System

Abstract

An object of a system according to an embodiment is to improve matching accuracy between a user and a driver and to provide a safe and secure ride sharing service.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a matching unit, a monitoring unit, an analysis unit, a route analysis unit, and an education unit. The collection unit collects information on a user and a driver. The analysis unit analyzes the information collected by the collection unit. The matching unit matches the user and the driver based on the analysis result obtained by the analysis unit. The collection unit collects information inside and outside the vehicle. The monitoring unit monitors information inside and outside the vehicle. The analysis unit analyzes the driving technique. The route analysis unit analyzes the driving route. The teaching department provides the driver with a teaching program.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there is room for improvement in the accuracy of matching users and drivers and in improving safety in ride-sharing services.

[0005] The system according to the embodiment aims to improve the accuracy of matching users and drivers and provide a safe and secure ride-sharing service. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a matching unit, a monitoring unit, an analysis unit, a route analysis unit, and an education unit. The collection unit collects information about users and drivers. The analysis unit analyzes the information collected by the collection unit. The matching unit matches users and drivers based on the analysis results obtained by the analysis unit. The collection unit collects information from inside and outside the vehicle. The monitoring unit monitors information from inside and outside the vehicle. The analysis unit analyzes driving techniques. The route analysis unit analyzes driving routes. The education unit provides education programs to drivers. [Effects of the Invention]

[0007] The system according to the embodiment can improve the accuracy of matching users and drivers and provide a safe and secure ride-sharing service. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A ride-sharing system according to an embodiment of the present invention analyzes user and driver information and performs optimal matching to provide services tailored to user needs. The ride-sharing system collects information such as user riding history and preferences, as well as driver driving history and ratings, and analyzes it using AI. Based on the analysis results, the system selects the optimal driver and matches the user. The ride-sharing system also uses connected cars to collect and monitor information inside and outside the vehicle and analyzes driving skills and driving routes. This provides safe and secure services. Furthermore, the ride-sharing system provides individualized training programs for drivers to improve their skills. For example, the ride-sharing system collects user riding history and preferences, as well as driver driving history and ratings. The ride-sharing system then uses AI to analyze the collected information and select the optimal driver. The ride-sharing system then matches users with drivers based on the analysis results. The ride-sharing system monitors passenger safety using in-vehicle cameras and sensors, and collects information outside the vehicle to monitor traffic conditions in real time. The ride-sharing system then analyzes the driver's driving skills and suggests an appropriate driving route. Based on the driver's driving history and ratings, the ride-sharing system suggests optimal training content. This allows the ride-sharing system to analyze user and driver information, provide optimal matching and safe and secure services, and also contribute to improving driver skills. This allows the ride-sharing system to provide services that meet user needs and realize safe and secure ride-sharing. For example, analyzing user and driver information and providing optimal matching can improve user satisfaction. In addition, collecting and monitoring information inside and outside the vehicle and analyzing driving techniques and driving routes can improve safety. Furthermore, providing individual educational programs for drivers can help improve driver skills.

[0029] A ride-sharing system according to an embodiment includes a collection unit, an analysis unit, a matching unit, a monitoring unit, an analysis unit, and an education unit. The collection unit collects information about users and drivers. The user information includes, for example, riding history and preferences. The driver information includes, for example, driving history and ratings. The collection unit, for example, acquires the user's riding history from a database. The collection unit can also acquire user preferences from questionnaires or app settings. The collection unit can also acquire the driver's driving history from a driving recording device. The collection unit can also acquire the driver's rating from user feedback. The analysis unit analyzes the information collected by the collection unit. The analysis unit, for example, analyzes the user's riding history and preferences using AI. The analysis unit can also analyze the driver's driving history and rating. For example, the analysis unit matches user preferences with driver ratings using a machine learning algorithm. The matching unit matches users with drivers based on the analysis results obtained by the analysis unit. The matching unit selects the optimal driver based on, for example, the user's preferences and the driver's rating. The matching unit can also match users based on their riding history and the driver's driving history. The monitoring unit collects and monitors information inside and outside the vehicle. For example, the monitoring unit monitors passenger safety using in-vehicle cameras and sensors. The monitoring unit can also collect information outside the vehicle to understand traffic conditions in real time. For example, the monitoring unit analyzes in-vehicle camera footage to detect abnormalities. The monitoring unit can also collect traffic conditions outside the vehicle in real time and provide congestion information. The analysis unit analyzes the driver's driving skills and suggests an appropriate driving route. For example, the analysis unit analyzes the driver's driving skills using AI. The analysis unit can also analyze the driving route and suggest an optimal route. For example, the analysis unit evaluates the driver's driving skills and provides advice for improving them. The analysis unit can also suggest an optimal driving route taking traffic conditions into account. The education unit provides educational programs to drivers. For example, the education unit suggests optimal educational content based on the driver's driving history and evaluation.The education department provides educational programs tailored to the needs of drivers, such as improving driving techniques and customer service manners. The education department can also help drivers improve their skills through online courses and on-the-job training. This allows the ride-sharing system according to the embodiment to analyze information on users and drivers, provide optimal matching, and provide safe and secure services. It also contributes to improving drivers' skills.

[0030] The collection unit can collect the user's riding history and preferences, as well as the driver's driving history and ratings. The collection unit, for example, acquires the user's riding history from a database. For example, the collection unit can collect the user's past riding dates and times and riding routes. The collection unit can also acquire the user's preferences from a questionnaire or app settings. For example, the collection unit can collect the user's preferred car model and the driver's gender. The collection unit can also acquire the driver's driving history from a driving recorder. For example, the collection unit can collect the driver's driving time and accident history. The collection unit can also acquire the driver's rating from user feedback. For example, the collection unit can collect star ratings and comments given by the user to the driver. This enables more accurate analysis and matching by collecting detailed information about the user and the driver. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's riding history into AI, which then automatically collects the data.

[0031] The analysis unit can analyze the collected information and select an appropriate driver. The analysis unit can, for example, use AI to analyze the user's riding history and preferences. For example, the analysis unit can use a machine learning algorithm to match the user's preferences with the driver's evaluation. The analysis unit can also analyze the driver's driving history and evaluation. For example, the analysis unit can analyze the driver's driving technique and evaluation score to select the optimal driver. The analysis unit can also perform matching based on the user's riding history and the driver's driving history. For example, the analysis unit can compare the user's past riding routes with the driver's driving routes to select the optimal driver. In this way, by analyzing the collected information, the optimal driver can be selected for the user. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected information into AI, which can then automatically perform the analysis.

[0032] The matching unit can match users and drivers based on the analysis results. The matching unit selects the optimal driver based on, for example, the user's preferences and the driver's evaluation. For example, the matching unit can perform matching taking into account the user's preferred car model and the driver's gender. The matching unit can also perform matching based on the user's riding history and the driver's driving history. For example, the matching unit can compare the user's past riding routes with the driver's driving routes to select the optimal driver. By matching users and drivers based on the analysis results, it is possible to provide services that meet the user's needs. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the analysis results into AI, and the AI ​​can automatically perform matching.

[0033] The monitoring unit monitors passenger safety using in-vehicle cameras and sensors and can collect information from outside the vehicle to grasp traffic conditions in real time. The monitoring unit, for example, analyzes in-vehicle camera footage to detect abnormalities. For example, the monitoring unit can monitor in-vehicle camera footage in real time and detect abnormal behavior. The monitoring unit can also collect information from outside the vehicle to grasp traffic conditions in real time. For example, the monitoring unit can collect traffic conditions outside the vehicle in real time and provide congestion information. The monitoring unit can also monitor passenger safety using in-vehicle sensors. For example, the monitoring unit can monitor seat belt fastening status and in-vehicle temperature to detect abnormalities. This allows for the collection and monitoring of information inside and outside the vehicle, thereby providing a safe and secure service. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input in-vehicle camera footage into AI, which can automatically detect abnormalities.

[0034] The analysis unit can analyze the driver's driving skills and suggest a driving route. The analysis unit, for example, analyzes the driver's driving skills using AI. For example, the analysis unit can evaluate the driver's driving skills and provide advice for improving the driving skills. The analysis unit can also analyze the driving route and suggest an optimal route. For example, the analysis unit can suggest an optimal driving route taking traffic conditions into consideration. The analysis unit can also evaluate the driver's driving skills and suggest a training program for improving the driving skills. For example, the analysis unit can evaluate the driver's driving skills and suggest a training program based on the number of sudden braking attempts and average speed. In this way, by analyzing the driver's driving skills, an optimal driving route can be suggested and efficient driving can be supported. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the driver's driving data into AI, which can automatically analyze the driving skills and suggest an optimal driving route.

[0035] The training department can propose optimal training content based on the driver's driving history and evaluation. The training department, for example, analyzes the driver's driving history and proposes training content to improve driving skills. For example, the training department can evaluate the number of sudden braking attempts and average speed based on the driver's driving history and propose courses to improve driving skills. The training department can also propose training content to improve customer service manners based on the driver's evaluation. For example, the training department can evaluate the driver's customer service attitude based on user feedback and propose training to improve customer service manners. This allows the driver's skills to be improved by providing an individual training program based on the driver's driving history and evaluation. Some or all of the above-described processing in the training department may be performed using, for example, AI, or may be performed without AI. For example, the training department can input the driver's driving history into AI, which can then automatically propose optimal training content.

[0036] The collection unit can analyze the user's past riding history and select the optimal collection method. For example, the collection unit retrieves the user's past riding history from a database and analyzes it using AI. For example, the collection unit can prioritize collection of routes that the user has frequently used in the past. The collection unit can also predict and collect routes that the user will use during a specific time period based on the user's past riding history. Furthermore, the collection unit can select the most efficient collection method based on the user's past riding history. For example, the collection unit can analyze the user's past riding history and develop an algorithm to select the optimal collection method. This allows the user's past riding history to be analyzed, thereby selecting the optimal collection method and efficiently collecting information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past riding history into AI, which then automatically analyzes the history and selects the optimal collection method.

[0037] When acquiring the ride history, the collection unit can filter the ride history based on the user's current living situation and areas of interest. The collection unit, for example, acquires the user's current living situation from a questionnaire or app settings. For example, the collection unit can collect the user's occupation and home environment. The collection unit can also acquire the user's areas of interest from social media or a questionnaire. For example, the collection unit can collect the user's hobbies and topics of interest. Furthermore, the collection unit can filter the ride history based on the user's current living situation and areas of interest. For example, the collection unit can prioritize collecting ride history related to the user's current living situation. The collection unit can also filter related ride history based on the user's areas of interest. In this way, by filtering the ride history based on the user's current living situation and areas of interest, more relevant information can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data on the user's living situation and areas of interest into AI, and the AI ​​can automatically perform filtering.

