PATENT DOCUMENT

Publication Number: US-10733375-B2
Application Number: US-201816012531-A
Country: US
Kind Code: B2

Title: Knowledge-based framework for improving natural language understanding

Abstract:
Systems and processes for operating an intelligent automated assistant are provided. An example process receives natural language input and determines a first and a second parsing result for the natural language input. The first and the second parsing results include respective mappings of one or more properties of a domain corresponding to the natural language input to one or more words of the natural language input. The process determines whether the second parsing result corresponds to a data item in a knowledge base, and in accordance with determining that the second parsing result corresponds to the data item in the knowledge base, the process ranks the second parsing result higher than the first parsing result. Based on the ranking, the process generates a task flow using the second parsing result and executes the task flow to provide an output based on the data item.

Claims:
What is claimed is: 
     
       1. 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:
 receiving natural language input; 
 determining, from a plurality of domains, a domain corresponding to the natural language input; 
 in accordance with determining that the domain corresponds to a predetermined type of domain:
 determining a first and a second parsing result for the natural language input, wherein each of the first and the second parsing results comprises a respective mapping of one or more properties of the domain to one or more words of the natural language input and wherein a first confidence score of the first parsing result is higher than a second confidence score of the second parsing result; 
 determining whether the first parsing result corresponds to any of a plurality of data items in a knowledge base; 
 determining whether the second parsing result corresponds to a data item of the plurality of data items in the knowledge base; and 
 in accordance with a determination that the first parsing result does not correspond to any of the plurality of data items in the knowledge base and in accordance with a determination that the second parsing result corresponds to the data item of the plurality of data items in the knowledge base, ranking, using the knowledge base, the second parsing result higher than the first parsing result; 
 
 after ranking, using the knowledge base, the second parsing result higher than the first parsing result, generating a task flow using the second parsing result based on the ranking; and 
 executing the task flow to provide an output based on the data item. 
 
 
     
     
       2. The device of  claim 1 , wherein the predetermined type of domain is a media domain. 
     
     
       3. The device of  claim 1 , wherein the one or more properties of the domain each correspond to a named entity. 
     
     
       4. The device of  claim 1 , wherein the first parsing result comprises a mapping of a first property of the domain to a first word of the natural language input, wherein the second parsing result comprises a mapping of a second property of the domain to the first word, and wherein the first property is different from the second property. 
     
     
       5. The device of  claim 1 , wherein the first parsing result and the second parsing result are each generated by a neural network, and wherein the first confidence score and the second confidence score are each generated by the neural network. 
     
     
       6. The device of  claim 5 , wherein the first parsing result and the second parsing result are each generated by a first output layer of the neural network, and wherein a third parsing result is generated by a second output layer of the neural network, the second output layer differing from the first output layer. 
     
     
       7. The device of  claim 1 , the one or more programs further including instructions for:
 determining a third parsing result for the natural language input, wherein the third parsing result comprises a mapping of a set of one or more properties of the domain to the one or more words of the natural language input. 
 
     
     
       8. The device of  claim 7 , the one or more programs further including instructions for:
 in accordance with ranking the second parsing result higher than the first parsing result, generating a final parsing result for the natural language input based on the second parsing result and the third parsing result, wherein generating the task flow using the second parsing result comprises generating the task flow using the final parsing result. 
 
     
     
       9. The device of  claim 8 , wherein the third parsing result comprises a mapping of a third property of the domain to a second word of the natural language input, wherein the second parsing result comprises a mapping of a fourth property of the domain to the second word, and wherein generating the final parsing result comprises:
 determining whether the second confidence score of the second parsing result exceeds a confidence threshold; and 
 in accordance with a determination that the second confidence score of the second parsing result exceeds the confidence threshold, using the mapping of the fourth property of the domain to the second word to generate the final parsing result. 
 
     
     
       10. The device of  claim 9 , wherein using the mapping of the fourth property of the domain to the second word to generate the final parsing result is performed in accordance with a determination that the third property of the domain is of a predetermined type. 
     
     
       11. The device of  claim 1 , wherein the second parsing result comprises a mapping of a fifth property of the domain to a first set of one or more words of the natural language input, wherein the data item is associated with a mapping of a sixth property of the domain to one or more words, and wherein determining whether the second parsing result corresponds to the data item of the plurality of data items in the knowledge base comprises determining whether the mapping of the fifth property of the domain to the first set of one or more words of the natural language input matches the mapping of the sixth property of the domain to the one or more words. 
     
     
       12. The device of  claim 1 , the one or more programs further including instructions for:
 receiving an updated natural language parsing model of the domain, wherein the first parsing result and the second parsing result are determined using an initial natural language parsing model of the domain, and wherein the updated natural language parsing model is trained using the natural language input and the data item. 
 
     
     
       13. The device of  claim 12 , wherein the updated natural language parsing model is received in accordance with a determination that the output satisfies a predetermined criterion. 
     
     
       14. The device of  claim 13 , wherein the predetermined criterion comprises a duration for which the output is provided. 
     
     
       15. A method for processing natural language requests, the method comprising:
 at an electronic device with one or more processors and memory:
 receiving natural language input; 
 determining, from a plurality of domains, a domain corresponding to the natural language input; 
 in accordance with determining that the domain corresponds to a predetermined type of domain:
 determining a first and a second parsing result for the natural language input, wherein each of the first and the second parsing results comprises a respective mapping of one or more properties of the domain to one or more words of the natural language input and wherein a first confidence score of the first parsing result is higher than a second confidence score of the second parsing result; 
 determining whether the first parsing result corresponds to any of a plurality of data items in a knowledge base; 
 determining whether the second parsing result corresponds to a data item of the plurality of data items in the knowledge base; and 
 in accordance with a determination that the first parsing result does not correspond to any of the plurality of data items in the knowledge base and in accordance with a determination that the second parsing result corresponds to the data item of the plurality of data items in the knowledge base, ranking, using the knowledge base, the second parsing result higher than the first parsing result; 
 
 after ranking, using the knowledge base, the second parsing result higher than the first parsing result, generating a task flow using the second parsing result based on the ranking; and 
 executing the task flow to provide an output based on the data item. 
 
 
     
     
       16. 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 a first electronic device, cause the first electronic device to:
 receive natural language input; 
 determine, from a plurality of domains, a domain corresponding to the natural language input; 
 in accordance with determining that the domain corresponds to a predetermined type of domain:
 determine a first and a second parsing result for the natural language input, wherein each of the first and the second parsing results comprises a respective mapping of one or more properties of the domain to one or more words of the natural language input and wherein a first confidence score of the first parsing result is higher than a second confidence score of the second parsing result; 
 determine whether the first parsing result corresponds to any of a plurality of data items in a knowledge base; 
 determine whether the second parsing result corresponds to a data item of the plurality of data items in the knowledge base; and 
 in accordance with a determination that the first parsing result does not correspond to any of the plurality of data items in the knowledge base and in accordance with a determination that the second parsing result corresponds to the data item of the plurality of data items in the knowledge base, rank, using the knowledge base, the second parsing result higher than the first parsing result; 
 
 after ranking, using the knowledge base, the second parsing result higher than the first parsing result, generate a task flow using the second parsing result based on the ranking; and 
 execute the task flow to provide an output based on the data item. 
 
     
     
       17. The method of  claim 15 , wherein the predetermined type of domain is a media domain. 
     
     
       18. The method of  claim 15 , wherein the one or more properties of the domain each correspond to a named entity. 
     
     
       19. The method of  claim 15 , wherein the first parsing result comprises a mapping of a first property of the domain to a first word of the natural language input, wherein the second parsing result comprises a mapping of a second property of the domain to the first word, and wherein the first property is different from the second property. 
     
     
       20. The method of  claim 15 , wherein the first parsing result and the second parsing result are each generated by a neural network, and wherein the first confidence score and the second confidence score are each generated by the neural network. 
     
     
       21. The method of  claim 20 , wherein the first parsing result and the second parsing result are each generated by a first output layer of the neural network, and wherein a third parsing result is generated by a second output layer of the neural network, the second output layer differing from the first output layer. 
     
     
       22. The method of  claim 15 , further comprising:
 determining a third parsing result for the natural language input, wherein the third parsing result comprises a mapping of a set of one or more properties of the domain to the one or more words of the natural language input. 
 
     
     
       23. The method of  claim 22 , further comprising:
 in accordance with ranking the second parsing result higher than the first parsing result, generating a final parsing result for the natural language input based on the second parsing result and the third parsing result, wherein generating the task flow using the second parsing result comprises generating the task flow using the final parsing result. 
 
     
     
       24. The method of  claim 23 , wherein the third parsing result comprises a mapping of a third property of the domain to a second word of the natural language input, wherein the second parsing result comprises a mapping of a fourth property of the domain to the second word, and wherein generating the final parsing result comprises:
 determining whether the second confidence score of the second parsing result exceeds a confidence threshold; and 
 in accordance with a determination that the second confidence score of the second parsing result exceeds the confidence threshold, using the mapping of the fourth property of the domain to the second word to generate the final parsing result. 
 
     
     
       25. The method of  claim 24 , wherein using the mapping of the fourth property of the domain to the second word to generate the final parsing result is performed in accordance with a determination that the third property of the domain is of a predetermined type. 
     
     
       26. The method of  claim 15 , wherein the second parsing result comprises a mapping of a fifth property of the domain to a first set of one or more words of the natural language input, wherein the data item is associated with a mapping of a sixth property of the domain to one or more words, and wherein determining whether the second parsing result corresponds to the data item of the plurality of data items in the knowledge base comprises determining whether the mapping of the fifth property of the domain to the first set of one or more words of the natural language input matches the mapping of the sixth property of the domain to the one or more words. 
     
     
       27. The method of  claim 15 , further comprising:
 receiving an updated natural language parsing model of the domain, wherein the first parsing result and the second parsing result are determined using an initial natural language parsing model of the domain, and wherein the updated natural language parsing model is trained using the natural language input and the data item. 
 
     
     
       28. The method of  claim 27 , wherein the updated natural language parsing model is received in accordance with a determination that the output satisfies a predetermined criterion. 
     
     
       29. The method of  claim 28 , wherein the predetermined criterion comprises a duration for which the output is provided. 
     
     
       30. The non-transitory computer-readable storage medium of  claim 16 , wherein the predetermined type of domain is a media domain. 
     
     
       31. The non-transitory computer-readable storage medium of  claim 16 , wherein the one or more properties of the domain each correspond to a named entity. 
     
     
       32. The non-transitory computer-readable storage medium of  claim 16 , wherein the first parsing result comprises a mapping of a first property of the domain to a first word of the natural language input, wherein the second parsing result comprises a mapping of a second property of the domain to the first word, and wherein the first property is different from the second property. 
     
     
       33. The non-transitory computer-readable storage medium of  claim 16 , wherein the first parsing result and the second parsing result are each generated by a neural network, and wherein the first confidence score and the second confidence score are each generated by the neural network. 
     
     
       34. The non-transitory computer-readable storage medium of  claim 33 , wherein the first parsing result and the second parsing result are each generated by a first output layer of the neural network, and wherein a third parsing result is generated by a second output layer of the neural network, the second output layer differing from the first output layer. 
     
     
       35. The non-transitory computer-readable storage medium of  claim 16 , wherein the one or more programs further comprise instructions, which when executed by the one or more processors of the first electronic device, cause the first electronic device to:
 determine a third parsing result for the natural language input, wherein the third parsing result comprises a mapping of a set of one or more properties of the domain to the one or more words of the natural language input. 
 
     
     
       36. The non-transitory computer-readable storage medium of  claim 35 , wherein the one or more programs further comprise instructions, which when executed by the one or more processors of the first electronic device, cause the first electronic device to:
 in accordance with ranking the second parsing result higher than the first parsing result, generate a final parsing result for the natural language input based on the second parsing result and the third parsing result, wherein generating the task flow using the second parsing result comprises generating the task flow using the final parsing result. 
 
