system

Abstract

The system according to the embodiment aims to enable a user to ask a question about directions and receive an appropriate answer. [Solution] A system according to an embodiment includes a reception unit, a guidance unit, a question reception unit, an answering unit, and a collection and analysis unit. The reception unit receives destination settings. The guidance unit provides route guidance based on the information received by the reception unit. The question reception unit receives questions from users. The answering unit answers questions received by the question reception unit. The collection and analysis unit collects and analyzes question information from multiple users.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem that route guidance services cannot respond to users' questions, causing problems for drivers when the route is difficult to understand.

[0005] The system according to the embodiment aims to enable a user to ask a question about directions and receive an appropriate answer. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a guidance unit, a question reception unit, an answering unit, and a collection and analysis unit. The reception unit receives a destination setting. The guidance unit provides route guidance based on the information received by the reception unit. The question reception unit receives questions from users. The answering unit answers the questions received by the question reception unit. The collection and analysis unit collects and analyzes question information from multiple users. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to ask questions about directions and receive appropriate answers. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A navigation system according to an embodiment of the present invention allows a user to set a destination and receive route guidance. In this navigation system, a user sets a destination and receives route guidance. If the route guidance is unclear, the driver can ask questions via voice input, and the AI ​​will respond verbally. For example, if an AI in the passenger seat guides the driver, saying, "Turn left at the intersection 100 meters ahead," and the user asks, "Is this the right place?", the AI ​​will respond, "It's the next intersection. It's the intersection with a specific facility on the left." Unlike conventional navigation services, this system allows users to ask questions, making it significantly more convenient. Furthermore, since the interaction is entirely voice-based, it also helps prevent distracted driving. Furthermore, the system collects and analyzes all user questions to identify areas that many users found difficult to understand, and updates the navigation method accordingly. For example, a user sets a destination and receives route guidance. Next, if the route guidance is unclear, the driver can ask questions via voice input, and the AI ​​will respond verbally. For example, if an AI driver in the passenger seat tells the driver, "Turn left at the intersection 100 meters ahead," and the user asks, "Are you at this facility?", the AI ​​will respond, "It's the next intersection. It's the intersection with a specific facility on the left." Unlike traditional navigation services, this service is significantly more convenient because users can ask questions. Furthermore, since the interaction is entirely voice-based, it also helps prevent distracted driving. Furthermore, all user questions are collected and analyzed, identifying areas that many users found difficult to understand and updating the navigation method. This allows the navigation system to efficiently set users' destinations, provide directions, receive and answer questions, and collect and analyze question information.

[0029] A route guidance system according to an embodiment includes a reception unit, a guidance unit, a question reception unit, a response unit, and a collection and analysis unit. The reception unit provides an interface for a user to set a destination. For example, the reception unit may provide a text box for a user to input an address or a drop-down menu for a user to select a landmark. The reception unit also includes a microphone for accepting voice input, allowing the user to set a destination by voice. For example, when a user vocally inputs, "My destination is ____," the reception unit analyzes the voice and sets the destination. The guidance unit provides route guidance based on the destination set by the reception unit. For example, the guidance unit includes a speaker for providing voice guidance, and may instruct the user, "Turn left at the intersection 100 meters ahead." The guidance unit also includes a display for displaying a map, allowing the user to visually provide route guidance. For example, the guidance unit may display the current location and destination on a map and draw a line indicating the route. The question reception unit receives a user's question. For example, the question receiving unit includes a microphone that receives voice input. When a user asks, "Is this the right facility?", the answering unit analyzes the voice and understands the question. The answering unit responds to the question received by the question receiving unit. For example, the answering unit uses voice synthesis technology to respond, "It's the next intersection. It's the intersection where a specific facility is on the left." The answering unit also includes a display that displays answers in text, allowing the answering unit to provide the user with a visual answer. The collection and analysis unit collects question information from all users and analyzes it using AI. For example, the collection and analysis unit stores the questions asked by users in a database, and the AI ​​analyzes the data to identify points that many users found difficult to understand. This allows the collection and analysis unit to provide feedback to update the navigation method. As a result, the navigation system according to the embodiment can efficiently set a user's destination, provide directions, receive and answer questions, and collect and analyze question information.

[0030] The question acceptance unit can accept voice input from a user. The question acceptance unit includes, for example, a microphone through which a user can input a question by voice. For example, when a user asks, "Is this the right facility?" by voice, the question acceptance unit analyzes the voice and understands the question. The question acceptance unit can also convert the user's voice into text using voice recognition technology. For example, the question acceptance unit can convert the user's voice into text in real time using voice recognition software, and analyze the text to understand the question. Furthermore, the question acceptance unit not only accepts the user's voice input but also includes a feedback function for improving the accuracy of the voice input. For example, when the user makes a voice input, the question acceptance unit displays the results of the voice recognition, allowing the user to confirm and correct the results. This allows the question acceptance unit to accurately accept the user's voice input. This allows the user to input a question by voice.

[0031] The answering unit can respond by voice. For example, the answering unit uses voice synthesis technology to respond by voice to the user's question. For example, if the user asks, "Are you at this facility?", the answering unit responds by voice, saying, "It's the next intersection. It's the intersection where the specific facility is on the left." The answering unit can also use voice synthesis technology to respond to the user's question in a natural voice. For example, the answering unit uses voice synthesis software to respond to the user's question in a voice with natural intonation and inflection. Furthermore, the answering unit not only responds by voice but also has a feedback function to improve the quality of the voice. For example, when the user receives a voice answer, the answering unit can evaluate the quality of the voice and adjust the voice synthesis parameters. This allows the answering unit to provide the user with a high-quality voice answer. This allows the user to receive a voice answer.

[0032] The collection and analysis unit can collect question information from multiple users and have the AI ​​analyze it. For example, the collection and analysis unit stores question information from all users in a database, and the AI ​​analyzes the data. For example, the collection and analysis unit stores what questions users have asked in a database, and the AI ​​analyzes the data to identify parts that many users found difficult to understand. The collection and analysis unit also has an algorithm that allows the AI ​​to analyze the question information. For example, the collection and analysis unit uses a machine learning algorithm to analyze the user's question information and identify parts that are difficult to understand. Furthermore, the collection and analysis unit provides feedback to update the route guidance method based on the results of the AI's analysis. For example, the collection and analysis unit makes suggestions to improve the route guidance method based on the results of the AI's analysis. In this way, the collection and analysis unit can provide feedback to improve the accuracy of route guidance. In this way, collecting question information from all users and having the AI ​​analyze it improves the accuracy of route guidance.

[0033] The collection and analysis unit can update the route guidance method based on the results of the AI ​​analysis. For example, the collection and analysis unit provides feedback for updating the route guidance method based on the results of the AI ​​analysis. For example, the collection and analysis unit makes suggestions for improving the route guidance method based on the results of the AI ​​analysis. The collection and analysis unit also has a software update function for updating the route guidance method. For example, the collection and analysis unit automatically updates the route guidance software based on the results of the AI ​​analysis to provide the latest route guidance method. Furthermore, the collection and analysis unit establishes a feedback loop for updating the route guidance method. For example, the collection and analysis unit improves the route guidance method based on the results of the AI ​​analysis, re-analyzes the results of the improvement, and establishes a feedback loop for further improvement. This allows the collection and analysis unit to continuously update the route guidance method. This causes the route guidance method to be continuously updated.

[0034] The reception unit can analyze the user's past destination history and suggest optimal destination candidates. The reception unit, for example, stores the user's past destination history in a database and analyzes the data. For example, the reception unit stores places the user has visited in the past in a database and analyzes the data to suggest optimal destination candidates. The reception unit can also predict places the user will visit on specific days of the week or time periods based on the user's past destination history. For example, the reception unit stores places the user will visit on specific days of the week or time periods in a database and analyzes the data to suggest optimal destination candidates. The reception unit can also analyze the user's past movement patterns and suggest optimal destination candidates. For example, the reception unit stores the user's past movement patterns in a database and analyzes the data to suggest optimal destination candidates. This allows the reception unit to suggest optimal destination candidates based on the user's past destination history. This makes destination setting more efficient by suggesting optimal destination candidates based on past history.

[0035] When setting a destination, the reception unit can propose an optimal route taking into consideration the user's current traffic conditions and weather information. The reception unit, for example, proposes an optimal route based on real-time traffic congestion information. For example, the reception unit acquires real-time traffic congestion information from a traffic information service and proposes an optimal route based on that information. The reception unit can also consider current weather information and prioritize proposals of covered routes when it is raining. For example, the reception unit acquires weather data and prioritizes proposals of covered routes when it is raining. The reception unit can also propose an optimal route taking into consideration the real-time operation status of public transportation. For example, the reception unit acquires the operation status of public transportation and proposes an optimal route based on that information. This allows the reception unit to propose an optimal route taking into consideration the user's current traffic conditions and weather information. This improves user convenience by proposing an optimal route based on real-time information.

