Patent Publication Number: US-2021193129-A1

Title: Agent device, agent system, and computer-readable storage medium

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is based on and claims priority under 35 USC § 119 from Japanese Patent Application No. 2019-228518 filed on Dec. 18, 2019, the disclosure of which is incorporated by reference herein. 
     TECHNICAL FIELD 
     The present disclosure relates to an agent device, an agent system, and a computer-readable storage medium stored with an agent program that are configured to provide explanation regarding devices, operation, and so on. 
     RELATED ART 
     Japanese Patent Application Laid-Open (JP-A) No. 2001-141500 (Patent Document 1) discloses a vehicle agent processing device that provides information regarding the operation of various onboard devices. 
     Specifically, the vehicle agent processing device employs a syntax analysis section to interpret the meaning of a signal received by a wireless communication section, either after performing speech recognition when provided with audio information including a natural voice, or as-is in the case of an email. The vehicle agent processing device selects information regarding operation of various onboard devices inside a vehicle to be summarized with priority, and synthesizes this information into audio to be output through a speaker. When a requirement for operation of one of the various onboard devices is inferred, this is automatically determined by an agent processing section, and audio guidance appended with operation guidance information regarding this onboard device is given. 
     However, if a user is unable to word a question clearly, resulting in an ambiguous question, sometimes a suitable response cannot be formulated. For example, in the case of an ambiguous question such as “I want to know what that switch on the door is”, it may not be possible to formulate a suitable response. 
     SUMMARY 
     In consideration of the above circumstances, an object of the present disclosure is to provide an agent device, an agent system, and a computer-readable storage medium stored with an agent program that are capable of increasing the probability of being able to formulate a suitable response, even in cases in which a user is unable to word a question clearly. 
     An agent device according to a first aspect includes a recognition section configured to recognize spoken content, and an output section configured to output response information for spoken content recognized by the recognition section. Moreover, in cases in which response information for the spoken content cannot be prepared and in which the recognition section has recognized in the spoken content a predetermined word used to identify an individual function pertaining to a vehicle, the output section outputs a relevant list that is relevant to the word as the response information. 
     According to the first aspect, the recognition section recognizes spoken content. The output section outputs response information for spoken content recognized by the recognition section, and, in cases in which response information for the spoken content cannot be prepared and in which the recognition section has recognized in the spoken content a predetermined word used to identify an individual function pertaining to a vehicle, the output section outputs a relevant list that is relevant to the word as the response information. Namely, even in cases in which a user is unable to word a question clearly, outputting the relevant list that is relevant to the predetermined word used to identify an individual function pertaining to the vehicle that was present in the spoken content enables the probability of being able to formulate a suitable response to be increased. 
     Note that as in a second aspect, configuration may be made such that in cases in which response information for the spoken content cannot be prepared and in which the recognition section has recognized in the spoken content the predetermined word and a word pertaining to a predetermined individual function, the output section outputs a relevant list that is relevant to both the predetermined word and the predetermined individual function as the response information. This increases the probability of being able to formulate a suitable response compared to a case in which a relevant list that is relevant to only the predetermined word is output as the response information. 
     Moreover, as in a third aspect, configuration may be made such that the predetermined word is at least one of a word expressing a location where the individual function is disposed, a word expressing a shape of the individual function, or a word expressing a color of the individual function. This enables a response to be obtained for an individual function of which the name is not known by posing a question including a word expressing at least one out of the location, the shape of the individual function, or the color of the individual function. 
     As in a fourth aspect, configuration may be made such that the output section is configured to output pre-prepared candidate images as the relevant list. This enables an object to be specified by looking at the candidate images when a question has been asked about an object of which the name is not known. 
     As in a fifth aspect, configuration may be made such that in cases in which there is no corresponding relevant list, the output section is configured to output an error message including content requesting an utterance including a word used to identify the individual function. This enables response candidates to a question to be found more easily. 
     As in a sixth aspect, configuration may be made such that the recognition section is configured to recognize spoken content regarding a manual of a vehicle. The recognition section recognizing specialized spoken content regarding a vehicle manual in this manner enables the precision with which utterances relating to a vehicle manual are recognized to be improved. 
