system

Abstract

The system according to this embodiment aims to provide a safe and secure ride-sharing service. [Solution] The system according to the embodiment comprises a confirmation unit, a provision unit, a monitoring unit, and a resolution unit. The confirmation unit confirms the permission criteria for the driver and vehicle. The provision unit provides information using generating AI. The monitoring unit monitors the vehicle status using connected cars. The resolution unit resolves the needs of both the user and the driver.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems regarding regulations, safety, social acceptance, handling of personal information, etc. of ride-sharing services, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a safe and secure ride-sharing service.

Means for Solving the Problems

[0006] The system according to the embodiment includes a confirmation unit, a provision unit, a monitoring unit, and a resolution unit. The confirmation unit confirms the permission criteria for drivers and vehicles. The provision unit provides information using generative AI. The monitoring unit monitors the vehicle state by utilizing a connected car. The resolution unit resolves the needs of both the user and the driver. [Effects of the Invention]

[0007] The system according to this embodiment can provide a safe and secure ride-sharing service. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 ride-sharing system according to an embodiment of the present invention is a system that provides a safe and secure ride-sharing service by combining AI ride-sharing and connected cars. By combining AI ride-sharing and connected cars, this ride-sharing system solves issues such as legal regulations, safety, social acceptance, and handling of personal information. Furthermore, it aims to address the needs of both users and drivers by introducing a sophisticated chatbot using generative AI. For example, the ride-sharing system uses AI to verify that drivers and vehicles meet permission standards, reducing liability and crime risk in the event of an accident. In addition, to improve driver quality, the ride-sharing system uses AI to evaluate drivers and select appropriate drivers. Next, the ride-sharing system introduces a chatbot using generative AI. This chatbot accurately provides information ranging from FAQs to specialized information tailored to the needs of both users and drivers. For example, if a user asks a question about how to use ride-sharing, the chatbot provides an appropriate answer. Also, if a driver encounters trouble while driving, the chatbot quickly guides them on how to deal with it. Furthermore, by utilizing connected cars, the ride-sharing system monitors the vehicle status in real time to ensure safety. For example, a ride-sharing system can track vehicle location, speed, fuel level, and other information in real time, enabling immediate response in case of any abnormalities. By combining AI ride-sharing, connected cars, and chatbots utilizing generative AI, a safe and secure ride-sharing service can be provided, addressing the needs of both users and drivers. This allows the ride-sharing system to verify driver and vehicle licensing standards, provide information, monitor vehicle conditions, and resolve user needs.

[0029] The ride-sharing system according to this embodiment comprises a verification unit, a provision unit, a monitoring unit, and a resolution unit. The verification unit verifies the permit criteria for the driver and vehicle. The verification unit evaluates the permit criteria based, for example, on the driver's qualifications and the condition of the vehicle. The verification unit can also use AI to analyze the driver's past driving history and the vehicle's maintenance history to evaluate compliance with the permit criteria. For example, the verification unit analyzes past traffic violation history to evaluate compliance with the permit criteria. The verification unit can also refer to past accident history to evaluate safe driving performance. Furthermore, the verification unit can analyze past driving time and distance to evaluate the driver's driving experience. The provision unit provides information using generative AI. For example, the provision unit provides appropriate answers when a user asks a question about how to use ride-sharing. The provision unit can generate quick and accurate answers to user questions using generative AI. For example, when a user asks a question about how to use ride-sharing, the generative AI refers to an FAQ database and provides an appropriate answer. The provision unit can also quickly guide drivers on how to deal with problems encountered while driving. For example, if a driver encounters trouble while driving, the service provider's AI generates a troubleshooting guide and quickly provides instructions on how to respond. The monitoring unit utilizes connected cars to monitor the vehicle's status. The monitoring unit, for example, obtains real-time information such as the vehicle's location, speed, and fuel level. Using AI, the monitoring unit can monitor the vehicle's status in real time and respond immediately if an abnormality occurs. For example, the monitoring unit obtains real-time information about the vehicle's location based on GPS data and issues an alert if an abnormality occurs. The monitoring unit can also monitor the vehicle's speed and fuel level in real time using sensor technology and respond immediately if an abnormality occurs. The resolution unit addresses the needs of both users and drivers. Using AI, the resolution unit accurately provides information ranging from FAQs to specialized information to address the needs of both users and drivers. For example, if a user asks a question about how to use rideshare, the AI ​​generates an appropriate answer.Furthermore, the resolution unit can also quickly guide the driver on how to respond if they encounter trouble while driving, thanks to its generated AI. This enables the ride-sharing system according to the embodiment to verify driver and vehicle permission standards, provide information, monitor vehicle status, and resolve needs.

[0030] The verification unit verifies the licensing standards for drivers and vehicles. For example, it evaluates licensing standards based on factors such as the driver's qualifications and the vehicle's condition. Specifically, it analyzes the driver's past driving history in detail to assess the validity of their driver's license, past traffic violation history, and driving experience. Using AI, this data can be analyzed quickly and accurately to determine if the driver meets the licensing standards. For instance, the AI ​​scans the driver's past traffic violation history to check for serious violations. It can also refer to past accident history to assess safe driving performance. Furthermore, it analyzes the driver's past driving time and mileage to assess driving experience. Regarding vehicle condition, the verification unit evaluates the vehicle's maintenance history and current condition. For example, it checks the vehicle's periodic inspection and repair history to determine if the vehicle is in a safe operating condition. The AI ​​integrates and analyzes this data to evaluate whether the vehicle meets the licensing standards. This allows the verification unit to confirm that both the driver and the vehicle are in a state to safely and appropriately provide rideshare services. Additionally, the verification unit can store these evaluation results in a database and share them with other departments as needed. For example, the evaluation results are used in collaboration with the service and monitoring departments to improve the overall safety and reliability of the system.

[0031] The service provider uses generative AI to provide information. For example, it can provide appropriate answers when a user asks a question about how to use rideshare services. Specifically, when a user enters a question through the application, the generative AI consults an FAQ database and generates the most appropriate answer. The generative AI uses natural language processing technology to understand the user's question and quickly retrieve relevant information. For example, if a user asks, "How do I book a rideshare?", the generative AI provides a detailed explanation of the booking procedure. The service provider can also quickly guide drivers on how to deal with problems they encounter while driving. For example, if a driver encounters a vehicle breakdown or traffic congestion, the generative AI consults a troubleshooting guide and provides specific solutions. This allows drivers to quickly resolve problems and continue driving safely. Furthermore, the service provider can collect feedback from users and drivers to continuously improve the accuracy of the generative AI's answers. For example, users can rate the answers provided, and the generative AI will use that rating to improve the quality of its answers. The service provider can also support multiple languages, providing appropriate information to users who speak different languages. This allows the service provider to provide timely and accurate information to both users and drivers, improving the convenience and reliability of the ride-sharing system.

[0032] The monitoring department utilizes connected cars to monitor the vehicle's status. For example, it obtains real-time information such as the vehicle's location, speed, and fuel level. Specifically, it collects data from GPS and various sensors installed in the vehicle and transmits it to a central monitoring system. Using AI, it analyzes this data in real time to monitor the vehicle's status. For example, based on the vehicle's location, it can determine the current driving route and the distance to the destination, and immediately issue an alert if an abnormality occurs. It can also monitor the vehicle's speed and fuel level in real time using sensor technology and respond immediately if an abnormality occurs. For example, if the fuel level is low, it can guide the driver to the nearest gas station. Also, if the vehicle's speed is abnormally high, it can warn the driver to slow down. Furthermore, the monitoring department can monitor the vehicle's maintenance status and notify the driver if regular inspections or repairs are needed. This helps maintain vehicle safety and reduce the risk of accidents. The monitoring department centrally manages this data and can collaborate with other departments as needed. For example, if an abnormality occurs, it can work with the resolution department and the service provision department to respond quickly. Furthermore, the monitoring unit can perform trend analysis based on past data and predict future risks. This allows the monitoring unit to monitor vehicle status in real time and respond quickly if an anomaly occurs, thereby improving the safety and reliability of the rideshare system.

[0033] The resolution unit addresses the needs of both users and drivers. Using generative AI, the resolution unit accurately provides information ranging from FAQs to specialized knowledge to address the needs of both users and drivers. Specifically, if a user asks a question about how to use rideshare, the generative AI provides an appropriate answer. For example, if a user asks, "How is rideshare pricing calculated?", the generative AI explains the pricing mechanism and details. Furthermore, if a driver encounters trouble while driving, the generative AI can quickly guide them on how to resolve the issue. For example, if a driver experiences a vehicle breakdown or traffic congestion, the generative AI will refer to a troubleshooting guide and provide specific solutions. This allows drivers to quickly resolve problems and continue driving safely. In addition, the resolution unit can collect feedback from users and drivers to continuously improve the accuracy of the generative AI's responses. For example, users can rate the provided answers, and the generative AI will improve the quality of its responses based on that rating. The resolution unit also supports multiple languages, enabling it to provide appropriate information to users who speak different languages. This allows the resolution unit to provide timely and accurate information to both users and drivers, improving the convenience and reliability of the rideshare system. Furthermore, the resolution unit can understand the needs of users and drivers and use this information to improve the entire system. For example, if users request specific functions or services, new functions can be added based on that feedback. In this way, the resolution unit can accurately understand the needs of users and drivers and contribute to the improvement of the entire system.

