System and method for processing voicemail

Information

  • Patent Grant
  • 10652394
  • Patent Number
    10,652,394
  • Date Filed
    Friday, March 14, 2014
    10 years ago
  • Date Issued
    Tuesday, May 12, 2020
    4 years ago
Abstract
In one example, a recorded voicemail is first converted from speech to text, and a proposed action to be performed by the user is extracted from the voice message. For example, in the voicemail “this is John, call me at 650.987.0987 at 9 am tomorrow,” the action is to call John. At least one action parameter for undertaking the action is determined. For example, the action parameters may include the 650.987.0987 telephone number and the 9 am time the following morning, The at least one action parameter may be extracted from the voicemail message or it may be determined by other means, e.g., from a user's contact book. Finally, the user is presented with a prompt to facilitate undertaking the action using the at least one the parameter. For example, the user may be given the option to set a reminder to call John the following morning at 9 am.
Description
TECHNICAL FIELD

The subject matter described herein relates to systems and methods for automatically recognizing and acting on the content of voicemail messages.


BACKGROUND

Most modern cellular telephone carriers offer a voicemail service. Conventional voicemail, however, is cumbersome to use, especially when a user needs to extract and/or act on information contained in a voicemail message. For example, if a user receives a voicemail message, and the user needs to extract and act on information contained in a voicemail message, the user needs to perform numerous steps to deal with the message. For example, if a caller leaves a voicemail message asking the user to email the caller a copy of a presentation that the user gave the day before, the user must first listen to the voicemail message; open an email application; locate the email address of the caller; attach the presentation to the email; and then send the email to the caller. This process is inefficient and time consuming. Accordingly, it would be desirable to have an automated system perform as many of these tasks as possible.


SUMMARY

According to some implementations there is provided a method of operating a digital assistant. The method occurs at a device having one or more processors and memory, such as at a mobile telephone. A recorded voice message is provided from a caller to a user. For example a caller leaves a voicemail message for the user of the mobile device. In some embodiments, the recorder voicemail is first converted from speech to text.


A proposed action to be performed by the user is then extracted from the voice message. For example, the voicemail may state “this is John, call me at 650.987.0987 at 9 am tomorrow.” Here, the action is to call John.


At least one action parameter for undertaking the action is determined. Using the same example, the at least one action parameter includes (i) the telephone number of 650.987.0987, and 9 am the following morning. The at least one action parameter may be extracted from the voicemail message or it may be determined by other means. For example, the caller's telephone number may be obtained from caller identification, or by looking-up the caller's telephone number in the user's contact book.


Finally, the user is presented with a prompt to facilitate undertaking the action using the at least one the parameter. For example, the user may be given the option to set a reminder to call John the following morning at 9 am.


Some implementations provide a non-transitory computer-readable storage medium storing one or more programs for execution by the one or more processors. The one or more programs comprise instructions for performing the methods described herein.


Finally, some implementations provide a mobile or cellular telephone that includes a processor and memory coupled to the processor. The memory includes instructions for performing the methods described herein.


In some implementations, many or all of these steps occur automatically without user intervention.


The automatic processing of incoming voicemail messages realizes one or more of the following potential advantages. First, it reduces or eliminates the user having to remember, write down or type in contacts details left by callers in voicemail messages. Second, it provides a useful and convenient mechanism for users to process and respond to incoming voicemail messages. Accordingly, automatic processing of incoming voicemail messages saves the user time and effort, and greatly improves the efficiency of responding to or acting on information contained in received voicemail messages.


Details of one or more implementations 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.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 is a block diagram illustrating example communication devices.



FIG. 2 is a block diagram illustrating an example communication module.



FIG. 3 is a block diagram that includes an example voice command controller.



FIG. 4 is a flow chart showing an example process for performing actions in response to voice commands.



FIG. 5 is a block diagram illustrating an example architecture of an interactive device that can be utilized to implement the systems and methods described herein.



FIG. 6A is a screenshot that includes an example user interface.



FIG. 6B is a screenshot that includes another example user interface. Like reference numbers and designations in the various drawings indicate like elements.



FIG. 7 is a flow chart of an exemplary method for operating a digital assistant to automatically act on incoming voicemail messages received on a user's interactive device.



FIGS. 8 and 9 show exemplary screenshots of presented to a user.





DETAILED DESCRIPTION


FIG. 1 is a block diagram illustrating example communication devices 110 and 120. The communication devices 110 and 120 can each be configured to receive and transmit data using a communication channel 100, e.g., an electronic channel such as a channel in a wired or wireless network. As an example, the communication devices 110 and 120 can receive and transmit digital audio signals over a multi-media broadband network. The communication devices 110 and 120 can connect over the communication channel 100 automatically, or in response to user input requesting a connection. For example, a first user operating the communication device 110 can input a second user's phone number to establish a connection with the communication device 120 operated by a second user. The communication devices 110 and 120 can receive and transmit multimedia data, including video content, audio content, image content, textual content, or any combination thereof over the communication channel 100.


In some implementations, a communication device is a physical device implemented as hardware and configured to operate a software program. In some alternative implementations, a communication device is a virtual device that is implemented as a software application or module that is configured to establish a connection with another communication device. As examples, the communication devices 110 and 120 can be or be implemented as software in a mobile phone, personal digital assistant, portable computer, desktop computer, or other electronic communication device. Examples of communication channels 100 include Internet Protocol-based networks, cellular telephone networks, satellite networks, and other communication networks. Note that one or more other communication devices, in addition to the communication devices 110 and 120, can be connected over the communication channel 100.


The communication devices 110 and 120 can communicate in real-time or near real-time over the communication channel 100. For example, a real-time communication session, such as a phone conversation, can be conducted using two or more communication devices. In addition, a communication session can be established using voice-over Internet Protocol full duplex communications. The communication devices 110 and 120 can be implemented to permit full duplex conversations that include any electronically assisted communication mechanism or channel, e.g., over a mobile broadband network. The bidirectional nature of the communication devices 110 and 120 can enable two or more users to simultaneously exchange voice information during a communication session, e.g., a phone call. Voice information corresponds to the voice communication, e.g., conversation, between the parties to the communication session.


A communication device can include a communication module. In FIG. 1, communication device 110 includes a communication module 200a, and communication device 120 includes a communication module 200b. A communication module (e.g., communication module 200a, communication module 200b) can be configured to process audio data, e.g., digital audio data, received from a communication device. For example, the communication module can process audio data communicated: by a user operating the communication device 110; in a voicemail message; and from an interactive voice response device. The communication module can be located internal or external to a communication device. In some implementations, the communication module can be implemented in hardware and configured to operate a software program. In some alternative implementations, the communication module is a software application or module that is configured to process digital audio data. The communication module can also be configured to process commands received from a user through a microphone operatively coupled to the communication device in which the communication module is located.



FIG. 2 is a block diagram illustrating an example communication module 200. The communication module 200 includes a recording unit 230, a storage unit 240, a recognizer unit 250, and a voice command controller 300. These functions performed by these components can be combined or subdivided into components in ways other than those shown in FIG. 2. In addition, these components can be implemented in hardware and configured to operate a software program, or can be implemented as a software application or module.


Each of the components 230, 240, 250, and 300 can be interconnected, for example, using a data communication link 260. The communication module 200 can receive input 210 and produce output 220. The received input 210 can be audio data, e.g., in the form of digital or analog audio signals. For example, the communication module 200 can receive voice information input 210 encoded in a digital audio signal. The output 220 can include audio data, visual data, textual data, or any combination thereof. The output 220 can be displayed graphically in a display screen, or a user interface provided by a software application running on the communication device. For example, the communication module 200 can generate textual data output corresponding to the received digital audio signals and can display the textual data output in a display, e.g., a touch screen display of a smart phone. In some implementations, one or more of the communication module components 230, 240, 250, and 300 are located external to the communication device in which the communication module is located. The recording unit 230 records audio data. The audio data can include both received and transmitted voice information.


The recording unit 230 can be implemented to record a communication session between two or more communication devices. For example, the recording unit 230 can record a portion, or the entirety, of a phone conversation between two users communicating with mobile phones.


The recording unit 230 can be configured, e.g., by setting user preferences, to record voice information originating from one or more participants, e.g., callers using different communication devices, of a communication session. In some implementations, user preferences are used to select one or more particular participants for which voice information is recorded by the recording unit 230.


As an example, the recording unit 230 can be configured to record only one side of the phone conversation. The recording unit 230 can be configured to capture voice information spoken only by a first caller on a far end of a phone call and not by a second caller on a near end. The first caller on the far end is a caller using a first communication device that is exchanging voice information during a communication session with a second caller using a second communication device. The second caller on the near end is a caller using the second communication device in which the recording unit 230 is located. Alternatively, the recording unit 230 can capture voice information spoken only by the second caller on the near end.


In some implementations, the recording unit 230 automatically records the communication session. In some implementations, the recording unit 230 records the communication session in response to user input. For example, the recording unit 230 can continuously record one or more sides of a conversation in response to a user pressing a hardware button, a virtual button, or a soft record button, or issuing a voice command.


In these and other implementations, the communication module 200 can provide a notification to each participant of which voice information is being recorded. The notification can be a visual notification displayed in a display of the communication module of each participant, or an audio notification played by the communication module of each participant. In FIG. 2, the communication module 200, e.g., the recording unit 230, can produce an output 220, such as an audible, visual or textual indicator notifying the caller on the far end, the near end, or both, that the conversation is being recorded.


In some implementations, the recording unit 230 determines an identifier that indicates a date and time, e.g., a time stamp, associated with the recorded audio data. In addition, the recording unit 230 can associate the recorded audio data with one or more other identifiers. Examples of identifiers include an identifier for a particular communication session, a particular communication device, or a particular user of a communication device, from which the recorded audio data was derived. The identifiers can be used to identify particular recorded audio data for processing.


The storage unit 240 can be implemented to store data, e.g., the recorded audio data. The storage unit 240 can receive audio data captured by the recording unit 230. For example, the storage unit 240 can store audio data and information associated with the audio data, e.g., the identifiers described above. The storage unit 240 can be implemented as a local storage device or local memory cache. In some implementations, the storage unit 240 is located external to both the communication module 200 and the communication device 120. For example, the storage unit 240 can reside in a server, e.g., a network device, located remotely from the communication device 120. Audio data stored at the storage unit 240 can be played back. Additionally, audio data stored at the storage unit 240 can be transcoded into textual data and can be provided as output 220.


The recognizer unit 250 can be implemented to automatically identify terms, e.g., identify without further user intervention one or more words, in the audio data received from a remote source, such as the communication device 110. In some implementations, the recognizer unit 250 uses conventional techniques and one or more language models to identify key words, e.g., part of speech, subject-verb-object word order (e.g., identifying declarative sentences) in the audio data. The recognizer unit 250 provides the key words as input to an application or service external to the communication module. As an example, the following conversation may occur:

    • User I: “We should have dinner tonight at eight.”
    • User 2: “I want sushi.”[


The recognizer unit 250 can identify the key words “dinner”, “eight”, and “sushi”. Furthermore, the recognizer unit 250 can work with a location based service to determine a geographical location of one or more of the communication devices being used by the users in the communication session. The recognizer unit 250 can determine that, based on the detected key words, that a restaurant reservation service (e.g., a web application that makes restaurant reservations) may be useful for the user.


In some implementations, the recognizer unit 250 sends the input to a suggestion service external to the communication device that makes this type of determination. In some implementations, pattern matching can be used to identify the terms. An example pattern for a term representing a city, state, and zip code is “City, State NNNNN”, where N is a digit. An example pattern for a term representing an address is “X Y Drive”, where X is a number and Y is one or more words associated with the name of the drive. An example pattern for a term representing a phone number is “NNN NNN NNNN”, where N is a digit. Other patterns are possible.


In some implementations, the communication module 200 provides a notification to the user that the particular key words were detected and provide suggestions on how to act on the key words. For example, the communication module can provide visual feedback in the screen of the communication device that asks, “Would you like to make dinner reservations at 8:00 pm at a sushi restaurant?”. In some implementations, the communication module 200 automatically provides, e.g., without further user intervention, the key words to an application or service external to the communication module. In the example, the communication module may work with a restaurant reservation service to generate a request for the reservation. In particular, the communication may initiate, at the restaurant reservation service, a request to search for sushi restaurants with reservations available at 8:00 pm in a predetermined proximity to the geographical location (e.g., within 10 miles).


Other implementations are possible. For example, the recognizer unit 250 can send the input to applications or services local or external to the communication device, e.g., email applications, web browsers, and work with the local applications or services to provide a suggested operation or automatically initiate a subsequent action, e.g., generate a draft email, request a particular web page.


In some implementations, the recognizer unit 250 can identify the terms as being commands, e.g., voice commands, or target information, e.g., information upon which a command operates or performs an action. Upon detecting a command and target information, the recognizer unit 250 can provide the command and target information as output 220 (e.g., audible, visual, textual output) indicating to the user of the communication device that the command and target information were detected, and request instructions from the user whether to store the command and target information in an information log.


The commands and target information can be detected by the recognizer unit 250 using various techniques. In some implementations, the recognizer unit 250 identifies commands by comparing terms in the audio data to a collection of terms specified as being commands, e.g., in a dictionary of commands. In some implementations, the recognizer unit 250 uses conventional techniques and one or more language models to identify commands and target information based on linguistics, e.g., part of speech, subject-verb-object word order (e.g., identifying declarative sentences). In these and other implementations, pattern matching can also be used to identify commands and target information. For example, a predetermined number of tokens, e.g., characters or words that follow a detected command can be identified as being target information.


As an example, the recognizer unit 250 can be configured to identify, in the audio data received from the remote source, the term “phone number” as being a command and the next ten numerals following the words “phone number” as being target information. Upon identifying the term “phone number,” the recognizer unit 250 can be implemented to produce any of audible, visual, and textual output 220, indicating that the ten numerals associated with the words “phone number” have been recognized. The audio data from the remote source can be monitored by the recognizer unit 250 during any portion of the communication session. For example, the recognizer unit 250 can be implemented to continuously monitor spoken voice information transmitted from one or more communication devices during a phone conversation.


In some implementations, the recognizer unit 250 can detect key words in the audio data and send portions of the audio data associated with the detected key words to a recognizer service external to the communication device, e.g., a recognizer service located on a server device. The key words can be specified, for example, in a dictionary of key words. The portion of the audio data can be defined, for example, based on an amount of time before the key word occurs in the corresponding audio and an amount of time after the key word occurs, e.g., a portion of audio data that corresponds to the audio from seconds before the key word occurs to seconds after the key word occurs. The recognizer service can determine commands and target information and provide the commands and target information to the recognizer unit 250.


In some implementations, the recognizer unit 250 can generate an information log based on the identified terms, e.g., identified commands and target information, key words. In some implementations, the information log is a list or queue of information items (e.g., commands and target information) recognized during a communication session. When an information item is detected, the item of information can be added to the information log.


Additional information also can be associated with the item of information, such as a time stamp and/or an indication of the item's source, e.g., an identifier of a user or a communication device. The information log can be displayed, e.g., in a user interface of a communication device.


In some implementations, the communication module 200 provides a notification to a user of the communication device, e.g., a tone or haptic feedback, when a new information item is added to the information log. Once added to the information log, an item of information can be acted on. For example, a phone number recognized during a communication session and added to the information log can be dialed during the communication session, such as to initiate a three-way call. Also, an e-mail address can be accessed to generate a message or message template during the communication session.


The information log also can be accessed after the corresponding communication session ends. For example, a recognized telephone number can be used to initiate a new communication session or an item of contact information can be used to generate a new contact or update an existing contact. One or more items of information included in the information log also can be altered, including through editing and deleting. For example, the spelling of a recognized name can be corrected.


The information log can be stored to permit subsequent retrieval and processing. For example, a link to the information log corresponding to a communication session can be included in a call history list or a file structure, such as a folder or directory. In some implementations, an audio recording of the communication session can be accessed in conjunction with the information log, such as for verification of one or more recognized information items, In addition, a time stamp associated with an information item can be used to access the corresponding portion of the audio recording, permitting the information item to be compared with the corresponding recorded audio.


In some implementations, the recognizer unit 250 automatically stores the identified terms at the storage unit 240. In some implementations, the recognizer unit 250 stores the recognized words and phrases when a user responds to the audible, visual or textual output 220. In some implementations, the user responds to the output 220 with a response, such as by issuing a voice command or by pressing a hardware button, a virtual button, or a soft button to store the recognized words. Alternatively, the user can respond with a gesture, such as by holding the communication device 120 and making a pointing gesture, or with motion, such as by shaking the communication device 120.


The recognizer unit 250 can be implemented to receive audio data as the audio data is input 210 into the communication module 200. The recognizer unit 250 also can receive audio data captured by the recording unit 230. Additionally, the recognizer unit 250 can receive audio data stored at the storage unit 240. In some implementations, the recognizer unit 250 uses a Hidden-Markov speech recognition model.


The data communication link 260 can be implemented as a system bus or a signal line. Audio data and information associated with the audio data can be transmitted on the data communication link 260. The voice command controller 300 can be implemented to receive one or more commands. The one or more commands can be received from a user operating the communication device in which the voice command controller 300 is located.



FIG. 3 is a block diagram that includes an example voice command controller 300. The voice command controller 300 can be implemented to receive input and produce output, and to parse one or more commands from audio data received during a communication session.


In some implementations, the voice command controller 300 differentiates between voice information associated with a phone conversation and one or more voice commands spoken into a microphone operatively coupled to a communication device in which the voice command controller 300 is installed. The voice command controller can be implemented to recognize voice commands spoken by a caller on the near end, e.g., the originating source, from the real-time voice information transmitted during a communication session.


The voice command controller 300 also can be implemented to ignore voice commands spoken by a caller on the far end, e.g., the secondary source, of the phone conversation. The voice command controller 300 includes a detection device 310. The detection device 310 can be implemented to parse one or more voice commands included in audio data received from the local source (i.e., the device user) during the communication session. The one or more voice commands can be received during a connected and active communication session. The voice command controller 300 can receive the one or more voice commands without causing the communication device to switch from a conversational mode to a command mode. In some implementations, the detection device 310 filters out ambient noise during the communication session.


