PATENT DOCUMENT

Publication Number: US-11069347-B2
Application Number: US-201816035169-A
Country: US
Kind Code: B2

Title: Intelligent automated assistant for media exploration

Abstract:
Systems and processes for operating an intelligent automated assistant to explore media items are provided. In one example process, a speech input representing a request for one or more media items is received from a user. The process determines whether the speech input corresponds to a user intent of obtaining personalized recommendations for media items. In response to determining that the speech input corresponds to a user intent of obtaining personalized recommendations for media items, at least one media item is obtained from a user-specific corpus of media items. The user-specific corpus of media items is generate based on data associated with the user. The at least one media item is provided.

Claims:
What is claimed is: 
     
       1. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors 
       of an electronic device, cause the electronic device to:
 determine at least one media item frequently requested by a user, wherein the at least one media item frequently requested by the user is associated with at least one first parameter; 
 determine at least one media item frequently rejected by the user, wherein the at least one media item frequently rejected by the user is associated with at least one second parameter; 
 generate a user-specific corpus of media items by identifying media items including the at least one first parameter and not including the at least one second parameter; 
 receive, from a user, speech input representing a request for one or more media items; 
 determine whether the speech input corresponds to a user intent of obtaining personalized recommendations for media items by evaluating a text representation of the speech input against a set of rules associated with one or more actionable intent nodes; 
 in accordance with a determination that the text representation satisfies the set of rules:
 obtain at least one media item from the user-specific corpus of media items; and 
 provide the at least one media item from the user-specific corpus of media items. 
 
 
     
     
       2. The non-transitory computer-readable storage medium of  claim 1 , wherein evaluating the text representation of the speech input against the set of rules further comprises determining whether a number of parameters defined in the text representation is less than a threshold number. 
     
     
       3. The non-transitory computer-readable storage medium of  claim 1 , wherein evaluating the text representation of the speech input against the set of rules further comprises determining whether the text representation includes one of a plurality of phrases corresponding to the user intent of obtaining personalized recommendations for media items. 
     
     
       4. The non-transitory computer-readable storage medium of  claim 1 , wherein the instructions further cause the one or more processors to:
 in accordance with the determination that the text representation satisfies the set of rules:
 rank a plurality of candidate media items from the user-specific corpus of media items using a user-specific media ranking model, the user-specific media ranking model generated based on a plurality of previous media-related requests from the user, wherein obtaining the at least one media item from the user-specific corpus of media items includes selecting the at least one media item from the plurality of candidate media items based on the ranking. 
 
 
     
     
       5. The non-transitory computer-readable storage medium of  claim 1 , wherein each media item in the user-specific corpus of media items includes metadata that indicates an activity associated with the media item, and wherein the activity is associated with the media item based on a music tempo of the media item. 
     
     
       6. The non-transitory computer-readable storage medium of  claim 1 , wherein each media item in the user-specific corpus of media items includes metadata that indicates a mood associated with the media item, and wherein the mood is associated with the media item based on a music key of the media item. 
     
     
       7. The non-transitory computer-readable storage medium of  claim 1 , wherein the instructions further cause the one or more processors to:
 determine whether the text representation defines an occasion associated with a time period; and 
 in accordance with the determination that the text representation satisfies the set of rules and that the text representation defines an occasion associated with a time period, obtain the at least one media item from the user-specific corpus of media items based the occasion, wherein the at least one media item from the user-specific corpus of media items includes metadata indicating the occasion. 
 
     
     
       8. The non-transitory computer-readable storage medium of  claim 1 , wherein the instructions further cause the one or more processors to:
 determine whether the text representation defines an editorial list associated with a media establishment; and 
 in accordance with the determination that the text representation satisfies the set of rules and that the text representation defines an editorial list associated with a media establishment, obtain the at least one media item from the user-specific corpus of media items based on the editorial list associated with the media establishment, wherein the at least one media item from the user-specific corpus of media items includes metadata indicating the editorial list associated with the media establishment. 
 
     
     
       9. The non-transitory computer-readable storage medium of  claim 1 , wherein the instructions further cause the one or more processors to:
 determine whether the text representation defines a mood; and 
 in accordance with the determination that the text representation satisfies the set of rules and that the text representation defines a mood, obtain the at least one media item from the user-specific corpus of media items based on the mood, wherein the at least one media item from the user-specific corpus of media items includes metadata indicating the mood. 
 
     
     
       10. The non-transitory computer-readable storage medium of  claim 1 , wherein the instructions further cause the one or more processors to:
 determine whether the text representation defines an activity; and 
 in accordance with the determination that the text representation satisfies the set of rules and that the text representation defines an activity, obtain the at least one media item from the user-specific corpus of media items based on the activity, wherein the at least one media item from the user-specific corpus of media items includes metadata indicating the activity. 
 
     
     
       11. The non-transitory computer-readable storage medium of  claim 1 , wherein the instructions further cause the one or more processors to:
 determine whether the text representation defines a time period; 
 in response to determining that the text representation defines a time period, determine whether the text representation defines a genre in association with the time period; and 
 in response to determining that the text representation defines a genre in association with the time period, determine a subgenre based on the time period and the genre, wherein in accordance with the determination that the text representation satisfies the set of rules, the at least one media item is obtained from the user-specific corpus of media items based on the subgenre, and wherein the at least one media item from the user-specific corpus of media items includes metadata indicating the subgenre. 
 
     
     
       12. The non-transitory computer-readable storage medium of  claim 1 , wherein the text representation defines a category of media items, and wherein the instructions further cause the one or more processors to:
 in accordance with the determination that the text representation satisfies the set of rules, obtain the at least one media item from the user-specific corpus of media items includes obtaining a plurality of media items associated with the category of media items; and 
 determine a degree of familiarity of the user with the category of media items, wherein an average popularity rating of the plurality of media items is based on the degree of familiarity of the user with the category of media items. 
 
     
     
       13. The non-transitory computer-readable storage medium of  claim 1 , wherein the instructions further cause the one or more processors to:
 determine an identity of the user by performing speaker identification using the speech input; and 
 in accordance with the determination that the text representation satisfies the set of rules, determine, based on the determined identity of the user, the user-specific corpus of media items from a plurality of user-specific corpuses of media items. 
 
     
     
       14. The non-transitory computer-readable storage medium of  claim 1 , wherein in accordance with the determination that the text representation satisfies the set of rules, obtaining the at least one media item from the user-specific corpus of media items comprises sending an encrypted token to a remote server, the encrypted token containing user identification information, and wherein the encrypted token is required to access the user-specific corpus of media items via the remote server. 
     
     
       15. The non-transitory computer-readable storage medium of  claim 1 , wherein the instructions further cause the one or more processors to:
 determine whether the one or more actionable intent nodes include at least one of a plurality of predetermined actionable intent nodes; 
 in accordance with the determination that the text representation satisfies the set of rules and that the one or more actionable intent nodes include at least one of a plurality of predetermined actionable intent nodes:
 upon providing the at least one media item from the user-specific corpus of media items, receive audio input; 
 determine whether the audio input contains speech; and 
 in response to determining that the audio input does not contain speech, cease to receive audio input after a predetermined amount of time. 
 
 
     
     
       16. The non-transitory computer-readable storage medium of  claim 15 , wherein the instructions further cause the one or more processors to:
 in response to determining that the audio input contains speech:
 determine whether the speech of the audio input corresponds to a same actionable intent node as the determined actionable intent node; 
 in response to determining that the speech of the audio input corresponds to a same actionable intent node as the determined actionable intent node:
 determine a user intent corresponding to the speech of the audio input; and 
 provide a response to the audio input in accordance with the user intent corresponding to the speech of the audio input. 
 
 
 
     
     
       17. The non-transitory computer-readable storage medium of  claim 1 , wherein the instructions further cause the one or more processors to:
 in accordance with the determination that the text representation satisfies the set of rules, provide the at least one media item from the user-specific corpus of media items comprises playing a media item of the at least one media item from the user-specific corpus of media items; and 
 while playing the media item, receiving a third speech input; 
 determine, based on the playing media item and the third speech input, a user intent corresponding to the third speech input; and 
 provide a response in accordance with the user intent that corresponds to the third speech input. 
 
     
     
       18. The non-transitory computer-readable storage medium of  claim 1 , wherein the instructions further cause the one or more processors to:
 in accordance with a determination that the text representation does not satisfy the set of rules: 
 obtain at least one media item from a general corpus of media items, the general corpus of media items generated according to inferred media preferences of a plurality of users; and 
 provide the at least one media item from the general corpus of media items. 
 
     
     
       19. An electronic device, comprising:
 one or more processors; 
 a memory; and 
 one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for:
 determining at least one media item frequently requested by a user, wherein the at least one media item frequently requested by the user is associated with at least one first parameter; 
 determining at least one media item frequently rejected by the user, wherein the at least one media item frequently rejected by the user is associated with at least one second parameter; 
 generating a user-specific corpus of media items data by identifying media items including the at least one first parameter and not including the at least one second parameter; 
 receiving, from a user, speech input representing a request for one or more media items; 
 determining whether the speech input corresponds to a user intent of obtaining personalized recommendations for media items by evaluating a text representation of the speech input against a set of rules associated with one or more actionable intent nodes; 
 in accordance with a determination that the text representation satisfies the set of rules:
 obtaining at least one media item from the user-specific corpus of media items; and 
 providing the at least one media item from the user-specific corpus of media items. 
 
 
 
     
     
       20. The device of  claim 19 , wherein evaluating the text representation of the speech input against the set of rules further comprises determining whether a number of parameters defined in the text representation is less than a threshold number. 
     
     
       21. The device of  claim 19 , wherein evaluating the text representation of the speech input against the set of rules further comprises determining whether the text representation includes one of a plurality of phrases corresponding to the user intent of obtaining personalized recommendations for media items. 
     
     
       22. The device of  claim 19 , wherein the one or more programs further comprise instructions for:
 in accordance with the determination that the text representation satisfies the set of rules: 
 ranking a plurality of candidate media items from the user-specific corpus of media items using a user-specific media ranking model, the user-specific media ranking model generated based on a plurality of previous media-related requests from the user, wherein obtaining the at least one media item from the user-specific corpus of media items includes selecting the at least one media item from the plurality of candidate media items based on the ranking. 
 
     
     
       23. The device of  claim 19 , wherein each media item in the user-specific corpus of media items includes metadata that indicates an activity associated with the media item, and wherein the activity is associated with the media item based on a music tempo of the media item. 
     
     
       24. The device of  claim 19 , wherein each media item in the user-specific corpus of media items includes metadata that indicates a mood associated with the media item, and wherein the mood is associated with the media item based on a music key of the media item. 
     
     
       25. The device of  claim 19 , wherein the one or more programs further comprise instructions for:
 determining whether the text representation defines an occasion associated with a time period; and 
 in accordance with the determination that the text representation satisfies the set of rules and that the text representation defines an occasion associated with a time period, obtaining the at least one media item from the user-specific corpus of media items based the occasion, wherein the at least one media item from the user-specific corpus of media items includes metadata indicating the occasion. 
 
     
     
       26. The device of  claim 19 , wherein the one or more programs further comprise instructions for:
 determining whether the text representation defines an editorial list associated with a media establishment; and 
 in accordance with the determination that the text representation satisfies the set of rules and that the text representation defines an editorial list associated with a media establishment, obtaining the at least one media item from the user-specific corpus of media items based on the editorial list associated with the media establishment, wherein the at least one media item from the user-specific corpus of media items includes metadata indicating the editorial list associated with the media establishment. 
 
     
     
       27. The device of  claim 19 , wherein the one or more programs further comprise instructions for:
 determining whether the text representation defines a mood; and 
 in accordance with the determination that the text representation satisfies the set of rules and that the text representation defines a mood, obtaining the at least one media item from the user-specific corpus of media items based on the mood, wherein the at least one media item from the user-specific corpus of media items includes metadata indicating the mood. 
 
     
     
       28. The device of  claim 19 , wherein the one or more programs further comprise instructions for:
 determining whether the text representation defines an activity; and 
 in accordance with the determination that the text representation satisfies the set of rules and that the text representation defines an activity, obtaining the at least one media item from the user-specific corpus of media items based on the activity, wherein the at least one media item from the user-specific corpus of media items includes metadata indicating the activity. 
 
     
     
       29. The device of  claim 19 , wherein the one or more programs further comprise instructions for:
 determining whether the text representation defines a time period; 
 in response to determining that the text representation defines a time period, determining whether the text representation defines a genre in association with the time period; and 
 in response to determining that the text representation defines a genre in association with the time period, determining a subgenre based on the time period and the genre, wherein in accordance with the determination that the text representation satisfies the set of rules, the at least one media item is obtained from the user-specific corpus of media items based on the subgenre, and wherein the at least one media item from the user-specific corpus of media items includes metadata indicating the subgenre. 
 
     
     
       30. The device of  claim 19 , wherein the text representation defines a category of media items, and wherein the one or more programs further comprise instructions for:
 in accordance with the determination that the text representation satisfies the set of rules, obtaining the at least one media item from the user-specific corpus of media items includes obtaining a plurality of media items associated with the category of media items; and 
 determining a degree of familiarity of the user with the category of media items, wherein an average popularity rating of the plurality of media items is based on the degree of familiarity of the user with the category of media items. 
 
     
     
       31. The device of  claim 19 , wherein the one or more programs further comprise instructions for:
 determining an identity of the user by performing speaker identification using the speech input; and 
 in accordance with the determination that the text representation satisfies the set of rules, determining, based on the determined identity of the user, the user-specific corpus of media items from a plurality of user-specific corpuses of media items. 
 
     
     
       32. The device of  claim 19 , wherein in accordance with the determination that the text representation satisfies the set of rules, obtaining the at least one media item from the user-specific corpus of media items comprises sending an encrypted token to a remote server, the encrypted token containing user identification information, and wherein the encrypted token is required to access the user-specific corpus of media items via the remote server. 
     
     
       33. The device of  claim 19 , wherein the one or more programs further comprise instructions for:
 determining whether the one or more actionable intent nodes include at least one of a plurality of predetermined actionable intent nodes; 
 in accordance with the determination that the text representation satisfies the set of rules and that the one or more actionable intent nodes include at least one of a plurality of predetermined actionable intent nodes:
 upon providing the at least one media item from the user-specific corpus of media items, receiving audio input; 
 determining whether the audio input contains speech; and 
 in response to determining that the audio input does not contain speech, cease to receive audio input after a predetermined amount of time. 
 
 
     
     
       34. The device of  claim 33 , wherein the one or more programs further comprise instructions for:
 in response to determining that the audio input contains speech:
 determining whether the speech of the audio input corresponds to a same actionable intent node as the determined actionable intent node; 
 in response to determining that the speech of the audio input corresponds to a same actionable intent node as the determined actionable intent node: 
 determining a user intent corresponding to the speech of the audio input; and 
 providing a response to the audio input in accordance with the user intent corresponding to the speech of the audio input. 
 
 
     
     
       35. The device of  claim 19 , wherein the one or more programs further comprise instructions for:
 in accordance with the determination that the text representation satisfies the set of rules, providing the at least one media item from the user-specific corpus of media items comprises playing a media item of the at least one media item from the user-specific corpus of media items; and; 
 while playing the media item, receiving a third speech input; 
 determining, based on the playing media item and the third speech input, a user intent corresponding to the third speech input; and 
 providing a response in accordance with the user intent that corresponds to the third speech input. 
 
     
     
       36. The device of  claim 19 , wherein the one or more programs further comprise instructions for:
 in accordance with a determination that the text representation does not satisfy the set of rules: 
 obtaining at least one media item from a general corpus of media items, the general corpus of media items generated according to inferred media preferences of a plurality of users; and
 provide the at least one media item from the general corpus of media items. 
 
 
     
     
       37. A method for operating a digital assistant to explore media items, comprising:
 at an electronic device with one or more processors and memory:
 determining at least one media item frequently requested by a user, wherein the at least one media item frequently requested by the user is associated with at least one first parameter; 
 determining at least one media item frequently rejected by the user, wherein the at least one media item frequently rejected by the user is associated with at least one second parameter; 
 generating a user-specific corpus of media items by identifying media items including the at least one first parameter and not including the at least one second parameter; 
 receiving, from a user, speech input representing a request for one or more media items; 
 determining whether the speech input corresponds to a user intent of obtaining personalized recommendations for media items by evaluating a text representation of the speech input against a set of rules associated with one or more actionable intent nodes; 
 in accordance with a determination that the text representation satisfies the set of rules: 
 obtaining at least one media item from the user-specific corpus of media items; and 
 providing the at least one media item from the user-specific corpus of media items. 
 
 
     
     
       38. The method of  claim 37 , wherein the one or more programs further comprise instructions for:
 in accordance with a determination that the text representation does not satisfy the set of rules: 
 obtaining at least one media item from a general corpus of media items, the general corpus of media items generated according to inferred media preferences of a plurality of users; and 
 provide the at least one media item from the general corpus of media items. 
 
     
     
       39. The method of  claim 37 , wherein evaluating the text representation of the speech input against the set of rules further comprises determining whether a number of parameters defined in the text representation is less than a threshold number. 
     
     
       40. The method of  claim 37 , wherein evaluating the text representation of the speech input against the set of rules further comprises determining whether the text representation includes one of a plurality of phrases corresponding to the user intent of obtaining personalized recommendations for media items. 
     
     
       41. The method of  claim 37 , further comprising:
 in accordance with the determination that the text representation satisfies the set of rules: 
 ranking a plurality of candidate media items from the user-specific corpus of media items using a user-specific media ranking model, the user-specific media ranking model generated based on a plurality of previous media-related requests from the user, wherein obtaining the at least one media item from the user-specific corpus of media items includes selecting the at least one media item from the plurality of candidate media items based on the ranking. 
 
     
     
       42. The method of  claim 37 , wherein each media item in the user-specific corpus of media items includes metadata that indicates an activity associated with the media item, and wherein the activity is associated with the media item based on a music tempo of the media item. 
     
     
       43. The method of  claim 37 , wherein each media item in the user-specific corpus of media items includes metadata that indicates a mood associated with the media item, and wherein the mood is associated with the media item based on a music key of the media item. 
     
     
       44. The method of  claim 37 , further comprising:
 determining whether the text representation defines an occasion associated with a time period; and 
 in accordance with the determination that the text representation satisfies the set of rules and that the text representation defines an occasion associated with a time period, obtaining the at least one media item from the user-specific corpus of media items based the occasion, wherein the at least one media item from the user-specific corpus of media items includes metadata indicating the occasion. 
 
     
     
       45. The method of  claim 37 , further comprising:
 determining whether the text representation defines an editorial list associated with a media establishment; and 
 in accordance with the determination that the text representation satisfies the set of rules and that the text representation defines an editorial list associated with a media establishment, obtaining the at least one media item from the user-specific corpus of media items based on the editorial list associated with the media establishment, wherein the at least one media item from the user-specific corpus of media items includes metadata indicating the editorial list associated with the media establishment. 
 
     
     
       46. The method of  claim 37 , further comprising:
 determining whether the text representation defines a mood; and 
 in accordance with the determination that the text representation satisfies the set of rules and that the text representation defines a mood, obtaining the at least one media item from the user-specific corpus of media items based on the mood, wherein the at least one media item from the user-specific corpus of media items includes metadata indicating the mood. 
 
     
     
       47. The method of  claim 37 , further comprising:
 determining whether the text representation defines an activity; and 
 in accordance with the determination that the text representation satisfies the set of rules and that the text representation defines an activity, obtaining the at least one media item from the user-specific corpus of media items based on the activity, wherein the at least one media item from the user-specific corpus of media items includes metadata indicating the activity. 
 
     
     
       48. The method of  claim 37 , further comprising:
 determining whether the text representation defines a time period; 
 in response to determining that the text representation defines a time period, determining whether the text representation defines a genre in association with the time period; and 
 in response to determining that the text representation defines a genre in association with the time period, determining a subgenre based on the time period and the genre, wherein in accordance with the determination that the text representation satisfies the set of rules, the at least one media item is obtained from the user-specific corpus of media items based on the subgenre, and wherein the at least one media item from the user-specific corpus of media items includes metadata indicating the subgenre. 
 
     
     
       49. The method of  claim 37 , wherein the text representation defines a category of media items, further comprising:
 in accordance with the determination that the text representation satisfies the set of rules, obtaining the at least one media item from the user-specific corpus of media items includes obtaining a plurality of media items associated with the category of media items; and 
 determining a degree of familiarity of the user with the category of media items, wherein an average popularity rating of the plurality of media items is based on the degree of familiarity of the user with the category of media items. 
 
     
     
       50. The method of  claim 37 , further comprising:
 determining an identity of the user by performing speaker identification using the speech input; and 
 in accordance with the determination that the text representation satisfies the set of rules, determining, based on the determined identity of the user, the user-specific corpus of media items from a plurality of user-specific corpuses of media items. 
 
     
     
       51. The method of  claim 37 , wherein in accordance with the determination that the text representation satisfies the set of rules, obtaining the at least one media item from the user-specific corpus of media items comprises sending an encrypted token to a remote server, the encrypted token containing user identification information, and wherein the encrypted token is required to access the user-specific corpus of media items via the remote server. 
     
     
       52. The method of  claim 37 , further comprising:
 determining whether the one or more actionable intent nodes include at least one of a plurality of predetermined actionable intent nodes; 
 in accordance with the determination that the text representation satisfies the set of rules and that the one or more actionable intent nodes include at least one of a plurality of predetermined actionable intent nodes:
 upon providing the at least one media item from the user-specific corpus of media items, receiving audio input; 
 determining whether the audio input contains speech; and 
 in response to determining that the audio input does not contain speech, cease to receive audio input after a predetermined amount of time. 
 
 
     
     
       53. The method of  claim 52 , further comprising:
 in response to determining that the audio input contains speech:
 determining whether the speech of the audio input corresponds to a same actionable intent node as the determined actionable intent node; 
 in response to determining that the speech of the audio input corresponds to a same actionable intent node as the determined actionable intent node: 
 determining a user intent corresponding to the speech of the audio input; and 
 providing a response to the audio input in accordance with the user intent corresponding to the speech of the audio input. 
 
 
     
     
       54. The method of  claim 37 , further comprising:
 in accordance with the determination that the text representation satisfies the set of rules, providing the at least one media item from the user-specific corpus of media items comprises playing a media item of the at least one media item from the user-specific corpus of media items; 
 and; 
 while playing the media item, receiving a third speech input; 
 determining, based on the playing media item and the third speech input, a user intent corresponding to the third speech input; and 
 providing a response in accordance with the user intent that corresponds to the third speech input.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 15/266,956, filed on Sep. 15, 2016, entitled INTELLIGENT AUTOMATED ASSISTANT FOR MEDIA EXPLORATION, which claims priority to U.S. Provisional Ser. No. 62/347,480, filed on Jun. 8, 2016, entitled INTELLIGENT AUTOMATED ASSISTANT FOR. MEDIA EXPLORATION, both of which are hereby incorporated by reference in their entirety for all purposes. 
    
