system

Abstract

The system according to this embodiment aims to suggest the optimal route based on the user's driving history, propose tourist spots and restaurants during the trip, and support emergency response. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, a sightseeing suggestion unit, and a support unit. The collection unit collects the user's driving history. The analysis unit analyzes the driving history collected by the collection unit to understand the user's preferences and driving patterns. The suggestion unit proposes the optimal route based on the analysis results obtained by the analysis unit. The sightseeing suggestion unit proposes sightseeing spots and restaurants based on the travel destination and route. The support unit assists in contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that personalized route proposals based on the user's driving history, proposals for tourist spots during travel, and emergency response support are not sufficiently provided.

[0005] The system according to the embodiment aims to propose an optimal route based on the user's driving history, propose tourist spots and restaurants during travel, and support emergency response.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, a sightseeing suggestion unit, and a support unit. The data collection unit collects the user's driving history. The analysis unit analyzes the driving history collected by the data collection unit to understand the user's preferences and driving patterns. The suggestion unit proposes the optimal route based on the analysis results obtained by the analysis unit. The sightseeing suggestion unit proposes sightseeing spots and restaurants based on the travel destination and route. The support unit assists in contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the optimal route based on the user's driving history, suggest sightseeing spots and restaurants during the trip, and assist in responding to emergencies. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The personalized car navigation system according to an embodiment of the present invention is a system that proposes the optimal route based on the user's driving history, suggests tourist spots and restaurants based on the travel destination and route, and assists in contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies. The personalized car navigation system proposes the optimal route based on the user's driving history. For example, it prioritizes guiding the user to cafes they frequently visit or routes with good scenery, and predicts a new destination if the user deviates from the route on the way to the destination. Next, it suggests tourist spots and restaurants that the user should stop at along the way based on the travel destination and route. It utilizes generative AI and MR (Mixed Reality) technology to provide real-time information. Furthermore, in emergencies such as accidents, it assists in contacting hospitals, police, and JAF, provides route guidance, and offers advice on first aid. First, the personalized car navigation system collects the user's driving history, which is then analyzed by AI. For example, by collecting data on cafes and scenic routes that the user has visited in the past and analyzing it with AI, the system understands the user's preferences and driving patterns. This allows the system to propose the optimal route to the user. For example, it prioritizes guiding the user to routes that pass through cafes they frequently visit or routes with good scenery. Next, it suggests tourist spots and restaurants that the user should stop at along the way based on the travel destination and route. For example, when a user sets a travel destination, the AI ​​collects information on tourist attractions and restaurants along the route and suggests them in real time. By utilizing generative AI and MR technology, users can obtain information overlaid on the real-world scenery. For instance, while driving, a user can check the location and details of tourist attractions and restaurants through information displayed on the windshield. Furthermore, in emergencies, the AI ​​assists in contacting hospitals, police, and JAF (Japan Automobile Federation). For example, in the event of an accident, the AI ​​automatically searches for and provides contact information for the nearest hospital, police, and JAF. It also provides route guidance and first aid advice. For example, the AI ​​guides the user to the nearest hospital and advises on first aid procedures.Thus, the present invention can provide users with a personalized driving experience by suggesting the optimal route based on the user's driving history, suggesting tourist spots and restaurants based on the travel destination and route, and assisting in contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies. This allows users to reduce time and stress, and enjoy a comfortable and fulfilling drive or trip. In this way, the personalized car navigation system can provide users with a personalized driving experience by suggesting the optimal route based on the user's driving history, suggesting tourist spots and restaurants based on the travel destination and route, and assisting in contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies.

[0029] The personalized car navigation system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, a sightseeing suggestion unit, and a support unit. The data collection unit collects the user's driving history. The user's driving history includes, but is not limited to, distance traveled, speed, route, and stopping time. The data collection unit collects, for example, data on cafes the user has visited in the past and scenic routes. The data collection unit can also collect data such as driving time, speed changes, and braking frequency in order to understand the user's driving patterns. For example, the data collection unit collects location information of cafes the user frequently visits to understand the user's preferences. The analysis unit analyzes the driving history collected by the data collection unit to understand the user's preferences and driving patterns. For example, the analysis unit analyzes data on cafes the user has visited in the past to identify the user's preferences. The analysis unit can also analyze driving patterns to understand the user's driving style. For example, the analysis unit analyzes data on the user's driving time and speed changes to identify the user's driving patterns. The suggestion unit proposes the optimal route based on the analysis results obtained by the analysis unit. The suggestion department prioritizes routes that pass by cafes the user frequently visits or routes with scenic views. It can also suggest optimal routes based on the user's driving patterns. For example, it might suggest the best route based on data regarding the user's driving time and speed changes. The sightseeing suggestion department suggests tourist spots and restaurants based on the travel destination and route. For example, once a user sets a travel destination, the sightseeing suggestion department collects information on tourist spots and restaurants along that route and suggests them in real time. The sightseeing suggestion department utilizes generative AI and MR technology to provide real-time information. For example, while driving, the sightseeing suggestion department allows users to check the location and details of tourist spots and restaurants through information displayed on the windshield. The support department assists with contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies. For example, in the event of an accident, the support department searches for and provides contact information for the nearest hospital, police, and JAF. The support department also provides route guidance and first aid advice. For example, it can guide the user to the nearest hospital and advise on first aid procedures.As a result, the personalized car navigation system according to the embodiment can provide the user with an individualized driving experience by suggesting the optimal route based on the user's driving history, suggesting tourist spots and restaurants based on the travel destination and route, and assisting in contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies.

[0030] The data collection unit collects the user's driving history. This history includes, but is not limited to, distance traveled, speed, route, and stopping time. Specifically, it uses GPS and various sensors installed in the vehicle to collect detailed user driving data. For example, distance traveled is obtained from the vehicle's odometer, and speed is obtained in real time from the vehicle's speed sensor. Route information is accurately recorded based on GPS data, showing which roads the user has traveled. Stopping time is determined by recording the time the vehicle's engine is off and the time spent stopped at specific locations. The data collection unit also collects data on cafes the user has visited in the past and scenic routes. This includes analyzing the user's tendency to frequently stop at specific locations and prefer certain routes. Furthermore, the data collection unit can also collect data such as driving time, speed changes, and braking frequency to understand the user's driving patterns. For example, if a user frequently drives during the morning commute, data from that time period will be collected intensively to gain a detailed understanding of the user's driving style. This allows the data collection unit to gather basic data to understand the user's driving history and preferences in detail and to provide personalized navigation services.

[0031] The analysis unit analyzes driving history collected by the data collection unit to understand the user's preferences and driving patterns. Specifically, it uses the collected data to analyze the user's driving style and preferences in detail. For example, it analyzes data on cafes the user has visited in the past to identify what type of cafe the user prefers. This uses data such as cafe location, length of stay, and frequency of visits. The analysis unit can also analyze driving patterns to understand the user's driving style. For example, it analyzes data on the user's driving time and speed changes to identify when and at what speed the user tends to drive. Furthermore, by analyzing braking frequency and acceleration patterns, it can obtain information about the safety and comfort of the user's driving. Based on this data, the analysis unit provides basic information for providing optimal routes and suggestions tailored to the user's driving style and preferences. For example, if the user prefers highways or scenic routes, the analysis unit can suggest the optimal route based on this information. In this way, the analysis unit can understand the user's driving history and preferences in detail and analyze the basic data to provide personalized navigation services.

[0032] The suggestion unit proposes the optimal route based on the analysis results obtained by the analysis unit. Specifically, it calculates and proposes the best route for the user, taking into account the user's preferences and driving patterns. For example, it may prioritize routes that pass by cafes the user frequently visits or routes with scenic views. The suggestion unit can also propose the optimal route based on the user's driving patterns. For example, it may propose the optimal route based on data on the user's driving time and speed changes. This includes, if the user drives during rush hour, proposing a route that avoids congestion by considering the traffic conditions during that time. Furthermore, the suggestion unit can also propose routes that allow for comfortable driving according to the user's driving style. For example, if the user prefers highways, it will propose a route that prioritizes highways. Also, if the user prefers routes with scenic views, it will prioritize such routes. In this way, the suggestion unit can propose the optimal route according to the user's preferences and driving patterns, providing the user with a comfortable and efficient driving experience.

