System

Abstract

An object of a system according to an embodiment is to appropriately evaluate the health condition of a driver and the driving ability of an elderly driver to prevent an accident.SOLUTION: A system according to an embodiment includes a vehicle information acquisition unit, a health condition measurement unit, an engine control unit, and a pre-driving test unit. The vehicle information acquisition unit acquires information on the vehicle. The health condition measurement unit measures a health condition of the driver. The engine control unit controls the engine when an abnormality is found in the health condition of the driver measured by the health condition measurement unit. The pre-driving test unit performs a pre-driving test of an elderly 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 that there are not enough systems in place to properly assess the health of drivers and the driving ability of elderly drivers and prevent accidents.

[0005] The system according to the embodiment aims to appropriately evaluate the health condition of drivers and the driving ability of elderly drivers, thereby preventing accidents. [Means for solving the problem]

[0006] The system according to the embodiment includes a vehicle information acquisition unit, a health condition measurement unit, an engine control unit, and a pre-driving test unit. The vehicle information acquisition unit acquires vehicle information. The health condition measurement unit measures the driver's health condition. The engine control unit controls the engine when an abnormality is detected in the driver's health condition measured by the health condition measurement unit. The pre-driving test unit conducts a pre-driving test for elderly drivers. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately evaluate the health condition of the driver and the driving ability of elderly drivers, thereby preventing accidents. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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 driving assistance system according to the embodiment of the present invention is a system that works in conjunction with a smartphone or a smartwatch to measure vehicle information and the driver's health condition and share the information with the driver. This allows the driving assistance system to monitor the driver's health condition in real time and prevent driving if an abnormality is detected.

[0029] A driving assistance system according to an embodiment includes a vehicle information acquisition unit, a health condition measurement unit, an engine control unit, and a pre-driving test unit. The vehicle information acquisition unit acquires vehicle information. For example, the vehicle information acquisition unit measures the mileage and remaining gasoline and shares the information with the driver in real time. The vehicle information acquisition unit can also acquire vehicle speed and location information. For example, the vehicle information acquisition unit identifies the vehicle's location using a GPS and notifies the driver. The health condition measurement unit measures the driver's health condition. For example, the health condition measurement unit measures the driver's heart rate and body temperature using a smartwatch and shares the information with the driver. The health condition measurement unit can also check the driver's sleep condition and whether the driver has a driver's license. For example, the health condition measurement unit analyzes the driver's sleep data and displays a warning if the driver is sleep deprived. The engine control unit controls the engine if an abnormality is detected in the driver's health condition measured by the health condition measurement unit. For example, the engine control unit prevents the engine from starting if the driver's heart rate is abnormally high or if the driver's body temperature is high. The engine control unit can also control the engine if the driver does not have a driver's license. For example, the engine control unit checks the expiration date of the driver's license and locks the engine if it has expired. The pre-driving test unit conducts a pre-driving test for the elderly driver. For example, the pre-driving test unit conducts tests to check reaction speed, vision, and cognitive function, and starts the engine only if the tests are passed. The pre-driving test unit can also use a driving simulator to reproduce actual driving conditions for a more realistic evaluation. For example, the pre-driving test unit can simulate heavy traffic roads and bad weather to evaluate the elderly driver's driving ability. In this way, the driving assistance system according to the embodiment can monitor the driver's health condition in real time and prevent driving if an abnormality is detected. For example, if the driver's heart rate is abnormally high or their body temperature is high, the engine can be prevented from starting, ensuring the driver's safety. Furthermore, by conducting a pre-driving test for elderly drivers to check their driving ability, the risk of accidents can be reduced.