[0038] When acquiring the ride history, the collection unit can select the optimal acquisition means depending on the user's input method. For example, when the user uses voice input, the collection unit acquires the ride history using voice recognition technology. For example, the collection unit can record the user's voice with a microphone and have AI analyze the voice and convert it into text data. Furthermore, when the user uses text input, the collection unit can also acquire the ride history using text analysis technology. For example, the collection unit can acquire the ride history by having AI analyze the text entered by the user. Furthermore, when the user uses image input, the collection unit can also acquire the ride history using image recognition technology. For example, the collection unit can acquire the ride history by having AI analyze images taken by the user. This allows information to be collected efficiently by selecting the optimal acquisition means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's input data into AI, and the AI ​​can automatically select the optimal acquisition means.

[0039] When acquiring a ride history, the collection unit can prioritize acquisition of highly relevant history by taking into account the user's geographical location information. The collection unit, for example, acquires the user's geographical location information from GPS data. For example, the collection unit can prioritize acquisition of highly relevant ride history based on the user's current location. Furthermore, if the user frequently visits a specific area, the collection unit can prioritize acquisition of ride history in that area. Furthermore, the collection unit can select the most efficient collection method based on the user's geographical location information. For example, the collection unit can analyze the user's geographical location information and develop an algorithm for selecting the optimal collection method. This allows for efficient information collection by prioritizing acquisition of highly relevant history by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which can automatically analyze the information and prioritize acquisition of highly relevant history.

[0040] When acquiring the ride history, the collection unit can analyze the user's social media activity and acquire related history. The collection unit, for example, analyzes the user's social media activity. For example, the collection unit can prioritize acquiring ride history for places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts to acquire related ride history. Furthermore, the collection unit can acquire related ride history by referring to the activities of the user's friends on social media. For example, the collection unit can prioritize acquiring ride history for places visited by the user's friends. This makes it possible to efficiently collect related history by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into AI, which can automatically analyze the data and acquire related history.

[0041] When acquiring the ride history, the collection unit can customize the acquisition method by reflecting the user's past feedback. The collection unit, for example, acquires the user's past feedback from a database. For example, the collection unit can select the optimal acquisition method based on feedback provided by the user in the past. The collection unit can also preferentially use a specific acquisition method based on the user's past feedback. Furthermore, the collection unit can continuously improve the acquisition method by reflecting the user's feedback. For example, the collection unit can analyze the user's feedback and develop an algorithm to customize the acquisition method. This allows the acquisition method to be optimized by reflecting the user's past feedback, and information to be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's feedback data into AI, which can automatically analyze and customize the acquisition method.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit adjusts the level of detail of the analysis, for example, using an algorithm that evaluates the importance of the information. For example, the analysis unit can perform a detailed analysis of information with high importance. The analysis unit can also perform a simplified analysis of information with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. For example, the analysis unit can develop an algorithm that evaluates the importance of the information in real time and adjusts the level of detail of the analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data into AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit performs analysis using, for example, an algorithm that classifies the category of information. For example, the analysis unit can apply a traffic analysis algorithm to traffic information. The analysis unit can also apply an evaluation analysis algorithm to user evaluation information. Furthermore, the analysis unit can apply a driving analysis algorithm to the driver's driving history. For example, the analysis unit can develop an algorithm that selects the optimal analysis algorithm depending on the category of information. This allows for more accurate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data into AI, and the AI ​​can automatically apply the optimal analysis algorithm.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit retrieves the user's past analysis results from a database and analyzes them using AI. For example, the analysis unit can improve the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the analysis. Furthermore, the analysis unit can optimize the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit can analyze the user's past analysis results and develop an algorithm that selects the optimal analysis algorithm. This allows the current analysis accuracy to be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI, which can automatically improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. For example, the analysis unit obtains the time of information submission from a database and analyzes it using AI. For example, the analysis unit can prioritize analysis of the most recent information. The analysis unit can also postpone information submitted earlier. Furthermore, the analysis unit can dynamically adjust the priority of analysis depending on the time of information submission. For example, the analysis unit can develop an algorithm that evaluates the time of information submission in real time and determines the priority of analysis. This allows for efficient analysis by determining the priority of analysis based on the time of information submission. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without AI. For example, the analysis unit can input data on the time of information submission into AI, and the AI ​​can automatically determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit adjusts the order of analysis using, for example, an algorithm that evaluates the relevance of information. For example, the analysis unit can prioritize analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of information. For example, the analysis unit can develop an algorithm that evaluates the relevance of information in real time and adjusts the order of analysis. This allows for efficient analysis by adjusting the order of analysis based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of information into AI, and the AI ​​can automatically adjust the order of analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit performs the analysis using, for example, an algorithm that evaluates the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results that avoid technical terms. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis results according to the user's level of expertise. For example, the analysis unit can develop an algorithm that evaluates the user's level of expertise in real time and adjusts the use of technical terms in the analysis results. This allows for adjusting the use of technical terms in the analysis according to the user's level of expertise, thereby providing analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into AI, which can then automatically adjust the use of technical terms in the analysis.

[0048] The matching unit can improve the accuracy of matching by taking into account the relationship between the user and the driver during matching. The matching unit, for example, selects a driver with good compatibility based on the user and driver's past matching history. For example, the matching unit can analyze the number of past matches and evaluations between the user and the driver, and prioritize matching drivers with good compatibility. The matching unit can also perform optimal matching based on the evaluations of the user and the driver. Furthermore, the matching unit can analyze the relationship between the user and the driver and improve the accuracy of matching. For example, the matching unit can analyze the relationship data between the user and the driver and develop an optimal matching algorithm. This allows for more accurate matching by taking the relationship between the user and the driver into consideration. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the relationship data between the user and the driver into AI, which then automatically analyzes the data and performs optimal matching.

[0049] The matching unit can perform matching by taking into account attribute information of the user and the driver. The matching unit, for example, acquires attribute information of the user and the driver from a database. For example, the matching unit can perform matching by taking into account the age and gender of the user and the driver. The matching unit can also perform matching by taking into account the hobbies and interests of the user and the driver. Furthermore, the matching unit can perform optimal matching based on the attribute information of the user and the driver. For example, the matching unit can analyze the attribute information of the user and the driver and develop an optimal matching algorithm. This allows for more appropriate matching by taking into account the attribute information of the user and the driver. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input attribute information data of the user and the driver into AI, which then automatically analyzes the data and performs optimal matching.

[0050] During matching, the matching unit can weight the matching based on the past matching history between the user and the driver. The matching unit, for example, retrieves the past matching history between the user and the driver from a database. For example, the matching unit can prioritize matching drivers who are compatible with the user and the driver based on the number of past matchings and evaluations between the user and the driver. The matching unit can also prioritize selecting a specific driver from the past matching history between the user and the driver. Furthermore, the matching unit can weight the matching based on the past matching history between the user and the driver. For example, the matching unit can analyze the past matching history between the user and the driver and develop an optimal matching algorithm. This allows for more accurate matching by weighting the matching based on the past matching history between the user and the driver. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without AI. For example, the matching unit can input past matching history data between the user and the driver into AI, which then automatically analyzes and weights the matching.

[0051] The matching unit can perform matching by taking into account the geographical distribution of users and drivers. For example, the matching unit acquires the geographical distribution of users and drivers from GPS data. For example, the matching unit can prioritize matching when the current locations of users and drivers are close to each other. The matching unit can also perform optimal matching based on the geographical distribution of users and drivers. Furthermore, the matching unit can perform efficient matching by taking into account the geographical distribution of users and drivers. For example, the matching unit can analyze the geographical distribution of users and drivers and develop an optimal matching algorithm. This allows for more efficient matching by taking the geographical distribution of users and drivers into consideration. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input geographical distribution data of users and drivers into AI, which can then automatically analyze the data and perform optimal matching.

[0052] The matching unit can improve the accuracy of matching by referring to literature related to the user and the driver during matching. The matching unit, for example, retrieves literature related to the user and the driver from a database. For example, the matching unit can perform optimal matching based on the literature related to the user and the driver. The matching unit can also improve the accuracy of matching by referring to the literature related to the user and the driver. Furthermore, the matching unit can analyze literature related to the user and the driver to improve the accuracy of matching. For example, the matching unit can analyze literature related to the user and the driver and develop an optimal matching algorithm. This allows for more accurate matching by referring to the literature related to the user and the driver. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input literature data related to the user and the driver into AI, which then automatically analyzes the data and performs optimal matching.

[0053] The matching unit can perform matching by taking into account the market value of the user and the driver. For example, the matching unit obtains the market value of the user and the driver from a database. For example, the matching unit can perform optimal matching based on the market value of the user and the driver. The matching unit can also perform efficient matching by taking into account the market value of the user and the driver. Furthermore, the matching unit can analyze the market value of the user and the driver and develop an optimal matching algorithm. For example, the matching unit can analyze the market value of the user and the driver and develop an algorithm for optimal matching. This allows for more efficient matching by taking into account the market value of the user and the driver. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input market value data of the user and the driver into AI, which then automatically analyzes the data and performs optimal matching.

[0054] During monitoring, the monitoring unit can optimize current monitoring by referring to past monitoring data. For example, the monitoring unit obtains past monitoring data from a database and analyzes it using AI. For example, the monitoring unit can improve current monitoring accuracy based on the past monitoring data. The monitoring unit can also extract specific patterns from the past monitoring data and reflect them in current monitoring. Furthermore, the monitoring unit can optimize monitoring algorithms by referring to past monitoring data. For example, the monitoring unit can analyze past monitoring data and develop optimal monitoring algorithms. This makes it possible to improve current monitoring accuracy by referring to past monitoring data. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input past monitoring data into AI, which then automatically analyzes the data and optimizes current monitoring.

[0055] During monitoring, the monitoring unit can apply different monitoring methods to each information category inside and outside the vehicle. The monitoring unit performs monitoring using, for example, an algorithm that classifies information categories inside and outside the vehicle. For example, the monitoring unit can apply an interior monitoring algorithm to information inside the vehicle. The monitoring unit can also apply an exterior monitoring algorithm to information outside the vehicle. Furthermore, the monitoring unit can dynamically apply different monitoring methods depending on the information category inside and outside the vehicle. For example, the monitoring unit can develop an algorithm that evaluates information categories inside and outside the vehicle in real time and applies the optimal monitoring method. This allows for more accurate monitoring by applying different monitoring methods depending on the information category inside and outside the vehicle. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input information category data inside and outside the vehicle into AI, which then automatically applies the optimal monitoring method.