The detection device 310 can be programmed to recognize pre-defined key words and phrases associated with the one or more voice commands. The pre-defined key words and phrases can include words and/or phrases defined by either or both of the manufacturer and one or more device users. For example, the pre-defined key word “phone” can be programmed such that when the detection device 310 detects the key word “phone,” the detection device 310 recognizes that the key word is associated with a command and informs the voice command controller 300 that one or more actions corresponding to the command should be taken. Actions performed by the voice command controller 300 can include generating audible, visual or textual data corresponding to the received audio data. For example, the voice command controller 300 can output textual data corresponding to the ten digits associated with the audio data triggering the key word “phone” audio data, in a similar manner as described above with respect to the recognizer unit 250.


The detection device 310 can include a detection filter that recognizes the differences between a voice at the near end of the phone conversation, the local source, and a voice at a far end, a remote source. For example, the detection filter can include speech recognition software based on the Hidden-Markov model that can distinguish between one or more voices during a communication session. In some implementations, audio signals are detected without the detection filter. For example, audio signals received from the near end can be received through a microphone operatively coupled to the communication device and can be routed to the communication module 200.


In some implementations, a dictation recognition system (e.g., a parser) included in the detection device 310 interprets text from a phone conversation. The dictation recognition system can include a text post-processor, or data detector that is configured to parse through the generated text to obtain useful textual information, e.g., target information. Examples of useful textual information include phone numbers, email addresses, dates and home addresses. In some implementations, the useful textual information is highlighted, or otherwise enhanced, such that a user can perform one or more actions on the textual information. For example, a user can click on a phone number that was recognized and highlighted by a data detector, to call the party associated with the phone number.


In some implementations, the detection device 310 can detect and extract useful information from a live or automated conversation and can store the information in an information log. For example, information such as a physical address, an email address, a phone number, a date, and a uniform resource locator can be detected and inserted into the information log. The information log can be implemented as a list or queue of information items recognized during a communication session. For example, the information log can be configured to include information items associated with a list of pre-defined or programmed words and phrases that are detected and identified by the detection device 310 in the course of a communication session. When an item of information is detected, e.g. a phone number, the item of information can be inserted into the information log. Additional information also can be associated with the item of information, such as a time stamp and/or an indication of the item's source. The information log can be displayed, e.g., in a user interface display of a device, such as an interactive device.


The device also can be configured to output a signal, such as a tone or haptic feedback, when a new information item is added to the information log. Each information item can also be associated with an identifier that identifies a particular user or communication device from which the information item was derived. Once added to the information log, an item of information can be acted on, such as through a voice command or tactile input. For example, a phone number recognized during a communication session and added to the information log can be dialed during the communication session, such as to initiate a three-way call. Also, an e-mail address can be accessed to generate a message or message template during the communication session.


The information log also can be accessed after the corresponding communication session ends. For example, a recognized telephone number can be used to initiate a new communication session or an item of contact information can be used to generate a new contact or update an existing contact. One or more items of information included in the information log also can be altered, including through editing and deleting. For example, the spelling of a recognized name can be corrected. A user can also associate particular commands with one or more items of target information.


Further, the information log can be stored to permit subsequent retrieval and processing. For example, a link to the information log corresponding to a communication session can be included in a call history list or a file structure, such as a folder or directory.


In some implementations, an audio recording of the communication session is accessed in conjunction with the information log, such as for verification of one or more recognized information items. In addition, a time stamp associated with an information item can be used to access the corresponding portion of the audio recording, permitting the information item to be compared with the corresponding recorded audio.


The detection device 310 can be implemented to process the one or more voice commands concurrent with the phone conversation. The one or more voice commands also can be recorded and time stamped by the detection device 310 for later execution. The recorded time stamped voice commands can be stored and displayed in a command list in, e.g., a user interface display. The detection device 310 also can record and time stamp the detected key words associated with the one or more voice commands. The recorded time stamped key words further can be stored and displayed in an information log. In some implementations, the information log and the command list can be integrated.


The voice command controller 300 can receive input from an input unit 320. The input unit 320 can be implemented to provide one or more types of input to the voice command controller 300. The input received from the input unit 320 can include one or more of: voice input 322; tactile input 324; gesture input 326; and motion input 328. The voice input 322 can include one or more voice commands directing the voice command controller 300 to perform one or more actions corresponding to the one or more voice commands.


For example, the voice input 322 can include a command to the voice command controller 300 to prepare an electronic message for dissemination to a particular person. Upon receipt of the command, the voice command controller 300 can be implemented to generate a shell electronic message to a particular contact named as a part of the command. For example, in response to a command to prepare an email for “Greg,” the voice command controller 300 can generate an email addressed to Greg.


The voice input 322 also can include a command to initiate dictation, e.g., to generate an information log that is not associated with a particular communication session. For example, the voice command controller 300 can be implemented to transcribe and record Greg's email address as Greg's email address is dictated into the phone. The voice command controller 300 also can be implemented to read and recite stored information. For example, during a phone call with “Bob,” the near end user can provide voice input 322 commanding the voice command controller 300 to “recite Greg's phone number”; in response to receiving the voice input 322, the voice command controller 300 can produce output 330 reciting Greg's phone number that is audible to Bob, the near end user, or both.


The tactile input 324, gesture input 326 and motion input 328 can be implemented as physical inputs. The physical inputs can be used in conjunction with the voice input 322 to differentiate the one or more voice commands, e.g., commands, from the real-time voice information, including target information. The physical inputs can be received before, concurrently with, or after the voice input 322, i.e., one or more voice commands, is received. For example, as a user speaks one or more voice commands into the communication device, the user also can press a button located on the communication device to indicate that the spoken words are distinct from regular voice information associated with the phone conversations and should be treated as a command.


Tactile input 324, such as pressing a hardware, virtual or soft button, also can be used to determine whether one or more voice commands should be treated as a string of commands, or distinguished as separate individual commands. Gesture input 326, such as gesturing with one or more fingers while holding the communication device in the gesturing hand, also can be used to indicate that spoken words should be treated as a command, in addition to determining the difference between a string of commands and separate individual commands.


Additionally, motion input 328, such as moving or shaking the communication device, also can be used to indicate that spoken words should be treated as a command, as well as determining the difference between a string of commands and separate individual commands. In some implementations, the voice input 322, as well as the physical inputs, can cause one or more processors at the voice command controller 300 to generate a new file corresponding to the received input. In some implementations, the physical inputs can be the sole input instructing the voice command controller 300 to perform the one or more actions corresponding to the received input.


The voice command controller 300 can produce output at an output unit 330. The output unit 330 can be implemented to provide one or more types of output from the voice command controller 300. The output unit 330 can include producing textual data corresponding to the received audio data. The textual data can be displayed on a display screen of, e.g., the communication device 120 depicted in FIG. 1. For example, in response to receiving voice input 322 directing the voice command controller 300 to perform an action, the voice command controller 300 can instruct the output unit 330 to produce textual data corresponding to the received voice information on the touch screen of, e.g., a smart phone. The output unit 330 also can produce audible and visual data based on the one or more actions performed by the voice command controller 300 in response to the one or more voice commands. In some implementations, the output unit 330 can be used to provide the output 220 depicted in FIG. 2.


Output from the voice command controller 300 can be stored in a storage unit 340. In some implementations, the storage unit 340 can be integrated physically and/or logically with the storage unit 240. In other implementations, the storage unit 340 can be both physically and logically separate from the storage unit 240. The storage unit 340 can be implemented to store information associated with the one or more actions taken by the voice command controller 300. For example, in response to receiving voice input 322 from a user directing the voice command controller 300 to “remember the phone number” recited by the caller on the far end, the voice command controller 300 can produce textual data corresponding to the phone number at the output unit 330 and also can store the phone number at the storage unit 340. The storage unit 340 can be implemented as the storage unit 240 in the communication module 200 depicted in FIG. 2. The storage unit 340 can be configured as a local storage device or local memory cache. In some implementations, the storage unit 340 can be located remotely from the communication device 120 depicted in FIG. 1. For example, the storage unit 340 can be located on a server maintained by a network provider. In some implementations, upon detecting particular pre-defined words and phrases, such as numbers, the communication module 200 can transmit a portion, or the entirety, of the particular audio data to a remote server. The dedicated server can be configured to recognize particular programmed utterances with greater clarity, and also can store the audio data. in some implementations, the server can transmit the clarified detected utterance to the communication device.



FIG. 4 is a flow chart showing an example process for performing actions in response to voice commands. The process 400 can, for example, be implemented in the communication devices 110, 120 depicted in FIG. 1, the communication module 200 depicted in FIG. 2 and the voice command controller 300 depicted in FIGS. 2-3.


Voice information, including one or more voice commands, can be received during a real-time full duplex phone conversation or from a voicemail left for the user by a caller (as described above in relation to FIG. 1) (405). The voice information can be received by a communication device. The communication device can be a mobile phone or other verbal communication device. The real-time full duplex phone conversation can be a bidirectional communication session. A user operating the mobile phone can speak the one or more voice commands into the mobile phone during the communication session and the mobile phone can identify or parse the commands from the conversation. The one or more voice commands can direct the communication device to take one or more actions corresponding to the one or more voice commands.


In some implementations, the one or more voice commands can be received after the communication session has ended. For example, at the conclusion of a communication session, a user can instruct the communication device to take an action based on information received during the communication session. In some implementations, the one or more voice commands can be accompanied by tactile, gesture or motion input. The tactile, gesture and motion input can be associated with the one or more voice commands and can be used to differentiate the one or more voice commands from other portions of the phone conversation. The accompanying input also can be received by the communication device during an active communication session or after the communication session has ended.


Voice information received during the real-time full duplex phone conversation (or voicemail) can be recorded (410). The voice information can be received by the communication device. The voice information can be encoded in digital audio data. The recording can occur automatically, or in response to input initiating the recording. In some implementations, the voice information can be continuously monitored during a real-time communication session, including a bidirectional communication session. The voice information also can be recorded continuously for the duration of the real-time full duplex phone conversation.


A source of the one or more voice commands (or keywords) can be determined (415). A speech recognition algorithm, such as the Hidden-Markov model, implemented in a detection device can filter voice information in audio data to determine the source of the one or more voice commands. In some implementations, the source can be the caller operating the communication device, e.g., the originating source.


The one or more voice commands (or keywords) can be parsed from the audio data received from the source (420). Two or more users operating communication devices can participate in the communication session. For example, the communication session can include a telephone conversation between two or more users operating smart phones. Audio data can include any and all voice information exchanged during the telephone conversation. The one or more voice commands can be detected by a detection module in the communication device.


In some implementations, the detection module can be located external from the communication device. The detection module can be implemented to identify the one or more voice commands in the voice information received from the source during the real-time full duplex phone conversation. For example, the detection module can identify key words and phrases associated with the one or more voice commands, such as “phone, remember that street address,” from the remainder of the telephone conversation. The detection module can extract the information associated with the one or more voice commands and can manage the extracted information differently than the received audio data.


One or more actions based on the one or more voice commands (or keywords) can be performed (425). The one or more voice commands can cause a processing module (see, e.g., FIG. 5) at the communication device to perform one or more actions corresponding to the one or more voice commands. The one or more actions can include generating textual data corresponding to the voice information received during the real-time full duplex phone conversation. For example, the processing module can produce textual data corresponding to the street address recited by the caller on the other end of a telephone conversation. The one or more actions also can include generating audible or visual data corresponding to the audio data received during the communication session. For example, in response to a voice command directing the communication device to “repeat the street address,” the processing module can produce audible data corresponding to the street address recited during the communication session. In some implementations, audio data received during the communication session also can be provided to one or more applications. For example, audio data received during a communication session can be used to populate one or more fields of an electronic mail message or inserted into a contact record. Further, audible data also can be provided to another device, such as another device participating in the communication session.


Information associated with the one or more actions can be stored (430). The information associated with the one or more actions can be stored in a storage unit located within or outside the communication device. For example, the storage unit can be implemented as a local storage device or local memory cache within the communication device. In some implementations, the information can be stored in a particular location of the storage unit based on the one or more commands. For example, in response to a voice command directing the communication device to “store the street address in my contacts folder,” the processing module can store the audio data corresponding to the street address in the contacts folder portion of the storage unit. In some implementations, physical commands can be used to direct the communication device to perform one or more actions. For example, a user can interact with, e.g., touch or press, a command button in an communication device user interface to store the street address in the contacts folder.


Information associated with the one or more actions can be displayed (435). For example, the generated textual data corresponding to the voice information recorded during the real-time full duplex phone conversation can be displayed in, e.g., a user interface of a data processing apparatus (e.g., a smart phone, an interactive device, or other electronic devices with display components). The information associated with the one or more actions also can include the corresponding voice commands and key words. In some implementations, the information can be presented in an information log.



FIG. 5 is a block diagram illustrating an example architecture of an interactive device 500 that can be utilized to implement the systems and methods described herein. The interactive device 500 can include a memory interface 502, one or more data processors, image processors and/or central processing units 504, and a peripherals interface 506. The memory interface 502, the one or more processors 504 and/or the peripherals interface 506 can be separate components or can be integrated in one or more integrated circuits. Various components in the interactive device 500 can be coupled together by one or more communication buses or signal lines.


Sensors, devices, and subsystems can be coupled to the peripherals interface 506 to facilitate multiple functionalities. For example, a motion sensor 510, a light sensor 512, and a proximity sensor 514 can be coupled to the peripherals interface 506 to facilitate orientation, lighting, and proximity functions. A location processor 515 (e.g., GPS receiver) can be connected to the peripherals interface 506 to provide geopositioning. A magnetic compass integrated circuit 516 can also be connected to the peripherals interface 506 to provide orientation (e.g., to determine the direction of due North).


A camera subsystem 520 and an optical sensor 522, e.g., a charged coupled device (CCD) or a complementary metal-oxide semiconductor (CMOS) optical sensor, can be utilized to facilitate camera functions, such as recording photographs and video clips.


Communication functions can be facilitated through one or more wireless communication subsystems 524, which can include radio frequency receivers and transmitters and/or optical (e.g., infrared) receivers and transmitters. The specific design and implementation of the communication subsystem 524 can depend on the communication network(s) over which the interactive device 500 is intended to operate. For example, an interactive device 500 can include communication subsystems 524 designed to operate over a wireless network, such as a GSM network, a GPRS network, an EDGE network, a Wi-Fi or WiMax network, and a Bluetooth™ network, or a wired network. In particular, the wireless communication subsystems 524 may include hosting protocols such that the device 500 may be configured as a base station for other wireless devices.


An audio subsystem 526 can be coupled to a speaker 528 and a microphone 530 to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording, and telephony functions. The I/O subsystem 540 can include a touch screen controller 542 and/or other input controller(s) 544. The touch-screen controller 542 can be coupled to a touch screen 546. The touch screen 546 and touch screen controller 542 can, for example, detect contact and movement or break thereof using any of a plurality of touch sensitivity technologies, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with the touch screen 546.


The other input controller(s) 544 can be coupled to other input/control devices 548, such as one or more buttons, rocker switches, thumb-wheel, infrared port, LISS port, and/or a pointer device such as a stylus. The one or more buttons (not shown) can include an up/down button for volume control of the speaker 528 and/or the microphone 530.


In one implementation, a pressing of the button for a first duration may disengage a lock of the touch screen 546; and a pressing of the button for a second duration that is longer than the first duration may turn power to the interactive device 500 on or off. The user may be able to customize a functionality of one or more of the buttons. The touch screen 546 can, for example, also be used to implement virtual or soft buttons and/or a keyboard.


In some implementations, the interactive device 500 can present recorded audio and/or video files, such as MP3, AAC, and MPEG files. In some implementations, the interactive device 500 can include the functionality of an MP3 player.


The memory interface 502 can be coupled to memory 550. The memory 550 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). The memory 550 can store an operating system 552, such as Darwin, RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such as VxWorks. The operating system 552 may include instructions for handling basic system services and for performing hardware dependent tasks. In some implementations, the operating system 552 can be a kernel (e.g., UNIX kernel). The memory 550 may also store communication instructions 554 to facilitate communicating with one or more additional devices, one or more computers and/or one or more servers.


The memory 550 may include graphical user interface instructions 556 to facilitate graphic user interface processing; sensor processing instructions 558 to facilitate sensor-related processing and functions; phone instructions 560 to facilitate phone-related processes and functions; electronic messaging instructions 562 to facilitate electronic messaging related processes and functions; web browsing instructions 564 to facilitate web browsing-related processes and functions; media processing instructions 566 to facilitate media processing-related processes and functions; GPS Navigation instructions 568 to facilitate GPS and navigation-related processes and instructions; camera instructions 570 to facilitate camera-related processes and functions; interactive game instructions 572 to facilitate interactive gaming; calibration instructions 574 to facilitate calibrating interactive devices; speech recognition instructions 576 to facilitate recognizing speech; voice command instructions 578 to facilitate detecting and distinguishing voice commands or keywords, as described in reference to FIGS. 1-4 and FIGS. 7-9, and voicemail messages 579. In some implementations, the GUI instructions 556 and the media processing instructions 566 implement the features and operations described in reference to FIGS. 1-4.


In some implementations, the voicemail messages 579 are stored locally in memory 550, while in other implementations, voicemail pointers are stored in memory 550, where the pointers point to voicemail messages stored on a remote sever. In some implementations, the voicemail messages 579 are audio recordings of voicemail messages left for the user of the device by one or more callers. In other implementations, the voicemail messages 579 are text files of audio messages that have been converted from speech to text by the speech recognition instructions 576. In some implementations, the voice commands or keywords detected by the voice command instructions 578 are an action and one or more associated action parameters as described in further detail in relation to FIGS. 7-9 below.


The memory 550 may also store other software instructions (not shown), such as web video instructions to facilitate web video-related processes and functions; and/or web shopping instructions to facilitate web shopping-related processes and functions. In some implementations, the media processing instructions 566 are divided into audio processing instructions and video processing instructions to facilitate audio processing-related processes and functions and video processing-related processes and functions, respectively. An activation record and International Mobile Equipment Identity (IMEI) or similar hardware identifier can also be stored in memory 550.