[0036] When setting a destination, the reception unit can prioritize suggesting highly relevant destinations in consideration of the user's geographical location information. The reception unit, for example, prioritizes suggesting locations close to the user's current location. For example, the reception unit acquires GPS data and prioritizes suggesting locations close to the user's current location. The reception unit can also prioritize suggesting locations with good transportation access from the user's current location. For example, the reception unit acquires transportation access information from a transportation information service and prioritizes suggesting locations with good transportation access from the user's current location. Furthermore, the reception unit can suggest an optimal destination in consideration of the distance from the user's current location. For example, the reception unit calculates the distance from the user's current location and suggests an optimal destination based on the distance. This allows the reception unit to prioritize suggesting highly relevant destinations in consideration of the user's geographical location information. This improves convenience by suggesting optimal destinations based on the user's current location.

[0037] When setting a destination, the reception unit can analyze the user's social media activity and suggest related destinations. For example, the reception unit suggests places where the user has checked in on social media as candidate destinations. For example, the reception unit uses a social media API to obtain data on places where the user has checked in and suggests candidate destinations based on the data. The reception unit can also suggest places and events that the user follows on social media as candidate destinations. For example, the reception unit uses a social media API to obtain data on places and events that the user follows and suggests candidate destinations based on the data. Furthermore, the reception unit can analyze the content of the user's social media posts and suggest places of interest. For example, the reception unit uses a social media API to obtain the content of the user's posts and analyzes the data to suggest places of interest. In this way, the reception unit can analyze the user's social media activity and suggest related destinations. In this way, by utilizing social media information, destinations that match the user's interests can be suggested.

[0038] The guidance unit can analyze the user's driving style and select the optimal guidance method when providing route guidance. The guidance unit, for example, has a data collection function for analyzing the user's driving style. For example, the guidance unit collects data such as the vehicle's speed and braking usage, and analyzes the data to identify the user's driving style. The guidance unit also has an algorithm for selecting a guidance method according to the user's driving style. For example, the guidance unit provides detailed guidance if the user has a cautious driving style. On the other hand, if the user has a smooth driving style, the guidance unit provides concise guidance. Furthermore, if the user is in a hurry, the guidance unit provides guidance that emphasizes the shortest route. This allows the guidance unit to provide guidance according to the user's driving style. As a result, providing guidance according to the user's driving style improves driving safety.

[0039] The guidance unit can dynamically change the route when providing route guidance, taking real-time traffic information into consideration. The guidance unit, for example, proposes an optimal route based on real-time traffic congestion information. For example, the guidance unit acquires real-time traffic congestion information from a traffic information service and proposes an optimal route based on that information. The guidance unit can also propose an optimal route taking into consideration the real-time operation status of public transportation. For example, the guidance unit acquires the operation status of public transportation and proposes an optimal route based on that information. Furthermore, the guidance unit can propose a detour route based on real-time road construction information. For example, the guidance unit acquires road construction information and proposes a detour route based on that information. This allows the guidance unit to dynamically change the route taking real-time traffic information into consideration. This improves user convenience by providing an optimal route based on real-time information.

[0040] When providing route guidance, the guidance unit can provide an optimal guidance method by referring to the user's past driving history. The guidance unit, for example, stores the user's past driving history in a database and analyzes the data. For example, the guidance unit proposes an optimal guidance method based on routes the user has used in the past. The guidance unit can also propose a route that avoids congestion based on the user's past driving history. For example, the guidance unit analyzes the user's past driving history and proposes a route that avoids congestion. Furthermore, the guidance unit can analyze the user's past driving history and propose the most efficient route. For example, the guidance unit proposes the most efficient route based on the user's past driving history. This allows the guidance unit to provide an optimal guidance method by referring to the user's past driving history. This improves user convenience by providing optimal guidance based on the past driving history.

[0041] When providing route guidance, the guidance unit can propose an optimal route taking into consideration the user's vehicle information. The guidance unit, for example, proposes the most economical route based on fuel efficiency information of the user's vehicle. For example, the guidance unit acquires fuel efficiency information of the user's vehicle and proposes the most economical route based on that information. The guidance unit can also propose a route that avoids narrow roads taking into consideration the size of the user's vehicle. For example, the guidance unit acquires size information of the user's vehicle and proposes a route that avoids narrow roads based on that information. The guidance unit can also propose an optimal route taking into consideration the performance of the user's vehicle. For example, the guidance unit acquires performance information of the user's vehicle and proposes an optimal route based on that information. This allows the guidance unit to propose an optimal route taking into consideration the user's vehicle information. This improves user convenience by providing an optimal route based on vehicle information.

[0042] When receiving a question, the question receiving unit can analyze the user's past question history and present optimal question candidates. The question receiving unit, for example, stores the user's past question history in a database and analyzes the data. For example, the question receiving unit stores the content of questions frequently asked by the user in a database and analyzes the data to present optimal question candidates. The question receiving unit can also predict the content of questions that will be asked on a specific day of the week or time of day based on the user's past question history. For example, the question receiving unit stores the content of questions that will be asked by the user on a specific day of the week or time of day in a database and analyzes the data to present optimal question candidates. The question receiving unit can also analyze the user's past question history and present optimal question candidates. For example, the question receiving unit stores the user's past question history in a database and analyzes the data to present optimal question candidates. This allows the question receiving unit to present optimal question candidates based on the user's past question history. This allows efficient question asking by presenting optimal question candidates based on the past question history.

[0043] When receiving a question, the question receiving unit can determine the priority of the question taking into consideration the user's current driving situation. The question receiving unit, for example, stores the user's current driving situation in a database and analyzes the data. For example, when the user is driving on a highway, the question receiving unit can preferentially receive important questions. Furthermore, when the user is driving in an urban area, the question receiving unit can receive detailed questions. For example, when the user is driving in an urban area, the question receiving unit receives detailed questions. Furthermore, when the user is parked, the question receiving unit can receive all questions. For example, when the user is parked, the question receiving unit receives all questions. In this way, the question receiving unit can determine the priority of the question taking into consideration the user's current driving situation. In this way, by determining the priority of questions according to the driving situation, important questions are preferentially processed.

[0044] When accepting a question, the question acceptance unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. The question acceptance unit, for example, stores the user's geographical location information in a database and analyzes the data. For example, if the user is in a specific area, the question acceptance unit can prioritize accepting questions related to the area. Furthermore, if the user is close to a specific facility, the question acceptance unit can prioritize accepting questions related to the facility. For example, if the user is close to a specific facility, the question acceptance unit can prioritize accepting questions related to the facility. Furthermore, the question acceptance unit can prioritize accepting highly relevant questions by taking into account the distance from the user's current location. For example, the question acceptance unit calculates the distance from the user's current location and prioritizes accepting highly relevant questions based on the distance. This allows the question acceptance unit to prioritize accepting highly relevant questions by taking into account the user's geographical location information. This improves user convenience by preferentially accepting questions based on geographical location information.

[0045] The question acceptance unit can analyze the user's social media activity when accepting a question and accept related questions. The question acceptance unit accepts, for example, questions related to places where the user has checked in on social media. For example, the question acceptance unit uses a social media API to acquire data on places where the user has checked in and accepts related questions based on the data. The question acceptance unit can also accept questions related to places or events the user follows on social media. For example, the question acceptance unit uses a social media API to acquire data on places or events the user follows and accepts related questions based on the data. The question acceptance unit can also analyze the content of the user's posts on social media and accept related questions. For example, the question acceptance unit uses a social media API to acquire the content of the user's posts and analyzes the data to accept related questions. In this way, the question acceptance unit can analyze the user's social media activity and accept related questions. In this way, by utilizing social media information, questions that match the user's interests can be accepted.

[0046] When answering a question, the answering unit can provide the optimal answer by referring to the user's past question history. The answering unit, for example, stores the user's past question history in a database and analyzes the data. For example, the answering unit stores the content of questions asked by the user in a database and analyzes the data to provide the optimal answer. The answering unit can also provide a related answer based on the user's past question history. For example, the answering unit analyzes the user's past question history and provides a related answer. The answering unit can also analyze the user's past question history and provide the most appropriate answer. For example, the answering unit provides the most appropriate answer based on the user's past question history. This allows the answering unit to provide the optimal answer by referring to the user's past question history. This improves user convenience by providing the optimal answer based on the past question history.

