US20120309363A1 - Triggering notifications associated with tasks items that represent tasks to perform - Google Patents

Triggering notifications associated with tasks items that represent tasks to perform Download PDF

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US20120309363A1
US20120309363A1 US13/251,104 US201113251104A US2012309363A1 US 20120309363 A1 US20120309363 A1 US 20120309363A1 US 201113251104 A US201113251104 A US 201113251104A US 2012309363 A1 US2012309363 A1 US 2012309363A1
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task
device
user
particular
location
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US13/251,104
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Thomas R. Gruber
Alessandro F. Sabatelli
Alexandre A. Aybes
Donald W. Pitschel
Edward D. Voas
Freddy A. Anzures
Paul D. Marcos
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Apple Inc
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Apple Inc
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Priority to US13/251,104 priority patent/US20120309363A1/en
Assigned to APPLE INC. reassignment APPLE INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: GRUBER, THOMAS R.
Assigned to APPLE INC. reassignment APPLE INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: MARCOS, PAUL D., VOAS, EDWARD D., AYBES, ALEXANDRE A., ANZURES, FREDDY A., SABATELLI, ALESSANDRO F., PITSCHEL, DONALD W.
Assigned to APPLE INC. reassignment APPLE INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: GRUBER, THOMAS R.
Publication of US20120309363A1 publication Critical patent/US20120309363A1/en
Priority claimed from US15/193,971 external-priority patent/US20170083179A1/en
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • G06Q10/0631Resource planning, allocation or scheduling for a business operation
    • G06Q10/06311Scheduling, planning or task assignment for a person or group
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/10Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
    • G06Q10/109Time management, e.g. calendars, reminders, meetings, time accounting
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W4/00Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/16Communication-related supplementary services, e.g. call-transfer or call-hold

Abstract

Techniques for processing task items are provided. A task item is electronic data that represents a task to be performed, whether manually or automatically. A task item includes one or more details about its corresponding task, such as a description of the task and a location of the task. Specifically, techniques for generating task items, organizing task items, triggering notifications of task items, and consuming task items are described. In one approach, a task item is generated based on input from a user and context of the input. In another approach, different attributes of task items are used to organize the task items intelligently into multiple lists. In another approach, one or more criteria, such as location, are used to determine when to notify a user of a task. In another approach, actions other than generating notifications are enabled or automatically performed, actions such as emailing, calling, and searching.