[0030] The vehicle information acquisition unit can simultaneously collect the vehicle's driving history and maintenance history and make preventive maintenance suggestions to the driver. The vehicle information acquisition unit, for example, records the vehicle's driving history on a smart device and notifies the driver when regular maintenance is required. For example, it may suggest an oil change when the mileage reaches a certain value. The vehicle information acquisition unit also records the maintenance history and notifies the driver when the next maintenance is due. For example, it may record the tire change history and suggest a tire change after a certain period of time has passed. This makes it possible to improve the safety of the vehicle by making preventive maintenance suggestions based on the driving history and maintenance history.

[0031] The vehicle information acquisition unit not only shares vehicle information in real time, but can also implement an algorithm that compares it with past data to detect abnormalities. The vehicle information acquisition unit, for example, implements an algorithm that compares past driving data with current driving data to detect abnormalities in fuel economy. For example, if fuel economy suddenly deteriorates, the unit notifies the driver. The vehicle information acquisition unit can also compare past maintenance history with the current vehicle condition to detect abnormalities. For example, the brake wear condition can be compared with past data, and a warning displayed if an abnormality is found. This allows for early detection of vehicle abnormalities by comparing it with past data.

[0032] The health condition measurement unit can measure the driver's heart rate, body temperature, sleep state, and driver's license status and share the information with the driver. The health condition measurement unit, for example, measures the driver's heart rate using a smartwatch and shares the information with the driver. For example, it notifies the driver that the heart rate is within the normal range. The health condition measurement unit also measures the driver's body temperature and shares the information with the driver. For example, it notifies the driver that the body temperature is 36.5 degrees. The health condition measurement unit also measures the driver's sleep status and shares the information with the driver. For example, it notifies the driver that the amount of sleep is sufficient. The health condition measurement unit also checks whether the driver has a driver's license and notifies the driver. For example, it notifies the driver that the license is valid. In this way, by measuring the driver's health status in detail and sharing the information with the driver, it is possible to support the driver's health management.

[0033] The engine control unit can prevent the engine from starting if an abnormality is detected in the driver's health. For example, the engine control unit prevents the engine from starting if the heart rate is abnormally high. For example, the engine control unit locks engine start if the heart rate exceeds a certain range. The engine control unit also prevents the engine from starting if the driver has a high body temperature. For example, the engine control unit locks engine start if the body temperature exceeds 38 degrees Celsius. The engine control unit also prevents the engine from starting if the driver does not have a driver's license. For example, the engine control unit locks engine start if the license has expired. In this way, the safety of the driver can be ensured by preventing the engine from starting if an abnormality is detected in the driver's health.

[0034] The pre-driving test unit can conduct a simple test before the elderly driver drives to confirm their driving ability. The pre-driving test unit, for example, conducts a reaction speed test to confirm the driving ability of the elderly driver. For example, the engine starts when the reaction speed meets a certain standard. The pre-driving test unit also conducts a vision test to confirm the driving ability of the elderly driver. For example, the engine starts when the vision meets a certain standard. The pre-driving test unit also conducts a cognitive function test to confirm the driving ability of the elderly driver. For example, the engine starts when the cognitive function meets a certain standard. In this way, by conducting a simple test before the elderly driver drives to confirm their driving ability, the risk of an accident can be reduced.

[0035] The pre-driving test unit can provide an individual driving training program to the elderly driver based on the test results. For example, the pre-driving test unit provides a program for reaction training to the elderly driver based on the results of a reaction speed test. For example, reaction training is provided when reaction speed is slow. The pre-driving test unit also provides a program for vision training to the elderly driver based on the results of a vision test. For example, vision training is provided when vision is impaired. The pre-driving test unit also provides a program for cognitive training to the elderly driver based on the results of a cognitive function test. For example, cognitive training is provided when cognitive function is impaired. In this way, by providing an individual driving training program based on the test results, the driving ability of the elderly driver can be improved.

