Patent Grant
11151899
The disclosed embodiments relate generally to digital assistants, and more specifically to digital assistants that intelligently provide training opportunities and assistance to users.
Just like human personal assistants, digital assistants or virtual assistants can perform requested tasks and provide requested advice, information, or services. An assistant's ability to fulfill a user's request is dependent on the assistant's correct comprehension of the request or instruction. Recent advances in natural language processing have enabled users to interact with digital assistants using natural language, in spoken or textual forms, rather than employing a conventional user interface (e.g., menus or programmed commands). Such digital assistants can interpret the user's input to infer the user's intent; translate the inferred intent into actionable tasks and parameters; execute operations or deploy services to perform the tasks; and produce outputs that are intelligible to the user. Ideally, the outputs produced by a digital assistant should fulfill the user's intent expressed during the natural language interaction between the user and the digital assistant. A digital assistant can perform searches in a selection domain (e.g., a restaurant domain, etc.) and present qualifying selection items (e.g., restaurants) in response to a search request received from a user.
The ability of a digital assistant system to produce satisfactory responses to user requests depends on the natural language processing, knowledge base, and artificial intelligence implemented by the system. Conventional digital assistants respond to user commands of a single language, and provide responses in the same language. These digital assistants are not adequate when a user visits a region where a different language is used. In addition, although some conventional digital assistants respond to training or customization by a user, conventional digital assistants have not been useful in providing training to a user by leveraging its natural language and intent processing capabilities.
The embodiments disclosed herein provide methods, systems, a computer readable storage medium and user interfaces for a digital assistant to intelligently and proactively provide training opportunities and assistance to a user by leveraging its natural language processing and intent processing capabilities, particularly in foreign language training and assistance, and/or in introducing locale-specific accents/slangs to the user. An intelligent digital assistant with multi-lingual capabilities can be more effective in foreign language training because it does not simply perform a direct literal translation of the user's input; instead, the training samples and foreign language assistance are provided based on user intent inferred based on the user's input. Intent inference may be contextual, and can utilize relevant information about the user's current needs from many different sources. Many limitations of direct translation (e.g., mistakes due to awkward and incorrect sentence structure, grammar, and usage in the input) may be avoided. In addition, digital assistants are aware of the current context associated with the user, and may provide more appropriate foreign language training exercises to the user for the current context and provide motivation and real life practice opportunities for the user's learning.
Accordingly, some embodiments provide a method for operating a digital assistant, the method including, at a device including one or more processors and memory storing one or more programs: receiving, from a user, a first speech input spoken in a first language; inferring a user intent based on at least the first speech input in the first language; based on the inferred user intent, generating one or more alternative expressions of the first speech input in the first language; and providing feedback to the user introducing the alternative expressions as a more preferred input to express the inferred user intent than the first speech input provided by the user.
In some embodiments, providing the feedback further includes: providing the feedback in a second language different from the first language, wherein the second language is a primary language associated with the user, and the first language is a secondary language associated with the user.
In some embodiments, the one or more alternative expressions of the first speech input includes at least a respective alternative expression that corrects a pronunciation of at least one word in the first speech input.
In some embodiments, the one or more alternative expressions of the first speech input includes at least a respective alternative expression that corrects a grammatical usage of at least one word in the first speech input.
In some embodiments, the one or more alternative expressions of the first speech input includes at least a respective alternative expression that replaces at least one word or phrase in the first speech input with another word or phrase.
In some embodiments, the method further includes: providing at least a command mode and a foreign language training mode, wherein the digital assistant executes a task flow to fulfill the inferred user intent in the command mode, and wherein the digital assistant generates the one or more alternative expressions and provides the feedback to the user in the foreign Language training mode.
In some embodiments, the method further includes: concurrently providing both the command mode and the foreign language training mode, wherein the digital assistant executes the task flow to fulfill the inferred user intent, in addition to generating the one or more alternative expressions and providing the feedback to the user.
In some embodiments, the method further includes: receiving user selection of the foreign language training mode; and enabling the foreign language training mode in response to the user selection of the foreign language training mode.
In some embodiments, the method further includes: automatically, without user intervention, enabling the foreign language training mode based on a current location of the user, wherein a primary language associated with the current location of the user is the first language.
In some embodiments, inferring the user intent based on the first speech input in the first language further includes: identifying a customized speech-to-text model of the first language for the user, wherein the customized speech-to-text model has been established based on training samples provided by native speakers of a second language (of which language the user is also a native speaker); processing the first speech input to generate a text string using the customized speech-to-text model; and using the text string as input for an intent inference model of the digital assistant.
In some embodiments, generating the one or more alternative expressions of the first speech input in the first language further includes: identifying a second speech input previously provided by a native speaker of the first language, wherein the second speech input had been associated with a respective user intent that is identical to the inferred user intent of the first speech input, and wherein a task flow executed for the respective user intent had been satisfactory to said native speaker; and utilizing the second speech input as one of the alternative expressions of the first speech input. The speech input previously provided by the native speakers are good source of example expressions showing customary usage of language and vocabulary in a particular region.
In some embodiments, providing the feedback to the user introducing the alternative expressions as a more preferred input to express the inferred user intent further includes: providing, in a second language, an explanation of a difference between a first alternative expression and the first speech input, wherein the second language is a primary language associated with the user, and the first language is a secondary language associated with the user.
In some embodiments, the method further includes: receiving a second speech input in the first language from the user, the second speech input utilizing at least one of the alternative expressions; determining whether the second speech input is a satisfactory vocal utterance of the at least one alternative expression; and upon determining that the second speech input is a satisfactory vocal utterance of the at least one alternative expression, executing a task flow to fulfill the inferred user intent.
In some embodiments, the method further includes: providing, in a second language, a paraphrase of the first speech input based on the inferred user intent to confirm the correctness of the inferred user intent, wherein the digital assistant generates the alternative expressions and provides the feedback after receiving user confirmation that the inferred user intent is the correct user intent.
In some embodiments, inferring the user intent based on at least the first speech input in the first language further includes inferring the user intent further based on a current context associated with the user.
In some embodiments, the current context associated with the user includes at least a current location of the user.
In some embodiments, the current context associated with the user includes at least a current time at which the first speech input was received.
In some embodiments, the current context associated with the user includes at least a type of place that is located at the user's current location.
In some embodiments, the current context associated with the user includes at least a correlation between a schedule item of the user and the current location.
In some embodiments, the current context associated with the user includes at least a correlation between a schedule item of the user and the current time.
In some embodiments, the current context associated with the user includes at least a current transportation mode of the user.
In some embodiments, the current context associated with the user includes at least a correlation between a directions request entered by the user and the user's current location.
In some embodiments, the method further includes storing the one or more alternative expressions for future review by the user.
In some embodiments, the method further implements features of any combination of the methods described above and in the remainder of this specification.
Accordingly, some embodiments provide a method for operating a digital assistant, the method including, at a device including one or more processors and memory storing one or more programs: receiving, from a user, a first speech input spoken in a first language; inferring a user intent based on at least the first speech input; based on the inferred user intent, generating one or more alternative expressions of the first speech input in a second language; and providing feedback to the user introducing the alternative expressions as a means to accomplish the inferred user intent when the user speaks at least one of the one or more alternative expressions to another user who understands the second language. In these embodiments, the digital assistant does not provide information or perform the task requested by the user, instead, the digital assistant teaches what the user needs to say to another person to obtain the information and/or to get the task accomplished. This is useful when the digital assistant can only correctly infer the user's intent, but does not have sufficient capabilities to accomplish the task for the user. Instead, the digital assistant teaches the user the correct foreign language expressions, such that the user can solicit and employ the help of another person who does not speak the native language of the user.
In some embodiments, the first language is a primary language associated with the user, and the second language is a primary language associated with a geographic area in which the user is currently located.
In some embodiments, the first language is a primary language associated with the user, and the second language is a secondary language associated with the user.
In some embodiments, the second language is different from the first language and at least one of the alternative expressions is not a translation of the first speech input from the first language to the second language.
In some embodiments, the digital assistant generates the alternative expressions and provides the feedback in a foreign language assistance mode in response to user selection of the foreign language assistance mode.
In some embodiments, the digital assistant initiates a foreign language assistance mode in response to detecting that the user's current location is outside of a geographic area for which the first language is a primary language, and wherein the digital assistant generates the alternative expressions and provides the feedback in the foreign language assistance mode.
In some embodiments, the digital assistant initiates a foreign language assistance mode in response to detecting that the user's current location is outside of a geographic area for which the first language is a primary language, and that the digital assistant is not able to fulfill the inferred user intent.
In some embodiments, the method further includes: in the feedback provided to the user, presenting, in the first language, a name of the second language as a respective language of the one or more alternative expressions.
