System

Abstract

An object of a system according to an embodiment is to comprehensively analyze user input information, real-time traffic information, and weather information and propose an optimal route.SOLUTION: A system according to an embodiment includes a user input analysis unit, a route suggestion unit, a traffic information analysis unit, and a weather information analysis unit. The user input analysis unit analyzes input information of a user. The route proposal unit proposes an optimal route based on the information analyzed by the user input analysis unit. The traffic information analysis unit analyzes traffic information provided in real time, and recalculates or proposes a route. The weather information analysis unit analyzes the weather information and reflects the analysis result in the route proposal.SELECTED DRAWING: Figure 1

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 technologies do not adequately propose optimal routes by comprehensively analyzing user input information, real-time traffic information, and weather information, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal route by comprehensively analyzing information input by a user, real-time traffic information, and weather information. [Means for solving the problem]

[0006] The system according to the embodiment includes a user input analysis unit, a route suggestion unit, a traffic information analysis unit, and a weather information analysis unit. The user input analysis unit analyzes information input by a user. The route suggestion unit suggests an optimal route based on the information analyzed by the user input analysis unit. The traffic information analysis unit analyzes traffic information provided in real time and recalculates or suggests routes. The weather information analysis unit analyzes weather information and reflects it in the route suggestion. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively analyze user input information, real-time traffic information, and weather information, and propose the optimal route. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 car navigation system according to an embodiment of the present invention uses a generative AI to analyze user input information and provide information on optimal routes and destinations, enabling the car navigation system to provide users with more advanced and personalized guidance.

[0029] A car navigation system according to an embodiment includes a user input analysis unit, a route suggestion unit, a traffic information analysis unit, and a weather information analysis unit. The user input analysis unit analyzes user input information. For example, when a user inputs a destination, the information can be received in the form of text input, voice input, touch input, or the like. The user input analysis unit can also analyze the user's desired conditions (e.g., shortest time, shortest distance, avoiding toll roads, etc.). The route suggestion unit proposes an optimal route based on the information analyzed by the user input analysis unit. For example, if a user instructs the generation AI to "arrive at the destination in the shortest time," the AI ​​calculates the most efficient route taking into account current traffic conditions and road congestion. The traffic information analysis unit analyzes traffic information provided in real time and recalculates or proposes a route. For example, if a traffic jam or accident occurs, the AI ​​proposes a new route based on that information. The weather information analysis unit analyzes weather information and reflects it in the proposed route. For example, if bad weather such as heavy rain or snow is predicted, the AI ​​proposes a safe route based on that information. This allows the system to analyze user input, traffic information, and weather information and suggest the optimal route.

[0030] The route suggestion unit can learn the user's driving history and preferences and reflect them in the next route suggestion. For example, the route suggestion unit learns places the user has visited in the past and routes frequently used by the user and reflects them in the next route suggestion. For example, the route suggestion unit may propose a route taking into account cafes and gas stations frequently used by the user. The route suggestion unit may also analyze the user's driving history and learn driving patterns for specific time periods and days of the week. For example, the route suggestion unit may distinguish between weekday commute routes and weekend leisure routes and suggest them accordingly. The route suggestion unit may also learn the user's preferences and reflect them in the next route suggestion. For example, the route suggestion unit may prioritize scenic routes and quiet roads that the user prefers. This enables more personalized route suggestions based on the user's past driving history and preferences.

[0031] The route suggestion unit can suggest a route that prioritizes safety based on the user's driving style. The route suggestion unit, for example, analyzes the user's driving style and suggests a safe route taking into account the frequency of sudden braking and sudden acceleration. For example, it selects a route that avoids sharp curves and steep slopes. The route suggestion unit also suggests a route that prioritizes safety based on the user's driving data. For example, it suggests a route that avoids areas where traffic accidents frequently occur. The route suggestion unit also learns the user's driving style and suggests a route that prioritizes safety. For example, for a user who is not good at driving at night, it suggests a route with many bright street lights. This makes it possible to suggest a route that prioritizes safety based on the user's driving style.

[0032] The route suggestion unit can suggest event information or special promotions around the destination based on the information input by the user. For example, the generation AI of the route suggestion unit suggests event information around the destination based on the information input by the user. For example, it provides information about concerts and festivals held near the destination. The route suggestion unit also analyzes the information input by the user and suggests special promotions around the destination. For example, it provides information about discount coupons for restaurants and sales at shopping malls. The route suggestion unit also suggests event information and promotions around the destination based on the information input by the user. For example, it provides information about special events at tourist spots and exhibitions at art museums. This makes it possible to suggest event information and special promotions around the destination based on the information input by the user.

[0033] When analyzing the information input by the user, the route suggestion unit can suggest popular routes and spots by referring to reviews and ratings from other users. For example, the route suggestion unit analyzes the information input by the user and suggests popular routes by referring to reviews and ratings from other users. For example, it selects a route that includes highly rated tourist spots. In addition, the route suggestion unit uses the generation AI to analyze reviews from other users and suggest popular spots. For example, it incorporates restaurants and cafes with good word-of-mouth reviews into the route. In addition, the route suggestion unit suggests the optimal route based on the information input by the user and by referring to ratings from other users. For example, it selects tourist spots based on ratings on review sites. This makes it possible to suggest popular routes and spots by referring to reviews and ratings from other users.

[0034] The traffic information analysis unit can analyze real-time traffic information and propose an optimal avoidance route based on the user's driving patterns. For example, the generation AI in the traffic information analysis unit analyzes real-time traffic information and proposes an optimal avoidance route based on the user's driving patterns. For example, if the user tends to avoid sudden braking, a smooth route is selected. The traffic information analysis unit also learns the user's driving patterns and proposes an optimal avoidance route based on real-time traffic information. For example, if the user prefers highways, a high-speed route that avoids traffic jams is proposed. The traffic information analysis unit also analyzes real-time traffic information and proposes an optimal avoidance route based on the user's driving patterns. For example, if the user wants to arrive at their destination in the shortest time, the most efficient route is selected. This makes it possible to propose an optimal avoidance route based on the user's driving patterns.

