the natural language processing module 332, the dialogue flow processing module 334, the task flow processing module 336, and/or the service processing module 338), data, and models implemented in the digital assistant module 326, the digital assistant system 300 performs at least some of the following: identifying a user's intent expressed in a natural language input received from the user; actively eliciting and obtaining information needed to fully deduce the user's intent (e.g., by disambiguating words, names, intentions, etc.); determining the task flow for fulfilling the deduced intent; and executing the task flow to fulfill the deduced intent. In some implementations, the digital assistant also takes appropriate actions when a satisfactory response was not or could not be provided to the user for various reasons.
In some implementations, as discussed below, the digital assistant system 300 identifies, from a natural language input, a user's intent to tag a digital photograph, and processes the natural language input so as to tag the digital photograph with appropriate information. In some implementations, the digital assistant system 300 performs other tasks related to photographs as well, such as searching for digital photographs using natural language input, auto-tagging photographs, and the like.
As shown in
In some implementations, the speech-to-text processing module 330 receives speech input (e.g., a user utterance captured in a voice recording) through the I/O processing module 328. In some implementations, the speech-to-text processing module 330 uses various acoustic and language models to recognize the speech input as a sequence of phonemes, and ultimately, a sequence of words or tokens written in one or more languages. The speech-to-text processing module 330 is implemented using any suitable speech recognition techniques, acoustic models, and language models, such as Hidden Markov Models, Dynamic Time Warping (DTW)-based speech recognition, and other statistical and/or analytical techniques. In some implementations, the speech-to-text processing can be performed at least partially by a third party service or on the user's device. Once the speech-to-text processing module 330 obtains the result of the speech-to-text processing (e.g., a sequence of words or tokens), it passes the result to the natural language processing module 332 for intent deduction.
The natural language processing module 332 (“natural language processor”) of the digital assistant 326 takes the sequence of words or tokens (“token sequence”) generated by the speech-to-text processing module 330, and attempts to associate the token sequence with one or more “actionable intents” recognized by the digital assistant. As used herein, an “actionable intent” represents a task that can be performed by the digital assistant 326 and/or the digital assistant system 300 (
In some implementations, in addition to the sequence of words or tokens obtained from the speech-to-text processing module 330, the natural language processor 332 also receives context information associated with the user request (e.g., from the I/O processing module 328). The natural language processor 332 optionally uses the context information to clarify, supplement, and/or further define the information contained in the token sequence received from the speech-to-text processing module 330. The context information includes, for example, user preferences, hardware and/or software states of the user device (e.g., voice activated device 102), 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.
In some implementations, the natural language processing is based on an ontology 360. The ontology 360 is a hierarchical structure containing a plurality of nodes, each node representing either an “actionable intent” or a “property” relevant to one or more of the “actionable intents” or other “properties.” As noted above, an “actionable intent” represents a task that the digital assistant system 300 is capable of performing (e.g., a task that is “actionable” or can be acted on). A “property” represents a parameter associated with an actionable intent or a sub-aspect of another property. A linkage between an actionable intent node and a property node in the ontology 360 defines how a parameter represented by the property node pertains to the task represented by the actionable intent node.
In some implementations, the ontology 360 is made up of actionable intent nodes and property nodes. Within the ontology 360, each actionable intent node is linked to one or more property nodes either directly or through one or more intermediate property nodes. Similarly, each property node is linked to one or more actionable intent nodes either directly or through one or more intermediate property nodes. For example, the ontology 360 shown in
An actionable intent node, along with its linked concept nodes, may be described as a “domain.” In the present discussion, each domain is associated with a respective actionable intent, and refers to the group of nodes (and the relationships therebetween) associated with the particular actionable intent. For example, the ontology 360 shown in
While
In some implementations, the ontology 360 includes all the domains (and hence actionable intents) that the digital assistant is capable of understanding and acting upon. In some implementations, the ontology 360 may be modified, such as by adding or removing domains or nodes, or by modifying relationships between the nodes within the ontology 360.
In some implementations, nodes associated with multiple related actionable intents may be clustered under a “super domain” in the ontology 360. For example, a “travel” super-domain may include a cluster of property nodes and actionable intent nodes related to travels. The actionable intent nodes related to travels may 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 “travels” super domain) may have many property nodes in common. For example, the actionable intent nodes for “airline reservation,” “hotel reservation,” “car rental,” “get directions,” “find points of interest” may share one or more of the property nodes “start location,” “destination,” “departure date/time,” “arrival date/time,” and “party size.”
In some implementations, each node in the ontology 360 is associated with a set of words and/or phrases that are relevant to the property or actionable intent represented by the node. The respective set of words and/or phrases associated with each node is the so-called “vocabulary” associated with the node. The respective set of words and/or phrases associated with each node can be stored in the vocabulary index 344 (
In some implementations, the natural language processor 332 shown in
In some implementations, the digital assistant system 300 also stores names of specific entities in the vocabulary index 344, so that when one of these names is detected in the user request, the natural language processor 332 will be able to recognize that the name refers to a specific instance of a property or sub-property in the ontology. In some implementations, the names of specific entities are names of businesses, restaurants, people, movies, and the like. In some implementations, the digital assistant system 300 can search and identify specific entity names from other data sources, such as the user's address book or contact list, a movies database, a musicians database, and/or a restaurant database. In some implementations, when the natural language processor 332 identifies that a word in the token sequence is a name of a specific entity (such as a name in the user's address book or contact list), that word is given additional significance in selecting the actionable intent within the ontology for the user request.
For example, when the words “Mr. Santo” are recognized from the user request, and the last name “Santo” is found in the vocabulary index 344 as one of the contacts in the user's contact list, then it is likely that the user request corresponds to a “send a message” or “initiate a phone call” domain. For another example, when the words “ABC Café” are found in the user request, and the term “ABC Café” is found in the vocabulary index 344 as the name of a particular restaurant in the user's city, then it is likely that the user request corresponds to a “restaurant reservation” domain.
User data 348 includes user-specific information, such as user-specific vocabulary, user preferences, user address, user's default and secondary languages, user's contact list, and other short-term in or long-term information for each user. The natural language processor 332 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,” the natural language processor 332 is able to access user data 348 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.
In some implementations, natural language processor 332 includes categorization module 349. In some implementations, the categorization module 349 determines whether each of the one or more terms in a text string (e.g., corresponding to a speech input associated with a digital photograph) is one of an entity, an activity, or a location, as discussed in greater detail below. In some implementations, the categorization module 349 classifies each term of the one or more terms as one of an entity, an activity, or a location.
Once the natural language processor 332 identifies an actionable intent (or domain) based on the user request, the natural language processor 332 generates a structured query to represent the identified actionable intent. In some implementations, the structured query includes parameters for one or more nodes within the domain for the actionable intent, and at least some of the parameters are populated with the specific information and requirements specified in the user request. For example, the user may say “Make me a dinner reservation at a sushi place at 7.” In this case, the natural language processor 332 may 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. Based on the information contained in the user's utterance, the natural language processor 332 may 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} are not specified in the structured query based on the information currently available. In some implementations, the natural language processor 332 populates some parameters of the structured query with received context information. For example, if the user requested a sushi restaurant “near me,” the natural language processor 332 may populate a {location} parameter in the structured query with GPS coordinates from the user device 104.
In some implementations, the natural language processor 332 passes the structured query (including any completed parameters) to the task flow processing module 336 (“task flow processor”). The task flow processor 336 is configured to perform one or more of: receiving the structured query from the natural language processor 332, completing the structured query, and performing the actions required to “complete” the user's ultimate request. In some implementations, the various procedures necessary to complete these tasks are provided in task flow models 354. In some implementations, the task flow models 354 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, the task flow processor 336 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, the task flow processor 336 invokes the dialogue processing module 334 (“dialogue processor”) to engage in a dialogue with the user. In some implementations, the dialogue processing module 334 determines how (and/or when) to ask the user for the additional information, and receives and processes the user responses. In some implementations, the questions are provided to and answers are received from the users through the I/O processing module 328. For example, the dialogue processing module 334 presents dialogue output to the user via audio and/or visual output, and receives input from the user via spoken or physical (e.g., touch gesture) responses. Continuing with the example above, when the task flow processor 336 invokes the dialogue processor 334 to determine the “party size” and “date” information for the structured query associated with the domain “restaurant reservation,” the dialogue processor 334 generates questions such as “For how many people?” and “On which day?” to pass to the user. Once answers are received from the user, the dialogue processing module 334 populates the structured query with the missing information, or passes the information to the task flow processor 336 to complete the missing information from the structured query.
In some cases, the task flow processor 336 may receive a structured query that has one or more ambiguous properties. For example, a structured query for the “send a message” domain may indicate that the intended recipient is “Bob,” and the user may have multiple contacts named “Bob.” The task flow processor 336 will request that the dialogue processor 334 disambiguate this property of the structured query. In turn, the dialogue processor 334 may ask the user “Which Bob?”, and display (or read) a list of contacts named “Bob” from which the user may choose.
In some implementations, dialogue processor 334 includes disambiguation module 350. In some implementations, disambiguation module 350 disambiguates one or more ambiguous terms (e.g., one or more ambiguous terms in a text string corresponding to a speech input associated with a digital photograph). In some implementations, disambiguation module 350 identifies that a first term of the one or more terms has multiple candidate meanings, prompts a user for additional information about the first term, receives the additional information from the user in response to the prompt and identifies the entity, activity, or location associated with the first term in accordance with the additional information.
In some implementations, disambiguation module 350 disambiguates pronouns. In such implementations, disambiguation module 350 identifies one of the one or more terms as a pronoun and determines a noun to which the pronoun refers. In some implementations, disambiguation module 350 determines a noun to which the pronoun refers by using a contact list associated with a user of the voice activated device. Alternatively, or in addition, disambiguation module 350 determines a noun to which the pronoun refers as a name of an entity, an activity, or a location identified in a previous speech input associated with a previously tagged digital photograph. Alternatively, or in addition, disambiguation module 350 determines a noun to which the pronoun refers as a name of a person identified based on a previous speech input associated with a previously tagged digital photograph.
In some implementations, disambiguation module 350 accesses information obtained from one or more sensors (e.g., motion sensor 230, light sensor 234, fire sensor 236, and other sensors 232) of a user device (e.g., voice activated device 102) for determining a meaning of one or more of the terms. In some implementations, disambiguation module 350 identifies two terms each associated with one of an entity, an activity, or a location. For example, a first of the two terms refers to a person, and a second of the two terms refers to a location. In some implementations, disambiguation module 350 identifies three terms each associated with one of an entity, an activity, or a location.
Once the task flow processor 336 has completed the structured query for an actionable intent, the task flow processor 336 proceeds to perform the ultimate task associated with the actionable intent. Accordingly, the task flow processor 336 executes the steps and instructions in the task flow model according to the specific parameters contained in the structured query. For example, the task flow model for the actionable intent of “restaurant reservation” may 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}, the task flow processor 336 may perform the steps of: (1) logging onto a server of the ABC Café or a restaurant reservation system that is configured to accept reservations for multiple restaurants, such as the ABC Café, (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 another example, described in greater detail below, the task flow processor 336 executes steps and instructions associated with tagging or searching for digital photographs in response to a voice input, e.g., in conjunction with photo module 132.
In some implementations, the task flow processor 336 employs the assistance of a service processing module 338 (“service processor”) to complete a task requested in the user input or to provide an informational answer requested in the user input. For example, the service processor 338 can act on behalf of the task flow processor 336 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 (e.g., voice activated device 102), and invoke or interact with third party services (e.g. a restaurant reservation portal, a social networking website or service, a banking portal, etc.). In some implementations, the protocols and application programming interfaces (API) required by each service can be specified by a respective service model among the service models 356. The service processor 338 accesses the appropriate service model for a service and generates requests for the service in accordance with the protocols and APIs required by the service according to the service model.
For example, if a restaurant has enabled an online reservation service, the restaurant can submit a service model specifying the necessary parameters for making a reservation and the APIs for communicating the values of the necessary parameters to the online reservation service. When requested by the task flow processor 336, the service processor 338 can establish a network connection with the online reservation service using the web address stored in the service models 356, 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 implementations, the natural language processor 332, dialogue processor 334, and task flow processor 336 are used collectively and iteratively to deduce and define the user's intent, obtain information to further clarify and refine the user intent, and finally generate a response (e.g., provide an output to the user, or complete a task) to fulfill the user's intent.
In some implementations, after all of the tasks needed to fulfill the user's request have been performed, the digital assistant 326 formulates a confirmation response, and sends the response back to the user through the I/O processing module 328. If the user request seeks an informational answer, the confirmation response presents the requested information to the user. In some implementations, the digital assistant also requests the user to indicate whether the user is satisfied with the response produced by the digital assistant 326.
Attention is now directed to
In some implementations, audio subsystem 214 includes analog to digital converter (ADC) 410, digital to analog converter (DAC) 412, and voice trigger system 220. In some implementations, the audio subsystem 226 is coupled to one or more microphones 218 (
In some implementations, voice trigger system 220 includes a noise detector 402, a sound-type detector 404, a trigger sound detector 406, and a speech-based service 408, each coupled to an audio bus 401. In some implementations, more or fewer of these modules are used. The detectors (e.g., noise detector 402, sound-type detector 404, and trigger sound detector 406) are, optionally, referred to as modules, and optionally include hardware (e.g., circuitry, memory, processors, etc.), software (e.g., programs, software-on-a-chip, firmware, etc.), and/or any combinations thereof for performing the functionality described herein. In some implementations, the sound detectors are communicatively, programmatically, physically, and/or operationally coupled to one another (e.g., via a communications bus), as illustrated in
In some implementations, the speech-based service 408 is a voice-based digital assistant, and corresponds to one or more components or functionalities of the digital assistant system described above with reference to
In some implementations, the noise detector 402 monitors an audio channel to determine whether a sound input satisfies a predetermined condition, such as an amplitude threshold. The audio channel corresponds to a stream of audio information received by one or more sound pickup devices, such as the one or more microphones 218 (
In some implementations, the predetermined condition is whether the sound input is above a certain volume for a predetermined amount of time. In some implementations, the noise detector uses time-domain analysis of the sound input, which requires relatively little computational and battery resources as compared to other types of analysis (e.g., as performed by the sound-type detector 404, the trigger word detector 406, and/or the speech-based service 408). In some implementations, other types of signal processing and/or audio analysis are used, including, for example, frequency-domain analysis. If the noise detector 402 determines that the sound input satisfies the predetermined condition, it initiates an upstream sound detector, such as the sound-type detector 404 (e.g., by providing a control signal to initiate one or more processing routines, and/or by providing power to the upstream sound detector). In some implementations, the upstream sound detector is initiated in response to other conditions being satisfied. For example, in some implementations, the upstream sound detector is initiated in response to determining that the voice activated device is not being stored in an enclosed space (e.g., based on a light detector detecting a threshold level of light).
The sound-type detector 404 monitors the audio channel to determine whether a sound input corresponds to a certain type of sound, such as sound that is characteristic of a human voice, whistle, clap, etc. The type of sound that the sound-type detector 404 is configured to recognize will correspond to the particular trigger sound(s) that the voice trigger is configured to recognize. In implementations where the trigger sound is a spoken word or phrase, the sound-type detector 404 includes a “voice activity detector” (VAD). In some implementations, the sound-type detector 404 uses frequency-domain analysis of the sound input. For example, the sound-type detector 404 generates a spectrogram of a received sound input (e.g., using a Fourier transform), and analyzes the spectral components of the sound input to determine whether the sound input is likely to correspond to a particular type or category of sounds (e.g., human speech). Thus, in implementations where the trigger sound is a spoken word or phrase, if the audio channel is picking up ambient sound (e.g., traffic noise) but not human speech, the voice activity detector will not initiate the trigger sound detector 406.
In some implementations, the sound-type detector 404 remains active for as long as predetermined conditions of any downstream sound detector (e.g., the noise detector 402) are satisfied. For example, in some implementations, the sound-type detector 404 remains active as long as the sound input includes sound above a predetermined amplitude threshold (as determined by the noise detector 402), and is deactivated when the sound drops below the predetermined threshold. In some implementations, once initiated, the sound-type detector 404 remains active until a condition is met, such as the expiration of a timer (e.g., for 1, 2, 5, or 10 seconds, or any other appropriate duration), the expiration of a certain number of on/off cycles of the sound-type detector 404, or the occurrence of an event (e.g., the amplitude of the sound falls below a second threshold, as determined by the noise detector 402 and/or the sound-type detector 404).
As mentioned above, if the sound-type detector 404 determines that the sound input corresponds to a predetermined type of sound, it initiates an upstream sound detector (e.g., by providing a control signal to initiate one or more processing routines, and/or by providing power to the upstream sound detector), such as the trigger sound detector 406.
The trigger sound detector 406 is configured to determine whether a sound input includes at least part of certain predetermined content (e.g., at least part of the trigger word, phrase, or sound). In some implementations, the trigger sound detector 406 compares a representation of the sound input (an “input representation”) to one or more reference representations of the trigger word. If the input representation matches at least one of the one or more reference representations with an acceptable confidence, the trigger sound detector 406 initiates the speech-based service 408 (e.g., by providing a control signal to initiate one or more processing routines, and/or by providing power to the upstream sound detector). In some implementations, the input representation and the one or more reference representations are spectrograms (or mathematical representations thereof), which represent how the spectral density of a signal varies with time. In some implementations, the representations are other types of audio signatures or voiceprints. In some implementations, initiating the speech-based service 408 includes bringing one or more circuits, programs, and/or processors out of a standby mode, and invoking the sound-based service. The sound-based service is then ready to provide more comprehensive speech recognition, speech-to-text processing, and/or natural language processing.
In some implementations, the voice-trigger system 220 includes voice authentication functionality, so that it can determine if a sound input corresponds to a voice of a particular person, such as an owner/user of voice activated device 102. For example, in some implementations, the sound-type detector 404 uses a voiceprinting technique to determine that the sound input was uttered by an authorized user. Voice authentication and voiceprinting are described in more detail in U.S. patent application Ser. No. 13/053,144, which is hereby incorporated by reference in its entirety. In some implementations, voice authentication is included in any of the sound detectors described herein (e.g., the noise detector 402, the sound-type detector 404, the trigger sound detector 406, and/or the speech-based service 408). In some implementations, voice authentication is implemented as a separate module from the sound detectors listed above (e.g., as voice authentication module 428,
In some implementations, the trigger sound detector 406 remains active for as long as conditions of any downstream sound detector(s) (e.g., the noise detector 402 and/or the sound-type detector 404) are satisfied. For example, in some implementations, the trigger sound detector 406 remains active as long as the sound input includes sound above a predetermined threshold (as detected by the noise detector 402). In some implementations, it remains active as long as the sound input includes sound of a certain type (as detected by the sound-type detector 404). In some implementations, it remains active as long as both the foregoing conditions are met.
In some implementations, once initiated, the trigger sound detector 406 remains active until a condition is met, such as the expiration of a timer (e.g., for 1, 2, 5, or 10 seconds, or any other appropriate duration), the expiration of a certain number of on/off cycles of the trigger sound detector 406, or the occurrence of an event (e.g., the amplitude of the sound falls below a second threshold).
In some implementations, when one sound detector initiates another detector, both sound detectors remain active. However, the sound detectors may be active or inactive at various times, and it is not necessary that all of the downstream (e.g., the lower power and/or sophistication) sound detectors be active (or that their respective conditions are met) in order for upstream sound detectors to be active. For example, in some implementations, after the noise detector 402 and the sound-type detector 404 determine that their respective conditions are met, and the trigger sound detector 406 is initiated, one or both of the noise detector 402 and the sound-type detector 404 are deactivated and/or enter a standby mode while the trigger sound detector 406 operates. In other implementations, both the noise detector 402 and the sound-type detector 404 (or one or the other) stay active while the trigger sound detector 406 operates. In various implementations, different combinations of the sound detectors are active at different times, and whether one is active or inactive may depend on the state of other sound detectors, or may be independent of the state of other sound detectors.
While
Moreover, different combinations of sound detectors may be used at different times. For example, the particular combination of sound detectors and how they interact may depend on one or more conditions, such as the context or operating state of a voice activated device. As a specific example, if a voice activated device is plugged in (and thus not relying exclusively on battery power), the trigger sound detector 406 is active, while the noise detector 402 and the sound-type detector 404 remain inactive. In another example, if the voice activated device is in a pocket or backpack, all sound detectors are inactive.