[0047] When providing an answer, the answering unit can determine the priority of the answer taking into account the user's current driving situation. The answering unit, for example, stores the user's current driving situation in a database and analyzes the data. For example, the answering unit can provide important answers preferentially when the user is driving on a highway. Furthermore, the answering unit can provide detailed answers when the user is driving in an urban area. For example, the answering unit provides detailed answers when the user is driving in an urban area. Furthermore, the answering unit can provide all answers when the user is parked. For example, the answering unit provides all answers when the user is parked. This allows the answering unit to determine the priority of the answer taking into account the user's current driving situation. Thus, by determining the priority of answers according to the driving situation, important answers are preferentially provided.

[0048] When answering a question, the answering unit can provide the optimal answer by taking into account the user's geographical location information. The answering unit, for example, stores the user's geographical location information in a database and analyzes the data. For example, if the user is in a specific area, the answering unit can provide information related to the area. Furthermore, if the user is close to a specific facility, the answering unit can provide information related to the facility. For example, if the user is close to a specific facility, the answering unit can provide information related to the facility. Furthermore, the answering unit can provide the optimal answer by taking into account the distance from the user's current location. For example, the answering unit calculates the distance from the user's current location and provides the optimal answer based on that distance. This allows the answering unit to provide the optimal answer by taking into account the user's geographical location information. This improves user convenience by providing the optimal answer based on the geographical location information.

[0049] When answering a question, the answer unit can analyze the user's social media activity and provide a relevant answer. The answer unit can provide, for example, information related to places where the user has checked in on social media. For example, the answer unit can use a social media API to obtain data on places where the user has checked in and provide relevant information based on that data. The answer unit can also provide information related to places and events the user is following on social media. For example, the answer unit can use a social media API to obtain data on places and events the user is following and provide relevant information based on that data. The answer unit can also analyze the content of the user's social media posts and provide a relevant answer. For example, the answer unit can use a social media API to obtain the content of the user's posts and analyze that data to provide a relevant answer. This allows the answer unit to analyze the user's social media activity and provide a relevant answer. This makes it possible to provide answers that match the user's interests by utilizing social media information.

[0050] The collection and analysis unit can optimize the analysis algorithm by referring to past question data during collection and analysis. The collection and analysis unit, for example, stores past question data in a database and analyzes the data. For example, the collection and analysis unit selects an optimal analysis algorithm based on the past question data. The collection and analysis unit can also analyze the past question data to improve the accuracy of the algorithm. For example, the collection and analysis unit analyzes the past question data and adjusts algorithm parameters. Furthermore, the collection and analysis unit can customize the analysis algorithm by referring to the past question data. For example, the collection and analysis unit develops an algorithm that meets specific conditions based on the past question data. In this way, the collection and analysis unit can optimize the analysis algorithm by referring to the past question data. In this way, optimizing the analysis algorithm based on the past question data improves the accuracy of the analysis.

[0051] The collection and analysis unit can customize the analysis results by taking into account the user's attribute information during collection and analysis. The collection and analysis unit, for example, stores the user's attribute information in a database and analyzes the data. For example, the collection and analysis unit stores the user's age and gender in a database and customizes the analysis results based on that data. The collection and analysis unit can also customize the analysis results by taking into account the user's driving history. For example, the collection and analysis unit stores the user's driving history in a database and customizes the analysis results based on that data. The collection and analysis unit can also customize the analysis results by taking into account the user's geographical location information. For example, the collection and analysis unit stores the user's geographical location information in a database and customizes the analysis results based on that data. This allows the collection and analysis unit to customize the analysis results by taking into account the user's attribute information. This improves user convenience by providing analysis results based on the user's attribute information.

[0052] The collection and analysis unit can provide optimal analysis results by taking into account the user's geographical location information during collection and analysis. The collection and analysis unit, for example, stores the user's geographical location information in a database and analyzes the data. For example, if the user is in a specific area, the collection and analysis unit can provide analysis results related to the area. Furthermore, if the user is close to a specific facility, the collection and analysis unit can provide analysis results related to the facility. For example, if the user is close to a specific facility, the collection and analysis unit can provide analysis results related to the facility. Furthermore, the collection and analysis unit can provide optimal analysis results by taking into account the distance from the user's current location. For example, the collection and analysis unit calculates the distance from the user's current location and provides optimal analysis results based on the distance. This allows the collection and analysis unit to provide optimal analysis results by taking into account the user's geographical location information. This improves user convenience by providing optimal analysis results based on geographical location information.

[0053] During collection and analysis, the collection and analysis unit can analyze the user's social media activities and provide related analysis results. The collection and analysis unit can provide, for example, analysis results related to places where the user has checked in on social media. For example, the collection and analysis unit can use a social media API to obtain data on places where the user has checked in and provide related analysis results based on the data. The collection and analysis unit can also provide analysis results related to places and events the user follows on social media. For example, the collection and analysis unit can use a social media API to obtain data on places and events the user follows and provide related analysis results based on the data. The collection and analysis unit can also analyze the content of the user's posts on social media and provide related analysis results. For example, the collection and analysis unit can use a social media API to obtain the content of the user's posts, analyze the data, and provide related analysis results. This allows the collection and analysis unit to analyze the user's social media activities and provide related analysis results. This makes it possible to provide analysis results that match the user's interests by utilizing social media information.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit can analyze the user's past driving history and suggest optimal destination candidates. For example, the reception unit stores places the user has visited in the past in a database and analyzes the data to suggest optimal destination candidates. The reception unit can also predict places the user will visit on specific days of the week or during specific times of the day based on the user's past driving history. Furthermore, the reception unit can analyze the user's past movement patterns and suggest optimal destination candidates. This allows the reception unit to suggest optimal destination candidates based on the user's past driving history. This makes destination setting more efficient by suggesting optimal destination candidates based on past history.

[0056] When setting a destination, the reception unit can propose an optimal route by taking into consideration the user's current traffic conditions and weather information. For example, the reception unit proposes an optimal route based on real-time traffic congestion information. For example, the reception unit acquires real-time traffic congestion information from a traffic information service and proposes an optimal route based on that information. The reception unit can also consider current weather information and prioritize proposals of covered routes when it is raining. For example, the reception unit acquires weather data and prioritizes proposals of covered routes when it is raining. The reception unit can also propose an optimal route by taking into consideration the real-time operation status of public transportation. For example, the reception unit acquires the operation status of public transportation and proposes an optimal route based on that information. In this way, the reception unit can propose an optimal route by taking into consideration the user's current traffic conditions and weather information. In this way, by proposing an optimal route based on real-time information, convenience for the user is improved.

[0057] The guidance unit can analyze the user's driving style and select the optimal guidance method when providing route guidance. For example, the guidance unit has a data collection function for analyzing the user's driving style. For example, the guidance unit collects data such as the vehicle's speed and braking usage, and analyzes the data to identify the user's driving style. The guidance unit also has an algorithm for selecting a guidance method according to the user's driving style. For example, the guidance unit provides detailed guidance if the user has a cautious driving style. On the other hand, if the user has a smooth driving style, the guidance unit provides concise guidance. Furthermore, if the user is in a hurry, the guidance unit provides guidance that emphasizes the shortest route. This allows the guidance unit to provide guidance according to the user's driving style. As a result, providing guidance according to the user's driving style improves driving safety.

[0058] During collection and analysis, the collection and analysis unit can optimize the analysis algorithm by referring to past question data. For example, the collection and analysis unit stores past question data in a database and analyzes the data. For example, the collection and analysis unit selects an optimal analysis algorithm based on the past question data. The collection and analysis unit can also analyze the past question data to improve the accuracy of the algorithm. For example, the collection and analysis unit analyzes the past question data and adjusts algorithm parameters. Furthermore, the collection and analysis unit can customize the analysis algorithm by referring to the past question data. For example, the collection and analysis unit develops an algorithm that meets specific conditions based on the past question data. In this way, the collection and analysis unit can optimize the analysis algorithm by referring to the past question data. In this way, optimizing the analysis algorithm based on the past question data improves the accuracy of the analysis.

[0059] When answering a question, the answering unit can provide the optimal answer by referring to the user's past question history. For example, the answering unit stores the user's past question history in a database and analyzes the data. For example, the answering unit stores the content of questions asked by the user in a database and analyzes the data to provide the optimal answer. The answering unit can also provide a related answer based on the user's past question history. For example, the answering unit analyzes the user's past question history and provides a related answer. The answering unit can also analyze the user's past question history and provide the most appropriate answer. For example, the answering unit provides the most appropriate answer based on the user's past question history. This allows the answering unit to provide the optimal answer by referring to the user's past question history. This improves user convenience by providing the optimal answer based on the past question history.