     
     
       37. The non-transitory computer-readable storage medium of  claim 16 , wherein the third parsing result comprises a mapping of a third property of the domain to a second word of the natural language input, wherein the second parsing result comprises a mapping of a fourth property of the domain to the second word, and wherein generating the final parsing result comprises:
 determining whether the second confidence score of the second parsing result exceeds a confidence threshold; and 
 in accordance with a determination that the second confidence score of the second parsing result exceeds the confidence threshold, using the mapping of the fourth property of the domain to the second word to generate the final parsing result. 
 
     
     
       38. The non-transitory computer-readable storage medium of  claim 37 , wherein using the mapping of the fourth property of the domain to the second word to generate the final parsing result is performed in accordance with a determination that the third property of the domain is of a predetermined type. 
     
     
       39. The non-transitory computer-readable storage medium of  claim 16 , wherein the second parsing result comprises a mapping of a fifth property of the domain to a first set of one or more words of the natural language input, wherein the data item is associated with a mapping of a sixth property of the domain to one or more words, and wherein determining whether the second parsing result corresponds to the data item of the plurality of data items in the knowledge base comprises determining whether the mapping of the fifth property of the domain to the first set of one or more words of the natural language input matches the mapping of the sixth property of the domain to the one or more words. 
     
     
       40. The non-transitory computer-readable storage medium of  claim 16 , wherein the one or more programs further comprise instructions, which when executed by the one or more processors of the first electronic device, cause the first electronic device to:
 receive an updated natural language parsing model of the domain, wherein the first parsing result and the second parsing result are determined using an initial natural language parsing model of the domain, and wherein the updated natural language parsing model is trained using the natural language input and the data item. 
 
     
     
       41. The non-transitory computer-readable storage medium of  claim 40 , wherein the updated natural language parsing model is received in accordance with a determination that the output satisfies a predetermined criterion. 
     
     
       42. The non-transitory computer-readable storage medium of  claim 41 , wherein the predetermined criterion comprises a duration for which the output is provided.

Description:
This application claims priority to U.S. Application No. 62/624,464, entitled “KNOWLEDGE-BASED FRAMEWORK FOR IMPROVING NATURAL LANGUAGE UNDERSTANDING,” filed on Jan. 31, 2018, the content of which is hereby incorporated by reference herein in its entirety. 
    
    
     FIELD 
     This relates generally to intelligent automated assistants and, more specifically, to using knowledge bases to improve the natural language understanding capabilities of intelligent automated assistants. 
     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 natural language input representing a user request to a digital assistant operating on an electronic device. The digital assistant can interpret the user&#39;s intent from the 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. 
     Digital assistants can interpret natural language input by parsing the natural language input to obtain one or more natural language parsing results. The one or more parsing results can identify relevant semantic information in the natural language input and can be used to generate one or more tasks corresponding to the natural language input. In some instances, the one or more generated tasks, when performed by the digital assistant, may fail to accomplish the user request represented by the natural language input. This may be because the one or more parsing results contain insufficient and/or erroneous semantic information about the natural language input. Accordingly, improved natural language processing techniques are desirable. 
     SUMMARY 
     Example methods are disclosed herein. An example method includes, at an electronic device having one or more processors and memory, receiving natural language input and determining, from a plurality of domains, a domain corresponding to the natural language input. The method further includes, in accordance with determining that the domain corresponds to a predetermined type of domain, determining a first and a second parsing result for the natural language input, wherein each of the first and the second parsing results comprises a respective mapping of one or more properties of the domain to one or more words of the natural language input, and wherein a first confidence score of the first parsing result is higher than a second confidence score of the second parsing result. The method further includes determining whether the second parsing result corresponds to a data item of a plurality of data items in a knowledge base, and in accordance with a determination that the second parsing result corresponds to the data item of the plurality of data items in the knowledge base, ranking the second parsing result higher than the first parsing result. The method further includes, based on the ranking, generating a task flow using the second parsing result and executing the task flow to provide an output based on the data item. 
     An example method includes at an electronic device with one or more processors and memory, obtaining a natural language input and metadata corresponding to an output provided responsive to the natural language input, wherein the natural language input corresponds to a domain of a predetermined type, the output includes a data item, and the metadata includes a set of attributes corresponding to the data item, the set of attributes defining a set of values for a respective set of properties of the domain. The method further includes determining, from the metadata, whether the output satisfies a predetermined criterion and, in accordance with a determination that the output satisfies the predetermined criterion, comparing the set of values to the natural language input to determine a parsing result, the parsing result mapping one or more words of the natural language input to one or more properties of the respective set of properties of the domain. The method further includes providing the natural language input and the determined parsing result to generate a natural language parsing model of the domain. 
     Exemplary non-transitory computer-readable media are disclosed herein. An example non-transitory computer-readable storage medium stores one or more programs. The one or more programs comprise instructions which, when executed by one or more processors of an electronic device, cause the electronic device to receive natural language input; determine, from a plurality of domains, a domain corresponding to the natural language input; in accordance with determining that the domain corresponds to a predetermined type of domain, determine a first and a second parsing result for the natural language input, wherein each of the first and the second parsing results comprises a respective mapping of one or more properties of the domain to one or more words of the natural language input, and wherein a first confidence score of the first parsing result is higher than a second confidence score of the second parsing result; determine whether the second parsing result corresponds to a data item of a plurality of data items in a knowledge base; in accordance with a determination that the second parsing result corresponds to the data item of the plurality of data items in the knowledge base, rank the second parsing result higher than the first parsing result; based on the ranking, generate a task flow using the second parsing result; and execute the task flow to provide an output based on the data item. 
     An example non-transitory computer readable storage medium stores one or more programs. The one or more programs comprise instructions which, when executed by one or more processors of an electronic device, cause the electronic device to obtain a natural language input and metadata corresponding to an output provided responsive to the natural language input, wherein the natural language input corresponds to a domain of a predetermined type, the output includes a data item, and the metadata includes a set of attributes corresponding to the data item, the set of attributes defining a set of values for a respective set of properties of the domain; determine from the metadata whether the output satisfies a predetermined criterion; in accordance with a determination that the output satisfies the predetermined criterion, compare the set of values to the natural language input to determine a parsing result, the parsing result mapping one or more words of the natural language input to one or more properties of the respective set of properties of the domain; and provide the natural language input and the determined parsing result to generate a natural language parsing model of the domain. 
     Exemplary electronic devices are disclosed herein. An example electronic device comprises one or more processors; a memory; and one or more programs, where 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 receiving natural language input; determining, from a plurality of domains, a domain corresponding to the natural language input; in accordance with determining that the domain corresponds to a predetermined type of domain, determining a first and a second parsing result for the natural language input, wherein each of the first and the second parsing results comprises a respective mapping of one or more properties of the domain to one or more words of the natural language input, and wherein a first confidence score of the first parsing result is higher than a second confidence score of the second parsing result; determining whether the second parsing result corresponds to a data item of a plurality of data items in a knowledge base; in accordance with a determination that the second parsing result corresponds to the data item of the plurality of data items in the knowledge base, ranking the second parsing result higher than the first parsing result based on the ranking, generating a task flow using the second parsing result; and executing the task flow to provide an output based on the data item. 
     An example electronic device comprises one or more processors; a memory; and one or more programs, where 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 obtaining a natural language input and metadata corresponding to an output provided responsive to the natural language input, wherein the natural language input corresponds to a domain of a predetermined type, the output includes a data item, and the metadata includes a set of attributes corresponding to the data item, the set of attributes defining a set of values for a respective set of properties of the domain; determining, from the metadata, whether the output satisfies a predetermined criterion; in accordance with a determination that the output satisfies the predetermined criterion, comparing the set of values to the natural language input to determine a parsing result, the parsing result mapping one or more words of the natural language input to one or more properties of the respective set of properties of the domain; and providing the natural language input and the determined parsing result to generate a natural language parsing model of the domain. 
     An example electronic device comprises means for receiving natural language input; determining, from a plurality of domains, a domain corresponding to the natural language input; in accordance with determining that the domain corresponds to a predetermined type of domain, determining a first and a second parsing result for the natural language input, wherein each of the first and the second parsing results comprises a respective mapping of one or more properties of the domain to one or more words of the natural language input, and wherein a first confidence score of the first parsing result is higher than a second confidence score of the second parsing result; determining whether the second parsing result corresponds to a data item of a plurality of data items in a knowledge base; in accordance with a determination that the second parsing result corresponds to the data item of the plurality of data items in the knowledge base, ranking the second parsing result higher than the first parsing result based on the ranking, generating a task flow using the second parsing result; and executing the task flow to provide an output based on the data item. 
     An example electronic device comprises means for obtaining a natural language input and metadata corresponding to an output provided responsive to the natural language input, wherein the natural language input corresponds to a domain of a predetermined type, the output includes a data item, and the metadata includes a set of attributes corresponding to the data item, the set of attributes defining a set of values for a respective set of properties of the domain; determining, from the metadata, whether the output satisfies a predetermined criterion; in accordance with a determination that the output satisfies the predetermined criterion, comparing the set of values to the natural language input to determine a parsing result, the parsing result mapping one or more words of the natural language input to one or more properties of the respective set of properties of the domain; and providing the natural language input and the determined parsing result to generate a natural language parsing model of the domain. 
     Determining one or more parsing results for natural language input and determining whether the one or more parsing results for the natural language input correspond to respective data items of a plurality of data items in a knowledge base allow an electronic device to validate and/or rank parsing results using data items contained in the knowledge base. Accordingly, parsing results determined to correspond to data items in a knowledge base accessible to the electronic device may be used to execute tasks. This reduces the chance that the electronic device incorrectly, or fails to, provide data items responsive to natural language inputs requesting data items and also increases the accuracy of the response provided to the natural language inputs. Improving the accuracy of the response in this manner enhances the operability of the electronic device and makes the user-device interface more efficient (e.g., by improving interpretation of user requests, by reducing mistakes in interpreting natural language input, and by reducing repetitive user inputs) which, additionally, reduces power usage and improves battery life of the device by enabling the user to user the device more quickly and efficiently. 
     Obtaining a natural language input and metadata corresponding to an output provided responsive to the natural language input, determining whether the output satisfies a predetermined criterion, and determining a parsing result for the natural language input using the metadata allows an electronic device to generate accurate parsing results that can be used to generate and/or train natural language parsing models. In particular, the above techniques allow the electronic device to use information contained in the metadata to infer whether the output provided responsive to the natural language input was correct. If the output is inferred to be correct, the metadata corresponding to the output can be used to determine an accurate parsing result for the natural language input. Determining accurate parsing results in this manner allows the electronic device to collect large amounts of accurate training data (e.g., the accurate parsing results) and to train natural language parsing models using the training data. Collecting accurate training data and training natural language parsing models in this manner improves the accuracy of parsing results, and therefore the accuracy of the response provided to natural language inputs. Improving the accuracy of response provided to natural language inputs enhances the operability of the electronic device and makes the user-device interface more efficient (e.g., by improving interpretation of user requests, by reducing mistakes in interpreting natural language input, and by reducing repetitive user inputs) which, additionally, reduces power usage and improves battery life of the device by enabling the user to user the device more quickly and efficiently. 
    