     An agent system according to a seventh aspect includes a recognition section configured to recognize spoken content, and an output section configured to output response information for spoken content recognized by the recognition section, and, in cases in which response information for the spoken content cannot be prepared and in which the recognition section has recognized in the spoken content a predetermined word used to identify an individual function pertaining to a vehicle, the output section outputs a relevant list that is relevant to the word as the response information. The agent system further includes an information provision device that is installed in a vehicle and that includes functionality to detect an utterance of an occupant, provide the detected utterance to the recognition section, and report the response information output from the output section. 
     According to the seventh aspect, the recognition section recognizes spoken content. The output section outputs response information for the spoken content recognized by the recognition section, and, in cases in which response information for the spoken content cannot be prepared and in which the recognition section has recognized in the spoken content a predetermined word used to identify an individual function pertaining to a vehicle, the output section outputs a relevant list that is relevant to the word as the response information. 
     The information provision device is installed in a vehicle and detects an utterance of an occupant, provides detected spoken content to the recognition section, and reports the response information output from the output section. When a user makes an utterance so as to pose a question to the information provision device installed in the vehicle in order to obtain a response, even in cases in which the user is unable to word the question clearly, a relevant list that is relevant to the predetermined word present in the spoken content used to identify an individual function pertaining to the vehicle is output to the information provision device and reported thereby, enabling the probability of being able to formulate a suitable response to be increased. 
     Note that as in an eighth aspect, a computer-readable storage medium may be stored with an agent program for causing a computer to function as the respective sections of the agent device of any one of the first aspect to the sixth aspect. 
     As described above, the present disclosure provides an agent device, an agent system, and a computer-readable storage medium stored with an agent program that are capable of increasing the probability of being able to formulate a suitable response, even in cases in which a user is unable to word a question clearly. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       An exemplary embodiment of the present invention will be described in detail based on the following figures, wherein: 
         FIG. 1  is a diagram illustrating a schematic configuration of a manual provision system according to an exemplary embodiment; 
         FIG. 2  is a block diagram illustrating a hardware configuration of a vehicle of an exemplary embodiment; 
         FIG. 3  is a block diagram illustrating a hardware configuration of a server of an exemplary embodiment; 
         FIG. 4  is a functional block diagram to explain functionality of a manual provision system of an exemplary embodiment; 
         FIG. 5  is a table illustrating an example of individual function identification data in a case in which words expressing locations where individual functions are disposed are applied as words for identifying individual functions; 
         FIG. 6  is a flowchart illustrating an example of a flow of processing performed by an agent server of a manual provision system according to an exemplary embodiment; and 
         FIG. 7  is a flowchart illustrating a modified example of a flow of processing performed by an agent server of a manual provision system according to an exemplary embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Detailed explanation follows regarding an example of an exemplary embodiment of the present disclosure, with reference to the drawings. In the present exemplary embodiment, as an example of an agent system, explanation is given regarding a manual provision system that provides vehicle manuals.  FIG. 1  is a diagram illustrating a schematic configuration of the manual provision system according to the present exemplary embodiment. 
     As illustrated in  FIG. 1 , a manual provision system  10  of the present exemplary embodiment is configured including plural vehicles  12  and plural servers  30 . An onboard device  20 , serving as an information provision device, is installed in each of the vehicles  12 . The servers  30  include a human machine interface (hereafter referred to as HMI) server  14  that functions as a human machine interface, an agent server  16 , and an owner&#39;s manual (hereafter also abbreviated to OM) server  18 . Note that the agent server  16  corresponds to an agent device. 
     The onboard devices  20  of the respective vehicles  12 , the HMI server  14 , and the OM server  18  are connected to each other via a network N 1 . The HMI server  14  and the agent server  16  are connected to each other through a network N 2 . 
     Vehicle 
     First, explanation follows regarding hardware configuration of the vehicles  12 .  FIG. 2  is a block diagram illustrating a hardware configuration of a vehicle of the present exemplary embodiment. 