[0060] The guidance unit can dynamically change the route when providing route guidance, taking real-time traffic information into consideration. For example, the guidance unit proposes an optimal route based on real-time traffic congestion information. For example, the guidance unit acquires real-time traffic congestion information from a traffic information service and proposes an optimal route based on that information. The guidance unit can also propose an optimal route taking into consideration the real-time operation status of public transportation. For example, the guidance unit acquires the operation status of public transportation and proposes an optimal route based on that information. Furthermore, the guidance unit can propose a detour route based on real-time road construction information. For example, the guidance unit acquires road construction information and proposes a detour route based on that information. This allows the guidance unit to dynamically change the route taking real-time traffic information into consideration. This improves user convenience by providing an optimal route based on real-time information.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit provides an interface for the user to set a destination. For example, it provides a text box for the user to enter an address and a drop-down menu for selecting a landmark. It also has a microphone that accepts voice input, so the user can set a destination by voice. Step 2: The guidance unit provides route guidance based on the destination set by the reception unit. For example, it may be equipped with a speaker that provides voice guidance, and may provide guidance to the user, such as "Turn left at the intersection 100 meters ahead." It may also be equipped with a display that displays a map, and can provide visual guidance to the user. Step 3: The question receiving unit receives a question from the user. For example, if the unit is equipped with a microphone that receives voice input, and the user asks, "Is this the correct facility?", the voice is analyzed and the question is understood. Step 4: The answering unit responds to the question received by the question receiving unit. For example, it uses voice synthesis technology to respond with, "It's the next intersection. It's the intersection where a specific facility is on the left." It also has a display that displays the answer in text, so it can provide the user with a visual answer. Step 5: The collection and analysis unit collects question information from all users, which is then analyzed by AI. For example, the types of questions users have asked are stored in a database, and the AI ​​analyzes the data to identify areas that many users found difficult to understand. This allows it to provide feedback to update the navigation method.

[0063] (Example 2) A navigation system according to an embodiment of the present invention allows a user to set a destination and receive route guidance. In this navigation system, a user sets a destination and receives route guidance. If the route guidance is unclear, the driver can ask questions via voice input, and the AI ​​will respond verbally. For example, if an AI in the passenger seat guides the driver, saying, "Turn left at the intersection 100 meters ahead," and the user asks, "Is this the right place?", the AI ​​will respond, "It's the next intersection. It's the intersection with a specific facility on the left." Unlike conventional navigation services, this system allows users to ask questions, making it significantly more convenient. Furthermore, since the interaction is entirely voice-based, it also helps prevent distracted driving. Furthermore, the system collects and analyzes all user questions to identify areas that many users found difficult to understand, and updates the navigation method accordingly. For example, a user sets a destination and receives route guidance. Next, if the route guidance is unclear, the driver can ask questions via voice input, and the AI ​​will respond verbally. For example, if an AI driver in the passenger seat tells the driver, "Turn left at the intersection 100 meters ahead," and the user asks, "Are you at this facility?", the AI ​​will respond, "It's the next intersection. It's the intersection with a specific facility on the left." Unlike traditional navigation services, this service is significantly more convenient because users can ask questions. Furthermore, since the interaction is entirely voice-based, it also helps prevent distracted driving. Furthermore, all user questions are collected and analyzed, identifying areas that many users found difficult to understand and updating the navigation method. This allows the navigation system to efficiently set users' destinations, provide directions, receive and answer questions, and collect and analyze question information.

[0064] A route guidance system according to an embodiment includes a reception unit, a guidance unit, a question reception unit, a response unit, and a collection and analysis unit. The reception unit provides an interface for a user to set a destination. For example, the reception unit may provide a text box for a user to input an address or a drop-down menu for a user to select a landmark. The reception unit also includes a microphone for accepting voice input, allowing the user to set a destination by voice. For example, when a user vocally inputs, "My destination is ____," the reception unit analyzes the voice and sets the destination. The guidance unit provides route guidance based on the destination set by the reception unit. For example, the guidance unit includes a speaker for providing voice guidance, and may instruct the user, "Turn left at the intersection 100 meters ahead." The guidance unit also includes a display for displaying a map, allowing the user to visually provide route guidance. For example, the guidance unit may display the current location and destination on a map and draw a line indicating the route. The question reception unit receives a user's question. For example, the question receiving unit includes a microphone that receives voice input. When a user asks, "Is this the right facility?", the answering unit analyzes the voice and understands the question. The answering unit responds to the question received by the question receiving unit. For example, the answering unit uses voice synthesis technology to respond, "It's the next intersection. It's the intersection where a specific facility is on the left." The answering unit also includes a display that displays answers in text, allowing the answering unit to provide the user with a visual answer. The collection and analysis unit collects question information from all users and analyzes it using AI. For example, the collection and analysis unit stores the questions asked by users in a database, and the AI ​​analyzes the data to identify points that many users found difficult to understand. This allows the collection and analysis unit to provide feedback to update the navigation method. As a result, the navigation system according to the embodiment can efficiently set a user's destination, provide directions, receive and answer questions, and collect and analyze question information.

[0065] The question acceptance unit can accept voice input from a user. The question acceptance unit includes, for example, a microphone through which a user can input a question by voice. For example, when a user asks, "Is this the right facility?" by voice, the question acceptance unit analyzes the voice and understands the question. The question acceptance unit can also convert the user's voice into text using voice recognition technology. For example, the question acceptance unit can convert the user's voice into text in real time using voice recognition software, and analyze the text to understand the question. Furthermore, the question acceptance unit not only accepts the user's voice input but also includes a feedback function for improving the accuracy of the voice input. For example, when the user makes a voice input, the question acceptance unit displays the results of the voice recognition, allowing the user to confirm and correct the results. This allows the question acceptance unit to accurately accept the user's voice input. This allows the user to input a question by voice.

[0066] The answering unit can respond by voice. For example, the answering unit uses voice synthesis technology to respond by voice to the user's question. For example, if the user asks, "Are you at this facility?", the answering unit responds by voice, saying, "It's the next intersection. It's the intersection where the specific facility is on the left." The answering unit can also use voice synthesis technology to respond to the user's question in a natural voice. For example, the answering unit uses voice synthesis software to respond to the user's question in a voice with natural intonation and inflection. Furthermore, the answering unit not only responds by voice but also has a feedback function to improve the quality of the voice. For example, when the user receives a voice answer, the answering unit can evaluate the quality of the voice and adjust the voice synthesis parameters. This allows the answering unit to provide the user with a high-quality voice answer. This allows the user to receive a voice answer.

[0067] The collection and analysis unit can collect question information from multiple users and have the AI ​​analyze it. For example, the collection and analysis unit stores question information from all users in a database, and the AI ​​analyzes the data. For example, the collection and analysis unit stores what questions users have asked in a database, and the AI ​​analyzes the data to identify parts that many users found difficult to understand. The collection and analysis unit also has an algorithm that allows the AI ​​to analyze the question information. For example, the collection and analysis unit uses a machine learning algorithm to analyze the user's question information and identify parts that are difficult to understand. Furthermore, the collection and analysis unit provides feedback to update the route guidance method based on the results of the AI's analysis. For example, the collection and analysis unit makes suggestions to improve the route guidance method based on the results of the AI's analysis. In this way, the collection and analysis unit can provide feedback to improve the accuracy of route guidance. In this way, collecting question information from all users and having the AI ​​analyze it improves the accuracy of route guidance.

[0068] The collection and analysis unit can update the route guidance method based on the results of the AI ​​analysis. For example, the collection and analysis unit provides feedback for updating the route guidance method based on the results of the AI ​​analysis. For example, the collection and analysis unit makes suggestions for improving the route guidance method based on the results of the AI ​​analysis. The collection and analysis unit also has a software update function for updating the route guidance method. For example, the collection and analysis unit automatically updates the route guidance software based on the results of the AI ​​analysis to provide the latest route guidance method. Furthermore, the collection and analysis unit establishes a feedback loop for updating the route guidance method. For example, the collection and analysis unit improves the route guidance method based on the results of the AI ​​analysis, re-analyzes the results of the improvement, and establishes a feedback loop for further improvement. This allows the collection and analysis unit to continuously update the route guidance method. This causes the route guidance method to be continuously updated.

[0069] The reception unit can estimate the user's emotions and adjust the destination setting interface based on the estimated user emotions. The reception unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the reception unit captures the user's facial expressions using a camera and estimates the user's emotions using a facial expression recognition algorithm. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit records the user's voice and estimates the user's emotions using a voice analysis algorithm. The reception unit further adjusts the destination setting interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. On the other hand, if the user is relaxed, the reception unit provides detailed input options and suggests a customizable input method. Furthermore, if the user is in a hurry, the reception unit prioritizes voice input, allowing the user to quickly set a destination. This allows the reception unit to provide an interface that corresponds to the user's emotions. By providing an interface that corresponds to the user's emotions, destination setting can be performed smoothly.