[0056] The monitoring unit can perform monitoring while taking into account the user's attribute information. The monitoring unit, for example, acquires the user's attribute information from a database. For example, the monitoring unit can perform monitoring while taking into account the user's age and gender. The monitoring unit can also perform monitoring while taking into account the user's hobbies and interests. Furthermore, the monitoring unit can perform optimal monitoring based on the user's attribute information. For example, the monitoring unit can analyze the user's attribute information and develop an optimal monitoring algorithm. This allows for more appropriate monitoring by taking the user's attribute information into account. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's attribute information data into AI, which then automatically analyzes the data and performs optimal monitoring.

[0057] During monitoring, the monitoring unit can analyze changes in monitoring based on the timing of acquisition of information inside and outside the vehicle. For example, the monitoring unit acquires the timing of acquisition of information inside and outside the vehicle from a database, and AI analyzes the information. For example, the monitoring unit can analyze changes in monitoring based on the timing of acquisition of information inside and outside the vehicle. The monitoring unit can also postpone information acquired at an older time. Furthermore, the monitoring unit can dynamically analyze changes in monitoring based on the timing of acquisition of information. For example, the monitoring unit can develop an algorithm that evaluates the timing of information acquisition in real time and analyzes changes in monitoring. This enables more accurate monitoring by analyzing changes in monitoring based on the timing of acquisition of information inside and outside the vehicle. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input data on the timing of acquisition of information inside and outside the vehicle into AI, which then automatically analyzes and analyzes changes in monitoring.

[0058] During monitoring, the monitoring unit can analyze the monitoring by referring to relevant market data inside and outside the vehicle. For example, the monitoring unit obtains the relevant market data inside and outside the vehicle from a database, and the AI ​​analyzes the data. For example, the monitoring unit can improve the accuracy of the monitoring based on the relevant market data inside and outside the vehicle. The monitoring unit can also analyze changes in the monitoring by referring to the relevant market data inside and outside the vehicle. Furthermore, the monitoring unit can analyze the relevant market data inside and outside the vehicle to improve the accuracy of the monitoring. For example, the monitoring unit can analyze the relevant market data inside and outside the vehicle and develop an optimal monitoring algorithm. As a result, more accurate monitoring can be performed by referring to the relevant market data inside and outside the vehicle. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the relevant market data inside and outside the vehicle into AI, which then automatically analyzes the data, thereby improving the accuracy of the monitoring.

[0059] During monitoring, the monitoring unit can analyze the monitoring taking into account the technological maturity inside and outside the vehicle. For example, the monitoring unit obtains the technological maturity inside and outside the vehicle from a database, and AI analyzes the data. For example, the monitoring unit can improve the accuracy of monitoring based on the technological maturity inside and outside the vehicle. The monitoring unit can also analyze changes in monitoring by referring to the technological maturity inside and outside the vehicle. Furthermore, the monitoring unit can analyze the technological maturity inside and outside the vehicle and improve the accuracy of monitoring. For example, the monitoring unit can analyze the technological maturity inside and outside the vehicle and develop an optimal monitoring algorithm. This enables more accurate monitoring by taking the technological maturity inside and outside the vehicle into consideration. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input technological maturity data inside and outside the vehicle into AI, which then automatically analyzes the data and improves the accuracy of monitoring.

[0060] During the analysis, the analysis unit can analyze the driver's past driving skills and propose an optimal driving route. The analysis unit, for example, retrieves the driver's past driving skills from a database and analyzes them using AI. For example, the analysis unit can propose an optimal driving route based on the driver's past driving skills. The analysis unit can also propose a driving route that avoids congestion based on the driver's past driving skills. Furthermore, the analysis unit can analyze the driver's past driving skills and propose the most efficient driving route. For example, the analysis unit can analyze the driver's past driving skills and develop an algorithm that proposes an optimal driving route. This makes it possible to propose an optimal driving route and support efficient driving by analyzing the driver's past driving skills. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the driver's past driving skill data into AI, which then automatically analyzes the data and proposes an optimal driving route.

[0061] During analysis, the analysis unit can customize the analysis means based on the driver's current driving situation. For example, the analysis unit retrieves the driver's current driving situation from a database and analyzes it using AI. For example, the analysis unit can select the optimal analysis means based on the driver's current driving situation. The analysis unit can also prioritize the use of specific analysis means based on the driver's current driving situation. Furthermore, the analysis unit can continuously improve the analysis means by referring to the driver's current driving situation. For example, the analysis unit can analyze the driver's current driving situation and develop an algorithm to select the optimal analysis means. This allows the analysis means to be customized based on the driver's current driving situation, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the driver's current driving situation into AI, which then automatically analyzes and selects the optimal analysis means.

[0062] The analysis unit can improve the analysis method by reflecting the driver's feedback during analysis. For example, the analysis unit obtains the driver's feedback from a database and analyzes it using AI. For example, the analysis unit can select the optimal analysis method based on the feedback provided by the driver. The analysis unit can also prioritize the use of a specific analysis method based on the driver's feedback. Furthermore, the analysis unit can continuously improve the analysis method by reflecting the driver's feedback. For example, the analysis unit can analyze the driver's feedback and develop an algorithm to select the optimal analysis method. This allows the analysis method to be continuously improved by reflecting the driver's feedback, and more appropriate analysis results to be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the driver's feedback data into AI, which then automatically analyzes and improves the analysis method.

[0063] During analysis, the analysis unit can select the optimal analysis method by taking into account the driver's geographical location information. For example, the analysis unit obtains the driver's geographical location information from GPS data and analyzes it using AI. For example, the analysis unit can select the optimal analysis method based on the driver's current location. The analysis unit can also prioritize the use of a specific analysis method based on the driver's geographical location information. Furthermore, the analysis unit can continuously improve the analysis method by referring to the driver's geographical location information. For example, the analysis unit can analyze the driver's geographical location information and develop an algorithm to select the optimal analysis method. This makes it possible to select a more appropriate analysis method by taking the driver's geographical location information into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the driver's geographical location information data into AI, which can then automatically analyze and select the optimal analysis method.

[0064] During analysis, the analysis unit can analyze the driver's social media activity and suggest analysis methods. For example, the analysis unit retrieves the driver's social media activity from a database and analyzes it using AI. For example, the analysis unit can select the optimal analysis method based on the driver's social media activity. The analysis unit can also prioritize the use of specific analysis methods based on the driver's social media activity. Furthermore, the analysis unit can continuously improve the analysis method by referring to the driver's social media activity. For example, the analysis unit can analyze the driver's social media activity and develop an algorithm to select the optimal analysis method. This makes it possible to suggest more appropriate analysis methods by analyzing the driver's social media activity. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the driver's social media activity data into AI, which can automatically analyze the data and suggest the optimal analysis method.

[0065] During analysis, the analysis unit can customize the analysis method by reflecting the driver's past feedback. For example, the analysis unit retrieves the driver's past feedback from a database and analyzes it using AI. For example, the analysis unit can select the optimal analysis method based on the driver's past feedback. The analysis unit can also prioritize the use of a specific analysis method based on the driver's past feedback. Furthermore, the analysis unit can continuously improve the analysis method by reflecting the driver's past feedback. For example, the analysis unit can analyze the driver's past feedback and develop an algorithm to select the optimal analysis method. This allows the analysis method to be continuously improved by reflecting the driver's past feedback, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the driver's past feedback data into AI, which then automatically analyzes and customizes the analysis method.

[0066] During training, the training department can analyze the driver's past driving history and select the most appropriate training content. For example, the training department retrieves the driver's past driving history from a database and analyzes it using AI. For example, the training department can select the most appropriate training content based on the driver's past driving history. The training department can also prioritize specific training content based on the driver's past driving history. Furthermore, the training department can analyze the driver's past driving history and select the most effective training content. For example, the training department can analyze the driver's past driving history and develop an algorithm for selecting the most appropriate training content. This allows for the selection of more appropriate training content by analyzing the driver's past driving history. Some or all of the above-mentioned processing in the training department can be performed using, for example, AI, or without AI. For example, the training department can input the driver's past driving history data into AI, which can automatically analyze the data and select the most appropriate training content.

[0067] During training, the training department can customize training methods based on the driver's current skill level. For example, the training department retrieves the driver's current skill level from a database and analyzes it using AI. For example, the training department can select the optimal training method based on the driver's current skill level. The training department can also prioritize specific training methods based on the driver's current skill level. Furthermore, the training department can continuously improve the training method based on the driver's current skill level. For example, the training department can analyze the driver's current skill level and develop an algorithm to select the optimal training method. This allows for customizing the training method based on the driver's current skill level, thereby providing more appropriate training. Some or all of the above-mentioned processing in the training department can be performed using, for example, AI, or without AI. For example, the training department can input the driver's current skill level data into AI, which can then automatically analyze and select the optimal training method.

[0068] The training department can improve the training content by reflecting driver feedback during training. For example, the training department obtains driver feedback from a database and analyzes it using AI. For example, the training department can select the optimal training content based on the feedback provided by the driver. The training department can also prioritize specific training content based on the driver feedback. Furthermore, the training department can continuously improve the training content by reflecting driver feedback. For example, the training department can develop an algorithm that analyzes driver feedback and selects the optimal training content. This allows the training content to be continuously improved by reflecting driver feedback, and more appropriate training can be provided. Some or all of the above-mentioned processing in the training department can be performed using AI, for example, or without AI. For example, the training department can input driver feedback data into AI, which can automatically analyze and improve the training content.

[0069] During training, the Ministry of Education can select the most appropriate training content by taking into account the driver's geographical location information. For example, the Ministry of Education may obtain the driver's geographical location information from GPS data and analyze it using AI. For example, the Ministry of Education can select the most appropriate training content based on the driver's current location. The Ministry of Education can also prioritize providing specific training content based on the driver's geographical location information. Furthermore, the Ministry of Education can continuously improve the training content by referring to the driver's geographical location information. For example, the Ministry of Education can analyze the driver's geographical location information and develop an algorithm to select the most appropriate training content. This allows for the selection of more appropriate training content by taking the driver's geographical location information into consideration. Some or all of the above-mentioned processing by the Ministry of Education may be performed using, for example, AI, or may be performed without using AI. For example, the Ministry of Education can input the driver's geographical location information data into AI, which can then automatically analyze and select the most appropriate training content.

[0070] During training, the Ministry of Education can analyze the driver's social media activity and suggest training methods. For example, the Ministry of Education retrieves the driver's social media activity from a database and analyzes it using AI. For example, the Ministry of Education can select the most appropriate training method based on the driver's social media activity. The Ministry of Education can also prioritize certain training methods based on the driver's social media activity. Furthermore, the Ministry of Education can continuously improve the training methods based on the driver's social media activity. For example, the Ministry of Education can analyze the driver's social media activity and develop an algorithm to select the most appropriate training method. This makes it possible to suggest more appropriate training methods by analyzing the driver's social media activity. Some or all of the above-mentioned processing by the Ministry of Education may be performed using, for example, AI, or may be performed without AI. For example, the Ministry of Education can input the driver's social media activity data into AI, which can automatically analyze it and suggest the most appropriate training method.

