System

Abstract

An object of a system according to an embodiment is to ensure safety in ridesharing.SOLUTION: A system according to an embodiment includes a driving technique analyzer, an action analyzer, and a driver selector. The driving technique analyzer analyzes the driving technique of the driver in real time. The behavior analysis unit analyzes the behavior of the customer in real time. The driver selection unit selects an appropriate driver.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of not being able to adequately analyze the driving skills of drivers and customer behavior in real time in ride-sharing services to ensure safety.

[0005] The system according to the embodiment aims to ensure safety in ride sharing. [Means for solving the problem]

[0006] The system according to the embodiment includes a driving technique analysis unit, a behavior analysis unit, and a driver selection unit. The driving technique analysis unit analyzes the driving technique of a driver in real time. The behavior analysis unit analyzes the behavior of a customer in real time. The driver selection unit selects an appropriate driver. [Effects of the Invention]

[0007] The system according to the embodiment can ensure safety in ride sharing. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The SafeRide AI system according to an embodiment of the present invention utilizes generative AI to ensure safe ride-sharing. This system analyzes driver skills and customer behavior in real time to minimize the risk of accidents and trouble. In addition, when a customer uses a ride-sharing service, the generative AI selects an appropriate driver to ensure safety. As a result, the SafeRide AI system can ensure safe ride-sharing by analyzing driver skills and customer behavior in real time and selecting an appropriate driver.

[0029] The Safe Ride AI system according to the embodiment includes a driving technique analysis unit, a behavior analysis unit, and a driver selection unit. The driving technique analysis unit analyzes a driver's driving technique in real time. For example, it collects data such as the driver's speed, braking technique, and steering operation using sensors and cameras, and the generation AI analyzes this data. For example, the generation AI evaluates speed control based on the driver's speed data. The generation AI can also analyze braking technique and evaluate appropriate braking operation. Furthermore, the generation AI can analyze steering operation data and evaluate smooth steering operation. The behavior analysis unit analyzes customer behavior in real time. For example, it monitors whether customers are wearing seat belts and whether their behavior in the car is safe using cameras and sensors. For example, the generation AI detects whether customers are wearing seat belts and evaluates safety. The generation AI can also monitor customers' movements in the car and evaluate safe behavior. Furthermore, the generation AI can analyze customer behavior data and detect abnormal behavior. The driver selection unit selects an appropriate driver. For example, the generation AI selects the safest driver based on the driver's past driving history and evaluations, current driving conditions, etc. The generation AI may, for example, analyze the driver's driving history data to select a driver with safe driving skills. The generation AI may also select a highly rated driver based on driver evaluation data. Furthermore, the generation AI may analyze current driving conditions data to select an appropriate driver. As a result, the Safe Ride AI system according to the embodiment can realize safe ride sharing by analyzing the driver's driving skills and customer behavior in real time and selecting an appropriate driver.

[0030] The driving technique analysis unit can collect data such as the driver's speed, braking technique, and steering operation using sensors and cameras, and analyze this data. For example, the driving technique analysis unit measures the driver's speed using a sensor, and the generation AI analyzes the data. For example, a speed sensor collects the driver's speed in real time, and the generation AI evaluates speed control. The driving technique analysis unit also measures braking technique using a sensor, and the generation AI analyzes the data. For example, a brake sensor collects braking strength and timing, and the generation AI evaluates appropriate braking operation. The driving technique analysis unit also monitors steering operation using a camera, and the generation AI analyzes the data. For example, a camera collects the steering wheel rotation angle and operation smoothness, and the generation AI evaluates smooth steering operation. This enables a more accurate evaluation by analyzing the driver's driving technique in detail.

[0031] The behavior analysis unit can use cameras and sensors to monitor whether customers are wearing seat belts and whether their behavior in the car is safe. For example, the behavior analysis unit uses cameras to monitor whether customers are wearing seat belts, and the generation AI analyzes the data. For example, a camera collects information on whether customers are wearing seat belts in real time, and the generation AI evaluates safety. The behavior analysis unit also uses sensors to monitor customers' behavior in the car, and the generation AI analyzes the data. For example, a sensor collects information on customers' movements in real time, and the generation AI evaluates safe behavior. The behavior analysis unit can also analyze customers' behavior data and detect abnormal behavior. For example, the generation AI compares it with past behavior data to detect abnormal behavior and issue a warning. In this way, safety is ensured by closely monitoring customer behavior.

[0032] The driver selection unit can select the safest driver based on the driver's past driving history and evaluations, current driving conditions, etc. In the driver selection unit, for example, the generation AI analyzes the driver's past driving history and selects a driver with safe driving skills. For example, the generation AI selects a driver with no history of accidents or violations based on the driver's driving history data. In addition, the driver selection unit analyzes the driver's evaluation data and selects a highly rated driver. For example, the generation AI selects a highly rated driver based on customer feedback and evaluation scores. In addition, the driver selection unit analyzes the current driving conditions and selects an appropriate driver. For example, the generation AI selects a driver with stable driving skills based on current driving condition data. This ensures customer safety by selecting drivers more accurately.