    
     FIELD 
     This relates generally to intelligent automated assistants and, more specifically, to intelligent automated assistants for media exploration. 
     BACKGROUND 
     Intelligent automated assistants (or digital assistants) can provide a beneficial interface between human users and electronic devices. Such assistants can allow users to interact with devices or systems using natural language in spoken and/or text forms. For example, a user can provide a speech input containing a user request to a digital assistant operating on an electronic device. The digital assistant can interpret the user s intent from the speech input and operationalize the user&#39;s intent into tasks. The tasks can then be performed by executing one or more services of the electronic device, and a relevant output responsive to the user request can be returned to the user. 
     When managing music or other media, a digital assistant can be helpful in searching for or playing back specific media, particularly in a hands-free environment. In particular, a digital assistant can respond effectively to a request to play a specific media item, such as an album or a song identified specifically by title or by artist. However, digital assistants can struggle with discovering relevant media items based on vague open-ended natural language requests, such as, for example, a request to recommend a song or album. 
     SUMMARY 
     Systems and processes for operating an intelligent automated assistant to explore media items are provided. In one example process, a speech input representing a request for one or more media items is received from a user. The process determines whether the speech input corresponds to a user intent of obtaining personalized recommendations for media items. In response to determining that the speech input corresponds to a user intent of obtaining personalized recommendations for media items, at least one media item is obtained from a user-specific corpus of media items. The user-specific corpus of media items is generated based on data associated with the user. The at least one media item is provided. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram illustrating a system and environment for implementing a digital assistant, according to various examples. 
         FIG. 2A  is a block diagram illustrating a portable multifunction device implementing the client-side portion of a digital assistant, according to various examples. 
         FIG. 2B  is a block diagram illustrating exemplary components for event handling, according to various examples. 
         FIG. 3  illustrates a portable multifunction device implementing the client-side portion of a digital assistant, according to various examples. 
         FIG. 4  is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface, according to various examples. 
         FIG. 5A  illustrates an exemplary user interface for a menu of applications on a portable multifunction device, according to various examples. 
         FIG. 5B  illustrates an exemplary user interface for a multifunction device with a touch-sensitive surface that is separate from the display, according to various examples. 
         FIG. 6A  illustrates a personal electronic device, according to various examples. 
         FIG. 6B  is a block diagram illustrating a personal electronic device, according to various examples. 
         FIG. 7A  is a block diagram illustrating a digital assistant system or a server portion thereof, according to various examples. 
         FIG. 7B  illustrates the functions of the digital assistant shown in  FIG. 7A , according to various examples. 
         FIG. 7C  illustrates a portion of an ontology, according to various examples. 
         FIGS. 8A-C  illustrate a process for operating a digital assistant for media exploration, according to various examples. 
         FIGS. 9A-B  illustrate a user operating a digital assistant for media exploration, according to various examples. 
         FIG. 10  illustrates a user operating a digital assistant for media exploration, according to various examples. 
         FIG. 11  illustrates a user operating a digital assistant for media exploration, according to various examples. 
         FIG. 12  illustrates a functional block diagram of an electronic device, according to various examples. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description of examples, reference is made to the accompanying drawings in which it is shown by way of illustration specific examples that can be practiced. It is to be understood that other examples can be used and structural changes can be made without departing from the scope of the various examples. 
     Conventional techniques for exploring media content using digital assistants can be generally cumbersome and inefficient. In particular, media-related requests that are in natural language form are, for example, overly broad or ambiguous, and thus it is difficult to accurately infer the user&#39;s intent that corresponds to the request. For example, the media-related request “Play me something good” is vague and open-ended, and thus with existing techniques, digital assistants may retrieve media items that are incompatible with the user&#39;s preferences, may overwhelm the user with too many media items, or may return nothing at all. This can result in a large number of follow-up interactions between the user and the digital assistant to clarify the user&#39;s intent. This can negatively impact user experience. Additionally, a large number of follow-up interactions is inefficient with respect to energy consumption of the device. This is consideration is particularly important, for battery-operated devices. 
     In accordance with some systems, computer-readable media, and processes described herein, media exploration is performed by a digital assistant in a more efficient and accurate manner. In one example process, a speech input, representing a request for one or more media items is received from a user. The process determines whether the speech input corresponds to a user intent of obtaining personalized recommendations for media items. In response to determining that the speech input corresponds to a user intent of obtaining personalized recommendations for media items, at least one media item is obtained from a user-specific corpus of media items. The at least one media item is obtained using a user-specific media ranking model. The user-specific corpus of media items and the user-specific media ranking model are generated based on data associated with the user. The at least one media item is then provided to the user. By using a user-specific corpus of media items and a user-specific media ranking model to obtain the at least one media item, the likelihood that the at least one media item matches the preferences of the user increases. As a result, media items that are more relevant to the user are recommended, which increases the efficiency and usefulness of the digital assistant. 
     Although the following description uses terms “first,” “second,” etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another. For example, a first input could be termed a second input, and, similarly, a second input could be termed a first input, without departing from the scope of the various described examples. The first input and the second input are both inputs and in some cases, are separate and different inputs. 
     The terminology used in the description of the various described examples herein is for the purpose of describing particular examples only and is not intended to be limiting. As used in the description of the various described examples and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     The term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” may be construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context. 
     1. System and Environment 
       FIG. 1  illustrates a block diagram of system  100  according to various examples. In some examples, system  100  implements a digital assistant. The terms “digital assistant,” “virtual assistant,” “intelligent automated assistant,” or “automatic digital assistant” refer to any information processing system that interprets natural language input in spoken and/or textual form to infer user intent, and performs actions based on the inferred user intent. For example, to act on an inferred user intent, the system performs one or more of the following: identifying a task flow with steps and parameters designed to accomplish the inferred user intent, inputting specific requirements from the inferred user intent into the task flow; executing the task flow by invoking programs, methods, services, APIs, or the like; and generating output responses to the user in an audible (e.g., speech) and/or visual form. 
     Specifically, a digital assistant is capable of accepting a user request at least partially in the form of a natural language command, request, statement, narrative, and/or inquiry. Typically, the user request seeks either an informational answer or performance of a task by the digital assistant. A satisfactory response to the user request includes a provision of the requested informational answer, a performance of the requested task, or a combination of the two. For example, a user asks the digital assistant a question, such as “Where am I right now?” Based on the user&#39;s current location, the digital assistant answers, “You are in Central Park near the west gate.” The user also requests the performance of a task, for example, “Please invite my friends to my girlfriend&#39;s birthday party next week.” In response, the digital assistant can acknowledge the request by saying “Yes, right away,” and then send a suitable calendar invite on behalf of the user to each of the user&#39;s friends listed in the user&#39;s electronic address book. During performance of a requested task, the digital assistant sometimes interacts with the user in a continuous dialogue involving multiple exchanges of information over an extended period of time. There are numerous other ways of interacting with a digital assistant to request information or performance of various tasks. In addition to providing verbal responses and taking programmed actions, the digital assistant also provides responses in other visual or audio forms, e.g., as text, alerts, music, videos, animations, etc. 
     As shown in  FIG. 1 , in some examples, a digital assistant is implemented according to a client-server model. The digital assistant includes client-side portion  102  (hereafter “DA client  102 ”) executed on user device  104  and server-side portion  106  (hereafter “DA server  106 ”) executed on server system  108 . DA client  102  communicates with DA server  106  through one or more networks  110 . DA client  102  provides client-side functionalities such as user-facing input and output processing and communication with DA server  106 . DA server  106  provides server-side functionalities for any number of DA clients  102  each residing on a respective user device  104 . 
     In some examples, DA server  106  includes client-facing I/O interface  112 , one or more processing modules  114 , data and models  116 , and I/O interface to external services  118 . The client-facing I/O interface  112  facilitates the client-facing input and output processing for DA server  106 . One or more processing modules  114  utilize data and models  116  to process speech input and determine the user&#39;s intent based on natural language input. Further, one or more processing modules  114  perform task execution based on inferred user intent. In some examples, DA server  106  communicates with external services  120  (e.g., media service(s)  120 - 1 , navigation service(s)  120 - 2 , messaging service(s)  120 - 3 , information service(s)  120 - 4 , calendar service  120 - 5 , telephony service  120 - 6 , etc.) through network(s)  110  for task completion or information acquisition. I/O interface to external services  118  facilitates such communications. 
     In particular, DA server  106  communicates to media service(s) to perform tasks that include searching for and obtaining media items. Media service(s)  120 - 1  is implemented, for example, on one or more remote media servers and is configured to provide media items, such as songs, albums, playlists, videos, or the like. For example, media service(s) includes media streaming services, such as Apple Music or iTunes Radio™ (services of. Apple, Inc. of Cupertino, Calif.). Media service(s)  120 - 1  is configured to receive a media search query (e.g., from DA server  106 ) and in response, provide one or more media items that satisfy the media search queries. Specifically, in accordance with the media search query, one or more corpuses of media items rue searched to identify one or more media items and the identified one or more media items are provided. Further, media service(s) are configured to provide media information associated with media items, such as the names of artists associated with specific media items, the release dates of specific media items, or the lyrics of specific media items. 
     Media service(s)  120 - 1  include various corpuses of media items. The corpuses of media items include a plurality of user-specific corpuses of media items. Each user-specific corpus of media items is generated based on media-related data associated with a respective user. The media-related data includes, for example, user input indicating media items that were previously viewed, selected, requested, acquired, or rejected by the user. Additionally, the media-related data includes the media items found in the personal library of media items associated with the user. Thus, the media items contained in each user-specific corpus of media items reflect the media preferences of the respective user. In some examples, each user-specific corpus of media items is identified and accessed based on user information, such as user log-in information and/or user password information. In some examples, the corpuses of media items in media service(s)  120 - 1  further include one or more second corpuses of media items generated based on the release dates of the media items. For example, the one or more second corpuses of media items only contain media items having release dates that are within a predetermined time range from the current date. 
     In some examples, each media item in the corpuses of media items includes metadata that indicates one or more media parameters. The media parameters include, for example, {title}, {artist}, {subgenre}, {release date}, {mood}, {occasion}, {editorial list}, {political preference}, {technical proficiency}, or the like. Media items in the corpuses of media items are thus searched and retrieved based on the media parameters indicated in the metadata of the media items. Additional description regarding media parameters associated with media items is provided below with reference to  FIGS. 8A-C . 
     User device  104  can be any suitable electronic device. In some examples, user device is a portable multifunctional device (e.g., device  200 , described below with reference to  FIG. 2A ), a multifunctional device (e.g., device  400 , described below with reference to  FIG. 4 ), or a personal electronic device (e.g., device  600 , described below with reference to  FIG. 6A-B .) A portable multifunctional device is, for example, a mobile telephone that also contains other functions, such as PDA and/or music player functions. Specific examples of portable multifunction devices include the iPhone®, iPod Touch®, and iPad® devices from Apple Inc. of Cupertino, Calif. Other examples of portable multifunction devices include, without limitation, laptop or tablet computers. Further, in some examples, user device  104  is a non-portable multifunctional device. In particular, user device  104  is a desktop computer, a game console, a television, or a television set-top box. In some examples, user device  104  includes a touch-sensitive surface (e.g., touch screen displays and/or touchpads). Further, user device  104  optionally includes one or more other physical user-interface devices, such as a physical keyboard, a mouse, and/or a joystick. Various examples of electronic devices, such as multifunctional devices, are described below in greater detail. 
     Examples of communication network(s)  110  include local area networks (LAN) and wide area networks (WAN), e.g., the internet. Communication network(s)  110  is implemented using any known network protocol, including various wired or wireless protocols, such as, for example. Ethernet, Universal Serial Bus (USB), FIREWIRE, Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP), Wi-MAX, or any other suitable communication protocol. 
     Server system  108  is implemented on one or more standalone data processing apparatus or a distributed network of computers. In some examples, server system  108  also employs various virtual devices and/or services of third-party service providers (e.g., third-party cloud service providers) to provide the underlying computing resources and/or infrastructure resources of server system  108 . 
     In some examples, user device  104  communicates with DA server  106  via second user device  122 . Second user device  122  is similar or identical to user device  104 . For example, second user device  122  is similar to devices  200 ,  400 , or  600  described below with reference to  FIGS. 2A, 4, and 6A -B. User device  104  is configured to communicatively couple to second user device  122  via a direct communication connection, such as Bluetooth, NFC, BTLE, or the like, or via a wired or wireless network, such as a local Wi-Fi network. In some examples, second user device  122  is configured to act as a proxy between user device  104  and DA server  106 . For example, DA client  102  of user device  104  is configured to transmit information (e.g., a user request received at user device  104 ) to DA server  106  via second user device  122 . DA server  106  processes the information and return relevant data (e.g., data content responsive to the user request) to user device  104  via second user device  122 . 
     In some examples, user device  104  is configured to communicate abbreviated requests for data to second user device  122  to reduce the amount of information transmitted from user device  104 . Second user device  122  is configured to determine supplemental information to add to the abbreviated request to generate a complete request to transmit to DA server  106 . This system architecture can advantageously allow user device  104  having limited communication capabilities and/or limited battery power (e.g., a watch or a similar compact electronic device) to access services provided by DA server  106  by using second user device  122 , having greater communication capabilities and/or battery power (e.g., a mobile phone, laptop computer, tablet computer, or the like), as a proxy to DA server  106 . While only two user devices  104  and  122  are shown in  FIG. 1 , it should be appreciated that system  100 , in some examples, includes any number and type of user devices configured in this proxy configuration to communicate with DA server system  106 . 
     Although the digital assistant shown in  FIG. 1  includes both a client-side portion (e.g., DA client  102 ) and a server-side portion (e.g., DA server  106 ), in some examples, the functions of a digital assistant are implemented as a standalone application installed on a user device. In addition, the divisions of functionalities between the client and server portions of the digital assistant cart vary in different implementations. For instance, in some examples, the DA client is a thin-client that provides only user-facing input and output processing functions, and delegates all other functionalities of the digital assistant to a backend server. 
     2. Electronic Devices 
     Attention is now directed toward embodiments of electronic devices for implementing the client-side portion of a digital assistant.  FIG. 2A  is a block diagram illustrating portable multifunction device  200  with touch-sensitive display system  212  in accordance with some embodiments. Touch-sensitive display  212  is sometimes called a “touch screen” for convenience and is sometimes known as or called a “touch-sensitive display system.” Device  200  includes memory  202  (which optionally includes one or more computer-readable storage mediums), memory controller  222 , one or more processing units (CPUs)  220 , peripherals interface  218 , RF circuitry  208 , audio circuitry  210 , speaker  211 , microphone  213 , input/output (I/O) subsystem  206 , other input control devices  216 , and external port  224 . Device  200  optionally includes one or more optical sensors  264 . Device  200  optionally includes one or more contact intensity sensors  265  for delecting intensity of contacts on device  200  (e.g., a touch-sensitive surface such as touch-sensitive display system  212  of device  200 ). Device  200  optionally includes one or mom tactile output generators  26  for generating tactile outputs on device  200  (e.g., generating tactile outputs on a touch-sensitive surface such as touch-sensitive display system  212  of device  200  or touchpad  455  of device  400 ). These components optionally communicate over one or mom communication buses or signal lines  203 . 
     As used in the specification and claims, the term “intensity” of a contact on a touch-sensitive surface refers to the force or pressure (force per unit area) of a contact (e.g., a finger contact) on the touch-sensitive surface, or to a substitute (proxy) for the force or pressure of a contact on the touch-sensitive surface. The intensity of a contact has a range of values that includes at least four distinct values and more typically includes hundreds of distinct values (e.g., at least  256 ). Intensity of a contact is, optionally, determined (or measured) using various approaches and various sensors or combinations of sensors. For example, one or more force sensors underneath or adjacent to the touch-sensitive surface are, optionally, used to measure force at various points on the touch-sensitive surface, in some implementations, force measurements from multiple force sensors are combined (e.g., a weighted average) to determine an estimated force of a contact. Similarly, a pressure-sensitive tip of a stylus is, optionally, used to determine a pressure of the stylus on the touch-sensitive surface. Alternatively, the size of the contact area detected on the touch-sensitive surface and/or changes thereto, the capacitance of the touch-sensitive surface proximate to the contact and/or changes thereto, and/or the resistance of the touch-sensitive surface proximate to the contact and/or changes thereto are, optionally, used as a substitute for the force or pressure of the contact on the touch-sensitive surface, in some implementations, the substitute measurements for contact force or pressure are used directly to determine whether an intensity threshold has been exceeded (e.g., the intensity threshold is described in units corresponding to the substitute measurements). In some implementations, the substitute measurements for contact force or pressure are converted to an estimated force or pressure, and the estimated force or pressure is used to determine whether an intensity threshold has been exceeded (e.g., the intensity threshold is a pressure threshold measured in units of pressure). Using the intensity of a contact as an attribute of a user input allows for user access to additional device functionality that may otherwise not be accessible by the user on a reduced-size device with limited real estate for displaying affordances (e.g., on a touch-sensitive display) and/or receiving user input (e.g., via a touch-sensitive display, a touch-sensitive surface, or a physical/mechanical control such as a knob or a button). 
     As used in the specification and claims, the term “tactile output” refers to physical displacement of a device relative to a previous position of the device, physical displacement of a component (e.g., a touch-sensitive surface) of a device relative to another component (e.g., housing) of the device, or displacement of the component relative to a center of mass of the device that will be detected by a user with the user&#39;s sense of touch. For example, in situations where the device or the component of the device is in contact with a surface of a user that is sensitive to touch (e.g., a finger, palm, or other part of a user&#39;s hand), the tactile output generated by the physical displacement will be interpreted by the user as a tactile sensation corresponding to a perceived change in physical characteristics of the device or the component of the device. For example, movement of a touch-sensitive surface (e.g., a touch-sensitive display or trackpad) is, optionally, interpreted by the user as a “down click” or “up click” of a physical actuator button. In some cases, a user will feel a tactile sensation such as an “down click” or “up click” even when there is no movement of a physical actuator button associated with the touch-sensitive surface that is physically pressed (e.g., displaced) by the user&#39;s movements. As another example, movement of the touch-sensitive surface is, optionally, interpreted or sensed by the user as “roughness” of the touch-sensitive surface, even when there is no change in smoothness of the touch-sensitive surface. While such interpretations of touch by a user will be subject to the individualized sensory perceptions of the user, there are many sensors perceptions of touch that are common to a large majority of users. Thus, when a tactile output is described as corresponding to a particular sensory perception of a user (e.g., an “up click,” a “down click,” “roughness”), unless otherwise stated, the generated tactile output corresponds to physical displacement of the device or a component thereof that will generate the described sensory perception for a typical (or average) user. 
     It should be appreciated that device  200  is only one example of a portable multifunction device, and that device  200  optionally has mom or fewer components than shown, optionally combines two or more components, or optionally has a different configuration or arrangement of the components. The various components shown in  FIG. 2A  are implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application specific integrated circuits. 
     Memory  202  includes one or mote computer-readable storage mediums. The computer-readable storage mediums are, for example, tangible and non-transitory. Memory  202  includes high-speed random access memory and also includes non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices. Memory controller  222  controls access to memory  202  by other components of device  200 . 
     In some examples, a non-transitory computer-readable storage medium of memory  202  is used to store instructions (e.g., for performing aspects of processes described below) for use by or in connection with an instruction execution system, apparatus, or device, such as a computer based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In other examples, the instructions (e.g., for performing aspects of the processes described below) am stored on a non-transitory computer-readable storage medium (not shown) of the server system  108  or are divided between the non-transitory computer-readable storage medium of memory  202  and the non-transitory computer-readable storage medium of server system  108 . 
     Peripherals interface  218  is used to couple input and output peripherals of the device to CPU  220  and memory  202 . The one or more processors  220  am or execute various software programs and/or sets of instructions stored in memory  202  to perform various functions for device  200  and to process data. In some embodiments, peripherals interface  218 . CPU  220 , and memory controller  222  are implemented on a single chip, such as chip  204 . In some other embodiments, they are implemented on separate chips. 