[0036] The pre-driving test unit can combine the pre-driving test with other health checks to perform a comprehensive health evaluation. The pre-driving test unit, for example, combines blood pressure measurement with the pre-driving test to build a system that performs a comprehensive health evaluation. For example, if the blood pressure is high, the driver is notified to refrain from driving. The pre-driving test unit also combines a vision test to build a system that performs a comprehensive health evaluation. For example, if the driver's eyesight is poor, the driver is notified to refrain from driving. The pre-driving test unit also combines other health checks (for example, blood glucose measurement) to build a system that performs a comprehensive health evaluation. For example, if the blood glucose level is high, the driver is notified to refrain from driving. In this way, by combining the pre-driving test with other health checks, a comprehensive health evaluation is possible.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The driving assistance system may further include a driving style analysis unit that analyzes the driver's driving style. The driving style analysis unit may, for example, analyze the frequency of the driver's acceleration and braking to evaluate the driving style. For example, if there are many sudden accelerations and sudden braking, it may determine that the driving style is rough. The driving style analysis unit may also analyze the driver's steering operation to evaluate the driving style. For example, if there are frequent steering operations, it may determine that the driving style is unstable. The driving style analysis unit may also analyze the frequency of the driver's lane changes to evaluate the driving style. For example, if there are frequent lane changes, it may determine that the driving style is risky. In this way, the driver's driving style may be analyzed and improvements may be suggested, thereby promoting safe driving.

[0039] The driving assistance system may further include an advice providing unit that provides driving advice based on the driver's health condition. The advice providing unit may, for example, analyze the driver's heart rate and body temperature and suggest appropriate rest timing. For example, if the heart rate is high, the advice providing unit may notify the driver to take a break. The advice providing unit may also analyze the driver's sleep state and suggest that the driver take sufficient rest before driving. For example, if the driver is sleep deprived, the advice providing unit may notify the driver to refrain from driving. The advice providing unit may also analyze the driver's dietary state and suggest appropriate nutritional supplementation. For example, if the diet is insufficient, the advice providing unit may notify the driver to replenish nutrition. In this way, by providing driving advice based on the driver's health condition, the driver's safety can be ensured.

[0040] The driving assistance system can further include an insurance premium calculation unit that calculates insurance premiums based on the driver's driving history. The insurance premium calculation unit, for example, analyzes the driver's driving history and discounts the insurance premium if the driver drives safely. For example, the insurance premium is discounted if there is little sudden acceleration or braking. The insurance premium calculation unit can also analyze the driver's driving history and increase the insurance premium if the driver engages in risky driving. For example, the insurance premium is increased if the driver frequently exceeds the speed limit. The insurance premium calculation unit can also analyze the driver's driving history and propose an insurance plan according to the driver's driving style. For example, if the driver frequently drives long distances, an insurance plan for long distance driving is proposed. In this way, calculating insurance premiums based on the driver's driving history enables fair insurance premium setting.

[0041] The driving assistance system can further include a maintenance optimization unit that optimizes the maintenance schedule based on the driver's driving history. The maintenance optimization unit, for example, analyzes the driver's driving history and suggests appropriate maintenance times. For example, it suggests the timing of an oil change based on mileage and engine operating hours. The maintenance optimization unit can also analyze the driver's driving history and suggest the timing of part replacement. For example, it suggests the timing of replacement based on the wear state of brake pads. The maintenance optimization unit can also analyze the driver's driving history and suggest the timing of tire replacement. For example, it suggests the timing of replacement based on the wear state of tires. In this way, the safety and performance of the vehicle can be maintained by optimizing the maintenance schedule based on the driver's driving history.

