Patent Publication Number: US-11646029-B2

Title: Multiple digital assistant coordination in vehicular environments

Description:
BACKGROUND 
     Excessive network transmissions, packet-based or otherwise, of network traffic data between computing devices can prevent a computing device from properly processing the network traffic data, completing an operation related to the network traffic data, or responding timely to the network traffic data. The excessive network transmissions of network traffic data can also complicate data routing or degrade the quality of the response when the responding computing device is at or above its processing capacity, which may result in inefficient bandwidth utilization. A portion of the excessive network transmissions can include transmissions for requests that are not valid requests. 
     SUMMARY 
     According to an aspect of the disclosure, a system to selectively invoke applications for execution can include a data processing system of a vehicle. A natural language processor component executed by the data processing system can receive, via an interface, an input audio signal. The natural language processor component can parse the input audio signal to identify a request and a command corresponding to the request. A configuration tracker executed by the data processing system can identify a first functionality of a first digital assistant application hosted on the data processing system in the vehicle and a second functionality of a second digital assistant application accessible via a client device communicatively coupled to the data processing system. An application selector executed by the data processing system can determine that one of the first functionality or the second functionality supports the command corresponding to the request. The application selector can select one of the first digital assistant application hosted on the data processing system or the second digital assistant application accessible via the client device based on the determination that one of the first functionality or the second functionality supports the command. The application selector can invoke one of the first digital assistant application or the second digital assistant application, selected based on the determination that one of the first functionality or the second functionality supports the command. 
     According to an aspect of the disclosure, a method to validate vehicular functions can include a natural language processor component executed by a data processing system of a vehicle receiving, via an interface, an input audio signal. The method can include the natural language processor component parsing the input audio signal to identify a request and a command corresponding to the request. The method can include a configuration tracker executed by the data processing system identifying a first functionality of a first digital assistant application hosted on the data processing system in the vehicle and a second functionality of a second digital assistant application accessible via a client device communicatively coupled to the data processing system. The method can include an application selector executed by the data processing system, determining that one of the first functionality or the second functionality supports the command corresponding to the request. The method can include the application selector selecting one of the first digital assistant application hosted on the data processing system or the second digital assistant application accessible via the client device based on the determination that one of the first functionality or the second functionality supports the command. The method can include the application selector invoking one of the first digital assistant application or the second digital assistant application, selected based on the determination that one of the first functionality or the second functionality supports the command. 
     These and other aspects and implementations are discussed in detail below. The foregoing information and the following detailed description include illustrative examples of various aspects and implementations and provide an overview or framework for understanding the nature and character of the claimed aspects and implementations. The drawings provide illustration and a further understanding of the various aspects and implementations, and are incorporated in and constitute a part of this specification. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings are not intended to be drawn to scale. Like reference numbers and designations in the various drawings indicate like elements. For purposes of clarity, not every component may be labeled in every drawing. In the drawings: 
         FIG.  1    illustrates an example system to selectively invoke applications for execution. 
         FIG.  2    illustrates a top view of the vehicle and illustrates the interior cabin of the vehicle illustrated in  FIG.  1   . 
         FIG.  3    illustrates a method to selectively invoke applications for execution using the example system illustrated in  FIG.  1   . 
         FIG.  4    is a block diagram of an example computer system. 
     
    
    
     DETAILED DESCRIPTION 
     Following below are more detailed descriptions of various concepts related to, and implementations of, methods, apparatuses, and systems to selectively invoke applications for execution in vehicular environments. The vehicular environment can include the vehicle and its surroundings, including a radius around the vehicle (e.g. 100 feet) as well as devices that are connected to the vehicle via wired or wireless electronic communication systems. The various concepts introduced above and discussed in greater detail below may be implemented in any of numerous ways. 
     The present disclosure is generally directed to a data processing system to selectively invoke applications for execution in vehicular environments. The systems and methods described herein can include a data processing system in a vehicle that can receive an audio input query, which can also be referred to herein as an audio input signal. From the audio input query, the data processing system can identify a request. The request can correspond to a command to perform one of various functions, such as a vehicular function (e.g., to open a window or to activate a windshield wiper) or a non-vehicular function (e.g., to make a phone call or to access a calendar). Digital assistant applications can carry, obtain, respond to, or process the command extracted from the audio input query. The digital assistant application can be a program or script executed on the data processing system, the vehicle, and the client device (e.g., a smartphone, tablet, laptop, desktop, etc.) interfacing with the data processing system via the cloud. The digital assistant application can receive audio input queries, process the requests associated with such queries using natural language processing algorithms, and then present an audio response in a conversational manner. 
     There can be multiple digital assistant applications available in the vehicular environment, each with various functionalities to carry out the requested command. Certain commands may be performed by a primary digital assistant application hosted on the data processing system in the vehicle, while other commands can be performed by a secondary digital assistant application accessible via a client device (e.g., smartphone or tablet). The client device and the data processing system can have established a communication link with each other. Even with commands that may be carried out either on the data processing system or through the client device, some commands can be executed faster or more efficiently at one digital assistant application versus the other digital assistant application because such commands may be performed via network access. With vehicular environments, however, there may be limited connectivity to the network. The data processing system on the vehicle itself may not be connected to the network. In addition, the client device may move in and out of range of wireless networks. As such, the functionalities of either the primary or secondary digital assistant application may vary. 
     To carry out the identified command, the data processing system can identify functionalities of the primary digital assistant application hosted in the vehicle and functionalities of the secondary digital assistant application accessible via the client device. With the functionalities of both applications identified, the data processing system can determine which digital assistant application supports the command as requested in the audio input query. Based on the determination, the data processing system can select which of the primary or secondary digital assistant application to use in carrying out the requested command. Once selected, the data processing system can invoke the corresponding digital assistant application. When the primary digital assistant application is selected, the data processing system can execute the requested command via the primary digital assistant application. Conversely, when the secondary digital assistant application is selected, the data processing system can send a command to the client device via the communication link to carry out the requested command using the secondary digital assistant application. 
     The present solution can improve efficiency and effectiveness of auditory data packet processing by selecting the appropriate digital assistant application to perform the command of the request contained in the auditory data packet. As the functionalities may vary across the multiple digital assistant applications, the present solution also can reduce network bandwidth and computing resource consumption by selecting the digital assistant application that can perform the command more efficiently than the other applications. From a human-computer interaction (HCI) perspective, the seamless transition in selectively invoking one of multiple digital assistant applications may improve the utility of all the digital assistant applications available in the vehicular environment. 
       FIG.  1    illustrates an example system  100  to selectively invoke applications for execution in vehicular environments. The system  100  can include at least one data processing system  102 , one or more client devices  132 , and one or more vehicles  144 . The one or more client devices  132  can be communicatively coupled to the one or more vehicles  144 , and vice-versa. The at least one data processing system  102 , one or more client devices  132 , and one or more vehicles  144  can be communicatively coupled to one another via the network  146 . 