     RF (radio frequency) circuitry  208  receives and sends RF signals, also called electromagnetic signals. RF circuitry  208  converts electrical signals to/from electromagnetic signals and communicates with communications networks and other communications devices via the electromagnetic signals. RF circuitry  208  optionally includes well-known circuitry for performing these functions, including but not limited to an antenna system, an RF transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a CODEC chipset, a subscriber identity module (SIM) card, memory, and so forth. RF circuitry  208  optionally communicates with networks, such as the Internet, also referred to as the World Wide Web (WWW), an intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN), and other devices by wireless communication. The RF circuitry  208  optionally includes well-known circuitry for detecting near field communication (NFC) fields, such as by a short-range communication radio. The wireless communication optionally uses any of a plurality of communications standards, protocols, and technologies, including but not limited to Global System for Mobile Communications (GSM). Enhanced Data GSM Environment (EDGE), high-speed downlink packet access (HSDPA), high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO), HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), near field communication (NFC), wideband code division multiple access (W-CDMA), code division multiple access (CDMA), time division multiple access (TDMA). Bluetooth. Bluetooth Low Energy (BTLE), Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n, and/or IEEE 802.11ac), voice over Internet Protocol (VoIP), Wi-MAX, a protocol fore mail (e.g., Internet message access protocol (IMAP) and/or post office protocol (POP)), instant messaging (e.g., extensible messaging and presence protocol (XMPP). Session Initiation Protocol for Instant Messaging and Presence leveraging Extensions (SIMPLE). Instant Messaging and Presence Service (IMPS)), and/or Short Message Service (SMS), or any other suitable communication protocol, including communication protocols not yet developed as of the filing date of this document. 
     Audio circuitry  210 , speaker  211 , and microphone  213  provide an audio interface between a user and device  200 . Audio circuitry  210  receives audio data from peripherals interface  218 , converts the audio data to an electrical signal, and transmits the electrical signal to speaker  211 . Speaker  211  converts the electrical signal to human-audible sound waves. Audio circuitry  210  also receives electrical signals converted by microphone  213  from sound waves. Audio circuitry  210  converts the electrical signal to audio data and transmits the audio data to peripherals interface  218  for processing. Audio data are retrieved from and/or transmitted to memory  202  and/or RE circuitry  208  by peripherals interface  218 . In some embodiments, audio circuitry  210  also includes a headset jack (e.g.,  312 ,  FIG. 3 ). The headset jack provides an interface between audio circuitry  210  and removable audio input/output peripherals, such as output-only headphones or a headset with both output (e.g., a headphone for one or both ears) and input (e.g., a microphone). 
     I/O subsystem  206  couples input/output peripherals on device  200 , such as touch screen  212  and other input control devices  216 , to peripherals interface  218 . I/O subsystem  206  optionally includes display controller  256 , optical sensor controller  258 , intensity sensor controller  259 , haptic feedback controller  261 , and one or more input controllers  260  for other input or control devices. The one or more input controllers  260  receive/send electrical signals from/to other input control devices  216 . The other input control devices  216  optionally include physical buttons (e.g., push buttons, rocker buttons, etc.), dials, slider switches, joysticks, click wheels, and so forth. In some alternate embodiments, input controllers)  260  are, optionally, coupled to any (or none) of the following: a keyboard, an infrared port, a USB port, and a pointer device such as a mouse. The one or more buttons (e.g.,  308 .  FIG. 3 ) optionally include an up/down button for volume control of speaker  211  and/or microphone  213 . The one or more buttons optionally include a push button (e.g.,  306 ,  FIG. 3 ). 
     A quick press of the push button disengages a lock of touch screen  212  or begin a process that uses gestures on the touch screen to unlock the device, as described in U.S. patent application Ser. No. 11/322,549, “Unlocking a Device by Performing Gestures on an Unlock Image,” filed Dec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated by reference in its entirety. A longer press of the push button (e.g.,  306 ) turns power to device  200  on or off. The user is able to customize a functionality of one or more of the buttons. Touch screen  212  is used to implement virtual or soft buttons and one or more soft keyboards. 
     Touch sensitive display  212  provides an input interface and an output interface between the device and a user. Display controller  256  receives and/or sends electrical signals from/to touch screen  212 . Touch screen  212  displays visual output to the user. The visual output includes graphics, text, icons, video, and any combination thereof (collectively termed “graphics”). In some embodiments, some or all of the visual output correspond to user-interface objects. 
     Touch screen  212  has a touch-sensitive surface, sensor, or set of sensors that accepts input from the user based on haptic and/or tactile contact. Touch screen  212  and display controller  256  (along with any associated modules and/or sets of instructions in memory  202 ) detect contact (and any movement or breaking of the contact) on touch screen  212  and convert the detected contact into interaction with user-interface objects (e.g., one or more soft keys, icons, web pages, or images) that are displayed on touch screen  212 . In an exemplary embodiment, a point of contact between touch screen  212  and (lie user corresponds to a finger of the user. 
     Touch screen  212  uses LCD (liquid crystal display) technology, LPD (light emitting polymer display) technology, or LED (light emitting diode) technology, although other display technologies may be used in other embodiments. Touch screen  212  and display controller  256  detect contact and any movement or breaking thereof using any of a plurality of touch sensing technologies now known or later developed, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touch screen  212 . In an exemplary embodiment, projected mutual capacitance sensing technology is used, such as that found in the iPhone® and iPod Touch® from Apple Inc. of Cupertino, Calif. 
     A touch-sensitive display in some embodiments of touch screen  212  is analogous to the multi-touch sensitive touchpads described in the following U.S. Pat. Nos. 6,323,846 (Westerman et ah), 6,570,557 (Westerman et ah), and/or 6,677,932 (Westerman), and/or U.S. Patent Publication 2002/0015024A1, each of which is hereby incorporated by reference in its entirety. However, touch screen  212  displays visual output from device  200 , whereas touch-sensitive touchpads do not provide visual output. 
     A touch-sensitive display in some embodiments of touch screen  212  is as described in the following applications: (1) U.S. patent application Ser. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2, 2006; (2) U.S. patent application Ser. No. 10/840,862, “Multipoint Touchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No. 10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30, 2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures For Touch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patent application Ser. No. 11/038,590, “Mode-Based Graphical User Interfaces For Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patent application Ser. No. 11/228,758. “Virtual Input Device Placement On A Touch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patent application Ser. No. 11/228.700. “Operation Of A Computer With A Touch Screen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser. No. 11/228,737. “Activating Virtual Keys Of A Touch-Screen Virtual Keyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No. 11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. All of these applications are incorporated by reference herein in their entirety. 
     Touch screen  212  has, for example, a video resolution in excess of 100 dpi. In some embodiments, the touch screen has a video resolution of approximately 160 dpi. The user makes contact with touch screen  212  using any suitable object or appendage, such as a stylus, a finger, and so forth. In some embodiments, the user interface is designed to work primarily with finger-based contacts and gestures, which can be less precise than stylus-based input due to the larger area of contact of a finger on the touch screen, in some embodiments, the device translates the rough finger-based input into a precise pointer/cursor position or command for performing the actions desired by the user. 
     In some embodiments, in addition to the touch screen, device  200  includes a touchpad (not shown) for activating or deactivating particular functions. In some embodiments, the touchpad is a touch-sensitive area of the device that, unlike the touch screen, does not display visual output. The touchpad is a touch-sensitive surface that is separate from much screen  212  or an extension of the touch-sensitive surface formed by the touch screen. 
     Device  200  also includes power system  262  for powering the various components. Power system  262  includes a power management system, one or more power sources (e.g., battery, alternating current (AC)), a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator (e.g., a light-emitting diode (LED)) and any other components associated with the generation, management and distribution of power in portable devices. 
     Device  200  also includes one or more optical sensors  264 .  FIG. 2A  shows an optical sensor coupled to optical sensor controller  258  in I/O subsystem  206 . Optical sensor  264  includes charge-coupled device (CCD) or complementary metal-oxide semiconductor (CMOS) phototransistors. Optical sensor  264  receives light from the environment, projected through one or more lenses, and converts the light to data representing an image. In conjunction with imaging module  243  (also called a camera module), optical sensor  264  captures still images or video. In some embodiments, an optical sensor is located on the back of device  200 , opposite touch screen display  212  on the front of the device so that the touch screen display is used as a viewfinder for still and/or video image acquisition. In some embodiments, an optical sensor is located on the front of the device so that the user&#39;s image is obtained for video conferencing while the user views the other video conference participants on the touch screen display. In some embodiments, the position of optical sensor  264  can be changed by the user (e.g., by rotating the lens and the sensor in the device housing) so that a single optical sensor  264  is used along with the touch screen display for both video conferencing and still and/or video image acquisition. 
     Device  200  optionally also includes one or more contact intensity sensors  265 .  FIG. 2A  shows a contact intensity sensor coupled to intensity sensor controller  259  in I/O subsystem  206 . Contact intensity sensor  265  optionally includes one or more piezoresistive strain gauges, capacitive force sensors, electric force sensors, piezoelectric force sensors, optical force sensors, capacitive touch-sensitive surfaces, or other intensity sensors (e.g., sensors used to measure the force (or pressure) of a contact on a touch-sensitive surface). Contact intensity sensor  265  receives contact intensity information (e.g., pressure information or a proxy for pressure information) from the environment. In some embodiments, at least one contact intensity sensor is collocated with, or proximate to, a touch-sensitive surface (e.g., touch-sensitive display system  212 ). In some embodiments, at least one contact intensity sensor is located on the back of device  200 , opposite touch screen display  212 , which is located on the front of device  200 . 
     Device  200  also includes one or more proximity sensors  266 .  FIG. 2A  shows proximity sensor  266  coupled to peripherals interface  218 . Alternately, proximity sensor  266  is coupled to input controller  260  in I/O subsystem  206 . Proximity sensor  266  is performed as described in U.S. patent application Ser. Nos. 11/241,839, “Proximity Detector In Handheld Device”; 11/240,788, “Proximity Detector In Handheld Device”: 11/620,702, “Using Ambient Light Sensor To Augment Proximity Sensor Output”; 11/586,862, “Automated Response To And Sensing Of User Activity In Portable Devices”; and 11/638,251, “Methods And Systems For Automatic Configuration Of Peripherals,” which are hereby incorporated by reference in their entirety. In some embodiments, the proximity sensor turns off and disables touch screen  212  when the multifunction device is placed near the user&#39;s car (e.g., when the user is making a phone call). 
     Device  200  optionally also includes one or more tactile output generators  267 .  FIG. 2A  shows a tactile output generator coupled to haptic feedback controller  261  in I/O subsystem  206 . Tactile output generator  267  optionally includes one or more electroacoustic devices such as speakers or other audio components and/or electromechanical devices that convert energy into linear motion such as a motor, solenoid, electroactive polymer, piezoelectric actuator, electrostatic actuator, or other tactile output generating component (e.g., a component that converts electrical signals into tactile outputs on the device). Contact intensity sensor  265  receives tactile feedback generation instructions from haptic feedback module  233  and generates tactile outputs on device  200  that are capable of being sensed by a user of device  200 . In some embodiments, at least one tactile output generator is collocated with, or proximate to, a touch-sensitive surface (e.g., touch-sensitive display system  212 ) and, optionally, generates a tactile output by moving the touch-sensitive surface vertically (e.g., in/out of a surface of device  200 ) or laterally (e.g., back and forth in the same plane as a surface of device  200 ). In some embodiments, at least one tactile output generator sensor is located on the back of device  200 , opposite touch screen display  212 , which is located on the from of device  200 . 
     Device  200  also includes one or more accelerometers  268 .  FIG. 2A  shows accelerometer  268  coupled to peripherals interface  218 . Alternately, accelerometer  268  is coupled to an input controller  260  in I/O subsystem  206 . Accelerometer  268  performs, for example, as described in U.S. Patent Publication No. 20050190059. “Acceleration-based Theft Detection System for Portable Electronic Devices,” and U.S. Patent Publication No. 20069017692. “Methods And Apparatuses For Operating A Portable Device Based On An Accelerometer” both of which are incorporated by reference herein in their entirety. In some embodiments, information is displayed on the touch screen display in a portrait view or a landscape view based on an analysis of data received from the one or more accelerometers. Device  200  optionally includes, in addition to accelerometer(s)  268 , a magnetometer (not shown) and a GPS (or GLONASS or other global navigation system) receiver (not shown) for obtaining information concerning the location and orientation (e.g., portrait or landscape) of device  200 . 
     In some embodiments, the soft ware components stored in memory  202  include operating system  226 , communication module (or set of instructions)  228 , contact/motion module (or set of instructions)  230 , graphics module (or set of instructions)  232 , text input module (or set of instructions)  234 . Global Positioning System (GPS) module (or set of instructions)  235 , Digital Assistant Client Module  229 , and applications (or sets of instructions)  236 . Further, memory  202  stoics data and models, such as user data and models  231 . Furthermore, in some embodiments, memory  202  ( FIG. 2A ) or  470  ( FIG. 4 ) stores device/global internal state  257 , as shown in  FIGS. 2A and 4 . Device/global internal state  257  includes one or more of: active application state, indicating which applications, if any, are currently active; display state, indicating what applications, views or other information occupy various regions of touch screen display  212 : sensor state, including information obtained from the device&#39;s various sensors and input control devices  216 ; and location information concerning the device&#39;s location and/or attitude. 
     Operating system  226  (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communication between various hardware and software components. 
     Communication module  228  facilitates communication with other devices over one or more external ports  224  and also includes various software components for handling data received by RF circuitry  208  and/or external port  224 . External port  224  (e.g., Universal Serial Bus (USB). FIREWIRE, etc.) is adapted for coupling directly to other devices or indirectly over a network (e.g., the Internet, wireless LAN, etc.). In some embodiments, the external port is a multi-pin (e.g., 30-pin) connector that is the same as, or similar to and/or compatible with, the 30-pin connector used on iPod® (trademark of Apple Inc.) devices. 
     Contact/motion module  230  optionally detects contact with touch screen  212  (in conjunction with display controller  256 ) and other touch-sensitive devices (e.g., a touchpad or physical click wheel). Contact/motion module  230  includes various software components for performing various operations related to detection of contact, such as determining if contact has occurred (e.g., detecting a finger-down event), determining an intensity of the contact (e.g., the force or pressure of the contact or a substitute for the force or pressure of the contact), determining if there is movement of the contact and tracking the movement across the touch-sensitive surface (e.g., detecting one or more finger-dragging events), and determining if the contact has ceased (e.g., detecting a finger-up event or a break in contact). Contact/motion module  230  receives contact data from the touch sensitive surface. Determining movement of the point of contact, which is represented by a series of contact data, optionally includes determining speed (magnitude), velocity (magnitude and direction), and/or an acceleration (a change in magnitude and/or direction) of the point of contact. These operations are, optionally, applied to single contacts (e.g., one finger contacts) or to multiple simultaneous contacts (e.g., “multitouch”/multiple finger contacts). In some embodiments, contact/motion module  230  and display controller  256  detect contact on a touchpad. 
     In some embodiments, contact/motion module  230  uses a set of one or more intensity thresholds to determine whether an operation has been performed by a user (e.g., to determine whether a user has “clicked” on an icon). In some embodiments, at least a subset of the intensity thresholds are determined in accordance with software parameters (e.g., the intensity thresholds are not determined by the activation thresholds of particular physical actuators and can be adjusted without changing the physical hardware of device  200 ). For example, a mouse “click” threshold of a trackpad or touch screen display can be set to any of a large range of predefined threshold values without changing the trackpad or touch screen display hardware. Additionally, in some implementations, a user of the device is provided with software settings for adjusting one or more of the set of intensity thresholds (e.g., by adjusting individual intensity thresholds and/or by adjusting a plurality of intensity thresholds at once with a system-level click “intensity” parameter). 
     Contact/motion module  230  optionally detects a gesture input by a user. Different gestures on the touch-sensitive surface have different contact patterns (e.g., different motions, timings, and/or intensities of detected contacts). Thus, a gesture is, optionally, detected by detecting a particular contact pattern. For example, detecting a finger tap gesture includes detecting a finger-down event followed by detecting a finger-up (liftoff) event at the same position (or substantially the same position) as the finger-down event (e.g., at the position of an icon). As another example, detecting a finger swipe gesture on the touch-sensitive surface includes detecting a finger down event followed by detecting one or more finger-dragging events, and subsequently followed by detecting a finger-up (liftoff) event. 
     Graphics module  232  includes various known software components for rendering and displaying graphics on touch screen  212  or other display, including components for changing the visual impact (e.g., brightness, transparency, saturation, contrast, or other visual property) of graphics that are displayed. As used herein, the term “graphics” includes any object that can be displayed to a user, including, without limitation, text, web pages, icons (such as user-interface objects including soft keys), digital images, videos, animations, and the like. 
     In some embodiments, graphics module  232  stores data representing graphics to be used. Each graphic is, optionally, assigned a corresponding code. Graphics module  232  receives, from applications etc., one or more codes specifying graphics to be displayed along with, if necessary, coordinate data and other graphic property data, and then generates screen image data to output to display controller  256 . 
     Haptic feedback module  233  includes various software components for generating instructions used by tactile output generator(s)  267  to produce tactile outputs at one or more locations on device  200  in response to user interactions with device  200 . 
     Text input module  234 , which is, in some examples, a component of graphics module  232 , provides soft keyboards for entering text in various applications (e.g., contacts  237 , email  240 , IM  241 , browser  247 , and any other application that needs text input). 
     GPS module  235  determines the location of the device and provides this information for use in various applications (e.g., to telephone  238  for use in location-based dialing; to camera  243  as picture/video metadata; and to applications that provide location-based services such as weather widgets, local yellow page widgets, and map/navigation widgets). 
     Digital assistant client module  229  includes various client-side digital assistant instructions to provide the client-side functionalities of the digital assistant. For example, digital assistant client module  229  is capable of accepting voice input (e.g., speech input), text input, touch input, and/or gestural input through various user interfaces (e.g., microphone  213 , accelerometer(s)  268 , touch-sensitive display system  212 , optical sensor(s)  229 , other input control devices  216 , etc.) of portable multifunction device  200 . Digital assistant client module  229  is also capable of providing output in audio (e.g., speech output), visual, and/or tactile forms through various output interfaces (e.g., speaker  211 , touch-sensitive display system  212 , tactile output generators)  267 , etc.) of portable multifunction device  200 . For example, output is provided as voice, sound, alerts, text messages, menus, graphics, videos, animations, vibrations, and/or combinations of two or more of the above. During operation, digital assistant client module  229  communicates with DA server  106  using RF circuitry  208 . 
     User data and models  231  include various data associated with the user (e.g., user-specific vocabulary data, user preference data, user specified name pronunciations, data from the user&#39;s electronic address book, to-do lists, shopping lists, etc.) to provide the client-side functionalities of the digital assistant. Further, user data and models  231  include various models (e.g., speech recognition models, statistical language models, natural language processing models, ontology, task flow models, service models, etc.) for processing user input and determining user intent. 
     In some examples, digital assistant client module  229  utilizes the various sensors, subsystems, and peripheral devices of portable multifunction device  200  to gather additional information from the surrounding environment of the portable multifunction device  200  to establish a context associated with a user, the current user interaction, and/or the current user input. In some examples, digital assistant client module  229  provides the contextual information or a subset thereof with the user input to DA server  106  to help infer the user&#39;s intent. In some examples, the digital assistant also uses the contextual information to determine how to prepare and deliver outputs to the user. Contextual information is referred to as context data. 
     In some examples, the contextual information that accompanies the user input includes sensor information, e.g., lighting, ambient noise, ambient temperature, images or videos of the surrounding environment, etc. In some examples, the contextual information can also includes the physical state of the device, e.g., device orientation, device location, device temperature, power level, speed, acceleration, motion patterns, cellular signals strength, etc. In some examples, information related to the software state of DA server  106 , e.g., running processes, installed programs, past and present network activities, background services, error logs, resources usage, etc., and of portable multifunction device  200  is provided to DA server  106  as contextual information associated with a user input. 
     In some examples, the digital assistant client module  229  selectively provides information (e.g., user data  231 ) stored on the portable multifunction device  200  in response to requests from DA server  106 . In some examples, digital assistant client module  229  also elicits additional input from the user via a natural language dialogue or other user interfaces upon request by DA server  106 . Digital assistant, client module  229  passes the additional input to DA server  106  to help DA server  106  in intent deduction and/or fulfillment of the user&#39;s intent expressed in the user request. 
     A more detailed description of a digital assistant is described below with reference to  FIGS. 7A-C . It should be recognized that digital assistant client module  229  can include any number of the sub-modules of digital assistant module  726  described below. 
     Applications  236  include the following modules (or sets of instructions), or a subset or superset thereof:
         Contacts module  237  (sometimes called an address book or contact list);   Telephone module  238 ;   Video conference module  239 ;   E-mail client module  240 ;   Instant messaging (IM) module  241 ;   Workout, support module  242 ;   Camera module  243  for still and/or video images;   Image management module  244 ;   Video player module;   Music player module;   Browser module  247 ;   Calendar module  248 ;   Widget modules  249 , which includes, in some examples, one or more of: weather widget  249 - 1 , stocks widget  249 - 2 , calculator widget  249 - 3 , alarm clock widget  249 - 4 , dictionary widget  249 - 5 , and other widgets obtained by the user, as well as user-created widgets  249 - 6 ;   Widget creator module  250  for making user-created widgets  249 - 6 ;   Search module  251 ;   Video and music player module  252 , which merges video player module and music player module;   Notes module  253 ;   Map module  254 ; and/or   Online video module  255 .       