[0034] The service provider can provide appropriate answers when users ask questions about how to use rideshare services. For example, when a user asks a question about how to use rideshare services, the service provider's generating AI can refer to an FAQ database and provide an appropriate answer. The service provider can also use the generating AI to quickly and accurately generate answers to user questions. For example, when a user asks a question about how to use rideshare services, the service provider's generating AI can refer to an FAQ database and provide an appropriate answer. In addition, when a user asks a question about how to use rideshare services, the service provider's generating AI can collect relevant information and provide a detailed answer. This ensures that appropriate answers are provided when users ask questions about how to use rideshare services.

[0035] The service provider can quickly guide drivers on how to respond if they encounter trouble while driving. For example, if a driver encounters trouble while driving, the service provider's generating AI will refer to a troubleshooting guide and quickly guide them on how to respond. The service provider can also use the generating AI to generate quick and accurate responses to the driver's troubles. For example, if a driver encounters trouble while driving, the service provider's generating AI will refer to a troubleshooting guide and quickly guide them on how to respond. In addition, if a driver encounters trouble while driving, the service provider's generating AI can collect relevant information and provide detailed response instructions. This allows the service provider to quickly guide drivers on how to respond if they encounter trouble while driving.

[0036] The monitoring unit can grasp vehicle location information, speed, fuel level, and other data in real time. For example, the monitoring unit grasps vehicle location information in real time based on GPS data. The monitoring unit can also use AI to monitor vehicle location information in real time and issue alerts if an abnormality occurs. For example, the monitoring unit grasps vehicle location information in real time based on GPS data and issues alerts if an abnormality occurs. In addition, the monitoring unit can monitor vehicle speed and fuel level in real time using sensor technology and respond immediately if an abnormality occurs. For example, the monitoring unit monitors vehicle speed in real time using sensor technology and responds immediately if an abnormality occurs. In addition, the monitoring unit can monitor vehicle fuel level in real time using sensor technology and respond immediately if an abnormality occurs. This allows for real-time tracking of vehicle location information, speed, fuel level, and other data.

[0037] The monitoring unit can respond immediately if an anomaly occurs. For example, the monitoring unit monitors the vehicle's location information in real time based on GPS data and issues an alert if an anomaly occurs. The monitoring unit can also use AI to monitor the vehicle's location information in real time and respond immediately if an anomaly occurs. For example, the monitoring unit monitors the vehicle's location information in real time based on GPS data and responds immediately if an anomaly occurs. In addition, the monitoring unit can monitor the vehicle's speed and fuel level in real time using sensor technology and respond immediately if an anomaly occurs. For example, the monitoring unit monitors the vehicle's speed in real time using sensor technology and responds immediately if an anomaly occurs. In addition, the monitoring unit can monitor the vehicle's fuel level in real time using sensor technology and respond immediately if an anomaly occurs. This allows for an immediate response if an anomaly occurs.

[0038] The resolution unit can accurately provide information ranging from FAQs to specialized information to address the needs of both users and drivers. For example, if a user asks a question about how to use rideshare, the generation AI will provide an appropriate answer. The resolution unit can also use the generation AI to quickly generate accurate answers to user questions. For example, if a user asks a question about how to use rideshare, the generation AI will refer to the FAQ database and provide an appropriate answer. Furthermore, if a driver encounters trouble while driving, the generation AI can quickly guide them on how to deal with the situation. For example, if a driver encounters trouble while driving, the generation AI will refer to a troubleshooting guide and quickly guide them on how to deal with the situation. In this way, the resolution unit can accurately provide information ranging from FAQs to specialized information to address the needs of both users and drivers.

[0039] The verification unit can analyze a driver's past driving history and evaluate their compliance with the permit criteria. For example, the verification unit can analyze a driver's past traffic violation history and evaluate whether they comply with the permit criteria. The verification unit can also use AI to analyze a driver's past driving history and evaluate their compliance with the permit criteria. For example, the verification unit can analyze a driver's past traffic violation history and evaluate whether they comply with the permit criteria. The verification unit can also refer to a driver's past accident history and evaluate their safe driving record. For example, the verification unit can refer to a driver's past accident history and evaluate their safe driving record. The verification unit can also analyze a driver's past driving time and distance and evaluate their driving experience. For example, the verification unit can analyze a driver's past driving time and distance and evaluate their driving experience. This allows the system to analyze a driver's past driving history and evaluate their compliance with the permit criteria.

[0040] The verification unit can refer to the vehicle's maintenance history and confirm compliance with the permit standards. For example, the verification unit can review the history of periodic inspections and evaluate the vehicle's maintenance status. The verification unit can also use AI to refer to the vehicle's maintenance history and confirm compliance with the permit standards. For example, the verification unit can review the history of periodic inspections and evaluate the vehicle's maintenance status. The verification unit can also refer to past repair history and evaluate the vehicle's reliability. For example, the verification unit can refer to past repair history and evaluate the vehicle's reliability. The verification unit can also analyze the frequency of maintenance and evaluate the vehicle's maintenance status. For example, the verification unit can analyze the frequency of maintenance and evaluate the vehicle's maintenance status. This allows the vehicle's maintenance history to be referenced and compliance with the permit standards to be confirmed.

[0041] The verification unit can prioritize checking highly relevant criteria when verifying permit criteria, taking into account the driver's geographical location information. For example, if the driver operates in a specific area, the verification unit will prioritize checking permit criteria related to traffic regulations in that area. The verification unit can also use AI to prioritize checking highly relevant criteria, taking into account the driver's geographical location information. For example, if the driver operates in a specific area, the verification unit's AI will analyze the geographical location information and prioritize checking permit criteria related to traffic regulations in that area. Furthermore, if the driver operates on a long-distance route, the verification unit can prioritize checking permit criteria related to long-distance operation. For example, if the driver operates on a long-distance route, the verification unit's AI will analyze the geographical location information and prioritize checking permit criteria related to long-distance operation. Furthermore, if the driver operates in an urban area, the verification unit can prioritize checking permit criteria related to traffic regulations specific to urban areas. For example, if the driver operates in an urban area, the AI ​​will analyze the geographical location information and prioritize checking permit criteria related to traffic regulations specific to urban areas. This allows for prioritizing the verification of highly relevant criteria, taking into account the driver's geographical location.

[0042] The verification unit can analyze the driver's social media activity and obtain relevant information when verifying the permit criteria. For example, the verification unit can analyze the driver's social media posts and evaluate their reliability. The verification unit can also use AI to analyze the driver's social media activity and obtain relevant information. For example, the verification unit can analyze the driver's social media posts and evaluate their reliability. The verification unit can also refer to the frequency of the driver's social media activity and evaluate their compliance with the permit criteria. For example, the verification unit can refer to the frequency of the driver's social media activity and evaluate their compliance with the permit criteria. The verification unit can also check the driver's social media ratings and refer to feedback from other users. For example, the verification unit can check the driver's social media ratings and refer to feedback from other users. This allows the verification unit to analyze the driver's social media activity and obtain relevant information.

[0043] The information provider can adjust the level of detail based on the importance of the information being provided. For example, the provider can provide detailed explanations for important information. The provider can also use generative AI to adjust the level of detail based on the importance of the information. For example, the provider can use generative AI to provide detailed explanations for important information. The provider can also provide concise explanations for general information. For example, the provider can use generative AI to provide concise explanations for general information. The provider can also provide concise explanations for urgent information to ensure quick understanding. For example, the provider can use generative AI to provide concise explanations for urgent information to ensure quick understanding. This allows the level of detail of information to be adjusted based on its importance.

[0044] The information provider can apply different information provision algorithms depending on the category of information being provided. For example, for providing information related to FAQs, the provider can apply a concise and easy-to-understand algorithm. The provider can also use generative AI to apply different information provision algorithms depending on the category of information. For example, for providing information related to FAQs, the provider can use generative AI to apply a concise and easy-to-understand algorithm. The provider can also apply a detailed algorithm that includes technical terms for providing specialized information. For example, for providing specialized information, the provider can use generative AI to apply a detailed algorithm that includes technical terms. The provider can also apply an algorithm that quickly presents solutions for providing troubleshooting information. For example, for providing troubleshooting information, the provider can use generative AI to quickly present solutions. This allows for the application of different information provision algorithms depending on the category of information.

[0045] The information provider can prioritize information based on when it is submitted. For example, the provider can prioritize providing information with high urgency. The provider can also use generative AI to prioritize information based on when it is submitted. For example, the provider can prioritize providing information with high urgency. The provider can also provide information regularly at the appropriate time. For example, the provider can use generative AI to provide regularly necessary information at the appropriate time. The provider can also provide information according to the user's schedule. For example, the provider can use generative AI to provide information according to the user's schedule. This allows for the prioritization of information based on when it is submitted.

[0046] The information provider can adjust the order of information based on the relevance of the information it provides. For example, the provider can prioritize providing information that is most relevant to the user's current situation. The provider can also use generative AI to adjust the order of information based on its relevance. For example, the provider can prioritize providing information that is most relevant to the user's current situation. The provider can also provide information that is highly relevant based on the user's past usage history. For example, the provider can use generative AI to provide information that is highly relevant based on the user's past usage history. The provider can also provide information that is highly relevant depending on the content of the user's question. For example, the provider can use generative AI to provide information that is highly relevant depending on the content of the user's question. This allows the order of information to be adjusted based on its relevance.