[0042] The driving assistance system may further include an eco-driving advice unit that provides eco-driving advice based on the driver's driving history. The eco-driving advice unit may, for example, analyze the driver's driving history and provide advice to improve fuel efficiency. For example, it may advise the driver to avoid sudden acceleration and sudden braking. The eco-driving advice unit may also analyze the driver's driving history and suggest an optimal speed for eco-driving. For example, it may advise the driver to maintain a constant speed on a highway. The eco-driving advice unit may also analyze the driver's driving history and suggest a route for eco-driving. For example, it may suggest a route with fewer traffic lights. In this way, by providing eco-driving advice based on the driver's driving history, it is possible to improve fuel efficiency and reduce environmental impact.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The vehicle information acquisition unit acquires vehicle information. For example, it measures the mileage, remaining gasoline, vehicle speed, and location information, and shares this information with the driver in real time. It can also use GPS to identify the vehicle's location and notify the driver. Step 2: The health condition measurement unit measures the driver's health condition. For example, it uses a smartwatch to measure heart rate and body temperature and share the data with the driver. It also checks the driver's sleep status and license status, and displays a warning if the driver is sleep deprived. Step 3: The engine control unit controls the engine if the health condition measurement unit detects any abnormalities in the driver's health. For example, it prevents the engine from starting if the driver's heart rate is abnormally high or if the body temperature is high. It also controls the engine if the driver does not have a driver's license, checks the license's expiration date, and locks the engine if it has expired. Step 4: The pre-driving test department will conduct pre-driving tests for the elderly driver, such as tests to check reaction speed, eyesight, and cognitive function, and the engine will only be started if the tests pass. In addition, a driving simulator will be used to recreate real driving situations, simulating busy roads and bad weather, to evaluate the driving ability of the elderly driver.

[0045] (Example 2) The driving assistance system according to the embodiment of the present invention is a system that works in conjunction with a smartphone or a smartwatch to measure vehicle information and the driver's health condition and share the information with the driver. This allows the driving assistance system to monitor the driver's health condition in real time and prevent driving if an abnormality is detected.

[0046] A driving assistance system according to an embodiment includes a vehicle information acquisition unit, a health condition measurement unit, an engine control unit, and a pre-driving test unit. The vehicle information acquisition unit acquires vehicle information. For example, the vehicle information acquisition unit measures the mileage and remaining gasoline and shares the information with the driver in real time. The vehicle information acquisition unit can also acquire vehicle speed and location information. For example, the vehicle information acquisition unit identifies the vehicle's location using a GPS and notifies the driver. The health condition measurement unit measures the driver's health condition. For example, the health condition measurement unit measures the driver's heart rate and body temperature using a smartwatch and shares the information with the driver. The health condition measurement unit can also check the driver's sleep condition and whether the driver has a driver's license. For example, the health condition measurement unit analyzes the driver's sleep data and displays a warning if the driver is sleep deprived. The engine control unit controls the engine if an abnormality is detected in the driver's health condition measured by the health condition measurement unit. For example, the engine control unit prevents the engine from starting if the driver's heart rate is abnormally high or if the driver's body temperature is high. The engine control unit can also control the engine if the driver does not have a driver's license. For example, the engine control unit checks the expiration date of the driver's license and locks the engine if it has expired. The pre-driving test unit conducts a pre-driving test for the elderly driver. For example, the pre-driving test unit conducts tests to check reaction speed, vision, and cognitive function, and starts the engine only if the tests are passed. The pre-driving test unit can also use a driving simulator to reproduce actual driving conditions for a more realistic evaluation. For example, the pre-driving test unit can simulate heavy traffic roads and bad weather to evaluate the elderly driver's driving ability. In this way, the driving assistance system according to the embodiment can monitor the driver's health condition in real time and prevent driving if an abnormality is detected. For example, if the driver's heart rate is abnormally high or their body temperature is high, the engine can be prevented from starting, ensuring the driver's safety. Furthermore, by conducting a pre-driving test for elderly drivers to check their driving ability, the risk of accidents can be reduced.

[0047] The vehicle information acquisition unit can simultaneously collect the vehicle's driving history and maintenance history and make preventive maintenance suggestions to the driver. The vehicle information acquisition unit, for example, records the vehicle's driving history on a smart device and notifies the driver when regular maintenance is required. For example, it may suggest an oil change when the mileage reaches a certain value. The vehicle information acquisition unit also records the maintenance history and notifies the driver when the next maintenance is due. For example, it may record the tire change history and suggest a tire change after a certain period of time has passed. This makes it possible to improve the safety of the vehicle by making preventive maintenance suggestions based on the driving history and maintenance history.