     Examples of other applications  236  that are stored in memory  202  include other word processing applications, other image editing applications, drawing applications, presentation applications, JAVA-enabled applications, encryption, digital rights management, voice recognition, and voice replication. 
     In conjunction with touch screen  212 , display controller  256 , contact/motion module  230 , graphics module  232 , and text input module  234 , contacts module  237  are used to manage an address book or contact list (e.g., stored in application internal state  292  of contacts module  237  in memory  202  or memory  470 ), including: adding name(s) to the address book; deleting name(s) from the address book; associating telephone number(s), e-mail address(es), physical address(es) or other information with a name; associating an image with a name; categorizing and sorting names; providing telephone numbers or e-mail addresses to initiate and/or facilitate communications by telephone  238 , video conference module  239 , e-mail  240 , or IM  241 ; and so forth. 
     In conjunction with RE circuitry  208 , audio circuitry  210 , speaker  211 , microphone  213 , touch screen  212 , display controller  256 , contact/motion module  230 , graphics module  232 , and text input module  234 , telephone module  238  are used to enter a sequence of characters corresponding to a telephone number, access one or more telephone numbers in contacts module  237 , modify a telephone number that has been entered, dial a respective telephone number, conduct a conversation, and disconnect or hang up when the conversation is completed. As noted above, the wireless communication uses any of a plurality of communications standards, protocols, and technologies. 
     In conjunction with RF circuitry  208 , audio circuitry  210 , speaker  211 , microphone  213 , touch screen  212 , display controller  256 , optical sensor  264 , optical sensor controller  258 , contact/motion module  230 , graphics module  232 , text input module  234 , contacts module  237 , and telephone module  238 , video conference module  239  includes executable instructions to initiate, conduct, and terminate a video conference between a user and one or more other participants in accordance with user instructions. 
     In conjunction with RF circuitry  208 , touch screen  212 , display controller  256 , contact/motion module  230 , graphics module  232 , and text input module  234 , e-mail client module  240  includes executable instructions to create, send, receive, and manage e-mail in response to user instructions. In conjunction with image management module  244 , e-mail client module  240  makes it very easy to create and send e-mails with still or video images taken with camera module  243 . 
     In conjunction with RF circuitry  208 , touch scram  212 , display controller  256 , contact/motion module  230 , graphics module  232 , and text input module  234 , the instant messaging module  241  includes executable instructions to enter a sequence of characters corresponding to art instant message, to modify previously entered characters, to transmit a respective instant message (for example, using a Short Message Service (SMS) or Multimedia Message Service (MMS) protocol for telephony-based instant messages or using XMPP, SIMPLE, or IMPS for Internet-based instant messages), to receive instant messages, and to view received instant messages. In some embodiments, transmitted and/or received instant messages include graphics, photos, audio files, video files and/or other attachments as are supported in an MMS and/or an Enhanced Messaging Service (EMS). As used herein, “instant messaging” refers to both telephony-based messages (e.g., messages sent using SMS or MMS) and Internet-based messages (e.g., messages sent using XMPP, SIMPLE, or IMPS). 
     In conjunction with RF circuitry  208 , touch screen  212 , display controller  256 , contact/motion module  230 , graphics module  232 , text input module  234 , GPS module  235 , map module  254 , and music player module, workout support module  242  includes executable instructions to create workouts (e.g., with time, distance, and/or calorie burning goals); communicate with workout sensors (sports devices); receive workout sensor data; calibrate sensors used to monitor a workout; select and play music for a workout; and display, store, and transmit workout data. 
     In conjunction with touch screen  212 , display controller  256 , optical sensor(s)  264 , optical sensor controller  258 , contact/motion module  230 , graphics module  232 , and image management module  244 , camera module  243  includes executable instructions to capture still images or video (including a video stream) and store them into memory  202 , modify characteristics of a still image or video, or delete a still image or video from memory  202 . 
     In conjunction with touch screen  212 , display controller  256 , contact/motion module  230 , graphics module  232 , text input module  234 , and camera module  243 , image management module  244  includes executable instructions to arrange, modify (e.g., edit), or otherwise manipulate, label, delete, present (e.g., in a digital slide show or album), and store still and/or video images. 
     In conjunction with RF circuitry  208 , touch screen  212 , display controller  256 , contact/motion module  230 , graphics module  232 , and text input module  234 , browser module  247  includes executable instructions to browse the Internet in accordance with user instructions, including searching, linking to, receiving, and displaying web pages or portions thereof, as well as attachments and other files linked to web pages. 
     In conjunction with RF circuitry  208 , touch screen  212 , display controller  256 , contact/motion module  230 , graphics module  232 , text input module  234 , e-mail client module  240 , and browser module  247 , calendar module  248  includes executable instructions to create, display, modify, and store calendars and data associated with calendars (e.g., calendar entries, to-do lists, etc.) in accordance with user instructions. 
     In conjunction with RF circuitry  208 , touch screen  212 , display controller  256 , contact/motion module  230 , graphics module  232 , text input module  234 , and browser module  247 , widget modules  249  are mini applications that can be downloaded and used by a user (e.g., weather widget  249 - 1 , stocks widget  249 - 2 , calculator widget  249 - 3 , alarm clock widget  249 - 4 , and dictionary widget  249 - 5 ) or created by the user (e.g., user-created widget  249 - 6 ). In some embodiments, a widget includes an HTML (Hypertext Markup language) file, a CSS (Cascading Style Sheets) file, and a JavaScript file. In some embodiments, a widget includes an XML (Extensible Markup Language) file and a JavaScript, file (e.g., Yahoo! Widgets). 
     In conjunction with RF circuitry  208 , touch screen  212 , display controller  256 , contact/motion module  230 , graphics module  232 , text input module  234 , and browser module  247 , the widget creator module  250  are used by a user to create widgets (e.g., turning a user-specified portion of a web page into a widget). 
     In conjunction with touch screen  212 , display controller  256 , contact/motion module  230 , graphics module  232 , and text input module  234 , search module  251  includes executable instructions to search for text, music, sound, image, video, and/or other files in memory  202  that match one or more search criteria (e.g., one or more user-specified search terms) in accordance with user instructions. 
     In conjunction with touch screen  212 , display controller  256 , contact/motion module  230 , graphics module  232 , audio circuitry  210 , speaker  211 , RF circuitry  208 , and browser module  247 , video and music player module  252  includes executable instructions that allow the user to download and play back recorded music and other sound files stored in one or more file formats, such as MP3 or AAC files, and executable instructions to display, present, or otherwise play back videos (e.g., on touch screen  212  or on an external, connected display via external port  224 ). In some embodiments, device  200  optionally includes the functionality of an MP3 player, such as an iPod (trademark of Apple Inc.). 
     In conjunction with touch screen  212 , display controller  256 , contact/motion module  230 , graphics module  232 , and text input module  234 , notes module  253  includes executable instructions to create and manage notes, to-do lists, and the like in accordance with user instructions. 
     In conjunction with RF circuitry  208 , touch screen  212 , display controller  256 , contact/motion module  230 , graphics module  232 , text input module  234 , GPS module  235 , and browser module  247 , map module  254  are used to receive, display, modify, and store maps and data associated with maps (e.g., driving directions, data on stores and other points of interest at or near a particular location, and other location-based data) in accordance with user instructions. 
     In conjunction with touch screen  212 , display controller  256 , contact/motion module  230 , graphics module  232 , audio circuitry  210 , speaker  211 , RF circuitry  208 , text input module  234 , e-mail client module  240 , and browser module  247 , online video module  255  includes instructions that allow the user to access, browse, receive (e.g., by streaming and/or download), play back (e.g., on the touch screen or on an external, connected display via external port  224 ), send an e-mail with a link to a particular online video, and otherwise manage online videos in one or more file formats, such as H.264. In some embodiments, instant messaging module  241 , rather than e-mail client module  240 , is used to send a link to a particular online video. Additional description of the online video application can be found in U.S. Provisional Patent Application No. 60/936,562, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Jun. 20, 2007, and U.S. patent application Ser. No. 11/968.067. “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Dec. 31, 2007, the contents of which are hereby incorporated by reference in their entirety. 
     Each of the above-identified modules and applications corresponds to a set of executable instructions for performing one or more functions described above and the methods described in this application (e.g., the computer-implemented methods and other information processing methods described herein). These modules (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules can be combined or otherwise rearranged in various embodiments. For example, video player module can be combined with music player module into a single module (e.g., video and music player module  252 ,  FIG. 2A ). In some embodiments, memory  202  stores a subset of the modules and data structures identified above. Furthermore, memory  202  stores additional modules and data structures not described above. 
     In some embodiments, device  200  is a device where operation of a predefined set of functions on the device is performed exclusively through a touch screen and/or a touchpad. By using a touch screen and/or a touchpad as the primary input control device for operation of device  200 , the number of physical input control devices (such as push buttons, dials, and the like) on device  200  is reduced. 
     The predefined set of functions that are performed exclusively through a touch screen and/or a touchpad optionally include navigation between user interfaces. In some embodiments, the touchpad, when touched by the user, navigates device  200  to a main, home, or root menu from any user interface that is displayed on device  200 . In such embodiments, a “menu button” is implemented using a touchpad. In some other embodiments, the menu button is a physical push button or other physical input control device instead of a touchpad. 
       FIG. 2B  is a block diagram illustrating exemplary components for event handling in accordance with some embodiments. In some embodiments, memory  202  ( FIG. 2A ) or  470  ( FIG. 4 ) includes event sorter  270  (e.g., in operating system  226 ) and a respective application  236 - 1  (e.g., any of the aforementioned applications  237 - 251 ,  255 ,  480 - 490 ). 
     Event sorter  270  receives event information and determines the application  236 - 1  and application view  291  of application  236 - 1  to which to deliver the event information. Event sorter  270  includes event monitor  271  and event dispatcher module  274 . In some embodiments, application  236 - 1  includes application internal state  292 , which indicates the current application view(s) displayed on touch-sensitive display  212  when the application is active or executing. In some embodiments, device/global internal state  257  is used by event sorter  270  to determine which application(s) is (are) currently active, and application internal state  292  is used by event sorter  270  to determine application views  291  to which to deliver event information. 
     In some embodiments, application internal state  292  includes additional information, such as one or more of: resume information to be used when application  236 - 1  resumes execution, user interface state information that indicates information being displayed or that is ready for display by application  236 - 1 , a state queue for enabling the user to go back to a prior state or view of application  236 - 1 , and a redo/undo queue of previous actions taken by the user. 
     Event monitor  271  receives event information from peripherals interface  218 . Event information includes information about a sub-event (e.g., a user touch on touch-sensitive display  212 , as part of a multi-touch gesture). Peripherals interface  218  transmits information it receives from I/O subsystem  206  or a sensor, such as proximity sensor  266 , accelerometer(s)  268 , and/or microphone  213  (through audio circuitry  210 ). Information that peripherals interface  218  receives from I/O subsystem  206  includes information from touch-sensitive display  212  or a touch-sensitive surface. 
     In some embodiments, event monitor  271  sends requests to the peripherals interface  218  at predetermined intervals. In response, peripherals interface  218  transmits event information. In other embodiments, peripherals interface  218  transmits event information only when there is a significant event (e.g., receiving an input above a predetermined noise threshold and/or for more than a predetermined duration). 
     In some embodiments, event sorter  270  also includes a hit view determination module  272  and/or an active event recognizer determination module  273 . 
     Hit view determination module  272  provides software procedures for determining where a sub-event has taken place within one or more views when touch-sensitive display  212  displays more than one view. Views are made up of controls and other elements that a user can see on the display. 
     Another aspect of the user interface associated with an application is a set of views, sometimes herein called application views or user interface windows in which information is displayed and touch-based gestures occur. The application views (of a respective application) in which a touch is detected correspond to programmatic levels within a programmatic or view hierarchy of the application. For example, the lowest level view in which a touch is detected is called the hit view, and the set of events that are recognized as proper inputs is determined based, at least in part, on the hit view of the initial touch that begins a touch-based gesture. 
     Hit view determination module  272  receives information related to sub events of a touch based gesture. When an application has multiple views organized in a hierarchy, hit view determination module  272  identifies a hit view as the lowest view in the hierarchy which should handle the sub-event. In most circumstances, the hit view is the lowest level view in which an initiating sub-event occurs (e.g., the first sub-event in the sequence of sub-events that form an event or potential event). Once the hit view is identified by the hit view determination module  272 , the hit view typically receives all sub-events related to the same touch or input source for which it was identified as the hit view. 
     Active event recognizer determination module  273  determines which view or views within a view hierarchy should receive a particular sequence of sub-events. In some embodiments, active event recognizer determination module  273  determines that only the hit view should receive a particular sequence of sub-events. In other embodiments, active event recognizer determination module  273  determines that all views that include the physical location of a sub-event are actively involved views, and therefore determines that all actively involved views should receive a particular sequence of sub-events. In other embodiments, even if touch sub-events were entirely confined to the area associated with one particular view, views higher in the hierarchy would still remain as actively involved views. 
     Event dispatcher module  274  dispatches the event information to an event recognizer (e.g., event recognizer  280 ). In embodiments including active event recognizer determination module  273 , event dispatcher module  274  delivers the event information to an event recognizer determined by active event recognizer determination module  273 . In some embodiments, event dispatcher module  274  stores in an event queue the event information, which is retrieved by a respective event receiver  282 . 
     In some embodiments, operating system  226  includes event sorter  270 . Alternatively, application  236 - 1  includes event sorter  270 . In yet other embodiments, event sorter  270  is a stand-alone module, or a part of another module stored in memory  202 , such as contact/motion module  230 . 
     In some embodiments, application  236 - 1  includes a plurality of event handlers  290  and one or more application views  291 , each of which includes instructions for handling touch events that occur within a respective view of the application&#39;s user interface. Each application view  291  of the application  236 - 1  includes one or more event recognizers  280 . Typically, a respective application view  291  includes a plurality of event recognizers  280 . In other embodiments, one or more of event recognizers  280  are part of a separate module, such as a user interface kit (not shown) or a higher level object from which application  236 - 1  inherits methods and other properties. In some embodiments, a respective event handler  290  includes one or more of: data updater  276 , object updater  277 , GUI updater  278 , and/or event data  279  received from event sorter  270 . Event handler  290  utilizes or calls data updater  276 , object updater  277 , or GUI updater  278  to update the application internal state  292 . Alternatively, one or more of the application views  291  include one or more respective event handlers  290 . Also, in some embodiments, one or more of data updater  276 , object updater  277 , and GUI updater  278  are included in a respective application view  291 . 
     A respective event recognizer  280  receives event information (e.g., event data  279 ) from event sorter  270  and identifies an event from the event information. Event recognizer  280  includes event receiver  282  and event comparator  284 . In some embodiments, event recognizer  280  also includes at least a subset of: metadata  283 , and event delivery instructions  288  (which include sub-event delivery instructions). 
     Event receiver  282  receives event information from event sorter  270 . The event information includes information about a sub-event, for example, a touch or a touch movement. Depending on the sub-event, the event information also includes additional information, such as location of the sub-event. When the sub-event concerns motion of a touch, the event information also includes speed and direction of the sub-event. In some embodiments, events include rotation of the device from one orientation to another (e.g., from a portrait orientation to a landscape orientation, or vice versa), and the event information includes corresponding information about the current orientation (also called device altitude) of the device. 
     Event comparator  284  compares the event information to predefined event or sub-event definitions and, based on the comparison, determines an event or sub event, or determines or updates the state of an event or sub event. In some embodiments, event comparator  284  includes event definitions  286 . Event definitions  286  contain definitions of events (e.g., predefined sequences of sub-events), for example, event  1  ( 287 - 1 ), event  2  ( 287 - 2 ), and others. In some embodiments, sub-events in an event ( 287 ) include, for example, touch begin, touch end, touch movement, touch cancellation, and multiple touching. In one example, the definition for event  1  ( 287 - 1 ) is a double tap on a displayed object. The double tap, for example, comprises a first touch (touch begin) on the displayed object for a predetermined phase, a first liftoff (touch end) for a predetermined phase, a second touch (touch begin) on the displayed object for a predetermined phase, and a second liftoff (touch end) for a predetermined phase. In another example, the definition for event  2  ( 287 - 2 ) is a dragging on a displayed object. The dragging, for example, comprises a touch (or contact) on the displayed object for a predetermined phase, a movement of the touch across touch-sensitive display  212 , and liftoff of the touch (touch end). In some embodiments, the event also includes information for one or more associated event handlers  290 . 
     In some embodiments, event definition  287  includes a definition of an event for a respective user-interface object. In some embodiments, event comparator  284  performs a hit test to determine which user-interface object is associated with a sub-event. For example, in an application view in which three user-interface objects are displayed on touch-sensitive display  212 , when a touch is detected on touch-sensitive display  212 , event comparator  284  performs a hit test to determine which of the three user-interface objects is associated with the touch (sub-event). If each displayed object is associated with a respective event handler  290 , the event comparator uses the result of the hit test to determine which event handler  290  should be activated. For example, event comparator  284  selects an event handler associated with the sub-event and the object triggering the hit test. 
     In some embodiments, the definition for a respective event ( 287 ) also includes delayed actions that delay delivery of the event information until after it has been determined whether the sequence of sub-events does or does not correspond to the event recognizer&#39;s event type. 
     When a respective event recognizer  280  determines that the series of sub-events do not match any of the events in event definitions  286 , the respective event recognizer  280  enters an event impossible, event failed, or event ended state, after which it disregards subsequent sub-events of the touch-based gesture. In this situation, other event recognizers, if any, that remain active for the hit view continue to track and process sub-events of an ongoing touch-based gesture. 
     In some embodiments, a respective event recognizer  280  includes metadata  283  with configurable properties, flags, and/or lists that indicate how the event delivery system should perform sub-event delivery to actively involved event recognizers. In some embodiments, metadata  283  includes configurable properties, flags, and/or lists that indicate how event recognizers interact, or are enabled to interact, with one another. In some embodiments, metadata  283  includes configurable properties, flags, and/or lists that indicate whether sub-events are delivered to varying levels in the view or programmatic hierarchy. 
     In some embodiments, a respective event recognizer  280  activates event handler  290  associated with an event when one or more particular sub-events of an event are recognized. In some embodiments, a respective event recognizer  280  delivers event information associated with the event to event handler  290 . Activating an event handler  290  is distinct from sending (and deferred sending) sub-events to a respective hit, view. In some embodiments, event recognizer  280  throws a flag associated with the recognized event, and event handler  290  associated with the flag catches the flag and performs a predefined process. 
     In some embodiments, event delivery instructions  288  include sub-event delivery instructions that deliver event information about a sub-event without activating an event handler. Instead, the sub-event delivery instructions deliver event information to event handlers associated with the series of sub-events or to actively involved views. Event handlers associated with the series of sub-events or with actively involved views receive the event information and perform a predetermined process. 
     In some embodiments, data updater  276  creates and updates data used in application  236 - 1 . For example, data updater  276  updates the telephone number used in contacts module  237 , or stores a video file used in video player module. In some embodiments, object updater  277  creates and updates objects used in application  236 - 1 . For example, object updater  277  creates a new user-interface object or updates the position of a user-interface object. GUI updater  278  updates the GUI. For example, GUI updater  278  prepares display information and sends it to graphics module  232  for display on a touch-sensitive display. 
     In some embodiments, event handler(s)  290  includes or has access to data updater  276 , object updater  277 , and GUI updater  278 . In some embodiments, data updater  276 , object updater  277 , and GUI updater  278  are included in a single module of a respective application  236 - 1  or application view  291 . In other embodiments, they are included in two or more software modules. 
     It shall be understood that the foregoing discussion regarding event handling of user touches on touch-sensitive displays also applies to other forms of user inputs to operate multifunction devices  200  with input devices, not all of which are initialed on touch screens. For example, mouse movement and mouse button presses, optionally coordinated with single or multiple keyboard presses or holds; contact movements such as taps, drags, scrolls, etc. on touchpads; pen stylus inputs: movement of the device: oral instructions: detected eye movements; biometric inputs; and/or any combination thereof are optionally utilized as inputs corresponding to sub events which define an event to be recognized. 
       FIG. 3  illustrates a portable multifunction device  200  having a touch screen  212  in accordance with some embodiments. The touch screen optionally displays one or more graphics within user interface (UI)  300 . In this embodiment, as well as others described below, a user is enabled to select one or more of the graphics by making a gesture on the graphics, for example, with one or more fingers  302  (not drawn to scale in the figure) or one or more styluses  303  (not drawn to scale in the figure). In some embodiments, selection of one or more graphics occurs when the user breaks contact with the one or more graphics. In some embodiments, the gesture optionally includes one or more taps, one or more swipes (from left to right, right to left, upward and/or downward), and/or a rolling of a finger (from right to left, left to right, upward and/or downward) that has made contact with device  200 . In some implementations or circumstances, inadvertent contact with a graphic does not select the graphic. For example, a swipe gesture that sweeps over an application icon optionally docs not select the corresponding application when the gesture corresponding to selection is a tap. 
     Device  200  also includes one or more physical buttons, such as “home” or menu button  304 . As described previously, menu button  304  is used to navigate to any application  236  in a set of applications that is executed on device  200 . Alternatively, in some embodiments, the menu button is implemented as a soft key in a GUI displayed on touch screen  212 . 
     In one embodiment, device  200  includes touch screen  212 , menu button  304 , push button  306  for powering the device on/off and locking the device, volume adjustment button(s)  308 , subscriber identity module (SIM) card slot  310 , headset jack  312 , and docking/charging external port  224 . Push button  306  is, optionally, used to turn the power on/off on the device by depressing the button and holding the button in the depressed state for a predefined time interval; to lock the device by depressing the button and releasing the button before the predefined time interval has elapsed; and/or to unlock the device or initiate an unlock process. In an alternative embodiment, device  200  also accepts verbal input for activation or deactivation of some functions through microphone  213 . Device  200  also, optionally, includes one or more contact intensity sensors  265  for detecting intensity of contacts on touch screen  212  and/or one or more tactile output generators  26  for generating tactile outputs for a user of device  200 . 
       FIG. 4  is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface in accordance with some embodiments. Device  400  need not be portable. In some embodiments, device  400  is a laptop computer, a desktop computer, a tablet computer, a multimedia player device, a navigation device, an educational device (such as a child&#39;s learning toy), a gaming system, or a control device (e.g., a home or industrial controller). Device  400  typically includes one or more processing units (CPUs)  410 , one or more network or other communications interfaces  460 , memory  470 , and one or more communication buses  420  for interconnecting these components. Communication buses  420  optionally include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. Device  400  includes input/output (I/O) interface  430  comprising display  440 , which is typically a touch screen display. I/O interlace  430  also optionally includes a keyboard and/or mouse (or other pointing device)  450  and touchpad  455 , tactile output generator  457  for generating tactile outputs on device  400  (e.g., similar to tactile output generators)  267  described above with reference to  FIG. 2A ), sensors  459  (e.g., optical, acceleration, proximity, touch-sensitive, and/or contact intensity sensors similar to contact intensity sensor(s)  265  described above with reference to  FIG. 2A ). Memory  470  includes high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid state memory devices; and optionally includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid stale storage devices. Memory  470  optionally includes one or more storage devices remotely located from CPU(s)  410 . In some embodiments, memory  470  stores programs, modules, and data structures analogous to the programs, modules, and data structures stored in memory  202  of portable multifunction device  200  ( FIG. 2A ), or a subset thereof. Furthermore, memory  470  optionally stoics additional programs, modules, and data structures not present in memory  202  of portable multifunction device  200 . For example, memory  470  of device  400  optionally stores drawing module  480 , presentation module  482 , word processing module  484 , website creation module  486 , disk authoring module  488 , and/or spreadsheet module  490 , while memory  202  of portable multifunction device  200  ( FIG. 2A ) optionally docs not store these modules. 
     Each of the above-identified elements in  FIG. 4  is, in some examples, stored in one or more of the previously mentioned memory devices. Each of the above identified modules corresponds to a set of instructions for performing a function described above. The above-identified modules or programs (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules are combined or otherwise rearranged in various embodiments. In some embodiments, memory  470  stores a subset of the modules and data structures identified above. Furthermore, memory  470  stores additional modules and data structures not described above. 
     Attention is now directed towards embodiments of user interfaces that can be implemented on, for example, portable multifunction device  200 . 
       FIG. 5A  illustrates an exemplary user interface for a menu of applications on portable multifunction device  200  in accordance with some embodiments. Similar user interfaces are implemented on device  400 . In some embodiments, user interface  500  includes the following elements, or a subset or superset thereof: 
     Signal strength indicator(s)  502  for wireless communication(s), such as cellular and Wi-Fi signals:
         Time  504 ;   Bluetooth indicator  505 ;   Battery status indicator  506 ;   Tray  508  with icons for frequently used applications, such as:
           Icon  516  for telephone module  238 , labeled “Phone,” which optionally includes an indicator  514  of the number of missed calls or voicemail messages;   Icon  518  for e-mail client module  240 , labeled “Mail,” which optionally includes an indicator  510  of the number of unread e-mails;   Icon  520  for browser module  247 , labeled “Browser,” and   Icon  522  for video and music player module  252 , also referred to as iPod (trademark of Apple Inc.) module  252 , labeled “iPod;” and   
           Icons for other applications, such as:
           Icon  524  for IM module  241 , labeled “Messages;”   Icon  526  for calendar module  248 , labeled “Calendar;”   Icon  528  for image management module  244 , labeled “Photos;”   Icon  530  for camera module  243 , labeled “Camera;”   Icon  532  for online video module  255 , labeled “Online Video;”   Icon  534  for stocks widget  249 - 2 , labeled “Stocks;”   Icon  536  for map module  254 , labeled “Maps;”   Icon  538  for weather widget  249 - 1 , labeled “Weather;”   Icon  540  for alarm clock widget  249 - 4 , labeled “Clock;”   Icon  542  for workout support module  242 , labeled “Workout Support;”   Icon  544  for notes module  253 , labeled “Notes;” and   Icon  546  for a settings application or module, labeled “Settings,” which provides access to settings for device  200  and its various applications  236 .   
               