     The data processing system  102  can include an interface  104 . The data processing system  102  can include a natural language processor (NLP) component  106  to parse audio-based inputs. The data processing system  102  can include an interface management component  108  to detect and manage the interfaces of other devices in the system  100 . The data processing system  102  can include an audio signal generator component  110  to generate audio-based signals. The data processing system  102  can include a direct action application programming interface (API)  112 . The data processing system  102  can include a response selector component  114  to select responses to audio-based input signals. The NLP component  106 , the interface management component  108 , the audio signal generator component  110 , the data repository  122 , the direct action API  112 , and the response selector component  114  may be part of a digital assistant application  142 . The data processing system  102  can include a data repository  122 . The data repository  122  can store parameters  124 , policies  126 , response data  128 , and templates  130 . The data processing system  102  can also include a configuration tracker component  116 , an application selector component  118 , and a performance evaluator component  120 , among others. 
     The functionalities of the data processing system  102 , such as the digital assistant application  142 , can be included or otherwise be accessible from the one or more client devices  132  and the one or more vehicles  144 . The functionalities of the data processing system  102  may correspond to the functionalities or interface with the digital assistant application  142  executing on the client devices  132  or the vehicles  144 . The client devices  132  can each include and execute a separate instance of the one or more components of the digital assistant application  142 . The client devices  132  can otherwise have access to the functionalities of the components of the digital assistant application  142  on a remote data processing system  102  via the network  146 . For example, the client device  132  can include the functionalities of the NLP component  106  and access the remainder of the components of the digital assistant application  142  via the network  146  to the data processing system  102 . The vehicle  144  can include and execute a separate instance of the components of the data processing system  102 , such as the digital assistant application  142 , the configuration tracker component  116 , the application selector component  118 , and the performance evaluator component  120 . For example, the digital assistant application  142  may be pre-installed on a head unit of a vehicle  144 . The digital assistant applications  142  accessible or executing on the client devices  132  and the vehicles  144  may each have different functionalities. 
     The client devices  132  and the vehicles  144  can each include at least one logic device such as a computing device having a processor to communicate with each other with the data processing system  102  via the network  146 . The client devices  132  and the vehicles  144  can include an instance of any of the components described in relation to the data processing system  102 . The client devices  132  and the vehicles  144  can include an instance of the digital assistant application  142 . The client devices  132  can include a desktop computer, laptop, tablet computer, personal digital assistant, smartphone, mobile device, portable computer, thin client computer, virtual server, speaker-based digital assistant, or other computing device. The vehicle  144  can be a car, truck, motorcycle, airplane, blimp, boat, submarine, or other transportation device. The vehicle  144  can include one or more processors that can execute an instance of the digital assistant application  142  or any component thereof. The processors can be a component of the head unit disposed in the vehicle  144 , such as a head unit in an automobile. 
     The components of the system  100  can communicate over a network  146 . In some implementations, one or more of the client devices  132  can be located within an instance of the vehicle  144 . For example, the client devices  132  can be the mobile phone of a driver driving the vehicle  144 . In some implementations, one or more of the client devices  132  can be remote to the vehicles  144 . For example, after the driver parks and exits the vehicle  144  for work, the driver&#39;s mobile phone may be remote to the vehicle  144 . 
     The network  146  can include, for example, a point-to-point network, a broadcast network, a wide area network, a local area network, a telecommunications network, a data communication network, a computer network, an ATM (Asynchronous Transfer Mode) network, a SONET (Synchronous Optical Network) network, a SDH (Synchronous Digital Hierarchy) network, an NFC (Near-Field Communication) network, a local area network (LAN), a wireless network or a wireline network, and combinations thereof. The network  146  can include a wireless link, such as an infrared channel or satellite band. The topology of the network  146  may include a bus, star, or ring network topology. The network  146  can include mobile telephone networks using any protocol or protocols used to communicate among mobile devices, including advanced mobile phone protocol (AMPS), time division multiple access (TDMA), code-division multiple access (CDMA), global system for mobile communication (GSM), general packet radio services (GPRS), or universal mobile telecommunications system (UMTS). Different types of data may be transmitted via different protocols, or the same types of data may be transmitted via different protocols. 
     The network  146  can include a short-range communication link (e.g., ranging up to 30 meters) established between the client devices  132  and the vehicles  144 , using Bluetooth, Bluetooth Low Energy, Dedicated Short-Range Communications (DSRC), or Near-Field Communications (NFC) protocols. Using such protocols, the data processing system  102  can establish a communication link with one or more client devices  132  via the interface  104 . The data processing system  102  can establish a communication link with one or more vehicles  144  via the interface  104 . The short-range communication link may be established between the client devices  132  and the vehicles  144  via pairing protocol. 
     The client devices  132  can include sensors  134 , speakers  136 , peripheral devices  138 , interfaces  104 , and transducers  140 . The client devices  132  can execute an instance of the digital assistant application  142 . The vehicles  144  can include sensors  134 , speakers  136 , peripheral devices  138 , interfaces  104 , and transducers  140 . The vehicles  144  can execute an instance of the NLP component  106 . The peripheral devices  138  for the client device  132  can include user input/output devices, such as a keyboard, a monitor, and headphones, among others. The peripheral devices  138  for the vehicle  144  can include any other devices, such as a garage door opener, a window opener, a passenger door opener, a trunk opener, a multimedia device player, and a temperature control, among others. 
     The client devices  132  and the vehicle  144  can include at least one sensor  134 , at least one transducer  140 , at least one audio driver, and at least one speaker  136 . The sensor  134  can include a microphone or audio input sensor. The sensor  134  can also include at least one of a GPS sensor, proximity sensor, ambient light sensor, temperature sensor, motion sensor, accelerometer, or gyroscope. The sensor can include an occupancy or weight sensor. The transducer  140  can convert the audio input into an electronic signal. The audio driver can include a script or program executed by one or more processors of the client devices  132  or the vehicle  144  to control the speaker  136 . The speaker  136  can render audio signals by converting electrical signals into audible waves. 
     The client devices  132  and the vehicle  144  can be associated with an end user that enters voice queries as input audio signals into the client devices  132  or the vehicle  144  (via the sensor  134 ) and receives audio output in the form of a computer generated voice that can be provided from the data processing system  102 . In response to the input audio signals, the client devices  132  and the vehicle  144  can also receive action data structures to perform predetermined functions or actions. The interface  104  can receive or provide data messages to the direct action API  112  of the data processing system  102  and enable communication between the components of the system  100 . The client devices  132  and the vehicle  144  can also include a user interface that enables a user to interact with the components of the system  100 . 
     The data processing system  102  can include at least one server having at least one processor. For example, the data processing system  102  can include a plurality of servers located in at least one data center or server farm. The data processing system  102  can determine from an audio input signal a request and a trigger keyword associated with the request. Based on the request and trigger keyword, the data processing system  102  can generate or select response data. The response data can be audio-based or text-based. For example, the response data can include one or more audio files that, when rendered, provide an audio output or acoustic wave. The data within the response data can also be referred to as content items. The response data can include other content (e.g., text, video, or image content) in addition to audio content. 