    
     
       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. 
         FIG. 8  illustrates a natural language parsing system, according to various examples. 
         FIG. 9  illustrates a neural network configured to generate natural language parsing results for natural language input, according to various examples. 
         FIG. 10  illustrates a process for using a knowledge base to perform natural language processing, according to various examples. 
         FIG. 11  illustrates a process for natural language parsing model generation based on user engagement data, according to various examples. 
         FIG. 12  illustrates parsing results determined for natural language input, according to various examples. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description of examples, reference is made to the accompanying drawings in which are 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. 
     Digital assistants can determine one or more parsing results for user natural language inputs to generate task flows to accomplish tasks requested by the user. The parsing results depend on a domain corresponding to the natural language input and map one or more properties of the domain to one or more words of the natural language input. For example, the natural language input “play Hello by Adele” can be determined to correspond to a media domain. In this example, a first parsing result can map “Hello by Adele” to the “musicTitle” property of the media domain and a second parsing result can map “Hello” to the “musicTitle” property of the media domain. 
     In the present example, a task flow generated using the first parsing result mapping the “musicTitle” domain property to “Hello by Adele” can lead to an error message (e.g., “Sorry I couldn&#39;t find the song “Hello by Adele”) when executed because there is no song titled “Hello by Adele.” However, a task flow generated using the second parsing result can correctly cause playback of the song titled “Hello” by the artist “Adele” when executed because there is a song titled “Hello” by the artist “Adele.” Accordingly, before generating task flows using one or more parsing results for natural language input, it may be desirable to validate whether the one or more parsing results correspond to a data item (e.g., a song) available to the digital assistant. 
     Thus, the present disclosure contemplates using knowledge bases (e.g., information databases comprising data items, such as the iTunes® database by Apple Inc.) to rank and/or validate parsing results before generating and executing task flows based on the parsing results. For example, it can be determined whether the first and the second parsing results for the natural language input “play Hello by Adele” respectively correspond to a data item in a knowledge base. Here, the first parsing result mapping the domain property “musicTitle” to “Hello by Adele” can be determined to not correspond to any data items in the knowledge base, while the second parsing result mapping the domain property “musicTitle” to “Hello” can be determined to correspond to a data item in the knowledge base. Accordingly, the second parsing result can be ranked higher than the first parsing result, and the second parsing result can be used to generate a task flow. The task flow generated using the second parsing result can cause correct playback of the song “Hello” by the artist “Adele” when executed. 
     The present disclosure further contemplates that parsing results correctly determined for natural language inputs can be used to generate and/or train natural language parsing models that generate parsing results. For example, the correct parsing result mapping “musicTitle” to “Hello” and the natural language input “play Hello by Adele” can be provided to train a natural language parsing model so that subsequent parsing results for the natural language input “play Hello by Adele” correctly map “musicTitle” to “Hello,” instead of incorrectly mapping “musicTitle” to “Hello by Adele.” 
     To determine correct parsing results for a natural language input, the present disclosure contemplates using “user engagement data” corresponding to outputs provided responsive to the natural language input. User engagement data corresponding to outputs can indicate that the output was played for greater than a predetermined duration (e.g., 30 seconds). For example, user engagement data corresponding to the song “Rolling in the Deep,” correctly provided responsive to the natural language input “play Rolling in the Deep,” can indicate that the song was played for greater than 30 seconds. In some examples, an output provided for greater than the predetermined duration is interpreted as an indication that the output was correct (e.g., because otherwise the user would cancel the output within the predetermined duration). Accordingly, information associated with a data item (e.g., a song) included in the output can be used to determine a correct parsing result for the natural language input. For example, information associated with the song “Rolling in the Deep” can specify that “Rolling in the Deep” is mapped to the “musicTitle” property of the media domain. This information can be compared to the natural language input “play Rolling in the Deep” to determine the correct parsing result mapping “Rolling in the Deep” to the “musicTitle” property of the domain. The correct parsing result for the natural language input and the natural language input can then be provided to train and/or generate a natural language parsing model. 
     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  through network(s)  110  for task completion or information acquisition. I/O interface to external services  118  facilitates such communications. 
     User device  104  can be any suitable electronic device. In some examples, user device  104  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  FIGS. 6A-6B .) 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 Apple Watch®, iPhone®, iPod Touch®, and iPad® devices from Apple Inc. of Cupertino, Calif. Other examples of portable multifunction devices include, without limitation, earphones/headphones, speakers, and 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 speaker, 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-6B . 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 returns 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 can 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 detecting 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 more tactile output generators  267  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 more 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 sensory 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 more 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 more 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) are 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  run 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 for e 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 RF 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 controller(s)  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 the 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. No. 6,323,846 (Westerman et al.), U.S. Pat. No. 6,570,557 (Westerman et al.), and/or U.S. Pat. No. 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 touch 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. No. 11/241,839, “Proximity Detector In Handheld Device”; Ser. No. 11/240,788, “Proximity Detector In Handheld Device”; Ser. No. 11/620,702, “Using Ambient Light Sensor To Augment Proximity Sensor Output”; Ser. No. 11/586,862, “Automated Response To And Sensing Of User Activity In Portable Devices”; and Ser. No. 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 ear (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 front 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. 20060017692, “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 software 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  stores 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 generator(s)  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 include 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-7C . 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 RF 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 screen  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 an 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 attitude) 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 initiated 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 does 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  267  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 interface  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 generator(s)  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 state 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 stores 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 does 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-4 ). 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, 2B, 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. 2A, 4, and 6A-6B ). 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 “focus 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 intensity 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 detecting 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-6B , 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 RF 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/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communications between various hardware, firmware, and software components. 
     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  FIGS. 2A, 4, 6A-6B , 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 processing 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  758 . 
     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  758 . The one or more ASR systems  758  can process the speech input that is received through I/O processing module  728  to produce a recognition result. Each ASR system  758  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  758  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. In some examples, STT processing module  730  produces multiple candidate text representations of the speech input. Each candidate text representation is a sequence of words or tokens corresponding to the speech input. In some examples, each candidate text representation is associated with a speech recognition confidence score. Based on the speech recognition confidence scores, STT processing module  730  ranks the candidate text representations and provides the n-best (e.g., n highest ranked) candidate text representation(s) to natural language processing module  732  for intent deduction, where n is a predetermined integer greater than zero. For example, in one example, only the highest ranked (n=1) candidate text representation is passed to natural language processing module  732  for intent deduction. In another example, the five highest ranked (n=5) candidate text representations are 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 . Each 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 / /. 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  determines 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 takes the n-best candidate text representation(s) (“word sequence(s)” or “token sequence(s)”) generated by STT processing module  730 , and attempts to associate each of the candidate text representations with one or more “actionable intents” recognized by the digital assistant. An “actionable intent” (or “user 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 candidate text representations 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 property 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, “search media,” “play media,” “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 “recipient(s),” “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, 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 another example, a “media” super-domain includes a cluster of property nodes and actionable intent nodes related to media. The actionable intent nodes related to media include, for example, “search media,” “play media item,” “buy media item,” or the like. 
     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 candidate text representations (e.g., text string(s) or token sequence(s)) from STT processing module  730 , and for each candidate representation, determines what nodes are implicated by the words in the candidate text representation. In some examples, if a word or phrase in the candidate text representation 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. 
     It should be recognized that in some examples, natural language processing module  732  is implemented using one or more machine learning mechanisms (e.g., neural networks). In particular, the one or more machine learning mechanisms are configured to receive a candidate text representation and contextual information associated with the candidate text representation. Based on the candidate text representation and the associated contextual information, the one or more machine learning mechanisms are configured to determine intent confidence scores over a set of candidate actionable intents. Natural language processing module  732  can select one or more candidate actionable intents from the set of candidate actionable intents based on the determined intent confidence scores. In some examples, an ontology (e.g., ontology  760 ) is also used to select the one or more candidate actionable intents from the set of candidate actionable intents. 
     Other details of searching an ontology based on a token string are 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} are 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  identifies multiple candidate actionable intents for each candidate text representation received from STT processing module  730 . Further, in some examples, a respective structured query (partial or complete) is generated for each identified candidate actionable intent. Natural language processing module  732  determines an intent confidence score for each candidate actionable intent and ranks the candidate actionable intents based on the intent confidence scores. In some examples, natural language processing module  732  passes the generated structured query (or queries), including any completed parameters, to task flow processing module  736  (“task flow processor”). In some examples, the structured query (or queries) for the m-best (e.g., m highest ranked) candidate actionable intents are provided to task flow processing module  736 , where m is a predetermined integer greater than zero. In some examples, the structured query (or queries) for the m-best candidate actionable intents are provided to task flow processing module  736  with the corresponding candidate text representation(s). 
     Other details of inferring a user intent based on multiple candidate actionable intents determined from multiple candidate text representations of a speech input are described in U.S. Utility application Ser. No. 14/298,725 for “System and Method for Inferring User Intent From Speech Inputs,” filed Jun. 6, 2014, the entire disclosure of which is incorporated herein by reference. 
     Task flow processing module  736  is configured to receive the structured query (or queries) 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=3/12/2012, 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 interfaces (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 generates requests for the service in accordance with the protocols and APIs required by the service according to the service model. 
     For example, if a restaurant has enabled an online reservation service, the restaurant 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 sends 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&#39;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 processing 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. 
     In examples where task flow processing module  736  receives multiple structured queries from natural language processing module  732 , task flow processing module  736  initially processes the first structured query of the received structured queries to attempt to complete the first structured query and/or execute one or more tasks or actions represented by the first structured query. In some examples, the first structured query corresponds to the highest ranked actionable intent. In other examples, the first structured query is selected from the received structured queries based on a combination of the corresponding speech recognition confidence scores and the corresponding intent confidence scores. In some examples, if task flow processing module  736  encounters an error during processing of the first structured query (e.g., due to an inability to determine a necessary parameter), the task flow processing module  736  can proceed to select and process a second structured query of the received structured queries that corresponds to a lower ranked actionable intent. The second structured query is selected, for example, based on the speech recognition confidence score of the corresponding candidate text representation, the intent confidence score of the corresponding candidate actionable intent, a missing necessary parameter in the first structured query, or any combination thereof. 