     It should be noted that the icon labels illustrated in  FIG. 5A  are merely exemplary. For example, icon  522  for video and music player module  252  is optionally labeled “Music” or “Music Player.” Other labels are, optionally, used for various application icons. In some embodiments, a label for a respective application icon includes a name of an application corresponding to the respective application icon. In some embodiments, a label for a particular application icon is distinct from a name of an application corresponding to the particular application icon. 
       FIG. 5B  illustrates an exemplary user interface on a device (e.g., device  400 ,  FIG. 4 ) with a touch-sensitive surface  551  (e.g., a tablet or touchpad  455 ,  FIG. 4 ) that is separate from the display  550  (e.g., touch screen display  212 ). Device  400  also, optionally, includes one or more contact intensity sensors (e.g., one or more of sensors  457 ) for detecting, intensity of contacts on touch-sensitive surface  551  and/or one or more tactile output generators  459  for generating tactile outputs for a user of device  400 . 
     Although some of the examples which follow will be given with reference to inputs on touch screen display  212  (where the touch-sensitive surface and the display are combined), in some embodiments, the device detects inputs on a touch-sensitive surface that is separate from the display, as shown in  FIG. 5B . In some embodiments, the touch-sensitive surface (e.g.,  551  in  FIG. 5B ) has a primary axis (e.g.,  552  in  FIG. 5B ) that corresponds to a primary axis (e.g.,  553  in  FIG. 5B ) on the display (e.g.,  550 ). In accordance with these embodiments, the device detects contacts (e.g.,  560  and  562  in  FIG. 5B ) with the touch-sensitive surface  551  at locations that correspond to respective locations on the display (e.g., in  FIG. 5B, 560  corresponds to  568  and  562  corresponds to  570 ). In this way, user inputs (e.g., contacts  560  and  562 , and movements thereof) detected by the device on the touch-sensitive surface (e.g.,  551  in  FIG. 5B ) are used by the device to manipulate the user interface on the display (e.g.,  550  in  FIG. 5B ) of the multifunction device when the touch-sensitive surface is separate from the display. It should be understood that similar methods are, optionally, used for other user interfaces described herein. 
     Additionally, while the following examples are given primarily with reference to finger inputs (e.g., finger contacts, finger tap gestures, finger swipe gestures), it should be understood that, in some embodiments, one or more of the finger inputs are replaced with input from another input device (e.g., a mouse-based input or stylus input). For example, a swipe gesture is, optionally, replaced with a mouse click (e.g., instead of a contact) followed by movement of the cursor along the path of the swipe (e.g., instead of movement of the contact). As another example, a tap gesture is, optionally, replaced with a mouse click while the cursor is located over the location of the tap gesture (e.g., instead of detection of the contact followed by ceasing to detect the contact). Similarly, when multiple user inputs are simultaneously detected, it should be understood that multiple computer mice are, optionally, used simultaneously, or a mouse: and finger contacts are, optionally, used simultaneously. 
       FIG. 6A  illustrates exemplary personal electronic device  600 . Device  600  includes body  602 . In some embodiments, device  600  includes some or all of the features described with respect to devices  200  and  400  (e.g.,  FIGS. 2A-4B ). In some embodiments, device  600  has touch-sensitive display screen  604 , hereafter touch screen  604 . Alternatively, or in addition to touch screen  604 , device  600  has a display and a touch-sensitive surface. As with devices  200  and  400 , in some embodiments, touch screen  604  (or the touch-sensitive surface) has one or more intensity sensors for detecting intensity of contacts (e.g., touches) being applied. The one or more intensity sensors of touch screen  604  (or the touch-sensitive surface) provide output data that represents the intensity of touches. The user interface of device  600  responds to touches based on their intensity, meaning that touches of different intensities can invoke different user interface operations on device  600 . 
     Techniques for detecting and processing touch intensity are found, for example, in related applications: International Patent Application Serial No. PCT/US2013/040061, titled “Device, Method, and Graphical User Interface for Displaying User Interface Objects Corresponding to an Application,” filed May 8, 2013, and International Patent Application Serial No. PCT/US2013/069483, titled “Device. Method, and Graphical User Interface for Transitioning Between Touch Input to Display Output Relationships,” filed Nov. 11, 2013, each of which is hereby incorporated by reference in their entirety. 
     In some embodiments, device  600  has one or more input mechanisms  606  and  608 . Input mechanisms  606  and  608 , if included, are physical. Examples of physical input mechanisms include push buttons and rotatable mechanisms. In some embodiments, device  600  has one or more attachment mechanisms. Such attachment mechanisms, if included, can permit attachment of device  600  with, for example, hats, eyewear, earrings, necklaces, shirts, jackets, bracelets, watch straps, chains, trousers, belts, shoes, purses, backpacks, and so forth. These attachment mechanisms permit device  600  to be worn by a user. 
       FIG. 6B  depicts exemplary personal electronic device  600 . In some embodiments, device  600  includes some or all of the components described with respect to  FIGS. 2A .  2 B, and  4 . Device  600  has bus  612  that operatively couples I/O section  614  with one or more computer processors  616  and memory  618 . I/O section  614  is connected to display  604 , which can have touch-sensitive component  622  and, optionally, touch-intensity sensitive component  624 . In addition, I/O section  614  is connected with communication unit  630  for receiving application and operating system data, using Wi-Fi, Bluetooth, near field communication (NFC), cellular, and/or other wireless communication techniques. Device  600  includes input mechanisms  606  and/or  608 . Input mechanism  606  is a rotatable input device or a depressible and rotatable input device, for example. Input mechanism  608  is a button, in some examples. 
     Input mechanism  608  is a microphone, in some examples. Personal electronic device  600  includes, for example, various sensors, such as GPS sensor  632 , accelerometer  634 , directional sensor  640  (e.g., compass), gyroscope  636 , motion sensor  638 , and/or a combination thereof, all of which are operatively connected to I/O section  614 . 
     Memory  618  of personal electronic device  600  is a non-transitory computer-readable storage medium, for storing computer-executable instructions, which, when executed by one or more computer processors  616 , for example, cause the computer processors to perform the techniques and processes described below. The computer-executable instructions, for example, are also stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. Personal electronic device  600  is not limited to the components and configuration of  FIG. 6B , but can include other or additional components in multiple configurations. 
     As used here, the term “affordance” refers to a user-interactive graphical user interface object that is, for example, displayed on the display screen of devices  200 ,  400 , and/or  600  ( FIGS. 2, 4, and 6 ). For example, an image (e.g., icon), a button, and text (e.g., hyperlink) each constitutes an affordance. 
     As used herein, the term “focus selector” refers to an input element that indicates a current part of a user interface with which a user is interacting. In some implementations that include a cursor or other location marker, the cursor acts as a “locus selector” so that when an input (e.g., a press input) is detected on a touch-sensitive surface (e.g., touchpad  455  in  FIG. 4  or touch-sensitive surface  551  in  FIG. 5B ) while the cursor is over a particular user interface element (e.g., a button, window, slider or other user interface element), the particular user interface element is adjusted in accordance with the detected input. In some implementations that include a touch screen display (e.g., touch-sensitive display system  212  in  FIG. 2A  or touch screen  212  in  FIG. 5A ) that enables direct interaction with user interface elements on the touch screen display, a detected contact on the touch screen acts as a “focus selector” so that when an input (e.g., a press input by the contact) is detected on the touch screen display at a location of a particular user interface element (e.g., a button, window, slider, or other user interface element), the particular user interface element is adjusted in accordance with the detected input. In some implementations, focus is moved from one region of a user interface to another region of the user interface without corresponding movement of a cursor or movement of a contact on a touch screen display (e.g., by using a tab key or arrow keys to move focus from one button to another button); in these implementations, the focus selector moves in accordance with movement of focus between different regions of the user interface. Without regard to the specific form taken by the focus selector, the focus selector is generally the user interface element (or contact on a touch screen display) that is controlled by the user so as to communicate the user&#39;s intended interaction with the user interface (e.g., by indicating, to the device, the element of the user interface with which the user is intending to interact). For example, the location of a focus selector (e.g., a cursor, a contact, or a selection box) over a respective button while a press input is detected on the touch-sensitive surface (e.g., a touchpad or touch screen) will indicate that the user is intending to activate the respective button (as opposed to other user interface elements shown on a display of the device). 
     As used in the specification and claims, the term “characteristic intensity” of a contact refers to a characteristic of the contact based on one or more intensities of the contact. In some embodiments, the characteristic intensity is based on multiple intensity samples. The characteristic intensity is, optionally, based on a predefined number of intensity samples, or a set of intensity samples collected during a predetermined time period (e.g., 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10 seconds) relative to a predefined event (e.g., after detecting the contact, prior to detecting liftoff of the contact, before or after detecting a start of movement of the contact, prior to detecting an end of the contact, before or after detecting an increase in intensity of the contact, and/or before or after detecting a decrease in intensity of the contact). A characteristic intensity of a contact is, optionally based on one or more of: a maximum value of the intensities of the contact, a mean value of the intensities of the contact, an average value of the intensities of the contact, a top 10 percentile value of the intensities of the contact, a value at the half maximum of the intensities of the contact, a value at the 90 percent maximum of the intensities of the contact, or the like. In some embodiments, the duration of the contact is used in determining the characteristic intensity (e.g., when the characteristic intensity is an average of the intensity of the contact over time), in some embodiments, the characteristic intensity is compared to a set of one or more intensity thresholds to determine whether an operation has been performed by a user. For example, the set of one or more intensity thresholds includes a first intensity threshold and a second intensify threshold. In this example, a contact with a characteristic intensity that does not exceed the first threshold results in a first operation, a contact with a characteristic intensity that exceeds the first intensity threshold and does not exceed the second intensity threshold results in a second operation, and a contact with a characteristic intensity that exceeds the second threshold results in a third operation. In some embodiments, a comparison between the characteristic intensity and one or more thresholds is used to determine whether or not to perform one or more operations (e.g., whether to perform a respective operation or forgo performing the respective operation) rather than being used to determine whether to perform a first operation or a second operation. 
     In some embodiments, a portion of a gesture is identified for purposes of determining a characteristic intensity. For example, a touch-sensitive surface receives a continuous swipe contact transitioning from a start location and reaching an end location, at which point the intensity of the contact increases. In this example, the characteristic intensity of the contact at the end location is based on only a portion of the continuous swipe contact, and not the entire swipe contact (e.g., only the portion of the swipe contact at the end location). In some embodiments, a smoothing algorithm is applied to the intensities of the swipe contact prior to determining the characteristic intensity of the contact. For example, the smoothing algorithm optionally includes one or more of: an unweighted sliding-average smoothing algorithm, a triangular smoothing algorithm, a median filter smoothing algorithm, and/or an exponential smoothing algorithm. In some circumstances, these smoothing algorithms eliminate narrow spikes or dips in the intensities of the swipe contact for purposes of determining a characteristic intensity. 
     The intensity of a contact on the touch-sensitive surface is characterized relative to one or more intensity thresholds, such as a contact-detection intensity threshold, a light press intensity threshold, a deep press intensity threshold, and/or one or more other intensity thresholds. In some embodiments, the light press intensity threshold corresponds to an intensity at which the device will perform operations typically associated with clicking a button of a physical mouse or a trackpad. In some embodiments, the deep press intensity threshold corresponds to an intensity at which the device will perform operations that are different from operations typically associated with clicking a button of a physical mouse or a trackpad. In some embodiments, when a contact is detected with a characteristic intensity below the light press intensity threshold (e.g., and above a nominal contact-detection intensity threshold below which the contact is no longer detected), the device will move a focus selector in accordance with movement of the contact on the touch sensitive surface without performing an operation associated with the light press intensity threshold or the deep press intensity threshold. Generally, unless otherwise stated, these intensity thresholds are consistent between different sets of user interface figures. 
     An increase of characteristic intensity of the contact from an intensity below the light press intensity threshold to an intensity between the light press intensity threshold and the deep press intensity threshold is sometimes referred to as a “light press” input. An increase of characteristic intensity of the contact from an intensity below the deep press intensity threshold to an intensity above the deep press intensity threshold is sometimes referred to as a “deep press” input. An increase of characteristic intensity of the contact from an intensity below the contact-detection intensity threshold to an intensity between the contact-detection intensity threshold and the light press intensity threshold is sometimes referred to as detecting the contact on the touch-surface. A decrease of characteristic intensity of the contact from an intensity above the contact-detection intensity threshold to an intensity below the contact detection intensity threshold is sometimes referred to as detecting liftoff of the contact from the touch-surface. In some embodiments, the contact-detection intensity threshold is zero. In some embodiments, the contact-detection intensity threshold is greater than zero. 
     In some embodiments described herein, one or more operations are performed in response to detecting a gesture that includes a respective press input or in response to detecting the respective press input performed with a respective contact (or a plurality of contacts), where the respective press input is detected based at least in part on detecting an increase in intensity of the contact (or plurality of contacts) above a press-input intensity threshold. In some embodiments, the respective operation is performed in response to detecting the increase in intensity of the respective contact above the press-input intensity threshold (e.g., a “down stroke” of the respective press input). In some embodiments, the press input includes an increase in intensity of the respective contact above the press-input intensity threshold and a subsequent decrease in intensity of the contact below the press-input intensity threshold, and the respective operation is performed in response to detecting the subsequent decrease in intensity of the respective contact below the press-input threshold (e.g., an “up stroke” of the respective press input). 
     In some embodiments, the device employs intensity hysteresis to avoid accidental inputs sometimes termed “jitter,” where the device defines or selects a hysteresis intensity threshold with a predefined relationship to the press-input intensity threshold (e.g., the hysteresis intensity threshold is X intensity units lower than the press-input intensity threshold or the hysteresis intensity threshold is 75%, 90%, or some reasonable proportion of the press-input intensity threshold). Thus, in some embodiments, the press input includes an increase in intensity of the respective contact above the press-input intensity threshold and a subsequent decrease in intensity of the contact below the hysteresis intensity threshold that corresponds to the press-input intensity threshold, and the respective operation is performed in response to delecting the subsequent decrease in intensity of the respective contact below the hysteresis intensity threshold (e.g., an “up stroke” of the respective press input). Similarly, in some embodiments, the press input is detected only when the device detects an increase in intensity of the contact from an intensity at or below the hysteresis intensity threshold to an intensity at or above the press-input intensity threshold and, optionally, a subsequent decrease in intensity of the contact to an intensity at or below the hysteresis intensity, and the respective operation is performed in response to detecting the press input (e.g., the increase in intensity of the contact or the decrease in intensity of the contact, depending on the circumstances). 
     For ease of explanation, the descriptions of operations performed in response to a press input associated with a press-input intensity threshold or in response to a gesture including the press input are, optionally, triggered in response to detecting either: an increase in intensity of a contact above the press-input intensity threshold, an increase in intensity of a contact from an intensity below the hysteresis intensity threshold to an intensity above the press-input intensity threshold, a decrease in intensity of the contact below the press-input intensity threshold, and/or a decrease in intensity of the contact below the hysteresis intensity threshold corresponding to the press-input intensity threshold. Additionally, in examples where an operation is described as being performed in response to detecting a decrease in intensity of a contact below the press-input intensity threshold, the operation is, optionally, performed in response to detecting a decrease in intensity of the contact below a hysteresis intensity threshold corresponding to, and lower than, the press-input intensity threshold. 
     3. Digital Assistant System 
       FIG. 7A  illustrates a block diagram of digital assistant system  700  in accordance with various examples. In some examples, digital assistant system  700  is implemented on a standalone computer system. In some examples, digital assistant system  700  is distributed across multiple computers. In some examples, some of the modules and functions of the digital assistant are divided into a server portion and a client portion, where the client portion resides on one or more user devices (e.g., devices  104 ,  122 ,  200 ,  400 , or  600 ) and communicates with the server portion (e.g., server system  108 ) through one or more networks, e.g., as shown in  FIG. 1 . In some examples, digital assistant system  700  is an implementation of server system  108  (and/or DA server  106 ) shown in  FIG. 1 . It should be noted that digital assistant, system  700  is only one example of a digital assistant system, and that digital assistant system  700  can have more or fewer components than shown, can combine two or more components, or can have a different configuration or arrangement of the components. The various components shown in  FIG. 7A  are implemented in hardware, software instructions for execution by one or more processors, firmware, including one or more signal processing and/or application specific integrated circuits, or a combination thereof. 
     Digital assistant system  700  includes memory  702 , one or more processors  704 , input/output (I/O) interface  706 , and network communications interface  708 . These components can communicate with one another over one or more communication buses or signal lines  710 . 
     In some examples, memory  702  includes a non-transitory computer-readable medium, such as high-speed random access memory and/or a non-volatile computer-readable storage medium (e.g., one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices). 
     In some examples, I/O interface  706  couples input/output devices  716  of digital assistant system  700 , such as displays, keyboards, touch screens, and microphones, to user interface module  722 . I/O interface  706 , in conjunction with user interface module  722 , receives user inputs (e.g., voice input, keyboard inputs, touch inputs, etc.) and processes them accordingly. In some examples, e.g., when the digital assistant is implemented on a standalone user device, digital assistant system  700  includes any of the components and I/O communication interfaces described with respect to devices  200 ,  400 , or  600  in  FIGS. 2A, 4, 6A -B, respectively. In some examples, digital assistant system  700  represents the server portion of a digital assistant implementation, and can interact with the user through a client-side portion residing on a user device (e.g., devices  104 ,  200 ,  400 , or  600 ). 
     In some examples, the network communications interface  708  includes wired communication port(s)  712  and/or wireless transmission and reception circuitry  714 . The wired communication port(s) receives and send communication signals via one or more wired interfaces, e.g., Ethernet. Universal Serial Bus (USB), FIREWIRE, etc. The wireless circuitry  714  receives and sends RE signals and/or optical signals from/to communications networks and other communications devices. The wireless communications use any of a plurality of communications standards, protocols, and technologies, such as GSM, EDGE, CDMA, TDMA, Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communication protocol. Network communications interface  708  enables communication between digital assistant system  700  with networks, such as the Internet, an intranet, and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN), and/or a metropolitan area network (MAN), and other devices. 
     In some examples, memory  702 , or the computer-readable storage media of memory  702 , stores programs, modules, instructions, and data structures including all or a subset of: operating system  718 , communications module  720 , user interface module  722 , one or more applications  724 , and digital assistant module  726 . In particular, memory  702 , or the computer-readable storage media of memory  702 , stores instructions for performing the processes described below. One or more processors  704  execute these programs, modules, and instructions, and reads/writes from/to the data structures. 
     Operating system  718  (e.g., Darwin, RTXC, LINUX, UNIX, iOS, OS X, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and/of drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communications between various hardware, firmware, and software components. 
     Communications module  720  facilitates communications between digital assistant system  700  with other devices over network communications interface  708 . For example, communications module  720  communicates with RF circuitry  208  of electronic devices such as devices  200 ,  400 , and  600  shown in  FIG. 2A, 4, 6A -B, respectively. Communications module  720  also includes various components for handling data received by wireless circuitry  714  and/or wired communications port  712 . 
     User interface module  722  receives commands and/or inputs from a user via I/O interface  706  (e.g., from a keyboard, touch screen, pointing device, controller, and/or microphone), and generate user interface objects on a display. User interface module  722  also prepares and delivers outputs (e.g., speech, sound, animation, text, icons, vibrations, haptic feedback, light, etc.) to the user via the I/O interface  706  (e.g., through displays, audio channels, speakers, touch-pads, etc.). 
     Applications  724  include programs and/or modules that are configured to be executed by one or more processors  704 . For example, if the digital assistant system is implemented on a standalone user device, applications  724  include user applications, such as games, a calendar application, a navigation application, or an email application. If digital assistant system  700  is implemented on a server, applications  724  include resource management applications, diagnostic applications, or scheduling applications, for example. 
     Memory  702  also stores digital assistant module  726  (or the server portion of a digital assistant). In some examples, digital assistant module  726  includes the following sub modules, or a subset or superset thereof: input/output processing module  728 , speech-to-text (STT) processing module  730 , natural language processing module  732 , dialogue flow processing module  734 , task flow processing module  736 , service processing module  738 , and speech synthesis module  740 . Each of these modules has access to one or more of the following systems or data and models of the digital assistant module  726 , or a subset or superset thereof; ontology  760 , vocabulary index  744 , user data  748 , task flow models  754 , service models  756 , and ASR systems. 
     In some examples, using the processing modules, data, and models implemented in digital assistant module  726 , the digital assistant can perform at least some of the following: converting speech input into text; identifying a user&#39;s intent expressed in a natural language input received from the user; actively eliciting and obtaining information needed to fully infer the user&#39;s intent (e.g., by disambiguating words, games, intentions, etc.); determining the task flow for fulfilling the inferred intent; and executing the task flow to fulfill the inferred intent. 
     In some examples, as shown in  FIG. 7B , I/O processing module  728  interacts with the user through I/O devices  716  in  FIG. 7A  or with a user device (e.g., devices  104 ,  200 ,  400 , or  600 ) through network communications interface  708  in  FIG. 7A  to obtain user input (e.g., a speech input) and to provide responses (e.g., as speech outputs) to the user input. I/O processing module  728  optionally obtains contextual information associated with the user input from the user device, along with or shortly after the receipt of the user input. The contextual information includes user-specific data, vocabulary, and/or preferences relevant to the user input. In some examples, the contextual information also includes software and hardware states of the user device at the time the user request is received, and/or information related to the surrounding environment of the user at the time that the user request was received. In some examples, I/O processing module  728  also sends follow-up questions to, and receive answers from, the user regarding the user request. When a user request is received by I/O processing module  728  and the user request includes speech input. I/O processing module  728  forwards the speech input to STT processing module  730  (or speech recognizer) for speech to-text conversions. 
     STT processing module  730  includes one or more ASR systems. The one or more ASR systems can process the speech input that is received through I/O processing module  728  to produce a recognition result. Each ASR system includes a front-end speech pre-processor. The front-end speech pre-processor extracts representative features from the speech input. For example, the front-end speech pre-processor performs a Fourier transform on the speech input to extract spectral features that characterize the speech input as a sequence of representative multi-dimensional vectors. Further, each ASR system includes one or more speech recognition models (e.g., acoustic models and/or language models) and implements one or more speech recognition engines. Examples of speech recognition models include Hidden Markov Models, Gaussian-Mixture Models, Deep Neural Network Models, n-gram language models, and other statistical models. Examples of speech recognition engines include the dynamic time warping based engines and weighted finite-state transducers (WFST) based engines. The one or more speech recognition models and the one or more speech recognition engines are used to process the extracted representative features of the front-end speech pre-processor to produce intermediate recognitions results (e.g., phonemes, phonemic strings, and sub-words), and ultimately, text recognition results (e.g., words, word strings, or sequence of tokens). In some examples, the speech input is processed at least partially by a third-party service or on the user&#39;s device (e.g., device  104 ,  200 ,  400 , or  600 ) to produce the recognition result. Once STT processing module  730  produces recognition results containing a text string (e.g., words, or sequence of words, or sequence of tokens), the recognition result is passed to natural language processing module  732  for intent deduction. 
     More details on the speech-to-text processing are described in U.S. Utility application Ser. No. 13/236,942 for “Consolidating Speech Recognition Results,” filed on Sep. 20, 2011, the entire disclosure of which is incorporated herein by reference. 
     In some examples, STT processing module  730  includes and/or accesses a vocabulary of recognizable words via phonetic alphabet conversion module  731 . Fuel vocabulary word is associated with one or more candidate pronunciations of the word represented in a speech recognition phonetic alphabet. In particular, the vocabulary of recognizable words includes a word that is associated with a plurality of candidate pronunciations. For example, the vocabulary includes the word “tomato” that is associated with the candidate pronunciations of / / and and / /. Further, vocabulary words are associated with custom candidate pronunciations that are based on previous speech inputs from the user. Such custom candidate pronunciations are stored in STT processing module  730  and are associated with a particular user via the user&#39;s profile on the device. In some examples, the candidate pronunciations for words are determined based on the spelling of the word and one or more linguistic and/or phonetic rules. In some examples, the candidate pronunciations are manually generated, e.g., based on known canonical pronunciations. 
     In some examples, the candidate pronunciations are ranked based on the commonness of the candidate pronunciation. For example, the candidate pronunciation / / is ranked higher than / /, because the former is a more commonly used pronunciation (e.g., among all users, for users in a particular geographical region, or for any other appropriate subset of users). In some examples, candidate pronunciations are ranked based on whether the candidate pronunciation is a custom candidate pronunciation associated with the user. For example, custom candidate pronunciations are ranked higher than canonical candidate pronunciations. This can be useful for recognizing proper nouns having a unique pronunciation that deviates from canonical pronunciation. In some examples, candidate pronunciations are associated with one or more speech characteristics, such as geographic origin, nationality, or ethnicity. For example, the candidate pronunciation / / is associated with the United States, whereas the candidate pronunciation / / is associated with Great Britain. Further, the rank of the candidate pronunciation is based on one or more characteristics (e.g., geographic origin, nationality, ethnicity, etc.) of the user stored in the user&#39;s profile on the device. For example, it can be determined from the user&#39;s profile that the user is associated with the United States. Based on the user being associated with the United States, the candidate pronunciation / / (associated with the United States) is ranked higher than the candidate pronunciation / / (associated with Great Britain). In some examples, one of the ranked candidate pronunciations is selected as a predicted pronunciation (e.g., the most likely pronunciation). 