[0035] The traffic information analysis unit learns from past traffic data, predicts future traffic conditions, and proposes routes. For example, the generation AI in the traffic information analysis unit learns from past traffic data, predicts future traffic conditions, and proposes routes. For example, it selects a route that avoids areas where congestion is likely to occur during specific time periods. The traffic information analysis unit also uses the generation AI to predict future traffic conditions based on past traffic data and proposes the optimal route. For example, it proposes a route that avoids areas with heavy traffic on weekends. The traffic information analysis unit also uses the generation AI to learn from past traffic data, predicts future traffic conditions, and proposes routes. For example, it selects a route that avoids areas where specific events are being held. This makes it possible to predict future traffic conditions based on past traffic data and propose the optimal route.

[0036] The traffic information analysis unit can analyze real-time traffic information and propose routes that take into consideration cooperation with public transportation. In the traffic information analysis unit, for example, the generation AI analyzes real-time traffic information and proposes routes that take into consideration cooperation with public transportation. For example, if there is congestion, the generation AI proposes a route to the nearest station. The traffic information analysis unit also analyzes the operation status of public transportation in real time, and the generation AI proposes the optimal route. For example, the route is selected taking into consideration the operation status of trains and buses. In addition, the traffic information analysis unit also analyzes real-time traffic information and proposes routes that take into consideration cooperation with public transportation. For example, the generation AI proposes using public transportation as the shortest route to the destination. This makes it possible to propose routes that take into consideration cooperation with public transportation.

[0037] The traffic information analysis unit can suggest the optimal route by referring to the driving data of other users. In the traffic information analysis unit, for example, the generation AI analyzes the driving data of other users and suggests the optimal route. For example, a route that avoids traffic jams is selected based on the data of other users who drive during the same time period. In addition, the traffic information analysis unit can suggest the optimal route by referring to the driving data of other users. For example, the optimal route is selected based on the data of users who have used the same route in the past. In addition, the traffic information analysis unit can suggest the optimal route by analyzing the driving data of other users. For example, the route is selected by referring to the data of other users who are heading to the same destination. In this way, the optimal route can be suggested by referring to the driving data of other users.

[0038] The weather information analysis unit can suggest a safe route according to the user's driving skill. In the weather information analysis unit, for example, the generation AI analyzes weather information and suggests a safe route according to the user's driving skill. For example, if the user is a beginner, it selects a road that is less slippery on rainy days. The weather information analysis unit also takes the user's driving skill into consideration, and the generation AI suggests a safe route based on weather information. For example, on snowy days, it selects a route that avoids steep slopes. In addition, the weather information analysis unit analyzes weather information and suggests a safe route according to the user's driving skill. For example, if the user is not good at driving at night, it selects a route with many streetlights. This makes it possible to suggest a safe route according to the user's driving skill.

[0039] The weather information analysis unit learns past weather data, predicts future weather changes, and proposes a route. For example, the generation AI of the weather information analysis unit learns past weather data, predicts future weather changes, and proposes a route. For example, the generation AI selects a route taking into account weather patterns that occur frequently in a particular season. The weather information analysis unit also predicts future weather changes based on past weather data and proposes the optimal route. For example, the generation AI predicts the path of a typhoon and proposes a safe route. The weather information analysis unit also learns past weather data, predicts future weather changes, and proposes a route. For example, the generation AI selects a route that avoids fog and strong winds that frequently occur in a particular area. This makes it possible to predict future weather changes based on past weather data and propose the optimal route.

[0040] The weather information analysis unit can provide the user with driving advice and cautionary points. In the weather information analysis unit, for example, the generation AI analyzes weather information and provides the user with appropriate driving advice. For example, it instructs the user to slow down on days with heavy rain. In addition, the weather information analysis unit uses the generation AI to provide the user with cautionary points based on the weather information. For example, it recommends the use of tire chains on snowy days. In addition, the weather information analysis unit uses the generation AI to analyze weather information and provides the user with appropriate driving advice and cautionary points. For example, it instructs the user to use fog lights when there is fog. In this way, it is possible to provide the user with appropriate driving advice and cautionary points.

[0041] The weather information analysis unit can suggest the optimal route by referring to the driving data of other users. In the weather information analysis unit, for example, the generation AI analyzes the driving data of other users and suggests the optimal route based on the weather information. For example, it refers to the data of other users who drove under the same weather conditions. In addition, the weather information analysis unit analyzes weather information based on the driving data of other users and suggests the optimal route. For example, it refers to the data of users who have driven under the same weather conditions in the past. In addition, the weather information analysis unit analyzes the driving data of other users and suggests the optimal route based on the weather information. For example, it refers to the data of other users who are heading to the same destination. In this way, it is possible to suggest the optimal route by referring to the driving data of other users.

[0042] The route suggestion unit can learn the user's driving history and preferences and reflect them in the next route suggestion. In the route suggestion unit, for example, the generation AI learns the user's past driving history and reflects it in the next route suggestion. For example, it prioritizes suggesting routes that the user frequently uses. The route suggestion unit also learns the user's preferences and the generation AI reflects them in the next route suggestion. For example, it proposes routes with beautiful scenery that the user prefers. In addition, the route suggestion unit learns the user's past driving history and preferences and reflects them in the next route suggestion. For example, it proposes a route taking into account places that the user frequently visits. This enables more personalized route suggestions based on the user's past driving history and preferences.

[0043] The route suggestion unit can analyze the user's driving style and provide personalized guidance that emphasizes safety. For example, the generation AI in the route suggestion unit analyzes the user's driving style and provides personalized guidance that emphasizes safety. For example, the generation AI proposes a route that matches a driving style that avoids sudden braking and sudden acceleration. Furthermore, the route suggestion unit provides guidance that emphasizes safety based on the user's driving data. For example, the generation AI proposes a route that avoids areas where traffic accidents are frequent. Furthermore, the route suggestion unit analyzes the user's driving style and provides personalized guidance that emphasizes safety. For example, for a user who is not good at driving at night, the generation AI proposes a route with many streetlights. This makes it possible to provide personalized guidance that emphasizes safety based on the user's driving style.