By cascading sound detectors as described above, where the detectors that require more power are invoked only when necessary by detectors that require lower power, power efficient voice triggering functionality can be provided. As described above, additional power efficiency is achieved by operating one or more of the sound detectors according to a duty cycle. For example, in some implementations, the noise detector 402 operates according to a duty cycle so that it performs effectively continuous noise detection, even though the noise detector is off for at least part of the time. In some implementations, the noise detector 402 is on for 10 milliseconds and off for 90 milliseconds. In some implementations, the noise detector 402 is on for 20 milliseconds and off for 500 milliseconds. Other on and off durations are also possible.
In some implementations, if the noise detector 402 detects a noise during its “on” interval, the noise detector 402 will remain on in order to further process and/or analyze the sound input. For example, the noise detector 402 may be configured to initiate an upstream sound detector if it detects sound above a predetermined amplitude for a predetermined amount of time (e.g., 100 milliseconds). Thus, if the noise detector 402 detects sound above a predetermined amplitude during its 10 millisecond “on” interval, it will not immediately enter the “off” interval. Instead, the noise detector 402 remains active and continues to process the sound input to determine whether it exceeds the threshold for the full predetermined duration (e.g., 100 milliseconds).
In some implementations, the sound-type detector 404 operates according to a duty cycle. In some implementations, the sound-type detector 404 is on for 20 milliseconds and off for 100 milliseconds. Other on and off durations are also possible. In some implementations, the sound-type detector 404 is able to determine whether a sound input corresponds to a predetermined type of sound within the “on” interval of its duty cycle. Thus, the sound-type detector 404 will initiate the trigger sound detector 406 (or any other upstream sound detector) if the sound-type detector 404 determines, during its “on” interval, that the sound is of a certain type. Alternatively, in some implementations, if the sound-type detector 404 detects, during the “on” interval, sound that may correspond to the predetermined type, the detector will not immediately enter the “off” interval. Instead, the sound-type detector 404 remains active and continues to process the sound input and determine whether it corresponds to the predetermined type of sound. In some implementations, if the sound detector determines that the predetermined type of sound has been detected, it initiates the trigger sound detector 406 to further process the sound input and determine if the trigger sound has been detected.
Similar to the noise detector 402 and the sound-type detector 404, in some implementations, the trigger sound detector 406 operates according to a duty cycle. In some implementations, the trigger sound detector 406 is on for 50 milliseconds and off for 50 milliseconds. Other on and off durations are also possible. If the trigger sound detector 406 detects, during its “on” interval, that there is sound that may correspond to a trigger sound, the detector will not immediately enter the “off” interval. Instead, the trigger sound detector 406 remains active and continues to process the sound input and determine whether it includes the trigger sound. In some implementations, if such a sound is detected, the trigger sound detector 406 remains active to process the audio for a predetermined duration, such as 1, 2, 5, or 10 seconds, or any other appropriate duration. In some implementations, the duration is selected based on the length of the particular trigger word or sound that it is configured to detect. For example, if the trigger phrase is “Hey, Assistant,” the trigger word detector is operated for about 2 seconds to determine whether the sound input includes that phrase.
In some implementations, some of the sound detectors are operated according to a duty cycle, while others operate continuously when active. For example, in some implementations, only the first sound detector is operated according to a duty cycle (e.g., the noise detector 402 in
In some implementations, the voice trigger includes noise, echo, and/or sound cancellation functionality (referred to collectively as noise cancellation). In some implementations, noise cancellation is performed by audio subsystem 214 (e.g., by an audio DSP). Noise cancellation reduces or removes unwanted noise or sounds from the sound input prior to it being processed by the sound detectors. In some cases, the unwanted noise is background noise from the user's environment, such as a fan or the clicking from a keyboard. In some implementations, the unwanted noise is any sound above, below, or at predetermined amplitudes or frequencies. For example, in some implementations, sound above the typical human vocal range (e.g., 3,000 Hz) is filtered out or removed from the signal. In some implementations, multiple microphones (e.g., the microphones 218) are used to help determine what components of received sound should be reduced and/or removed. For example, in some implementations, the audio subsystem 214 uses beam forming techniques to identify sounds or portions of sound inputs that appear to originate from a single point in space (e.g., a user's mouth). The audio subsystem 214 then focuses on this sound by removing from the sound input sounds that are received equally by all microphones (e.g., ambient sound that does not appear to originate from any particular direction).
In some implementations, the DSP is configured to cancel or remove from the sound input sounds that are being output by the voice activated device on which the digital assistant is operating. For example, if the audio subsystem 214 is outputting music, radio, a podcast, a voice output, or any other audio content (e.g., via the speaker 216), the DSP removes any of the outputted sound that was picked up by a microphone and included in the sound input. Thus, the sound input is free of the outputted audio (or at least contains less of the outputted audio). Accordingly, the sound input that is provided to the sound detectors will be cleaner, and the triggers more accurate. Aspects of noise cancellation are described in more detail in U.S. Pat. No. 7,272,224, which is hereby incorporated by reference in its entirety.
In some implementations, different sound detectors require that the sound input be filtered and/or preprocessed in different ways. For example, in some implementations, the noise detector 402 is configured to analyze time-domain audio signal between 60 and 20,000 Hz, and the sound-type detector is configured to perform frequency-domain analysis of audio between 60 and 3,000 Hz. Thus, in some implementations, an audio DSP of device 102 preprocesses received audio according to the respective needs of the sound detectors. In some implementations, on the other hand, the sound detectors are configured to filter and/or preprocess the audio from the audio subsystem 214 according to their specific needs. In such cases, the audio DSP may still perform noise cancellation prior to providing the sound input to the sound detectors.
In some implementations, the context of the voice activated device is used to help determine whether and how to operate the voice trigger. For example, it may be unlikely that users will invoke a speech-based service, such as a voice-based digital assistant, when the voice activated device is stored in their pocket, purse, or backpack. Also, it may be unlikely that users will invoke a speech-based service when they are listening to a loud rock concert. For some users, it is unlikely that they will invoke a speech-based service at certain times of the day (e.g., late at night). On the other hand, there are also contexts in which it is more likely that a user will invoke a speech-based service using a voice trigger. For example, some users will be more likely to use a voice trigger when they are alone, when they are at home, or the like. Various techniques are used to determine the context of a voice activated device. In various implementations, the voice activated device uses information from any one or more of the following components or information sources to determine the context of a voice activated device: light sensors, microphones, proximity sensors, motion sensors, cameras, communications circuitry and/or antennas, charging and/or power circuitry, temperature sensors, calendars, user preferences, etc.
The context of the voice activated device can then be used to adjust how and whether the voice trigger operates. For example, in certain contexts, the voice trigger will be deactivated (or operated in a different mode) as long as that context is maintained. For example, in some implementations, the voice trigger is deactivated when the voice activated device is unplugged, during predetermined time periods (e.g., between 10:00 PM and 8:00 AM), when the voice activated device is in a substantially enclosed space (e.g., a pocket, bag, purse, drawer, or glove box), when the voice activated device is near other devices that have a voice trigger and/or speech-based services (e.g., based on acoustic/wireless/infrared communications), and the like. In some implementations, instead of being deactivated, the voice trigger system 220 is operated in a low-power mode (e.g., by operating the noise detector 402 according to a duty cycle with a 10 millisecond “on” interval and a 5 second “off” interval). In some implementations, an audio channel is monitored more infrequently when the voice trigger system 220 is operated in a low-power mode. In some implementations, a voice trigger uses a different sound detector or combination of sound detectors when it is in a low-power mode than when it is in a normal mode. (The voice trigger may be capable of numerous different modes or operating states, each of which may use a different amount of power, and different implementations will use them according to their specific designs.)
On the other hand, when the voice activated device is in some other contexts, the voice trigger will be activated (or operated in a different mode) so long as that context is maintained. For example, in some implementations, the voice trigger remains active while it is plugged into a power source, during predetermined time periods (e.g., between 8:00 AM and 10:00 PM), when the voice activated device is travelling in a vehicle (e.g., based on GPS signals, BLUETOOTH connection or coupling with a vehicle, etc.), and the like. Aspects of determining when a device is in a vehicle are described in more detail in U.S. Provisional Patent Application No. 61/657,744, which is hereby incorporated by reference in its entirety. Several specific examples of how to determine certain contexts are provided below. In various embodiments, different techniques and/or information sources are used to detect these and other contexts.
As noted above, whether or not the voice trigger system 220 is active (e.g., listening) can depend on the physical orientation of a voice activated device. This provides a user with an easy way to activate and/or deactivate the voice trigger without requiring manipulation of switches or buttons. In some implementations, the voice activated device detects whether it is face-up or face-down on a surface using light sensors (e.g., based on the difference in incident light on a front and a back face of device 102), proximity sensors, cameras, and the like.
In some implementations, other operating modes, settings, parameters, or preferences are affected by the orientation and/or position of the voice activated device. In some implementations, the particular trigger sound, word, or phrase of the voice trigger is listening for depends on the orientation and/or position of the voice activated device. For example, in some implementations, the voice trigger listens for a first trigger word, phrase, or sound when the voice activated device is in one orientation (e.g., laying face-up on a surface), and a different trigger word, phrase, or sound when the voice activated device is in another orientation (e.g., laying face-down). In some implementations, the trigger phrase for a face-down orientation is longer and/or more complex than for a face-up orientation. Thus, a user can place a voice activated device face-down when they are around other people or in a noisy environment so that the voice trigger can still be operational while also reducing false accepts, which may be more frequent for shorter or simpler trigger words. As a specific example, a face-up trigger phrase may be “Hey, Assistant,” while a face-down trigger phrase may be “Hey, Assistant, this is Andrew, please wake up.” The longer trigger phrase also provides a larger voice sample for the sound detectors and/or voice authenticators to process and/or analyze, thus increasing the accuracy of the voice trigger and decreasing false accepts.
In some implementations, the voice activated device detects whether the voice activated device is stored (e.g., in a pocket, purse, bag, a drawer, or the like) by determining whether it is in a substantially enclosed space. In some implementations, the voice activated device uses light sensors (e.g., dedicated ambient light sensors and/or cameras) to determine that it is stored. For example, in some implementations, the voice activated device is likely being stored if light sensors detect little or no light. In some implementations, the time of day and/or location of the voice activated device are also considered. For example, if the light sensors detect low light levels when high light levels would be expected (e.g., during the day), the voice activated device may be in storage and the voice trigger system 220 not needed. Thus, the voice trigger system 220 will be placed in a low-power or standby state.
In some implementations, the difference in light detected by sensors located on opposite faces of a voice activated device can be used to determine its position, and hence whether or not it is stored. Specifically, users are likely to attempt to activate a voice trigger when the voice activated device is resting on a table or surface rather than when it is being stored in a pocket or bag. But when a voice activated device is lying face-down (or face-up) on a surface such as a table or desk, one surface of the voice activated device will be occluded so that little or no light reaches that surface, while the other surface will be exposed to ambient light. Thus, if light sensors on the front and back face of a voice activated device detect significantly different light levels, the voice activated device determines that it is not being stored. On the other hand, if light sensors on opposite faces detect the same or similar light levels, the voice activated device determines that it is being stored in a substantially enclosed space. Also, if the light sensors both detect a low light level during the daytime (or when the voice activated device would expect the phone to be in a bright environment), the voice activated device determines with a greater confidence that it is being stored.
In some implementations, other techniques are used (instead of or in addition to light sensors) to determine whether the voice activated device is stored. For example, in some implementations, the voice activated device emits one or more sounds (e.g., tones, clicks, pings, etc.) from a speaker or transducer (e.g., speaker 216), and monitors one or more microphones or transducers (e.g., microphone 218) to detect echoes of the omitted sound(s). (In some implementations, the voice activated device emits inaudible signals, such as sound outside of the human hearing range.) From the echoes, the voice activated device determines characteristics of the surrounding environment. For example, a relatively large environment (e.g., a room or a vehicle) will reflect the sound differently than a relatively small, enclosed environment (e.g., a pocket, purse, bag, a drawer, or the like).
In some implementations, the voice trigger system 220 operates differently if it is near other devices (such as other devices that have voice triggers and/or speech-based services) than if it is not near other devices. This may be useful, for example, to shut down or decrease the sensitivity of the voice trigger system 220 when many devices are close together so that if one person utters a trigger word, other surrounding devices are not triggered as well. In some implementations, a voice activated device determines proximity to other devices using RFID, near-field communications, infrared/acoustic signals, or the like.
Because people's voices vary greatly, it may be necessary or beneficial to tune a voice trigger to improve its accuracy in recognizing the voice of a particular user. Also, people's voices may change over time, for example, because of illnesses, natural voice changes relating to aging or hormonal changes, and the like. Thus, in some implementations, the voice trigger system 220 is able to adapt its voice and/or sound recognition profiles for a particular user or group of users.
As described above, sound detectors (e.g., the sound-type detector 404 and/or the trigger sound detector 406) may be configured to compare a representation of a sound input (e.g., the sound or utterance provided by a user) to one or more reference representations. For example, if an input representation matches the reference representation to a predetermined confidence level, the sound detector will determine that the sound input corresponds to a predetermined type of sound (e.g., the sound-type detector 404), or that the sound input includes predetermined content (e.g., the trigger sound detector 406). In order to tune the voice trigger system 220, in some implementations, the voice activated device adjusts the reference representation to which the input representation is compared. In some implementations, the reference representation is adjusted (or created) as part of a voice enrollment or “training” procedure, where a user outputs the trigger sound several times so that the voice activated device can adjust (or create) the reference representation. The voice activated device can then create a reference representation using that person's actual voice.
In some implementations, the voice activated device uses trigger sounds that are received under normal use conditions to adjust the reference representation. For example, after a successful voice triggering event (e.g., where the sound input was found to satisfy all of the triggering criteria) the voice activated device will use information from the sound input to adjust and/or tune the reference representation. In some implementations, only sound inputs that were determined to satisfy all or some of the triggering criteria with a certain confidence level are used to adjust the reference representation. Thus, when the voice trigger is less confident that a sound input corresponds to or includes a trigger sound, that voice input may be ignored for the purposes of adjusting the reference representation. On the other hand, in some implementations, sound inputs that satisfied the voice trigger system 220 to a lower confidence are used to adjust the reference representation.
In some implementations, device 102 iteratively adjusts the reference representation (using these or other techniques) as more and more sound inputs are received so that slight changes in a user's voice over time can be accommodated. For example, in some implementations, device 102 (and/or associated devices or services) adjusts the reference representation after each successful triggering event. In some implementations, device 102 analyzes the sound input associated with each successful triggering event and determines if the reference representations should be adjusted based on that input (e.g., if certain conditions are met), and only adjusts the reference representation if it is appropriate to do so. In some implementations, device 102 maintains a moving average of the reference representation over time.
In some implementations, the voice trigger system 220 detects sounds that do not satisfy one or more of the triggering criteria (e.g., as determined by one or more of the sound detectors), but that may actually be attempts by an authorized user to do so. For example, voice trigger system 220 may be configured to respond to a trigger phrase such as “Hey, Assistant,” but if a user's voice has changed (e.g., due to sickness, age, accent/inflection changes, etc.), the voice trigger system 220 may not recognize the user's attempt to activate the voice activated device. (This may also occur when the voice trigger system 220 has not been properly tuned for that user's particular voice, such as when the voice trigger system 220 is set to default conditions and/or the user has not performed an initialization or training procedure to customize the voice trigger system 220 for his or her voice.) If the voice trigger system 220 does not respond to the user's first attempt to active the voice trigger, the user is likely to repeat the trigger phrase. The voice activated device detects that these repeated sound inputs are similar to one another, and/or that they are similar to the trigger phrase (though not similar enough to cause the voice trigger system 220 to activate the speech-based service). If such conditions are met, the voice activated device determines that the sound inputs correspond to valid attempts to activate the voice trigger system 220. Accordingly, in some implementations, the voice trigger system 220 uses those received sound inputs to adjust one or more aspects of the voice trigger system 220 so that similar utterances by the user will be accepted as valid triggers in the future. In some implementations, these sound inputs are used to adapt the voice trigger system 220 only if a certain conditions or combinations of conditions are met. For example, in some implementations, the sound inputs are used to adapt the voice trigger system 220 when a predetermined number of sound inputs are received in succession (e.g., 2, 3, 4, 5, or any other appropriate number), when the sound inputs are sufficiently similar to the reference representation, when the sound inputs are sufficiently similar to each other, when the sound inputs are close together (e.g., when they are received within a predetermined time period and/or at or near a predetermined interval), and/or any combination of these or other conditions.
While the adaptation techniques described above refer to adjusting a reference representation, other aspects of the trigger sound detecting techniques may be adjusted in the same or similar manner in addition to or instead of adjusting the reference representation. For example, in some implementations, the voice activated device adjusts how sound inputs are filtered and/or what filters are applied to sound inputs, such as to focus on and/or eliminate certain frequencies or ranges of frequencies of a sound input. In some implementations, the voice activated device adjusts an algorithm that is used to compare the input representation with the reference representation. For example, in some implementations, one or more terms of a mathematical function used to determine the difference between an input representation and a reference representation are changed, added, or removed, or a different mathematical function is substituted.
In some implementations, adaptation techniques such as those described above require more resources than the voice trigger system 220 is able to or is configured to provide. In particular, the sound detectors may not have, or have access to, the amount or the types of processors, data, or memory that are necessary to perform the iterative adaptation of a reference representation and/or a sound detection algorithm (or any other appropriate aspect of the voice trigger system 220). Thus, in some implementations, one or more of the above described adaptation techniques are performed by a more powerful processor, such as an application processor (e.g., the processor(s) 204), or by a different device (e.g., the server system 108). However, the voice trigger system 220 is designed to operate even when the application processor is in a standby mode. Thus, the sound inputs which are to be used to adapt the voice trigger system 220 are received when the application processor is not active and cannot process the sound input. Accordingly, in some implementations, the sound input is stored by the voice activated device so that it can be further processed and/or analyzed after it is received. In some implementations, the sound input is stored in system memory (e.g., memory 206,
In response to the change in light level, the voice activated device audibly requests (506) a passcode. For example, the voice activated device prompts the user (e.g., using speaker 216) to speak a preset password which will be captured by microphone 218.
In accordance with a determination that the passcode was received within a predetermined amount of time (e.g., 0.5, 1, 5, 10, 20 seconds or another reasonable time period) and that the received passcode matches a preset security code, the voice activated device disables (508) the security mode. For example, the user speaks the passcode within a predetermined amount of time and the voice activated device verifies the passcode then enters a default or preset mode.
In accordance with a determination that the passcode was not received within a predetermined amount of time or that the received passcode did not match the preset security code, the voice activated device activates (510) an alarm routine. In some implementations, activating the alarm routine includes notifying the police that an intruder is preset. In some implementations, activating the alarm routine includes playing an audile alarm. In some implementations, activating the alarm routine includes sending an alert notification to coupled devices. In some implementations, activating the alarm routine includes locking all coupled devices.
In response to the detected movement, the voice activated device audibly requests (606) a passcode. In accordance with a determination that the passcode was received within a predetermined amount of time (e.g., 0.5, 1, 5, 10, 20 seconds or another reasonable time period) and that the received passcode matches a preset security code, the voice activated device disables (608) the security mode. In accordance with a determination that the passcode was not received within a predetermined amount of time or that the received passcode did not match the preset security code, the voice activated device activates 610) an alarm routine. In some implementations, activating the alarm routine includes notifying the police that an intruder is preset. In some implementations, activating the alarm routine includes playing an audile alarm. In some implementations, activating the alarm routine includes sending an alert notification to coupled devices. In some implementations, the voice activated device is coupled to a plurality of user devices and activating the alarm routine includes locking at least a subset of the coupled devices.
The foregoing description, for purpose of explanation, has been described with reference to specific implementations. However, the illustrative discussions above are not intended to be exhaustive or to limit the disclosed implementations to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The implementations were chosen and described in order to best explain the principles and practical applications of the disclosed ideas, to thereby enable others skilled in the art to best utilize them with various modifications as are suited to the particular use contemplated.
It will be understood that, although the terms “first,” “second,” etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one clement from another. For example, a first sound detector could be termed a second sound detector, and, similarly, a second sound detector could be termed a first sound detector, without changing the meaning of the description, so long as all occurrences of the “first sound detector” are renamed consistently and all occurrences of the “second sound detector” are renamed consistently. The first sound detector and the second sound detector are both sound detectors, but they are not the same sound detector.
The terminology used herein is for the purpose of describing particular implementations only and is not intended to be limiting of the claims. As used in the description of the implementations 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 “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. As used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in accordance with a determination” or “in response to detecting,” that a stated condition precedent is true, depending on the context. Similarly, the phrase “if it is determined [that a stated condition precedent is true]” or “if [a stated condition precedent is true]” or “when [a stated condition precedent is true]” may be construed to mean “upon determining” or “upon a determination that” or “in response to determining” or “in accordance with a determination” or “upon detecting” or “in response to detecting” that the stated condition precedent is true, depending on the context.