[0071] During training, the training department can customize the training content by reflecting the driver's past feedback. For example, the training department retrieves the driver's past feedback from a database and analyzes it using AI. For example, the training department can select the optimal training content based on the driver's past feedback. The training department can also prioritize specific training content based on the driver's past feedback. Furthermore, the training department can continuously improve the training content by reflecting the driver's past feedback. For example, the training department can analyze the driver's past feedback and develop an algorithm to select the optimal training content. This allows the training content to be continuously improved by reflecting the driver's past feedback, and more appropriate training can be provided. Some or all of the above-mentioned processing in the training department may be performed using, for example, AI, or may be performed without AI. For example, the training department can input the driver's past feedback data into AI, which then automatically analyzes and customizes the training content.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The ride-sharing system may further include a weather information unit that collects real-time weather information and analyzes it in an analysis unit. For example, the weather information unit may collect current weather information and predicted weather changes, and the analysis unit may use this information to evaluate the safety of driving routes. The weather information unit may also analyze weather patterns in a specific area and provide appropriate driving advice to the driver. Furthermore, the weather information unit may combine the user's riding history with weather data to preferentially match rides under weather conditions preferred by the user. This allows for a safe and comfortable ride-sharing service that takes weather information into consideration.

[0074] The ride-sharing system may further include a feedback analysis unit that customizes the service based on the user's past feedback. For example, the feedback analysis unit may collect feedback provided by the user in the past and analyze it in the analysis unit. The feedback analysis unit may also make suggestions to improve the driver's service content based on the user's feedback. Furthermore, the feedback analysis unit may preferentially match specific drivers based on the user's feedback. This makes it possible to provide a high-quality ride-sharing service that reflects the user's feedback.

[0075] The ride-sharing system may further include a travel support unit that supports the user's travel plans. For example, the travel support unit may collect the user's travel destinations and schedule and analyze them using the analysis unit. The travel support unit may also suggest tourist spots and restaurants at the user's travel destinations. Furthermore, the travel support unit may suggest optimal driving routes and transportation methods based on the user's travel plans. This supports the user's travel plans and provides a comfortable travel experience.

[0076] The ride-sharing system may further include a fitness support unit that supports the user's exercise habits. For example, the fitness support unit may collect the user's exercise history and goals and analyze them using an analysis unit. The fitness support unit may also suggest an appropriate exercise program based on the user's exercise habits. Furthermore, the fitness support unit may suggest the optimal place and time for the user to exercise. This may support the user's exercise habits and promote a healthy lifestyle.

[0077] The ride-sharing system may further include an education support unit that supports the user's learning needs. For example, the education support unit may collect the user's learning history and goals and analyze them using an analysis unit. The education support unit may also provide appropriate learning content based on the user's learning needs. Furthermore, the education support unit may suggest the optimal time and place for the user to study. This supports the user's learning needs and provides an efficient learning environment.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The collection unit collects user and driver information. User information includes riding history and preferences, and driver information includes driving history and ratings. The collection unit obtains the user's riding history from the database and obtains the user's preferences from questionnaires and app settings. It also obtains the driver's driving history from the driving recorder and obtains the driver's rating from user feedback. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses AI to analyze the user's riding history and preferences, as well as the driver's driving history and ratings. For example, it uses machine learning algorithms to match the user's preferences with the driver's ratings. Step 3: The matching unit matches users and drivers based on the analysis results obtained by the analysis unit. The matching unit selects the most suitable driver based on the user's preferences and driver ratings, and performs matching based on the user's riding history and the driver's driving history. Step 4: The monitoring unit collects and monitors information inside and outside the vehicle. The monitoring unit uses cameras and sensors inside the vehicle to monitor passenger safety, and collects information outside the vehicle to understand traffic conditions in real time. For example, it analyzes camera footage inside the vehicle to detect abnormalities, collects traffic conditions outside the vehicle, and provides congestion information. Step 5: The analysis unit analyzes the driver's driving skills and suggests an appropriate driving route. The analysis unit uses AI to analyze the driver's driving skills and the driving route to suggest the optimal route. For example, it evaluates the driver's driving skills, provides advice to improve them, and suggests the optimal driving route taking traffic conditions into account. Step 6: The Education Department provides the driver with an education program. Based on the driver's driving history and evaluation, the Education Department proposes the most appropriate education content and provides an education program tailored to the driver's needs, such as improving driving skills and customer service manners. For example, the department aims to improve the driver's skills through online courses and on-the-job training.

[0080] (Example 2) A ride-sharing system according to an embodiment of the present invention analyzes user and driver information and performs optimal matching to provide services tailored to user needs. The ride-sharing system collects information such as user riding history and preferences, as well as driver driving history and ratings, and analyzes it using AI. Based on the analysis results, the system selects the optimal driver and matches the user. The ride-sharing system also uses connected cars to collect and monitor information inside and outside the vehicle and analyzes driving skills and driving routes. This provides safe and secure services. Furthermore, the ride-sharing system provides individualized training programs for drivers to improve their skills. For example, the ride-sharing system collects user riding history and preferences, as well as driver driving history and ratings. The ride-sharing system then uses AI to analyze the collected information and select the optimal driver. The ride-sharing system then matches users with drivers based on the analysis results. The ride-sharing system monitors passenger safety using in-vehicle cameras and sensors, and collects information outside the vehicle to monitor traffic conditions in real time. The ride-sharing system then analyzes the driver's driving skills and suggests an appropriate driving route. Based on the driver's driving history and ratings, the ride-sharing system suggests optimal training content. This allows the ride-sharing system to analyze user and driver information, provide optimal matching and safe and secure services, and also contribute to improving driver skills. This allows the ride-sharing system to provide services that meet user needs and realize safe and secure ride-sharing. For example, analyzing user and driver information and providing optimal matching can improve user satisfaction. In addition, collecting and monitoring information inside and outside the vehicle and analyzing driving techniques and driving routes can improve safety. Furthermore, providing individual educational programs for drivers can help improve driver skills.

[0081] A ride-sharing system according to an embodiment includes a collection unit, an analysis unit, a matching unit, a monitoring unit, an analysis unit, and an education unit. The collection unit collects information about users and drivers. The user information includes, for example, riding history and preferences. The driver information includes, for example, driving history and ratings. The collection unit, for example, acquires the user's riding history from a database. The collection unit can also acquire user preferences from questionnaires or app settings. The collection unit can also acquire the driver's driving history from a driving recording device. The collection unit can also acquire the driver's rating from user feedback. The analysis unit analyzes the information collected by the collection unit. The analysis unit, for example, analyzes the user's riding history and preferences using AI. The analysis unit can also analyze the driver's driving history and rating. For example, the analysis unit matches user preferences with driver ratings using a machine learning algorithm. The matching unit matches users with drivers based on the analysis results obtained by the analysis unit. The matching unit selects the optimal driver based on, for example, the user's preferences and the driver's rating. The matching unit can also match users based on their riding history and the driver's driving history. The monitoring unit collects and monitors information inside and outside the vehicle. For example, the monitoring unit monitors passenger safety using in-vehicle cameras and sensors. The monitoring unit can also collect information outside the vehicle to understand traffic conditions in real time. For example, the monitoring unit analyzes in-vehicle camera footage to detect abnormalities. The monitoring unit can also collect traffic conditions outside the vehicle in real time and provide congestion information. The analysis unit analyzes the driver's driving skills and suggests an appropriate driving route. For example, the analysis unit analyzes the driver's driving skills using AI. The analysis unit can also analyze the driving route and suggest an optimal route. For example, the analysis unit evaluates the driver's driving skills and provides advice for improving them. The analysis unit can also suggest an optimal driving route taking traffic conditions into account. The education unit provides educational programs to drivers. For example, the education unit suggests optimal educational content based on the driver's driving history and evaluation.The education department provides educational programs tailored to the needs of drivers, such as improving driving techniques and customer service manners. The education department can also help drivers improve their skills through online courses and on-the-job training. This allows the ride-sharing system according to the embodiment to analyze information on users and drivers, provide optimal matching, and provide safe and secure services. It also contributes to improving drivers' skills.

[0082] The collection unit can collect the user's riding history and preferences, as well as the driver's driving history and ratings. The collection unit, for example, acquires the user's riding history from a database. For example, the collection unit can collect the user's past riding dates and times and riding routes. The collection unit can also acquire the user's preferences from a questionnaire or app settings. For example, the collection unit can collect the user's preferred car model and the driver's gender. The collection unit can also acquire the driver's driving history from a driving recorder. For example, the collection unit can collect the driver's driving time and accident history. The collection unit can also acquire the driver's rating from user feedback. For example, the collection unit can collect star ratings and comments given by the user to the driver. This enables more accurate analysis and matching by collecting detailed information about the user and the driver. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's riding history into AI, which then automatically collects the data.

[0083] The analysis unit can analyze the collected information and select an appropriate driver. The analysis unit can, for example, use AI to analyze the user's riding history and preferences. For example, the analysis unit can use a machine learning algorithm to match the user's preferences with the driver's evaluation. The analysis unit can also analyze the driver's driving history and evaluation. For example, the analysis unit can analyze the driver's driving technique and evaluation score to select the optimal driver. The analysis unit can also perform matching based on the user's riding history and the driver's driving history. For example, the analysis unit can compare the user's past riding routes with the driver's driving routes to select the optimal driver. In this way, by analyzing the collected information, the optimal driver can be selected for the user. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected information into AI, which can then automatically perform the analysis.

[0084] The matching unit can match users and drivers based on the analysis results. The matching unit selects the optimal driver based on, for example, the user's preferences and the driver's evaluation. For example, the matching unit can perform matching taking into account the user's preferred car model and the driver's gender. The matching unit can also perform matching based on the user's riding history and the driver's driving history. For example, the matching unit can compare the user's past riding routes with the driver's driving routes to select the optimal driver. By matching users and drivers based on the analysis results, it is possible to provide services that meet the user's needs. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the analysis results into AI, and the AI ​​can automatically perform matching.

[0085] The monitoring unit monitors passenger safety using in-vehicle cameras and sensors and can collect information from outside the vehicle to grasp traffic conditions in real time. The monitoring unit, for example, analyzes in-vehicle camera footage to detect abnormalities. For example, the monitoring unit can monitor in-vehicle camera footage in real time and detect abnormal behavior. The monitoring unit can also collect information from outside the vehicle to grasp traffic conditions in real time. For example, the monitoring unit can collect traffic conditions outside the vehicle in real time and provide congestion information. The monitoring unit can also monitor passenger safety using in-vehicle sensors. For example, the monitoring unit can monitor seat belt fastening status and in-vehicle temperature to detect abnormalities. This allows for the collection and monitoring of information inside and outside the vehicle, thereby providing a safe and secure service. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input in-vehicle camera footage into AI, which can automatically detect abnormalities.