[0048] The vehicle information acquisition unit not only shares vehicle information in real time, but can also implement an algorithm that compares it with past data to detect abnormalities. The vehicle information acquisition unit, for example, implements an algorithm that compares past driving data with current driving data to detect abnormalities in fuel economy. For example, if fuel economy suddenly deteriorates, the unit notifies the driver. The vehicle information acquisition unit can also compare past maintenance history with the current vehicle condition to detect abnormalities. For example, the brake wear condition can be compared with past data, and a warning displayed if an abnormality is found. This allows for early detection of vehicle abnormalities by comparing it with past data.

[0049] The health condition measurement unit can measure the driver's heart rate, body temperature, sleep state, and driver's license status and share the information with the driver. The health condition measurement unit, for example, measures the driver's heart rate using a smartwatch and shares the information with the driver. For example, it notifies the driver that the heart rate is within the normal range. The health condition measurement unit also measures the driver's body temperature and shares the information with the driver. For example, it notifies the driver that the body temperature is 36.5 degrees. The health condition measurement unit also measures the driver's sleep status and shares the information with the driver. For example, it notifies the driver that the amount of sleep is sufficient. The health condition measurement unit also checks whether the driver has a driver's license and notifies the driver. For example, it notifies the driver that the license is valid. In this way, by measuring the driver's health status in detail and sharing the information with the driver, it is possible to support the driver's health management.

[0050] The engine control unit can prevent the engine from starting if an abnormality is detected in the driver's health. For example, the engine control unit prevents the engine from starting if the heart rate is abnormally high. For example, the engine control unit locks engine start if the heart rate exceeds a certain range. The engine control unit also prevents the engine from starting if the driver has a high body temperature. For example, the engine control unit locks engine start if the body temperature exceeds 38 degrees Celsius. The engine control unit also prevents the engine from starting if the driver does not have a driver's license. For example, the engine control unit locks engine start if the license has expired. In this way, the safety of the driver can be ensured by preventing the engine from starting if an abnormality is detected in the driver's health.

[0051] The pre-driving test unit can conduct a simple test before the elderly driver drives to confirm their driving ability. The pre-driving test unit, for example, conducts a reaction speed test to confirm the driving ability of the elderly driver. For example, the engine starts when the reaction speed meets a certain standard. The pre-driving test unit also conducts a vision test to confirm the driving ability of the elderly driver. For example, the engine starts when the vision meets a certain standard. The pre-driving test unit also conducts a cognitive function test to confirm the driving ability of the elderly driver. For example, the engine starts when the cognitive function meets a certain standard. In this way, by conducting a simple test before the elderly driver drives to confirm their driving ability, the risk of an accident can be reduced.

[0052] The pre-driving test unit can provide an individual driving training program to the elderly driver based on the test results. For example, the pre-driving test unit provides a program for reaction training to the elderly driver based on the results of a reaction speed test. For example, reaction training is provided when reaction speed is slow. The pre-driving test unit also provides a program for vision training to the elderly driver based on the results of a vision test. For example, vision training is provided when vision is impaired. The pre-driving test unit also provides a program for cognitive training to the elderly driver based on the results of a cognitive function test. For example, cognitive training is provided when cognitive function is impaired. In this way, by providing an individual driving training program based on the test results, the driving ability of the elderly driver can be improved.