In some embodiments, the method further includes: providing a practice session for the user to vocally practice at least one of the one or more alternative expressions; and during the practice session: receiving a second speech input from the user speaking at least one of the one or more alternative expressions; determining whether the second speech input is a satisfactory vocal utterance of the at least one alternative expressions; and upon determining that the second speech input is a satisfactory vocal utterance of the at least one alternative expressions, providing an output to the user indicating that the second speech input is satisfactory.
In some embodiments, the method further includes: during the practice session: providing, to the user, a sample vocal utterance for at least one of the one or more alternative expressions.
In some embodiments, the method further includes during the practice session: receiving a third speech input from the user speaking at least one of the one or more alternative expressions; detecting an error in the third speech input based on a difference between the third speech input and a standard vocal utterance of the at least one alternative expressions; and providing a sample vocal utterance to the user one or more times, the sample vocal utterance tailored for correcting the error in the third speech input.
In some embodiments, the first language is a first dialect of a respective language associated with the user, and the second language is a second dialect of the respective language, and wherein the second dialect is different from the first dialect and is associated with a respective geographic area in which the user is currently located.
In some embodiments, the one or more alternative expressions of the first speech input includes at least a respective alternative expression that changes a pronunciation of at least one word in the first speech input.
In some embodiments, the one or more alternative expressions of the first speech input includes at least a respective alternative expression that changes a grammatical usage of at least one word in the first speech input.
In some embodiments, the one or more alternative expressions of the first speech input includes at least a respective alternative expression that replaces at least one word in the first speech input.
In some embodiments, the respective alternative expression that replaces at least one word or expression in the first speech input is a local slang for the at least one word or expression in the geographic area in which the user is currently located.
In some embodiments, the digital assistant generates the alternative expressions and provides the feedback in a foreign language assistance mode, and the method further includes: while in the foreign language assistance mode: receiving input from the user for entering a live session for the user to utilize at least one of the alternative expressions to accomplish the inferred user intent; and providing the live session for the user; and during the live session: listening for the user speaking the at least one of the alternative expression to a second user; listening for a verbal response from the second user; based on the verbal response received from the second user, determining that additional foreign language assistance is needed by the user; and providing one or more speech outputs in the second language to assist the user in accomplishing the inferred user intent.
In some embodiments, the method further includes: providing, to the user, a textual transcript of a verbal exchange between the digital assistant and the second user in a user interface displayed on the device.
In some embodiments, the method further includes: providing, to the user, a translation of the textual transcript from the second language to the first language in the user interface displayed on the device.
In some embodiments, the method further includes storing a transcript of a user session conducted in the foreign language assistance mode for future review by the user.
In some embodiments, the digital assistant generates a different set of alternative expressions for the inferred user intent depending on a respective current context associated with the user.
In some embodiments, the current context associated with the user includes a current location of the user.
In some embodiments, the current context associated with the user includes a current time at which the first speech input was received.
In some embodiments, the current context associated with the user includes a type of place that is located at the user's current location.
In some embodiments, the current context associated with the user includes a correlation between a schedule item of the user and the current location.
In some embodiments, the current context associated with the user includes a correlation between a schedule item of the user and the current time.
In some embodiments, the current context associated with the user includes a current transportation mode of the user.
In some embodiments, the current context associated with the user includes a correlation between a directions request entered by the user and the user's current location.
In some embodiments, the method further implements features of any combination of the methods described above and in the remainder of this specification.
Accordingly, some embodiments provide a method for operating a digital assistant, the method including, at a device including one or more processors and memory storing one or more programs: (1) during a first interaction with a user: receiving a first speech input from the user while the user is located in a first geographic area; inferring a first user intent based on the first speech input; providing a first paraphrase of the first speech input based on the inferred first user intent; and optionally executing a respective task flow to accomplish the inferred first user intent; (2) during a second interaction with the user: receiving a second speech input from the user while the user is located in a second geographic, the second speech input being substantially identical to the first speech input; inferring a second user intent based on the second speech input, the inferred second user intent being identical to the inferred first user intent; determining that a location change from the first geographic area to the second geographic area is associated with a change in language or locale-specific vocabulary for at least one word or expression in the second speech input; in response to said determination, providing a second paraphrase based on the second inferred user intent, wherein the second paraphrase is different from the first paraphrase based on the change in language or vocabulary; and optionally executing the respective task flow to accomplish the inferred second user intent.
In some embodiments, the first geographic area and the second geographic area are both associated with a primary language of the user.
In some embodiments, the change in locale-specific vocabulary includes use of a respective local slang in the second geographic area for the at least one word or expression in the second speech input, and wherein the second paraphrase utilizes the respective local slang.
In some embodiments, the method further includes: receiving user input to start a learning session regarding the respective local slang provided in the second paraphrase; and in response to receiving the user input, providing an explanation of the usage of the respective local slang in the second geographic area.
In some embodiments, the change in language includes use of a respective local accent in the second geographic area for the at least one word or expression in the second speech input, and wherein the second paraphrase utilizes the respective local accent.
In some embodiments, the method further includes: receiving user input to start a learning session regarding the respective local accent provided in the second paraphrase; and in response to receiving the user input, providing one or more additional examples of the usage of the respective local accent in the second geographic area.
In some embodiments, the method further implements features of any combination of the methods described above and in the remainder of this specification.
Accordingly, some embodiments provide a method for operating a digital assistant, the method including, at a device including one or more processors and memory storing one or more programs: evaluating a present context associated with a user; identifying a respective foreign language training scenario associate with the present context; and providing a foreign language training session for the user, the foreign language training session containing one or more foreign language exercises tailored for the current context. In some embodiments, the digital assistant keeps track of the present context associated with the user based on various sensors (e.g., GPS, temperature sensors, light sensors, accelerometers, compass, etc.) and events occurring on the device (e.g., phone calls, email communications, notifications generated, alerts by schedule items, searches performed, and directions request fulfilled, etc.).
In some embodiments, the method further includes: automatically, without user intervention, selecting a respective language for the one or more foreign language exercises based on a primary language associated with a geographic area in which the user is currently located; and generating the one or more foreign language exercises in the automatically selected language.
In some embodiments, the method further includes: receiving user input selecting a respective language for the one or more foreign language exercises; and generating the one or more foreign language exercises in the user-selected language.
In some embodiments, the present context associated with the user includes the user's presence inside a store located in a geographic area in which a respective foreign language is a primary language, and the one or more foreign language exercises include at least vocabulary or dialogue in the respective foreign language that is associated with shopping in the store.
In some embodiments, the present context associated with the user includes the user's presence in proximity to a terminal of public transportation located in a geographic area in which a respective foreign language is a primary language, and the one or more foreign language exercises include at least vocabulary or dialogue in the respective foreign language that is associated with use of the public transportation.
In some embodiments, the present context associated with the user includes the user's presence inside a dining facility located in a geographic area in which a respective foreign language is a primary language, and the one or more foreign language exercises include at least vocabulary or dialogue in the respective foreign language that is associated with dining at the dining facility.
In some embodiments, the present context associated with the user includes the user's presence inside a lodging facility located in a geographic area in which a respective foreign language is a primary language, and the one or more foreign language exercises include at least vocabulary or dialogue in the respective foreign language that is associated with lodging at the lodging facility.
In some embodiments, the present context associated with the user includes the user's presence inside a public transport vehicle moving toward a destination for which the user has recently requested directions and the destination is located in a geographic area in which a respective foreign language is a primary language, and wherein the one or more foreign language exercises include at least vocabulary or dialogue in the respective foreign language that is associated with visiting to said destination.
In some embodiments, the present context associated with the user includes the user's presence inside a healthcare facility, and wherein the one or more foreign language exercises include at least vocabulary or dialogue in the respective foreign language that is associated with obtaining healthcare services at the healthcare facility.
In some embodiments, the present context associated with the user includes the user's presence inside a business premise offering beverage services, and wherein the one or more foreign language exercises include at least vocabulary or dialogue in the respective foreign language that is associated with ordering beverages at the business premise.
In some embodiments, the method further includes presenting images associated with vocabulary used in the foreign language exercises.
In some embodiments, the method further implements features of any combination of the methods described above and in the remainder of this specification.
The above embodiments, and other embodiments described in this specification may help realize one or more of the following advantages.
In some embodiments, the digital assistant provides alternative expressions for a user input based on the user intent inferred from the user input. The alternative expressions may be more grammatically correct, have better pronunciation, and/or are more customary to the geographic region in which the user is currently located. This is helpful for non-native speakers visiting a foreign country (e.g., overseas students, business travelers, tourists, etc.), to learn and remember the local language and usage in context.
In addition, in some embodiments, the alternative expressions are not necessarily a direct translation of the user input, but are based on the user intent inferred from the user input. Alternative expressions generated based on intent is more tolerant of informalities, errors, and missing information in the user input than results produced by direct translation. Thus, many limitations of direct translation may be avoided.