Description

    CROSS-REFERENCE TO RELATED APPLICATIONS
  • The present application claims priority to U.S. Provisional Patent Application No. 61/493,201, entitled “Generating And Processing Data Items That Represent Tasks To Perform,” filed on Jun. 3, 2011, invented by Thomas R. Gruber, et al., the entire disclosure of which is incorporated by reference for all purposes as if fully set forth herein.
  • This application is related to U.S. patent application Ser. No. 12/479,477, filed Jun. 5, 2009, the entire contents of which are hereby incorporated by reference as if fully set forth herein.
  • This application is related to U.S. patent application Ser. No. 12/987,982, filed Jan. 10, 2011, the entire contents of which are hereby incorporated by reference as if fully set forth herein.
  • FIELD OF THE INVENTION
  • The present invention relates to electronic reminders and, more particularly to, the intelligent generation, organization, triggering, and delivery of reminders and tasks in electronic to-do lists.
  • BACKGROUND
  • People have devised numerous ways to remind themselves of certain tasks or events. Many people have and still do write on physical media, such as sticky notes and calendars. With the ubiquity of electronic devices, many people have turned to computers to help manage their to-do lists and keep of record of upcoming events. Numerous reminder and to-do applications are available, both for desktop computers as well as handheld devices, such as laptop computers, tablet computers, and “smart” phones.
  • However, the timeliness and accuracy of a notification provided to a user of a reminder application depends almost entirely on input received from the user. For example, if a user enters, in a reminder application, a wrong date for an important event, then the user might not receive a notification of the event until after the event occurs. As another example, if a user provides a generic description of a task (e.g., “send him an email”) in a to-do application, then, when the user later reads the description, the user might not remember who “him” is and/or what the content of the email should be. In other words, when it comes to reminder and to-do applications, the old adage of “garbage in garbage out” is applicable.
  • The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • In the drawings:
  • FIG. 1 is a block diagram that depicts a system that is used for creating and processing task items, according to an embodiment of the invention;
  • FIG. 2 is a flow diagram that depicts a process for generating a task item based on context of user input, according to an embodiment of the invention;
  • FIG. 3 is a flow diagram that depicts a process for determining a time to provide a reminder to a user ahead of a scheduled time for a task, according to an embodiment of the invention;
  • FIG. 4 is a view of a travel reminder, according to an embodiment of the invention;
  • FIGS. 5-15 depict views of various types of lists, according to an embodiment of the invention; and
  • FIG. 16 is a block diagram that illustrates a computer system upon which an embodiment of the invention may be implemented.
  • DETAILED DESCRIPTION
  • In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, that the present invention may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the present invention.
  • General Overview
  • Multiple techniques are provided below for assisting a user in managing his/her tasks. Tasks are represented as task items in a computing system. Specifically, approaches for generating task items, organizing task items, triggering the notification of tasks based on corresponding task items, and consuming task items are described in detail below.
  • With respect to generating a task item, a task item is generated based on input from a user. The input (whether voice or text) includes one or more details about a task while context of the input is used to determine one or more other details about the task. The one or more other details are not reflected in the input. Context may include data that is stored on a device of the user. For example, a user provides the following voice input: “Call George at 5 PM today.” The user may have a contact list that includes information about multiple people with the first name of George. However, based on the context of the input, such as a recent voice message from a phone number associated with a particular George, it can be determined which George the user intends to call. In this way, a user is not required to be specific about each detail of a task when providing input about the task.
  • With respect to triggering a notification of a task item, one or more characteristics of a device may be used to determine whether to trigger the notification. Thus, time is not the sole factor (if at all) of whether a notification should be provided to a user. Examples of such characteristics may include where the device is located, what the device is displaying or processing, and specific movements of the device. For example, the fact that the device is in a car or at work may trigger the generation of a reminder of a task. As another example, if the device is currently displaying web content, then a user of the device may be considered to be “online,” which status might trigger a notification of a task to be provided to the user.
  • With respect to “consuming” task items, instead of simply providing a reminder of a task, the task is automated so that a user is not required to perform the steps typically required to perform the task. For example, a user may want to call a particular person at a particular time. When the particular time equals the current time, instead of simply reminding the user about the call, the call can be set up, ready to make without the user having to specify the person's phone number.
  • With respect to organizing task items, task items may be organized automatically or manually into multiple lists. Each list corresponds to a different attribute of a task item, such as the type of task, the type of criteria that is used to trigger a notification of a task, and the location of where the task is to be performed.
  • Task Items
  • Again, a “task item” is an electronic data item that contains one or more details about a task to perform, whether by a user or automatically by a process. A task item is generated based on input from a user. A task item may be one of two types: tasks associated with reminders (“reminder task”) and tasks not associated with reminders (“non-reminder task”). A reminder task is associated with an action, such as a notification being generated and provided to a user, while a non-reminder task is not associated with any action. A non-reminder task may be associated with a “complete-by” date or time. However, the complete-by date or time does not trigger the creation of a notification or other action. In other words, while a reminder task is associated with one or more triggering criteria that, when satisfied, trigger an action, a non-reminder task is not. Thus, a “task item” may or may not be associated with one or more triggering criteria that, when satisfied, triggers an action.
  • System Overview
  • FIG. 1 is a block diagram that depicts a system 100 that is used for creating task items and processing task items, according to an embodiment of the invention. System 100 includes a device 110, a network 120, and a cloud 130.
  • Device 110 is any computing device that is capable of receiving input from a user and displaying information about tasks. Non-limiting examples of device 110 include a desktop computer and a handheld device, such as a laptop computer, a tablet computer, and a “smart” phone. In the illustrated embodiment, device 110 includes a task manager 112. Task manager 112 processes task items, both of the reminder type or of the non-reminder type. Task manager 112 may be implemented in software, hardware, or any combination of software and hardware.
  • Device 110 includes communication technology (e.g., wireless technology) for sharing information with other devices. Device 110 can include a variety of integrated user interface units or can be coupled to user interface units through one or more communication ports or data links of the device. Non-limiting examples of user interface units include a voice input unit (e.g., a microphone), physical input units (e.g., a keyboard, a mouse, a track ball, a rotary dial or wheel, a touchpad, or a touch-screen), and motion sensors (e.g., an accelerometer, magnetometer, or a gyroscope). Any of these user interface units can be implemented as an external unit that communicates with device 110 to provide user input using a wired or wireless communication technology. Examples of wired communication technology include Universal Serial Bus (USB) interface, FireWire interface, etc. Examples of wireless communication technology include Bluetooth, Wi-Fi, and WiMax, infrared. Through these user interface units, device 110 can receive physical or voice inputs from the user.
  • Device 110 includes one or more output units to present visual and audio information to a user. Non-limiting examples of output units include a display unit for displaying visual data and a speaker for playing audio.
  • Cloud 130 is implemented by one or more computing devices. Cloud 130 hosts multiple services, such as a NLP (natural language processing) service 132 and one or more other services 134A-N. NLP service 132 uses one or more models of real-world things that a user can talk about in order to make sense of what the user is trying to say. For example, NLP service 132 can determine, based on models and context, what a user may be referring to when the user uses terms like, “him,” “there,” or “that.” An example of how NLP service 132 might operate is described in U.S. patent application Ser. No. 12/987,982, referenced above.
  • NLP service 132 may employ numerous APIs to communicate with and initiate actions performed by the one or more other services 134A-N and, optionally, other services not hosted in cloud 130. For example, in response to voice data sent from device 110, where the voice data reflects the user command “Reserve two seats at Maribella's in San Jose at 7 PM tonight,” NLP service 132 makes an API call to an online reservation service provided by Maribella's restaurant to initiate the creation of two reservations at that restaurant for 7 PM. Thus, NLP service 132 allows many operations to be performed automatically without requiring a user of device 110 to manually input text data and interact with numerous applications.
  • Communication between device 110 and services hosted in cloud 130 is made possible via network 120. Network 120 may be implemented by any medium or mechanism that provides for the exchange of data between various computing devices. Examples of such a network include, without limitation, a network such as a Local Area Network (LAN), Wide Area Network (WAN), Ethernet or the Internet, or one or more terrestrial, satellite, or wireless links. The network may include a combination of networks such as those described. Without limitation, the network may transmit data according to Transmission Control Protocol (TCP), User Datagram Protocol (UDP), and/or Internet Protocol (IP).
  • The following description includes numerous examples where both device 110 and cloud 130 take part in generating task items, organizing task items, triggering notifications of task items, and consuming task items. Instead, one or more of the techniques described herein may be implemented wholly on device 110 (making network 120 and cloud 130 unnecessary, wholly in cloud 130, or using some combination of device 110 and cloud 130.
  • Processing of Task Items
  • Task items may be created on device 110 or in cloud 130 based on input received at device 110. Although not depicted, task items may be stored on device 110 or in cloud 130, or synchronized to both. If task items are stored in cloud 130, then task manager 112 may retrieve the task items in response to, for example, input from a user or the one or more triggering criteria associated with one or more task items being satisfied.
  • In the scenario where task items are created and stored in cloud 130, task manager 112 may be, primarily, a set of one or more user interfaces that display information about tasks. Thus, a task service (not shown) in cloud 130 would be responsible for maintaining task items and triggering any notifications when triggering events occur.
  • Alternatively, task manager 112 creates and stores task items on device 110. In this scenario, task manager 112 may be entirely responsible for maintaining task items and generating any notifications when triggering events occur. One advantage of this scenario is that device 110 may be operating in an “offline” mode where device 110 is not capable of communicating with any service hosted in cloud 130.
  • Further, in this scenario, device 110 may include a service like NLP service 132, which may be part of task manager 112 or may execute separately from task manager 112. Such a service acts as a conversational interface to allow a user to quickly and easily create tasks. Such a service may be implemented by a process that is continually executing in the background without requiring a user of device 110 to provide input to cause the service to execute. Thus, whenever device 110 starts up (or restarts), the service is automatically started.
  • Alternatively, information needed to create task items may be identified by NLP service 132 (i.e., hosted in cloud 130). Device 110 may include a user input interface that continuously executes in the background, identifies input (e.g., voice or text) from a user, and sends the input over network 120 to NLP service 132. Once NLP service 132 identifies task details in the input, NLP service 132 may send task information (e.g., a description of a task and a time to complete the task) (a) over network 120 to task manager 112, which creates and stores a task item based on the information or (b) to a task service in cloud 130 to create a task item based on the information.
  • Most of the examples provided herein involve NLP service 132 receiving input data from device 110, identifying details (about a task) reflected in the input data, and providing those details to task manager 112. However, embodiments of the invention are not limited to this scenario. Such examples may alternatively involve only device 110 or may involve device 110 as merely an input and display device where NLP service 132 and a task service in cloud 130 provide the primary functionality.
  • I. Generating Task Items Based on Context
  • According to an embodiment of the invention, a task item is generated based on input and context of the input. “Context” of input refers to data that is currently or recently (relative to input, from a user, that initiated the generation of a task item) displayed or processed at device 110. Thus, context data is not reflected in the input from the user. For example, a user of device 110 may provide the following voice input: “Send him an email about the project when I get home.” The pronoun “him” is ambiguous because it is not clear, from the input alone, to whom “him” refers. However, the context of the voice input may be that device 110 currently displays (or just recently displayed) an email from an individual named Jack Bauer where the email includes a request for a status update about a project named “Bunny.” Based on the voice input and the context, task manager 112 (or a task service in cloud 130) creates a task item that includes the description “Send Jack Bauer an email about Project Bunny” and that includes the triggering criterion of device 110 being at a geographical location that is at or near the user's home. When device 110 is at or near the user's home, task manager 112 causes a notification to be displayed on device 110 where the notification includes the description from the task item.
  • FIG. 2 is a flow diagram that depicts a process 200 for generating a task item based on context of user input, according to an embodiment of the invention. At step 210, input that expressly specifies one or more first attributes for a task is received from a user. The input may be text input or voice input. The text input may be from a user of device 110 pressing physical keys on device 110 or pressing a touch screen of device 110 that includes a graphical keyboard. Additionally or alternatively, device 110 includes a microphone that accepts, from a user, voice input that device 110 converts into voice data. Device 110 may send the input data (whether voice data or text data) to NLP service 132, which analyzes the input data to identify the one or more first attributes for the task. Instead, as noted previously, device 110 may include functionality to analyze the input data to identify the one or more first attributes for the task. (Although many of the examples herein refer to natural language processing, natural language processing is not required.)
  • At step 220, a task item is generated for the task based on the input data. At step 230, one or more second attributes for the task are determined based on context data that is separate from the input. Although step 230 is depicted as occurring after step 220, step 230 may occur before step 220.
  • At step 240, the one or more first attributes and the one or more second attributes are stored in association with the task item.
  • The steps of process 200 may be performed by one or multiple devices. For example, the input in step 210 may be processed at device 110 to generate the task item. In this scenario, task manager 112 (or another process executing on device 110) identifies the context data associated with the input to determine the one or more second attributes, for the task, that are not identified in the input. Task manager 112 then stores the one or more second attributes in or in association with the task item.
  • Alternatively, in another scenario, device 110 sends the user input over network 120 to NLP service 132. NLP service 132 accepts, as input, context data associated with the input to determine the one or more second attributes, for the task, that are not identified in the input. Context data may have been sent to NLP service 132 prior to the input that initiates the generation of the task item (in step 220). NLP service 132 sends the one or more second attributes to task manager 112 (or a task service in cloud 130). Task manager 112 stores the one or more second attributes in or in association with a newly-generated task item.
  • Certain words or phrases may be used to cue NLP service 132 to communicate with manager 112. For example, user commands that begin with “Remind me . . . ” and “I need to . . . ” are used by NLP service 132 to determine to communicate with task manager 112. In response to detecting one of those user commands, NLP service 132 analyzes the input data (from device 110) and, optionally, context data for certain types of task details, such as a location, time, description, and/or action. NLP service 132 then determines to communicate with task manager 112 and sends, to task manager 112, the task details as part of the communication(s).
  • Sources of Context Data
  • Context data associated with user input that initiates the generation of a task item may come from one of many sources. Non-limiting examples of context data include data that is or was displayed on device 110 (“display data”), data that is stored on or in association with device 110 (“personalized data”), data that is or was processed by device 110 (“process data”), data that was previously provided by a user of device 110 (“input data”), data that indicates the location of device 110 (“location data”).
  • The following is an example of display data, or data that is or was displayed on device 110. Device 110 displays a map that includes a marker associated with a specific location on the map. A user of device 110 then says, while the map is displayed or soon after the map was displayed, “I need to be there by 5 today.” NLP service 132 (or a voice analyzer on device 110) analyzes voice data that reflects the voice input. NLP service 132 analyzes data that is currently displayed on device 110 to determine what “there” refers to. NLP service 132 identifies the marker and the associated location and replaces “there” with the location. NLP service 132 sends, to task manager 112, task data that indicates 5 PM today as the completion time of the task and the specified location as the location of the task. Task manager 112 generates a task item based on the task data.
  • As another example of display data, device 110 displays an email that is from a particular sender and includes a subject line. A user of device 110 then says, “I need to email him about that subject in two hours.” Device 110 sends voice data that reflects this input and an image of what is displayed to NLP service 132. In response, NLP service 132 identifies the email address of the sender of the email and the subject of the email. NLP service 132 sends, to task manager 112, task data that indicates a time of two hours from the current time as the completion time of the task and
  • The following is an example of personalized data, or data that is stored on or in association with device 110. A user of device 110 says, “I will have lunch with Rachelle tomorrow at 12 noon.” Device 110 sends voice data that reflects this input to NLP service 132, which identifies “Rachelle” in the voice data. NLP service 132 looks up “Rachelle” in contact data or an “address book” (stored on device 110 or in cloud 130) and determines that the last name of Rachelle is Goodwin. NLP service 132 then causes “Rachelle Goodwin” to be associated with a task item that is generated for the task. In addition to or instead of being stored on device 110, personalized data may be stored in cloud 130, i.e., remote to device 110.
  • The following is an example of process data, or data that was recently processed by device 110. For example, a user of device 110 used device 110 as a phone to communicate with a friend. Device 110 keeps track of who the user recently spoke with. After ending the call, the user says, “Remind me to call her back in 30 minutes.” NLP service 132, in addition to analyzing the voice input, analyzes data that indicates who recently established a phone call with device 110 (e.g., the last five phone calls). NLP service 132 determines the phone number of the most recently established phone call with device 110. NLP service 132 then determines, based on contact data, that the phone number is associated with particular individual. NLP service 132 sends, to task manager 112, task data that indicates a task of calling, a time of 30 minutes from the current time as the completion time of the task, the name of the particular individual, and, optionally, the phone number of the particular individual. Task manager 112 generates a task item based on the task item.
  • The following is an example of input data, or data that was recently (e.g., the last 5 minutes) provided by a user of device 110. The input from the user may be text input or voice input. Device 110 or NLP service 132 keeps track of recently entered input and may use that input to determine the identity of certain terms reflected in current input. For example, a user of device 110 says, “Remind me to meet him there at 7 tonight.” NLP service 132 receives voice data that reflects that voice input and identifies the terms “him” and “there.” Although it is not clear who “him” is and where “there” is, NLP service 132 accesses input that was recently received from the user. Such recently-received input reflects the names “George Reed” (identified as a name of a person) and “Starbucks” (identified as a place). In response, NLP service 132 causes a task item to be generated where the task is to “Meet George Reed at Starbucks” where the time is 7 PM of the current day.
  • The following is example of location data, or data that indicates a location of device 110, whether current or past. A user of device 110 says, “Remind me to meet Julian here next Thursday for lunch.” Device 110 sends voice data that reflects this input to NLP service 132. NLP service 132 identifies the term “here” and, in response, determines where device 110 is currently located. The current location may be determined in numerous ways. For example, device 110 may provide, to NLP service 132, a geographical location, such as longitude and latitude coordinates. NLP service 132 may then determine, based on the coordinates, a name of the place or establishment that is located at those coordinates. NLP service 132 causes a name of the place or establishment to be associated with a task item for the task to meet Julian for lunch on the date indicated.
  • Alternatively, the user may say, “I need to meet Josh Peters tomorrow at the same place where I was last Thursday at noon.” Device 110 sends voice data that reflects this input to NLP service 132. NLP service identifies the phrase “at the same place where I was last Thursday at noon” and, in response, determines where device 110 was located last Thursday at noon. NLP service 132 accesses location history data (stored in cloud 130 or stored on device 110 and sent to NLP service 132) and determines where device 110 was located last Thursday at noon. The location history may indicate the name of a place or may consist of geographical coordinates. If geographical coordinates, then NLP service 132 determines a name of the place or establishment that is located at those coordinates. NLP service 132 causes that name to be associated with a task item for the task to meet Josh Peters on the date indicated.
  • Events that occur with respect to device 110 may also be used to create task items. Such events may fall into one or more categories (or types) of context data described above, such as display data, presentation data, and process data. For example, device 110 detects an incoming call and notifies the user of the call by causing a phone number or other identifying information about the call or caller to be displayed on a screen of device 110. In addition to this information, the display may include three selectable options: “Answer”, “Ignore”, and “Call Back Later.” If the user selects “Call Back Later”, then a task item is created where the task item identifies the caller and, optionally, a time of the call and/or a time to make a call to the caller. Also, the task item may be automatically categorized as a task of type “To Call.”
  • Many of the examples herein regarding generating task items include a user providing voice or text input that includes details about a task. Another non-limiting example of how a task item may be generated is a user selecting (or highlighting) text that is displayed on a screen of device 110. The selected text is considered context data. After the text is selected, the user may be presented with one or more options, one of which is a “Remind” option which, when selected, causes a task item to be generated. Task manager 112 generates the task item based on the information reflected in the selected text. Details of the task item may be also determined from other context data, such as a time or event to trigger a notification of the task.
  • Virtual Dialogue
  • In some situations, NLP service 132 is unable to determine one or more details about a task based on input received from device 110 and the context associated with the input. Thus, in an embodiment, NLP service 132 prompts a user of device 110 for further input to determine the one or more details. The one or more details may pertain to any attribute of a task item, such as the description of the task, the location of the task, the location of a reminder (if any), or the time of the task.
  • For example, NLP service 132 receives, from device 110, voice data that reflects a user's command to “Remind me to call Steve at 7.” NLP service 132 may have access to information (e.g., an address book) about numerous contacts, of the user, that have the name of Steve. Further, nothing in the address book can be used to disambiguate which of the Steve contacts to call. Therefore, NLP service 132 sends, to device 110, the following message to be displayed (or played audibly) by device 110: “Do you mean Steve Anderson, Steve Hanson, or Steve Jobs?” The user then provides, to device 110, voice or text input that indicates one of the three Steve contacts. In response, device 110 sends the corresponding voice or text data over network 120 to NLP service 132.
  • As another example, NLP service 132 receives, from device 110, voice data that reflects a user's command to “I need to pick up bread at Whole Foods.” In response, NLP service 132 performs a lookup of the nearest Whole Foods stores to (a) the current location of device 110 or (b) the user's home. There may be multiple Whole Foods stores that are near device 110's current location and near the user's home. Therefore, NLP service 132 sends, to device 110, the following message to be displayed by device 110: “Which Whole Food's? The one on Almaden Rd, Chester Expressway, or Green Street?” The user then provides, to device 110, voice or text input that indicates one of the three Whole Foods stores. In response, device 110 sends the corresponding voice or text data over network 120 to NLP service 132.
  • As another example, NLP service 132 receives, from device 110, voice data that reflects a user's command to “Remind me to text Jerry by 8.” In response, NLP service 132 determines, based on the voice data and the context of the input that Jerry is Jerry Wall, indicated in the user's contact list (or address book). However, it is unclear whether the user intended 8 AM or 8 PM as the time to send an SMS message to Jerry. Therefore, NLP service 132 sends, to device 110, the following message to be displayed by device 110: “Do you want to text Jerry Wall at 8 AM or 8 PM?” The user then provides, to device 110, voice or text input that selects one of the two times. In response, device 110 sends the corresponding voice or text data over network 120 to NLP service 132.
  • Autocategorization of Task Items
  • In an embodiment, NLP service 132 determines, based on input from a user of device 110, one or more categories to associate with a task item. The one or more categories may be one of many different categories, which may be virtually limitless. Non-limiting examples of categories with which a task item may be associated include things to purchase, things to do on vacation, things to do at work, and things to do while driving. Each category may be associated with a sub-category. For example, a “purchase category” may be divided into a grocery category indicating items to purchase at a grocery store, a book category indicating books to purchase, and a music category indicating songs to purchase.
  • For example, a user may provide the following voice input to device 110: “Remind me to get milk.” Device 110 sends voice data that reflects that input to NLP service 132. NLP service 132 determines that a task item should be created and that “get milk” should be the description associated with the task item. NLP service 132 may also determine that milk is a grocery item and that the task item should be associated with a grocery category and/or a purchase category. Thus, NLP service 132 may send, to task manager 112, category data that indicates one or more categories with which the task item (whether created by NLP service 132, by a task service in cloud 130, or by task manager 112) should be associated.
  • As will be described hereinafter, the one or more categories associated with each task item may be used to organize task items that belong to the same category and display, on device 110, task items of the same category. This will allow a user of device 110 to view task items by category, in addition to or instead of by completion time, by creation time, by trigger type (described hereinafter), by location, by type (e.g., reminder task v. non-reminder task), or by some other criterion.
  • II. Triggering Notifications of Task Items
  • As noted previously, a task item may be associated with one or more triggering criteria (or triggers) that, when satisfied, causes a notification to be presented to a user of device 110 or some other action to be performed. When one or more triggering criteria of a task item are satisfied, a notification (or other action) is “triggered.” Non-limiting examples of triggering criteria include time, location, relative travel time, context triggers, and exogenous triggers, each of which is described in more detail below.
  • Time Trigger
  • The time of a time trigger may be an absolute time, a relative time, a recurring time, or a symbolic deadline. An example of an absolute time is Jun. 6, 2011, 9 AM Pacific Time. An example of a relative time is “10 minutes before the Patriots-Jets football game.” An example of a recurring time is “Every Thursday at LOAM.” An example of a symbolic deadline is “end of business day”.
  • Location Trigger
  • According to an embodiment of the invention, the location of device 110 is a triggering criterion associated with a task item. Such a triggering criterion is referred to herein as a “location trigger.” The location of device 110 may be determined in one of many ways. For example, the location of device 110 may be automatically determined based on Wi-Fi positioning, cell positioning, and/or GPS (global positioning system) positioning. Device 110 may determine its current location with or without input from a service in cloud 130.
  • In an embodiment, a user may provide input that indicates a label to be associated with a certain geographical location. For example, a user of device 110 may speak the following sentence, “I am home” or “I am at Whole Foods.” NLP service 132 may then associate the word “home” or phrase “Whole Foods” with the current location of device 110, as determined based on one of the three positioning methods mentioned previously. This association of a word with a location may be later leveraged to determine where “home” or “Whole Foods” is located.
  • A location trigger may not be associated with a specific geographic location or area. Instead, a location trigger may be associated with a place that is not limited to a specific geographic location or area. For example, a location trigger of a task item may be “on the road” or “while driving.” Device 110 (or a process executing on device 110) determines that the current location of device 110 is on a freeway or another busy road. Thus, this determination can be made regardless of the speed at which device 110 is moving or whether device 110 is paired with another device that would indicate that the user is traveling. Based on this determination, task manager 112 analyzes one or more task items to determine whether any task items are associated with the “on the road” or “while driving” location trigger.
  • As another example, a location trigger of a task item may be the user's car. Specifically, the user may have provided the following voice command: “Remind me to call my mom while driving.” NLP service 132 analyzes voice data that reflects that command and determines that “while driving” refers to the user's car. The user's car may have a Bluetooth-enabled component to allow device 110 to communicate with the user's car. When device 110 comes into range of a Bluetooth signal propagated by a Bluetooth-enabled component in the user's car, device 110 determines that device 110 is located in (or at least near) the user's car. In response to this determination, task manager 112 triggers the location trigger of the task item. Task manager 112 causes a reminder message to be displayed on device 110, where the reminder message informs the user to call his mother. The user may then provide a single tap or a voice response that causes a phone application executing on device 110 to initiate a call to a phone number associated with the user's mom.
  • While establishing a connection (or “pairing”) with another Bluetooth-enabled device is one example of pairing that can be used to determine device 110's location, other types of pairings are possible. For example, device 110 may detect certain network data during the evening and morning hours. The network data indicates one or more networks to which device 110 may connect. The network data may include the names of one or more networks or MAC addresses of one or more routers. Device 110 may then determine that whenever that network data is detected, device 110 is considered to be at the user's home. Thus, actual pairing is not required since pairing entails the establishment of a connection between device 110 and another device, such as a router. As another example, device 110 may detect a Wi-Fi signal on a train, subway, or bus. The Wi-Fi signal might indicate the type of transportation that corresponds to the Wi-Fi signal. Thus, device 110 might detect, based on the Wi-Fi signal, that its location is “on a train,” “in a subway,” or “on a bus.” If a triggering criterion of a task item indicates one or more of these locations, then an action associated with the task item may be triggered. Further, such “transit-oriented” locations may also be considered to be associated with specific contexts (described in more detail below), such as “in transit” or “while traveling.” Thus, detection by task manager 112 of such contexts may cause actions associated with certain task items to be performed.
  • The foregoing examples of location triggers can be categorized as “arrival triggers,” such as are found in user input to “Remind me to do X when I arrive at Y.” Another type of location trigger is a “departure trigger,” an example of which is found in the user command to “Remind me to do X when I leave work” or “ . . . when I leave here.” In an embodiment, in the departure trigger scenario, a minimum distance from the current location and the location of the departure is required before a particular departure trigger “fires.” Such a minimum distance may be helpful to avoid the performance of corresponding actions when there are false starts.
  • Additionally, a location trigger may be one of multiple conditions that trigger an action of a task item. Examples of user commands that include multiple conditions include “Remind me to do X when I get home or at 8 PM at the latest,” “Remind me to do X before 8 PM or when I leave, whichever is first,” and “Remind me to do X before 8 PM or while I am driving, whichever is first.”
  • Travel Time Trigger
  • In an embodiment, the location of device 110 and a time associated with a task item is used to provide a notification to a user of device 110. Thus, while the time may be one of the one or more triggering criteria associated with the task item, the location of device 110 may not be, at least explicitly so.
  • FIG. 3 is a flow diagram that depicts a process 300 for determining a time to provide a reminder to a user ahead of a scheduled time for a task, according to an embodiment of the invention. Process 300 may be performed by one or more processes executing on device 110 or in cloud 130. However, for ease of explanation, all the steps in process 300 are performed by task manager 112.
  • At step 310, task manager 112 determines a current location of device 110. At step 320, task manager 112 determines a location of a destination (or “destination location”) associated with (or identify by) a task item. At step 320, based on the distance between the two locations, task manager 112 determines a “travel time,” or the time it might take for the user of device 110 to travel to the destination location. At step 330, task manager 112 determines a “difference time,” or the difference between the current time and the time triggering criterion associated with the task item. At step 340, if the travel time is the same as or near the difference time, then task manager 112 provides a notification to the user. This notification acts as a reminder for the user to begin (if s/he has not already done so) traveling to the destination.
  • For example, a task item may be for a reminder to meet Sue at a particular restaurant at 2 PM. Task manager 112 determines the location of device 110 and the location of the particular restaurant. The location of the particular restaurant may be determined by initiating, e.g., an Internet search and identifying the closest restaurant, with the same name, to device 110's location. Alternatively, an address of the particular restaurant may already be stored in association with the task item. Based on the distance between device 110's location and the particular restaurant, task manager 112 determines how long it will take for the user of device 110 to travel to the particular restaurant (or “travel time”). When the travel time is the same as or near (e.g., within 10 minutes) the difference between the current time and the time trigger (i.e., 2 PM), then task manager 112 causes, to be displayed on device 110, a message that indicates that the user should leave soon to arrive at the particular restaurant at 2 PM.
  • In an embodiment, the time of when to leave for a destination changes based on the current location of device 110. For example, when the current location of device 110 is at location A and the destination is at location B, task manager 112 determines that the user should begin traveling 50 minutes before the time of a scheduled task. However, in response to detecting that the current location of device 110 is now at location C, task manager 112 determines that the user should begin traveling 20 minutes before the time of the scheduled task. For example, a user of device 110 may be at home at the beginning of the day and task manager 112 determines that it will take 50 minutes to travel, from the user's home, to the location of a dinner event in the evening. Later in the day, the user of device 110 travels to work, which is closer to the location of the dinner event. In response to device 110 being at a different location, task manager 112 determines that it will take 20 minutes to travel, from the user's work, to the location of the dinner event.
  • In an embodiment, the time of when to leave for a destination changes based on current traffic information. For example, at 2:30 PM, task manager 112 determines that the time of when a user of device 110 should leave for a restaurant is 5:00 PM. However, due to a car accident on a freeway that the user can take to arrive at the restaurant, the traffic slows considerably. Task manager 112 determines, at 3:30 PM, that the time of when the user should leave for the restaurant is 4:00 PM.
  • FIG. 4 is a view 400 of a travel reminder, according to an embodiment of the invention. View 400 is displayed by device 110. The travel reminder of view 400 contains six data items. The six data items include: (1) a description 410 of the corresponding task (“pick up Chloe”); (2) a time 420 of when to complete the task (“5:00 PM Today”); (3) an action 430 to perform when the user of device 110 should begin traveling to the destination; (4) a reminder time 440 that indicates that the user would like to be reminded of when the user should begin traveling to arrive at the destination on time; (5) a start time 450 that indicates when the user should begin traveling to arrive at the destination on time; and (6) a location 460 that indicates a name of the destination and an address of the destination. Another travel reminder that device 110 displays may contain more or less data items.
  • The action associated with action 430 may be triggered (or performed) in response to task manager 112 determining that the current time (indicated at the top of travel reminder) equals the time indicated by start time 450. In the illustrated example, action 430 is a map-related action where task manager 112 causes a map to be generated at start time 450 and displayed to the user of device 110. The map includes an indication of the address of location 460, an indication of the user's current location, or both. Instead of automatically causing the map to be displayed at start time 450, task manager 112 might first cause a message to be displayed on device 110, wherein the message includes an option to generate the map. If the user selects the option (e.g., through voice input or tapping on the screen), then task manager 112 causes the map to be generated and displayed.
  • A reminder setting may be in an “on” or “off” mode. In FIG. 4, reminder time 440 is in an “on” mode. If reminder time 440 is in an “off” mode, then the travel reminder of view 400 might not include reminder time 440 or start time 450.
  • As indicated previously, task manager 112 might change start time 450 in response to changes in device 110's location. Thus, while start time 450 may indicate “3:30 PM Today” when device 110 is located at the user's home in the morning, start time 450 may indicate “4:20 PM Today” when device 110 is located at the user's work office in the afternoon.
  • In an embodiment, task manager 112 checks for changes in computed start time 450 in response to significant changes in device 110's location. Significant changes in location may be determined as a result of other events that are already being computed. For example, device 110 might already process events when it transitions between cell towers, and these events could trigger the re-computation of a change in location and, therefore, in an updated start time 450. Other non-limiting examples of events that indicate a potential significant change in location are changes in Wi-Fi signatures detected, the computation of accurate GPS locations for some other application (such as maps or navigation), a power cycle event, turning on or off radios on the device, alerts based on accelerometer signals, and the receipt of text messages or push notifications that contain location information.
  • In an embodiment, task manager 112 combines strategies for detecting significant event changes. For example, in a low power/low resolution mode, task manager 112 only checks for significant location changes every N minutes or only when some periodic computation occurs, such as checking for incoming data. In a high power/high resolution mode, task manager 112 uses cell tower positioning and/or GPS. A combined strategy might run the low power solution by default and then invoke the high power solution when the estimated start time is soon or when other events occur (for example, a change in Wi-Fi or Bluetooth signatures is detected).
  • In an embodiment, a travel reminder or start time item in a travel reminder may be associated with one or more modes of transportation. Non-limiting examples of modes of transportation include driving a car, riding a bus, bicycling, and walking. A default transportation mode may be driving a car. For example, task manager 112 may provide the option for a user to view start time 450 in a “car” mode, a “bus” mode, a “bike” mode, a “walking” mode, or multiple modes simultaneously. Depending on the current mode(s) selected for start time 450, the start time may vary widely. For example, in FIG. 4, while start time 450 indicates “4:20 PM Today” for a car mode, start time 450 may indicate “3:15 PM Today” for a bus mode, “3:45 PM Today” for a biking mode, and “11:30 AM Today” for a walking mode.
  • In a related embodiment, a task item is associated with both a location and a date/time and a notification of the task may be triggered by either the user (or, rather, the user's device) being at the location or by the date/time. For example, if the user's device is at the location, (either on the date or regardless of the date), then a notification is triggered. If the user has not arrived at the location on the day indicated by the date (or at the location by the time), then the time is used as a “last resort” for triggering a notification.
  • Context Triggers
  • As described previously, time and location are examples of types of triggering criteria associated with a task item. Another type of triggering criteria associated with a task item is context. A “context trigger” refers to one or more characteristics of device 110 other than simply the device 110's location. Thus, like context triggers, travel triggers and travel time triggers also refer to one or more characteristics of device 110.
  • Context triggers may be categorized into one of two types: data-oriented context triggers and spatial-oriented context triggers. Non-limiting examples of data-oriented context triggers include the kind or type of data that device 110 is displaying on its screen (e.g., video), the specific application(s) or type of application(s) that are currently executing on device 110 (e.g., a texting application or a music application), the type of input device 110 is receiving from a user (e.g., voice or data), and the type of network connections available to device 110 (e.g., Wi-Fi or cellular network).
  • For example, a user command that device 110 receives may be “Remind me to call my mom next time I am on the phone.” The phrase “on the phone” is presumed to mean that when the user is using device 110 as a phone, a reminder will be sent to the user to inform the user to call his/her mom.
  • As another example, a user command that device 110 receives may be “I need to email Bob when I am surfing the Internet.” The phrase “surfing the Internet” is presumed to mean that when the user is interacting with a web browser on device 110, the context of device 110 (or of the user) is “when online.” In response to determining the context of the device or of the user, a reminder will be sent to the user to inform the user to email Bob. Additionally another reminder may be provided to the user for any other task items that are associated with the “when online” context trigger.
  • As another example, a user command that device 110 receives may be “Text Mom when I am talking to my sister Sarah.” The phrase “when I am talking to my sister Sarah” is presumed to mean that when the user is using device 110 as a phone and a phone call is established with Sarah, a reminder will be sent to the user to remind the user to send a text (or SMS) message to the user's mother.
  • As another example, a user command that device 110 receives may be “Remind me to email Jane Smith when I have a Wi-Fi connection.” In response to device 110 detecting a Wi-Fi signal that does not require a password or that requires a password accessible to device 110, task manager 112 causes a notification to be displayed on a screen of device 110, where the notification indicates that Jane is to email Jane Smith.
  • Non-limiting examples of spatial-oriented context triggers include the speed at which device 110 is moving (e.g., over 30 mph indicating driving, or less than 3 mph indicating walking), a direction (absolute or relative) at which device 110 is moving, and a set of movements of device 110 (e.g., short vertical movements while moving continuously in a horizontal direction). In other words, device 110 may be configured to detect how device 110 is moving through space.
  • For example, device 110 (or rather a process executing on device 110) determines, based on detecting changes in its location over a period of time, that device 110 is moving at 60 mph. Based on this information, device 110 determines that the device's context is “while driving” or “on the road.” Task manager 112 analyzes one or more task items to determine whether any task items are associated with a “while driving” or “on the road” context trigger. If a task item is associated with a “while driving” or “on the road” context trigger, then an action (e.g., displaying a notification) associated with the task item is performed.
  • As another example, device 110 determines, based on detecting changes in its location over a period of time, that device 110 is moving towards his home over a certain period of time (e.g., 5 minutes). Based on this information, device 110 determines that the context is “on my way home.” Task manager 112 analyzes one or more task items to determine whether any task items are associated with a “on my way home” context trigger. If a task item is associated with a “on my way home” context trigger, then an action (e.g., displaying a notification) associated with the task item is performed.
  • As another example, device 110 includes an accelerator that detects certain repetitive movements. Device 110 may determine, based on these repetitive movements over a period of time, that the user of device 110 might be running at a slow pace. Based on this determination, device 110 determines that the context is “while jogging.” Task manager 112 analyzes one or more task items to determine whether any task items are associated with a “while jogging” or “while walking” context trigger. If a task item is associated with a “while jogging” or “while walking” context trigger, then an action (e.g., displaying a notification) associated with the task item is performed.
  • As another example, device 110 might detect that it has not moved for a period of time (e.g., 3 hours). A user of device 110 might be interested in being alert and non-movement of device 110 might indicate that the user is asleep. Thus, the user might issue the command, “Alert me if the phone doesn't move for 3 hours.”
  • In addition to data-oriented and spatial-oriented triggers, other kinds of triggers may be based on any sensor on device 110. Device 110 may include multiple sensors, such as temperature sensors and light sensors. For example, device 110 might include a thermometer for detecting the outside temperature or an internal temperature of device 110. Thus, a user of device 110 might issue the command, “Remind me to call Harold when it reaches 100 degrees.”
  • Exogenous Triggers
  • Another type of triggering criteria that may be associated with a task item is exogenous criteria. An “exogenous trigger” is a triggering criterion that depends on one or more factors that exist outside and separate from device 110 and the user of device 110. Such factors may be considered “events” that occur with respect to devices other than device 110 or with respect to data that is stored on one or more devices other than device 110. Non-limiting examples of exogenous triggers include social location, social proximity, standing queries, and local events.
  • An example of a social location trigger is when a friend or associate of the user of device 110 arrives or leaves a certain location. For example, a user command that initiated the creation of a task item may have been “Notify me if Sarah leaves the mall.” Thus, the location of Sarah (or Sarah's mobile device) is an essential factor in setting off this type of exogenous trigger. Specifically, task manager 112 determines the current location of Sarah's device. The current location of Sarah's device may be provided by a cloud service (e.g., in cloud 130) to which both Sarah's device and device 110 are subscribed. Device 110 receives, from the cloud service, updates as to the location of Sarah's device. Task manager 112 uses that location information to determine whether the social location trigger should be activated. A similar user command is “Remind me when my daughter gets home.”
  • An example of a social proximity trigger is when a friend or associate of the user of device 110 is within a certain distance of the user (or device 110). For example, a user command that initiated the creation of a task item may have been “Remind me to call George when he is within 100 feet of me.” Thus, the location of George (or George's mobile device) is an essential factor in setting off this exogenous trigger. Specifically, task manager 112 or another process executing on device 110 compares the current location of device 110 with the current location of George's device to determine the distance that separates the two devices. Alternatively, George's device may transmit its location to a cloud service to which both George's device and device 110 are subscribed. Device 110 receives, from the cloud service, updates as to a distance between George's device and device 110. Task manager 112 uses that distance information to determine whether the social proximity trigger should be activated.
  • An example of a standing query trigger is when a webpage mentions a particular term or phrase, such as a company name. To detect this, a standing query is generated and issued continuously (e.g., once a day). For example, a user command that initiated the creation of a task item may have been “Tell me when cnn.com mentions Berkman Industries.” Task manager 112 or another process executing on device 110 issues a search query (e.g., to a search engine) and receives results. When task manager 112 determines that the results include a webpage from cnn.com that includes the name “Berkman Industries,” task manager 112 provides a notification to the user of device 110.
  • An example of a local event trigger is when a certain local event occurs. To detect this, task manager 112 receives data from an online service. Task manager 112 (or a task service in cloud 130) may periodically send a request to the online service (via one or more communication protocols). Alternatively, task manager 112 may subscribe with the online service to receive information about certain events. For example, a user command that initiated the creation of a task item may have been “Tell me when Beatles tickets go on sale at Shoreline.” In response, task manager 112, another process executing on device 110, or NLP service 132 sends a subscription request to an online ticket service to receive a notification when Beatles tickets for a performance at Shoreline Amphitheatre become available for purchase. When task manager 112 is determines Beatles tickets are available for purchase, task manager 112 provides a notification to the user of device 110.
  • As another example, a user might be interested in knowing when the surf is up. Thus, the user might issue the command, “Remind me an hour before the surf is up.” Task service 112 (or a task service in cloud 130) might regularly issue a query of a surfing site or might subscribe for alerts from the surfing site.
  • Based on the foregoing, the types and examples of exogenous triggers are virtually endless. As long as task manager 112 (or a task service in cloud 130) can make a determination about an event that occurs separate from device 110, that event can be used to trigger the performance of an action associated with a task item.
  • III. Consuming Task Items (Active Payloads)
  • A task item is “consumed” when an action associated with the task item is performed. Such an action may be a notification that is displayed (or played, if the notification is an audio notification) on device 110. In addition to or instead of providing a notification to a user of device 110, other possible actions include initiating a phone call or a search query, sending an HTTP request (that includes a Uniform Resource Location (URL)), sending an email or a text (SMS) message, causing an application to execute, and causing a purchase to be made on the user's behalf. Such actions that can be associated with task items are referred to as “active payloads.” The processing of an active payload causes some action to be performed, whether by task manager 112 or by another process, whether local or remote to device 110. In other words, instead of simply notifying the user of a task associated with a task item, task manager 112 (or a service in cloud 130) can automate the action part of the task item.
  • As alluded to above, causing an action to be performed may involve task manager 112 causing another application or process to perform the action. The calling or invoking of the other application (e.g., via an API of the other application) may be performed with or without further input, as indicated in the following examples.
  • The types of “other” applications can vary greatly. Non-limiting examples of applications that might be available on device 110 include a phone application, an email application, a Web browser, a music player application, a media player application, a music download application, an image processing application, a geopositioning application, a contacts application, an SMS application, a video game application, and a text processing application.
  • For example, a user of device 110 says aloud, “Remind me to call her back this afternoon.” This voice input is converted into voice data that device 110 sends (along with context data) over network 120 to NLP service 132. NLP service 132 analyzes the voice data and the context data to determine that “her” refers to Marilyn Merlot. NLP service 132 determines that “afternoon” is 2 PM (whether based on context data, a pre-defined setting, or prior history) and determines a phone number for Marilyn Merlot based on a contacts list (or address book), associated with the user, that includes one or more phone numbers for Marilyn Merlot. The contacts list may be stored on device 110 or in cloud 130. NLP 132 sends, to task manager 112 (or to a task service in cloud 130), reminder data used to generate a task item. The reminder data includes the date of “Today”, time of 2 PM, and an instruction to call Marilyn Merlot using a particular phone number. When task manager 112 determines that the current time is 2 PM, task manager 112 may cause a message to be displayed that prompts the user to call Marilyn Merlot. The message may include a “Later” button and a “Call Now” button. If the user selects the “Later” button, then task manager 112 will send the message again later in the afternoon (e.g., in 1 hour). If the user selects the “Call Now” button, then task manager 112 initiates a call to Marilyn Merlot. This initiation may involve task manager 112 making an API call to a phone application (not shown) executing on device 110 and passing the phone number as an argument of the API call. The phone application then uses the phone number to call a device associated with the phone number.
  • As another example, a user of device 110 says aloud, “Text Lindsay that I love her at 5 o'clock.” This voice input is converted into voice data that device 110 sends over network 120 to NLP service 132. NLP service 132 analyzes the voice data to determine that a cell phone number of Lindsay is necessary and that “5 o'clock” refers to 5 PM of the current day. Task manager 112 (or a task service in cloud 130) creates a task item that includes the following data items: (1) a completion time of 5 PM today, (2) an action of sending a text (or SMS) message, (3) a number of Lindsay's cell phone, and (4) a text string of “I love you” that will be part of the text message. In response to determining that the current time is 5 PM, task manager 112 analyzes the task item to determine the action that needs to be performed. Task manager 112 then causes a text message that includes the text string associated with the task item to be sent to Lindsay's cell phone. This step may comprise task manager 112 invoking an API call of a texting application (not shown) executing on device 110, where the text string (“I love you”) is an argument of the API call.
  • As another example, a user of device 110 says aloud, “Show me directions on how to get to Rachel's Restaurant in San Jose when I leave the office.” This voice input is converted into voice data that device 110 sends over network 120 to NLP service 132. NLP service 132 analyzes the voice data to determine that a cell phone number of Lindsay is necessary and that “5 o'clock” refers to 5 PM of the current day. Task manager 112 (or a task service in cloud 130) creates a task item that includes the following data items: (1) a location trigger of leaving the user's office and (2) an action of displaying instructions (and, optionally, a map) on how to arrive at Rachel's Restaurant from the user's office. In response to determining that the user of device 110 has left his/her office, task manager 112 analyzes the task item to determine the action that needs to be performed. Task manager 112 then causes (without further input from the user) a travel directions request to be sent to a travel directions service. The travel directions request includes the name of the restaurant, any address information of the restaurant, or both. The travel directions service may be hosted on device 110 or on another device (not shown).
  • As another example, a user of device 110 says aloud, “Order a cheese only pizza at Pizza Heaven in San Jose, home delivered, 30 minutes before the Bulls-Pacers game starts.” This voice input is converted into voice data that device 110 sends over network 120 to NLP service 132. NLP service 132 analyzes the voice data to determine that a Bulls-Pacers game starts at 6 PM local time; thus, the time trigger is 5:30 PM local time. NLP service 132 also determines that Pizza Heaven in San Jose allows online ordering. Task manager 112 (or a task service in cloud 130) creates a task item that includes the following data items: (1) a time trigger of 5:30 PM and (2) an action of ordering a cheese only pizza from Pizza Heaven with home delivery as an option. In response to determining that the current time is 5:30 PM, task manager 112 (or a task service in cloud 13) analyzes the task item to determine the action that needs to be performed. Task manager 112 then causes a pizza order request to be sent to Pizza Heaven's online ordering service. The pizza order request includes the pizza type of cheese only, the delivery option of home delivery, and the user's home address. The pizza order request may be in the form of an API call to the online ordering service, where arguments of the API call include indications of cheese only topping, home delivery, and the user's home address. Alternatively, before causing the pizza order required to be sent, task manager 112 may formulate a message that is displayed on (or played by) device 110, where the message informs the user about this task. If the user provides affirmative input, then task manager 112 causes the pizza request order to be sent. If the user provides negative input, then no pizza request order is sent.
  • As another example, a user of device 110 says aloud, “Play my classical station on Pandora at 3 PM tomorrow.” The time of “3 PM tomorrow” coincides with the end of a scheduled exam for the user. This voice input is converted into voice data that device 110 sends over network 120 to NLP service 132. NLP service 132 analyzes the voice data to determine a date and time that the intended action is going to be performed. Task manager 112 (or a task service in cloud 130) creates a task item that includes the following data items: (1) a time trigger of 3 PM with a date that identifies the following day and (2) an action of playing a classical “station” of the user's Pandora music application, where the classical station was established by the user and associated with the user's Pandora account. In response to determining that the current time is 3:00 PM on the proper date, task manager 112 (or a task service in cloud 13) analyzes the task item to determine the action that needs to be performed. Task manager 112 then causes the Pandora music application (not shown) to begin executing on device 110 and to “play” the user's classical station. Task manager 112 may cause the classical station to play by invoking an API call to the Pandora music application, where an argument of the API call includes an indication of a classical station.
  • Automated Task Completion
  • In an embodiment, task manager 112 (or a task service in cloud 130) “marks” the task item as complete in response to detecting that a task item is consumed. In other words, a task item may be associated with a complete or an incomplete status. Task manager 112 may provide an interface for a user to view task items managed by task manager 112 and determine whether a task item is complete or not. Task manager 112 may provide an option for a user of device 110 to view all completed task items. The completed task items may be ordered based on when the task items were created, consumed (or completed), or some other criteria.
  • Additionally or alternatively, task items that are consumed are deleted from storage. For example, task manager 112 deletes, from storage on device 110, any task items that have been consumed. The deletion of a task item may occur a certain period of time (e.g., 1 month) after the corresponding task has been completed to allow a user of device 110 to review recently-consumed task items. If a task service in cloud 130 manages task items that are stored in cloud 130, then that task service may delete consumed task items.
  • Delayed Task Interpretation
  • In an embodiment, when a task item is created, only some details of the corresponding task may be known and stored in association with the task item. Other details regarding the description, address (if any), trigger, and/or action may be determined later, whether automatically or via a manual process.
  • For example, device 110 sends, to NLP service 132, voice data that reflects a user command to “Call Sarah at 5.” NLP service 132 determines that 5 PM of the current day is a time trigger and causes task manager 112 (or a task service in cloud 130) to create a task item with that time trigger. However, an action item associated with the task item is “Call Sarah” without any indication of a phone number. NLP service 132 has not yet determined who Sarah is and, thus, what phone number to use to call her. Instead, those details are determined later; for example, when the current time is 5 PM and the action is triggered or sometime before the trigger activates. At 5 PM, task manager 112 sends the action item “Call Sarah” (whether in text form or audio form) to NLP service 132 or another service to identify information about a particular Sarah (if there are many) and to determine a phone number for Sarah. When a phone number for Sarah is determined, task manager 112 (or another process) causes a phone application on device 110 to initiate a call using the phone number. In this example, the disambiguation of (a) the identity of an individual and (b) a phone number for that individual is delayed until after the task item is generated.
  • As another example, device 110 sends, to NLP service 132, voice data that reflects a user command to “Check the weather in San Jose tomorrow morning.” NLP service 132 determines that 7 AM of the next day is a time trigger and causes task manager 112 (or a task service in cloud 130) to create a task item with that time trigger. However, an action item associated with the task item is “Check the weather in San Jose” without any indication of how to perform the action. NLP service 132 has not yet interpreted that portion of the user command to determine how the weather in San Jose is to be checked. Instead, those details are determined later; for example, when the current time is 7 AM of the next day and the action is triggered or sometime before the trigger activates. At 7 AM of the next day, task manager 112 sends the action item “Check the weather in San Jose” (whether in text form or audio form) to NLP service 132 or another service to identify how the weather in San Jose is to be checked. In response, NLP service 132 or another service retrieves information about the weather in San Jose and provides that information to device 110 to be displayed. In this example, the determination of how the action is to be performed is delayed until after the task item is generated.
  • Response to Alert
  • As alluded to previously, for a task item that is associated with an action that is more than a mere notification, instead of performing the action, a user of device 110 is first alerted of a task and the user is allowed to respond with an affirmative or negative response. For example, an action of a task item is to email Jane Smith about Project Knuckles. Task manager 112 causes, to be displayed on device 110, a message that indicates that the user of device 110 is suppose to email Jane Smith. The user may press a physical or graphical button that indicates an affirmative response. Alternatively, the user may speak the command, “Do it” or “Yes” indicating an affirmative response. In response to the input (whether via a touch screen of device 110, a keyboard selection, or voice input), task manager 112 causes an email application on device 110 to compose an email message addressed to Jane Smith with a subject line that refers to Project Knuckles. Alternatively, the user may decide to be reminded later of the task to email Jane Smith. Thus, in response to the notification, the user provides input (via device 110) that indicates that s/he would like to email Jane Smith some time later, such as in one hour or the next day. Such input may be the user saying “Remind me later” or simply “later.”
  • In an embodiment, when the action is to respond to an act of communication such as an email message, task manager 112 stores the context of the communication at the time of task creation and retrieves the context at the time of performing the action. The context of communication might be, in various embodiments, a Universal Resource Identifier or other reference to the context or a copy of the data of the context. For example, task manager 112 stores a reference to or copy of the email message that is to be replied to. When the action is performed, the contents of the email message can be recreated just as if the user had performed a reply when initially reading it. Other examples of context data that can be stored and retrieved in this manner include without limitation text messages, documents, web pages, voicemail messages, photographs, audio recordings, and videos.
  • As another example, an action of a task item is to call George Burt. In response to determining to trigger the action to call, task manager 112 provides an indication that a reminder is available for a user of device 110. The indication may be device 110 buzzing/shaking, generating an audible noise, and/or displaying a notification message. Without holding device 110, the user says aloud, “Read it.” In response to task manager 112 (or another process) processing this input, device 110 plays an audible version of the following statement: “Reminder . . . call George Burt.” The audible version may be based on a playback of the original input from the user or may reflect a computer-generated voice. If the user decides to call George Burt, then the user may simply say, “Okay” or “Do it,” which causes a phone application on device 110 to call George Burt. If the user decides not to call George Burt, then the user may say, “Ignore” or “remind me later.”
  • IV. Organizing Task Items Using Lists
  • According to an embodiment of the invention, a task item may be associated with one or more lists. A list is a set of one or more task items that are associated with (or belong to) the same category. Lists are ways that a user of device 110 can view task items in an organized way. The different lists allow the user to intelligently and intuitively browse the tasks that s/he would like to perform (or have performed on his/her behalf). FIGS. 6-14 depict views of various types of lists, according to an embodiment of the invention.
  • When a new task item is created, task manager 112 (or a service in cloud 130) identifies one or more attributes associated with the new task item and assigns the new task item to one or more lists. For example, if the new task item includes the action “to call,” then task manager 112 (or other process) adds the new task item to a To Call list. Similarly, if the new task item includes a certain context and a particular location, then task manager 112 might identify the context and/or the particular location and add the new task item to a location list and/or a context list. Alternatively, a user might manually identify one or more of the lists, which are described in detail below, to which a new task item is to be added.
  • All Lists View
  • FIG. 5A depicts an All Lists view 500 that device 110 might display, according to an embodiment of the invention. All List view 500 does not contain information about any specific task items. Instead, All Lists view 500 includes references to multiple lists maintained by task manager 112 (or a task service in cloud 130): a Today list 510, an All To Do list 520, a Nearby list 530, an In Car list 540, a To Call list 550, a To Email list 560, a Groceries list 570, a To Buy list 580, and a Completed list 590. As noted previously, a task item may be associated with (or belong to) multiple lists. For example, a task item whose description is to buy milk and whose time trigger is today may belong to Today list 510, All To Do list 520, Groceries list 570, and To Buy list 580.
  • Lists may be characterized as one of three types: built-in or predefined list, smart list, or custom list. Today list 510, All To Do list 520, and Completed list 590 are examples of built-in or pre-defined lists.
  • Smart lists are based on different characteristics or attributes that a task item might have, such as an action (e.g., call, email, text, alert), a location, and/or a context in which the action is to be performed. Examples of smart lists include By Action lists, By Location lists, and By Context lists. In Car list 540, To Call list 550, and To Email list 560 are examples of By Action lists. Other examples of By Actions lists might include a To Text list, a To Lookup list, and a To Visit list.
  • Examples of custom lists include lists that are based on categories identified by NLP service 132 and lists that are created by a user. Groceries list 570 and To Buy list 580 are examples of custom lists. Another example of a custom list is a wine list (not shown) that includes a list of the user's favorite wines.
  • Returning to the lists depicted in FIG. 5A, task items that belong to Today list 510 are associated with a triggering criterion that indicates a time during the current day that the corresponding task must or should be performed. All task items belong to All To Do list 520. Task items that belong to Nearby list 530 are associated with locations that are considered to be within a certain distance (e.g., 1 mile) from the current location of device 110. Task items that belong to In Car list 540 are associated with tasks that are to be performed in a car or while traveling. Task items that belong to To Call list 550 are associated with the action to call a person or entity. Task items that belong to To Email list 560 are associated with the action to email a person or entity. Task items that belong to Groceries list 570 are associated with grocery items (e.g., milk, eggs, fruit) to purchase. Task items that belong to To Buy list 580 are associated with items to purchase, such as clothing, books, songs, or groceries. Task items that belong to Completed list 590 are considered completed, which may indicate that the corresponding tasks have been performed or at least that an action (e.g., an alert or notification) associated with each task item has been performed.
  • All Lists view 500 also includes a “+” image that when selected, allows a user of device 110 to create another custom list so that current and/or future task items can be added thereto.
  • FIG. 5B depicts some of the lists depicted in FIG. 5A, but with a search field 502 to allow a user of device 110 to search for a specific task item. A task item may be searched for based on, for example, the task item's associated creation date, completion date (if known), completion status, context trigger (if any), location (if any), and/or action type (e.g., notify only, call, email, or buy).
  • Today List
  • FIG. 6 depicts a view 600 of a Today list that device 110 displays, for example, in response to user selection of Today list 510. View 600 includes a list of tasks that are divided into two sections: a section 610 for task items that are associated with a specific time and a section 620 for task items that are not associated with a specific time. Each of the task items in section 610 is associated with a travel time reminder. The third task item in section 610 and the second through fourth task items in section 620 are associated with actions that are more than mere reminders or alerts.
  • For example, the third task item in section 610 is to “pick up Chloe” at 5:00 PM. The icon to the right of that description is an image of a compass, indicating that the action associated with this task item is to generate travel directions to help guide the user of device 110 to the intended destination, which is Pinewood School in this example.
  • As another example, the second task item in section 620 is to “call John Appleseed.” The icon to the right of that description is an image of a phone, indicating that the action associated with this task item is to call John Appleseed. The image adjacent to the phone image is of a car, indicating that the user of device 110 is to call John Appleseed when the user is in a car or while the user is traveling.
  • As another example, the last task item in section 620 is to “reply to Pablo Marc.” The icon to the right of that description is an image of an envelope, indicating that the action associated with this task item is to send an email to Pablo Marc. View 600 also indicates that this task item is overdue, or rather, that the originally-scheduled time to email Pablo Marc has passed.
  • Single Task Item View
  • FIG. 7 depicts a view 700 that device 110 displays and that includes details about a particular task item. View 700 may have been generated based on a user selection of the second task item in section 620 in view 600 of FIG. 6. The displayed task item contains four data items: a description item 710, an action item 720, a reminder item 730, and a list assignment item 740.
  • Description item 710 contains a high-level description of the task (“Call John Appleseed”) and includes details about the subject matter (“Discuss the almond deal”). Selection of description item 710 may allow a user of device 110 to edit the description.
  • Action item 720 contains a description of the action (“Call”) and includes which phone (“mobile”) of John Appleseed to use. Selection of action item 720 may allow the user of device 110 to view the phone number associated with John Appleseed and/or provide other contact options, such as another phone number associated with John Appleseed, an email address of John Appleseed, etc. Furthermore, selection of the phone icon in action item 720 may cause task manager 112 to initiate a call phone to John Appleseed right then instead of waiting for the one or more triggering criteria associated with the task item to be satisfied.
  • Reminder item 730 indicates the type of trigger (“when in car”) that, when detected, will cause the action to be performed, or at least an alert about the task. Selection of reminder item 730 may allow a user to change the type of reminder.
  • List assignment item 740 indicates the list to which the task item belongs, which is the “Nut to Crack Project” list in this example. This list is an example of a customized list. Selection of list assignment item 740 may cause device 110 to display multiple task items that belong to the “Nut to Crack Project” list.
  • All to do List
  • FIG. 8 depicts a view 800 of an All To Do list that device 110 displays and that includes information about multiple task items. In this example, the multiple task items are ordered by date. View 800 may have been generated based on a user selection of All To Do list 820 in view 800 of FIG. 8A. View 800 is divided into two sections: section 810 that contains task items (or references thereto) to be completed on one day and section 820 that contains task items to be completed on the following day.
  • Some of the task items referenced in view 800 have been completed. Such completed task items are shown with a lighter gray image to the left of the corresponding description. Task items that have been completed may be distinguished from not-yet-completed task items by other techniques, such as check marks.
  • In the example depicted in FIG. 8, the task items are organized by the date on which the corresponding tasks should be performed (or “due date”). However, the task items referenced in view 800 may be organized by the date on which a user of device 110 is to be alerted or reminded of the corresponding tasks (“alert date”), the date on which the task items were created (“created date”), the date on which the task items were modified (“modified date”), or the date on which the corresponding tasks were performed (“completed date”).
  • Nearby List
  • FIG. 9 depicts a view 900 of a “Nearby” list that device 110 displays. View 900 may have been generated based on a user selection of Nearby list 830 in view 800 of FIG. 8A. View 900 contains information about multiple locations that are ordered based on distance from device 110's current location. The location indicated at the top of the list (“Home”) is closest to the current location of device 110 while the location indicated at the bottom of the list (“Pinewood School”) is furthest from the current location of device 110.
  • Each location indicated in view 900 is associated with a different location list. Each location list may be associated with one or more task items. For example, the “Home” location may be associated with four task items (which may be displayed on user selected of the “Home” location) while the “Atherton Dry Cleaning” location may be associated with just one task item.
  • Because the locations indicated in view 900 are ordered based on distance from the current location of device 110, when the current location of device 110 changes, the location indicators may be re-ordered, some may be removed from view 900, and others not currently displayed in view 900 may appear in view 900. For example, if device 110 is currently located in a store that is next to the Whole Foods store identified by the second location indicated in view 900, then, if device 110 displays view 900, that Whole Foods location indicator will be at the top of the list.
  • As indicated above, view 900 includes a “Home” location and a “Work” location. The association of a location labeled “Home” (or “Work”) with a particular address may be made in numerous ways. For example, many mobile devices store profile information about a user of the mobile device. This information is referred to as a “me card.” A me card typically stores a user's home address and the user's work address. Thus, task manager 112 (or another process) analyzes the me card that is stored on device 110 to determine a home address and a work address (if any) of the user.
  • In an embodiment, a radius is associated with a particular location and any task items that are associated with a location that is within the distance indicated by the radius is considered to be associated with the particular location. For example, a radius associated with a home of a user of device 110 is 2 miles. If a task item is associated with a park and the park is within 2 miles from the home, then the task item is associated with a “home” list, along with other task items that are associated with the home.
  • Location List View
  • As noted previously, a location list is an example of a smart list. In an embodiment, any task item that is associated with a location (e.g., as part of the one or more triggering criteria) is automatically associated with a location list that is associated with the same location as the location of the task item. Task manager 112 (or a task service in cloud 130) may maintain multiple location lists.
  • FIG. 10A depicts a Location List view 1000 that device 110 displays. Location list view 1000 may have been generated based on a user selection the “Home” location indicator in Nearby view 900 of FIG. 9. Location list view 1000 contains six task items. The bell image adjacent to each of the first four task items indicates that a reminder (or alert) for those task items will be generated when device 110 is at or near the user's home or at least sometime on a specified date. A reminder or alert will not be generated for the last two task items.
  • Location List view 1000 also includes a map icon 1002 which, when selected, causes task manager 112 to communicate with a map application that generates a map of the location associated with the map icon. In this example, a map of the user's home would be generated.
  • FIG. 10B depicts a Location List view 1050 that device 110 displays. Location List view 1050 may have been generated based on a user selection the “Whole Foods” location indicator in Nearby view 900 of FIG. 9. Location List view 1050 contains six data items, each of which may or may not be a task item. Instead, each data item in Location List view 1050 simply identifies a grocery item to purchase at a Whole Foods grocery store. None of the grocery items are associated with a reminder (although they could be) or a completion date (although they could be).
  • The grocery items identified in Location List view 1050 was associated with the Whole Foods grocery list in response to input from a user of device 110. For example, a user spoke the following command: “Add almond milk to my grocery list” or “Remember to pick up almond milk at Whole Foods near my house.” Device 110 transmits voice data that reflects this command to NLP service 132. NLP service 132 determines, based on the voice data, that the user intends to purchase almond milk. NLP service 132 may cause task manager 112 to (a) create a task item for the task of purchasing almond milk and add the task item to the Whole Foods list or (b) simply add “almond milk” to the Whole Foods list.
  • Location List view 1050 also includes a map icon 1052 which, when selected, causes task manager 112 to communicate with a map application that generates a map of the location associated with the map icon. In this example, a map of the Whole Foods store identified by the displayed address would be generated.
  • Smart Lists
  • As noted previously, By Location lists, By Action lists, and By Context lists are examples of smart lists. FIG. 11A depicts a view 1100 of a By Context list; specifically, an In Car list. FIG. 11B and FIG. 11D depict views of different By Action lists; specifically, a To Call list and a To Email list.
  • View 1100 contains task items that are associated with tasks that are to be performed in a specific context, i.e., the “In Car” context. The task items in the In Car list may be associated with different actions, such as calling and getting directions.
  • In contrast, view 1110, depicted in FIG. 11B, contains task items that are associated with the same action, which, in this example, is to call a person or entity. The first three task items in view 1110 have a phone icon, indicating that a phone number for the person indicated in the corresponding task is known to task manager 112. However, the last task item in view 1110 is not associated with a phone icon, indicating that a phone number for “Bob” is not positively known to task manager 112, probably because many contacts in the user's contact list may have the name of Bob. Selection of the “call Bob” task item in view 1110 causes device 110 to display a view 1120 depicted in FIG. 11C.
  • View 1120 indicates two data items that are contained in (or associated with) the “call Bob” task item: a description item and an action item. The action item indicates that multiple contacts are known as “Bob.” As a result, the action item includes a call button that is disabled, whereas the call buttons associated with the other task items in view 1110 are not disabled. Selection of the action item may initiate a process for disambiguating the identity of “Bob.” For example, selection of the action item may cause task manager 112 to display a list of names, each of which have the name of Bob or Robert. In this way, the disambiguation of an identity or of a phone number may occur much later than the creation of the corresponding task item.
  • View 1130, depicted in FIG. 11D, includes six task items, each of which includes an action to email. The active payload arguments of a To Email task item include a “To” or email address and, optionally, a subject for the subject line of the email.
  • In an embodiment, an “email” task item is created from an email application that is separate from task manager 112. The email application may invoke an API call of task manager 112 to create a task item whose action is to email, where the action includes an active payload that includes an email address and a subject.
  • Custom Lists
  • As noted previously, custom lists are one of the three main types of lists, including built-in lists and smart lists. Examples of custom lists indicated above include Grocery list 570 and To Buy list 580 (referenced in FIG. 5A). FIG. 12 depicts a view 1200 that might be generated in response to user selection of Grocery list 570. View 1200 includes six data items, each referring a different grocery item to purchase. Each of these data items may be task items that only have a description. The data items may have been associated with the grocery list based on input from NLP service 132. For example, NLP service receives, from device 110, voice data that reflects the user command to “pick up fresh bread from the store.” NLP service 132 determines that the user of device 110 intends to purchase fresh bread from a grocery store and associates “fresh bread” with a grocery category. In response, NLP service 132 sends, to task manager 112, a create task item command to create a task item that includes the description “fresh bread” and that is associated with the grocery category. In response, task manager 112 creates a task item and associates the task item with a grocery list that task manager 112 maintains.
  • FIG. 13 depicts a view 1300 of another type of custom list: a user-defined list. This user-defined list is entitled, “Nut to Crack Project,” and contains three task items, the first of which is associated with an action (i.e., call) and a context trigger (e.g. “in car” or “while driving”). A user of device 110 may “manually” associate a task item with a user-defined list. For example, after task manager 112 creates a task item, the user selects the task item and, via one or more selectable (e.g., menu) options displayed on device 110, selects a particular user-defined list, which causes task manager 112 to associate the task item with the particular user-defined list.
  • Alternatively, NLP service 132 may determine, based on input data (whether voice or text) received from device 110, a specific list to associate with a task item. For example, voice data may reflect a user command to “I need to write a proposal for the Nut to Crack Project.” NLP service 132 determines that “write a proposal” is the task and that “Nut to Crack Project” is the name of a list, which task manager 112 may or may not have yet created. NLP service 132 then sends, to task manager 112, the description (“write proposal”) and the name of a possible list to which the to-be-created task item may be added (“Nut to Crack Project”). Task manager 112 determines whether there is a list that has the same or similar name as “Nut to Crack Project.” If so, then task manager 112 creates a new task item and associates the task item with that list. If not, then task manager 112 creates a new list with that name, creates a new task item, and associates that task item with the new list.
  • Lists and Notes
  • As noted previously, a list may contain items that are not tasks. Such “non-task” are referred to as “notes” that consist only of a description. FIG. 14 depicts a view 1400 of a Favorite Wines list, which contains six notes, each referring to a different wine.
  • Also as noted previously, NLP service 132 may be configured to recognize list names so that task manager 112 can easily assign tasks and notes to the appropriate list(s).
  • Calendar Events
  • In an embodiment, calendar events created in the context of a calendar application are used to create task items that are managed by task manager 112. The calendar application may be part of task manager 112 or may be separately executing applications. For example, the calendar application might be configured to send newly-created calendar events to task manager 112, e.g., via one or more API calls that cause task manager 112 to create a task item based on the details of a calendar event, such as a description, a date, a location (if any), a duration (if any), and a reminder (if any). Alternatively, task manager 112 might provide a calendar service that allows a user to view a calendar and create events that are associated with a specific date and time or set of dates. Upon creation of events, task manager 112 also creates task items for the events.
  • FIG. 15 depicts a view 1500 of a task item that was generated based on a calendar event. The task item includes four data items: a description (“lunch with Elizabeth Reid”), a begin time (“12:00 PM Today”), a duration (“1 hour”), and a reminder (“10 minutes before”). Selection of any of the four data items may allow a user of device 110 to edit the corresponding data items. In an embodiment, if a change is made to a task item that was generated based on a calendar event, then that change is “pushed” to the calendar event that is managed by a calendar application.
  • In either scenario, if a calendar event that is created and maintained by the calendar service is associated with a location, then a task item that is generated based on the calendar event might also be associated with the location. In that case, task manager 112 might automatically associate the task item with a location list, such as the location list in view 1000 of FIG. 10A.
  • Combinations
  • While the foregoing description includes four main approaches (generating task items, organizing task items, triggering notifications, and consuming task items), each of these approaches may be implemented individually or may be used together, as noted in many of the examples. For example, natural language processing may be used to generate a task item, but none of the approaches described herein for processing the task item (i.e., organizing the task item, triggering a notification, and consuming the task item) are used. As another example, natural language processing may be used to generate a task item and an approach for organizing the task item as described herein may be used, but none of the approaches for triggering a notification or consuming the task item described herein are used. As another example, none of the approaches for generating and organizing task items and triggering a notification is used, but the approach for consuming the task item as described herein is used.
  • Hardware Overview
  • According to one embodiment, the techniques described herein are implemented by one or more special-purpose computing devices. The special-purpose computing devices may be hard-wired to perform the techniques, or may include digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are persistently programmed to perform the techniques, or may include one or more general purpose hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Such special-purpose computing devices may also combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the techniques. The special-purpose computing devices may be desktop computer systems, portable computer systems, handheld devices, networking devices or any other device that incorporates hard-wired and/or program logic to implement the techniques.
  • For example, FIG. 16 is a block diagram that illustrates a computer system 1600 upon which an embodiment of the invention may be implemented. Computer system 1600 includes a bus 1602 or other communication mechanism for communicating information, and a hardware processor 1604 coupled with bus 1602 for processing information. Hardware processor 1604 may be, for example, a general purpose microprocessor.
  • Computer system 1600 also includes a main memory 1606, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 1602 for storing information and instructions to be executed by processor 1604. Main memory 1606 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 1604. Such instructions, when stored in non-transitory storage media accessible to processor 1604, render computer system 1600 into a special-purpose machine that is customized to perform the operations specified in the instructions.
  • Computer system 1600 further includes a read only memory (ROM) 1608 or other static storage device coupled to bus 1602 for storing static information and instructions for processor 1604. A storage device 1610, such as a magnetic disk or optical disk, is provided and coupled to bus 1602 for storing information and instructions.
  • Computer system 1600 may be coupled via bus 1602 to a display 1612, such as a cathode ray tube (CRT), for displaying information to a computer user. An input device 1614, including alphanumeric and other keys, is coupled to bus 1602 for communicating information and command selections to processor 1604. Another type of user input device is cursor control 1616, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 1604 and for controlling cursor movement on display 1612. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane.
  • Computer system 1600 may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system causes or programs computer system 1600 to be a special-purpose machine. According to one embodiment, the techniques herein are performed by computer system 1600 in response to processor 1604 executing one or more sequences of one or more instructions contained in main memory 1606. Such instructions may be read into main memory 1606 from another storage medium, such as storage device 1610. Execution of the sequences of instructions contained in main memory 1606 causes processor 1604 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.
  • The term “storage media” as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operation in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device 1610. Volatile media includes dynamic memory, such as main memory 1606. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge.
  • Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 1602. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.
  • Various forms of media may be involved in carrying one or more sequences of one or more instructions to processor 1604 for execution. For example, the instructions may initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 1600 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus 1602. Bus 1602 carries the data to main memory 1606, from which processor 1604 retrieves and executes the instructions. The instructions received by main memory 1606 may optionally be stored on storage device 1610 either before or after execution by processor 1604.
  • Computer system 1600 also includes a communication interface 1618 coupled to bus 1602. Communication interface 1618 provides a two-way data communication coupling to a network link 1620 that is connected to a local network 1622. For example, communication interface 1618 may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 1618 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, communication interface 1618 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
  • Network link 1620 typically provides data communication through one or more networks to other data devices. For example, network link 1620 may provide a connection through local network 1622 to a host computer 1624 or to data equipment operated by an Internet Service Provider (ISP) 1626. ISP 1626 in turn provides data communication services through the world wide packet data communication network now commonly referred to as the “Internet” 1628. Local network 1622 and Internet 1628 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link 1620 and through communication interface 1618, which carry the digital data to and from computer system 1600, are example forms of transmission media.
  • Computer system 1600 can send messages and receive data, including program code, through the network(s), network link 1620 and communication interface 1618. In the Internet example, a server 1630 might transmit a requested code for an application program through Internet 1628, ISP 1626, local network 1622 and communication interface 1618.
  • The received code may be executed by processor 1604 as it is received, and/or stored in storage device 1610, or other non-volatile storage for later execution.
  • In the foregoing specification, embodiments of the invention have been described with reference to numerous specific details that may vary from implementation to implementation. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what is intended by the applicants to be the scope of the invention, is the literal and equivalent scope of the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction.