     The data processing system  102  can include multiple, logically grouped servers and facilitate distributed computing techniques. The logical group of servers may be referred to as a data center, server farm, or a machine farm. The servers can be geographically dispersed. A data center or machine farm may be administered as a single entity, or the machine farm can include a plurality of machine farms. The servers within each machine farm can be heterogeneous—one or more of the servers or machines can operate according to one or more type of operating system platform. The data processing system  102  can include servers in a data center that are stored in one or more high-density rack systems, along with associated storage systems, located for example in an enterprise data center. In this way, the data processing system  102  with consolidated servers can improve system manageability, data security, the physical security of the system, and system performance by locating servers and high performance storage systems on localized high performance networks. Centralization of all or some of the data processing system  102  components, including servers and storage systems, and coupling them with advanced system management tools allows more efficient use of server resources, which saves power and processing requirements and reduces bandwidth usage. Each of the components of the data processing system  102  can include at least one processing unit, server, virtual server, circuit, engine, agent, appliance, or other logic device such as programmable logic arrays configured to communicate with the data repository  122  and with other computing devices. 
     The data processing system  102  can include the data repository  122 . The data repository  122  can include one or more local or distributed databases and can include a database management system. The data repository  122  can include computer data storage or memory and can store one or more parameters  124 , one or more policies  126 , response data  128 , and templates  130 , among other data. The parameters  124 , policies  126 , and templates  130  can include information such as rules about a voice based session between the client devices  132 , the data processing system  102 , and the vehicle  144 . The parameters  124 , policies  126 , and templates  130  can also include information for another digital assistant application  142  received via the interface  104  from another source (e.g., the data processing system  102 , the client devices  132 , and the vehicles  144 ). For example, the parameters  124 , policies  126 , and templates  130  stored in the data repository  122  of a digital assistant application  142  hosted on a vehicle  144  can include the parameters  124 , policies  126 , and templates  130  from the data repository  122  of a digital assistant application  142  accessible via the client device  132 , and vice-versa. In this manner, the parameters  124 , policies  126 , and templates  130  of different digital assistant applications  142  may be shared and used by one another. The response data  128  can include content items for audio output or associated metadata, as well as input audio messages that can be part of one or more communication sessions with the client devices  132 . 
     An application, script, program, or other components that are associated with the data processing system  102  can be installed at the client devices  132  or the vehicle  144 . The application can enable the client devices  132  or vehicle  144  to communicate input audio signals (and other data) to the interface  104  of the data processing system  102 . The application can enable the client devices  132  and the vehicle  144  to drive components of the client devices  132  and the vehicle  144  to render the output audio signals. 
     The NLP component  106  can receive input audio signals. The data processing system  102  can receive the input audio signal from the client devices  132  or the vehicle  144  (e.g., via the transducers  140 ) included in a data packet. A first device can execute the NLP component  106 , and the NLP component  106  can receive the input audio signal from a second device. For example, a client device  132  can receive the input audio signal from and the data processing system  102  can execute the NLP component  106 . 
     The NLP component  106  can convert input audio signals into recognized text by comparing the input audio signal against a stored, representative set of audio waveforms and choosing the closest matches. The representative waveforms can be generated across a large set of input audio signals. Once the input audio signal is converted into recognized text, the NLP component  106  can match the text to words that are associated, for example, via a learning phase, with actions or output audio signals. 
     From the input audio signal, the NLP component  106  can identify at least one request or at least one command corresponding to the request. The request can indicate intent or subject matter of the input audio signal. The command can indicate a type of action likely to be taken. For example, the NLP component  106  can parse the input audio signal to identify at least one request to open the vehicle&#39;s windows or skip to a next audio file in a music playlist. The command can include at least one word, phrase, root or partial word, or derivative indicating an action to be taken. The request can also include a trigger keyword. The trigger keyword may be associated with a particular digital assistant application accessible via the client device  132  or running on the vehicle  144 . For example, the trigger keywords “go” may be associated with the digital assistant application accessible via the client device  132 , and “ok” may be associated with the digital assistant application running on the vehicle  144 . Which digital assistant application is selected to execute the indicated command is detailed herein. 
     The NLP component  106  can also determine whether the at least one command corresponding to the request of the input audio signal relates to a vehicle functionality of the vehicle  144  or to a non-vehicle functionality of the client device  132 . Upon extraction of the intent, subject matter, and the command from the input audio signal, the NLP component  106  can compare the word, phrases, root or partial words, or derivatives to a semantic knowledge graph. The semantic knowledge graph may specify a set of words, phrases, root or partial words, or derivatives relating to a vehicle functionality and non-vehicle functionality. For example, “windshield” may be marked as related to a vehicle functionality and “phonebook” may be marked as related to a non-vehicle functionality. The semantic knowledge graph may also specify a relationship between each word, phrase, root or partial word, or derivative. The semantic knowledge graph may be maintained at the data repository  122 . Based on the comparison with the semantic knowledge graph, the NLP component  106  can determine whether the word, phrase, root or partial word, or derivative of the command relates to a vehicle functionality or a non-vehicle functionality. 
     The response selector component  114  can obtain information from the data repository  122  where it can be stored as part of the response data  128 . The response selector component  114  can query the data repository  122  to select or otherwise identify response phrases or content items, e.g., from the response data  128 . 
     The audio signal generator component  110  can generate or otherwise obtain an output signal that includes the content item. The data processing system  102  can execute the audio signal generator component  110  to generate or create an output signal corresponding to the content item or request. For example, once a request is fulfilled, the audio signal generator component  110  can generate an audio output signal that includes the phrase “The action was completed.” 
     The interface  104  can be a data interface or a network interface that enables the components of the system  100  to communicate with one another. The interface  104  of the data processing system  102  can provide or transmit one or more data packets that include the action data structure, audio signals, or other data via the network  146  to the client devices  132  or vehicle  144 . For example, the data processing system  102  can provide the output signal from the data repository  122  or from the audio signal generator component  110  to the client devices  132 . The data processing system  102  can also instruct, via data packet transmissions, the client devices  132  or the vehicle  144  to perform the functions indicated in the action data structure. The output signal can be obtained, generated, transformed to, or transmitted as one or more data packets (or other communications protocol) from the data processing system  102  (or other computing device) to the client devices  132  or the vehicle  144 . 
     The direct action API  112  of the data processing system  102  can generate, based on, for example, the request, action data structures. The action data structure can include data or instructions for the execution of a specified action to satisfy the request. In some implementations, the action data structure can be a JSON formatted data structure or an XML formatted data structure. The direct action API  112  may be prevented from carrying out the action specified in the request until invocation by the application selector component  118 . The details of the functionalities of the application selector component  118  in relation to the direct action API  112  are explained below. 
     Depending on the action specified in the request, the direct action API  112  can execute code or a dialog script that identifies the parameters required to fulfill the request. The action data structures can be generated responsive to the request. The action data structure can be included in messages that are transmitted to or received by the client devices  132  or the vehicle  144 . Based on the request parsed by the NLP component  106 , the direct action API  112  can determine to which of the client devices  132  or the vehicles  144  the message should be sent for processing. Whether the client device  132  or the vehicle  144  is selected to process the message is detailed below with respect to the application selector component  118 . The direct action API  112  can package the request into an action data structure for transmission to the vehicle  144 . The direct action API  112  can access a vehicle ID from the response data  128  to determine which vehicle is associated with the user that generated the request. Once received, the vehicle  144  can process the action data structure and can perform the indicated action. The direct action API  112  can also package the request into an action data structure for execution by the client device  132 . Once received, the client device  132  can process the action data structure using the digital assistant application  142  or one or more applications running on the client device  132 . 