     As illustrated in  FIG. 2 , the vehicle  12  according to the present exemplary embodiment is configured including the onboard device  20 , plural ECUs  22 , a microphone  24 , an input switch  26 , and a monitor  28 . 
     The onboard device  20  is configured including a central processing unit (CPU)  20 A, Read Only Memory (ROM)  20 B, random access memory (RAM)  20 C, an in-vehicle communication interface (I/F)  20 D, a wireless communication I/F  20 E, and an input/output I/F  20 F. The CPU  20 A, the ROM  20 B, the RAM  20 C, the in-vehicle communication I/F  20 D, the wireless communication I/F  20 E, and the input/output I/F  20 F are connected together so as to be capable of communicating with each other via an internal bus  20 G. 
     The CPU  20 A is a central processing unit (central processing circuitry) that executes various programs and controls various sections. Namely, the CPU  20 A reads a program from the ROM  20 B and executes this program using the RAM  20 C as a workspace. 
     The ROM  20 B stores various programs and various data. A control program for controlling the onboard device  20  is stored in the ROM  20 B of the present exemplary embodiment. 
     As described above, the RAM  20 C acts as a workspace to temporarily store various programs and data while the CPU  20 A is executing the programs. 
     The in-vehicle communication I/F  20 D is an interface for connecting with the ECUs  22 . CAN protocol communication standards are employed for this interface. The in-vehicle communication I/F  20 D is connected to an external bus  20 H. Plural of the ECUs  22  are provided corresponding to the respective functions of the vehicle  12 . Examples of the ECUs  22  of the present exemplary embodiment include a vehicle control ECU, an engine ECU, a brake ECU, a body ECU, a camera ECU, and a multimedia ECU. 
     The wireless communication OF  20 E is a wireless communication module for communicating with the servers  30 . A communication standard such as 5G LTE, or Wi-Fi (registered trademark) is employed for this wireless communication module. The wireless communication OF  20 E is connected to the network N 1 . 
     The input/output OF  20 F is an interface for communicating with the microphone  24 , the input switch  26 , and the monitor  28  installed in the vehicle  12 . 
     The microphone  24  is a device that is for example provided to a front pillar or a dashboard of the vehicle  12  in order to pick up sounds from an occupant. 
     The input switch  26  is a switch that is for example provided to a center console or a steering wheel and is input with operation by the fingers of an occupant. A push-button ten-key pad, a touch pad, or the like may be adopted as the input switch  26 . 
     The monitor  28  is a liquid crystal monitor that is provided to the center console or to a meter panel in order to display an owner&#39;s manual or response information, described later. The monitor  28  may be provided in the form of a touch panel that also serves as the input switch  26 . 
     Server 
     Next, explanation follows regarding hardware configuration of the servers  30 .  FIG. 3  is a block diagram illustrating a hardware configuration of a server of the present exemplary embodiment. 
     As illustrated in  FIG. 3 , each of the servers  30  is configured including a CPU  30 A, ROM  30 B, RANI  30 C, storage  30 D, and a communication OF  30 E. The CPU  30 A, the ROM  30 B, the RAM  30 C, the storage  30 D, and the communication OF  30 E are connected together so as to be capable of communicating with each other through an internal bus  30 G The CPU  30 A, the ROM  30 B, the RAM  30 C, and the communication OF  30 E have functionality equivalent to that of the CPU  20 A, the ROM  20 B, the RAM  20 C, and the wireless communication OF  20 E of the onboard device  20  previously described. 
     The storage  30 D is configured by a hard disk drive (HDD) or a solid state drive (SSD), and stores various programs and various data. 
     The CPU  30 A reads a program from the storage  30 D and executes this program using the RANI  30 C as a workspace. 
     A processing program  100  and a data group  110  are stored in the storage  30 D of the present exemplary embodiment. The processing program  100  is a program for implementing the various functionality included in the server  30 . 