Claims (24)

1. A method comprising:
determining one or more characteristics of a device;
determining whether any of the one or more characteristics of the device satisfy one or more triggering criteria associated with a task item in a plurality of task items;
wherein each task item of the plurality of task items is associated with an action and one or more triggering criteria that must occur before the action is performed; and
in response to determining that the one or more characteristics of the device satisfy one or more triggering criteria of a particular task item in the plurality of task items, performing an action associated with the particular task item;
wherein the method is performed by one or more computing devices.
2. The method of claim 1, wherein:
the one or more characteristics of the device indicate a first location of the device and the one or more triggering criteria indicates a second location; and
determining that the one or more characteristics of the device satisfy the one or more triggering criteria comprises determining that the second location is the same as or near the first location.
3. The method of claim 1, wherein the one or more characteristics of the device include at least one of a speed of the device, a set of movements of the device, or a lack of movement of the device for a period of time.
4. The method of claim 1, wherein the one or more characteristics of the device indicate that device is in a car and the one or more triggering criteria indicates that the device must be in a car.
5. The method of claim 1, wherein the one or more characteristics of the device indicate that the device detects one or more wireless networks or MAC addresses.
6. The method of claim 1, wherein the action includes at least one of (a) causing, to be displayed on the device, a notification that includes information associated with the task item, (b) sending an email, (c) initiating a phone call, (d) sending a text message, or (e) initiating a search based on a search query associated with the particular task item.
7. A method comprising:
analyzing first data that indicates that an event that is remote relative to a handheld device has occurred;
determining, based on the first data, whether one or more particular triggering criteria associated with a particular task item in a plurality of task items are satisfied;
wherein each task item of the plurality of task items is associated with an action and one or more triggering criteria that indicate when the action is to be performed;
determining, based on the first data, that the one or more particular triggering criteria are satisfied; and
in response to determining that the one or more particular triggering criteria are satisfied, the handheld device causing an action associated with the particular task item to be performed;
wherein the method is performed by one or more computing devices.
8. The method of claim 7, wherein the first data does not indicate a time.
9. The method of claim 7, wherein:
the first data indicates that the handheld device is a first distance from a second device that is different than the handheld device;
the one or more particular triggering criteria indicates that the handheld device must be within a particular distance from the second device in order for the action associated with the particular task item; and
determining that the one or more particular triggering criteria are satisfied comprises determining that the first distance is the same as or less than the particular distance.
10. The method of claim 7, wherein:
the first data indicates that a second device that is different than the handheld device is located at a particular location;
the one or more particular triggering criteria indicates that the second device must be within or near a particular geographical area; and
determining that the one or more particular triggering criteria are satisfied comprises determining that the particular location is within or near the particular geographical area.
11. The method of claim 7, wherein:
the first data indicates particular results of a search that is performed on one or more devices that are remote relative to the handheld device;
the one or more particular triggering criteria specify particular data; and
determining that the one or more particular triggering criteria are satisfied comprises determining that the particular results include the particular data.
12. The method of claim 7, wherein:
the first data indicates that a particular event occurred remotely relative to the handheld;
the one or more particular triggering criteria indicates that the particular event must occur; and
determining that the one or more particular triggering criteria are satisfied comprises determining that the particular event occurred.
13. One or more non-transitory storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 1.
14. One or more non-transitory storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 2.
15. One or more non-transitory storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 3.
16. One or more non-transitory storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 4.
17. One or more non-transitory storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 5.
18. One or more non-transitory storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 6.
19. One or more non-transitory storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 7.
20. One or more non-transitory storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 8.
21. One or more non-transitory storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 9.
22. One or more non-transitory storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 10.
23. One or more non-transitory storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 11.
24. One or more non-transitory storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 12.
US13/251,104 2011-06-03 2011-09-30 Triggering notifications associated with tasks items that represent tasks to perform Abandoned US20120309363A1 (en)

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US13/251,104 US20120309363A1 (en) 2011-06-03 2011-09-30 Triggering notifications associated with tasks items that represent tasks to perform
JP2014513765A JP6144256B2 (en) 2011-06-03 2012-06-01 Generation and processing of a task item representing a task to be executed
EP18213462.7A EP3483807A1 (en) 2011-06-03 2012-06-01 Generating and processing task items that represent tasks to perform
KR1020177017149A KR101915185B1 (en) 2011-06-03 2012-06-01 Generating and processing task items that represent tasks to perform
KR1020187031467A KR20180121686A (en) 2011-06-03 2012-06-01 Generating and processing task items that represent tasks to perform
EP12727027.0A EP2715625A4 (en) 2011-06-03 2012-06-01 Generating and processing task items that represent tasks to perform
PCT/US2012/040571 WO2012167168A2 (en) 2011-06-03 2012-06-01 Generating and processing task items that represent tasks to perform
AU2012261958A AU2012261958B2 (en) 2011-06-03 2012-06-01 Generating and processing task items that represent tasks to perform
KR1020137034856A KR101752035B1 (en) 2011-06-03 2012-06-01 Generating and processing task items that represent tasks to perform
CN201280027176.5A CN103582896A (en) 2011-06-03 2012-06-01 Generating and processing task items that represent tasks to perform
AU2016204091A AU2016204091B2 (en) 2011-06-03 2016-06-17 Triggering notifications associated with tasks to perform
US15/193,971 US20170083179A1 (en) 2009-06-05 2016-06-27 Intelligent organization of tasks items
JP2017068594A JP2017142833A (en) 2011-06-03 2017-03-30 Triggering notifications associated with tasks to perform
AU2018204265A AU2018204265A1 (en) 2011-06-03 2018-06-14 Triggering notifications associated with tasks to perform

