system

Abstract

The system according to this embodiment aims to generate an optimal route based on the driver's hobbies and preferences, and to recommend tourist spots, restaurants, and accommodations. [Solution] The system according to the embodiment comprises an understanding unit, an acquisition unit, a generation unit, and a provision unit. The understanding unit understands the driver's hobbies and preferences. The acquisition unit acquires real-time data. The generation unit generates an optimal route based on the data acquired by the acquisition unit. The provision unit recommends tourist spots, restaurants, and accommodations based on the route generated by the generation unit.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, recommendations for tourist spots, restaurants, and accommodation facilities based on the hobbies and preferences of drivers have not been sufficiently made, and there is room for improvement.

[0005] The system according to the embodiment aims to generate an optimal route based on the hobbies and preferences of a driver and make recommendations for tourist spots, restaurants, and accommodation facilities.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an understanding unit, an acquisition unit, a generation unit, and a provision unit. The understanding unit understands the driver's hobbies and preferences. The acquisition unit acquires real-time data. The generation unit generates an optimal route based on the data acquired by the acquisition unit. The provision unit recommends tourist spots, restaurants, and accommodations based on the route generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate an optimal route based on the driver's hobbies and preferences, and can recommend tourist spots, restaurants, and accommodations. [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 controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 reception 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 reception 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 travel navigation agent system according to an embodiment of the present invention is a system that uses a generated AI installed in a car to provide recommendations for tourist spots, restaurants, and accommodations, as well as route guidance, based on the driver's hobbies and preferences. The travel navigation agent system understands the driver's hobbies and preferences using natural language processing and generates the optimal route considering real-time traffic and weather information. Furthermore, it recommends tourist spots, restaurants, and accommodations to the driver. For example, if the driver inputs "I want to go somewhere where I can see the ocean," the generated AI analyzes this information and understands the driver's preferences. Next, the travel navigation agent system generates the optimal route considering real-time traffic and weather information. For example, to avoid traffic jams and bad weather, the generated AI analyzes real-time data and proposes the optimal route. Furthermore, based on the driver's preferences, the travel navigation agent system recommends tourist spots with ocean views, seafood restaurants, and accommodations along the coast. Through this mechanism, the travel navigation agent system maximizes the enjoyment of the drive and improves driver satisfaction. For example, if the driver visits a recommended tourist spot and enjoys a wonderful view, their satisfaction with the drive will increase. Furthermore, the travel navigation agent system can contribute to reducing traffic accidents by suggesting optimal routes that take real-time traffic and weather information into account. This allows the travel navigation agent system to provide recommendations for tourist spots, restaurants, and accommodations, along with route guidance, based on the driver's interests and preferences.

[0029] The travel navigation agent system according to this embodiment comprises an understanding unit, an acquisition unit, a generation unit, and a provision unit. The understanding unit understands the driver's hobbies and preferences. The understanding unit analyzes the driver's hobbies and preferences using, for example, natural language processing. If the driver inputs "I want to go somewhere where I can see the sea," the understanding unit can analyze that information and understand the driver's preferences. The acquisition unit acquires real-time data. The acquisition unit acquires, for example, traffic information and weather information in real time. The acquisition unit can acquire information on traffic congestion and bad weather and provide data for generating the optimal route. The generation unit generates the optimal route based on the data acquired by the acquisition unit. The generation unit analyzes real-time data to avoid traffic congestion and bad weather and proposes the optimal route. Based on the driver's preferences, the generation unit can generate a route that includes tourist spots with ocean views, seafood restaurants, and seaside accommodations. The provision unit recommends tourist spots, restaurants, and accommodations based on the route generated by the generation unit. Based on the driver's preferences, the provision unit recommends tourist spots with ocean views, seafood restaurants, and seaside accommodations. The provisioning unit can provide drivers with information on recommended tourist spots, restaurants, and accommodations. This allows the travel navigation agent system according to the embodiment to provide recommendations for tourist spots, restaurants, and accommodations, as well as route guidance, based on the driver's hobbies and preferences. Some or all of the above-described processes in the understanding unit, acquisition unit, generation unit, and provisioning unit may be performed using, for example, AI, or without AI. For example, the understanding unit can use natural language processing to understand the driver's hobbies and preferences. The acquisition unit can use traffic information services and weather information services to acquire real-time data. The generation unit can use an AI model that analyzes real-time data to generate the optimal route. The provisioning unit can use an AI model that provides information based on the driver's preferences to recommend tourist spots, restaurants, and accommodations.

[0030] The understanding unit uses natural language processing technology to analyze the driver's input in order to understand the driver's hobbies and preferences. For example, if a driver inputs "I want to go somewhere where I can see the ocean," the understanding unit analyzes the text and understands that the driver likes the ocean. Specifically, it uses natural language processing technology to extract keywords from the input text and uses those keywords to identify the driver's hobbies and preferences. Furthermore, the understanding unit can learn from past input history and the driver's behavior patterns to understand hobbies and preferences with greater accuracy. For example, it accumulates data on places the driver has visited and routes they have selected in the past, and uses this data to predict the driver's preferences. As a result, the understanding unit can accurately grasp the driver's hobbies and preferences and make optimal suggestions for each individual driver.

[0031] The data acquisition unit utilizes traffic information and weather information services to obtain real-time data. Specifically, it acquires traffic congestion and accident information from traffic information services and weather information, temperature, and precipitation data from weather information services. This data is updated in real time and continuously collected by the data acquisition unit. Furthermore, the data acquisition unit also acquires information about the driver's current location and destination, and uses this as basic data to generate the optimal route. For example, the data acquisition unit uses GPS data to determine the driver's current location and calculates the distance and travel time to the destination. The data acquisition unit also acquires information such as the driver's vehicle status and fuel level, and can propose the optimal route based on this information. In this way, the data acquisition unit can collect diverse data that is updated in real time, improving the accuracy and reliability of the entire system.

[0032] The generation unit generates the optimal route based on the driver's preferences and current conditions, using real-time data acquired by the acquisition unit. Specifically, the generation unit analyzes real-time data to avoid traffic congestion and bad weather and proposes the best route. For example, based on traffic congestion information, the generation unit calculates detour routes to avoid congestion and based on weather information, it proposes routes to avoid bad weather. The generation unit also generates routes that include tourist spots, restaurants, and accommodations based on the driver's preferences. For example, if the driver prefers places with ocean views, the generation unit proposes a route that includes tourist spots with ocean views, seafood restaurants, and accommodations along the coast. Furthermore, the generation unit uses an AI model to analyze the acquired data and generate the optimal route. The AI ​​model predicts the optimal route based on past data and statistical information and proposes it to the driver. In this way, the generation unit can generate the optimal route based on the driver's preferences and current conditions, supporting a comfortable trip.

[0033] The service provider recommends tourist attractions, restaurants, and accommodations to drivers based on routes generated by the route generation unit. Specifically, the service provider recommends tourist attractions with ocean views, seafood restaurants, and seaside accommodations based on the driver's preferences. For example, the service provider provides information on the most suitable tourist attractions, restaurants, and accommodations based on the driver's current location and destination. The service provider provides drivers with detailed information on the recommended tourist attractions, restaurants, and accommodations to support their selection. For example, the service provider provides photos and reviews of tourist attractions, restaurant menus and opening hours, and accommodation rates and facilities information. Furthermore, the service provider can collect driver feedback to continuously improve the accuracy and effectiveness of recommendations. For example, it collects evaluations and impressions from drivers after they visit the recommended tourist attractions, restaurants, and accommodations, and revises the recommendations based on this data. In addition, the service provider can reliably transmit information using multiple communication methods. For example, it uses not only smartphone notifications but also voice calls, SMS, and email to ensure that important information is delivered reliably. This allows the service provider to provide information to drivers quickly and reliably, supporting a comfortable trip.

[0034] The travel navigation agent system according to the embodiment further comprises a learning unit that performs continuous data updates and learning. The learning unit performs continuous data updates and learning. For example, the learning unit learns to improve the accuracy of the system based on data acquired in real time. The learning unit can continuously update data and learn in order to respond to changes in the driver's hobbies and preferences. For example, the learning unit analyzes the driver's past travel history and real-time statements to grasp changes in hobbies and preferences. The learning unit can continuously update data and learn in order to provide recommendations based on the driver's hobbies and preferences. As a result, the accuracy of the system is improved through continuous data updates and learning. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can use a machine learning algorithm to perform continuous data updates and learning. The learning unit can learn to improve the accuracy of the system based on data acquired in real time. The learning unit can continuously update data and learn in order to respond to changes in the driver's hobbies and preferences.