     Next, explanation follows regarding functional configuration of the manual provision system  10  of the present exemplary embodiment.  FIG. 4  is a functional block diagram to explain the functionality of the manual provision system of an exemplary embodiment. 
     HMI Server 
     The RMI server  14  includes functionality to receive a question from one of the onboard devices  20 , and to refer to the agent server  16  for a response to a question relating to an owner&#39;s manual. 
     As illustrated in  FIG. 4 , in the HMI server  14  of the present exemplary embodiment, the CPU  30 A functions as an HMT interaction control section  32  by executing the processing program  100 . 
     The RMI interaction control section  32  communicates with the onboard devices  20 . The HMI interaction control section  32  acquires audio information from an occupant of one of the vehicles  12  transmitted from the corresponding onboard device  20 , and performs speech recognition to convert the audio information to text information. The HMI interaction control section  32  ascertains the intent of the words spoken by the occupant based on the converted text information. In cases in which the HMI interaction control section  32  ascertains that the occupant has asked a question relating to the owner&#39;s manual, the HMI interaction control section  32  refers to the agent server  16  regarding this question. 
     Agent Server 
     The agent server  16  functions as an agent (owner&#39;s manual agent (hereafter referred to as OMA)) that processes questions relating to owner&#39;s manuals. The agent server  16  acquires vehicle information for the corresponding vehicle  12  and text information relating to the question from the HMI server  14 , and provides the HMI server  14  with response information relating to a response to the question. 
     In the agent server  16  of the present exemplary embodiment, the CPU  30 A functions as an OMA interaction control section  36  and an intent inference section  38  by executing the processing program  100 . Note that the OMA interaction control section  36  and the intent inference section  38  correspond to a recognition section and an output section. 
     The data group  110  of the agent server  16  includes vehicle OM association data  200 , intent inference ID management data  210 , QA data  220 , word normalization data  230 , a trained model  240 , and individual function identification data  250 . 
     The vehicle OM association data  200  is data in which vehicle information relating to the vehicle type, grade, equipment, and so on of each of the vehicles  12  is linked to OM item codes allocated in the respective owner&#39;s manuals. 
     The intent inference ID management data  210  is data to manage OM item codes and intent inference engine IDs associated with the OM item codes. The intent inference engine IDs are IDs allocated to each intent inference engine used to execute intent inference processing, described later. An intent inference engine is provided for each similar or relevant owner&#39;s manual. 
     The QA data  220  is data retaining response information linked to intent labels allocated to each response. Note that the intent labels are label numbers provided as a result of inferring intent from spoken content of an occupant. The response information relates to question responses, and includes displayable text, display images, text for text-to-speech readout, URLs to display owner&#39;s manuals, and so on. 
     The word normalization data  230  is data employed to normalize words, and is used in intent inference pre-processing. This pre-processing refers to processing to standardize differences in notation and expression. For example, the word normalization data  230  may include data for standardizing differences in notation, such as information indicating that “off” and “OFF” correspond to the same word. As another example, the word normalization data  230  may include data for standardizing different expressions, such as information indicating that “tire pressure warning lamp” and “air pressure warning lamp” refer to the same thing. 
     The trained model  240  is data generated by performing machine learning employing training data in which text information corresponding to plural ways of expressing a given question is the input, and an intent label relating to a response to this question is the output. The trained model  240  is prepared by advance training for each OMA intent inference engine. The trained model  240  is equipped with training data in which abbreviations, formal names, and similar expressions are linked to a single response. 
     The individual function identification data  250  is data in which each individual function pertaining to a vehicle (such as a switch or a lamp), predetermined words used to identify the individual functions, and relevant intent labels are associated with each other. The individual function identification data  250  is prepared in advance for each OM item code. As an example, the predetermined words to identify an individual function pertaining to the vehicle may include at least one out of a word expressing the location where the individual function is disposed, a word expressing the shape of the individual function, or a word expressing the color of the individual function. Other words may also be applied. 