[0033] The Tourism Recommendation Department suggests tourist spots and restaurants based on the travel destination and route. Specifically, when a user sets a travel destination, it collects information on tourist spots and restaurants along that route and suggests them in real time. The Tourism Recommendation Department utilizes generative AI and MR technology to provide real-time information. For example, while driving, the Tourism Recommendation Department allows users to check the location and details of tourist spots and restaurants through information displayed on the windshield. The generative AI selects the most suitable tourist spots and restaurants based on the user's preferences and past visit history. For example, it analyzes data on tourist spots the user has visited in the past to identify the type of tourist spots the user prefers. It can also suggest the most suitable restaurants based on the user's dining preferences. By using MR technology, users can visually check information while driving, ensuring driving safety while obtaining information. As a result, the Tourism Recommendation Department can provide information in real time to enhance the user's travel experience and suggest attractive tourist spots and restaurants for the user.

[0034] The support department assists with contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies. Specifically, in the event of an accident, it searches for and provides the contact information of the nearest hospital, police, and JAF. The support department also provides route guidance and first aid advice. For example, the support department guides the user to the nearest hospital and advises on first aid methods. This includes providing detailed explanations of first aid procedures and how to use necessary tools so that the user can respond quickly at the accident scene. Furthermore, the support department can provide accurate location information to emergency services based on the user's location data. This allows emergency services to arrive at the scene quickly and provide appropriate assistance. The support department also provides information using multiple communication methods so that users can quickly obtain the information they need in an emergency. For example, it uses a combination of voice calls, SMS, and email to ensure that important information is delivered reliably. In this way, the support department can provide users with prompt and reliable emergency assistance and ensure their safety.

[0035] The data collection unit can collect data on cafes and scenic routes that the user has visited in the past. For example, the data collection unit can collect location information of cafes that the user has visited in the past. The data collection unit can also collect data on scenic routes that the user has visited in the past. For example, the data collection unit can collect location information of cafes that the user frequently visits to understand the user's preferences. The data collection unit can also collect data on scenic routes that the user has visited in the past to understand the user's preferences. This makes it possible to provide more personalized route suggestions by collecting data based on the user's past visit history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input location information of cafes that the user has visited in the past into a generating AI and have the generating AI collect the cafe data.

[0036] The analysis unit can analyze data collected by the collection unit to understand the user's preferences and driving patterns. For example, the analysis unit can analyze café data collected by the collection unit to identify the user's preferences. The analysis unit can also analyze driving pattern data collected by the collection unit to understand the user's driving style. For example, the analysis unit can analyze data on the user's driving time and speed changes to identify the user's driving pattern. In addition, the analysis unit can analyze data such as driving time, speed changes, and braking frequency to understand the user's driving pattern. By understanding the user's preferences and driving patterns, it becomes possible to suggest more appropriate routes. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input café data collected by the collection unit into a generating AI and have the generating AI identify the user's preferences.

[0037] The suggestion unit can prioritize guiding users to routes that pass through cafes they frequently visit or routes with scenic views, based on the analysis results obtained by the analysis unit. For example, the suggestion unit can guide users to routes that pass through cafes they frequently visit based on cafe data obtained by the analysis unit. The suggestion unit can also prioritize guiding users to routes with scenic views based on driving pattern data obtained by the analysis unit. For example, the suggestion unit can guide users to routes with scenic views based on data on the user's driving time and speed changes. This allows for a more satisfying driving experience by suggesting routes based on the user's preferences. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input cafe data obtained by the analysis unit into a generating AI and have the generating AI perform route suggestions.

[0038] The tourism suggestion department can suggest tourist spots and restaurants to visit along the way, based on the travel destination and route. For example, when a user sets a travel destination, the tourism suggestion department can collect information on tourist spots and restaurants along that route and suggest them in real time. The tourism suggestion department can also collect information on tourist spots and restaurants based on the user's travel destination and route and suggest them. For example, when a user sets a travel destination, the tourism suggestion department can collect information on tourist spots and restaurants along that route and suggest them in real time. This can enhance the enjoyment of a trip by suggesting tourist spots and restaurants based on the travel destination and route. Some or all of the above processing in the tourism suggestion department may be performed using AI, for example, or not using AI. For example, the tourism suggestion department can input information on tourist spots and restaurants based on the travel destination and route into a generating AI and have the generating AI execute the suggestions.

[0039] The Tourism Proposal Department can provide real-time information using generative AI and MR technology. For example, when the generative AI performs summarization, the Tourism Proposal Department can automatically collect relevant background information and refer to it to understand the context. For example, the Tourism Proposal Department can collect relevant news articles and academic papers. The Tourism Proposal Department can also use topic models to understand the context when the generative AI performs summarization. For example, the Tourism Proposal Department can extract relevant keywords and phrases based on topic models. Topic models can be implemented using techniques such as LDA (Latent Dirichlet Allocation), non-negative matrix factorization, or topic clustering. The Tourism Proposal Department can also build a system to understand the context by referring to relevant background information and topic models when the generative AI performs summarization. For example, the Tourism Proposal Department can automatically collect relevant information and reflect it in the summary. As a result, by utilizing generative AI and MR technology, users can obtain information superimposed on real-world scenery. Some or all of the above processing in the Tourism Proposal Department may be performed using generative AI, or without using generative AI. For example, the Tourism Proposal Department can input background information related to the Generative AI and have the Generative AI perform contextual understanding.

[0040] The support unit can search for and provide the nearest hospital, police, and JAF contact information to the user in the event of an accident. For example, if an accident occurs, the support unit can search for and provide the nearest hospital, police, and JAF contact information to the user. The support unit can also search for and provide the nearest hospital, police, and JAF contact information to the user in the event of an accident. For example, if an accident occurs, the support unit can search for and provide the nearest hospital, police, and JAF contact information to the user. This ensures the user's safety by providing appropriate contact information quickly in the event of an accident. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the nearest hospital, police, and JAF contact information into a generating AI in the event of an accident and have the generating AI perform the contact information search.

[0041] The support unit can provide route guidance and first aid advice. For example, in the event of an accident, the support unit can guide the user to the nearest hospital. The support unit can also advise on first aid methods in the event of an accident. For example, the support unit can guide the user to the nearest hospital and advise on first aid methods. This ensures the user's safety by providing appropriate route guidance and first aid advice in emergencies. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, in the event of an accident, the support unit can input route guidance to the nearest hospital into a generating AI and have the generating AI execute the route guidance.

[0042] The data collection unit can analyze the user's past driving history and select the optimal data collection method. For example, the data collection unit can set collection points for driving history based on places the user has frequently visited in the past. The data collection unit can also analyze the user's driving patterns and determine the optimal collection interval. For example, the data collection unit can concentrate data collection during specific time periods based on the user's driving history. This allows for efficient data collection by selecting the optimal data collection method through analysis of the user's past driving history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past driving history into a generating AI and have the generating AI select the optimal data collection method.

[0043] The data collection unit can filter the collected driving history based on the user's current driving status and areas of interest. For example, if the user is driving on a highway, the data collection unit will prioritize collecting specific data. The data collection unit can also collect data related to tourist spots if the user is visiting a tourist destination. For example, if the user is commuting, the data collection unit will collect data related to their commute route. By filtering the data based on the user's current driving status and areas of interest, more relevant data can be collected. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current driving status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0044] The data collection unit can prioritize the collection of highly relevant driving history by considering the user's geographical location information when collecting driving history. For example, if the user is visiting a specific area, the data collection unit will prioritize the collection of driving history related to that area. Similarly, if the user is visiting a tourist destination, the data collection unit can prioritize the collection of driving history related to tourist spots. For example, if the user is commuting, the data collection unit will prioritize the collection of driving history related to their commute route. This allows for the priority collection of highly relevant driving history by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant driving history.