In addition, in some embodiments, intent inference is contextual, and can leverage many sources of information that the digital assistant has about the user. Thus, more appropriate expressions to accomplish the true intent of the user can be provided by the digital assistant.
In some embodiments, the better expressions may be introduced to the user in the user's native language, such that the user can better understand the subtle differences between the alternative expressions provided by the digital assistant and his/her direct input.
In some embodiments, the digital assistant teaches foreign language alternative expressions to a user after the user has expressed his/her intent in his/her native language. This is useful when the user wishes to speak to a real person about his/her needs in the foreign language (e.g., when the real person only understands the foreign language), and wants the digital assistant to teach him/her the correct expressions in that foreign language. Since the capabilities of a digital assistant are sometimes limited and some assistance is better provided by a real person. The ability to let the user to express his/her needs in his/her native language to the digital assistant, and learn the correct foreign language expressions needed to be said to a real person, can greatly expand the helpfulness of the digital assistant in many scenarios (e.g., when the user is traveling abroad).
In some embodiments, after teaching the foreign language expressions to the user, the digital assistant can continue to listen to the conversation between the user and the third-party native speaker, and provides additional language assistance when needed.
In some embodiments, the digital assistant service provider already supports inputs and responses in multiple languages, and implementing foreign language or mixed language assistance to a user can effectively leverage the existing natural language processing and intent processing capabilities of the digital assistant.
In some embodiments, the digital assistant teaches the user about a locale-specific accent or slang when the user is found at that location. For example, if the user provides an input in one location, and the input is completely congruent with the local language usage, the digital assistant performs the task without providing any language training. However, when the user provides the same input at a different location, and the user input differs from the local language usage in one or more aspects, the digital assistant optionally provides some “in-context language training” about those differences, in addition to performing the task. This is helpful for both native speakers of a language and non-native speakers for a language, since there are many regional variations (e.g., expressions, word usage, accents, slangs, etc.) even for the same language.
In some embodiments, in order to be less intrusive in the training process, the digital assistant provides the differences between the user's input and the locale-specific expressions in a paraphrase of the user input, such that the user can hear the differences, and learn the new language information without expending too much time and efforts. In some embodiments, additional information about those locale-specific language differences may be provided to the user upon user request.
In some embodiments, the digital assistant keeps track of the current context associated with the user, and provides foreign language training opportunities to the user based on the user's current context. For example, training exercises related to shopping can be provided to the user, when the digital assistant detects that the user is inside a store. More specific training exercises and vocabulary may be provided to the user depending on the type of store that the user is in. Sometimes, in addition to the type of places that the user is current visiting, previous directions requests, the user's calendar items, and searches performed by the user can also be used to determine the current context, which is then used to generate relevant foreign language training exercises for the user. These contextually relevant foreign language exercises can help motivate the user's learning, and the surrounding environment also provides additional opportunities and visual cues to help the user practice and memorize the content of the foreign language exercises.
The details of one or more embodiments of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims.
Like reference numerals refer to corresponding parts throughout the drawings.
Specifically, a digital assistant is capable of accepting a user request at least partially in the form of a natural language command, request, statement, narrative, and/or inquiry. Typically, the user request seeks either an informational answer or performance of a task by the digital assistant. A satisfactory response to the user request is 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 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.” The user may also request the performance of a task, for example, “Please remind me to call mom at 4 pm today.” In response, the digital assistant may acknowledge the request and then creates an appropriate reminder item in the user's electronic schedule. During performance of a requested task, the digital assistant sometimes interacts with the user in a continuous dialogue involving multiple exchanges of information over an extended period of time. There are numerous other ways of interacting with a digital assistant to request information or performance of various tasks. In addition to providing verbal responses and taking programmed actions, the digital assistant also provides responses in other visual or audio forms, e.g., as text, alerts, music, videos, animations, etc. In some embodiments, the digital assistant accepts input in more than one language, and provides responses in the language of the input, the user's primary language, a user's selected language, and/or a mixture of multiple languages.
An example of a digital assistant is described in Applicant's U.S. Utility application Ser. No. 12/987,982 for “Intelligent Automated Assistant,” filed Jan. 10, 2011, the entire disclosure of which is incorporated herein by reference.
As shown in
In some embodiments, the DA server 106 includes a client-facing I/O interface 112, one or more processing modules 114, data and models 116, and an I/O interface to external services 118. 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 inferred user intent. In some embodiments, the DA-server 106 communicates with external services 120 through the network(s) 110 for task completion or information acquisition. The I/O interface to 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 smart phone, 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 other data processing devices. More details on the user device 104 are provided in reference to an exemplary user device 104 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 are, optionally, implemented using any known network protocol, including various wired or wireless protocols, such as e.g., 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 is implemented on one or more standalone data processing apparatus or a distributed network of computers. In some embodiments, 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 shown in
For example, a motion sensor 210, a light sensor 212, and a proximity sensor 214 are coupled to the peripherals interface 206 to facilitate orientation, light, and proximity sensing functions. One or more other sensors 216, such as a positioning system (e.g., GPS receiver), a temperature sensor, a biometric sensor, a gyro, a compass, an accelerometer, and the like, are also connected to the peripherals interface 206, to facilitate related functionalities.
In some embodiments, a camera subsystem 220 and an optical sensor 222 are utilized to facilitate camera functions, such as taking photographs and recording video clips. Communication functions are facilitated through one or more wired and/or wireless communication subsystems 224, which can include various communication ports, radio frequency receivers and transmitters, and/or optical (e.g., infrared) receivers and transmitters. An audio subsystem 226 is coupled to speakers 228 and a microphone 230 to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording, and telephony functions.
In some embodiments, an I/O subsystem 240 is also coupled to the peripheral interface 206. The I/O subsystem 240 includes a touch screen controller 242 and/or other input controller(s) 244. The touch-screen controller 242 is coupled to a touch screen 246. The touch screen 246 and the touch screen controller 242 can, 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. The other input controller(s) 244 can be coupled to other input/control devices 248, such as one or more buttons, rocker switches, thumb-wheel, infrared port, USB port, and/or a pointer device such as a stylus.
In some embodiments, the memory interface 202 is coupled to memory 250. The memory 250 can include high-speed random access memory and/or non-volatile memory, such as one or more magnetic disk storage devices, one or more optical storage devices, and/or flash memory (e.g., NAND, NOR).
In some embodiments, the memory 250 stores an operating system 252, a communication module 254, a graphical user interface module 256, a sensor processing module 258, a phone module 260, and applications 262. The operating system 252 includes instructions for handling basic system services and for performing hardware dependent tasks. The communication module 254 facilitates communicating with one or more additional devices, one or more computers and/or one or more services. The graphical user interface module 256 facilitates graphic user interface processing. The sensor processing module 258 facilitates sensor-related processing and functions. The phone module 260 facilitates phone-related processes and functions. The application module 262 facilitates various functionalities of user applications, such as electronic-messaging, web browsing, media processing, Navigation, imaging and/or other processes and functions.
As described in this specification, the memory 250 also stores client-side digital assistant instructions (e.g., in a digital assistant client module 264) and various user data 266 (e.g., user-specific vocabulary data, preference data, and/or other data such as the user's electronic address book, to-do lists, shopping lists, etc.) to provide the client-side functionalities of the digital assistant.
In various embodiments, the digital assistant client module 264 is capable of accepting voice input (e.g., speech input), text input, touch input, and/or gestural input through various user interfaces (e.g., the I/O subsystem 244) of the user device 104. The digital assistant client module 264 is also capable of providing output in audio (e.g., speech output), visual, and/or tactile forms. For example, output can be provided as voice, sound, alerts, text messages, menus, graphics, videos, animations, vibrations, and/or combinations of two or more of the above. During operation, the digital assistant client module 264 communicates with the digital assistant server using the communication subsystems 224.
In some embodiments, the digital assistant client module 264 utilizes the various sensors, subsystems peripheral devices to gather additional information from the surrounding environment of the user device 104 to establish a context associated with a user, the current user interaction, and/or the current user input. In some embodiments, the digital assistant client module 264 provides the context information or a subset thereof with the user input to the digital assistant server to help infer the user's intent. In some embodiments, the digital assistant also uses the context information to determine how to prepare and delivery outputs to the user.
In some embodiments, the context information that accompanies the user input includes sensor information, e.g., lighting, ambient noise, ambient temperature, images or videos of the surrounding environment, etc. in some embodiments, the context information also includes the physical state of the device, e.g., device orientation, device location, device temperature, power level, speed, acceleration, motion patterns, cellular signals strength, etc. In some embodiments, information related to the software state of the user device 106, e.g., running processes, installed programs, past and present network activities, background services, error logs, resources usage, etc., of the user device 104 are provided to the digital assistant server as context information associated with a user input.