[0047] The monitoring unit can analyze past vehicle operation data to improve monitoring accuracy. For example, the monitoring unit can analyze vehicle anomaly occurrence patterns based on past operation data. The monitoring unit can also use AI to analyze past vehicle operation data and improve monitoring accuracy. For example, the monitoring unit can analyze vehicle anomaly occurrence patterns based on past operation data. The monitoring unit can also refer to past operation data to predict vehicle maintenance timing. For example, the monitoring unit can refer to past operation data to predict vehicle maintenance timing. The monitoring unit can also analyze past operation data to evaluate vehicle operation efficiency. For example, the monitoring unit analyzes past operation data to evaluate vehicle operation efficiency. This allows for the analysis of past vehicle operation data and improves monitoring accuracy.

[0048] The monitoring unit can refer to the vehicle's maintenance history to determine monitoring priorities. For example, the monitoring unit can identify areas that should be monitored intensively based on the maintenance history. The monitoring unit can also use AI to refer to the vehicle's maintenance history to determine monitoring priorities. For example, the monitoring unit can identify areas that should be monitored intensively based on the maintenance history. The monitoring unit can also refer to the maintenance history to predict the timing of the next maintenance. For example, the monitoring unit can refer to the maintenance history to predict the timing of the next maintenance. The monitoring unit can also analyze the maintenance history to evaluate the reliability of the vehicle. For example, the monitoring unit analyzes the maintenance history to evaluate the reliability of the vehicle. This allows the monitoring unit to refer to the vehicle's maintenance history and determine monitoring priorities.

[0049] The monitoring unit can perform monitoring while considering the geographical distribution of vehicles. For example, if vehicles are concentrated in a particular area, the monitoring unit will perform monitoring while considering the traffic conditions in that area. The monitoring unit can also use AI to perform monitoring while considering the geographical distribution of vehicles. For example, if vehicles are concentrated in a particular area, the monitoring unit's AI will analyze the geographical distribution and perform monitoring while considering the traffic conditions in that area. Furthermore, if vehicles are distributed over a wide area, the monitoring unit can perform monitoring according to the characteristics of each area. For example, if vehicles are distributed over a wide area, the monitoring unit's AI will analyze the geographical distribution and perform monitoring according to the characteristics of each area. In addition, if vehicles are concentrated in an urban area, the monitoring unit can perform monitoring while considering traffic regulations specific to urban areas. For example, if vehicles are concentrated in an urban area, the monitoring unit's AI will analyze the geographical distribution and perform monitoring while considering traffic regulations specific to urban areas. This allows monitoring to be performed while considering the geographical distribution of vehicles.

[0050] The monitoring unit can improve the accuracy of monitoring by referring to relevant vehicle documentation. For example, the monitoring unit can refer to vehicle technical documentation and apply the latest monitoring technologies. The monitoring unit can also use AI to improve the accuracy of monitoring by referring to relevant vehicle documentation. For example, the monitoring unit can refer to vehicle technical documentation and apply the latest monitoring technologies. The monitoring unit can also refer to vehicle maintenance guides and select appropriate monitoring methods. For example, the monitoring unit can refer to vehicle maintenance guides and select appropriate monitoring methods. The monitoring unit can also refer to vehicle operation manuals and perform monitoring according to operating conditions. For example, the monitoring unit can refer to vehicle operation manuals and perform monitoring according to operating conditions. This allows for improved monitoring accuracy by referring to relevant vehicle documentation.

[0051] The resolution unit can analyze the past behavioral history of users and drivers and select the optimal solution to their needs. For example, the resolution unit can propose the optimal solution based on services that the user has frequently used in the past. The resolution unit can also use generative AI to analyze the past behavioral history of users and drivers and select the optimal solution to their needs. For example, the resolution unit uses generative AI to propose the optimal solution based on services that the user has frequently used in the past. The resolution unit can also refer to the driver's past trouble response history and select an appropriate solution. For example, the resolution unit refers to the driver's past trouble response history and the generative AI selects an appropriate solution. The resolution unit can also analyze the past evaluations of users and drivers and select the most effective solution. For example, the resolution unit analyzes the past evaluations of users and drivers and the generative AI selects the most effective solution. This allows the resolution unit to analyze the past behavioral history of users and drivers and select the optimal solution to their needs.

[0052] The resolution unit can customize the means of resolving needs based on the current situation of the user and driver. For example, the resolution unit can suggest the optimal solution based on the user's current situation. The resolution unit can also customize the means of resolving needs based on the current situation of the user and driver using generative AI. For example, the resolution unit can have the generative AI suggest the optimal solution based on the user's current situation. The resolution unit can also select an appropriate solution considering the driver's current driving situation. For example, the resolution unit can have the generative AI select an appropriate solution considering the driver's current driving situation. The resolution unit can also suggest an optimal solution based on the current location information of the user and driver. For example, the resolution unit can have the generative AI suggest an optimal solution based on the current location information of the user and driver. This allows the means of resolving needs to be customized based on the current situation of the user and driver.

[0053] The resolution unit can select the optimal solution to address the needs by considering the geographical location information of the user and the driver. For example, if the user is in a specific area, the resolution unit will propose a solution that is appropriate to the characteristics of that area. The resolution unit can also use generative AI to select the optimal solution to address the needs by considering the geographical location information of the user and the driver. For example, if the user is in a specific area, the generative AI will propose a solution that is appropriate to the characteristics of that area. Furthermore, if the driver is operating in a specific area, the resolution unit can select a solution that is appropriate to the traffic conditions of that area. For example, if the driver is operating in a specific area, the generative AI will select a solution that is appropriate to the traffic conditions of that area. In addition, the resolution unit can propose the optimal solution based on the location information of the user and the driver. For example, the generative AI will propose the optimal solution based on the location information of the user and the driver. This allows the resolution unit to select the optimal solution to address the needs by considering the geographical location information of the user and the driver.

[0054] The resolution unit can analyze the social media activities of users and drivers and propose means to resolve needs. For example, the resolution unit can analyze users' social media posts and propose the optimal solution. The resolution unit can also use generative AI to analyze the social media activities of users and drivers and propose means to resolve needs. For example, the resolution unit analyzes users' social media posts and the generative AI proposes the optimal solution. The resolution unit can also refer to the social media activities of drivers and select an appropriate solution. For example, the resolution unit refers to the social media activities of drivers and the generative AI selects an appropriate solution. The resolution unit can also propose effective solutions based on the evaluations of users and drivers on social media. For example, the resolution unit uses the evaluations of users and drivers on social media and the generative AI proposes an effective solution. In this way, the resolution unit can analyze the social media activities of users and drivers and propose means to resolve needs.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The rideshare system can also include a health management unit that monitors the user's health. This unit can, for example, monitor the user's heart rate and blood pressure in real time and issue alerts if abnormalities occur. The health management unit can also use AI to analyze the user's health data and take appropriate action. For example, if the user's heart rate increases rapidly, the health management unit can send a notification to emergency contacts. Furthermore, the health management unit can instruct drivers on appropriate driving methods based on the user's health status. For example, if the user appears fatigued, the health management unit can instruct the driver to take a break. This allows for real-time monitoring of the user's health and enables the provision of a safe rideshare service.

[0057] Ridesharing systems can also include an entertainment unit that provides music and entertainment based on user preferences. For example, the entertainment unit could analyze a user's music preferences and generate appropriate playlists. It could also use AI to analyze a user's past music playback history and provide music that matches their preferences. For instance, if a user wants to relax, the entertainment unit could provide relaxing music. Furthermore, the entertainment unit could offer games or video content that users can enjoy while driving. For example, it could offer simple games that users can enjoy while driving. This allows for entertainment tailored to user preferences, providing a comfortable ridesharing experience.

[0058] The ride-sharing system can further analyze the user's past usage history and suggest the optimal route. For example, the route suggestion unit can suggest the optimal route based on routes the user has frequently used in the past. The route suggestion unit can also use AI to analyze the user's past usage history and suggest the optimal route. For example, the route suggestion unit can suggest the optimal route based on routes the user has frequently used in the past. Furthermore, the route suggestion unit can suggest the optimal route based on the user's current location information. For example, the route suggestion unit can suggest the optimal route based on the user's current location information. Additionally, the route suggestion unit can suggest the optimal route considering traffic conditions. For example, the route suggestion unit can suggest the optimal route considering traffic conditions. This allows the system to analyze the user's past usage history and suggest the optimal route.

[0059] The ride-sharing system can further suggest the optimal pickup point based on the user's current location. The pickup point suggestion unit can, for example, suggest the pickup point closest to the user's current location. The pickup point suggestion unit can also use AI to suggest the optimal pickup point based on the user's current location. Furthermore, the pickup point suggestion unit can suggest the optimal pickup point considering traffic conditions. It can also suggest the optimal pickup point based on the user's preferences. This allows the system to suggest the optimal pickup point based on the user's current location.

[0060] The ride-sharing system can further analyze users' past ratings and select the most suitable driver. For example, the driver selection unit might prioritize drivers who have received high ratings from users in the past. The driver selection unit can also use AI to analyze users' past ratings and select the most suitable driver. For example, the driver selection unit might prioritize drivers who have received high ratings from users in the past. Furthermore, the driver selection unit can select the most suitable driver based on the user's current situation. For example, the driver selection unit might select the most suitable driver based on the user's current situation. Additionally, the driver selection unit can select the most suitable driver based on the driver's past ratings. For example, the driver selection unit might select the most suitable driver based on the driver's past ratings. This allows the system to analyze users' past ratings and select the most suitable driver.