     When a speech input is received, STT processing module  730  is used to determine the phonemes corresponding to the speech input (e.g., using an acoustic model), and then attempt to determine words that match the phonemes (e.g., using a language model). For example, if STT processing module  730  first identifies the sequence of phonemes / / corresponding to a portion of the speech input, it can then determine, based on vocabulary index  744 , that this sequence corresponds to the word “tomato.” 
     In some examples, STT processing module  730  uses approximate matching techniques to determine words in an utterance. Thus, for example, the STT processing module  730  can determine that the sequence of phonemes / / corresponds to the word “tomato,” even if that particular sequence of phonemes is not one of the candidate sequence of phonemes for that word. 
     Natural language processing module  732  (“natural language processor”) of the digital assistant can take the sequence of words or tokens (“token sequence”) generated by STT processing module  730 , and attempt to associate the token sequence with one or more “actionable intents” recognized by the digital assistant. An “actionable intent” represents a task that can be performed by the digital assistant, and can have an associated task flow implemented in task flow models  754 . The associated task flow is a series of programmed actions and steps that the digital assistant takes in order to perform the task. The scope of a digital assistant&#39;s capabilities is dependent on the number and variety of task flows that have been implemented and stored in task flow models  754 , or in other words, on the number and variety of “actionable intents” that the digital assistant recognizes. The effectiveness of the digital assistant, however, also dependents on the assistant&#39;s ability to infer the correct “actionable intent(s)” from the user request expressed in natural language. 
     In some examples, in addition to the sequence of words or tokens obtained from STT processing module  730 , natural language processing module  732  also receives contextual information associated with the user request, e.g., from I/O processing module  728 . The natural language processing module  732  optionally uses the contextual information to clarify, supplement, and/or further define the information contained in the token sequence received from STT processing module  730 . The contextual information includes, for example, user preferences, hardware, and/or software states of the user device, sensor information collected before, during, or shortly after the user request, prior interactions (e.g., dialogue) between the digital assistant and the user, and the like. As described herein, contextual information is, in some examples, dynamic, and changes with time, location, content of the dialogue, and other factors. 
     In some examples, the natural language processing is based on, e.g., ontology  760 . Ontology  760  is a hierarchical structure containing many nodes, each node representing either an “actionable intent” or a “property” relevant to one or more of the “actionable intents” or other “properties.” As noted above, an “actionable intent” represents a task that the digital assistant is capable of performing, i.e., it is “actionable” or can be acted on. A “property” represents a parameter associated with an actionable intent or a sub-aspect of another property. A linkage between an actionable intent node and a property node in ontology  760  defines how a parameter represented by the property node pertains to the task represented by the actionable intent node. 
     In some examples, ontology  760  is made up of actionable intent nodes and property nodes. Within ontology  760 , each actionable intent node is linked to one or more property nodes either directly or through one or more intermediate property nodes. Similarly, each property node is linked to one or more actionable intent nodes either directly or through one or more intermediate property nodes. For example, as shown in  FIG. 7C , ontology  760  includes a “restaurant reservation” node (i.e., an actionable intent node). Property nodes “restaurant,” “date/time” (for the reservation), and “party size” are each directly linked to the actionable intent node (i.e., the “restaurant reservation” node). 
     In addition, property nodes “cuisine,” “price range,” “phone number,” and “location” are sub-nodes of the property node “restaurant,” and are each linked to the “restaurant reservation” node (i.e., the actionable intent node) through the intermediate property node “restaurant.” For another example, as shown in  FIG. 7C , ontology  760  also includes a “set reminder” node (i.e., another actionable intent node). Property nodes “date/time” (for setting the reminder) and “subject” (for the reminder) are each linked to the “set reminder” node. Since the property “date/time” is relevant to both the task of making a restaurant reservation and the task of setting a reminder, the property node “date/time” is linked to both the “restaurant reservation” node and the “set reminder” node in ontology  760 . 
     An actionable intent node, along with its linked concept nodes, is described as a “domain.” In the present discussion, each domain is associated with a respective actionable intent, and refers to the group of nodes (and the relationships there between) associated with the particular actionable intent. For example, ontology  760  shown in  FIG. 7C  includes an example of restaurant reservation domain  762  and an example of reminder domain  764  within ontology  760 . The restaurant reservation domain includes the actionable intent node “restaurant reservation,” property nodes “restaurant,” “date/time,” and “party size,” and sub-property nodes “cuisine,” “price range,” “phone number,” and “location.” Reminder domain  764  includes the actionable intent node “set reminder,” and property nodes “subject” and “date/time.” In some examples, ontology  760  is made up of many domains. Each domain shares one or more property nodes with one or more other domains. For example, the “date/time” property node is associated with many different domains (e.g., a scheduling domain, a travel reservation domain, a movie ticket domain, etc.), in addition to restaurant reservation domain  762  and reminder domain  764 . 
     While  FIG. 7C  illustrates two example domains within ontology  760 , other domains include, for example, “find a movie,” “initiate a phone call,” “find directions,” “schedule a meeting,” “send a message,” and “provide an answer to a question,” “read a list,” “providing navigation instructions,” “provide instructions for a task” and so on. A “send a message” domain is associated with a “send a message” actionable intent node, and further includes property nodes such as “recipients),” “message type,” and “message body.” The property node “recipient” is further defined, for example, by the sub-property nodes such as “recipient name” and “message address.” 
     In some examples, the “find media items” domain includes a super domain containing many actionable intent nodes associated with finding or obtaining media items. For example, the “find media items” domain includes the actionable intent nodes, such as “obtain media items having recent release data,” “obtain personalized recommendations for media items,” or “obtain information associated with media items.” 
     In some examples, ontology  760  includes all the domains (and hence actionable intents) that the digital assistant is capable of understanding and acting upon. In some examples, ontology  760  is modified, such as by adding or removing entire domains or nodes, or by modifying relationships between the nodes within the ontology  760 . 
     In some examples, nodes associated with multiple related actionable intents are clustered under a “super domain” in ontology  760 . For example, a “travel” super-domain includes a cluster of property nodes and actionable intent nodes related to travel. The actionable intent nodes related to travel includes “airline reservation,” “hotel reservation,” “car rental,” “get directions,” “find points of interest,” and so on. The actionable intent nodes under the same super domain (e.g., the “travel” super domain) have many property nodes in common. For example, the actionable intent nodes for “airline reservation,” “hotel reservation,” “car rental,” “get directions,” and “find points of interest” share one or more of the property nodes “start location,” “destination,” “departure date/time,” “arrival date/time,” and “party size.” 
     In some examples, each node in ontology  760  is associated with a set of words and/or phrases that are relevant to the property or actionable intent represented by the node. The respective set of words and/or phrases associated with each node are the so-called “vocabulary” associated with the node. The respective set of words and/or phrases associated with each node are stored in vocabulary index  744  in association with the property or actionable intent represented by the node. For example, returning to  FIG. 7B , the vocabulary associated with the node for the property of “restaurant” includes words such as “food,” “drinks,” “cuisine,” “hungry,” “eat,” “pizza”, “fast food,” “meal,” and so on. For another example, the vocabulary associated with the node for the actionable intent of “initiate a phone call” includes words and phrases such as “call,” “phone,” “dial,” “ring,” “call this number”, “make a call to,” and so on. The vocabulary index  744  optionally includes words and phrases in different languages. 
     Natural language processing module  732  receives the token sequence (e.g., a text string) from STT processing module  730 , and determines what nodes are implicated by the words in the token sequence. In some examples, if a word or phrase in the token sequence is found to be associated with one or more nodes in ontology  760  (via vocabulary index  744 ), the word or phrase “triggers” or “activates” those nodes. Based on the quantity and/or relative importance of the activated nodes, natural language processing module  732  selects one of the actionable intents as the task that the user intended the digital assistant to perform. In some examples, the domain that has the most “triggered” nodes is selected. In some examples, the domain having the highest confidence value (e.g., based on the relative importance of its various triggered nodes) is selected. In some examples, the domain is selected based on a combination of the number and the importance of the triggered nodes. In some examples, additional factors are considered in selecting the node as well, such as whether the digital assistant has previously correctly interpreted a similar request from a user. 
     User data  748  includes user-specific information, such as user-specific vocabulary, user preferences, user address, user&#39;s default and secondary languages, user&#39;s contact list, and other short-term or long-term information for each user. In some examples, natural language processing module  732  uses the user-specific information to supplement the information contained in the user input to further define the user intent. For example, for a user request “invite my friends to my birthday party,” natural language processing module  732  is able to access user data  748  to determine who the “friends” are and when and where the “birthday party” would be held, rather than requiring the user to provide such information explicitly in his/her request. 
     Other details of searching an ontology based on a token string is described in U.S. Utility application Ser. No. 12/341,743 for “Method and Apparatus for Searching Using An Active Ontology,” filed Dec. 22, 2008, the entire disclosure of which is incorporated herein by reference. 
     In some examples, once natural language processing module  732  identifies an actionable intent (or domain) based on the user request, natural language processing module  732  generates a structured query to represent the identified actionable intent. In some examples, the structured query includes parameters for one or more nodes within the domain for the actionable intent, and at least some of the parameters are populated with the specific information and requirements specified in the user request. For example, the user says “Make me a dinner reservation at a sushi place at 7.” In this case, natural language processing module  732  is able to correctly identify the actionable intent to be “restaurant reservation” based on the user input. According to the ontology, a structured query for a “restaurant reservation” domain includes parameters such as {Cuisine}, {Time}, {Date}, {Party Size}, and the like. In some examples, based on the speech input and the text derived from the speech input using STT processing module  730 , natural language processing module  732  generates a partial structured query for the restaurant reservation domain, where the partial structured query includes the parameters {Cuisine=“Sushi”} and {Time=“7 pm”}. However, in this example, the user&#39;s utterance contains insufficient information to complete the structured query associated with the domain. Therefore, other necessary parameters such as {Party Size} and {Date} is not specified in the structured query based on the information currently available. In some examples, natural language processing module  732  populates some parameters of the structured query with received contextual information. For example, in some examples, if the user requested a sushi restaurant “near me,” natural language processing module  732  populates a {location} parameter in the structured query with GPS coordinates from the user device. 
     In some examples, natural language processing module  732  passes the generated structured query (including any completed parameters) to task flow processing module  736  (“task flow processor”). Task How processing module  736  is configured to receive the structured query from natural language processing module  732 , complete the structured query, if necessary, and perform the actions required to “complete” the user&#39;s ultimate request. In some examples, the various procedures necessary to complete these tasks are provided in task flow models  754 . In some examples, task flow models  754  include procedures for obtaining additional information from the user and task flows for performing actions associated with the actionable intent. 
     As described above, in order to complete a structured query, task flow processing module  736  needs to initiate additional dialogue with the user in order to obtain additional information, and/or disambiguate potentially ambiguous utterances. When such interactions are necessary, task flow processing module  736  invokes dialogue flow processing module  734  to engage in a dialogue with the user. In some examples, dialogue flow processing module  734  determines how (and/or when) to ask the user for the additional information and receives and processes the user responses. The questions are provided to and answers are received from the users through I/O processing module  728 . In some examples, dialogue flow processing module  734  presents dialogue output to the user via audio and/or visual output, and receives input from the user via spoken or physical (e.g., clicking) responses. Continuing with the example above, when task flow processing module  736  invokes dialogue flow processing module  734  to determine the “party size” and “date” information for the structured query associated with the domain “restaurant reservation,” dialogue flow processing module  734  generates questions such as “For how many people” and “On which day” to pass to the user. Once answers are received from the user, dialogue flow processing module  734  then populates the structured query with the missing information, or pass the information to task flow processing module  736  to complete the missing information from the structured query. 
     Once task flow processing module  736  has completed the structured query for an actionable intent, task flow processing module  736  proceeds to perform the ultimate task associated with the actionable intent. Accordingly, task flow processing module  736  executes the steps and instructions in the task flow model according to the specific parameters contained in the structured query. For example, the task flow model for the actionable intent of “restaurant reservation” includes steps and instructions for contacting a restaurant and actually requesting a reservation for a particular party size at a particular time. For example, using a structured query such as: {restaurant reservation, restaurant=ABC Café, date=Mar. 12, 2012, time=7 pm, party size=5}, task flow processing module  736  performs the steps of: (1) logging onto a server of the ABC Café or a restaurant reservation system such as OPENTABLE®, (2) entering the date, time, and party size information in a form on the website, (3) submitting the form, and (4) making a calendar entry for the reservation in the user&#39;s calendar. 
     In some examples, task flow processing module  736  employs the assistance of service processing module  738  (“service processing module”) to complete a task requested in the user input or to provide an informational answer requested in the user input. For example, service processing module  738  acts on behalf of task flow processing module  736  to make a phone call set a calendar entry, invoke a map search, invoke or interact with other user applications installed on the user device, and invoke or interact with third party services (e.g., a restaurant reservation portal, a social networking website, a banking portal, etc.). In some examples, the protocols and application programming interlaces (API) required by each service are specified by a respective service model among service models  756 . Service processing module  738  accesses the appropriate service model, for a service and generate requests for the service in accordance with the protocols and APIs required by the service according to the service model. 
     For example, if a restaurant has enabled an online reservation service, the restaurant Submits a service model specifying the necessary parameters for making a reservation and the APIs for communicating the values of the necessary parameter to the online reservation service. When requested by task flow processing module  736 , service processing module  738  establishes a network connection with the online reservation service using the web address stored in the service model, and send the necessary parameters of the reservation (e.g., time, date, party size) to the online reservation interface in a format according to the API of the online reservation service. 
     In some examples, natural language processing module  732 , dialogue flow processing module  734 , and task flow processing module  736  are used collectively and iteratively to infer and define the user s intent, obtain information to further clarify and refine the user intent, and finally generate a response (i.e., an output to the user, or the completion of a task) to fulfill the user&#39;s intent. The generated response is a dialogue response to the speech input that at least partially fulfills the user&#39;s intent. Further, in some examples, the generated response is output as a speech output. In these examples, the generated response is sent to speech synthesis module  740  (e.g., speech synthesizer) where it can be processed to synthesize the dialogue response in speech form. In yet other examples, the generated response is data content, relevant to satisfying a user request in the speech input. 
     Speech synthesis module  740  is configured to synthesize speech outputs for presentation to the user. Speech synthesis module  740  synthesizes speech outputs based on text provided by the digital assistant. For example, the generated dialogue response is in the form of a text string. Speech synthesis module  740  converts the text string to an audible speech output. Speech synthesis module  740  uses any appropriate speech synthesis technique in order to generate speech outputs from text, including, but not limited, to concatenate synthesis, unit selection synthesis, diphone synthesis, domain-specific synthesis, formant synthesis, articulatory synthesis, hidden Markov model (HMM) based synthesis, and sinewave synthesis. In some examples, speech synthesis module  740  is configured to synthesize individual words based on phonemic strings corresponding to the words. For example, a phonemic string is associated with a word in the generated dialogue response. The phonemic string is stored in metadata associated with the word. Speech synthesis model  740  is configured to directly process the phonemic string in the metadata to synthesize the word in speech form. 
     In some examples, instead of (or in addition to) using speech synthesis module  740 , speech synthesis is performed on a remote device (e.g., the server system  108 ), and the synthesized speech is sent to the user device, for output to the user. For example, this can occur in some implementations where outputs for a digital assistant are generated at a server system. And because server systems generally have more processing power or resources than a user device, it is possible to obtain higher quality speech outputs than would be practical with client-side synthesis. 
     Additional details on digital assistants can be found in the U.S. Utility application Ser. No. 12/987,982, entitled “Intelligent Automated Assistant,” filed Jan. 10, 2011, and U.S. Utility application Ser. No. 13/251,088, entitled “Generating and Processing Task Items That Represent Tasks to Perform,” filed Sep. 30, 2011, the entire disclosures of which are incorporated herein by reference. 
     4. Processes for Operating Digital Assistants for Media Exploration 
       FIGS. 8A-C  illustrate process  800  for operating a digital assistant for media exploration, according to various examples.  FIGS. 9A-B ,  10 , and  11  illustrate interactions user  901  operating a digital assistant on user device  903  for media exploration, according to various examples. Process  800  is performed, for example, using one or mom electronic devices implementing a digital assistant. In some examples, the process is performed at a client-server system (e.g., system  100 ) implementing a digital assistant. In some examples, the process is performed at a user device (e.g., device  104 ,  200 ,  400 , or  600 ). In process  800 , some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. Further, it should be recognized that in some examples, only a subset of the features described below with reference to  FIGS. 8A-C  are performed in process  800 . 
     At block  802 , a speech input is received (e.g., at I/O processing module  728  and via microphone  213 ) from a user. The speech input represents a request for one or mom media items. For example, with reference to  FIG. 9A , the speech input is “Hey Siri, play me some hip-hop music I&#39;d like.” In another example shown in  FIG. 10 , the speech input is “Hey Siri, play me some music for barbecuing.” In yet another example shown in  FIG. 11 , the speech input is “Hey Siri, play me some pop music that just came out.” Other examples of speech input that represent a request for one or more media items include, “What should I listen to,” “Recommend some music,” “What&#39;s for me today,” “Hey Siri, be my DJ,” “Spin me some tasty beats,” “Find a recommended playlist,” “Any good album I should play. “Play me something I&#39;d like,” “Any recommended workout music.” “Find recently released music,” Hot new rock tracks please,” or the like. 
     At block  804 , a determination is made (e.g., using natural language processing module  732 ) as to whether the speech input of block  802  corresponds to the user intent of obtaining personalized recommendations for media items. In particular, the determination includes determining the user intent (e.g., actionable intent) corresponding to the speech input. The user intent is determined in a similar manner as discussed above with reference to  FIGS. 7A-C . In particular, the words or phrases in the speech input are parsed and compared to the words or phrases of a vocabulary index (e.g., vocabulary index  744 ). The words or phrases of the vocabulary index are associated with the various nodes (e.g., actionable intent nodes or domains) of an ontology (e.g., ontology  760 ) and thus, based on the comparison, nodes corresponding to the words or phrases in the speech input are “triggered” or “activated.” The node having the highest confidence value among the activated nodes is selected. The determined user intent corresponding to the speech input of block  802  is thus the actionable intent corresponding to the selected node. 
     Determining whether the speech input corresponds to the user intent of obtaining personalized recommendations for media items is based on the selected actionable intent node, if the selected node has a corresponding actionable intent of obtaining personalized recommendations for media items, then the speech input is determined to correspond to the user intent of obtaining personalized recommendations for media items. Conversely, if the selected node has a corresponding actionable intent other than obtaining personalized recommendations for media items, then the speech input is determined to not correspond to the user intent of obtaining personalized recommendations for media items. 
     In some examples, determining whether the speech input corresponds to the user intent of obtaining personalized recommendations for media items includes determining whether the speech input includes one or more of a plurality of predetermined phrases. In particular, the vocabulary index includes a plurality of predetermined phrases that correspond to the actionable intent node of obtaining personalized recommendations for media items. The plurality of predetermined phrases include, for example, “Recommend me . . . (music),” “Be my DJ,” “Spin some tunes/beats,” “What should I play,” “Play me some [music] I&#39;d like,” “Find some good [music] for . . . .” or the like. Based on the speech input, containing one or more of these phrases, the speech input is mapped to the actionable intent of obtaining personalized recommendations for media items, and the speech input is determined to correspond to the user intent of obtaining personalized recommendations for media items. For example, in  FIG. 9A , speech input  902  contains the phrase “play me some [music] I&#39;d like,” which is one of the plurality of predetermined phrases corresponding to the actionable intent node of obtaining personalized recommendations for media items. Thus, in this example, speech input  902  is determined to correspond to the user intent of obtaining personalized recommendations for media items. 
     In some examples, determining whether the speech input corresponds to the user intent of obtaining personalized recommendations for media items includes determining whether a number of parameters defined in the speech input is less than a predetermined threshold number. In particular, the speech input is determined to correspond to the user intent of obtaining personalized recommendations for media items if the number of parameters (e.g., media parameters) defined in the speech input is less than a predetermined threshold number. For example, the speech input “What should I play” is a request related to playing music. However, the request is broad and vague as it does not define any media parameters, such as the desired artist, album, genre, or release date. In this example, the speech input is determined to correspond to the user intent of obtaining personalized recommendations for media items, because the number of media parameters defined in the speech input is less than a predetermined threshold number (e.g., one). 
     In some examples, determining whether the speech input corresponds to the user intent of obtaining personalized recommendations for media includes determining whether the speech input refers to the user. Specifically, the speech input is parsed to determine whether it contains words or phrases referring to the user (e.g., “me,” “for me,” “I,” “my,” etc.). For example, the following phrases are determined to contain words referring to the user: “What&#39;s for me,” “Surprise me,” “Anything for me today” In some examples, the determination is based on determining whether the speech input contains words or phrases referring to the user in combination with words or phrases related to media (e.g., “listen,” “music,” “play,” “tunes,” “DJ,” etc.). For example, the following phrases are determined to contain words referring to the user in combination with words or phrases related to media: “Recommend me some hip-hop,” “Be my DJ,” “What should I listen to,” “What do songs do you have for me,” or “Play me some tunes.” Thus, the speech input is determined to correspond to the user intent of obtaining personalized recommendations for media based on the speech inputs containing words or phrases that refer to the user. 
     In response to determining that the speech input corresponds to the user intent of obtaining personalized recommendations for media items, block  806  is performed. At block  806 , at least one media item is obtained from a user-specific corpus of media items (e.g., using natural language processing module  732 , task flow processing module  736 , and/or service processing module  738 ). In some examples, the at least one media item includes a song, album, video, movie, or playlist. The user-specific corpus of media items is a personalized corpus of media items that is specific to the user. In particular, the user-specific corpus of media items is generated based on data associated with the user. A more detailed description of the user-specific corpus of media items is provided below with reference to block  810 . Obtaining the at least one media item from the user-specific corpus of media items at block  806  includes performing one or more of blocks  808 - 816 , described below. Blocks  808 - 816  are performed, for example, using one or more of natural language processing module  732 , task flow processing module  736 , and/or service processing module  738 . 
     At block  808 , media parameters defined in the speech input ace determined (e.g., using natural language processing module  732 ). A structured query corresponding to the actionable intent of obtaining personalized recommendations for media items is then generated with the defined media parameters. In particular, the vocabulary index (e.g., vocabulary index  744 ) includes words or phrases corresponding to each of a plurality of media parameters. Media parameters defined in the speech input are thus determined by comparing the words or phrases of the speech input with the words or phrases in the vocabulary index. For example, the vocabulary index includes words or phrases associated with the media parameter {genre}. The words or phrases include, for example, “hip-hop,” “R&amp;B,” “jazz,” “punk,” “rock,” “pop,” “classical,” “bluegrass,” or the like. In the example of  FIG. 9A , speech input  902  is determined to define the media parameter {genre}=“hip-hop” based on detecting the phrase “hip-hop” in speech input  902 . Thus, in this example, the structured query corresponding to the actionable intent of obtaining personalized recommendations for media items are generated to include the media parameter {genre}=“hip-hop.” 
     Another media parameter that can be determined from the speech input is {release date}. The media parameter {release date} refers to the release dates of the media items the user is interested in. The release date is, for example, a specific date or a range of dates. Words or phrase associated with the media parameter {release date} include, for example, “seventies,” “eighties,” “nineties,” “the last ten years,” “2008,” “after March 2016,” or the like. In one example, based on the word “eighties” in the speech input “Play some eighties tunes for me,” the speech input is determined to define the media parameter {release date}=“1980-1989.” Thus, in this example, the structured query corresponding to the actionable intent of obtaining personalized recommendations for media items is generated to include the media parameter {release date}=“1980-1989.” 
     In some examples, based on the context of the speech input, dates or time periods in the speech input are interpreted as defining a subgenre rather than defining a release date. For example, based on the phrase “&#39;70s” in the speech input “Play me some &#39;70s punk,” the speech input is determined to define the time period “1970-1979.” In response to determining that the speech input defines this time period, a determination is made as to whether the speech input defines a genre in association with this time period. In the present example, the speech input includes the phrase “punk,” which corresponds to the media parameter {genre}. Since the time period “&#39;70s” modifies the genre “punk” in the speech input, it is determined that the speech input defines the genre “punk” in associated with the time period “&#39;70s.” In response to determining that the speech input defines a genre in association with the defined time period, a subgenre is determine based on the defined time period and the defined genre. For example, based on the defined lime period “&#39;70s” and the defined genre “punk,” the subgenre “&#39;70s punk” is determined. Thus, in this example, the structured query generated for the actionable intent of obtaining personalized recommendations for media items includes the media parameter {subgenre}=“&#39;70s punk.” Notably, rather than interpreting the defined time period as the media parameter {release date}, the defined time period is interpreted more accurately as part of the media parameter {subgenre}. In this way, the speech input is interpreted to more accurately reflect the user&#39;s actual intent and thus, more relevant media items are provided to the user. For example, the at least one media item is obtained at block  806  based on the determined subgenre “&#39;70s punk,” which includes media items with release dates outside of the time period 1970-1979. Specifically, each media item in the at least one media item includes metadata that indicates the subgenre of “&#39;70s punk.” 