[0033] The driving technique analysis unit uses a biometric sensor to measure the driver's stress level while driving, and the generation AI analyzes the data to issue a warning if stress levels increase. For example, the driving technique analysis unit collects heart rate and electrodermal activity in real time from a biometric sensor worn by the driver while driving, and the generation AI analyzes this data. For example, a heart rate sensor collects the driver's heart rate in real time, and the generation AI evaluates the stress level. A electrodermal response sensor also collects the driver's electrodermal activity in real time, and the generation AI evaluates the stress level. If the stress level exceeds a certain threshold, the generation AI issues a warning. For example, the generation AI issues an audio warning to encourage the driver to take a break. The generation AI also issues a visual warning to alert the driver. This improves safety by monitoring the driver's stress level in real time and issuing warnings at the appropriate time.

[0034] When evaluating a driver's driving skills, the driving skill analysis unit compares them with past driving data to track improvements or deterioration in the skills, and the generation AI can provide feedback. For example, the driving skill analysis unit stores the driver's past driving data in a database and compares it with current driving data. The generation AI tracks improvements or deterioration in skills and provides specific feedback to the driver. For example, the generation AI compares past driving data with current driving data to evaluate improvements or deterioration in speed control. The generation AI also compares braking operation data to evaluate improvements or deterioration in appropriate braking operation. The generation AI also compares steering operation data to evaluate improvements or deterioration in smooth steering operation. Based on these evaluation results, the generation AI provides the driver with specific improvements and advice. For example, the generation AI points out improvements in speed control and provides specific advice. The generation AI also points out improvements in braking operation and provides specific advice. The generation AI also points out improvements in steering operation and provides specific advice. In this way, the system tracks changes in the driver's driving skills and provides appropriate feedback to promote improvement.

[0035] When evaluating a driver's driving skills, the driving technique analysis unit collects driving data under different weather and road conditions, and the generation AI evaluates driving techniques according to those conditions. For example, when collecting a driver's driving data, the driving technique analysis unit takes weather conditions (rain, snow, fog, etc.) into consideration, and the generation AI evaluates driving techniques according to those conditions. For example, the generation AI analyzes braking and steering operation in the rain and evaluates appropriate driving techniques. The generation AI also analyzes speed control and braking operation in snowy weather and evaluates appropriate driving techniques. The generation AI also analyzes visibility and steering operation in foggy weather and evaluates appropriate driving techniques. The driving technique analysis unit also considers road conditions (traffic jams, construction, accidents, etc.), and the generation AI evaluates driving techniques according to those conditions. For example, the generation AI analyzes speed control and braking operation in traffic jams and evaluates appropriate driving techniques. The generation AI also analyzes steering operation and speed control in construction zones and evaluates appropriate driving techniques. The generation AI also analyzes evasive maneuvers and speed control at accident sites and evaluates appropriate driving techniques. This allows for a more accurate assessment by assessing driving skills under different weather and road conditions.

[0036] The driving technique analysis unit can perform a relative evaluation using comparative data with other drivers to evaluate a driver's driving technique. For example, to evaluate a driver's driving technique, the driving technique analysis unit collects driving data of other drivers, and the generation AI performs a relative evaluation based on that data. For example, the generation AI performs an evaluation by comparing it with data from other drivers who drove the same route. The generation AI also performs an evaluation by comparing it with data from other drivers under the same weather conditions and road conditions. The generation AI also performs an evaluation by comparing it with data from other drivers under the same time of day and day of the week. This makes it possible to perform a relative evaluation by using comparative data with other drivers.

[0037] When analyzing customer behavior, the behavior analysis unit compares it with past behavioral data to detect abnormal behavior, and the generation AI can then issue a warning. For example, the behavior analysis unit stores past behavioral data of customers in a database and compares it with current behavioral data. The generation AI detects abnormal behavior and issues a warning to the customer. For example, the generation AI compares the status of seat belt use with past data to detect abnormal behavior. The generation AI also compares the customer's movements in the car with past data to detect abnormal behavior. If abnormal behavior is detected, the generation AI issues a warning to the customer. For example, the generation AI issues an audio warning to alert the customer. The generation AI also issues a visual warning to alert the customer. In this way, safety is improved by detecting abnormal customer behavior and issuing a warning.

[0038] The behavior analysis unit collects environmental data such as in-car temperature and lighting to evaluate customer behavior, and the generation AI can analyze the impact of these environmental factors on behavior. For example, the behavior analysis unit collects in-car temperature and lighting data in real time, and the generation AI analyzes that data. For example, a temperature sensor collects in-car temperature in real time, and the generation AI evaluates the impact of temperature changes on customer behavior. In addition, a lighting sensor collects in-car lighting data in real time, and the generation AI evaluates the impact of lighting brightness on customer behavior. The generation AI adjusts the in-car environment based on these evaluation results. For example, the generation AI adjusts the temperature appropriately to improve customer comfort. In addition, the generation AI adjusts the lighting appropriately to improve customer comfort. In this way, safety is improved by analyzing the impact of in-car environmental factors on customer behavior.

[0039] When evaluating customer behavior, the behavior analysis unit collects behavioral data from different times of the day and days of the week, and the generation AI can analyze behavioral patterns according to those conditions. For example, the behavior analysis unit collects customer behavioral data from different times of the day and days of the week, and the generation AI analyzes that data. For example, the generation AI evaluates the differences in behavioral patterns between weekdays and weekends. The generation AI also evaluates the differences in behavioral patterns between mornings and evenings. Furthermore, the generation AI analyzes behavioral patterns from specific days of the week and time periods to evaluate customer behavior. This allows for a more accurate evaluation by analyzing behavioral patterns from different times of the day and days of the week.