[0070] The reception unit can analyze the user's past destination history and suggest optimal destination candidates. The reception unit, for example, stores the user's past destination history in a database and analyzes the data. For example, the reception unit stores places the user has visited in the past in a database and analyzes the data to suggest optimal destination candidates. The reception unit can also predict places the user will visit on specific days of the week or time periods based on the user's past destination history. For example, the reception unit stores places the user will visit on specific days of the week or time periods in a database and analyzes the data to suggest optimal destination candidates. The reception unit can also analyze the user's past movement patterns and suggest optimal destination candidates. For example, the reception unit stores the user's past movement patterns in a database and analyzes the data to suggest optimal destination candidates. This allows the reception unit to suggest optimal destination candidates based on the user's past destination history. This makes destination setting more efficient by suggesting optimal destination candidates based on past history.

[0071] When setting a destination, the reception unit can propose an optimal route taking into consideration the user's current traffic conditions and weather information. The reception unit, for example, proposes an optimal route based on real-time traffic congestion information. For example, the reception unit acquires real-time traffic congestion information from a traffic information service and proposes an optimal route based on that information. The reception unit can also consider current weather information and prioritize proposals of covered routes when it is raining. For example, the reception unit acquires weather data and prioritizes proposals of covered routes when it is raining. The reception unit can also propose an optimal route taking into consideration the real-time operation status of public transportation. For example, the reception unit acquires the operation status of public transportation and proposes an optimal route based on that information. This allows the reception unit to propose an optimal route taking into consideration the user's current traffic conditions and weather information. This improves user convenience by proposing an optimal route based on real-time information.

[0072] The reception unit can estimate the user's emotions and determine the priority of destination setting based on the estimated user's emotions. The reception unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the reception unit captures the user's facial expressions using a camera and estimates the user's emotions using a facial expression recognition algorithm. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit records the user's voice and estimates the user's emotions using a voice analysis algorithm. The reception unit then determines the priority of destination setting based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit prioritizes suggesting places where the user can relax. If the user is in a hurry, the reception unit prioritizes suggesting destinations that can be reached in the shortest time. If the user is having fun, the reception unit prioritizes suggesting tourist spots and leisure facilities. This allows the reception unit to suggest destinations in a priority order according to the user's emotions. By suggesting destinations in a priority order according to the user's emotions, user satisfaction is improved.

[0073] When setting a destination, the reception unit can prioritize suggesting highly relevant destinations in consideration of the user's geographical location information. The reception unit, for example, prioritizes suggesting locations close to the user's current location. For example, the reception unit acquires GPS data and prioritizes suggesting locations close to the user's current location. The reception unit can also prioritize suggesting locations with good transportation access from the user's current location. For example, the reception unit acquires transportation access information from a transportation information service and prioritizes suggesting locations with good transportation access from the user's current location. Furthermore, the reception unit can suggest an optimal destination in consideration of the distance from the user's current location. For example, the reception unit calculates the distance from the user's current location and suggests an optimal destination based on the distance. This allows the reception unit to prioritize suggesting highly relevant destinations in consideration of the user's geographical location information. This improves convenience by suggesting optimal destinations based on the user's current location.

[0074] When setting a destination, the reception unit can analyze the user's social media activity and suggest related destinations. For example, the reception unit suggests places where the user has checked in on social media as candidate destinations. For example, the reception unit uses a social media API to obtain data on places where the user has checked in and suggests candidate destinations based on the data. The reception unit can also suggest places and events that the user follows on social media as candidate destinations. For example, the reception unit uses a social media API to obtain data on places and events that the user follows and suggests candidate destinations based on the data. Furthermore, the reception unit can analyze the content of the user's social media posts and suggest places of interest. For example, the reception unit uses a social media API to obtain the content of the user's posts and analyzes the data to suggest places of interest. In this way, the reception unit can analyze the user's social media activity and suggest related destinations. In this way, by utilizing social media information, destinations that match the user's interests can be suggested.

[0075] The guidance unit can estimate the user's emotions and adjust the way in which route guidance is presented based on the estimated user's emotions. The guidance unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the guidance unit captures the user's facial expressions using a camera and estimates the user's emotions using a facial expression recognition algorithm. The guidance unit can also estimate the user's emotions using voice analysis technology. For example, the guidance unit records the user's voice and estimates the user's emotions using a voice analysis algorithm. The guidance unit further adjusts the way in which route guidance is presented based on the estimated user's emotions. For example, if the user is nervous, the guidance unit provides a simple, highly visible display method. If the user is relaxed, the guidance unit provides a display method including detailed information. If the user is in a hurry, the guidance unit provides a display method that focuses on the main points. This allows the guidance unit to provide route guidance that corresponds to the user's emotions. By providing route guidance that corresponds to the user's emotions, the user's understanding is deepened.

[0076] The guidance unit can analyze the user's driving style and select the optimal guidance method when providing route guidance. The guidance unit, for example, has a data collection function for analyzing the user's driving style. For example, the guidance unit collects data such as the vehicle's speed and braking usage, and analyzes the data to identify the user's driving style. The guidance unit also has an algorithm for selecting a guidance method according to the user's driving style. For example, the guidance unit provides detailed guidance if the user has a cautious driving style. On the other hand, if the user has a smooth driving style, the guidance unit provides concise guidance. Furthermore, if the user is in a hurry, the guidance unit provides guidance that emphasizes the shortest route. This allows the guidance unit to provide guidance according to the user's driving style. As a result, providing guidance according to the user's driving style improves driving safety.

[0077] The guidance unit can dynamically change the route when providing route guidance, taking real-time traffic information into consideration. The guidance unit, for example, proposes an optimal route based on real-time traffic congestion information. For example, the guidance unit acquires real-time traffic congestion information from a traffic information service and proposes an optimal route based on that information. The guidance unit can also propose an optimal route taking into consideration the real-time operation status of public transportation. For example, the guidance unit acquires the operation status of public transportation and proposes an optimal route based on that information. Furthermore, the guidance unit can propose a detour route based on real-time road construction information. For example, the guidance unit acquires road construction information and proposes a detour route based on that information. This allows the guidance unit to dynamically change the route taking real-time traffic information into consideration. This improves user convenience by providing an optimal route based on real-time information.

[0078] The guidance unit can estimate the user's emotions and adjust the level of detail of route guidance based on the estimated user's emotions. The guidance unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the guidance unit captures the user's facial expressions using a camera and estimates the user's emotions using a facial expression recognition algorithm. The guidance unit can also estimate the user's emotions using voice analysis technology. For example, the guidance unit records the user's voice and estimates the user's emotions using a voice analysis algorithm. The guidance unit further adjusts the level of detail of route guidance based on the estimated user's emotions. For example, if the user is nervous, the guidance unit provides a simple, highly visible display method. If the user is relaxed, the guidance unit provides a display method including detailed information. If the user is in a hurry, the guidance unit provides a display method that focuses on the main points. This allows the guidance unit to provide detailed route guidance that corresponds to the user's emotions. By providing detailed route guidance that corresponds to the user's emotions, the user's understanding is deepened.

[0079] When providing route guidance, the guidance unit can provide an optimal guidance method by referring to the user's past driving history. The guidance unit, for example, stores the user's past driving history in a database and analyzes the data. For example, the guidance unit proposes an optimal guidance method based on routes the user has used in the past. The guidance unit can also propose a route that avoids congestion based on the user's past driving history. For example, the guidance unit analyzes the user's past driving history and proposes a route that avoids congestion. Furthermore, the guidance unit can analyze the user's past driving history and propose the most efficient route. For example, the guidance unit proposes the most efficient route based on the user's past driving history. This allows the guidance unit to provide an optimal guidance method by referring to the user's past driving history. This improves user convenience by providing optimal guidance based on the past driving history.

[0080] When providing route guidance, the guidance unit can propose an optimal route taking into consideration the user's vehicle information. The guidance unit, for example, proposes the most economical route based on fuel efficiency information of the user's vehicle. For example, the guidance unit acquires fuel efficiency information of the user's vehicle and proposes the most economical route based on that information. The guidance unit can also propose a route that avoids narrow roads taking into consideration the size of the user's vehicle. For example, the guidance unit acquires size information of the user's vehicle and proposes a route that avoids narrow roads based on that information. The guidance unit can also propose an optimal route taking into consideration the performance of the user's vehicle. For example, the guidance unit acquires performance information of the user's vehicle and proposes an optimal route based on that information. This allows the guidance unit to propose an optimal route taking into consideration the user's vehicle information. This improves user convenience by providing an optimal route based on vehicle information.