This application claims the benefit of U.S. Provisional Application No. 61/799,722, filed on Mar. 15, 2013, entitled VOICE ACTIVATED DEVICE FOR USE WITH A VOICE-BASED DIGITAL ASSISTANT, which is hereby incorporated by reference in its entity for all purposes.
| Number | Name | Date | Kind |
|---|---|---|---|
| 3704345 | Coker et al. | Nov 1972 | A |
| 3828132 | Flanagan et al. | Aug 1974 | A |
| 3979557 | Schulman et al. | Sep 1976 | A |
| 4278838 | Antonov | Jul 1981 | A |
| 4282405 | Taguchi | Aug 1981 | A |
| 4310721 | Manley et al. | Jan 1982 | A |
| 4348553 | Baker et al. | Sep 1982 | A |
| 4653021 | Takagi | Mar 1987 | A |
| 4688195 | Thompson et al. | Aug 1987 | A |
| 4692941 | Jacks et al. | Sep 1987 | A |
| 4718094 | Bahl et al. | Jan 1988 | A |
| 4724542 | Williford | Feb 1988 | A |
| 4726065 | Froessl | Feb 1988 | A |
| 4727354 | Lindsay | Feb 1988 | A |
| 4776016 | Hansen | Oct 1988 | A |
| 4783807 | Marley | Nov 1988 | A |
| 4811243 | Racine | Mar 1989 | A |
| 4819271 | Bahl et al. | Apr 1989 | A |
| 4827518 | Feustel et al. | May 1989 | A |
| 4827520 | Zeinstra | May 1989 | A |
| 4829576 | Porter | May 1989 | A |
| 4833712 | Bahl et al. | May 1989 | A |
| 4839853 | Deerwester et al. | Jun 1989 | A |
| 4852168 | Sprague | Jul 1989 | A |
| 4862504 | Nomura | Aug 1989 | A |
| 4878230 | Murakami et al. | Oct 1989 | A |
| 4903305 | Gillick et al. | Feb 1990 | A |
| 4905163 | Garber et al. | Feb 1990 | A |
| 4914586 | Swinehart et al. | Apr 1990 | A |
| 4914590 | Loatman et al. | Apr 1990 | A |
| 4944013 | Gouvianakis et al. | Jul 1990 | A |
| 4955047 | Morganstein et al. | Sep 1990 | A |
| 4965763 | Zamora | Oct 1990 | A |
| 4974191 | Amirghodsi et al. | Nov 1990 | A |
| 4977598 | Doddington et al. | Dec 1990 | A |
| 4992972 | Brooks et al. | Feb 1991 | A |
| 5010574 | Wang | Apr 1991 | A |
| 5020112 | Chou | May 1991 | A |
| 5021971 | Lindsay | Jun 1991 | A |
| 5022081 | Hirose et al. | Jun 1991 | A |
| 5027406 | Roberts et al. | Jun 1991 | A |
| 5031217 | Nishimura | Jul 1991 | A |
| 5032989 | Tornetta | Jul 1991 | A |
| 5040218 | Vitale et al. | Aug 1991 | A |
| 5047617 | Shepard et al. | Sep 1991 | A |
| 5057915 | Von Kohorn | Oct 1991 | A |
| 5072452 | Brown et al. | Dec 1991 | A |
| 5091945 | Kleijn | Feb 1992 | A |
| 5127053 | Koch | Jun 1992 | A |
| 5127055 | Larkey | Jun 1992 | A |
| 5128672 | Kaehler | Jul 1992 | A |
| 5133011 | McKiel, Jr. | Jul 1992 | A |
| 5142584 | Ozawa | Aug 1992 | A |
| 5164900 | Bernath | Nov 1992 | A |
| 5165007 | Bahl et al. | Nov 1992 | A |
| 5179627 | Sweet et al. | Jan 1993 | A |
| 5179652 | Rozmanith et al. | Jan 1993 | A |
| 5194950 | Murakami et al. | Mar 1993 | A |
| 5197005 | Shwartz et al. | Mar 1993 | A |
| 5199077 | Wilcox et al. | Mar 1993 | A |
| 5202952 | Gillick et al. | Apr 1993 | A |
| 5208862 | Ozawa | May 1993 | A |
| 5216747 | Hardwick et al. | Jun 1993 | A |
| 5220639 | Lee | Jun 1993 | A |
| 5220657 | Bly et al. | Jun 1993 | A |
| 5222146 | Bahl et al. | Jun 1993 | A |
| 5230036 | Akamine et al. | Jul 1993 | A |
| 5235680 | Bijnagte | Aug 1993 | A |
| 5267345 | Brown et al. | Nov 1993 | A |
| 5268990 | Cohen et al. | Dec 1993 | A |
| 5282265 | Rohra Suda et al. | Jan 1994 | A |
| RE34562 | Murakami et al. | Mar 1994 | E |
| 5291286 | Murakami et al. | Mar 1994 | A |
| 5293448 | Honda | Mar 1994 | A |
| 5293452 | Picone et al. | Mar 1994 | A |
| 5297170 | Eyuboglu et al. | Mar 1994 | A |
| 5301109 | Landauer et al. | Apr 1994 | A |
| 5303406 | Hansen et al. | Apr 1994 | A |
| 5309359 | Katz et al. | May 1994 | A |
| 5317507 | Gallant | May 1994 | A |
| 5317647 | Pagallo | May 1994 | A |
| 5325297 | Bird et al. | Jun 1994 | A |
| 5325298 | Gallant | Jun 1994 | A |
| 5327498 | Hamon | Jul 1994 | A |
| 5333236 | Bahl et al. | Jul 1994 | A |
| 5333275 | Wheatley et al. | Jul 1994 | A |
| 5345536 | Hoshimi et al. | Sep 1994 | A |
| 5349645 | Zhao | Sep 1994 | A |
| 5353377 | Kuroda et al. | Oct 1994 | A |
| 5377301 | Rosenberg et al. | Dec 1994 | A |
| 5384892 | Strong | Jan 1995 | A |
| 5384893 | Hutchins | Jan 1995 | A |
| 5386494 | White | Jan 1995 | A |
| 5386556 | Hedin et al. | Jan 1995 | A |
| 5390279 | Strong | Feb 1995 | A |
| 5396625 | Parkes | Mar 1995 | A |
| 5400434 | Pearson | Mar 1995 | A |
| 5404295 | Katz et al. | Apr 1995 | A |
| 5412756 | Bauman et al. | May 1995 | A |
| 5412804 | Krishna | May 1995 | A |
| 5412806 | Du et al. | May 1995 | A |
| 5418951 | Damashek | May 1995 | A |
| 5424947 | Nagao et al. | Jun 1995 | A |
| 5434777 | Luciw | Jul 1995 | A |
| 5444823 | Nguyen | Aug 1995 | A |
| 5455888 | Iyengar et al. | Oct 1995 | A |
| 5469529 | Bimbot et al. | Nov 1995 | A |
| 5471611 | McGregor | Nov 1995 | A |
| 5475587 | Anick et al. | Dec 1995 | A |
| 5479488 | Lennig et al. | Dec 1995 | A |
| 5491772 | Hardwick et al. | Feb 1996 | A |
| 5493677 | Balogh et al. | Feb 1996 | A |
| 5495604 | Harding et al. | Feb 1996 | A |
| 5502790 | Yi | Mar 1996 | A |
| 5502791 | Nishimura et al. | Mar 1996 | A |
| 5515475 | Gupta et al. | May 1996 | A |
| 5536902 | Serra et al. | Jul 1996 | A |
| 5537618 | Boulton et al. | Jul 1996 | A |
| 5574823 | Hassanein et al. | Nov 1996 | A |
| 5577241 | Spencer | Nov 1996 | A |
| 5578808 | Taylor | Nov 1996 | A |
| 5579436 | Chou et al. | Nov 1996 | A |
| 5581655 | Cohen et al. | Dec 1996 | A |
| 5584024 | Shwartz | Dec 1996 | A |
| 5596676 | Swaminathan et al. | Jan 1997 | A |
| 5596994 | Bro | Jan 1997 | A |
| 5608624 | Luciw | Mar 1997 | A |
| 5613036 | Strong | Mar 1997 | A |
| 5617507 | Lee et al. | Apr 1997 | A |
| 5619694 | Shimazu | Apr 1997 | A |
| 5621859 | Schwartz et al. | Apr 1997 | A |
| 5621903 | Luciw et al. | Apr 1997 | A |
| 5642464 | Yue et al. | Jun 1997 | A |
| 5642519 | Martin | Jun 1997 | A |
| 5644727 | Atkins | Jul 1997 | A |
| 5664055 | Kroon | Sep 1997 | A |
| 5675819 | Schuetze | Oct 1997 | A |
| 5682539 | Conrad et al. | Oct 1997 | A |
| 5687077 | Gough, Jr. | Nov 1997 | A |
| 5696962 | Kupiec | Dec 1997 | A |
| 5701400 | Amado | Dec 1997 | A |
| 5706442 | Anderson et al. | Jan 1998 | A |
| 5710886 | Christensen et al. | Jan 1998 | A |
| 5712957 | Waibel et al. | Jan 1998 | A |
| 5715468 | Budzinski | Feb 1998 | A |
| 5721827 | Logan et al. | Feb 1998 | A |
| 5727950 | Cook et al. | Mar 1998 | A |
| 5729694 | Holzrichter et al. | Mar 1998 | A |
| 5732216 | Logan et al. | Mar 1998 | A |
| 5732390 | Katayanagi et al. | Mar 1998 | A |
| 5734791 | Acero et al. | Mar 1998 | A |
| 5737734 | Schultz | Apr 1998 | A |
| 5748974 | Johnson | May 1998 | A |
| 5749081 | Whiteis | May 1998 | A |
| 5759101 | Von Kohorn | Jun 1998 | A |
| 5790978 | Olive et al. | Aug 1998 | A |
| 5794050 | Dahlgren et al. | Aug 1998 | A |
| 5794182 | Manduchi et al. | Aug 1998 | A |
| 5794207 | Walker et al. | Aug 1998 | A |
| 5794237 | Gore, Jr. | Aug 1998 | A |
| 5799276 | Komissarchik et al. | Aug 1998 | A |
| 5822743 | Gupta et al. | Oct 1998 | A |
| 5825881 | Colvin, Sr. | Oct 1998 | A |
| 5826261 | Spencer | Oct 1998 | A |
| 5828999 | Bellegarda et al. | Oct 1998 | A |
| 5835893 | Ushioda | Nov 1998 | A |
| 5839106 | Bellegarda | Nov 1998 | A |
| 5845255 | Mayaud | Dec 1998 | A |
| 5857184 | Lynch | Jan 1999 | A |
| 5860063 | Gorin et al. | Jan 1999 | A |
| 5862233 | Poletti | Jan 1999 | A |
| 5864806 | Mokbel et al. | Jan 1999 | A |
| 5864844 | James et al. | Jan 1999 | A |
| 5867799 | Lang et al. | Feb 1999 | A |
| 5873056 | Liddy et al. | Feb 1999 | A |
| 5875437 | Atkins | Feb 1999 | A |
| 5884323 | Hawkins et al. | Mar 1999 | A |
| 5895464 | Bhandari et al. | Apr 1999 | A |
| 5895466 | Goldberg et al. | Apr 1999 | A |
| 5899972 | Miyazawa et al. | May 1999 | A |
| 5913193 | Huang et al. | Jun 1999 | A |
| 5915249 | Spencer | Jun 1999 | A |
| 5930769 | Rose | Jul 1999 | A |
| 5933822 | Braden-Harder et al. | Aug 1999 | A |
| 5936926 | Yokouchi et al. | Aug 1999 | A |
| 5940811 | Norris | Aug 1999 | A |
| 5941944 | Messerly | Aug 1999 | A |
| 5943670 | Prager | Aug 1999 | A |
| 5948040 | DeLorme et al. | Sep 1999 | A |
| 5956699 | Wong et al. | Sep 1999 | A |
| 5960394 | Gould et al. | Sep 1999 | A |
| 5960422 | Prasad | Sep 1999 | A |
| 5963924 | Williams et al. | Oct 1999 | A |
| 5966126 | Szabo | Oct 1999 | A |
| 5970474 | LeRoy et al. | Oct 1999 | A |
| 5974146 | Randle et al. | Oct 1999 | A |
| 5982891 | Ginter et al. | Nov 1999 | A |
| 5987132 | Rowney | Nov 1999 | A |
| 5987140 | Rowney et al. | Nov 1999 | A |
| 5987404 | Della Pietra et al. | Nov 1999 | A |
| 5987440 | O'Neil et al. | Nov 1999 | A |
| 5999908 | Abelow | Dec 1999 | A |
| 6016471 | Kuhn et al. | Jan 2000 | A |
| 6023684 | Pearson | Feb 2000 | A |
| 6024288 | Gottlich et al. | Feb 2000 | A |
| 6026345 | Shah et al. | Feb 2000 | A |
| 6026375 | Hall et al. | Feb 2000 | A |
| 6026388 | Liddy et al. | Feb 2000 | A |
| 6026393 | Gupta et al. | Feb 2000 | A |
| 6029132 | Kuhn et al. | Feb 2000 | A |
| 6038533 | Buchsbaum et al. | Mar 2000 | A |
| 6052656 | Suda et al. | Apr 2000 | A |
| 6055514 | Wren | Apr 2000 | A |
| 6055531 | Bennett et al. | Apr 2000 | A |
| 6064960 | Bellegarda et al. | May 2000 | A |
| 6070139 | Miyazawa et al. | May 2000 | A |
| 6070147 | Harms et al. | May 2000 | A |
| 6076051 | Messerly et al. | Jun 2000 | A |
| 6076088 | Paik et al. | Jun 2000 | A |
| 6078914 | Redfern | Jun 2000 | A |
| 6081750 | Hoffberg et al. | Jun 2000 | A |
| 6081774 | de Hita et al. | Jun 2000 | A |
| 6088731 | Kiraly et al. | Jul 2000 | A |
| 6094649 | Bowen et al. | Jul 2000 | A |
| 6105865 | Hardesty | Aug 2000 | A |
| 6108627 | Sabourin | Aug 2000 | A |
| 6119101 | Peckover | Sep 2000 | A |
| 6122616 | Henton | Sep 2000 | A |
| 6125356 | Brockman et al. | Sep 2000 | A |
| 6144938 | Surace et al. | Nov 2000 | A |
| 6173261 | Arai et al. | Jan 2001 | B1 |
| 6173279 | Levin et al. | Jan 2001 | B1 |
| 6188999 | Moody | Feb 2001 | B1 |
| 6195641 | Loring et al. | Feb 2001 | B1 |
| 6205456 | Nakao | Mar 2001 | B1 |
| 6208971 | Bellegarda et al. | Mar 2001 | B1 |
| 6233559 | Balakrishnan | May 2001 | B1 |
| 6233578 | Machihara et al. | May 2001 | B1 |
| 6246981 | Papineni et al. | Jun 2001 | B1 |
| 6260024 | Shkedy | Jul 2001 | B1 |
| 6266637 | Donovan et al. | Jul 2001 | B1 |
| 6275824 | O'Flaherty et al. | Aug 2001 | B1 |
| 6285786 | Seni et al. | Sep 2001 | B1 |
| 6308149 | Gaussier et al. | Oct 2001 | B1 |
| 6311189 | deVries et al. | Oct 2001 | B1 |
| 6317594 | Gossman et al. | Nov 2001 | B1 |
| 6317707 | Bangalore et al. | Nov 2001 | B1 |
| 6317831 | King | Nov 2001 | B1 |
| 6321092 | Fitch et al. | Nov 2001 | B1 |
| 6324514 | Matulich | Nov 2001 | B2 |
| 6334103 | Surace et al. | Dec 2001 | B1 |
| 6356854 | Schubert et al. | Mar 2002 | B1 |
| 6356905 | Gershman et al. | Mar 2002 | B1 |
| 6366883 | Campbell et al. | Apr 2002 | B1 |
| 6366884 | Bellegarda et al. | Apr 2002 | B1 |
| 6421672 | McAllister et al. | Jul 2002 | B1 |
| 6434524 | Weber | Aug 2002 | B1 |
| 6446076 | Burkey et al. | Sep 2002 | B1 |
| 6449620 | Draper et al. | Sep 2002 | B1 |
| 6453281 | Walters et al. | Sep 2002 | B1 |
| 6453292 | Ramaswamy et al. | Sep 2002 | B2 |
| 6460029 | Fries et al. | Oct 2002 | B1 |
| 6466654 | Cooper et al. | Oct 2002 | B1 |
| 6477488 | Bellegarda | Nov 2002 | B1 |
| 6487534 | Thelen et al. | Nov 2002 | B1 |
| 6499013 | Weber | Dec 2002 | B1 |
| 6501937 | Ho et al. | Dec 2002 | B1 |
| 6505158 | Conkie | Jan 2003 | B1 |
| 6505175 | Silverman et al. | Jan 2003 | B1 |
| 6505183 | Loofbourrow et al. | Jan 2003 | B1 |
| 6510417 | Woods et al. | Jan 2003 | B1 |
| 6513063 | Julia et al. | Jan 2003 | B1 |
| 6519565 | Clements et al. | Feb 2003 | B1 |
| 6523061 | Halverson et al. | Feb 2003 | B1 |
| 6523172 | Martinez-Guerra et al. | Feb 2003 | B1 |
| 6526382 | Yuschik | Feb 2003 | B1 |
| 6526395 | Morris | Feb 2003 | B1 |
| 6532444 | Weber | Mar 2003 | B1 |
| 6532446 | King | Mar 2003 | B1 |
| 6546388 | Edlund et al. | Apr 2003 | B1 |
| 6553344 | Bellegarda et al. | Apr 2003 | B2 |
| 6556983 | Altschuler et al. | Apr 2003 | B1 |
| 6584464 | Warthen | Jun 2003 | B1 |
| 6598039 | Livowsky | Jul 2003 | B1 |
| 6601026 | Appelt et al. | Jul 2003 | B2 |
| 6601234 | Bowman-Amuah | Jul 2003 | B1 |
| 6604059 | Strubbe et al. | Aug 2003 | B2 |
| 6615172 | Bennett et al. | Sep 2003 | B1 |
| 6615175 | Gazdzinski | Sep 2003 | B1 |
| 6615220 | Austin et al. | Sep 2003 | B1 |
| 6625583 | Silverman et al. | Sep 2003 | B1 |
| 6631346 | Karaorman et al. | Oct 2003 | B1 |
| 6633846 | Bennett et al. | Oct 2003 | B1 |
| 6647260 | Dusse et al. | Nov 2003 | B2 |
| 6650735 | Burton et al. | Nov 2003 | B2 |
| 6654740 | Tokuda et al. | Nov 2003 | B2 |
| 6665639 | Mozer et al. | Dec 2003 | B2 |
| 6665640 | Bennett et al. | Dec 2003 | B1 |
| 6665641 | Coorman et al. | Dec 2003 | B1 |
| 6684187 | Conkie | Jan 2004 | B1 |
| 6691064 | Vroman | Feb 2004 | B2 |
| 6691111 | Lazaridis et al. | Feb 2004 | B2 |
| 6691151 | Cheyer et al. | Feb 2004 | B1 |
| 6697780 | Beutnagel et al. | Feb 2004 | B1 |
| 6697824 | Bowman-Amuah | Feb 2004 | B1 |
| 6701294 | Ball et al. | Mar 2004 | B1 |
| 6711585 | Copperman et al. | Mar 2004 | B1 |
| 6718324 | Edlund et al. | Apr 2004 | B2 |
| 6721728 | McGreevy | Apr 2004 | B2 |
| 6735632 | Kiraly et al. | May 2004 | B1 |
| 6742021 | Halverson et al. | May 2004 | B1 |
| 6757362 | Cooper et al. | Jun 2004 | B1 |
| 6757718 | Halverson et al. | Jun 2004 | B1 |
| 6766320 | Wang et al. | Jul 2004 | B1 |
| 6772123 | Cooklev et al. | Aug 2004 | B2 |
| 6778951 | Contractor | Aug 2004 | B1 |
| 6778952 | Bellegarda | Aug 2004 | B2 |
| 6778962 | Kasai et al. | Aug 2004 | B1 |
| 6778970 | Au | Aug 2004 | B2 |
| 6792082 | Levine | Sep 2004 | B1 |
| 6807574 | Partovi et al. | Oct 2004 | B1 |
| 6810379 | Vermeulen et al. | Oct 2004 | B1 |
| 6813491 | McKinney | Nov 2004 | B1 |
| 6829603 | Chai et al. | Dec 2004 | B1 |
| 6832194 | Mozer et al. | Dec 2004 | B1 |
| 6839669 | Gould | Jan 2005 | B1 |
| 6842767 | Partovi et al. | Jan 2005 | B1 |
| 6847966 | Sommer et al. | Jan 2005 | B1 |
| 6847979 | Allemang et al. | Jan 2005 | B2 |
| 6851115 | Cheyer et al. | Feb 2005 | B1 |
| 6859931 | Cheyer et al. | Feb 2005 | B1 |
| 6895380 | Sepe, Jr. | May 2005 | B2 |
| 6895558 | Loveland | May 2005 | B1 |
| 6901399 | Corston et al. | May 2005 | B1 |
| 6912499 | Sabourin et al. | Jun 2005 | B1 |
| 6924828 | Hirsch | Aug 2005 | B1 |
| 6928614 | Everhart | Aug 2005 | B1 |
| 6931384 | Horvitz et al. | Aug 2005 | B1 |
| 6934684 | Alpdemir et al. | Aug 2005 | B2 |
| 6937975 | Elworthy | Aug 2005 | B1 |
| 6937986 | Denenberg et al. | Aug 2005 | B2 |
| 6964023 | Maes et al. | Nov 2005 | B2 |
| 6980949 | Ford | Dec 2005 | B2 |
| 6980955 | Okutani et al. | Dec 2005 | B2 |
| 6985865 | Packingham et al. | Jan 2006 | B1 |
| 6988071 | Gazdzinski | Jan 2006 | B1 |
| 6996531 | Korall et al. | Feb 2006 | B2 |
| 6999927 | Mozer et al. | Feb 2006 | B2 |
| 7020685 | Chen et al. | Mar 2006 | B1 |
| 7024363 | Comerford et al. | Apr 2006 | B1 |
| 7027974 | Busch et al. | Apr 2006 | B1 |
| 7036128 | Julia et al. | Apr 2006 | B1 |
| 7050977 | Bennett | May 2006 | B1 |
| 7058569 | Coorman et al. | Jun 2006 | B2 |
| 7062428 | Hogenhout et al. | Jun 2006 | B2 |
| 7069560 | Cheyer et al. | Jun 2006 | B1 |
| 7092887 | Mozer et al. | Aug 2006 | B2 |
| 7092928 | Elad et al. | Aug 2006 | B1 |
| 7093693 | Gazdzinski | Aug 2006 | B1 |
| 7127046 | Smith et al. | Oct 2006 | B1 |
| 7127403 | Saylor et al. | Oct 2006 | B1 |
| 7136710 | Hoffberg et al. | Nov 2006 | B1 |
| 7137126 | Coffman et al. | Nov 2006 | B1 |
| 7139714 | Bennett et al. | Nov 2006 | B2 |
| 7139722 | Perrella et al. | Nov 2006 | B2 |
| 7152070 | Musick et al. | Dec 2006 | B1 |
| 7177798 | Hsu et al. | Feb 2007 | B2 |
| 7197460 | Gupta et al. | Mar 2007 | B1 |
| 7200559 | Wang | Apr 2007 | B2 |
| 7203646 | Bennett | Apr 2007 | B2 |
| 7216073 | Lavi et al. | May 2007 | B2 |
| 7216080 | Tsiao et al. | May 2007 | B2 |
| 7225125 | Bennett et al. | May 2007 | B2 |
| 7233790 | Kjellberg et al. | Jun 2007 | B2 |
| 7233904 | Luisi | Jun 2007 | B2 |
| 7266496 | Wang et al. | Sep 2007 | B2 |
| 7277854 | Bennett et al. | Oct 2007 | B2 |
| 7290039 | Lisitsa et al. | Oct 2007 | B1 |
| 7299033 | Kjellberg et al. | Nov 2007 | B2 |
| 7310600 | Garner et al. | Dec 2007 | B1 |
| 7324947 | Jordan et al. | Jan 2008 | B2 |
| 7349953 | Lisitsa et al. | Mar 2008 | B2 |
| 7376556 | Bennett | May 2008 | B2 |
| 7376645 | Bernard | May 2008 | B2 |
| 7379874 | Schmid et al. | May 2008 | B2 |
| 7386449 | Sun et al. | Jun 2008 | B2 |
| 7389224 | Elworthy | Jun 2008 | B1 |
| 7392185 | Bennett | Jun 2008 | B2 |
| 7398209 | Kennewick et al. | Jul 2008 | B2 |
| 7403938 | Harrison et al. | Jul 2008 | B2 |
| 7409337 | Potter et al. | Aug 2008 | B1 |
| 7415100 | Cooper et al. | Aug 2008 | B2 |