[0040] The behavioral analysis unit can use comparative data with other customers to evaluate customer behavior and make a relative evaluation. The behavioral analysis unit, for example, collects customer behavior data and compares it with data from other customers. The generation AI makes a relative evaluation based on that data. For example, the generation AI makes an evaluation by comparing it with other customers who used the same route. The generation AI also makes an evaluation by comparing it with data from other customers at the same time of day or day of the week. The generation AI also makes an evaluation by comparing it with data from other customers under the same weather conditions or road conditions. In this way, using comparative data with other customers makes it possible to make a relative evaluation.

[0041] When selecting drivers, the driver selection unit uses biosensors to measure not only the driver's past driving history but also their health condition and fatigue level, and the generation AI can take this data into consideration. For example, the driver selection unit measures the driver's health condition and fatigue level using biosensors, and the generation AI analyzes the data. For example, a heart rate sensor collects the driver's heart rate in real time, and the generation AI evaluates their health condition. Also, a skin electrodermal response sensor collects the driver's skin activity in real time, and the generation AI evaluates their fatigue level. Based on these evaluation results, the generation AI selects drivers with good health and fatigue levels. For example, the generation AI selects drivers with good health based on heart rate and skin activity data. Also, the generation AI selects drivers with low fatigue levels based on fatigue level data. In this way, by taking the driver's health condition and fatigue level into consideration, safer drivers can be selected.

[0042] When selecting a driver, the driver selection unit performs a detailed analysis of customers' past feedback and evaluations, allowing the generation AI to select the most suitable driver. For example, the driver selection unit stores customers' past feedback and evaluation data in a database, and the generation AI analyzes this data. For example, the generation AI selects the most suitable driver based on customers' evaluation scores and comments. The generation AI also selects drivers with high customer satisfaction based on customer feedback data. Furthermore, the generation AI selects drivers who can ensure customer safety based on customer evaluation data. This makes it possible to select a more suitable driver by taking customer feedback and evaluations into consideration.

[0043] When selecting a driver, the driver selection unit collects data on drivers from different regions and cultural spheres, and the generation AI can select the optimal driver based on those conditions. The driver selection unit, for example, collects data on drivers from different regions and cultural spheres, and the generation AI analyzes that data. For example, the generation AI selects the optimal driver by taking into account the driving habits and cultural background of each region. The generation AI also selects the optimal driver by taking into account the driving skills and evaluation criteria of each cultural sphere. Furthermore, the generation AI selects a driver who can ensure customer safety based on the characteristics of the region and cultural sphere. This makes it possible to select a more appropriate driver by taking into account data from different regions and cultural spheres.

[0044] When selecting drivers, the driver selection unit integrates data from other ride-sharing services, allowing the generation AI to make selections based on a wider range of data. The driver selection unit, for example, collects data from other ride-sharing services, and the generation AI analyzes that data. For example, the generation AI selects the optimal driver by taking into account ratings and driving history on other services. The generation AI also selects the optimal driver based on customer feedback and evaluation scores from other services. Furthermore, the generation AI selects a driver who can ensure customer safety based on driving skills and safety data from other services. In this way, by integrating data from other ride-sharing services, it is possible to select an appropriate driver based on a wider range of data.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The SafeRide AI system can further be equipped with a health management unit that monitors the driver's health. The health management unit collects biometric data such as the driver's heart rate, blood pressure, and body temperature in real time, and the generation AI analyzes this data. For example, a heart rate sensor collects the driver's heart rate in real time, and the generation AI evaluates their stress level and fatigue level. A blood pressure sensor collects the driver's blood pressure in real time, and the generation AI evaluates their health condition. Furthermore, a body temperature sensor collects the driver's body temperature in real time, and the generation AI evaluates their health condition. This improves safety by monitoring the driver's health condition in real time and issuing a warning if an abnormality is detected.

[0047] When evaluating a driver's driving skills, the driving technique analysis unit collects driving data under different weather and road conditions, and the generation AI can evaluate driving techniques according to those conditions. For example, the generation AI analyzes braking and steering operations in rainy weather and evaluates appropriate driving techniques. The generation AI also analyzes speed control and braking operations in snowy weather and evaluates appropriate driving techniques. The generation AI also analyzes visibility and steering operations in foggy weather and evaluates appropriate driving techniques. This allows for more accurate evaluations by evaluating driving techniques under different weather and road conditions.

[0048] The behavior analysis unit collects environmental data such as in-car temperature and lighting to evaluate customer behavior, and the generation AI can analyze the impact of these environmental factors on behavior. For example, a temperature sensor collects in-car temperature in real time, and the generation AI evaluates the impact of temperature changes on customer behavior. In addition, a lighting sensor collects in-car lighting data in real time, and the generation AI evaluates the impact of lighting brightness on customer behavior. This allows for the analysis of the impact of in-car environmental factors on customer behavior, thereby improving safety.

[0049] When selecting a driver, the driver selection unit integrates data from other ride-sharing services, allowing the generation AI to make a selection based on a wider range of data. For example, the generation AI selects the optimal driver by taking into account ratings and driving history on other services. The generation AI also selects the optimal driver based on customer feedback and evaluation scores on other services. In this way, by integrating data from other ride-sharing services, it is possible to select an appropriate driver based on a wider range of data.