Each of the above identified instructions and applications can correspond to a set of instructions for performing one or more functions described above. These instructions need not be implemented as separate software programs, procedures, or modules. The memory 550 can include additional instructions or fewer instructions. Furthermore, various functions of the interactive device 500 may be implemented in hardware and/or in software, including in one or more signal processing and/or application specific integrated circuits.



FIG. 6A is a screenshot that includes an example user interface 600. In particular, the user interface 600 displays a transcript 610 of a conversation, e.g., the example conversation described above with respect to FIG. 2, that is currently occurring between the user of communication device 620 and another user “Dad”, as indicated by the label “Dad” and a user interface element “End Call” configured to end the communication session. Note that particular words, e.g., key words, in the transcript 610 are enhanced, as illustrated in FIG. 6A by bold formatting. A suggestion 630, in the form of visual feedback, is generated based on the key words and displayed in the user interface 600. In the example, if the user selects “Yes”, the key words can be sent to a restaurant reservation service to obtain available reservations.



FIG. 6B is a screenshot that includes another example user interface 650. The user interface 650 displays an example information log 660. Note that, in some implementations, the information log can be rendered in a similar manner as other logs, e.g., logs for dialed calls, received calls, missed calls, are displayed in the communication device 620. In the example, identified commands and suggested commands, e.g., “Reservation” and “Call” are displayed in a first column of the information log 660. Identified target information, e.g., “Tonight”, “Eight”, “Sushi”, “Brother” is displayed in a second column that is adjacent to the first column.


In some implementations, each call in a call log, e.g., the logs for dialed calls, received calls, and missed calls, that has an associated information log can have a selectable interface element (e.g., a virtual button such as a chevron (>>rendered next to a call). A user selection of the selectable interface element causes a respective call log to be displayed in the communication device 620. As an example, the call log can be displayed in a pop-up window that is superimposed over the call log.


Associations between target information and particular commands can be indicated by aligning a particular command with associated target information. In FIG. 6B, the command “Reservation” to request a reservation can be performed on one or more of the target information items “Tonight”, “Eight”, and “Sushi.” Similarly, the command “Call” can be performed on the target information item “Brother.” The user interface 650 also includes a virtual keyboard, e.g., a virtual representation of a keyboard that facilitates editing of the information log 660 as described above.


As described above, these same systems and methods can be applied to recorded information, like voicemail messages. The systems and methods may be implemented on the device itself, on a remote server, or on a combination of the device and a remote server. Further details of such a system are also described in U.S. Provisional Application Ser. No. 61/646,831, filed May 14, 2012, which is incorporated by reference herein.



FIG. 7 is a flow chart of an exemplary method 700 for operating a digital assistant to automatically act on incoming voicemail messages received on a user's interactive device. In some implementations the interactive device is a user's mobile device, like a smartphone, such as that described in relation to FIG. 5. Initially, recorded voice messages (e.g., voicemail messages) are provided (702) from one or more callers to a user of the device. For example, the device receives one or more voicemail messages 579 of FIG. 5. In some implementations, these voicemail messages are audio files of recorded voice messages, while in other implementations they are pointers to audio recordings stored on a remote server.


In some implementations, the recorded voice messages are then converted (704) from speech to text. In some implementations, this conversion occurs automatically without user intervention as soon as the voicemail message is received at the device, while in other implementations, this occurs at any other suitable time, e.g., when the device has processing cycles to spare.


hereafter, a number of steps occur automatically without user intervention. First, a proposed action to be performed by the user is extracted (706) from the voice message. In some implementations, the voice command instructions 578 of FIG. 5 extract the proposed action from the converted text of the voicemail message as described above. For example, the voicemail message may state the following: “John, please can you email me a copy of yesterday's presentation.” The voice command instructions 578 of FIG. 5 determine the proposed action is to send the caller an email message. In other words, the action proposed by the caller is to email the caller with certain information.


Second, at least one action parameter for undertaking the action is determined (708). The action parameters are any parameters that are necessary or optional for performing or undertaking the action. For example, in the above example, the action parameters are the caller's email address and “yesterday's presentation.” Both of these parameters may be required for performing or undertaking the action of sending via email a copy of yesterday's presentation to the caller. In some implementations, the one or more action parameters are also extracted from the voice message.


Finally, the user of the device is presented (710) with a prompt facilitate undertaking the action using the at least one the parameter. Completing the example above, the voice command instructions 578 of FIG. 5 presents the user with an option to send the caller an email_ An example of such a prompt is shown in FIG. 8. If the user selects to send the caller an email, a draft email is generated as shown in FIG. 9.

    • Some examples of actions and action parameters include:
    • 1. Action: calling the caller back;
      • Action parameter: the caller's name, telephone number, time to call back, date to call back, etc.
    • 2. Action: calling another person (not the caller);
      • Action parameter: the person's name, telephone number, time to call back, date to call back, etc.
    • 3. Action: sending an email message to the caller (or another person); Action parameter: caller's/person's name or email address; content of the email message, a desired attachment, a time to email, a date to email, etc.
    • 4. Action: sending a message (e.g., SMS) to the caller (or another person); Action parameter: caller's/person's name, email address or phone number; content of the message, a desired attachment, a time or date to send the message, etc.
    • 5. Action: visit a webpage;
      • Action parameter: the webpage's uniform resource locator (URL) name of the webpage or website, etc.
    • 6. Action: watch a online video;
      • Action parameter: an identifier or name of the video; a URL of the video; etc.
    • 7. Action: a recommendation to download or purchase a software application; Action parameter: the name or location of the application, etc.
    • 8. Action: to remember to do something (i.e., a reminder);
      • Action parameter: the name of the task to be performed; the time period (e.g., time of day and date) that the task should be performed.
    • 9. Action: to perform a task;
      • Action parameter: the task to be performed;
    • 10. Action: to enter a calendar entry;
      • Action parameter: task name, date/time, etc.
    • 11. Action: to meet;
      • Action parameter: meeting name, location, time/date, etc.
    • 12. Action: to lookup or go to (navigate to) to a specific geographic location; Action parameter: location, etc.
    • 13. Action: adding contact details to the user's contact book;
    • Action parameter: contact details.
    • 14. Action: checking on the user's availability;
    • Action parameter: time period (date and time).


In those implementations where the action is to call or send a text message to the caller or another person (e.g., “Dave, morn asked that you call her tonight”), a telephone number is required. If the telephone number is provided in the voicemail message, then that number may be used to call the caller/person. If a number is not provided (see example above), then the number (action parameter) is first obtained from the user's contact or address book. For example, if the voicemail is to call “mom” and no number is provided, then a search is performed (712) of the user's contact book for an entry matching “mom, “mother”, etc. The same method can be performed for any other contact details, such as an email address, physical address, alternative phone numbers, etc. Similarly, any other action parameter may be looked-up in the same way. For example, a URL, calendar entry, application identifier, online video, etc., may all be looked-up based on another action parameter extracted or inferred from the message (e.g., “look at today's WALL STREET JOURNAL” may initiate a search for a URL associated with “WALL STREET JOURNAL”). For example, if the voice mail says “Check out the XYZ website for Linda's new profile” without specifying the URL of the website, the URL of the XYZ website is looked-up and displayed to the user in a user interface element (e.g., a hyperlink) for accessing the website from the voicemail interface.


In some implementations, a source telephone number of the caller may be obtained from automatic caller identification, performing a reverse lookup etc. In other implementations, an existing contact is identified in a contact list or book associated with the user based on at least one of a source telephone number from which the recorded voice message originated and a name extracted from the recorded voice message.


In the implementations where the action is to send an email, the at least one action parameter is an email address of the caller, and the prompt presents the user with an option to send an email message to the email address. For example, the voicemail message may say “Dave, please can you email me at mark@newco.com to let me know if you are coming for dinner.” The at least one parameter is the email address (markgnewco.com) of the caller. If the email address is not given by the caller, e.g., “Dave, please can you email me to let me know if you are coming for dinner,” then the email address is obtained by first determining the name of the caller from caller identification (or any other means), and thereafter looking up the person's name in the user's contact book to locate an address. A prompt is then presented to the user with the option to email the caller. For example, in a voice mail retrieval user interface, the user is requested to confirm that he wants an email prepared to the caller's email address. Upon user confirmation by the user, a draft email is presented to the user, where the email includes the email address as a destination address (e.g., pre-populated into the “to” field).


In some implementations, a prompt presents the user with an option to store an email address extracted from the recorded voice message in the user's contact book (or update an existing contact entry). If the identity of the person leaving the voicemail message can be ascertained from the source phone number, or the voice mail message, the device optionally supplements existing contact information of the contact based on the email address left in the voice mail. In another implementation, the prompt provides the user with the option to store any other contact detail extracted from the voicemail message in the user's contact book. For example, where Mr. Smith calls from his office phone, and says “This is Kevin Smith, please call me at my cell 650-888-5889”, the device finds an existing contact “K. Smith” in the user's contact list with an office phone number different from the number left in the voicemail message, the device offers to store the number “650-888-5889” as an additional contact number for the contact “K. Smith.”


In some implementations, a transcript of the recorded voice message is also included in the body of the message, so that the user can easily see what they need to respond to, e.g., a question from the caller.


In implementations where a caller has left a voicemail about a previous email sent to the user, and where the user requests the user to write back, the user is presented with the option to prepare a reply email to the previously received incoming email mentioned in the recorded voice message. Upon user confirmation, a draft reply email to the incoming email mentioned in the recorded voice message is presented to the user.


In those implementations where the action is to send the caller certain information in a text message, e.g., and SMS message, the at least one parameter is a telephone number or email address of the caller. Here, the prompt presents the user with an option to send a text message to the telephone number or email address.


In some implementations where contact details are mentioned in the voicemail and other contact details exist for the same person in the user's contact book, the user may be presented with (i) only one or the other of the contact details, or (ii) the option to respond using one of multiple contact details. For example, if a caller leaves a callback number that is different from the source phone number, the device presents a user interface element to call either the callback number extracted from the voicemail message or the source phone number. In some implementations, the user interface element includes a “CALL” or “SEND” button or icon followed by the person's name or contact details. In another example where a caller has left a callback number that is different from the source phone number logged for the voicemail message, the device presents a user interface element to call the callback number extracted from the voicemail message, rather than the source phone number for the voice mail message. In some implementations, a determination is first made that the source phone number is a masked phone number (e.g., a company's main phone number), when choosing to not to display an option to call the source telephone number.


In some implementations, the prompt to the user is a speech prompt. In these implementations, the prompt is first generated as text. The prompt is then converted from text to speech, where after it is played to the user.


In the implementations where the action is to visit an online application store, the at least one parameter is a name of an application. Here, the prompt presents the user with an option to visit a page associated with the application at the online application store.


In the implementations where the action is to watch an online video, the at least one action parameter is a name of an online video. The prompt presents the user with an option to watch the online video. In some implementations, the device determines the correct video portal directly from the voice mail message. In some implementations, the device searches for the video mentioned in the message on one or more major or preferred online video portals beforehand, and presents the video from a suitable source that has been identified. In some implementations, the device merely takes the user to a default video portal, and enters the search for the user. The user can then browser through the search results that are returned. For example, after the user has viewed the video, the device presents an option for the user to callback the caller to discuss his/her opinions of the video. In some embodiments, the device determines the telephone number associated with the caller based on the contact list of the user, or the source phone number of the voice mail message.


In some implementations, instead of calling the caller, the device also allows the user to contact the caller via a text or email message.


In implementations where the action is to meet at a specified geographic location, the at least one action parameter comprises a name or an address of the geographic location. In some implementations, presenting the prompt further comprises presenting an option to the user to provide navigation to the specified geographic location. In some implementations, presenting the prompt further comprises presenting the user with an option to store the specified geographic location as a reminder or calendar entry. In some implementations, the at least one action parameter also includes a time period and the prompt presents the user with an option to store a reminder or calendar entry for meeting at the specified geographic location at the time period. For example, a reminder for “meet me at Pizza Hut in Cupertino in an hour” is created for the user.


In some implementations, the action is to perform a task at a later time, and the at least one action parameter is an action and a time for the task. Here, the prompt presents the user with an option to store a reminder to perform the task at the time. For example, if the voice mail message says, “This is morn, please call me tonight.” The device prepares a reminder to call a number associated with “mom” at 8 pm that night. The time of 8 pm may be arbitrarily chosen for “tonight” or a time that the user normally makes calls to mom in the evening is used instead. In another example, if the caller left a message at 4:30 pm saying “meet me at Pizza Hut in Cupertino in an hour” and the user did not look at the device until 6:30 pm, the device offers an option to call the caller immediately, without setting a reminder.


It may also be determined whether the recorded voice message requires immediate attention from the user based on the action and the at least one action parameter. If it is determined that the recorded voice message requires immediate user attention, the prompt is immediately presented to the user. However, if it is determined that the recorded voice message does not require immediate user attention, the prompt is presented to the user at the time that the user accesses the recorded voice message. For example, if the caller left a message at 4:30 pm saying “meet me at Pizza Hut in Cupertino in an hour” and the device detects that the user has not checked his voice mail at 4:00 pm, the device proactively presents a prompt for the user to review the voice mail message, and optionally provides the user directions to the location of the meeting.


In implementations where a time is provided in a voicemail message, the system may first determine the address of the caller from the user's contact book, and then determine the appropriate time taking time zones into account. For example, the voicemail may state “this is John Goodman, call me at work at 1 pm.” Here the system determines that John Goodman lives in California, while the user lives in Virginia; and offers to set a reminder to call John Goodman at 4 pm EST (1 pm PST) the following day.


Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a data processing apparatus, or programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.


The term “data processing apparatus” encompasses all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.


These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor.


To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device, a keyboard, and a pointing device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.


The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), and the Internet.


The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.


Although implementations have been described in detail above, other modifications are possible. For example, the flow diagrams depicted in the figures do not require the particular order shown, or sequential order, to achieve desirable results. In addition, other steps may be provided, or steps may be eliminated, from the described flow diagrams, and other components may be added to, or removed from, the described systems. Accordingly, various modifications may be made to the disclosed implementations and still be within the scope of the following claims.

Claims
  • 1. A method of operating a digital assistant, comprising: at a device having one or more processors and memory: providing a recorded voice message from a caller to a user; andautomatically, without user input: extracting from the voice message a proposed action to be performed by the user, wherein extracting the proposed action comprises determining the proposed action from a plurality of proposed actions, wherein the proposed actions include at least one of: calling back the caller, calling a person other than the caller, storing contact details in a contact book of the user, sending an email to the caller, sending a text message to the caller, visiting a webpage, watching an online video, recommending to download or purchase a software application, reminding to perform a task, performing a task, entering a calendar entry, attending a meeting, and navigating to a geographic location;determining at least one action parameter for undertaking the action; andpresenting the user with a prompt to facilitate undertaking the action using the at least one parameter.
  • 2. The method of claim 1, wherein the extracting further comprises: converting the recorded voice message from speech to text; and extracting the action from the text.
  • 3. The method of claim 2, wherein the extracting further comprises extracting the action parameter from the text.
  • 4. The method of claim 1, wherein the action parameter is a source telephone number associated with the caller.
  • 5. The method of claim 4, wherein the source telephone number is obtained from a contact book of the user.
  • 6. The method of claim 4, wherein the source telephone number is obtained from caller identification.
  • 7. The method of claim 1, wherein the action is for the user to call back the caller, and the at least one action parameter comprises the name or telephone number of the caller, and wherein the prompt asks the user if they would like to call back the caller.
  • 8. The method of claim 1, wherein the action is for the user to call a person other than the caller, and the at least one action parameter comprises the name and telephone number of the person, and wherein the prompt asks the user if they would like to call back the person.
  • 9. The method of claim 1, wherein the at least one parameter includes a specific time period, and the presenting of the prompt occurs at or before the specific time period.
  • 10. The method of claim 1, wherein the action is to store contact details in a contact book of the user, and the at least one parameter comprises contact details.
  • 11. The method of claim 1, wherein the action is to check on the user's availability, and the at least one parameter is retrieved from a calendar of the user.
  • 12. The method of claim 1, wherein the action is send an email to the caller, and the at least one parameter is an email address of the caller, and wherein the prompt presents the user with an option to send an email message to the email address.
  • 13. The method of claim 12, wherein presenting the prompt further comprises: in a voice mail retrieval user interface, requesting user confirmation regarding whether to prepare an email to the email address; and upon user confirmation, presenting a draft email including the email address as a destination address and a transcript of the recorded voice message in a body of the draft email on the user device.
  • 14. The method of claim 12, further comprising: identifying an existing contact in a contact list associated with the user based on at least one of a source telephone number from which the recorded voice message originated and a name extracted from the recorded voice message, wherein the prompt offers to the user an option to store the email address extracted from the recorded voice message in association with the identified existing contact in the contact list.
  • 15. The method of claim 12, wherein presenting the prompt further comprises: in a voice mail retrieval user interface, requesting user confirmation regarding whether to prepare a reply email to an incoming email mentioned in the recorded voice message; andupon user confirmation, presenting a draft reply email to the incoming email mentioned in the recorded voice message.
  • 16. The method of claim 1, wherein the action is to send the caller certain information in a text message, and the at least one parameter is an telephone number or email address of the caller, and wherein the prompt presents the user with an option to send a text message to the telephone number.
  • 17. The method of claim 1, wherein presenting the prompt further comprises: in a voicemail retrieval user interface, presenting a user interface clement for initiating a telephone call to a telephone number extracted from the recorded voice message, in addition to a user interface element for initiating a telephone call to a source telephone number from which the recorded voice message originated.
  • 18. The method of claim 1, wherein the extracting further comprises: identifying an existing contact in a contact list associated with the user based on at least one of a source telephone number from which the recorded voice message originated and a name extracted from the recorded voice message; andin accordance with a determination that the recorded voice message contains a telephone number that is different from an existing telephone number associated with the identified existing contact, offering to the user an option to store the telephone number extracted from the recorded voice message in association with the identified existing contact in the contact list.
  • 19. A method of operating a digital assistant, comprising: at a device having one or more processors and memory: providing a recorded voice message from a caller to a user; andwithout user input:extracting from the voice message a proposed action to be performed by the user;determining at least one action parameter for undertaking the action; andpresenting the user with a prompt to facilitate undertaking the action using the at least one parameter, wherein presenting the prompt further comprises presenting the user with an option to store the specified geographic location as a reminder or calendar entry, wherein the action is to meet at a specified geographic location, and the at least one action parameter comprises a name or an address of the geographic location, and wherein the at least one action parameter also includes a time period and the prompt presents the user with an option to store a reminder or calendar entry for meeting at the specified geographic location at the time period.
  • 20. The method of claim 19, wherein presenting the prompt further comprises presenting an option to the user to provide navigation to the specified geographic location.
  • 21. A non-transitory computer-readable storage medium storing one or more programs for execution by the one or more processors, the one or more programs comprising instructions for: providing a recorded voice message from a caller to a user; andwithout user input:extracting from the recorded voice message a proposed action to be performed by the user, wherein extracting the proposed action comprises determining the proposed action from a plurality of proposed actions, wherein the proposed actions include at least one of: calling back the caller, calling a person other than the caller, storing contact details in a contact book of the user, sending an email to the caller, sending a text message to the caller, visiting a webpage, watching an online video, recommending to download or purchase a software application, reminding to perform a task, performing a task, entering a calendar entry, attending a meeting, and navigating to a geographic location;determining at least one action parameter for undertaking the action; andpresenting a prompt to the user to perform the action in accordance with the parameter.
  • 22. The non-transitory computer-readable storage medium of claim 21, wherein the extracting further comprises: converting the recorded voice message from speech to text; and extracting the action from the text.
  • 23. The non-transitory computer-readable storage medium of claim 21, wherein the extracting further comprises extracting the action parameter from the text.
  • 24. The non-transitory computer-readable storage medium of claim 21, wherein the action parameter is a source telephone number associated with the caller.
  • 25. The non-transitory computer-readable storage medium of claim 24, wherein the source telephone number is obtained from a contact book of the user.
  • 26. A cellular telephone comprising: a processor; andmemory coupled to the processor, the memory comprising instructions for:receiving a voice message from a caller to a user of the telephone; andautomatically, without user input:extracting from the recorded voice message a proposed action to be performed by the user, wherein extracting the proposed action comprises determining the proposed action from a plurality of proposed actions, wherein the proposed actions include at least one of: calling back the caller, calling a person other than the caller, storing contact details in a contact book of the user, sending an email to the caller, sending a text message to the caller, visiting a webpage, watching an online video, recommending to download or purchase a software application, reminding to perform a task, performing a task, entering a calendar entry, attending a meeting, and navigating to a geographic location;determining at least one action parameter for undertaking the action; andpresenting a prompt to the user to perform the action in accordance with the parameter.
  • 27. The cellular telephone of claim 26, wherein the extracting further comprises: converting the recorded voice message from speech to text; and extracting the action from the text.
  • 28. The cellular telephone of claim 26, wherein the extracting further comprises extracting the action parameter from the text.
  • 29. The cellular telephone of claim 26, wherein the action parameter is a source telephone number associated with the caller.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 61/783,984, filed on Mar. 14, 2013, entitled SYSTEM AND METHOD FOR PROCESSING VOICEMAIL, which is hereby incorporated by reference in its entity for all purposes. This application is related to U.S. patent application Ser. No. 12/794,650, and U.S. Provisional Patent Application No. 61/184,717, entitled SMART DEDUCTION OF VOICE COMMANDS, filed Jun. 5, 2009, which are both hereby incorporated by reference in their entireties and for all purposes.