[0053] The pre-driving test unit uses an emotion estimation function to monitor the emotional state of the elderly driver during the test and can suspend the test if the stress level is high. The pre-driving test unit, for example, uses the emotion estimation function to monitor the emotional state of the elderly driver during the pre-driving test in real time and suspends the test if the stress level is high. For example, a system is constructed in which facial expression recognition technology is used to analyze changes in the elderly driver's facial expressions and evaluate the stress level. The pre-driving test unit also uses voice analysis technology to analyze the elderly driver's voice tone and speed to evaluate the stress level. For example, a high-pitched voice and a fast speed may be determined to indicate high stress. The pre-driving test unit also collects biometric data (heart rate and electrodermal activity) using a sensor and evaluates the stress level using an emotion estimation algorithm. For example, large fluctuations in heart rate may be determined to indicate high stress. This allows the emotional state of the elderly driver during the test to be monitored and the test to be suspended if the stress level is high, thereby reducing the driver's burden.

[0054] The pre-driving test unit can combine the pre-driving test with other health checks to perform a comprehensive health evaluation. The pre-driving test unit, for example, combines blood pressure measurement with the pre-driving test to build a system that performs a comprehensive health evaluation. For example, if the blood pressure is high, the driver is notified to refrain from driving. The pre-driving test unit also combines a vision test to build a system that performs a comprehensive health evaluation. For example, if the driver's eyesight is poor, the driver is notified to refrain from driving. The pre-driving test unit also combines other health checks (for example, blood glucose measurement) to build a system that performs a comprehensive health evaluation. For example, if the blood glucose level is high, the driver is notified to refrain from driving. In this way, by combining the pre-driving test with other health checks, a comprehensive health evaluation is possible.

[0055] The pre-driving test unit can use the emotion estimation function to analyze the elderly driver's emotional response to the test results and provide positive feedback. For example, the pre-driving test unit uses the emotion estimation function to analyze the elderly driver's emotional response to the pre-driving test results in real time and build a system to provide positive feedback. For example, if the test results are good, the pre-driving test unit can display words of praise. The pre-driving test unit can also use the emotion estimation function to analyze the elderly driver's emotional response to the test results and provide words of encouragement. For example, if the test results are insufficient, the pre-driving test unit can display a message such as "Try harder next time." The pre-driving test unit can also use the emotion estimation function to analyze the elderly driver's emotional response to the test results and share successful experiences. For example, the pre-driving test unit can review past good test results to boost the driver's confidence. In this way, the elderly driver's emotional response to the test results can be analyzed and positive feedback can be provided to improve the driver's motivation.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The driving assistance system may further include a stress evaluation unit that evaluates the driver's stress level. The stress evaluation unit may, for example, analyze the driver's voice tone and speed to evaluate the stress level. For example, if the voice is high-pitched and fast, it may determine that the driver is under high stress. The stress evaluation unit may also analyze the driver's facial expression to evaluate the stress level. For example, it may detect facial tension or wrinkles between the eyebrows to determine that the driver is under high stress. The stress evaluation unit may also analyze the driver's biometric data (heart rate and electrodermal activity) to evaluate the stress level. For example, it may determine that the driver is under high stress if the heart rate fluctuates greatly. This allows the driver's stress level to be evaluated and, if necessary, may suggest relaxation methods to ensure the driver's safety.

[0058] The driving assistance system may further include a driving style analysis unit that analyzes the driver's driving style. The driving style analysis unit may, for example, analyze the frequency of the driver's acceleration and braking to evaluate the driving style. For example, if there are many sudden accelerations and sudden braking, it may determine that the driving style is rough. The driving style analysis unit may also analyze the driver's steering operation to evaluate the driving style. For example, if there are frequent steering operations, it may determine that the driving style is unstable. The driving style analysis unit may also analyze the frequency of the driver's lane changes to evaluate the driving style. For example, if there are frequent lane changes, it may determine that the driving style is risky. In this way, the driver's driving style may be analyzed and improvements may be suggested, thereby promoting safe driving.