| 7418392 | Mozer et al. | Aug 2008 | B1 |
| 7426467 | Nashida et al. | Sep 2008 | B2 |
| 7427024 | Gazdzinski et al. | Sep 2008 | B1 |
| 7447635 | Konopka et al. | Nov 2008 | B1 |
| 7454351 | Jeschke et al. | Nov 2008 | B2 |
| 7467087 | Gillick et al. | Dec 2008 | B1 |
| 7475010 | Chao | Jan 2009 | B2 |
| 7483894 | Cao | Jan 2009 | B2 |
| 7487089 | Mozer | Feb 2009 | B2 |
| 7496498 | Chu et al. | Feb 2009 | B2 |
| 7496512 | Zhao et al. | Feb 2009 | B2 |
| 7502738 | Kennewick et al. | Mar 2009 | B2 |
| 7508373 | Lin et al. | Mar 2009 | B2 |
| 7522927 | Fitch et al. | Apr 2009 | B2 |
| 7523108 | Cao | Apr 2009 | B2 |
| 7526466 | Au | Apr 2009 | B2 |
| 7529671 | Rockenbeck et al. | May 2009 | B2 |
| 7529676 | Koyama | May 2009 | B2 |
| 7539656 | Fratkina et al. | May 2009 | B2 |
| 7546382 | Healey et al. | Jun 2009 | B2 |
| 7548895 | Pulsipher | Jun 2009 | B2 |
| 7552055 | Lecoeuche | Jun 2009 | B2 |
| 7555431 | Bennett | Jun 2009 | B2 |
| 7558730 | Davis et al. | Jul 2009 | B2 |
| 7571106 | Cao et al. | Aug 2009 | B2 |
| 7599918 | Shen et al. | Oct 2009 | B2 |
| 7620549 | Di Cristo et al. | Nov 2009 | B2 |
| 7624007 | Bennett | Nov 2009 | B2 |
| 7634409 | Kennewick et al. | Dec 2009 | B2 |
| 7636657 | Ju et al. | Dec 2009 | B2 |
| 7640160 | Di Cristo et al. | Dec 2009 | B2 |
| 7647225 | Bennett et al. | Jan 2010 | B2 |
| 7657424 | Bennett | Feb 2010 | B2 |
| 7664638 | Cooper et al. | Feb 2010 | B2 |
| 7672841 | Bennett | Mar 2010 | B2 |
| 7676026 | Baxter, Jr. | Mar 2010 | B1 |
| 7684985 | Dominach et al. | Mar 2010 | B2 |
| 7693715 | Hwang et al. | Apr 2010 | B2 |
| 7693720 | Kennewick et al. | Apr 2010 | B2 |
| 7698131 | Bennett | Apr 2010 | B2 |
| 7702500 | Blaedow | Apr 2010 | B2 |
| 7702508 | Bennett | Apr 2010 | B2 |
| 7707027 | Balchandran et al. | Apr 2010 | B2 |
| 7707032 | Wang et al. | Apr 2010 | B2 |
| 7707267 | Lisitsa et al. | Apr 2010 | B2 |
| 7711565 | Gazdzinski | May 2010 | B1 |
| 7711672 | Au | May 2010 | B2 |
| 7716056 | Weng et al. | May 2010 | B2 |
| 7720674 | Kaiser et al. | May 2010 | B2 |
| 7720683 | Vermeulen et al. | May 2010 | B1 |
| 7721301 | Wong et al. | May 2010 | B2 |
| 7725307 | Bennett | May 2010 | B2 |
| 7725318 | Gavalda et al. | May 2010 | B2 |
| 7725320 | Bennett | May 2010 | B2 |
| 7725321 | Bennett | May 2010 | B2 |
| 7729904 | Bennett | Jun 2010 | B2 |
| 7729916 | Coffman et al. | Jun 2010 | B2 |
| 7734461 | Kwak et al. | Jun 2010 | B2 |
| 7747616 | Yamada et al. | Jun 2010 | B2 |
| 7752152 | Paek et al. | Jul 2010 | B2 |
| 7756868 | Lee | Jul 2010 | B2 |
| 7774204 | Mozer et al. | Aug 2010 | B2 |
| 7783486 | Rosser et al. | Aug 2010 | B2 |
| 7801729 | Mozer | Sep 2010 | B2 |
| 7809570 | Kennewick et al. | Oct 2010 | B2 |
| 7809610 | Cao | Oct 2010 | B2 |
| 7818176 | Freeman et al. | Oct 2010 | B2 |
| 7822608 | Cross, Jr. et al. | Oct 2010 | B2 |
| 7826945 | Zhang et al. | Nov 2010 | B2 |
| 7831426 | Bennett | Nov 2010 | B2 |
| 7840400 | Lavi et al. | Nov 2010 | B2 |
| 7840447 | Kleinrock et al. | Nov 2010 | B2 |
| 7853574 | Kraenzel et al. | Dec 2010 | B2 |
| 7853664 | Wang et al. | Dec 2010 | B1 |
| 7873519 | Bennett | Jan 2011 | B2 |
| 7873654 | Bernard | Jan 2011 | B2 |
| 7881936 | Longe et al. | Feb 2011 | B2 |
| 7890652 | Bull et al. | Feb 2011 | B2 |
| 7912702 | Bennett | 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 |
| 7925525 | Chin | Apr 2011 | B2 |
| 7930168 | Weng et al. | Apr 2011 | B2 |
| 7949529 | Weider et al. | May 2011 | B2 |
| 7949534 | Davis et al. | May 2011 | B2 |
| 7974844 | Sumita | Jul 2011 | B2 |
| 7974972 | Cao | Jul 2011 | B2 |
| 7983915 | Knight et al. | Jul 2011 | B2 |
| 7983917 | Kennewick et al. | Jul 2011 | B2 |
| 7983997 | Allen et al. | Jul 2011 | B2 |
| 7986431 | Emori et al. | Jul 2011 | B2 |
| 7987151 | Schott et al. | Jul 2011 | B2 |
| 7996228 | Miller et al. | Aug 2011 | B2 |
| 8000453 | Cooper et al. | Aug 2011 | B2 |
| 8005679 | Jordan et al. | Aug 2011 | B2 |
| 8015006 | Kennewick et al. | Sep 2011 | B2 |
| 8024195 | Mozer et al. | Sep 2011 | B2 |
| 8032383 | Bhardwaj et al. | Oct 2011 | B1 |
| 8036901 | Mozer | Oct 2011 | B2 |
| 8041570 | Mirkovic et al. | Oct 2011 | B2 |
| 8041611 | Kleinrock et al. | Oct 2011 | B2 |
| 8055708 | Chitsaz et al. | Nov 2011 | B2 |
| 8065155 | Gazdzinski | Nov 2011 | B1 |
| 8065156 | Gazdzinski | Nov 2011 | B2 |
| 8069046 | Kennewick et al. | Nov 2011 | B2 |
| 8073681 | Baldwin et al. | Dec 2011 | B2 |
| 8078473 | Gazdzinski | Dec 2011 | B1 |
| 8082153 | Coffman et al. | Dec 2011 | B2 |
| 8095364 | Longe et al. | Jan 2012 | B2 |
| 8099289 | Mozer et al. | Jan 2012 | B2 |
| 8107401 | John et al. | Jan 2012 | B2 |
| 8112275 | Kennewick et al. | Feb 2012 | B2 |
| 8112280 | Lu | Feb 2012 | B2 |
| 8117037 | Gazdzinski | Feb 2012 | B2 |
| 8131557 | Davis et al. | Mar 2012 | B2 |
| 8140335 | Kennewick et al. | Mar 2012 | B2 |
| 8165886 | Gagnon et al. | Apr 2012 | B1 |
| 8166019 | Lee et al. | Apr 2012 | B1 |
| 8190359 | Bourne | May 2012 | B2 |
| 8195467 | Mozer et al. | Jun 2012 | B2 |
| 8204238 | Mozer | Jun 2012 | B2 |
| 8205788 | Gazdzinski et al. | Jun 2012 | B1 |
| 8219407 | Roy et al. | Jul 2012 | B1 |
| 8285551 | Gazdzinski | Oct 2012 | B2 |
| 8285553 | Gazdzinski | Oct 2012 | B2 |
| 8290778 | Gazdzinski | Oct 2012 | B2 |
| 8290781 | Gazdzinski | Oct 2012 | B2 |
| 8296146 | Gazdzinski | Oct 2012 | B2 |
| 8296153 | Gazdzinski | Oct 2012 | B2 |
| 8296383 | Lindahl | Oct 2012 | B2 |
| 8301456 | Gazdzinski | Oct 2012 | B2 |
| 8311834 | Gazdzinski | Nov 2012 | B1 |
| 8340975 | Rosenberger | Dec 2012 | B1 |
| 8370158 | Gazdzinski | Feb 2013 | B2 |
| 8371503 | Gazdzinski | Feb 2013 | B2 |
| 8374871 | Ehsani et al. | Feb 2013 | B2 |
| 8447612 | Gazdzinski | May 2013 | B2 |
| 8452597 | Bringert | May 2013 | B2 |
| 8762145 | Ouchi | Jun 2014 | B2 |
| 8798995 | Edara | Aug 2014 | B1 |
| 8983383 | Haskin | Mar 2015 | B1 |
| 8996381 | Mozer | Mar 2015 | B2 |
| 9098467 | Blanksteen | Aug 2015 | B1 |
| 20010047264 | Roundtree | Nov 2001 | A1 |
| 20020010584 | Schultz et al. | Jan 2002 | A1 |
| 20020032564 | Ehsani et al. | Mar 2002 | A1 |
| 20020035474 | Alpdemir | Mar 2002 | A1 |
| 20020046025 | Hain | Apr 2002 | A1 |
| 20020069063 | Buchner et al. | Jun 2002 | A1 |
| 20020077817 | Atal | Jun 2002 | A1 |
| 20020103641 | Kuo et al. | Aug 2002 | A1 |
| 20020116185 | Cooper et al. | Aug 2002 | A1 |
| 20020116189 | Yeh et al. | Aug 2002 | A1 |
| 20020164000 | Cohen et al. | Nov 2002 | A1 |
| 20020198714 | Zhou | Dec 2002 | A1 |
| 20030088414 | Huang et al. | May 2003 | A1 |
| 20030097210 | Horst et al. | May 2003 | A1 |
| 20030135740 | Talmor et al. | Jul 2003 | A1 |
| 20040135701 | Yasuda et al. | Jul 2004 | A1 |
| 20040199387 | Wang et al. | Oct 2004 | A1 |
| 20040210442 | Glynn | Oct 2004 | A1 |
| 20040220798 | Chi et al. | Nov 2004 | A1 |
| 20040236778 | Junqua et al. | Nov 2004 | A1 |
| 20050002507 | Timmins et al. | Jan 2005 | A1 |
| 20050055403 | Brittan | Mar 2005 | A1 |
| 20050071332 | Ortega et al. | Mar 2005 | A1 |
| 20050080625 | Bennett et al. | Apr 2005 | A1 |
| 20050091118 | Fano | Apr 2005 | A1 |
| 20050102614 | Brockett et al. | May 2005 | A1 |
| 20050108001 | Aarskog | May 2005 | A1 |
| 20050114124 | Liu et al. | May 2005 | A1 |
| 20050119897 | Bennett et al. | Jun 2005 | A1 |
| 20050143972 | Gopalakrishnan et al. | Jun 2005 | A1 |
| 20050165607 | Di Fabbrizio et al. | Jul 2005 | A1 |
| 20050182629 | Coorman et al. | Aug 2005 | A1 |
| 20050196733 | Budra et al. | Sep 2005 | A1 |
| 20050273626 | Pearson et al. | Dec 2005 | A1 |
| 20050288936 | Busayapongchai et al. | Dec 2005 | A1 |
| 20060018492 | Chiu et al. | Jan 2006 | A1 |
| 20060053007 | Niemisto | Mar 2006 | A1 |
| 20060106592 | Brockett et al. | May 2006 | A1 |
| 20060106594 | Brockett et al. | May 2006 | A1 |
| 20060106595 | Brockett et al. | May 2006 | A1 |
| 20060117002 | Swen | Jun 2006 | A1 |
| 20060122834 | Bennett | Jun 2006 | A1 |
| 20060143007 | Koh et al. | Jun 2006 | A1 |
| 20060217967 | Goertzen et al. | Sep 2006 | A1 |
| 20060235700 | Wong et al. | Oct 2006 | A1 |
| 20070041361 | Iso-Sipila | Feb 2007 | A1 |
| 20070055529 | Kanevsky et al. | Mar 2007 | A1 |
| 20070058832 | Hug et al. | Mar 2007 | A1 |
| 20070088556 | Andrew | Apr 2007 | A1 |
| 20070100790 | Cheyer et al. | May 2007 | A1 |
| 20070106674 | Agrawal et al. | May 2007 | A1 |
| 20070118377 | Badino et al. | May 2007 | A1 |
| 20070135949 | Snover et al. | Jun 2007 | A1 |
| 20070174188 | Fish | Jul 2007 | A1 |
| 20070185917 | Prahlad et al. | Aug 2007 | A1 |
| 20070282595 | Tunning et al. | Dec 2007 | A1 |
| 20080015863 | Agapi | Jan 2008 | A1 |
| 20080015864 | Ross et al. | Jan 2008 | A1 |
| 20080021708 | Bennett et al. | Jan 2008 | A1 |
| 20080034032 | Healey et al. | Feb 2008 | A1 |
| 20080046250 | Agapi | Feb 2008 | A1 |
| 20080052063 | Bennett et al. | Feb 2008 | A1 |
| 20080082332 | Mallett et al. | Apr 2008 | A1 |
| 20080082338 | O'Neil et al. | Apr 2008 | A1 |
| 20080120112 | Jordan et al. | May 2008 | A1 |
| 20080129520 | Lee | Jun 2008 | A1 |
| 20080140416 | Shostak | Jun 2008 | A1 |
| 20080140657 | Azvine et al. | Jun 2008 | A1 |
| 20080221903 | Kanevsky et al. | Sep 2008 | A1 |
| 20080228496 | Yu et al. | Sep 2008 | A1 |
| 20080247519 | Abella et al. | Oct 2008 | A1 |
| 20080249770 | Kim et al. | Oct 2008 | A1 |
| 20080256613 | Grover | Oct 2008 | A1 |
| 20080300878 | Bennett | Dec 2008 | A1 |
| 20080319763 | Di Fabbrizio | Dec 2008 | A1 |
| 20090006100 | Badger et al. | Jan 2009 | A1 |
| 20090006343 | Platt et al. | Jan 2009 | A1 |
| 20090018835 | Cooper et al. | Jan 2009 | A1 |
| 20090030800 | Grois | Jan 2009 | A1 |
| 20090043580 | Mozer | Feb 2009 | A1 |
| 20090055179 | Cho et al. | Feb 2009 | A1 |
| 20090058823 | Kocienda | Mar 2009 | A1 |
| 20090076796 | Daraselia | Mar 2009 | A1 |
| 20090077165 | Rhodes et al. | Mar 2009 | A1 |
| 20090100049 | Cao | Apr 2009 | A1 |
| 20090112677 | Rhett | Apr 2009 | A1 |
| 20090150156 | Kennewick et al. | Jun 2009 | A1 |
| 20090157401 | Bennett | Jun 2009 | A1 |
| 20090164441 | Cheyer | Jun 2009 | A1 |
| 20090171664 | Kennewick et al. | Jul 2009 | A1 |
| 20090204409 | Mozer et al. | Aug 2009 | A1 |
| 20090216540 | Tessel | Aug 2009 | A1 |
| 20090287583 | Holmes | Nov 2009 | A1 |
| 20090290718 | Kahn et al. | Nov 2009 | A1 |
| 20090299745 | Kennewick et al. | Dec 2009 | A1 |
| 20090299849 | Cao et al. | Dec 2009 | A1 |
| 20090307162 | Bui et al. | Dec 2009 | A1 |
| 20100005081 | Bennett | Jan 2010 | A1 |
| 20100023320 | Di Cristo et al. | Jan 2010 | A1 |
| 20100036660 | Bennett | Feb 2010 | A1 |
| 20100042400 | Block et al. | Feb 2010 | A1 |
| 20100088020 | Sano et al. | Apr 2010 | A1 |
| 20100088100 | Lindahl | Apr 2010 | A1 |
| 20100131273 | Aley-Raz et al. | May 2010 | A1 |
| 20100138215 | Williams | Jun 2010 | A1 |
| 20100145700 | Kennewick et al. | Jun 2010 | A1 |
| 20100204986 | Kennewick et al. | Aug 2010 | A1 |
| 20100217604 | Baldwin et al. | Aug 2010 | A1 |
| 20100228540 | Bennett | Sep 2010 | A1 |
| 20100235341 | Bennett | Sep 2010 | A1 |
| 20100257160 | Cao | Oct 2010 | A1 |
| 20100262599 | Nitz | Oct 2010 | A1 |
| 20100277579 | Cho et al. | Nov 2010 | A1 |
| 20100278320 | Arsenault et al. | Nov 2010 | A1 |
| 20100280983 | Cho et al. | Nov 2010 | A1 |
| 20100286985 | Kennewick et al. | Nov 2010 | A1 |
| 20100299142 | Freeman et al. | Nov 2010 | A1 |
| 20100312547 | Van Os | Dec 2010 | A1 |
| 20100318576 | Kim | Dec 2010 | A1 |
| 20100332235 | David | Dec 2010 | A1 |
| 20100332348 | Cao | Dec 2010 | A1 |
| 20110047072 | Ciurea | Feb 2011 | A1 |
| 20110060807 | Martin et al. | Mar 2011 | A1 |
| 20110082688 | Kim et al. | Apr 2011 | A1 |
| 20110112827 | Kennewick et al. | May 2011 | A1 |
| 20110112921 | Kennewick et al. | May 2011 | A1 |
| 20110119049 | Ylonen | May 2011 | A1 |
| 20110125540 | Jang et al. | May 2011 | A1 |
| 20110130958 | Stahl et al. | Jun 2011 | A1 |
| 20110131036 | DiCristo et al. | Jun 2011 | A1 |
| 20110131045 | Cristo et al. | Jun 2011 | A1 |
| 20110143811 | Rodriguez | Jun 2011 | A1 |
| 20110144999 | Jang et al. | Jun 2011 | A1 |
| 20110161076 | Davis et al. | Jun 2011 | A1 |
| 20110161309 | Lung et al. | Jun 2011 | A1 |
| 20110175810 | Markovic et al. | Jul 2011 | A1 |
| 20110184730 | LeBeau et al. | Jul 2011 | A1 |
| 20110201385 | Higginbotham | Aug 2011 | A1 |
| 20110218855 | Cao et al. | Sep 2011 | A1 |
| 20110231182 | Weider et al. | Sep 2011 | A1 |
| 20110231188 | Kennewick et al. | Sep 2011 | A1 |
| 20110264643 | Cao | Oct 2011 | A1 |
| 20110279368 | Klein et al. | Nov 2011 | A1 |
| 20110298585 | Barry | Dec 2011 | A1 |
| 20110306426 | Novak et al. | Dec 2011 | A1 |
| 20110313775 | Laligand | Dec 2011 | A1 |
| 20120002820 | Leichter | Jan 2012 | A1 |
| 20120016678 | Gruber et al. | Jan 2012 | A1 |
| 20120020490 | Leichter | Jan 2012 | A1 |
| 20120022787 | LeBeau et al. | Jan 2012 | A1 |
| 20120022857 | Baldwin et al. | Jan 2012 | A1 |
| 20120022860 | Lloyd et al. | Jan 2012 | A1 |
| 20120022868 | LeBeau et al. | Jan 2012 | A1 |
| 20120022869 | Lloyd et al. | Jan 2012 | A1 |
| 20120022870 | Kristjansson et al. | Jan 2012 | A1 |
| 20120022874 | Lloyd et al. | Jan 2012 | A1 |
| 20120022876 | LeBeau et al. | Jan 2012 | A1 |
| 20120023088 | Cheng et al. | Jan 2012 | A1 |
| 20120034904 | LeBeau et al. | Feb 2012 | A1 |
| 20120035908 | LeBeau et al. | Feb 2012 | A1 |
| 20120035924 | Jitkoff et al. | Feb 2012 | A1 |
| 20120035931 | LeBeau et al. | Feb 2012 | A1 |
| 20120035932 | Jitkoff et al. | Feb 2012 | A1 |
| 20120042343 | Laligand et al. | Feb 2012 | A1 |
| 20120137367 | Dupont et al. | May 2012 | A1 |
| 20120173464 | Tur et al. | Jul 2012 | A1 |
| 20120265528 | Gruber et al. | Oct 2012 | A1 |
| 20120271640 | Basir | Oct 2012 | A1 |
| 20120271676 | Aravamudan et al. | Oct 2012 | A1 |
| 20120311583 | Gruber et al. | Dec 2012 | A1 |
| 20120330661 | Lindahl | Dec 2012 | A1 |
| 20130006638 | Lindahl | Jan 2013 | A1 |
| 20130110518 | Gruber et al. | May 2013 | A1 |
| 20130110520 | Cheyer et al. | May 2013 | A1 |
| 20130191117 | Atti | Jul 2013 | A1 |
| 20130218560 | Hsiao | Aug 2013 | A1 |
| 20130289994 | Newman | Oct 2013 | A1 |
| 20140136195 | Abdossalami | May 2014 | A1 |
| 20140278435 | Ganong, III | Sep 2014 | A1 |
| Number | Date | Country |
|---|---|---|
| 681573 | Apr 1993 | CH |
| 3837590 | May 1990 | DE |
| 19841541 | Dec 2007 | DE |
| 0138061 | Apr 1985 | EP |
| 0218859 | Apr 1987 | EP |
| 0262938 | Apr 1988 | EP |
| 0138061 | Jun 1988 | EP |
| 0293259 | Nov 1988 | EP |
| 0299572 | Jan 1989 | EP |
| 0313975 | May 1989 | EP |
| 0314908 | May 1989 | EP |
| 0327408 | Aug 1989 | EP |
| 0389271 | Sep 1990 | EP |
| 0411675 | Feb 1991 | EP |
| 0559349 | Sep 1993 | EP |
| 0570660 | Nov 1993 | EP |
| 0863453 | Sep 1998 | EP |
| 0559349 | Jan 1999 | EP |
| 1229496 | Aug 2002 | EP |
| 1245023 | Oct 2002 | EP |
| 2109295 | Oct 2009 | EP |
| 2293667 | Apr 1996 | GB |
| 6-19965 | Jan 1994 | JP |
| 2001-125896 | May 2001 | JP |
| 2002-24212 | Jan 2002 | JP |
| 2003-517158 | May 2003 | JP |
| 2009-36999 | Feb 2009 | JP |
| 10-0757496 | Sep 2007 | KR |
| 10-0776800 | Nov 2007 | KR |
| 10-0801227 | Feb 2008 | KR |
| 10-0810500 | Mar 2008 | KR |
| 10-2008-0109322 | Dec 2008 | KR |
| 10-2009-0086805 | Aug 2009 | KR |
| 10-0920267 | Oct 2009 | KR |
| 10-2010-0119519 | Nov 2010 | KR |
| 10-1032792 | May 2011 | KR |
| 10-2011-0113414 | Oct 2011 | KR |
| 1995002221 | Jan 1995 | WO |
| 1997026612 | Jul 1997 | WO |
| 1998041956 | Sep 1998 | WO |
| 1999001834 | Jan 1999 | WO |
| 1999008238 | Feb 1999 | WO |
| 1999056227 | Nov 1999 | WO |
| 2000060435 | Oct 2000 | WO |
| 2000060435 | Apr 2001 | WO |
| 2002073603 | Sep 2002 | WO |
| 2006129967 | Dec 2006 | WO |
| 2008085742 | Jul 2008 | WO |
| 2008109835 | Sep 2008 | WO |
| 2010075623 | Jul 2010 | WO |
| 2011088053 | Jul 2011 | WO |
| Entry |
|---|
| CastleOS—Whole House Voice Control Demonstration, Youtube Video, https://www.youtube.com/watch?v=9SRCoxrZ_W4 Jun. 2, 2012. |
| Cheyer, Adam, “About Adam Cheyer”, available online at <http://www.adam.cheyer.com/about.html>, retrieved from Internet on Sep. 17, 2012, 2 pages. |
| Codd, E. F., “Databases: Improving Usability and Responsiveness—How About Recently”, 1978, pp. 3-28. |
| Cohen et al., “An Open Agent Architecture”, SRI International, 1994, pp. 1-8. |
| Coles et al., “Chemistry Question-Answering”, Technical Note 9, Jun. 1969, 15 pages. |
| Coles et al., “Techniques for Information Retrieval Using an Inferential Question-Answering System with Natural Language Input”, Technical Note74, Nov. 1972, 198 Pages. |
| Coles Stephen L., “The Application of Theorem Proving to Information Retrieval”, Technical Note 51, Jan. 1971, 21 pages. |
| Conklin, “Hypertext: An Introduction and Survey”, Computer Magazine, Sep. 1987, 25 pages. |