US Referenced Citations (1263)
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
4677570 Taki Jun 1987 A
4680805 Scott Jul 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
4829583 Monroe et al. 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
4975975 Filipski Dec 1990 A
4977598 Doddington et al. Dec 1990 A
4992972 Brooks et al. Feb 1991 A
5007098 Kumagai Apr 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
5047614 Bianco Sep 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
5231670 Goldhor 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
5289562 Mizuta et al. Feb 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
5296642 Konishi 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
5377103 Lamberti et al. Dec 1994 A
5377301 Rosenberg et al. Dec 1994 A
5377303 Firman 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
5491758 Bellegarda et al. Feb 1996 A
5491772 Hardwick et al. Feb 1996 A
5493677 Balogh et al. Feb 1996 A
5495604 Harding et al. Feb 1996 A
5500905 Martin et al. Mar 1996 A
5502790 Yi Mar 1996 A
5502791 Nishimura et al. Mar 1996 A
5515475 Gupta et al. May 1996 A
5533182 Bates et al. Jul 1996 A
5536902 Serra et al. Jul 1996 A
5537618 Boulton et al. Jul 1996 A
5544264 Bellegarda et al. Aug 1996 A
5555343 Luther Sep 1996 A
5574823 Hassanein et al. Nov 1996 A
5577164 Kaneko 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
5649060 Ellozy et al. Jul 1997 A
5661787 Pocock Aug 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
5734750 Arai et al. Mar 1998 A
5734791 Acero et al. Mar 1998 A
5737734 Schultz Apr 1998 A
5742705 Parthasarathy Apr 1998 A
5748974 Johnson May 1998 A
5749081 Whiteis May 1998 A
5757979 Hongo et al. May 1998 A
5759101 Von Kohorn Jun 1998 A
5777614 Ando et al. Jul 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
5802526 Fawcett et al. Sep 1998 A
5812697 Sakai et al. Sep 1998 A
5812698 Platt et al. Sep 1998 A
5822730 Roth et al. Oct 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
5841902 Tu Nov 1998 A
5845255 Mayaud Dec 1998 A
5857184 Lynch Jan 1999 A
5860063 Gorin et al. Jan 1999 A
5860064 Henton Jan 1999 A
5862223 Walker 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
5890122 Van 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
5909666 Gould et al. Jun 1999 A
5912952 Brendzel Jun 1999 A
5913193 Huang et al. Jun 1999 A
5915236 Gould et al. Jun 1999 A
5915249 Spencer Jun 1999 A
5920836 Gould et al. Jul 1999 A
5920837 Gould et al. Jul 1999 A
5930408 Seto Jul 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
5950123 Schwelb 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
5983179 Gould et al. Nov 1999 A
5987132 Rowney et al. 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
5991441 Jourjine Nov 1999 A
5999895 Forest Dec 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
6035267 Watanabe et al. Mar 2000 A
6035336 Lu et al. Mar 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
6064767 Muir et al. May 2000 A
6064959 Young et al. May 2000 A
6064960 Bellegarda et al. May 2000 A
6064963 Gainsboro May 2000 A
6070139 Miyazawa et al. May 2000 A
6070147 Harms et al. May 2000 A
6073097 Gould et al. Jun 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 et al. Jun 2000 A
6088671 Gould et al. Jul 2000 A
6088731 Kiraly et al. Jul 2000 A
6092043 Squires et al. Jul 2000 A
6094649 Bowen et al. Jul 2000 A
6101468 Gould et al. Aug 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
6138098 Shieber et al. Oct 2000 A
6144938 Surace et al. Nov 2000 A
6154720 Onishi et al. Nov 2000 A
6161084 Messerly et al. Dec 2000 A
6173251 Ito et al. Jan 2001 B1
6173261 Arai et al. Jan 2001 B1
6173279 Levin et al. Jan 2001 B1
6177905 Welch Jan 2001 B1
6188999 Moody Feb 2001 B1
6195641 Loring et al. Feb 2001 B1
6205456 Nakao Mar 2001 B1
6208956 Motoyama Mar 2001 B1
6208971 Bellegarda et al. Mar 2001 B1
6226403 Parthasarathy May 2001 B1
6233545 Datig May 2001 B1
6233559 Balakrishnan May 2001 B1
6233578 Machihara et al. May 2001 B1
6246981 Papineni et al. Jun 2001 B1
6249606 Kiraly et al. Jun 2001 B1
6259826 Pollard et al. Jul 2001 B1
6260011 Heckerman et al. Jul 2001 B1
6260013 Sejnoha Jul 2001 B1
6260024 Shkedy Jul 2001 B1
6266637 Donovan et al. Jul 2001 B1
6275824 O'Flaherty et al. Aug 2001 B1
6282507 Horiguchi et al. Aug 2001 B1
6285785 Bellegarda et al. Sep 2001 B1
6285786 Seni et al. Sep 2001 B1
6289124 Okamoto 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
6324512 Junqua et al. Nov 2001 B1
6334103 Surace et al. Dec 2001 B1
6345250 Martin Feb 2002 B1
6353794 Davis et al. Mar 2002 B1
6356854 Schubert et al. Mar 2002 B1
6356905 Gershman et al. Mar 2002 B1
6360237 Schulz et al. Mar 2002 B1
6366883 Campbell et al. Apr 2002 B1
6366884 Bellegarda et al. Apr 2002 B1
6397186 Bush et al. May 2002 B1
6401065 Kanevsky et al. Jun 2002 B1
6421672 McAllister et al. Jul 2002 B1
6430551 Thelen et al. Aug 2002 B1
6434522 Tsuboka Aug 2002 B1
6434524 Weber Aug 2002 B1
6438523 Oberteuffer et al. Aug 2002 B1
6442518 Van Thong et al. Aug 2002 B1
6446076 Burkey et al. Sep 2002 B1
6448485 Barile 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
6463128 Elwin Oct 2002 B1
6466654 Cooper et al. Oct 2002 B1
6477488 Bellegarda Nov 2002 B1
6487534 Thelen et al. Nov 2002 B1
6489951 Wong et al. Dec 2002 B1
6493428 Hillier Dec 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
6519566 Boyer et al. Feb 2003 B1
6523061 Halverson et al. Feb 2003 B1
6523172 Martinez-Guerra et al. Feb 2003 B1
6526351 Whitham Feb 2003 B2
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
6556971 Rigsby et al. Apr 2003 B1
6556983 Altschuler et al. Apr 2003 B1
6563769 Van Der Meulen May 2003 B1
6584464 Warthen Jun 2003 B1
6598022 Yuschik Jul 2003 B2
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
6606388 Townsend et al. Aug 2003 B1
6615172 Bennett et al. Sep 2003 B1
6615175 Gazdzinski Sep 2003 B1
6615220 Austin et al. Sep 2003 B1
6622121 Crepy et al. Sep 2003 B1
6622136 Russell Sep 2003 B2
6625583 Silverman et al. Sep 2003 B1
6628808 Bach et al. Sep 2003 B1
6631346 Karaorman et al. Oct 2003 B1
6633846 Bennett et al. Oct 2003 B1
6643401 Kashioka et al. Nov 2003 B1
6647260 Dusse et al. Nov 2003 B2
6650735 Burton et al. Nov 2003 B2
6654740 Tokuda et al. Nov 2003 B2
6658389 Alpdemir Dec 2003 B1
6665639 Mozer et al. Dec 2003 B2
6665640 Bennett et al. Dec 2003 B1
6665641 Coorman et al. Dec 2003 B1
6680675 Suzuki Jan 2004 B1
6684187 Conkie Jan 2004 B1
6691064 Vroman Feb 2004 B2
6691090 Laurila et al. Feb 2004 B1
6691111 Lazaridis et al. Feb 2004 B2
6691151 Cheyer et al. Feb 2004 B1
6694295 Lindholm et al. Feb 2004 B2
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
6720980 Lui et al. Apr 2004 B1
6721728 McGreevy Apr 2004 B2
6721734 Subasic et al. Apr 2004 B1
6728675 Maddalozzo, Jr. et al. Apr 2004 B1
6728729 Jawa et al. Apr 2004 B1
6731312 Robbin May 2004 B2
6735632 Kiraly et al. May 2004 B1
6741264 Lesser May 2004 B1
6742021 Halverson et al. May 2004 B1
6751595 Busayapongchai et al. Jun 2004 B2
6754504 Reed Jun 2004 B1
6757362 Cooper et al. Jun 2004 B1
6757718 Halverson et al. Jun 2004 B1
6760754 Isaacs et al. Jul 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
6839464 Hawkins et al. Jan 2005 B2
6839669 Gould et al. Jan 2005 B1
6839670 Stammler et al. Jan 2005 B1
6842767 Partovi et al. Jan 2005 B1
6847966 Sommer et al. Jan 2005 B1
6847979 Allemang et al. Jan 2005 B2
6851115 Cheyer et al. Feb 2005 B1
6859931 Cheyer et al. Feb 2005 B1
6865533 Addison et al. Mar 2005 B2
6882971 Craner Apr 2005 B2
6885734 Eberle et al. Apr 2005 B1
6895380 Sepe, Jr. May 2005 B2
6895558 Loveland May 2005 B1
6901364 Nguyen et al. May 2005 B2
6901399 Corston et al. May 2005 B1
6912498 Stevens et al. Jun 2005 B2
6912499 Sabourin et al. Jun 2005 B1
6915246 Gusler et al. Jul 2005 B2
6917373 Vong et al. Jul 2005 B2
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
6944593 Kuzunuki et al. Sep 2005 B2
6954755 Reisman Oct 2005 B2
6957076 Hunzinger Oct 2005 B2
6960734 Park Nov 2005 B1
6964023 Maes et al. Nov 2005 B2
6968311 Knockeart et al. Nov 2005 B2
6970935 Maes Nov 2005 B1
6978127 Bulthuis et al. Dec 2005 B1
6980949 Ford Dec 2005 B2
6980955 Okutani et al. Dec 2005 B2
6983251 Umemoto et al. Jan 2006 B1
6985865 Packingham et al. Jan 2006 B1
6988071 Gazdzinski Jan 2006 B1
6996520 Levin Feb 2006 B2
6996531 Korall et al. Feb 2006 B2
6999066 Litwiller Feb 2006 B2
6999914 Boerner et al. Feb 2006 B1
6999927 Mozer et al. Feb 2006 B2
7000189 Dutta et al. Feb 2006 B2
7003463 Maes et al. Feb 2006 B1
7010581 Brown et al. Mar 2006 B2
7020685 Chen et al. Mar 2006 B1
7024363 Comerford et al. Apr 2006 B1
7024364 Guerra et al. Apr 2006 B2
7027974 Busch et al. Apr 2006 B1
7027990 Sussman Apr 2006 B2
7031530 Driggs et al. Apr 2006 B2
7036128 Julia et al. Apr 2006 B1
7050977 Bennett May 2006 B1
7054888 LaChapelle et al. May 2006 B2
7058569 Coorman et al. Jun 2006 B2
7058888 Gjerstad et al. Jun 2006 B1
7058889 Trovato et al. Jun 2006 B2
7062428 Hogenhout et al. Jun 2006 B2
7069220 Coffman et al. Jun 2006 B2
7069560 Cheyer et al. Jun 2006 B1
7084758 Cole Aug 2006 B1
7085723 Ross et al. Aug 2006 B2
7092887 Mozer et al. Aug 2006 B2
7092928 Elad et al. Aug 2006 B1
7093693 Gazdzinski Aug 2006 B1
7107204 Liu et al. Sep 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
7149319 Roeck Dec 2006 B2
7152070 Musick et al. Dec 2006 B1
7166791 Robbin et al. Jan 2007 B2
7174295 Kivimaki Feb 2007 B1
7177794 Mani et al. Feb 2007 B2
7177798 Hsu et al. Feb 2007 B2
7190794 Hinde Mar 2007 B2
7197120 Luehrig et al. Mar 2007 B2
7197460 Gupta et al. Mar 2007 B1
7200559 Wang Apr 2007 B2
7203646 Bennett Apr 2007 B2
7216008 Sakata May 2007 B2
7216073 Lavi et al. May 2007 B2
7216080 Tsiao et al. May 2007 B2
7219063 Schalk et al. May 2007 B2
7219123 Fiechter et al. May 2007 B1
7225125 Bennett et al. May 2007 B2
7228278 Nguyen et al. Jun 2007 B2
7231343 Treadgold et al. Jun 2007 B1
7233790 Kjellberg et al. Jun 2007 B2
7233904 Luisi Jun 2007 B2
7246151 Isaacs et al. Jul 2007 B2
7251313 Miller Jul 2007 B1
7260529 Lengen Aug 2007 B1
7266496 Wang et al. Sep 2007 B2
7269556 Kiss et al. Sep 2007 B2
7277854 Bennett et al. Oct 2007 B2
7290039 Lisitsa et al. Oct 2007 B1
7292579 Morris Nov 2007 B2
7299033 Kjellberg et al. Nov 2007 B2
7302392 Thenthiruperai et al. Nov 2007 B1
7302686 Togawa Nov 2007 B2
7310600 Garner et al. Dec 2007 B1
7315818 Stevens et al. Jan 2008 B2
7319957 Robinson et al. Jan 2008 B2
7324947 Jordan et al. Jan 2008 B2
7349953 Lisitsa et al. Mar 2008 B2
7362738 Taube et al. Apr 2008 B2
7376556 Bennett May 2008 B2
7376632 Sadek et al. May 2008 B1
7376645 Bernard May 2008 B2
7379874 Schmid et al. May 2008 B2
7380203 Keely 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
7460652 Chang Dec 2008 B2
7467087 Gillick et al. Dec 2008 B1
7475010 Chao Jan 2009 B2
7478037 Strong Jan 2009 B2
7483832 Tischer 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
7528713 Singh et al. May 2009 B2
7529671 Rockenbeck et al. May 2009 B2
7529676 Koyama May 2009 B2
7536565 Girish et al. May 2009 B2
7539656 Fratkina et al. May 2009 B2
7543232 Easton, Jr. et al. Jun 2009 B2
7546382 Healey et al. Jun 2009 B2
7546529 Reynar et al. Jun 2009 B2
7548895 Pulsipher Jun 2009 B2
7552055 Lecoeuche Jun 2009 B2
7555431 Bennett Jun 2009 B2
7558730 Davis et al. Jul 2009 B2
7559026 Girish et al. Jul 2009 B2
7561069 Horstemeyer Jul 2009 B2
7571106 Cao et al. Aug 2009 B2
7577522 Rosenberg Aug 2009 B2
7580551 Srihari et al. Aug 2009 B1
7580576 Wang et al. Aug 2009 B2
7599918 Shen et al. Oct 2009 B2
7603381 Burke et al. Oct 2009 B2
7613264 Wells et al. Nov 2009 B2
7617094 Aoki et al. Nov 2009 B2
7620549 Di Cristo et al. Nov 2009 B2
7624007 Bennett Nov 2009 B2
7627481 Kuo et al. Dec 2009 B1
7630901 Omi Dec 2009 B2
7634409 Kennewick et al. Dec 2009 B2
7634413 Kuo et al. Dec 2009 B1
7636657 Ju et al. Dec 2009 B2
7640160 Di Cristo et al. Dec 2009 B2
7647225 Bennett et al. Jan 2010 B2
7649454 Singh et al. Jan 2010 B2
7657424 Bennett Feb 2010 B2
7664558 Lindahl et al. Feb 2010 B2
7664638 Cooper et al. Feb 2010 B2
7672841 Bennett Mar 2010 B2
7673238 Girish et al. Mar 2010 B2
7676026 Baxter, Jr. Mar 2010 B1
7684985 Dominach et al. Mar 2010 B2
7684990 Caskey et al. Mar 2010 B2
7693715 Hwang et al. Apr 2010 B2
7693719 Chu et al. Apr 2010 B2
7693720 Kennewick et al. Apr 2010 B2
7698131 Bennett Apr 2010 B2
7702500 Blaedow Apr 2010 B2
7702508 Bennett Apr 2010 B2
7707027 Balchandran et al. Apr 2010 B2
7707032 Wang et al. Apr 2010 B2
7707267 Lisitsa et al. Apr 2010 B2
7711129 Lindahl et al. May 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
7778632 Kurlander 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
7818291 Ferguson et al. Oct 2010 B2
7822608 Cross, Jr. et al. Oct 2010 B2
7823123 Sabbouh Oct 2010 B2
7826945 Zhang et al. Nov 2010 B2
7827047 Anderson et al. Nov 2010 B2
7831426 Bennett Nov 2010 B2
7840400 Lavi et al. Nov 2010 B2
7840447 Kleinrock et al. Nov 2010 B2
7853444 Wang et al. Dec 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
7885844 Cohen et al. Feb 2011 B1
7890652 Bull et al. Feb 2011 B2
7899666 Varone Mar 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
7920682 Byrne et al. Apr 2011 B2
7920857 Lau et al. Apr 2011 B2
7925525 Chin Apr 2011 B2
7930168 Weng et al. Apr 2011 B2
7930197 Ozzie 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
7999669 Singh et al. Aug 2011 B2
8000453 Cooper et al. Aug 2011 B2
8005664 Hanumanthappa Aug 2011 B2
8005679 Jordan et al. Aug 2011 B2
8015006 Kennewick et al. Sep 2011 B2
8015144 Zheng 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
8050500 Batty et al. Nov 2011 B1
8055502 Clark et al. Nov 2011 B2
8055708 Chitsaz et al. Nov 2011 B2
8065143 Yanagihara Nov 2011 B2
8065155 Gazdzinski Nov 2011 B1
8065156 Gazdzinski Nov 2011 B2
8069046 Kennewick et al. Nov 2011 B2
8069422 Sheshagiri 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
8103510 Sato Jan 2012 B2
8107401 John et al. Jan 2012 B2
8112275 Kennewick et al. Feb 2012 B2
8112280 Lu Feb 2012 B2
8117037 Gazdzinski Feb 2012 B2
8122353 Bouta Feb 2012 B2
8131557 Davis et al. Mar 2012 B2
8138912 Singh et al. Mar 2012 B2
8140335 Kennewick et al. Mar 2012 B2
8150700 Shin et al. Apr 2012 B2
8165886 Gagnon et al. Apr 2012 B1
8166019 Lee et al. Apr 2012 B1
8170790 Lee et al. May 2012 B2
8179370 Yamasani et al. May 2012 B1
8188856 Singh et al. May 2012 B2
8190359 Bourne May 2012 B2
8195467 Mozer et al. Jun 2012 B2
8204238 Mozer Jun 2012 B2
8205788 Gazdzinski et al. Jun 2012 B1
8219406 Yu et al. Jul 2012 B2
8219407 Roy et al. Jul 2012 B1
8219608 alSafadi et al. Jul 2012 B2
8224649 Chaudhari et al. Jul 2012 B2
8239207 Seligman et al. Aug 2012 B2
8285551 Gazdzinski Oct 2012 B2
8285553 Gazdzinski Oct 2012 B2
8290777 Nguyen et al. Oct 2012 B1
8290778 Gazdzinski Oct 2012 B2
8290781 Gazdzinski Oct 2012 B2
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
8406745 Upadhyay Mar 2013 B1
8447612 Gazdzinski May 2013 B2
8498857 Kopparapu et al. Jul 2013 B2
20010029455 Chin et al. Oct 2001 A1
20010030660 Zainoulline Oct 2001 A1
20010047264 Roundtree Nov 2001 A1
20020002039 Qureshey et al. Jan 2002 A1
20020002461 Tetsumoto Jan 2002 A1
20020004703 Gaspard, II Jan 2002 A1
20020010584 Schultz et al. Jan 2002 A1
20020013852 Janik Jan 2002 A1
20020031262 Imagawa et al. Mar 2002 A1