[0086] The analysis unit can analyze the driver's driving skills and suggest a driving route. The analysis unit, for example, analyzes the driver's driving skills using AI. For example, the analysis unit can evaluate the driver's driving skills and provide advice for improving the driving skills. The analysis unit can also analyze the driving route and suggest an optimal route. For example, the analysis unit can suggest an optimal driving route taking traffic conditions into consideration. The analysis unit can also evaluate the driver's driving skills and suggest a training program for improving the driving skills. For example, the analysis unit can evaluate the driver's driving skills and suggest a training program based on the number of sudden braking attempts and average speed. In this way, by analyzing the driver's driving skills, an optimal driving route can be suggested and efficient driving can be supported. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the driver's driving data into AI, which can automatically analyze the driving skills and suggest an optimal driving route.

[0087] The training department can propose optimal training content based on the driver's driving history and evaluation. The training department, for example, analyzes the driver's driving history and proposes training content to improve driving skills. For example, the training department can evaluate the number of sudden braking attempts and average speed based on the driver's driving history and propose courses to improve driving skills. The training department can also propose training content to improve customer service manners based on the driver's evaluation. For example, the training department can evaluate the driver's customer service attitude based on user feedback and propose training to improve customer service manners. This allows the driver's skills to be improved by providing an individual training program based on the driver's driving history and evaluation. Some or all of the above-described processing in the training department may be performed using, for example, AI, or may be performed without AI. For example, the training department can input the driver's driving history into AI, which can then automatically propose optimal training content.

[0088] The collection unit can estimate the user's emotions and adjust the timing of acquiring the ride history based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the collection unit can capture the user's facial expressions with a camera and use AI to analyze the expressions to estimate the emotions. The collection unit can also adjust the timing of acquiring the ride history based on the user's emotions. For example, if the user is feeling stressed, the collection unit can delay acquiring the ride history and acquire it when the user is relaxed. If the user is relaxed, the collection unit can immediately acquire the ride history and quickly reflect this in their next action. Furthermore, if the user is in a hurry, the collection unit can quickly acquire the ride history and immediately analyze it. This allows information to be collected at a more appropriate time by adjusting the timing of acquiring the ride history according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without AI. For example, the collection unit can input the user's emotional data into AI, which can automatically analyze the emotions and adjust the timing of acquiring the ride history.

[0089] The collection unit can analyze the user's past riding history and select the optimal collection method. For example, the collection unit retrieves the user's past riding history from a database and analyzes it using AI. For example, the collection unit can prioritize collection of routes that the user has frequently used in the past. The collection unit can also predict and collect routes that the user will use during a specific time period based on the user's past riding history. Furthermore, the collection unit can select the most efficient collection method based on the user's past riding history. For example, the collection unit can analyze the user's past riding history and develop an algorithm to select the optimal collection method. This allows the user's past riding history to be analyzed, thereby selecting the optimal collection method and efficiently collecting information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past riding history into AI, which then automatically analyzes the history and selects the optimal collection method.

[0090] When acquiring the ride history, the collection unit can filter the ride history based on the user's current living situation and areas of interest. The collection unit, for example, acquires the user's current living situation from a questionnaire or app settings. For example, the collection unit can collect the user's occupation and home environment. The collection unit can also acquire the user's areas of interest from social media or a questionnaire. For example, the collection unit can collect the user's hobbies and topics of interest. Furthermore, the collection unit can filter the ride history based on the user's current living situation and areas of interest. For example, the collection unit can prioritize collecting ride history related to the user's current living situation. The collection unit can also filter related ride history based on the user's areas of interest. In this way, by filtering the ride history based on the user's current living situation and areas of interest, more relevant information can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data on the user's living situation and areas of interest into AI, and the AI ​​can automatically perform filtering.

[0091] When acquiring the ride history, the collection unit can select the optimal acquisition means depending on the user's input method. For example, when the user uses voice input, the collection unit acquires the ride history using voice recognition technology. For example, the collection unit can record the user's voice with a microphone and have AI analyze the voice and convert it into text data. Furthermore, when the user uses text input, the collection unit can also acquire the ride history using text analysis technology. For example, the collection unit can acquire the ride history by having AI analyze the text entered by the user. Furthermore, when the user uses image input, the collection unit can also acquire the ride history using image recognition technology. For example, the collection unit can acquire the ride history by having AI analyze images taken by the user. This allows information to be collected efficiently by selecting the optimal acquisition means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's input data into AI, and the AI ​​can automatically select the optimal acquisition means.

[0092] The collection unit can estimate the user's emotions and determine the priority of the ride histories to be acquired based on the estimated user emotions. The collection unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the collection unit can capture the user's facial expressions with a camera and have AI analyze the expressions to estimate the emotions. The collection unit can also determine the priority of the ride histories to be acquired based on the user's emotions. For example, if the user is feeling stressed, the collection unit can postpone acquiring less important ride histories. Furthermore, if the user is relaxed, the collection unit can prioritize acquiring more important ride histories. Furthermore, if the user is in a hurry, the collection unit can quickly acquire the most important ride histories. Thus, by determining the priority of the ride histories according to the user's emotions, important information can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's emotional data into AI, which can automatically analyze the emotions and determine the priority of the ride histories.

[0093] When acquiring a ride history, the collection unit can prioritize acquisition of highly relevant history by taking into account the user's geographical location information. The collection unit, for example, acquires the user's geographical location information from GPS data. For example, the collection unit can prioritize acquisition of highly relevant ride history based on the user's current location. Furthermore, if the user frequently visits a specific area, the collection unit can prioritize acquisition of ride history in that area. Furthermore, the collection unit can select the most efficient collection method based on the user's geographical location information. For example, the collection unit can analyze the user's geographical location information and develop an algorithm for selecting the optimal collection method. This allows for efficient information collection by prioritizing acquisition of highly relevant history by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which can automatically analyze the information and prioritize acquisition of highly relevant history.

[0094] When acquiring the ride history, the collection unit can analyze the user's social media activity and acquire related history. The collection unit, for example, analyzes the user's social media activity. For example, the collection unit can prioritize acquiring ride history for places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts to acquire related ride history. Furthermore, the collection unit can acquire related ride history by referring to the activities of the user's friends on social media. For example, the collection unit can prioritize acquiring ride history for places visited by the user's friends. This makes it possible to efficiently collect related history by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into AI, which can automatically analyze the data and acquire related history.

[0095] When acquiring the ride history, the collection unit can customize the acquisition method by reflecting the user's past feedback. The collection unit, for example, acquires the user's past feedback from a database. For example, the collection unit can select the optimal acquisition method based on feedback provided by the user in the past. The collection unit can also preferentially use a specific acquisition method based on the user's past feedback. Furthermore, the collection unit can continuously improve the acquisition method by reflecting the user's feedback. For example, the collection unit can analyze the user's feedback and develop an algorithm to customize the acquisition method. This allows the acquisition method to be optimized by reflecting the user's past feedback, and information to be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's feedback data into AI, which can automatically analyze and customize the acquisition method.

[0096] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the analysis unit can capture the user's facial expressions with a camera and use AI to analyze the expressions to estimate the emotions. The analysis unit can also adjust the presentation method of the analysis based on the user's emotions. For example, the analysis unit can provide simple, highly visible analysis results when the user is nervous. The analysis unit can also provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide analysis results that focus on the main points when the user is in a hurry. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotional data into AI, which can then automatically adjust the presentation method of the analysis.

[0097] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit adjusts the level of detail of the analysis, for example, using an algorithm that evaluates the importance of the information. For example, the analysis unit can perform a detailed analysis of information with high importance. The analysis unit can also perform a simplified analysis of information with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. For example, the analysis unit can develop an algorithm that evaluates the importance of the information in real time and adjusts the level of detail of the analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data into AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0098] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit performs analysis using, for example, an algorithm that classifies the category of information. For example, the analysis unit can apply a traffic analysis algorithm to traffic information. The analysis unit can also apply an evaluation analysis algorithm to user evaluation information. Furthermore, the analysis unit can apply a driving analysis algorithm to the driver's driving history. For example, the analysis unit can develop an algorithm that selects the optimal analysis algorithm depending on the category of information. This allows for more accurate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data into AI, and the AI ​​can automatically apply the optimal analysis algorithm.

[0099] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit retrieves the user's past analysis results from a database and analyzes them using AI. For example, the analysis unit can improve the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the analysis. Furthermore, the analysis unit can optimize the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit can analyze the user's past analysis results and develop an algorithm that selects the optimal analysis algorithm. This allows the current analysis accuracy to be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI, which can automatically improve the accuracy of the analysis.

[0100] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the analysis unit can capture the user's facial expressions with a camera and use AI to analyze the expressions to estimate the emotions. The analysis unit can also adjust the length of the analysis based on the user's emotions. For example, the analysis unit can provide a short and concise analysis result if the user is in a hurry. The analysis unit can provide a detailed analysis result if the user is relaxed. Furthermore, the analysis unit can provide a visually stimulating analysis result if the user is excited. This allows the analysis unit to adjust the length of the analysis according to the user's emotions, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotional data into AI, which can then automatically adjust the length of the analysis.

[0101] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. For example, the analysis unit obtains the time of information submission from a database and analyzes it using AI. For example, the analysis unit can prioritize analysis of the most recent information. The analysis unit can also postpone information submitted earlier. Furthermore, the analysis unit can dynamically adjust the priority of analysis depending on the time of information submission. For example, the analysis unit can develop an algorithm that evaluates the time of information submission in real time and determines the priority of analysis. This allows for efficient analysis by determining the priority of analysis based on the time of information submission. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without AI. For example, the analysis unit can input data on the time of information submission into AI, and the AI ​​can automatically determine the priority of analysis.

[0102] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit adjusts the order of analysis using, for example, an algorithm that evaluates the relevance of information. For example, the analysis unit can prioritize analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of information. For example, the analysis unit can develop an algorithm that evaluates the relevance of information in real time and adjusts the order of analysis. This allows for efficient analysis by adjusting the order of analysis based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of information into AI, and the AI ​​can automatically adjust the order of analysis.

[0103] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit performs the analysis using, for example, an algorithm that evaluates the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results that avoid technical terms. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis results according to the user's level of expertise. For example, the analysis unit can develop an algorithm that evaluates the user's level of expertise in real time and adjusts the use of technical terms in the analysis results. This allows for adjusting the use of technical terms in the analysis according to the user's level of expertise, thereby providing analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into AI, which can then automatically adjust the use of technical terms in the analysis.