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US13/251,104 Abandoned US20120309363A1 (en) 2011-06-03 2011-09-30 Triggering notifications associated with tasks items that represent tasks to perform
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Cited By (101)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130246419A1 (en) * 2012-03-13 2013-09-19 Samsung Electronics Co., Ltd. Method and apparatus for tagging contents in a portable electronic device
US8892446B2 (en) 2010-01-18 2014-11-18 Apple Inc. Service orchestration for intelligent automated assistant
EP2824618A1 (en) * 2013-07-12 2015-01-14 Samsung Electronics Co., Ltd Electronic device for reminding of task and controlling method thereof
US8977255B2 (en) 2007-04-03 2015-03-10 Apple Inc. Method and system for operating a multi-function portable electronic device using voice-activation
US9147001B1 (en) * 2012-06-27 2015-09-29 Google Inc. Automatic user-based query generation and execution
CN105051674A (en) * 2012-12-24 2015-11-11 微软技术许可有限责任公司 Discreetly displaying contextually relevant information
US9190062B2 (en) 2010-02-25 2015-11-17 Apple Inc. User profiling for voice input processing
EP2955672A1 (en) * 2014-06-11 2015-12-16 Honeywell International Inc. Computer-generated speech device for site survey and maintenance
US9262612B2 (en) 2011-03-21 2016-02-16 Apple Inc. Device access using voice authentication
US9300784B2 (en) 2013-06-13 2016-03-29 Apple Inc. System and method for emergency calls initiated by voice command
US9330720B2 (en) 2008-01-03 2016-05-03 Apple Inc. Methods and apparatus for altering audio output signals
US9338493B2 (en) 2014-06-30 2016-05-10 Apple Inc. Intelligent automated assistant for TV user interactions
US9368114B2 (en) 2013-03-14 2016-06-14 Apple Inc. Context-sensitive handling of interruptions
US9430463B2 (en) 2014-05-30 2016-08-30 Apple Inc. Exemplar-based natural language processing
US9467521B2 (en) 2014-04-02 2016-10-11 David S. Owens System and computer implemented method of personal monitoring
US9483461B2 (en) 2012-03-06 2016-11-01 Apple Inc. Handling speech synthesis of content for multiple languages
US9495129B2 (en) 2012-06-29 2016-11-15 Apple Inc. Device, method, and user interface for voice-activated navigation and browsing of a document
US9502031B2 (en) 2014-05-27 2016-11-22 Apple Inc. Method for supporting dynamic grammars in WFST-based ASR
US9513867B1 (en) 2015-06-19 2016-12-06 Honda Motor Co., Ltd. System and method for managing communications on a mobile communication device based upon a user's behavior
US9535906B2 (en) 2008-07-31 2017-01-03 Apple Inc. Mobile device having human language translation capability with positional feedback
US9576574B2 (en) 2012-09-10 2017-02-21 Apple Inc. Context-sensitive handling of interruptions by intelligent digital assistant
US9582608B2 (en) 2013-06-07 2017-02-28 Apple Inc. Unified ranking with entropy-weighted information for phrase-based semantic auto-completion
WO2017044163A1 (en) * 2015-09-08 2017-03-16 Apple Inc. Distributed personal assistant
US9620105B2 (en) 2014-05-15 2017-04-11 Apple Inc. Analyzing audio input for efficient speech and music recognition
US9620104B2 (en) 2013-06-07 2017-04-11 Apple Inc. System and method for user-specified pronunciation of words for speech synthesis and recognition
US9626955B2 (en) 2008-04-05 2017-04-18 Apple Inc. Intelligent text-to-speech conversion
US9633674B2 (en) 2013-06-07 2017-04-25 Apple Inc. System and method for detecting errors in interactions with a voice-based digital assistant
US9633004B2 (en) 2014-05-30 2017-04-25 Apple Inc. Better resolution when referencing to concepts
US9646609B2 (en) 2014-09-30 2017-05-09 Apple Inc. Caching apparatus for serving phonetic pronunciations
US9646614B2 (en) 2000-03-16 2017-05-09 Apple Inc. Fast, language-independent method for user authentication by voice
US9668121B2 (en) 2014-09-30 2017-05-30 Apple Inc. Social reminders
WO2017099978A1 (en) * 2015-12-07 2017-06-15 Microsoft Technology Licensing, Llc Providing reminders related to contextual data on lock screens
US9697820B2 (en) 2015-09-24 2017-07-04 Apple Inc. Unit-selection text-to-speech synthesis using concatenation-sensitive neural networks
US9697822B1 (en) 2013-03-15 2017-07-04 Apple Inc. System and method for updating an adaptive speech recognition model
US9704282B1 (en) 2013-06-19 2017-07-11 Google Inc. Texture blending between view-dependent texture and base texture in a geographic information system
US9711141B2 (en) 2014-12-09 2017-07-18 Apple Inc. Disambiguating heteronyms in speech synthesis
US9715875B2 (en) 2014-05-30 2017-07-25 Apple Inc. Reducing the need for manual start/end-pointing and trigger phrases
US9721566B2 (en) 2015-03-08 2017-08-01 Apple Inc. Competing devices responding to voice triggers
US9734193B2 (en) 2014-05-30 2017-08-15 Apple Inc. Determining domain salience ranking from ambiguous words in natural speech
US9760559B2 (en) 2014-05-30 2017-09-12 Apple Inc. Predictive text input
US9785630B2 (en) 2014-05-30 2017-10-10 Apple Inc. Text prediction using combined word N-gram and unigram language models
US9798393B2 (en) 2011-08-29 2017-10-24 Apple Inc. Text correction processing
US9818400B2 (en) 2014-09-11 2017-11-14 Apple Inc. Method and apparatus for discovering trending terms in speech requests
US9842101B2 (en) 2014-05-30 2017-12-12 Apple Inc. Predictive conversion of language input
US9842105B2 (en) 2015-04-16 2017-12-12 Apple Inc. Parsimonious continuous-space phrase representations for natural language processing
US9858925B2 (en) 2009-06-05 2018-01-02 Apple Inc. Using context information to facilitate processing of commands in a virtual assistant
US9865280B2 (en) 2015-03-06 2018-01-09 Apple Inc. Structured dictation using intelligent automated assistants
US9886953B2 (en) 2015-03-08 2018-02-06 Apple Inc. Virtual assistant activation
US9886432B2 (en) 2014-09-30 2018-02-06 Apple Inc. Parsimonious handling of word inflection via categorical stem + suffix N-gram language models
US9899019B2 (en) 2015-03-18 2018-02-20 Apple Inc. Systems and methods for structured stem and suffix language models
US9922642B2 (en) 2013-03-15 2018-03-20 Apple Inc. Training an at least partial voice command system
EP3158691A4 (en) * 2014-06-06 2018-03-28 Obschestvo S Ogranichennoy Otvetstvennostiyu "Speactoit" Proactive environment-based chat information system
US9934775B2 (en) 2016-05-26 2018-04-03 Apple Inc. Unit-selection text-to-speech synthesis based on predicted concatenation parameters
US9953088B2 (en) 2012-05-14 2018-04-24 Apple Inc. Crowd sourcing information to fulfill user requests
US9959870B2 (en) 2008-12-11 2018-05-01 Apple Inc. Speech recognition involving a mobile device
US9966068B2 (en) 2013-06-08 2018-05-08 Apple Inc. Interpreting and acting upon commands that involve sharing information with remote devices
US9966065B2 (en) 2014-05-30 2018-05-08 Apple Inc. Multi-command single utterance input method
US9971774B2 (en) 2012-09-19 2018-05-15 Apple Inc. Voice-based media searching
US9972304B2 (en) 2016-06-03 2018-05-15 Apple Inc. Privacy preserving distributed evaluation framework for embedded personalized systems
US10043516B2 (en) 2016-09-23 2018-08-07 Apple Inc. Intelligent automated assistant
US10049663B2 (en) 2016-06-08 2018-08-14 Apple, Inc. Intelligent automated assistant for media exploration
US10049668B2 (en) 2015-12-02 2018-08-14 Apple Inc. Applying neural network language models to weighted finite state transducers for automatic speech recognition
US10057736B2 (en) 2011-06-03 2018-08-21 Apple Inc. Active transport based notifications
US10067938B2 (en) 2016-06-10 2018-09-04 Apple Inc. Multilingual word prediction
US10074360B2 (en) 2014-09-30 2018-09-11 Apple Inc. Providing an indication of the suitability of speech recognition
US10078631B2 (en) 2014-05-30 2018-09-18 Apple Inc. Entropy-guided text prediction using combined word and character n-gram language models
US10079014B2 (en) 2012-06-08 2018-09-18 Apple Inc. Name recognition system
US10083688B2 (en) 2015-05-27 2018-09-25 Apple Inc. Device voice control for selecting a displayed affordance
US10089072B2 (en) 2016-06-11 2018-10-02 Apple Inc. Intelligent device arbitration and control
US10101822B2 (en) 2015-06-05 2018-10-16 Apple Inc. Language input correction
US10127911B2 (en) 2014-09-30 2018-11-13 Apple Inc. Speaker identification and unsupervised speaker adaptation techniques
US10127220B2 (en) 2015-06-04 2018-11-13 Apple Inc. Language identification from short strings
US10134385B2 (en) 2012-03-02 2018-11-20 Apple Inc. Systems and methods for name pronunciation
WO2018231412A1 (en) * 2017-06-13 2018-12-20 Microsoft Technology Licensing, Llc Providing suggestions for task completion through intelligent canvas
US10169554B2 (en) 2015-08-03 2019-01-01 Casio Computer Co., Ltd. Work support system, work support method and computer-readable recording medium
US10170123B2 (en) 2014-05-30 2019-01-01 Apple Inc. Intelligent assistant for home automation
US10176167B2 (en) 2013-06-09 2019-01-08 Apple Inc. System and method for inferring user intent from speech inputs
US10185542B2 (en) 2013-06-09 2019-01-22 Apple Inc. Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant
US10186254B2 (en) 2015-06-07 2019-01-22 Apple Inc. Context-based endpoint detection
US10192552B2 (en) 2016-06-10 2019-01-29 Apple Inc. Digital assistant providing whispered speech
US10199051B2 (en) 2013-02-07 2019-02-05 Apple Inc. Voice trigger for a digital assistant
US10223066B2 (en) 2015-12-23 2019-03-05 Apple Inc. Proactive assistance based on dialog communication between devices
US10241752B2 (en) 2011-09-30 2019-03-26 Apple Inc. Interface for a virtual digital assistant
US10241644B2 (en) 2011-06-03 2019-03-26 Apple Inc. Actionable reminder entries
US10249300B2 (en) 2016-06-06 2019-04-02 Apple Inc. Intelligent list reading
US10255907B2 (en) 2015-06-07 2019-04-09 Apple Inc. Automatic accent detection using acoustic models
US10255566B2 (en) 2011-06-03 2019-04-09 Apple Inc. Generating and processing task items that represent tasks to perform
US10268972B2 (en) 2015-08-21 2019-04-23 Casio Computer Co., Ltd. Work support system, work support method and computer-readable recording medium
US10269345B2 (en) 2016-06-11 2019-04-23 Apple Inc. Intelligent task discovery
US10276170B2 (en) 2010-01-18 2019-04-30 Apple Inc. Intelligent automated assistant
US10283110B2 (en) 2009-07-02 2019-05-07 Apple Inc. Methods and apparatuses for automatic speech recognition
US10289433B2 (en) 2014-05-30 2019-05-14 Apple Inc. Domain specific language for encoding assistant dialog
US10297253B2 (en) 2016-06-11 2019-05-21 Apple Inc. Application integration with a digital assistant
US10303715B2 (en) 2017-05-16 2019-05-28 Apple Inc. Intelligent automated assistant for media exploration
US10311144B2 (en) 2017-05-16 2019-06-04 Apple Inc. Emoji word sense disambiguation
US10318871B2 (en) 2005-09-08 2019-06-11 Apple Inc. Method and apparatus for building an intelligent automated assistant
US10332518B2 (en) 2017-05-09 2019-06-25 Apple Inc. User interface for correcting recognition errors
US10354011B2 (en) 2016-06-09 2019-07-16 Apple Inc. Intelligent automated assistant in a home environment
US10356243B2 (en) 2015-06-05 2019-07-16 Apple Inc. Virtual assistant aided communication with 3rd party service in a communication session
US10366158B2 (en) 2015-09-29 2019-07-30 Apple Inc. Efficient word encoding for recurrent neural network language models
US10381016B2 (en) 2016-03-29 2019-08-13 Apple Inc. Methods and apparatus for altering audio output signals

Families Citing this family (83)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
ITFI20010199A1 (en) 2001-10-22 2003-04-22 Riccardo Vieri System and method for transforming text into voice communications and send them with an internet connection to any telephone set
US7633076B2 (en) 2005-09-30 2009-12-15 Apple Inc. Automated response to and sensing of user activity in portable devices
US9053089B2 (en) 2007-10-02 2015-06-09 Apple Inc. Part-of-speech tagging using latent analogy
US8620662B2 (en) 2007-11-20 2013-12-31 Apple Inc. Context-aware unit selection
US8065143B2 (en) 2008-02-22 2011-11-22 Apple Inc. Providing text input using speech data and non-speech data
US9965035B2 (en) 2008-05-13 2018-05-08 Apple Inc. Device, method, and graphical user interface for synchronizing two or more displays
US8464150B2 (en) 2008-06-07 2013-06-11 Apple Inc. Automatic language identification for dynamic text processing
US8768702B2 (en) 2008-09-05 2014-07-01 Apple Inc. Multi-tiered voice feedback in an electronic device
US8898568B2 (en) 2008-09-09 2014-11-25 Apple Inc. Audio user interface
US8712776B2 (en) 2008-09-29 2014-04-29 Apple Inc. Systems and methods for selective text to speech synthesis
US8676904B2 (en) 2008-10-02 2014-03-18 Apple Inc. Electronic devices with voice command and contextual data processing capabilities
US8862252B2 (en) 2009-01-30 2014-10-14 Apple Inc. Audio user interface for displayless electronic device
US8380507B2 (en) 2009-03-09 2013-02-19 Apple Inc. Systems and methods for determining the language to use for speech generated by a text to speech engine
EP3392876A1 (en) * 2011-09-30 2018-10-24 Apple Inc. Using context information to facilitate processing of commands in a virtual assistant
US8682649B2 (en) 2009-11-12 2014-03-25 Apple Inc. Sentiment prediction from textual data
US8600743B2 (en) 2010-01-06 2013-12-03 Apple Inc. Noise profile determination for voice-related feature
US8311838B2 (en) 2010-01-13 2012-11-13 Apple Inc. Devices and methods for identifying a prompt corresponding to a voice input in a sequence of prompts
US8381107B2 (en) 2010-01-13 2013-02-19 Apple Inc. Adaptive audio feedback system and method
CN105284099B (en) * 2013-06-08 2019-05-17 苹果公司 For hands-free interaction come adjust automatically user interface
US8977584B2 (en) 2010-01-25 2015-03-10 Newvaluexchange Global Ai Llp Apparatuses, methods and systems for a digital conversation management platform
US8713021B2 (en) 2010-07-07 2014-04-29 Apple Inc. Unsupervised document clustering using latent semantic density analysis
US8719006B2 (en) 2010-08-27 2014-05-06 Apple Inc. Combined statistical and rule-based part-of-speech tagging for text-to-speech synthesis
US20120057689A1 (en) * 2010-09-07 2012-03-08 Research In Motion Limited Callback option
US8719014B2 (en) 2010-09-27 2014-05-06 Apple Inc. Electronic device with text error correction based on voice recognition data
JP2012142825A (en) * 2011-01-05 2012-07-26 Sony Corp Information processing apparatus, information display method and computer program
US8781836B2 (en) 2011-02-22 2014-07-15 Apple Inc. Hearing assistance system for providing consistent human speech
US9134930B2 (en) 2011-03-30 2015-09-15 Hewlett-Packard Development Company, L.P. Delayed content production
US8812294B2 (en) 2011-06-21 2014-08-19 Apple Inc. Translating phrases from one language into another using an order-based set of declarative rules
US8706472B2 (en) 2011-08-11 2014-04-22 Apple Inc. Method for disambiguating multiple readings in language conversion
US8762156B2 (en) 2011-09-28 2014-06-24 Apple Inc. Speech recognition repair using contextual information
US10192176B2 (en) * 2011-10-11 2019-01-29 Microsoft Technology Licensing, Llc Motivation of task completion and personalization of tasks and lists
US9323483B2 (en) 2011-10-28 2016-04-26 Hewlett-Packard Development Company, L.P. Location-based print notifications
US20130181828A1 (en) * 2012-01-17 2013-07-18 Rajan Lukose Delivering an item of interest
US8775442B2 (en) 2012-05-15 2014-07-08 Apple Inc. Semantic search using a single-source semantic model
US20130307681A1 (en) * 2012-05-15 2013-11-21 Research In Motion Limited Methods and devices for providing action item reminders
WO2013185109A2 (en) 2012-06-08 2013-12-12 Apple Inc. Systems and methods for recognizing textual identifiers within a plurality of words
US20140032728A1 (en) * 2012-07-30 2014-01-30 John Conor O'neil Location-based task activation
US10345765B2 (en) * 2012-09-14 2019-07-09 Ademco Inc. System and method of overriding a scheduled task in an intrusion system to reduce false alarms
US10042603B2 (en) 2012-09-20 2018-08-07 Samsung Electronics Co., Ltd. Context aware service provision method and apparatus of user device
US8935167B2 (en) 2012-09-25 2015-01-13 Apple Inc. Exemplar-based latent perceptual modeling for automatic speech recognition
US9313162B2 (en) 2012-12-13 2016-04-12 Microsoft Technology Licensing, Llc Task completion in email using third party app
US20140173602A1 (en) * 2012-12-14 2014-06-19 Microsoft Corporation Matching Opportunity to Context
EP2940683A4 (en) * 2012-12-28 2016-08-10 Sony Corp Information processing device, information processing method and program
CN103903618B (en) * 2012-12-28 2017-08-29 联想(北京)有限公司 A speech input method and an electronic device
US20140188729A1 (en) * 2013-01-02 2014-07-03 Ricoh Company, Ltd. Remote notification and action system with event generating
US10255327B2 (en) 2013-02-22 2019-04-09 Nokia Technology Oy Apparatus and method for providing contact-related information items
US9378437B2 (en) 2013-02-27 2016-06-28 Hewlett-Packard Development Company, L.P. Sending print jobs using trigger distances
US9733821B2 (en) 2013-03-14 2017-08-15 Apple Inc. Voice control to diagnose inadvertent activation of accessibility features
US9977779B2 (en) 2013-03-14 2018-05-22 Apple Inc. Automatic supplementation of word correction dictionaries
KR101904293B1 (en) 2013-03-15 2018-10-05 애플 인크. Context-sensitive handling of interruptions
US9626658B2 (en) * 2013-03-15 2017-04-18 Thomas W. Mustaine System and method for generating a task list
US20150356614A1 (en) * 2013-06-05 2015-12-10 Iouri Makedonov Method for displaying advertising and task reminders on a portable electronic device
US10083009B2 (en) 2013-06-20 2018-09-25 Viv Labs, Inc. Dynamically evolving cognitive architecture system planning
US9594542B2 (en) 2013-06-20 2017-03-14 Viv Labs, Inc. Dynamically evolving cognitive architecture system based on training by third-party developers
US9633317B2 (en) 2013-06-20 2017-04-25 Viv Labs, Inc. Dynamically evolving cognitive architecture system based on a natural language intent interpreter
US20150006632A1 (en) * 2013-06-27 2015-01-01 Google Inc. Determining additional information for an intended action of a user
CN104281623A (en) * 2013-07-12 2015-01-14 武汉好味道科技有限公司 Method and system for predicting hot dishes and recommending personalized dishes on internet
US10296160B2 (en) 2013-12-06 2019-05-21 Apple Inc. Method for extracting salient dialog usage from live data
US9940099B2 (en) 2014-01-03 2018-04-10 Oath Inc. Systems and methods for content processing
US9971756B2 (en) 2014-01-03 2018-05-15 Oath Inc. Systems and methods for delivering task-oriented content
US9558180B2 (en) 2014-01-03 2017-01-31 Yahoo! Inc. Systems and methods for quote extraction
US9742836B2 (en) 2014-01-03 2017-08-22 Yahoo Holdings, Inc. Systems and methods for content delivery
US20150195233A1 (en) * 2014-01-08 2015-07-09 Microsoft Corporation Reminder service for email selected for follow-up actions
US20150286383A1 (en) 2014-04-03 2015-10-08 Yahoo! Inc. Systems and methods for delivering task-oriented content using a desktop widget
US9413707B2 (en) 2014-04-11 2016-08-09 ACR Development, Inc. Automated user task management
US8942727B1 (en) 2014-04-11 2015-01-27 ACR Development, Inc. User Location Tracking
US9509799B1 (en) * 2014-06-04 2016-11-29 Grandios Technologies, Llc Providing status updates via a personal assistant
US8995972B1 (en) 2014-06-05 2015-03-31 Grandios Technologies, Llc Automatic personal assistance between users devices
KR20150144189A (en) 2014-06-16 2015-12-24 주식회사 테라텍 Control system and method for project management in the smart work of cloud-based ECM
US10152987B2 (en) 2014-06-23 2018-12-11 Google Llc Remote invocation of mobile device actions
GB2528901B (en) 2014-08-04 2017-02-08 Ibm Uncorrectable memory errors in pipelined CPUs
US9990610B2 (en) * 2014-08-29 2018-06-05 Google Llc Systems and methods for providing suggested reminders
US20170371727A1 (en) * 2014-12-22 2017-12-28 Hewlett Packard Enterprise Development Lp Execution of interaction flows
CN106155496A (en) * 2015-04-27 2016-11-23 阿里巴巴集团控股有限公司 Information display method and device
US9552229B2 (en) * 2015-05-14 2017-01-24 Atlassian Pty Ltd Systems and methods for task scheduling
CN105706126A (en) * 2015-05-22 2016-06-22 深圳市博安达信息技术股份有限公司 Monitoring task generation method and system
US20160350306A1 (en) * 2015-05-28 2016-12-01 Google Inc. World knowledge triggers
CN105023051B (en) * 2015-07-24 2018-12-14 金华观瑞科技有限公司 Seat predetermined ordering system method and system
JP6499044B2 (en) * 2015-08-25 2019-04-10 日本電信電話株式会社 Task management apparatus, task management method, task management program, and user interface providing method
JP6354715B2 (en) * 2015-09-08 2018-07-11 カシオ計算機株式会社 Work support system, work support method, and program
US20170263249A1 (en) 2016-03-14 2017-09-14 Apple Inc. Identification of voice inputs providing credentials
JP2017167366A (en) * 2016-03-16 2017-09-21 Kddi株式会社 Communication terminal, communication method, and program
US20190050775A1 (en) * 2017-08-11 2019-02-14 Uber Technologies, Inc. Configuring an application feature using event records

Citations (99)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3704345A (en) * 1971-03-19 1972-11-28 Bell Telephone Labor Inc Conversion of printed text into synthetic speech
US3828132A (en) * 1970-10-30 1974-08-06 Bell Telephone Labor Inc Speech synthesis by concatenation of formant encoded words
US3979557A (en) * 1974-07-03 1976-09-07 International Telephone And Telegraph Corporation Speech processor system for pitch period extraction using prediction filters
US4278838A (en) * 1976-09-08 1981-07-14 Edinen Centar Po Physika Method of and device for synthesis of speech from printed text
US4282405A (en) * 1978-11-24 1981-08-04 Nippon Electric Co., Ltd. Speech analyzer comprising circuits for calculating autocorrelation coefficients forwardly and backwardly
US4310721A (en) * 1980-01-23 1982-01-12 The United States Of America As Represented By The Secretary Of The Army Half duplex integral vocoder modem system
US4348553A (en) * 1980-07-02 1982-09-07 International Business Machines Corporation Parallel pattern verifier with dynamic time warping
US4653021A (en) * 1983-06-21 1987-03-24 Kabushiki Kaisha Toshiba Data management apparatus
US4688195A (en) * 1983-01-28 1987-08-18 Texas Instruments Incorporated Natural-language interface generating system
US4692941A (en) * 1984-04-10 1987-09-08 First Byte Real-time text-to-speech conversion system
US4718094A (en) * 1984-11-19 1988-01-05 International Business Machines Corp. Speech recognition system
US4724542A (en) * 1986-01-22 1988-02-09 International Business Machines Corporation Automatic reference adaptation during dynamic signature verification
US4726065A (en) * 1984-01-26 1988-02-16 Horst Froessl Image manipulation by speech signals
US4727354A (en) * 1987-01-07 1988-02-23 Unisys Corporation System for selecting best fit vector code in vector quantization encoding
US4776016A (en) * 1985-11-21 1988-10-04 Position Orientation Systems, Inc. Voice control system
US4783807A (en) * 1984-08-27 1988-11-08 John Marley System and method for sound recognition with feature selection synchronized to voice pitch
US4811243A (en) * 1984-04-06 1989-03-07 Racine Marsh V Computer aided coordinate digitizing system
US4819271A (en) * 1985-05-29 1989-04-04 International Business Machines Corporation Constructing Markov model word baseforms from multiple utterances by concatenating model sequences for word segments
US4827520A (en) * 1987-01-16 1989-05-02 Prince Corporation Voice actuated control system for use in a vehicle
US4829576A (en) * 1986-10-21 1989-05-09 Dragon Systems, Inc. Voice recognition system
US4833712A (en) * 1985-05-29 1989-05-23 International Business Machines Corporation Automatic generation of simple Markov model stunted baseforms for words in a vocabulary
US4839853A (en) * 1988-09-15 1989-06-13 Bell Communications Research, Inc. Computer information retrieval using latent semantic structure
US4852168A (en) * 1986-11-18 1989-07-25 Sprague Richard P Compression of stored waveforms for artificial speech
US4862504A (en) * 1986-01-09 1989-08-29 Kabushiki Kaisha Toshiba Speech synthesis system of rule-synthesis type
US4878230A (en) * 1986-10-16 1989-10-31 Mitsubishi Denki Kabushiki Kaisha Amplitude-adaptive vector quantization system
US4903305A (en) * 1986-05-12 1990-02-20 Dragon Systems, Inc. Method for representing word models for use in speech recognition
US4905163A (en) * 1988-10-03 1990-02-27 Minnesota Mining & Manufacturing Company Intelligent optical navigator dynamic information presentation and navigation system
US4914586A (en) * 1987-11-06 1990-04-03 Xerox Corporation Garbage collector for hypermedia systems
US4944013A (en) * 1985-04-03 1990-07-24 British Telecommunications Public Limited Company Multi-pulse speech coder
US4965763A (en) * 1987-03-03 1990-10-23 International Business Machines Corporation Computer method for automatic extraction of commonly specified information from business correspondence
US4977598A (en) * 1989-04-13 1990-12-11 Texas Instruments Incorporated Efficient pruning algorithm for hidden markov model speech recognition
US4992972A (en) * 1987-11-18 1991-02-12 International Business Machines Corporation Flexible context searchable on-line information system with help files and modules for on-line computer system documentation
US5010574A (en) * 1989-06-13 1991-04-23 At&T Bell Laboratories Vector quantizer search arrangement
US5020112A (en) * 1989-10-31 1991-05-28 At&T Bell Laboratories Image recognition method using two-dimensional stochastic grammars
US5022081A (en) * 1987-10-01 1991-06-04 Sharp Kabushiki Kaisha Information recognition system
US5021971A (en) * 1989-12-07 1991-06-04 Unisys Corporation Reflective binary encoder for vector quantization
US5027406A (en) * 1988-12-06 1991-06-25 Dragon Systems, Inc. Method for interactive speech recognition and training
US5031217A (en) * 1988-09-30 1991-07-09 International Business Machines Corporation Speech recognition system using Markov models having independent label output sets
US5032989A (en) * 1986-03-19 1991-07-16 Realpro, Ltd. Real estate search and location system and method
US5040218A (en) * 1988-11-23 1991-08-13 Digital Equipment Corporation Name pronounciation by synthesizer
US5072452A (en) * 1987-10-30 1991-12-10 International Business Machines Corporation Automatic determination of labels and Markov word models in a speech recognition system
US5091945A (en) * 1989-09-28 1992-02-25 At&T Bell Laboratories Source dependent channel coding with error protection
US5127053A (en) * 1990-12-24 1992-06-30 General Electric Company Low-complexity method for improving the performance of autocorrelation-based pitch detectors
US5127055A (en) * 1988-12-30 1992-06-30 Kurzweil Applied Intelligence, Inc. Speech recognition apparatus & method having dynamic reference pattern adaptation
US5142584A (en) * 1989-07-20 1992-08-25 Nec Corporation Speech coding/decoding method having an excitation signal
US5164900A (en) * 1983-11-14 1992-11-17 Colman Bernath Method and device for phonetically encoding Chinese textual data for data processing entry
US5165007A (en) * 1985-02-01 1992-11-17 International Business Machines Corporation Feneme-based Markov models for words
US5179652A (en) * 1989-12-13 1993-01-12 Anthony I. Rozmanith Method and apparatus for storing, transmitting and retrieving graphical and tabular data
US5194950A (en) * 1988-02-29 1993-03-16 Mitsubishi Denki Kabushiki Kaisha Vector quantizer
US5199077A (en) * 1991-09-19 1993-03-30 Xerox Corporation Wordspotting for voice editing and indexing
US5202952A (en) * 1990-06-22 1993-04-13 Dragon Systems, Inc. Large-vocabulary continuous speech prefiltering and processing system
US5208862A (en) * 1990-02-22 1993-05-04 Nec Corporation Speech coder
US5216747A (en) * 1990-09-20 1993-06-01 Digital Voice Systems, Inc. Voiced/unvoiced estimation of an acoustic signal
US5220657A (en) * 1987-12-02 1993-06-15 Xerox Corporation Updating local copy of shared data in a collaborative system
US5220639A (en) * 1989-12-01 1993-06-15 National Science Council Mandarin speech input method for Chinese computers and a mandarin speech recognition machine
US5222146A (en) * 1991-10-23 1993-06-22 International Business Machines Corporation Speech recognition apparatus having a speech coder outputting acoustic prototype ranks
US5230036A (en) * 1989-10-17 1993-07-20 Kabushiki Kaisha Toshiba Speech coding system utilizing a recursive computation technique for improvement in processing speed
US5235680A (en) * 1987-07-31 1993-08-10 Moore Business Forms, Inc. Apparatus and method for communicating textual and image information between a host computer and a remote display terminal
US5267345A (en) * 1992-02-10 1993-11-30 International Business Machines Corporation Speech recognition apparatus which predicts word classes from context and words from word classes
US5268990A (en) * 1991-01-31 1993-12-07 Sri International Method for recognizing speech using linguistically-motivated hidden Markov models
US5293448A (en) * 1989-10-02 1994-03-08 Nippon Telegraph And Telephone Corporation Speech analysis-synthesis method and apparatus therefor
US5293452A (en) * 1991-07-01 1994-03-08 Texas Instruments Incorporated Voice log-in using spoken name input
US5297170A (en) * 1990-08-21 1994-03-22 Codex Corporation Lattice and trellis-coded quantization
US5301109A (en) * 1990-06-11 1994-04-05 Bell Communications Research, Inc. Computerized cross-language document retrieval using latent semantic indexing
US5303406A (en) * 1991-04-29 1994-04-12 Motorola, Inc. Noise squelch circuit with adaptive noise shaping
US5317647A (en) * 1992-04-07 1994-05-31 Apple Computer, Inc. Constrained attribute grammars for syntactic pattern recognition
US5317507A (en) * 1990-11-07 1994-05-31 Gallant Stephen I Method for document retrieval and for word sense disambiguation using neural networks
US5325298A (en) * 1990-11-07 1994-06-28 Hnc, Inc. Methods for generating or revising context vectors for a plurality of word stems
US5325297A (en) * 1992-06-25 1994-06-28 System Of Multiple-Colored Images For Internationally Listed Estates, Inc. Computer implemented method and system for storing and retrieving textual data and compressed image data
US5327498A (en) * 1988-09-02 1994-07-05 Ministry Of Posts, Tele-French State Communications & Space Processing device for speech synthesis by addition overlapping of wave forms
US5333236A (en) * 1992-09-10 1994-07-26 International Business Machines Corporation Speech recognizer having a speech coder for an acoustic match based on context-dependent speech-transition acoustic models
US5333275A (en) * 1992-06-23 1994-07-26 Wheatley Barbara J System and method for time aligning speech
US5345536A (en) * 1990-12-21 1994-09-06 Matsushita Electric Industrial Co., Ltd. Method of speech recognition
US5349645A (en) * 1991-12-31 1994-09-20 Matsushita Electric Industrial Co., Ltd. Word hypothesizer for continuous speech decoding using stressed-vowel centered bidirectional tree searches
US5353377A (en) * 1991-10-01 1994-10-04 International Business Machines Corporation Speech recognition system having an interface to a host computer bus for direct access to the host memory
US5377301A (en) * 1986-03-28 1994-12-27 At&T Corp. Technique for modifying reference vector quantized speech feature signals
US5377303A (en) * 1989-06-23 1994-12-27 Articulate Systems, Inc. Controlled computer interface
US5384892A (en) * 1992-12-31 1995-01-24 Apple Computer, Inc. Dynamic language model for speech recognition
US5384893A (en) * 1992-09-23 1995-01-24 Emerson & Stern Associates, Inc. Method and apparatus for speech synthesis based on prosodic analysis
US5386494A (en) * 1991-12-06 1995-01-31 Apple Computer, Inc. Method and apparatus for controlling a speech recognition function using a cursor control device
US5390279A (en) * 1992-12-31 1995-02-14 Apple Computer, Inc. Partitioning speech rules by context for speech recognition
US5396625A (en) * 1990-08-10 1995-03-07 British Aerospace Public Ltd., Co. System for binary tree searched vector quantization data compression processing each tree node containing one vector and one scalar to compare with an input vector
US5400434A (en) * 1990-09-04 1995-03-21 Matsushita Electric Industrial Co., Ltd. Voice source for synthetic speech system
US5424947A (en) * 1990-06-15 1995-06-13 International Business Machines Corporation Natural language analyzing apparatus and method, and construction of a knowledge base for natural language analysis
US5455888A (en) * 1992-12-04 1995-10-03 Northern Telecom Limited Speech bandwidth extension method and apparatus
US5469529A (en) * 1992-09-24 1995-11-21 France Telecom Establissement Autonome De Droit Public Process for measuring the resemblance between sound samples and apparatus for performing this process
US5475587A (en) * 1991-06-28 1995-12-12 Digital Equipment Corporation Method and apparatus for efficient morphological text analysis using a high-level language for compact specification of inflectional paradigms
US5491772A (en) * 1990-12-05 1996-02-13 Digital Voice Systems, Inc. Methods for speech transmission
US5502790A (en) * 1991-12-24 1996-03-26 Oki Electric Industry Co., Ltd. Speech recognition method and system using triphones, diphones, and phonemes
US5502791A (en) * 1992-09-29 1996-03-26 International Business Machines Corporation Speech recognition by concatenating fenonic allophone hidden Markov models in parallel among subwords
US5515475A (en) * 1993-06-24 1996-05-07 Northern Telecom Limited Speech recognition method using a two-pass search
US5536902A (en) * 1993-04-14 1996-07-16 Yamaha Corporation Method of and apparatus for analyzing and synthesizing a sound by extracting and controlling a sound parameter
US5574823A (en) * 1993-06-23 1996-11-12 Her Majesty The Queen In Right Of Canada As Represented By The Minister Of Communications Frequency selective harmonic coding
US5577164A (en) * 1994-01-28 1996-11-19 Canon Kabushiki Kaisha Incorrect voice command recognition prevention and recovery processing method and apparatus
US5579436A (en) * 1992-03-02 1996-11-26 Lucent Technologies Inc. Recognition unit model training based on competing word and word string models
US5596676A (en) * 1992-06-01 1997-01-21 Hughes Electronics Mode-specific method and apparatus for encoding signals containing speech
US5613036A (en) * 1992-12-31 1997-03-18 Apple Computer, Inc. Dynamic categories for a speech recognition system
US20120242482A1 (en) * 2011-03-25 2012-09-27 Microsoft Corporation Contextually-Appropriate Task Reminders
US8560229B1 (en) * 2010-09-15 2013-10-15 Google Inc. Sensor based activity detection