[0035] The travel navigation agent system according to this embodiment further comprises an analysis unit that performs real-time data analysis. The analysis unit performs real-time data analysis. For example, the analysis unit analyzes real-time traffic information and weather information and provides data for generating the optimal route. By analyzing real-time data, the analysis unit can provide more accurate information. For example, the analysis unit analyzes information on traffic congestion and bad weather and proposes the optimal route. By analyzing real-time data, the analysis unit can provide more accurate information to the driver. This makes it possible to provide more accurate information through the analysis of real-time data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can use a data analysis algorithm to perform real-time data analysis. The analysis unit can analyze real-time traffic information and weather information and provide data for generating the optimal route. By analyzing real-time data, the analysis unit can provide more accurate information.

[0036] The understanding unit uses natural language processing to understand the driver's hobbies and preferences. For example, if the driver inputs "I want to go somewhere where I can see the ocean," the understanding unit analyzes that information and understands the driver's preferences. By using natural language processing, the understanding unit can accurately understand the driver's hobbies and preferences. Some or all of the processing described above in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can use techniques such as morphological analysis, grammatical analysis, and semantic analysis to understand the driver's hobbies and preferences using natural language processing. This allows the understanding unit to accurately understand the driver's hobbies and preferences by using natural language processing.

[0037] The acquisition unit acquires real-time traffic and weather information. The acquisition unit acquires real-time traffic and weather information, for example, by using traffic information provision services and weather information provision services. By acquiring real-time traffic and weather information, the acquisition unit can provide the optimal route. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can use traffic sensors and weather sensors to acquire real-time traffic and weather information. This allows the acquisition unit to provide the optimal route by acquiring real-time traffic and weather information.

[0038] The generation unit generates the optimal route based on the data acquired by the acquisition unit. For example, the generation unit analyzes real-time data to avoid traffic congestion or bad weather and proposes the optimal route. By generating the optimal route based on the acquired data, the generation unit can provide the driver with the best possible route guidance. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can use an AI model to analyze real-time data in order to generate the optimal route. This allows the generation unit to provide the driver with the best possible route guidance by generating the optimal route based on the acquired data.

[0039] The service provider recommends tourist attractions, restaurants, and accommodations based on the route generated by the generation unit. For example, the service provider recommends tourist attractions with ocean views, seafood restaurants, and seaside accommodations based on the driver's preferences. By making recommendations based on the generated route, the service provider can improve driver satisfaction. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use an AI model that provides information based on the driver's preferences to recommend tourist attractions, restaurants, and accommodations. This can improve driver satisfaction by making recommendations based on the generated route.

[0040] The understanding unit analyzes the driver's past travel history to grasp changes in their hobbies and preferences. For example, the understanding unit can analyze the history of tourist spots the driver has visited in the past to grasp changes in their hobbies and preferences. The understanding unit can analyze the history of restaurants the driver has used in the past to grasp changes in their food preferences. The understanding unit can analyze the history of accommodations the driver has stayed at in the past to grasp changes in their accommodation preferences. By analyzing past travel history, the understanding unit can grasp changes in hobbies and preferences and make more appropriate recommendations. Some or all of the above processing in the understanding unit may be performed using AI, for example, or not using AI. For example, the understanding unit can use a data analysis algorithm to analyze the driver's past travel history. By analyzing past travel history, the understanding unit can grasp changes in hobbies and preferences and make more appropriate recommendations.

[0041] The understanding unit analyzes the driver's real-time statements and actions and immediately reflects their hobbies and preferences. For example, if the driver says, "I want to go somewhere where I can see the ocean," the understanding unit analyzes that information in real time and immediately reflects it in their hobbies and preferences. If the driver plays a specific piece of music, the understanding unit can analyze the genre of that music and immediately reflect it in their hobbies and preferences. If the driver searches for a specific restaurant, the understanding unit can analyze that information and immediately reflect it in their food preferences. In this way, by analyzing real-time statements and actions, hobbies and preferences can be reflected immediately. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can use technologies such as speech recognition and behavior tracking to analyze the driver's real-time statements and actions. The understanding unit can immediately reflect hobbies and preferences by analyzing real-time statements and actions.

[0042] The understanding unit analyzes the driver's social media activity and reflects this understanding in their hobbies and preferences. For example, the understanding unit can analyze photos the driver shares on social media and reflect this understanding in their hobbies and preferences. The understanding unit can analyze accounts the driver follows on social media and reflect this understanding in their hobbies and preferences. The understanding unit can analyze posts the driver "likes" on social media and reflect this understanding in their hobbies and preferences. In this way, by analyzing social media activity, it is possible to reflect this understanding in their hobbies and preferences. Some or all of the above processing in the understanding unit may be performed using AI, for example, or not using AI. For example, the understanding unit can use a data analysis algorithm to analyze the driver's social media activity. By analyzing social media activity, the understanding unit can reflect this understanding in their hobbies and preferences.

[0043] The understanding unit takes into account the hobbies and preferences of the driver's family and friends to accommodate group travel. For example, the understanding unit can consider the tourist spots that the driver's family likes and suggest the best route for group travel. The understanding unit can consider the restaurants that the driver's friends like and suggest the best restaurants for group travel. The understanding unit can consider the accommodations that the driver's family and friends would like to stay at and suggest the best accommodations for group travel. In this way, by taking into account the hobbies and preferences of family and friends, it can accommodate group travel. Some or all of the above processing in the understanding unit may be performed using AI, for example, or not using AI. For example, the understanding unit can analyze survey results and past travel history to take into account the hobbies and preferences of the driver's family and friends. By taking into account the hobbies and preferences of family and friends, the understanding unit can accommodate group travel.

[0044] The acquisition unit improves the accuracy of data acquisition by referring to past traffic and weather information. The acquisition unit can improve the accuracy of real-time data acquisition by referring to past traffic congestion information, for example. The acquisition unit can improve the accuracy of real-time data acquisition by referring to past weather information. The acquisition unit can improve the accuracy of real-time data acquisition by referring to past traffic accident information. In this way, the accuracy of data acquisition can be improved by referring to past information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can use a data analysis algorithm to refer to past traffic and weather information. The acquisition unit can improve the accuracy of data acquisition by referring to past information.

[0045] The data acquisition unit acquires optimal data by considering the driver's current location information when acquiring data. For example, the data acquisition unit can acquire optimal traffic information based on the driver's current location. The data acquisition unit can acquire optimal weather information based on the driver's current location. The data acquisition unit can acquire optimal tourist spot information based on the driver's current location. In this way, optimal data can be acquired by considering the current location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without using AI. For example, the data acquisition unit can use GPS data or location information services to acquire optimal data by considering the driver's current location information when acquiring data. The data acquisition unit can acquire optimal data by considering the current location information.

[0046] The acquisition unit acquires information about the driver's vehicle status and incorporates it into the travel plan. For example, the acquisition unit can acquire the vehicle's fuel level and suggest the optimal refueling point. The acquisition unit can acquire the vehicle's tire status and suggest a safe route. The acquisition unit can acquire the vehicle's engine status and suggest the optimal route if maintenance is required. As a result, acquiring vehicle status information enables safer and more efficient travel planning. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can use vehicle sensors or a vehicle diagnostic system to acquire information about the driver's vehicle status. By acquiring vehicle status information, the acquisition unit enables safer and more efficient travel planning.

[0047] The data acquisition unit refers to the driver's past travel history and prioritizes the acquisition of relevant data. For example, the data acquisition unit may prioritize the acquisition of information on tourist spots the driver has visited in the past. The data acquisition unit may prioritize the acquisition of information on restaurants the driver has used in the past. The data acquisition unit may prioritize the acquisition of information on accommodations the driver has stayed at in the past. In this way, relevant data can be prioritized by referring to past travel history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without using AI. For example, the data acquisition unit may use a data analysis algorithm to refer to the driver's past travel history. The data acquisition unit can prioritize the acquisition of relevant data by referring to past travel history.

[0048] The generation unit optimizes the route generation algorithm by referring to past route generation history. For example, the generation unit constructs an optimal route generation algorithm by referring to the history of previously generated routes. The generation unit can prioritize generating routes that match the driver's preferences from past route generation history. The generation unit can improve the accuracy of the route generation algorithm by analyzing past route generation history. This allows the route generation algorithm to be optimized by referring to past route generation history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can use a data analysis algorithm to refer to past route generation history. The generation unit can optimize the route generation algorithm by referring to past route generation history.

[0049] The generation unit collects driver feedback on the generated routes to improve the accuracy of route generation. For example, the generation unit can collect feedback from drivers who evaluate the generated routes. The generation unit can collect feedback from drivers who leave comments on the generated routes. The generation unit can collect feedback from drivers who point out areas for improvement on the generated routes. In this way, the accuracy of route generation can be improved by collecting driver feedback. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may use survey results or evaluation comments to collect driver feedback. The generation unit can improve the accuracy of route generation by collecting driver feedback.