[0044] The route suggestion unit can suggest event information and special promotions around the destination based on the user's preferences. In the route suggestion unit, for example, the generation AI suggests event information around the destination based on the user's preferences. For example, it provides information about concerts and festivals held near the destination. The route suggestion unit also analyzes the user's preferences and the generation AI suggests special promotions around the destination. For example, it provides discount coupons for restaurants and sale information for shopping malls. In addition, the route suggestion unit also suggests event information and promotions around the destination based on the user's preferences. For example, it provides information about special events at tourist spots and exhibitions at art museums. This makes it possible to suggest event information and special promotions around the destination based on the user's preferences.

[0045] The route suggestion unit can suggest popular spots by referring to reviews and ratings from other users. In the route suggestion unit, for example, the generation AI analyzes reviews from other users and suggests popular spots. For example, restaurants and cafes with good word-of-mouth reviews are incorporated into the route. The route suggestion unit also analyzes the user's preferences, and the generation AI suggests optimal spots by referring to ratings from other users. For example, it selects tourist spots based on ratings on review sites. In addition, the route suggestion unit also suggests popular spots by referring to reviews and ratings from other users. For example, it selects a route that includes highly rated tourist spots. This makes it possible to suggest popular spots by referring to reviews and ratings from other users.

[0046] The voice guidance unit can analyze the user's driving style and provide voice guidance at the optimal timing. For example, the generation AI of the voice guidance unit analyzes the user's driving style and provides voice guidance at the optimal timing. For example, it issues instructions when the user needs to avoid sudden braking. Furthermore, the generation AI of the voice guidance unit provides voice guidance at the optimal timing based on the user's driving data. For example, it issues instructions before the user approaches an intersection. Furthermore, the generation AI of the voice guidance unit analyzes the user's driving style and provides voice guidance at the optimal timing. For example, it issues instructions at the appropriate timing while the user is driving on a highway. This makes it possible to provide voice guidance at the optimal timing based on the user's driving style.

[0047] The audio guide unit can customize the tone and language of the audio guide based on the user's preferences. For example, the generation AI customizes the tone of the audio guide based on the user's preferences. For example, if the user wants to relax, the audio guide will be provided in a calm tone. The audio guide unit also customizes the language of the audio guide based on the user's language settings. For example, the audio guide will be provided in the user's native language, such as English, French, or Chinese. The audio guide unit also customizes the tone and language of the audio guide based on the user's preferences. For example, if the user wants to cheer up, the audio guide will be provided in a bright tone. This makes it possible to customize the tone and language of the audio guide based on the user's preferences.

[0048] The audio guide unit can provide visual guidance in addition to the audio guidance. For example, the generation AI of the audio guide unit provides visual guidance in addition to the audio guidance. For example, the generation AI uses AR display to superimpose route guidance on the user's field of vision. The audio guide unit also combines audio guidance and visual guidance to provide more intuitive guidance to the user. For example, the route is displayed on the screen of a smartphone or car navigation system. The generation AI of the audio guide unit also provides visual guidance in addition to the audio guidance. For example, the generation AI uses a head-up display (HUD) to display route guidance on the windshield. This makes it possible to provide visual guidance in addition to the audio guidance.

[0049] The audio guide unit can provide the optimal audio guide by taking into account feedback from other users. For example, the generation AI of the audio guide unit analyzes the feedback from other users and provides the optimal audio guide. For example, it refers to the tone and content of audio guides that have been highly rated by users. Furthermore, the audio guide unit improves the content of the audio guide by taking into account feedback from other users. For example, it reflects the user's opinions and provides easier-to-understand instructions. Furthermore, the audio guide unit can provide the optimal audio guide by taking into account feedback from other users. For example, it provides customization options that match the user's preferences. This allows the optimal audio guide to be provided by taking into account feedback from other users.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The car navigation system may further include a health management unit that monitors the user's health condition. The health management unit, for example, measures the user's heart rate and blood pressure and monitors the user's health condition while driving. For example, if the user's heart rate becomes high, an alert is displayed urging the user to take a break. The health management unit also analyzes the user's health data and provides advice to reduce risks while driving. For example, it may suggest avoiding long periods of driving. The health management unit also suggests optimal rest spots based on the user's health condition. For example, it may suggest parks or cafes where the user can relax. This makes it possible to provide driving support that takes the user's health condition into consideration.

[0052] The car navigation system may further include an entertainment provider. The entertainment provider may suggest music or podcasts based on the user's preferences. For example, if the user wants to relax, it may play calm music. The entertainment provider may also provide content to boost the user's mood while driving. For example, if the user wants to cheer up, it may suggest upbeat music. The entertainment provider may also provide audiobooks or news based on the user's preferences. For example, it may suggest audiobooks in genres that the user may be interested in. This may improve the entertainment experience while driving.

[0053] The car navigation system may further include a fuel economy management unit. The fuel economy management unit, for example, analyzes the user's driving style and provides advice to optimize fuel economy. For example, it may instruct the user to avoid sudden acceleration and braking. The fuel economy management unit also suggests routes to achieve optimal fuel economy based on the user's driving data. For example, it may prioritize flat roads. The fuel economy management unit also analyzes the fuel economy data of the user's vehicle and provides maintenance advice to improve fuel economy. For example, it may suggest maintaining appropriate tire pressure. This makes it possible to optimize the user's fuel economy and support economical driving.

[0054] The car navigation system may further include an emergency response unit. The emergency response unit has a function of automatically notifying emergency contacts in the event of an accident or breakdown, for example. For example, it may automatically call an ambulance when an airbag is deployed. The emergency response unit may also suggest the nearest hospital or repair shop when the user faces an emergency. For example, if the user complains of feeling unwell, it may provide directions to the nearest hospital. The emergency response unit may also use the user's location information to enable a rapid response in an emergency. For example, if the user is involved in an accident, it may send the location information to the emergency contact. This allows for a rapid and accurate response in an emergency.