     The OMA interaction control section  36  consults the vehicle OM association data  200  to find and acquire an OM item code associated with a given vehicle  12  based on the vehicle information for this vehicle  12 . In cases in which an OM item code cannot be acquired, the OMA interaction control section  36  notifies the HMI interaction control section  32  that “this service is unavailable”. The OMA interaction control section  36  also consults the intent inference ID management data  210  to find and acquire an OMA intent inference engine ID applicable to the given vehicle  12  based on the acquired OM item code. 
     The OMA interaction control section  36  also refers to the intent inference section  38 , using text information and the OMA intent inference engine ID as input values, in order to acquire the intent label. In cases in which no corresponding intent label is found, the OMA interaction control section  36  notifies the HMI interaction control section  32  that “no results could be found”. The OMA interaction control section  36  consults the QA data  220  to find and acquire associated response information based on the acquired intent label and OM item code. 
     The intent inference section  38  uses the intent inference engine with the corresponding intent inference engine ID to acquire the intent label, and supplies this to the OMA interaction control section  36 . First, the intent inference section  38  performs pre-processing on text relating to the text information acquired via the OMA interaction control section  36 . Differences in notation and expression are standardized during this pre-processing. The intent inference section  38  then uses the trained models  240  prepared for each OMA intent inference engine to perform intent inference processing on the pre-processed text information so as to output an intent label associated with the text information, and also computes a confidence score for this intent label. The OMA interaction control section  36  is notified of intent labels having a confidence score of a predetermined value or greater. 
     OM Server 
     The OM server  18  is a server that provides owner&#39;s manuals. The data group  110  of the OM server  18  includes OM data  300 , this being HTML data relating to the respective the owner&#39;s manuals. The OM data  300  is read by specifying a URL or the like for display of the owner&#39;s manual included in the response information retained in the QA data  220 . 
     However, in the manual provision system  10  according to the present exemplary embodiment, in cases in which the user has posed an ambiguous question, the intent inference section  38  is unable to find a corresponding intent label and notifies the HMI interaction control section  32  that “no results could be found”. Therefore, a suitable response cannot be returned to the onboard device  20 . 
     To address this, in the present exemplary embodiment, in cases in which spoken content uttered by a user cannot be recognized and in which a predetermined word used to identify an individual function is present within an ambiguous question, responses pertaining to an individual function relevant to this word are presented as a list, thereby increasing the probability of being able to formulate a response. In the following explanation, a location where an individual function is disposed is given as an example of a predetermined word used to identify the individual function. 
     Specifically, in cases in which the confidence score of an inferred intent label is a predetermined threshold or less, namely, in cases in which the result of inference is “no results could be found”, the intent inference section  38  searches for whether or not a predetermined word used to identify an individual function is present in the question. For example, the intent inference section  38  searches for whether or not wording pertaining to a location, such as “on the door”, is present in the question. In cases in which a word relating to a location is present, the individual function identification data  250  associated with the OM item codes is consulted in order to find intent labels including individual functions relevant to the location (such as “door”). In cases in which an intent label including an individual function relevant to the location is found, the OMA interaction control section  36  consults the QA data  220  to find and acquire associated response information based on the found intent label and the OM item code, and notifies the HMI interaction control section  32  of such response information. In this manner, a list of response information pertaining to the individual functions relevant to the location can thus be displayed on the corresponding onboard device  20  via the HMI interaction control section  32 . The user selects response information corresponding to their intent from the list of response information, thereby increasing the probability of the user obtaining a response to their question. In other words, the user is able to obtain a response regarding an individual function such as a switch or a lamp whose name they do not know by posing a question including a predetermined word used to identify the individual function. 
     Explanation follows regarding an example of individual function identification data employed in the present exemplary embodiment.  FIG. 5  is a diagram illustrating examples of individual function identification data  250  in a case in which words expressing locations where individual functions are disposed are applied as the predetermined words used to identify the individual functions pertaining to the vehicle. 