[0081] The question acceptance unit can estimate the user's emotions and adjust the question acceptance interface based on the estimated user emotions. The question acceptance unit uses, for example, facial expression recognition technology to estimate the user's emotions. For example, the question acceptance unit captures the user's facial expressions using a camera and estimates the user's emotions using a facial expression recognition algorithm. The question acceptance unit can also estimate the user's emotions using voice analysis technology. For example, the question acceptance unit records the user's voice and estimates the user's emotions using a voice analysis algorithm. Furthermore, the question acceptance unit adjusts the question acceptance interface based on the estimated user emotions. For example, if the user is nervous, the question acceptance unit provides a simple, highly visible interface. On the other hand, if the user is relaxed, the question acceptance unit provides detailed input options. Furthermore, if the user is in a hurry, the question acceptance unit prioritizes voice input and accepts questions quickly. This allows the question acceptance unit to provide an interface that corresponds to the user's emotions. By providing an interface that corresponds to the user's emotions, questions can be accepted smoothly.

[0082] When receiving a question, the question receiving unit can analyze the user's past question history and present optimal question candidates. The question receiving unit, for example, stores the user's past question history in a database and analyzes the data. For example, the question receiving unit stores the content of questions frequently asked by the user in a database and analyzes the data to present optimal question candidates. The question receiving unit can also predict the content of questions that will be asked on a specific day of the week or time of day based on the user's past question history. For example, the question receiving unit stores the content of questions that will be asked by the user on a specific day of the week or time of day in a database and analyzes the data to present optimal question candidates. The question receiving unit can also analyze the user's past question history and present optimal question candidates. For example, the question receiving unit stores the user's past question history in a database and analyzes the data to present optimal question candidates. This allows the question receiving unit to present optimal question candidates based on the user's past question history. This allows efficient question asking by presenting optimal question candidates based on the past question history.

[0083] When receiving a question, the question receiving unit can determine the priority of the question taking into consideration the user's current driving situation. The question receiving unit, for example, stores the user's current driving situation in a database and analyzes the data. For example, when the user is driving on a highway, the question receiving unit can preferentially receive important questions. Furthermore, when the user is driving in an urban area, the question receiving unit can receive detailed questions. For example, when the user is driving in an urban area, the question receiving unit receives detailed questions. Furthermore, when the user is parked, the question receiving unit can receive all questions. For example, when the user is parked, the question receiving unit receives all questions. In this way, the question receiving unit can determine the priority of the question taking into consideration the user's current driving situation. In this way, by determining the priority of questions according to the driving situation, important questions are preferentially processed.

[0084] The question acceptance unit can estimate a user's emotions and adjust the question acceptance method based on the estimated user's emotions. The question acceptance unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the question acceptance unit captures the user's facial expressions using a camera and estimates the user's emotions using a facial expression recognition algorithm. The question acceptance unit can also estimate the user's emotions using voice analysis technology. For example, the question acceptance unit records the user's voice and estimates the user's emotions using a voice analysis algorithm. Furthermore, the question acceptance unit adjusts the question acceptance method based on the estimated user's emotions. For example, if the user is nervous, the question acceptance unit provides a simple, highly visible interface. On the other hand, if the user is relaxed, the question acceptance unit provides detailed input options. Furthermore, if the user is in a hurry, the question acceptance unit prioritizes voice input and accepts questions quickly. This allows the question acceptance unit to provide a question acceptance method that corresponds to the user's emotions. By providing a question acceptance method that corresponds to the user's emotions, questions can be asked smoothly.

[0085] When accepting a question, the question acceptance unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. The question acceptance unit, for example, stores the user's geographical location information in a database and analyzes the data. For example, if the user is in a specific area, the question acceptance unit can prioritize accepting questions related to the area. Furthermore, if the user is close to a specific facility, the question acceptance unit can prioritize accepting questions related to the facility. For example, if the user is close to a specific facility, the question acceptance unit can prioritize accepting questions related to the facility. Furthermore, the question acceptance unit can prioritize accepting highly relevant questions by taking into account the distance from the user's current location. For example, the question acceptance unit calculates the distance from the user's current location and prioritizes accepting highly relevant questions based on the distance. This allows the question acceptance unit to prioritize accepting highly relevant questions by taking into account the user's geographical location information. This improves user convenience by preferentially accepting questions based on geographical location information.

[0086] The question acceptance unit can analyze the user's social media activity when accepting a question and accept related questions. The question acceptance unit accepts, for example, questions related to places where the user has checked in on social media. For example, the question acceptance unit uses a social media API to acquire data on places where the user has checked in and accepts related questions based on the data. The question acceptance unit can also accept questions related to places or events the user follows on social media. For example, the question acceptance unit uses a social media API to acquire data on places or events the user follows and accepts related questions based on the data. The question acceptance unit can also analyze the content of the user's posts on social media and accept related questions. For example, the question acceptance unit uses a social media API to acquire the content of the user's posts and analyzes the data to accept related questions. In this way, the question acceptance unit can analyze the user's social media activity and accept related questions. In this way, by utilizing social media information, questions that match the user's interests can be accepted.

[0087] The answering unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. The answering unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the answering unit captures the user's facial expressions using a camera and estimates the user's emotions using a facial expression recognition algorithm. The answering unit can also estimate the user's emotions using voice analysis technology. For example, the answering unit records the user's voice and estimates the user's emotions using a voice analysis algorithm. Furthermore, the answering unit adjusts the way the answer is expressed based on the estimated user's emotions. For example, if the user is nervous, the answering unit provides a simple, highly visible display method. If the user is relaxed, the answering unit provides a display method including detailed information. If the user is in a hurry, the answering unit provides a display method that focuses on the main points. This allows the answering unit to provide an answer that corresponds to the user's emotions. As a result, providing an answer that corresponds to the user's emotions deepens the user's understanding.

[0088] When answering a question, the answering unit can provide the optimal answer by referring to the user's past question history. The answering unit, for example, stores the user's past question history in a database and analyzes the data. For example, the answering unit stores the content of questions asked by the user in a database and analyzes the data to provide the optimal answer. The answering unit can also provide a related answer based on the user's past question history. For example, the answering unit analyzes the user's past question history and provides a related answer. The answering unit can also analyze the user's past question history and provide the most appropriate answer. For example, the answering unit provides the most appropriate answer based on the user's past question history. This allows the answering unit to provide the optimal answer by referring to the user's past question history. This improves user convenience by providing the optimal answer based on the past question history.

[0089] When providing an answer, the answering unit can determine the priority of the answer taking into account the user's current driving situation. The answering unit, for example, stores the user's current driving situation in a database and analyzes the data. For example, the answering unit can provide important answers preferentially when the user is driving on a highway. Furthermore, the answering unit can provide detailed answers when the user is driving in an urban area. For example, the answering unit provides detailed answers when the user is driving in an urban area. Furthermore, the answering unit can provide all answers when the user is parked. For example, the answering unit provides all answers when the user is parked. This allows the answering unit to determine the priority of the answer taking into account the user's current driving situation. Thus, by determining the priority of answers according to the driving situation, important answers are preferentially provided.

[0090] The answering unit can estimate the user's emotions and adjust the level of detail of the answer based on the estimated user's emotions. The answering unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the answering unit captures the user's facial expressions using a camera and estimates the user's emotions using a facial expression recognition algorithm. The answering unit can also estimate the user's emotions using voice analysis technology. For example, the answering unit records the user's voice and estimates the user's emotions using a voice analysis algorithm. Furthermore, the answering unit adjusts the level of detail of the answer based on the estimated user's emotions. For example, if the user is nervous, the answering unit provides a simple, highly visible answer. On the other hand, if the user is relaxed, the answering unit provides an answer that includes detailed information. On the other hand, if the user is in a hurry, the answering unit provides an answer that focuses on the main points. This allows the answering unit to provide a detailed answer that corresponds to the user's emotions. As a result, providing a detailed answer that corresponds to the user's emotions deepens the user's understanding.

[0091] When answering a question, the answering unit can provide the optimal answer by taking into account the user's geographical location information. The answering unit, for example, stores the user's geographical location information in a database and analyzes the data. For example, if the user is in a specific area, the answering unit can provide information related to the area. Furthermore, if the user is close to a specific facility, the answering unit can provide information related to the facility. For example, if the user is close to a specific facility, the answering unit can provide information related to the facility. Furthermore, the answering unit can provide the optimal answer by taking into account the distance from the user's current location. For example, the answering unit calculates the distance from the user's current location and provides the optimal answer based on that distance. This allows the answering unit to provide the optimal answer by taking into account the user's geographical location information. This improves user convenience by providing the optimal answer based on the geographical location information.