[0050] When selecting drivers, the driver selection unit collects data on drivers from different regions and cultural spheres, and the generation AI can select the optimal driver based on those conditions. For example, the generation AI selects the optimal driver by taking into account the driving habits and cultural background of each region. The generation AI also selects the optimal driver by taking into account the driving skills and evaluation criteria of each cultural sphere. This makes it possible to select a more appropriate driver by taking into account data from different regions and cultural spheres.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The driving technique analysis unit analyzes the driver's driving technique in real time. Specifically, sensors and cameras are used to collect data on the driver's speed, braking technique, steering operation, etc., and the generation AI analyzes this data. The generation AI evaluates speed control based on the speed data, analyzes braking technique to evaluate appropriate braking operation, and analyzes steering operation data to evaluate smooth steering operation. Step 2: The behavioral analysis unit analyzes customer behavior in real time. Specifically, it uses cameras and sensors to monitor whether customers are wearing seat belts and whether their behavior in the car is safe. The generation AI detects whether seat belts are being worn, evaluates safety, monitors customer movements in the car to evaluate safe behavior, and detects abnormal behavior. Step 3: The driver selection unit selects an appropriate driver. Specifically, the generation AI selects the safest driver based on the driver's past driving history and evaluations, current driving conditions, etc. The generation AI analyzes driving history data to select drivers with safe driving skills, selects highly rated drivers based on evaluation data, and analyzes current driving conditions data to select an appropriate driver.

[0053] (Example 2) The SafeRide AI system according to an embodiment of the present invention utilizes generative AI to ensure safe ride-sharing. This system analyzes driver skills and customer behavior in real time to minimize the risk of accidents and trouble. In addition, when a customer uses a ride-sharing service, the generative AI selects an appropriate driver to ensure safety. As a result, the SafeRide AI system can ensure safe ride-sharing by analyzing driver skills and customer behavior in real time and selecting an appropriate driver.

[0054] The Safe Ride AI system according to the embodiment includes a driving technique analysis unit, a behavior analysis unit, and a driver selection unit. The driving technique analysis unit analyzes a driver's driving technique in real time. For example, it collects data such as the driver's speed, braking technique, and steering operation using sensors and cameras, and the generation AI analyzes this data. For example, the generation AI evaluates speed control based on the driver's speed data. The generation AI can also analyze braking technique and evaluate appropriate braking operation. Furthermore, the generation AI can analyze steering operation data and evaluate smooth steering operation. The behavior analysis unit analyzes customer behavior in real time. For example, it monitors whether customers are wearing seat belts and whether their behavior in the car is safe using cameras and sensors. For example, the generation AI detects whether customers are wearing seat belts and evaluates safety. The generation AI can also monitor customers' movements in the car and evaluate safe behavior. Furthermore, the generation AI can analyze customer behavior data and detect abnormal behavior. The driver selection unit selects an appropriate driver. For example, the generation AI selects the safest driver based on the driver's past driving history and evaluations, current driving conditions, etc. The generation AI may, for example, analyze the driver's driving history data to select a driver with safe driving skills. The generation AI may also select a highly rated driver based on driver evaluation data. Furthermore, the generation AI may analyze current driving conditions data to select an appropriate driver. As a result, the Safe Ride AI system according to the embodiment can realize safe ride sharing by analyzing the driver's driving skills and customer behavior in real time and selecting an appropriate driver.

[0055] The driving technique analysis unit can collect data such as the driver's speed, braking technique, and steering operation using sensors and cameras, and analyze this data. For example, the driving technique analysis unit measures the driver's speed using a sensor, and the generation AI analyzes the data. For example, a speed sensor collects the driver's speed in real time, and the generation AI evaluates speed control. The driving technique analysis unit also measures braking technique using a sensor, and the generation AI analyzes the data. For example, a brake sensor collects braking strength and timing, and the generation AI evaluates appropriate braking operation. The driving technique analysis unit also monitors steering operation using a camera, and the generation AI analyzes the data. For example, a camera collects the steering wheel rotation angle and operation smoothness, and the generation AI evaluates smooth steering operation. This enables a more accurate evaluation by analyzing the driver's driving technique in detail.

[0056] The behavior analysis unit can use cameras and sensors to monitor whether customers are wearing seat belts and whether their behavior in the car is safe. For example, the behavior analysis unit uses cameras to monitor whether customers are wearing seat belts, and the generation AI analyzes the data. For example, a camera collects information on whether customers are wearing seat belts in real time, and the generation AI evaluates safety. The behavior analysis unit also uses sensors to monitor customers' behavior in the car, and the generation AI analyzes the data. For example, a sensor collects information on customers' movements in real time, and the generation AI evaluates safe behavior. The behavior analysis unit can also analyze customers' behavior data and detect abnormal behavior. For example, the generation AI compares it with past behavior data to detect abnormal behavior and issue a warning. In this way, safety is ensured by closely monitoring customer behavior.