     As illustrated in  FIG. 5 , a location, an individual function classification, an individual function name, and intent labels are registered in association with each other in the individual function identification data  250 . In the example in  FIG. 5 , “switch” and “handle” are registered as “individual function classifications” relevant to “door” as an example of a “location”, and “door lock switch” and “power window switch” are registered under “switch”. The associated intent label numbers are also registered. Namely, the individual function classifications and intent labels relevant to a given location can be obtained from the individual function identification data  250 , thereby enabling the associated intent labels to be extracted and a list of response candidates to be provided in cases in which a word relevant to the “location” is present in the question. Note that the associated intent labels can be narrowed down further when providing response candidates in cases in which not only a “location” but also a word used to identify an individual function, such “switch” is present in the question. 
     Next, explanation follows regarding specific processing performed by the agent server  16  of the manual provision system  10  according to the present exemplary embodiment configured as described above.  FIG. 6  is a flowchart illustrating an example of a flow of processing performed by the agent server  16  of the manual provision system  10  according to the present exemplary embodiment. Note that processing in  FIG. 6  is initiated in cases in which the HMI interaction control section  32  refers to the agent server  16  regarding a question relating to the owner&#39;s manual posed by an occupant, and the OMA interaction control section  36  consults the vehicle OM association data  200  to find and acquire an OM item code associated with the vehicle  12  based on the vehicle information for the vehicle  12 . 
     At step S 100 , the CPU  30 A of the agent server  16  receives an utterance and analyzes the spoken content. Processing then transitions to step S 102 . Namely, the OMA interaction control section  36  consults the vehicle OM association data  200  to find and acquire an OM item code associated with the vehicle  12  based on the vehicle information for the vehicle  12 . The OMA interaction control section  36  consults the intent inference ID management data  210  to find and acquire an OMA intent inference engine ID applicable to the vehicle  12  based on the acquired OM item code. The OMA interaction control section  36  then refers to the intent inference section  38  by inputting the text information, converted by speech recognition performed by the HMI interaction control section  32 , and the OMA intent inference engine ID as input values, in order to analyze the spoken content. Note that the processing of step S 100  corresponds to that of a recognition section. 
     At step S 102 , the CPU  30 A of the agent server  16  determines whether or not the spoken content has been recognized. This determination is made by the OMA interaction control section  36  determining whether or not the confidence score of an intent label inferred by the intent inference section  38  exceeds the predetermined threshold. Processing transitions to step S 104  in cases in which affirmative determination is made, and processing transitions to step S 106  in cases in which negative determination is made. 
     At step S 104 , the CPU  30 A of the agent server  16  outputs the associated response to the HMI server  14 , and the processing routine is ended. Namely, the OMA interaction control section  36  consults the QA data  220  to find and acquire the associated response information based on the intent label acquired from the intent inference section  38  and the OM item code, and outputs this to the HMI server  14 . A response to the question is thereby output from the HMI server  14  to the corresponding onboard device  20 . Note that the response to the question is not limited to a single response, and for example plural responses may be provided to the onboard device  20  in cases in which plural intent labels with a confidence score exceeding the threshold are present. 
     At step S 106 , the CPU  30 A of the agent server  16  determines whether or not a word pertaining to a “location” has been recognized. This determination is made by the intent inference section  38  consulting the individual function identification data  250  to determine whether or not a word pertaining to a “location” is present in the question. For example, determination is made as to whether or not a word that matches a “location” registered in the individual function identification data  250  is present in the question. Processing transitions to step S 108  in cases in which a negative determination is made, and processing transitions to step S 110  in cases in which an affirmative determination is made. 
     At step S 108 , the CPU  30 A of the agent server  16  outputs an error message to the HMI server  14 , and the processing routine is ended. The HMI server  14  accordingly sends an error notification to the onboard device  20  stating that “no results could be found” in response to the question. Note that content such as “Please ask another question specifying a location” may also be reported as the error message. This facilitates the search for response candidates to the question. 