[0050] The generation unit analyzes the driver's real-time response to the generated route and dynamically adjusts the route. For example, if the driver expresses dissatisfaction with the generated route, the generation unit will regenerate the route in real time. If the driver is satisfied with the generated route, the generation unit can maintain the route as is. If the driver wishes to change the generated route, the generation unit can adjust the route in real time. In this way, the route can be dynamically adjusted by analyzing the driver's real-time response. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can use technologies such as speech recognition and behavior tracking to analyze the driver's real-time response. The generation unit can dynamically adjust the route by analyzing the real-time response.

[0051] The generation unit references the driver's past evaluations of generated routes and reflects them in route generation. For example, the generation unit can prioritize generating routes that the driver has previously given high ratings to. The generation unit can also avoid generating routes that the driver has previously given low ratings to. The generation unit can reference the history of routes that the driver has previously evaluated and generate the optimal route. This allows the generation unit to reflect the driver's past evaluations in route generation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can use evaluation comments or evaluation scores to reference the driver's past evaluations. The generation unit can reflect the driver's past evaluations in route generation.

[0052] The service provider optimizes the recommendation algorithm by referring to past recommendation history. For example, the service provider can optimize the recommendation algorithm by referring to the history of previously recommended tourist spots. The service provider can optimize the recommendation algorithm by referring to the history of previously recommended restaurants. The service provider can optimize the recommendation algorithm by referring to the history of previously recommended accommodations. In this way, the recommendation algorithm can be optimized by referring to past recommendation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can use a data analysis algorithm to refer to past recommendation history. The service provider can optimize the recommendation algorithm by referring to past recommendation history.

[0053] The service provider collects driver feedback on recommendations to improve the accuracy of those recommendations. For example, the service provider can collect feedback from drivers who rate recommended tourist spots. The service provider can collect feedback from drivers who leave comments on recommended restaurants. The service provider can collect feedback from drivers who point out areas for improvement on recommended accommodations. By collecting driver feedback, the accuracy of recommendations can be improved. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can use survey results and evaluation comments to collect driver feedback. By collecting driver feedback, the service provider can improve the accuracy of recommendations.

[0054] The service provider analyzes the driver's real-time response to recommendations and dynamically adjusts them. For example, if the driver expresses dissatisfaction with a recommended tourist spot, the service provider will recommend an alternative tourist spot in real time. If the driver is satisfied with a recommended restaurant, the service provider can maintain that recommendation. If the driver wishes to change their recommended accommodation, the service provider can recommend an alternative accommodation in real time. In this way, recommendations can be dynamically adjusted by analyzing the driver's real-time response. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use technologies such as speech recognition and behavior tracking to analyze the driver's real-time response. The service provider can dynamically adjust recommendations by analyzing real-time responses.

[0055] The recommendation system references drivers' past ratings of recommended items and incorporates them into its recommendations. For example, the system may prioritize recommending tourist spots that drivers have previously given high ratings to. The system may also avoid recommending restaurants that drivers have previously given low ratings to. The system may also reference the history of accommodations that drivers have previously rated and recommend the most suitable accommodations. This allows the system to incorporate drivers' past ratings into its recommendations. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the system may use rating comments or rating scores to reference drivers' past ratings. The system can incorporate drivers' past ratings into its recommendations.

[0056] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can construct an optimal learning algorithm by referring to past learning data. The learning unit can construct a learning algorithm that suits the driver's preferences from past learning data. The learning unit can analyze past learning data to improve the accuracy of the learning algorithm. This allows the learning algorithm to be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can use a data analysis algorithm to refer to past learning data. The learning unit can optimize the learning algorithm by referring to past learning data.

[0057] The learning unit weights the training data based on the timing of real-time data acquisition during training. For example, the learning unit can weight the latest data based on the timing of real-time data acquisition and perform training. The learning unit can also weight past data based on the timing of real-time data acquisition and perform training. The learning unit can weight specific data based on the timing of real-time data acquisition and perform training. This allows for more appropriate training by weighting the training data based on the timing of real-time data acquisition. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can use a data analysis algorithm to weight the training data based on the timing of real-time data acquisition. The learning unit can weight the training data based on the timing of real-time data acquisition.

[0058] The analysis unit optimizes the analysis algorithm by referring to past analysis history. For example, the analysis unit constructs an optimal analysis algorithm by referring to past analysis history. The analysis unit can construct an analysis algorithm that matches the driver's preferences from past analysis history. The analysis unit can improve the accuracy of the analysis algorithm by analyzing past analysis history. This allows the analysis algorithm to be optimized by referring to past analysis history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use a data analysis algorithm to refer to past analysis history. The analysis unit can optimize the analysis algorithm by referring to past analysis history.

[0059] The analysis unit collects driver feedback on the analysis results to improve the accuracy of the analysis. For example, the analysis unit can collect feedback from drivers who evaluate the analysis results. The analysis unit can collect feedback from drivers who leave comments on the analysis results. The analysis unit can collect feedback from drivers who point out areas for improvement in the analysis results. In this way, the accuracy of the analysis can be improved by collecting driver feedback. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use survey results or evaluation comments to collect driver feedback. The analysis unit can improve the accuracy of the analysis by collecting driver feedback.

[0060] The analysis unit analyzes the driver's real-time response to the analysis results and dynamically adjusts the analysis method. For example, if the driver expresses dissatisfaction with the analysis results, the analysis unit readjusts the analysis method in real time. If the driver is satisfied with the analysis results, the analysis unit can maintain the same analysis method. If the driver wishes to change the analysis results, the analysis unit can adjust the analysis method in real time. In this way, the analysis method can be dynamically adjusted by analyzing the driver's real-time response. 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 use technologies such as speech recognition and behavior tracking to analyze the driver's real-time response. The analysis unit can dynamically adjust the analysis method by analyzing the real-time response.

[0061] The analysis unit refers to the driver's past evaluations of the analysis results and incorporates them into the analysis. For example, the analysis unit prioritizes providing analysis results that the driver has previously given high ratings to. The analysis unit can avoid providing analysis results that the driver has previously given low ratings to. The analysis unit can refer to the history of analysis results that the driver has previously evaluated and provide the optimal analysis results. This allows the analysis to be influenced by the driver's past evaluations. 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 use evaluation comments or evaluation scores to refer to the driver's past evaluations. The analysis unit can influence the analysis by referring to the driver's past evaluations.

[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 driver understanding unit can also analyze the driver's music playlists to understand their hobbies and preferences. For example, if the driver frequently plays a particular genre of music, it can recommend tourist attractions and events related to that genre. Similarly, if the driver has attended concerts by a particular artist, it can recommend related events and exhibitions by that artist. Furthermore, it can analyze playlists created by the driver on music streaming services and suggest travel plans based on the playlist's theme. This enables more personalized recommendations that take the driver's musical tastes into account.

[0064] The learning function can analyze a driver's driving style and incorporate that understanding into their hobbies and preferences. For example, if a driver prefers using highways, it can recommend tourist spots and service areas along the highway. Similarly, if a driver prefers driving on mountain roads or country roads, it can recommend nature-rich tourist spots and outdoor activities. Furthermore, if a driver tends to take frequent breaks, it can recommend cafes and parks suitable for resting. This allows for more appropriate recommendations based on the driver's driving style.

[0065] The analysis unit can analyze the driver's vehicle condition information and incorporate it into the travel plan. For example, if the vehicle's fuel level is low, it can suggest the optimal refueling point. If the vehicle's tires are in poor condition, it can suggest a safer route. Furthermore, if the vehicle's engine is malfunctioning, it can suggest the optimal route for maintenance. This enables safer and more efficient travel planning that takes vehicle condition information into account.

[0066] The understanding unit can analyze a driver's social media activity and reflect this in understanding their hobbies and preferences. For example, it can analyze photos a driver shares on social media and reflect this in understanding their hobbies and preferences. It can also analyze accounts a driver follows on social media and reflect this in understanding their hobbies and preferences. Furthermore, it can analyze posts a driver "likes" on social media and reflect this in understanding their hobbies and preferences. In this way, by analyzing social media activity, it is possible to reflect this in understanding a driver's hobbies and preferences.

[0067] The data acquisition unit can refer to the driver's past travel history and prioritize the acquisition of relevant data. For example, it can prioritize the acquisition of information on tourist spots the driver has visited in the past. It can also prioritize the acquisition of information on restaurants the driver has used in the past. Furthermore, it can prioritize the acquisition of information on accommodations the driver has stayed at in the past. In this way, by referring to past travel history, relevant data can be prioritized.