[0061] Ridesharing systems can further provide optimal services based on the user's current situation. For example, the service provider can provide the optimal service according to the user's current circumstances. The service provider can also use AI to provide optimal services based on the user's current situation. Furthermore, the service provider can provide optimal services based on the user's past usage history. For example, the service provider can provide optimal services based on the user's past usage history. Additionally, the service provider can provide optimal services based on the user's preferences. This allows for the provision of optimal services based on the user's current situation.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The verification unit checks the driver and vehicle's licensing criteria. The verification unit evaluates the licensing criteria based on the driver's qualifications and the vehicle's condition, and uses AI to analyze past driving history and vehicle maintenance history. For example, it analyzes past traffic violation history, accident history, driving time, and distance to evaluate compliance with licensing criteria. Step 2: The service provider uses a generative AI to provide information. The service provider provides appropriate answers when users ask questions about how to use rideshare services and quickly guides drivers on how to deal with problems they encounter while driving. The generative AI generates answers by referring to an FAQ database and troubleshooting guides. Step 3: The monitoring unit utilizes connected cars to monitor the vehicle's status. The monitoring unit grasps the vehicle's location, speed, fuel level, etc., in real time and uses AI to respond immediately if an anomaly occurs. For example, it determines location based on GPS data and monitors speed and fuel level using sensor technology. Step 4: The resolution unit addresses the needs of both users and drivers. Using generated AI, the resolution unit accurately provides information ranging from FAQs to specialized information, quickly guiding users on how to use rideshare services and drivers on how to respond when they encounter problems while driving.

[0064] (Example of form 2) The ride-sharing system according to an embodiment of the present invention is a system that provides a safe and secure ride-sharing service by combining AI ride-sharing and connected cars. By combining AI ride-sharing and connected cars, this ride-sharing system solves issues such as legal regulations, safety, social acceptance, and handling of personal information. Furthermore, it aims to address the needs of both users and drivers by introducing a sophisticated chatbot using generative AI. For example, the ride-sharing system uses AI to verify that drivers and vehicles meet permission standards, reducing liability and crime risk in the event of an accident. In addition, to improve driver quality, the ride-sharing system uses AI to evaluate drivers and select appropriate drivers. Next, the ride-sharing system introduces a chatbot using generative AI. This chatbot accurately provides information ranging from FAQs to specialized information tailored to the needs of both users and drivers. For example, if a user asks a question about how to use ride-sharing, the chatbot provides an appropriate answer. Also, if a driver encounters trouble while driving, the chatbot quickly guides them on how to deal with it. Furthermore, by utilizing connected cars, the ride-sharing system monitors the vehicle status in real time to ensure safety. For example, a ride-sharing system can track vehicle location, speed, fuel level, and other information in real time, enabling immediate response in case of any abnormalities. By combining AI ride-sharing, connected cars, and chatbots utilizing generative AI, a safe and secure ride-sharing service can be provided, addressing the needs of both users and drivers. This allows the ride-sharing system to verify driver and vehicle licensing standards, provide information, monitor vehicle conditions, and resolve user needs.

[0065] The ride-sharing system according to this embodiment comprises a verification unit, a provision unit, a monitoring unit, and a resolution unit. The verification unit verifies the permit criteria for the driver and vehicle. The verification unit evaluates the permit criteria based, for example, on the driver's qualifications and the condition of the vehicle. The verification unit can also use AI to analyze the driver's past driving history and the vehicle's maintenance history to evaluate compliance with the permit criteria. For example, the verification unit analyzes past traffic violation history to evaluate compliance with the permit criteria. The verification unit can also refer to past accident history to evaluate safe driving performance. Furthermore, the verification unit can analyze past driving time and distance to evaluate the driver's driving experience. The provision unit provides information using generative AI. For example, the provision unit provides appropriate answers when a user asks a question about how to use ride-sharing. The provision unit can generate quick and accurate answers to user questions using generative AI. For example, when a user asks a question about how to use ride-sharing, the generative AI refers to an FAQ database and provides an appropriate answer. The provision unit can also quickly guide drivers on how to deal with problems encountered while driving. For example, if a driver encounters trouble while driving, the service provider's AI generates a troubleshooting guide and quickly provides instructions on how to respond. The monitoring unit utilizes connected cars to monitor the vehicle's status. The monitoring unit, for example, obtains real-time information such as the vehicle's location, speed, and fuel level. Using AI, the monitoring unit can monitor the vehicle's status in real time and respond immediately if an abnormality occurs. For example, the monitoring unit obtains real-time information about the vehicle's location based on GPS data and issues an alert if an abnormality occurs. The monitoring unit can also monitor the vehicle's speed and fuel level in real time using sensor technology and respond immediately if an abnormality occurs. The resolution unit addresses the needs of both users and drivers. Using AI, the resolution unit accurately provides information ranging from FAQs to specialized information to address the needs of both users and drivers. For example, if a user asks a question about how to use rideshare, the AI ​​generates an appropriate answer.Furthermore, the resolution unit can also quickly guide the driver on how to respond if they encounter trouble while driving, thanks to its generated AI. This enables the ride-sharing system according to the embodiment to verify driver and vehicle permission standards, provide information, monitor vehicle status, and resolve needs.

[0066] The verification unit verifies the licensing standards for drivers and vehicles. For example, it evaluates licensing standards based on factors such as the driver's qualifications and the vehicle's condition. Specifically, it analyzes the driver's past driving history in detail to assess the validity of their driver's license, past traffic violation history, and driving experience. Using AI, this data can be analyzed quickly and accurately to determine if the driver meets the licensing standards. For instance, the AI ​​scans the driver's past traffic violation history to check for serious violations. It can also refer to past accident history to assess safe driving performance. Furthermore, it analyzes the driver's past driving time and mileage to assess driving experience. Regarding vehicle condition, the verification unit evaluates the vehicle's maintenance history and current condition. For example, it checks the vehicle's periodic inspection and repair history to determine if the vehicle is in a safe operating condition. The AI ​​integrates and analyzes this data to evaluate whether the vehicle meets the licensing standards. This allows the verification unit to confirm that both the driver and the vehicle are in a state to safely and appropriately provide rideshare services. Additionally, the verification unit can store these evaluation results in a database and share them with other departments as needed. For example, the evaluation results are used in collaboration with the service and monitoring departments to improve the overall safety and reliability of the system.

[0067] The service provider uses generative AI to provide information. For example, it can provide appropriate answers when a user asks a question about how to use rideshare services. Specifically, when a user enters a question through the application, the generative AI consults an FAQ database and generates the most appropriate answer. The generative AI uses natural language processing technology to understand the user's question and quickly retrieve relevant information. For example, if a user asks, "How do I book a rideshare?", the generative AI provides a detailed explanation of the booking procedure. The service provider can also quickly guide drivers on how to deal with problems they encounter while driving. For example, if a driver encounters a vehicle breakdown or traffic congestion, the generative AI consults a troubleshooting guide and provides specific solutions. This allows drivers to quickly resolve problems and continue driving safely. Furthermore, the service provider can collect feedback from users and drivers to continuously improve the accuracy of the generative AI's answers. For example, users can rate the answers provided, and the generative AI will use that rating to improve the quality of its answers. The service provider can also support multiple languages, providing appropriate information to users who speak different languages. This allows the service provider to provide timely and accurate information to both users and drivers, improving the convenience and reliability of the ride-sharing system.

[0068] The monitoring department utilizes connected cars to monitor the vehicle's status. For example, it obtains real-time information such as the vehicle's location, speed, and fuel level. Specifically, it collects data from GPS and various sensors installed in the vehicle and transmits it to a central monitoring system. Using AI, it analyzes this data in real time to monitor the vehicle's status. For example, based on the vehicle's location, it can determine the current driving route and the distance to the destination, and immediately issue an alert if an abnormality occurs. It can also monitor the vehicle's speed and fuel level in real time using sensor technology and respond immediately if an abnormality occurs. For example, if the fuel level is low, it can guide the driver to the nearest gas station. Also, if the vehicle's speed is abnormally high, it can warn the driver to slow down. Furthermore, the monitoring department can monitor the vehicle's maintenance status and notify the driver if regular inspections or repairs are needed. This helps maintain vehicle safety and reduce the risk of accidents. The monitoring department centrally manages this data and can collaborate with other departments as needed. For example, if an abnormality occurs, it can work with the resolution department and the service provision department to respond quickly. Furthermore, the monitoring unit can perform trend analysis based on past data and predict future risks. This allows the monitoring unit to monitor vehicle status in real time and respond quickly if an anomaly occurs, thereby improving the safety and reliability of the rideshare system.

[0069] The resolution unit addresses the needs of both users and drivers. Using generative AI, the resolution unit accurately provides information ranging from FAQs to specialized knowledge to address the needs of both users and drivers. Specifically, if a user asks a question about how to use rideshare, the generative AI provides an appropriate answer. For example, if a user asks, "How is rideshare pricing calculated?", the generative AI explains the pricing mechanism and details. Furthermore, if a driver encounters trouble while driving, the generative AI can quickly guide them on how to resolve the issue. For example, if a driver experiences a vehicle breakdown or traffic congestion, the generative AI will refer to a troubleshooting guide and provide specific solutions. This allows drivers to quickly resolve problems and continue driving safely. In addition, the resolution unit can collect feedback from users and drivers to continuously improve the accuracy of the generative AI's responses. For example, users can rate the provided answers, and the generative AI will improve the quality of its responses based on that rating. The resolution unit also supports multiple languages, enabling it to provide appropriate information to users who speak different languages. This allows the resolution unit to provide timely and accurate information to both users and drivers, improving the convenience and reliability of the rideshare system. Furthermore, the resolution unit can understand the needs of users and drivers and use this information to improve the entire system. For example, if users request specific functions or services, new functions can be added based on that feedback. In this way, the resolution unit can accurately understand the needs of users and drivers and contribute to the improvement of the entire system.