| Connolly et al., “Fast Algorithms for Complex Matrix Multiplication Using Surrogates”, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 37, No. 6, Jun. 1989, 13 pages. |
| Constantinides et al., “A Schema Based Approach to Dialog Control”, Proceedings of the International Conference on Spoken Language Processing, 1998, 4 pages. |
| Cox et al., “Speech and Language Processing for Next-Millennium Communications Services”, Proceedings of the IEEE, vol. 88, No. 8, Aug. 2000, pp. 1314-1337. |
| Craig et al., “Deacon: Direct English Access and Control”, AFIPS Conference Proceedings, vol. 29, Nov. 7-10, 1966, pp. 365-380. |
| Cutkosky et al., “PACT: An Experiment in Integrating Concurrent Engineering Systems”, Jan. 1993, pp. 1-13. |
| Dar et al., “DTL's DataSpot: Database Exploration Using Plain Language”, Proceedings of the 24th VLDB Conference, 1998, pp. 645-649. |
| Davis et al., “A Personal Handheld Multi-Modal Shopping Assistant”, IEEE, 2006, 9 pages. |
| Decker et al., “Designing Behaviors for Information Agents”, Jul. 6, 1996, pp. 1-15. |
| Decker et al., “Matchmaking and Brokering”, May 16, 1996, pp. 1-19. |
| Deerwester et al., “Indexing by Latent Semantic Analysis”, Journal of the American Society for Information Science, vol. 41, No. 6, Sep. 1990, 19 pages. |
| Deller, Jr., et al., “Discrete-Time Processing of Speech Signals”, May 13, 1993, pp. 114-137. |
| Digital Equipment Corporation, “Open VMS Software Overview”, Dec. 1995, 159 pages. |
| Domingue et al., “Web Service Modeling Ontology (WSMO)—An Ontology for Semantic Web Services”, Position paper at the W3C Workshop on Frameworks for Semantics in Web Services, Jun. 9-10, 2005, 6 pages. |
| Donovan, Robert E., “A New Distance Measure for Costing Spectral Discontinuities in Concatenative Speech Synthesisers”, 2001, 4 pages. |
| Dowding et al., “Gemini: A Natural Language System for Spoken-Language Understanding”, Proceedings of the Thirty-First Annual Meeting of the Association for Computational Linguistics, 1993, 8 pages. |
| Dowding et al., “Interleaving Syntax and Semantics in an Efficient Bottom-Up Parser”, Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, 1994, 7 pages. |
| Elio et al., “On Abstract Task Models and Conversation Policies”, 1999, pp. 1-10. |
| Epstein et al., “Natural Language Access to a Melanoma Data Base”, Technical Note 171, Sep. 1978, 7 pages. |
| Ericsson et al., “Software Illustrating a Unified Approach to Multimodality and Multilinguality in the in-home Domain”, Talk and Look: Tools for Ambient Linguistic Knowledge, Dec. 22, 2006, 127 pages. |
| Evi, “Meet Evi: The One Mobile App that Provides Solutions for Your Everyday Problems”, avialable online at <http://www.evi.com/>, retrieved on Feb. 8, 2012, 3 pages. |
| Grosz et al., “Dialogic: A Core Natural-Language Processing System”, SRI International, Nov. 9, 1982, 17 pages. |
| Exhibit, 1, “Natural Language Interface Using Constrained Intermediate Dictionary of Results”, List of Publications Manually reviewed for the Search of U.S. Pat. No. 7,177,798, Mar. 22, 2013, 1 page. |
| Feigenbaum et al., “Computer-assisted Semantic Annotation of Scientific Life Works”, Oct. 15, 2007, 22 pages. |
| Ferguson et al., “TRIPS: An Integrated Intelligent Problem-Solving Assistant,”, Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98) and Tenth Conference on Innovative Applications of Artificial Intelligence (IAAI-98), 1998, 7 pages. |
| Fikes et al., “A Network-based knowledge Representation and its Natural Deduction System”, SRI International, Jul. 1977, 43 pages. |
| Frisse, M. E., “Searching for Information in a Hypertext Medical Handbook”, Communications of the ACM, vol. 31, No. 7, Jul. 1988, 8 pages. |
| Gamback et al., “The Swedish Core Language Engine”, NOTEX Conference, 1992, 17 pages. |
| Gannes, Liz, “Alfred App Gives Personalized Restaurant Recommendations”, AllThingsD, Jul. 18, 2011, pp. 1-3. |
| Gautier et al., “Generating Explanations of Device Behavior Using Compositional Modeling and Causal Ordering”, CiteSeerx, 1993, pp. 89-97. |
| Gervasio et al., “Active Preference Learning for Personalized Calendar Scheduling Assistancae”, CiteSeerx, In Proc. of IUI'05, Jan. 9-12, 2005, pp. 90-97. |
| Glass et al., “Multilingual Language Generation Across Multiple Domains”, International Conference on Spoken Language Processing, Japan, Sep. 18-22, 1994, 5 pages. |
| Glass et al., “Multilingual Spoken-Language Understanding in the Mit Voyager System”, <http://groups.csail.mit.edu/sls/publications/1995/speechcomm95-voyager.pdf>, Aug. 1995, 29 pages. |
| Glass, Alyssa, “Explaining Preference Learning”, CiteSeerx, 2006, pp. 1-5. |
| Goddeau et al., “A Form-Based Dialogue Manager for Spoken Language Applications”, http://phasedance.com/pdf!icslp96.pdf, Oct. 1996, 4 pages. |
| Goddeau et al., “Galaxy: A Human-Language Interface to On-Line Travel Information”, International Conference on Spoken Language Processing 1994, Yokohama, 1994, pp. 707-710. |
| Goldberg et al., “Using Collaborative Filtering to Weave an Information Tapestry”, Communications of the ACM, vol. 35, No. 12, Dec. 1992, 10 pages. |
| Gorin et al., “On Adaptive Acquisition of Language”, International Conference on Acoustics, Speech, and Signal Processing (ICASSP'90), vol. 1, Apr. 3-6, 1990, 5 pages. |
| Gotoh et al., “Document Space Models Using Latent Semantic Analysis”, In Proceedings of Eurospeech, 1997, 4 pages. |
| Gray, R. M., “Vector Quantization”, IEEE ASSP Magazine, Apr. 1984, 26 pages. |
| Green, C., “The Application of Theorem Proving to Question-Answering Systems”, SRI Stanford Research Institute, Artificial Intelligence Group, Jun. 1969, 169 pages. |
| Gregg et al., “DSS Access on the WWW: An Intelligent Agent Prototype”, Proceedings of the Americas Conference on Information Systems—Association for Information Systems, 1998, 3 pages. |
| Grishman et al., “Computational Linguistics: An Introduction”, © Cambridge University Press, 1986, 172 pages. |
| Lieberman et al., “Out of Context: Computer Systems that Adapt to, and Learn from, Context”, IBM Systems Journal, vol. 39, No. 3&4, 2000, pp. 617-632. |
| Lin et al., “A Distributed Architecture for Cooperative Spoken Dialogue Agents with Coherent Dialogue State and History”, <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=1 0.1.1.42.272>, 1999, 4 pages. |
| Lin et al., “A New Framework for Recognition of Mandarin Syllables With Tones Using Sub-syllabic Units”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-93), Apr. 27-30, 1993, pp. 227-230. |
| Linde, “An Algorithm for Vector Quantizer Design”, IEEE Transactions on Communications, vol. 28, No. 1, Jan. 1980, pp. 84-95. |
| Liu et al., “Efficient Joint Compensation of Speech for the Effects of Additive Noise and Linear Filtering”, IEEE International Conference of Acoustics, Speech, and Signal Processing,ICASSP-92, Mar. 23-26, 1992, pp. 257-260. |
| LNTRASPECT Software, “The Intraspect Knowledge Management Solution: Technical Overview”, <http://tomgruber.org/writing/intraspect-whitepaper-1998.pdf>, 1998, 18 pages. |
| Logan et al., “Mel Frequency Cepstral Coefficients for Music Modeling”, In International Symposium on Music Information Retrieval, 2000, 2 pages. |
| Lowegian International, “FIR Filter Properties”, dspGuro, Digital Signal Processing Central, available online at <httJ;>://www.dspQuru.com/dso/taas/fir/orooerties, > retrived from internet on Jul. 28, 2010, 6 pages. |
| Lowerre, B. T., “The-HARPY Speech Recognition System”, Doctoral Dissertation, Department of Computer Science, Carnegie Mellon University, Apr. 1976, 20 pages. |
| Maghbouleh, A., “An Empirical Comparison of Automatic Decision Tree and Linear Regression Models for Vowel Durations”, Revised Version of a Paper Presented at the Computational Phonology in Speech Technology workshop, 1996 annual meeting of the Association for Computational Linguistics in Santa Cruz, California, 7 pages. |
| Markel et al., “Linear Prediction of Speech”, Springer-Verlag, Berlin Heidelberg New York, 1976, 12 pages. |
| Martin et al., “The Open Agent Architecture: A Framework for Building Distributed Software Systems”, Applied Artificial Intelligence: An International Journal, vol. 13, No. 1-2, pp. 1-38. |
| Martin et al., “Building and Using Practical Agent Applications”, SRI International, PAAM Tutorial, 1998, pp. 1-26. |
| Martin et al., “Building Distributed Software Systems with the Open Agent Architecture”, Proceedings of the Third International Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology, Mar. 23-25, 1998, pp. 355-376. |
| Martin et al., “Development Tools for the Open Agent Architecture”, Proceedings of the International Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology, Apr. 1996, pp. 1-17. |
| Martin et al., “Information Brokering in an Agent Architecture”, Proceedings of the second International Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology, Apr. 1997, pp. 1-20. |
| Martin et al., “Transportability and Generality in a Natural-Language Interface System”, Proceedings of the Eight International Joint Conference on Artificial Intelligence, Technical Note 293, Aug. 8-12, 1983, 21 pages. |
| Matiasek et al., “Tamic-P: A System for NL Access to Social Insurance Database”, 4th International Conference on Applications of Natural Language to Information Systems, Jun. 17-19, 1999, 7 pages. |
| McGuire et al., “SHADE: Technology for Knowledge-Based Collaborative Engineering”, Journal of Concurrent Engineering˜r Applications and Research (CERA), 1993, 18 pages. |
| Meng et al., “Wheels: A Conversational System in the Automobile Classified Domain”, Proceedings Fourth International Conference on Spoken Language, 1996. ICSLP 96, Oct. 1996, pp. 542-545. |
| Michos et al., “Towards an Adaptive Natural Language Interface to Command Languages”, Natural Language Engineering, vol. 2, No. 3, 1996, pp. 191-209. |
| Milstead et al., “Metadata: Cataloging by Any Other Name”, Online, Information Today, Inc., Jan. 1999, 18 pages. |
| Milward et al., “D2.2: Dynamic MultimodalInterface Reconfiguration” Talk and Look: Tools for Ambient Linguistic Knowledge, IST-507802 Deliverable D2.2, Aug. 8, 2006, 69 pages. |
| Minker et al., “Hidden Understanding Models for Machine Translation”, Proceedings of ETRW on Interactive Dialogue in Multi-Modal Systems, Jun. 1999, pp. 1-4. |
| Mitra et al., “A Graph-Oriented Model for Articulation of Ontology Interdependencies”, Lecture Notes in Computer Science, vol. 1777, 2000, pp. 86-100. |
| Modi et al., “CMRadar: A Personal Assistant Agent for Calendar Management”, AAAI, Intelligent Systems Demonstrations, 2004, pp. 1020-1021. |
| Moore et al., “Combining Linguistic and Statistical Knowledge Sources in Natural-Language Processing for ATIS”, SRI International, ArtificialIntelliqence Center, 1995, 4 pages. |
| Moore et al., “SRI's Experience with the ATIS Evaluation”, Proceedings of the workshop on Speech and Natural Language, Jun. 24-27, 1990, pp. 147-148. |
| Moore et al., “The Information Warefare Advisor: An Architecture for Interacting with Intelligent Agents Across the Web”, Proceedings of Americas Conference on Information Systems (AMCIS), Dec. 31, 1998, pp. 186-188. |
| Moore, Robert C., “Handling Complex Queries in a Distributed Data Base”, SRI International, Technical Note 170, Oct. 8, 1979, 38 pages. |
| Moore, Robert C., “Practical Natural-Language Processing by Computer”, SRI International, Technical Note 251, Oct. 1981, 34 pages. |
| Moore, Robert C., “The Role of Logic in Knowledge Representation and Commonsense Reasoning”, SRI International, Technical Note 264, Jun. 1982, 19 pages. |
| Moore, Robert C., “Using Natural-Language Knowledge Sources in Speech Recognition”, SRI International, Artificial Intelligence Center, Jan. 1999, pp. 1-24. |
| Moran et al., “Intelligent Agent-based User Interfaces”, Proceedings of International Workshop on Human Interface Technology, Oct. 12-13, 1995, pp. 1-4. |
| Moran et al., “Multimodal User Interfaces in the Open Agent Architecture”, International Conference on Intelligent User Interfaces (IUI97), 1997, 8 pages. |
| Moran, Douglas B., “Quantifier Scoping in the SRI Core Language Engine”, Proceedings of the 26th Annual Meeting on Association for Computational Linguistics, 1988, pp. 33-40. |
| Morgan, B., “Business Objects (Business Objects for Windows) Business Objects Inc.”, DBMS, vol. 5, No. 10, Sep. 1992, 3 pages. |
| Motro, Amihai, “Flex: A Tolerant and Cooperative User Interface to Databases”, IEEE Transactions on Knowledge and Data Engineering, vol. 2, No. 2, Jun. 1990, pp. 231-246. |
| Mountford et al., “Talking and Listening to Computers”, The Art of Human-Computer Interface Design, Copyright © 1990 Apple Computer, Inc. Addison-Wesley Publishing Company, Inc, 1990, 17 pages. |
| Mozer, Michael C., “An Intelligent Environment Must be Adaptive”, IEEE Intelligent Systems, Mar./Apr. 1999, pp. 11-13. |
| Muhlhauser, Max, “Context Aware Voice User Interfaces for Workflow Support”, 2007, 254 pages. |
| Murty, “Combining Evidence from Residual Phase and MFCC Features for Speaker Recognition”, IEEE Signal Processing Letters, vol. 13, No. 1, Jan. 2006, pp. 52-55. |
| Murveit et al., “Integrating Natural Language Constraints into HMM-based Speech Recognition”, 1990 International Conference on Acoustics, Speech, and Signal Processing, Apr. 3-6, 1990, 5 pages. |
| Murveit et al., “Speech Recognition in SRI's Resource Management and ATIS Systems”, Proceedings of the Workshop on Speech and Natural Language, 1991, pp. 94-100. |
| Nakagawa et al., “Speaker Recognition by Combining MFCC and Phase Information”, IEEE International Conference on Acoustics Speech and Signal Processing ICASSP, Mar. 14-19, 2010, pp. 4502-4505. |
| Naone, Erica, “TR10: Intelligent Software Assistant”, Technology Review, Mar.-Apr. 2009, 2 pages. |
| Neches et al., “Enabling Technology for Knowledge Sharing”, Fall, 1991, pp. 37-56. |
| Niesler et al., “A Variable-Length Category-Based N-Gram Language Model”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'96), vol. 1, May 7-10, 1996, pp. 164-167. |
| Noth et al., “Verbmobil: The Use of Prosody in the Linguistic Components of a Speech Understanding System”, IEEE Transactions on Speech and Audio Processing, vol. 8, No. 5, Sep. 2000, pp. 519-532. |
| Odubiyi et al., “SAIRE—A Scalable Agent-Based Information Retrieval Engine”, Proceedings of the First International Conference on Autonomous Agents, 1997, 12 pages. |
| Owei et al., “Natural Language Query Filtration in the Conceptual Query Language”, IEEE, 1997, pp. 539-549. |
| Pannu et al., “A Learning Personal Agent for Text Filtering and Notification”, Proceedings of the International Conference of Knowledge Based Systems, 1996, pp. 1-11. |
| Papadimitriou et al., “Latent Semantic Indexing: A Probabilistic Analysis”, available online at <http://citeseerx.ist.psu.edu/messaqes/downloadsexceeded.html>, Nov. 14, 1997, 21 pages. |
| Parsons, T. W., “Voice and Speech Processing”, Pitch and Formant Estimation © 1987, McGraw-Hill, Inc., ISBN: 0-07-0485541-0, 1987, 15 pages. |
| Parsons,, T. W., “Voice and Speech Processin”, Linguistics and Technical Fundamentals, Articulatory Phonetics and Phonemics, © 1987 McGraw-Hill, Inc., ISBN: 0-07-0485541-0, 5 pages. |
| International Preliminary Report on Patentability received for PCT Patent Application No. PCT/US1993/012637, dated Apr. 10, 1995, 7 pages. |
| International Preliminary Report on Patentability received for PCT Patent Application No. PCT/US1993/012666, dated Mar. 1, 1995, 5 pages. |
| International Search Report received for PCT Patent Application No. PCT/US1993/012666, dated Nov. 9, 1994, 8 pages. |
| International Preliminary Report on Patentability received for PCT Patent Application No. PCT/US1994/011011, dated Feb. 28, 1996, 4 pages. |
| International Search Report received for PCT Patent Application No. PCT/US1994/011011, dated Feb. 8, 1995, 7 pages. |
| Written Opinion received for PCT Patent Application No. PCT/US1994/011011, dated Aug. 21, 1995, 4 pages. |
| International Preliminary Report on Patentability received for PCT Patent Application No. PCT/US1995/008369, dated Oct. 9, 1996, 4 pages. |
| International Search Report received for PCT Patent Application No. PCT/US1995/008369, dated Nov. 8, 1995, 6 pages. |
| International Search Report and Written Opinion received for PCT Patent Application No. PCT/US2011/020861, dated Nov. 29, 2011, 12 pages. |
| International Preliminary Report on Patentability received for PCT Patent Application No. PCT/US2012/029810, dated Oct. 3, 2013, 9 pages. |
| International Search Report and Written Opinion received for PCT Patent Application No. PCT/US2012/029810, dated Aug. 17, 2012, 11 pages. |