[0055] The car navigation system may further include an eco-driving support unit. The eco-driving support unit, for example, analyzes the user's driving style and suggests environmentally friendly driving methods. For example, it instructs the user to avoid sudden acceleration and braking. The eco-driving support unit also provides advice to improve fuel efficiency based on the user's driving data. For example, it recommends driving at a constant speed. The eco-driving support unit also analyzes the fuel efficiency data of the user's vehicle and visualizes the effects of eco-driving. For example, it displays the improvement in fuel efficiency after driving. This can increase the user's awareness of eco-driving and support environmentally friendly driving.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The user input analysis unit analyzes the user's input information. For example, when the user inputs a destination, the information can be received in the form of text input, voice input, touch input, etc. The user input analysis unit can also analyze the user's desired conditions (e.g., shortest time, shortest distance, avoiding toll roads, etc.). Step 2: The route suggestion unit proposes the optimal route based on the information analyzed by the user input analysis unit. For example, if the user instructs the AI ​​to "arrive at the destination in the shortest time possible," the AI ​​will calculate the most efficient route, taking into account current traffic conditions and road congestion. Step 3: The traffic information analysis unit analyzes traffic information provided in real time and recalculates or proposes routes. For example, if a traffic jam or accident occurs, the generation AI will propose a new route based on that information. Step 4: The weather information analysis unit analyzes the weather information and reflects it in the route proposal. For example, if bad weather such as heavy rain or snow is predicted, the generation AI will use that information to propose a safe route.

[0058] (Example 2) A car navigation system according to an embodiment of the present invention uses a generative AI to analyze user input information and provide information on optimal routes and destinations, enabling the car navigation system to provide users with more advanced and personalized guidance.

[0059] A car navigation system according to an embodiment includes a user input analysis unit, a route suggestion unit, a traffic information analysis unit, and a weather information analysis unit. The user input analysis unit analyzes user input information. For example, when a user inputs a destination, the information can be received in the form of text input, voice input, touch input, or the like. The user input analysis unit can also analyze the user's desired conditions (e.g., shortest time, shortest distance, avoiding toll roads, etc.). The route suggestion unit proposes an optimal route based on the information analyzed by the user input analysis unit. For example, if a user instructs the generation AI to "arrive at the destination in the shortest time," the AI ​​calculates the most efficient route taking into account current traffic conditions and road congestion. The traffic information analysis unit analyzes traffic information provided in real time and recalculates or proposes a route. For example, if a traffic jam or accident occurs, the AI ​​proposes a new route based on that information. The weather information analysis unit analyzes weather information and reflects it in the proposed route. For example, if bad weather such as heavy rain or snow is predicted, the AI ​​proposes a safe route based on that information. This allows the system to analyze user input, traffic information, and weather information and suggest the optimal route.

[0060] The route suggestion unit can learn the user's driving history and preferences and reflect them in the next route suggestion. For example, the route suggestion unit learns places the user has visited in the past and routes frequently used by the user and reflects them in the next route suggestion. For example, the route suggestion unit may propose a route taking into account cafes and gas stations frequently used by the user. The route suggestion unit may also analyze the user's driving history and learn driving patterns for specific time periods and days of the week. For example, the route suggestion unit may distinguish between weekday commute routes and weekend leisure routes and suggest them accordingly. The route suggestion unit may also learn the user's preferences and reflect them in the next route suggestion. For example, the route suggestion unit may prioritize scenic routes and quiet roads that the user prefers. This enables more personalized route suggestions based on the user's past driving history and preferences.

[0061] The route suggestion unit can suggest a route that prioritizes safety based on the user's driving style. The route suggestion unit, for example, analyzes the user's driving style and suggests a safe route taking into account the frequency of sudden braking and sudden acceleration. For example, it selects a route that avoids sharp curves and steep slopes. The route suggestion unit also suggests a route that prioritizes safety based on the user's driving data. For example, it suggests a route that avoids areas where traffic accidents frequently occur. The route suggestion unit also learns the user's driving style and suggests a route that prioritizes safety. For example, for a user who is not good at driving at night, it suggests a route with many bright street lights. This makes it possible to suggest a route that prioritizes safety based on the user's driving style.

[0062] The route suggestion unit can use the emotion estimation function to analyze the user's current emotional state and suggest a relaxing route to reduce stress. The route suggestion unit, for example, uses the emotion estimation function to analyze the user's current emotional state and suggest a relaxing route to reduce stress. For example, if the user is feeling stressed, it suggests a route with a beautiful view. The route suggestion unit also analyzes the user's emotional state in real time and suggests a relaxing route. For example, if the user is tired, it suggests a route with many rest spots. The route suggestion unit also uses the emotion estimation function to analyze the user's emotional state and suggest a route to reduce stress. For example, if the user is feeling impatient, it suggests a route with little traffic. This makes it possible to suggest a relaxing route to reduce stress based on the user's emotional state.

[0063] The route suggestion unit can suggest event information or special promotions around the destination based on the information input by the user. For example, the generation AI of the route suggestion unit suggests event information around the destination based on the information input by the user. For example, it provides information about concerts and festivals held near the destination. The route suggestion unit also analyzes the information input by the user and suggests special promotions around the destination. For example, it provides information about discount coupons for restaurants and sales at shopping malls. The route suggestion unit also suggests event information and promotions around the destination based on the information input by the user. For example, it provides information about special events at tourist spots and exhibitions at art museums. This makes it possible to suggest event information and special promotions around the destination based on the information input by the user.

[0064] When analyzing the information input by the user, the route suggestion unit can suggest popular routes and spots by referring to reviews and ratings from other users. For example, the route suggestion unit analyzes the information input by the user and suggests popular routes by referring to reviews and ratings from other users. For example, it selects a route that includes highly rated tourist spots. In addition, the route suggestion unit uses the generation AI to analyze reviews from other users and suggest popular spots. For example, it incorporates restaurants and cafes with good word-of-mouth reviews into the route. In addition, the route suggestion unit suggests the optimal route based on the information input by the user and by referring to ratings from other users. For example, it selects tourist spots based on ratings on review sites. This makes it possible to suggest popular routes and spots by referring to reviews and ratings from other users.