     The action data structure can include information for completing the request. For example, the action data structure can be an XML or JSON formatted data structure that includes attributes used in completing or otherwise fulfilling the request. The attributes can include a location of the vehicle  144 , a location of the client devices  132 , an authorization level of a user associated with a client devices  132 , a vehicle identifier, an interface identifier, a vehicular state, or a request state. In some implementations, the request state includes one or more attributes that should be satisfied before the action is fulfilled. For example, with the request “Ok, change the song,” the request state may have the attribute {requester: [authorized, passenger]}, indicating that the request should be an explicitly authorized user or a passenger in the vehicle. 
     The direct action API  112  can retrieve a template  126  from the repository  122  to determine which fields or attributes to include in the action data structure. The direct action API  112  can determine necessary parameters and can package the information into an action data structure. The direct action API  112  can retrieve content from the repository  122  to obtain information for the attributes of the data structure. 
     The direct action API  112  can populate the fields with data from the input audio signal. The direct action API  112  can also populate the fields with data from the client devices  132  or the vehicle  144 , or from another source. The direct action API  112  can prompt a user for additional information when populating the fields. The templates  130  can be standardized for different types of actions, such as playing media files through the vehicle&#39;s head unit, responding to messages, and performing functions within the vehicle  144 . The action data structure can initially be generated by a direct action API  112  executed by a remote data processing system  102 . The remote data processing system  102  can transmit the action data structure to the data processing system  102  of the vehicle  144 , which can add fields and attributes to the action data structure. 
     The direct action API  112  can obtain response data  128  (or parameters  124  or policies  126 ) from the data repository  122 , as well as data received with end user consent from the client devices  132  to determine location, time, user accounts, and logistical or other information in order to reserve a car from the car share service. The response data  128  (or parameters  124  or policies  126 ) can be included in the action data structure. When the content included in the action data structure includes end user data that is used for authentication, the data can be passed through a hashing function before being stored in the data repository  122 . 
     The data processing system  102  can include, interface, or otherwise communicate with the configuration tracker component  116 . The configuration tracker component  116  can be hosted on and executed from the vehicle  144 . The configuration tracker component  116  can identify a functionality of each digital assistant application. The configuration tracker component  116  can identify each digital assistant application  142  running on or otherwise accessible via a client device  132 . The configuration tracker component  116  can identify each digital assistant application  142  running on the vehicle  144 . The client device  132  may be communicatively coupled (e.g., via network  146  or via a short-range communication link) to the data processing system  102  or the vehicle  144 . With the communication link established, each digital assistant application  142  accessible from the vehicle  144 , such as the digital assistant application  142  running on the vehicle  144  and the digital assistant application  142  accessible via the client device  132 , may be prevented from executing the action specified by the command until invoked by the application selector component  118 . In addition, the transducer  140  at the client device  132  may also be disabled until the digital assistant application  142  is invoked by the application selector component  118 . Details of the functionality of the application selector component  118  are explained below. 
     The functionalities of the digital assistant application  142  hosted on the vehicle  144  (sometimes referred to as a “primary digital assistant application”) may differ from the functionalities of the digital assistant application  142  accessible via the client device  132  (sometimes referred to as a “secondary digital assistant application”). For example, the digital assistant application  142  installed on the vehicle  144  may activate various function on the vehicle  144 , such as opening a door, turning on a windshield, or changing temperature controls. On the other hand, the digital assistant application  142  accessible through the client device  132  may carry out other functions, such as making a telephone call or accessing a calendar. For functionalities of the digital assistant application  142  accessible via the client device  132  that do not differ from the functionalities of the digital assistant application  142  hosted on the vehicle  144 , one of the digital assistant applications  142  may be able to execute the functionality faster or more efficiently. 
     The configuration tracker component  116  can identify the functionalities of each digital assistant application  142  accessible via the client device  132 . There may be multiple digital assistant applications  142  accessible via multiple client devices  132 . The configuration tracker component  116  can identify an application profile of the digital assistant application  142 . The application profile may include metadata for the digital assistant application  142  and may indicate a name and a type for the application. The configuration tracker component  116  can identify a version number of the digital assistant application  142 . The configuration tracker component  116  can identify a connectivity of the client device  132  to the vehicle  144  or to an external network (e.g., the network  146 ) for accessing the remote data processing system  102 . The configuration tracker component  116  can identify one or more routines or processes (e.g., application programming interface (API) calls) supported or used by the digital assistant application  142 . The configuration tracker component  116  can identify one or more peripheral devices  138  interfacing with the client device  132 . The one or more peripheral devices  138  may include input/output devices, such as a monitor, keyboard, and card-reader, among others, connected to the client device  132 . The configuration tracker component  116  can identify consumption of computing resources at the client device  132  (e.g., CPU time, remaining battery life, or memory use, for example) through which the digital assistant application  142  is accessed. 
     The configuration tracker component  116  can identify the functionalities of the digital assistant application  142  hosted on the vehicle  144 . The configuration tracker component  116  can identify an application profile of the digital assistant application  142 . The application profile may include metadata for the digital assistant application  142  and may indicate a name and a type for the application. The configuration tracker component  116  can identify a version number of the digital assistant application  142 . The version number of the digital assistant application  142  hosted on the vehicle  144  may differ from the version number of the digital assistant application  142  accessible via the client device  132 . The configuration tracker component  116  can identify a connectivity of the vehicle  144  to the client device  132  or to an external network (e.g., the network  146 ) for accessing the remote data processing system  102 . The configuration tracker component  116  can identify one or more routines or processes (e.g., application programming interface (API) calls) supported or used by the digital assistant application  142 . The configuration tracker component  116  can identify one or more peripheral devices  138  interfacing with the vehicle  144 . The one or more peripheral devices  138  may include various devices in the vehicle  144 , such as a temperature control, passenger door opener, trunk opener, garage door opener, a steering wheel, and multimedia device, among others accessible by the digital assistant application  142  running from the vehicle  144 . The configuration tracker component  116  can identify consumption of computing resources (e.g., CPU time, remaining battery life, memory use, etc.) at the vehicle  144  hosting the digital assistant application  142 . 
     The data processing system  102  can include, interface, or otherwise communicate with the application selector component  118 . The application selector component  118  can be hosted on and executed from the vehicle  144 . The application selector component  118  can determine whether the functionality of the digital assistant application  142  accessible via the client device  132  or the functionality of the digital assistant application  142  hosted on the vehicle  144  can support the command corresponding to the request. The application selector component  118  can identify an action specified by the command as processed by the NLP component  106 . The application selector component  118  can parse the action data structure generated by the direct action API  112  to identify the specified action. 