[0057] The driver selection unit can select the safest driver based on the driver's past driving history and evaluations, current driving conditions, etc. In the driver selection unit, for example, the generation AI analyzes the driver's past driving history and selects a driver with safe driving skills. For example, the generation AI selects a driver with no history of accidents or violations based on the driver's driving history data. In addition, the driver selection unit analyzes the driver's evaluation data and selects a highly rated driver. For example, the generation AI selects a highly rated driver based on customer feedback and evaluation scores. In addition, the driver selection unit analyzes the current driving conditions and selects an appropriate driver. For example, the generation AI selects a driver with stable driving skills based on current driving condition data. This ensures customer safety by selecting drivers more accurately.

[0058] The driving technique analysis unit uses a biometric sensor to measure the driver's stress level while driving, and the generation AI analyzes the data to issue a warning if stress levels increase. For example, the driving technique analysis unit collects heart rate and electrodermal activity in real time from a biometric sensor worn by the driver while driving, and the generation AI analyzes this data. For example, a heart rate sensor collects the driver's heart rate in real time, and the generation AI evaluates the stress level. A electrodermal response sensor also collects the driver's electrodermal activity in real time, and the generation AI evaluates the stress level. If the stress level exceeds a certain threshold, the generation AI issues a warning. For example, the generation AI issues an audio warning to encourage the driver to take a break. The generation AI also issues a visual warning to alert the driver. This improves safety by monitoring the driver's stress level in real time and issuing warnings at the appropriate time.

[0059] When evaluating a driver's driving skills, the driving skill analysis unit compares them with past driving data to track improvements or deterioration in the skills, and the generation AI can provide feedback. For example, the driving skill analysis unit stores the driver's past driving data in a database and compares it with current driving data. The generation AI tracks improvements or deterioration in skills and provides specific feedback to the driver. For example, the generation AI compares past driving data with current driving data to evaluate improvements or deterioration in speed control. The generation AI also compares braking operation data to evaluate improvements or deterioration in appropriate braking operation. The generation AI also compares steering operation data to evaluate improvements or deterioration in smooth steering operation. Based on these evaluation results, the generation AI provides the driver with specific improvements and advice. For example, the generation AI points out improvements in speed control and provides specific advice. The generation AI also points out improvements in braking operation and provides specific advice. The generation AI also points out improvements in steering operation and provides specific advice. In this way, the system tracks changes in the driver's driving skills and provides appropriate feedback to promote improvement.

[0060] The driving technique analysis unit uses an emotion estimation function to analyze the driver's emotional state in real time and can prompt the driver to stop driving if their emotions are unstable. The driving technique analysis unit, for example, analyzes the driver's facial expressions and voice and analyzes their emotional state in real time using the emotion estimation function. For example, the generation AI evaluates the driver's emotional state based on the driver's facial expression data. The generation AI also evaluates the driver's emotional state based on the driver's voice data. If the driver's emotions are unstable, the generation AI issues a warning prompting the driver to stop driving. For example, the generation AI issues an audio warning to prompt the driver to take a break. The generation AI also issues a visual warning to alert the driver. This improves safety by monitoring the driver's emotional state in real time and prompting the driver to stop driving if their emotions are unstable.

[0061] When evaluating a driver's driving skills, the driving technique analysis unit collects driving data under different weather and road conditions, and the generation AI evaluates driving techniques according to those conditions. For example, when collecting a driver's driving data, the driving technique analysis unit takes weather conditions (rain, snow, fog, etc.) into consideration, and the generation AI evaluates driving techniques according to those conditions. For example, the generation AI analyzes braking and steering operation in the rain and evaluates appropriate driving techniques. The generation AI also analyzes speed control and braking operation in snowy weather and evaluates appropriate driving techniques. The generation AI also analyzes visibility and steering operation in foggy weather and evaluates appropriate driving techniques. The driving technique analysis unit also considers road conditions (traffic jams, construction, accidents, etc.), and the generation AI evaluates driving techniques according to those conditions. For example, the generation AI analyzes speed control and braking operation in traffic jams and evaluates appropriate driving techniques. The generation AI also analyzes steering operation and speed control in construction zones and evaluates appropriate driving techniques. The generation AI also analyzes evasive maneuvers and speed control at accident sites and evaluates appropriate driving techniques. This allows for a more accurate assessment by assessing driving skills under different weather and road conditions.

[0062] The driving technique analysis unit can perform a relative evaluation using comparative data with other drivers to evaluate a driver's driving technique. For example, to evaluate a driver's driving technique, the driving technique analysis unit collects driving data of other drivers, and the generation AI performs a relative evaluation based on that data. For example, the generation AI performs an evaluation by comparing it with data from other drivers who drove the same route. The generation AI also performs an evaluation by comparing it with data from other drivers under the same weather conditions and road conditions. The generation AI also performs an evaluation by comparing it with data from other drivers under the same time of day and day of the week. This makes it possible to perform a relative evaluation by using comparative data with other drivers.

[0063] The driving technique analysis unit can use the emotion estimation function to monitor changes in the driver's emotions while driving and provide music or messages to elicit positive emotions. The driving technique analysis unit, for example, analyzes the driver's facial expressions and voice and monitors emotional changes in real time using the emotion estimation function. For example, the generation AI evaluates emotional changes based on the driver's facial expression data. The generation AI also evaluates emotional changes based on the driver's voice data. The generation AI provides music tailored to the driver's preferences to elicit positive emotions. For example, the generation AI plays relaxing music to reduce the driver's stress. The generation AI also plays up-tempo music to lift the driver's spirits. Furthermore, the generation AI provides encouraging messages to improve the driver's motivation. In this way, safety is improved by monitoring changes in the driver's emotions and eliciting positive emotions.