In some embodiments, the DA client module 264 selectively provides information (e.g., user data 266) stored on the user device 104 in response to requests from the digital assistant server. In some embodiments, the digital assistant client module 264 also elicits additional input from the user via a natural language dialogue or other user interfaces upon request by the digital assistant server 106. The digital assistant client module 264 passes the additional input to the digital assistant server 106 to help the digital assistant server 106 in intent inference and/or fulfillment of the user's intent expressed in the user request.
In various embodiments, the memory 250 includes additional instructions or fewer instructions. Furthermore, various functions of the user device 104 may be implemented in hardware and/or in firmware, including in one or more signal processing and/or application specific integrated circuits.
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 embodiments, the 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, flash memory devices, or other non-volatile solid-state memory devices).
In some embodiments, 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, receive user inputs (e.g., voice input, keyboard inputs, touch inputs, etc.) and process them accordingly. In some embodiments, e.g., 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 user device 104 in
In some embodiments, the network communications interface 308 includes 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 receives and sends RF signals and/or optical signals from/to communications networks and other communications devices. The wireless communications, optionally, 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 embodiments, memory 302, or the computer readable storage media 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, OS X, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communications between various hardware, firmware, and software components.
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, optionally, communicates with the communication interface 254 of the device 104 shown in
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, pointing device, controller, and/or microphone), and generates user interface objects on a display. The user interface module 322 also prepares and delivers outputs (e.g., speech, sound, animation, text, icons, vibrations, haptic feedback, and light, etc.) to the user via the I/O interface 306 (e.g., through displays, audio channels, speakers, and touch-pads, etc.).
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, optionally, 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, optionally, include resource management applications, diagnostic applications, or scheduling applications, for example.
The memory 302 also stores the digital assistant module (or the server portion of a digital assistant) 326. In some embodiments, 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 user training module 340. Each of these 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, task flow models 354, service models 356, and user training data 358.
In some embodiments, using the processing modules, data, and models implemented in the digital assistant module 326, the digital assistant 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 infer the user's intent (e.g., by disambiguating words, names, intentions, etc.); determining the task flow for fulfilling the inferred intent; and executing the task flow to fulfill the inferred intent. In this specifications, more details regarding the user training module 340 and its use of the user training data 358 are provided later in
In some embodiments, as shown in
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 embodiments, 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 can be 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 embodiments, the speech-to-text processing can be performed at least partially by a third party service or on the user's device. In some embodiments, the speech-to-text processing module 330 handles input in multiple languages and have locale-specific acoustic models and language models tailored for each language and locale-specific inputs. In some embodiments, the speech-to-text processing module 330 includes multiple acoustic and language models for each single language to accommodate regional and other variations. 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 inference.
More details on the speech-to-text processing are described in U.S. Utility application Ser. No. 13/236,942 for “Consolidating Speech Recognition Results,” filed on Sep. 20, 2011, the entire disclosure of which is incorporated herein by reference.
The natural language processing module 332 (“natural language processor”) of the digital assistant 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. An “actionable intent” represents a task that can be performed by the digital assistant, and has an associated task flow implemented in the task flow models 354. The associated task flow is a series of programmed actions and steps that the digital assistant takes in order to perform the task. The scope of a digital assistant's capabilities is dependent on the number and variety of task flows that have been implemented and stored in the task flow models 354, or in other words, on the number and variety of “actionable intents” that the digital assistant recognizes. The effectiveness of the digital assistant, however, is also dependent on the assistant's ability to infer the correct “actionable intent(s)” from the user request expressed in natural language.
In some embodiments, the natural language processing module 332 handles input in multiple languages and have locale-specific vocabulary and language usage models tailored for each language and locale-specific inputs. In some embodiments, the natural language processing module 332 includes multiple locale-specific vocabulary and language usage models for each single language to accommodate regional and other variations.
In some embodiments, 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, 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 embodiments, the natural language processing is based on ontology 360. The ontology 360 is a hierarchical structure containing many nodes, each node representing either an “actionable intent” or a “property” relevant to one or more of the “actionable intents” or other “properties”. As noted above, an “actionable intent” represents a task that the digital assistant is capable of performing, i.e., it is “actionable” or can be acted on. A “property” represents a parameter associated with an actionable intent, a domain concept or entity, 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 embodiments, 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, as 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 embodiments, the ontology 360 includes all the domains (and hence actionable intents) that the digital assistant is capable of understanding and acting upon. In some embodiments, the ontology 360 is optionally modified, such as by adding or removing entire domains or nodes, or by modifying relationships between the nodes within the ontology 360.
In some embodiments, nodes associated with multiple related actionable intents are optionally clustered under a “super domain” in the ontology 360. For example, a “travel” super-domain optionally includes a cluster of property nodes and actionable intent nodes related to travels. The actionable intent nodes related to travels optionally includes “airline reservation,” “hotel reservation,” “car rental,” “get directions,” “find points of interest,” and so on. The actionable intent nodes under the same super domain (e.g., the “travels” super domain) sometimes 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” sometimes share one or more of the property nodes “start location,” “destination,” “departure date/time,” “arrival date/time,” and “party size.”
In some embodiments, 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 association with the property or actionable intent represented by the node. For example, returning to
The natural language processor 332 receives the token sequence (e.g., a text string) from the speech-to-text processing module 330, and determines what nodes are implicated by the words in the token sequence. In some embodiments, if a word or phrase in the token sequence is found to be associated with one or more nodes in the ontology 360 (via the vocabulary index 344), the word or phrase will “trigger” or “activate” those nodes. Based on the quantity and/or relative importance of the activated nodes, the natural language processor 332 will select one of the actionable intents as the task that the user intended the digital assistant to perform. In some embodiments, the domain that has the most “triggered” nodes is selected. In some embodiments, the domain having the highest confidence value (e.g., based on the relative importance of its various triggered nodes) is selected. In some embodiments, the domain is selected based on a combination of the number and the importance of the triggered nodes. In some embodiments, additional factors are considered in selecting the node as well, such as whether the digital assistant has previously correctly interpreted a similar request from a user.
In some embodiments, the digital assistant 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 embodiments, the names of specific entities are names of businesses, restaurants, people, movies, and the like. In some embodiments, the digital assistant searches and identifies specific entity names from other data sources, such as the user's address book, a movies database, a musicians database, and/or a restaurant database. In some embodiments, 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), 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 or long-term information for each user. In some embodiments, the natural language processor 332 uses the user-specific information to supplement the information contained in the user input to further define the user intent. For example, for a user request “invite my friends to my birthday party,” 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.
Other details of searching an ontology based on a token string is described in U.S. Utility application Ser. No. 12/341,743 for “Method and Apparatus for Searching Using An Active Ontology,” filed Dec. 22, 2008, the entire disclosure of which is incorporated herein by reference.
In some embodiments, 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 embodiments, 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 seven o'clock.” 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 optionally includes parameters such as {Cuisine}, {Time}, {Date}, {Party Size}, and the like. In some embodiments, based on the information contained in the user's utterance, the natural language processor 332 generates a partial structured query for the restaurant reservation domain, where the partial structured query includes the parameters {Cuisine=“Sushi”} and {Time=“7 pm”}. However, in this example, the user's utterance contains insufficient information to complete the structured query associated with the domain. Therefore, other necessary parameters such as {Party Size} and {Date} are not specified in the structured query based on the information currently available. In some embodiments, the natural language processor 332 populates some parameters of the structured query with received context information. For example, in some embodiments, if the user requested a sushi restaurant “near me,” the natural language processor 332 populates a {location} parameter in the structured query with GPS coordinates from the user device 104.
In some embodiments, 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 receive the structured query from the natural language processor 332, complete the structured query, if necessary, and perform the actions required to “complete” the user's ultimate request. In some embodiments, the various procedures necessary to complete these tasks are provided in task flow models 354. In some embodiments, the task flow models 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 sometimes needs to initiate additional dialogue with the user in order to obtain additional information, and/or disambiguate potentially ambiguous utterances. When such interactions are necessary, the task flow processor 336 invokes the dialogue processing module 334 (“dialogue processor 334”) to engage in a dialogue with the user. In some embodiments, the dialogue processor 334 determines how (and/or when) to ask the user for the additional information, and receives and processes the user responses. The questions are provided to and answers are received from the users through the I/O processing module 328. In some embodiments, the dialogue processor 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., clicking) responses. Continuing with the example above, when the task flow processor 336 invokes the dialogue flow processor 334 to determine the “party size” and “date” information for the structured query associated with the domain “restaurant reservation,” the dialogue flow processor 335 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 flow processor 334 can then populate the structured query with the missing information, or pass 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 receives a structured query that has one or more ambiguous properties, e.g., when a structured query for the “send a message” domain indicates that the intended recipient is “Bob,” and the user happens to have multiple contacts named “Bob.” In such cases, the task flow processor 336, optionally, requests that the dialogue processor 334 disambiguate this property of the structured query. In turn, the dialogue processor 334, optionally, asks the user “Which Bob?”, and displays (or reads) a list of contacts named “Bob” from which the user may choose.