[0070] The service provider can provide appropriate answers when users ask questions about how to use rideshare services. For example, when a user asks a question about how to use rideshare services, the service provider's generating AI can refer to an FAQ database and provide an appropriate answer. The service provider can also use the generating AI to quickly and accurately generate answers to user questions. For example, when a user asks a question about how to use rideshare services, the service provider's generating AI can refer to an FAQ database and provide an appropriate answer. In addition, when a user asks a question about how to use rideshare services, the service provider's generating AI can collect relevant information and provide a detailed answer. This ensures that appropriate answers are provided when users ask questions about how to use rideshare services.

[0071] The service provider can quickly guide drivers on how to respond if they encounter trouble while driving. For example, if a driver encounters trouble while driving, the service provider's generating AI will refer to a troubleshooting guide and quickly guide them on how to respond. The service provider can also use the generating AI to generate quick and accurate responses to the driver's troubles. For example, if a driver encounters trouble while driving, the service provider's generating AI will refer to a troubleshooting guide and quickly guide them on how to respond. In addition, if a driver encounters trouble while driving, the service provider's generating AI can collect relevant information and provide detailed response instructions. This allows the service provider to quickly guide drivers on how to respond if they encounter trouble while driving.

[0072] The monitoring unit can grasp vehicle location information, speed, fuel level, and other data in real time. For example, the monitoring unit grasps vehicle location information in real time based on GPS data. The monitoring unit can also use AI to monitor vehicle location information in real time and issue alerts if an abnormality occurs. For example, the monitoring unit grasps vehicle location information in real time based on GPS data and issues alerts if an abnormality occurs. In addition, the monitoring unit can monitor vehicle speed and fuel level in real time using sensor technology and respond immediately if an abnormality occurs. For example, the monitoring unit monitors vehicle speed in real time using sensor technology and responds immediately if an abnormality occurs. In addition, the monitoring unit can monitor vehicle fuel level in real time using sensor technology and respond immediately if an abnormality occurs. This allows for real-time tracking of vehicle location information, speed, fuel level, and other data.

[0073] The monitoring unit can respond immediately if an anomaly occurs. For example, the monitoring unit monitors the vehicle's location information in real time based on GPS data and issues an alert if an anomaly occurs. The monitoring unit can also use AI to monitor the vehicle's location information in real time and respond immediately if an anomaly occurs. For example, the monitoring unit monitors the vehicle's location information in real time based on GPS data and responds immediately if an anomaly occurs. In addition, the monitoring unit can monitor the vehicle's speed and fuel level in real time using sensor technology and respond immediately if an anomaly occurs. For example, the monitoring unit monitors the vehicle's speed in real time using sensor technology and responds immediately if an anomaly occurs. In addition, the monitoring unit can monitor the vehicle's fuel level in real time using sensor technology and respond immediately if an anomaly occurs. This allows for an immediate response if an anomaly occurs.

[0074] The resolution unit can accurately provide information ranging from FAQs to specialized information to address the needs of both users and drivers. For example, if a user asks a question about how to use rideshare, the generation AI will provide an appropriate answer. The resolution unit can also use the generation AI to quickly generate accurate answers to user questions. For example, if a user asks a question about how to use rideshare, the generation AI will refer to the FAQ database and provide an appropriate answer. Furthermore, if a driver encounters trouble while driving, the generation AI can quickly guide them on how to deal with the situation. For example, if a driver encounters trouble while driving, the generation AI will refer to a troubleshooting guide and quickly guide them on how to deal with the situation. In this way, the resolution unit can accurately provide information ranging from FAQs to specialized information to address the needs of both users and drivers.

[0075] The verification unit can estimate the user's emotions and adjust the verification method for driver and vehicle permission criteria based on the estimated user emotions. For example, if the user is feeling anxious, the verification unit can provide a detailed explanation of the permission criteria to reassure them. The verification unit can use generative AI to estimate the user's emotions and take appropriate action. For example, if the user is feeling anxious, the generative AI uses facial recognition technology to estimate the emotions and provides a detailed explanation of the permission criteria. The verification unit can also provide a concise method for verifying permission criteria if the user is in a hurry, allowing for quick verification. For example, if the user is in a hurry, the generative AI uses voice analysis technology to estimate the emotions and provides a concise method for verifying permission criteria. The verification unit can also provide a standard method for verifying permission criteria if the user is relaxed, allowing for verification using the usual procedure. For example, if the user is relaxed, the generative AI uses text analysis technology to estimate the emotions and provides a standard method for verifying permission criteria. This allows the verification method for permission criteria to be adjusted based on the user's emotions.

[0076] The verification unit can analyze a driver's past driving history and evaluate their compliance with the permit criteria. For example, the verification unit can analyze a driver's past traffic violation history and evaluate whether they comply with the permit criteria. The verification unit can also use AI to analyze a driver's past driving history and evaluate their compliance with the permit criteria. For example, the verification unit can analyze a driver's past traffic violation history and evaluate whether they comply with the permit criteria. The verification unit can also refer to a driver's past accident history and evaluate their safe driving record. For example, the verification unit can refer to a driver's past accident history and evaluate their safe driving record. The verification unit can also analyze a driver's past driving time and distance and evaluate their driving experience. For example, the verification unit can analyze a driver's past driving time and distance and evaluate their driving experience. This allows the system to analyze a driver's past driving history and evaluate their compliance with the permit criteria.

[0077] The verification unit can refer to the vehicle's maintenance history and confirm compliance with the permit standards. For example, the verification unit can review the history of periodic inspections and evaluate the vehicle's maintenance status. The verification unit can also use AI to refer to the vehicle's maintenance history and confirm compliance with the permit standards. For example, the verification unit can review the history of periodic inspections and evaluate the vehicle's maintenance status. The verification unit can also refer to past repair history and evaluate the vehicle's reliability. For example, the verification unit can refer to past repair history and evaluate the vehicle's reliability. The verification unit can also analyze the frequency of maintenance and evaluate the vehicle's maintenance status. For example, the verification unit can analyze the frequency of maintenance and evaluate the vehicle's maintenance status. This allows the vehicle's maintenance history to be referenced and compliance with the permit standards to be confirmed.

[0078] The verification unit can estimate the user's emotions and determine the priority of the permission criteria to be verified based on the estimated emotions. For example, if the user is feeling anxious, the verification unit will prioritize verification of safety-related permission criteria. The verification unit can use generative AI to estimate the user's emotions and take appropriate action. For example, if the user is feeling anxious, the verification unit will use facial recognition technology to estimate the emotions and prioritize verification of safety-related permission criteria. The verification unit can also prioritize permission criteria that can be verified quickly if the user is in a hurry. For example, if the user is in a hurry, the verification unit will use voice analysis technology to estimate the emotions and prioritize permission criteria that can be verified quickly. The verification unit can also maintain the standard order of verification of permission criteria if the user is relaxed. For example, if the user is relaxed, the verification unit will use text analysis technology to estimate the emotions and maintain the standard order of verification of permission criteria. This allows the verification unit to determine the priority of permission criteria based on the user's emotions.

[0079] The verification unit can prioritize checking highly relevant criteria when verifying permit criteria, taking into account the driver's geographical location information. For example, if the driver operates in a specific area, the verification unit will prioritize checking permit criteria related to traffic regulations in that area. The verification unit can also use AI to prioritize checking highly relevant criteria, taking into account the driver's geographical location information. For example, if the driver operates in a specific area, the verification unit's AI will analyze the geographical location information and prioritize checking permit criteria related to traffic regulations in that area. Furthermore, if the driver operates on a long-distance route, the verification unit can prioritize checking permit criteria related to long-distance operation. For example, if the driver operates on a long-distance route, the verification unit's AI will analyze the geographical location information and prioritize checking permit criteria related to long-distance operation. Furthermore, if the driver operates in an urban area, the verification unit can prioritize checking permit criteria related to traffic regulations specific to urban areas. For example, if the driver operates in an urban area, the AI ​​will analyze the geographical location information and prioritize checking permit criteria related to traffic regulations specific to urban areas. This allows for prioritizing the verification of highly relevant criteria, taking into account the driver's geographical location.

[0080] The verification unit can analyze the driver's social media activity and obtain relevant information when verifying the permit criteria. For example, the verification unit can analyze the driver's social media posts and evaluate their reliability. The verification unit can also use AI to analyze the driver's social media activity and obtain relevant information. For example, the verification unit can analyze the driver's social media posts and evaluate their reliability. The verification unit can also refer to the frequency of the driver's social media activity and evaluate their compliance with the permit criteria. For example, the verification unit can refer to the frequency of the driver's social media activity and evaluate their compliance with the permit criteria. The verification unit can also check the driver's social media ratings and refer to feedback from other users. For example, the verification unit can check the driver's social media ratings and refer to feedback from other users. This allows the verification unit to analyze the driver's social media activity and obtain relevant information.