[0064] When analyzing customer behavior, the behavior analysis unit compares it with past behavioral data to detect abnormal behavior, and the generation AI can then issue a warning. For example, the behavior analysis unit stores past behavioral data of customers in a database and compares it with current behavioral data. The generation AI detects abnormal behavior and issues a warning to the customer. For example, the generation AI compares the status of seat belt use with past data to detect abnormal behavior. The generation AI also compares the customer's movements in the car with past data to detect abnormal behavior. If abnormal behavior is detected, the generation AI issues a warning to the customer. For example, the generation AI issues an audio warning to alert the customer. The generation AI also issues a visual warning to alert the customer. In this way, safety is improved by detecting abnormal customer behavior and issuing a warning.

[0065] The behavior analysis unit collects environmental data such as in-car temperature and lighting to evaluate customer behavior, and the generation AI can analyze the impact of these environmental factors on behavior. For example, the behavior analysis unit collects in-car temperature and lighting data in real time, and the generation AI analyzes that data. For example, a temperature sensor collects in-car temperature in real time, and the generation AI evaluates the impact of temperature changes on customer behavior. In addition, a lighting sensor collects in-car lighting data in real time, and the generation AI evaluates the impact of lighting brightness on customer behavior. The generation AI adjusts the in-car environment based on these evaluation results. For example, the generation AI adjusts the temperature appropriately to improve customer comfort. In addition, the generation AI adjusts the lighting appropriately to improve customer comfort. In this way, safety is improved by analyzing the impact of in-car environmental factors on customer behavior.

[0066] The behavior analysis unit uses an emotion estimation function to analyze the customer's emotional state in real time, and can make suggestions to help them relax if they are emotionally unstable. The behavior analysis unit, for example, analyzes the customer's facial expressions and voice, and analyzes their emotional state in real time using the emotion estimation function. For example, the generation AI evaluates the customer's emotional state based on their facial expression data. The generation AI also evaluates the customer's emotional state based on their voice data. If the customer is emotionally unstable, the generation AI makes suggestions to help them relax. For example, the generation AI plays relaxing music to reduce the customer's stress. The generation AI also suggests deep breathing to encourage the customer to relax. Furthermore, the generation AI provides encouraging messages to stabilize the customer's mood. In this way, safety is improved by monitoring the customer's emotional state in real time and making suggestions to help them relax if they are emotionally unstable.

[0067] When evaluating customer behavior, the behavior analysis unit collects behavioral data from different times of the day and days of the week, and the generation AI can analyze behavioral patterns according to those conditions. For example, the behavior analysis unit collects customer behavioral data from different times of the day and days of the week, and the generation AI analyzes that data. For example, the generation AI evaluates the differences in behavioral patterns between weekdays and weekends. The generation AI also evaluates the differences in behavioral patterns between mornings and evenings. Furthermore, the generation AI analyzes behavioral patterns from specific days of the week and time periods to evaluate customer behavior. This allows for a more accurate evaluation by analyzing behavioral patterns from different times of the day and days of the week.

[0068] The behavioral analysis unit can use comparative data with other customers to evaluate customer behavior and make a relative evaluation. The behavioral analysis unit, for example, collects customer behavior data and compares it with data from other customers. The generation AI makes a relative evaluation based on that data. For example, the generation AI makes an evaluation by comparing it with other customers who used the same route. The generation AI also makes an evaluation by comparing it with data from other customers at the same time of day or day of the week. The generation AI also makes an evaluation by comparing it with data from other customers under the same weather conditions or road conditions. In this way, using comparative data with other customers makes it possible to make a relative evaluation.

[0069] The behavior analysis unit can use the emotion estimation function to monitor changes in the customer's emotions and provide music or messages to elicit positive emotions. The behavior analysis unit, for example, analyzes the customer's facial expressions and voice and monitors emotional changes in real time using the emotion estimation function. For example, the generation AI evaluates emotional changes based on the customer's facial expression data. The generation AI also evaluates emotional changes based on the customer's voice data. The generation AI provides music tailored to the customer's preferences to elicit positive emotions. For example, the generation AI plays relaxing music to reduce the customer's stress. The generation AI also plays up-tempo music to lift the customer's spirits. Furthermore, the generation AI provides encouraging messages to increase the customer's motivation. In this way, safety is improved by monitoring changes in the customer's emotions and eliciting positive emotions.

[0070] When selecting drivers, the driver selection unit uses biosensors to measure not only the driver's past driving history but also their health condition and fatigue level, and the generation AI can take this data into consideration. For example, the driver selection unit measures the driver's health condition and fatigue level using biosensors, and the generation AI analyzes the data. For example, a heart rate sensor collects the driver's heart rate in real time, and the generation AI evaluates their health condition. Also, a skin electrodermal response sensor collects the driver's skin activity in real time, and the generation AI evaluates their fatigue level. Based on these evaluation results, the generation AI selects drivers with good health and fatigue levels. For example, the generation AI selects drivers with good health based on heart rate and skin activity data. Also, the generation AI selects drivers with low fatigue levels based on fatigue level data. In this way, by taking the driver's health condition and fatigue level into consideration, safer drivers can be selected.