[0045] The data collection unit can analyze the user's social media activity and collect relevant history when collecting driving history. For example, the data collection unit can collect driving history related to places the user has shared on social media. The data collection unit can also collect driving history related to places the user has checked in to on social media. For example, the data collection unit can collect driving history related to places the user follows on social media. This allows the data collection unit to collect relevant driving history by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data on the user's social media activity into a generating AI and have the generating AI perform the collection of relevant driving history.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the driving history. For example, the analysis unit performs a detailed analysis for important driving history. The analysis unit can also perform a simplified analysis for general driving history. For example, the analysis unit performs a special analysis for driving history under specific conditions. By adjusting the level of detail of the analysis based on the importance of the driving history, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the driving history into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the category of the driving history during analysis. For example, the analysis unit can apply a commuting-specific analysis algorithm to driving history on commuting routes. It can also apply a tourism-specific analysis algorithm to driving history on sightseeing routes. For example, the analysis unit can apply a highway-specific analysis algorithm to driving history on highways. By applying different analysis algorithms depending on the category of the driving history, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of the driving history into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0048] The analysis unit can determine the priority of analysis based on the submission timing of the driving history during the analysis. For example, the analysis unit may prioritize the analysis of recent driving history. The analysis unit can also prioritize the analysis of driving history within a specific period. For example, the analysis unit may prioritize the analysis of driving history within a period specified by the user. This enables efficient analysis by determining the priority of analysis based on the submission timing of the driving history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission timing of the driving history into a generating AI and have the generating AI perform the determination of the analysis priority.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the driving history during the analysis. For example, the analysis unit can prioritize the analysis of important driving history. The analysis unit can also prioritize the analysis of driving history of high interest to the user. For example, the analysis unit can prioritize the analysis of driving history under specific conditions. This allows for the prioritization of important data by adjusting the order of analysis based on the relevance of the driving history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the driving history into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0050] The proposal unit can adjust the level of detail of its proposals based on the importance of the route. For example, it can provide detailed proposals for important routes, and concise proposals for general routes. For example, it can provide special proposals for routes under specific conditions. By adjusting the level of detail of proposals based on the importance of the route, efficient proposals become possible. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the route into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0051] The proposal unit can apply different proposal algorithms depending on the route category when making a proposal. For example, the proposal unit can apply a proposal algorithm specialized for commuting to commuting routes. It can also apply a proposal algorithm specialized for sightseeing routes. For example, the proposal unit can apply a proposal algorithm specialized for highway routes to highway routes. By applying different proposal algorithms depending on the route category, more accurate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the route category into a generation AI and have the generation AI execute the application of the proposal algorithm.

[0052] The proposal unit can determine the priority of proposals based on the submission date of the routes. For example, the proposal unit may prioritize recently submitted routes. It can also prioritize routes within a specific period. For example, the proposal unit may prioritize routes within a period specified by the user. This enables efficient proposals by prioritizing proposals based on the submission date of the routes. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the submission date of the routes into a generating AI and have the generating AI determine the priority of the proposals.

[0053] The suggestion unit can adjust the order of suggestions based on the relevance of the routes when making suggestions. For example, the suggestion unit can prioritize suggesting important routes. It can also prioritize suggesting routes of high user interest. For example, the suggestion unit can prioritize suggesting routes under specific conditions. This allows for prioritizing important routes by adjusting the order of suggestions based on the relevance of the routes. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of routes into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0054] The tourism suggestion department can provide optimal suggestions for tourist spots by referring to the user's past visit history. For example, the tourism suggestion department can make optimal suggestions based on tourist spots the user has visited in the past. The tourism suggestion department can also suggest relevant tourist spots based on the user's past visit history. For example, the tourism suggestion department can analyze the user's past visit history and suggest the tourist spots that are most interesting to the user. This makes it possible to suggest more relevant tourist spots by referring to the user's past visit history. Some or all of the above processing in the tourism suggestion department may be performed using AI, for example, or not using AI. For example, the tourism suggestion department can input the user's past visit history into a generating AI and have the generating AI perform the task of providing optimal suggestions.

[0055] The tourism suggestion department can customize the method of suggestion based on the user's current travel situation when suggesting tourist spots. For example, if the user is traveling by car, the tourism suggestion department will suggest tourist spots that are easily accessible by car. It can also suggest tourist spots that are accessible on foot if the user is traveling on foot. For example, if the user is using public transport, the tourism suggestion department will suggest tourist spots that are easily accessible by public transport. This allows for more appropriate tourist spot suggestions by customizing the method of suggestion based on the user's current travel situation. Some or all of the above processing in the tourism suggestion department may be performed using AI, or not. For example, the tourism suggestion department can input the user's current travel situation into a generating AI and have the generating AI customize the method of suggestion.

[0056] The tourism suggestion department can provide optimal suggestions for tourist spots by considering the user's geographical location. For example, if the user is visiting a specific region, the tourism suggestion department will prioritize suggesting tourist spots related to that region. Furthermore, if the user is visiting a tourist destination, the tourism suggestion department can also provide suggestions related to tourist spots. For example, if the user is commuting, the tourism suggestion department will suggest tourist spots related to their commute route. This allows for the suggestion of more relevant tourist spots by considering the user's geographical location. Some or all of the above processing in the tourism suggestion department may be performed using AI, or not. For example, the tourism suggestion department can input the user's geographical location information into a generating AI and have the generating AI provide optimal suggestions.

[0057] The tourism suggestion department can analyze a user's social media activity to suggest tourist spots. For example, it can suggest tourist spots related to places the user has shared on social media. It can also suggest tourist spots related to places the user has checked into on social media. For example, it can suggest tourist spots related to places the user follows on social media. By analyzing the user's social media activity, it becomes possible to suggest more relevant tourist spots. Some or all of the above processing in the tourism suggestion department may be performed using AI, for example, or not using AI. For example, the tourism suggestion department can input data on the user's social media activity into a generating AI and have the generating AI perform the task of providing suggestions.

[0058] The support unit can select the optimal response method in the event of an emergency by referring to the user's past emergency response history. For example, the support unit can propose the optimal response method based on the emergency response methods the user has used in the past. The support unit can also propose relevant response methods from the user's past emergency response history. For example, the support unit can analyze the user's past emergency response history and propose the most effective response method. In this way, by referring to the user's past emergency response history, a more appropriate response method can be provided. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past emergency response history into a generating AI and have the generating AI select the optimal response method.

[0059] The support unit can customize the response based on the user's current situation during an emergency. For example, if a user is involved in a car accident, the support unit can suggest how to contact the nearest hospital or police. It can also suggest first aid procedures if a user is involved in an accident while walking. For example, if a user is using public transportation, the support unit can suggest the nearest emergency contact. This allows for a more appropriate response by customizing the response based on the user's current situation. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current situation into a generating AI and have the generating AI customize the response.

[0060] The support unit can select the most appropriate response method in the event of an emergency, taking into account the user's geographical location. For example, if a user has an accident in a specific area, the support unit can propose an emergency response method relevant to that area. Similarly, if a user has an accident in a tourist area, the support unit can propose an emergency response method relevant to that tourist area. For example, if a user has an accident while commuting, the support unit can propose an emergency response method relevant to their commute route. This allows for the provision of a more appropriate emergency response method by considering the user's geographical location. Some or all of the above processing in the support unit may be performed using AI, or not. For example, the support unit can input the user's geographical location into a generating AI and have the generating AI select the most appropriate response method.

[0061] The support unit can analyze a user's social media activity and propose response measures during an emergency. For example, the support unit can propose emergency response methods related to locations shared by the user on social media. It can also propose emergency response methods related to locations checked in by the user on social media. For example, the support unit can propose emergency response methods related to locations followed by the user on social media. This allows for the provision of more appropriate emergency response methods by analyzing the user's social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input data on the user's social media activity into a generating AI and have the generating AI provide response measures.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The suggestion unit can analyze the user's past driving history and select the optimal suggestion method. For example, the suggestion unit can customize the suggested content based on places the user has frequently visited in the past. The suggestion unit can also analyze the user's driving patterns and determine the optimal timing for suggestions. For example, the suggestion unit can concentrate suggestions during specific time periods based on the user's driving history. This allows for efficient suggestions by selecting the optimal suggestion method through analysis of the user's past driving history. Some or all of the above processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's past driving history into a generating AI and have the generating AI select the optimal suggestion method.