Family Cites Families (610)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5047617A (en) 1982-01-25 1991-09-10 Symbol Technologies, Inc. Narrow-bodied, single- and twin-windowed portable laser scanning head for reading bar code symbols
EP0097858B1 (en) 1982-06-11 1991-12-18 Mitsubishi Denki Kabushiki Kaisha Vector quantizer
DE3335358A1 (en) 1983-09-29 1985-04-11 Siemens Ag A method of determining speech spectra for automatic speech recognition and speech coding
US4955047A (en) 1984-03-26 1990-09-04 Dytel Corporation Automated attendant with direct inward system access
EP0218859A3 (en) 1985-10-11 1989-09-06 International Business Machines Corporation Signal processor communication interface
US5759101A (en) 1986-03-10 1998-06-02 Response Reward Systems L.C. Central and remote evaluation of responses of participatory broadcast audience with automatic crediting and couponing
US5057915A (en) 1986-03-10 1991-10-15 Kohorn H Von System and method for attracting shoppers to sales outlets
DE3788488D1 (en) 1986-10-03 1994-01-27 British Telecomm Language translation system.
US5644727A (en) 1987-04-15 1997-07-01 Proprietary Financial Products, Inc. System for the operation and management of one or more financial accounts through the use of a digital communication and computation system for exchange, investment and borrowing
CA1295064C (en) 1987-05-29 1992-01-28 Kuniyoshi Marui Voice recognition system used in telephone apparatus
DE3723078A1 (en) 1987-07-11 1989-01-19 Philips Patentverwaltung Method for detecting spoken words zusammenhaengend
US4974191A (en) 1987-07-31 1990-11-27 Syntellect Software Inc. Adaptive natural language computer interface system
US4852173A (en) 1987-10-29 1989-07-25 International Business Machines Corporation Design and construction of a binary-tree system for language modelling
DE3876379D1 (en) 1987-10-30 1993-01-14 Ibm Automatic determination of identification and markov-word models in a speech recognition system.
US4984177A (en) 1988-02-05 1991-01-08 Advanced Products And Technologies, Inc. Voice language translator
US4914590A (en) 1988-05-18 1990-04-03 Emhart Industries, Inc. Natural language understanding system
US5282265A (en) 1988-10-04 1994-01-25 Canon Kabushiki Kaisha Knowledge information processing system
DE3837590A1 (en) 1988-11-05 1990-05-10 Ant Nachrichtentech A method for reducing the data rate of digital image data
SE466029B (en) 1989-03-06 1991-12-02 Ibm Svenska Ab Device and foerfarande Foer analysis naturally the Language in a computer-based information processing systems
JPH0782544B2 (en) 1989-03-24 1995-09-06 インターナショナル・ビジネス・マシーンズ・コーポレーション dp Matsuchingu method and apparatus using a multi-template
US5197005A (en) 1989-05-01 1993-03-23 Intelligent Business Systems Database retrieval system having a natural language interface
CH681573A5 (en) 1990-02-13 1993-04-15 Astral Automatic teller arrangement involving bank computers - is operated by user data card carrying personal data, account information and transaction records
US5404295A (en) 1990-08-16 1995-04-04 Katz; Boris Method and apparatus for utilizing annotations to facilitate computer retrieval of database material
US5309359A (en) 1990-08-16 1994-05-03 Boris Katz Method and apparatus for generating and utlizing annotations to facilitate computer text retrieval
US5128672A (en) 1990-10-30 1992-07-07 Apple Computer, Inc. Dynamic predictive keyboard
US5133011A (en) 1990-12-26 1992-07-21 International Business Machines Corporation Method and apparatus for linear vocal control of cursor position
GB9105367D0 (en) 1991-03-13 1991-04-24 Univ Strathclyde Computerised information-retrieval database systems
US5687077A (en) 1991-07-31 1997-11-11 Universal Dynamics Limited Method and apparatus for adaptive control
JPH05197573A (en) 1991-08-26 1993-08-06 Hewlett Packard Co <Hp> Task management system based on task-oriented paradigm
KR940002854B1 (en) 1991-11-06 1994-04-04 이해욱 Sound synthesizing system
US6081750A (en) 1991-12-23 2000-06-27 Hoffberg; Steven Mark Ergonomic man-machine interface incorporating adaptive pattern recognition based control system
US5903454A (en) 1991-12-23 1999-05-11 Hoffberg; Linda Irene Human-factored interface corporating adaptive pattern recognition based controller apparatus
US6055514A (en) 1992-03-20 2000-04-25 Wren; Stephen Corey System for marketing foods and services utilizing computerized centraland remote facilities
US5412804A (en) 1992-04-30 1995-05-02 Oracle Corporation Extending the semantics of the outer join operator for un-nesting queries to a data base
JPH07506908A (en) 1992-05-20 1995-07-27
US5293584A (en) 1992-05-21 1994-03-08 International Business Machines Corporation Speech recognition system for natural language translation
US5390281A (en) 1992-05-27 1995-02-14 Apple Computer, Inc. Method and apparatus for deducing user intent and providing computer implemented services
US5434777A (en) 1992-05-27 1995-07-18 Apple Computer, Inc. Method and apparatus for processing natural language
JPH0619965A (en) 1992-07-01 1994-01-28 Canon Inc Natural language processor
US5999908A (en) 1992-08-06 1999-12-07 Abelow; Daniel H. Customer-based product design module
GB9220404D0 (en) 1992-08-20 1992-11-11 Nat Security Agency Method of identifying,retrieving and sorting documents
US5412806A (en) 1992-08-20 1995-05-02 Hewlett-Packard Company Calibration of logical cost formulae for queries in a heterogeneous DBMS using synthetic database
US5758313A (en) 1992-10-16 1998-05-26 Mobile Information Systems, Inc. Method and apparatus for tracking vehicle location
US5412756A (en) 1992-12-22 1995-05-02 Mitsubishi Denki Kabushiki Kaisha Artificial intelligence software shell for plant operation simulation
US5734791A (en) 1992-12-31 1998-03-31 Apple Computer, Inc. Rapid tree-based method for vector quantization
US6122616A (en) 1993-01-21 2000-09-19 Apple Computer, Inc. Method and apparatus for diphone aliasing
US5864844A (en) 1993-02-18 1999-01-26 Apple Computer, Inc. System and method for enhancing a user interface with a computer based training tool
CA2091658A1 (en) 1993-03-15 1994-09-16 Matthew Lennig Method and apparatus for automation of directory assistance using speech recognition
US6055531A (en) 1993-03-24 2000-04-25 Engate Incorporated Down-line transcription system having context sensitive searching capability
US5444823A (en) 1993-04-16 1995-08-22 Compaq Computer Corporation Intelligent search engine for associated on-line documentation having questionless case-based knowledge base
JPH0756933A (en) 1993-06-24 1995-03-03 Xerox Corp Document retrieval method
JP3685812B2 (en) 1993-06-29 2005-08-24 ソニー株式会社 Audio signal transmitting and receiving device
WO1995002221A1 (en) 1993-07-07 1995-01-19 Inference Corporation Case-based organizing and querying of a database
US5495604A (en) 1993-08-25 1996-02-27 Asymetrix Corporation Method and apparatus for the modeling and query of database structures using natural language-like constructs
US5619694A (en) 1993-08-26 1997-04-08 Nec Corporation Case database storage/retrieval system
US5940811A (en) 1993-08-27 1999-08-17 Affinity Technology Group, Inc. Closed loop financial transaction method and apparatus
US5377258A (en) 1993-08-30 1994-12-27 National Medical Research Council Method and apparatus for an automated and interactive behavioral guidance system
US5873056A (en) 1993-10-12 1999-02-16 The Syracuse University Natural language processing system for semantic vector representation which accounts for lexical ambiguity
US5578808A (en) 1993-12-22 1996-11-26 Datamark Services, Inc. Data card that can be used for transactions involving separate card issuers
EP0736203A1 (en) 1993-12-23 1996-10-09 Diacom Technologies, Inc. Method and apparatus for implementing user feedback
US5621859A (en) 1994-01-19 1997-04-15 Bbn Corporation Single tree method for grammar directed, very large vocabulary speech recognizer
US5584024A (en) 1994-03-24 1996-12-10 Software Ag Interactive database query system and method for prohibiting the selection of semantically incorrect query parameters
US5642519A (en) 1994-04-29 1997-06-24 Sun Microsystems, Inc. Speech interpreter with a unified grammer compiler
DE69520302T2 (en) 1994-05-25 2001-08-09 Victor Company Of Japan A data reproducing apparatus with a variable transmission rate
US5493677A (en) 1994-06-08 1996-02-20 Systems Research & Applications Corporation Generation, archiving, and retrieval of digital images with evoked suggestion-set captions and natural language interface
US5675819A (en) 1994-06-16 1997-10-07 Xerox Corporation Document information retrieval using global word co-occurrence patterns
JPH0869470A (en) 1994-06-21 1996-03-12 Canon Inc Natural language processing device and method
US5948040A (en) 1994-06-24 1999-09-07 Delorme Publishing Co. Travel reservation information and planning system
US5682539A (en) 1994-09-29 1997-10-28 Conrad; Donovan Anticipated meaning natural language interface
US5715468A (en) 1994-09-30 1998-02-03 Budzinski; Robert Lucius Memory system for storing and retrieving experience and knowledge with natural language
GB2293667B (en) 1994-09-30 1998-05-27 Intermation Limited Database management system
US5777614A (en) 1994-10-14 1998-07-07 Hitachi, Ltd. Editing support system including an interactive interface
US5845255A (en) 1994-10-28 1998-12-01 Advanced Health Med-E-Systems Corporation Prescription management system
US5577241A (en) 1994-12-07 1996-11-19 Excite, Inc. Information retrieval system and method with implementation extensible query architecture
US5748974A (en) 1994-12-13 1998-05-05 International Business Machines Corporation Multimodal natural language interface for cross-application tasks
US5794050A (en) 1995-01-04 1998-08-11 Intelligent Text Processing, Inc. Natural language understanding system
CN100365535C (en) 1995-02-13 2008-01-30 英特特拉斯特技术公司 Systems and methods for secure transaction management and electronic rights protection
US5701400A (en) 1995-03-08 1997-12-23 Amado; Carlos Armando Method and apparatus for applying if-then-else rules to data sets in a relational data base and generating from the results of application of said rules a database of diagnostics linked to said data sets to aid executive analysis of financial data
US5749081A (en) 1995-04-06 1998-05-05 Firefly Network, Inc. System and method for recommending items to a user
US5642464A (en) 1995-05-03 1997-06-24 Northern Telecom Limited Methods and apparatus for noise conditioning in digital speech compression systems using linear predictive coding
US5664055A (en) 1995-06-07 1997-09-02 Lucent Technologies Inc. CS-ACELP speech compression system with adaptive pitch prediction filter gain based on a measure of periodicity
US5710886A (en) 1995-06-16 1998-01-20 Sellectsoft, L.C. Electric couponing method and apparatus
JP3284832B2 (en) 1995-06-22 2002-05-20 セイコーエプソン株式会社 Speech recognition dialogue processing method and speech recognition dialogue system
US6038533A (en) 1995-07-07 2000-03-14 Lucent Technologies Inc. System and method for selecting training text
US6026388A (en) 1995-08-16 2000-02-15 Textwise, Llc User interface and other enhancements for natural language information retrieval system and method
JP3697748B2 (en) 1995-08-21 2005-09-21 セイコーエプソン株式会社 Terminal, the voice recognition device
US5712957A (en) 1995-09-08 1998-01-27 Carnegie Mellon University Locating and correcting erroneously recognized portions of utterances by rescoring based on two n-best lists
US5790978A (en) 1995-09-15 1998-08-04 Lucent Technologies, Inc. System and method for determining pitch contours
US5737734A (en) 1995-09-15 1998-04-07 Infonautics Corporation Query word relevance adjustment in a search of an information retrieval system
US5884323A (en) 1995-10-13 1999-03-16 3Com Corporation Extendible method and apparatus for synchronizing files on two different computer systems
US5799276A (en) 1995-11-07 1998-08-25 Accent Incorporated Knowledge-based speech recognition system and methods having frame length computed based upon estimated pitch period of vocalic intervals
US5799279A (en) 1995-11-13 1998-08-25 Dragon Systems, Inc. Continuous speech recognition of text and commands
US5794237A (en) 1995-11-13 1998-08-11 International Business Machines Corporation System and method for improving problem source identification in computer systems employing relevance feedback and statistical source ranking
US5706442A (en) 1995-12-20 1998-01-06 Block Financial Corporation System for on-line financial services using distributed objects
CA2242874A1 (en) 1996-01-17 1997-07-24 Personal Agents, Inc. Intelligent agents for electronic commerce
US6119101A (en) 1996-01-17 2000-09-12 Personal Agents, Inc. Intelligent agents for electronic commerce
US6125356A (en) 1996-01-18 2000-09-26 Rosefaire Development, Ltd. Portable sales presentation system with selective scripted seller prompts
US5987404A (en) 1996-01-29 1999-11-16 International Business Machines Corporation Statistical natural language understanding using hidden clumpings
US5729694A (en) 1996-02-06 1998-03-17 The Regents Of The University Of California Speech coding, reconstruction and recognition using acoustics and electromagnetic waves
US6076088A (en) 1996-02-09 2000-06-13 Paik; Woojin Information extraction system and method using concept relation concept (CRC) triples
US5835893A (en) 1996-02-15 1998-11-10 Atr Interpreting Telecommunications Research Labs Class-based word clustering for speech recognition using a three-level balanced hierarchical similarity
US5901287A (en) 1996-04-01 1999-05-04 The Sabre Group Inc. Information aggregation and synthesization system
US5867799A (en) 1996-04-04 1999-02-02 Lang; Andrew K. Information system and method for filtering a massive flow of information entities to meet user information classification needs
US5987140A (en) 1996-04-26 1999-11-16 Verifone, Inc. System, method and article of manufacture for secure network electronic payment and credit collection
US5963924A (en) 1996-04-26 1999-10-05 Verifone, Inc. System, method and article of manufacture for the use of payment instrument holders and payment instruments in network electronic commerce
US5913193A (en) 1996-04-30 1999-06-15 Microsoft Corporation Method and system of runtime acoustic unit selection for speech synthesis
US5857184A (en) 1996-05-03 1999-01-05 Walden Media, Inc. Language and method for creating, organizing, and retrieving data from a database
US5828999A (en) 1996-05-06 1998-10-27 Apple Computer, Inc. Method and system for deriving a large-span semantic language model for large-vocabulary recognition systems
FR2748342B1 (en) 1996-05-06 1998-07-17 France Telecom Method and filter device by equalizing a speech signal, using a statistical model of the signal
US5826261A (en) 1996-05-10 1998-10-20 Spencer; Graham System and method for querying multiple, distributed databases by selective sharing of local relative significance information for terms related to the query
US6366883B1 (en) 1996-05-15 2002-04-02 Atr Interpreting Telecommunications Concatenation of speech segments by use of a speech synthesizer
US5727950A (en) 1996-05-22 1998-03-17 Netsage Corporation Agent based instruction system and method
US5966533A (en) 1996-06-11 1999-10-12 Excite, Inc. Method and system for dynamically synthesizing a computer program by differentially resolving atoms based on user context data
US5915249A (en) 1996-06-14 1999-06-22 Excite, Inc. System and method for accelerated query evaluation of very large full-text databases
US5987132A (en) 1996-06-17 1999-11-16 Verifone, Inc. System, method and article of manufacture for conditionally accepting a payment method utilizing an extensible, flexible architecture
US5825881A (en) 1996-06-28 1998-10-20 Allsoft Distributing Inc. Public network merchandising system
US6070147A (en) 1996-07-02 2000-05-30 Tecmark Services, Inc. Customer identification and marketing analysis systems
DE69735486D1 (en) 1996-07-22 2006-05-11 Cyva Res Corp Tool for safety and austauch of personal data
JPH1069578A (en) 1996-08-29 1998-03-10 Tec Corp Data processing device
US5794207A (en) 1996-09-04 1998-08-11 Walker Asset Management Limited Partnership Method and apparatus for a cryptographically assisted commercial network system designed to facilitate buyer-driven conditional purchase offers
EP0829811A1 (en) 1996-09-11 1998-03-18 Nippon Telegraph And Telephone Corporation Method and system for information retrieval
US6181935B1 (en) 1996-09-27 2001-01-30 Software.Com, Inc. Mobility extended telephone application programming interface and method of use
US5794182A (en) 1996-09-30 1998-08-11 Apple Computer, Inc. Linear predictive speech encoding systems with efficient combination pitch coefficients computation
US5721827A (en) 1996-10-02 1998-02-24 James Logan System for electrically distributing personalized information
US5913203A (en) 1996-10-03 1999-06-15 Jaesent Inc. System and method for pseudo cash transactions
US5930769A (en) 1996-10-07 1999-07-27 Rose; Andrea System and method for fashion shopping
US5836771A (en) 1996-12-02 1998-11-17 Ho; Chi Fai Learning method and system based on questioning
US6665639B2 (en) 1996-12-06 2003-12-16 Sensory, Inc. Speech recognition in consumer electronic products
US6078914A (en) 1996-12-09 2000-06-20 Open Text Corporation Natural language meta-search system and method
US5839106A (en) 1996-12-17 1998-11-17 Apple Computer, Inc. Large-vocabulary speech recognition using an integrated syntactic and semantic statistical language model
US5966126A (en) 1996-12-23 1999-10-12 Szabo; Andrew J. Graphic user interface for database system
US5932869A (en) 1996-12-27 1999-08-03 Graphic Technology, Inc. Promotional system with magnetic stripe and visual thermo-reversible print surfaced medium
JP3579204B2 (en) 1997-01-17 2004-10-20 富士通株式会社 Article summarizing apparatus and method
US5941944A (en) 1997-03-03 1999-08-24 Microsoft Corporation Method for providing a substitute for a requested inaccessible object by identifying substantially similar objects using weights corresponding to object features
US6076051A (en) 1997-03-07 2000-06-13 Microsoft Corporation Information retrieval utilizing semantic representation of text
US5930801A (en) 1997-03-07 1999-07-27 Xerox Corporation Shared-data environment in which each file has independent security properties
AU6566598A (en) 1997-03-20 1998-10-12 Schlumberger Technologies, Inc. System and method of transactional taxation using secure stored data devices
US5822743A (en) 1997-04-08 1998-10-13 1215627 Ontario Inc. Knowledge-based information retrieval system
US5970474A (en) 1997-04-24 1999-10-19 Sears, Roebuck And Co. Registry information system for shoppers
US5895464A (en) 1997-04-30 1999-04-20 Eastman Kodak Company Computer program product and a method for using natural language for the description, search and retrieval of multi-media objects
EP1008084A1 (en) 1997-07-02 2000-06-14 Philippe J. M. Coueignoux System and method for the secure discovery, exploitation and publication of information
US5860063A (en) 1997-07-11 1999-01-12 At&T Corp Automated meaningful phrase clustering
US5933822A (en) 1997-07-22 1999-08-03 Microsoft Corporation Apparatus and methods for an information retrieval system that employs natural language processing of search results to improve overall precision
US5974146A (en) 1997-07-30 1999-10-26 Huntington Bancshares Incorporated Real time bank-centric universal payment system
US6016476A (en) 1997-08-11 2000-01-18 International Business Machines Corporation Portable information and transaction processing system and method utilizing biometric authorization and digital certificate security
US5895466A (en) 1997-08-19 1999-04-20 At&T Corp Automated natural language understanding customer service system
US6081774A (en) 1997-08-22 2000-06-27 Novell, Inc. Natural language information retrieval system and method
US6404876B1 (en) 1997-09-25 2002-06-11 Gte Intelligent Network Services Incorporated System and method for voice activated dialing and routing under open access network control
US6023684A (en) 1997-10-01 2000-02-08 Security First Technologies, Inc. Three tier financial transaction system with cache memory
EP0911808B1 (en) 1997-10-23 2002-05-08 Sony International (Europe) GmbH Speech interface in a home network environment
US6108627A (en) 1997-10-31 2000-08-22 Nortel Networks Corporation Automatic transcription tool
US5943670A (en) 1997-11-21 1999-08-24 International Business Machines Corporation System and method for categorizing objects in combined categories
US5960422A (en) 1997-11-26 1999-09-28 International Business Machines Corporation System and method for optimized source selection in an information retrieval system
US6026375A (en) 1997-12-05 2000-02-15 Nortel Networks Corporation Method and apparatus for processing orders from customers in a mobile environment
US6064960A (en) 1997-12-18 2000-05-16 Apple Computer, Inc. Method and apparatus for improved duration modeling of phonemes
US6094649A (en) 1997-12-22 2000-07-25 Partnet, Inc. Keyword searches of structured databases
US6173287B1 (en) 1998-03-11 2001-01-09 Digital Equipment Corporation Technique for ranking multimedia annotations of interest
US6195641B1 (en) 1998-03-27 2001-02-27 International Business Machines Corp. Network universal spoken language vocabulary
US6026393A (en) 1998-03-31 2000-02-15 Casebank Technologies Inc. Configuration knowledge as an aid to case retrieval
US6233559B1 (en) 1998-04-01 2001-05-15 Motorola, Inc. Speech control of multiple applications using applets
US6173279B1 (en) 1998-04-09 2001-01-09 At&T Corp. Method of using a natural language interface to retrieve information from one or more data resources
US6088731A (en) 1998-04-24 2000-07-11 Associative Computing, Inc. Intelligent assistant for use with a local computer and with the internet
WO1999056227A1 (en) 1998-04-27 1999-11-04 British Telecommunications Public Limited Company Database access tool
US6016471A (en) 1998-04-29 2000-01-18 Matsushita Electric Industrial Co., Ltd. Method and apparatus using decision trees to generate and score multiple pronunciations for a spelled word
US6029132A (en) 1998-04-30 2000-02-22 Matsushita Electric Industrial Co. Method for letter-to-sound in text-to-speech synthesis
US6285786B1 (en) 1998-04-30 2001-09-04 Motorola, Inc. Text recognizer and method using non-cumulative character scoring in a forward search
US6144938A (en) 1998-05-01 2000-11-07 Sun Microsystems, Inc. Voice user interface with personality
US7711672B2 (en) 1998-05-28 2010-05-04 Lawrence Au Semantic network methods to disambiguate natural language meaning
US6778970B2 (en) 1998-05-28 2004-08-17 Lawrence Au Topological methods to organize semantic network data flows for conversational applications
US20070094224A1 (en) 1998-05-28 2007-04-26 Lawrence Au Method and system for determining contextual meaning for network search applications
US6144958A (en) 1998-07-15 2000-11-07 Amazon.Com, Inc. System and method for correcting spelling errors in search queries
US6105865A (en) 1998-07-17 2000-08-22 Hardesty; Laurence Daniel Financial transaction system with retirement saving benefit
US6499013B1 (en) 1998-09-09 2002-12-24 One Voice Technologies, Inc. Interactive user interface using speech recognition and natural language processing
US6434524B1 (en) 1998-09-09 2002-08-13 One Voice Technologies, Inc. Object interactive user interface using speech recognition and natural language processing
DE29825146U1 (en) 1998-09-11 2005-08-18 Püllen, Rainer Audio on demand system
US6266637B1 (en) 1998-09-11 2001-07-24 International Business Machines Corporation Phrase splicing and variable substitution using a trainable speech synthesizer
US6792082B1 (en) 1998-09-11 2004-09-14 Comverse Ltd. Voice mail system with personal assistant provisioning
US6317831B1 (en) 1998-09-21 2001-11-13 Openwave Systems Inc. Method and apparatus for establishing a secure connection over a one-way data path
US6173261B1 (en) 1998-09-30 2001-01-09 At&T Corp Grammar fragment acquisition using syntactic and semantic clustering
EP1163576A4 (en) 1998-10-02 2005-11-30 Ibm Conversational computing via conversational virtual machine
US6275824B1 (en) 1998-10-02 2001-08-14 Ncr Corporation System and method for managing data privacy in a database management system
GB9821969D0 (en) 1998-10-08 1998-12-02 Canon Kk Apparatus and method for processing natural language
US6928614B1 (en) 1998-10-13 2005-08-09 Visteon Global Technologies, Inc. Mobile office with speech recognition
US6453292B2 (en) 1998-10-28 2002-09-17 International Business Machines Corporation Command boundary identifier for conversational natural language
US6208971B1 (en) 1998-10-30 2001-03-27 Apple Computer, Inc. Method and apparatus for command recognition using data-driven semantic inference
US6321092B1 (en) 1998-11-03 2001-11-20 Signal Soft Corporation Multiple input data management for wireless location-based applications
US6446076B1 (en) 1998-11-12 2002-09-03 Accenture Llp. Voice interactive web-based agent system responsive to a user location for prioritizing and formatting information
US6665641B1 (en) 1998-11-13 2003-12-16 Scansoft, Inc. Speech synthesis using concatenation of speech waveforms
US6246981B1 (en) 1998-11-25 2001-06-12 International Business Machines Corporation Natural language task-oriented dialog manager and method
US7082397B2 (en) 1998-12-01 2006-07-25 Nuance Communications, Inc. System for and method of creating and browsing a voice web
US6260024B1 (en) 1998-12-02 2001-07-10 Gary Shkedy Method and apparatus for facilitating buyer-driven purchase orders on a commercial network system
US7881936B2 (en) 1998-12-04 2011-02-01 Tegic Communications, Inc. Multimodal disambiguation of speech recognition
US8095364B2 (en) 2004-06-02 2012-01-10 Tegic Communications, Inc. Multimodal disambiguation of speech recognition
US6317707B1 (en) 1998-12-07 2001-11-13 At&T Corp. Automatic clustering of tokens from a corpus for grammar acquisition
US6177905B1 (en) * 1998-12-08 2001-01-23 Avaya Technology Corp. Location-triggered reminder for mobile user devices
US6308149B1 (en) 1998-12-16 2001-10-23 Xerox Corporation Grouping words with equivalent substrings by automatic clustering based on suffix relationships
US6523172B1 (en) 1998-12-17 2003-02-18 Evolutionary Technologies International, Inc. Parser translator system and method
US6460029B1 (en) 1998-12-23 2002-10-01 Microsoft Corporation System for improving search text
US6606599B2 (en) 1998-12-23 2003-08-12 Interactive Speech Technologies, Llc Method for integrating computing processes with an interface controlled by voice actuated grammars
US6742021B1 (en) 1999-01-05 2004-05-25 Sri International, Inc. Navigating network-based electronic information using spoken input with multimodal error feedback
US6513063B1 (en) 1999-01-05 2003-01-28 Sri International Accessing network-based electronic information through scripted online interfaces using spoken input
US6523061B1 (en) 1999-01-05 2003-02-18 Sri International, Inc. System, method, and article of manufacture for agent-based navigation in a speech-based data navigation system
US6851115B1 (en) 1999-01-05 2005-02-01 Sri International Software-based architecture for communication and cooperation among distributed electronic agents
US6757718B1 (en) 1999-01-05 2004-06-29 Sri International Mobile navigation of network-based electronic information using spoken input
US7036128B1 (en) 1999-01-05 2006-04-25 Sri International Offices Using a community of distributed electronic agents to support a highly mobile, ambient computing environment
US7152070B1 (en) 1999-01-08 2006-12-19 The Regents Of The University Of California System and method for integrating and accessing multiple data sources within a data warehouse architecture
US6505183B1 (en) 1999-02-04 2003-01-07 Authoria, Inc. Human resource knowledge modeling and delivery system
US6317718B1 (en) 1999-02-26 2001-11-13 Accenture Properties (2) B.V. System, method and article of manufacture for location-based filtering for shopping agent in the physical world
GB9904662D0 (en) 1999-03-01 1999-04-21 Canon Kk Natural language search method and apparatus
US6356905B1 (en) 1999-03-05 2002-03-12 Accenture Llp System, method and article of manufacture for mobile communication utilizing an interface support framework
US6928404B1 (en) 1999-03-17 2005-08-09 International Business Machines Corporation System and methods for acoustic and language modeling for automatic speech recognition with large vocabularies
US6584464B1 (en) 1999-03-19 2003-06-24 Ask Jeeves, Inc. Grammar template query system
EP1088299A2 (en) 1999-03-26 2001-04-04 Philips Corporate Intellectual Property GmbH Client-server speech recognition
US6356854B1 (en) 1999-04-05 2002-03-12 Delphi Technologies, Inc. Holographic object position and type sensing system and method
US6631346B1 (en) 1999-04-07 2003-10-07 Matsushita Electric Industrial Co., Ltd. Method and apparatus for natural language parsing using multiple passes and tags
WO2000060435A2 (en) 1999-04-07 2000-10-12 Rensselaer Polytechnic Institute System and method for accessing personal information
US6647260B2 (en) 1999-04-09 2003-11-11 Openwave Systems Inc. Method and system facilitating web based provisioning of two-way mobile communications devices
US6924828B1 (en) 1999-04-27 2005-08-02 Surfnotes Method and apparatus for improved information representation
US6697780B1 (en) 1999-04-30 2004-02-24 At&T Corp. Method and apparatus for rapid acoustic unit selection from a large speech corpus
AU5451800A (en) 1999-05-28 2000-12-18 Sehda, Inc. Phrase-based dialogue modeling with particular application to creating recognition grammars for voice-controlled user interfaces
US6931384B1 (en) 1999-06-04 2005-08-16 Microsoft Corporation System and method providing utility-based decision making about clarification dialog given communicative uncertainty
US6598039B1 (en) 1999-06-08 2003-07-22 Albert-Inc. S.A. Natural language interface for searching database
US6615175B1 (en) 1999-06-10 2003-09-02 Robert F. Gazdzinski “Smart” elevator system and method
US7093693B1 (en) 1999-06-10 2006-08-22 Gazdzinski Robert F Elevator access control system and method
US8065155B1 (en) 1999-06-10 2011-11-22 Gazdzinski Robert F Adaptive advertising apparatus and methods
US7711565B1 (en) 1999-06-10 2010-05-04 Gazdzinski Robert F “Smart” elevator system and method
US6711585B1 (en) 1999-06-15 2004-03-23 Kanisa Inc. System and method for implementing a knowledge management system
JP3361291B2 (en) 1999-07-23 2003-01-07 コナミ株式会社 Speech synthesis method, recording a computer-readable medium speech synthesis apparatus and the speech synthesis program
US6421672B1 (en) 1999-07-27 2002-07-16 Verizon Services Corp. Apparatus for and method of disambiguation of directory listing searches utilizing multiple selectable secondary search keys
EP1079387A3 (en) 1999-08-26 2003-07-09 Matsushita Electric Industrial Co., Ltd. Mechanism for storing information about recorded television broadcasts
US6912499B1 (en) 1999-08-31 2005-06-28 Nortel Networks Limited Method and apparatus for training a multilingual speech model set
US6697824B1 (en) 1999-08-31 2004-02-24 Accenture Llp Relationship management in an E-commerce application framework
US6601234B1 (en) 1999-08-31 2003-07-29 Accenture Llp Attribute dictionary in a business logic services environment
US7127403B1 (en) 1999-09-13 2006-10-24 Microstrategy, Inc. System and method for personalizing an interactive voice broadcast of a voice service based on particulars of a request
US6601026B2 (en) 1999-09-17 2003-07-29 Discern Communications, Inc. Information retrieval by natural language querying
US6625583B1 (en) 1999-10-06 2003-09-23 Goldman, Sachs & Co. Handheld trading system interface
US6505175B1 (en) 1999-10-06 2003-01-07 Goldman, Sachs & Co. Order centric tracking system
US7020685B1 (en) 1999-10-08 2006-03-28 Openwave Systems Inc. Method and apparatus for providing internet content to SMS-based wireless devices
JP5118280B2 (en) 1999-10-19 2013-01-16 ソニー エレクトロニクス インク Natural language interface control system
US6807574B1 (en) 1999-10-22 2004-10-19 Tellme Networks, Inc. Method and apparatus for content personalization over a telephone interface
JP2001125896A (en) 1999-10-26 2001-05-11 Victor Co Of Japan Ltd Natural language interactive system
US7310600B1 (en) 1999-10-28 2007-12-18 Canon Kabushiki Kaisha Language recognition using a similarity measure
US7392185B2 (en) 1999-11-12 2008-06-24 Phoenix Solutions, Inc. Speech based learning/training system using semantic decoding
US7050977B1 (en) 1999-11-12 2006-05-23 Phoenix Solutions, Inc. Speech-enabled server for internet website and method
US7725307B2 (en) 1999-11-12 2010-05-25 Phoenix Solutions, Inc. Query engine for processing voice based queries including semantic decoding
US6665640B1 (en) 1999-11-12 2003-12-16 Phoenix Solutions, Inc. Interactive speech based learning/training system formulating search queries based on natural language parsing of recognized user queries
US9076448B2 (en) 1999-11-12 2015-07-07 Nuance Communications, Inc. Distributed real time speech recognition system
US6615172B1 (en) 1999-11-12 2003-09-02 Phoenix Solutions, Inc. Intelligent query engine for processing voice based queries
US6633846B1 (en) 1999-11-12 2003-10-14 Phoenix Solutions, Inc. Distributed realtime speech recognition system
US6532446B1 (en) 1999-11-24 2003-03-11 Openwave Systems Inc. Server based speech recognition user interface for wireless devices
US6526382B1 (en) 1999-12-07 2003-02-25 Comverse, Inc. Language-oriented user interfaces for voice activated services
US6526395B1 (en) 1999-12-31 2003-02-25 Intel Corporation Application of personality models and interaction with synthetic characters in a computing system
US6556983B1 (en) 2000-01-12 2003-04-29 Microsoft Corporation Methods and apparatus for finding semantic information, such as usage logs, similar to a query using a pattern lattice data space
US6546388B1 (en) 2000-01-14 2003-04-08 International Business Machines Corporation Metadata search results ranking system
US6701294B1 (en) 2000-01-19 2004-03-02 Lucent Technologies, Inc. User interface for translating natural language inquiries into database queries and data presentations
US6829603B1 (en) 2000-02-02 2004-12-07 International Business Machines Corp. System, method and program product for interactive natural dialog
US6895558B1 (en) 2000-02-11 2005-05-17 Microsoft Corporation Multi-access mode electronic personal assistant
US6640098B1 (en) 2000-02-14 2003-10-28 Action Engine Corporation System for obtaining service-related information for local interactive wireless devices
AU4327701A (en) 2000-02-25 2001-09-03 Synquiry Technologies Ltd Conceptual factoring and unification of graphs representing semantic models
US6449620B1 (en) 2000-03-02 2002-09-10 Nimble Technology, Inc. Method and apparatus for generating information pages using semi-structured data stored in a structured manner
US6895380B2 (en) 2000-03-02 2005-05-17 Electro Standards Laboratories Voice actuation with contextual learning for intelligent machine control
US6757362B1 (en) 2000-03-06 2004-06-29 Avaya Technology Corp. Personal virtual assistant
WO2001067225A2 (en) 2000-03-06 2001-09-13 Kanisa Inc. A system and method for providing an intelligent multi-step dialog with a user
US6466654B1 (en) 2000-03-06 2002-10-15 Avaya Technology Corp. Personal virtual assistant with semantic tagging
US6477488B1 (en) 2000-03-10 2002-11-05 Apple Computer, Inc. Method for dynamic context scope selection in hybrid n-gram+LSA language modeling
US6615220B1 (en) 2000-03-14 2003-09-02 Oracle International Corporation Method and mechanism for data consolidation
US6510417B1 (en) 2000-03-21 2003-01-21 America Online, Inc. System and method for voice access to internet-based information
GB2366009B (en) 2000-03-22 2004-07-21 Canon Kk Natural language machine interface
JP3728172B2 (en) 2000-03-31 2005-12-21 キヤノン株式会社 Speech synthesis method and apparatus
US7177798B2 (en) 2000-04-07 2007-02-13 Rensselaer Polytechnic Institute Natural language interface using constrained intermediate dictionary of results
US20020032564A1 (en) 2000-04-19 2002-03-14 Farzad Ehsani Phrase-based dialogue modeling with particular application to creating a recognition grammar for a voice-controlled user interface
US6810379B1 (en) 2000-04-24 2004-10-26 Sensory, Inc. Client/server architecture for text-to-speech synthesis
US20060143007A1 (en) 2000-07-24 2006-06-29 Koh V E User interaction with voice information services
US6680675B1 (en) * 2000-06-21 2004-01-20 Fujitsu Limited Interactive to-do list item notification system including GPS interface
US6684187B1 (en) 2000-06-30 2004-01-27 At&T Corp. Method and system for preselection of suitable units for concatenative speech
US6691111B2 (en) 2000-06-30 2004-02-10 Research In Motion Limited System and method for implementing a natural language user interface
US6505158B1 (en) 2000-07-05 2003-01-07 At&T Corp. Synthesis-based pre-selection of suitable units for concatenative speech
US6240362B1 (en) 2000-07-10 2001-05-29 Iap Intermodal, Llc Method to schedule a vehicle in real-time to transport freight and passengers
JP3949356B2 (en) 2000-07-12 2007-07-25 三菱電機株式会社 Voice dialogue system
US7139709B2 (en) 2000-07-20 2006-11-21 Microsoft Corporation Middleware layer between speech related applications and engines
JP2002041276A (en) 2000-07-24 2002-02-08 Sony Corp Interactive operation-supporting system, interactive operation-supporting method and recording medium
JP2002041624A (en) 2000-07-31 2002-02-08 Living First:Kk System and method for processing real estate information and recording medium recorded with software for real estate information processing
US7092928B1 (en) 2000-07-31 2006-08-15 Quantum Leap Research, Inc. Intelligent portal engine
US6778951B1 (en) 2000-08-09 2004-08-17 Concerto Software, Inc. Information retrieval method with natural language interface
US6766320B1 (en) 2000-08-24 2004-07-20 Microsoft Corporation Search engine with natural language-based robust parsing for user query and relevance feedback learning
DE10042944C2 (en) 2000-08-31 2003-03-13 Siemens Ag Grapheme-phoneme conversion
WO2002023523A2 (en) 2000-09-15 2002-03-21 Lernout & Hauspie Speech Products N.V. Fast waveform synchronization for concatenation and time-scale modification of speech
US7101185B2 (en) 2001-09-26 2006-09-05 Scientific Learning Corporation Method and apparatus for automated training of language learning skills
WO2002027712A1 (en) 2000-09-29 2002-04-04 Professorq, Inc. Natural-language voice-activated personal assistant
US6832194B1 (en) 2000-10-26 2004-12-14 Sensory, Incorporated Audio recognition peripheral system
US7027974B1 (en) 2000-10-27 2006-04-11 Science Applications International Corporation Ontology-based parser for natural language processing
US7006969B2 (en) 2000-11-02 2006-02-28 At&T Corp. System and method of pattern recognition in very high-dimensional space
US6957076B2 (en) * 2000-11-22 2005-10-18 Denso Corporation Location specific reminders for wireless mobiles
US20020067308A1 (en) 2000-12-06 2002-06-06 Xerox Corporation Location/time-based reminder for personal electronic devices
WO2002050816A1 (en) 2000-12-18 2002-06-27 Koninklijke Philips Electronics N.V. Store speech, select vocabulary to recognize word
US6937986B2 (en) 2000-12-28 2005-08-30 Comverse, Inc. Automatic dynamic speech recognition vocabulary based on external sources of information
WO2002054239A2 (en) 2000-12-29 2002-07-11 General Electric Company Method and system for identifying repeatedly malfunctioning equipment