[0065] The route suggestion unit can use the emotion estimation function to suggest tourist spots or restaurants that the user might be interested in. For example, the route suggestion unit uses the emotion estimation function to suggest tourist spots that the user might be interested in. For example, if the user wants to relax, natural parks or hot spring resorts are suggested. The route suggestion unit also analyzes the user's emotional state to suggest restaurants that the user might be interested in. For example, if the user wants to have fun, popular restaurants or cafes are suggested. The route suggestion unit also uses the emotion estimation function to suggest tourist spots and restaurants that the user might be interested in. For example, if the user has a sense of adventure, places with plenty of activities are suggested. In this way, tourist spots and restaurants that the user might be interested in can be suggested based on the user's emotional state.

[0066] The traffic information analysis unit can analyze real-time traffic information and propose an optimal avoidance route based on the user's driving patterns. For example, the generation AI in the traffic information analysis unit analyzes real-time traffic information and proposes an optimal avoidance route based on the user's driving patterns. For example, if the user tends to avoid sudden braking, a smooth route is selected. The traffic information analysis unit also learns the user's driving patterns and proposes an optimal avoidance route based on real-time traffic information. For example, if the user prefers highways, a high-speed route that avoids traffic jams is proposed. The traffic information analysis unit also analyzes real-time traffic information and proposes an optimal avoidance route based on the user's driving patterns. For example, if the user wants to arrive at their destination in the shortest time, the most efficient route is selected. This makes it possible to propose an optimal avoidance route based on the user's driving patterns.

[0067] The traffic information analysis unit learns from past traffic data, predicts future traffic conditions, and proposes routes. For example, the generation AI in the traffic information analysis unit learns from past traffic data, predicts future traffic conditions, and proposes routes. For example, it selects a route that avoids areas where congestion is likely to occur during specific time periods. The traffic information analysis unit also uses the generation AI to predict future traffic conditions based on past traffic data and proposes the optimal route. For example, it proposes a route that avoids areas with heavy traffic on weekends. The traffic information analysis unit also uses the generation AI to learn from past traffic data, predicts future traffic conditions, and proposes routes. For example, it selects a route that avoids areas where specific events are being held. This makes it possible to predict future traffic conditions based on past traffic data and propose the optimal route.

[0068] The traffic information analysis unit can use the emotion estimation function to suggest a route that avoids congestion so that the user does not feel stressed. The traffic information analysis unit, for example, uses the emotion estimation function to suggest a route that avoids congestion so that the user does not feel stressed. For example, if the user is feeling stressed, a route with less traffic is selected. The traffic information analysis unit also analyzes the user's emotional state to suggest a route that avoids congestion. For example, if the user wants to relax, a route with a beautiful view is suggested. The traffic information analysis unit also uses the emotion estimation function to suggest a route that avoids congestion so that the user does not feel stressed. For example, if the user is in a hurry, a route that will allow the user to arrive in the shortest time is selected. In this way, a route that avoids congestion can be suggested based on the user's emotional state.

[0069] The traffic information analysis unit can analyze real-time traffic information and propose routes that take into consideration cooperation with public transportation. In the traffic information analysis unit, for example, the generation AI analyzes real-time traffic information and proposes routes that take into consideration cooperation with public transportation. For example, if there is congestion, the generation AI proposes a route to the nearest station. The traffic information analysis unit also analyzes the operation status of public transportation in real time, and the generation AI proposes the optimal route. For example, the route is selected taking into consideration the operation status of trains and buses. In addition, the traffic information analysis unit also analyzes real-time traffic information and proposes routes that take into consideration cooperation with public transportation. For example, the generation AI proposes using public transportation as the shortest route to the destination. This makes it possible to propose routes that take into consideration cooperation with public transportation.

[0070] The traffic information analysis unit can suggest the optimal route by referring to the driving data of other users. In the traffic information analysis unit, for example, the generation AI analyzes the driving data of other users and suggests the optimal route. For example, a route that avoids traffic jams is selected based on the data of other users who drive during the same time period. In addition, the traffic information analysis unit can suggest the optimal route by referring to the driving data of other users. For example, the optimal route is selected based on the data of users who have used the same route in the past. In addition, the traffic information analysis unit can suggest the optimal route by analyzing the driving data of other users. For example, the route is selected by referring to the data of other users who are heading to the same destination. In this way, the optimal route can be suggested by referring to the driving data of other users.

[0071] The traffic information analysis unit can use the emotion estimation function to suggest a scenic route that will help the user relax. The traffic information analysis unit, for example, uses the emotion estimation function to suggest a scenic route that will help the user relax. For example, if the user is feeling stressed, a route with abundant natural scenery is selected. The traffic information analysis unit also analyzes the user's emotional state and suggests a relaxing route. For example, if the user is tired, a route that takes in quiet roads is suggested. The traffic information analysis unit also uses the emotion estimation function to suggest a scenic route that will help the user relax. For example, if the user wants to refresh themselves, a route that takes in the sea or mountain paths is selected. In this way, a scenic route that will help the user relax can be suggested based on the user's emotional state.

[0072] The weather information analysis unit can suggest a safe route according to the user's driving skill. In the weather information analysis unit, for example, the generation AI analyzes weather information and suggests a safe route according to the user's driving skill. For example, if the user is a beginner, it selects a road that is less slippery on rainy days. The weather information analysis unit also takes the user's driving skill into consideration, and the generation AI suggests a safe route based on weather information. For example, on snowy days, it selects a route that avoids steep slopes. In addition, the weather information analysis unit analyzes weather information and suggests a safe route according to the user's driving skill. For example, if the user is not good at driving at night, it selects a route with many streetlights. This makes it possible to suggest a safe route according to the user's driving skill.

[0073] The weather information analysis unit learns past weather data, predicts future weather changes, and proposes a route. For example, the generation AI of the weather information analysis unit learns past weather data, predicts future weather changes, and proposes a route. For example, the generation AI selects a route taking into account weather patterns that occur frequently in a particular season. The weather information analysis unit also predicts future weather changes based on past weather data and proposes the optimal route. For example, the generation AI predicts the path of a typhoon and proposes a safe route. The weather information analysis unit also learns past weather data, predicts future weather changes, and proposes a route. For example, the generation AI selects a route that avoids fog and strong winds that frequently occur in a particular area. This makes it possible to predict future weather changes based on past weather data and propose the optimal route.