[0081] The information provider can estimate the user's emotions and adjust the way information is presented based on those emotions. For example, if the user is feeling anxious, the information provider can provide information in a way that provides reassurance. The information provider can use generative AI to estimate the user's emotions and select an appropriate way of presenting the information. For example, if the user is feeling anxious, the generative AI uses facial recognition technology to estimate the emotion and provides information in a way that provides reassurance. The information provider can also provide information in a concise and quick manner if the user is in a hurry. For example, if the user is in a hurry, the generative AI uses voice analysis technology to estimate the emotion and provides information in a concise and quick manner. The information provider can also provide information in a detailed and polite manner if the user is relaxed. For example, if the user is relaxed, the generative AI uses text analysis technology to estimate the emotion and provides information in a detailed and polite manner. This allows the information provider to adjust the way information is presented based on the user's emotions.

[0082] The information provider can adjust the level of detail based on the importance of the information being provided. For example, the provider can provide detailed explanations for important information. The provider can also use generative AI to adjust the level of detail based on the importance of the information. For example, the provider can use generative AI to provide detailed explanations for important information. The provider can also provide concise explanations for general information. For example, the provider can use generative AI to provide concise explanations for general information. The provider can also provide concise explanations for urgent information to ensure quick understanding. For example, the provider can use generative AI to provide concise explanations for urgent information to ensure quick understanding. This allows the level of detail of information to be adjusted based on its importance.

[0083] The information provider can apply different information provision algorithms depending on the category of information being provided. For example, for providing information related to FAQs, the provider can apply a concise and easy-to-understand algorithm. The provider can also use generative AI to apply different information provision algorithms depending on the category of information. For example, for providing information related to FAQs, the provider can use generative AI to apply a concise and easy-to-understand algorithm. The provider can also apply a detailed algorithm that includes technical terms for providing specialized information. For example, for providing specialized information, the provider can use generative AI to apply a detailed algorithm that includes technical terms. The provider can also apply an algorithm that quickly presents solutions for providing troubleshooting information. For example, for providing troubleshooting information, the provider can use generative AI to quickly present solutions. This allows for the application of different information provision algorithms depending on the category of information.

[0084] The information provider can estimate the user's emotions and adjust the length of the information provided based on those emotions. For example, if the user is feeling anxious, the provider can provide detailed information to reassure them. The provider can use generative AI to estimate the user's emotions and select an appropriate length of information. For example, if the user is feeling anxious, the generative AI uses facial recognition technology to estimate the emotion and provides detailed information. The provider can also provide concise and to-the-point information if the user is in a hurry. For example, if the user is in a hurry, the generative AI uses voice analysis technology to estimate the emotion and provides concise and to-the-point information. The provider can also provide detailed and polite information if the user is relaxed. For example, if the user is relaxed, the generative AI uses text analysis technology to estimate the emotion and provides detailed and polite information. This allows the length of the information provided to be adjusted based on the user's emotions.

[0085] The information provider can prioritize information based on when it is submitted. For example, the provider can prioritize providing information with high urgency. The provider can also use generative AI to prioritize information based on when it is submitted. For example, the provider can prioritize providing information with high urgency. The provider can also provide information regularly at the appropriate time. For example, the provider can use generative AI to provide regularly necessary information at the appropriate time. The provider can also provide information according to the user's schedule. For example, the provider can use generative AI to provide information according to the user's schedule. This allows for the prioritization of information based on when it is submitted.

[0086] The information provider can adjust the order of information based on the relevance of the information it provides. For example, the provider can prioritize providing information that is most relevant to the user's current situation. The provider can also use generative AI to adjust the order of information based on its relevance. For example, the provider can prioritize providing information that is most relevant to the user's current situation. The provider can also provide information that is highly relevant based on the user's past usage history. For example, the provider can use generative AI to provide information that is highly relevant based on the user's past usage history. The provider can also provide information that is highly relevant depending on the content of the user's question. For example, the provider can use generative AI to provide information that is highly relevant depending on the content of the user's question. This allows the order of information to be adjusted based on its relevance.

[0087] The monitoring unit can estimate the user's emotions and adjust the vehicle status monitoring method based on the estimated user emotions. For example, if the user is feeling anxious, the monitoring unit can provide detailed vehicle status monitoring information. The monitoring unit can use generative AI to estimate the user's emotions and select an appropriate monitoring method. For example, if the user is feeling anxious, the generative AI uses facial recognition technology to estimate the emotions and provides detailed vehicle status monitoring information. The monitoring unit can also provide concise vehicle status monitoring information if the user is in a hurry. For example, if the user is in a hurry, the generative AI uses voice analysis technology to estimate the emotions and provides concise vehicle status monitoring information. The monitoring unit can also provide standard vehicle status monitoring information if the user is relaxed. For example, if the user is relaxed, the generative AI uses text analysis technology to estimate the emotions and provides standard vehicle status monitoring information. This allows the vehicle status monitoring method to be adjusted based on the user's emotions.

[0088] The monitoring unit can analyze past vehicle operation data to improve monitoring accuracy. For example, the monitoring unit can analyze vehicle anomaly occurrence patterns based on past operation data. The monitoring unit can also use AI to analyze past vehicle operation data and improve monitoring accuracy. For example, the monitoring unit can analyze vehicle anomaly occurrence patterns based on past operation data. The monitoring unit can also refer to past operation data to predict vehicle maintenance timing. For example, the monitoring unit can refer to past operation data to predict vehicle maintenance timing. The monitoring unit can also analyze past operation data to evaluate vehicle operation efficiency. For example, the monitoring unit analyzes past operation data to evaluate vehicle operation efficiency. This allows for the analysis of past vehicle operation data and improves monitoring accuracy.

[0089] The monitoring unit can refer to the vehicle's maintenance history to determine monitoring priorities. For example, the monitoring unit can identify areas that should be monitored intensively based on the maintenance history. The monitoring unit can also use AI to refer to the vehicle's maintenance history to determine monitoring priorities. For example, the monitoring unit can identify areas that should be monitored intensively based on the maintenance history. The monitoring unit can also refer to the maintenance history to predict the timing of the next maintenance. For example, the monitoring unit can refer to the maintenance history to predict the timing of the next maintenance. The monitoring unit can also analyze the maintenance history to evaluate the reliability of the vehicle. For example, the monitoring unit analyzes the maintenance history to evaluate the reliability of the vehicle. This allows the monitoring unit to refer to the vehicle's maintenance history and determine monitoring priorities.

[0090] The monitoring unit can estimate the user's emotions and adjust how the monitoring results are displayed based on those emotions. For example, if the user is feeling anxious, the monitoring unit can display detailed monitoring results to provide reassurance. The monitoring unit can use generative AI to estimate the user's emotions and select an appropriate display method. For example, if the user is feeling anxious, the generative AI uses facial recognition technology to estimate the emotions and displays detailed monitoring results. The monitoring unit can also display concise monitoring results and provide information quickly if the user is in a hurry. For example, if the user is in a hurry, the generative AI uses voice analysis technology to estimate the emotions and displays concise monitoring results. The monitoring unit can also display standard monitoring results and provide information in the usual way if the user is relaxed. For example, if the user is relaxed, the generative AI uses text analysis technology to estimate the emotions and displays standard monitoring results. This allows the monitoring unit to adjust how the monitoring results are displayed based on the user's emotions.

[0091] The monitoring unit can perform monitoring while considering the geographical distribution of vehicles. For example, if vehicles are concentrated in a particular area, the monitoring unit will perform monitoring while considering the traffic conditions in that area. The monitoring unit can also use AI to perform monitoring while considering the geographical distribution of vehicles. For example, if vehicles are concentrated in a particular area, the monitoring unit's AI will analyze the geographical distribution and perform monitoring while considering the traffic conditions in that area. Furthermore, if vehicles are distributed over a wide area, the monitoring unit can perform monitoring according to the characteristics of each area. For example, if vehicles are distributed over a wide area, the monitoring unit's AI will analyze the geographical distribution and perform monitoring according to the characteristics of each area. In addition, if vehicles are concentrated in an urban area, the monitoring unit can perform monitoring while considering traffic regulations specific to urban areas. For example, if vehicles are concentrated in an urban area, the monitoring unit's AI will analyze the geographical distribution and perform monitoring while considering traffic regulations specific to urban areas. This allows monitoring to be performed while considering the geographical distribution of vehicles.

[0092] The monitoring unit can improve the accuracy of monitoring by referring to relevant vehicle documentation. For example, the monitoring unit can refer to vehicle technical documentation and apply the latest monitoring technologies. The monitoring unit can also use AI to improve the accuracy of monitoring by referring to relevant vehicle documentation. For example, the monitoring unit can refer to vehicle technical documentation and apply the latest monitoring technologies. The monitoring unit can also refer to vehicle maintenance guides and select appropriate monitoring methods. For example, the monitoring unit can refer to vehicle maintenance guides and select appropriate monitoring methods. The monitoring unit can also refer to vehicle operation manuals and perform monitoring according to operating conditions. For example, the monitoring unit can refer to vehicle operation manuals and perform monitoring according to operating conditions. This allows for improved monitoring accuracy by referring to relevant vehicle documentation.