[0092] When answering a question, the answer unit can analyze the user's social media activity and provide a relevant answer. The answer unit can provide, for example, information related to places where the user has checked in on social media. For example, the answer unit can use a social media API to obtain data on places where the user has checked in and provide relevant information based on that data. The answer unit can also provide information related to places and events the user is following on social media. For example, the answer unit can use a social media API to obtain data on places and events the user is following and provide relevant information based on that data. The answer unit can also analyze the content of the user's social media posts and provide a relevant answer. For example, the answer unit can use a social media API to obtain the content of the user's posts and analyze that data to provide a relevant answer. This allows the answer unit to analyze the user's social media activity and provide a relevant answer. This makes it possible to provide answers that match the user's interests by utilizing social media information.

[0093] The collection and analysis unit can estimate the user's emotions and determine the priority of the question information to be collected based on the estimated user's emotions. The collection and analysis unit uses, for example, facial expression recognition technology to estimate the user's emotions. For example, the collection and analysis unit captures the user's facial expressions using a camera and estimates the user's emotions using a facial expression recognition algorithm. The collection and analysis unit can also estimate the user's emotions using voice analysis technology. For example, the collection and analysis unit records the user's voice and estimates the user's emotions using a voice analysis algorithm. The collection and analysis unit further determines the priority of the question information to be collected based on the estimated user's emotions. For example, if the user is nervous, the collection and analysis unit prioritizes collecting important question information. If the user is relaxed, the collection and analysis unit collects detailed question information. If the user is in a hurry, the collection and analysis unit prioritizes collecting question information that covers the main points. This allows the collection and analysis unit to determine the priority of the question information according to the user's emotions. By prioritizing the question information according to the user's emotions, important information is preferentially collected.

[0094] The collection and analysis unit can optimize the analysis algorithm by referring to past question data during collection and analysis. The collection and analysis unit, for example, stores past question data in a database and analyzes the data. For example, the collection and analysis unit selects an optimal analysis algorithm based on the past question data. The collection and analysis unit can also analyze the past question data to improve the accuracy of the algorithm. For example, the collection and analysis unit analyzes the past question data and adjusts algorithm parameters. Furthermore, the collection and analysis unit can customize the analysis algorithm by referring to the past question data. For example, the collection and analysis unit develops an algorithm that meets specific conditions based on the past question data. In this way, the collection and analysis unit can optimize the analysis algorithm by referring to the past question data. In this way, optimizing the analysis algorithm based on the past question data improves the accuracy of the analysis.

[0095] The collection and analysis unit can customize the analysis results by taking into account the user's attribute information during collection and analysis. The collection and analysis unit, for example, stores the user's attribute information in a database and analyzes the data. For example, the collection and analysis unit stores the user's age and gender in a database and customizes the analysis results based on that data. The collection and analysis unit can also customize the analysis results by taking into account the user's driving history. For example, the collection and analysis unit stores the user's driving history in a database and customizes the analysis results based on that data. The collection and analysis unit can also customize the analysis results by taking into account the user's geographical location information. For example, the collection and analysis unit stores the user's geographical location information in a database and customizes the analysis results based on that data. This allows the collection and analysis unit to customize the analysis results by taking into account the user's attribute information. This improves user convenience by providing analysis results based on the user's attribute information.

[0096] The collection and analysis unit can estimate the user's emotions and adjust the display method of the collected question information based on the estimated user's emotions. The collection and analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the collection and analysis unit captures the user's facial expressions using a camera and estimates the user's emotions using a facial expression recognition algorithm. The collection and analysis unit can also estimate the user's emotions using voice analysis technology. For example, the collection and analysis unit records the user's voice and estimates the user's emotions using a voice analysis algorithm. The collection and analysis unit further adjusts the display method of the collected question information based on the estimated user's emotions. For example, if the user is nervous, the collection and analysis unit provides a simple, highly visible display method. If the user is relaxed, the collection and analysis unit provides a display method including detailed information. If the user is in a hurry, the collection and analysis unit provides a display method that focuses on the main points. In this way, the collection and analysis unit can provide a display method that corresponds to the user's emotions. By providing a display method that corresponds to the user's emotions, the visibility of information is improved.

[0097] The collection and analysis unit can provide optimal analysis results by taking into account the user's geographical location information during collection and analysis. The collection and analysis unit, for example, stores the user's geographical location information in a database and analyzes the data. For example, if the user is in a specific area, the collection and analysis unit can provide analysis results related to the area. Furthermore, if the user is close to a specific facility, the collection and analysis unit can provide analysis results related to the facility. For example, if the user is close to a specific facility, the collection and analysis unit can provide analysis results related to the facility. Furthermore, the collection and analysis unit can provide optimal analysis results by taking into account the distance from the user's current location. For example, the collection and analysis unit calculates the distance from the user's current location and provides optimal analysis results based on the distance. This allows the collection and analysis unit to provide optimal analysis results by taking into account the user's geographical location information. This improves user convenience by providing optimal analysis results based on geographical location information.

[0098] During collection and analysis, the collection and analysis unit can analyze the user's social media activities and provide related analysis results. The collection and analysis unit can provide, for example, analysis results related to places where the user has checked in on social media. For example, the collection and analysis unit can use a social media API to obtain data on places where the user has checked in and provide related analysis results based on the data. The collection and analysis unit can also provide analysis results related to places and events the user follows on social media. For example, the collection and analysis unit can use a social media API to obtain data on places and events the user follows and provide related analysis results based on the data. The collection and analysis unit can also analyze the content of the user's posts on social media and provide related analysis results. For example, the collection and analysis unit can use a social media API to obtain the content of the user's posts, analyze the data, and provide related analysis results. This allows the collection and analysis unit to analyze the user's social media activities and provide related analysis results. This makes it possible to provide analysis results that match the user's interests by utilizing social media information. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, guidance unit, question reception unit, answering unit, and collection and analysis unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, allowing the user to set a destination by voice. The guidance unit is realized, for example, by the speaker 40B and display 40A of the smart device 14, providing audio and visual guidance. The question reception unit is realized, for example, by the microphone 38B of the smart device 14, accepting a user's question. The answering unit is realized, for example, by the specific processing unit 290 of the data processing device 12, providing an answer using voice synthesis technology. The collection and analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, collecting question information from all users and analyzing it using AI. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, guidance unit, question reception unit, answering unit, and collection and analysis unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, allowing the user to set a destination by voice. The guidance unit is realized, for example, by the speaker 240 and display of the smart glasses 214, providing audio and visual guidance. The question reception unit is realized, for example, by the microphone 238 of the smart glasses 214, accepting a user's question. The answering unit is realized, for example, by the specific processing unit 290 of the data processing device 12, providing an answer using voice synthesis technology. The collection and analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, collecting question information from all users and analyzing it using AI. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, guidance unit, question reception unit, answering unit, and collection and analysis unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314, and allows the user to set a destination by voice. The guidance unit is realized, for example, by the speaker 240 and display 343 of the headset-type terminal 314, and provides audio and visual guidance. The question reception unit is realized, for example, by the microphone 238 of the headset-type terminal 314, and receives a user's question. The answering unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides an answer using voice synthesis technology. The collection and analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects question information from all users and analyzes it using AI. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, guidance unit, question reception unit, answering unit, and collection and analysis unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and allows the user to set a destination by voice. The guidance unit is realized, for example, by the speaker 240 and display of the robot 414, and provides audio and visual guidance. The question reception unit is realized, for example, by the microphone 238 of the robot 414, and receives questions from the user. The answering unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides answers using voice synthesis technology. The collection and analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects question information from all users and analyzes it using AI.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The reception unit can analyze the user's past driving history and suggest optimal destination candidates. For example, the reception unit stores places the user has visited in the past in a database and analyzes the data to suggest optimal destination candidates. The reception unit can also predict places the user will visit on specific days of the week or during specific times of the day based on the user's past driving history. Furthermore, the reception unit can analyze the user's past movement patterns and suggest optimal destination candidates. This allows the reception unit to suggest optimal destination candidates based on the user's past driving history. This makes destination setting more efficient by suggesting optimal destination candidates based on past history.

[0101] The question acceptance unit can estimate the user's emotions and adjust the question acceptance interface based on the estimated user's emotions. For example, the question acceptance unit uses facial expression recognition technology to estimate the user's emotions. For example, the question acceptance unit captures the user's facial expressions using a camera and estimates the user's emotions using a facial expression recognition algorithm. The question acceptance unit can also estimate the user's emotions using voice analysis technology. Furthermore, the question acceptance unit adjusts the question acceptance interface based on the estimated user's emotions. For example, if the user is nervous, the question acceptance unit provides a simple, highly visible interface. On the other hand, if the user is relaxed, the question acceptance unit provides detailed input options. Furthermore, if the user is in a hurry, the question acceptance unit prioritizes voice input and accepts questions quickly. This allows the question acceptance unit to provide an interface that corresponds to the user's emotions. By providing an interface that corresponds to the user's emotions, questions can be accepted smoothly.