| Pereira, Fernando, “Logic for Natural Language Analysis”, SRI International, Technical Note 275, Jan. 1983, 194 pages. |
| Perrault et al., “Natural-Language Interfaces”, SRI International, Technical Note 393, Aug. 22, 1986, 48 pages. |
| Phoenix Solutions, Inc., A, “Declaration of Christopher Schmandt Regarding the MIT Galaxy System”, West Interactive Corporation, a Delaware Corporation, Document 40, Jul. 2, 2010, 162 pages. |
| Picone, J., “Continuous Speech Recognition Using Hidden Markov Models”, IEEE ASSP Magazine, vol. 7, No. 3, Jul. 1990, 16 pages. |
| Pulman et al., “Clare: A Combined Language and Reasoning Engine”, Proceedings of JFIT Conference, available online at <URL: http://www.cam.sri.com/tr/crc042/paper.ps.Z>, 1993, 8 pages. |
| Rabiner et al., “Fundamental of Speech Recognition”, © 1993 AT&T, Published by Prentice-Hall, Inc., ISBN: 0-13-285826-6, 1993, 17 pages. |
| Rabiner et al., “Note on the Properties of a Vector Quantizer for LPC Coefficients”, Bell System Technical Journal, vol. 62, No. 8, Oct. 1983, 9 pages. |
| Ratcliffe, M., “CiearAccess 2.0 allows SQL searches off-line (Structured Query Language)”, ClearAcess Corp., MacWeek, vol. 6, No. 41, Nov. 16, 1992, 2 pages. |
| Ravishankar, “Efficient Algorithms for Speech Recognition”, Doctoral Thesis submitted to School of Computer Science, Computer Science Division, Carnegie Mellon University, Pittsburg, May 15, 1996, 146 pages. |
| Rayner et al., “Adapting the Core Language Engine to French and Spanish”, Cornell University Library, available online at <http:l/arxiv.org/abs/cmp-lg/9605015.>, May 10, 1996, 9 pages. |
| Rayner et al., “Deriving Database Queries from Logical Forms by Abductive Definition Expansion”, Proceedings of the Third Conference on Applied Natural Language Processing, ANLC, 1992, 8 pages. |
| Rayner et al., “Spoken Language Translation With Mid-90's Technology: A Case Study”, EUROSPEECH, ISCA, available online at <http:// db l p. u n i-trier. de/d b/conf/interspeech/eurospeech 1993.html#RaynerBCCDGKKLPPS93>, 1993, 4 pages. |
| Rayner, M., “Abductive Equivalential Translation and its application to Natural Language Database Interfacing”, Dissertation paper, SRI International, Sep. 1993, 163 pages. |
| Rayner, M., “Linguistic Domain Theories: Natural-Language Database Interfacing from First Principles”, SRI International, Cambridge, 1993, 11 pages. |
| Remde et al., “SuperBook: An Automatic Tool for Information Exploration—Hypertext?”, In Proceedings of Hypertext '87, Nov. 13-15, 1987, 14 pages. |
| Reynolds, C. F., “On-Line Reviews: A New Application of the HICOM Conferencing System”, IEE Colloquium on Human Factors in Electronic Mail and Conferencing Systems, Feb. 3, 1989, 4 pages. |
| Rice et al., “Monthly Program: Nov. 14, 1995”, The San Francisco Bay Area Chapter of ACM SIGCHI, Available at <http://www.baychi.org/calendar/19951114>, Nov. 14, 1995, 2 pages. |
| Rice et al., “Using the Web Instead of a Window System”, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI'96, 1996, pp. 1-14. |
| Rigoll, G., “Speaker Adaptation for Large Vocabulary Speech Recognition Systems Using Speaker Markov Models”, International Conference on Acoustics, Speech, and Signal Processing (ICASSP'89), May 23-26, 1989, 4 pages. |
| Riley, M D., “Tree-Based Modelling of Segmental Durations”, Talking Machines Theories, Models, and Designs, 1992 © Elsevier Science Publishers B.V., North-Holland, ISBN: 08-444-89115.3, 1992, 15 pages. |
| Rivlin et al., “Maestro: Conductor of Multimedia Analysis Technologies”, SRI International, 1999, 7 pages. |
| Rivoira et al., “Syntax and Semantics in a Word-Sequence Recognition System”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'79), Apr. 1979, 5 pages. |
| Roddy et al., “Communication and Collaboration in a Landscape of B2B eMarketplaces”, VerticaiNet Solutions, white paper, Jun. 15, 2000, 23 pages. |
| Rosenfeld, R., “A Maximum Entropy Approach to Adaptive Statistical Language Modelling”, Computer Speech and Language, vol. 10, No. 3, Jul. 1996, 25 pages. |
| Roszkiewicz, A., “Extending your Apple”, Back Talk—Lip Service, A+ Magazine, The Independent Guide for Apple Computing, vol. 2, No. 2, Feb. 1984, 5 pages. |
| Rudnicky et al., “Creating Natural Dialogs in the Carnegie Mellon Communicator System”, Proceedings of Eurospeech, vol. 4, 1999, pp. 1531-1534. |
| Russell et al., “Artificial Intelligence, A Modern Approac”, © 1995 Prentice Hall, Inc., 1995, 121 pages. |
| Sacerdoti et al., “A Ladder User's Guide (Revised)”, SRI International Artificial Intelligence Center, Mar. 1980, 39 pages. |
| Sagalowicz, D., “AD-Ladder User's Guide”, SRI International, Sep. 1980, 42 pages. |
| Sakoe et al., “Dynamic Programming Algorithm Optimization for Spoken Word Recognition”, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-26 No. 1, Feb. 1978, 8 pages. |
| Salton et al., “On the Application of Syntactic Methodologies in Automatic Text Analysis”, Information Processing and Management, vol. 26, No. 1, Great Britain, 1990, 22 pages. |
| Sameshima et al., “Authorization with security attributes and privilege delegation Access control beyond the ACL”, Computer Communications, vol. 20, 1997, 9 pages. |
| San-Segundo et al., “Confidence Measures for Dialogue Management in the CU Communicator System”, Proceedings of Acoustics, Speech, and Signal Processing (ICASSP'00), Jun. 5-9, 2000, 4 pages. |
| Sato, H., “A Data Model, Knowledge Base, and Natural Language Processing for Sharing a Large Statistical Database,”, Statistical and Scientific Database Management, Lecture Notes in Computer Science, vol. 339, 1989, 20 pages. |
| Ward et al., “Recent Improvements in the CMU Spoken Language Understanding System”, ARPA Human Language Technology Workshop, 1994, pp. 213-216. |
| Ward, Wayne, “The CMU Air Travel Information Service: Understanding Spontaneous Speech”, Proceedings of the Workshop on Speech and Natural Language, HLT '90, 1990, pp. 127-129. |
| Warren, “An Efficient Easily Adaptable System for Interpreting Natural Language Queries”, American Journal of Computational Linguistics, vol. 8, No. 3-4, Jul.-Dec. 1982, pp. 110-119. |
| Weizenbaum, Joseph, “ELIZA—A Computer Program for the Study of Natural Language Communication Between Man and Machine”, Communications of the ACM, vol. 9, No. 1, Jan. 1966, pp. 36-45. |
| Werner et al., “Prosodic Aspects of Speech,” Universite de Lausanne, Switzerland, 1994, Fundamentals of Speech Synthesis and Speech Recognition: Basic Concepts, State of the Art, and Future Challenges, 1994, pp. 23-40. |
| Winiwarter et al., “Adaptive Natural Language Interfaces to FAQ Knowledge Bases”, Proceedings of 4th International Conference on Applications of Natural Language to Information Systems, Austria, Jun. 17-19, 1999, 22 pages. |
| Wolff, Mark, “Poststructuralism and the ARTFUL Database: Some Theoretical Considerations”, Information Technology and Libraries, vol. 13, No. 1, Mar. 1994, 10 pages. |
| Wu et al., “KDA: A Knowledge-based Database Assistant”, Data Engineering, Proceeding of the Fifth International Conference on Engineering (IEEE Cat No. 89CH2695-5), Feb. 6-10, 1989, pp. 402-409. |
| Wu, Min, “Digital Speech Processing and Coding”, ENEE408G Capstone-Multimedia Signal Processing, Spring 2003, Lecture-2 Course Presentation, University of Maryland, College Park, 2003, pp. 1-8. |
| Wu, Min, “Speech Recognition, Synthesis, and H.C.I.”, ENEE408G Capstone-Multimedia Signal Processing, Spring 2003, Lecture-3 course presentation, University of Maryland, College Park, 2003, pp. 1-11. |
| Wyle, M. F., “A Wide Area Network Information Filter”, In Proceedings of First International Conference on Artificial Intelligence on Wall Street, Oct. 9-11, 1991, pp. 10-15. |
| Yang et al., “Smart Sight: A Tourist Assistant System”, Proceedings of Third International Symposium on Wearable Computers, 1999, 6 pages. |
| Yankelovich et al., “Intermedia: The Concept and the Construction of a Seamless Information Environment”, IEEE Computer Magazine, Jan. 1988, pp. 81-96. |
| Yoon et al., “Letter-to-Sound Rules for Korean”, Department of Linguistics, The Ohio State University, 2002, 4 pages. |
| Zeng et al., “Cooperative Intelligent Software Agents”, The Robotics Institute, Carnegie-Mellon University, Mar. 1995, 13 pages. |
| Zhao Leon., “Intelligent Agents for Flexible Workflow Systems”, Proceedings of the Americas Conference on Information Systems (AMCIS), 1998, pp. 237-239. |
| Zhao, Y., “An Acoustic-Phonetic-Based Speaker Adaptation Technique for Improving Speaker-Independent Continuous Speech Recognition”, IEEE Transactions on Speech and Audio Processing, vol. 2, No. 3, Jul. 1994, pp. 380-394. |
| Zovato et al., “Towards Emotional Speech Synthesis: A Rule based Approach”, Proceedings 5th ISCA Speech Synthesis Workshop—Pittsburgh, 2004, pp. 219-220. |
| Zue et al., “From Interface to Content: Translingual Access and Delivery of On-Line Information”, EUROSPEECH, 1997, 4 pages. |
| Zue et al., “Jupiter: A Telephone-Based Conversational Interface for Weather Information”, IEEE Transactions on Speech and Audio Processing, Jan. 2000, pp. 100-112. |
| Zue et al., “Pegasus: A Spoken Dialogue Interface for On-Line Air Travel Planning”, Elsevier, Speech Communication, vol. 15, 1994, 10 pages. |
| Zue et al., “The Voyager Speech Understanding System: Preliminary Development and Evaluation”, Proceedings of IEEE 1990 International Conference on Acoustics, Speech, and Signal Processing, 1990, pp. 73-76. |
| Zue, Victor, “Conversational Interfaces: Advances and Challenges”, Spoken Language System Group <http://www.cs.cmu.edu/-dod/papers/zue97.pdf>, Sep. 1997, 10 pages. |
| Zue, Victor W., “Toward Systems that Understand Spoken Language”, ARPA Strategic Computing Institute, Feb. 1994, pp. 51-59. |
| Grosz et al., “Research on Natural-Language Processing at SRI”, SRI International Technical Note 257, Nov. 1981, 21 pages. |
| Grosz et al., “TEAM: An Experiment in the Design of Transportable Natural-Language Interfaces”, Artificial Intelligence, vol. 32, 1987, pp. 173-243. |
| Grosz, Barbara J., “Team: A Transportable Natural-Language Interface System”, Proceedings of the First Conference on Applied Natural Language Processing, 1983, pp. 39-45. |
| Gruber et al., “An Ontology for Engineering Mathematics”, Fourth International Conference on Principles of Knowledge Representation and Reasoning, Available at <http://www-ksl.stanford.edu/knowledge-sharing/papers/engmath.html>, 1994, pp. 1-22. |
| Gruber et al., “Generative Design Rationale: Beyond the Record and Replay Paradigm”, Knowledge Systems Laboratory, Technical Report KSL 92-59, Dec. 1991, Updated Feb. 1993, 24 pages. |
| Gruber et al., “Machine-Generated Explanations of Engineering Models: A Compositional Modeling Approach”, In Proc. International Joint Conference on Artificial Intelligence, 1993, 7 pages. |
| Gruber et al., “NIKE: A National Infrastructure for Knowledge Exchange”, A Whitepaper Advocating and ATP Initiative on Technologies for Lifelong Learning, Oct. 1994, pp. 1-10. |
| Gruber et al., “Toward a Knowledge Medium for Collaborative Product Development”, Proceedings of the Second International Conference on Artificial Intelligence in Design, Jun. 22-25, 1992, pp. 1-19. |
| Gruber et al., “A Translation Approach to Portable Ontology Specifications”, Knowledge Systems Laboratory, Technical Report KSL 92-71, Sep. 1992, Revised Apr. 1993, 27 pages. |
| Gruber, Thomas R., “Automated Knowledge Acquisition for Strategic Knowledge”, Machine Learning, vol. 4, 1989, pp. 293-336. |
| Gruber, Thomas R., Interactive Acquisition of Justifications: Learning “Why” by Being Told “What”, Knowledge Systems Laboratory, Technical Report KSL 91-17, Original Oct. 1990, Revised Feb. 1991, pp. 1-23. |
| Gruber, Thomas R., “Toward Principles for the Design of Ontologies Used for Knowledge Sharing”, In International Journal Human-Computer Studies, vol. 43, p. 907-928, substantial revision of paper presented at the International Workshop on Formal Ontology, Available as Technical Report KSL 93-04, Knowledge Systems Laboratory, revised Aug. 23, 1993, 23 pages. |
| Gruber, Tom, “(Avoiding) the Travesty of the Commons”, Presentation at NPUC, New Paradigms for User Computing, IBM Almaden Research Center, Jul. 24, 2006, 52 pages. |
| Gruber, Tom, “2021: Mass Collaboration and the Really New Economy”, TNTY Futures, vol. 1, No. 6, Available Online at <http://tomgruber.org/writing/tnty2001.htm>, Aug. 2001, 5 pages. |
| Gruber, Tom, “Big Think Small Screen: How Semantic Computing in the Cloud will Revolutionize the Consumer Experience on the Phone”, Keynote presentation at Web 3.0 conference, 2009, 41 pages. |
| Gruber, Tom, “Collaborating around Shared Content on the WWW”, W3C Workshop on WWW and Collaboration, Available at <http://www.w3.org/Collaboration/Workshop/Proceedings/P9.html>, Sep. 11, 1995, 1 page. |
| Gruber, Tom, “Collective Knowledge Systems: Where the Social Web meets the Semantic Web”, Web Semantics: Science, Services and Agents on the World Wide Web, 2007, pp. 1-19. |
| Gruber, Tom, “Despite our Best Efforts, Ontologies are not the Problem”, AAAI Spring Symposium, Available at <http://tomgruber.org/writing/aaai-ss08.htm>, 2008, pp. 1-40. |
| Gruber, Tom, “Enterprise Collaboration Management with Intraspect”, Intraspect Technical White Paper, Jul. 2001, pp. 1-24. |
| Gruber, Tom, “Every Ontology is a Treaty—A Social Agreement—Among People with Some Common Motive in Sharing”, Official Quarterly Bulletin of AIS Special Interest Group on Semantic Web and Information Systems, vol. 1, No. 3, 2004, pp. 1-5. |
| Gruber, Tom, “Helping Organizations Collaborate, Communicate, and Learn”, Presentation to NASA Ames Research, intraspect, available at <http://tomgruber.org/writing/organizational-intelligence-talk.htm>, Mar.-Oct. 2003, 30 pages. |
| Gruber, Tom, “Intelligence at the Interface: Semantic Technology and the Consumer Internet Experience”, Presentation at Semantic Technologies Conference, Available at <http://tomgruber.org/writing/semtech08.htm>, 2008, pp. 1-40. |
| Gruber, Tom, “It Is What It Does: The Pragmatics of Ontology for Knowledge Sharing”, Proceedings of the International CIDOC CRM Symposium, Available at <http://tomgruber.org/writing/cidoc-ontology.htm>, Mar. 26, 2003, 21 pages. |
| Gruber, Tom, “Ontologies, Web 2.0 and Beyond”, Ontology Summit, available at <http://tomgruber.org/writing/ontolog-social-web-keynote.htm>, Apr. 24, 2007, 17 pages. |
| Gruber, Tom, “Ontology of Folksonomy: A Mash-up of Apples and Oranges”, Int'l Journal on Semantic Web & Information Systems, vol. 3, No. 2, 2007, 7 pages. |
| Gruber, Tom, “Siri, a Virtual Personal Assistant—Bringing Intelligence to the Interface”, Semantic Technologies conference, Jun. 16, 2009, 21 pages. |
| Gruber, Tom, “TagOntology”, Presentation to Tag Camp, Oct. 29, 2005, 20 pages. |
| Gruber, Tom, “Where the Social Web meets the Semantic Web”, Presentation at the 5th International Semantic Web Conference, Nov. 7, 2006, 38 pages. |
| Guida et al., “NLI: A Robust Interface for Natural Language Person-Machine Communication”, Int. J. Man-Machine Studies, vol. 17, 1982, pp. 417-433. |
| Guzzoni et al., “A Unified Platform for Building Intelligent Web Interaction Assistants”, Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Computer Society, 2006, 4 pages. |
| Guzzoni et al., “Active, A Platform for Building Intelligent Operating Rooms”, Surgetica 2007 Computer-Aided Medical Interventions: tools and Applications, Paris, 2007, Sauramps Medical, http://lsro.epfl.ch/page-68384-en.html,, 2007, 8 pages. |
| Guzzoni et al., “Active, A Platform for Building Intelligent Software,”, Computational Intelligence, available online at <http://www.informatik.uni-trier.del-ley/pers/hd/g/Guzzoni:Didier >, 2006, 5 pages. |
| Guzzoni et al., “Active, A Tool for Building Intelligent User Interfaces”, ASC 2007, Palma de Mallorca, <http://lsro.epfl.ch/page-34241.html>, 2007, 6 pages. |
| Guzzoni et al., “Many Robots Make Short Work”, AAAI Robot Contest, SRI International, 1996, pp. 1-9. |
| Guzzoni et al., “Modeling Human-Agent Interaction with Active Ontologies”, AAAI Spring Symposium, Interaction Challenges for Intelligent Assistants, Stanford University, Palo Alto, California, 2007, 8 pages. |
| Guzzoni, D., “Active: A unified platform for building intelligent assistant applications”, Oct. 25, 2007, 262 pages. |
| Haas et al., “An Approach to Acquiring and Applying Knowledge”, SRI international Technical Note 227, Nov. 1980, 22 pages. |
| Hadidi et al., “Students' Acceptance of Web-Based Course Offerings: An Empirical Assessment”, Proceedings of the Americas Conference on Information Systems (AMCIS), 1998, 4 pages. |