     Speech synthesis processing module  740  is configured to synthesize speech outputs for presentation to the user. Speech synthesis processing 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 processing module  740  converts the text string to an audible speech output. Speech synthesis processing module  740  uses any appropriate speech synthesis technique in order to generate speech outputs from text, including, but not limited, to concatenative 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 processing 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 processing module  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 processing 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. 
       FIG. 8  illustrates a block diagram of a natural language parsing system  800  in accordance with some examples. In some examples, natural language parsing system  800  is implemented on a standalone computer system. In some examples, natural language parsing system  800  is distributed across multiple devices. In some examples, some of the modules and functions of the natural language parsing system 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 . It should be noted that natural language parsing system  800  is only one example of a natural language parsing system, and that natural language parsing system  800  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. Further, although the below discussion describes functions being performed at a single component of natural language parsing system  800 , it is to be understood that such functions can be performed at other components of natural language parsing system  808  and that such functions can be performed at more than one component of natural language parsing system  808 . 
     Natural language parsing system  800  includes natural language parsing module  802 , knowledge base  804 , post-processing module  806 , and natural language parsing model generating module  808 . In some examples, natural language parsing system  800  is at least partially implemented within natural language processing module  732  discussed above and the output of natural language parsing system  800  (e.g., the output of post-processing module  806 ) is provided as a structured query to task flow processing module  736 . 
     As discussed in greater detail below, in some examples, natural language parsing module  802  uses information contained in knowledge base  804  and a natural language parsing model to generate parsing results for natural language input. In some examples, the parsing results have confidence scores and are ranked based on their respective confidence scores. In some examples, post-processing module  806  re-ranks parsing results generated by natural language parsing module  802  (e.g., by adjusting the respective confidence scores of the parsing results) and generates new parsing results using the parsing results generated by natural language parsing module  802 . In some examples, natural language parsing model generating module  808  generates and/or trains natural language parsing models. For example, generating module  808  generates any natural language parsing model that is implemented within natural language parsing module  802 . 
     Natural language parsing module  802  generates parsing results for natural language input based on a domain determined to correspond to the natural language input. In particular, natural language parsing module  802  generates one or more parsing results for natural language input using a determined domain corresponding to the natural language input (e.g., determined by natural language processing module  732 ) and a received text representation of the natural language input (e.g., received from STT processing module  730 ). Each parsing result comprises a mapping of one or more properties of the determined domain to one or more words of the natural language input. In some examples, the one or more parsing results are generated using a neural network (e.g., neural network  900  discussed with respect to  FIG. 9  below) implemented within natural language parsing module  802 . 
     For example, the natural language input “play Don&#39;t Let Me down by the Chainsmokers” is determined by natural language processing module  732  to correspond to the media domain. The media domain is a domain (e.g., a domain of ontology  760 ) associated with the actionable intents of searching for and playing media items such as songs, movies, books, video games etc. The media domain includes properties such as “default,” “mediaPlayerItem,” “mediaPlayerMood,” “musicArtist,” “musicAlbum,” “musicTitle,” and “musicGenre.” When these properties are mapped to words of the natural language input, they respectively indicate one or more default words, one or more words associated with media items, one or more words associated with a music artist, one or more words associated with a music album, one or more words associated with a music title (e.g., a song title), and one or more words associated with a music genre. 
     In some examples, natural language parsing module  802  generates a first and a second parsing result for the natural language input. For example, a first parsing result comprises the mapping of “Don&#39;t Let Me Down by the Chainsmokers” to “musicArtist” and a second parsing result comprises the mappings of “Don&#39;t Let Me Down” to “musicTitle” and “Chainsmokers” to “musicArtist.” In the present example, the first parsing result indicates that the words “Don&#39;t Let Me Down by the Chainsmokers” are interpreted as a music artist and the second parsing result indicates that the words “Don&#39;t Let Me Down” are interpreted as a music title (e.g., a song title) and that the word “Chainsmokers” is interpreted as a music artist. 
     In some examples, natural language parsing module  802  generates respective confidence scores and/or rankings for the parsing results it generates (e.g., using neural network  900 ). For example, a first confidence score of the first parsing result is higher than a second confidence score of the second parsing result and the first parsing result is thus initially ranked higher than the second parsing result. 
     In some examples, natural language parsing module  802  uses information contained in knowledge base  804  when generating and/or ranking parsing results. Knowledge base  804  includes a plurality of data items (e.g., songs, books, audiobooks, movies, video games, emails, websites, contacts, calendar events, etc.) and, in some examples, includes additional information related to the data items (e.g., the number of views, plays, and/or purchases of the data items, the ranking of the data items, the popularity of the data items, the length of the data items, the source of the data items, etc.). In one example, knowledge base  804  is a media database (e.g., iTunes® by Apple Inc.). In some examples, the data items comprising knowledge base  804  and the additional information about the data items in knowledge base  804  are updated in real-time (e.g., live updates, daily updates, etc.). For example, a media database such as iTunes® is updated live as new data items are added and information associated with the data items changes (e.g., a song gets more plays and/or purchases). 
     In some examples, knowledge base  804  is embedded in natural language parsing module  802 . For example, knowledge base  804  is embedded into neural network  900  implemented within natural language parsing module  802 . Exemplary techniques for embedding knowledge bases into neural networks are described in Bishan Yang&#39;s and Tom Mitchell&#39;s, “Leveraging Knowledge Bases in LSTMs for Improving Machine Reading,”  Proceedings of the  55 th Annual Meeting of the Association for Computational Linguistics  ( Volume  1 : Long Papers ), 2017, pp. 1436-1446, the content of which is incorporated by reference herein in its entirety. 
     Embedding knowledge base  804  in natural language parsing module  802  enables natural language parsing module  802  to use information stored in knowledge base  804  when determining parsing results and their respective confidence scores. For example, because knowledge base  804  includes the song “Don&#39;t Let Me Down” by the artist “The Chainsmokers,” natural language parsing module  802  (with embedded knowledge base  804 ) determines a high confidence score for a parsing result indicating that the words “Don&#39;t Let Me Down” are interpreted as a music title and that the word “Chainsmokers” is interpreted as an artist. In contrast, natural language parsing module  802  determines a low confidence score for a parsing result indicating that “Don&#39;t Let Me Down by the Chainsmokers” is interpreted as a music artist because knowledge base  804  does not include a music artist titled “Don&#39;t Let Me Down by the Chainsmokers.” 
     In some examples, knowledge base  804  validates parsing results generated by natural language parsing module  802 . In particular, natural language parsing module  802  determines whether generated parsing results correspond to any one of a plurality of data items included in knowledge base  804 . For example, natural language parsing module  802  determines that the first parsing result comprising the mapping of “Don&#39;t Let Me Down by the Chainsmokers” to “musicArtist” does not correspond to any one of the plurality of data items in knowledge base  804  because there is no data item in knowledge base  804  by the music artist “Don&#39;t Let Me Down by the Chainsmokers.” However, natural language parsing module  802  determines that the second parsing result comprising the mappings of “Don&#39;t Let Me Down” to “musicTitle” and “Chainsmokers” to “musicArtist corresponds to one of the plurality of data items in knowledge base  804  because knowledge base  804  includes the song “Don&#39;t′ Let Me Down” by the artist “The Chainsmokers.” 
     Post-processing module  806  re-ranks parsing results generated by natural language parsing module  802  using validation results determined from knowledge base  804 . For example, because the first parsing result comprising the mapping of “Don&#39;t Let Me Down by the Chainsmokers” to “musicArtist” does not correspond to any one of the plurality of data items in knowledge base  804  and the second parsing result comprising the mappings of “Don&#39;t Let Me Down” to “musicTitle” and “Chainsmokers” to “musicArtist” is determined to correspond to a data item in knowledge base  804 , post-processing module  806  ranks the second parsing result higher than the first parsing result. As discussed, the second parsing result is initially ranked lower than the first parsing result (e.g., because the first parsing result had a higher confidence value). Accordingly, in some examples, post-processing module  806  adjusts the respective confidence scores associated with parsing results generated by natural language parsing module  802  to re-rank the parsing results. 
     In some examples, post-processing module  806  generates a final parsing result using parsing results generated by natural language parsing module  802 . In particular, in some examples, the final parsing result is generated using parsing results output from different output layers of a neural network (e.g., output layers  906  and  908  of neural network  900  within parsing module  802 ). For example, the second parsing result discussed above is generated by a first output layer of a neural network (e.g., output layer  906 ) and a third parsing result is generated by a second output layer of a neural network (e.g., output layer  908 ). Post-processing module  806  uses the first parsing result and the second parsing result to generate the final parsing result. In the present example, the third parsing result for the natural language input “play Don&#39;t Let Me Down by the Chainsmokers” comprises the mapping of the “default” property to “play” and the mappings of the “mediaPlayerItem” property to “Don&#39;t Let Me Down” and to “Chainsmokers.” To generate the final parsing result, post-processing module  806  replaces the mapping of the “mediaPlayerItem” property to “Don&#39;t Let Me Down” with the mapping of “musicTitle” to “Don&#39;t Let Me Down” and replaces the mapping of the “mediaPlayerItem” property to “Chainsmokers” with the mapping of “musicArtist” to “Chainsmokers.” Accordingly, the final parsing result comprises the mappings of “default” to “play,” “musicTitle” to “Don&#39;t Let Me Down,” and “musicArtist” to “Chainsmokers.” 
     In some examples, post-processing module  806  provides one or more parsing results (e.g., the first, second, third, and/or final parsing results) to task flow processing module  736 . In some examples, natural language parsing module  802  provides one or more parsing results to task flow processing module  736  without having post-processing  806  module re-rank parsing results and/or generate a final parsing result. 
     Natural language parsing model generating module  808  is configured to generate and/or train natural language parsing models of domains (e.g., a natural language parsing model within natural language parsing module  802 ). In some examples, generating module  808  obtains a data item (e.g., a song) and a natural language input that caused the data item to be provided. Generating module  808  then trains a natural language parsing model using the data item and the natural language input. For example, generating module  808  uses the data item and the natural language input to train a neural network (e.g., neural network  900  discussed below). 
     In some examples, natural language parsing model generating module  808  uses natural language input and metadata corresponding to an output provided responsive to the natural language input (e.g., from I/O processing module  728  and/or from STT processing module  730 ) to generate training data for natural language parsing models and to train natural language parsing models. For example, natural language parsing model generating module  808  uses information included in the metadata to determine parsing results for the natural language input as training data. Generating module  808  then uses the natural language input and the determined parsing result to train a natural language parsing model. In some examples, before determining the parsing result using the metadata, generating module  808  determines, using the metadata, whether the output satisfies a predetermined criterion, such as a predetermined duration of the output (e.g., 30 seconds). 
     For example, generating module  808  obtains the natural language input “Play Hello by Adele” and metadata corresponding to the song “Hello” by “Adele” provided responsive to the natural language input. The metadata specifies the song “Hello” by “Adele” is played for over 30 seconds and that “Hello” is mapped to “musicTitle” and “Adele” is mapped to “musicArtist.” Because the output of the song “Hello” is played for over 30 seconds, the output satisfies the predetermined criterion, and thus generating module  808  determines a parsing result for “Play Hello by Adele” by comparing the mappings of “Hello” to “musicTitle” and “Adele” to “musicArtist” to “Play Hello by Adele.” In the present example, the parsing result is determined to include the mappings of “Hello” to “musicTitle” and “Adele” to “musicArtist.” In some examples, generating module  808  compares the mappings specified in the metadata to the natural language input to determine the parsing result using fuzzy matching techniques. Generating module  808  then uses the natural language input “Play Hello by Adele” and the determined parsing result to generate and/or train a natural language parsing model. 
       FIG. 9 . illustrates neural network  900  in accordance with some examples. Neural network  900  is configured to receive a textual representation of natural language input (e.g., the text representations from STT processing module  730 ) and generate one or more parsing results for the natural language input. In some examples, neural network  900  is implemented within natural language parsing module  802  described above and neural network  900  generates the one or more parsing results for natural language parsing module  802 . Further, in some examples, neural network  900  generates respective confidence scores for the one or more parsing results. 
     Neural network  900  includes multiple layers. In particular, neural network  900  includes input layer  902 , one or more hidden layers  904 , first output layer  906 , and second output layer  908 . As shown in  FIG. 9 , one or more hidden layers  904  includes four hidden layers. However, in some examples, one or more hidden layers  904  includes any number of hidden layers. 