20020032564 Ehsani et al. Mar 2002 A1
20020032751 Bharadwaj Mar 2002 A1
20020035474 Alpdemir Mar 2002 A1
20020042707 Zhao et al. Apr 2002 A1
20020045438 Tagawa et al. Apr 2002 A1
20020046025 Hain Apr 2002 A1
20020046315 Miller et al. Apr 2002 A1
20020052747 Sarukkai May 2002 A1
20020059068 Rose et al. May 2002 A1
20020067308 Robertson Jun 2002 A1
20020069063 Buchner et al. Jun 2002 A1
20020072816 Shdema et al. Jun 2002 A1
20020077817 Atal Jun 2002 A1
20020080163 Morey Jun 2002 A1
20020099552 Rubin et al. Jul 2002 A1
20020103641 Kuo et al. Aug 2002 A1
20020107684 Gao Aug 2002 A1
20020116171 Russell Aug 2002 A1
20020116185 Cooper et al. Aug 2002 A1
20020116189 Yeh et al. Aug 2002 A1
20020128827 Bu et al. Sep 2002 A1
20020133347 Schoneburg et al. Sep 2002 A1
20020135565 Gordon et al. Sep 2002 A1
20020138265 Stevens et al. Sep 2002 A1
20020143533 Lucas et al. Oct 2002 A1
20020143551 Sharma et al. Oct 2002 A1
20020151297 Remboski et al. Oct 2002 A1
20020154160 Hosokawa Oct 2002 A1
20020164000 Cohen et al. Nov 2002 A1
20020169605 Damiba et al. Nov 2002 A1
20020173889 Odinak et al. Nov 2002 A1
20020184189 Hay et al. Dec 2002 A1
20020198714 Zhou Dec 2002 A1
20030001881 Mannheimer et al. Jan 2003 A1
20030016770 Trans et al. Jan 2003 A1
20030020760 Takatsu et al. Jan 2003 A1
20030026392 Brown Feb 2003 A1
20030033153 Olson et al. Feb 2003 A1
20030046401 Abbott et al. Mar 2003 A1
20030074198 Sussman Apr 2003 A1
20030078766 Appelt et al. Apr 2003 A1
20030079038 Robbin et al. Apr 2003 A1
20030083884 Odinak et al. May 2003 A1
20030088414 Huang et al. May 2003 A1
20030097210 Horst et al. May 2003 A1
20030098892 Hiipakka May 2003 A1
20030099335 Tanaka et al. May 2003 A1
20030101045 Moffatt et al. May 2003 A1
20030115060 Junqua et al. Jun 2003 A1
20030115064 Gusler et al. Jun 2003 A1
20030115552 Jahnke et al. Jun 2003 A1
20030117365 Shteyn Jun 2003 A1
20030120494 Jost et al. Jun 2003 A1
20030125927 Seme Jul 2003 A1
20030126559 Fuhrmann Jul 2003 A1
20030135740 Talmor et al. Jul 2003 A1
20030144846 Denenberg et al. Jul 2003 A1
20030157968 Boman et al. Aug 2003 A1
20030158737 Csicsatka Aug 2003 A1
20030167318 Robbin et al. Sep 2003 A1
20030167335 Alexander Sep 2003 A1
20030190074 Loudon et al. Oct 2003 A1
20030197744 Irvine Oct 2003 A1
20030212961 Soin et al. Nov 2003 A1
20030233230 Ammicht et al. Dec 2003 A1
20030233237 Garside et al. Dec 2003 A1
20030234824 Litwiller Dec 2003 A1
20040051729 Borden, IV Mar 2004 A1
20040052338 Celi, Jr. et al. Mar 2004 A1
20040054535 Mackie et al. Mar 2004 A1
20040054690 Hillerbrand et al. Mar 2004 A1
20040055446 Robbin et al. Mar 2004 A1
20040061717 Menon et al. Apr 2004 A1
20040085162 Agarwal et al. May 2004 A1
20040114731 Gillett et al. Jun 2004 A1
20040127241 Shostak Jul 2004 A1
20040135701 Yasuda et al. Jul 2004 A1
20040145607 Alderson Jul 2004 A1
20040162741 Flaxer et al. Aug 2004 A1
20040176958 Salmenkaita et al. Sep 2004 A1
20040186714 Baker Sep 2004 A1
20040193420 Kennewick et al. Sep 2004 A1
20040193426 Maddux et al. Sep 2004 A1
20040199375 Ehsani et al. Oct 2004 A1
20040199387 Wang et al. Oct 2004 A1
20040205671 Sukehiro et al. Oct 2004 A1
20040218451 Said et al. Nov 2004 A1
20040220798 Chi et al. Nov 2004 A1
20040225746 Niell et al. Nov 2004 A1
20040236778 Junqua et al. Nov 2004 A1
20040243419 Wang Dec 2004 A1
20040257432 Girish et al. Dec 2004 A1
20050002507 Timmins et al. Jan 2005 A1
20050015254 Beaman Jan 2005 A1
20050015772 Saare et al. Jan 2005 A1
20050033582 Gadd et al. Feb 2005 A1
20050045373 Born Mar 2005 A1
20050049880 Roth et al. Mar 2005 A1
20050055403 Brittan Mar 2005 A1
20050058438 Hayashi Mar 2005 A1
20050071332 Ortega et al. Mar 2005 A1
20050080625 Bennett et al. Apr 2005 A1
20050080780 Colledge et al. Apr 2005 A1
20050086059 Bennett Apr 2005 A1
20050091118 Fano Apr 2005 A1
20050100214 Zhang et al. May 2005 A1
20050102614 Brockett et al. May 2005 A1
20050102625 Lee et al. May 2005 A1
20050108001 Aarskog May 2005 A1
20050108074 Bloechl et al. May 2005 A1
20050108338 Simske et al. May 2005 A1
20050114124 Liu et al. May 2005 A1
20050114140 Brackett et al. May 2005 A1
20050119897 Bennett et al. Jun 2005 A1
20050125216 Chitrapura et al. Jun 2005 A1
20050125235 Lazay et al. Jun 2005 A1
20050132301 Ikeda Jun 2005 A1
20050143972 Gopalakrishnan et al. Jun 2005 A1
20050149332 Kuzunuki et al. Jul 2005 A1
20050152602 Chen et al. Jul 2005 A1
20050165607 Di Fabbrizio et al. Jul 2005 A1
20050182616 Kotipalli Aug 2005 A1
20050182628 Choi Aug 2005 A1
20050182629 Coorman et al. Aug 2005 A1
20050192801 Lewis et al. Sep 2005 A1
20050196733 Budra et al. Sep 2005 A1
20050201572 Lindahl et al. Sep 2005 A1
20050203747 Lecoeuche Sep 2005 A1
20050203991 Kawamura et al. Sep 2005 A1
20050222843 Kahn et al. Oct 2005 A1
20050228665 Kobayashi et al. Oct 2005 A1
20050273626 Pearson et al. Dec 2005 A1
20050283364 Longe et al. Dec 2005 A1
20050288934 Omi Dec 2005 A1
20050288936 Busayapongchai et al. Dec 2005 A1
20050289463 Wu et al. Dec 2005 A1
20060009973 Nguyen et al. Jan 2006 A1
20060018492 Chiu et al. Jan 2006 A1
20060061488 Dunton Mar 2006 A1
20060067535 Culbert et al. Mar 2006 A1
20060067536 Culbert et al. Mar 2006 A1
20060074660 Waters et al. Apr 2006 A1
20060077055 Basir Apr 2006 A1
20060095846 Nurmi May 2006 A1
20060095848 Naik May 2006 A1
20060106592 Brockett et al. May 2006 A1
20060106594 Brockett et al. May 2006 A1
20060106595 Brockett et al. May 2006 A1
20060111906 Cross et al. May 2006 A1
20060116874 Samuelsson et al. Jun 2006 A1
20060117002 Swen Jun 2006 A1
20060119582 Ng et al. Jun 2006 A1
20060122834 Bennett Jun 2006 A1
20060122836 Cross et al. Jun 2006 A1
20060143007 Koh et al. Jun 2006 A1
20060143576 Gupta et al. Jun 2006 A1
20060153040 Girish et al. Jul 2006 A1
20060156252 Sheshagiri et al. Jul 2006 A1
20060190269 Tessel et al. Aug 2006 A1
20060193518 Dong Aug 2006 A1
20060200253 Hoffberg et al. Sep 2006 A1
20060200342 Corston-Oliver et al. Sep 2006 A1
20060217967 Goertzen et al. Sep 2006 A1
20060221788 Lindahl et al. Oct 2006 A1
20060235700 Wong et al. Oct 2006 A1
20060239471 Mao et al. Oct 2006 A1
20060242190 Wnek Oct 2006 A1
20060262876 LaDue Nov 2006 A1
20060274051 Longe et al. Dec 2006 A1
20060274905 Lindahl et al. Dec 2006 A1
20060277058 J″maev et al. Dec 2006 A1
20060282264 Denny et al. Dec 2006 A1
20060293876 Kamatani et al. Dec 2006 A1
20060293886 Odell et al. Dec 2006 A1
20070006098 Krumm et al. Jan 2007 A1
20070021956 Qu et al. Jan 2007 A1
20070027732 Hudgens Feb 2007 A1
20070033003 Morris Feb 2007 A1
20070038436 Cristoe et al. Feb 2007 A1
20070038609 Wu Feb 2007 A1
20070041361 Iso-Sipila Feb 2007 A1
20070043568 Dhanakshirur et al. Feb 2007 A1
20070044038 Horentrup et al. Feb 2007 A1
20070047719 Dhawan et al. Mar 2007 A1
20070050191 Weider et al. Mar 2007 A1
20070050712 Hull et al. Mar 2007 A1
20070052586 Horstemeyer Mar 2007 A1
20070055514 Beattie et al. Mar 2007 A1
20070055525 Kennewick et al. Mar 2007 A1
20070055529 Kanevsky et al. Mar 2007 A1
20070058832 Hug et al. Mar 2007 A1
20070073540 Hirakawa et al. Mar 2007 A1
20070083467 Lindahl et al. Apr 2007 A1
20070088556 Andrew Apr 2007 A1
20070094026 Ativanichayaphong et al. Apr 2007 A1
20070100635 Mahajan et al. May 2007 A1
20070100790 Cheyer et al. May 2007 A1
20070106674 Agrawal et al. May 2007 A1
20070118377 Badino et al. May 2007 A1
20070118378 Skuratovsky May 2007 A1
20070124149 Shen et al. May 2007 A1
20070135949 Snover et al. Jun 2007 A1
20070157268 Girish et al. Jul 2007 A1
20070174188 Fish Jul 2007 A1
20070180383 Naik Aug 2007 A1
20070182595 Ghasabian Aug 2007 A1
20070185754 Schmidt Aug 2007 A1
20070185917 Prahlad et al. Aug 2007 A1
20070198269 Braho et al. Aug 2007 A1
20070208569 Subramanian et al. Sep 2007 A1
20070208579 Peterson Sep 2007 A1
20070208726 Krishnaprasad et al. Sep 2007 A1
20070211071 Slotznick et al. Sep 2007 A1
20070225980 Sumita Sep 2007 A1
20070239429 Johnson et al. Oct 2007 A1
20070265831 Dinur et al. Nov 2007 A1
20070276651 Bliss et al. Nov 2007 A1
20070276714 Beringer Nov 2007 A1
20070276810 Rosen Nov 2007 A1
20070282595 Tunning et al. Dec 2007 A1
20070286399 Ramamoorthy Dec 2007 A1
20070288241 Cross et al. Dec 2007 A1
20070291108 Huber et al. Dec 2007 A1
20070294263 Punj et al. Dec 2007 A1
20070299664 Peters et al. Dec 2007 A1
20080012950 Lee et al. Jan 2008 A1
20080015864 Ross et al. Jan 2008 A1
20080021708 Bennett et al. Jan 2008 A1
20080034032 Healey et al. Feb 2008 A1
20080040339 Zhou et al. Feb 2008 A1
20080042970 Liang et al. Feb 2008 A1
20080048908 Sato Feb 2008 A1
20080052063 Bennett et al. Feb 2008 A1
20080052073 Goto et al. Feb 2008 A1
20080056579 Guha Mar 2008 A1
20080071544 Beaufays et al. Mar 2008 A1
20080075296 Lindahl et al. Mar 2008 A1
20080077384 Agapi et al. Mar 2008 A1
20080077406 Ganong Mar 2008 A1
20080079566 Singh et al. Apr 2008 A1
20080082332 Mallett et al. Apr 2008 A1
20080082338 O'Neil et al. Apr 2008 A1
20080082390 Hawkins et al. Apr 2008 A1
20080082651 Singh et al. Apr 2008 A1
20080091406 Baldwin et al. Apr 2008 A1
20080109222 Liu May 2008 A1
20080118143 Gordon et al. May 2008 A1
20080120102 Rao May 2008 A1
20080120112 Bennett et al. May 2008 A1
20080120342 Reed et al. May 2008 A1
20080126100 Grost et al. May 2008 A1
20080129520 Lee Jun 2008 A1
20080131006 Oliver Jun 2008 A1
20080133215 Sarukkai Jun 2008 A1
20080133228 Rao Jun 2008 A1
20080140413 Millman et al. Jun 2008 A1
20080140416 Shostak Jun 2008 A1
20080140652 Millman et al. Jun 2008 A1
20080140657 Azvine et al. Jun 2008 A1
20080154612 Evermann et al. Jun 2008 A1
20080157867 Krah Jul 2008 A1
20080165980 Pavlovic et al. Jul 2008 A1
20080189106 Low et al. Aug 2008 A1
20080189114 Fail et al. Aug 2008 A1
20080208585 Ativanichayaphong et al. Aug 2008 A1
20080208587 Ben-David et al. Aug 2008 A1
20080221866 Katragadda et al. Sep 2008 A1
20080221880 Cerra et al. Sep 2008 A1
20080221889 Cerra et al. Sep 2008 A1
20080221903 Kanevsky et al. Sep 2008 A1
20080228463 Mori et al. Sep 2008 A1
20080228490 Fischer et al. Sep 2008 A1
20080228496 Yu et al. Sep 2008 A1
20080240569 Tonouchi Oct 2008 A1
20080247519 Abella et al. Oct 2008 A1
20080249770 Kim et al. Oct 2008 A1
20080253577 Eppolito Oct 2008 A1
20080255845 Bennett Oct 2008 A1
20080256613 Grover Oct 2008 A1
20080270118 Kuo et al. Oct 2008 A1
20080281510 Shahine Nov 2008 A1
20080300878 Bennett Dec 2008 A1
20080313335 Jung et al. Dec 2008 A1
20080319763 Di Fabbrizio et al. Dec 2008 A1
20090003115 Lindahl et al. Jan 2009 A1
20090005891 Batson et al. Jan 2009 A1
20090006100 Badger et al. Jan 2009 A1
20090006343 Platt et al. Jan 2009 A1
20090006488 Lindahl et al. Jan 2009 A1
20090006671 Batson et al. Jan 2009 A1
20090011709 Akasaka et al. Jan 2009 A1
20090012775 El Hady et al. Jan 2009 A1
20090018835 Cooper et al. Jan 2009 A1
20090022329 Mahowald Jan 2009 A1
20090028435 Wu et al. Jan 2009 A1
20090030800 Grois Jan 2009 A1
20090048845 Burckart et al. Feb 2009 A1
20090055179 Cho et al. Feb 2009 A1
20090058823 Kocienda Mar 2009 A1
20090060472 Bull et al. Mar 2009 A1
20090070097 Wu et al. Mar 2009 A1
20090076792 Lawson-Tancred Mar 2009 A1
20090076796 Daraselia Mar 2009 A1
20090077165 Rhodes et al. Mar 2009 A1
20090083047 Lindahl et al. Mar 2009 A1
20090092239 MacWan Apr 2009 A1
20090092260 Powers Apr 2009 A1
20090092261 Bard Apr 2009 A1
20090092262 Costa et al. Apr 2009 A1
20090094033 Mozer et al. Apr 2009 A1
20090100049 Cao Apr 2009 A1
20090106026 Ferrieux Apr 2009 A1
20090112572 Thorn Apr 2009 A1
20090112677 Rhett Apr 2009 A1
20090112892 Cardie et al. Apr 2009 A1
20090123071 Iwasaki May 2009 A1
20090125477 Lu et al. May 2009 A1
20090144049 Haddad et al. Jun 2009 A1
20090146848 Ghassabian Jun 2009 A1
20090150156 Kennewick et al. Jun 2009 A1
20090154669 Wood et al. Jun 2009 A1
20090157401 Bennett Jun 2009 A1
20090164441 Cheyer Jun 2009 A1
20090164655 Pettersson et al. Jun 2009 A1
20090167508 Fadell et al. Jul 2009 A1
20090167509 Fadell et al. Jul 2009 A1
20090171664 Kennewick et al. Jul 2009 A1
20090172542 Girish et al. Jul 2009 A1
20090177461 Ehsani et al. Jul 2009 A1
20090182445 Girish et al. Jul 2009 A1
20090187577 Reznik et al. Jul 2009 A1
20090191895 Singh et al. Jul 2009 A1
20090192782 Drewes Jul 2009 A1
20090204409 Mozer et al. Aug 2009 A1
20090216704 Zheng et al. Aug 2009 A1
20090222488 Boerries et al. Sep 2009 A1
20090228273 Wang et al. Sep 2009 A1
20090234655 Kwon Sep 2009 A1
20090239552 Churchill et al. Sep 2009 A1
20090248182 Logan et al. Oct 2009 A1
20090252350 Seguin Oct 2009 A1
20090253457 Seguin Oct 2009 A1
20090253463 Shin et al. Oct 2009 A1
20090254339 Seguin Oct 2009 A1
20090271109 Lee et al. Oct 2009 A1
20090271175 Bodin et al. Oct 2009 A1
20090271178 Bodin et al. Oct 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
20090306967 Nicolov et al. Dec 2009 A1
20090306980 Shin Dec 2009 A1
20090306981 Cromack et al. Dec 2009 A1
20090306985 Roberts et al. Dec 2009 A1
20090306989 Kaji Dec 2009 A1
20090307162 Bui et al. Dec 2009 A1
20090313026 Coffman et al. Dec 2009 A1
20090319266 Brown et al. Dec 2009 A1
20090326936 Nagashima Dec 2009 A1
20090326938 Marila et al. Dec 2009 A1
20100005081 Bennett Jan 2010 A1
20100023320 Di Cristo et al. Jan 2010 A1
20100030928 Conroy et al. Feb 2010 A1
20100031143 Rao et al. Feb 2010 A1
20100036660 Bennett Feb 2010 A1
20100042400 Block et al. Feb 2010 A1
20100049514 Kennewick et al. Feb 2010 A1
20100057457 Ogata et al. Mar 2010 A1
20100060646 Unsal et al. Mar 2010 A1
20100063825 Williams et al. Mar 2010 A1
20100064113 Lindahl et al. Mar 2010 A1
20100070899 Hunt et al. Mar 2010 A1
20100076760 Kraenzel et al. Mar 2010 A1
20100081456 Singh et al. Apr 2010 A1
20100081487 Chen et al. Apr 2010 A1
20100082970 Lindahl et al. Apr 2010 A1
20100088020 Sano et al. Apr 2010 A1
20100088100 Lindahl Apr 2010 A1
20100100212 Lindahl et al. Apr 2010 A1
20100100384 Ju et al. Apr 2010 A1
20100106500 McKee et al. Apr 2010 A1
20100125460 Mellott et al. May 2010 A1
20100131273 Aley-Raz et al. May 2010 A1
20100138215 Williams Jun 2010 A1
20100138224 Bedingfield, Sr. Jun 2010 A1
20100138416 Bellotti Jun 2010 A1
20100145694 Ju et al. Jun 2010 A1
20100145700 Kennewick et al. Jun 2010 A1
20100146442 Nagasaka et al. Jun 2010 A1
20100161554 Datuashvili et al. Jun 2010 A1
20100185448 Meisel Jul 2010 A1
20100204986 Kennewick et al. Aug 2010 A1
20100217604 Baldwin et al. Aug 2010 A1
20100228540 Bennett Sep 2010 A1
20100231474 Yamagajo et al. Sep 2010 A1
20100235341 Bennett Sep 2010 A1
20100257160 Cao Oct 2010 A1
20100257478 Longe et al. Oct 2010 A1
20100262599 Nitz Oct 2010 A1
20100277579 Cho et al. Nov 2010 A1
20100278320 Arsenault et al. Nov 2010 A1
20100278453 King Nov 2010 A1
20100280983 Cho et al. Nov 2010 A1
20100286985 Kennewick et al. Nov 2010 A1
20100299133 Kopparapu et al. Nov 2010 A1
20100299142 Freeman et al. Nov 2010 A1
20100312547 Van Os et al. Dec 2010 A1
20100312566 Odinak et al. Dec 2010 A1
20100318576 Kim Dec 2010 A1
20100324905 Kurzweil et al. Dec 2010 A1
20100332235 David Dec 2010 A1
20100332280 Bradley et al. Dec 2010 A1
20100332348 Cao Dec 2010 A1
20110010178 Lee et al. Jan 2011 A1
20110022952 Wu et al. Jan 2011 A1
20110047072 Ciurea Feb 2011 A1
20110054901 Qin et al. Mar 2011 A1
20110060584 Ferrucci et al. Mar 2011 A1
20110060807 Martin et al. Mar 2011 A1
20110076994 Kim et al. Mar 2011 A1
20110082688 Kim et al. Apr 2011 A1