[0064] The tourism suggestion department can provide optimal suggestions by referring to the user's past visit history. For example, the tourism suggestion department can make optimal suggestions based on the tourist spots the user has visited in the past. The tourism suggestion department can also suggest relevant tourist spots based on the user's past visit history. For example, the tourism suggestion department can analyze the user's past visit history and suggest the tourist spots that are most interesting to the user. This makes it possible to suggest more relevant tourist spots by referring to the user's past visit history. Some or all of the above processing in the tourism suggestion department may be performed using AI, for example, or not using AI. For example, the tourism suggestion department can input the user's past visit history into a generating AI and have the generating AI perform the task of providing optimal suggestions.

[0065] The support unit can select the optimal response method in the event of an emergency by referring to the user's past emergency response history. For example, the support unit can propose the optimal response method based on the emergency response methods the user has used in the past. The support unit can also propose relevant response methods from the user's past emergency response history. For example, the support unit can analyze the user's past emergency response history and propose the most effective response method. In this way, by referring to the user's past emergency response history, a more appropriate response method can be provided. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past emergency response history into a generating AI and have the generating AI select the optimal response method.

[0066] The data collection unit can filter the collected driving history based on the user's current driving status and areas of interest. For example, if the user is driving on a highway, the data collection unit will prioritize collecting specific data. The data collection unit can also collect data related to tourist spots if the user is visiting a tourist destination. For example, if the user is commuting, the data collection unit will collect data related to their commute route. By filtering the data based on the user's current driving status and areas of interest, more relevant data can be collected. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current driving status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0067] The analysis unit can apply different analysis algorithms depending on the category of the driving history during analysis. For example, the analysis unit can apply a commuting-specific analysis algorithm to driving history on commuting routes. It can also apply a tourism-specific analysis algorithm to driving history on sightseeing routes. For example, the analysis unit can apply a highway-specific analysis algorithm to driving history on highways. By applying different analysis algorithms depending on the category of the driving history, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of the driving history into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects the user's driving history. This history includes, for example, distance traveled, speed, route, and stopping time. The data collection unit also collects data on cafes the user has visited in the past and scenic routes. It can also collect data such as driving time, speed changes, and braking frequency. Step 2: The analysis unit analyzes the driving history collected by the data collection unit to understand the user's preferences and driving patterns. For example, it analyzes data on cafes the user has visited in the past to identify the user's preferences. It also analyzes data on driving time and speed changes to identify the user's driving patterns. Step 3: The suggestion unit proposes the optimal route based on the analysis results obtained by the analysis unit. For example, it prioritizes routes that pass by cafes the user frequently visits or routes with scenic views. It also proposes the optimal route based on data on the user's driving time and speed changes. Step 4: The Tourism Recommendation Department suggests tourist spots and restaurants based on the travel destination and route. For example, when a user sets a travel destination, it collects information on tourist spots and restaurants along that route and suggests them in real time. The Tourism Recommendation Department provides real-time information using generative AI and MR technology. Step 5: The support department assists with contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies. For example, in the event of an accident, they search for and provide the contact information of the nearest hospital, police, and JAF. They also provide route guidance and first aid advice.

[0070] (Example of form 2) The personalized car navigation system according to an embodiment of the present invention is a system that proposes the optimal route based on the user's driving history, suggests tourist spots and restaurants based on the travel destination and route, and assists in contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies. The personalized car navigation system proposes the optimal route based on the user's driving history. For example, it prioritizes guiding the user to cafes they frequently visit or routes with good scenery, and predicts a new destination if the user deviates from the route on the way to the destination. Next, it suggests tourist spots and restaurants that the user should stop at along the way based on the travel destination and route. It utilizes generative AI and MR (Mixed Reality) technology to provide real-time information. Furthermore, in emergencies such as accidents, it assists in contacting hospitals, police, and JAF, provides route guidance, and offers advice on first aid. First, the personalized car navigation system collects the user's driving history, which is then analyzed by AI. For example, by collecting data on cafes and scenic routes that the user has visited in the past and analyzing it with AI, the system understands the user's preferences and driving patterns. This allows the system to propose the optimal route to the user. For example, it prioritizes guiding the user to routes that pass through cafes they frequently visit or routes with good scenery. Next, it suggests tourist spots and restaurants that the user should stop at along the way based on the travel destination and route. For example, when a user sets a travel destination, the AI ​​collects information on tourist attractions and restaurants along the route and suggests them in real time. By utilizing generative AI and MR technology, users can obtain information overlaid on the real-world scenery. For instance, while driving, a user can check the location and details of tourist attractions and restaurants through information displayed on the windshield. Furthermore, in emergencies, the AI ​​assists in contacting hospitals, police, and JAF (Japan Automobile Federation). For example, in the event of an accident, the AI ​​automatically searches for and provides contact information for the nearest hospital, police, and JAF. It also provides route guidance and first aid advice. For example, the AI ​​guides the user to the nearest hospital and advises on first aid procedures.Thus, the present invention can provide users with a personalized driving experience by suggesting the optimal route based on the user's driving history, suggesting tourist spots and restaurants based on the travel destination and route, and assisting in contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies. This allows users to reduce time and stress, and enjoy a comfortable and fulfilling drive or trip. In this way, the personalized car navigation system can provide users with a personalized driving experience by suggesting the optimal route based on the user's driving history, suggesting tourist spots and restaurants based on the travel destination and route, and assisting in contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies.

[0071] The personalized car navigation system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, a sightseeing suggestion unit, and a support unit. The data collection unit collects the user's driving history. The user's driving history includes, but is not limited to, distance traveled, speed, route, and stopping time. The data collection unit collects, for example, data on cafes the user has visited in the past and scenic routes. The data collection unit can also collect data such as driving time, speed changes, and braking frequency in order to understand the user's driving patterns. For example, the data collection unit collects location information of cafes the user frequently visits to understand the user's preferences. The analysis unit analyzes the driving history collected by the data collection unit to understand the user's preferences and driving patterns. For example, the analysis unit analyzes data on cafes the user has visited in the past to identify the user's preferences. The analysis unit can also analyze driving patterns to understand the user's driving style. For example, the analysis unit analyzes data on the user's driving time and speed changes to identify the user's driving patterns. The suggestion unit proposes the optimal route based on the analysis results obtained by the analysis unit. The suggestion department prioritizes routes that pass by cafes the user frequently visits or routes with scenic views. It can also suggest optimal routes based on the user's driving patterns. For example, it might suggest the best route based on data regarding the user's driving time and speed changes. The sightseeing suggestion department suggests tourist spots and restaurants based on the travel destination and route. For example, once a user sets a travel destination, the sightseeing suggestion department collects information on tourist spots and restaurants along that route and suggests them in real time. The sightseeing suggestion department utilizes generative AI and MR technology to provide real-time information. For example, while driving, the sightseeing suggestion department allows users to check the location and details of tourist spots and restaurants through information displayed on the windshield. The support department assists with contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies. For example, in the event of an accident, the support department searches for and provides contact information for the nearest hospital, police, and JAF. The support department also provides route guidance and first aid advice. For example, it can guide the user to the nearest hospital and advise on first aid procedures.As a result, the personalized car navigation system according to the embodiment can provide the user with an individualized driving experience by suggesting the optimal route based on the user's driving history, suggesting tourist spots and restaurants based on the travel destination and route, and assisting in contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies.