     At step S 110 , the CPU  30 A of the agent server  16  outputs a relevant list that is relevant to the recognized “location” to the HMI server  14 , and the processing routine is ended. Namely, the intent inference section  38  acquires intent labels associated with the “location” registered in the individual function identification data  250 . The OMA interaction control section  36  then consults the QA data  220  to find and acquire the associated response information based on the found intent labels and OM item codes, and notifies the HMI interaction control section  32  of this. Thus, a list of response information associated with the individual functions relevant to the “location” can be displayed on the onboard device  20  via the HMI interaction control section  32 . The user selects the intended response information from the list of response information, thereby increasing the probability of obtaining a response to their question. Note that the processing of step S 110  corresponds to that of an output section. 
     Next, explanation follows regarding a case in which not only a “location”, but also a word such as “switch” used to identify an “individual function” is present in the question, and the associated intent labels are narrowed down accordingly in order to provide response candidates.  FIG. 7  is a flowchart illustrating a modified example of a flow of processing performed by the agent server  16  of the manual provision system  10  according to the present exemplary embodiment. Note that the processing in  FIG. 7  is initiated in cases in which the HMI interaction control section  32  refers to the agent server  16  regarding a question relating to an owner&#39;s manual posed by an occupant, and the OMA interaction control section  36  consults the vehicle OM association data  200  to find and acquire an OM item code associated with the vehicle  12  based on the vehicle information for the vehicle  12 . Processing similar to that in  FIG. 6  is allocated the same reference numerals in the forthcoming explanation. 
     At step S 100 , the CPU  30 A of the agent server  16  receives an utterance and analyzes the spoken content. Processing then transitions to step S 102 . Namely, the OMA interaction control section  36  consults the vehicle OM association data  200  to find and acquire an OM item code associated with the vehicle  12  based on the vehicle information for the vehicle  12 . The OMA interaction control section  36  then consults the intent inference ID management data  210  to find and acquire an OMA intent inference engine ID applicable to the vehicle  12  based on the acquired OM item code. The OMA interaction control section  36  then refers to the intent inference section  38  by inputting the text information, converted by speech recognition performed by the HMI interaction control section  32 , and the OMA intent inference engine ID as input values, in order to analyze the spoken content. Note that the processing of step S 100  corresponds to that of a recognition section. 
     At step S 102 , the CPU  30 A of the agent server  16  determines whether or not the spoken content has been recognized. This determination is made by the OMA interaction control section  36  determining whether or not the confidence score of an intent label inferred by the intent inference section  38  exceeds the predetermined threshold. Processing transitions to step S 104  in cases in which affirmative determination is made, and processing transitions to step S 106  in cases in which negative determination is made. 
     At step S 104 , the CPU  30 A of the agent server  16  outputs the associated response to the HMI server  14 , and the processing routine is ended. Namely, the OMA interaction control section  36  consults the QA data  220  to find and acquire the associated response information based on the intent label acquired from the intent inference section  38  and the OM item code, and outputs this to the HMI server  14 . A response to the question is thereby output from the HMI server  14  to the corresponding onboard device  20 . Note that the response to the question is not limited to a single response, and for example plural responses may be provided to the onboard device  20  in cases in which plural intent labels with a confidence score exceeding the threshold are present. 
     At step S 106 , the CPU  30 A of the agent server  16  determines whether or not a word pertaining to a “location” has been recognized. This determination is made by the intent inference section  38  consulting the individual function identification data  250  to determine whether or not a word pertaining to a “location” is present in the question. For example, determination is made as to whether or not a word that matches a “location” registered in the individual function identification data  250  is present in the question. Processing transitions to step S 108  in cases in which a negative determination is made, and processing transitions to step S 109  in cases in which an affirmative determination is made. 
     At step S 108 , the CPU  30 A of the agent server  16  outputs an error message to the HMI server  14 , and the processing routine is ended. The HMI server  14  accordingly sends an error notification to the onboard device  20  stating that “no results could be found” in response to the question. Note that content requesting an utterance including a location, such as “Please ask another question specifying a location” may also be reported as the error message. This facilitates the search for response candidates to the question. 
     At step S 109 , the CPU  30 A of the agent server  16  determines whether or not a word pertaining to an “individual function” has been recognized. This determination is made by the intent inference section  38  consulting the individual function identification data  250  to determine whether or not a word pertaining to an “individual function” is present in the question. For example, determination is made as to whether or not a word that matches an “individual function” registered in the individual function identification data  250  is present in the question. Processing transitions to step S 110  in cases in which negative determination is made, and processing transitions to step S 112  in cases in which affirmative determination is made. 