[0074] The weather information analysis unit can use the emotion estimation function to suggest a relaxing route according to the weather so that the user can drive with peace of mind. The weather information analysis unit, for example, uses the emotion estimation function to suggest a relaxing route according to the weather so that the user can drive with peace of mind. For example, if the user is feeling anxious, a route with stable weather is selected. The weather information analysis unit also analyzes the user's emotional state and suggests a relaxing route according to the weather. For example, if the user is feeling stressed, a route with calm weather is suggested. The weather information analysis unit also uses the emotion estimation function to suggest a relaxing route according to the weather so that the user can drive with peace of mind. For example, if the user is tired, a route with good weather is selected. In this way, a relaxing route according to the weather can be suggested based on the user's emotional state.

[0075] The weather information analysis unit can provide the user with driving advice and cautionary points. In the weather information analysis unit, for example, the generation AI analyzes weather information and provides the user with appropriate driving advice. For example, it instructs the user to slow down on days with heavy rain. In addition, the weather information analysis unit uses the generation AI to provide the user with cautionary points based on the weather information. For example, it recommends the use of tire chains on snowy days. In addition, the weather information analysis unit uses the generation AI to analyze weather information and provides the user with appropriate driving advice and cautionary points. For example, it instructs the user to use fog lights when there is fog. In this way, it is possible to provide the user with appropriate driving advice and cautionary points.

[0076] The weather information analysis unit can suggest the optimal route by referring to the driving data of other users. In the weather information analysis unit, for example, the generation AI analyzes the driving data of other users and suggests the optimal route based on the weather information. For example, it refers to the data of other users who drove under the same weather conditions. In addition, the weather information analysis unit analyzes weather information based on the driving data of other users and suggests the optimal route. For example, it refers to the data of users who have driven under the same weather conditions in the past. In addition, the weather information analysis unit analyzes the driving data of other users and suggests the optimal route based on the weather information. For example, it refers to the data of other users who are heading to the same destination. In this way, it is possible to suggest the optimal route by referring to the driving data of other users.

[0077] The weather information analysis unit can use the emotion estimation function to suggest rest spots according to the weather where the user can spend their time comfortably. The weather information analysis unit, for example, uses the emotion estimation function to suggest rest spots according to the weather where the user can spend their time comfortably. For example, if the user is tired, it suggests rest spots with good weather. The weather information analysis unit also analyzes the user's emotional state and suggests comfortable rest spots according to the weather. For example, if the user wants to relax, it suggests a place with calm weather. The weather information analysis unit also uses the emotion estimation function to suggest rest spots according to the weather where the user can spend their time comfortably. For example, if the user is feeling stressed, it suggests a place with stable weather. In this way, it is possible to suggest comfortable rest spots according to the weather based on the user's emotional state.

[0078] The route suggestion unit can learn the user's driving history and preferences and reflect them in the next route suggestion. In the route suggestion unit, for example, the generation AI learns the user's past driving history and reflects it in the next route suggestion. For example, it prioritizes suggesting routes that the user frequently uses. The route suggestion unit also learns the user's preferences and the generation AI reflects them in the next route suggestion. For example, it proposes routes with beautiful scenery that the user prefers. In addition, the route suggestion unit learns the user's past driving history and preferences and reflects them in the next route suggestion. For example, it proposes a route taking into account places that the user frequently visits. This enables more personalized route suggestions based on the user's past driving history and preferences.

[0079] The route suggestion unit can analyze the user's driving style and provide personalized guidance that emphasizes safety. For example, the generation AI in the route suggestion unit analyzes the user's driving style and provides personalized guidance that emphasizes safety. For example, the generation AI proposes a route that matches a driving style that avoids sudden braking and sudden acceleration. Furthermore, the route suggestion unit provides guidance that emphasizes safety based on the user's driving data. For example, the generation AI proposes a route that avoids areas where traffic accidents are frequent. Furthermore, the route suggestion unit analyzes the user's driving style and provides personalized guidance that emphasizes safety. For example, for a user who is not good at driving at night, the generation AI proposes a route with many streetlights. This makes it possible to provide personalized guidance that emphasizes safety based on the user's driving style.

[0080] The route suggestion unit can use the emotion estimation function to analyze the user's current emotional state and provide a relaxation guide to reduce stress. The route suggestion unit, for example, uses the emotion estimation function to analyze the user's current emotional state and provide a relaxation guide to reduce stress. For example, if the user is feeling stressed, a scenic route is suggested. The route suggestion unit also analyzes the user's emotional state in real time and provides a relaxation guide. For example, if the user is tired, a route with many rest spots is suggested. The route suggestion unit also uses the emotion estimation function to analyze the user's emotional state and provide a relaxation guide to reduce stress. For example, if the user is feeling impatient, a route with less traffic is suggested. In this way, a relaxation guide to reduce stress can be provided based on the user's emotional state.

[0081] The route suggestion unit can suggest event information and special promotions around the destination based on the user's preferences. In the route suggestion unit, for example, the generation AI suggests event information around the destination based on the user's preferences. For example, it provides information about concerts and festivals held near the destination. The route suggestion unit also analyzes the user's preferences and the generation AI suggests special promotions around the destination. For example, it provides discount coupons for restaurants and sale information for shopping malls. In addition, the route suggestion unit also suggests event information and promotions around the destination based on the user's preferences. For example, it provides information about special events at tourist spots and exhibitions at art museums. This makes it possible to suggest event information and special promotions around the destination based on the user's preferences.

[0082] The route suggestion unit can suggest popular spots by referring to reviews and ratings from other users. In the route suggestion unit, for example, the generation AI analyzes reviews from other users and suggests popular spots. For example, restaurants and cafes with good word-of-mouth reviews are incorporated into the route. The route suggestion unit also analyzes the user's preferences, and the generation AI suggests optimal spots by referring to ratings from other users. For example, it selects tourist spots based on ratings on review sites. In addition, the route suggestion unit also suggests popular spots by referring to reviews and ratings from other users. For example, it selects a route that includes highly rated tourist spots. This makes it possible to suggest popular spots by referring to reviews and ratings from other users.