[0104] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated user's emotions. The matching unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the matching unit can capture the user's facial expressions with a camera and use AI to analyze the expressions to estimate the emotions. The matching unit can also adjust the matching criteria based on the user's emotions. For example, if the user is nervous, the matching unit can prioritize matching with a reliable driver. If the user is relaxed, the matching unit can prioritize matching with a driver who suits the user's preferences. Furthermore, if the user is in a hurry, the matching unit can prioritize matching with a driver who can respond quickly. This allows for the selection of a more appropriate driver by adjusting the matching criteria according to the user's emotions. Some or all of the above-described processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input the user's emotional data into AI, which then automatically adjusts the matching criteria.

[0105] The matching unit can improve the accuracy of matching by taking into account the relationship between the user and the driver during matching. The matching unit, for example, selects a driver with good compatibility based on the user and driver's past matching history. For example, the matching unit can analyze the number of past matches and evaluations between the user and the driver, and prioritize matching drivers with good compatibility. The matching unit can also perform optimal matching based on the evaluations of the user and the driver. Furthermore, the matching unit can analyze the relationship between the user and the driver and improve the accuracy of matching. For example, the matching unit can analyze the relationship data between the user and the driver and develop an optimal matching algorithm. This allows for more accurate matching by taking the relationship between the user and the driver into consideration. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the relationship data between the user and the driver into AI, which then automatically analyzes the data and performs optimal matching.

[0106] The matching unit can perform matching by taking into account attribute information of the user and the driver. The matching unit, for example, acquires attribute information of the user and the driver from a database. For example, the matching unit can perform matching by taking into account the age and gender of the user and the driver. The matching unit can also perform matching by taking into account the hobbies and interests of the user and the driver. Furthermore, the matching unit can perform optimal matching based on the attribute information of the user and the driver. For example, the matching unit can analyze the attribute information of the user and the driver and develop an optimal matching algorithm. This allows for more appropriate matching by taking into account the attribute information of the user and the driver. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input attribute information data of the user and the driver into AI, which then automatically analyzes the data and performs optimal matching.

[0107] During matching, the matching unit can weight the matching based on the past matching history between the user and the driver. The matching unit, for example, retrieves the past matching history between the user and the driver from a database. For example, the matching unit can prioritize matching drivers who are compatible with the user and the driver based on the number of past matchings and evaluations between the user and the driver. The matching unit can also prioritize selecting a specific driver from the past matching history between the user and the driver. Furthermore, the matching unit can weight the matching based on the past matching history between the user and the driver. For example, the matching unit can analyze the past matching history between the user and the driver and develop an optimal matching algorithm. This allows for more accurate matching by weighting the matching based on the past matching history between the user and the driver. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without AI. For example, the matching unit can input past matching history data between the user and the driver into AI, which then automatically analyzes and weights the matching.

[0108] The matching unit can estimate the user's emotions and adjust the order in which the matching results are displayed based on the estimated user's emotions. The matching unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the matching unit can capture the user's facial expressions with a camera and use AI to analyze the expressions to estimate the emotions. The matching unit can also adjust the order in which the matching results are displayed based on the user's emotions. For example, if the user is nervous, the matching unit can first display reliable drivers. If the user is relaxed, the matching unit can first display drivers that match the user's preferences. Furthermore, if the user is in a hurry, the matching unit can first display drivers who can respond quickly. This allows for the selection of a more appropriate driver by adjusting the order in which the matching results are displayed based on the user's emotions. Some or all of the above-described processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input the user's emotional data into AI, and the AI ​​can automatically adjust the order in which the matching results are displayed.

[0109] The matching unit can perform matching by taking into account the geographical distribution of users and drivers. For example, the matching unit acquires the geographical distribution of users and drivers from GPS data. For example, the matching unit can prioritize matching when the current locations of users and drivers are close to each other. The matching unit can also perform optimal matching based on the geographical distribution of users and drivers. Furthermore, the matching unit can perform efficient matching by taking into account the geographical distribution of users and drivers. For example, the matching unit can analyze the geographical distribution of users and drivers and develop an optimal matching algorithm. This allows for more efficient matching by taking the geographical distribution of users and drivers into consideration. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input geographical distribution data of users and drivers into AI, which can then automatically analyze the data and perform optimal matching.

[0110] The matching unit can improve the accuracy of matching by referring to literature related to the user and the driver during matching. The matching unit, for example, retrieves literature related to the user and the driver from a database. For example, the matching unit can perform optimal matching based on the literature related to the user and the driver. The matching unit can also improve the accuracy of matching by referring to the literature related to the user and the driver. Furthermore, the matching unit can analyze literature related to the user and the driver to improve the accuracy of matching. For example, the matching unit can analyze literature related to the user and the driver and develop an optimal matching algorithm. This allows for more accurate matching by referring to the literature related to the user and the driver. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input literature data related to the user and the driver into AI, which then automatically analyzes the data and performs optimal matching.

[0111] The matching unit can perform matching by taking into account the market value of the user and the driver. For example, the matching unit obtains the market value of the user and the driver from a database. For example, the matching unit can perform optimal matching based on the market value of the user and the driver. The matching unit can also perform efficient matching by taking into account the market value of the user and the driver. Furthermore, the matching unit can analyze the market value of the user and the driver and develop an optimal matching algorithm. For example, the matching unit can analyze the market value of the user and the driver and develop an algorithm for optimal matching. This allows for more efficient matching by taking into account the market value of the user and the driver. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input market value data of the user and the driver into AI, which then automatically analyzes the data and performs optimal matching.

[0112] The monitoring unit can estimate the user's emotions and adjust the monitoring display method based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the monitoring unit can capture the user's facial expressions with a camera and use AI to analyze the expressions to estimate the emotions. The monitoring unit can also adjust the monitoring display method based on the user's emotions. For example, the monitoring unit can provide simple, highly visible monitoring results when the user is nervous. The monitoring unit can also provide detailed monitoring results when the user is relaxed. Furthermore, the monitoring unit can provide monitoring results that focus on the main points when the user is in a hurry. This allows the monitoring display method to be adjusted according to the user's emotions, thereby providing more appropriate monitoring results. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's emotional data into AI, which can then automatically adjust the monitoring display method.

[0113] During monitoring, the monitoring unit can optimize current monitoring by referring to past monitoring data. For example, the monitoring unit obtains past monitoring data from a database and analyzes it using AI. For example, the monitoring unit can improve current monitoring accuracy based on the past monitoring data. The monitoring unit can also extract specific patterns from the past monitoring data and reflect them in current monitoring. Furthermore, the monitoring unit can optimize monitoring algorithms by referring to past monitoring data. For example, the monitoring unit can analyze past monitoring data and develop optimal monitoring algorithms. This makes it possible to improve current monitoring accuracy by referring to past monitoring data. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input past monitoring data into AI, which then automatically analyzes the data and optimizes current monitoring.

[0114] During monitoring, the monitoring unit can apply different monitoring methods to each information category inside and outside the vehicle. The monitoring unit performs monitoring using, for example, an algorithm that classifies information categories inside and outside the vehicle. For example, the monitoring unit can apply an interior monitoring algorithm to information inside the vehicle. The monitoring unit can also apply an exterior monitoring algorithm to information outside the vehicle. Furthermore, the monitoring unit can dynamically apply different monitoring methods depending on the information category inside and outside the vehicle. For example, the monitoring unit can develop an algorithm that evaluates information categories inside and outside the vehicle in real time and applies the optimal monitoring method. This allows for more accurate monitoring by applying different monitoring methods depending on the information category inside and outside the vehicle. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input information category data inside and outside the vehicle into AI, which then automatically applies the optimal monitoring method.

[0115] The monitoring unit can perform monitoring while taking into account the user's attribute information. The monitoring unit, for example, acquires the user's attribute information from a database. For example, the monitoring unit can perform monitoring while taking into account the user's age and gender. The monitoring unit can also perform monitoring while taking into account the user's hobbies and interests. Furthermore, the monitoring unit can perform optimal monitoring based on the user's attribute information. For example, the monitoring unit can analyze the user's attribute information and develop an optimal monitoring algorithm. This allows for more appropriate monitoring by taking the user's attribute information into account. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's attribute information data into AI, which then automatically analyzes the data and performs optimal monitoring.

[0116] The monitoring unit can estimate the user's emotions and adjust the importance of monitoring based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the monitoring unit can capture the user's facial expressions with a camera and use AI to analyze the expressions to estimate the emotions. The monitoring unit can also adjust the importance of monitoring based on the user's emotions. For example, if the user is nervous, the monitoring unit can prioritize displaying monitoring items with high importance. Also, if the user is relaxed, the monitoring unit can postpone monitoring items with low importance. Furthermore, if the user is in a hurry, the monitoring unit can quickly display the most important monitoring items. In this way, by adjusting the importance of monitoring according to the user's emotions, more important information can be prioritized for monitoring. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's emotional data into AI, which can then automatically adjust the importance of monitoring.

[0117] During monitoring, the monitoring unit can analyze changes in monitoring based on the timing of acquisition of information inside and outside the vehicle. For example, the monitoring unit acquires the timing of acquisition of information inside and outside the vehicle from a database, and AI analyzes the information. For example, the monitoring unit can analyze changes in monitoring based on the timing of acquisition of information inside and outside the vehicle. The monitoring unit can also postpone information acquired at an older time. Furthermore, the monitoring unit can dynamically analyze changes in monitoring based on the timing of acquisition of information. For example, the monitoring unit can develop an algorithm that evaluates the timing of information acquisition in real time and analyzes changes in monitoring. This enables more accurate monitoring by analyzing changes in monitoring based on the timing of acquisition of information inside and outside the vehicle. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input data on the timing of acquisition of information inside and outside the vehicle into AI, which then automatically analyzes and analyzes changes in monitoring.

[0118] During monitoring, the monitoring unit can analyze the monitoring by referring to relevant market data inside and outside the vehicle. For example, the monitoring unit obtains the relevant market data inside and outside the vehicle from a database, and the AI ​​analyzes the data. For example, the monitoring unit can improve the accuracy of the monitoring based on the relevant market data inside and outside the vehicle. The monitoring unit can also analyze changes in the monitoring by referring to the relevant market data inside and outside the vehicle. Furthermore, the monitoring unit can analyze the relevant market data inside and outside the vehicle to improve the accuracy of the monitoring. For example, the monitoring unit can analyze the relevant market data inside and outside the vehicle and develop an optimal monitoring algorithm. As a result, more accurate monitoring can be performed by referring to the relevant market data inside and outside the vehicle. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the relevant market data inside and outside the vehicle into AI, which then automatically analyzes the data, thereby improving the accuracy of the monitoring.