[0072] The data collection unit collects the user's driving history. This history includes, but is not limited to, distance traveled, speed, route, and stopping time. Specifically, it uses GPS and various sensors installed in the vehicle to collect detailed user driving data. For example, distance traveled is obtained from the vehicle's odometer, and speed is obtained in real time from the vehicle's speed sensor. Route information is accurately recorded based on GPS data, showing which roads the user has traveled. Stopping time is determined by recording the time the vehicle's engine is off and the time spent stopped at specific locations. The data collection unit also collects data on cafes the user has visited in the past and scenic routes. This includes analyzing the user's tendency to frequently stop at specific locations and prefer certain routes. Furthermore, the data collection unit can also collect data such as driving time, speed changes, and braking frequency to understand the user's driving patterns. For example, if a user frequently drives during the morning commute, data from that time period will be collected intensively to gain a detailed understanding of the user's driving style. This allows the data collection unit to gather basic data to understand the user's driving history and preferences in detail and to provide personalized navigation services.

[0073] The analysis unit analyzes driving history collected by the data collection unit to understand the user's preferences and driving patterns. Specifically, it uses the collected data to analyze the user's driving style and preferences in detail. For example, it analyzes data on cafes the user has visited in the past to identify what type of cafe the user prefers. This uses data such as cafe location, length of stay, and frequency of visits. The analysis unit can also analyze driving patterns to understand the user's driving style. For example, it analyzes data on the user's driving time and speed changes to identify when and at what speed the user tends to drive. Furthermore, by analyzing braking frequency and acceleration patterns, it can obtain information about the safety and comfort of the user's driving. Based on this data, the analysis unit provides basic information for providing optimal routes and suggestions tailored to the user's driving style and preferences. For example, if the user prefers highways or scenic routes, the analysis unit can suggest the optimal route based on this information. In this way, the analysis unit can understand the user's driving history and preferences in detail and analyze the basic data to provide personalized navigation services.

[0074] The suggestion unit proposes the optimal route based on the analysis results obtained by the analysis unit. Specifically, it calculates and proposes the best route for the user, taking into account the user's preferences and driving patterns. For example, it may prioritize routes that pass by cafes the user frequently visits or routes with scenic views. The suggestion unit can also propose the optimal route based on the user's driving patterns. For example, it may propose the optimal route based on data on the user's driving time and speed changes. This includes, if the user drives during rush hour, proposing a route that avoids congestion by considering the traffic conditions during that time. Furthermore, the suggestion unit can also propose routes that allow for comfortable driving according to the user's driving style. For example, if the user prefers highways, it will propose a route that prioritizes highways. Also, if the user prefers routes with scenic views, it will prioritize such routes. In this way, the suggestion unit can propose the optimal route according to the user's preferences and driving patterns, providing the user with a comfortable and efficient driving experience.

[0075] The Tourism Recommendation Department suggests tourist spots and restaurants based on the travel destination and route. Specifically, when a user sets a travel destination, it collects information on tourist spots and restaurants along that route and suggests them in real time. The Tourism Recommendation Department utilizes generative AI and MR technology to provide real-time information. For example, while driving, the Tourism Recommendation Department allows users to check the location and details of tourist spots and restaurants through information displayed on the windshield. The generative AI selects the most suitable tourist spots and restaurants based on the user's preferences and past visit history. For example, it analyzes data on tourist spots the user has visited in the past to identify the type of tourist spots the user prefers. It can also suggest the most suitable restaurants based on the user's dining preferences. By using MR technology, users can visually check information while driving, ensuring driving safety while obtaining information. As a result, the Tourism Recommendation Department can provide information in real time to enhance the user's travel experience and suggest attractive tourist spots and restaurants for the user.

[0076] The support department assists with contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies. Specifically, in the event of an accident, it searches for and provides the contact information of the nearest hospital, police, and JAF. The support department also provides route guidance and first aid advice. For example, the support department guides the user to the nearest hospital and advises on first aid methods. This includes providing detailed explanations of first aid procedures and how to use necessary tools so that the user can respond quickly at the accident scene. Furthermore, the support department can provide accurate location information to emergency services based on the user's location data. This allows emergency services to arrive at the scene quickly and provide appropriate assistance. The support department also provides information using multiple communication methods so that users can quickly obtain the information they need in an emergency. For example, it uses a combination of voice calls, SMS, and email to ensure that important information is delivered reliably. In this way, the support department can provide users with prompt and reliable emergency assistance and ensure their safety.

[0077] The data collection unit can collect data on cafes and scenic routes that the user has visited in the past. For example, the data collection unit can collect location information of cafes that the user has visited in the past. The data collection unit can also collect data on scenic routes that the user has visited in the past. For example, the data collection unit can collect location information of cafes that the user frequently visits to understand the user's preferences. The data collection unit can also collect data on scenic routes that the user has visited in the past to understand the user's preferences. This makes it possible to provide more personalized route suggestions by collecting data based on the user's past visit history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input location information of cafes that the user has visited in the past into a generating AI and have the generating AI collect the cafe data.

[0078] The analysis unit can analyze data collected by the collection unit to understand the user's preferences and driving patterns. For example, the analysis unit can analyze café data collected by the collection unit to identify the user's preferences. The analysis unit can also analyze driving pattern data collected by the collection unit to understand the user's driving style. For example, the analysis unit can analyze data on the user's driving time and speed changes to identify the user's driving pattern. In addition, the analysis unit can analyze data such as driving time, speed changes, and braking frequency to understand the user's driving pattern. By understanding the user's preferences and driving patterns, it becomes possible to suggest more appropriate routes. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input café data collected by the collection unit into a generating AI and have the generating AI identify the user's preferences.

[0079] The suggestion unit can prioritize guiding users to routes that pass through cafes they frequently visit or routes with scenic views, based on the analysis results obtained by the analysis unit. For example, the suggestion unit can guide users to routes that pass through cafes they frequently visit based on cafe data obtained by the analysis unit. The suggestion unit can also prioritize guiding users to routes with scenic views based on driving pattern data obtained by the analysis unit. For example, the suggestion unit can guide users to routes with scenic views based on data on the user's driving time and speed changes. This allows for a more satisfying driving experience by suggesting routes based on the user's preferences. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input cafe data obtained by the analysis unit into a generating AI and have the generating AI perform route suggestions.

[0080] The tourism suggestion department can suggest tourist spots and restaurants to visit along the way, based on the travel destination and route. For example, when a user sets a travel destination, the tourism suggestion department can collect information on tourist spots and restaurants along that route and suggest them in real time. The tourism suggestion department can also collect information on tourist spots and restaurants based on the user's travel destination and route and suggest them. For example, when a user sets a travel destination, the tourism suggestion department can collect information on tourist spots and restaurants along that route and suggest them in real time. This can enhance the enjoyment of a trip by suggesting tourist spots and restaurants based on the travel destination and route. Some or all of the above processing in the tourism suggestion department may be performed using AI, for example, or not using AI. For example, the tourism suggestion department can input information on tourist spots and restaurants based on the travel destination and route into a generating AI and have the generating AI execute the suggestions.

[0081] The Tourism Proposal Department can provide real-time information using generative AI and MR technology. For example, when the generative AI performs summarization, the Tourism Proposal Department can automatically collect relevant background information and refer to it to understand the context. For example, the Tourism Proposal Department can collect relevant news articles and academic papers. The Tourism Proposal Department can also use topic models to understand the context when the generative AI performs summarization. For example, the Tourism Proposal Department can extract relevant keywords and phrases based on topic models. Topic models can be implemented using techniques such as LDA (Latent Dirichlet Allocation), non-negative matrix factorization, or topic clustering. The Tourism Proposal Department can also build a system to understand the context by referring to relevant background information and topic models when the generative AI performs summarization. For example, the Tourism Proposal Department can automatically collect relevant information and reflect it in the summary. As a result, by utilizing generative AI and MR technology, users can obtain information superimposed on real-world scenery. Some or all of the above processing in the Tourism Proposal Department may be performed using generative AI, or without using generative AI. For example, the Tourism Proposal Department can input background information related to the Generative AI and have the Generative AI perform contextual understanding.