| Hardwar, Devemder, “Driving App Waze Builds its own Siri for Hands-Free Voice Control”, retrieved from internet on Feb. 9, 2012 <http ://ventu rebeat. com/20 12/0 2/09/ driving-a pp-waze-bu i lds-its-own-siri-for -hands-freevoice-control/>, 4 pages. |
| Harris, F. J., “On the Use of Windows for Harmonic Analysis with the Discrete Fourier Transform”, In Proceedings of the IEEE, vol. 66, No. 1, Jan. 1978, 34 pages. |
| Hawkins et al., “Hierarchical Temporal Memory: Concepts, Theory, and Terminology”, Numenta, Inc., Mar. 27, 2007, pp. 1-20. |
| He et al., “Personal Security Agent: KQML-Based PKI”, The Robotics Institute, Carnegie-Mellon University, paper, 1997, pp. 1-14. |
| Helm et al., “Building Visual Language Parsers”, In Proceedings of CHI'91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1991, pp. 105-112. |
| Hendrix et al., “Developing a Natural Language Interface to Complex Data”, ACM Transactions on Database Systems, vol. 3, No. 2, Jun. 1978, pp. 105-147. |
| Hendrix et al., “Transportable Natural-Language Interfaces to Databases”, SRI International, Technical Note 228, Apr. 30, 1981, 18 pages. |
| Hendrix et al., “Human Engineering for Applied Natural Language Processing”, SRI International, Technical Note 139, Feb. 1977, 27 pages. |
| Hendrix, Gary G., “Klaus: A System for Managing Information and Computational Resources”, SRI International, Technical Note 230, Oct. 1980, 34 pages. |
| Hendrix, Gary G., “Lifer: A Natural Language Interface Facility”, SRI Stanford Research Institute, Technical Note 135, Dec. 1976, 9 pages. |
| Hendrix, Gary G., “Natural-Language Interface”, American Journal of Computational Linguistics, vol. 8, No. 2, Apr.-Jun. 1982, pp. 56-61. |
| Hendrix, Gary G., “The Lifer Manual: A Guide to Building Practical Natural Language Interfaces”, SRI International, Technical Note 138, Feb. 1977, 76 pages. |
| Savoy, J., “Searching Information in Hypertext Systems Using Multiple Sources of Evidence”, International Journal of Man-Machine Studies, vol. 38, No. 6, Jun. 1996, pp. 1017-1030. |
| Scagliola, C., “Language Models and Search Algorithms for Real-Time Speech Recognition”, International Journal of Man-Machine Studies, vol. 22, No. 5, 1985, pp. 523-547. |
| Schmandt et al., “Augmenting a Window System with Speech Input”, IEEE Computer Society, Computer, vol. 23, No. 8, Aug. 1990, pp. 50-56. |
| Ward et al., “A Class Based Language Model for Speech Recognition”, Conference Proceedings International Conference on Acoustics, Speech, and Signal Processing, 1996, ICASSP-96 , pp. 416-418. |
| Schütze, H., “Dimensions of Meaning”, Proceedings of Supercomputing'92 Conference, Nov. 16-20, 1992, 10 pages. |
| Seneff et al., “A New Restaurant Guide Conversational System: Issues in Rapid Prototyping for Specialized Domains”, Proceedings of Fourth International Conference on Spoken Language, 1996. ICSLP 96, 1996, pp. 665-668. |
| Sharoff et al., “Register-domain Separation as a Methodology for Development of Natural Language Interfaces to Databases”, Proceedings of Human-Computer Interaction (INTERACT'99), 1999, 7 pages. |
| Sheth et al., “Evolving Agents for Personalized Information Filtering”, In Proceedings of the Ninth Conference on Artificial Intelligence for Applications, Mar. 1-5, 1993, 9 pages. |
| Sheth et al., “Relationships at the Heart of Semantic Web: Modeling, Discovering, and Exploiting Complex Semantic Relationships”, Enhancing the Power of the Internet: Studies in Fuzziness and Soft Computing, Oct. 13, 2002, pp. 1-38. |
| Shikano et al., “Speaker Adaptation Through Vector Quantization”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'86), vol. 11, Apr. 1986, 4 pages. |
| Shimazu et al., “CAPIT: Natural Language Interface Design Tool with Keyword Analyzer and Case-Based Parser,”, NEG Research & Development, vol. 33, No. 4, Oct. 1992, 11 pages. |
| Shinkle, L., “Team User's Guide”, SRI International, Artificial Intelligence Center, Nov. 1984, 78 pages. |
| Shklar et al., “Info Harness: Use of Automatically Generated Metadata for Search and Retrieval of Heterogeneous Information”, 1995 Proceedings of CAiSE'95, Finland, 1995. |
| Sigurdsson et al., “Mel Frequency Cepstral Coefficients: An Evaluation of Robustness of MP3 Encoded Music”, In Proceedings of the 7th International Conference on Music Information Retrieval ISMIR, 2006, 4 pages. |
| Silverman et al., “Using a Sigmoid Transformation for Improved Modeling of Phoneme Duration”, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Mar. 15-19, 1999, 5 pages. |
| Simonite, Tom, “One Easy Way to Make Siri Smarter”, Technology Review, Oct. 18, 2011, 2 pages. |
| Singh, N., “Unifying Heterogeneous Information Models”, Communications of the ACM, 1998, 13 pages. |
| SRI International, “The Open Agent Architecture.TM. 1.0 Distribution”, Open Agent Architecture (OAA), 1999, 2 pages. |
| SRI2009, “SRI Speech: Products: Software Development Kits: EduSpeak”, available a <http://web.archive.org/web/20090828084033/http://www.speechatsri.com/products/eduspeak> shtml., 2009, 2 pages. |
| Starr et al., “Knowledge-Intensive Query Processing”, Proceedings of the 5th KRDB Workshop, Seattle, May 31, 1998, 6 pages. |
| Stent et al., “The CommandTalk Spoken Dialogue System”, SRI International, 1999, pp. 183-190. |
| Stern et al., “Multiple Approaches to Robust Speech Recognition”, Proceedings of Speech and Natural Language Workshop, 1992, 6 pages. |
| Stickel, “A Nonclausal Connection-Graph Resolution Theorem-Proving Program”, Proceedings of AAAI'82, 1982, pp. 1-13. |
| Sugumaran, V., “A Distributed Intelligent Agent-Based Spatial Decision Support System”, Proceedings of the Americas Conference on Information systems (AMCIS), Dec. 31, 1998, 4 pages. |
| Sycara et al., “Coordination of Multiple Intelligent Software Agents”, International Journal of Cooperative Information Systems (IJCIS), vol. 5, No. 2 & 3, Jun. & Sep. 1996, 33 pages. |
| Sycara et al., “Distributed Intelligent Agents”, IEEE Expert, vol. 11, No. 6, Dec. 1996, 32 pages. |
| Sycara et al., “Dynamic Service Matchmaking Among Agents in Open Information Environments”, SIGMOD Record, 1999, 7 pages. |
| Sycara et al., “The RETSINA MAS Infrastructure”, Autonomous Agents and Multi-Agent Systems, vol. 7, 2003, 20 pages. |
| Tenenbaum et al., “Data Structure Using Pascal”, 1981 Prentice-Hall, Inc, 1981, 34 pages. |
| Tofel et al., “SpeakToIt: A Personal Assistant for Older iPhones, iPads”, Apple News, Tips and Reviews, Feb. 9, 2012, 7 pages. |
| Tsai et al., “Attributed Grammar—A Tool for Combining Syntactic and Statistical Approaches to Pattern Recognition”, IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-10, No. 12, Dec. 1980, 13 pages. |
| Tucker, Joshua, “Too Lazy to Grab Your TV Remote? Use Siri Instead”, Engadget, Nov. 30, 2011, 8 pages. |
| Tur et al., “The CALO Meeting Assistant System”, IEEE Transactions on Audio, Speech, and Language Processing, vol. 18, No. 6, Aug. 2010, pp. 1601-1611. |
| Tur et al., “The CALO Meeting Speech Recognition and Understanding System”, Proc. IEEE Spoken Language Technology Workshop, 2008, 4 pages. |
| Tyson et al., “Domain-Independent Task Specification in the TACITUS Natural Language System”, SRI International, Artificial Intelligence Center, May 1990, 16 pages. |
| Udell, J., “Computer Telephony”, BYTE, vol. 19, No. 7, Jul. 1, 1994, 9 pages. |
| Van Santen, J. P., “Contextual Effects on Vowel Duration”, Journal Speech Communication, vol. 11, No. 6, Dec. 1992, 34 pages. |
| Vepa et al., “New Objective Distance Measures for Spectral Discontinuities in Concatenative Speech Synthesis”, In Proceedings of the IEEE 2002 Workshop on Speech Synthesis, 2002, 4 pages. |
| Verschelde, J., “MATLAB Lecture 8. Special Matrices in MATLAB,”, UIC Dept. of Math., Stat. & C.S., MCS 320, Introduction to Symbolic Computation, Nov. 23, 2005, 4 pages. |
| Vingron, M., “Near-Optimal Sequence Alignment”, Deutsches Krebsforschungszentrum (DKFZ), Abteilung Theoretische Bioinformatik, Heidelberg, Germany, Jun. 1996, 20 pages. |
| Vlingo, “Vlingo Launches Voice Enablement Application on Apple App Store”, Press Release, Dec. 3, 2008, 2 pages. |
| Vlingo LNCAR, “Distracted Driving Solution with Vlingo InCar”, YouTube by Vlingo Voice on (Oct. 6, 2010) 2:38 minute video uploaded to Retrieved from Internet on Jun. 6, 2012<http://www.youtube.com/watch?v=Vqs8XfXxgz4?, 2 pages. |
| Wahlster et al., “Smartkom: Multimodal Communication with a Life-Like Character,”, EUROSPEECH—Scandinavia, 7th European Conference on Speech Communication and Technology, 2001, 5 pages. |
| Waldinger et al., “Deductive Question Answering from Multiple Resources”, New Directions in Question Answering, published by AAAI, Menlo Park, 2003, 22 pages. |
| Walker et al., “Natural Language Access to Medical Text”, SRI International, Artificial Intelligence Center, Mar. 1981, 23 pages. |
| Waltz, D., “An English Language Question Answering System for a Large Relational Database”, © 1978 ACM, vol. 21, No. 7, 1978, 14 pages. |
| Hermansky, H., “Perceptual Linear Predictive (PLP) Analysis of Speech”, Journal of the Acoustical Society of America, vol. 87, No. 4, Apr. 1990, pp. 1738-1752. |
| Hermansky, H., “Recognition of Speech in Additive and Convolutional Noise Based on Rasta Spectral Processing”, In proceedings of IEEE International Conference on Acoustics, speech and Signal Processing (ICASSP'93), Apr. 27-30, 1993, pp. II-83-II-86. |
| Hirschman et al., “Multi-Site Data Collection and Evaluation in Spoken Language Understanding”, Proceedings of the workshop on Human Language Technology, 1993, pp. 19-24. |
| Hobbs et al., “Fastus: A System for Extracting Information from Natural-Language Text”, SRI International, Technical Note 519, Nov. 19, 1992, 26 pages. |
| Hobbs et al., “Fastus: Extracting Information from Natural-Language Texts”, SRI International, 1992, pp. 1-22. |
| Hobbs, Jerry R., “Sublanguage and Knowledge”, SRI International, Technical Note 329, Jun. 1984, 30 pages. |
| Hodjat et al., “Iterative Statistical Language Model Generation for Use with an Agent-Oriented Natural Language Interface”, Proceedings of HCI International, vol. 4, Jun. 22-27, 2003, pp. 1422-1426. |
| Hoehfeld et al., “Learning with Limited Numerical Precision Using the Cascade-Correlation Algorithm”, IEEE Transactions on Neural Networks, vol. 3, No. 4, Jul. 1992, 18 pages. |
| Holmes, J. N., “Speech Synthesis and Recognition—Stochastic Models for Word Recognition”, Published by Chapman & Hall, London, ISBN 0 412 534304, 1998, 7 pages. |
| Hon et al., “CMU Robust Vocabulary-Independent Speech Recognition System”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-91), Apr. 14-17, 1991, pp. 889-892. |
| Huang et al., “The SPHINX-II Speech Recognition System: An Overview”, Computer, Speech and Language, Jan. 15, 1992, pp. 1-12. |
| Issar et al., “CMU's Robust Spoken Language Understanding System”, Proceedings of Eurospeech, 1993, 4 pages. |
| Issar, Sunil, “Estimation of Language Models for New Spoken Language Applications”, Proceedings of 4th International Conference on Spoken language Processing, Oct. 3-6, 1996, 4 pages. |
| Jacobs et al., “Scisor: Extracting Information from On-Line News”, Communications of the ACM, vol. 33, No. 11, Nov. 1990, pp. 88-97. |
| Janas, Jurgen M., “The Semantics-Based Natural Language Interface to Relational Databases”, Chapter 6, Cooperative Interfaces to Information Systems, 1986, pp. 143-188. |
| Jelinek, F., “Self-Organized Language Modeling for Speech Recognition,”, Readings in Speech Recognition, edited by Alex Waibel and Kai-Fu Lee, Morgan Kaufmann Publishers, Inc., ISBN: 1-55860-124-4, May 15, 1990, 63 pages. |
| Jennings et al., “A Personal News Service Based on a User Model Neural Network”, IEICE Transactions on Information and Systems, vol. E75-D, No. 2, Tokyo, JP, Mar. 1992, pp. 198-209. |
| Ji et al., “A Method for Chinese Syllables Recognition based upon Sub-syllable Hidden Markov Model”, 1994 International Symposium on Speech, Image Processing and Neural Networks, Hong Kong, Apr. 13-16, 1994, pp. 730-733. |
| Johnson, Julia A., “A Data Management Strategy for Transportable Natural Language Interfaces”, Doctoral thesis submitted to the Department of Computer Science, University of British Columbia, Canada, Jun. 1989, 285 pages. |
| Jones, J., “Speech Recognition for Cyclone”, Apple Computer, Inc., E.R.S.Revision 2.9, Sep. 10, 1992, 93 pages. |
| Julia et al., “http://www.speech.siri.com/demos/atis.html”, Proceedings of AAAI, Spring Symposium, 1997, 5 pages. |
| Julia et al., “Un editeur interactif de tableaux dessines a main levee (An Interactive Editor for Hand-Sketched Tables)”, Traitement du Signal, vol. 12, No. 6, 1995, pp. 619-626. |
| Kahn et al., “CoABS Grid Scalability Experiments”, Autonomous Agents and Multi-Agent Systems, vol. 7, 2003, pp. 171-178. |
| Kamel et al., “A Graph Based Knowledge Retrieval System”, IEEE International Conference on Systems, Man and Cybernetics, 1990, pp. 269-275. |
| Karp, P. D., “A Generic Knowledge-Base Access Protocol”, <http://lecture.cs.buu.ac.th/-f50353/Document/gfp.pdf>, May 12, 1994, 66 pages. |
| Kats et al., “Exploiting Lexical Regularities in Designing Natural Language Systems”, In the Proceedings of the 12th International Conference on Computational Linguistics, 1988, pp. 1-22. |
| Katz et al., “REXTOR: A System for Generating Relations from Natural Language”, In Proceedings of the ACL Workshop on Natural Language Processing and Information Retrieval (NLP&IR), Oct. 2000, 11 pages. |
| Katz, Boris, “A Three-Step Procedure for Language Generation”, Massachusetts Institute of Technology, A.I. Memo No. 599, Dec. 1980, pp. 1-40. |
| Katz, Boris, “Annotating the World Wide Web Using Natural Language”, In Proceedings of the 5th RIAO Conference on Computer Assisted Information Searching on the Internet, 1997, 7 pages. |
| Katz, Boris, “Using English for Indexing and Retrieving”, Proceedings of the 1st RIAO Conference on User-Oriented Content-Based Text and Image Handling, 1988, pp. 314-332. |
| Katz, Slava. M., “Estimation of Probabilities from Sparse Data for the Language Model Component of a Speech Recognizer”, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-35, No. 3, Mar. 1987, 3 pages. |
| Kickstarter, “Ivee Sleek: Wi-Fi Voice-Activated Assistant”, available online at <https://www.kickstarter.com/projects/ivee/ivee-sleek-wi-fi-voice-activated-assistant> retrived from internet on Feb. 10, 2014, 13 pages. |
| Kitano, H., “ϕDM-Dialog: An Experimental Speech-to-Speech Dialog Translation System”, Computer, vol. 24, No. 6, Jun. 1991, 13 pages. |
| Klabbers et al., “Reducing Audible Spectral Discontinuities”, IEEE Transactions on Speech and Audio Processing, vol. 9, No. 1, Jan. 2001, pp. 39-51. |
| Klatt Dennis H., “Linguistic Uses of Segmental Duration in English: Acoustic and Perpetual Evidence”, Journal of the Acoustical Society of America, vol. 59, No. 5, May 1976, pp. 1208-1221. |
| Kominek et al., “Impact of Durational Outlier Removal from Unit Selection Catalogs”, 5th ISCA Speech Synthesis Workshop, Jun. 14-16, 2004, pp. 155-159. |
| Konolige, Kurt, “A Framework for a Portable Natural-Language Interface to Large Data Bases”, SRI International, Technical Note 197, Oct. 12, 1979, 54 pages. |
| Kubala et al., “Speaker Adaptation from a Speaker-Independent Training Corpus”, International Conference on Acoustics, Speech, and Signal Processing (ICASSP'90), Apr. 3-6, 1990, 4 pages. |
| Kubala et al., “The Hub and Spoke Paradigm for CSR Evaluation”, Proceedings of the Spoken Language Technology Workshop, Mar. 6-8, 1994, 9 pages. |
| Laird et al., “SOAR: An Architecture for General Intelligence”, Artificial Intelligence, vol. 33, 1987, pp. 1-64. |
| Langly et al., “A Design for the ICARUS Architechture”, SIGART Bulletin, vol. 2, No. 4, 1991, pp. 104-109. |
| Larks, “Intelligent Software Agents”, Available Online at <http://www.cs.cmu.edu/˜softagents/larks.html> retrieved on Mar. 15, 2013, 2006, 2 pages. |
| Lee et al., “A Real-Time Mandarin Dictation Machine for Chinese Language with Unlimited Texts and Very Large Vocabulary”, International Conference on Acoustics, Speech and Signal Processing, vol. 1, Apr. 3-6, 1990, 5 pages. |
| Lee et al., “Golden Mandarin(II)—An Improved Single-Chip Real-Time Mandarin Dictation Machine for Chinese Language with Very Large Vocabulary”, 0-7803-0946-4/93 © 19931EEE, 1993, 4 pages. |
| Lee et al., “Golden Mandarin(II)—An Intelligent Mandarin Dictation Machine for Chinese Character Input with Adaptation/Learning Functions”, International Symposium on Speech, Image Processing and Neural Networks, Hong Kong, Apr. 13-16, 1994, 5 pages. |