[0083] The route suggestion unit can use the emotion estimation function to suggest tourist spots or restaurants that the user might be interested in. For example, the route suggestion unit uses the emotion estimation function to suggest tourist spots that the user might be interested in. For example, if the user wants to relax, natural parks or hot spring resorts are suggested. The route suggestion unit also analyzes the user's emotional state to suggest restaurants that the user might be interested in. For example, if the user wants to have fun, popular restaurants or cafes are suggested. The route suggestion unit also uses the emotion estimation function to suggest tourist spots and restaurants that the user might be interested in. For example, if the user has a sense of adventure, places with plenty of activities are suggested. In this way, tourist spots and restaurants that the user might be interested in can be suggested based on the user's emotional state.

[0084] The voice guidance unit can analyze the user's driving style and provide voice guidance at the optimal timing. For example, the generation AI of the voice guidance unit analyzes the user's driving style and provides voice guidance at the optimal timing. For example, it issues instructions when the user needs to avoid sudden braking. Furthermore, the generation AI of the voice guidance unit provides voice guidance at the optimal timing based on the user's driving data. For example, it issues instructions before the user approaches an intersection. Furthermore, the generation AI of the voice guidance unit analyzes the user's driving style and provides voice guidance at the optimal timing. For example, it issues instructions at the appropriate timing while the user is driving on a highway. This makes it possible to provide voice guidance at the optimal timing based on the user's driving style.

[0085] The audio guide unit can customize the tone and language of the audio guide based on the user's preferences. For example, the generation AI customizes the tone of the audio guide based on the user's preferences. For example, if the user wants to relax, the audio guide will be provided in a calm tone. The audio guide unit also customizes the language of the audio guide based on the user's language settings. For example, the audio guide will be provided in the user's native language, such as English, French, or Chinese. The audio guide unit also customizes the tone and language of the audio guide based on the user's preferences. For example, if the user wants to cheer up, the audio guide will be provided in a bright tone. This makes it possible to customize the tone and language of the audio guide based on the user's preferences.

[0086] The audio guide unit can use the emotion estimation function to analyze the user's current emotional state and provide an audio guide that helps the user relax. The audio guide unit, for example, uses the emotion estimation function to analyze the user's current emotional state and provide an audio guide that helps the user relax. For example, if the user is feeling stressed, an audio guide in a calm tone is provided. The audio guide unit can also analyze the user's emotional state in real time and provide an audio guide that helps the user relax. For example, if the user is tired, an audio guide in a calm tone is provided. The audio guide unit can also use the emotion estimation function to analyze the user's emotional state and provide an audio guide that helps the user relax. For example, if the user is impatient, an audio guide in a slow tone is provided. This makes it possible to provide an audio guide that helps the user relax based on the user's emotional state.

[0087] The audio guide unit can provide visual guidance in addition to the audio guidance. For example, the generation AI of the audio guide unit provides visual guidance in addition to the audio guidance. For example, the generation AI uses AR display to superimpose route guidance on the user's field of vision. The audio guide unit also combines audio guidance and visual guidance to provide more intuitive guidance to the user. For example, the route is displayed on the screen of a smartphone or car navigation system. The generation AI of the audio guide unit also provides visual guidance in addition to the audio guidance. For example, the generation AI uses a head-up display (HUD) to display route guidance on the windshield. This makes it possible to provide visual guidance in addition to the audio guidance.

[0088] The audio guide unit can provide the optimal audio guide by taking into account feedback from other users. For example, the generation AI of the audio guide unit analyzes the feedback from other users and provides the optimal audio guide. For example, it refers to the tone and content of audio guides that have been highly rated by users. Furthermore, the audio guide unit improves the content of the audio guide by taking into account feedback from other users. For example, it reflects the user's opinions and provides easier-to-understand instructions. Furthermore, the audio guide unit can provide the optimal audio guide by taking into account feedback from other users. For example, it provides customization options that match the user's preferences. This allows the optimal audio guide to be provided by taking into account feedback from other users.

[0089] The audio guide unit can use the emotion estimation function to provide information that the user is likely to be interested in through the audio guide. The audio guide unit, for example, uses the emotion estimation function to provide information that the user is likely to be interested in through the audio guide. For example, if the user wants to relax, information about nearby tourist spots and natural landscapes is provided. The audio guide unit also analyzes the user's emotional state and provides information that the user is likely to be interested in through the audio guide. For example, if the user wants to have fun, information about nearby restaurants and cafes is provided. The audio guide unit also uses the emotion estimation function to provide information that the user is likely to be interested in through the audio guide. For example, if the user is adventurous, information about places with plenty of activities is provided. In this way, it is possible to provide information that the user is likely to be interested in through the audio guide based on the user's emotional state.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The car navigation system may further include a health management unit that monitors the user's health condition. The health management unit, for example, measures the user's heart rate and blood pressure and monitors the user's health condition while driving. For example, if the user's heart rate becomes high, an alert is displayed urging the user to take a break. The health management unit also analyzes the user's health data and provides advice to reduce risks while driving. For example, it may suggest avoiding long periods of driving. The health management unit also suggests optimal rest spots based on the user's health condition. For example, it may suggest parks or cafes where the user can relax. This makes it possible to provide driving support that takes the user's health condition into consideration.

[0092] The car navigation system may further include an entertainment provider. The entertainment provider may suggest music or podcasts based on the user's preferences. For example, if the user wants to relax, it may play calm music. The entertainment provider may also provide content to boost the user's mood while driving. For example, if the user wants to cheer up, it may suggest upbeat music. The entertainment provider may also provide audiobooks or news based on the user's preferences. For example, it may suggest audiobooks in genres that the user may be interested in. This may improve the entertainment experience while driving.