[0082] The support unit can search for and provide the nearest hospital, police, and JAF contact information to the user in the event of an accident. For example, if an accident occurs, the support unit can search for and provide the nearest hospital, police, and JAF contact information to the user. The support unit can also search for and provide the nearest hospital, police, and JAF contact information to the user in the event of an accident. For example, if an accident occurs, the support unit can search for and provide the nearest hospital, police, and JAF contact information to the user. This ensures the user's safety by providing appropriate contact information quickly in the event of an accident. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the nearest hospital, police, and JAF contact information into a generating AI in the event of an accident and have the generating AI perform the contact information search.

[0083] The support unit can provide route guidance and first aid advice. For example, in the event of an accident, the support unit can guide the user to the nearest hospital. The support unit can also advise on first aid methods in the event of an accident. For example, the support unit can guide the user to the nearest hospital and advise on first aid methods. This ensures the user's safety by providing appropriate route guidance and first aid advice in emergencies. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, in the event of an accident, the support unit can input route guidance to the nearest hospital into a generating AI and have the generating AI execute the route guidance.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of driving history collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect driving history more frequently to obtain detailed data. Conversely, if the user is stressed, the data collection unit can reduce the user's burden by reducing the frequency of driving history collection. For example, if the user is in a hurry, the data collection unit will prioritize collecting only important data and shorten the collection time. In this way, the user's burden can be reduced by adjusting the timing of driving history collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The data collection unit can analyze the user's past driving history and select the optimal data collection method. For example, the data collection unit can set collection points for driving history based on places the user has frequently visited in the past. The data collection unit can also analyze the user's driving patterns and determine the optimal collection interval. For example, the data collection unit can concentrate data collection during specific time periods based on the user's driving history. This allows for efficient data collection by selecting the optimal data collection method through analysis of the user's past driving history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past driving history into a generating AI and have the generating AI select the optimal data collection method.

[0086] The data collection unit can filter the collected driving history based on the user's current driving status and areas of interest. For example, if the user is driving on a highway, the data collection unit will prioritize collecting specific data. The data collection unit can also collect data related to tourist spots if the user is visiting a tourist destination. For example, if the user is commuting, the data collection unit will collect data related to their commute route. By filtering the data based on the user's current driving status and areas of interest, more relevant data can be collected. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current driving status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0087] The data collection unit can estimate the user's emotions and determine the priority of the driving history to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed driving history. Alternatively, if the user is stressed, the data collection unit can prioritize collecting only essential data. For example, if the user is in a hurry, the data collection unit will reduce the amount of data collected and shorten the collection time. This allows for the priority collection of essential data by prioritizing the driving history according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The data collection unit can prioritize the collection of highly relevant driving history by considering the user's geographical location information when collecting driving history. For example, if the user is visiting a specific area, the data collection unit will prioritize the collection of driving history related to that area. Similarly, if the user is visiting a tourist destination, the data collection unit can prioritize the collection of driving history related to tourist spots. For example, if the user is commuting, the data collection unit will prioritize the collection of driving history related to their commute route. This allows for the priority collection of highly relevant driving history by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant driving history.

[0089] The data collection unit can analyze the user's social media activity and collect relevant history when collecting driving history. For example, the data collection unit can collect driving history related to places the user has shared on social media. The data collection unit can also collect driving history related to places the user has checked in to on social media. For example, the data collection unit can collect driving history related to places the user follows on social media. This allows the data collection unit to collect relevant driving history by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data on the user's social media activity into a generating AI and have the generating AI perform the collection of relevant driving history.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. Conversely, if the user is stressed, the analysis unit can provide concise analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis unit can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the driving history. For example, the analysis unit performs a detailed analysis for important driving history. The analysis unit can also perform a simplified analysis for general driving history. For example, the analysis unit performs a special analysis for driving history under specific conditions. By adjusting the level of detail of the analysis based on the importance of the driving history, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the driving history into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0092] The analysis unit can apply different analysis algorithms depending on the category of the driving history during analysis. For example, the analysis unit can apply a commuting-specific analysis algorithm to driving history on commuting routes. It can also apply a tourism-specific analysis algorithm to driving history on sightseeing routes. For example, the analysis unit can apply a highway-specific analysis algorithm to driving history on highways. By applying different analysis algorithms depending on the category of the driving history, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of the driving history into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit will perform a detailed analysis. Conversely, if the user is stressed, the analysis unit can perform a concise analysis. For example, if the user is in a hurry, the analysis unit will perform a concise analysis. By adjusting the length of the analysis according to the user's emotions, the analysis results can be provided to the user in an easy-to-understand manner. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The analysis unit can determine the priority of analysis based on the submission timing of the driving history during the analysis. For example, the analysis unit may prioritize the analysis of recent driving history. The analysis unit can also prioritize the analysis of driving history within a specific period. For example, the analysis unit may prioritize the analysis of driving history within a period specified by the user. This enables efficient analysis by determining the priority of analysis based on the submission timing of the driving history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission timing of the driving history into a generating AI and have the generating AI perform the determination of the analysis priority.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the driving history during the analysis. For example, the analysis unit can prioritize the analysis of important driving history. The analysis unit can also prioritize the analysis of driving history of high interest to the user. For example, the analysis unit can prioritize the analysis of driving history under specific conditions. This allows for the prioritization of important data by adjusting the order of analysis based on the relevance of the driving history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the driving history into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0096] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, it can provide concise suggestions. For example, if the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, the system can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The proposal unit can adjust the level of detail of its proposals based on the importance of the route. For example, it can provide detailed proposals for important routes, and concise proposals for general routes. For example, it can provide special proposals for routes under specific conditions. By adjusting the level of detail of proposals based on the importance of the route, efficient proposals become possible. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the route into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0098] The proposal unit can apply different proposal algorithms depending on the route category when making a proposal. For example, the proposal unit can apply a proposal algorithm specialized for commuting to commuting routes. It can also apply a proposal algorithm specialized for sightseeing routes. For example, the proposal unit can apply a proposal algorithm specialized for highway routes to highway routes. By applying different proposal algorithms depending on the route category, more accurate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the route category into a generation AI and have the generation AI execute the application of the proposal algorithm.

[0099] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. Conversely, if the user is stressed, the suggestion unit can provide concise suggestions. For example, if the user is in a hurry, the suggestion unit will provide concise suggestions. By adjusting the length of suggestions according to the user's emotions, suggestions that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The proposal unit can determine the priority of proposals based on the submission date of the routes. For example, the proposal unit may prioritize recently submitted routes. It can also prioritize routes within a specific period. For example, the proposal unit may prioritize routes within a period specified by the user. This enables efficient proposals by prioritizing proposals based on the submission date of the routes. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the submission date of the routes into a generating AI and have the generating AI determine the priority of the proposals.

[0101] The suggestion unit can adjust the order of suggestions based on the relevance of the routes when making suggestions. For example, the suggestion unit can prioritize suggesting important routes. It can also prioritize suggesting routes of high user interest. For example, the suggestion unit can prioritize suggesting routes under specific conditions. This allows for prioritizing important routes by adjusting the order of suggestions based on the relevance of the routes. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of routes into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0102] The tourism suggestion department can estimate the user's emotions and adjust the way it suggests tourist spots based on those emotions. For example, if the user is relaxed, the tourism suggestion department can suggest detailed tourist spots. Conversely, if the user is stressed, it can suggest concise tourist spots. For example, if the user is in a hurry, the tourism suggestion department can suggest tourist spots that get straight to the point. By adjusting the way tourist spots are suggested according to the user's emotions, the department can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tourism suggestion department may be performed using AI or not. For example, the tourism suggestion department can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The tourism suggestion department can provide optimal suggestions for tourist spots by referring to the user's past visit history. For example, the tourism suggestion department can make optimal suggestions based on tourist spots the user has visited in the past. The tourism suggestion department can also suggest relevant tourist spots based on the user's past visit history. For example, the tourism suggestion department can analyze the user's past visit history and suggest the tourist spots that are most interesting to the user. This makes it possible to suggest more relevant tourist spots by referring to the user's past visit history. Some or all of the above processing in the tourism suggestion department may be performed using AI, for example, or not using AI. For example, the tourism suggestion department can input the user's past visit history into a generating AI and have the generating AI perform the task of providing optimal suggestions.