[0071] When selecting a driver, the driver selection unit performs a detailed analysis of customers' past feedback and evaluations, allowing the generation AI to select the most suitable driver. For example, the driver selection unit stores customers' past feedback and evaluation data in a database, and the generation AI analyzes this data. For example, the generation AI selects the most suitable driver based on customers' evaluation scores and comments. The generation AI also selects drivers with high customer satisfaction based on customer feedback data. Furthermore, the generation AI selects drivers who can ensure customer safety based on customer evaluation data. This makes it possible to select a more suitable driver by taking customer feedback and evaluations into consideration.

[0072] The driver selection unit uses the emotion estimation function to analyze the driver's emotional state in real time and can prioritize the selection of emotionally stable drivers. The driver selection unit, for example, analyzes the driver's facial expressions and voice and analyzes the emotional state in real time using the emotion estimation function. For example, the generation AI evaluates the driver's emotional state based on facial expression data. The generation AI also evaluates the emotional state based on the driver's voice data. The generation AI prioritizes the selection of emotionally stable drivers. For example, the generation AI selects drivers with stable emotional states to ensure customer safety. Furthermore, the generation AI avoids drivers with unstable emotional states, reducing the risk of accidents and trouble. This improves safety by prioritizing the selection of emotionally stable drivers.

[0073] When selecting a driver, the driver selection unit collects data on drivers from different regions and cultural spheres, and the generation AI can select the optimal driver based on those conditions. The driver selection unit, for example, collects data on drivers from different regions and cultural spheres, and the generation AI analyzes that data. For example, the generation AI selects the optimal driver by taking into account the driving habits and cultural background of each region. The generation AI also selects the optimal driver by taking into account the driving skills and evaluation criteria of each cultural sphere. Furthermore, the generation AI selects a driver who can ensure customer safety based on the characteristics of the region and cultural sphere. This makes it possible to select a more appropriate driver by taking into account data from different regions and cultural spheres.

[0074] When selecting drivers, the driver selection unit integrates data from other ride-sharing services, allowing the generation AI to make selections based on a wider range of data. The driver selection unit, for example, collects data from other ride-sharing services, and the generation AI analyzes that data. For example, the generation AI selects the optimal driver by taking into account ratings and driving history on other services. The generation AI also selects the optimal driver based on customer feedback and evaluation scores from other services. Furthermore, the generation AI selects a driver who can ensure customer safety based on driving skills and safety data from other services. In this way, by integrating data from other ride-sharing services, it is possible to select an appropriate driver based on a wider range of data.

[0075] The driver selection unit can use the emotion estimation function to analyze the customer's emotional state in real time and select a driver who is most suited to the customer's emotions. The driver selection unit, for example, analyzes the customer's facial expressions and voice and analyzes the emotional state in real time using the emotion estimation function. For example, the generation AI evaluates the customer's emotional state based on facial expression data. The generation AI also evaluates the customer's emotional state based on voice data. The generation AI selects a driver who is most suited to the customer's emotions. For example, if the customer's emotional state is relaxed, the generation AI selects a similarly relaxed driver. Also, if the customer's emotional state is tense, the generation AI selects a calm driver. This makes it possible to select a more appropriate driver by taking the customer's emotional state into consideration.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The SafeRide AI system can further be equipped with a health management unit that monitors the driver's health. The health management unit collects biometric data such as the driver's heart rate, blood pressure, and body temperature in real time, and the generation AI analyzes this data. For example, a heart rate sensor collects the driver's heart rate in real time, and the generation AI evaluates their stress level and fatigue level. A blood pressure sensor collects the driver's blood pressure in real time, and the generation AI evaluates their health condition. Furthermore, a body temperature sensor collects the driver's body temperature in real time, and the generation AI evaluates their health condition. This improves safety by monitoring the driver's health condition in real time and issuing a warning if an abnormality is detected.

[0078] When evaluating a driver's driving skills, the driving technique analysis unit collects driving data under different weather and road conditions, and the generation AI can evaluate driving techniques according to those conditions. For example, the generation AI analyzes braking and steering operations in rainy weather and evaluates appropriate driving techniques. The generation AI also analyzes speed control and braking operations in snowy weather and evaluates appropriate driving techniques. The generation AI also analyzes visibility and steering operations in foggy weather and evaluates appropriate driving techniques. This allows for more accurate evaluations by evaluating driving techniques under different weather and road conditions.

[0079] The behavior analysis unit collects environmental data such as in-car temperature and lighting to evaluate customer behavior, and the generation AI can analyze the impact of these environmental factors on behavior. For example, a temperature sensor collects in-car temperature in real time, and the generation AI evaluates the impact of temperature changes on customer behavior. In addition, a lighting sensor collects in-car lighting data in real time, and the generation AI evaluates the impact of lighting brightness on customer behavior. This allows for the analysis of the impact of in-car environmental factors on customer behavior, thereby improving safety.

[0080] When selecting a driver, the driver selection unit integrates data from other ride-sharing services, allowing the generation AI to make a selection based on a wider range of data. For example, the generation AI selects the optimal driver by taking into account ratings and driving history on other services. The generation AI also selects the optimal driver based on customer feedback and evaluation scores on other services. In this way, by integrating data from other ride-sharing services, it is possible to select an appropriate driver based on a wider range of data.