US7257537B2 (en) * 2001-01-12 2007-08-14 International Business Machines Corporation Method and apparatus for performing dialog management in a computer conversational interface
US6964023B2 (en) 2001-02-05 2005-11-08 International Business Machines Corporation System and method for multi-modal focus detection, referential ambiguity resolution and mood classification using multi-modal input
US7290039B1 (en) 2001-02-27 2007-10-30 Microsoft Corporation Intent based processing
US6721728B2 (en) 2001-03-02 2004-04-13 The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration System, method and apparatus for discovering phrases in a database
US7216073B2 (en) 2001-03-13 2007-05-08 Intelligate, Ltd. Dynamic natural language understanding
JP2002281562A (en) 2001-03-21 2002-09-27 Sony Corp Portable information terminal equipment
JP3925611B2 (en) 2001-03-22 2007-06-06 セイコーエプソン株式会社 Information providing system, information providing apparatus, information storage medium and the user interface setting
US6996531B2 (en) 2001-03-30 2006-02-07 Comverse Ltd. Automated database assistance using a telephone for a speech based or text based multimedia communication mode
US6654740B2 (en) 2001-05-08 2003-11-25 Sunflare Co., Ltd. Probabilistic information retrieval based on differential latent semantic space
US7085722B2 (en) 2001-05-14 2006-08-01 Sony Computer Entertainment America Inc. System and method for menu-driven voice control of characters in a game environment
US6944594B2 (en) 2001-05-30 2005-09-13 Bellsouth Intellectual Property Corporation Multi-context conversational environment system and method
US20020194003A1 (en) 2001-06-05 2002-12-19 Mozer Todd F. Client-server security system and method
US20020198714A1 (en) 2001-06-26 2002-12-26 Guojun Zhou Statistical spoken dialog system
US7139722B2 (en) 2001-06-27 2006-11-21 Bellsouth Intellectual Property Corporation Location and time sensitive wireless calendaring
US7302686B2 (en) * 2001-07-04 2007-11-27 Sony Corporation Task management system
US6526351B2 (en) * 2001-07-09 2003-02-25 Charles Lamont Whitham Interactive multimedia tour guide
US6604059B2 (en) 2001-07-10 2003-08-05 Koninklijke Philips Electronics N.V. Predictive calendar
US7987151B2 (en) 2001-08-10 2011-07-26 General Dynamics Advanced Info Systems, Inc. Apparatus and method for problem solving using intelligent agents
US6813491B1 (en) 2001-08-31 2004-11-02 Openwave Systems Inc. Method and apparatus for adapting settings of wireless communication devices in accordance with user proximity
US7403938B2 (en) 2001-09-24 2008-07-22 Iac Search & Media, Inc. Natural language query processing
US6985865B1 (en) 2001-09-26 2006-01-10 Sprint Spectrum L.P. Method and system for enhanced response to voice commands in a voice command platform
US6650735B2 (en) 2001-09-27 2003-11-18 Microsoft Corporation Integrated voice access to a variety of personal information services
US7324947B2 (en) 2001-10-03 2008-01-29 Promptu Systems Corporation Global speech user interface
US7167832B2 (en) * 2001-10-15 2007-01-23 At&T Corp. Method for dialog management
US20040054535A1 (en) 2001-10-22 2004-03-18 Mackie Andrew William System and method of processing structured text for text-to-speech synthesis
GB2381409B (en) 2001-10-27 2004-04-28 Hewlett Packard Ltd Asynchronous access to synchronous voice services
NO316480B1 (en) 2001-11-15 2004-01-26 Forinnova As A method and system for textual investigation and detection
US20030101054A1 (en) 2001-11-27 2003-05-29 Ncc, Llc Integrated system and method for electronic speech recognition and transcription
US20030177046A1 (en) * 2001-12-03 2003-09-18 John Socha-Leialoha Method and system for reusing components
GB2388209C (en) 2001-12-20 2005-08-23 Canon Kk Control apparatus
TW541517B (en) 2001-12-25 2003-07-11 Univ Nat Cheng Kung Speech recognition system
US20030140088A1 (en) * 2002-01-24 2003-07-24 Robinson Scott H. Context-based information processing
WO2003077079A2 (en) 2002-03-08 2003-09-18 Enleague Systems, Inc Methods and systems for modeling and using computer resources over a heterogeneous distributed network using semantic ontologies
US7197460B1 (en) 2002-04-23 2007-03-27 At&T Corp. System for handling frequently asked questions in a natural language dialog service
US6847966B1 (en) 2002-04-24 2005-01-25 Engenium Corporation Method and system for optimally searching a document database using a representative semantic space
US7221937B2 (en) 2002-05-06 2007-05-22 Research In Motion Limited Event reminder method
US6986106B2 (en) 2002-05-13 2006-01-10 Microsoft Corporation Correction widget
US7546382B2 (en) 2002-05-28 2009-06-09 International Business Machines Corporation Methods and systems for authoring of mixed-initiative multi-modal interactions and related browsing mechanisms
US7398209B2 (en) 2002-06-03 2008-07-08 Voicebox Technologies, Inc. Systems and methods for responding to natural language speech utterance
US7233790B2 (en) 2002-06-28 2007-06-19 Openwave Systems, Inc. Device capability based discovery, packaging and provisioning of content for wireless mobile devices
US7299033B2 (en) 2002-06-28 2007-11-20 Openwave Systems Inc. Domain-based management of distribution of digital content from multiple suppliers to multiple wireless services subscribers
US7693720B2 (en) 2002-07-15 2010-04-06 Voicebox Technologies, Inc. Mobile systems and methods for responding to natural language speech utterance
US20080101584A1 (en) 2003-08-01 2008-05-01 Mitel Networks Corporation Method of providing context aware announcements
US7467087B1 (en) 2002-10-10 2008-12-16 Gillick Laurence S Training and using pronunciation guessers in speech recognition
JP4109091B2 (en) 2002-11-19 2008-06-25 株式会社山武 Schedule management apparatus and method, program
US7783486B2 (en) 2002-11-22 2010-08-24 Roy Jonathan Rosser Response generator for mimicking human-computer natural language conversation
EP1614102A4 (en) 2002-12-10 2006-12-20 Kirusa Inc Techniques for disambiguating speech input using multimodal interfaces
US7386449B2 (en) 2002-12-11 2008-06-10 Voice Enabling Systems Technology Inc. Knowledge-based flexible natural speech dialogue system
US7956766B2 (en) 2003-01-06 2011-06-07 Panasonic Corporation Apparatus operating system
US20040162741A1 (en) 2003-02-07 2004-08-19 David Flaxer Method and apparatus for product lifecycle management in a distributed environment enabled by dynamic business process composition and execution by rule inference
US7529671B2 (en) 2003-03-04 2009-05-05 Microsoft Corporation Block synchronous decoding
US6980949B2 (en) 2003-03-14 2005-12-27 Sonum Technologies, Inc. Natural language processor
US7496498B2 (en) 2003-03-24 2009-02-24 Microsoft Corporation Front-end architecture for a multi-lingual text-to-speech system
US7561069B2 (en) 2003-11-12 2009-07-14 Legalview Assets, Limited Notification systems and methods enabling a response to change particulars of delivery or pickup
US7200559B2 (en) 2003-05-29 2007-04-03 Microsoft Corporation Semantic object synchronous understanding implemented with speech application language tags
US7720683B1 (en) 2003-06-13 2010-05-18 Sensory, Inc. Method and apparatus of specifying and performing speech recognition operations
US20050015772A1 (en) * 2003-07-16 2005-01-20 Saare John E. Method and system for device specific application optimization via a portal server
JP2005080094A (en) 2003-09-02 2005-03-24 Canon Inc Communication apparatus and subject matter notification method therefor
US7475010B2 (en) 2003-09-03 2009-01-06 Lingospot, Inc. Adaptive and scalable method for resolving natural language ambiguities
US20050125235A1 (en) 2003-09-11 2005-06-09 Voice Signal Technologies, Inc. Method and apparatus for using earcons in mobile communication devices
US7418392B1 (en) 2003-09-25 2008-08-26 Sensory, Inc. System and method for controlling the operation of a device by voice commands
US7155706B2 (en) 2003-10-24 2006-12-26 Microsoft Corporation Administrative tool environment
US7412385B2 (en) 2003-11-12 2008-08-12 Microsoft Corporation System for identifying paraphrases using machine translation
US20050108074A1 (en) * 2003-11-14 2005-05-19 Bloechl Peter E. Method and system for prioritization of task items
US7248900B2 (en) 2003-11-18 2007-07-24 Nokia Corporation Compound ring tunes
US7447630B2 (en) 2003-11-26 2008-11-04 Microsoft Corporation Method and apparatus for multi-sensory speech enhancement
US20050114140A1 (en) 2003-11-26 2005-05-26 Brackett Charles C. Method and apparatus for contextual voice cues
DE602004016681D1 (en) 2003-12-05 2008-10-30 Kenwood Corp , Audio device-control method Audio Setup control device and program
ES2312851T3 (en) 2003-12-16 2009-03-01 Loquendo Spa Procedure and text voice system and associated software.
US7427024B1 (en) 2003-12-17 2008-09-23 Gazdzinski Mark J Chattel management apparatus and methods
US7552055B2 (en) 2004-01-10 2009-06-23 Microsoft Corporation Dialog component re-use in recognition systems
EP1704558B8 (en) 2004-01-16 2011-09-21 Nuance Communications, Inc. Corpus-based speech synthesis based on segment recombination
US20050165607A1 (en) 2004-01-22 2005-07-28 At&T Corp. System and method to disambiguate and clarify user intention in a spoken dialog system
DE602004017955D1 (en) 2004-01-29 2009-01-08 Daimler Ag Method and system for speech dialog interface
JP2005223782A (en) 2004-02-09 2005-08-18 Nec Access Technica Ltd Mobile portable terminal
KR100462292B1 (en) 2004-02-26 2004-12-08 엔에이치엔(주) A method for providing search results list based on importance information and a system thereof
US7421393B1 (en) 2004-03-01 2008-09-02 At&T Corp. System for developing a dialog manager using modular spoken-dialog components
US7693715B2 (en) 2004-03-10 2010-04-06 Microsoft Corporation Generating large units of graphonemes with mutual information criterion for letter to sound conversion
US7084758B1 (en) * 2004-03-19 2006-08-01 Advanced Micro Devices, Inc. Location-based reminders
US7409337B1 (en) 2004-03-30 2008-08-05 Microsoft Corporation Natural language processing interface
US7496512B2 (en) 2004-04-13 2009-02-24 Microsoft Corporation Refining of segmental boundaries in speech waveforms using contextual-dependent models
US7720674B2 (en) 2004-06-29 2010-05-18 Sap Ag Systems and methods for processing natural language queries
US7823123B2 (en) 2004-07-13 2010-10-26 The Mitre Corporation Semantic system for integrating software components
TWI252049B (en) 2004-07-23 2006-03-21 Inventec Corp Sound control system and method
US7725318B2 (en) 2004-07-30 2010-05-25 Nice Systems Inc. System and method for improving the accuracy of audio searching
US7853574B2 (en) 2004-08-26 2010-12-14 International Business Machines Corporation Method of generating a context-inferenced search query and of sorting a result of the query
US20060061488A1 (en) * 2004-09-17 2006-03-23 Dunton Randy R Location based task reminder
US7716056B2 (en) 2004-09-27 2010-05-11 Robert Bosch Corporation Method and system for interactive conversational dialogue for cognitively overloaded device users
US8107401B2 (en) 2004-09-30 2012-01-31 Avaya Inc. Method and apparatus for providing a virtual assistant to a communication participant
US7603381B2 (en) * 2004-09-30 2009-10-13 Microsoft Corporation Contextual action publishing
US7788589B2 (en) 2004-09-30 2010-08-31 Microsoft Corporation Method and system for improved electronic task flagging and management
US9100776B2 (en) 2004-10-06 2015-08-04 Intelligent Mechatronic Systems Inc. Location based event reminder for mobile device
US7543232B2 (en) 2004-10-19 2009-06-02 International Business Machines Corporation Intelligent web based help system
US7546235B2 (en) 2004-11-15 2009-06-09 Microsoft Corporation Unsupervised learning of paraphrase/translation alternations and selective application thereof
US7584092B2 (en) 2004-11-15 2009-09-01 Microsoft Corporation Unsupervised learning of paraphrase/translation alternations and selective application thereof
US7552046B2 (en) 2004-11-15 2009-06-23 Microsoft Corporation Unsupervised learning of paraphrase/translation alternations and selective application thereof
US7885844B1 (en) * 2004-11-16 2011-02-08 Amazon Technologies, Inc. Automatically generating task recommendations for human task performers
US7650284B2 (en) 2004-11-19 2010-01-19 Nuance Communications, Inc. Enabling voice click in a multimodal page
US7702500B2 (en) 2004-11-24 2010-04-20 Blaedow Karen R Method and apparatus for determining the meaning of natural language
CN1609859A (en) 2004-11-26 2005-04-27 孙斌 Search results clustering method
US7873654B2 (en) 2005-01-24 2011-01-18 The Intellection Group, Inc. Multimodal natural language query system for processing and analyzing voice and proximity-based queries
US7376645B2 (en) 2004-11-29 2008-05-20 The Intellection Group, Inc. Multimodal natural language query system and architecture for processing voice and proximity-based queries
JP4282591B2 (en) 2004-11-30 2009-06-24 株式会社東芝 Schedule management system, schedule management method and program
US20060122834A1 (en) 2004-12-03 2006-06-08 Bennett Ian M Emotion detection device & method for use in distributed systems
US8214214B2 (en) 2004-12-03 2012-07-03 Phoenix Solutions, Inc. Emotion detection device and method for use in distributed systems
JP2006166118A (en) 2004-12-08 2006-06-22 Nec Access Technica Ltd Portable communication terminal and its information providing method
US7636657B2 (en) 2004-12-09 2009-12-22 Microsoft Corporation Method and apparatus for automatic grammar generation from data entries
US7483692B2 (en) 2004-12-28 2009-01-27 Sony Ericsson Mobile Communications Ab System and method of predicting user input to a mobile terminal
US8510737B2 (en) 2005-01-07 2013-08-13 Samsung Electronics Co., Ltd. Method and system for prioritizing tasks made available by devices in a network
US8069422B2 (en) * 2005-01-10 2011-11-29 Samsung Electronics, Co., Ltd. Contextual task recommendation system and method for determining user's context and suggesting tasks
WO2007080559A2 (en) 2006-01-16 2007-07-19 Zlango Ltd. Iconic communication
US7508373B2 (en) 2005-01-28 2009-03-24 Microsoft Corporation Form factor and input method for language input
GB0502259D0 (en) 2005-02-03 2005-03-09 British Telecomm Document searching tool and method
US7676026B1 (en) 2005-03-08 2010-03-09 Baxtech Asia Pte Ltd Desktop telephony system
US7925525B2 (en) 2005-03-25 2011-04-12 Microsoft Corporation Smart reminders
JP2006309457A (en) 2005-04-27 2006-11-09 Nec Corp Method for reporting schedule, intelligent scheduler and portable communication equipment
US7292579B2 (en) 2005-04-29 2007-11-06 Scenera Technologies, Llc Processing operations associated with resources on a local network
WO2006129967A1 (en) 2005-05-30 2006-12-07 Daumsoft, Inc. Conversation system and method using conversational agent
US8041570B2 (en) 2005-05-31 2011-10-18 Robert Bosch Corporation Dialogue management using scripts
KR100712808B1 (en) 2005-06-08 2007-04-30 에스케이 텔레콤주식회사 Mobile terminal for supporting the context-aware service and Method of providing the context-aware service in the mobile terminal
US8024195B2 (en) 2005-06-27 2011-09-20 Sensory, Inc. Systems and methods of performing speech recognition using historical information
US7925995B2 (en) * 2005-06-30 2011-04-12 Microsoft Corporation Integration of location logs, GPS signals, and spatial resources for identifying user activities, goals, and context
US7826945B2 (en) 2005-07-01 2010-11-02 You Zhang Automobile speech-recognition interface
US7881283B2 (en) 2005-07-13 2011-02-01 Research In Motion Limited Customizability of event notification on telephony-enabled devices
US20070027732A1 (en) 2005-07-28 2007-02-01 Accu-Spatial, Llc Context-sensitive, location-dependent information delivery at a construction site
US7640160B2 (en) 2005-08-05 2009-12-29 Voicebox Technologies, Inc. Systems and methods for responding to natural language speech utterance
WO2007019480A2 (en) 2005-08-05 2007-02-15 Realnetworks, Inc. System and computer program product for chronologically presenting data
US7844037B2 (en) 2005-08-08 2010-11-30 Palm, Inc. Method and device for enabling message responses to incoming phone calls
US7362738B2 (en) * 2005-08-09 2008-04-22 Deere & Company Method and system for delivering information to a user
US7620549B2 (en) 2005-08-10 2009-11-17 Voicebox Technologies, Inc. System and method of supporting adaptive misrecognition in conversational speech
US7949529B2 (en) 2005-08-29 2011-05-24 Voicebox Technologies, Inc. Mobile systems and methods of supporting natural language human-machine interactions
EP1934971A4 (en) 2005-08-31 2010-10-27 Voicebox Technologies Inc Dynamic speech sharpening
US8265939B2 (en) 2005-08-31 2012-09-11 Nuance Communications, Inc. Hierarchical methods and apparatus for extracting user intent from spoken utterances
US8677377B2 (en) 2005-09-08 2014-03-18 Apple Inc. Method and apparatus for building an intelligent automated assistant
US8275399B2 (en) * 2005-09-21 2012-09-25 Buckyball Mobile Inc. Dynamic context-data tag cloud
WO2007036762A1 (en) 2005-09-30 2007-04-05 Nokia Corporation A method, device, computer program and graphical user interface used for the selection, movement and de-selection of an item
JP4908094B2 (en) 2005-09-30 2012-04-04 株式会社リコー Information processing system, information processing method, and an information processing program
US7930168B2 (en) 2005-10-04 2011-04-19 Robert Bosch Gmbh Natural language processing of disfluent sentences
US8620667B2 (en) 2005-10-17 2013-12-31 Microsoft Corporation Flexible speech-activated command and control
US7707032B2 (en) 2005-10-20 2010-04-27 National Cheng Kung University Method and system for matching speech data
US20070106674A1 (en) 2005-11-10 2007-05-10 Purusharth Agrawal Field sales process facilitation systems and methods
US20070185926A1 (en) 2005-11-28 2007-08-09 Anand Prahlad Systems and methods for classifying and transferring information in a storage network
TWI298844B (en) 2005-11-30 2008-07-11 Delta Electronics Inc User-defines speech-controlled shortcut module and method
KR20070057496A (en) 2005-12-02 2007-06-07 삼성전자주식회사 Liquid crystal display
US7577522B2 (en) 2005-12-05 2009-08-18 Outland Research, Llc Spatially associated personal reminder system and method
EP1958377B1 (en) * 2005-12-05 2009-05-27 Telefonaktiebolaget LM Ericsson (publ) A method and a system relating to network management
KR100810500B1 (en) 2005-12-08 2008-03-07 한국전자통신연구원 Method for enhancing usability in a spoken dialog system
US7461043B2 (en) 2005-12-14 2008-12-02 Siemens Aktiengesellschaft Methods and apparatus to abstract events in software applications or services
US20070143163A1 (en) 2005-12-16 2007-06-21 Sap Ag Systems and methods for organizing and monitoring data collection
DE102005061365A1 (en) 2005-12-21 2007-06-28 Siemens Ag Background applications e.g. home banking system, controlling method for use over e.g. user interface, involves associating transactions and transaction parameters over universal dialog specification, and universally operating applications
US7996228B2 (en) 2005-12-22 2011-08-09 Microsoft Corporation Voice initiated network operations
US7599918B2 (en) 2005-12-29 2009-10-06 Microsoft Corporation Dynamic search with implicit user intention mining
JP2007183864A (en) 2006-01-10 2007-07-19 Fujitsu Ltd File retrieval method and system therefor
US8972494B2 (en) 2006-01-19 2015-03-03 International Business Machines Corporation Scheduling calendar entries via an instant messaging interface
US20070174188A1 (en) 2006-01-25 2007-07-26 Fish Robert D Electronic marketplace that facilitates transactions between consolidated buyers and/or sellers
IL174107D0 (en) 2006-02-01 2006-08-01 Grois Dan Method and system for advertising by means of a search engine over a data network
US7818291B2 (en) * 2006-02-03 2010-10-19 The General Electric Company Data object access system and method using dedicated task object
US8595041B2 (en) * 2006-02-07 2013-11-26 Sap Ag Task responsibility system
US7541940B2 (en) * 2006-02-16 2009-06-02 International Business Machines Corporation Proximity-based task alerts
US20070208726A1 (en) 2006-03-01 2007-09-06 Oracle International Corporation Enhancing search results using ontologies
KR100764174B1 (en) 2006-03-03 2007-10-08 삼성전자주식회사 Apparatus for providing voice dialogue service and method for operating the apparatus
US7752152B2 (en) 2006-03-17 2010-07-06 Microsoft Corporation Using predictive user models for language modeling on a personal device with user behavior models based on statistical modeling
JP4734155B2 (en) 2006-03-24 2011-07-27 株式会社東芝 Speech recognition device, speech recognition method and a speech recognition program
US7707027B2 (en) 2006-04-13 2010-04-27 Nuance Communications, Inc. Identification and rejection of meaningless input during natural language classification
US20070276714A1 (en) * 2006-05-15 2007-11-29 Sap Ag Business process map management
US20070276810A1 (en) 2006-05-23 2007-11-29 Joshua Rosen Search Engine for Presenting User-Editable Search Listings and Ranking Search Results Based on the Same
US8423347B2 (en) 2006-06-06 2013-04-16 Microsoft Corporation Natural language personal information management
US20100257160A1 (en) 2006-06-07 2010-10-07 Yu Cao Methods & apparatus for searching with awareness of different types of information
US7483894B2 (en) 2006-06-07 2009-01-27 Platformation Technologies, Inc Methods and apparatus for entity search
US7523108B2 (en) 2006-06-07 2009-04-21 Platformation, Inc. Methods and apparatus for searching with awareness of geography and languages
KR100776800B1 (en) 2006-06-16 2007-11-19 한국전자통신연구원 Method and system (apparatus) for user specific service using intelligent gadget
KR20080001227A (en) 2006-06-29 2008-01-03 엘지.필립스 엘시디 주식회사 Apparatus for fixing a lamp of the back-light
US7548895B2 (en) 2006-06-30 2009-06-16 Microsoft Corporation Communication-prompted user assistance
US8170790B2 (en) 2006-09-05 2012-05-01 Garmin Switzerland Gmbh Apparatus for switching navigation device mode
WO2008034111A2 (en) 2006-09-14 2008-03-20 Google Inc. Integrating voice-enabled local search and contact lists
US7930197B2 (en) 2006-09-28 2011-04-19 Microsoft Corporation Personal data mining
US7649454B2 (en) * 2006-09-28 2010-01-19 Ektimisi Semiotics Holdings, Llc System and method for providing a task reminder based on historical travel information
US7528713B2 (en) * 2006-09-28 2009-05-05 Ektimisi Semiotics Holdings, Llc Apparatus and method for providing a task reminder based on travel history
US20080082390A1 (en) * 2006-10-02 2008-04-03 International Business Machines Corporation Methods for Generating Auxiliary Data Operations for a Role Based Personalized Business User Workplace
US8073681B2 (en) 2006-10-16 2011-12-06 Voicebox Technologies, Inc. System and method for a cooperative conversational voice user interface
US7697922B2 (en) 2006-10-18 2010-04-13 At&T Intellectual Property I., L.P. Event notification systems and related methods
JP2008134949A (en) 2006-11-29 2008-06-12 Fujitsu Ltd Portable terminal device and method for displaying schedule preparation screen
US20080129520A1 (en) 2006-12-01 2008-06-05 Apple Computer, Inc. Electronic device with enhanced audio feedback
US7676249B2 (en) 2006-12-05 2010-03-09 Research In Motion Limited Alert methods and apparatus for call appointments in a calendar application based on communication conditions of a mobile station
US8060824B2 (en) 2007-01-05 2011-11-15 Starz Entertainment Llc User interface for a multimedia service
WO2008085742A2 (en) 2007-01-07 2008-07-17 Apple Inc. Portable multifunction device, method and graphical user interface for interacting with user input elements in displayed content
KR100883657B1 (en) 2007-01-26 2009-02-18 삼성전자주식회사 Method and apparatus for searching a music using speech recognition
US20080186196A1 (en) 2007-02-01 2008-08-07 Sony Ericsson Mobile Communications Ab Non-time based snooze
US7818176B2 (en) 2007-02-06 2010-10-19 Voicebox Technologies, Inc. System and method for selecting and presenting advertisements based on natural language processing of voice-based input
US7941133B2 (en) 2007-02-14 2011-05-10 At&T Intellectual Property I, L.P. Methods, systems, and computer program products for schedule management based on locations of wireless devices
US7822608B2 (en) 2007-02-27 2010-10-26 Nuance Communications, Inc. Disambiguating a speech recognition grammar in a multimodal application
JP2008217468A (en) 2007-03-05 2008-09-18 Mitsubishi Electric Corp Information processor and menu item generation program
US20080221880A1 (en) 2007-03-07 2008-09-11 Cerra Joseph P Mobile music environment speech processing facility
US7801729B2 (en) 2007-03-13 2010-09-21 Sensory, Inc. Using multiple attributes to create a voice search playlist
US8219406B2 (en) 2007-03-15 2012-07-10 Microsoft Corporation Speech-centric multimodal user interface design in mobile technology
JP4713532B2 (en) 2007-03-29 2011-06-29 株式会社エヌ・ティ・ティ・ドコモ Communication terminal and the program
US7809610B2 (en) 2007-04-09 2010-10-05 Platformation, Inc. Methods and apparatus for freshness and completeness of information
US7983915B2 (en) 2007-04-30 2011-07-19 Sonic Foundry, Inc. Audio content search engine
US9292807B2 (en) 2007-05-10 2016-03-22 Microsoft Technology Licensing, Llc Recommending actions based on context
US8055708B2 (en) 2007-06-01 2011-11-08 Microsoft Corporation Multimedia spaces
US8204238B2 (en) 2007-06-08 2012-06-19 Sensory, Inc Systems and methods of sonic communication
US20080313335A1 (en) * 2007-06-15 2008-12-18 Searete Llc, A Limited Liability Corporation Of The State Of Delaware Communicator establishing aspects with context identifying
KR20080109322A (en) 2007-06-12 2008-12-17 엘지전자 주식회사 Method and apparatus for providing services by comprehended user's intuited intension
US8190627B2 (en) 2007-06-28 2012-05-29 Microsoft Corporation Machine assisted query formulation
US8019606B2 (en) 2007-06-29 2011-09-13 Microsoft Corporation Identification and selection of a software application via speech
JP2009036999A (en) 2007-08-01 2009-02-19 Gengo Rikai Kenkyusho:Kk Interactive method using computer, interactive system, computer program and computer-readable storage medium
KR101359715B1 (en) 2007-08-24 2014-02-10 삼성전자주식회사 Method and apparatus for providing mobile voice web
WO2009029910A2 (en) 2007-08-31 2009-03-05 Proxpro, Inc. Situation-aware personal information management for a mobile device
US20090058823A1 (en) 2007-09-04 2009-03-05 Apple Inc. Virtual Keyboards in Multi-Language Environment
US8838760B2 (en) 2007-09-14 2014-09-16 Ricoh Co., Ltd. Workflow-enabled provider
KR100920267B1 (en) 2007-09-17 2009-10-05 한국전자통신연구원 System for voice communication analysis and method thereof
US8706476B2 (en) 2007-09-18 2014-04-22 Ariadne Genomics, Inc. Natural language processing method by analyzing primitive sentences, logical clauses, clause types and verbal blocks
US8165886B1 (en) 2007-10-04 2012-04-24 Great Northern Research LLC Speech interface system and method for control and interaction with applications on a computing system
US8036901B2 (en) 2007-10-05 2011-10-11 Sensory, Incorporated Systems and methods of performing speech recognition using sensory inputs of human position
US20090112677A1 (en) 2007-10-24 2009-04-30 Rhett Randolph L Method for automatically developing suggested optimal work schedules from unsorted group and individual task lists
US7840447B2 (en) 2007-10-30 2010-11-23 Leonard Kleinrock Pricing and auctioning of bundled items among multiple sellers and buyers
US20090112572A1 (en) 2007-10-30 2009-04-30 Karl Ola Thorn System and method for input of text to an application operating on a device
US7983997B2 (en) 2007-11-02 2011-07-19 Florida Institute For Human And Machine Cognition, Inc. Interactive complex task teaching system that allows for natural language input, recognizes a user's intent, and automatically performs tasks in document object model (DOM) nodes
US8112280B2 (en) 2007-11-19 2012-02-07 Sensory, Inc. Systems and methods of performing speech recognition with barge-in for use in a bluetooth system
JP2009134409A (en) 2007-11-29 2009-06-18 Sony Ericsson Mobilecommunications Japan Inc Reminder device, reminder method, reminder program, and portable terminal device
AT516658T (en) 2007-12-07 2011-07-15 Research In Motion Ltd System and method for event-dependent state activation for a mobile communication device
US8140335B2 (en) 2007-12-11 2012-03-20 Voicebox Technologies, Inc. System and method for providing a natural language voice user interface in an integrated voice navigation services environment
US10002189B2 (en) 2007-12-20 2018-06-19 Apple Inc. Method and apparatus for searching using an active ontology
US8219407B1 (en) 2007-12-27 2012-07-10 Great Northern Research, LLC Method for processing the output of a speech recognizer
KR101334066B1 (en) 2008-02-11 2013-11-29 이점식 Self-evolving Artificial Intelligent cyber robot system and offer method
US8099289B2 (en) 2008-02-13 2012-01-17 Sensory, Inc. Voice interface and search for electronic devices including bluetooth headsets and remote systems
US8015144B2 (en) 2008-02-26 2011-09-06 Microsoft Corporation Learning transportation modes from raw GPS data
EP2096840B1 (en) 2008-02-29 2012-07-04 Research In Motion Limited Visual event notification on a handheld communications device
US9049255B2 (en) 2008-02-29 2015-06-02 Blackberry Limited Visual event notification on a handheld communications device
US20090235176A1 (en) 2008-03-14 2009-09-17 Madhavi Jayanthi Social interaction system for facilitating display of current location of friends and location of businesses of interest
US20090239552A1 (en) 2008-03-24 2009-09-24 Yahoo! Inc. Location-based opportunistic recommendations
US8958848B2 (en) 2008-04-08 2015-02-17 Lg Electronics Inc. Mobile terminal and menu control method thereof
US8666824B2 (en) 2008-04-23 2014-03-04 Dell Products L.P. Digital media content location and purchasing system
US8219115B1 (en) * 2008-05-12 2012-07-10 Google Inc. Location based reminders
US8285344B2 (en) 2008-05-21 2012-10-09 DP Technlogies, Inc. Method and apparatus for adjusting audio for a user environment
US8589161B2 (en) 2008-05-27 2013-11-19 Voicebox Technologies, Inc. System and method for an integrated, multi-modal, multi-device natural language voice services environment
US8694355B2 (en) 2008-05-30 2014-04-08 Sri International Method and apparatus for automated assistance with task management
JP5136228B2 (en) 2008-06-05 2013-02-06 日本電気株式会社 Work environment autosave recovery system, the working environment autosave recovery method and working environment autosave restoration program
KR100988397B1 (en) 2008-06-09 2010-10-19 엘지전자 주식회사 Mobile terminal and text correcting method in the same
US8166019B1 (en) 2008-07-21 2012-04-24 Sprint Communications Company L.P. Providing suggested actions in response to textual communications
KR101005074B1 (en) 2008-09-18 2010-12-30 주식회사 수현테크 Plastic pipe connection fixing device
US9200913B2 (en) 2008-10-07 2015-12-01 Telecommunication Systems, Inc. User interface for predictive traffic
US8122094B1 (en) 2008-11-05 2012-02-21 Kotab Dominic M Methods for performing an action relating to the scheduling of an event by performing one or more actions based on a response to a message
KR20100053149A (en) 2008-11-12 2010-05-20 삼성전자주식회사 Apparatus and method for scheduling considering each attendees' context in mobile communicatiion terminal
US8140328B2 (en) 2008-12-01 2012-03-20 At&T Intellectual Property I, L.P. User intention based on N-best list of recognition hypotheses for utterances in a dialog
US8489599B2 (en) * 2008-12-02 2013-07-16 Palo Alto Research Center Incorporated Context and activity-driven content delivery and interaction
JP5257311B2 (en) * 2008-12-05 2013-08-07 ソニー株式会社 Information processing apparatus, an information processing method
US8068604B2 (en) 2008-12-19 2011-11-29 Computer Product Introductions Corporation Method and system for event notifications
US8254972B2 (en) 2009-02-13 2012-08-28 Sony Mobile Communications Ab Device and method for handling messages
US8326637B2 (en) 2009-02-20 2012-12-04 Voicebox Technologies, Inc. System and method for processing multi-modal device interactions in a natural language voice services environment
US20100223131A1 (en) 2009-02-27 2010-09-02 Research In Motion Limited Communications system providing mobile device notification based upon contact web pages and related methods
US8155630B2 (en) 2009-02-27 2012-04-10 Research In Motion Limited Communications system providing mobile device notification based upon personal interest information and calendar events
US8805823B2 (en) 2009-04-14 2014-08-12 Sri International Content processing systems and methods
US8606735B2 (en) 2009-04-30 2013-12-10 Samsung Electronics Co., Ltd. Apparatus and method for predicting user's intention based on multimodal information
KR101581883B1 (en) 2009-04-30 2016-01-11 삼성전자주식회사 Voice detection apparatus using the motion information and the way
US20100312547A1 (en) 2009-06-05 2010-12-09 Apple Inc. Contextual voice commands
US9858925B2 (en) 2009-06-05 2018-01-02 Apple Inc. Using context information to facilitate processing of commands in a virtual assistant
KR101562792B1 (en) 2009-06-10 2015-10-23 삼성전자주식회사 Target prediction interface providing apparatus and method
JP2010287063A (en) 2009-06-11 2010-12-24 Zenrin Datacom Co Ltd Information provision device, information provision system and program
US9754224B2 (en) 2009-06-26 2017-09-05 International Business Machines Corporation Action based to-do list
US8527278B2 (en) 2009-06-29 2013-09-03 Abraham Ben David Intelligent home automation
US20110016421A1 (en) 2009-07-20 2011-01-20 Microsoft Corporation Task oriented user interface platform
US20110047072A1 (en) 2009-08-07 2011-02-24 Visa U.S.A. Inc. Systems and Methods for Propensity Analysis and Validation
US8768313B2 (en) 2009-08-17 2014-07-01 Digimarc Corporation Methods and systems for image or audio recognition processing
WO2011028844A2 (en) 2009-09-02 2011-03-10 Sri International Method and apparatus for tailoring the output of an intelligent automated assistant to a user
US8321527B2 (en) 2009-09-10 2012-11-27 Tribal Brands System and method for tracking user location and associated activity and responsively providing mobile device updates
US8768308B2 (en) 2009-09-29 2014-07-01 Deutsche Telekom Ag Apparatus and method for creating and managing personal schedules via context-sensing and actuation
KR20110036385A (en) 2009-10-01 2011-04-07 삼성전자주식회사 Apparatus for analyzing intention of user and method thereof
US8451112B2 (en) 2009-10-19 2013-05-28 Qualcomm Incorporated Methods and apparatus for estimating departure time based on known calendar events
US20110099507A1 (en) 2009-10-28 2011-04-28 Google Inc. Displaying a collection of interactive elements that trigger actions directed to an item
US20120137367A1 (en) 2009-11-06 2012-05-31 Cataphora, Inc. Continuous anomaly detection based on behavior modeling and heterogeneous information analysis
US9171541B2 (en) 2009-11-10 2015-10-27 Voicebox Technologies Corporation System and method for hybrid processing in a natural language voice services environment
WO2011059997A1 (en) 2009-11-10 2011-05-19 Voicebox Technologies, Inc. System and method for providing a natural language content dedication service
US8712759B2 (en) 2009-11-13 2014-04-29 Clausal Computing Oy Specializing disambiguation of a natural language expression
KR101960835B1 (en) 2009-11-24 2019-03-21 삼성전자주식회사 Schedule Management System Using Interactive Robot and Method Thereof
US8423288B2 (en) 2009-11-30 2013-04-16 Apple Inc. Dynamic alerts for calendar events
US8396888B2 (en) 2009-12-04 2013-03-12 Google Inc. Location-based searching using a search area that corresponds to a geographical location of a computing device
KR101622111B1 (en) 2009-12-11 2016-05-18 삼성전자 주식회사 Dialog system and conversational method thereof
US20110161309A1 (en) 2009-12-29 2011-06-30 Lx1 Technology Limited Method Of Sorting The Result Set Of A Search Engine
US9197736B2 (en) 2009-12-31 2015-11-24 Digimarc Corporation Intuitive computing methods and systems
US8494852B2 (en) 2010-01-05 2013-07-23 Google Inc. Word-level correction of speech input
US8334842B2 (en) 2010-01-15 2012-12-18 Microsoft Corporation Recognizing user intent in motion capture system
US20120022872A1 (en) 2010-01-18 2012-01-26 Apple Inc. Automatically Adapting User Interfaces For Hands-Free Interaction
US9318108B2 (en) 2010-01-18 2016-04-19 Apple Inc. Intelligent automated assistant
US8626511B2 (en) 2010-01-22 2014-01-07 Google Inc. Multi-dimensional disambiguation of voice commands
US9413869B2 (en) 2010-02-10 2016-08-09 Qualcomm Incorporated Mobile device having plurality of input modes
US20110218855A1 (en) 2010-03-03 2011-09-08 Platformation, Inc. Offering Promotions Based on Query Analysis
KR101369810B1 (en) 2010-04-09 2014-03-05 이초강 Empirical Context Aware Computing Method For Robot
US8265928B2 (en) 2010-04-14 2012-09-11 Google Inc. Geotagged environmental audio for enhanced speech recognition accuracy
US20110279368A1 (en) 2010-05-12 2011-11-17 Microsoft Corporation Inferring user intent to engage a motion capture system
US8694313B2 (en) 2010-05-19 2014-04-08 Google Inc. Disambiguation of contact information using historical data
US8522283B2 (en) 2010-05-20 2013-08-27 Google Inc. Television remote control data transfer
US8468012B2 (en) 2010-05-26 2013-06-18 Google Inc. Acoustic model adaptation using geographic information
US20110306426A1 (en) 2010-06-10 2011-12-15 Microsoft Corporation Activity Participation Based On User Intent
US20110307810A1 (en) 2010-06-11 2011-12-15 Isreal Hilerio List integration
US8234111B2 (en) 2010-06-14 2012-07-31 Google Inc. Speech and noise models for speech recognition
US20120136572A1 (en) 2010-06-17 2012-05-31 Norton Kenneth S Distance and Location-Aware Reminders in a Calendar System
US9009592B2 (en) 2010-06-22 2015-04-14 Microsoft Technology Licensing, Llc Population of lists and tasks from captured voice and audio content
US8375320B2 (en) * 2010-06-22 2013-02-12 Microsoft Corporation Context-based task generation
US8411874B2 (en) 2010-06-30 2013-04-02 Google Inc. Removing noise from audio
US8775156B2 (en) 2010-08-05 2014-07-08 Google Inc. Translating languages in response to device motion
US8473289B2 (en) 2010-08-06 2013-06-25 Google Inc. Disambiguating input based on context
US8359020B2 (en) 2010-08-06 2013-01-22 Google Inc. Automatically monitoring for voice input based on context
US20120124126A1 (en) 2010-11-17 2012-05-17 Microsoft Corporation Contextual and task focused computing
US20120158422A1 (en) * 2010-12-21 2012-06-21 General Electric Company Methods and systems for scheduling appointments in healthcare systems
US20120158293A1 (en) 2010-12-21 2012-06-21 General Electric Company Methods and systems for dynamically providing users with appointment reminders
EP2661699B1 (en) 2011-01-07 2017-06-28 BlackBerry Limited System and method for controlling mobile communication devices
JP2014520297A (en) 2011-04-25 2014-08-21 ベベオ,インク. System and method for advanced personal timetable assistant
US20120311584A1 (en) 2011-06-03 2012-12-06 Apple Inc. Performing actions associated with task items that represent tasks to perform
US10057736B2 (en) 2011-06-03 2018-08-21 Apple Inc. Active transport based notifications
US10241644B2 (en) 2011-06-03 2019-03-26 Apple Inc. Actionable reminder entries
US20120317498A1 (en) 2011-06-07 2012-12-13 Research In Motion Limited Electronic communication device and method for displaying icons
US10163058B2 (en) 2012-08-14 2018-12-25 Sri International Method, system and device for inferring a mobile user's current context and proactively providing assistance