     Each layer of neural network  900  includes multiple units. The units are the basic computational elements of neural network  900  and are referred to as dimensions, neurons, or nodes. As shown in  FIG. 9 , input layer  902  includes input units  910 , one or more hidden layers  904  includes hidden units  912 , first output layer  906  includes first output layer units  914 , and second output layer  908  includes second output layer units  916 . The units are interconnected by connections  918 . Specifically, connections  918  connect the units of one layer to the units of a subsequent layer. Further, each connection  918  is associated with a weighting value. For simplicity, the weighting values are not shown in  FIG. 9 . 
     Input layer  902  is configured to receive a textual representation of natural language input and the output of input layer  902  is provided to one or more hidden layers  904 . The output of one or more hidden layers  904  is provided to first output layer  906  and to second output layer  908 . In particular, as shown in  FIG. 9 , first output layer  906  and second output layer  908  both receive the same output from one or more hidden layers  904 . 
     In some examples, first output layer  906  and second output layer  908  each generate one or more parsing results for the natural language input. In some examples, first output layer  906  is a named entity recognition (NER) output layer configured to map properties of the domain corresponding to named entities (e.g., domain properties such as “musicTitle,” “musicArtist,” and “musicAlbum”) to one or more words of the natural language input. In some examples, second output layer  908  is a shallow parsing output layer configured to map properties of the domain to the natural language input. 
     In some examples, first output layer  906  and second output layer  908  map different properties of the domain to one or more words of the natural language input. For example, second output layer  908  maps the media domain property “mediaPlayerMood” to one or more words of the natural language input, while first output layer  906  is not configured to map the “mediaPlayerMood” property to one or more words of natural language input. Similarly, in some examples, first output layer  906  maps the media domain property “musicArtist” to one or more words of the natural language input, while second output layer  908  is not configured to map the “musicArtist” property to one or more words of natural language input. 
     Because in some examples first output layer  906  and second output layer  908  respectively map different properties of a domain to one or more words of the natural language input in some examples, using both first output layer  906  and second output layer  908  to generate parsing results enables use of many different domain properties when generating parsing results for natural language input. This can improve the accuracy of the parsing results and thus the accuracy of the task flows generated using the parsing results (e.g., generated by task flow processing module  754 ) by providing more detailed information about the natural language input (e.g., which words correspond to song titles, which words correspond to moods, etc.). 
     4. Process for Using a Knowledge Base to Perform Natural Language Processing 
       FIG. 10  illustrates process  1000  for using a knowledge base to perform natural language processing according to various examples. Process  1000  is performed, for example, using one or more electronic devices implementing a digital assistant. In some examples, process  1000  is performed using a client-server system (e.g., system  100 ), and the blocks of process  1000  are divided up in any manner between the server (e.g., DA server  106 ) and a client device. In other examples, the blocks of process  1000  are divided up between the server and multiple client devices (e.g., a mobile phone and a smart watch). Thus, while portions of process  1000  are described herein as being performed by particular devices of a client-server system, it will be appreciated that process  1000  is not so limited. In other examples, process  1000  is performed using only a client device (e.g., user device  104 ) or only multiple client devices. In process  1000 , some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process  1000 . 
     At block  1002 , the electronic device (e.g., device  200 ,  400 ,  600  and/or server system  108 ) receives natural language input. In some examples, the natural language input comprises a request for a media item (e.g., “play Hello by Adele”). In some examples, the natural language input is received from a user of the electronic device, or from a second electronic device separate from the electronic device. Exemplary natural language inputs include speech, text, or a combination of speech and text. 
     At block  1004 , the electronic device determines, from a plurality of domains, a domain corresponding to the natural language input. For example, the electronic device determines that the natural language input “play Hello by Adele” corresponds to the media domain. In some examples, the plurality of domains are domains of an ontology (e.g., ontology  760 ). The domain corresponding to the natural language input is determined, for example, using any of the techniques described with respect to  FIGS. 7A-7C  above. 
     At block  1006 , the electronic device determines whether the domain corresponds to a predetermined type of domain. The predetermined type of domain corresponds to a specific domain of the ontology. For example, the predetermined type of domain is the media domain. As discussed, in some examples, the media domain is associated with the actionable intents of searching for and playing media items such as music, videos, books, video games, etc. The determined domain is associated with one or more properties. In some examples, the one or more properties each correspond to a named entity (e.g., any object that can be given a proper name). For instance, in examples where the determined domain corresponds to a media domain, the one or more properties each correspond to media-related named entities, such as artists, song titles, albums, authors and the like. 
     In some examples, in accordance with determining that the domain does not correspond to a predetermined type of domain, the device forgoes performing the operations described with respect to blocks  1008 - 1016  below. Rather, for example, the electronic device processes the natural language input to determine parsing results using a domain that is not of the predetermined type (e.g., domains other than the media domain). In some examples, parsing results determined using domains not of the predetermined type do not include mappings of domain properties corresponding to named entities to one or more words of the natural language input. In some examples, parsing results determined using domains not of the predetermined type are not validated and/or re-ranked using a knowledge base. Processing the natural language input with respect to domains not of the predetermined type to determine parsing results to execute one or more tasks is described with respect to  FIGS. 7A-7C  above. 
     At block  1008 , in accordance with determining that the domain corresponds to a predetermined type of domain, the device determines parsing results for the natural language input (e.g., using neural network  900  and/or using natural language parsing system  800 ). As discussed, each parsing result represents a semantic interpretation of the natural language input and each parsing result comprises respective mappings of one or more domain properties to one or more words of the natural language input. In determining the parsing results, one or more words of the natural language input are mapped to one or more properties of the domain to obtain the parsing results. The parsing results represent, for example, which words in the natural language input correspond to song titles, music albums, music artists, etc. 
     For example, at block  1008 , in accordance with determining that the domain corresponds to a predetermined type of domain, the device determines a first and a second parsing result for the natural language input. The first and the second parsing results each comprise a respective mapping of one or more properties of the domain to one or more words of the natural language input. A first confidence score of the first parsing result is higher than a second confidence score of the second parsing result. 
     For example, as shown in  FIG. 12 , an exemplary first parsing result for the natural language input “play Hello by Adele” comprises the mapping of “Hello by Adele” to the “musicArtist” property of the domain. The first parsing result represents the semantic interpretation of “Hello by Adele” as a music artist. An exemplary second parsing result for the natural language input “play Hello by Adele” comprises the mappings of “Hello” to the “musicTitle” property of the domain and “Adele” to the “musicArtist” property of the domain. The second parsing result represents the semantic interpretations of “Hello” as a music title (e.g., a song title) and “Adele” as a music Artist. In some examples, one or more words in the natural language input that do not correspond to named entities and do not correspond to other properties of the domain, are mapped to the “default” property. For example, as shown in  FIG. 12 , the word “Play” is mapped to the “default” property in both the first and the second parsing results. 
     As shown in  FIG. 12 , in some examples, the first parsing result comprises a mapping of a first property of the domain (e.g., “musicArtist”) to a first word of the natural language input (e.g., “Hello”) and the second parsing result comprises a mapping of a second property of the domain (e.g., “musicTitle”) to the first word (e.g., “Hello”). In some examples, the second property differs from the first property. Accordingly, in some examples, the first parsing result and the second parsing result respectively comprise one or more mappings of different properties of the domain to the same one or more words in the natural language input. 
     In some examples, the first parsing result and the second parsing result respectively comprise one or more mappings of the same property of the domain to the same one or more words in the natural language input. For example, for the natural language input “play Frozen Let It Go by Idina Menzel” (determined to correspond to the media domain) a first parsing result maps the media domain property “musicArtist” to “Idina Menzel” while a second parsing result also maps the media domain property “musicArtist” to “Idina Menzel.” 
     Although block  1008  is described above with respect to a first and a second parsing result for the natural language input, it is to be understood that, in some examples, more than two parsing results are determined for the natural language input. In particular, in some examples, a plurality of parsing results are determined for the natural language input, where each parsing result of the plurality of parsing results respectively includes a mapping of one or more properties of the domain to one or more words of the natural language input. In some examples, each parsing result of the plurality of parsing results is generated using a neural network (e.g., neural network  900 ). For example, the first parsing result and the second parsing result are each generated by neural network  900 . In some examples, the first and the second parsing result for the natural language input are each generated by a first output layer of a neural network (e.g., output layer  906  of neural network  900 ), while a third parsing result for the natural language input is generated by a second output layer of a neural network (e.g., output layer  908  of neural network  900 ) different from the first output layer. The third parsing result comprises a mapping of a set of one or more properties of the domain (e.g., the domain determined at step  1004 ) to the one or more words of the natural language input. 
     As discussed, in some examples, the first output layer and the second output layer are configured to map different properties of the domain to one or more words of the natural language input. For example, the first output layer is a named entity recognition (NER) output layer configured to map properties of the domain corresponding to named entities (e.g., domain properties such as “musicTitle,” “musicArtist,” and “musicAlbum,”) to one or more words of the natural language input and the second output layer is a shallow parsing output layer configured to coarsely map properties of the domain to one or more words of the natural language input. For the exemplary natural language input “Play Hello by Adele,” the first output layer generates the second parsing result mapping “Hello” to “musicTitle” and “Adele” to “musicArtist shown in  FIG. 12  and the second output layer generates the third parsing result shown in  FIG. 12  mapping “Play” to the property “default” of the media domain and mapping “Hello by Adele” to the property “mediaPlayerItem” of the domain. 
     In some examples, the parsing results generated by the second output layer (e.g., the third parsing result) provide coarser information about the natural language input than the parsing results generated using the first output layer. For example, the third parsing result mapping the domain property “mediaPlayerItem” to “Hello by Adele” generated by the second output layer does not provide as detailed information about the natural language input as the parsing result mapping the domain properties “musicTitle” and “musicArtist” respectively to “Hello” and “Adele” generated by the first output layer. However, in some examples, the parsing results generated by the second output layer provide semantic interpretations of the natural language input that the parsing results generated by the first output layer are unable to provide. In particular, in some examples, the parsing results generated using the second output layer map a set of properties of the domain to the one or more words of the natural language input, where the first output layer is not configured to map the set of properties of the domain to the one or more words. 
     For example, for the natural language input “play me a sad song by Adele,” (determined to correspond to the media domain) the second output layer generates a parsing result mapping the “mediaPlayerMood” property of the media domain to “sad song.” The “mediaPlayerMood” property of the domain indicates a mood (e.g., sad, happy, angry) of a media item specified in the natural language input. In the present example, the first output layer is not configured to map the “mediaPlayerMood” property of the domain to the one or more words of the natural language input. Accordingly, using both the first output layer and the second output layer to generate parsing results for natural language input can allow for a greater variety of domain properties to be mapped to one or more words of the natural language input. This in turn can allow for more accurate processing and generation of task flows from natural language input. 
     As discussed, in some examples, each of the parsing results of the plurality of the parsing results has a respective confidence score. The respective confidence score indicates a likelihood or probability (e.g., determined by the device) that the respective parsing result is correct for the natural language input. For example, the first parsing result mapping “Hello by Adele” to the “musicArtist” property of the domain has a first confidence score and the second parsing result mapping “Hello” to the “musicTitle” property of the domain and mapping “Adele” to the “musicArtist” property of the domain has a second confidence score. In some examples, the confidence score of the first parsing result is higher than the confidence score of the second parsing result when the first and the second parsing result are initially generated. 
     In some examples, each of the parsing results of the plurality of parsing results is initially ranked based on their respective confidence scores. For example, because the confidence score of the first parsing result is higher than the confidence score of the second parsing result, the first parsing result is ranked higher than the second parsing result. In some examples including more than two parsing results, the respective confidence score of each parsing result determines the respective ranking of the respective parsing result. In some examples, the ranking of the plurality of parsing results is independent of the respective confidence scores of the plurality of parsing results or the respective confidence scores of the plurality of parsing results are only one factor considered in ranking the plurality of parsing results. 