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

[0069] Step 1: The understanding unit understands the driver's hobbies and preferences. For example, it uses natural language processing to analyze the driver's hobbies and preferences. If the driver inputs "I want to go somewhere where I can see the ocean," the unit analyzes that information to understand the driver's preferences. Step 2: The acquisition unit acquires real-time data. For example, it acquires traffic information and weather information in real time, and provides data to generate the optimal route by acquiring information on traffic congestion and bad weather. Step 3: The generation unit generates the optimal route based on the data acquired by the acquisition unit. For example, it analyzes real-time data to avoid traffic congestion and bad weather, and generates a route that includes tourist spots with ocean views, seafood restaurants, and seaside accommodations based on the driver's preferences. Step 4: The provisioning unit recommends tourist spots, restaurants, and accommodations based on the route generated by the generation unit. For example, it recommends tourist spots with ocean views, seafood restaurants, and seaside accommodations based on the driver's preferences, and provides the driver with information on the recommended tourist spots, restaurants, and accommodations.

[0070] (Example of form 2) The travel navigation agent system according to an embodiment of the present invention is a system that uses a generated AI installed in a car to provide recommendations for tourist spots, restaurants, and accommodations, as well as route guidance, based on the driver's hobbies and preferences. The travel navigation agent system understands the driver's hobbies and preferences using natural language processing and generates the optimal route considering real-time traffic and weather information. Furthermore, it recommends tourist spots, restaurants, and accommodations to the driver. For example, if the driver inputs "I want to go somewhere where I can see the ocean," the generated AI analyzes this information and understands the driver's preferences. Next, the travel navigation agent system generates the optimal route considering real-time traffic and weather information. For example, to avoid traffic jams and bad weather, the generated AI analyzes real-time data and proposes the optimal route. Furthermore, based on the driver's preferences, the travel navigation agent system recommends tourist spots with ocean views, seafood restaurants, and accommodations along the coast. Through this mechanism, the travel navigation agent system maximizes the enjoyment of the drive and improves driver satisfaction. For example, if the driver visits a recommended tourist spot and enjoys a wonderful view, their satisfaction with the drive will increase. Furthermore, the travel navigation agent system can contribute to reducing traffic accidents by suggesting optimal routes that take real-time traffic and weather information into account. This allows the travel navigation agent system to provide recommendations for tourist spots, restaurants, and accommodations, along with route guidance, based on the driver's interests and preferences.

[0071] The travel navigation agent system according to this embodiment comprises an understanding unit, an acquisition unit, a generation unit, and a provision unit. The understanding unit understands the driver's hobbies and preferences. The understanding unit analyzes the driver's hobbies and preferences using, for example, natural language processing. If the driver inputs "I want to go somewhere where I can see the sea," the understanding unit can analyze that information and understand the driver's preferences. The acquisition unit acquires real-time data. The acquisition unit acquires, for example, traffic information and weather information in real time. The acquisition unit can acquire information on traffic congestion and bad weather and provide data for generating the optimal route. The generation unit generates the optimal route based on the data acquired by the acquisition unit. The generation unit analyzes real-time data to avoid traffic congestion and bad weather and proposes the optimal route. Based on the driver's preferences, the generation unit can generate a route that includes tourist spots with ocean views, seafood restaurants, and seaside accommodations. The provision unit recommends tourist spots, restaurants, and accommodations based on the route generated by the generation unit. Based on the driver's preferences, the provision unit recommends tourist spots with ocean views, seafood restaurants, and seaside accommodations. The provisioning unit can provide drivers with information on recommended tourist spots, restaurants, and accommodations. This allows the travel navigation agent system according to the embodiment to provide recommendations for tourist spots, restaurants, and accommodations, as well as route guidance, based on the driver's hobbies and preferences. Some or all of the above-described processes in the understanding unit, acquisition unit, generation unit, and provisioning unit may be performed using, for example, AI, or without AI. For example, the understanding unit can use natural language processing to understand the driver's hobbies and preferences. The acquisition unit can use traffic information services and weather information services to acquire real-time data. The generation unit can use an AI model that analyzes real-time data to generate the optimal route. The provisioning unit can use an AI model that provides information based on the driver's preferences to recommend tourist spots, restaurants, and accommodations.

[0072] The understanding unit uses natural language processing technology to analyze the driver's input in order to understand the driver's hobbies and preferences. For example, if a driver inputs "I want to go somewhere where I can see the ocean," the understanding unit analyzes the text and understands that the driver likes the ocean. Specifically, it uses natural language processing technology to extract keywords from the input text and uses those keywords to identify the driver's hobbies and preferences. Furthermore, the understanding unit can learn from past input history and the driver's behavior patterns to understand hobbies and preferences with greater accuracy. For example, it accumulates data on places the driver has visited and routes they have selected in the past, and uses this data to predict the driver's preferences. As a result, the understanding unit can accurately grasp the driver's hobbies and preferences and make optimal suggestions for each individual driver.

[0073] The data acquisition unit utilizes traffic information and weather information services to obtain real-time data. Specifically, it acquires traffic congestion and accident information from traffic information services and weather information, temperature, and precipitation data from weather information services. This data is updated in real time and continuously collected by the data acquisition unit. Furthermore, the data acquisition unit also acquires information about the driver's current location and destination, and uses this as basic data to generate the optimal route. For example, the data acquisition unit uses GPS data to determine the driver's current location and calculates the distance and travel time to the destination. The data acquisition unit also acquires information such as the driver's vehicle status and fuel level, and can propose the optimal route based on this information. In this way, the data acquisition unit can collect diverse data that is updated in real time, improving the accuracy and reliability of the entire system.

[0074] The generation unit generates the optimal route based on the driver's preferences and current conditions, using real-time data acquired by the acquisition unit. Specifically, the generation unit analyzes real-time data to avoid traffic congestion and bad weather and proposes the best route. For example, based on traffic congestion information, the generation unit calculates detour routes to avoid congestion and based on weather information, it proposes routes to avoid bad weather. The generation unit also generates routes that include tourist spots, restaurants, and accommodations based on the driver's preferences. For example, if the driver prefers places with ocean views, the generation unit proposes a route that includes tourist spots with ocean views, seafood restaurants, and accommodations along the coast. Furthermore, the generation unit uses an AI model to analyze the acquired data and generate the optimal route. The AI ​​model predicts the optimal route based on past data and statistical information and proposes it to the driver. In this way, the generation unit can generate the optimal route based on the driver's preferences and current conditions, supporting a comfortable trip.

[0075] The service provider recommends tourist attractions, restaurants, and accommodations to drivers based on routes generated by the route generation unit. Specifically, the service provider recommends tourist attractions with ocean views, seafood restaurants, and seaside accommodations based on the driver's preferences. For example, the service provider provides information on the most suitable tourist attractions, restaurants, and accommodations based on the driver's current location and destination. The service provider provides drivers with detailed information on the recommended tourist attractions, restaurants, and accommodations to support their selection. For example, the service provider provides photos and reviews of tourist attractions, restaurant menus and opening hours, and accommodation rates and facilities information. Furthermore, the service provider can collect driver feedback to continuously improve the accuracy and effectiveness of recommendations. For example, it collects evaluations and impressions from drivers after they visit the recommended tourist attractions, restaurants, and accommodations, and revises the recommendations based on this data. In addition, the service provider can reliably transmit information using multiple communication methods. For example, it uses not only smartphone notifications but also voice calls, SMS, and email to ensure that important information is delivered reliably. This allows the service provider to provide information to drivers quickly and reliably, supporting a comfortable trip.

[0076] The travel navigation agent system according to the embodiment further comprises a learning unit that performs continuous data updates and learning. The learning unit performs continuous data updates and learning. For example, the learning unit learns to improve the accuracy of the system based on data acquired in real time. The learning unit can continuously update data and learn in order to respond to changes in the driver's hobbies and preferences. For example, the learning unit analyzes the driver's past travel history and real-time statements to grasp changes in hobbies and preferences. The learning unit can continuously update data and learn in order to provide recommendations based on the driver's hobbies and preferences. As a result, the accuracy of the system is improved through continuous data updates and learning. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can use a machine learning algorithm to perform continuous data updates and learning. The learning unit can learn to improve the accuracy of the system based on data acquired in real time. The learning unit can continuously update data and learn in order to respond to changes in the driver's hobbies and preferences.