| Lee et al., “System Description of Golden Mandarin (I) Voice Input for Unlimited Chinese Characters”, International Conference on Computer Processing of Chinese & Oriental Languages, vol. 5, Nos. 3 & 4, Nov. 1991, 16 pages. |
| Lee, K. F., “Large-Vocabulary Speaker-Independent Continuous Speech Recognition: The SPHINX System”, Partial Fulfillment of the requirements for the degree of Doctor of Philosophy, Computer Science Department, Carnegie Mellon University, Apr. 18, 1988, 195 pages. |
| Lemon et al., “Multithreaded Context for Robust Conversational Interfaces: Context-Sensitive Speech Recognition and Interpretation of Corrective Fragments”, ACM Transactions on Computer-Human Interaction, vol. 11, No. 3, Sep. 2004, pp. 241-267. |
| Leong et al., “CASIS: A Context-Aware Speech Interface System”, Proceedings of the 10th International Conference on Intelligent user Interfaces, San Diego, California, Jan. 9-12, 2005, pp. 231-238. |
| “Integrated Audio-Graphics User Interface”, IBM Technical Disclosure Bulletin, vol. 33, No. 11, Apr. 1991, pp. 368-371. |
| “Interactive Voice”, available online at <http://www.helloivee.com/company/> retrieved from internet on Feb. 10, 2014, 2 pages. |
| “Meet Ivee Your Wi-Fi Voice Activated Assistant”, available online at <http://www.helloivee.com/> retrieved from internet on Feb. 10, 2014, 8 pages. |
| “Mel Scale”, Wikipedia the Free Encyclopedia, last modified on Oct. 13, 2009 and retrieved on Jul. 28, 2010, available online <http://en.wikipedia.org/wiki/Mel_scale>, 2 pages. |
| “Minimum Phase”, Wikipedia the free Encyclopedia, Last Modified on Jan. 12, 2010 and retrieved on Jul. 28, 2010, available online at <http://en.wikipedia.org/wiki/Minimum_phase>, 8 pages. |
| “Speech Editor”, IBM Technical Disclosure Bulletin, vol. 29, No. 10, Mar. 1987, pp. 4512-4514. |
| “Speech Recognition with Hidden Markov Models of Speech Waveforms”, IBM Technical Disclosure Bulletin, vol. 34, No. 1, Jun. 1991, pp. 7-16. |
| Ex-Parte Quayle Action received for U.S. Appl. No. 12/244,713, mailed on Sep. 10, 2012, 5 pages. |
| Non Final Office Action received for U.S. Appl. No. 12/244,713, dated Dec. 23, 2011, 11 pages. |
| Bahl et al., “Large Vocabulary Natural Language Continuous Speech Recognition”, In Proceedings of 1989 International Conference on Acoustics, Speech, and Signal Processing, vol. 1, May 23-26, 1989, 6 pages. |
| Notice of Allowance received for U.S. Appl. No. 12/244,713, dated Aug. 7, 2012, 7 pages. |
| Notice of Allowance received for U.S. Appl. No. 12/244,713, dated May 10, 2012, 5 pages. |
| Final Office Action received for U.S. Appl. No. 13/053,144, dated Nov. 19, 2013, 24 pages. |
| Non Final Office Action received for U.S. Appl. No. 13/053,144, dated Mar. 22, 2013, 18 pages. |
| Notice of Allowance received for U.S. Appl. No. 13/480,422, dated Jul. 19, 2012, 9 pages. |
| Supplemental Notice of Allowance received for U.S. Appl. No. 13/480,422, dated Sep. 27, 2012, 2 pages. |
| Notice of Allowance received for U.S. Appl. No. 13/604,556, dated Apr. 1, 2013, 9 pages. |
| Notice of Allowance received for U.S. Appl. No. 13/604,556, dated Jul. 31, 2013, 9 pages. |
| Acero et al., “Environmental Robustness in Automatic Speech Recognition”, International Conference on Acoustics, Speech, and Signal Processing (ICASSP'90), Apr. 3-6, 1990, 4 pages. |
| Acero et al., “Robust Speech Recognition by Normalization of The Acoustic Space”, International Conference on Acoustics, Speech, and Signal Processing, 1991, 4 pages. |
| Agnes et al., “Spoken Language Translator: First-Year Report”, SICS Research Report, Jan. 1994, 161 pages. |
| Ahlborn et al., “Modeling Spectral Speech Transitions Using Temporal Decomposition Techniques”, IEEE International Conference of Acoustics, Speech, and Signal Processing (ICASSP'87), vol. 12, Apr. 1987, 4 pages. |
| Aikawa et al., “Speech Recognition Using Time-Warping Neural Networks”, Proceedings of the 1991 IEEE Workshop on Neural Networks for Signal Processing, Sep. 30 to Oct. 1, 1991, 10 pages. |
| Alfred App, available online at <http://www.alfredapp.com/>, retrieved on Feb. 8, 2012, 5 pages. |
| Allen, James, “Natural Language Understanding”, 2nd Edition, 1995, 671 pages. |
| Alshawi et al., “CLARE: A Contextual Reasoning and Cooperative Response Framework for the Core Language Engine—Final Report”, SRI International, available online at <http://www.cam.sri.com/tr/crc028/paper.ps.Z>, Dec. 1992, pp. 1-272. |
| Alshawi et al., “Declarative Derivation of Database Queries from Meaning Representations”, Proceedings of the BANKAI Workshop, Oct. 1991, pp. 1-12. |
| Alshawi et al., “Logical Forms in the Core Language Engine”, Proceedings of the 27th Annual Meeting of the Association for Computational Linguistics, 1989, pp. 25-32. |
| Alshawi et al., “Overview of the Core Language Engine”, Proceedings of Future Generation Computing Systems, Sep. 1988, pp. 1-13. |
| Alshawi, Hiyan, “Translation and Monotonic Interpretation/Generation”, SRI International, available online at <http://www.cam.sri.com/tr/crc024/paper.ps.Z>, Jul. 1992, pp. 1-18. |
| Ambite et al., Design and Implementation of the CALO Query Manager, American Association for Artificial Intelligence, 2006, 8 pages. |
| Ambite et al., “Integration of Heterogeneous Knowledge Sources in the CALO Query Manager”, The 4th International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE), 2005, 18 pages. |
| Anastasakos et al., “Duration Modeling in Large Vocabulary Speech Recognition”, International Conference on Acoustics, Speech, and Signal Processing (ICASSP'95), May 9-12, 1995, 4 pages. |
| Anderson et al., “Syntax-Directed Recognition of Hand-Printed Two-Dimensional Mathematics”, In Proceedings of Symposium on Interactive Systems for Experimental Applied Mathematics: Proceedings of the Association for Computing Machinery Inc. Symposium, 1967, 12 pages. |
| Anonymous, “Speaker Recognition”, Wikipedia, The Free Enclyclopedia, Nov. 2, 2010, 3 pages. |
| Ansari et al., “Pitch Modification of Speech using a Low-Sensitivity Inverse Filter Approach”, IEEE Signal Processing Letters, vol. 5, No. 3, Mar. 1998, 3 pages. |
| Anthony et al., “Supervised Adaption for Signature Verification System”, IBM Technical Disclosure, Jun. 1, 1978, 3 pages. |
| Appelt et al., “Fastus: A Finite-state Processor for Information Extraction from Real-world Text”, Proceedings of IJCAI, 1993, 8 pages. |
| Appelt et al., “SRI International Fastus System MUC-6 Test Results and Analysis”, SRI International, 1995, 12 pages. |
| Appelt et al., “SRI: Description of the JV-FASTUS System Used for MUC-5”, SRI International, 1993, pp. 1-19. |
| Apple Computer, “Guide Maker User's Guide”, © Apple Computer, Inc., Apr. 27, 1994, 8 pages. |
| Apple Computer, “Introduction to Apple Guide”, © Apple Computer, Inc., Apr. 28, 1994, 20 pages. |
| Archbold et al., “A Team User's Guide”, SRI International, Technical Note 254, Dec. 21, 1981, 70 pages. |
| Asanovic et al., “Experimental Determination of Precision Requirements for Back-Propagation Training of Artificial Neural Networks”, In Proceedings of the 2nd International Conference of Microelectronics for Neural Networks, 1991, www.ICSI.Berkelev.EDU, 1991, 7 pages. |
| Atal et al., “Efficient Coding of LPC Parameters by Temporal Decomposition”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'83), Apr. 1983, 4 pages. |
| Bahl et al., “A Maximum Likelihood Approach to Continuous Speech Recognition”, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. PAMI-5, No. 2, Mar. 1983, 13 pages. |
| Bahl et al., “A Tree-Based Statistical Language Model for Natural Language Speech Recognition”, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 37, No., Jul. 1989, 8 pages. |
| Bahl et al., “Acoustic Markov Models Used in the Tangora Speech Recognition System”, In Proceeding of International Conference on Acoustics, Speech, and Signal Processing (ICASSP'88), vol. 1, Apr. 11-14, 1988, 4 pages. |
| Bulyko et al., “Error-Correction Detection and Response Generation in a Spoken Dialogue System”, Speech Communication, vol. 45, 2005, pp. 271-288. |
| Bahl et al., “Multonic Markov Word Models for Large Vocabulary Continuous Speech Recognition”, IEEE Transactions on Speech and Audio Processing, vol. 1, No. 3, Jul. 1993, 11 pages. |
| Bahl et al., “Speech Recognition with Continuous-Parameter Hidden Markov Models”, In Proceeding of International Conference on Acoustics, Speech, and Signal Processing (ICASSP'88), vol. 1, Apr. 11-14, 1988, 8 pages. |
| Banbrook, M., “Nonlinear Analysis of Speech from a Synthesis Perspective”, A Thesis Submitted for the Degree of Doctor of Philosophy, The University of Edinburgh, Oct. 15, 1996, 35 pages. |
| Bear et al., “A System for Labeling Self-Repairs in Speech”, SRI International, Technical Note 522, Feb. 22, 1993, pp. 1-8. |
| Bear et al., “Detection and Correction of Repairs in Human-Computer Dialog”, SRI International, Technical Note 518, May 5, 1992, pp. 1-10. |
| Bear et al., “Integrating Multiple Knowledge Sources for Detection and Correction of Repairs in Human-Computer Dialog”, Proceedings of the 30th annual meeting on Association for Computational Linguistics (ACL), 1992, 8 pages. |
| Bear et al., “Using Information Extraction to Improve Document Retrieval”, SRI International, Jan. 9, 1998, pp. 1-11. |
| Belaid et al., “A Syntactic Approach for Handwritten Mathematical Formula Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-6, No. 1, Jan. 1984, 7 pages. |
| Bellegarda et al., “A Latent Semantic Analysis Framework for Large-Span Language Modeling”, 5th European Conference on Speech, Communication and Technology, (EUROSPEECH'97), Sep. 22-25, 1997, 4 pages. |
| Bellegarda et al., “A Multispan Language Modeling Framework for Large Vocabulary Speech Recognition”, IEEE Transactions on Speech and Audio Processing vol. 6, No. 5, Sep. 1998, 12 pages. |
| Bellegarda et al., “A Novel Word Clustering Algorithm Based on Latent Semantic Analysis”, In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'96}, vol. 1, 1996, 4 pages. |
| Bellegarda et al., “Experiments Using Data Augmentation for Speaker Adaptation”, International Conference on Acoustics, Speech, and Signal Processing (ICASSP'95), May 9-12, 1995, 4 pages. |
| Bellegarda et al., “On-Line Handwriting Recognition Using Statistical Mixtures”, Advances in Handwriting and Drawings: A Multidisciplinary Approach, Europia, 6th International IGS Conference on Handwriting and Drawing, Paris-France, Jul. 1993, 11 pages. |
| Bellegarda et al., “Performance of the IBM Large Vocabulary Continuous Speech Recognition System on the ARPA Wall Street Journal Task”, Signal Processing VII: Theories and Applications, © 1994 European Association for Signal Processing, 1994, 4 pages. |
| Bellegarda et al., “The Metamorphic Algorithm: A Speaker Mapping Approach to Data Augmentation”, IEEE Transactions on Speech and Audio Processing, vol. 2, No. 3, Jul. 1994, 8 pages. |
| Bellegarda, J. R., “Exploiting Both Local and Global Constraints for Multi-Span Statistical Language Modeling”, Proceeding of the 1998 IEEE International Conference on Acoustics, Speech, and Signal Processing (1CASSP'98), vol. 2, May 12-15, 1998, 5 pages. |
| Bellegarda, J. R., “Exploiting Latent Semantic Information in Statistical Language Modeling”, Proceedings of the IEEE, vol. 88, No. 8, Aug. 2000, 18 pages. |
| Bellegarda, J. R., “Interaction-Driven Speech Input—A Data-Driven Approach to the Capture of Both Local and Global Language Constraints”, available online at <http:// old.sig.chi.ora/bulletin/1998.2/bellegarda.html>, 1992, 7 pages. |
| Bellegarda, J. R., “Large Vocabulary Speech Recognition with Multispan Statistical Language Models”, IEEE Transactions on Speech and Audio Processing, vol. 8, No. 1, Jan. 2000, 9 pages. |
| Belvin et al., “Development of the HRL Route Navigation Dialogue System”, In Proceedings of the First International Conference on Human Language Technology Research, Paper, 2001, 5 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. |
| Berry et al., “Task Management under Change and Uncertainty Constraint Solving Experience with the CALO Project”, Proceedings of CP'05 Workshop on Constraint Solving under Change, 2005, 5 pages. |
| Black et al., “Automatically Clustering Similar Units for Unit Selection in Speech Synthesis”, In Proceedings of Eurospeech 1997, vol. 2, 1997, 4 pages. |
| Blair et al., “An Evaluation of Retrieval Effectiveness for a Full-Text Document-Retrieval System”, Communications of the ACM, vol. 28, No. 3, Mar. 1985, 11 pages. |
| Bobrow et al., “Knowledge Representation for Syntactic/Semantic Processing”, AAA-80 Proceedings, 1980, pp. 316-323. |
| Bouchou et al., “Using Transducers in Natural Language Database Query”, Proceedings of 4th International Conference on Applications of Natural Language to Information Systems, Austria, Jun. 17-19, 1999, 17 pages. |
| Bratt et al., “The SRI Telephone-based ATIS System”, Proceedings of ARPA Workshop on Spoken Language Technology, 1995, 3 pages. |
| Briner, L. L., “Identifying Keywords in Text Data Processing”, In Zelkowitz, Marvin V., ED, Directions and Challenges, 15th Annual Technical Symposium, Gaithersbury, Maryland, Jun. 17, 1976, 7 pages. |
| Bulyko et al., “Joint Prosody Prediction and Unit Selection for Concatenative Speech Synthesis”, Electrical Engineering Department, University of Washington, Seattle, 2001, 4 pages. |
| Burke et al., “Question Answering from Frequently Asked Question Files”, AI Magazine, Spring, vol. 18, No. 2, 1997, pp. 57-66. |
| Burns et al., “Development of a Web-Based Intelligent Agent for the Fashion Selection and Purchasing Process via Electronic Commerce”, Proceedings of the Americas Conference on Information system (AMCIS), Dec. 31, 1998, pp. 140-142. |
| Bussey et al., “Service Architecture, Prototype Description, and Network Implications of a Personalized Information Grazing Service”, INFOCOM'90, Ninth Annual Joint Conference of the IEEE Computer and Communication Societies, available online at <http://slrohall.com/oublications/>, Jun. 3-7, 1990, 8 pages. |
| Bussler et al., “Web Service Execution Environment (WSMX)”, available online at <http://www.w3.org/Submission/WSMX/>, retrieved from internet on Sep. 17, 2012, 29 pages. |
| Butcher, Mike, “EVI Arrives in Town to go Toe-to-Toe with Siri”, TechCrunch, Jan. 23, 2012, 2 pages. |
| Buzo et al., “Speech Coding Based Upon Vector Quantization”, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. Assp-28, No. 5, Oct. 1980, 13 pages. |
| Caminero-Gil et al., “Data-Driven Discourse Modeling for Semantic Interpretation”, In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, May 7-10, 1996, 6 pages. |
| Carter et al., “The Speech-Language Interface in the Spoken Language Translator”, SRI International, Nov. 23, 1994, pp. 1-9. |
| Carter, David M., “Lexical Acquisition in the Core Language Engine”, Proceedings of the Fourth Conference of the European Chapter of the Association for Computational Linguistics, 1989, pp. 137-144. |
| Cawley, “The Application of Neural Networks to Phonetic Modelling”, PhD Thesis, University of Essex, Mar. 1996, 13 pages. |
| Chai et al., “Comparative Evaluation of a Natural Language Dialog Based System and a Menu Driven System for Information Access: a Case Study”, Proceedings of the International Conference on Multimedia Information Retrieval (RIAO), Apr. 2000, 11 pages. |
| Chang et al., “A Segment-based Speech Recognition System for Isolated Mandarin Syllables”, Proceedings TEN CON '93, IEEE Region 10 conference on Computer, Communication, Control and Power Engineering, vol. 3, Oct. 19-21, 1993, pp. 317-320. |
| Chen, Yi, “Multimedia Siri Finds and Plays Whatever You Ask for”, PSFK Report, Feb. 9, 2012, 9 pages. |
| Cheyer et al., “Multimodal Maps: An Agent-based Approach”, International Conference on Cooperative Multimodal Communication, Jun. 9, 1995, pp. 1-15. |
| Cheyer et al., “Spoken Language and Multimodal Applications for Electronic Realties”, Virtual Reality, vol. 3, 1999, pp. 1-15. |
| Cheyer et al., “The Open Agent Architecture,”, Autonomous Agents and Multi-Agent systems, vol. 4, Mar. 1, 2001, 6 pages. |
| Cheyer et al., “The Open Agent Architecture: Building Communities of Distributed Software Agents”, Artificial Intelligence Center SRI International, Power Point presentation, available online at <http://www.ai.sri.com/˜oaa>, Feb. 21, 1998, 25 pages. |
| Cheyer, Adam, “A Perspective on AI & Agent Technologies for SCM”, VerticaiNet presentation, 2001, 22 pages. |
| Number | Date | Country | |
|---|---|---|---|
| 61799722 | Mar 2013 | US |