[0119] During monitoring, the monitoring unit can analyze the monitoring taking into account the technological maturity inside and outside the vehicle. For example, the monitoring unit obtains the technological maturity inside and outside the vehicle from a database, and AI analyzes the data. For example, the monitoring unit can improve the accuracy of monitoring based on the technological maturity inside and outside the vehicle. The monitoring unit can also analyze changes in monitoring by referring to the technological maturity inside and outside the vehicle. Furthermore, the monitoring unit can analyze the technological maturity inside and outside the vehicle and improve the accuracy of monitoring. For example, the monitoring unit can analyze the technological maturity inside and outside the vehicle and develop an optimal monitoring algorithm. This enables more accurate monitoring by taking the technological maturity inside and outside the vehicle into consideration. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input technological maturity data inside and outside the vehicle into AI, which then automatically analyzes the data and improves the accuracy of monitoring.

[0120] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the analysis unit can capture the user's facial expressions with a camera and use AI to analyze the expressions to estimate the emotions. The analysis unit can also adjust the analysis method based on the user's emotions. For example, the analysis unit can provide simple, highly visible analysis results when the user is nervous. The analysis unit can also provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide analysis results that focus on the main points when the user is in a hurry. This allows the analysis method to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without AI. For example, the analysis unit can input the user's emotional data into AI, which then automatically adjusts the analysis method.

[0121] During the analysis, the analysis unit can analyze the driver's past driving skills and propose an optimal driving route. The analysis unit, for example, retrieves the driver's past driving skills from a database and analyzes them using AI. For example, the analysis unit can propose an optimal driving route based on the driver's past driving skills. The analysis unit can also propose a driving route that avoids congestion based on the driver's past driving skills. Furthermore, the analysis unit can analyze the driver's past driving skills and propose the most efficient driving route. For example, the analysis unit can analyze the driver's past driving skills and develop an algorithm that proposes an optimal driving route. This makes it possible to propose an optimal driving route and support efficient driving by analyzing the driver's past driving skills. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the driver's past driving skill data into AI, which then automatically analyzes the data and proposes an optimal driving route.

[0122] During analysis, the analysis unit can customize the analysis means based on the driver's current driving situation. For example, the analysis unit retrieves the driver's current driving situation from a database and analyzes it using AI. For example, the analysis unit can select the optimal analysis means based on the driver's current driving situation. The analysis unit can also prioritize the use of specific analysis means based on the driver's current driving situation. Furthermore, the analysis unit can continuously improve the analysis means by referring to the driver's current driving situation. For example, the analysis unit can analyze the driver's current driving situation and develop an algorithm to select the optimal analysis means. This allows the analysis means to be customized based on the driver's current driving situation, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the driver's current driving situation into AI, which then automatically analyzes and selects the optimal analysis means.

[0123] The analysis unit can improve the analysis method by reflecting the driver's feedback during analysis. For example, the analysis unit obtains the driver's feedback from a database and analyzes it using AI. For example, the analysis unit can select the optimal analysis method based on the feedback provided by the driver. The analysis unit can also prioritize the use of a specific analysis method based on the driver's feedback. Furthermore, the analysis unit can continuously improve the analysis method by reflecting the driver's feedback. For example, the analysis unit can analyze the driver's feedback and develop an algorithm to select the optimal analysis method. This allows the analysis method to be continuously improved by reflecting the driver's feedback, and more appropriate analysis results to be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the driver's feedback data into AI, which then automatically analyzes and improves the analysis method.

[0124] The analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the analysis unit can capture the user's facial expressions with a camera and use AI to analyze the expressions to estimate the emotions. The analysis unit can also determine the analysis priorities based on the user's emotions. For example, if the user is nervous, the analysis unit can prioritize displaying analysis items with high importance. Also, if the user is relaxed, the analysis unit can postpone analysis items with low importance. Furthermore, if the user is in a hurry, the analysis unit can quickly display the most important analysis items. In this way, by determining the analysis priorities according to the user's emotions, more important information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotional data into AI, which can then automatically determine the analysis priorities.

[0125] During analysis, the analysis unit can select the optimal analysis method by taking into account the driver's geographical location information. For example, the analysis unit obtains the driver's geographical location information from GPS data and analyzes it using AI. For example, the analysis unit can select the optimal analysis method based on the driver's current location. The analysis unit can also prioritize the use of a specific analysis method based on the driver's geographical location information. Furthermore, the analysis unit can continuously improve the analysis method by referring to the driver's geographical location information. For example, the analysis unit can analyze the driver's geographical location information and develop an algorithm to select the optimal analysis method. This makes it possible to select a more appropriate analysis method by taking the driver's geographical location information into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the driver's geographical location information data into AI, which can then automatically analyze and select the optimal analysis method.

[0126] During analysis, the analysis unit can analyze the driver's social media activity and suggest analysis methods. For example, the analysis unit retrieves the driver's social media activity from a database and analyzes it using AI. For example, the analysis unit can select the optimal analysis method based on the driver's social media activity. The analysis unit can also prioritize the use of specific analysis methods based on the driver's social media activity. Furthermore, the analysis unit can continuously improve the analysis method by referring to the driver's social media activity. For example, the analysis unit can analyze the driver's social media activity and develop an algorithm to select the optimal analysis method. This makes it possible to suggest more appropriate analysis methods by analyzing the driver's social media activity. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the driver's social media activity data into AI, which can automatically analyze the data and suggest the optimal analysis method.

[0127] During analysis, the analysis unit can customize the analysis method by reflecting the driver's past feedback. For example, the analysis unit retrieves the driver's past feedback from a database and analyzes it using AI. For example, the analysis unit can select the optimal analysis method based on the driver's past feedback. The analysis unit can also prioritize the use of a specific analysis method based on the driver's past feedback. Furthermore, the analysis unit can continuously improve the analysis method by reflecting the driver's past feedback. For example, the analysis unit can analyze the driver's past feedback and develop an algorithm to select the optimal analysis method. This allows the analysis method to be continuously improved by reflecting the driver's past feedback, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the driver's past feedback data into AI, which then automatically analyzes and customizes the analysis method.

[0128] The education unit can estimate the user's emotions and adjust the educational content based on the estimated user's emotions. The education unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the education unit can capture the user's facial expressions with a camera and use AI to analyze the expressions to estimate the emotions. The education unit can also adjust the educational content based on the user's emotions. For example, if the user is nervous, the education unit can provide simple, highly visible educational content. If the user is relaxed, the education unit can provide detailed educational content. Furthermore, if the user is in a hurry, the education unit can provide educational content that focuses on the main points. This allows for more appropriate education by adjusting the educational content according to the user's emotions. Some or all of the above-mentioned processing in the education unit may be performed using AI, for example, or may be performed without AI. For example, the education unit can input the user's emotional data into AI, which can then automatically adjust the educational content.

[0129] During training, the training department can analyze the driver's past driving history and select the most appropriate training content. For example, the training department retrieves the driver's past driving history from a database and analyzes it using AI. For example, the training department can select the most appropriate training content based on the driver's past driving history. The training department can also prioritize specific training content based on the driver's past driving history. Furthermore, the training department can analyze the driver's past driving history and select the most effective training content. For example, the training department can analyze the driver's past driving history and develop an algorithm for selecting the most appropriate training content. This allows for the selection of more appropriate training content by analyzing the driver's past driving history. Some or all of the above-mentioned processing in the training department can be performed using, for example, AI, or without AI. For example, the training department can input the driver's past driving history data into AI, which can automatically analyze the data and select the most appropriate training content.

[0130] During training, the training department can customize training methods based on the driver's current skill level. For example, the training department retrieves the driver's current skill level from a database and analyzes it using AI. For example, the training department can select the optimal training method based on the driver's current skill level. The training department can also prioritize specific training methods based on the driver's current skill level. Furthermore, the training department can continuously improve the training method based on the driver's current skill level. For example, the training department can analyze the driver's current skill level and develop an algorithm to select the optimal training method. This allows for customizing the training method based on the driver's current skill level, thereby providing more appropriate training. Some or all of the above-mentioned processing in the training department can be performed using, for example, AI, or without AI. For example, the training department can input the driver's current skill level data into AI, which can then automatically analyze and select the optimal training method.

[0131] The training department can improve the training content by reflecting driver feedback during training. For example, the training department obtains driver feedback from a database and analyzes it using AI. For example, the training department can select the optimal training content based on the feedback provided by the driver. The training department can also prioritize specific training content based on the driver feedback. Furthermore, the training department can continuously improve the training content by reflecting driver feedback. For example, the training department can develop an algorithm that analyzes driver feedback and selects the optimal training content. This allows the training content to be continuously improved by reflecting driver feedback, and more appropriate training can be provided. Some or all of the above-mentioned processing in the training department can be performed using AI, for example, or without AI. For example, the training department can input driver feedback data into AI, which can automatically analyze and improve the training content.

[0132] The education unit can estimate the user's emotions and determine the priority of education based on the estimated user emotions. The education unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the education unit can capture the user's facial expressions with a camera and use AI to analyze the expressions to estimate the emotions. The education unit can also determine the priority of education based on the user's emotions. For example, if the user is nervous, the education unit can prioritize displaying educational items with high importance. If the user is relaxed, the education unit can postpone educational items with low importance. Furthermore, if the user is in a hurry, the education unit can quickly display the most important educational items. This allows the priority of education to be determined according to the user's emotions, so that more important educational items can be provided preferentially. Some or all of the above-described processing in the education unit may be performed using AI, for example, or without AI. For example, the education unit can input the user's emotional data into AI, which can then automatically determine the priority of education.

[0133] During training, the Ministry of Education can select the most appropriate training content by taking into account the driver's geographical location information. For example, the Ministry of Education may obtain the driver's geographical location information from GPS data and analyze it using AI. For example, the Ministry of Education can select the most appropriate training content based on the driver's current location. The Ministry of Education can also prioritize providing specific training content based on the driver's geographical location information. Furthermore, the Ministry of Education can continuously improve the training content by referring to the driver's geographical location information. For example, the Ministry of Education can analyze the driver's geographical location information and develop an algorithm to select the most appropriate training content. This allows for the selection of more appropriate training content by taking the driver's geographical location information into consideration. Some or all of the above-mentioned processing by the Ministry of Education may be performed using, for example, AI, or may be performed without using AI. For example, the Ministry of Education can input the driver's geographical location information data into AI, which can then automatically analyze and select the most appropriate training content.