     At block  1010 , in accordance with determining that the domain corresponds to a predetermined type of domain, the electronic device determines whether the parsing results determined for the natural language input correspond to respective data items of a plurality of data items in a knowledge base. For example, the electronic device determines whether the second parsing result corresponds to a data item of a plurality of data items in a knowledge base (e.g., knowledge base  804 ). In some examples, in addition to determining whether the second parsing result corresponds to a data item of the plurality of data items in the knowledge base, the electronic device determines whether the first parsing result corresponds to any of the plurality of data items in the knowledge base. In some examples, the data item is a media item (e.g., a song, book, movie, video game, etc.) and the knowledge base is a database of media items (e.g., the iTunes® database by Apple Inc.). As will be discussed in greater detail below, in some examples, the parsing results are re-ranked based on whether they are determined to correspond to respective data items in the knowledge base. In this way, parsing results determined to correspond to respective data items have their rankings increased, which allows for execution of task flows using knowledge base validated parsing results. 
     In the example shown in  FIG. 12 , where the first parsing result comprises a mapping of the “musicArtist” property to “Hello by Adele,” and where the second parsing result comprises the mappings of the “musicTitle” property to “Hello,” and the “musicArtist” property to “Adele,” the first parsing result is determined to not correspond to any of the plurality of items in the knowledge base because no data item associated with the mapping of “musicArtist” to “Hello by Adele” exists in the knowledge base. In other words, the first parsing result is determined to not correspond to any of the plurality of data items in the knowledge base because there is no music artist named “Hello by Adele.” However, the second parsing result is determined to correspond to a data item of the plurality of data items in the knowledge base because a song exists in the knowledge base titled “Hello” by the music Artist “Adele.” 
     As discussed, determining whether a parsing result corresponds to any of a plurality of data items in a knowledge base comprises determining whether a data item exists in the knowledge base that is associated with a mapping that matches one or more mappings comprising the parsing result. In particular, in some examples, the second parsing result comprises a mapping of a fourth property of the domain to a first set of one or more words of the natural language input (e.g., the property “musicTitle” to the first set of one or more words “Hello”) and the data item is associated with a mapping of a fifth property of the domain to one or more words (e.g., the property “musicTitle” to the one or more words “Hello”). The device determines that the mapping of the fourth property of the domain to the first set of one or more words matches the mapping of the fifth property of the domain to the one or more words. In the present example, the second parsing result is determined to correspond to the data item because there is a data item (e.g., the song “Hello”) associated with the mapping of the fifth property of the domain to the one or more words that matches the mapping of the fourth property of the domain to the first set of one or more words. In other words, because the second parsing result indicates a song titled “Hello,” and there is a song titled “Hello” in the knowledge base, the second parsing result is determined to correspond to a data item of the plurality of data items in the knowledge base. 
     At block  1012 , in accordance with determining that the domain corresponds to a predetermined type of domain, the electronic device re-ranks parsing results in accordance with determining whether the parsing results respectively correspond to a data item in the knowledge base. In some examples, any parsing result determined to correspond to a data item in the knowledge base has its ranking increased relative to any parsing result determined not to correspond to a data item in the knowledge base. For example, the confidence scores of the parsing results determined to correspond to respective data items in the knowledge base are increased relative to the confidence scores of the parsing results determined not to correspond to respective data items in the knowledge base. In some examples, this is accomplished by decreasing the respective confidence scores of the parsing results determined to not correspond to respective data items and/or by increasing the respective confidence scores of the parsing results determined to correspond to data items. In the event that none of the parsing results are determined to correspond to a data item in the knowledge base, the respective ranking of each of the parsing results is unchanged, and the respective confidence score of each of the parsing results is also unchanged. 
     As an example of the process implemented at block  1012 , in accordance with a determination that the second parsing result corresponds to the data item of the plurality of data items in the knowledge base, and in accordance with a determination that the first parsing result does not correspond to any of the plurality of data items in the knowledge base, the electronic device ranks the second parsing result higher than the first parsing result (e.g., using post-processing module  806 ). As discussed, the first parsing result (mapping “Hello by Adele” to the “musicArtist” property) is initially ranked higher than the second parsing result (mapping “Hello” to the “musicTitle” property and “Adele” to the “musicArtist” property) because the first parsing result initially has a confidence score higher than the second parsing result. However, because the first parsing result is determined not to correspond to any of the plurality of items in the knowledge base and because the second parsing result is determined to correspond to the data item of the plurality of data items in the knowledge base, the second parsing result is ranked higher than the first parsing result. For example, the second confidence score of the second parsing result is adjusted to be greater than the first confidence score of the first parsing result. Thus, in the present example, the second parsing result is the highest ranked parsing result of a plurality of parsing results determined for the natural language input. 
     For some natural language inputs, more than one of the determined parsing results is determined to correspond to a respective data item in the knowledge base. This occurs, for example, when one or more words of the natural language input correspond to both a song name and a music artist name. For example, for the natural language input “play Death Cab for Cutie” a first parsing result comprises a mapping of “musicTitle” to “Death Cab for Cutie” and a second parsing result comprises a mapping of “musicArtist” to “Death Cab for Cutie.” Because “Death Cab for Cutie” refers to both a song name and an artist name, both the first parsing result and the second parsing result are determined to correspond to a respective data item of the plurality of data items of the knowledge base. 
     In examples where more than one parsing result corresponds to a respective data item in the knowledge base, the electronic device uses the multiple techniques discussed below, alone or in combination, to determine the respective rankings (and/or the respective confidence scores) of the parsing results. 
     In some examples, the electronic device uses the number of words of the natural language input mapped to a same property to determine the respective rankings of the parsing results. For example, for the natural language input “play Death Cab for Cutie,” a first parsing result maps “musicTitle” to “Death Cab” and a second parsing result maps “musicTitle” to “Death Cab for Cutie.” In this example, because the number of words the second parsing result maps to “musicTitle” (e.g., four words) is greater than the number of words the first parsing result maps to “musicTitle” (e.g., two words), the second parsing result is ranked higher than the first parsing result. In other examples, the parsing result that maps the lesser number of words to the same property is ranked higher. 
     In some examples, the electronic device uses the number of mapped domain properties of the natural language input to determine the respective rankings of the parsing results. For example, for the natural language input “play Thriller by Michael Jackson,” the first parsing result includes mapping of “Thriller by Michael Jackson” to “musicTitle” and the second parsing result includes the mappings of “Thriller” to “musicTitle” and “Michael Jackson” to “musicArtist.” Because the number of domain properties mapped to the natural language input in the second parsing result (e.g., two) is greater than the number of domain properties mapped to the natural language input in the first parsing result (e.g., one), the second parsing result is ranked higher than the first parsing result. In other examples, the parsing result mapping the lesser number of domain properties to the natural language input is ranked higher. 
     In some examples, the electronic device uses the popularity of the respective data items in the knowledge base to determine the respective rankings of the parsing results. Accordingly, in some examples, ranking the second parsing result higher than the first parsing result is performed in accordance with the electronic device determining that a popularity of a second data item corresponding to the second parsing result is greater than a popularity of a first data item corresponding to the first parsing result. In some examples, the popularity of a data item is determined using information in the knowledge base containing the data item. For example, the popularity of a data item is based on the number of views (and/or listens) of the data item, the number of purchases of the data item, the position of the data item on a chart, and the like. 
     Accordingly, in the example where the first parsing result comprises a mapping of “musicTitle” to “Death Cab for Cutie” and the second parsing result comprises a mapping of “musicArtist” to “Death Cab for Cutie,” the second parsing result is ranked higher than the first parsing result because the popularity of the music artist “Death Cab for Cutie” is greater than the popularity of the song “Death Cab for Cutie.” For example, there are more plays of songs by the artist “Death Cab for Cutie” than there are plays of the song “Death Cab for Cutie.” 
     As discussed, using knowledge base (e.g., knowledge base  804 ) information to rank parsing results for natural language input both validates parsing results (e.g., by ranking parsing results determined not to correspond to data items lower) and provides techniques for selecting between multiple parsing results each corresponding to a respective data item (e.g., by determining which parsing result corresponds to a data item with the greatest popularity). In addition, because exemplary knowledge bases contain real-time information about the popularity of data items, using a knowledge base to rank parsing results provides the benefit of selecting parsing results corresponding to relevant (e.g., currently popular) data items to execute task flows. This can increase the accuracy with which digital assistants process natural language inputs to execute user requests, thus improving the efficiency of the man-machine interface, and conserving battery power on electronic devices. 
     In some examples, instead of, or in addition to, using the knowledge base to re-rank and/or validate parsing results (e.g., block  1012 ), the knowledge base (e.g., knowledge base  804 ) is directly incorporated into the natural language processing module  732  (e.g., directly incorporated into neural network  900  and/or natural language parsing module  802 ). By directly incorporating knowledge base  804  into neural network  900  and/or natural language parsing module  802 , parsing results (and their respective confidence scores) generated by neural network  900  and/or natural language parsing module  802  for natural language inputs account for information about the data items in knowledge base  804 . For example, because the knowledge base indicates that “Death Cab for Cutie” is more likely to be a music artist than to be a song title (e.g., based on popularity), parsing results mapping “Death Cab for Cutie” to “musicArtist” have a greater confidence score than parsing results mapping “Death Cab for Cutie” to “musicTitle.” Exemplary techniques for incorporating knowledge bases into neural networks are described in Bishan Yang&#39;s and Tom Mitchell&#39;s, “Leveraging Knowledge Bases in LSTMs for Improving Machine Reading,”  Proceedings of the  55 th Annual Meeting of the Association for Computational Linguistics  ( Volume  1 : Long Papers ), 2017, pp. 1436-1446, the content of which is incorporated by reference herein in its entirety. 
     Additionally, in some examples, parsing results generated for natural language input (e.g., generated using parsing module  802 ) implicitly account for the popularity of data items respectively corresponding to the parsing results. In particular, as discussed below with respect to  FIG. 11 , in some examples, a parsing model that generates parsing results (e.g., a parsing model within parsing module  802 ) is trained using user-engagement data. Training a parsing model using user-engagement data allows for parsing results corresponding to data items preferred by users (e.g., more popular data items) to be ranked higher than parsing results corresponding to data items not preferred by users, even when the parsing results all correspond to respective data items in a knowledge base. In this way, the rankings (and respective confidence scores) of parsing results reflect the popularity of data items respectively corresponding to the parsing results. 
     For example, for the natural language input “play Death Cab for Cutie,” the first parsing result maps “musicTitle” to “Death Cab for Cutie,” and the second parsing result maps “musicArtist” to “Death Cab for Cutie.” The second parsing result is ranked higher than the first parsing result when the parsing results are initially generated because more people intend to play a song by the artist “Death Cab for Cutie” than the song “Death Cab for Cutie.” As discussed below, the parsing model generating the parsing results accounts for this popularity information because it is trained using user-engagement data. For example, user engagement data indicates that most users are dissatisfied with the output of the song “Death Cab for Cutie” responsive to “play Death Cab for Cutie” and instead intend to play a song by the artist “Death Cab for Cutie.” Accordingly, a parsing model is trained using this user engagement data to prefer (e.g., rank higher) parsing results mapping “Death Cab for Cutie” to “musicArtist.” 
     In some examples, in accordance with ranking the second parsing result higher than the first parsing result, the electronic device (e.g., post-processing module  806 ) generates a final parsing result for the natural language input based on the second parsing result and a third parsing result In some examples, the second parsing result is generated by a first output layer of a neural network (e.g., output layer  906 ) and the third parsing result is generated by a second output layer of a neural network (e.g., output layer  908 ). The third parsing result comprises a mapping of a third property of the domain to a second word of the natural language input and the second parsing result comprises a mapping of a sixth property of the domain to the second word. As discussed below, in some examples, the final parsing result combines mappings from a plurality of parsing results to provide the parsing result with the greatest specificity for the natural language input (e.g., when mapped to one or more words, the property “musicArtist” provides greater specificity than the property “default”). The parsing result with the greatest specificity may provide the most detailed information about the natural language input, which allows for accurate task flows to be generated responsive to the natural language input. 
     In some examples, to generate the final parsing result, the device determines whether the second confidence score of the second parsing result exceeds a confidence threshold. In accordance with a determination that the second confidence score of the second parsing result exceeds the confidence threshold, the device uses the mapping of the sixth property of the domain to the second word to generate the final parsing result. In some examples, the mapping of the third property of the domain to the second word is not used to generate the final parsing result. 