     Other media parameters that are determined as being defined in the speech input includes, for example, {activity}, {mood}, {occasion}, {editorial list}, {political preference}, or {technical proficiency}. Each of these media parameters is described below in turn. For example, the media parameter {activity} refers to an activity performed by the user and is associated with words or phrases, such as “working out,” “exercising,” “barbecuing,” “sleeping,” “driving,” “studying,” “painting,” or the like. In one example, based on the word “studying” in the speech input “Find some music that&#39;s good for studying,” the speech input is determined to define the media parameter {activity}=“studying.” In another example shown in  FIG. 10 , the phrase “barbecuing” is detected in speech input  1002  as corresponding to the media parameter {activity}. Thus, in this example, speech input  1002  is determined as defining the media parameter {activity}=“barbecuing.” 
     The media parameter {mood} refers to a feeling or state of mind of the user and is associated with words or phrases, such as “happy,” “sad,” “angry,” “relaxing,” “powerful,” “excited,” “romantic,” or the like. In one example, based on the word “happy” in the speech input “Recommend me some happy music,” the speech input is determined to define the media parameter {mood}=“happy.” 
     The media parameter {occasion} refers to an occasion associated with a specific time period and is associated with words or phrases, such as “Christmas,” “birthday,” “summer,” “winter,” “Halloween,” “New Years,” “Easter,” or the like. In one example, based on the word “Christmas” in the speech input “Play some Christmas music,” the speech input is determined to define the media parameter {occasion}=“Christmas.” 
     The media parameter {editorial list} refers to a predetermined list of media items compiled by a media establishment, such as Rolling Stones magazine. Billboard magazine, Shazam, or the like. Exemplary editorial lists include, for example, the Billboard Hot 100. Billboard Hit Parade. Billboard 200, American top 40, Rolling Stone&#39;s 500 Greatest Songs of All Time. Rolling Stone&#39;s 500 Greatest Albums of All Time. Rolling Stones 100 Greatest Artists, or the like. The media parameter {editorial list} is associated with words or phrases that correspond to these lists. For example, based on the phrase “Billboard Hot 100” in the speech input “Play me songs from the Billboard Hot 100,” the speech input is determined to define the media parameter {editorial list}=“Billboard Hot 100.” 
     The media parameter {political preference} refers to a political preference of the user and is associated with words or phrases, such as “conservative,” “liberal,” “right wing,” “right leaning,” “left wing,” left leaning,” or the like. In one example, based on the phrase “conservative” in the speech input “Find me conservative news,” the speech input is determined to define the media parameter {political preference}=“conservative.” In this example, the candidate media items determined at block  812  are more likely associated with conservative media sources (e.g., Fox news, drudge report, etc.) than liberal media sources (e.g., Huffington Post, New York Times, etc.). 
     The media parameter {technical proficiency} refers to how proficient the user is with technical subject matter. This is relevant when making requests for documentaries that discuss technical subject matter. In particular, the media parameter {technical proficiency} is associated with words and phrases, such as “very technical,” “layman,” “scientific,” “easily understandable,” “simple,” “advanced,” or the like. In one example, based on the phrase “very technical” in the speech input “Find me some very technical documentaries on space crafts,” the speech input, is determined to define the media parameter {technical proficiency}=“high.” In some examples, the media parameter {technical proficiency} is inferred based on a user&#39;s familiarity with the requested subject matter. In particular, if the user frequently requests documentaries on space crafts (e.g., based on user request logs) or if the user has a large collection of documentaries on space crafts in his/her personal media library, then it can be determined that the user is very familiar with the subject matter of space crafts and thus, in this example, the media parameter {technical proficiency} is inferred to be “high.”?? 
     At block  810 , a user-specific corpus of media items is determined. Determining the user-specific corpus of media items includes obtaining user identification information associated with the user. The user identification information contains, for example, user account log-in information or user password information for accessing a corresponding user-specific corpus of media items. The user identification information is then used to identify and access the appropriate user-specific corpus of media items from among a plurality of user-specific corpuses of media items to obtain the at least one media item. 
     In some examples, the user device at which the speech input of block  802  was received is associated with a unique user profile (e.g., stored in user data  748 ) that contains the user identification information. The user identification information is thus retrieved at block  810  based on the user profile associated with the user device. The corresponding user-specific corpus of media items is thus identified based on the retrieved user identification information. 
     In some examples, the user identification information is retrieved upon verifying the identity of the user. In particular, the identity of the user is verified by performing speaker identification using the speech input of block  802 . Speaker identification is performed, for example, by comparing a voice print generated from the speech input of block  802  with a reference voice print associated with a specific user. The identity of the user is verified if the voice print generated from the speech input of block  802  is determined to match the reference voice print beyond a threshold confidence value. It should be recognized that other methods of identity verification can be implemented, such as fingerprinting authentication, passcode verification, or the like. Upon successfully verifying the identity of the user, the user identification information corresponding to the verified identity of the user is retrieved (e.g., from the user profile). The user identification information is then used to identify and access the corresponding user-specific corpus of media items. Based on the determined identity of the user, the respective user-specific corpus of media items is determined from the plurality of user-specific corpuses of media items. 
     In some examples, the user-specific corpus of media items is stored on a remote server separate from the user device. For example, the user-specific corpus of media items is stored as part of a media service (e.g., media service(s)  120 - 1 ) that provides media items. The user identification information is required to access the user-specific corpus of media items. In some examples, an encrypted token containing the user identification information is generated at the user device and sent to the media service. The media service then decrypts the token and utilize the user identification information from the decrypted token to access the corresponding user-specific corpus of media items to obtain the at least one media item. 
     In some examples, the user-specific corpus of media items is customized to the media preferences of a specific user. For example, previous media-related data associated with the user is utilized to generate the user-specific corpus of media items. In particular, the user-specific corpus of media items is generated based on the media items previously selected, requested, or rejected by the user. For example, if the user is determined to frequently request, browse, select, or play media items having certain media parameters (e.g., {genre}=“pop” or {artist}=“Katy Perry”), then the user-specific corpus of media items is generated to favor media items having those parameters. Similarly, if the user is determined to consistently reject recommended media items having certain other parameters (e.g., {mood}=sad), then the user-specific corpus of media items is generated to disfavor media items having those other parameters. 
     In some examples, the user-specific corpus of media items is generated based on information from the user&#39;s profile. The user profile contains information that characterizes the user, such as the country associated with the user, the user&#39;s spoken languages, the user&#39;s age, or the activities frequently engaged by the user. Based on this information, the user-specific corpus of media items is generated to favor media items having media parameters that complement this information. For example, if the user profile indicates that the user primarily speaks English and is twenty years old, then the user-specific corpus of media items is generated to favor media items that are spoken or sung in English and have a recent (e.g., last 5 years) release date. 
     Further, in some examples, the user-specific corpus of media items is generated based on a personal library of media items associated with the user. The personal library of media items include media items (e.g., songs, movies, etc.) that were acquired by the user. The personal library of media items is stored on the user device and/or stored on a remote server in association with the user&#39;s account. The user-specific corpus of media items is generated to favor media items having media parameters that are similar to those in the user&#39;s personal library of media items. For example, if the user&#39;s personal library of media items contains many albums by the artist Katy Perry, then the user-specific corpus is generated to favor media items associated with the artist Katy Perry and/or artists similar to Katy Perry, such as Avril Lavigne. 
     In some examples, the user-specific corpus of media items is generated such that each media item in the user-specific corpus of media items includes metadata that indicates a plurality of media parameters corresponding to the respective media item. In particular, the metadata for each media item defines any of the media parameters discussed above, such as {artist}, {genre}, {subgenre}, {release date}, {activity}, {mood}, {occasion}, {editorial list}, {political preference}, or {technical proficiency}. The metadata is used to recommend suitable media items to the user based on the media parameters defined in the user&#39;s speech input. For example, the user-specific corpus of media items include the instrumental song “Chariots of Fire” having metadata that indicates the following media parameters: {title}=“Chariots of Fire”, {genre}=“soundtrack; instrumental,” {composer}=Vangelis, {release date}=“March 1981,” {activity}=“running,” and {mood}=“inspirational.” Thus, if the speech input received at block  802  were “Play me some inspirational instrumental music good for running,” then based on the media parameters defined in the speech input (i.e., {genre}=“instrumental” {activity}=“running,” and {mood}=“inspirational”) the song “Chariots of Fire” is a candidate media item that is identified in the user-specific corpus of media items and recommended to the user. 
     In some example, the metadata of the media items in the user-specific corpus of media items is intelligently generated based on analyzing specific characteristics associated with the media items. In particular, the music tempo (e.g., beats per minute) of each media item is determined by analyzing the audio data of the media items. Based on the determined music tempo, the {activity} media parameter is determined for media items in the user-specific corpus of media items. For example, media items having a faster music tempo are associated with more lively activities, such as working out, hiking, or the like. Conversely, media items having a slower music tempo are associated with more passive activities, such as sleeping, meditating, or the like. The relevant (activity) media parameter determined based on music tempo is thus included in the metadata of the respective media items. 
     Additionally, in some examples, the {mood} media parameter for media items in the user-specific corpus of media items is determined based on the music key of each media item. For example, the audio data of each media item is analyzed to determine the music key (e.g., C major, G major, A minor, etc.) associated with the audio data. Media items having a major music key are associated with more positive and happy moods, such as “happy,” “upbeat,” “cheerful,” “excited,” or the like, whereas media items having a minor music key are associated with more somber moods, such as “sad,” “mournful,” or the like. 
     At block  812 , a plurality of candidate media items are determined from the user-specific corpus of media items based on the determined media parameters of block  808 . For example, a search is performed using the determined media parameters of block  808  to identify candidate media items in the user-specific corpus of media items having metadata that include the determined media parameters of block  808 . For example, returning to  FIG. 9A , the media parameter {genre}=“hip-hop” is determined at block  808  to be defined in speech input  902 . In response to determining that the speech input defines the media parameter {genre}=“hip-hop,” the user-specific corpus of media items can be searched to identify media items having metadata that include the media parameter {genre}=“hip-hop.” For Example, media items such as “Tipsy” by J-Kwon. “99 Problems” by Jay-Z, and “Over” by Drake each have metadata that include the media parameter {genre}=“hip-hop.” Thus, in this example, the plurality of candidate media items determined from the user-specific corpus of media items include these media items. 
     In another example shown in  FIG. 10 , user  901  provides speech input  1002  “Hey Siri, play me some music for barbecuing.” In this example, the speech input is determined at block  808  to define the media parameter {activity}=“barbecuing.” In response to determining that the speech input defines the media parameter {activity}=“barbecuing,” the user-specific corpus of media items is searched to identify media items having metadata that include the media parameter {activity}=“barbecuing.” For example, media items such as “She Moves in Her Own Way” by The Kooks, “Hot n Cold” by Katy Perry, and “Fun Fun Fun” by The Beach Boys each has metadata that include the media parameter {activity}=“barbecuing.” Thus, in this example, the plurality of candidate media items determined from the user-specific corpus of media items include there media items. 
     Although the examples of  FIGS. 9A-B  and  10  are described with respect to specific media parameters, it should be recognized that the plurality of candidate media items are determined from the user-specific corpus of media items based on any media parameter(s) defined in the speech input of block  802 . For example, in addition to the media parameters {genre} and {barbecue} described in the examples of  FIGS. 9A-B  and  10 , the media parameters include, {artist}, {media type}, {subgenre}, {release date}, {mood}, {occasion}, {editorial list}, {political preference}. {technical proficiency}, or the like. 
     At block  814 , the plurality of candidate media items of block  812  are ranked using a user-specific media ranking model. User-specific media ranking model is stored, for example, in user data  748  or data &amp; models  116 . Using the user-specific media ranking model, a user-specific ranking score is generated for each of the plurality of candidate media items. The plurality of candidate media items are thus ranked based on the user-specific ranking scores. The user-specific ranking score represents a likelihood that the user will accept the candidate media item given the media parameters associated with the candidate media item. The user-specific media ranking model is a statistical machine-learned model (e.g., neural network model, Bayesian model, etc.) that is trained using user-specific data, such as information from the user profile, previous media-related inputs from the user, or media items associated with the user. Further, the user-specific media ranking model is continuously updated based on subsequently received user-specific data. For example, the user-specific media ranking model is updated based on the speech input of block  802  or any speech contained in the audio input of block  824 , described below. 
     Information from the user profile includes the user&#39;s age, ethnicity, location, profession, or the like. This information is used to generate the user-specific media ranking model. For example, if information from the user profile indicates that the user is a scientist living in the conservative state of Id., the user-specific media ranking model is trained to generate more favorable scores for media items that are associated with a higher technical proficiency or a more conservative political orientation. 
     Previous media-related inputs from the user are used to generate the user-specific media ranking model. In particular, previous media-related inputs from the user include media-related requests, selections, or rejections received prior to receiving the speech input of block  802 . For example, if previous media-related requests from the user indicate that the user typically requests for pop music and rejects rap music, then based on this previous media-related input, the user-specific media ranking model is trained to generate more favorable ranking scores for pop music and less favorable ranking scores for rap music. In another example, previous media-related input indicates that, when the user browses through online music stores, the user frequently views music items with release dates in the 1970s. Based on this determination, the user-specific media ranking model is trained to generate more favorable scores for media items having release dates that are in the 1970s. 
     Media items associated with the user include media items found in the user&#39;s personal media library. In some examples, the media items in the user&#39;s personal media library are used to generate the user-specific media ranking model. In particular, the user-specific media ranking model is trained to favor the media items having media parameters similar to those of the media items in the user&#39;s personal media library. For example, based on the user&#39;s personal media library having many albums by Jay-Z, the user-specific media ranking model is trained to generate more favorable scores for media items related to the artist Jay-Z or artists similar to Jay-Z. 
     In some examples, the ranking of block  814  is performed (additionally or alternatively) using a general media ranking model. In particular, a general ranking score is generated for each of the plurality of candidate media items using the general media ranking model. The plurality of candidate media items are thus ranked based on the general ranking scores. The general media ranking model is similar to the user-specific media ranking model except that the general media ranking model is trained using media-related data from a large population of users rather than from one specific user. The general ranking scores represent general popularity ratings of the media items. In particular, the general media ranking model generates more favorable ranking scores for media items that are most frequently requested, viewed, or selected by the large population of users. 
     It should be recognized that the ranking of block  814  is, in some examples, performed based on a combination of the user-specific ranking score from the user-specific media ranking model and the general ranking score from the general media ranking model. For example, the scores are interpolated to generate a combined ranking score for each candidate media item. The plurality of candidate media items are then ranked based on the combined ranking scores. Further, it should be recognized that in some examples, the general media ranking model is integrated with the user-specific ranking model. For example, the user-specific ranking model is generated using media-related data from a large population of users, but adjusted to favor user preferences indicated in user-specific data. 
     At block  816 , the at least one media item is selected from the plurality of candidate media items based on the ranking of block  814 . For example, the at least one media item includes the highest ranked candidate media item or the highest N ranked candidate media items among the plurality of candidate media items, wherein N is an integer greater than zero. The at least one media item obtained at block  806  is the selected at least one media item of block  816 . The selected at least one media item is retrieved from the user-specific corpus of media items (e.g., at media service(s)  120 - 1 ) and provided to the user at block  818 . 
     In some examples, the at least one media item is selected based on an inferred degree of familiarity of the user with one or more media parameters associated with the at least one media item. For example, the speech input received at block  802  is “Play me some Michael Jackson songs.” In this example, the speech input is determined at block  808  to define the media parameter {artist}=“Michael Jackson.” Based on this determination, a plurality of candidate Michael Jackson songs are identified at block  812  from the user-specific corpus of media items. The plurality of candidate Michael Jackson songs are ranked at block  814  based on general popularity (e.g., according to the general media ranking model) and/or based on the user-specific media ranking model. A degree of familiarity of the user with the artist “Michael Jackson” is determined. The determination is made based on user-specific data associated with the artist “Michael Jackson.” For example, a high degree of familiarity of the user with the artist “Michael Jackson” is determined based on previous media-related input from the user indicating that the user frequently browses, purchases, listens to, and/or requests songs by Michael Jackson or based on the user&#39;s personal media library containing a large number of songs by Michael Jackson. Conversely, a low degree of familiarity of the user with the artist “Michael Jackson” is determined based on media-related input from the user indicating that the user infrequently browses, purchases, listens to, and/or requests songs by Michael Jackson or based on the user&#39;s personal media library containing very few songs by Michael Jackson. The songs selected at block  816  from the plurality of candidate Michael Jackson songs are based on the determined degree of familiarity. For example, if the degree of familiarity of the user with the artist “Michael Jackson” is determined to be low, then the most popular or the highest ranked candidate Michael Jackson songs are selected at block  814 . In particular, the highest ranked N candidate Michael Jackson songs from the plurality of candidate Michael Jackson songs are selected to play as a playlist. In contrast, if the degree of familiarity of the user with the artist “Michael Jackson” is determined to be high, then a combination of popular (e.g., higher ranked) and less popular (e.g., lower ranked) candidate Michael Jackson songs are selected to play as a playlist. In particular, a greater proportion of less popular candidate Michael Jackson songs is selected based on a higher degree of familiarity of the user with the artist “Michael Jackson.” This is desirable because a user who is very familiar with the artist Michael Jackson would likely already be familiar with the most popular Michael Jackson songs. Such a user would enjoy listening to a combination of Michael Jackson songs that includes popular, highly commercialized songs and less popular, less commercialized (e.g., “deep cuts”) songs. Thus, in this example, the average popularity rating of the Michael Jackson songs selected at block  816  is based on the determined degree of familiarity of the user with the artist “Michael Jackson.” 
     It should be recognized that in some examples, the degree of familiarity of the user with the one or more media parameters associated with the at least one media item is directly factored into the user-specific media ranking model. For exxample, based on a determination that the user is very familiar with the artist “Michael Jackson,” the user-specific media ranking model is configured to generate higher ranking scores for some less popular Michael Jackson songs. In this way, the highest ranked N candidate Michael Jackson songs include a mix of highly commercialized popular Michael Jackson songs and lesser known Michael Jackson songs. In these examples, the selected at least one media item includes the highest ranked N candidate Michael Jackson songs. 
     Although block  806  is described above as being performed using a user-specific corpus of media items, it should be recognized and in other examples, other corpuses of media items can be used in lieu of the user-specific corpus of media items. For instance, in some examples, the at least one media item is obtained from a general (user-independent) corpus of media items or a corpus of media items generated based on one or more specific media parameters. 
     At block  818 , the at least one media item is provided. In particular, the at least one media item is provided at the user device. In some examples, the at least one media item is played at the user device (e.g., using speaker  211 ). In other examples, the at least one media item is displayed on the user device (e.g., on touchscreen  212 ) for the user to view and/or select. In yet other examples, the at least one media item is provided in a spoken response to the user (e.g., using speaker  211 ). 
     With reference back to the example shown in  FIG. 9A , the candidate hip-hop media items determined at block  812  are ranked at block  814  using the user-specific media ranking model. In particular, the candidate hip-hop media items are ranked such that the candidate media item “99 Problems” by Jay-Z is highest ranked among the candidate hip-hop media items determined at block  812 . Thus, in this example, the at least one media item selected at block  816  includes the media item “99 Problems” by Jay-Z and the media item is played to user  901  on user device  903 . 
     With reference now to the example shown in  FIG. 10 , the “barbecue” candidate media items determined at block  812  is ranked at block  814  using the user-specific media ranking model. In this example, the candidate media item “She Moves in Her Own Way” by The Kooks is the highest ranked among the candidate media items determined at block  812 . Thus, the at least one media item selected at block  816  includes the media item “She Moves in Her Own Way” by The Kooks and the media item is obtained and played on to user  901  on user device  903 . It should be recognized that the selected at least one media item can include other media items. For example, the candidate media items “Hot n Cold” by Katy Perry and “Fun Fun Fun” by The Beach Boys are the second and third highest ranked media items among the candidate media items determined at block  812 . The at least one media item selected at block  816  includes these media items. Thus, in these examples, the media items “Hot n Cold” by Katy Perry and “Fun Fun Fun” by The Beach Boys are played on user device after the media item “She Moves in Her Own Way” by The Kooks. 
     In some examples, process  800  enables the user to provide a follow-up request upon providing the at least one media item at block  818 . For example, the user rejects the at least one media item provided at block  818  or request additional information related to the at least, one media item. Blocks  820 - 826  describe aspects where a follow-up spoken request from the user is received and a response to the follow-up spoken request is provided. 
     At block  820 , a determination is made as to whether a domain corresponding to the speech input is one of a plurality of predetermined domains. In particular, only certain predetermined domains are likely to elicit a follow-up request from the user. Thus, to enhance efficiency, the capability to receive follow-up spoken requests from the user is implemented only for certain predetermined domains. For example, the plurality of predetermined domains include domains associated with items having a large amount of metadata, such as the “find media items” domain or the “find restaurant” domain. Items having a large amount of metadata, such as media items and restaurant items frequently elicit follow-up requests from users. In response to determining that a domain corresponding to the speech input, is one of a plurality of predetermined domains, audio input is received at block  820  (e.g., microphone  213  is activated). Conversely, in response to determining that a domain corresponding to the speech input is not one of a plurality of predetermined domains, process  800  forgoes receiving audio input at block  822  (e.g., microphone  213  is not activated). 
     At block  824 , audio input is received. Specifically, the audio input is received upon providing the at least one media item at block  818 . For example, with reference to  FIG. 9A , user device  903  begins receiving audio input via a microphone of user device  903  once the media item “99 Problems” by Jay-Z begins playing on user device  903 . 
     At block  826 , a determination is made as to whether the audio input contains speech. The determination is made while receiving the audio input. In particular, as audio input is received, the audio input is analyzed to determine whether it contains acoustic features that correspond to those of speech. In particular, time domain (e.g., zero crossing rates, short-time energy, spectral energy, or spectral flatness) and/or frequency domain features (e.g., mel-frequency cepstral coefficients, linear predictive cepstral coefficients, or mel-frequency discrete wavelet coefficients) are extracted from the received audio input and compared to a human speech model to determine the likelihood that the audio input contains speech. The audio input is determined to contain speech if the likelihood is determined to exceed a predetermined value. Conversely, the audio input is determined not to contain speech if the likelihood is less than the predetermined value. In response to determining that the audio input does not contain speech, process  800  ceases to receive audio input at block  828  after a predetermined amount of time. For example, with reference to  FIG. 9A , user device  903  ceases to receive audio input after receiving a predetermined duration of audio input that is determined not to contain any speech. 
     In some examples, the predetermined amount of time is based on the degree of ambient noise detected in the audio input. In particular, block  826  includes determining the amount of ambient noise (e.g., background noise) in the audio input. The predetermined amount of time in which audio input not containing any speech is received at block  824  decreases based on detecting a high degree of ambient noise in the audio input. For example, if the amplitude of ambient noise in the audio input is determined not to exceed a predetermined threshold value, process  800  ceases to receive audio input at block  828  after a predetermined amount of time (e.g., 7 seconds). However, if the amplitude of ambient noise in the audio input is determined to exceed a predetermined threshold value, process  800  ceases to receive audio input at block  828  after a second predetermined amount of time (e.g., 4 seconds) that is less than the predetermined amount of time. 
     In response to determining that the audio input contains speech, block  830  is performed. At block  830 , a determination is made as to whether the speech of the audio input corresponds to a same domain as the speech input. The determination includes determining the user intent corresponding to the speech of the audio input. The user intent is determined in a similar manner as described above in block  804 . Determining the user intent corresponding to the speech of the audio input, includes determine a domain corresponding to the speech of the audio input. A determination is then made as to whether the domain corresponding to the speech of the audio input is the same as the domain corresponding to the speech input of block  802 . In response to determining that the speech of the audio input does not correspond to a same domain as the speech input, process  800  forgoes providing a response to the audio input at block  832 . This is desirable to filler out babble noise. For example, with reference to  FIG. 9A , speech input  902  corresponds to the “find media items” domain, if audio input is received while playing the song “99 Problems” by Jay-Z and the audio input contains babble noise that is unrelated to finding media items, then the babble noise is determined to be irrelevant to speech input  902  and no follow-up response would be provided to the user (block  832 ). 
     In response to determining that the speech of the audio input corresponds to a same domain as the speech input, block  834  is performed. At block  834 , a response is provided in accordance with the user intent corresponding to the speech of the audio input. The response is provided in a similar manner as described above with respect to  FIGS. 7A-C . In particular, a structured query is generated based on the determined user intent corresponding to the speech of the audio input. One or more tasks corresponding to the user intent are then performed in accordance with the generated structured query. A response is provided based on the one or more performed tasks. 