[0077] The travel navigation agent system according to this embodiment further comprises an analysis unit that performs real-time data analysis. The analysis unit performs real-time data analysis. For example, the analysis unit analyzes real-time traffic information and weather information and provides data for generating the optimal route. By analyzing real-time data, the analysis unit can provide more accurate information. For example, the analysis unit analyzes information on traffic congestion and bad weather and proposes the optimal route. By analyzing real-time data, the analysis unit can provide more accurate information to the driver. This makes it possible to provide more accurate information through the analysis of real-time data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can use a data analysis algorithm to perform real-time data analysis. The analysis unit can analyze real-time traffic information and weather information and provide data for generating the optimal route. By analyzing real-time data, the analysis unit can provide more accurate information.

[0078] The understanding unit uses natural language processing to understand the driver's hobbies and preferences. For example, if the driver inputs "I want to go somewhere where I can see the ocean," the understanding unit analyzes that information and understands the driver's preferences. By using natural language processing, the understanding unit can accurately understand the driver's hobbies and preferences. Some or all of the processing described above in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can use techniques such as morphological analysis, grammatical analysis, and semantic analysis to understand the driver's hobbies and preferences using natural language processing. This allows the understanding unit to accurately understand the driver's hobbies and preferences by using natural language processing.

[0079] The acquisition unit acquires real-time traffic and weather information. The acquisition unit acquires real-time traffic and weather information, for example, by using traffic information provision services and weather information provision services. By acquiring real-time traffic and weather information, the acquisition unit can provide the optimal route. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can use traffic sensors and weather sensors to acquire real-time traffic and weather information. This allows the acquisition unit to provide the optimal route by acquiring real-time traffic and weather information.

[0080] The generation unit generates the optimal route based on the data acquired by the acquisition unit. For example, the generation unit analyzes real-time data to avoid traffic congestion or bad weather and proposes the optimal route. By generating the optimal route based on the acquired data, the generation unit can provide the driver with the best possible route guidance. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can use an AI model to analyze real-time data in order to generate the optimal route. This allows the generation unit to provide the driver with the best possible route guidance by generating the optimal route based on the acquired data.

[0081] The service provider recommends tourist attractions, restaurants, and accommodations based on the route generated by the generation unit. For example, the service provider recommends tourist attractions with ocean views, seafood restaurants, and seaside accommodations based on the driver's preferences. By making recommendations based on the generated route, the service provider can improve driver satisfaction. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use an AI model that provides information based on the driver's preferences to recommend tourist attractions, restaurants, and accommodations. This can improve driver satisfaction by making recommendations based on the generated route.

[0082] The understanding unit estimates the driver's emotions and improves the accuracy of understanding their hobbies and preferences based on the estimated emotions. For example, if the driver is relaxed, the understanding unit can analyze past travel history in detail to improve the accuracy of understanding their hobbies and preferences. If the driver is excited, the understanding unit can analyze real-time speech and behavior to immediately reflect their hobbies and preferences. If the driver is stressed, the understanding unit can reconfirm their hobbies and preferences through simple questions to improve the accuracy of understanding. This allows for more appropriate recommendations by improving the accuracy of understanding hobbies and preferences based on the driver's emotions. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can use technologies such as speech analysis, facial expression analysis, and behavioral analysis to estimate the driver's emotions. Based on the estimated emotions of the driver, the understanding unit can analyze past travel history and real-time speech to improve the accuracy of understanding their hobbies and preferences.

[0083] The understanding unit analyzes the driver's past travel history to grasp changes in their hobbies and preferences. For example, the understanding unit can analyze the history of tourist spots the driver has visited in the past to grasp changes in their hobbies and preferences. The understanding unit can analyze the history of restaurants the driver has used in the past to grasp changes in their food preferences. The understanding unit can analyze the history of accommodations the driver has stayed at in the past to grasp changes in their accommodation preferences. By analyzing past travel history, the understanding unit can grasp changes in hobbies and preferences and make more appropriate recommendations. Some or all of the above processing in the understanding unit may be performed using AI, for example, or not using AI. For example, the understanding unit can use a data analysis algorithm to analyze the driver's past travel history. By analyzing past travel history, the understanding unit can grasp changes in hobbies and preferences and make more appropriate recommendations.

[0084] The understanding unit analyzes the driver's real-time statements and actions and immediately reflects their hobbies and preferences. For example, if the driver says, "I want to go somewhere where I can see the ocean," the understanding unit analyzes that information in real time and immediately reflects it in their hobbies and preferences. If the driver plays a specific piece of music, the understanding unit can analyze the genre of that music and immediately reflect it in their hobbies and preferences. If the driver searches for a specific restaurant, the understanding unit can analyze that information and immediately reflect it in their food preferences. In this way, by analyzing real-time statements and actions, hobbies and preferences can be reflected immediately. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can use technologies such as speech recognition and behavior tracking to analyze the driver's real-time statements and actions. The understanding unit can immediately reflect hobbies and preferences by analyzing real-time statements and actions.

[0085] The understanding unit estimates the driver's emotions and, based on the estimated emotions, determines the priority of understood hobbies and preferences. For example, if the driver is relaxed, the understanding unit may prioritize suggesting relaxing tourist spots. If the driver is excited, the understanding unit may prioritize suggesting active tourist spots. If the driver is stressed, the understanding unit may prioritize suggesting relaxing restaurants. This allows for more appropriate recommendations by determining the priority of hobbies and preferences based on the driver's emotions. Some or all of the above processing in the understanding unit may be performed using AI, for example, or not using AI. For example, the understanding unit may use technologies such as voice analysis, facial expression analysis, and behavioral analysis to estimate the driver's emotions. Based on the estimated emotions of the driver, the understanding unit can determine the priority of understood hobbies and preferences.

[0086] The understanding unit analyzes the driver's social media activity and reflects this understanding in their hobbies and preferences. For example, the understanding unit can analyze photos the driver shares on social media and reflect this understanding in their hobbies and preferences. The understanding unit can analyze accounts the driver follows on social media and reflect this understanding in their hobbies and preferences. The understanding unit can analyze posts the driver "likes" on social media and reflect this understanding in their hobbies and preferences. In this way, by analyzing social media activity, it is possible to reflect this understanding in their hobbies and preferences. Some or all of the above processing in the understanding unit may be performed using AI, for example, or not using AI. For example, the understanding unit can use a data analysis algorithm to analyze the driver's social media activity. By analyzing social media activity, the understanding unit can reflect this understanding in their hobbies and preferences.

[0087] The understanding unit takes into account the hobbies and preferences of the driver's family and friends to accommodate group travel. For example, the understanding unit can consider the tourist spots that the driver's family likes and suggest the best route for group travel. The understanding unit can consider the restaurants that the driver's friends like and suggest the best restaurants for group travel. The understanding unit can consider the accommodations that the driver's family and friends would like to stay at and suggest the best accommodations for group travel. In this way, by taking into account the hobbies and preferences of family and friends, it can accommodate group travel. Some or all of the above processing in the understanding unit may be performed using AI, for example, or not using AI. For example, the understanding unit can analyze survey results and past travel history to take into account the hobbies and preferences of the driver's family and friends. By taking into account the hobbies and preferences of family and friends, the understanding unit can accommodate group travel.

[0088] The data acquisition unit estimates the driver's emotions and adjusts the timing of real-time data acquisition based on the estimated emotions. For example, if the driver is relaxed, the data acquisition unit periodically acquires real-time data to provide up-to-date information. If the driver is in a hurry, the data acquisition unit can acquire real-time data frequently to provide information quickly. If the driver is stressed, the data acquisition unit can acquire real-time data only when necessary to avoid information overload. This allows for more appropriate information to be provided by adjusting the timing of real-time data acquisition based on the driver's emotions. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or not. For example, the data acquisition unit can use technologies such as voice analysis, facial expression analysis, and behavioral analysis to estimate the driver's emotions. The data acquisition unit can adjust the timing of real-time data acquisition based on the estimated emotions of the driver.

[0089] The acquisition unit improves the accuracy of data acquisition by referring to past traffic and weather information. The acquisition unit can improve the accuracy of real-time data acquisition by referring to past traffic congestion information, for example. The acquisition unit can improve the accuracy of real-time data acquisition by referring to past weather information. The acquisition unit can improve the accuracy of real-time data acquisition by referring to past traffic accident information. In this way, the accuracy of data acquisition can be improved by referring to past information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can use a data analysis algorithm to refer to past traffic and weather information. The acquisition unit can improve the accuracy of data acquisition by referring to past information.

[0090] The data acquisition unit acquires optimal data by considering the driver's current location information when acquiring data. For example, the data acquisition unit can acquire optimal traffic information based on the driver's current location. The data acquisition unit can acquire optimal weather information based on the driver's current location. The data acquisition unit can acquire optimal tourist spot information based on the driver's current location. In this way, optimal data can be acquired by considering the current location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without using AI. For example, the data acquisition unit can use GPS data or location information services to acquire optimal data by considering the driver's current location information when acquiring data. The data acquisition unit can acquire optimal data by considering the current location information.