[0102] The answering unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, the answering unit uses facial expression recognition technology to estimate the user's emotions. For example, the answering unit can capture the user's facial expressions using a camera and estimate the user's emotions using a facial expression recognition algorithm. The answering unit can also estimate the user's emotions using voice analysis technology. Furthermore, the answering unit can adjust the way the answer is expressed based on the estimated user's emotions. For example, if the user is nervous, the answering unit provides a simple, highly visible display method. If the user is relaxed, the answering unit provides a display method including detailed information. If the user is in a hurry, the answering unit provides a display method that focuses on the main points. This allows the answering unit to provide an answer that corresponds to the user's emotions. As a result, providing an answer that corresponds to the user's emotions deepens the user's understanding.

[0103] The collection and analysis unit can estimate the user's emotions and determine the priority of the question information to be collected based on the estimated user's emotions. For example, the collection and analysis unit uses facial expression recognition technology to estimate the user's emotions. For example, the collection and analysis unit captures the user's facial expressions using a camera and estimates the user's emotions using a facial expression recognition algorithm. The collection and analysis unit can also estimate the user's emotions using voice analysis technology. Furthermore, the collection and analysis unit determines the priority of the question information to be collected based on the estimated user's emotions. For example, if the user is nervous, the collection and analysis unit prioritizes collecting important question information. Furthermore, if the user is relaxed, the collection and analysis unit prioritizes collecting detailed question information. Furthermore, if the user is in a hurry, the collection and analysis unit prioritizes collecting question information that covers the main points. In this way, the collection and analysis unit can determine the priority of the question information according to the user's emotions. Thus, by prioritizing the question information according to the user's emotions, important information is preferentially collected.

[0104] The guidance unit can estimate the user's emotions and adjust the way in which route guidance is presented based on the estimated user's emotions. For example, the guidance unit uses facial expression recognition technology to estimate the user's emotions. For example, the guidance unit captures the user's facial expressions using a camera and estimates the user's emotions using a facial expression recognition algorithm. The guidance unit can also estimate the user's emotions using voice analysis technology. Furthermore, the guidance unit adjusts the way in which route guidance is presented based on the estimated user's emotions. For example, if the user is nervous, the guidance unit provides a simple, highly visible display method. If the user is relaxed, the guidance unit provides a display method that includes detailed information. If the user is in a hurry, the guidance unit provides a display method that focuses on the main points. This allows the guidance unit to provide route guidance that corresponds to the user's emotions. As a result, providing route guidance that corresponds to the user's emotions deepens the user's understanding.

[0105] When setting a destination, the reception unit can propose an optimal route by taking into consideration the user's current traffic conditions and weather information. For example, the reception unit proposes an optimal route based on real-time traffic congestion information. For example, the reception unit acquires real-time traffic congestion information from a traffic information service and proposes an optimal route based on that information. The reception unit can also consider current weather information and prioritize proposals of covered routes when it is raining. For example, the reception unit acquires weather data and prioritizes proposals of covered routes when it is raining. The reception unit can also propose an optimal route by taking into consideration the real-time operation status of public transportation. For example, the reception unit acquires the operation status of public transportation and proposes an optimal route based on that information. In this way, the reception unit can propose an optimal route by taking into consideration the user's current traffic conditions and weather information. In this way, by proposing an optimal route based on real-time information, convenience for the user is improved.

[0106] The guidance unit can analyze the user's driving style and select the optimal guidance method when providing route guidance. For example, the guidance unit has a data collection function for analyzing the user's driving style. For example, the guidance unit collects data such as the vehicle's speed and braking usage, and analyzes the data to identify the user's driving style. The guidance unit also has an algorithm for selecting a guidance method according to the user's driving style. For example, the guidance unit provides detailed guidance if the user has a cautious driving style. On the other hand, if the user has a smooth driving style, the guidance unit provides concise guidance. Furthermore, if the user is in a hurry, the guidance unit provides guidance that emphasizes the shortest route. This allows the guidance unit to provide guidance according to the user's driving style. As a result, providing guidance according to the user's driving style improves driving safety.

[0107] During collection and analysis, the collection and analysis unit can optimize the analysis algorithm by referring to past question data. For example, the collection and analysis unit stores past question data in a database and analyzes the data. For example, the collection and analysis unit selects an optimal analysis algorithm based on the past question data. The collection and analysis unit can also analyze the past question data to improve the accuracy of the algorithm. For example, the collection and analysis unit analyzes the past question data and adjusts algorithm parameters. Furthermore, the collection and analysis unit can customize the analysis algorithm by referring to the past question data. For example, the collection and analysis unit develops an algorithm that meets specific conditions based on the past question data. In this way, the collection and analysis unit can optimize the analysis algorithm by referring to the past question data. In this way, optimizing the analysis algorithm based on the past question data improves the accuracy of the analysis.

[0108] When answering a question, the answering unit can provide the optimal answer by referring to the user's past question history. For example, the answering unit stores the user's past question history in a database and analyzes the data. For example, the answering unit stores the content of questions asked by the user in a database and analyzes the data to provide the optimal answer. The answering unit can also provide a related answer based on the user's past question history. For example, the answering unit analyzes the user's past question history and provides a related answer. The answering unit can also analyze the user's past question history and provide the most appropriate answer. For example, the answering unit provides the most appropriate answer based on the user's past question history. This allows the answering unit to provide the optimal answer by referring to the user's past question history. This improves user convenience by providing the optimal answer based on the past question history.

[0109] The guidance unit can dynamically change the route when providing route guidance, taking real-time traffic information into consideration. For example, the guidance unit proposes an optimal route based on real-time traffic congestion information. For example, the guidance unit acquires real-time traffic congestion information from a traffic information service and proposes an optimal route based on that information. The guidance unit can also propose an optimal route taking into consideration the real-time operation status of public transportation. For example, the guidance unit acquires the operation status of public transportation and proposes an optimal route based on that information. Furthermore, the guidance unit can propose a detour route based on real-time road construction information. For example, the guidance unit acquires road construction information and proposes a detour route based on that information. This allows the guidance unit to dynamically change the route taking real-time traffic information into consideration. This improves user convenience by providing an optimal route based on real-time information.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The reception unit provides an interface for the user to set a destination. For example, it provides a text box for the user to enter an address and a drop-down menu for selecting a landmark. It also has a microphone that accepts voice input, so the user can set a destination by voice. Step 2: The guidance unit provides route guidance based on the destination set by the reception unit. For example, it may be equipped with a speaker that provides voice guidance, and may provide guidance to the user, such as "Turn left at the intersection 100 meters ahead." It may also be equipped with a display that displays a map, and can provide visual guidance to the user. Step 3: The question receiving unit receives a question from the user. For example, if the unit is equipped with a microphone that receives voice input, and the user asks, "Is this the correct facility?", the voice is analyzed and the question is understood. Step 4: The answering unit responds to the question received by the question receiving unit. For example, it uses voice synthesis technology to respond with, "It's the next intersection. It's the intersection where a specific facility is on the left." It also has a display that displays the answer in text, so it can provide the user with a visual answer. Step 5: The collection and analysis unit collects question information from all users, which is then analyzed by AI. For example, the types of questions users have asked are stored in a database, and the AI ​​analyzes the data to identify areas that many users found difficult to understand. This allows it to provide feedback to update the navigation method.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0114] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0156] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0167] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0168] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0169] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0173] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0174] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0175] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0176] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0177] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0178] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0180] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0181] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0182] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0183] [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a destination setting; a guidance unit that provides route guidance based on the information received by the reception unit; a question receiving unit that receives questions from users; an answering unit that answers the questions received by the question receiving unit; a collection and analysis unit that collects and analyzes question information from multiple users; Equipped with A system characterized by:

2. The question receiving unit Accepts user voice input 2. The system of claim 1.

3. The answering section Answer by voice 2. The system of claim 1.

4. The collection and analysis unit Question information from multiple users is collected and analyzed by AI 2. The system of claim 1.

5. The collection and analysis unit Update route guidance methods based on AI analysis results 2. The system of claim 1.

6. The reception unit Estimating user emotions and adjusting the destination setting interface based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyzes the user's past destination history and suggests optimal destination candidates 2. The system of claim 1.

8. The reception unit When setting a destination, the system suggests the optimal route, taking into account the user's current traffic conditions and weather information.

2. The system of claim 1.

Images