20110090078 Kim et al. Apr 2011 A1
20110099000 Rai 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
20110184721 Subramanian et al. Jul 2011 A1
20110184730 LeBeau et al. Jul 2011 A1
20110195758 Damale et al. Aug 2011 A1
20110201387 Paek et al. Aug 2011 A1
20110218855 Cao et al. Sep 2011 A1
20110224972 Millett et al. Sep 2011 A1
20110228913 Cochinwala Sep 2011 A1
20110231182 Weider et al. Sep 2011 A1
20110231188 Kennewick et al. Sep 2011 A1
20110231474 Locker et al. Sep 2011 A1
20110260861 Singh et al. Oct 2011 A1
20110264643 Cao Oct 2011 A1
20110279368 Klein et al. Nov 2011 A1
20110288861 Kurzweil et al. Nov 2011 A1
20110298585 Barry Dec 2011 A1
20110306426 Novak et al. Dec 2011 A1
20110314404 Kotler et al. Dec 2011 A1
20120002820 Leichter Jan 2012 A1
20120016678 Gruber et al. Jan 2012 A1
20120020490 Leichter Jan 2012 A1
20120022787 LeBeau et al. Jan 2012 A1
20120022857 Baldwin et al. Jan 2012 A1
20120022860 Lloyd et al. Jan 2012 A1
20120022868 LeBeau et al. Jan 2012 A1
20120022869 Lloyd et al. Jan 2012 A1
20120022870 Kristjansson et al. Jan 2012 A1
20120022872 Gruber et al. Jan 2012 A1
20120022874 Lloyd et al. Jan 2012 A1
20120022876 LeBeau et al. Jan 2012 A1
20120023088 Cheng et al. Jan 2012 A1
20120034904 LeBeau et al. Feb 2012 A1
20120035908 Lebeau et al. Feb 2012 A1
20120035924 Jitkoff et al. Feb 2012 A1
20120035931 LeBeau et al. Feb 2012 A1
20120035932 Jitkoff et al. Feb 2012 A1
20120042343 Laligand et al. Feb 2012 A1
20120078627 Wagner Mar 2012 A1
20120084086 Gilbert et al. Apr 2012 A1
20120108221 Thomas et al. May 2012 A1
20120136572 Norton May 2012 A1
20120137367 Dupont et al. May 2012 A1
20120149394 Singh et al. Jun 2012 A1
20120150580 Norton Jun 2012 A1
20120173464 Tur et al. Jul 2012 A1
20120185237 Gajic et al. Jul 2012 A1
20120197998 Kessel et al. Aug 2012 A1
20120214517 Singh et al. Aug 2012 A1
20120221339 Wang et al. Aug 2012 A1
20120245719 Story, Jr. et al. Sep 2012 A1
20120245944 Gruber et al. Sep 2012 A1
20120265528 Gruber et al. Oct 2012 A1
20120271625 Bernard Oct 2012 A1
20120271635 Ljolje Oct 2012 A1
20120271676 Aravamudan et al. Oct 2012 A1
20120284027 Mallett et al. Nov 2012 A1
20120296649 Bansal et al. Nov 2012 A1
20120309363 Gruber et al. Dec 2012 A1
20120310642 Cao et al. Dec 2012 A1
20120310649 Cannistraro et al. Dec 2012 A1
20120311583 Gruber et al. Dec 2012 A1
20120311584 Gruber et al. Dec 2012 A1
20120311585 Gruber et al. Dec 2012 A1
20120330661 Lindahl Dec 2012 A1
20130006638 Lindahl Jan 2013 A1
20130007648 Gamon Jan 2013 A1
20130110505 Gruber et al. May 2013 A1
20130110515 Guzzoni et al. May 2013 A1
20130110518 Gruber et al. May 2013 A1
20130110519 Cheyer et al. May 2013 A1
20130110520 Cheyer et al. May 2013 A1
20130111348 Gruber et al. May 2013 A1
20130111487 Cheyer et al. May 2013 A1
20130115927 Gruber et al. May 2013 A1
20130117022 Chen et al. May 2013 A1
20130185074 Gruber et al. Jul 2013 A1
20130185081 Cheyer et al. Jul 2013 A1
20130325443 Begeja et al. Dec 2013 A1
Foreign Referenced Citations (73)
Number Date Country
681573 Apr 1993 CH
202035047 Nov 2011 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
0558312 Sep 1993 EP
0559349 Sep 1993 EP
0570660 Nov 1993 EP
0863453 Sep 1998 EP
0559349 Jan 1999 EP
0981236 Feb 2000 EP
1229496 Aug 2002 EP
1245023 Oct 2002 EP
1311102 May 2003 EP
1315084 May 2003 EP
1315086 May 2003 EP
2109295 Oct 2009 EP
2293667 Apr 1996 GB
60-19965 Jan 1994 JP
7-199379 Aug 1995 JP
11-6743 Jan 1999 JP
2001-125896 May 2001 JP
2002-14954 Jan 2002 JP
2002-24212 Jan 2002 JP
2003-517158 May 2003 JP
2004-152063 May 2004 JP
2007-4633 Jan 2007 JP
2008-236448 Oct 2008 JP
2008-271481 Nov 2008 JP
2009-36999 Feb 2009 JP
2009-294913 Dec 2009 JP
10-0757496 Sep 2007 KR
10-0776800 Nov 2007 KR
10-0801227 Feb 2008 KR
10-0810500 Mar 2008 KR
10-2008-0109322 Dec 2008 KR
10-2009-0086805 Aug 2009 KR
10-0920267 Oct 2009 KR
10-2010-0119519 Nov 2010 KR
10-1032792 May 2011 KR
10-2011-0113414 Oct 2011 KR
1014847 Oct 2001 NL
1995002221 Jan 1995 WO
1997010586 Mar 1997 WO
1997026612 Jul 1997 WO
1998041956 Sep 1998 WO
1999001834 Jan 1999 WO
1999008238 Feb 1999 WO
1999056227 Nov 1999 WO
2000029964 May 2000 WO
2000060435 Oct 2000 WO
2000060435 Apr 2001 WO
2001035391 May 2001 WO
2002073603 Sep 2002 WO
20041008801 Jan 2004 WO
2006129967 Dec 2006 WO
2007080559 Jul 2007 WO
2008085742 Jul 2008 WO
2008109835 Sep 2008 WO
2010075623 Jul 2010 WO
2011088053 Jul 2011 WO
2011133543 Oct 2011 WO
2012167168 Dec 2012 WO
Non-Patent Literature Citations (464)
Entry
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US2010/037378, dated Aug. 25, 2010, 14 pages.
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US2012/040571, dated Nov. 16, 2012, 14 pages.
Extended European Search Report and Search Opinion received for European Patent Application No. 12185276.8, dated Dec. 18, 2012, 4 pages.
Extended European Search Report received for European Patent Application No. 12186663.6, dated Jul. 16, 2013, 6 pages.
“Top 10 Best Practices for Voice User Interface Design” available at <http://www.developer.com/voice/article.php/1567051/Top-10-Best-Practices-for-Voice-UserInterface-Design.htm>, Nov. 1, 2002, 4 pages.
Apple Computer, “Knowledge Navigator”, published by Apple Computer no later than 2008, as depicted in “Exemplary Screenshots from video entitled “Knowledge Navigator””, 2008, 7 pages.
Bellegarda, Jerome R., “Latent Semantic Mapping”, IEEE Signal Processing Magazine, vol. 22, No. 5, Sep. 2005, pp. 70-80.
Car Working Group, “Hands-Free Profile 1.5 HFP1.5_SPEC”, Bluetooth Doc, available at <www.bluetooth.org>, Nov. 25, 2005, 93 pages.
Cohen et al., “Voice User Interface Design,”, Excerpts from Chapter 1 and Chapter 10, 2004, 36 pages.
“Mel Scale”, Wikipedia the Free Encyclopedia, Last modified on Oct. 13, 2009 Available and retrieved on Jul. 28, 2010, online at <http://en.wikipedia.org/wiki/Mel_scale>, 2 pages.
“Minimum Phase”, Wikipedia the free Encyclopedia, Last modified on Jan. 12, 2010 and retrieved on Jul. 28, 2010, Available online at <http://en.wikipedia.org/wiki/Minimum_phase>, 8 pages.
Busemann et al., “Natural Language Diaglogue Service for Appointment Scheduling Agents”, Technical Report RR-97-02, Deutsches Forschungszentrum fur Kunstliche Intelligenz GmbH, 1997, 8 pages.
Acero et al., “Environmental Robustness in Automatic Speech Recognition”, International Conference on Acoustics, Speech and Signal Processing (ICASSP'90), Apr. 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.
Agnas et al., “Spoken Language Translator: First-Year Report”, SICS (ISSN 0283-3638), SRI and Telia Research AB, Jan. 1994, 161 pages.
Ahlbom 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, 1991, 10 pages.
Alfred App, “Alfred”, Available online at <http://www.alfredapp.com/>, retrieved on Feb. 8, 2012, 5 pages.
Allen, J., “Natural Language Understanding”, 2nd Edition, The Benjamin/Cummings Publishing Company, Inc., 1995, 671 pages.
Alshawi et al., “CLARE: A Contextual Reasoning and Co-operative Response Framework for the Core Language Engine”, SRI International, Cambridge Computer Science Research Centre, Cambridge, Dec. 1992, 273 pages.
Alshawi et al., “Declarative Derivation of Database Queries from Meaning Representations”, Proceedings of the BANKAI Workshop on Intelligent Information Access, Oct. 1991, 12 pages.
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, Tokyo, 13 pages.
Alshawi, H., “Translation and Monotonic Interpretation/Generation”, SRI International, Cambridge Computer Science Research Centre, Cambridge, Available online at <http://www.cam.sri.com/tr/crc024/paper.ps.Z 1992>, Jul. 1992, 18 pages.
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 1995, pp. 628-631.
Anderson et al., “Syntax-Directed Recognition of Hand-Printed Two-Dimensional Mathematics”, Proceedings of Symposium on Interactive Systems for Experimental Applied Mathematics: Proceedings of the Association for Computing Machinery Inc. Symposium, 1967, 12 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, pp. 60-62.
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, Menlo Park, California, 1995, 12 pages.
Apple Computer, “Guide Maker User's Guide”, Apple Computer, Inc., Apr. 27, 1994, 8 pages.
Apple Computer, “Introduction to Apple Guide”, Apple Computer, Inc., Apr. 28, 1994, 20 pages.
Archbold et al., “A Team User's Guide”, SRI International, Dec. 21, 1981, 70 pages.
Asanovic et al., “Experimental Determination of Precision Requirements for Back-Propagation Training of Artificial Neural Networks”, 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. 7, Jul. 1989, 8 pages.
Bahl et al., “Acoustic Markov Models Used in the Tangora Speech Recognition System”, Proceeding of International Conference on Acoustics, Speech and Signal Processing (ICASSP'88), vol. 1, Apr. 1988, 4 pages.
Bahl et al., “Large Vocabulary Natural Language Continuous Speech Recognition”, Proceedings of 1989 International Conference on Acoustics, Speech and Signal Processing, vol. 1, May, 1989, 6 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”, Proceeding of International Conference on Acoustics, Speech and Signal Processing (ICASSP'88), vol. 1, Apr. 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, Feb. 22, 1993, 9 pages.
Bear et al., “Detection and Correction of Repairs in Human-Computer Dialog”, SRI International, May 1992, 11 pages.
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, Menlo Park, California, 1998, 11 pages.
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, Jerome 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 1998, 5 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. 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”, 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 1995, 4 pages.
Bellegarda, Jerome R., “Exploiting Latent Semantic Information in Statistical Language Modeling”, Proceedings of the IEEE, vol. 88, No. 8, Aug. 2000, 18 pages.
Bellegarda, Jerome 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, Jerome 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.
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.
Appelt et al., “SRI: Description of the JV-FASTUS System used for MUC-5”, SRI International, Artificial Intelligence Center, 1993, 19 pages.
Zue et al., “The Voyager Speech Understanding System: Preliminary Development and Evaluation”, Proceedings of IEEE, International Conference on Acoustics, Speech and Signal Processing, 1990, 4 pages.
Zue, Victor W., “Toward Systems that Understand Spoken Language”, ARPA Strategic Computing Institute, Feb. 1994, 9 pages.
Lyons et al., “Augmenting Conversations Using Dual-Purpose Speech”, available at <http://research.nokia.com/files/2004-LYONS-UIST04-DPS.pdf>, 2004, 10 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, available at <http://adam.cheyer.com/papers/oaa.pdf>>, retrieved from internet on Jan.-Mar. 1999.
Schnelle, D., “Context Aware Voice User Interfaces for Workflow Support”, Dissertation paper, Aug. 27, 2007, 254 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, 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.
Belvin et al., “Development of the HRL Route Navigation Dialogue System”, 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”, Proceedings of Eurospeech, 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”, From: AAA-80 Proceedings, Copyright 1980, AAAI, 1980, 8 pages.
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. 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., “Error-Correction Detection and Response Generation in a Spoken Dialogue System”, Speech Communication, vol. 45, 2005, pp. 271-288.
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, vol. 18, No. 2, 1997, 10 pages.
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, 4 pages.
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. 1990, 8 pages.
Bussler et al., “Web Service Execution Environment (WSMX)”, Retrieved from Internet on Sep. 17, 2012, Available online at <http://wwww3.org/Submission/WSMX>, Jun. 3, 2005, 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”, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, May 1996, 6 pages.
Carter, D., “Lexical Acquisition in the Core Language Engine”, Proceedings of the Fourth Conference of the European Chapter of the Association for Computational Linguistics, 1989, 8 pages.
Carter et al., “The Speech-Language Interface in the Spoken Language Translator”, SRI International, Nov. 23, 1994, 9 pages.
Cawley, Gavin C. “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), Paris, 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. 1993, 6 pages. (3 pages of English Translation and 3 pages of Office Action).
Chen, Yi, “Multimedia Siri Finds and Plays Whatever You Ask for”, PSFK Report, Feb. 9, 2012, 9 pages.
Cheyer, Adam, “A Perspective on AI & Agent Technologies for SCM”, VerticalNet Presentation, 2001, 22 pages.
Cheyer, Adam, “About Adam Cheyer”, Available online at <http://www.adam.cheyer.com/about.html>, retrieved on Sep. 17, 2012, 2 pages.
Cheyer et al., “Multimodal Maps: An Agent-Based Approach”, International Conference on Co-operative Multimodal Communication, 1995, 15 pages.
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/>, retrieved on Feb. 21, 1998, 25 pages.
Codd, E. F., “Databases: Improving Usability and Responsiveness—How About Recently”, Copyright 1978, Academic Press, Inc., 1978, 28 pages.
Cohen et al., “An Open Agent Architecture”, Available Online at <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.480>, 1994, 8 pages.
Coles et al., “Chemistry Question-Answering”, SRI International, Jun. 1969, 15 pages.
Coles et al., “Techniques for Information Retrieval Using an Inferential Question-Answering System with Natural-Language Input”, SRI International, Nov. 1972, 198 Pages.
Coles et al., “The Application of Theorem Proving to Information Retrieval”, SRI International, Jan. 1971, 21 pages.
Conklin, Jeff, “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, 24 pages.
Craig et al., “Deacon: Direct English Access and Control”, AFIPS Conference Proceedings, vol. 19, San Francisco, Nov. 1966, 18 pages.
Cutkosky et al., “PACT: An Experiment in Integrating Concurrent Engineering Systems”, Journal & Magazines, Computer, vol. 26, No. 1, Jan. 1993, 14 pages.
Dar et al., “DTL's DataSpot: Database Exploration Using Plain Language”, Proceedings of the 24th VLDB Conference, New York, 1998, 5 pages.
Davis et al., “A Personal Handheld Multi-Modal Shopping Assistant”, IEEE, 2006, 9 pages.
Decker et al., “Designing Behaviors for Information Agents”, The Robotics Institute, Carnegie-Mellon University, Paper, Jul. 1996, 15 pages.
Decker et al., “Matchmaking and Brokering”, The Robotics Institute, Carnegie-Mellon University, Paper, May 1996, 19 pages.
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”, Prentice Hall, ISBN: 0-02-328301-7, 1987, 14.
Digital Equipment Corporation, “Open Vms Software Overview”, Software Manual, Dec. 1995, 159 pages.
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, 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, Mar. 1992, 12 pages.
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. 1994, 4 pages.