[0104] The tourism suggestion department can customize the method of suggestion based on the user's current travel situation when suggesting tourist spots. For example, if the user is traveling by car, the tourism suggestion department will suggest tourist spots that are easily accessible by car. It can also suggest tourist spots that are accessible on foot if the user is traveling on foot. For example, if the user is using public transport, the tourism suggestion department will suggest tourist spots that are easily accessible by public transport. This allows for more appropriate tourist spot suggestions by customizing the method of suggestion based on the user's current travel situation. Some or all of the above processing in the tourism suggestion department may be performed using AI, or not. For example, the tourism suggestion department can input the user's current travel situation into a generating AI and have the generating AI customize the method of suggestion.

[0105] The tourism suggestion department can estimate the user's emotions and prioritize tourist spots based on those emotions. For example, if the user is relaxed, the tourism suggestion department can provide detailed suggestions for tourist spots. Conversely, if the user is stressed, it can provide concise suggestions. For example, if the user is in a hurry, the tourism suggestion department can provide concise suggestions for tourist spots. By prioritizing tourist spots according to the user's emotions, it is possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tourism suggestion department may be performed using AI or not. For example, the tourism suggestion department can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The tourism suggestion department can provide optimal suggestions for tourist spots by considering the user's geographical location. For example, if the user is visiting a specific region, the tourism suggestion department will prioritize suggesting tourist spots related to that region. Furthermore, if the user is visiting a tourist destination, the tourism suggestion department can also provide suggestions related to tourist spots. For example, if the user is commuting, the tourism suggestion department will suggest tourist spots related to their commute route. This allows for the suggestion of more relevant tourist spots by considering the user's geographical location. Some or all of the above processing in the tourism suggestion department may be performed using AI, or not. For example, the tourism suggestion department can input the user's geographical location information into a generating AI and have the generating AI provide optimal suggestions.

[0107] The tourism suggestion department can analyze a user's social media activity to suggest tourist spots. For example, it can suggest tourist spots related to places the user has shared on social media. It can also suggest tourist spots related to places the user has checked into on social media. For example, it can suggest tourist spots related to places the user follows on social media. By analyzing the user's social media activity, it becomes possible to suggest more relevant tourist spots. Some or all of the above processing in the tourism suggestion department may be performed using AI, for example, or not using AI. For example, the tourism suggestion department can input data on the user's social media activity into a generating AI and have the generating AI perform the task of providing suggestions.

[0108] The support unit can estimate the user's emotions and adjust emergency response methods based on the estimated emotions. For example, if the user is panicking, the support unit will guide the user through emergency response methods in a calm voice. If the user is calm, the support unit can also provide detailed emergency response methods. For example, if the user is confused, the support unit will provide concise and easy-to-understand emergency response methods. By adjusting emergency response methods according to the user's emotions, it is possible to provide responses that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0109] The support unit can select the optimal response method in the event of an emergency by referring to the user's past emergency response history. For example, the support unit can propose the optimal response method based on the emergency response methods the user has used in the past. The support unit can also propose relevant response methods from the user's past emergency response history. For example, the support unit can analyze the user's past emergency response history and propose the most effective response method. In this way, by referring to the user's past emergency response history, a more appropriate response method can be provided. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past emergency response history into a generating AI and have the generating AI select the optimal response method.

[0110] The support unit can customize the response based on the user's current situation during an emergency. For example, if a user is involved in a car accident, the support unit can suggest how to contact the nearest hospital or police. It can also suggest first aid procedures if a user is involved in an accident while walking. For example, if a user is using public transportation, the support unit can suggest the nearest emergency contact. This allows for a more appropriate response by customizing the response based on the user's current situation. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current situation into a generating AI and have the generating AI customize the response.

[0111] The support unit can estimate the user's emotions and determine the priority of emergency responses based on the estimated emotions. For example, if the user is in a state of panic, the support unit will prioritize providing the most important response. If the user is calm, the support unit can also provide detailed response instructions. For example, if the user is confused, the support unit will provide concise and easy-to-understand response instructions. This allows for the priority of important responses by determining the priority of emergency responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0112] The support unit can select the most appropriate response method in the event of an emergency, taking into account the user's geographical location. For example, if a user has an accident in a specific area, the support unit can propose an emergency response method relevant to that area. Similarly, if a user has an accident in a tourist area, the support unit can propose an emergency response method relevant to that tourist area. For example, if a user has an accident while commuting, the support unit can propose an emergency response method relevant to their commute route. This allows for the provision of a more appropriate emergency response method by considering the user's geographical location. Some or all of the above processing in the support unit may be performed using AI, or not. For example, the support unit can input the user's geographical location into a generating AI and have the generating AI select the most appropriate response method.

[0113] The support unit can analyze a user's social media activity and propose response measures during an emergency. For example, the support unit can propose emergency response methods related to locations shared by the user on social media. It can also propose emergency response methods related to locations checked in by the user on social media. For example, the support unit can propose emergency response methods related to locations followed by the user on social media. This allows for the provision of more appropriate emergency response methods by analyzing the user's social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input data on the user's social media activity into a generating AI and have the generating AI provide response measures.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated emotions. For example, if the user is relaxed, suggestions can be made frequently and detailed information can be provided. Conversely, if the user is stressed, the frequency of suggestions can be reduced and concise information can be provided. For example, if the user is in a hurry, only important suggestions can be prioritized and the timing of suggestions can be shortened. By adjusting the timing of suggestions according to the user's emotions, the burden on the user can be reduced and suggestions can be made more effective. Emotion estimation can be achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0116] The tourism suggestion department can estimate the user's emotions and adjust the suggested tourist spots based on those emotions. For example, if the user is relaxed, it can provide detailed information about tourist spots. If the user is stressed, it can provide concise information about tourist spots. If the user is in a hurry, it can provide information that gets straight to the point. By adjusting the suggested tourist spots according to the user's emotions, it is possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tourism suggestion department may be performed using AI or not using AI. For example, the tourism suggestion department can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0117] The support unit can estimate the user's emotions and adjust emergency response methods based on the estimated emotions. For example, if the user is panicking, it can provide emergency response instructions in a calm voice. If the user is calm, it can also provide detailed emergency response instructions. For example, if the user is confused, it can provide concise and easy-to-understand emergency response instructions. In this way, by adjusting emergency response methods according to the user's emotions, it is possible to provide responses that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0118] The data collection unit can estimate the user's emotions and adjust the timing of driving history collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect driving history more frequently to obtain detailed data. Conversely, if the user is stressed, the data collection unit can reduce the user's burden by reducing the frequency of driving history collection. For example, if the user is in a hurry, the data collection unit will prioritize collecting only important data and shorten the collection time. In this way, the user's burden can be reduced by adjusting the timing of driving history collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0119] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. Conversely, if the user is stressed, the analysis unit can provide concise analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis unit can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0120] The suggestion unit can analyze the user's past driving history and select the optimal suggestion method. For example, the suggestion unit can customize the suggested content based on places the user has frequently visited in the past. The suggestion unit can also analyze the user's driving patterns and determine the optimal timing for suggestions. For example, the suggestion unit can concentrate suggestions during specific time periods based on the user's driving history. This allows for efficient suggestions by selecting the optimal suggestion method through analysis of the user's past driving history. Some or all of the above processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's past driving history into a generating AI and have the generating AI select the optimal suggestion method.

[0121] The tourism suggestion department can provide optimal suggestions by referring to the user's past visit history. For example, the tourism suggestion department can make optimal suggestions based on the tourist spots the user has visited in the past. The tourism suggestion department can also suggest relevant tourist spots based on the user's past visit history. For example, the tourism suggestion department can analyze the user's past visit history and suggest the tourist spots that are most interesting to the user. This makes it possible to suggest more relevant tourist spots by referring to the user's past visit history. Some or all of the above processing in the tourism suggestion department may be performed using AI, for example, or not using AI. For example, the tourism suggestion department can input the user's past visit history into a generating AI and have the generating AI perform the task of providing optimal suggestions.