[0093] The resolution unit can estimate the user's emotions and adjust the method of resolving the needs based on the estimated emotions. For example, if the user is feeling anxious, the resolution unit can provide a detailed explanation and reassure the user. The resolution unit can use generative AI to estimate the user's emotions and select an appropriate method of resolving the needs. For example, if the user is feeling anxious, the generative AI uses facial recognition technology to estimate the emotions and provides a detailed explanation. The resolution unit can also quickly present a solution if the user is in a hurry. For example, if the user is in a hurry, the generative AI uses voice analysis technology to estimate the emotions and quickly presents a solution. The resolution unit can also provide a courteous response and resolve the needs if the user is relaxed. For example, if the user is relaxed, the generative AI uses text analysis technology to estimate the emotions and provides a courteous response to resolve the needs. This allows the method of resolving needs to be adjusted based on the user's emotions.

[0094] The resolution unit can analyze the past behavioral history of users and drivers and select the optimal solution to their needs. For example, the resolution unit can propose the optimal solution based on services that the user has frequently used in the past. The resolution unit can also use generative AI to analyze the past behavioral history of users and drivers and select the optimal solution to their needs. For example, the resolution unit uses generative AI to propose the optimal solution based on services that the user has frequently used in the past. The resolution unit can also refer to the driver's past trouble response history and select an appropriate solution. For example, the resolution unit refers to the driver's past trouble response history and the generative AI selects an appropriate solution. The resolution unit can also analyze the past evaluations of users and drivers and select the most effective solution. For example, the resolution unit analyzes the past evaluations of users and drivers and the generative AI selects the most effective solution. This allows the resolution unit to analyze the past behavioral history of users and drivers and select the optimal solution to their needs.

[0095] The resolution unit can customize the means of resolving needs based on the current situation of the user and driver. For example, the resolution unit can suggest the optimal solution based on the user's current situation. The resolution unit can also customize the means of resolving needs based on the current situation of the user and driver using generative AI. For example, the resolution unit can have the generative AI suggest the optimal solution based on the user's current situation. The resolution unit can also select an appropriate solution considering the driver's current driving situation. For example, the resolution unit can have the generative AI select an appropriate solution considering the driver's current driving situation. The resolution unit can also suggest an optimal solution based on the current location information of the user and driver. For example, the resolution unit can have the generative AI suggest an optimal solution based on the current location information of the user and driver. This allows the means of resolving needs to be customized based on the current situation of the user and driver.

[0096] The resolution unit can estimate the user's emotions and determine the priority of need resolution based on the estimated emotions. For example, if the user is feeling anxious, the resolution unit will prioritize solutions that provide a sense of security. The resolution unit can use generative AI to estimate the user's emotions and select appropriate priorities. For example, if the user is feeling anxious, the generative AI will use facial recognition technology to estimate the emotions and prioritize solutions that provide a sense of security. The resolution unit can also prioritize solutions that can be resolved quickly if the user is in a hurry. For example, if the user is in a hurry, the generative AI will use voice analysis technology to estimate the emotions and prioritize solutions that can be resolved quickly. The resolution unit can also prioritize standard solutions if the user is relaxed. For example, if the user is relaxed, the generative AI will use text analysis technology to estimate the emotions and prioritize standard solutions. This allows the resolution unit to determine the priority of need resolution based on the user's emotions.

[0097] The resolution unit can select the optimal solution to address the needs by considering the geographical location information of the user and the driver. For example, if the user is in a specific area, the resolution unit will propose a solution that is appropriate to the characteristics of that area. The resolution unit can also use generative AI to select the optimal solution to address the needs by considering the geographical location information of the user and the driver. For example, if the user is in a specific area, the generative AI will propose a solution that is appropriate to the characteristics of that area. Furthermore, if the driver is operating in a specific area, the resolution unit can select a solution that is appropriate to the traffic conditions of that area. For example, if the driver is operating in a specific area, the generative AI will select a solution that is appropriate to the traffic conditions of that area. In addition, the resolution unit can propose the optimal solution based on the location information of the user and the driver. For example, the generative AI will propose the optimal solution based on the location information of the user and the driver. This allows the resolution unit to select the optimal solution to address the needs by considering the geographical location information of the user and the driver.

[0098] The resolution unit can analyze the social media activities of users and drivers and propose means to resolve needs. For example, the resolution unit can analyze users' social media posts and propose the optimal solution. The resolution unit can also use generative AI to analyze the social media activities of users and drivers and propose means to resolve needs. For example, the resolution unit analyzes users' social media posts and the generative AI proposes the optimal solution. The resolution unit can also refer to the social media activities of drivers and select an appropriate solution. For example, the resolution unit refers to the social media activities of drivers and the generative AI selects an appropriate solution. The resolution unit can also propose effective solutions based on the evaluations of users and drivers on social media. For example, the resolution unit uses the evaluations of users and drivers on social media and the generative AI proposes an effective solution. In this way, the resolution unit can analyze the social media activities of users and drivers and propose means to resolve needs.

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

[0100] The rideshare system can also include a health management unit that monitors the user's health. This unit can, for example, monitor the user's heart rate and blood pressure in real time and issue alerts if abnormalities occur. The health management unit can also use AI to analyze the user's health data and take appropriate action. For example, if the user's heart rate increases rapidly, the health management unit can send a notification to emergency contacts. Furthermore, the health management unit can instruct drivers on appropriate driving methods based on the user's health status. For example, if the user appears fatigued, the health management unit can instruct the driver to take a break. This allows for real-time monitoring of the user's health and enables the provision of a safe rideshare service.

[0101] Ridesharing systems can also include an entertainment unit that provides music and entertainment based on user preferences. For example, the entertainment unit could analyze a user's music preferences and generate appropriate playlists. It could also use AI to analyze a user's past music playback history and provide music that matches their preferences. For instance, if a user wants to relax, the entertainment unit could provide relaxing music. Furthermore, the entertainment unit could offer games or video content that users can enjoy while driving. For example, it could offer simple games that users can enjoy while driving. This allows for entertainment tailored to user preferences, providing a comfortable ridesharing experience.

[0102] The ride-sharing system can further estimate the user's emotions and adjust the driver's response based on those estimates. For example, if a user is feeling anxious, the system can instruct the driver to act in a way that provides reassurance. Drivers can use generative AI to estimate the user's emotions and respond appropriately. For example, if a user is feeling anxious, the generative AI can use facial recognition technology to estimate their emotions and speak to them in a gentle manner. Drivers can also adjust their driving style to ensure a quick arrival at the destination if the user is in a hurry. For example, if a user is in a hurry, the generative AI can use voice analysis technology to estimate their emotions and select the shortest route. Drivers can also provide a comfortable ride if the user is relaxed. For example, if a user is relaxed, the generative AI can use text analysis technology to estimate their emotions and drive calmly. This allows the driver's response to be adjusted based on the user's emotions.

[0103] The ride-sharing system can further estimate the user's emotions and adjust the in-car environment based on those emotions. For example, if the user is feeling anxious, the system can soften the interior lighting to provide a relaxing environment. The in-car environment adjustment unit uses generative AI to estimate the user's emotions and provide an appropriate environment. For example, if the user is feeling anxious, the generative AI uses facial recognition technology to estimate the emotion and softens the lighting. The in-car environment adjustment unit can also adjust the temperature inside the car appropriately to provide a comfortable environment if the user is in a hurry. For example, if the user is in a hurry, the generative AI uses voice analysis technology to estimate the emotion and adjusts the temperature inside the car appropriately. Furthermore, if the user is relaxed, the in-car environment adjustment unit can provide music or scents to provide a comfortable environment. For example, if the user is relaxed, the generative AI uses text analysis technology to estimate the emotion and provides relaxing music or scents. This allows the in-car environment to be adjusted based on the user's emotions.

[0104] The ride-sharing system can further estimate the user's emotions and offer discounts based on those emotions. For example, if a user is feeling anxious, a portion of the fare can be discounted to provide reassurance. The fare discounting section can use generative AI to estimate the user's emotions and offer appropriate discounts. For example, if a user is feeling anxious, the generative AI uses facial recognition technology to estimate their emotions and discounts a portion of the fare. The fare discounting section can also discount additional charges to provide faster service if the user is in a hurry. For example, if a user is in a hurry, the generative AI uses voice analysis technology to estimate their emotions and discounts the additional charges. The fare discounting section can also apply the regular fare if the user is relaxed. For example, if a user is relaxed, the generative AI uses text analysis technology to estimate their emotions and applies the regular fare. This allows for fare discounts to be offered based on the user's emotions.

[0105] The ride-sharing system can further estimate the user's emotions and suggest destinations based on those emotions. For example, if the user wants to relax, it can suggest a place where they can relax. The destination suggestion unit uses generative AI to estimate the user's emotions and suggest an appropriate destination. For example, if the user wants to relax, the generative AI uses facial recognition technology to estimate their emotions and suggests a place where they can relax. The destination suggestion unit can also suggest a place that can be reached via the shortest route if the user is in a hurry. For example, if the user is in a hurry, the generative AI uses voice analysis technology to estimate their emotions and suggests a place that can be reached via the shortest route. Furthermore, if the user is adventurous, the destination suggestion unit can suggest a new place. For example, if the user is adventurous, the generative AI uses text analysis technology to estimate their emotions and suggests a new place. In this way, destination suggestions can be made based on the user's emotions.

[0106] The ride-sharing system can further analyze the user's past usage history and suggest the optimal route. For example, the route suggestion unit can suggest the optimal route based on routes the user has frequently used in the past. The route suggestion unit can also use AI to analyze the user's past usage history and suggest the optimal route. For example, the route suggestion unit can suggest the optimal route based on routes the user has frequently used in the past. Furthermore, the route suggestion unit can suggest the optimal route based on the user's current location information. For example, the route suggestion unit can suggest the optimal route based on the user's current location information. Additionally, the route suggestion unit can suggest the optimal route considering traffic conditions. For example, the route suggestion unit can suggest the optimal route considering traffic conditions. This allows the system to analyze the user's past usage history and suggest the optimal route.