[0091] The data acquisition unit estimates the driver's emotions and determines the priority of data to acquire based on the estimated emotions. For example, if the driver is relaxed, the data acquisition unit may prioritize acquiring tourist spot information. If the driver is in a hurry, the data acquisition unit may prioritize acquiring traffic information. If the driver is stressed, the data acquisition unit may prioritize acquiring weather information. This allows for the provision of more appropriate information by prioritizing data based on the driver's emotions. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit may use techniques such as voice analysis, facial expression analysis, and behavioral analysis to estimate the driver's emotions. The data acquisition unit can determine the priority of data to acquire based on the estimated emotions of the driver.

[0092] The acquisition unit acquires information about the driver's vehicle status and incorporates it into the travel plan. For example, the acquisition unit can acquire the vehicle's fuel level and suggest the optimal refueling point. The acquisition unit can acquire the vehicle's tire status and suggest a safe route. The acquisition unit can acquire the vehicle's engine status and suggest the optimal route if maintenance is required. As a result, acquiring vehicle status information enables safer and more efficient travel planning. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can use vehicle sensors or a vehicle diagnostic system to acquire information about the driver's vehicle status. By acquiring vehicle status information, the acquisition unit enables safer and more efficient travel planning.

[0093] The data acquisition unit refers to the driver's past travel history and prioritizes the acquisition of relevant data. For example, the data acquisition unit may prioritize the acquisition of information on tourist spots the driver has visited in the past. The data acquisition unit may prioritize the acquisition of information on restaurants the driver has used in the past. The data acquisition unit may prioritize the acquisition of information on accommodations the driver has stayed at in the past. In this way, relevant data can be prioritized by referring to past travel history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without using AI. For example, the data acquisition unit may use a data analysis algorithm to refer to the driver's past travel history. The data acquisition unit can prioritize the acquisition of relevant data by referring to past travel history.

[0094] The generation unit estimates the driver's emotions and adjusts the optimal route generation method based on the estimated emotions. For example, if the driver is relaxed, the generation unit may prioritize generating scenic routes. If the driver is in a hurry, the generation unit may prioritize generating the shortest route. If the driver is stressed, the generation unit may prioritize generating routes that avoid traffic congestion. In this way, by adjusting the route generation method based on the driver's emotions, a more appropriate route can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may use techniques such as voice analysis, facial expression analysis, and behavioral analysis to estimate the driver's emotions. The generation unit can adjust the optimal route generation method based on the estimated emotions of the driver.

[0095] The generation unit optimizes the route generation algorithm by referring to past route generation history. For example, the generation unit constructs an optimal route generation algorithm by referring to the history of previously generated routes. The generation unit can prioritize generating routes that match the driver's preferences from past route generation history. The generation unit can improve the accuracy of the route generation algorithm by analyzing past route generation history. This allows the route generation algorithm to be optimized by referring to past route generation history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can use a data analysis algorithm to refer to past route generation history. The generation unit can optimize the route generation algorithm by referring to past route generation history.

[0096] The generation unit collects driver feedback on the generated routes to improve the accuracy of route generation. For example, the generation unit can collect feedback from drivers who evaluate the generated routes. The generation unit can collect feedback from drivers who leave comments on the generated routes. The generation unit can collect feedback from drivers who point out areas for improvement on the generated routes. In this way, the accuracy of route generation can be improved by collecting driver feedback. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may use survey results or evaluation comments to collect driver feedback. The generation unit can improve the accuracy of route generation by collecting driver feedback.

[0097] The generation unit estimates the driver's emotions and determines the priority of the generated routes based on the estimated emotions. For example, if the driver is relaxed, the generation unit may prioritize scenic routes. If the driver is in a hurry, the generation unit may prioritize the shortest route. If the driver is stressed, the generation unit may prioritize routes that avoid traffic congestion. This allows for the provision of more appropriate routes by prioritizing routes based on the driver's emotions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may use techniques such as voice analysis, facial expression analysis, and behavioral analysis to estimate the driver's emotions. Based on the estimated emotions of the driver, the generation unit can determine the priority of the generated routes.

[0098] The generation unit analyzes the driver's real-time response to the generated route and dynamically adjusts the route. For example, if the driver expresses dissatisfaction with the generated route, the generation unit will regenerate the route in real time. If the driver is satisfied with the generated route, the generation unit can maintain the route as is. If the driver wishes to change the generated route, the generation unit can adjust the route in real time. In this way, the route can be dynamically adjusted by analyzing the driver's real-time response. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can use technologies such as speech recognition and behavior tracking to analyze the driver's real-time response. The generation unit can dynamically adjust the route by analyzing the real-time response.

[0099] The generation unit references the driver's past evaluations of generated routes and reflects them in route generation. For example, the generation unit can prioritize generating routes that the driver has previously given high ratings to. The generation unit can also avoid generating routes that the driver has previously given low ratings to. The generation unit can reference the history of routes that the driver has previously evaluated and generate the optimal route. This allows the generation unit to reflect the driver's past evaluations in route generation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can use evaluation comments or evaluation scores to reference the driver's past evaluations. The generation unit can reflect the driver's past evaluations in route generation.

[0100] The recommendation unit estimates the driver's emotions and adjusts the way recommendations are presented based on the estimated emotions. For example, if the driver is relaxed, the recommendation unit may provide recommendations with detailed explanations. If the driver is in a hurry, the recommendation unit may provide recommendations with concise explanations. If the driver is stressed, the recommendation unit may provide recommendations that are visually easy to understand. By adjusting the way recommendations are presented based on the driver's emotions, more appropriate recommendations become possible. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit may use technologies such as voice analysis, facial expression analysis, and behavioral analysis to estimate the driver's emotions. The recommendation unit may adjust the way recommendations are presented based on the estimated emotions of the driver.

[0101] The service provider optimizes the recommendation algorithm by referring to past recommendation history. For example, the service provider can optimize the recommendation algorithm by referring to the history of previously recommended tourist spots. The service provider can optimize the recommendation algorithm by referring to the history of previously recommended restaurants. The service provider can optimize the recommendation algorithm by referring to the history of previously recommended accommodations. In this way, the recommendation algorithm can be optimized by referring to past recommendation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can use a data analysis algorithm to refer to past recommendation history. The service provider can optimize the recommendation algorithm by referring to past recommendation history.

[0102] The service provider collects driver feedback on recommendations to improve the accuracy of those recommendations. For example, the service provider can collect feedback from drivers who rate recommended tourist spots. The service provider can collect feedback from drivers who leave comments on recommended restaurants. The service provider can collect feedback from drivers who point out areas for improvement on recommended accommodations. By collecting driver feedback, the accuracy of recommendations can be improved. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can use survey results and evaluation comments to collect driver feedback. By collecting driver feedback, the service provider can improve the accuracy of recommendations.

[0103] The service provider estimates the driver's emotions and prioritizes recommendations based on the estimated emotions. For example, if the driver is relaxed, the service provider will prioritize recommending relaxing tourist spots. If the driver is in a hurry, the service provider may prioritize recommending restaurants that are easily accessible. If the driver is stressed, the service provider may prioritize recommending relaxing accommodations. By prioritizing recommendations based on the driver's emotions, more appropriate recommendations can be made. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may use technologies such as voice analysis, facial expression analysis, and behavioral analysis to estimate the driver's emotions. The service provider may prioritize recommendations based on the estimated emotions of the driver.

[0104] The service provider analyzes the driver's real-time response to recommendations and dynamically adjusts them. For example, if the driver expresses dissatisfaction with a recommended tourist spot, the service provider will recommend an alternative tourist spot in real time. If the driver is satisfied with a recommended restaurant, the service provider can maintain that recommendation. If the driver wishes to change their recommended accommodation, the service provider can recommend an alternative accommodation in real time. In this way, recommendations can be dynamically adjusted by analyzing the driver's real-time response. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use technologies such as speech recognition and behavior tracking to analyze the driver's real-time response. The service provider can dynamically adjust recommendations by analyzing real-time responses.

[0105] The recommendation system references drivers' past ratings of recommended items and incorporates them into its recommendations. For example, the system may prioritize recommending tourist spots that drivers have previously given high ratings to. The system may also avoid recommending restaurants that drivers have previously given low ratings to. The system may also reference the history of accommodations that drivers have previously rated and recommend the most suitable accommodations. This allows the system to incorporate drivers' past ratings into its recommendations. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the system may use rating comments or rating scores to reference drivers' past ratings. The system can incorporate drivers' past ratings into its recommendations.