     In some examples, the device uses the mapping of the third property of the domain to the second word to generate the final parsing result. For example, as shown in  FIG. 12 , for the natural language input “play Hello by Adele,” the second parsing result comprises mappings of the media domain properties “musicTitle” and “musicArtist” respectively to “Hello” and “Adele.” The third parsing result comprises a mapping of the media domain property “mediaPlayerItem” to “Hello by Adele” (e.g., the “mediaPlayerItem” property is mapped to “Hello,” “by,” and “Adele”). In this example, the mapping of the third property of the domain to the second word of the natural language input is “mediaPlayerItem” to “Hello.” The mapping of the sixth property of the domain to the second word is “musicTitle” to “Hello.” The final parsing result is generated by replacing the mapping of the third property of the domain to the second word with the mapping of the sixth property of the domain to the second word. In particular, the final parsing result is generated by replacing the mapping of “mediaPlayerItem” to “Hello” comprising the third parsing result with the mapping of “musicTitle” to “Hello” comprising the second parsing result. 
     In the above example, the mapping of “mediaPlayerItem” property to “Hello” is replaced by the mapping of the “musicTitle” property to “Hello” because the “mediaPlayerItem” property does not provide as specific information about the word “Hello” as the “musicTitle” property does. Accordingly, in some examples, only properties that do not provide specific named-entity information about the natural language input (e.g., the “mediaPlayerItem” property and the “default” property) are replaced. As discussed, this may provide the advantage of determining a final parsing result for the natural language input that provides the greatest specificity for the natural language input. 
     Accordingly, in some examples, using the mapping (comprising the second parsing result) of the sixth property of the domain to the second word to generate the final parsing result is performed in accordance with a determination that the third property of the domain (comprising the third parsing result) is of a predetermined type. In some examples, the predetermined type of property is a property of the domain that does not provide named entity information about the natural language input (e.g., “default” or “mediaPlayerItem”). In some examples, the predetermined type of property is any property of a domain included in ontology  760  discussed above. For example, with respect to the media domain, the predetermined type of properties are selected from the “mediaPlayerItem,” “musicArtist,” “musicTitle,” “musicAlbum,” and “default” properties. 
     As discussed, in some examples, parsing results generated by the second output layer (e.g., the third parsing result) provide information that parsing results generated by the first output layer (e.g., the second parsing result) are unable to provide. Thus, in some examples, parsing results generated by the second output layer provide more specific information about the natural language input than parsing results generated by the first output layer. To prevent this specific information included in the parsing results generated by the second output layer from being excluded in the final parsing result, in some examples, replacing the mapping (comprising the third parsing result) of the third property of the domain to the second word with the mapping (comprising the second parsing result) of the sixth property of the domain to the second word is not performed if the third property of the domain is of a predetermined type. Exemplary predetermined types of properties of the domain are properties that only the second output layer is configured to map to the one or more words of the natural language input. For example, the predetermined type of property is the “mediaPlayerMood” property. Accordingly, the above technique preserves properties of the domain that the first output layer is not configured to map to the one or more words of the natural language input in the final parsing result. 
     For example, for the natural language input “play me a sad song by Adele,” the second parsing result (e.g., determined using the first output layer) maps the media domain properties “default” and “musicArtist” respectively to the words “sad” and “Adele.” The third parsing result (e.g., determined using the second output layer) maps the media domain properties “mediaPlayerMood” and “mediaPlayerItem” respectively to the words “sad” and “Adele.” Because the “mediaPlayerMood” property is a predetermined type of property, the mapping of “mediaPlayerMood” to “sad” is not replaced by the mapping of “default” to “sad” when the final parsing result is generated using the second parsing result and the third parsing result. Accordingly, in the present example, the final parsing result comprises the mappings of “sad” to “mediaPlayerMood” and “Adele” to “musicArtist.” 
     At block  1014 , in accordance with determining that the domain corresponds to a predetermined type of domain, based on the ranking (e.g., the ranking of the second parsing result higher than the first parsing result), the device generates a task flow using the second parsing result. For example, the second parsing result for the natural language input “play Hello by Adele” is used to generate the task flow of searching for and playing the song “Hello” by the artist “Adele.” In some examples, the device uses the final parsing result when generating the task flow at block  1014 . 
     At block  1016 , in accordance with determining that the domain corresponds to a predetermined type of domain, the device executes the task flow (e.g., using task flow processing module  736 ) to provide an output based on the data item. For example, the generated task flow is executed to provide the output of (e.g., play and/or display) the data item of the song “Hello” by the artist “Adele.” In some examples, the generated task flow is executed to provide output to an external electronic device, the output comprising instructions for the external electronic device (e.g., any of devices  200 ,  400 , and/or  600 ) to provide the data item. 
     In some examples, the device receives an updated natural language parsing model of the domain (e.g., an updated version of a natural language parsing model within natural language parsing module  802 ). In some examples, the first and the second parsing results have been determined using an initial natural language parsing model of the domain. The updated natural language parsing model is trained using the natural language input and the data item. 
     For example, the device provides the natural language input and the data item to natural language parsing model generating module  808 , and generating module  808  uses the data item and the natural language input to train a natural language parsing model within system  800  to obtain the updated natural language parsing model. For example, the natural language input and the data item are provided to train neural network  900 . Generating module  808  trains neural network  900  using the data item and the natural language input to update one or more weighting values associated with connections  918 . Updated neural network  900  is then provided to the device. 
     In some examples, the device receives the updated natural parsing language model in accordance with determining that the output satisfies a predetermined criterion. For example, the predetermined criterion comprises a duration for which the output is provided. 
     Receiving an updated natural language model in accordance with a determination that the output satisfies a predetermined criterion (e.g., a predetermined duration) may allow the received updated natural language parsing model to be trained and/or updated using a data item that was correctly determined to correspond to the natural language input. For example, if the electronic device correctly provides the output of playing the song “Hello” by the artist “Adele,” the output is provided for greater than a predetermined duration (e.g., 30 seconds) because the user was satisfied with the output. In other words, provision of outputs for greater than the predetermined duration indicates user satisfaction with the provided output, which in turn indicates that the correct data item was determined to correspond to the natural language input. Accordingly, in some examples, natural language parsing system  800  is trained using the data item of the song “Hello” by the “Artist” Adele and the natural language input of “play Hello by Adele,” where the data item was correctly determined to correspond to the natural language input. This provides the benefit of using both natural language input and the correct data item determined to correspond to the natural language input to train natural language parsing models, resulting in more accurate natural language parsing results. 
     The operations described above with reference to  FIG. 10  are optionally implemented by components depicted in  FIGS. 1-4, 6A-6B, 7A-7C, 8 and 9 . For example, the operations of process  1000  may be implemented by natural language parsing system  800 , neural network  900 , natural language processing module  732 , and task flow processing module  736 . 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,  7 A-C,  8 , and  9 . 
     5. Process for Natural Language Parsing Model Generation Based on User Engagement Data 
       FIG. 11  illustrates a process for natural language parsing model (e.g., a natural language parsing model included in natural language parsing system  800 ) training based on user engagement data. Process  1100  is performed, for example, using one or more electronic devices implementing a digital assistant. In some examples, process  1100  is performed using a client-server system (e.g., system  100 ), and the blocks of process  1100  are divided up in any manner between the server (e.g., DA server  106 ) and a client device. In other examples, the blocks of process  1100  are divided up between the server and multiple client devices (e.g., a mobile phone and a smart watch). Thus, while portions of process  1100  are described herein as being performed by particular devices of a client-server system, it will be appreciated that process  1100  is not so limited. In other examples, process  1100  is performed using only a client device (e.g., user device  104 ) or only multiple client devices. In process  1100 , some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process  1100 . 
     At block  1102 , an electronic device obtains a natural language input and metadata corresponding to an output provided responsive to the natural language input. For example, generating module  808  obtains the natural language input and the metadata (e.g., from I/O processing module  728  and/or STT processing module  730 .) The natural language input corresponds to a domain of a predetermined type (e.g., the media domain, or any other domain that can be within ontology  760 .) The output provided responsive to the natural language input includes a data item (e.g., a media item such as a song, movie, book, and the like). The metadata includes a set of attributes corresponding to the data item, where the set of attributes defines a set of values for a respective set of properties of the domain. For example, the metadata includes attributes such as the name of the data item (e.g., a song title), the artist of the data item (e.g., a music artist), and the album of the data item (e.g., the album a song is from). These attributes define a set of values for the respective “musicTitle,” “musicArtist,” and “musicAlbum” properties of the media domain. 
     In some examples, the metadata includes additional information about the output provided responsive to the natural language input. For example, the metadata includes one or more of: a duration for which the output is provided, whether the user cancelled or interrupted the output within a predetermined duration, and a timestamp of when the output was provided. This additional information about the output included in the metadata is referred to as “user engagement data” (e.g., user generated data about the output). In some examples, the user engagement data is collected (e.g., collected by any one of devices  200 ,  400 , and/or  600 ) for a predetermined duration after provision of the output. In some examples, the predetermined duration is 30 seconds, 1 minute, 2 minutes, 5 minutes, etc. As discussed below, user engagement data indicates a user&#39;s satisfaction with the output provided responsive to the natural language input. Accordingly, positive user engagement data can indicate that a correct output was provided responsive to the natural language input and thus information corresponding to the correct output and the natural language input is be provided as “user-validated” training data for a natural language parsing model of the domain. 
     As an example of the process described with respect to block  1102 , the electronic device obtains the natural language input “play Rolling in the Deep” that corresponds to the media domain. The output provided responsive to the natural language input includes the data item of the song “Rolling in the Deep” by the music artist “Adele.” The electronic device receives metadata corresponding to the output including the song “Rolling in the Deep.” For example, the metadata corresponding to the output includes the attributes of the song name “Rolling in the Deep,” the music artist name “Adele,” and the music album name “21.” These attributes respectively define a set of values for the respective “musicTitle,” “musicArtist,” and “musicAlbum” properties of the media domain. The additional information included in the metadata specifies that the song “Rolling in the Deep” by “Adele” was played for 3 minutes and 48 seconds (the entire length of “Rolling in the Deep” by Adele) and that the user did not cancel playback of the song for 3 minutes and 48 seconds. 
     At block  1104 , the electronic device determines, from the metadata, whether the output satisfies a predetermined criterion (e.g., using generating module  808 ). In some examples, the predetermined criterion is a duration for which the output is provided (e.g., 30 seconds). For example, the electronic device determines from the metadata specifying that the song “Rolling in the Deep” was played for 3 minutes and 48 seconds that the output including the song “Rolling in the Deep” satisfies the predetermined criterion. In some examples, the predetermined criterion is whether or not the user cancelled and/or interrupted the output within a predetermined duration (e.g., 30 seconds). For example, the electronic device determines from the metadata specifying that playback of the song “Rolling in the Deep” was not canceled by the user within 30 seconds that the output including the song “Rolling in the Deep” satisfies the predetermined criterion. 
     An output provided for more than a predetermined duration (e.g., 30 seconds) is interpreted by the device as a strong indication of user satisfaction with the output provided responsive to the natural language input. For example, if the output provided responsive to the natural language input “Play Rolling in the Deep” includes playback of the song “Rolling in the Deep” by “Adele” for more than 30 seconds, the user is likely satisfied with the output. In contrast, if the output provided responsive to “Play Rolling in the Deep” includes playback of the song “Hello” by “Adele,” the user quickly (e.g., within less than 30 seconds) cancels playback of the song because he or she is dissatisfied with the output. Accordingly, determining whether the output satisfies a predetermined criterion allows for determining whether a correct output was provided responsive to a natural language input based on user engagement data. As described below, in some examples, information corresponding to the correct output and the natural language input is thus provided as “user validated” training data for a natural language parsing model of the domain. 
     At block  1106 , in accordance with a determination that the output satisfies the predetermined criterion, the device compares the set of values to the natural language input to determine a parsing result (e.g., using generating module  808 ). The parsing result maps one or more words of the natural language input to one or more properties of the respective set of properties of the domain. For example, for the natural language input “play Rolling in the Deep,” the value “Rolling in the Deep” (for the “musicTitle” property of the domain) is compared to the natural language input to determine the parsing result mapping the domain property “musicTitle” to “Rolling in the Deep.” 