[0107] The ride-sharing system can further suggest the optimal pickup point based on the user's current location. The pickup point suggestion unit can, for example, suggest the pickup point closest to the user's current location. The pickup point suggestion unit can also use AI to suggest the optimal pickup point based on the user's current location. Furthermore, the pickup point suggestion unit can suggest the optimal pickup point considering traffic conditions. It can also suggest the optimal pickup point based on the user's preferences. This allows the system to suggest the optimal pickup point based on the user's current location.

[0108] The ride-sharing system can further analyze users' past ratings and select the most suitable driver. For example, the driver selection unit might prioritize drivers who have received high ratings from users in the past. The driver selection unit can also use AI to analyze users' past ratings and select the most suitable driver. For example, the driver selection unit might prioritize drivers who have received high ratings from users in the past. Furthermore, the driver selection unit can select the most suitable driver based on the user's current situation. For example, the driver selection unit might select the most suitable driver based on the user's current situation. Additionally, the driver selection unit can select the most suitable driver based on the driver's past ratings. For example, the driver selection unit might select the most suitable driver based on the driver's past ratings. This allows the system to analyze users' past ratings and select the most suitable driver.

[0109] Ridesharing systems can further provide optimal services based on the user's current situation. For example, the service provider can provide the optimal service according to the user's current circumstances. The service provider can also use AI to provide optimal services based on the user's current situation. Furthermore, the service provider can provide optimal services based on the user's past usage history. For example, the service provider can provide optimal services based on the user's past usage history. Additionally, the service provider can provide optimal services based on the user's preferences. This allows for the provision of optimal services based on the user's current situation.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The verification unit checks the driver and vehicle's licensing criteria. The verification unit evaluates the licensing criteria based on the driver's qualifications and the vehicle's condition, and uses AI to analyze past driving history and vehicle maintenance history. For example, it analyzes past traffic violation history, accident history, driving time, and distance to evaluate compliance with licensing criteria. Step 2: The service provider uses a generative AI to provide information. The service provider provides appropriate answers when users ask questions about how to use rideshare services and quickly guides drivers on how to deal with problems they encounter while driving. The generative AI generates answers by referring to an FAQ database and troubleshooting guides. Step 3: The monitoring unit utilizes connected cars to monitor the vehicle's status. The monitoring unit grasps the vehicle's location, speed, fuel level, etc., in real time and uses AI to respond immediately if an anomaly occurs. For example, it determines location based on GPS data and monitors speed and fuel level using sensor technology. Step 4: The resolution unit addresses the needs of both users and drivers. Using generated AI, the resolution unit accurately provides information ranging from FAQs to specialized information, quickly guiding users on how to use rideshare services and drivers on how to respond when they encounter problems while driving.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the verification unit, provision unit, monitoring unit, and resolution unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the verification unit evaluates the driver's qualifications and the vehicle's condition using the control unit 46A of the smart device 14, and analyzes past driving history and maintenance history using the identification processing unit 290 of the data processing unit 12. The provision unit answers user questions using generated AI with the control unit 46A of the smart device 14, and refers to an FAQ database using the identification processing unit 290 of the data processing unit 12. The monitoring unit grasps the vehicle's location information and speed in real time using the control unit 46A of the smart device 14, and detects abnormalities using the identification processing unit 290 of the data processing unit 12. The resolution unit resolves user and driver needs using generated AI with the control unit 46A of the smart device 14, and provides expert information using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0129] 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.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0131] Each of the multiple elements described above, including the verification unit, provision unit, monitoring unit, and resolution unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the verification unit evaluates the driver's qualifications and the vehicle's condition using the control unit 46A of the smart glasses 214, and analyzes past driving history and maintenance history using the identification processing unit 290 of the data processing unit 12. The provision unit answers user questions using generated AI with the control unit 46A of the smart glasses 214, and refers to an FAQ database using the identification processing unit 290 of the data processing unit 12. The monitoring unit grasps the vehicle's location information and speed in real time using the control unit 46A of the smart glasses 214, and detects abnormalities using the identification processing unit 290 of the data processing unit 12. The resolution unit resolves user and driver needs using generated AI with the control unit 46A of the smart glasses 214, and provides expert information using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0147] Each of the multiple elements described above, including the verification unit, provision unit, monitoring unit, and resolution unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the verification unit evaluates the driver's qualifications and the vehicle's condition using the control unit 46A of the headset terminal 314, and analyzes past driving history and maintenance history using the identification processing unit 290 of the data processing unit 12. The provision unit, for example, answers user questions using generated AI with the control unit 46A of the headset terminal 314, and refers to an FAQ database using the identification processing unit 290 of the data processing unit 12. The monitoring unit, for example, grasps the vehicle's location information and speed in real time using the control unit 46A of the headset terminal 314, and detects abnormalities using the identification processing unit 290 of the data processing unit 12. The resolution unit, for example, resolves the needs of the user and driver using generated AI with the control unit 46A of the headset terminal 314, and provides expert information using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[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 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.

[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 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.

[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 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.

[0164] Each of the multiple elements described above, including the verification unit, provision unit, monitoring unit, and resolution unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the verification unit evaluates the driver's qualifications and the vehicle's condition using the control unit 46A of the robot 414, and analyzes past driving history and maintenance history using the identification processing unit 290 of the data processing unit 12. The provision unit answers user questions using generated AI with the control unit 46A of the robot 414, and refers to an FAQ database using the identification processing unit 290 of the data processing unit 12. The monitoring unit grasps the vehicle's location information and speed in real time using the control unit 46A of the robot 414, and detects abnormalities using the identification processing unit 290 of the data processing unit 12. The resolution unit resolves user and driver needs using generated AI with the control unit 46A of the robot 414, and provides expert information using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

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

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A verification unit that checks the permit standards for drivers and vehicles, A provision department that provides information using generation AI, A monitoring unit that utilizes connected cars to monitor the vehicle's status, It includes a resolution unit that addresses the needs of both the user and the driver. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Provide appropriate answers when users ask questions about how to use rideshare services. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, To quickly guide drivers on how to deal with problems they encounter while driving. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned monitoring unit, The system tracks the vehicle's location, speed, fuel level, and other information in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned monitoring unit, Respond immediately if an anomaly occurs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned eliminating unit is We provide accurate information ranging from FAQs to specialized topics to address the needs of both users and drivers. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned verification unit is We estimate user sentiment and adjust the verification process for driver and vehicle approval criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned verification unit is We analyze the driver's past driving history and evaluate their compliance with the licensing criteria. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned verification unit is Refer to the vehicle's maintenance history to confirm compliance with the permit standards. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned verification unit is It estimates the user's emotions and determines the priority of permission criteria to confirm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned verification unit is When reviewing permit criteria, prioritize reviewing the most relevant criteria by considering the driver's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned verification unit is When verifying the permit criteria, we analyze the driver's social media activity and obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way information is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, Adjust the level of detail of the information based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, Apply different information delivery algorithms depending on the category of information being provided. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the length of information provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, Prioritize information based on when it is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, The order of information is adjusted based on the relevance of the information provided. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned monitoring unit, The system estimates the user's emotions and adjusts the vehicle status monitoring method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned monitoring unit, Analyzing past vehicle operation data improves the accuracy of monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned monitoring unit, Refer to the vehicle's maintenance history to determine monitoring priorities. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned monitoring unit, Monitoring is conducted while considering the geographical distribution of vehicles. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned monitoring unit, Improve monitoring accuracy by referring to relevant vehicle literature. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned eliminating unit is It estimates the user's emotions and adjusts the method of addressing their needs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned eliminating unit is We analyze the past behavioral history of users and drivers to select the most suitable method for addressing their needs. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned eliminating unit is Customize the means of addressing needs based on the current situation of the user and driver. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned eliminating unit is It estimates the user's emotions and determines the priority of addressing their needs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned eliminating unit is The optimal solution to address user and driver needs is selected by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned eliminating unit is We analyze the social media activity of users and drivers to propose solutions to address their needs. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A verification unit that checks the permit standards for drivers and vehicles, A provision unit that provides information using generation AI, A monitoring unit that utilizes connected cars to monitor the vehicle's status, It includes a resolution unit that addresses the needs of both the user and the driver. A system characterized by the following features.

2. The aforementioned supply unit is, Provide appropriate answers when users ask questions about how to use rideshare services. The system according to feature 1.

3. The aforementioned supply unit is, To quickly guide drivers on how to deal with problems they encounter while driving. The system according to feature 1.

4. The aforementioned monitoring unit, The system tracks the vehicle's location, speed, fuel level, and other information in real time. The system according to feature 1.

5. The aforementioned monitoring unit, Respond immediately if an anomaly occurs. The system according to feature 1.

6. The aforementioned eliminating unit is We provide accurate information ranging from FAQs to specialized topics to address the needs of both users and drivers. The system according to feature 1.

7. The aforementioned verification unit is We estimate user sentiment and adjust the verification process for driver and vehicle approval criteria based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned verification unit is We analyze the driver's past driving history and evaluate their compliance with the licensing criteria. The system according to feature 1.

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