[0059] The driving assistance system may further include a music selection unit that selects music based on the emotional state of the driver. The music selection unit, for example, analyzes the emotional state of the driver and selects music that has a relaxing effect. For example, if the driver is feeling stressed, classical music with a relaxing effect is played. The music selection unit can also analyze the emotional state of the driver and select music that enhances concentration. For example, if the driver is tired, upbeat music that enhances concentration is played. The music selection unit can also analyze the emotional state of the driver and select music that uplifts the driver's mood. For example, if the driver is feeling depressed, pop music that uplifts the driver's mood is played. In this way, by selecting music based on the emotional state of the driver, the driver's mood can be adjusted and safe driving can be supported.

[0060] The driving assistance system may further include an advice providing unit that provides driving advice based on the driver's health condition. The advice providing unit may, for example, analyze the driver's heart rate and body temperature and suggest appropriate rest timing. For example, if the heart rate is high, the advice providing unit may notify the driver to take a break. The advice providing unit may also analyze the driver's sleep state and suggest that the driver take sufficient rest before driving. For example, if the driver is sleep deprived, the advice providing unit may notify the driver to refrain from driving. The advice providing unit may also analyze the driver's dietary state and suggest appropriate nutritional supplementation. For example, if the diet is insufficient, the advice providing unit may notify the driver to replenish nutrition. In this way, by providing driving advice based on the driver's health condition, the driver's safety can be ensured.

[0061] The driving assistance system may further include a driving mode switching unit that switches driving modes based on the emotional state of the driver. The driving mode switching unit, for example, analyzes the emotional state of the driver and switches to a safe driving mode. For example, if the driver is feeling stressed, the driving mode switching unit switches to the safe driving mode and limits the vehicle speed. The driving mode switching unit may also analyze the emotional state of the driver and switch to an eco mode. For example, if the driver is relaxed, the driving mode switching unit switches to the eco mode to improve fuel efficiency. The driving mode switching unit may also analyze the emotional state of the driver and switch to a sports mode. For example, if the driver is excited, the driving mode switching unit switches to the sports mode to provide driving enjoyment. In this way, by switching driving modes based on the emotional state of the driver, it is possible to improve the safety and comfort of the driver.

[0062] The driving assistance system can further include an insurance premium calculation unit that calculates insurance premiums based on the driver's driving history. The insurance premium calculation unit, for example, analyzes the driver's driving history and discounts the insurance premium if the driver drives safely. For example, the insurance premium is discounted if there is little sudden acceleration or braking. The insurance premium calculation unit can also analyze the driver's driving history and increase the insurance premium if the driver engages in risky driving. For example, the insurance premium is increased if the driver frequently exceeds the speed limit. The insurance premium calculation unit can also analyze the driver's driving history and propose an insurance plan according to the driver's driving style. For example, if the driver frequently drives long distances, an insurance plan for long distance driving is proposed. In this way, calculating insurance premiums based on the driver's driving history enables fair insurance premium setting.

[0063] The driving assistance system may further include a navigation optimization unit that optimizes a navigation route based on the emotional state of the driver. The navigation optimization unit, for example, analyzes the emotional state of the driver and suggests a route with less stress. For example, if the driver is feeling stressed, a route with less traffic is suggested. The navigation optimization unit may also analyze the emotional state of the driver and suggest a route with good scenery. For example, if the driver wants to relax, a route with good scenery is suggested. The navigation optimization unit may also analyze the emotional state of the driver and suggest a route that includes rest spots. For example, if the driver is tired, a route that includes rest spots is suggested. In this way, optimizing the navigation route based on the emotional state of the driver can improve the comfort and safety of the driver.

[0064] The driving assistance system can further include a maintenance optimization unit that optimizes the maintenance schedule based on the driver's driving history. The maintenance optimization unit, for example, analyzes the driver's driving history and suggests appropriate maintenance times. For example, it suggests the timing of an oil change based on mileage and engine operating hours. The maintenance optimization unit can also analyze the driver's driving history and suggest the timing of part replacement. For example, it suggests the timing of replacement based on the wear state of brake pads. The maintenance optimization unit can also analyze the driver's driving history and suggest the timing of tire replacement. For example, it suggests the timing of replacement based on the wear state of tires. In this way, the safety and performance of the vehicle can be maintained by optimizing the maintenance schedule based on the driver's driving history.