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”, optionally, includes steps and instructions for contacting a restaurant and actually requesting a reservation for a particular party size at a particular time. For example, using a structured query such as: {restaurant reservation, restaurant=ABC Café, date=Mar. 12, 2012, time=7 pm, party size=5}, the task flow processor 336, optionally, performs the steps of: (1) logging onto a server of the ABC Café or a restaurant reservation system such as OPENTABLE®, (2) entering the date, time, and party size information in a form on the website, (3) submitting the form, and (4) making a calendar entry for the reservation in the user's calendar.
In some embodiments, 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, and invoke or interact with third party services (e.g. a restaurant reservation portal, a social networking website, a banking portal, etc.). In some embodiments, the protocols and application programming interfaces (API) required by each service can be specified by a respective service model among the services 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 parameter 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 model, and send the necessary parameters of the reservation (e.g., time, date, party size) to the online reservation interface in a format according to the API of the online reservation service.
in some embodiments, the natural language processor 332, dialogue processor 334, and task flow processor 336 are used collectively and iteratively to infer and define the user's intent, obtain information to further clarify and refine the user intent, and finally generate a response (i.e., an output to the user, or the completion of a task) to fulfill the user's intent.
In some embodiments, 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 embodiments, the digital assistant also requests the user to indicate whether the user is satisfied with the response produced by the digital assistant 326.
More details on the digital assistant can be found in the U.S. Utility application Ser. No. 12/987,982, entitled “Intelligent Automated Assistant”, filed Jan. 10, 2011, U.S. Provisional Application No. 61/493,201, entitled “Generating and Processing Data Items That Represent Tasks to Perform”, filed Jun. 3, 2011, the entire disclosures of which are incorporated herein by reference.
As described in this specification, in some embodiments, a digital assistant provides training, in particular, locale-specific language training and/or foreign language training and assistance to the user. The exemplary processes provided below may be implemented by the user training module 340, using the information stored in the user training data 358. In some embodiments, the user training data 358 includes suitable alternative expressions and vocabulary in various languages indexed by user intent, and templates for additional foreign language exercises.
In the process 400, the digital assistant receives (402), from a user, a first speech input spoken in a first language. The digital assistant infers (404) a user intent based on at least the first speech input in the first language. Based on the inferred user intent, the digital assistant generates (406) one or more alternative expressions of the first speech input in the first language. The digital assistant provides (408) feedback to the user introducing the alternative expressions as a more preferred input to express the inferred user intent than the first speech input provided by the user.
In an example scenario, the user provides a speech input in English to the digital assistant “Where can I buy a torch?” The user, being a non-native English speaker, may not be aware that the term “torch” has a different meaning in the United States than in other English-speaking countries. The digital assistant receiving the speech input is aware of the different meanings for the term “torch” in different locales (e.g., in England, “torch” refers to a type of illumination device relying on dry batteries (i.e., “flashlight” in the United States), but in the United States, “torch” refers to a type of illumination devices relying on burning an organic fuel). The digital assistant infers an intent based on the user's input, and determines that the user is more likely to be asking about the type of illumination devices using dry batteries. Based on the inferred intent and the user's current location (e.g., in the United States), the digital assistant generates at least one alternative expression for the term “torch,” such as “flashlight” and provides that alternative expression to the user. For example, in some embodiments, the digital assistant displays in a user interface names and directions to stores (e.g., hardware stores) that sell flashlights, and in addition, the digital assistant also teaches the user that the term “flashlight” is more customarily used in the United States than the term “torch.” In some embodiments, instead of teaching the user about the term “torch” directly, the digital assistant optionally provides a paraphrase of the user's speech input, where the paraphrase introduces the term “flashlight” to the user. For example, the paraphrase provided by the digital assistant can be in the form of a confirmation request “Did you mean you want to buy a “flashlight” which uses dry batteries instead of a burning fuel?” Alternatively, the digital assistant optionally says “Searching for stores nearby that sell flashlights . . . .”
In another example, when a non-native English speaker provides a speech input in English, but speaks one or more words with a heavy accent. The digital assistant can infer the user's intent based on other content in the speech input and the current context. Based on the inferred user intent, the digital assistant can generate alternative expressions that correct the accent of the particular words. For example, many proper nouns (e.g., names of international stores and businesses) are used worldwide, and the pronunciations for those proper nouns are localized in different countries and regions. When a non-native speaker speaks those proper nouns in a request to the digital assistant, they frequently use the pronunciations in their native languages, even though the rest of the request is spoken with a proper English accent. Based on the inferred user intent, the digital assistant can determine what those words are, and provide alternative expressions that have the correct pronunciation in an American or British accent. For example, when the user says “I want to fine a MaiDanglao to get a burger.” Although the pronunciation of the user is imperfect, the digital assistant infers that the user wishes to find a McDonald's to get a burger, and presents a speech output saying “OK, McDonald's is what you want to find. I will show you the directions.” The digital assistant optionally places vocal emphasis on the terms “McDonald's” and “find” to indicate to the user the proper pronunciation of these words. In some embodiments, spelling of the words “McDonald's” and “find” is shown to the user in a user interface as well.
In some embodiments, when providing the feedback to the user, the digital assistant provides (410) the feedback in a second language different from the first language, where the second language is a primary language associated with the user, and the first language is a secondary language associated with the user. For example, if the user is not a native English speaker, and provides a speech input in English, the digital assistant optionally provides the feedback in the native language of the speaker (e.g., Chinese). In other words, part of the response provided by the digital assistant is in the user's native language, while the alternative expressions are in English. Continuing with the earlier example, the digital assistant may provide a speech output “______ McDonald's ______.” The English translation of the speech output is “I think you want to find McDonald's. I found two nearby.”
In some embodiments, the one or more alternative expressions of the first speech input includes (412) at least a respective alternative expression that corrects a pronunciation of at least one word in the first speech input.
In some embodiments, the one or more alternative expressions of the first speech input includes (414) at least a respective alternative expression that corrects a grammatical usage of at least one word in the first speech input.
In some embodiments, the one or more alternative expressions of the first speech input includes (416) at least a respective alternative expression that replaces at least one word or phrase in the first speech input with another word or phrase.
In some embodiments, the digital assistant provide (418) at least a command mode and a foreign language training mode, where the digital assistant (1) executes a task flow to fulfill the inferred user intent in the command mode, and (2) generates the one or more alternative expressions and provides the feedback to the user in the foreign language training mode.
In some embodiments, the digital assistant concurrently provides (420) both the command mode and the foreign language training mode (e.g., in a hybrid mode), where the digital assistant executes the task flow to fulfill the inferred user intent, in addition to generating the one or more alternative expressions and providing the feedback to the user.
In some embodiments, the digital assistant receives (422) user selection of the foreign language training mode; and enables (424) the foreign language training mode in response to the user selection of the foreign language training mode.
In some embodiments, the digital assistant automatically, without user intervention, enables (426) the foreign language training mode based on a current location of the user, where a primary language associated with the current location of the user is the first language.
In some embodiments, to infer (428) the user intent based on the first speech input in the first language, the digital assistant identifies (430) a customized speech-to-text model of the first language for the user, where the customized speech-to-text model has been established based on training samples provided by native speakers of a second language of which the user is also a native speaker. The digital assistant then process (432) the first speech input to generate a text string using the customized speech-to-text model. In some embodiments, the digital assistant uses (434) the text string as input for an intent inference model of the digital assistant.
In some embodiments, to generate (436) the one or more alternative expressions of the first speech input in the first language, the digital assistant identifies (438) a second speech input previously provided by a native speaker of the first language, where the second speech input had been associated with a respective user intent that is identical to the inferred user intent of the first speech input, and where a task flow executed for the respective user intent had been satisfactory to said native speaker. The digital assistant then utilizes (440) the second speech input as one of the alternative expressions of the first speech input.
In some embodiments, to provide (442) the feedback to the user introducing the alternative expressions as a more preferred input to express the inferred user intent, the digital assistant provides (444), in a second language, an explanation of a difference between a first alternative expression and the first speech input, where the second language is a primary language associated with the user, and the first language is a secondary language associated with the user.
In some embodiments, the digital assistant receives (446) a second speech input in the first language from the user, the second speech input utilizing at least one of the alternative expressions provided to the user. In some embodiments, the digital assistant determines (448) whether the second speech input is a satisfactory vocal utterance of the at least one alternative expression. In some embodiments, upon determining that the second speech input is a satisfactory vocal utterance of the at least one alternative expression, the digital assistant executes (450) a task flow to fulfill the inferred user intent.
In some embodiments, the digital assistant provides (452), in a second language, a paraphrase of the first speech input based on the inferred user intent to confirm the correctness of the inferred user intent, where the digital assistant generates the alternative expressions and provides the feedback after receiving user confirmation that the inferred user intent is the correct user intent.