Johnson, Julia Ann., “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”, Available online at <http://lecture.cs.buu.ac.th/-f50353/Document/gfp.pdf>, May 12, 1994, 66 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”, Proceedings of the 5th RIAO Conference on Computer Assisted Information Searching on the Internet, 1997, 7 pages.
Katz, S. 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.
Katz et al., “Exploiting Lexical Regularities in Designing Natural Language Systems”, 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”, Proceedings of the ACL Workshop on Natural Language Processing and Information Retrieval (NLP&IR), Oct. 2000, 11 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.
Kitano, H., “PhiDM-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, 13 pages.
Klatt et al., “Linguistic Uses of Segmental Duration in English: Acoustic and Perpetual Evidence”, Journal of the Acoustical Society of America, vol. 59, No. 5, May 1976, 16 pages.
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. 1990, 4 pages.
Kubala et al., “The Hub and Spoke Paradigm for CSR Evaluation”, Proceedings of the Spoken Language Technology Workshop, Mar. 1994, 9 pages.
Laird et al., “SOAR: An Architecture for General Intelligence”, Artificial Intelligence, vol. 33, 1987, pp. 1-64.
Langley 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.eduk-softagents/larks.html> retrieved on Mar. 15, 2013, 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. 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”, IEEE International Conference of Acoustics, Speech and Signal Processing, vol. 2, 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. 1994, 5 pages.
Lee, K. F., “Large-Vocabulary Speaker-Independent Continuous Speech Recognition: The SPHINX System”, Partial Fulfillment of the Requirements for the Degree of Doctorof Philosophy, Computer Science Department, Carnegie Mellon University, Apr. 1988, 195 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, No. 3 & 4, Nov. 1991, 16 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, Jan. 2005, pp. 231-238.
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”, Available on line at <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.272>, 1999, 4 pages.
Lin et al., “A New Framework for Recognition of Mandarin Syllables with Tones Using Sub-syllabic Unites”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-93), Apr. 1993, 4 pages.
Linde et al., “An Algorithm for Vector Quantizer Design”, IEEE Transactions on Communications, vol. 28, No. 1, Jan. 1980, 12 pages.
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. 1992, 4 pages.
Logan et al., “Mel Frequency Cepstral Co-efficients for Music Modeling”, International Symposium on Music Information Retrieval, 2000, 2 pages.
Lowerre, B. T., “The-Harpy Speech Recognition System”, Doctoral Dissertation, Department of Computer Science, Carnegie Mellon University, Apr. 1976, 20 pages.
Maghbouleh, Arman, “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., “Building and Using Practical Agent Applications”, SRI International, PAAM Tutorial, 1998, 78 pages.
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. 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 Eighth International Joint Conference on Artificial Intelligence, Technical Note 293, Aug. 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. 1999, 7 pages.
McGuire et al., “Shade: Technology for Knowledge-Based Collaborative Engineering”, Journal of Concurrent Engineering Applications and Research (CERA), 1993, 18 pages.
Rabiner et al., “Fundamental of Speech Recognition”, 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., “ClearAccess 2.0 Allows SQL Searches Off-Line (Structured Query Language) (ClearAccess Corp. Preparing New Version of Data-Access Application with Simplified User Interface, New Features) (Product Announcement)”, MacWeek, vol. 6, No. 41, Nov. 16, 1992, 2 pages.
Ravishankar, Mosur K., “Efficient Algorithms for Speech Recognition”, Doctoral Thesis Submitted to School of Computer Science, Computer Science Division, Carnegie Mellon University, Pittsburgh, May 15, 1996, 146 pages.
Rayner, M., “Abductive Equivalential Translation and its Application to Natural Language Database Interfacing”, Dissertation Paper, SRI International, Sep. 1993, 162 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, Manny, “Linguistic Domain Theories: Natural-Language Database Interfacing from First Principles”, SRI International, Cambridge, 1993, 11 pages.
Rayner et al., “Spoken Language Translation with Mid-90's Technology: A Case Study”, Eurospeech, ISCA, Available online at <http://citeseerxist.psu.edu/viewdoc/summary?doi=10.1.1.54.8608>, 1993, 4 pages.
Remde et al., “SuperBook: An Automatic Tool for Information Exploration-Hypertext?”, In Proceedings of Hypertext, 87 Papers, Nov. 1987, 14 pages.
Reynolds, C. F., “On-Line Reviews: A New Application of the HICOM Conferencing System”, IEEE 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 online 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 1989, 4 pages.
Riley, M D., “Tree-Based Modelling of Segmental Durations”, Talking Machines Theories, Models and Designs, 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”, VerticalNet 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 Approach”, 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. 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.
Savoy, J., “Searching Information in Hypertext Systems Using Multiple Sources of Evidence”, International Journal of Man-Machine Studies, vol. 38, No. 6, Jun. 1996, 15 pages.
Scagliola, C., “Language Models and Search Algorithms for Real-Time Speech Recognition”, International Journal of Man-Machine Studies, vol. 22, No. 5, 1985, 25 pages.
Schmandt et al., “Augmenting a Window System with Speech Input”, IEEE Computer Society, Computer, vol. 23, No. 8, Aug. 1990, 8 pages.
Schütze, H., “Dimensions of Meaning”, Proceedings of Supercomputing'92 Conference, Nov. 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, vol. 2, 1996, 4 pages.
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”, Proceedings of the Ninth Conference on Artificial Intelligence for Applications, Mar. 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., “InfoHarness: Use of Automatically Generated Metadata for Search and Retrieval of Heterogeneous Information”, Proceedings of CAiSE'95, Finland, 1995, 14 pages.
Sigurdsson et al., “Mel Frequency Cepstral Co-efficients: An Evaluation of Robustness of MP3 Encoded Music”, Proceedings of the 7th International Conference on Music Information Retrieval, 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. 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 Distributiom”, Open Agent Architecture (OAA), 1999, 2 pages.
SRI2009, “SRI Speech: Products: Software Development Kits: EduSpeak”, Available online at <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, Mark E., “A Nonclausal Connection—Graph Resolution Theorem-Proving Program”, Proceedings of AAAI'82, 1982, 5 pages.
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, 1996, 31 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”, 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, Kevin C., “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. 1994, 9 pages.
Van Santen, J. P.H., “Contextual Effects on Vowel Duration”, Journal Speech Communication, vol. 11, No. 6, Dec. 1992, pp. 513-546.
Vepa et al., “New Objective Distance Measures for Spectral Discontinuities in Concatenative Speech Synthesis”, Proceedings of the IEEE 2002 Workshop on Speech Synthesis, 2002, 4 pages.
Verschelde, Jan, “MATLAB Lecture 8. Special Matrices in MATLAB”, UIC, Dept. of Math, Stat. & CS, MCS 320, Introduction to Symbolic Computation, 2007, 4 pages.
Vingron, Martin, “Near-Optimal Sequence Alignment”, Current Opinion in Structural Biology, vol. 6, No. 3, 1996, pp. 346-352.
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 Video, Available online at <http://www.youtube.com/watch?v=Vqs8XfXxgz4>, Oct. 2010, 2 pages.
Voiceassist, “Send Text, Listen to and Send E-Mail by Voice”, YouTube Video, Available online at <http://www.youtube.com/watch?v=0tEU61nHHA4>, Jul. 30, 2009, 1 page.
VoiceontheGo, “Voice on the Go (BlackBerry)”, YouTube Video, available online at <http://www.youtube.com/watch?v=pJqpWgQS98w>, Jul. 27, 2009, 1 page.
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”, ACM, vol. 21, No. 7, 1978, 14 pages.
Ward et al., “A Class Based Language Model for Speech Recognition”, IEEE, 1996, 3 pages.
Ward et al., “Recent Improvements in the CMU Spoken Language Understanding System”, ARPA Human Language Technology Workshop, 1994, 4 pages.
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 et al., “An Efficient Easily Adaptable System for Interpreting Natural Language Queries”, American Journal of Computational Linguistics, vol. 8, No. 3-4 , 1982, 11 pages.
Weizenbaum, J., “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, 10 pages.
Werner et al., “Prosodic Aspects of Speech, Universite de Lausanne”, Fundamentals of Speech Synthesis and Speech Recognition: Basic Concepts, State of the Art and Future Challenges, 1994, 18 pages.
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. 1999, 22 pages.
Wolff, M., “Post Structuralism and the Artful Database: Some Theoretical Considerations”, Information Technology and Libraries, vol. 13, No. 1, Mar. 1994, 10 pages.
Wu, M., “Digital Speech Processing and Coding”, Multimedia Signal Processing, Lecture-2 Course Presentation, University of Maryland, College Park, 2003, 8 pages.
Wu et al., “KDA: A Knowledge-Based Database Assistant”, Proceeding of the Fifth International Conference on Engineering (IEEE Cat.No. 89CH2695-5), 1989, 8 pages.
Wu, M., “Speech Recognition, Synthesis, and H.C.I.”, Multimedia Signal Processing, Lecture-3 Course Presentation, University of Maryland, College Park, 2003, 11 pages.
Wyle, M. F., “A Wide Area Network Information Filter”, Proceedings of First International Conference on Artificial Intelligence on Wall Street, Oct. 1991, 6 pages.
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”, Computer Magazine, IEEE, Jan. 1988, 16 pages.
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, 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.
Zhao et al., “Intelligent Agents for Flexible Workflow Systems”, Proceedings of the Americas Conference on Information Systems (AMCIS), Oct. 1998, 4 pages.
Zovato et al., “Towards Emotional Speech Synthesis: A Rule based Approach”, Proceedings of 5th Isca Speech Synthesis Workshop—Pittsburgh, 2004, pp. 219-220.
Zue, Victor, “Conversational Interfaces: Advances and Challenges”, Spoken Language System Group, Sep. 1997, 10 pages.
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, 13 pages.
Zue et al., “Pegasus: A Spoken Dialogue Interface for On-Line Air Travel Planning”, Speech Communication, vol. 15, 1994, 10 pages.
Meng et al., “Wheels: A Conversational System in the Automobile Classified Domain”, Proceedings of Fourth International Conference on Spoken Language, ICSLP 96, vol. 1, Oct. 1996, 4 pages.
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”, Available online at <http://www.iicm.tugraz.at/thesis/cguetl_diss/literatur/Kapitel06/References/Milstead_et_al._1999/metadata.html>, Jan. 1999, 18 pages.
Milward et al., “D2.2: Dynamic Multimodal Interface Reconfiguration, Talk and Look: Tools for Ambient Linguistic Knowledge”, Available online at <http://www.ihmc.us/users/nblaylock!Pubs/Files/talk d2.2.pdf>, 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”, Advances in Database Technology, Lecture Notes in Computer Science, vol. 1777, 2000, pp. 1-15.
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, Artificial Intelligence Center, 1995, 4 pages.
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 et al., “SRI's Experience with the ATIS Evaluation”, Proceedings of the Workshop on Speech and Natural Language, Jun. 1990, pp. 147-148.
Moore et al., “The Information Warfare 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., “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. 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, Apple Computer, Inc., Addison-Wesley Publishing Company, Inc., 1990, 17 pages.
Mozer, Michael C., “An Intelligent Environment must be Adaptive”, IEEE Intelligent Systems, 1999, pp. 11-13.
Muhlhauser, Max, “Context Aware Voice User Interfaces for Workflow Support”, 2007, 254 pages.
Murty et al., “Combining Evidence from Residual Phase and MFCC Features for Speaker Recognition”, IEEE Signal Processing Letters, vol. 13, No. 1, Jan. 2006, 4 pages.
Murveit et al., “Integrating Natural Language Constraints into HMM-Based Speech Recognition”, International Conference on Acoustics, Speech and Signal Processing, Apr. 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. 2010, 4 pages.
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 1996, 6 pages.
Noth et al., “Verbmobil: The Use of Prosody in the Linguistic Components of a Speech Understanding System”, IEEE Transactions on Speech and Audio Processing, vol. 8, No. 5, Sep. 2000, pp. 519-532.
Odubiyi et al., “SAIRE—A Scalable Agent-Based Information Retrieval Engine”, Proceedings of the First International Conference on Autonomous Agents, 1997, 12 pages.
Owei et al., “Natural Language Query Filtration in the Conceptual Query Language”, IEEE, 1997, pp. 539-549.
Pannu et al., “A Learning Personal Agent for Text Filtering and Notification”, Proceedings of the International Conference of Knowledge Based Systems, 1996, pp. 1-11.
Papadimitriou et al., “Latent Semantic Indexing: A Probabilistic Analysis”, Available online at <http://citeseerx.ist.psu.edu/messaqes/downloadsexceeded.html>, Nov. 14, 1997, 21 pages.
Parson, T. W., “Voice and Speech Processing”, Pitch and Formant Estimation, McGraw-Hill, Inc., ISBN: 0-07-0485541-0, 1987, 15 pages.
Parsons, T. W., “Voice and Speech Processing”, Linguistics and Technical Fundamentals, Articulatory Phonetics and Phonemics, McGraw-Hill, Inc., ISBN: 0-07-0485541-0, 1987, 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.
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., “Declaration of Christopher Schmandt Regarding the MIT Galaxy System”, West Interactive Corp., 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 <http://www.cam.sri.com/tr/crc042/paper.ps.Z>, 1993, 8 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, Innsbruck, Austria, Jun. 2005, 6 pages.
Donovan, R. E., “A New Distance Measure for Costing Spectral Discontinuities in Concatenative Speech Synthesisers”, Available online at <http://citeseerx.ist.osu.edu/viewdoc/summarv?doi=10.1.1.21.6398>, 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”, Proc. Workshop on Specifying and Implementing Conversation Policies, Autonomous Agents'99 Conference, 1999, pp. 1-10.
Epstein et al., “Natural Language Access to a Melanoma Data Base”, SRI International, 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. 2006, 127 pages.
Evi, “Meet Evi: The One Mobile Application that Provides Solutions for your Everyday Problems”, Feb. 2012, 3 pages.
Exhibit 1, “Natural Language Interface Using Constrained Intermediate Dictionary of Results”, List of Publications Manually Reviewed for the Search of U.S. Pat. No. 7,177,798, Mar. 22, 2013, 1 page.