[0093] The car navigation system may further include a fuel economy management unit. The fuel economy management unit, for example, analyzes the user's driving style and provides advice to optimize fuel economy. For example, it may instruct the user to avoid sudden acceleration and braking. The fuel economy management unit also suggests routes to achieve optimal fuel economy based on the user's driving data. For example, it may prioritize flat roads. The fuel economy management unit also analyzes the fuel economy data of the user's vehicle and provides maintenance advice to improve fuel economy. For example, it may suggest maintaining appropriate tire pressure. This makes it possible to optimize the user's fuel economy and support economical driving.

[0094] The car navigation system may further include an emergency response unit. The emergency response unit has a function of automatically notifying emergency contacts in the event of an accident or breakdown, for example. For example, it may automatically call an ambulance when an airbag is deployed. The emergency response unit may also suggest the nearest hospital or repair shop when the user faces an emergency. For example, if the user complains of feeling unwell, it may provide directions to the nearest hospital. The emergency response unit may also use the user's location information to enable a rapid response in an emergency. For example, if the user is involved in an accident, it may send the location information to the emergency contact. This allows for a rapid and accurate response in an emergency.

[0095] The car navigation system may further include an eco-driving support unit. The eco-driving support unit, for example, analyzes the user's driving style and suggests environmentally friendly driving methods. For example, it instructs the user to avoid sudden acceleration and braking. The eco-driving support unit also provides advice to improve fuel efficiency based on the user's driving data. For example, it recommends driving at a constant speed. The eco-driving support unit also analyzes the fuel efficiency data of the user's vehicle and visualizes the effects of eco-driving. For example, it displays the improvement in fuel efficiency after driving. This can increase the user's awareness of eco-driving and support environmentally friendly driving.

[0096] The car navigation system can further use the emotion estimation function to suggest music or podcasts that correspond to the user's emotional state. For example, if the user is feeling stressed, relaxing music can be played. The emotion estimation function can also be used to provide content to lift the user's mood while driving based on the user's emotional state. For example, if the user wants to cheer up, upbeat music can be suggested. The emotion estimation function can also be used to provide audiobooks or news that correspond to the user's emotional state. For example, audiobooks in genres that the user might be interested in can be suggested. This allows for an improved entertainment experience while driving based on the user's emotional state.

[0097] The car navigation system can further use the emotion estimation function to provide driving advice according to the user's emotional state. For example, if the user is feeling anxious, the system can provide advice to calm down. The emotion estimation function can also be used to provide advice to encourage safe driving based on the user's emotional state. For example, if the user is feeling irritated, the system can provide advice to encourage deep breathing. The emotion estimation function can also be used to suggest relaxation methods according to the user's emotional state. For example, if the user is feeling nervous, the system can play relaxing music. This makes it possible to support safe and comfortable driving based on the user's emotional state.

[0098] The car navigation system can further use the emotion estimation function to suggest rest spots according to the user's emotional state. For example, if the user is tired, it can suggest rest spots where the user can relax. The emotion estimation function can also be used to suggest the optimal timing for a break based on the user's emotional state. For example, if the user is feeling stressed, it can encourage the user to take an early break. The emotion estimation function can also be used to suggest a way to refresh according to the user's emotional state. For example, if the user wants to refresh themselves, it can suggest a spot where they can enjoy natural scenery. In this way, it is possible to suggest the optimal rest spot and timing based on the user's emotional state.

[0099] The car navigation system can further use the emotion estimation function to provide driving style advice based on the user's emotional state. For example, if the user is relaxed, it recommends driving gently. The emotion estimation function can also be used to provide points to be careful about while driving based on the user's emotional state. For example, if the user is tired, it can provide advice encouraging the user to take a break. The emotion estimation function can also be used to suggest ways to improve the user's driving style based on the user's emotional state. For example, if the user is impatient, it can recommend driving slowly. This makes it possible to support safe and comfortable driving based on the user's emotional state.

[0100] The car navigation system can further use an emotion estimation function to adjust the driving environment according to the user's emotional state. For example, if the user is feeling stressed, the lighting inside the car can be adjusted to create a relaxing environment. The emotion estimation function can also be used to adjust the temperature inside the car based on the user's emotional state. For example, if the user is feeling hot, the air conditioner can be adjusted to a comfortable temperature. The emotion estimation function can also be used to provide music or fragrances according to the user's emotional state. For example, if the user wants to relax, calm music or a fragrance with a relaxing effect can be provided. In this way, a comfortable driving environment can be provided based on the user's emotional state.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The user input analysis unit analyzes the user's input information. For example, when the user inputs a destination, the information can be received in the form of text input, voice input, touch input, etc. The user input analysis unit can also analyze the user's desired conditions (e.g., shortest time, shortest distance, avoiding toll roads, etc.). Step 2: The route suggestion unit proposes the optimal route based on the information analyzed by the user input analysis unit. For example, if the user instructs the AI ​​to "arrive at the destination in the shortest time possible," the AI ​​will calculate the most efficient route, taking into account current traffic conditions and road congestion. Step 3: The traffic information analysis unit analyzes traffic information provided in real time and recalculates or proposes routes. For example, if a traffic jam or accident occurs, the generation AI will propose a new route based on that information. Step 4: The weather information analysis unit analyzes the weather information and reflects it in the route proposal. For example, if bad weather such as heavy rain or snow is predicted, the generation AI will use that information to propose a safe route.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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. [Explanation of symbols]

[0170] 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 user input analysis unit that analyzes user input information; a route suggestion unit that suggests an optimal route based on the information analyzed by the user input analysis unit; A traffic information analysis section analyzes real-time traffic information and recalculates and proposes routes. A weather information analysis unit that analyzes weather information and reflects the information in route suggestions. A system characterized by:

2. The traffic information analysis unit Analyze real-time traffic information and suggest optimal avoidance routes based on the user's driving patterns 2. The system of claim 1.

3. The weather information analysis unit Proposing safe routes according to the user's driving skills 2. The system of claim 1.

4. The audio guide section is Analyzing the user's driving style and providing audio guidance at the optimal timing 2. The system of claim 1.

5. The route suggestion unit Analyzing the user's current emotional state and suggesting relaxing routes to reduce stress 2. The system of claim 1.

Images