[0065] The driving assistance system may further include a driving assist adjustment unit that adjusts the driving assist function based on the emotional state of the driver. The driving assist adjustment unit, for example, analyzes the emotional state of the driver and adjusts the sensitivity of the assist function. For example, if the driver is stressed, the sensitivity of the assist function is increased. The driving assist adjustment unit may also analyze the emotional state of the driver and adjust the frequency of intervention of the assist function. For example, if the driver is relaxed, the frequency of intervention of the assist function is reduced. The driving assist adjustment unit may also analyze the emotional state of the driver and adjust the type of assist function. For example, if the driver is tired, the lane keeping assist function is strengthened. In this way, adjusting the driving assist function based on the emotional state of the driver can improve the safety and comfort of the driver.

[0066] The driving assistance system may further include an eco-driving advice unit that provides eco-driving advice based on the driver's driving history. The eco-driving advice unit may, for example, analyze the driver's driving history and provide advice to improve fuel efficiency. For example, it may advise the driver to avoid sudden acceleration and sudden braking. The eco-driving advice unit may also analyze the driver's driving history and suggest an optimal speed for eco-driving. For example, it may advise the driver to maintain a constant speed on a highway. The eco-driving advice unit may also analyze the driver's driving history and suggest a route for eco-driving. For example, it may suggest a route with fewer traffic lights. In this way, by providing eco-driving advice based on the driver's driving history, it is possible to improve fuel efficiency and reduce environmental impact.

[0067] The processing flow of the second embodiment will be briefly explained below.

[0068] Step 1: The vehicle information acquisition unit acquires vehicle information. For example, it measures the mileage, remaining gasoline, vehicle speed, and location information, and shares this information with the driver in real time. It can also use GPS to identify the vehicle's location and notify the driver. Step 2: The health condition measurement unit measures the driver's health condition. For example, it uses a smartwatch to measure heart rate and body temperature and share the data with the driver. It also checks the driver's sleep status and license status, and displays a warning if the driver is sleep deprived. Step 3: The engine control unit controls the engine if the health condition measurement unit detects any abnormalities in the driver's health. For example, it prevents the engine from starting if the driver's heart rate is abnormally high or if the body temperature is high. It also controls the engine if the driver does not have a driver's license, checks the license's expiration date, and locks the engine if it has expired. Step 4: The pre-driving test department will conduct pre-driving tests for the elderly driver, such as tests to check reaction speed, eyesight, and cognitive function, and the engine will only be started if the tests pass. In addition, a driving simulator will be used to recreate real driving situations, simulating busy roads and bad weather, to evaluate the driving ability of the elderly driver.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0073] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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).

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0088] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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."

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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]

[0136] 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 vehicle information acquisition unit that acquires vehicle information; a health condition measurement unit for measuring the health condition of the driver; an engine control unit that controls the engine when an abnormality is found in the health condition of the driver measured by the health condition measurement unit; and a pre-driving test unit that conducts pre-driving tests for elderly drivers. A system characterized by:

2. The vehicle information acquisition unit Simultaneously collects vehicle driving history and maintenance history and provides preventive maintenance suggestions to the driver.

2. The system of claim 1.

3. The health condition measurement unit Measures the driver's heart rate, body temperature, sleep status, and license possession information and shares it with the driver 2. The system of claim 1.

4. The engine control unit If an abnormality is found in the health condition, the engine is prevented from starting.

2. The system of claim 1.

5. The pre-operation test unit The emotional state of the elderly driver is monitored during the test, and the test is stopped if stress levels become too high.

2. The system of claim 1.

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