     Blocks  820 - 834  are further described with reference to the examples of  FIGS. 9A-B  and  10 . In  FIG. 9A , while user device  903  is playing the obtained at least one media item “99 Problems” by Jay-Z (block  818 ), audio input containing second speech input  904  “Anything but Jay-Z!” is received from user  901  (block  824 ). A user intent corresponding to second speech input  904  is determined (block  830 ). Based on the phrase “Jay-Z” in second speech input  904  and the context of user device  903  playing the media item “99 Problems” by Jay-Z, second speech input  904  is determined to correspond to the same domain as speech input  902 . Specifically, the domain corresponding to speech input  904  is determined to be the “find media items” domain. Further, a determination is made as to whether second speech input  904  corresponds to a rejection of the media item “99 Problems.” The determination is made based on the user intent corresponding to second speech input  904 . In this example, based on interpreting the phrase “Anything but” in the context of the media item “99 Problems” being played, it is determined that second speech input  904  corresponds to the user intent of rejecting the media item “99 Problems” and obtaining alternative recommendations for media items. One or more tasks corresponding to the user intent are then performed (block  834 ). In particular, in response to determining that the second speech input corresponds to a rejection of the at least one media item, the candidate hip-hop media items previously determined and ranked (e.g., at blocks  812  and  814 ) are re-ranked based on the rejection. The re-ranking is similar to the ranking of block  814 , except that unfavorable ranking scores are generated for media items having media parameter {artist}=Jay-Z. For example, the candidate hip-hop media items are re-ranked such that the candidate media item “Tipsy” by J-Kwon is the highest ranked media item among the candidate hip-hop media items whereas the candidate media item “99 Problems” by Jay-Z is one of the lowest ranked media items among the candidate hip-hop media items. Based on this re-ranking, as shown in  FIG. 9B , the media item “Tipsy” by J-Kwon is obtained and played on user device  903 . Furthermore, as discussed above, the user-specific media ranking model is continuously updated based on any subsequent speech input received from the user. Thus, in response to determining that the second speech input corresponds to a rejection of the at least one media item, the user-specific media ranking model is updated in accordance with the rejection. For example, the user-specific media ranking model is updated to subsequently generate less favorable ranking score for candidate media items having media parameter {artist}=Jay-Z. Thus, digital assistant would be less likely to recommend media items associated with the artist Jay-Z when the user subsequently requests to obtain recommendations for media items. 
     Referring now to the example of  FIG. 10 , user  901  provides second speech input  1004  “When was this released” Specifically, while user device  903  plays the media item “She Moves in Her Own Way” by The Kooks, second speech input  1004  is received from user  901  (block  824 ). A user intent corresponding to second speech input  1004  is determined (block  830 ). Based on the word “this” in second speech input  1004  and the context of user device  903  playing the media item “She Moves in Her Own Way,” second speech input  1004  is determined to correspond to the same domain as speech input  902 . Specifically, the domain corresponding to speech input  904  is determined to be the “find media items” domain. Further, in this example, based on interpreting the words “this” and “released” in the context of the media item “She Moves in Her Own Way,” being played, it is determined (block  830 ) that second speech input  904  corresponds to the user intent of obtaining the release date associated with the media item “She Moves in Her Own Way.” In response to this determination, one or more tasks corresponding to the user intent is performed (block  834 ). In particular, the release date of the song “She Moves in Her Own Way” is retrieved (e.g., from media service(s)  120 - 1 ) and provided to the user (block  834 ). For example, as shown in  FIG. 10 , spoken response  1006  is provided at user device  903  to user  901  in accordance with the determined user intent. Specifically, spoken response  1006  indicates that the release date of the song “She Moves in Her Own Way” Is “June 2006.” In some examples, the release data is additionally or alternatively displayed on user device  903  in response to second speech input  1004 . 
     Returning back to block  804 , in response to determining that the speech input of block  802  does not correspond to the user intent of obtaining personalized recommendations for media items, block  836  of  FIG. 8C  is performed. At block  836 , a determination is made as to whether the speech input of block  802  corresponds to the user intent of obtaining media items having a recent release date. As discussed above, the user intent corresponding to the speech input is determined at block  804 . The determination of block  836  is based on the actionable intent node selected in the ontology (e.g., ontology  760 ). If the selected node has a corresponding actionable intent of obtaining media items having a recent release date, then the speech input is determined to correspond to the user intent of obtaining media items having a recent release date. Conversely, if the node has a corresponding actionable intent other than obtaining media items having a recent release date, then the speech input is determined to not correspond to the user intent of obtaining media items having a recent release date. 
     In some examples, determining whether the speech input corresponds to the user intent of obtaining media items having a recent release date includes determining whether the speech input includes one or more of a second plurality of predetermined phrases. In particular, the actionable intent node corresponding to the user intent of obtaining media items having a recent release date is associated with the second plurality of predetermined phrases. The second plurality of predetermined phrases are stored in a vocabulary index (vocabulary index  744 ) in association with the actionable intent node that corresponds to the user intent, of obtaining media items. The second plurality of predetermined phrases include phrases, such as, “new music,” “recently released,” “latest releases,” “just came out,” or the like. Based on the speech input of block  802  containing one or more of the second plurality of predetermined phrases, the speech input is mapped to the actionable intent node that corresponds to the user intent of obtaining media items having a recent release date. Thus, the speech input of block  802  is determined to correspond to the user intent of obtaining media items having a recent release date. For example, with reference to  FIG. 11 , speech input  1102  “Hey Siri, play me some pop music that just came out” is received from user  901 . Based on speech input  1102  containing the phrase “just came out,” the actionable intent node corresponding to the user intent of obtaining media items having a recent release date is selected. Thus, speech input  1102  is determined to correspond to the user intent of obtaining media items having a recent release date. 
     In response to determining that the speech input corresponds to the user intent of obtaining media items having a recent release date, block  838  is performed. Conversely, in response to determining that the speech input does not correspond to the user intent of obtaining media items having a recent release date, process  800  forgoes performing block  838 . For example, as shown in  FIG. 8C , process  800  ends in response to determining that the speech input does not correspond to the user intent of obtaining media items having a recent release date. 
     At block  838 , at least one second media item from a second corpus of media items is obtained. Block  838  is similar to block  806 , except that block  838  is performed using the second corpus of media items rather than the user-specific corpus of media items. Further, block  838  includes blocks similar to blocks  808 - 816 , except again that the blocks are performed with respect to the second corpus of media items rather than the user-specific corpus of media items. The second corpus of media items is, for example, a general corpus of media items that is generated based on the release date of media items. In particular, each media item in the second corpus of media items has a release date that is within a predetermined time range of a current date. For example, the second corpus of media items includes only media items having a release date that is within three months of the current date. In some examples, the second corpus of media items is generated based on other factors, such as the popularity of each media item. 
     At block  840 , the at least one second media item is provided. Block  840  is similar to block  818 . In particular, the at least one second media item is provided at the user device. In some examples, the at least one media item is played at the user device. In other examples, the at least one media item is displayed on the user device (e.g., on touchscreen  212 ) for the user to view and/or select. In yet other examples, the at least one media item is provided in a spoken response to the user. 
     Blocks  838 - 840  are further described with reference to  FIG. 11 . For example, in response to determining that speech input  1102  corresponds to the user intent of obtaining media items having a recent release date, the digital assistant implemented on user device  903  obtains at least one second media item from a second corpus of media items. The second corpus of media items contain only media items having a release date that is within three months of the current date. In the present example, if the current date is Jun. 1, 2016, then each media item in the second corpus of media items has a release date that is not earlier than Mar. 1, 2016. Thus, the at least one second media item obtained from the second corpus of media items also has a release data that is not earlier than Mar. 1, 2016. In this example, the at least one second media item includes the song “Dangerous Woman” by Ariana Grande, which has a release date of Mar. 11, 2016. As shown, the song “Dangerous Woman” is obtained (e.g., from media service(s)  120 - 1 ) and played on user device  903  in response to speech input  1102 . 
     5. Other Electronic Devices 
       FIG. 12  shows a functional block diagram of electronic device  1200  configured in accordance with the principles of the various described examples. The functional blocks of the device are optionally implemented by hardware, software, or a combination of hardware and soft ware to carry out the principles of the various described examples. It is understood by persons of skill in the art that the functional blocks described in  FIG. 12  are optionally combined or separated into sub-blocks to implement the principles of the various described examples. Therefore, the description herein optionally supports any possible combination, separation, or further definition of the functional blocks described herein. 
     As shown in  FIG. 12 , electronic device  1200  includes touch screen display unit  1202  configured to display a graphical user interface and to receive touch input from the user, audio input unit  1204  configured to receive audio input (e.g., speech input), speaker unit  1205  configured to output audio (e.g., speech and/or media content), and communication unit  1206  configured to transmit and receive information. Electronic device  1200  further includes processing unit  1208  coupled to touch screen display unit  1202 , audio input unit  1204 , and communication unit  1206 . In some examples, processing unit  1208  includes receiving unit  1210 , determining unit  1212 , obtaining unit  1214 , providing unit  1216 , ranking unit  1218 , updating unit  1220 , ceasing unit  1222 , forgoing unit  1224 , and selecting unit  1226 . 
     In accordance with some embodiments, processing unit  1208  is configured to receive (e.g., with receiving unit  1210  and via audio input unit  1204 ) from a user, speech input (e.g., speech input of block  802 ) representing a request for one or more media items. Processing unit  1208  is further configured to determine (e.g. with determining unit  1212 ) whether the speech input corresponds to a user intent of obtaining personalized recommendations for media items (e.g., block  804 ). Processing unit  1208  is further configured to, in response to determining that the speech input corresponds to a user intent of obtaining personalized recommendations for media items, obtain (e.g., with obtaining unit  1214 ) at least one media item (e.g., at least one media item of block  806 ) from a user-specific corpus of media items. The user-specific corpus of media items (e.g., user-specific corpus of media items of block  806 ) is generated based on data associated with the user. Processing unit  1208  is further configured to provide (e.g., with providing unit and using touch screen display unit  1202  and/or speaker unit  1205 ) the at least one media item (e.g., block  818 ). 
     In some examples, determining whether the speech input corresponds to a user intent of obtaining personalized recommendations for media items comprises determining whether a number of parameters defined in the speech input is less than a threshold number (e.g., block  804 ). 
     In some examples, determining whether the speech input corresponds to a user intent of obtaining personalized recommendations for media items comprises determining whether the speech input includes one of a plurality of phrases corresponding to the user intent of obtaining personalized recommendations for media (e.g., block  804 ). 
     In some examples, determining whether the speech input corresponds to a user intent of obtaining personalized recommendations for media comprises determining whether the speech input refers to the user (e.g., block  804 ). 
     In some examples, the user-specific corpus of media items (e.g., user-specific corpus of media items of block  806 ) is generated based on media items previously selected or requested by the user. 
     In some examples, the user-specific corpus of media items is generated based on media items previously rejected by the user (e.g., block  806 ). 
     In some examples, the user-specific corpus of media items is generated based on a personal library of media items associated with the user (e.g., block  806 ). 
     In some examples, processing unit  1208  is further configured to rank (e.g., with ranking unit  1218 ) a plurality of candidate media items from the user-specific corpus of media items using a user-specific media ranking model (e.g., block  814 ). The user-specific media ranking model is generated based on a plurality of previous media-related requests from the user. Obtaining the at least one media item includes selecting the at least one media item from the plurality of candidate media items based on the ranking (e.g., block  816 ). 
     In some examples, processing unit  1208  is further configured to, receive (e.g., with receiving unit  1210  and via audio input unit  1204 ) from the user, a second speech input. Processing unit  1208  is further configured to determine (e.g., with determining unit  1212 ) whether the second speech input corresponds to a rejection of the at least one media item. Processing unit  1208  is further configured to, in response to determining that the second speech input corresponds to a rejection of the at least one media item, update (e.g., with updating unit  1220 ) the user-specific media ranking model in accordance with the rejection. 
     In some examples, processing unit  1208  is further configured to re-rank (e.g., with ranking unit  1218 ) the plurality of candidate media items from the user-specific corpus of media items based on the rejection of the at least one media item. Processing unit  1208  is further configured to select (e.g., with selecting unit  1226 ) at least one second media item from the plurality of candidate media items based on the re-ranking. 
     In some examples, the plurality of candidate media items are ranked based on a popularity rating of each media item of the plurality of candidate media items (e.g., block  814 ). 
     In some examples, each media item in the user-specific corpus of media items includes metadata that indicates an activity associated with the media item. The activity is associated with the media item based on a music tempo of the media item. 
     In some examples, each media item in the user-specific corpus of media items includes metadata that indicates a mood associated with the media item. The mood is associated with the media item based on a music key of the media item. 
     In some examples, processing unit  1208  is further configured to determine (e.g., with determining unit  1212 ) whether the speech input defines an occasion associated with a time period (e.g., block  804 ). Processing unit  1208  is further configured to, in response to determining that the speech input defines an occasion associated with a time period, obtain (e.g., with obtaining unit  1214 ) the at least one media item based the occasion, wherein the at least one media item includes metadata indicating the occasion (e.g., block  806 ). 
     In some examples, processing unit  1208  is further configured to determine (e.g., with determining unit  1212 ) whether the speech input defines an editorial list associated with a media establishment (e.g., block  804 ). Processing unit  1208  is further configured to, in response to determining that the speech input defines an editorial list associated with a media establishment, obtain (e.g., with obtaining unit) the at least one media item based on the editorial list associated with the media establishment (e.g., block  806 ). The at least one media item includes metadata indicating the editorial list associated with the media establishment. 
     In some examples, processing unit  1208  is further configured to determine (e.g., with determining unit  1212 ) whether the speech input defines a mood (e.g., block  804 ). Processing unit  1208  is further configured to, in response to determining that the speech input defines a mood, obtain (e.g., with obtaining unit  1214 ) the at least one media item based on the mood, where the at least one media item includes metadata indicating the mood (e.g., block  806 ). 
     In some examples, processing unit  1208  is further configured to determine (e.g., with determining unit  1212 ) whether the speech input defines an activity (e.g., block  804 ). Processing unit  1208  is further configured to, in response to determining that the speech input defines an activity, obtain (e.g., with obtaining unit  1214 ) the at least one media item based on the activity, wherein the at least one media item includes metadata indicating the activity (e.g., block  806 ). 
     In some examples, processing unit  1208  is further configured to determine (e.g., with determining unit  1212 ) whether the speech input defines a time period (e.g., block  804 ). Processing unit  1208  is further configured to, in response to determining that the speech input defines a time period, determine (e.g., with determining unit  1212 ) whether the speech input defines a genre in association with the time period. Processing unit  1208  is further configured to, in response to determining that the speech input defines a genre in association with the time period, determine (e.g., with determining unit  1212 ) a subgenre based on the time period and the genre. The at least one media item is obtained based on the subgenre, and the at least one media item includes metadata indicating the subgenre (e.g., block  806 ). 
     In some examples, the speech input defines a category of media items and obtaining the at least one media item includes obtaining a plurality of media items associated with the category of media items. Processing unit  1208  is further configured to determine (e.g., with determining unit  1212 ) a degree of familiarity of the user with the category of media items (e.g., degree of familiarity of block  816 ). An average popularity rating of the plurality of media items is based on the degree of familiarity of the user with the category of media items. 
     In some examples, processing unit  1208  is further configured to determine (e.g., with determining unit  1212 ) an identity of the user by performing speaker identification using the speech input. Processing unit  1208  is further configured to, based on the determined identity of the user, determine (e.g., with determining unit  1212 ) the user-specific corpus of media items from a plurality of user-specific corpuses of media items. 
     In some examples, obtaining the at least one media item comprises sending an encrypted token to a remote server. The encrypted token contains user identification information. The encrypted token is required to access the user-specific corpus of media items via the remote server. 
     In some examples, processing unit  1208  is further configured to determine (e.g., with determining unit  1212 ) whether a domain (e.g., domain of block  820 ) corresponding to the speech input is one of a plurality of predetermined domains. Processing unit  1208  is further configured to, in response to determining that a domain corresponding to the speech input is one of a plurality of predetermined domains, receive (e.g., with receiving unit  1210  and via audio input unit  1204 ) audio input (e.g., audio input of block  824 ) upon providing the at least one media item. Processing unit  1208  is further configured to determine (e.g., with determining unit  1212 ) whether the audio input contains speech (e.g., block  826 ). Processing unit  1208  is further configured to, in response to determining that the audio input does not contain speech, cease (e.g., with ceasing unit  1222 ) to receive audio input after a predetermined amount of time (e.g., block  828 ). 
     In some examples, processing unit  1208  is further configured to, in response to determining that the audio input contains speech, determine (e.g., with determining unit  1212 ) whether the speech of the audio input corresponds to a same domain as the speech input (e.g., block  830 ). Processing unit  1208  is further configured to, in response to determining that the speech of the audio input corresponds to a same domain as the speech input, determine (e.g., with determining unit  1212 ) a user intent (e.g., user intent of block  820 ) corresponding to the speech of the audio input. Processing unit  1208  is further configured to provide (with providing unit  1216 ) a response (e.g., response of block  834 ) to the audio input in accordance with the user intent corresponding to the speech of the audio input. 
     In some examples, processing unit  1208  is further configured to, in response to determining that the speech of the audio input does not correspond to a same domain as the speech input, forgo (e.g., with forgoing unit  1224 ) providing a response to the audio input (e.g., block  832 ). 
     In some examples, the predetermined amount of time is based on a degree of ambient noise detected in the audio input. 
     In some examples, providing the at least one media item comprises playing a media item. Processing unit  1208  is further configured to, while playing the media item, receive (e.g., with receiving unit  1210  and via audio input unit  1204 ) a third speech input (e.g., speech input in audio input of block  824 ). Processing unit  1208  is further configured to, based on the playing media item and the third speech input, determine (e.g., with determining unit  1212 ) a user intent (e.g., user intent of block  820 ) that corresponds to the third speech input. Processing unit  1208  is further configured to, provide (e.g., with providing unit  1216 ) a response (e.g., response of block  834 ) in accordance with the user intent that corresponds to the third speech input. 
     In some examples, processing unit  1208  is further configured to, in response to determining that the speech input does not correspond to a user intent of obtaining personalized recommendations for media items, determine (e.g., with determining unit  1212 ) whether the speech input corresponds to a user intent of obtaining media items having a recent release date (e.g., block  836 ). Processing unit  1208  is further configured to, in response to determining that the speech input corresponds to a user intent of obtaining media items having a recent release date, obtain (e.g., with obtaining unit  1214 ) at least one second media item (e.g., at least one second media item of block  838 ) from a second corpus of media items. Each media item in the second corpus of media items has a release date that is within a predetermined time range of a current date. Processing unit  1208  is further configured to provide (e.g., with providing unit  1216 ) the at least one second media item (e.g., block  840 ). 
     In some examples, determining whether the speech input corresponds to a user intent of obtaining media items having a recent release date comprises determining whether the speech input includes one of a second plurality of phrases corresponding to the user intent of obtaining media items having a recent release date (e.g., block  836 ). 
     In some examples, processing unit  1208  is further configured to determine (e.g., with determining unit  1212 ) a political preference associated with the user (e.g., block  814 ). The determining is based on previous media items requested or consumed by the user. The at least one media item is obtained based on the determined political preference. 
     In some examples, processing unit  1208  is further configured to determine (e.g., with determining unit  1212 ) a technical proficiency associated with the user (e.g., block  814 ). The determining is based on previous media items requested or consumed by the user. The at least one media item is obtained based on the determined technical proficiency. 
     The operations described above with reference to  FIGS. 8A-C  are optionally implemented by components depicted in  FIGS. 1-4, 6A -B, and  7 A-C. For example, the operations of process  800  may be implemented by one or more of operating system  718 , applications module  724 , I/O processing module  728 , STT processing module  730 , natural language processing module  732 , vocabulary index  744 , task flow processing module  736 , service processing module  738 , media service(s)  120 - 1 , or processor(s)  220 ,  410 ,  704 . It would be clear to a person having ordinary skill in the art how other processes are implemented based on the components depicted in  FIGS. 1-4, 6A -B, and  7 A-C. 
     In accordance with some implementations, a computer-readable storage medium (e.g., a non-transitory computer readable storage medium) is provided, the computer-readable storage medium storing one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for performing any of the methods or processes described herein. 
     In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises means for performing any of the methods or processes described herein. 
     In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises a processing unit configured to perform any of the methods or processes described herein. 
     In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises one or more processors and memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for performing any of the methods or processes described herein. 
     The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the techniques and their practical applications. Others skilled in the art are thereby enabled to best utilize the techniques and various embodiments with various modifications as are suited to the particular use contemplated. 
     Although the disclosure and examples have been fully described with reference to the accompanying drawings, it is to be noted that various changes and modifications will become apparent, to those skilled in the art. Such changes and modifications are to be understood as being included within the scope of the disclosure and examples as defined by the claims. 
     As described above, one aspect of the present technology is the gathering and use of data available from various sources to improve the delivery to users of invitational content or any other content that may be of interest to them. The present disclosure contemplates that in some instances, this gathered data may include personal information data that uniquely identifies or can be used to contact or locate a specific person. Such personal information data can include demographic data, location-based data, telephone numbers, email addresses, home addresses, or any other identifying information. 
     The present disclosure recognizes that the use of such personal information data, in the present technology, can be used to the benefit of users. For example, the personal information data can be used to deliver targeted content that is of greater interest to the user. Accordingly, use of such personal information data enables calculated control of the delivered content. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure. 
     The present disclosure further contemplates that the entities responsible for the collection, analysis, disclosure, transfer, storage, or other use of such personal information data will comply with well-established privacy policies and/or privacy practices. In particular, such entities should implement and consistently use privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining personal information data private and secure. For example, personal information from users should be collected for legitimate and reasonable uses of the entity and not shared or sold outside of those legitimate uses. Further, such collection should occur only after receiving the informed consent of the users. Additionally, such entities would take any needed steps for safeguarding and securing access to such personal information data and ensuring that others with access to the personal information data adhere to their privacy policies and procedures. Further, such entities can subject themselves to evaluation by third parties to certify their adherence to widely accepted privacy policies and practices. 
     Despite the foregoing, the present disclosure also contemplates embodiments in which users selectively block the use of, or access to, personal information data. That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, in the case of advertisement delivery services, the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services. In another example, users can select not to provide location information for targeted content delivery services. In yet another example, users can select to not provide precise location information, but permit the transfer of location zone information. 
     Therefore, although the present disclosure broadly covers use of personal information data to implement one or more various disclosed embodiments, the present disclosure also contemplates that the various embodiments can also be implemented without the need for accessing such personal information data. That is, the various embodiments of the present technology are not rendered inoperable due to the lack of all or a portion of such personal information data. For example, content, can be selected and delivered to users by inferring preferences based on non-personal information data or a bare minimum amount of personal information, such as the content being requested by the device associated with a user, other non-personal information available to the content delivery services, or publically available information.

Metadata:
Filing Date: 20180713
Publication Date: 20210720
Grant Date: 20210720
Priority Date: 20160608
Inventors: ORR, RYAN M.
BERNARDO, Matthew P.
MANDEL, DANIEL J.
Assignee: APPLE INC
CPC Classifications: [{"code": "G06F16/433", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F16/00", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F16/90332", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L25/84", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/433", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/433", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L15/1815", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/167", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L17/00", "inventive": false, "first": false, "tree": "[]"}, {"code": "G10L15/22", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F3/167", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/90332", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L15/22", "inventive": true, "first": true, "tree": "[]"}, {"code": "G10L15/22", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/435", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L17/00", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06F16/435", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/90332", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L25/84", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/435", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L17/22", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L15/1815", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L17/22", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L15/22", "inventive": true, "first": true, "tree": "[]"}, {"code": "G10L15/1815", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/167", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/433", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L17/22", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L25/84", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L17/00", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06F16/90332", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/435", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 60573004