     In some examples, comparing the set of values to the natural language input to determine the parsing result comprises comparing the set of values to the natural language input using fuzzy matching. Comparing the set of values to the natural language input using fuzzy matching can be desirable in situations where the natural language input does not contain an exact match to one or more values specified in the metadata (corresponding to output provided responsive to the natural language input). For example, for the natural language input “play Roll in the Deep by Adele” and the output provided responsive to the natural language input including the song of “Rolling in the Deep,” the metadata specifies the set of values “Rolling in the Deep,” “Adele,” and “21.” However, despite the value “Rolling in the Deep” not matching “Roll in the Deep” of the natural language input, fuzzy matching techniques still associate the value “Rolling in the Deep” with the words “Roll in the Deep.” Accordingly, the parsing result maps the words “Roll in the Deep” to the “musicTitle” property of the domain. 
     In some examples, the parsing result determined by comparing the set of values to the natural language input is associated with a confidence score determined using the fuzzy matching. For example, the parsing result mapping the “musicTitle” property of the domain to “Roll in the Deep” is associated with a relatively high confidence score because the fuzzy matching algorithm determines that the value “Rolling in the Deep” (for the “musicTitle” property) closely matches the words “Roll in the Deep” of the natural language input. In contrast, if the value does not closely match one or more words of the natural language input, the parsing result mapping one or more words of natural language input to a property of the domain is determined to have a low confidence score. 
     At block  1108 , in accordance with a determination that the output satisfies the predetermined criterion, the electronic device provides the natural language input and the determined parsing result to generate and/or train a natural language parsing model of the domain. For example, the natural language input and the determined parsing result are provided to natural language parsing model generating module  808  and generating module  808  generates and/or trains a natural language model of the domain. For example, the electronic device provides the natural language input “play Rolling in the Deep” and the parsing result mapping “Rolling in the Deep” to “musicTitle” to generating module  808 . Accordingly, the generated natural language parsing model of the domain is trained to generate parsing results mapping the words “Rolling in the Deep” to “musicTitle.” 
     In some examples, the device provides the natural language input and the determined parsing result in accordance with a determination that the confidence score associated with the parsing result exceeds a threshold. Providing the natural language input and the determined parsing result in accordance with determining that the confidence score exceeds a threshold may be desirable to allow parsing results with sufficient confidence scores to be provided to train and/or generate a natural language parsing model of the domain. For example, for the natural language input “Play Rolling in the Deep by Adele,” the confidence score associated with the parsing result mapping “Rolling in the Deep” to the domain property “musicTitle” and “Adele” to the domain property “musicArtist” exceeds the threshold. Accordingly, the natural language input and the parsing result are provided to generate a natural language parsing model of the domain. 
     In some examples, generating and/or training the natural language parsing model of the domain using the natural language input and the determined parsing result comprises providing the natural language input and the determined parsing result as training data for a neural network (e.g., neural network  900 ). For example, a set of optimal weighting values for the connections (e.g., connections  918 ) in a neural network are determined using the training data. The neural network is thus trained (e.g., trained by generating module  808 ) to generate parsing results mapping, for example, “Rolling in the Deep” to “musicTitle” when the natural language input including “Rolling in the Deep” is input into the neural network. 
     In some examples, the natural language parsing model of the domain is configured to receive a second natural language input and to generate a plurality of candidate parsing results. Each candidate parsing result of the plurality of candidate parsing results comprises a respective mapping of one or more properties of the domain to one or more words of the second natural language input. For example, the second natural language input is a subsequent natural language input of “Play Rolling in the Deep by Adele” and the generated plurality of candidate parsing results includes a first parsing result mapping “Rolling in the Deep” to “musicTitle” and a second parsing result mapping “Rolling in the Deep by Adele” to “musicTitle.” Because the natural language parsing model of the domain has been trained to prefer parsing results mapping “Rolling in the Deep” to “musicTitle,” the first parsing result is ranked higher than the second parsing result. 
     In some examples, the electronic device provides the natural language parsing model of the domain to a second electronic device, the second electronic device configured to perform natural language processing services. For example, the device that performs blocks  1102 - 1108  provides the natural language parsing model to one or more devices (e.g., devices  200 ,  400 , or,  600 ). 
     In some examples, in accordance with a determination that the output does not satisfy the predetermined criterion, the electronic device forgoes performing the operations described with respect to blocks  1106  and  1108 . In particular, the parsing result is not determined using the metadata corresponding to the output and thus the parsing result is not provided to generate a natural language parsing model of the domain. Rather, in some examples, in accordance with a determination that the output does not satisfy the predetermined criterion, the metadata corresponding to the output is discarded. 
     Forgoing the operations in block  1106  and  1108  when the output provided responsive to the natural language input does not satisfy the predetermined criterion can be desirable, as the electronic device does not waste processing power and/or battery resources determining a parsing result using metadata corresponding to an output the device interprets as an incorrect response to the natural language input. In this way, the generation of erroneous parsing results as training data for the natural language parsing model may be avoided. 
     At block  1110 , in accordance with a determination that the output does not satisfy the predetermined criterion, the electronic device obtains a second set of metadata corresponding to a second output provided after the output responsive to the natural language input. The second output is output within a second predetermined duration of the first output ceasing to be provided. The second output includes a second data item and the second set of metadata includes a second set of attributes corresponding to the second data item. The second set of attributes defines a second set of values for a second respective set of properties of the domain. 
     For example, suppose the output provided responsive to the natural language input “Play Rolling in the Deep by Adele” erroneously includes playback of the data item “Hello” by “Adele.” In some examples, this is because the parsing result used to generate the task flow of searching for and playing a song erroneously maps “in the Deep” to “musicTitle” and “Adele” to “musicArtist.” Because “in the Deep” is not a song by the artist “Adele,” the electronic device causes playback of a random erroneous song by the artist “Adele” (e.g., the song “Hello”). A user cancels playback of “Hello” by “Adele” within a predetermined duration (e.g., within 30 seconds) because the user intended to play “Rolling in the Deep” by “Adele,” not “Hello” by “Adele.” Within the second predetermined duration (e.g., a short duration such as thirty seconds) of cancelling playback of “Hello” by “Adele,” the user manually searches for the song “Rolling in the Deep” by Adele and causes it to be played. Accordingly, in the present example, the electronic device determines from the metadata corresponding to the output including the song “Hello,” that the output does not satisfy the predetermined criterion (e.g., a duration of 30 seconds) and determines that the second output “Rolling in the Deep” is output within the second predetermined duration of canceling the output of the song “Hello.” Because the second output (e.g., “Rolling in the Deep”) is output within a short duration of the previous output ceasing to be provided, the second output is likely the correct output that should be provided responsive to the natural language input. Thus, in the present example, the electronic device obtains a second set of metadata corresponding to the second output including the song “Rolling in the Deep” by “Adele.” The second set of metadata includes the second set of attributes of “Rolling in the Deep,” “Adele,” and “21” corresponding to the song “Rolling in the Deep” by “Adele.” The second set of attributes defines a second set of values for the second set of respective “musicTitle,” “musicArtist,” and “musicAlbum” properties of the domain. 
     In some examples, after the electronic device obtains the second set of metadata corresponding to the second output provided responsive to the natural language input, process  1100  returns to block  1104 , where the electronic device determines from the second set of metadata, whether the second output satisfies the predetermined criterion. For example, because the user intended to play the song “Rolling in the Deep” by “Adele,” the song “Rolling in the Deep” is played for a predetermined duration that satisfies the predetermined criterion. Accordingly, the electronic device determines that the second output including the song “Rolling in the Deep” satisfies the predetermined criterion. At block,  1106  in accordance with a determination that the second output satisfies the predetermined criterion, the electronic device compares the second set of values to the natural language input to determine a second parsing result. The second parsing result maps one or more words of the natural language input to one or more properties of the second respective set of properties of the domain. For example, the electronic device compares the values of “Rolling in the Deep,” “Adele,” and, “21” to the natural language input “Play Rolling in the Deep by Adele” to determine the second parsing result mapping “Rolling in the Deep” to “musicTitle” and “Adele” to “musicArtist.” 
     At block  1108 , in accordance with a determination that the second output satisfies the predetermined criterion, the electronic device provides the natural language input and the determined second parsing result to generate a natural language parsing model of the domain. 
     As described, providing the natural language input and the determined second parsing result to generate a natural language parsing model of the domain can allow the device to generate correct training data even when the initial output provided responsive to the natural language input was incorrect (e.g., determined not to satisfy a predetermined criterion). In particular, as described, the device uses metadata corresponding to a correct output that should have been provided responsive to the natural language input to generate a second parsing result for the natural language input. The second parsing result for the natural language input and the natural language input can thus be provided as correct training data to generate a natural language parsing model of the domain. 
     The operations described above with reference to  FIG. 11  are optionally implemented by components depicted in  FIGS. 1-4, 6A-6B, 7A-7C, 8 and 9 . For example, the operations of process  1100  may be implemented by natural language parsing system  800 , neural network  900 , natural language processing module  732 , and task flow processing module  736 . 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-6B, 7A-7C, 8, and 9 . 
     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 natural language processing. 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, twitter IDs, home addresses, data or records relating to a user&#39;s health or level of fitness (e.g., vital signs measurements, medication information, exercise information), date of birth, or any other identifying or personal 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 improve natural language processing and to more accurately provide media items to users. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure. For instance, health and fitness data may be used to provide insights into a user&#39;s general wellness, or may be used as positive feedback to individuals using technology to pursue wellness goals. 
     The present disclosure 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. Such policies should be easily accessible by users, and should be updated as the collection and/or use of data changes. 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/sharing should occur after receiving the informed consent of the users. Additionally, such entities should consider taking 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. In addition, policies and practices should be adapted for the particular types of personal information data being collected and/or accessed and adapted to applicable laws and standards, including jurisdiction-specific considerations. For instance, in the US, collection of or access to certain health data may be governed by federal and/or state laws, such as the Health Insurance Portability and Accountability Act (HIPAA); whereas health data in other countries may be subject to other regulations and policies and should be handled accordingly. Hence different privacy practices should be maintained for different personal data types in each country. 
     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 collecting user engagement data, 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 or anytime thereafter. In another example, users can select not to provide user engagement data to train natural language parsing models. In yet another example, users can select to limit the length of time user engagement data is maintained or entirely prohibit the collection of user engagement data. In addition to providing “opt in” and “opt out” options, the present disclosure contemplates providing notifications relating to the access or use of personal information. For instance, a user may be notified upon downloading an app that their personal information data will be accessed and then reminded again just before personal information data is accessed by the app. 
     Moreover, it is the intent of the present disclosure that personal information data should be managed and handled in a way to minimize risks of unintentional or unauthorized access or use. Risk can be minimized by limiting the collection of data and deleting data once it is no longer needed. In addition, and when applicable, including in certain health related applications, data de-identification can be used to protect a user&#39;s privacy. De-identification may be facilitated, when appropriate, by removing specific identifiers (e.g., date of birth, etc.), controlling the amount or specificity of data stored (e.g., collecting location data at a city level rather than at an address level), controlling how data is stored (e.g., aggregating data across users), and/or other methods. 
     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, natural language parsing results and the presentation of media items can be 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 natural language parsing models, or publicly available information.

Metadata:
Filing Date: 20180619
Publication Date: 20200804
Grant Date: 20200804
Priority Date: 20180131
Inventors: LI, LIN
MURALIDHARAN, DEEPAK
YANG, XIAO
KAO, JUSTINE
COLINJIVADI VISWANATHAN, LAVANYA
IBRAHIM, MUBARAK ALI SEYED
GARG, ASHISH
Assignee: APPLE INC
CPC Classifications: [{"code": "G06N3/042", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N7/01", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06N3/044", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06N5/022", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/08", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L15/22", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06F40/205", "inventive": true, "first": true, "tree": "[]"}, {"code": "G10L2015/223", "inventive": false, "first": false, "tree": "[]"}, {"code": "G10L15/22", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N5/022", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F40/211", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06N5/022", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F40/211", "inventive": true, "first": true, "tree": "[]"}, {"code": "G10L15/22", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L2015/223", "inventive": false, "first": false, "tree": "[]"}]
Family ID: 67391426