     To determine that the specified action is (or is not) supported by the digital assistant application  142 , the application selector component  118  can compare the operational capabilities or functionality of the digital assistant application  142  accessible via the client device  132  to the action specified by the command. The application selector component  118  can also compare the operational capabilities or functionality of the digital assistant application  142  hosted on the vehicle  144 . The application selector component  118  can determine that the functionality of the digital assistant application  142  can support the action specified by the command based on combination of factors. The application selector component  118  can determine that the action corresponding to one or more routines or processes of the digital assistant application  142  corresponding to the action specified by the command. Responsive to the determination, the application selector component  118  can determine that the functionality of the digital assistant application  142  supports the action. The application selector component  118  can determine that the action specified by the command is performed via an external network. Responsive to the determination, the application selector component  118  can determine that the functionality of the digital assistant application  142  supports the action when there is access to the external network (e.g., network  146 ). The application selector component  118  can determine the action specified by the command is performed via a particular peripheral device  138 . Responsive to the determination, the application selector component  118  can identify whether the particular peripheral device  138  is interfacing with the client device  132 , and the particular peripheral devices  138  is interfacing with the vehicle  144 . The application selector component  118  can determine the action specified by the command is performed by a particular type of application (e.g., the digital assistant application  142  at the vehicle  144  or the digital assistant application  142  on the client device  132 ). Responsive to the determination, the application selector component  118  can determine whether the application profile of the digital assistant application  142  is a type of application that can carry out the specification action. 
     Based on the determination of the functionality of the respective digital applications  142 , the application selector component  118  can select one of the digital assistant application  142  accessible via the client device  132  or the functionality of the digital assistant application  142  hosted on the vehicle  144 . For example, the application selector component  118  can set the digital assistant application  142  hosted together on the vehicle  144  as the default digital assistant application and set the digital assistant application  142  accessible via the client device  132  as the secondary digital assistant application. In the absence of the secondary digital assistant application, the application selector component  118  can select the primary digital assistant application to carry out the command. The application selector component  118  can determine the functionality of the digital assistant application  142  accessible via the client device  132  can support the action specified by the command. Responsive to the determination, the application selector component  118  can select the digital assistant application  142  accessible via the client device  132 . The application selector component  118  can determine that there are multiple digital assistant applications  142  accessible via the one or more client devices  132 . Responsive to the determination that there are multiple digital assistant applications  14 , the application selector component  118  can select the one digital assistant application  142  accessible via the client device  132  whose functionality can support the action specified by the command. Conversely, the application selector component  118  can determine the functionality of the digital assistant application  142  hosted on the vehicle  144  but not that of the digital assistant application  142  accessible via the client device  132  can support the action specified by the command. Responsive to the determination, the application selector component  118  can select the digital assistant application  142  hosted on the vehicle  144 . 
     When both functionalities of the digital assistant applications  142  accessible via the client device  132  and hosted on the vehicle  144  are determined support the action specified by the command, the digital assistant application  142  can select based on which digital assistant application  142  can more quickly carry out the action. The application selector component  118  can compare the version number of the digital assistant application  142  hosted on the vehicle  144  with the version number of the digital assistant application  142  accessible via the client device  132 . The application selector component  118  can select the digital assistant application  142  with the higher version number, as the corresponding the digital assistant application  142  may be more up-to-date. The application selector component  118  can compare the computing resources consumption at the vehicle  144  with the computing resources consumption at the client device  132 . The application selector component  118  can select the client device  132  or the vehicle  144  based on which has the lower computing resource consumption, as the action specified by the command is likely to be executed faster when computing resource consumption is lower. 
     The application selector component  118  can also select the digital assistant application  142  accessible via the client device  132  or the functionality of the digital assistant application  142  hosted on the vehicle  144  based on other factors. These other factors can override the selection based on the determination on which digital assistant application  142  can support the action specified by the command. The application selector component  118  can also select the digital assistant application  142  accessible via the client device  132  or the digital assistant application  142  hosted on the vehicle  144  based on an input via a vehicle input interface. The vehicle input interface may be one of the peripheral devices  138  connected to the vehicle  144 , such as a button on a steering wheel or a touchscreen on a center stack in the vehicle  144 . For example, the selection may be based on a length of a button press on the button on the steering wheel of the vehicle  144 . The application selector component  118  can determine that the length of the button press is greater than or equal to a predetermined threshold (e.g., 5 seconds). In response to the determination, the application selector component  118  can select the digital assistant application  142  hosted on the vehicle  144 . The application selector component  118  can determine that the length of the button press is shorter than the predetermined threshold. In response to this determination, the application selector component  118  can select the digital assistant application  142  accessible via the client device  132 . A position of the interaction on the touchscreen may also be used to select the digital assistant application  142  accessible via the client device  132  or the digital assistant application  142  hosted on the vehicle  144 . 
     The application selector component  118  can also select the digital assistant application  142  accessible via the client device  132  or the functionality of the digital assistant application  142  hosted on the vehicle  144  based on the trigger keyword recognized in the audio input signal. The application selector component  118  can compare the trigger keyword to a set of keywords associated with corresponding digital assistant applications  148 . Based on the comparison of the trigger word detected from the audio input signal with the set of associated keywords, the application selector component  118  can select the corresponding digital assistant application  148 . For example, there may be two client devices  132  in the vehicle  144  with respective digital assistant applications  148  associated with different trigger keywords. The trigger word “okay” may be associated with the digital assistant application  148  on a first client device  132  and the trigger word “hey” may be associated with the digital assistant application  148  on a second client device  132 . Using the set of keywords, when the audio input signal including “hey” is received, the application selector component  118  can select the digital assistant application  148  on the second client device  132 . 
     The application selector component  118  can also select the digital assistant application  142  accessible via the client device  132  or the functionality of the digital assistant application  142  hosted on the vehicle  144  based on whether the command is related to a vehicular function. As explained above, the NLP component  106  can determine whether the command is related to a vehicular or non-vehicular function using a semantic knowledge graph. Using the determination, the application selector component  118  can select the corresponding digital assistant application  142 . The application selector component  118  can determine that the command is related to a vehicular function. In response to the determination, the application selector component  118  can select the digital assistant application  142  hosted on the vehicle  144 . The application selector component  118  can determine that the command is determined to relate to a non-vehicular function. In response to the determination, the application selector component  118  can select the digital assistant application  142  accessible via the client device  132 . The application selector component  118  can determine that there are multiple digital assistant applications  142  accessible via the one or more client devices  132 . In response to the determination, the application selector component  118  can select the one digital assistant application  142  accessible via the client device  132  whose functionality can support the action specified by the command identified to be related to a non-vehicular function. 
     The application selector component  118  can determine that there are multiple digital assistant applications  142  accessible whose functionalities can support the action specified by the command. In response to the determination, the application selector component  118  can select one of the digital assistant applications  142  based on a quality score. These multiple digital assistant applications  142  can include the digital assistant applications  142  accessible via the one or more client devices  132  and the digital assistant application  142  hosted on the vehicle  144 . The data processing system  102  can include, interface, or otherwise communicate with the performance evaluator component  120 . The performance evaluator component  120  can determine the quality score for each digital assistant application  142  accessible via one or more client devices  132  based on any number of factors. The performance evaluator component  120  can determine the quality score based on a feedback indicator. The feedback indicator may be a numerical value representing a user&#39;s response to a prompt inquiring about quality of interaction with the digital assistant application  142 . To obtain the feedback indicator, the digital assistant application  142  accessible via the client device  132  can display the prompt inquiring about quality of interaction on the client device  132  displaying the digital assistant application  142 . The digital assistant application  142  hosted on the vehicle  144  or the performance evaluator component  120  can also display the prompt inquiring about quality of interaction on the vehicle  144  displaying the digital assistant application  142 . The prompt may include one or more user interface elements (e.g., buttons, radio buttons, checkboxes, etc.), each indicating the quality of interaction. Once an interaction event is detected on one of the user interface elements on the prompt, the performance evaluator component  120  can identify the feedback indicator corresponding to the quality of interaction. The performance evaluator component  120  can determine the quality score using the feedback indicator. 