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 Assistance”, CiteSeerx, Proceedings of IUI'05, Jan. 2005, pp. 90-97.
Glass, Alyssa, “Explaining Preference Learning”, CiteSeerx, 2006, pp. 1-5.
Glass et al., “Multilingual Language Generation Across Multiple Domains”, International Conference on Spoken Language Processing, Japan, Sep. 1994, 5 pages.
Glass et al., “Multilingual Spoken-Language Understanding in the Mit Voyager System”, Available online at <http://groups.csail.mit.edu/sls/publications/1995/speechcomm95-voyager.pdf>, Aug. 1995, 29 pages.
Goddeau et al., “A Form-Based Dialogue Manager for Spoken Language Applications”, Available online at <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, 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. 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.
Grosz et al., “Dialogic: A Core Natural-Language Processing System”, SRI International, Nov. 1982, 17 pages.
Grosz et al., “Research on Natural-Language Processing at SRI”, SRI International, Nov. 1981, 21 pages.
Grosz, B., “Team: A Transportable Natural-Language Interface System”, Proceedings of the First Conference on Applied Natural Language Processing, 1983, 7 pages.
Grosz et al., “TEAM: An Experiment in the Design of Transportable Natural-Language Interfaces”, Artificial Intelligence, vol. 32, 1987, 71 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, Thomas R., “A Translation Approach to Portable Ontology Specifications”, Knowledge Acquisition, vol. 5, No. 2, Jun. 1993, pp. 199-220.
Gruber et al., “An Ontology for Engineering Mathematics”, Fourth International Conference on Principles of Knowledge Representation and Reasoning, Available online at <http://www-ksl.stanford.edu/knowledge-sharing/papers/engmath.html>, 1994, pp. 1-22.
Gruber, Thomas R., “Automated Knowledge Acquisition for Strategic Knowledge”, Machine Learning, vol. 4, 1989, pp. 293-336.
Gruber, Tom, “Big Think Small Screen: How Semantic Computing in the Cloud will Revolutionize the Consumer Experience on the Phone”, Keynote Presentation at Web 3.0 Conference, Jan. 2010, 41 pages.
Gruber, Tom, “Collaborating Around Shared Content on the WWW, W3C Workshop on WWW and Collaboration”, Available online at <http://wwww3.org/Collaboration/Workshop/Proceedings/P9.html>, Sep. 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 online at <http://tomgruber.org/writing/aaai-ss08.htm>, Mar. 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 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, Tom, “Helping Organizations Collaborate, Communicate, and Learn”, Presentation to NASA Ames Research, Available online 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 online at <http://tomgruber.org/writing/semtech08.htm>, May 20, 2008, pp. 1-40.
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, 24 pages.
Gruber, Tom, “It Is What It Does: The Pragmatics of Ontology for Knowledge Sharing”, Proceedings of the International CIDOC CRM Symposium, Available online at <http://tomgruber.org/writing/cidoc-ontology.htm>, Mar. 26, 2003, 21 pages.
Gruber et al., “Machine-Generated Explanations of Engineering Models: A Compositional Modeling Approach”, Proceedings of 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, Tom, “Ontologies, Web 2.0 and Beyond”, Ontology Summit, Available online at <http://tomgruber.org/writing/ontolog-social-web-keynote.htm>, Apr. 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 et al., “Toward a Knowledge Medium for Collaborative Product Development”, Proceedings of the Second International Conference on Artificial Intelligence in Design, Jun. 1992, pp. 1-19.
Gruber, Thomas R., “Toward Principles for the Design of Ontologies used for Knowledge Sharing?”, International Journal of Human-Computer Studies, vol. 43, No. 5-6, Nov. 1995, pp. 907-928.
Gruber, Tom, “Where the Social Web Meets the Semantic Web”, Presentation at the 5th International Semantic Web Conference, Nov. 2006, 38 pages.
Guida et al., “NLI: A Robust Interface for Natural Language Person-Machine Communication”, International Journal of Man-Machine Studies, vol. 17, 1982, 17 pages.
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, 2007, pp. 191-198.
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, Aug. 2007, 6 pages.
Guzzoni, D., “Active: A Unified Platform for Building Intelligent Assistant Applications”, Oct. 25, 2007, 262 pages.
Guzzoni et al., “Many Robots Make Short Work”, AAAI Robot Contest, SRI International, 1996, 9 pages.
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.
Haas et al., “An Approach to Acquiring and Applying Knowledge”, SRI international, Nov. 1980, 22 pages.
Hadidi et al., “Student's Acceptance of Web-Based Course Offerings: An Empirical Assessment”, Proceedings of the Americas Conference on Information Systems(AMCIS), 1998, 4 pages.
Hardwar, Devindra, “Driving App Waze Builds its own Siri for Hands-Free Voice Control”, Available online at <http://venturebeat.com/2012/02/09/driving-app-waze-builds-its-own-siri-for-hands-free-voice-control/>, retrieved on Feb. 9, 2012, 4 pages.
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, 20 pages.
He et al., “Personal Security Agent: KQML-Based PKI”, The Robotics Institute, Carnegie-Mellon University, Paper, 1997, 14 pages.
Helm et al., “Building Visual Language Parsers”, Proceedings of CHI'91, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1991, 8 pages.
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, Gary G., “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.
Hendrix et al., “Transportable Natural-Language Interfaces to Databases”, SRI International, Technical Note 228, Apr. 30, 1981, 18 pages.
Hermansky, H., “Perceptual Linear Predictive (PLP) Analysis of Speech”, Journal of the Acoustical Society of America, vol. 87, No. 4, Apr. 1990, 15 pages.
Hermansky, H., “Recognition of Speech in Additive and Convolutional Noise Based on Rasta Spectral Processing”, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'93), Apr. 1993, 4 pages.
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, 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. 1991, 4 pages.
Huang et al., “The SPHINX-II Speech Recognition System: An Overview”, Computer, Speech and Language, vol. 7, No. 2, 1993, 14 pages.
IBM, “Integrated Audio-Graphics User Interface”, IBM Technical Disclosure Bulletin, vol. 33, No. 11, Apr. 1991, 4 pages.
IBM, “Speech Editor”, IBM Technical Disclosure Bulletin, vol. 29, No. 10, Mar. 10, 1987, 3 pages.
IBM, “Speech Recognition with Hidden Markov Models of Speech Waveforms”, IBM Technical Disclosure Bulletin, vol. 34, No. 1, Jun. 1991, 10 pages.
Intraspect Software, “The Intraspect Knowledge Management Solution: Technical Overview”, Available online at <http://tomgruber.org/writing/intraspect-whitepaper-1998.pdf>, 1998, 18 pages.
Iowegian International, “FIR Filter Properties, DSPGuru, Digital Signal Processing Central”, Available online at <http://www.dspguru.com/dsp/faq/fir/properties> retrieved on Jul. 28, 2010, 6 pages.
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. 1996, 4 pages.
Jacobs et al., “Scisor: Extracting Information from On-Line News”, Communications of the ACM, vol. 33, No. 11, Nov. 1990, 10 pages.
Janas, Jurgen M., “The Semantics-Based Natural Language Interface to Relational Databases”, Chapter 6, Cooperative Interfaces to Information Systems, 1986, pp. 143-188.
Goliath, “2004 Chrysler Pacifica: U-Connect Hands-Free Communication System. (The Best and Brightest of 2004) (Brief Article)”, Automotive Industries, Sep. 2003, 1 pages.
Massy, Kevin, “2007 Lexus GS 450H, 4Dr Sedan (3.5L, 6cyl Gas/Electric Hybrid CVT)”, ZDNet Reviews, Reviewed on Aug. 3, 2006, 10 pages.
“All Music”, Available online at <http://www.allmusic.com/cg/amg.dll?p=amg&sql=32:amg/info_pages/a_about.html>, retrieved on Mar. 19, 2007, 2 pages.
“BluePhoneElite: About”, Available online at <http://www.reelintelligence.com/BluePhoneElite>, retrieved on Sep. 25, 2006, 2 pages.
“BluePhoneElite: Features”, Available online at <http://www.reelintelligence.com/BluePhoneElite/features.shtml>, retrieved on Sep. 25, 2006, 2 pages.
“Digital Audio in the New Era”, Electronic Design and Application, No. 6, Jun. 30, 2003, 3 pages.
“Interactive Voice”, Available online at <http://www.helloivee.com/company/>, retrieved on Feb. 10, 2014, 2 pages.
“Meet Ivee, Your Wi-Fi Voice Activated Assistant”, Available online at <http://www.helloivee.com/>, retrieved from on Feb. 10, 2014, 8 pages.
“Mobile Speech Solutions, Mobile Accessibility”, SVOX AG Product Information Sheet, Available online at <http://www.svox.com/site/bra840604/con782768/mob965831936.aSQ?osLang=1>, 1 page.
Wireless Ground, “N200 Hands-Free Bluetooth Car Kit”, Available on line at <www.wirelessground.com>, retrieved on Mar. 19, 2007, 3 pages.
“PhatNoise”, Voice Index on Tap, Kenwood Music Keg, Available online at <http://www.phatnoise.com/kenwood/kenwoodssamail.html>, retrieved on Jul. 13, 2006, 1 pages.
“What is Fuzzy Logic?”, Available online at <http://www.cs.cmu.edu/Groups/AI/html/faqs/ai/fuzzy/part1/faq-doc-2.html>, retrieved on Mar. 19, 2007, 5 pages.
“Windows XP: A Big Surprise!—Experiencing Amazement from Windows XP”, New Computer, No. 2, Feb. 28, 2002, 8 pages.
Aikawa et al., “Generation for Multilingual MT”, Available online at <http://mtarchive.info/MTS-2001-Aikawa.pdf>, retrieved on Sep. 18, 2001, 6 pages.
Anhui USTC IFL Ytek Co. Ltd., “Flytek Research Center Information Datasheet”, Available online at <http://www.iflttek.com/english/Research_htm>, retrieved on Oct. 15, 2004, 3 pages.
Anonymous, “Speaker Recognition”, Wikipedia, The Free Enclyclopedia, Nov. 2, 2010, 4 pages.
Applebaum et al., “Enhancing the Discrimination of Speaker Independent Hidden Markov Models with Corrective Training”, International Conference on Acoustics, Speech, and Signal Processing, May 23, 1989, pp. 302-305.
Bellegarda et al., “Tied Mixture Continuous Parameter Modeling for Speech Recognition”, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 38, No. 12, Dec. 1990, pp. 2033-2045.
Borden IV, G.R., “An Aural User Interface for Ubiquitous Computing”, Proceedings of the 6th International Symposium on Wearable Computers, IEEE, 2002, 2 pages.
Brain, Marshall, “How MP3 Files Work”, Available online at <http://computerhowstuffworks.com/mp31.htm>, retrieved on Mar. 19, 2007, 4 pages.
Chang et al., “Discriminative Training of Dynamic Programming Based Speech Recognizers”, IEEE Transactions on Speech and Audio Processing, vol. 1, No. 2, Apr. 1993, pp. 135-143.
Cheyer et al., “Demonstration Video of Multimodal Maps Using an Agent Architecture”, Published by SRI International no later than 1996, as Depicted in Exemplary Screenshots from Video Entitled “Demonstration Video of Multimodal Maps Using an Agent Architecture”, 1996, 6 pages.
Cheyer et al., “Demonstration Video of Multimodal Maps Using an Open-Agent Architecture”, Published by SRI International no later than 1996, as Depicted in Exemplary Screenshots from Video Entitled “Demonstration Video of Multimodal Maps Using an Open-Agent Architecture”, 6 pages.
Cheyer, A., “Demonstration Video of Vanguard Mobile Portal”, Published by SRI International no later than 2004, as Depicted in Exemplary Screenshots from Video Entitled “Demonstration Video of Vanguard Mobile Portal”, 2004, 10 pages.
Choi et al., “Acoustic and Visual Signal Based Context Awareness System for Mobile Application”, IEEE Transactions on Consumer Electronics, vol. 57, No. 2, May 2011, pp. 738-746.
Dusan et al., “Multimodal Interaction on PDA's Integrating Speech and Pen Inputs”, Eurospeech Geneva, 2003, 4 pages.
Kickstarter, “Ivee Sleek: Wi-Fi Voice-Activated Assistant”, Available online at <https://www.kickstarter.com/discover/categories/hardware?ref=category>, retrieved on Feb. 10, 2014, 13 pages.
Lamel et al., “Generation and Synthesis of Broadcast Messages”, Proceedings of ESCA-NATO Workshop: Applications of Speech Technology, Sep. 10, 1993, 4 pages.
Macsimum News, “Apple Files Patent for an Audio Interface for the iPod”, Available online at <http://www.macsimumnews.com/index.php/archive/apple_files_patent_for_an_audio_interface_for_the_ipod>, retrieved on May 4, 2006, 8 pages.
Navigli, Roberto, “Word Sense Disambiguation: A Survey”, ACM Computing Surveys, vol. 41, No. 2, Article 10, Feb. 2009, 70 pages.
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US2004/016519, dated Nov. 3, 2005, 16 pages.
Partial International Search Report and Invitation to Pay Additional Fees received for PCT Patent Application No. PCT/US2004/016519, dated Aug. 4, 2005, 6 pages.
International Search Report received for PCT Patent Application No. PCT/US2011/037014, dated Oct. 4, 2011, 6 pages.
Invitation to Pay Additional Search Fees received for PCT Application No. PCT/US2011/037014, dated Aug. 2, 2011, 6 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.
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US2012/043098, dated Nov. 14, 2012, 9 pages.
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US2012/056382, dated Dec. 20, 2012, 11 pages.
Gong et al., “Guidelines for Handheld Mobile Device Interface Design”, Proceedings of DSI 2004 Annual Meeting, 2004, pp. 3751-3756.
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US2013/040971, dated Nov. 12, 2013, 11 pages.
Quazza et al., “Actor: A Multilingual Unit-Selection Speech Synthesis System”, Proceedings of 4th ISCA Tutorial and Research Workshop on Speech Synthesis, Jan. 1, 2001, 6 pages.
Ricker, T., “Apple Patents Auciio User Interface”, Engadget, Available online at <http://www.engadget.com/2006/05/04/apple-patents-audio-user-interface>, May 4, 2006, 6 pages.
Santaholma, M., “Grammar Sharing Techniques for Rule-Based Multilingual NLP Systems”, Proceedings of the 16th Nordic Conference of Computational Linguistics, NODALIDA 2007, May 25, 2007, 8 pages.
Taylor et al., “Speech Synthesis by Phonological Structure Matching”, International Speech Communication Association, vol. 2, Section 3, 1999, 4 pages.
Xu, “Speech-Based Interactive Games for Language Learning: Reading, Translation and Question-Answering”, Computational Linguistics and Chinese Language Processing, vol. 14, No. 2, Jun. 2009, pp. 133-160.
Yunker, John, “Beyond Borders: Web Globalization Strategies”, New Riders, Aug. 22, 2002, 11 pages.
Combined Search Report and Examination Report under Sections 17 and 18(3) received for GB Patent Application No. 1009318.5, dated Oct. 8, 2010, 5 pages.
Combined Search Report and Examination Report under Sections 17 and 18(3) received for GB Patent Application No. 1217449.6, dated Jan. 17, 2013, 6 pages.
Horvitz et al., “Handsfree Decision Support: Toward a Non-invasive Human-Computer Interface”, Proceedings of the Symposium on Computer Applications in Medical Care, IEEE Computer Society Press, 1995, p. 955.
Horvitz et al., “In Pursuit of Effective Handsfree Decision Support: Coupling Bayesian Inference, Speech Understanding, and User Models”, 1995, 8 pages.
Related Publications (1)
Number Date Country
20140273979 A1 Sep 2014 US
Provisional Applications (1)
Number Date Country
61783984 Mar 2013 US