In some embodiments, inferring the user intent based on at least the first speech input in the first language further includes (454) inferring the user intent further based on a current context associated with the user.
In some embodiments, the current context associated with the user includes (456) at least a current location of the user.
In some embodiments, the current context associated with the user includes (458) at least a current time at which the first speech input was received.
In some embodiments, the current context associated with the user includes (460) at least a type of place that is located at the user's current location.
In some embodiments, the current context associated with the user includes (462) at least a correlation between a schedule item of the user and the current location.
In some embodiments, the current context associated with the user includes (464) at least a correlation between a schedule item of the user and the current time.
In some embodiments, the current context associated with the user includes (466) at least a current transportation mode of the user.
In some embodiments, the current context associated with the user includes (468) at least a correlation between a directions request entered by the user and the user's current location.
In some embodiments, the digital assistant stores (470) the one or more alternative expressions for future review by the user.
In some embodiments, the process 400 includes any combination of the features described above and in the remainder of this specification.
In the process 500, in some embodiments, the digital assistant receives (502), from a user, a first speech input spoken in a first language. The digital assistant infers (504) a user intent based on at least the first speech input. Based on the inferred user intent, the digital assistant generates (506) one or more alternative expressions of the first speech input in a second language. The digital assistant then provides (508) feedback to the user introducing the alternative expressions as a means to accomplish the inferred user intent when the user speaks at least one of the one or more alternative expressions to another user who understands the second language.
In an example scenario, the user is in a foreign country (e.g., China) and does not speak the native language (e.g., Chinese) of the region (e.g., southern China). The user can employ the assistance of his/her digital assistant, but sometimes, the capabilities of the digital assistant are not adequate in the current situation. For example, if the user is visiting a client “Mr. Santo,” and the user is already in Mr. Santo's office building. He cannot ask the digital assistant for information regarding Mr. Santo's whereabouts in the office building. Instead, the user needs to speak to a real person in the foreign language (e.g., Mandarin) that is understood in this region. In this case, the user asks the digital assistant for foreign language assistance. For example, the user may enable a foreign language assistance mode, and ask the digital assistant with a speech input in his native language (e.g., English), “I need to find Mr. Santo.” The digital assistant can correctly infer the user's intent based on the user's speech input. Instead of providing directions to the user, the digital assistant provides expressions in Chinese that would be useful for the user to enlist help of a Chinese person. For example, the digital assistant optionally provides sample speech outputs saying “______, ______ Santo ______? (meaning “Hello, which floor is Mr. Santo located?”), and/or “______, ______ Santo ______? (meaning “Sir, is Mr. Santo in?”), and/or “______, ______ Santo, ______, ______? (meaning “Miss, I am looking for Mr. Santo. Is he here today?”). In some embodiments, the digital assistant provides each of these alternative expressions in Chinese, and plays back sample recordings of these alternative Chinese expressions. In some embodiments, the digital assistant also helps the user to practice the pronunciation of these expressions in Chinese, e.g., by providing phonetic spellings of these sentences.”
In another example, if the user (e.g., an English speaker) is driving in a foreign country (e.g., Taiwan), and gets a flat tire. The user may ask the digital assistant to find tire shops or towing companies on a map, but may not be able to get the services needed without speaking the local language. In some embodiments, the user enables the foreign language assistance mode, and provides a speech input explaining his/her needs. For example, the user may say to the digital assistant in English “I have a flat tire and need to call a tow truck.” The digital assistant processes the speech input, and determines that the user needs to speak to a person at a towing service. Based on the user intent, the digital assistant generates a number of expressions in the local language (e.g., Mandarin), and provides the expressions to the user. For example, in an output interface of the digital assistant, the digital assistant optionally provides the following expressions: “______, ______ ______ A ______ 10 ______ (meaning “Hi, I need towing service. I am located at the intersection of A street and No. 10 road), and/or”______, ______, ______ (meaning “Hi, my tire blew. I need a tow truck or a mechanic to come.”). In some embodiments, the digital assistant teaches the user how to say these expressions in the foreign language properly, and let the user practice a few times before letting the user call the local roadside assistance services. In some embodiments, as shown in this example, the digital assistant optionally includes additional information the assistant has about the user (e.g., the user's current location) in the expressions, even though the user him/herself may not possess this information or have included this information in his/her speech input to the digital assistant.
In some embodiments, the first language is (510) a primary language associated with the user, and the second language is a primary language associated with a geographic area in which the user is currently located.
In some embodiments, the first language is (512) a primary language associated with the user, and the second language is a secondary language associated with the user.
In some embodiments, the second language is (514) different from the first language and at least one of the alternative expressions is not a translation of the first speech input from the first language to the second language.
In some embodiments, the digital assistant generates (516) the alternative expressions and provides the feedback in a foreign language assistance mode in response to user selection of the foreign language assistance mode.
In some embodiments, the digital assistant initiates (518) a foreign language assistance mode in response to detecting that the user's current location is outside of a geographic area for which the first language is a primary language, and wherein the digital assistant generates the alternative expressions and provides the feedback in the foreign language assistance mode.
In some embodiments, the digital assistant initiates (520) a foreign language assistance mode in response to detecting that the user's current location is outside of a geographic area for which the first language is a primary language, and that the digital assistant is not able to fulfill the inferred user intent.
In some embodiments, in the feedback provided to the user, the digital assistant presents (522), in the first language, a name of the second language as a respective language of the one or more alternative expressions.
In some embodiments, the digital assistant provides (524) a practice session for the user to vocally practice at least one of the one or more alternative expressions. During the practice session (526): the digital assistant receives (528) a second speech input from the user speaking at least one of the one or more alternative expressions; determines (530) whether the second speech input is a satisfactory vocal utterance of the at least one alternative expressions; and upon determining that the second speech input is a satisfactory vocal utterance of the at least one alternative expressions, provides (532) an output to the user indicating that the second speech input is satisfactory.
In some embodiments, during the practice session, the digital assistant provides (534), to the user, a sample vocal utterance for at least one of the one or more alternative expressions.
In some embodiments, during the practice session, the digital assistant receives (536) a third speech input from the user speaking at least one of the one or more alternative expressions. In some embodiments, the digital assistant detects (538) an error in the third speech input based on a difference between the third speech input and a standard vocal utterance of the at least one alternative expressions. In some embodiments, the digital assistant provides (540) a sample vocal utterance to the user one or more times, the sample vocal utterance tailored for correcting the error in the third speech input In some embodiments, the first language is (542) a first dialect of a respective language associated with the user, and the second language is a second dialect of the respective language, and where the second dialect is different from the first dialect and is associated with a respective geographic area in which the user is currently located.
In some embodiments, the one or more alternative expressions of the first speech input includes (544) at least a respective alternative expression that changes a pronunciation of at least one word in the first speech input.
In some embodiments, the one or more alternative expressions of the first speech input includes (546) at least a respective alternative expression that changes a grammatical usage of at least one word in the first speech input.
In some embodiments, the one or more alternative expressions of the first speech input includes (548) at least a respective alternative expression that replaces at least one word in the first speech input.
In some embodiments, the respective alternative expression that replaces at least one word or expression in the first speech input is (550) a local slang for the at least one word or expression in the geographic area in which the user is currently located.
In some embodiments, the digital assistant generates (552) the alternative expressions and provides the feedback in a foreign language assistance mode. In some embodiments, while in the foreign language assistance mode (554), the digital assistant receives (556) input from the user for entering a live session for the user to utilize at least one of the alternative expressions to accomplish the inferred user intent. In some embodiments, the digital assistant provides (558) the live session for the user. In some embodiments, during the live session (560), the digital assistant listens (562) for the user speaking the at least one of the alternative expression to a second user. The digital assistant also listens (564) for a verbal response from the second user. Based on the verbal response received from the second user, the digital assistant determines (566) that additional foreign language assistance is needed by the user; and provides (568) one or more speech outputs in the second language to assist the user in accomplishing the inferred user intent.
In some embodiments, the digital assistant provides (570), to the user, a textual transcript of a verbal exchange between the digital assistant and the second user in a user interface displayed on the device.
In some embodiments, the digital assistant provides (572), to the user, a translation of the textual transcript from the second language to the first language in the user interface displayed on the device.
In some embodiments, the digital assistant stores (574) a transcript of a user session conducted in the foreign language assistance mode for future review by the user.
In some embodiments, the digital assistant generates (576) a different set of alternative expressions for the inferred user intent depending on a respective current context associated with the user.
In some embodiments, the current context associated with the user includes (578) a current location of the user.
In some embodiments, the current context associated with the user includes (580) a current time at which the first speech input was received.