     At step S 110 , the CPU  30 A of the agent server  16  outputs a relevant list that is relevant to the recognized “location” to the HMI server  14 , and the processing routine is ended. Namely, the intent inference section  38  acquires intent labels associated with the “location” registered in the individual function identification data  250 . The OMA interaction control section  36  then consults the QA data  220  to find and acquire the associated response information based on the found intent labels and the OM item codes, and notifies the HMI interaction control section  32  of this. Thus, a list of response information associated with the individual functions relevant to the “location” can be displayed on the onboard device  20  via the HMI interaction control section  32 . The user selects the intended response information from the list of response information, thereby increasing the probability of obtaining a response to their question. Note that the processing of step S 110  corresponds to that of an output section. 
     At step S 112 , the CPU  30 A of the agent server  16  outputs a relevant list that is relevant to both the recognized “location” and “individual function” to the HMI server  14 , and the processing routine is ended. Namely, the intent inference section  38  acquires intent labels associated with both the “location” and “individual function” registered in the individual function identification data  250 . The OMA interaction control section  36  then consults the QA data  220  to find and acquire the associated response information based on the found intent labels and the OM item code, and notifies the HMI interaction control section  32  of this. Thus, a list of response information that is relevant to both the “location” and the “individual function” can be displayed on the corresponding onboard device  20  via the HMI interaction control section  32 . The user selects the intended response information from the list of response information, thereby increasing the probability of obtaining a response to their question. Note that the processing of step S 112  also corresponds to that of an output section. 
     Note that when outputting the relevant list at step S 110  or S 112  of the above exemplary embodiment, the HMI interaction control section  32  may be notified of display images included in the response information, and these display images may be displayed on the corresponding onboard device  20  as candidate images to prompt selection by the user. This enables the user to specify an object by viewing the candidate images in cases in which they are posing a question regarding an object whose name they are unaware of. 
     Although the HMI server  14 , the agent server  16 , and the OM server  18  configuring the manual provision system  10  are configured by separate servers  30  in the above exemplary embodiment, the functionality of each of the servers  30  may be consolidated into a single server  30 . For example, all or some of the servers  30  corresponding to the HMI server  14 , the agent server  16 , and the OM server  18  may be consolidated. Moreover, the functionality of the OMA interaction control section  36  and the intent inference section  38  of the agent server  16  may be distributed between different servers  30 . 
     Note that the various processing executed by the CPUs  20 A,  30 A reading and executing software (a program) in the above exemplary embodiment may be executed by various types of processor other than a CPU. Such processors include programmable logic devices (PLD) that allow circuit configuration to be modified post-manufacture, such as a field-programmable gate array (FPGA), and dedicated electric circuits, these being processors including a circuit configuration custom-designed to execute specific processing, such as an application specific integrated circuit (ASIC). The processing executed by the agent server  16  as previously described may be executed by any one of these various types of processor, or by a combination of two or more of the same type or different types of processor (such as plural FPGAs, or a combination of a CPU and an FPGA). The hardware structure of these various types of processors is more specifically an electric circuit combining circuit elements such as semiconductor elements. 
     In the above exemplary embodiment, the program is in a format pre-stored (installed) in a computer-readable non-transitory recording medium. For example, the processing program  100  of each of the servers  30  is pre-stored in the storage  30 D. However, there is no limitation thereto, and the respective programs may be provided in a format recorded on a non-transitory recording medium such as compact disc read only memory (CD-ROM), digital versatile disc read only memory (DVD-ROM), or universal serial bus (USB) memory. Alternatively, the program may be provided in a format downloadable from an external device through a network. 
     The processing flows explained in the above exemplary embodiment are merely examples, and superfluous steps may be omitted, new steps may be added, or the processing sequences may be changed within a range not departing from the spirit of the present disclosure.