[0106] The learning unit estimates the driver's emotions and selects training data based on the estimated emotions. For example, if the driver is relaxed, the learning unit analyzes past travel history in detail and selects training data. If the driver is in a hurry, the learning unit can quickly select training data and perform training. If the driver is stressed, the learning unit can select concise data and perform training. This allows for more appropriate training by selecting training data based on the driver's emotions. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can use techniques such as voice analysis, facial expression analysis, and behavioral analysis to estimate the driver's emotions. The learning unit can select training data based on the estimated emotions of the driver.

[0107] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can construct an optimal learning algorithm by referring to past learning data. The learning unit can construct a learning algorithm that suits the driver's preferences from past learning data. The learning unit can analyze past learning data to improve the accuracy of the learning algorithm. This allows the learning algorithm to be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can use a data analysis algorithm to refer to past learning data. The learning unit can optimize the learning algorithm by referring to past learning data.

[0108] The learning unit estimates the driver's emotions and adjusts the frequency of learning based on the estimated emotions. For example, if the driver is relaxed, the learning unit can learn regularly to provide up-to-date information. If the driver is in a hurry, the learning unit can learn frequently to provide information quickly. If the driver is stressed, the learning unit can learn only when necessary to avoid information overload. This allows for more appropriate learning by adjusting the frequency of learning based on the driver's emotions. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can use technologies such as voice analysis, facial expression analysis, and behavioral analysis to estimate the driver's emotions. The learning unit can adjust the frequency of learning based on the estimated emotions of the driver.

[0109] The learning unit weights the training data based on the timing of real-time data acquisition during training. For example, the learning unit can weight the latest data based on the timing of real-time data acquisition and perform training. The learning unit can also weight past data based on the timing of real-time data acquisition and perform training. The learning unit can weight specific data based on the timing of real-time data acquisition and perform training. This allows for more appropriate training by weighting the training data based on the timing of real-time data acquisition. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can use a data analysis algorithm to weight the training data based on the timing of real-time data acquisition. The learning unit can weight the training data based on the timing of real-time data acquisition.

[0110] The analysis unit estimates the driver's emotions and adjusts the real-time data analysis method based on the estimated emotions. For example, if the driver is relaxed, the analysis unit performs a detailed analysis and provides up-to-date information. If the driver is in a hurry, the analysis unit can perform a rapid analysis and provide information quickly. If the driver is stressed, the analysis unit can perform analysis only when necessary, avoiding information overload. This allows for more appropriate analysis by adjusting the analysis method based on the driver's emotions. 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 use techniques such as voice analysis, facial expression analysis, and behavioral analysis to estimate the driver's emotions. The analysis unit can adjust the real-time data analysis method based on the estimated emotions of the driver.

[0111] The analysis unit optimizes the analysis algorithm by referring to past analysis history. For example, the analysis unit constructs an optimal analysis algorithm by referring to past analysis history. The analysis unit can construct an analysis algorithm that matches the driver's preferences from past analysis history. The analysis unit can improve the accuracy of the analysis algorithm by analyzing past analysis history. This allows the analysis algorithm to be optimized by referring to past analysis history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use a data analysis algorithm to refer to past analysis history. The analysis unit can optimize the analysis algorithm by referring to past analysis history.

[0112] The analysis unit collects driver feedback on the analysis results to improve the accuracy of the analysis. For example, the analysis unit can collect feedback from drivers who evaluate the analysis results. The analysis unit can collect feedback from drivers who leave comments on the analysis results. The analysis unit can collect feedback from drivers who point out areas for improvement in the analysis results. In this way, the accuracy of the analysis can be improved by collecting driver feedback. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use survey results or evaluation comments to collect driver feedback. The analysis unit can improve the accuracy of the analysis by collecting driver feedback.

[0113] The analysis unit estimates the driver's emotions and prioritizes the analysis results based on the estimated emotions. For example, if the driver is relaxed, the analysis unit may prioritize providing detailed analysis results. If the driver is in a hurry, the analysis unit may prioritize providing quick analysis results. If the driver is stressed, the analysis unit may prioritize providing necessary analysis results. This allows for the provision of more appropriate analysis results by prioritizing the results based on the driver's emotions. 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 may use techniques such as voice analysis, facial expression analysis, and behavioral analysis to estimate the driver's emotions. The analysis unit may prioritize the analysis results based on the estimated emotions of the driver.

[0114] The analysis unit analyzes the driver's real-time response to the analysis results and dynamically adjusts the analysis method. For example, if the driver expresses dissatisfaction with the analysis results, the analysis unit readjusts the analysis method in real time. If the driver is satisfied with the analysis results, the analysis unit can maintain the same analysis method. If the driver wishes to change the analysis results, the analysis unit can adjust the analysis method in real time. In this way, the analysis method can be dynamically adjusted by analyzing the driver's real-time response. 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 use technologies such as speech recognition and behavior tracking to analyze the driver's real-time response. The analysis unit can dynamically adjust the analysis method by analyzing the real-time response.

[0115] The analysis unit refers to the driver's past evaluations of the analysis results and incorporates them into the analysis. For example, the analysis unit prioritizes providing analysis results that the driver has previously given high ratings to. The analysis unit can avoid providing analysis results that the driver has previously given low ratings to. The analysis unit can refer to the history of analysis results that the driver has previously evaluated and provide the optimal analysis results. This allows the analysis to be influenced by the driver's past evaluations. 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 use evaluation comments or evaluation scores to refer to the driver's past evaluations. The analysis unit can influence the analysis by referring to the driver's past evaluations.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The driver understanding unit can also analyze the driver's music playlists to understand their hobbies and preferences. For example, if the driver frequently plays a particular genre of music, it can recommend tourist attractions and events related to that genre. Similarly, if the driver has attended concerts by a particular artist, it can recommend related events and exhibitions by that artist. Furthermore, it can analyze playlists created by the driver on music streaming services and suggest travel plans based on the playlist's theme. This enables more personalized recommendations that take the driver's musical tastes into account.

[0118] The learning function can analyze a driver's driving style and incorporate that understanding into their hobbies and preferences. For example, if a driver prefers using highways, it can recommend tourist spots and service areas along the highway. Similarly, if a driver prefers driving on mountain roads or country roads, it can recommend nature-rich tourist spots and outdoor activities. Furthermore, if a driver tends to take frequent breaks, it can recommend cafes and parks suitable for resting. This allows for more appropriate recommendations based on the driver's driving style.

[0119] The analysis unit can analyze the driver's vehicle condition information and incorporate it into the travel plan. For example, if the vehicle's fuel level is low, it can suggest the optimal refueling point. If the vehicle's tires are in poor condition, it can suggest a safer route. Furthermore, if the vehicle's engine is malfunctioning, it can suggest the optimal route for maintenance. This enables safer and more efficient travel planning that takes vehicle condition information into account.

[0120] The understanding unit can analyze a driver's social media activity and reflect this in understanding their hobbies and preferences. For example, it can analyze photos a driver shares on social media and reflect this in understanding their hobbies and preferences. It can also analyze accounts a driver follows on social media and reflect this in understanding their hobbies and preferences. Furthermore, it can analyze posts a driver "likes" on social media and reflect this in understanding their hobbies and preferences. In this way, by analyzing social media activity, it is possible to reflect this in understanding a driver's hobbies and preferences.

[0121] The data acquisition unit can refer to the driver's past travel history and prioritize the acquisition of relevant data. For example, it can prioritize the acquisition of information on tourist spots the driver has visited in the past. It can also prioritize the acquisition of information on restaurants the driver has used in the past. Furthermore, it can prioritize the acquisition of information on accommodations the driver has stayed at in the past. In this way, by referring to past travel history, relevant data can be prioritized.

[0122] The understanding unit can estimate the driver's emotions and improve the accuracy of understanding their hobbies and preferences based on the estimated emotions. For example, if the driver is relaxed, it can analyze their past travel history in detail to improve the accuracy of understanding their hobbies and preferences. If the driver is excited, it can analyze their real-time speech and actions to immediately reflect their hobbies and preferences. Furthermore, if the driver is stressed, it can reconfirm their hobbies and preferences through simple questions to improve the accuracy of understanding. In this way, the accuracy of understanding hobbies and preferences can be improved based on the driver's emotions.

[0123] The data acquisition unit can estimate the driver's emotions and adjust the timing of real-time data acquisition based on the estimated emotions. For example, if the driver is relaxed, real-time data can be acquired regularly to provide up-to-date information. If the driver is in a hurry, real-time data can be acquired frequently to provide information quickly. Furthermore, if the driver is stressed, real-time data can be acquired only when necessary to avoid information overload. In this way, the timing of real-time data acquisition can be adjusted based on the driver's emotions.