In some embodiments, the current context associated with the user includes (582) a type of place that is located at the user's current location. Example types of places include places to shop (e.g., shopping mall, grocery stores, clothing outlets, shoe stores, electronic stores, supermarkets, etc.), places to get drinks (e.g., bars, pubs, etc.), places to get coffee or other beverages (e.g. coffee shops, juice bar, ice cream shops, tea houses, etc.), places to eat (e.g., fining dining restaurants, fast food restaurants, café, cafeteria, etc.) places to send mail (e.g., postal offices, mail boxes, commercial shipping services, etc.), places to get healthcare services (e.g., hospitals and clinics, emergency services, etc.), places to get banking services (e.g., banks, check cashing services, etc.), places to take public transportation (e.g., train stations, bus stops, airports, etc.), places to see movies (e.g., theatres, movie theatres, video stores, video rental stores, etc.), tourist sites, places to get police assistance (e.g., police station, through police dispatchers), etc.
In some embodiments, the current context associated with the user includes (584) a correlation between a schedule item of the user and the current location.
In some embodiments, the current context associated with the user includes (586) a correlation between a schedule item of the user and the current time.
In some embodiments, the current context associated with the user includes (588) a current transportation mode of the user.
In some embodiments, the current context associated with the user includes (590) a correlation between a directions request entered by the user and the user's current location.
In some embodiments, the process 500 further implements any combination of the features described above and in the remainder of this specification.
In the process 600, in some embodiments, during a first interaction with a user (602): the digital assistant receives (604) a first speech input from the user while the user is located in a first geographic area. The digital assistant infers (606) a first user intent based on the first speech input. The digital assistant provides (608) a first paraphrase of the first speech input based on the inferred first user intent. The digital assistant then executes (610) a respective task flow to accomplish the inferred first user intent. During a second interaction with the same user (612): the digital assistant receives (614) a second speech input from the user while the user is located in a second geographic, the second speech input being substantially identical to the first speech input. The digital assistant infers (616) a second user intent based on the second speech input, the inferred second user intent being identical to the inferred first user intent. The digital assistant determines (618) that a location change from the first geographic area to the second geographic area is associated with a change in language or locale-specific vocabulary for at least one word or expression in the second speech input. In response to said determination, the digital assistant provides (620) a second paraphrase based on the second inferred user intent, where the second paraphrase is different from the first paraphrase based on the change in language or vocabulary. In some embodiments, the digital assistant executes (622) the respective task flow to accomplish the inferred second user intent.
In an example scenario, during one user session, if the user says “I want to buy some pencils and erasers” while the user is in the United States, the digital assistant will infer that the user needs to find a stationery shop, and provides search results and directions to one or more stationery shops nearby. In some embodiments, to confirm the user intent, the digital assistant optionally provides a paraphrase of the user input, e.g., “Search for stores that sell erasers and pencils . . . . Here are a few stationery stores I found . . . ” When the user travels to a different location (e.g., England) where the language usage and vocabulary are somewhat different from the United States, the digital assistant optionally provides opportunities for the user to learn about the local language usage and vocabulary. For example, in another user session occurring while the user is in England, if the user says “I want to buy some erasers and pencils,” the digital assistant will infer the same user intent as before (e.g., the user needs to find a stationery shop). In addition to providing search results and directions to one or more stationery shops nearby, the digital assistant optionally provides a different paraphrase of the user input. In this paraphrase, the digital assistant can teach the user about the language difference in the U.S. and England for the term “eraser.” For example, the digital assistant optionally provides a paraphrase that says “Search for stores that sell rubbers and pencils . . . . Here are a few stationery stores I found . . . ” or “Erasers are called ‘rubbers’ in England. Here are a few stationery shops that sell rubbers and pencils.” By including the change in language or locale-specific vocabulary in the paraphrase, the digital assistant can provide some information to the user in context, without making the interaction with the user too cumbersome. Other similar examples include “gas” and “petrol,” “apartment” and “flat,” “can” and “tin,” “closet” and “wardrobe,” “elevator” and “lift,” etc.
In some embodiments, the first geographic area and the second geographic area are (624) both associated with a primary language of the user.
In some embodiments, the change in locale-specific vocabulary includes (626) use of a respective local slang in the second geographic area for the at least one word or expression in the second speech input, and wherein the second paraphrase utilizes the respective local slang.
In some embodiments, the digital assistant receives (628) user input to start a learning session regarding the respective local slang provided in the second paraphrase. In response to receiving the user input, the digital assistant provides (630) an explanation of the usage of the respective local slang in the second geographic area.
In some embodiments, the change in language includes (632) use of a respective local accent in the second geographic area for the at least one word or expression in the second speech input, and wherein the second paraphrase utilizes the respective local accent.
In some embodiments, the digital assistant receives (634) user input to start a learning session regarding the respective local accent provided in the second paraphrase. In response to receiving the user input, the digital assistant provides (636) one or more additional examples of the usage of the respective local accent in the second geographic area.
In some embodiments, the digital assistant further implements any combination of the features described above and in the remainder of this specification.
In the process 700, in some embodiments, the digital assistant evaluates (702) a present context associated with a user. The digital assistant identifies (704) a respective foreign language training scenario associate with the present context. The digital assistant then provides (706) a foreign language training session for the user, the foreign language training session containing one or more foreign language exercises tailored for the current context.
In an example scenario, when the user is in a foreign country, the digital assistant keeps track of the user's current location. When the user is located inside a grocery store in the foreign country, the digital assistant optionally generates foreign language exercises that are suitable for the current context. For example, the foreign language exercises may include vocabulary about food products (e.g., words for names of food products, categories of food products, description of food products, nutrition information of food products, prices, discounts, etc.). In addition, the foreign language exercises may also include dialogues and/or phrases related to interactions that commonly occur in a grocery store. For example, the digital assistant optionally generates foreign language exercises related to asking for help to locate a particular food item, asking for different alternatives for a food product, asking about origins or sources of a particular type of food product, asking about discounts for a particular product, and errors in prices for particular products and/or the total bill, etc.
In some embodiments, the digital assistant can carry out a foreign language dialogue with the user to practice various aspects of shopping in the grocery store. Dynamically generating foreign language exercises based on the current context is helpful for the user to learn a foreign language more quickly. The user may better remember words in the foreign language, when he or she can see in person the particular items that those words describe. Comprehension and memorization of the foreign language dialogues and vocabulary can also be enhanced when the user see the same words in context (e.g., food labels in the grocery store), and hear the dialogue spoken by native speakers in real life (e.g., other customers asking about prices and discounts in the grocery store).
In some embodiments, the digital assistant automatically, without user intervention, selects (708) a respective language for the one or more foreign language exercises based on a primary language associated with a geographic area in which the user is currently located. Then, the digital assistant generates (710) the one or more foreign language exercises in the automatically selected language.
In some embodiments, the digital assistant receives (712) user input selecting a respective language for the one or more foreign language exercises, and generates (714) the one or more foreign language exercises in the user-selected language.
In some embodiments, the present context associated with the user includes (716) the user's presence inside a store located in a geographic area in which a respective foreign language is a primary language, and the one or more foreign language exercises include at least vocabulary or dialogue in the respective foreign language that is associated with shopping in the store.
In some embodiments, the present context associated with the user includes (718) the user's presence in proximity to a terminal of public transportation located in a geographic area in which a respective foreign language is a primary language, and the one or more foreign language exercises include at least vocabulary or dialogue in the respective foreign language that is associated with use of the public transportation.
In some embodiments, the present context associated with the user includes (720) the user's presence inside a dining facility located in a geographic area in which a respective foreign language is a primary language, and the one or more foreign language exercises include at least vocabulary or dialogue in the respective foreign language that is associated with dining at the dining facility.
In some embodiments, the present context associated with the user includes (722) the user's presence inside a lodging facility located in a geographic area in which a respective foreign language is a primary language, and the one or more foreign language exercises include at least vocabulary or dialogue in the respective foreign language that is associated with lodging at the lodging facility.
In some embodiments, the present context associated with the user includes (724) the user's presence inside a public transport vehicle moving toward a destination for which the user has recently requested directions and the destination is located in a geographic area in which a respective foreign language is a primary language, and wherein the one or more foreign language exercises include at least vocabulary or dialogue in the respective foreign language that is associated with visiting to said destination.
In some embodiments, the present context associated with the user includes (726) the user's presence inside a healthcare facility, and wherein the one or more foreign language exercises include at least vocabulary or dialogue in the respective foreign language that is associated with obtaining healthcare services at the healthcare facility.
In some embodiments, the present context associated with the user includes (728) the user's presence inside a business premise offering beverage services, and wherein the one or more foreign language exercises include at least vocabulary or dialogue in the respective foreign language that is associated with ordering beverages at the business premise.
In some embodiments, the digital assistant presents (730) images associated with vocabulary used in the foreign language exercises.
In some embodiments, the digital assistant further implements any combination of the features described above.
The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated.