[0081] The driving technique analysis unit uses biosensors to measure the driver's stress level while driving, and the generation AI analyzes the data to issue a warning if stress increases. For example, a heart rate sensor collects the driver's heart rate in real time, and the generation AI evaluates the stress level. In addition, a galvanic skin response sensor collects the driver's electrodermal activity in real time, and the generation AI evaluates the stress level. If the stress level exceeds a certain threshold, the generation AI issues a warning to the driver. This allows for real-time monitoring of the driver's stress level and issuing warnings at the appropriate time, improving safety.

[0082] The driving technique analysis unit uses the emotion estimation function to analyze the driver's emotional state in real time and can prompt the driver to stop driving if their emotions are unstable. For example, the generation AI evaluates the driver's emotional state based on their facial expression data. The generation AI also evaluates the driver's emotional state based on their voice data. If their emotions are unstable, the generation AI issues a warning prompting the driver to stop driving. This allows the system to monitor the driver's emotional state in real time and prompt the driver to stop driving if their emotions are unstable, thereby improving safety.

[0083] The behavioral analysis unit uses the emotion estimation function to analyze the customer's emotional state in real time, and can make suggestions to help them relax if they are emotionally unstable. For example, the generation AI evaluates the customer's emotional state based on their facial expression data. The generation AI also evaluates the customer's emotional state based on their voice data. If they are emotionally unstable, the generation AI makes suggestions to help them relax. For example, the generation AI plays relaxing music to reduce the customer's stress. The generation AI also suggests deep breathing to encourage the customer to relax. This makes it possible to monitor the customer's emotional state in real time and make suggestions to help them relax if they are emotionally unstable, thereby improving safety.

[0084] The driving technique analysis unit uses the emotion estimation function to monitor changes in the driver's emotions while driving and can provide music or messages to elicit positive emotions. For example, the generation AI evaluates changes in emotions based on the driver's facial expression data. The generation AI also evaluates changes in emotions based on the driver's voice data. The generation AI provides music tailored to the driver's preferences to elicit positive emotions. This makes it possible to monitor changes in the driver's emotions and elicit positive emotions, thereby improving safety.

[0085] The driver selection unit uses an emotion estimation function to analyze the driver's emotional state in real time and prioritize the selection of emotionally stable drivers. For example, the generation AI evaluates the driver's emotional state based on facial expression data. The generation AI also evaluates the driver's emotional state based on voice data. The generation AI prioritizes the selection of emotionally stable drivers. This prioritizes the selection of emotionally stable drivers, thereby improving safety.

[0086] When selecting drivers, the driver selection unit collects data on drivers from different regions and cultural spheres, and the generation AI can select the optimal driver based on those conditions. For example, the generation AI selects the optimal driver by taking into account the driving habits and cultural background of each region. The generation AI also selects the optimal driver by taking into account the driving skills and evaluation criteria of each cultural sphere. This makes it possible to select a more appropriate driver by taking into account data from different regions and cultural spheres.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The driving technique analysis unit analyzes the driver's driving technique in real time. Specifically, sensors and cameras are used to collect data on the driver's speed, braking technique, steering operation, etc., and the generation AI analyzes this data. The generation AI evaluates speed control based on the speed data, analyzes braking technique to evaluate appropriate braking operation, and analyzes steering operation data to evaluate smooth steering operation. Step 2: The behavioral analysis unit analyzes customer behavior in real time. Specifically, it uses cameras and sensors to monitor whether customers are wearing seat belts and whether their behavior in the car is safe. The generation AI detects whether seat belts are being worn, evaluates safety, monitors customer movements in the car to evaluate safe behavior, and detects abnormal behavior. Step 3: The driver selection unit selects an appropriate driver. Specifically, the generation AI selects the safest driver based on the driver's past driving history and evaluations, current driving conditions, etc. The generation AI analyzes driving history data to select drivers with safe driving skills, selects highly rated drivers based on evaluation data, and analyzes current driving conditions data to select an appropriate driver.

[0089] 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.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0156] 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 driving technique analysis department that analyzes the driver's driving technique in real time, A behavioral analysis department that analyzes customer behavior in real time, A driver selection unit that selects an appropriate driver is provided. A system characterized by:

2. The driving technique analysis unit The driver's speed, braking, steering, etc. are collected using sensors and cameras, and this data is analyzed.

2. The system of claim 1.

3. The behavior analysis unit Cameras and sensors are used to monitor whether the customer is wearing a seatbelt and whether their behavior in the car is safe.

2. The system of claim 1.

4. The driver selection unit The safest driver is selected based on the driver's past driving history, evaluation, current driving situation, etc.

2. The system of claim 1.

5. The driving technique analysis unit When evaluating the driving skills of the driver, driving data under different weather conditions and road conditions is collected, and the generation AI evaluates the driving skills according to those conditions.

2. The system of claim 1.

6. The behavior analysis unit When analyzing the customer's behavior, the AI ​​compares it with past behavioral data to detect abnormal behavior and issues a warning.

2. The system of claim 1.

7. The driver selection unit When selecting the driver, not only the driver's past driving history but also the driver's health condition and fatigue level are measured using biometric sensors, and the generation AI takes this data into consideration.

2. The system of claim 1.

8. The driving technique analysis unit Using an emotion estimation function, the emotional state of the driver is analyzed in real time, and if the emotional state is unstable, the driver is prompted to stop driving.

2. The system of claim 1.

Images