Patent Citations (102)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3828132A (en) * 1970-10-30 1974-08-06 Bell Telephone Labor Inc Speech synthesis by concatenation of formant encoded words
US3704345A (en) * 1971-03-19 1972-11-28 Bell Telephone Labor Inc Conversion of printed text into synthetic speech
US3979557A (en) * 1974-07-03 1976-09-07 International Telephone And Telegraph Corporation Speech processor system for pitch period extraction using prediction filters
US4278838A (en) * 1976-09-08 1981-07-14 Edinen Centar Po Physika Method of and device for synthesis of speech from printed text
US4282405A (en) * 1978-11-24 1981-08-04 Nippon Electric Co., Ltd. Speech analyzer comprising circuits for calculating autocorrelation coefficients forwardly and backwardly
US4310721A (en) * 1980-01-23 1982-01-12 The United States Of America As Represented By The Secretary Of The Army Half duplex integral vocoder modem system
US4348553A (en) * 1980-07-02 1982-09-07 International Business Machines Corporation Parallel pattern verifier with dynamic time warping
US4688195A (en) * 1983-01-28 1987-08-18 Texas Instruments Incorporated Natural-language interface generating system
US4653021A (en) * 1983-06-21 1987-03-24 Kabushiki Kaisha Toshiba Data management apparatus
US5164900A (en) * 1983-11-14 1992-11-17 Colman Bernath Method and device for phonetically encoding Chinese textual data for data processing entry
US4726065A (en) * 1984-01-26 1988-02-16 Horst Froessl Image manipulation by speech signals
US4811243A (en) * 1984-04-06 1989-03-07 Racine Marsh V Computer aided coordinate digitizing system
US4692941A (en) * 1984-04-10 1987-09-08 First Byte Real-time text-to-speech conversion system
US4783807A (en) * 1984-08-27 1988-11-08 John Marley System and method for sound recognition with feature selection synchronized to voice pitch
US4718094A (en) * 1984-11-19 1988-01-05 International Business Machines Corp. Speech recognition system
US5165007A (en) * 1985-02-01 1992-11-17 International Business Machines Corporation Feneme-based Markov models for words
US4944013A (en) * 1985-04-03 1990-07-24 British Telecommunications Public Limited Company Multi-pulse speech coder
US4819271A (en) * 1985-05-29 1989-04-04 International Business Machines Corporation Constructing Markov model word baseforms from multiple utterances by concatenating model sequences for word segments
US4833712A (en) * 1985-05-29 1989-05-23 International Business Machines Corporation Automatic generation of simple Markov model stunted baseforms for words in a vocabulary
US4776016A (en) * 1985-11-21 1988-10-04 Position Orientation Systems, Inc. Voice control system
US4862504A (en) * 1986-01-09 1989-08-29 Kabushiki Kaisha Toshiba Speech synthesis system of rule-synthesis type
US4724542A (en) * 1986-01-22 1988-02-09 International Business Machines Corporation Automatic reference adaptation during dynamic signature verification
US5032989A (en) * 1986-03-19 1991-07-16 Realpro, Ltd. Real estate search and location system and method
US5377301A (en) * 1986-03-28 1994-12-27 At&T Corp. Technique for modifying reference vector quantized speech feature signals
US4903305A (en) * 1986-05-12 1990-02-20 Dragon Systems, Inc. Method for representing word models for use in speech recognition
US4878230A (en) * 1986-10-16 1989-10-31 Mitsubishi Denki Kabushiki Kaisha Amplitude-adaptive vector quantization system
US4829576A (en) * 1986-10-21 1989-05-09 Dragon Systems, Inc. Voice recognition system
US4852168A (en) * 1986-11-18 1989-07-25 Sprague Richard P Compression of stored waveforms for artificial speech
US4727354A (en) * 1987-01-07 1988-02-23 Unisys Corporation System for selecting best fit vector code in vector quantization encoding
US4827520A (en) * 1987-01-16 1989-05-02 Prince Corporation Voice actuated control system for use in a vehicle
US4965763A (en) * 1987-03-03 1990-10-23 International Business Machines Corporation Computer method for automatic extraction of commonly specified information from business correspondence
US5235680A (en) * 1987-07-31 1993-08-10 Moore Business Forms, Inc. Apparatus and method for communicating textual and image information between a host computer and a remote display terminal
US5235680B1 (en) * 1987-07-31 1999-06-22 Moore Business Forms Inc Apparatus and method for communicating textual and image information between a host computer and a remote display terminal
US5022081A (en) * 1987-10-01 1991-06-04 Sharp Kabushiki Kaisha Information recognition system
US5072452A (en) * 1987-10-30 1991-12-10 International Business Machines Corporation Automatic determination of labels and Markov word models in a speech recognition system
US4914586A (en) * 1987-11-06 1990-04-03 Xerox Corporation Garbage collector for hypermedia systems
US4992972A (en) * 1987-11-18 1991-02-12 International Business Machines Corporation Flexible context searchable on-line information system with help files and modules for on-line computer system documentation
US5220657A (en) * 1987-12-02 1993-06-15 Xerox Corporation Updating local copy of shared data in a collaborative system
US5194950A (en) * 1988-02-29 1993-03-16 Mitsubishi Denki Kabushiki Kaisha Vector quantizer
US5291286A (en) * 1988-02-29 1994-03-01 Mitsubishi Denki Kabushiki Kaisha Multimedia data transmission system
US5327498A (en) * 1988-09-02 1994-07-05 Ministry Of Posts, Tele-French State Communications & Space Processing device for speech synthesis by addition overlapping of wave forms
US4839853A (en) * 1988-09-15 1989-06-13 Bell Communications Research, Inc. Computer information retrieval using latent semantic structure
US5031217A (en) * 1988-09-30 1991-07-09 International Business Machines Corporation Speech recognition system using Markov models having independent label output sets
US4905163A (en) * 1988-10-03 1990-02-27 Minnesota Mining & Manufacturing Company Intelligent optical navigator dynamic information presentation and navigation system
US5040218A (en) * 1988-11-23 1991-08-13 Digital Equipment Corporation Name pronounciation by synthesizer
US5027406A (en) * 1988-12-06 1991-06-25 Dragon Systems, Inc. Method for interactive speech recognition and training
US5127055A (en) * 1988-12-30 1992-06-30 Kurzweil Applied Intelligence, Inc. Speech recognition apparatus & method having dynamic reference pattern adaptation
US4977598A (en) * 1989-04-13 1990-12-11 Texas Instruments Incorporated Efficient pruning algorithm for hidden markov model speech recognition
US5010574A (en) * 1989-06-13 1991-04-23 At&T Bell Laboratories Vector quantizer search arrangement
US5377303A (en) * 1989-06-23 1994-12-27 Articulate Systems, Inc. Controlled computer interface
US5142584A (en) * 1989-07-20 1992-08-25 Nec Corporation Speech coding/decoding method having an excitation signal
US5091945A (en) * 1989-09-28 1992-02-25 At&T Bell Laboratories Source dependent channel coding with error protection
US5293448A (en) * 1989-10-02 1994-03-08 Nippon Telegraph And Telephone Corporation Speech analysis-synthesis method and apparatus therefor
US5230036A (en) * 1989-10-17 1993-07-20 Kabushiki Kaisha Toshiba Speech coding system utilizing a recursive computation technique for improvement in processing speed
US5020112A (en) * 1989-10-31 1991-05-28 At&T Bell Laboratories Image recognition method using two-dimensional stochastic grammars
US5220639A (en) * 1989-12-01 1993-06-15 National Science Council Mandarin speech input method for Chinese computers and a mandarin speech recognition machine
US5021971A (en) * 1989-12-07 1991-06-04 Unisys Corporation Reflective binary encoder for vector quantization
US5179652A (en) * 1989-12-13 1993-01-12 Anthony I. Rozmanith Method and apparatus for storing, transmitting and retrieving graphical and tabular data
US5208862A (en) * 1990-02-22 1993-05-04 Nec Corporation Speech coder
US5301109A (en) * 1990-06-11 1994-04-05 Bell Communications Research, Inc. Computerized cross-language document retrieval using latent semantic indexing
US5424947A (en) * 1990-06-15 1995-06-13 International Business Machines Corporation Natural language analyzing apparatus and method, and construction of a knowledge base for natural language analysis
US5202952A (en) * 1990-06-22 1993-04-13 Dragon Systems, Inc. Large-vocabulary continuous speech prefiltering and processing system
US5396625A (en) * 1990-08-10 1995-03-07 British Aerospace Public Ltd., Co. System for binary tree searched vector quantization data compression processing each tree node containing one vector and one scalar to compare with an input vector
US5297170A (en) * 1990-08-21 1994-03-22 Codex Corporation Lattice and trellis-coded quantization
US5400434A (en) * 1990-09-04 1995-03-21 Matsushita Electric Industrial Co., Ltd. Voice source for synthetic speech system
US5216747A (en) * 1990-09-20 1993-06-01 Digital Voice Systems, Inc. Voiced/unvoiced estimation of an acoustic signal
US5325298A (en) * 1990-11-07 1994-06-28 Hnc, Inc. Methods for generating or revising context vectors for a plurality of word stems
US5317507A (en) * 1990-11-07 1994-05-31 Gallant Stephen I Method for document retrieval and for word sense disambiguation using neural networks
US5491772A (en) * 1990-12-05 1996-02-13 Digital Voice Systems, Inc. Methods for speech transmission
US5345536A (en) * 1990-12-21 1994-09-06 Matsushita Electric Industrial Co., Ltd. Method of speech recognition
US5127053A (en) * 1990-12-24 1992-06-30 General Electric Company Low-complexity method for improving the performance of autocorrelation-based pitch detectors
US5581655A (en) * 1991-01-31 1996-12-03 Sri International Method for recognizing speech using linguistically-motivated hidden Markov models
US5268990A (en) * 1991-01-31 1993-12-07 Sri International Method for recognizing speech using linguistically-motivated hidden Markov models
US5303406A (en) * 1991-04-29 1994-04-12 Motorola, Inc. Noise squelch circuit with adaptive noise shaping
US5475587A (en) * 1991-06-28 1995-12-12 Digital Equipment Corporation Method and apparatus for efficient morphological text analysis using a high-level language for compact specification of inflectional paradigms
US5293452A (en) * 1991-07-01 1994-03-08 Texas Instruments Incorporated Voice log-in using spoken name input
US5199077A (en) * 1991-09-19 1993-03-30 Xerox Corporation Wordspotting for voice editing and indexing
US5353377A (en) * 1991-10-01 1994-10-04 International Business Machines Corporation Speech recognition system having an interface to a host computer bus for direct access to the host memory
US5222146A (en) * 1991-10-23 1993-06-22 International Business Machines Corporation Speech recognition apparatus having a speech coder outputting acoustic prototype ranks
US5386494A (en) * 1991-12-06 1995-01-31 Apple Computer, Inc. Method and apparatus for controlling a speech recognition function using a cursor control device
US5502790A (en) * 1991-12-24 1996-03-26 Oki Electric Industry Co., Ltd. Speech recognition method and system using triphones, diphones, and phonemes
US5349645A (en) * 1991-12-31 1994-09-20 Matsushita Electric Industrial Co., Ltd. Word hypothesizer for continuous speech decoding using stressed-vowel centered bidirectional tree searches
US5267345A (en) * 1992-02-10 1993-11-30 International Business Machines Corporation Speech recognition apparatus which predicts word classes from context and words from word classes
US5579436A (en) * 1992-03-02 1996-11-26 Lucent Technologies Inc. Recognition unit model training based on competing word and word string models
US5317647A (en) * 1992-04-07 1994-05-31 Apple Computer, Inc. Constrained attribute grammars for syntactic pattern recognition
US5596676A (en) * 1992-06-01 1997-01-21 Hughes Electronics Mode-specific method and apparatus for encoding signals containing speech
US5333275A (en) * 1992-06-23 1994-07-26 Wheatley Barbara J System and method for time aligning speech
US5325297A (en) * 1992-06-25 1994-06-28 System Of Multiple-Colored Images For Internationally Listed Estates, Inc. Computer implemented method and system for storing and retrieving textual data and compressed image data
US5333236A (en) * 1992-09-10 1994-07-26 International Business Machines Corporation Speech recognizer having a speech coder for an acoustic match based on context-dependent speech-transition acoustic models
US5384893A (en) * 1992-09-23 1995-01-24 Emerson & Stern Associates, Inc. Method and apparatus for speech synthesis based on prosodic analysis
US5469529A (en) * 1992-09-24 1995-11-21 France Telecom Establissement Autonome De Droit Public Process for measuring the resemblance between sound samples and apparatus for performing this process
US5502791A (en) * 1992-09-29 1996-03-26 International Business Machines Corporation Speech recognition by concatenating fenonic allophone hidden Markov models in parallel among subwords
US5455888A (en) * 1992-12-04 1995-10-03 Northern Telecom Limited Speech bandwidth extension method and apparatus
US5613036A (en) * 1992-12-31 1997-03-18 Apple Computer, Inc. Dynamic categories for a speech recognition system
US5390279A (en) * 1992-12-31 1995-02-14 Apple Computer, Inc. Partitioning speech rules by context for speech recognition
US5384892A (en) * 1992-12-31 1995-01-24 Apple Computer, Inc. Dynamic language model for speech recognition
US5536902A (en) * 1993-04-14 1996-07-16 Yamaha Corporation Method of and apparatus for analyzing and synthesizing a sound by extracting and controlling a sound parameter
US5574823A (en) * 1993-06-23 1996-11-12 Her Majesty The Queen In Right Of Canada As Represented By The Minister Of Communications Frequency selective harmonic coding
US5515475A (en) * 1993-06-24 1996-05-07 Northern Telecom Limited Speech recognition method using a two-pass search
US5577164A (en) * 1994-01-28 1996-11-19 Canon Kabushiki Kaisha Incorrect voice command recognition prevention and recovery processing method and apparatus
US8560229B1 (en) * 2010-09-15 2013-10-15 Google Inc. Sensor based activity detection
US20120242482A1 (en) * 2011-03-25 2012-09-27 Microsoft Corporation Contextually-Appropriate Task Reminders