This application claims the benefit of U.S. Provisional Application No. 61/800,846, filed on Mar. 15, 2013, entitled USER TRAINING BY INTELLIGENT 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 |
| 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 | Cooperman 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 |
| 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 | Hoftberg 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 |
| 8370158 | Gazdzinski | Feb 2013 | B2 |
| 8371503 | Gazdzinski | Feb 2013 | B2 |
| 8374871 | Ehsani et al. | Feb 2013 | B2 |
| 8447612 | Gazdzinski | May 2013 | B2 |
| 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 |
| 20020086268 | Shpiro | Jul 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 |
| 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 |
| 20060080098 | Campbell | Apr 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 |
| 20060293876 | Kamatani | Dec 2006 | A1 |
| 20070038452 | Blair et al. | Feb 2007 | 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 |
| 20070106497 | Ramsey | May 2007 | A1 |
| 20070106674 | Agrawal et al. | May 2007 | A1 |
| 20070118351 | Sumita | May 2007 | A1 |
| 20070118377 | Badino et al. | May 2007 | A1 |
| 20070124131 | Chino | 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 |
| 20080015864 | Ross et al. | Jan 2008 | A1 |
| 20080021708 | Bennett et al. | Jan 2008 | A1 |
| 20080034032 | Healey et al. | 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 |
| 20080261572 | Tsui | Oct 2008 | A1 |
| 20080299523 | Chai et al. | Dec 2008 | A1 |
| 20080300878 | Bennett | Dec 2008 | A1 |
| 20080319763 | Di Fabbrizio et al. | 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 |
| 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 |
| 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 |
| 20100013760 | Hirai | 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 |
| 20100161311 | Massuh | 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 |
| 20100299138 | Kim | Nov 2010 | A1 |
| 20100299142 | Freeman et al. | Nov 2010 | A1 |
| 20100312547 | Van Os et al. | 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 |
| 20110218855 | Cao et al. | Sep 2011 | A1 |
| 20110231182 | Weider et al. | Sep 2011 | A1 |
| 20110231188 | Kennewick et al. | Sep 2011 | A1 |
| 20110231432 | Sata | Sep 2011 | A1 |
| 20110238407 | Kent | 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 |
| 20120002820 | Leichter | Jan 2012 | A1 |
| 20120016678 | Gruber | 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 |
| 20120035907 | 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 |
| 20120214141 | Raya et al. | Aug 2012 | A1 |
| 20120239761 | Linner | Sep 2012 | A1 |
| 20120265528 | Gruber et al. | Oct 2012 | A1 |
| 20120271676 | Aravamudan et al. | Oct 2012 | A1 |
| 20120290300 | Lee | Nov 2012 | A1 |
| 20120311583 | Gruber et al. | Dec 2012 | A1 |
| 20120330661 | Lindahl | Dec 2012 | A1 |
| 20130006638 | Lindahl | Jan 2013 | A1 |
| 20130030789 | Dalce | Jan 2013 | A1 |
| 20130110515 | Guzzoni et al. | May 2013 | A1 |
| 20130110518 | Gruber et al. | May 2013 | A1 |
| 20130110520 | Cheyer et al. | May 2013 | A1 |
| 20130152092 | Yadgar | Jun 2013 | A1 |
| 20130159847 | Banke | Jun 2013 | A1 |
| Number | Date | Country |
|---|---|---|
| 681573 | Apr 1993 | CH |
| 1912994 | Feb 2007 | CN |
| 101751387 | Jun 2010 | CN |
| 102368256 | Mar 2012 | CN |
| 102792320 | Nov 2012 | CN |
| 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 |
| 1020110068490 | Jun 2011 | KR |
| 10-2011-0113414 | Oct 2011 | KR |
| 10-1178310 | Aug 2012 | KR |
| 101193668 | Dec 2012 | KR |
| 1020120138826 | Dec 2012 | 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 |
|---|
| “Overcoming the Language Barrier with Speech Translation Technology”; Satoshi Nakamura; Quarterly Review No. 31 / Apr. 2009. pp. 35-48. |
| 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. |
| 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., “Demonstration Video of Multimodal Maps Using an Agent Architecture”, available online at <http://www.youtube.com/watch?v=x3TptMGT9EQ&feature=youtu.be>, published on 1996, 6 pages. |
| Cheyer et al., “Demonstration Video of Multimodal Maps Using an Open—Agent Architecture”, available online at <http://www.youtube.com/watch?v=JUxaKnyZyM&feature=youtu.be>, published on 1996, 6 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, A., “Demonstration Video of Vanguard Mobile Portal”, available online at <http://www.youtube.com/watch?v=ZTMsvg_0oLQ&feature=youtu.be>, published on 2004, 10 pages. |
| Cheyer, Adam, “A Perspective on AI & Agent Technologies for SCM”, VerticaiNet presentation, 2001, 22 pages. |
| 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. |
| 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. |
| Textndrive, “Text'nDrive App Demo—Listen and Reply to your Messages by Voice while Driving!”, YouTube Video available at <http://www.youtube.com/watch?v=WaGfzoHsAMw>, Apr. 27, 2010, 1 page. |
| Tofel et al., “SpeakTolt: 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. |
| Voiceassist, “Send Text, Listen to and Send E-Mail By Voice”, YouTube Video available at <http://www.youtube.com/watch?v=0tEU61nHHA4>, Jul. 30, 2009, 1 page. |
| Voiceonthego, “Voice on the Go (BlackBerry)”, YouTube available at <http://www.youtube.com/watch?v=pJqpWgQS98w>, Jul. 27, 2009, 1 page. |
| 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. |
| 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. |
| 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. |
| 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.sri.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. |
| Knownav, “Knowledge Navigator”, YouTube Video available at <http://www.youtube.com/watch?v=QRH8eimU_20>, Apr. 29, 2008, 1 page. |
| Kominek et al., “Impact of Durational Outlier Removal from Unit Selection Catalogs”, 5th ISCA Speech Synthesis Workshop, Jun. 14-16, 2004, 6 pages. |
| 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, dated 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. |
| Apple Computer, “Knowledge Navigator”, available online at <http://www.youtube.com/watch?v=QRH8eimU_20>, Uploaded on Apr. 29, 2008, 7 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. |
| Invitation to Pay Additional Fees received for PCT Patent Application No. PCT/US2014/028785, dated Jul. 4, 2014, 7 pages. |
| International Search Report and Written Opinion received for PCT Patent Application No. PCT/US2014/028785, dated Oct. 17, 2014, 23 pages. |
| International Preliminary Report on Patentability received for PCT Patent Application No. PCT/US2014/028785, dated Sep. 24, 2015, 15 pages. |
| Waibel, Alex, “Interactive Translation of Conversational Speech”, Computer, vol. 29, No. 7, Jul. 1996, pp. 41-48. |
| Office Action received for Korean Patent Application No. 10-2018-7013050, dated Jul. 9, 2018, 5 pages (2 pages of English Translation and 3 pages of Official Copy only). |
| Notice of Acceptance received for Australian Patent Application No. 2014227586, dated May 19, 2017, 3 pages. |
| Notice of Allowance received for Korean Patent Application No. 10-2015-7023441, dated Feb. 5, 2018, 3 pages (1 pages of English Translation and 2 pages of official copy). |
| Office Action received for Australian Patent Application No. 2014227586, dated May 16, 2016, 3 pages. |
| Office Action received for Chinese Patent Application No. 201480014613.9, dated Mar. 10, 2017, 17 pages (4 pages of English Translation and 13 pages of Official Copy). |
| Office Action received for Chinese Patent Application No. 201480014613.9, dated Dec. 21, 2017, 14 pages (6 pages of English Translation and 8 pages of Official Copy). |
| Office Action received for European Patent Application No. 14716736.5, dated Dec. 21, 2016, 4 pages. |
| Office Action received for European Patent Application No. 14716736.5, dated Oct. 5, 2017, 6 pages. |
| Office Action received for Korean Patent Application No. 10-2015-7023441, dated Jun. 21, 2016, 12 pages (6 pages of English Translation and 6 pages of Official Copy). |
| Office Action received for Korean Patent Application No. 10-2015-7023441, dated Apr. 27, 2017, 8 pages (4 pages of English Translation and 4 pages of Official Copy). |
| Office Action received for Korean Patent Application No. 10-2015-7023441, dated Sep. 7, 2017, 5 pages (2 page of English Translation and 3 pages of Official copy). |
| Office Action received for Australian Patent Application No. 2017221864, dated Aug. 2, 2018, 3 pages. |
| Intention to Grant received for European Patent Application No. 14716736.5, dated Jan. 8, 2019, 7 pages. |
| Decision to Grant received for European Patent Application No. 14716736.5, dated May 31, 2019, 1 page. |
| Office Action received for Australian Patent Application No. 2017221864, dated Jun. 5, 2019, 3 pages. |
| Number | Date | Country | |
|---|---|---|---|
| 20140272821 A1 | Sep 2014 | US |
| Number | Date | Country | |
|---|---|---|---|
| 61800846 | Mar 2013 | US |