     The performance evaluator component  120  can also determine the quality score for each digital assistant application  142  based on a rating score from an application distribution platform. The application distribution platform can run on an external server accessible via the network  146 . The external server can host applications, such as the digital assistant applications  142 , for download by the client device  132  or the vehicle  144 . The external server for the application distribution platform may store the rating scores for all the digital assistant applications  142 . From the application distribution platform running on the external server, the performance evaluator component  120  can identify the rating score for each digital assistant application  142  identified as accessible via the client device  132  or hosted on the vehicle  144 . The performance evaluator component  120  can send a request for the rating score to the external server, when there is connectivity with the network  146 . Using the rating score for each digital assistant application  142 , the performance evaluator component  120  can determine the quality score for the corresponding digital assistant application  142 . 
     The performance evaluator component  120  can also run an automated test evaluation to determine the quality score for the corresponding digital assistant application  142 . The automated test evaluation may be executed using an application or the performance evaluator component  120  interfacing with the digital assistant application  142 . The automated test evaluation may include a set of audio input signal stimuli and a corresponding expected action data structure for the audio input signal stimuli. The automated test evaluation may be run simultaneous to the execution of the digital assistant application  142 . While executing, the automated test evaluation can feed each audio input signal stimulus into the digital assistant application  142 . The automated test evaluation can identify an action data structure generated by the digital assistant application  142 . The automated test evaluation can compare the action data structure generated by the digital assistant application  142  with the expected action data structure. From the comparison, the automated test evaluation can identify a number of matches and a number of mismatches between the two action data structures. Based on the number of matches and the number of mismatches, the performance evaluator component  120  can determine the quality score for the digital assistant application  142 . 
     Once the quality score for each digital assistant application  142  is determined, the application selector component  118  can select the digital assistant application  142  with the highest quality score having the functionality to support the action specified by the command. The quality score may be based on any combination of the feedback indicator, the rating score, and the automated test evaluation. The application selector component  118  can identify a subset of digital assistant applications  142  whose functionality can support the action specified by the command. From the subset of digital assistant applications  142 , the application selector component  118  can identify the digital assistant application  142  with the highest quality score as determined by the performance evaluator component  120 . 
     The application selector component  118  can invoke the digital assistant application  142  selected based on the determination that the functionality of the digital assistant application  142  can support the action specified by the command. To invoke the selected digital assistant application  142 , the application selector component  118  can identify the action data structure corresponding to the command. The application selector component  118  can call the direct action API  112  of the selected digital assistant application  142  to execute and process the action data structure corresponding to the command in the audio input signal. The application selector component  118  can determine that the digital assistant application  142  selected is accessible via the client device  132 . Responsive to the determination, the application selector component  118  can assign control of the input interfaces (e.g., the sensor  134 , the interface  104 , the transducer  140 , and other peripheral devices  138 ) of the vehicle  144  to the client device  132 . For example, the command may be to play an audio file on the client device  132 , and the digital assistant application  142  selected is accessible via the client device  132 . To carry out the command, the application selector component  118  can assign control of the speakers  136  from the vehicle  144  to the digital assistant application  142  accessible via the client device  132  (sometimes referred to in such instances as a “brought-in digital assistant application”), such that the audio is played from the speakers  136  of the vehicle  144 . 
     In invoking the selected digital assistant application  142 , the application selector component  118  can use application data from a non-selected digital assistant application  142 . The application selector component  118  can access the data repository  122  of the non-selected digital assistant application  142  to retrieve the parameters  124 , the policies  126 , the response data  128 , the templates  130 , and other information (e.g., phone numbers, calendar events, etc.). For example, when the action specified by the command is to call a particular phone number, the application selector component  118  can retrieve the phone number from the non-selected digital assistant application  142  (or another application) running on a first client device  132 . The application selector component  118  can store the retrieved application data in the local data repository  122 . Prior to invocation, the application selector component  118  can modify the action data structure to include the application data retrieved from the non-selected digital assistant application  142 . The application selector component  118  can then call the direct action API  112  of the selected digital assistant application  142  using the direct action structure modified by the application data retrieved from the non-selected digital assistant application  142 . Continuing with the previous example, the application selector component  118  can call the selected digital assistant application  142  accessible via a second client device  132  to make the phone call using the phone number retrieved from the first client device  132 . 
       FIG.  2    illustrates a top view of the vehicle  144  and illustrates the interior cabin of the vehicle  144 . In the vehicular environment  200 , the interior cabin of the vehicle  144  can include four seats, a plurality of speakers  136 , and two client devices  132 ( 1 ) and  132 ( 2 ). The two client devices  132 ( 1 ) and  132 ( 2 ) may have been carried in by one or more passengers of the vehicle  144 . The vehicle  144  can also include a head unit  202 . The head unit  202  can execute one or more of the components described in relation to the data processing system  102 , such as a digital assistant application  142 ( 1 ), the configuration tracker component  116 , the application selector component  118 , and the performance valuator component  120 . The client device  132 ( 1 ) can execute or make accessible one or more of the components described in relation to the data processing system  102 , such a digital assistant application  142 ( 2 ). The client device  132 ( 2 ) also can execute or make accessible one or more of the components described in relation to the data processing system  102 , such a digital assistant application  142 ( 3 ). The client devices  132 ( 1 ) and  132 ( 2 ) may have established communication links (e.g., via pairing) with the head unit  202 . 
     Referring to  FIG.  1    among others, a transducer  140  within the vehicle  144  may detect an audio input signal uttered by a passenger within the vehicle  144 . Upon detection of the audio input signal, the NLP component  106  operating from the head unit  202  of the vehicle  144  can parse the audio input signal to identify a command. Using the command parsed from the audio input signal, the direct action API  112  running in the head unit  202  of the vehicle  144  can generate an action data structure. The action data structure can include data or instructions for the execution of a specified action to satisfy the command. 
     The configuration tracker component  116  operating from the head unit  202  of the vehicle  144  can identify each digital assistant application  141 ( 1 )—( 3 ) running on or in communication with the head unit  202 . The digital assistant applications  141 ( 1 )—( 3 ) may have differing functionalities. For example, the digital assistant application  141 ( 1 ) running in the head unit  202  may support vehicle-related commands (e.g., “pop the trunk”) while the digital assistant application  141 ( 2 ) of the client device  132 ( 1 ) and the digital assistant application  141 ( 2 ) of the client device  132 ( 2 ) may support non-vehicle related commands (e.g., “play a song”). The configuration tracker component  116  can identify one or more functionalities of each of the identified digital assistant applications  141 ( 1 )—( 3 ), such as the application profile, version number, connectivity to the network  146  or the vehicle  144 , peripheral devices  138 , consumption of computing resources, and supported routines, among others. 