[0124] The generation unit can estimate the driver's emotions and adjust the route generation method based on the estimated emotions. For example, if the driver is relaxed, it can prioritize generating scenic routes. If the driver is in a hurry, it can prioritize generating the shortest route. Furthermore, if the driver is stressed, it can prioritize generating routes that avoid traffic congestion. In this way, the route generation method can be adjusted based on the driver's emotions.

[0125] The recommendation system can estimate the driver's emotions and adjust the way recommendations are presented based on those emotions. For example, if the driver is relaxed, it can provide recommendations with detailed explanations. If the driver is in a hurry, it can provide recommendations with concise explanations. Furthermore, if the driver is stressed, it can provide recommendations that are visually easy to understand. In this way, the recommendation system can adjust the way recommendations are presented based on the driver's emotions.

[0126] The analysis unit can estimate the driver's emotions and adjust the real-time data analysis method based on the estimated emotions. For example, if the driver is relaxed, it can perform a detailed analysis and provide up-to-date information. If the driver is in a hurry, it can perform a rapid analysis and provide information quickly. Furthermore, if the driver is stressed, it can perform analysis only when necessary to avoid information overload. In this way, the analysis method can be adjusted based on the driver's emotions.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The understanding unit understands the driver's hobbies and preferences. For example, it uses natural language processing to analyze the driver's hobbies and preferences. If the driver inputs "I want to go somewhere where I can see the ocean," the unit analyzes that information to understand the driver's preferences. Step 2: The acquisition unit acquires real-time data. For example, it acquires traffic information and weather information in real time, and provides data to generate the optimal route by acquiring information on traffic congestion and bad weather. Step 3: The generation unit generates the optimal route based on the data acquired by the acquisition unit. For example, it analyzes real-time data to avoid traffic congestion and bad weather, and generates a route that includes tourist spots with ocean views, seafood restaurants, and seaside accommodations based on the driver's preferences. Step 4: The provisioning unit recommends tourist spots, restaurants, and accommodations based on the route generated by the generation unit. For example, it recommends tourist spots with ocean views, seafood restaurants, and seaside accommodations based on the driver's preferences, and provides the driver with information on the recommended tourist spots, restaurants, and accommodations.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the understanding unit, acquisition unit, generation unit, provision unit, learning unit, and analysis unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the understanding unit is implemented by the control unit 46A of the smart device 14 and analyzes the driver's hobbies and preferences using natural language processing. The acquisition unit is implemented by the specific processing unit 290 of the data processing device 12 and acquires real-time traffic and weather information. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates an optimal route based on the acquired data. The provision unit is implemented by the control unit 46A of the smart device 14 and recommends tourist spots, restaurants, and accommodations based on the generated route. The learning unit is implemented by the specific processing unit 290 of the data processing device 12 and performs continuous data updating and learning. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and performs real-time data analysis. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the understanding unit, acquisition unit, generation unit, provision unit, learning unit, and analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the understanding unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the driver's tastes and preferences using natural language processing. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and acquires real-time traffic and weather information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal route based on the acquired data. The provision unit is implemented by the control unit 46A of the smart glasses 214 and recommends tourist spots, restaurants, and accommodations based on the generated route. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs continuous data updating and learning. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs real-time data analysis. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the understanding unit, acquisition unit, generation unit, provision unit, learning unit, and analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the understanding unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the driver's hobbies and preferences using natural language processing. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and acquires real-time traffic and weather information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal route based on the acquired data. The provision unit is implemented by the control unit 46A of the headset terminal 314 and recommends tourist spots, restaurants, and accommodations based on the generated route. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs continuous data updating and learning. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs real-time data analysis. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the understanding unit, acquisition unit, generation unit, provision unit, learning unit, and analysis unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the understanding unit is implemented by the control unit 46A of the robot 414 and analyzes the driver's hobbies and preferences using natural language processing. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and acquires real-time traffic and weather information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal route based on the acquired data. The provision unit is implemented by the control unit 46A of the robot 414 and recommends tourist spots, restaurants, and accommodations based on the generated route. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs continuous data updating and learning. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs real-time data analysis. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) An understanding unit that understands the driver's hobbies and preferences, A data acquisition unit that acquires real-time data, A generation unit generates an optimal route based on the data acquired by the acquisition unit, The system includes a provisioning unit that recommends tourist spots, restaurants, and accommodations based on the routes generated by the generation unit. A system characterized by the following features. (Note 2) It also includes a learning unit that performs continuous data updates and learning. The system described in Appendix 1, characterized by the features described herein. (Note 3) It also includes an analysis unit for analyzing real-time data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned understanding unit is, Using natural language processing to understand drivers' hobbies and preferences The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, Get real-time traffic and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is The optimal route is generated based on the data acquired by the acquisition unit. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Based on the routes generated by the aforementioned generation unit, recommendations for tourist spots, restaurants, and accommodations are made. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned understanding unit is, It estimates the driver's emotions and improves the accuracy of understanding their hobbies and preferences based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned understanding unit is, Analyze the driver's past travel history to understand changes in their hobbies and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned understanding unit is, Analyze the driver's real-time statements and actions to instantly reflect their hobbies and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned understanding unit is, It estimates the driver's emotions and, based on those emotions, determines the priority of their hobbies and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned understanding unit is, Analyze drivers' social media activity to gain a better understanding of their hobbies and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned understanding unit is, We also take into account the hobbies and preferences of the driver's family and friends to accommodate group travel. The system described in Appendix 1, characterized by the features described herein. (Note 14) The acquisition unit is, The system estimates the driver's emotions and adjusts the timing of real-time data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The acquisition unit is, We improve the accuracy of data acquisition by referring to past traffic and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The acquisition unit is, When acquiring data, the system takes into account the driver's current location to select the most optimal data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The acquisition unit is, The system estimates the driver's emotions and prioritizes the data to be collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The acquisition unit is, Obtain information on the driver's vehicle status and incorporate it into the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 19) The acquisition unit is, Referencing the driver's past travel history prioritizes the retrieval of relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the driver's emotions and adjusts the optimal route generation method based on the estimated driver emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is Referencing past route generation history optimizes the route generation algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is Collect driver feedback on generated routes to improve route generation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the driver's emotions and determines the priority of the route generated based on the estimated driver's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is Analyze the driver's real-time response to the generated route and dynamically adjust the route. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is Refer to the driver's past evaluations of the generated route and incorporate them into route generation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, The system estimates the driver's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, Referencing past recommendation history optimizes the recommendation algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, We collect driver feedback on recommendations to improve their accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, The system estimates the driver's emotions and prioritizes recommendations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, Analyze the driver's real-time response to recommendations and dynamically adjust the recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, Refer to the driver's past evaluations of the recommendation and incorporate them into the recommendation. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned learning unit, The system estimates the driver's emotions and selects training data based on the estimated driver's emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned learning unit, It estimates the driver's emotions and adjusts the learning frequency based on the estimated driver's emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned learning unit, During training, the training data is weighted based on when the real-time data was acquired. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned analysis unit, It estimates the driver's emotions and adjusts the real-time data analysis method based on the estimated driver emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned analysis unit, Refer to past analysis history and optimize the analysis algorithm. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned analysis unit, Collect driver feedback on analysis results to improve the accuracy of the analysis. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned analysis unit, The system estimates the driver's emotions and prioritizes the analysis results based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned analysis unit, The system analyzes the driver's real-time response to the analysis results and dynamically adjusts the analysis method. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned analysis unit, Refer to the driver's past evaluations of the analysis results and incorporate them into the analysis. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0201] 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. An understanding unit that understands the driver's hobbies and preferences, A data acquisition unit that acquires real-time data, A generation unit generates an optimal route based on the data acquired by the acquisition unit, The system includes a provisioning unit that recommends tourist spots, restaurants, and accommodations based on the routes generated by the generation unit. A system characterized by the following features.

2. It also includes a learning unit that performs continuous data updates and learning. The system according to feature 1.

3. It also includes an analysis unit for analyzing real-time data. The system according to feature 1.

4. The aforementioned understanding unit is, Using natural language processing to understand drivers' hobbies and preferences The system according to feature 1.

5. The acquisition unit is, Get real-time traffic and weather information. The system according to feature 1.

6. The generating unit is The optimal route is generated based on the data acquired by the acquisition unit. The system according to feature 1.

7. The aforementioned supply unit is, Based on the routes generated by the aforementioned generation unit, recommendations for tourist spots, restaurants, and accommodations are made. The system according to feature 1.

8. The aforementioned understanding unit is, It estimates the driver's emotions and improves the accuracy of understanding their hobbies and preferences based on those estimated emotions. The system according to feature 1.

Images