[0122] The support unit can select the optimal response method in the event of an emergency by referring to the user's past emergency response history. For example, the support unit can propose the optimal response method based on the emergency response methods the user has used in the past. The support unit can also propose relevant response methods from the user's past emergency response history. For example, the support unit can analyze the user's past emergency response history and propose the most effective response method. In this way, by referring to the user's past emergency response history, a more appropriate response method can be provided. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past emergency response history into a generating AI and have the generating AI select the optimal response method.

[0123] The data collection unit can filter the collected driving history based on the user's current driving status and areas of interest. For example, if the user is driving on a highway, the data collection unit will prioritize collecting specific data. The data collection unit can also collect data related to tourist spots if the user is visiting a tourist destination. For example, if the user is commuting, the data collection unit will collect data related to their commute route. By filtering the data based on the user's current driving status and areas of interest, more relevant data can be collected. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current driving status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0124] The analysis unit can apply different analysis algorithms depending on the category of the driving history during analysis. For example, the analysis unit can apply a commuting-specific analysis algorithm to driving history on commuting routes. It can also apply a tourism-specific analysis algorithm to driving history on sightseeing routes. For example, the analysis unit can apply a highway-specific analysis algorithm to driving history on highways. By applying different analysis algorithms depending on the category of the driving history, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of the driving history into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The data collection unit collects the user's driving history. This history includes, for example, distance traveled, speed, route, and stopping time. The data collection unit also collects data on cafes the user has visited in the past and scenic routes. It can also collect data such as driving time, speed changes, and braking frequency. Step 2: The analysis unit analyzes the driving history collected by the data collection unit to understand the user's preferences and driving patterns. For example, it analyzes data on cafes the user has visited in the past to identify the user's preferences. It also analyzes data on driving time and speed changes to identify the user's driving patterns. Step 3: The suggestion unit proposes the optimal route based on the analysis results obtained by the analysis unit. For example, it prioritizes routes that pass by cafes the user frequently visits or routes with scenic views. It also proposes the optimal route based on data on the user's driving time and speed changes. Step 4: The Tourism Recommendation Department suggests tourist spots and restaurants based on the travel destination and route. For example, when a user sets a travel destination, it collects information on tourist spots and restaurants along that route and suggests them in real time. The Tourism Recommendation Department provides real-time information using generative AI and MR technology. Step 5: The support department assists with contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies. For example, in the event of an accident, they search for and provide the contact information of the nearest hospital, police, and JAF. They also provide route guidance and first aid advice.

[0127] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0128] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0129] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, tourism proposal unit, and support unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's driving history using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected driving history. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal route based on the analysis results. The tourism proposal unit is implemented, for example, by the control unit 46A of the smart device 14 and proposes tourist spots and restaurants based on the travel destination and route. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and assists in contacting hospitals, police, and JAF in emergencies. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, tourism proposal unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's driving history using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected driving history. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal route based on the analysis results. The tourism proposal unit is implemented, for example, by the control unit 46A of the smart glasses 214 and proposes tourist spots and restaurants based on the travel destination and route. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and assists in contacting hospitals, police, and JAF in emergencies. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, tourism proposal unit, and support unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's driving history using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected driving history. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal route based on the analysis results. The tourism proposal unit is implemented, for example, by the control unit 46A of the headset terminal 314 and proposes tourist spots and restaurants based on the travel destination and route. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and assists in contacting hospitals, police, and JAF in emergencies. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] As shown in Figure 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.

[0165] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0166] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0167] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0169] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0170] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0171] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0172] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0173] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0174] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0175] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0176] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0177] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0178] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0179] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, tourism proposal unit, and support unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's driving history using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected driving history. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal route based on the analysis results. The tourism proposal unit is implemented, for example, by the control unit 46A of the robot 414 and proposes tourist spots and restaurants based on the travel destination and route. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and assists in contacting hospitals, police, and JAF in emergencies. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0180] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0182] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0183] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0184] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0187] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0188] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0189] 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.

[0190] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0191] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0192] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0193] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0195] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0196] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0197] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0198] (Note 1) A collection unit that collects the user's driving history, An analysis unit analyzes the driving history collected by the aforementioned collection unit to understand the user's preferences and driving patterns, A proposal unit proposes the optimal route based on the analysis results obtained by the analysis unit, The Tourism Proposal Department suggests tourist spots and restaurants based on travel destinations and routes, It includes a support department that assists in contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects data on cafes and scenic routes that users have visited in the past. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The data collected by the aforementioned collection unit is analyzed to understand the user's preferences and driving patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results obtained by the aforementioned analysis unit, the system prioritizes guiding users along routes that pass by cafes they frequently visit or routes with scenic views. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned Tourism Proposal Department Based on your travel destination and route, we suggest sightseeing spots and restaurants you should stop at along the way. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned Tourism Proposal Department We will provide real-time information using generative AI and MR technology. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned support unit, In the event of an accident, the system searches for and provides users with contact information for the nearest hospital, police, and JAF (Japan Automobile Federation). The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned support unit, Provides route guidance and first aid advice. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of driving history collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past driving history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting driving history, filtering is performed based on the user's current driving status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and determines the priority of driving history to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting driving history, the system prioritizes collecting highly relevant history by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting driving history, the system analyzes the user's social media activity and collects relevant history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the driving history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the driving history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the driving history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the driving history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the route. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the route category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the routes were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, adjust the order of the proposals based on the relevance of the routes. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Tourism Proposal Department The system estimates the user's emotions and adjusts how tourist attractions are suggested based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Tourism Proposal Department When suggesting tourist spots, the system provides optimal suggestions by referencing the user's past visit history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Tourism Proposal Department When suggesting tourist spots, customize the suggestion method based on the user's current travel situation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned Tourism Proposal Department It estimates the user's emotions and prioritizes tourist attractions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned Tourism Proposal Department When suggesting tourist spots, we provide optimal suggestions by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned Tourism Proposal Department When suggesting tourist spots, we analyze users' social media activity to propose methods for making suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned support unit, It estimates the user's emotions and adjusts emergency response methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit, In emergency situations, the system selects the most appropriate response method by referring to the user's past emergency response history. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned support unit, In emergency situations, the response method is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned support unit, It estimates the user's emotions and determines the priority of emergency responses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned support unit, In emergency situations, the system selects the most appropriate response method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned support unit, In emergency situations, we analyze users' social media activity and propose appropriate response measures. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects the user's driving history, An analysis unit analyzes the driving history collected by the aforementioned collection unit to understand the user's preferences and driving patterns, A proposal unit proposes the optimal route based on the analysis results obtained by the analysis unit, The Tourism Proposal Department suggests tourist spots and restaurants based on travel destinations and routes, It includes a support department that assists in contacting hospitals, police, and JAF (Japan Automobile Federation) in emergencies. A system characterized by the following features.

2. The aforementioned collection unit is The system collects data on cafes and scenic routes that users have visited in the past. The system according to feature 1.

3. The aforementioned analysis unit, The data collected by the aforementioned collection unit is analyzed to understand the user's preferences and driving patterns. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results obtained by the aforementioned analysis unit, the system prioritizes guiding users along routes that pass by cafes they frequently visit or routes with scenic views. The system according to feature 1.

5. The aforementioned Tourism Proposal Department Based on your travel destination and route, we suggest sightseeing spots and restaurants you should stop at along the way. The system according to feature 1.

6. The aforementioned Tourism Proposal Department We will provide real-time information using generative AI and MR technology. The system according to feature 1.

7. The aforementioned support unit, In the event of an accident, the system searches for and provides users with contact information for the nearest hospital, police, and JAF (Japan Automobile Federation). The system according to feature 1.

8. The aforementioned support unit, Provides route guidance and first aid advice. The system according to feature 1.

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