     Having identified the functionalities of the digital assistant applications  141 ( 1 )—( 3 ), the application selector component  118  can determine which digital assistant applications  141 ( 1 )—( 3 ) can support the action specified by the command. The application selector component  118  can select the digital assistant application  141 ( 1 )—( 3 ) based on which can support the action specified by the command. When two or more of the digital assistant application  141 ( 1 )—( 3 ) can support the action specified by the command, the application selector component  118  can select one of the digital assistant applications  141 ( 1 )—( 3 ) using other factors, such as the version number and consumption of computing resources, among others. Once determined, the application selector component  118  can invoke the selected digital assistant application  141 ( 1 )—( 3 ) by calling the respective direct action API  112  to execute in accordance with the generated action data structure. When application data from one digital assistant application  141 ( 2 ) is to be used by the selected digital assistant application  141 ( 3 ), the application data may be transferred from the selected digital assistant application  141 ( 2 ) to the non-selected digital assistant application  141 ( 3 ). The application selector component  118  can modify the data structure using the transferred data structure. The direct action API  112  may be called to carry out the action specified by the command. 
       FIG.  3    illustrates an example method  300  to selectively invoke applications for execution. The method  300  may be implemented or executed by the system  100  described above in conjunction with  FIGS.  1  and  2    or system  400  detailed below in conjunction with  FIG.  4   . The method  300  can include receiving an audio signal (BLOCK  305 ). The method  300  can include parsing the audio signal to identify a command (BLOCK  310 ). The method  300  can include generating an action data structure (BLOCK  315 ). The method  300  can include identifying a functionality of a digital assistant application (BLOCK  320 ). The method  300  can include determining that the functionality supports the command (BLOCK  325 ). The method  300  can include selecting the digital assistant application (BLOCK  330 ). The method  300  can include invoking the selected digital assistant application (BLOCK  335 ). 
     The method  300  can include receiving an audio signal (BLOCK  305 ). A data processing system can receive the input audio signal. For example, an NLP component, executed by the data processing system, can receive the input audio signal. The data processing system (and the NLP component) can be a component of or otherwise executed by a client device, a vehicle, or be a standalone device. The sensor, such as a microphone, at the client device or the vehicle can detect the input audio signal and then the respective client device or vehicle can transmit the input audio signal to the data processing system. For example, an application executed on the client device can detect a user speaking “Ok, open the sunroof.” The detected utterance can be encoded into an input audio signal and transmitted to the NLP component of the data processing system or vehicle. 
     The method  300  can include parsing the audio signal to identify a command (BLOCK  310 ). The NLP component can parse the input audio signal to identify a request in the input audio signal. The NLP component can identify a vehicle associated with the request. The NLP component can identify a fulfillment interface associated with the request and the vehicle. The fulfillment interface can be the interface of one of the client device or the vehicle that will execute the action data structure to fulfill the request of the input audio signal. 
     In the above example, the request can be to open the sunroof. In this example, the fulfillment interface can be the interface of the vehicle that includes the sunroof. In some implementations, the vehicle can be explicitly stated in the input audio signal. For example, a user may assign nicknames to his vehicle (e.g., the user may name his red Toyota Camry the “red car”). When explicitly stated, the input audio signal could be “OK, open the sunroof of my red car.” In some implementations, the data processing system can determine which vehicles associated with the user&#39;s account can fulfill the action. For example, the user&#39;s red car may include a sun roof and the user&#39;s blue car may not. After receiving the input audio signal “Ok, open the sunroof.” The data processing system may automatically select the user&#39;s red car. In some implementations, the data processing system may ask the user for confirmation of the vehicle. 
     The method  300  can include generating an action data structure (BLOCK  315 ). The direct action API can generate the data structure that can be transmitted and processed by the client device or the vehicle to fulfil the request of the input audio signal. For example, continuing the above example, the direct action API can generate a first action data structure for opening the sunroof of the user&#39;s car. The direction action API can generate the action data structure using a template retrieved from the data processing system&#39;s data repository. The action data structure can include fields used to fulfill the request. For example, for the request to open the sunroof, the action data structure can include a field (or attribute) for the vehicle ID to which the action data structure should be transmitted when the action data structure is approved. 
     The method  300  can include identifying a functionality of a digital assistant application (BLOCK  320 ). For example, a configuration tracker component can identify each digital assistant application in the vehicle. The digital assistant applications can be hosted on the vehicle itself or may be accessible via the client device. All of the digital assistant applications in the vehicle may be communicatively linked via short-range communications. For each digital assistant application identified, the configuration tracker component can identify the functionality of the digital assistant application, such as the application profile, version number, connectivity to the network  146  or the vehicle  144 , peripheral devices  138 , consumption of computing resources, and supported routines, among others. 
     The method  300  can include determining that the functionality supports the command (BLOCK  325 ). For instance, an application selector component can determine whether the functionality identified for the corresponding digital assistant application supports the action specified by the command. Some commands may be related to a vehicle function (e.g., “open the sunroof”), while other commands may be related to a non-vehicle function (e.g., “call John”). The determination whether the command relates to a vehicle function or a non-vehicle function may be performed by the NLP component. The application selector component can compare the functionality of each digital assistant application with the action specified by the command. 
     The method  300  can include selecting the digital assistant application (BLOCK  330 ). For example, the application selector component can select the digital assistant application determined to be able to support the action specified by the command. When the functionality of the digital assistant application hosted on the vehicle can support the action specified by the command, the application selector component can select the digital assistant application hosted on the vehicle, in lieu of the application accessible via the client device. When the functionality of the digital assistant application accessible via the client device can support the action specified by the command, the application selector component can select the digital assistant application hosted on the vehicle, in lieu of the application hosted on the vehicle. When both digital assistant applications can support the action specified by the command, the application selector component can use other factors (e.g., latency or computing resources consumed) to select one of the multiple digital assistant applications. 
     The method  300  can include invoking the selected digital assistant application (BLOCK  335 ). For instance, the application selector component can invoke the digital assistant application selected based on the determination that the application can support the action supported by the command. The application selector component can call the direct action API of the selected digital assistant application using the action data structure for the command. The action data structure may be modified by the application selector component using application data from the non-selected digital assistant application. 
       FIG.  4    is a block diagram of an example computer system  400 . The computer system or computing device  400  can include or be used to implement the system  100  or its components such as the data processing system  102 . The computing system  400  includes a bus  405  or other communication component for communicating information and a processor  410  or processing circuit coupled to the bus  405  for processing information. The computing system  400  can also include one or more processors  410  or processing circuits coupled to the bus for processing information. The computing system  400  also includes main memory  415 , such as a random access memory (RAM) or other dynamic storage device, coupled to the bus  405  for storing information and instructions to be executed by the processor  410 . The main memory  415  can be or include the data repository  122 . The main memory  415  can also be used for storing position information, temporary variables, or other intermediate information during execution of instructions by the processor  410 . The computing system  400  may further include a read-only memory (ROM)  420  or other static storage device coupled to the bus  405  for storing static information and instructions for the processor  410 . A storage device  425 , such as a solid state device, magnetic disk or optical disk, can be coupled to the bus  405  to persistently store information and instructions. The storage device  425  can include or be part of the data repository  122 . 