Cited By (124)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9646614B2 (en) 2000-03-16 2017-05-09 Apple Inc. Fast, language-independent method for user authentication by voice
US10318871B2 (en) 2005-09-08 2019-06-11 Apple Inc. Method and apparatus for building an intelligent automated assistant
US8930191B2 (en) 2006-09-08 2015-01-06 Apple Inc. Paraphrasing of user requests and results by automated digital assistant
US8942986B2 (en) 2006-09-08 2015-01-27 Apple Inc. Determining user intent based on ontologies of domains
US9117447B2 (en) 2006-09-08 2015-08-25 Apple Inc. Using event alert text as input to an automated assistant
US8977255B2 (en) 2007-04-03 2015-03-10 Apple Inc. Method and system for operating a multi-function portable electronic device using voice-activation
US9330720B2 (en) 2008-01-03 2016-05-03 Apple Inc. Methods and apparatus for altering audio output signals
US9865248B2 (en) 2008-04-05 2018-01-09 Apple Inc. Intelligent text-to-speech conversion
US9626955B2 (en) 2008-04-05 2017-04-18 Apple Inc. Intelligent text-to-speech conversion
US9535906B2 (en) 2008-07-31 2017-01-03 Apple Inc. Mobile device having human language translation capability with positional feedback
US10108612B2 (en) 2008-07-31 2018-10-23 Apple Inc. Mobile device having human language translation capability with positional feedback
US9959870B2 (en) 2008-12-11 2018-05-01 Apple Inc. Speech recognition involving a mobile device
US9858925B2 (en) 2009-06-05 2018-01-02 Apple Inc. Using context information to facilitate processing of commands in a virtual assistant
US10283110B2 (en) 2009-07-02 2019-05-07 Apple Inc. Methods and apparatuses for automatic speech recognition
US10276170B2 (en) 2010-01-18 2019-04-30 Apple Inc. Intelligent automated assistant
US9318108B2 (en) 2010-01-18 2016-04-19 Apple Inc. Intelligent automated assistant
US8892446B2 (en) 2010-01-18 2014-11-18 Apple Inc. Service orchestration for intelligent automated assistant
US8903716B2 (en) 2010-01-18 2014-12-02 Apple Inc. Personalized vocabulary for digital assistant
US9548050B2 (en) 2010-01-18 2017-01-17 Apple Inc. Intelligent automated assistant
US10049675B2 (en) 2010-02-25 2018-08-14 Apple Inc. User profiling for voice input processing
US9633660B2 (en) 2010-02-25 2017-04-25 Apple Inc. User profiling for voice input processing
US9190062B2 (en) 2010-02-25 2015-11-17 Apple Inc. User profiling for voice input processing
US10102359B2 (en) 2011-03-21 2018-10-16 Apple Inc. Device access using voice authentication
US9262612B2 (en) 2011-03-21 2016-02-16 Apple Inc. Device access using voice authentication
US10057736B2 (en) 2011-06-03 2018-08-21 Apple Inc. Active transport based notifications
US10255566B2 (en) 2011-06-03 2019-04-09 Apple Inc. Generating and processing task items that represent tasks to perform
US10241644B2 (en) 2011-06-03 2019-03-26 Apple Inc. Actionable reminder entries
US9798393B2 (en) 2011-08-29 2017-10-24 Apple Inc. Text correction processing
US10241752B2 (en) 2011-09-30 2019-03-26 Apple Inc. Interface for a virtual digital assistant
US10134385B2 (en) 2012-03-02 2018-11-20 Apple Inc. Systems and methods for name pronunciation
US9483461B2 (en) 2012-03-06 2016-11-01 Apple Inc. Handling speech synthesis of content for multiple languages
US20130246419A1 (en) * 2012-03-13 2013-09-19 Samsung Electronics Co., Ltd. Method and apparatus for tagging contents in a portable electronic device
US9953088B2 (en) 2012-05-14 2018-04-24 Apple Inc. Crowd sourcing information to fulfill user requests
US10079014B2 (en) 2012-06-08 2018-09-18 Apple Inc. Name recognition system
US9147001B1 (en) * 2012-06-27 2015-09-29 Google Inc. Automatic user-based query generation and execution
US10169421B1 (en) * 2012-06-27 2019-01-01 Google Llc Automatic user-based query generation and execution
US9495129B2 (en) 2012-06-29 2016-11-15 Apple Inc. Device, method, and user interface for voice-activated navigation and browsing of a document
US9576574B2 (en) 2012-09-10 2017-02-21 Apple Inc. Context-sensitive handling of interruptions by intelligent digital assistant
US9971774B2 (en) 2012-09-19 2018-05-15 Apple Inc. Voice-based media searching
EP2936299A4 (en) * 2012-12-24 2016-01-13 Microsoft Technology Licensing Llc Discreetly displaying contextually relevant information
CN105051674A (en) * 2012-12-24 2015-11-11 微软技术许可有限责任公司 Discreetly displaying contextually relevant information
US10199051B2 (en) 2013-02-07 2019-02-05 Apple Inc. Voice trigger for a digital assistant
US9368114B2 (en) 2013-03-14 2016-06-14 Apple Inc. Context-sensitive handling of interruptions
US9697822B1 (en) 2013-03-15 2017-07-04 Apple Inc. System and method for updating an adaptive speech recognition model
US9922642B2 (en) 2013-03-15 2018-03-20 Apple Inc. Training an at least partial voice command system
US9633674B2 (en) 2013-06-07 2017-04-25 Apple Inc. System and method for detecting errors in interactions with a voice-based digital assistant
US9620104B2 (en) 2013-06-07 2017-04-11 Apple Inc. System and method for user-specified pronunciation of words for speech synthesis and recognition
US9582608B2 (en) 2013-06-07 2017-02-28 Apple Inc. Unified ranking with entropy-weighted information for phrase-based semantic auto-completion
US9966060B2 (en) 2013-06-07 2018-05-08 Apple Inc. System and method for user-specified pronunciation of words for speech synthesis and recognition
US9966068B2 (en) 2013-06-08 2018-05-08 Apple Inc. Interpreting and acting upon commands that involve sharing information with remote devices
US10176167B2 (en) 2013-06-09 2019-01-08 Apple Inc. System and method for inferring user intent from speech inputs
US10185542B2 (en) 2013-06-09 2019-01-22 Apple Inc. Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant
US9300784B2 (en) 2013-06-13 2016-03-29 Apple Inc. System and method for emergency calls initiated by voice command
US9704282B1 (en) 2013-06-19 2017-07-11 Google Inc. Texture blending between view-dependent texture and base texture in a geographic information system
EP2824618A1 (en) * 2013-07-12 2015-01-14 Samsung Electronics Co., Ltd Electronic device for reminding of task and controlling method thereof
US9959740B2 (en) 2013-07-12 2018-05-01 Samsung Electronics Co., Ltd. Electronic device for reminding of task and controlling method thereof
US9467521B2 (en) 2014-04-02 2016-10-11 David S. Owens System and computer implemented method of personal monitoring
US9620105B2 (en) 2014-05-15 2017-04-11 Apple Inc. Analyzing audio input for efficient speech and music recognition
US9502031B2 (en) 2014-05-27 2016-11-22 Apple Inc. Method for supporting dynamic grammars in WFST-based ASR
US9785630B2 (en) 2014-05-30 2017-10-10 Apple Inc. Text prediction using combined word N-gram and unigram language models
US9715875B2 (en) 2014-05-30 2017-07-25 Apple Inc. Reducing the need for manual start/end-pointing and trigger phrases
US10170123B2 (en) 2014-05-30 2019-01-01 Apple Inc. Intelligent assistant for home automation
US10169329B2 (en) 2014-05-30 2019-01-01 Apple Inc. Exemplar-based natural language processing
US9430463B2 (en) 2014-05-30 2016-08-30 Apple Inc. Exemplar-based natural language processing
US9734193B2 (en) 2014-05-30 2017-08-15 Apple Inc. Determining domain salience ranking from ambiguous words in natural speech
US9842101B2 (en) 2014-05-30 2017-12-12 Apple Inc. Predictive conversion of language input
US9760559B2 (en) 2014-05-30 2017-09-12 Apple Inc. Predictive text input
US10083690B2 (en) 2014-05-30 2018-09-25 Apple Inc. Better resolution when referencing to concepts
US9966065B2 (en) 2014-05-30 2018-05-08 Apple Inc. Multi-command single utterance input method
US9633004B2 (en) 2014-05-30 2017-04-25 Apple Inc. Better resolution when referencing to concepts
US10078631B2 (en) 2014-05-30 2018-09-18 Apple Inc. Entropy-guided text prediction using combined word and character n-gram language models
US10289433B2 (en) 2014-05-30 2019-05-14 Apple Inc. Domain specific language for encoding assistant dialog
EP3158691A4 (en) * 2014-06-06 2018-03-28 Obschestvo S Ogranichennoy Otvetstvennostiyu "Speactoit" Proactive environment-based chat information system
EP2955672A1 (en) * 2014-06-11 2015-12-16 Honeywell International Inc. Computer-generated speech device for site survey and maintenance
US10121470B2 (en) 2014-06-11 2018-11-06 Honeywell International Inc. Computer-generated speech device for site survey and maintenance
US9338493B2 (en) 2014-06-30 2016-05-10 Apple Inc. Intelligent automated assistant for TV user interactions
US9668024B2 (en) 2014-06-30 2017-05-30 Apple Inc. Intelligent automated assistant for TV user interactions
US9818400B2 (en) 2014-09-11 2017-11-14 Apple Inc. Method and apparatus for discovering trending terms in speech requests
US9646609B2 (en) 2014-09-30 2017-05-09 Apple Inc. Caching apparatus for serving phonetic pronunciations
US10074360B2 (en) 2014-09-30 2018-09-11 Apple Inc. Providing an indication of the suitability of speech recognition
US9986419B2 (en) 2014-09-30 2018-05-29 Apple Inc. Social reminders
US9886432B2 (en) 2014-09-30 2018-02-06 Apple Inc. Parsimonious handling of word inflection via categorical stem + suffix N-gram language models
US9668121B2 (en) 2014-09-30 2017-05-30 Apple Inc. Social reminders
US10127911B2 (en) 2014-09-30 2018-11-13 Apple Inc. Speaker identification and unsupervised speaker adaptation techniques
US9711141B2 (en) 2014-12-09 2017-07-18 Apple Inc. Disambiguating heteronyms in speech synthesis
US9865280B2 (en) 2015-03-06 2018-01-09 Apple Inc. Structured dictation using intelligent automated assistants
US9721566B2 (en) 2015-03-08 2017-08-01 Apple Inc. Competing devices responding to voice triggers
US10311871B2 (en) 2015-03-08 2019-06-04 Apple Inc. Competing devices responding to voice triggers
US9886953B2 (en) 2015-03-08 2018-02-06 Apple Inc. Virtual assistant activation
US9899019B2 (en) 2015-03-18 2018-02-20 Apple Inc. Systems and methods for structured stem and suffix language models
US9842105B2 (en) 2015-04-16 2017-12-12 Apple Inc. Parsimonious continuous-space phrase representations for natural language processing
US10083688B2 (en) 2015-05-27 2018-09-25 Apple Inc. Device voice control for selecting a displayed affordance
US10127220B2 (en) 2015-06-04 2018-11-13 Apple Inc. Language identification from short strings
US10101822B2 (en) 2015-06-05 2018-10-16 Apple Inc. Language input correction
US10356243B2 (en) 2015-06-05 2019-07-16 Apple Inc. Virtual assistant aided communication with 3rd party service in a communication session
US10255907B2 (en) 2015-06-07 2019-04-09 Apple Inc. Automatic accent detection using acoustic models
US10186254B2 (en) 2015-06-07 2019-01-22 Apple Inc. Context-based endpoint detection
US9513867B1 (en) 2015-06-19 2016-12-06 Honda Motor Co., Ltd. System and method for managing communications on a mobile communication device based upon a user's behavior
US10169554B2 (en) 2015-08-03 2019-01-01 Casio Computer Co., Ltd. Work support system, work support method and computer-readable recording medium
US10268972B2 (en) 2015-08-21 2019-04-23 Casio Computer Co., Ltd. Work support system, work support method and computer-readable recording medium
WO2017044163A1 (en) * 2015-09-08 2017-03-16 Apple Inc. Distributed personal assistant
US9697820B2 (en) 2015-09-24 2017-07-04 Apple Inc. Unit-selection text-to-speech synthesis using concatenation-sensitive neural networks
US10366158B2 (en) 2015-09-29 2019-07-30 Apple Inc. Efficient word encoding for recurrent neural network language models
US10354652B2 (en) 2015-12-02 2019-07-16 Apple Inc. Applying neural network language models to weighted finite state transducers for automatic speech recognition
US10049668B2 (en) 2015-12-02 2018-08-14 Apple Inc. Applying neural network language models to weighted finite state transducers for automatic speech recognition
WO2017099978A1 (en) * 2015-12-07 2017-06-15 Microsoft Technology Licensing, Llc Providing reminders related to contextual data on lock screens
US10223066B2 (en) 2015-12-23 2019-03-05 Apple Inc. Proactive assistance based on dialog communication between devices
US10381016B2 (en) 2016-03-29 2019-08-13 Apple Inc. Methods and apparatus for altering audio output signals
US9934775B2 (en) 2016-05-26 2018-04-03 Apple Inc. Unit-selection text-to-speech synthesis based on predicted concatenation parameters
US9972304B2 (en) 2016-06-03 2018-05-15 Apple Inc. Privacy preserving distributed evaluation framework for embedded personalized systems
US10249300B2 (en) 2016-06-06 2019-04-02 Apple Inc. Intelligent list reading
US10049663B2 (en) 2016-06-08 2018-08-14 Apple, Inc. Intelligent automated assistant for media exploration
US10354011B2 (en) 2016-06-09 2019-07-16 Apple Inc. Intelligent automated assistant in a home environment
US10192552B2 (en) 2016-06-10 2019-01-29 Apple Inc. Digital assistant providing whispered speech
US10067938B2 (en) 2016-06-10 2018-09-04 Apple Inc. Multilingual word prediction
US10297253B2 (en) 2016-06-11 2019-05-21 Apple Inc. Application integration with a digital assistant
US10089072B2 (en) 2016-06-11 2018-10-02 Apple Inc. Intelligent device arbitration and control
US10269345B2 (en) 2016-06-11 2019-04-23 Apple Inc. Intelligent task discovery
US10043516B2 (en) 2016-09-23 2018-08-07 Apple Inc. Intelligent automated assistant
US10332518B2 (en) 2017-05-09 2019-06-25 Apple Inc. User interface for correcting recognition errors
US10311144B2 (en) 2017-05-16 2019-06-04 Apple Inc. Emoji word sense disambiguation
US10303715B2 (en) 2017-05-16 2019-05-28 Apple Inc. Intelligent automated assistant for media exploration
WO2018231412A1 (en) * 2017-06-13 2018-12-20 Microsoft Technology Licensing, Llc Providing suggestions for task completion through intelligent canvas
US10390213B2 (en) 2018-05-24 2019-08-20 Apple Inc. Social reminders

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