[0134] During training, the Ministry of Education can analyze the driver's social media activity and suggest training methods. For example, the Ministry of Education retrieves the driver's social media activity from a database and analyzes it using AI. For example, the Ministry of Education can select the most appropriate training method based on the driver's social media activity. The Ministry of Education can also prioritize certain training methods based on the driver's social media activity. Furthermore, the Ministry of Education can continuously improve the training methods based on the driver's social media activity. For example, the Ministry of Education can analyze the driver's social media activity and develop an algorithm to select the most appropriate training method. This makes it possible to suggest more appropriate training methods by analyzing the driver's social media activity. Some or all of the above-mentioned processing by the Ministry of Education may be performed using, for example, AI, or may be performed without AI. For example, the Ministry of Education can input the driver's social media activity data into AI, which can automatically analyze it and suggest the most appropriate training method.

[0135] During training, the training department can customize the training content by reflecting the driver's past feedback. For example, the training department retrieves the driver's past feedback from a database and analyzes it using AI. For example, the training department can select the optimal training content based on the driver's past feedback. The training department can also prioritize specific training content based on the driver's past feedback. Furthermore, the training department can continuously improve the training content by reflecting the driver's past feedback. For example, the training department can analyze the driver's past feedback and develop an algorithm to select the optimal training content. This allows the training content to be continuously improved by reflecting the driver's past feedback, and more appropriate training can be provided. Some or all of the above-mentioned processing in the training department may be performed using, for example, AI, or may be performed without AI. For example, the training department can input the driver's past feedback data into AI, which then automatically analyzes and customizes the training content. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, matching unit, monitoring unit, analysis unit, and education unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information about the user and the driver using the camera 42 and sensors of the smart device 14, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using AI. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and matches the user and the driver based on the analysis results. The monitoring unit collects and monitors information inside and outside the vehicle using, for example, the camera 42 and sensors of the smart device 14, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the driver's driving technique and suggests an appropriate driving route. The education unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides an individual education program to the driver. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, matching unit, monitoring unit, analysis unit, and education unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information about the user and the driver using the camera 42 and sensors of the smart glasses 214, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using AI. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and matches the user and the driver based on the analysis results. The monitoring unit collects and monitors information inside and outside the vehicle using, for example, the camera 42 and sensors of the smart glasses 214, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the driver's driving technique and suggests an appropriate driving route. The education unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides an individual education program to the driver. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, matching unit, monitoring unit, analysis unit, and education unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects information about the user and the driver using the camera 42 and sensors of the headset-type terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using AI. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and matches the user and the driver based on the analysis results. The monitoring unit collects and monitors information inside and outside the vehicle using, for example, the camera 42 and sensors of the headset-type terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the driver's driving technique and suggests an appropriate driving route. The education section is realized by, for example, the specific processing section 290 of the data processing device 12, and provides an individual education program to the driver. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, matching unit, monitoring unit, analysis unit, and education unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information about the user and the driver using the camera 42 and sensors of the robot 414, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using AI. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and matches the user and the driver based on the analysis results. The monitoring unit collects and monitors information inside and outside the vehicle using, for example, the camera 42 and sensors of the robot 414, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the driver's driving technique and suggests an appropriate driving route. The education unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides an individual education program to the driver.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] The ride-sharing system may further include a weather information unit that collects real-time weather information and analyzes it in an analysis unit. For example, the weather information unit may collect current weather information and predicted weather changes, and the analysis unit may use this information to evaluate the safety of driving routes. The weather information unit may also analyze weather patterns in a specific area and provide appropriate driving advice to the driver. Furthermore, the weather information unit may combine the user's riding history with weather data to preferentially match rides under weather conditions preferred by the user. This allows for a safe and comfortable ride-sharing service that takes weather information into consideration.

[0138] The ride-sharing system can also be equipped with a health management unit that monitors the user's health condition. For example, the health management unit can collect the user's heart rate and stress level from a wearable device and analyze them with an analysis unit. The health management unit can also provide relaxing music or aromas based on the user's health condition. Furthermore, if the user complains of feeling unwell, the health management unit can suggest a route to the nearest medical institution. This makes it possible to provide a safe and secure ride-sharing service that takes the user's health condition into consideration.

[0139] The ride-sharing system may further include an entertainment unit that provides customized entertainment based on the user's hobbies and interests. For example, the entertainment unit may collect the user's music preferences and provide an appropriate playlist during the ride. The entertainment unit may also suggest movies or TV shows that the user may be interested in and make them available for viewing in the car. Furthermore, the entertainment unit may provide e-books based on the user's reading preferences. This allows for a comfortable ride-sharing experience tailored to the user's hobbies and interests.

[0140] The ride-sharing system may further include a social network unit that optimizes matching by utilizing the user's social network. For example, the social network unit may collect ratings of drivers previously used by the user's friends and acquaintances and analyze the ratings in the analysis unit. The social network unit may also prioritize matching with drivers currently used by the user's friends. Furthermore, the social network unit may analyze the user's social media activity and match with drivers who share common hobbies and interests. This makes it possible to provide reliable matching by utilizing the user's social network.

[0141] The ride-sharing system may further include a feedback analysis unit that customizes the service based on the user's past feedback. For example, the feedback analysis unit may collect feedback provided by the user in the past and analyze it in the analysis unit. The feedback analysis unit may also make suggestions to improve the driver's service content based on the user's feedback. Furthermore, the feedback analysis unit may preferentially match specific drivers based on the user's feedback. This makes it possible to provide a high-quality ride-sharing service that reflects the user's feedback.

[0142] The ride-sharing system may further include a travel support unit that supports the user's travel plans. For example, the travel support unit may collect the user's travel destinations and schedule and analyze them using the analysis unit. The travel support unit may also suggest tourist spots and restaurants at the user's travel destinations. Furthermore, the travel support unit may suggest optimal driving routes and transportation methods based on the user's travel plans. This supports the user's travel plans and provides a comfortable travel experience.

[0143] The ride-sharing system may further include an environmental adjustment unit that estimates the user's emotions and adjusts the in-car environment based on the estimated emotions. For example, the environmental adjustment unit may estimate the user's emotions using facial expression recognition technology and analyze the emotions using an analysis unit. The environmental adjustment unit may also adjust the lighting and temperature in the car to provide a comfortable environment when the user is relaxed. Furthermore, the environmental adjustment unit may provide relaxing music or aromas when the user is feeling stressed. This allows a comfortable in-car environment to be provided according to the user's emotions.

[0144] The ride-sharing system may further include a fitness support unit that supports the user's exercise habits. For example, the fitness support unit may collect the user's exercise history and goals and analyze them using an analysis unit. The fitness support unit may also suggest an appropriate exercise program based on the user's exercise habits. Furthermore, the fitness support unit may suggest the optimal place and time for the user to exercise. This may support the user's exercise habits and promote a healthy lifestyle.

[0145] The ride-sharing system may further include a service adjustment unit that estimates the user's emotions and adjusts the driver's attitude toward the user based on the estimated emotions. For example, the service adjustment unit may estimate the user's emotions using facial expression recognition technology and analyze the user's emotions using the analysis unit. The service adjustment unit may also recommend a friendly attitude toward the driver when the user is relaxed. Furthermore, the service adjustment unit may recommend a quieter attitude toward the driver when the user is stressed. This allows the system to provide an appropriate attitude toward the user based on the user's emotions.

[0146] The ride-sharing system may further include an education support unit that supports the user's learning needs. For example, the education support unit may collect the user's learning history and goals and analyze them using an analysis unit. The education support unit may also provide appropriate learning content based on the user's learning needs. Furthermore, the education support unit may suggest the optimal time and place for the user to study. This supports the user's learning needs and provides an efficient learning environment.

[0147] The processing flow of the second embodiment will be briefly explained below.

[0148] Step 1: The collection unit collects user and driver information. User information includes riding history and preferences, and driver information includes driving history and ratings. The collection unit obtains the user's riding history from the database and obtains the user's preferences from questionnaires and app settings. It also obtains the driver's driving history from the driving recorder and obtains the driver's rating from user feedback. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses AI to analyze the user's riding history and preferences, as well as the driver's driving history and ratings. For example, it uses machine learning algorithms to match the user's preferences with the driver's ratings. Step 3: The matching unit matches users and drivers based on the analysis results obtained by the analysis unit. The matching unit selects the most suitable driver based on the user's preferences and driver ratings, and performs matching based on the user's riding history and the driver's driving history. Step 4: The monitoring unit collects and monitors information inside and outside the vehicle. The monitoring unit uses cameras and sensors inside the vehicle to monitor passenger safety, and collects information outside the vehicle to understand traffic conditions in real time. For example, it analyzes camera footage inside the vehicle to detect abnormalities, collects traffic conditions outside the vehicle, and provides congestion information. Step 5: The analysis unit analyzes the driver's driving skills and suggests an appropriate driving route. The analysis unit uses AI to analyze the driver's driving skills and the driving route to suggest the optimal route. For example, it evaluates the driver's driving skills, provides advice to improve them, and suggests the optimal driving route taking traffic conditions into account. Step 6: The Education Department provides the driver with an education program. Based on the driver's driving history and evaluation, the Education Department proposes the most appropriate education content and provides an education program tailored to the driver's needs, such as improving driving skills and customer service manners. For example, the department aims to improve the driver's skills through online courses and on-the-job training.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0177] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0178] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0179] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0180] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0181] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0184] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0187] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0188] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0189] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0190] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0191] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0192] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0193] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0194] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0195] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0196] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0197] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0198] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0199] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0200] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0201] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0202] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0203] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0204] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0205] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0206] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0207] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0208] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0209] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0210] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0211] 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.

[0212] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0213] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0214] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0215] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0216] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0217] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0218] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0219] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0220] [Explanation of symbols]

[0221] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit for collecting user and driver information; an analysis unit that analyzes the information collected by the collection unit; a matching unit that matches users with drivers based on the analysis results obtained by the analysis unit; a collection unit that collects information inside and outside the vehicle, and a monitoring unit that monitors the information inside and outside the vehicle; an analysis unit that analyzes driving techniques, a route analysis unit that analyzes driving routes, An education department that provides education programs to drivers. A system characterized by:

2. The collecting unit Collect user riding history and preferences, driver driving history and ratings 2. The system of claim 1.

3. The analysis unit Analyze the collected information and select the appropriate driver 2. The system of claim 1.

4. The matching unit Matching users and drivers based on analysis results 2. The system of claim 1.

5. The monitoring unit A monitoring section that uses cameras and sensors inside the vehicle to monitor passenger safety and collects information from outside the vehicle to understand traffic conditions in real time.

2. The system of claim 1.

6. The analysis unit Analyzes the driver's driving technique and suggests driving routes 2. The system of claim 1.

7. The Ministry of Education, Propose optimal training content based on the driver's driving history and evaluation 2. The system of claim 1.

8. The collecting unit Estimate the user's emotions and adjust the timing of acquiring the ride history based on the estimated user emotions.

2. The system of claim 1.

9. The collecting unit Analyze the user's past riding history and select the optimal collection method 2. The system of claim 1.

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