     The computing system  400  may be coupled via the bus  405  to a display  435 , such as a liquid crystal display or active matrix display, for displaying information to a user. An input device  430 , such as a keyboard including alphanumeric and other keys, may be coupled to the bus  405  for communicating information and command selections to the processor  410 . The input device  430  can include a touch screen display  435 . The input device  430  can also include a cursor control, such as a mouse, a trackball, or cursor direction keys, for communicating direction information and command selections to the processor  410  and for controlling cursor movement on the display  435 . The display  435  can be part of the data processing system  102 , the client computing devices  132 , or other component of  FIG.  1   , for example. 
     The processes, systems and methods described herein can be implemented by the computing system  400  in response to the processor  410  executing an arrangement of instructions contained in main memory  415 . Such instructions can be read into main memory  415  from another computer-readable medium, such as the storage device  425 . Execution of the arrangement of instructions contained in main memory  415  causes the computing system  400  to perform the illustrative processes described herein. One or more processors in a multi-processing arrangement may also be employed to execute the instructions contained in main memory  415 . Hard-wired circuitry can be used in place of or in combination with software instructions together with the systems and methods described herein. Systems and methods described herein are not limited to any specific combination of hardware circuitry and software. 
     Although an example computing system has been described in  FIG.  4   , the subject matter including the operations described in this specification can be implemented in other types of digital electronic circuitry or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. 
     For situations in which the systems discussed herein collect personal information about users, or may make use of personal information, the users may be provided with an opportunity to control whether programs or features that may collect personal information (e.g., information about a user&#39;s social network, social actions, or activities; a user&#39;s preferences; or a user&#39;s location), or to control whether or how to receive content from a content server or other data processing system that may be more relevant to the user. In addition, certain data may be anonymized in one or more ways before it is stored or used, so that personally identifiable information is removed when generating parameters. For example, a user&#39;s identity may be anonymized so that no personally identifiable information can be determined for the user, or a user&#39;s geographic location may be generalized where location information is obtained (such as to a city, postal code, or state level), so that a particular location of a user cannot be determined. Thus, the user may have control over how information is collected about him or her and used by the content server. 
     The subject matter and the operations described in this specification can be implemented in digital electronic circuitry or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. The subject matter described in this specification can be implemented as one or more computer programs, e.g., one or more circuits of computer program instructions, encoded on one or more computer storage media for execution by, or to control the operation of, data processing apparatuses. Alternatively or in addition, the program instructions can be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. A computer storage medium can be, or be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial-access memory array or device, or a combination of one or more of them. While a computer storage medium is not a propagated signal, a computer storage medium can be a source or destination of computer program instructions encoded in an artificially generated propagated signal. The computer storage medium can also be, or be included in, one or more separate components or media (e.g., multiple CDs, disks, or other storage devices). The operations described in this specification can be implemented as operations performed by a data processing apparatus on data stored on one or more computer-readable storage devices or received from other sources. 
     The terms “data processing system,” “computing device,” “component,” or “data processing apparatus” encompass various apparatuses, devices, and machines for processing data, including, by way of example, a programmable processor, a computer, a system on a chip, or multiple ones, or combinations of the foregoing. The apparatus can include special-purpose logic circuitry, e.g., an FPGA (field-programmable gate array) or an ASIC (application-specific integrated circuit). The apparatus can also include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them. The apparatus and execution environment can realize various different computing model infrastructures, such as web services, distributed computing and grid computing infrastructures. The components of system  100  can include or share one or more data processing apparatuses, systems, computing devices, or processors. 
     A computer program (also known as a program, software, software application, app, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program can correspond to a file in a file system. A computer program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network. 
     The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs (e.g., components of the data processing system  102 ) to perform actions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatuses can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field-programmable gate array) or an ASIC (application-specific integrated circuit). Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry. 
     The subject matter described herein can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a web browser through which a user can interact with an implementation of the subject matter described in this specification, or a combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), an inter-network (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks). 
     The computing system such as system  100  or system  400  can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network (e.g., the network  146 ). The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some implementations, a server transmits data (e.g., data packets representing a content item) to a client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., a result of the user interaction) can be received from the client device at the server (e.g., received by the data processing system  102  from the client devices  132  or the vehicle  144 ). 
     While operations are depicted in the drawings in a particular order, such operations are not required to be performed in the particular order shown or in sequential order, and all illustrated operations are not required to be performed. Actions described herein can be performed in a different order. 
     The separation of various system components does not require separation in all implementations, and the described program components can be included in a single hardware or software product. For example, the NLP component  106  and the direct action API  112  can be a single component, app, or program, or a logic device having one or more processing circuits, or part of one or more servers of the data processing system  102 . 
     Having now described some illustrative implementations, it is apparent that the foregoing is illustrative and not limiting, having been presented by way of example. In particular, although many of the examples presented herein involve specific combinations of method acts or system elements, those acts and those elements may be combined in other ways to accomplish the same objectives. Acts, elements, and features discussed in connection with one implementation are not intended to be excluded from a similar role in other implementations. 
     The phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” “having,” “containing,” “involving,” “characterized by,” “characterized in that,” and variations thereof herein, is meant to encompass the items listed thereafter, equivalents thereof, and additional items, as well as alternate implementations consisting of the items listed thereafter exclusively. In one implementation, the systems and methods described herein consist of one, each combination of more than one, or all of the described elements, acts, or components. 
     Any references to implementations, elements, or acts of the systems and methods herein referred to in the singular may also embrace implementations including a plurality of these elements, and any references in plural to any implementation, element, or act herein may also embrace implementations including only a single element. References in the singular or plural form are not intended to limit the presently disclosed systems or methods, their components, acts, or elements to single or plural configurations. References to any act or element being based on any information, act, or element may include implementations where the act or element is based at least in part on any information, act, or element. 
     Any implementation disclosed herein may be combined with any other implementation or embodiment, and references to “an implementation,” “some implementations,” “one implementation,” or the like are not necessarily mutually exclusive and are intended to indicate that a particular feature, structure, or characteristic described in connection with the implementation may be included in at least one implementation or embodiment. Such terms as used herein are not necessarily all referring to the same implementation. Any implementation may be combined with any other implementation, inclusively or exclusively, in any manner consistent with the aspects and implementations disclosed herein. 
     References to “or” may be construed as inclusive so that any terms described using “or” may indicate any of a single, more than one, and all of the described terms. A reference to “at least one of ‘A’ and ‘B’” can include only ‘A’, only ‘B’, as well as both ‘A’ and ‘B’. Such references used in conjunction with “comprising” or other open terminology can include additional items. 
     Where technical features in the drawings, detailed description, or any claim are followed by reference signs, the reference signs have been included to increase the intelligibility of the drawings, detailed description, and claims. Accordingly, neither the reference signs nor their absence have any limiting effect on the scope of any claim elements. 
     The systems and methods described herein may be embodied in other specific forms without departing from the characteristics thereof. The foregoing implementations are illustrative rather than limiting of the described systems and methods. Scope of the systems and methods described herein is thus indicated by the appended claims, rather than the foregoing description, and changes that come within the meaning and range of equivalency of the claims are embraced therein.