System

Abstract

An object of the system is to analyze traffic data and realize risk prediction of a traffic accident and efficient traffic control.SOLUTION: The traffic data analysis unit analyzes traffic data. The risk prediction unit predicts a risk of a traffic accident based on the data analyzed by the traffic data analysis unit. The alerting unit alerts the driver based on the risk predicted by the risk predicting unit. The route guidance unit guides a driver along an appropriate route based on the data analyzed by the traffic data analysis unit. The traffic jam prediction unit predicts a traffic jam and a peak of transportation demand based on the data analyzed by the traffic data analysis unit. The traffic control suggestion unit suggests efficient traffic control on the basis of the data predicted by the congestion prediction unit. The monitoring unit monitors a state of a traffic infrastructure or an automobile. The maintenance planning unit supports planning of maintenance and inspection based on the data collected by the monitoring unit.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 technologies do not adequately predict the risk of traffic accidents or provide efficient traffic control, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze traffic data and realize risk prediction of traffic accidents and efficient traffic control. [Means for solving the problem]

[0006] The system according to the embodiment includes a traffic data analysis unit, a risk prediction unit, a warning unit, a route guidance unit, a congestion prediction unit, a traffic control proposal unit, a monitoring unit, and a maintenance planning unit. The traffic data analysis unit analyzes traffic data. The risk prediction unit predicts the risk of a traffic accident based on the data analyzed by the traffic data analysis unit. The warning unit warns the driver based on the risk predicted by the risk prediction unit. The route guidance unit guides the driver to an appropriate route based on the data analyzed by the traffic data analysis unit. The congestion prediction unit predicts congestion and peaks in transportation demand based on the data analyzed by the traffic data analysis unit. The traffic control proposal unit proposes efficient traffic control based on the data predicted by the congestion prediction unit. The monitoring unit monitors the condition of traffic infrastructure and vehicles. The maintenance planning unit supports maintenance and inspection planning based on the data collected by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze traffic data, predict the risk of traffic accidents, and realize efficient traffic control. [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 Traffic AI Mind System according to an embodiment of the present invention is a system that analyzes traffic data, predicts the risk of traffic accidents, warns drivers and provides appropriate route guidance, predicts congestion and peak transport demand, supports efficient traffic control, monitors the status of automobiles and transportation infrastructure, and supports the planned implementation of maintenance and inspection. As a result, the Traffic AI Mind System reduces the risk of traffic accidents, realizes efficient traffic control, and enables planned maintenance and inspection of automobiles and transportation infrastructure.

[0029] The traffic AI mind system according to the embodiment includes a traffic data analysis unit, a risk prediction unit, an attention warning unit, a route guidance unit, a congestion prediction unit, a traffic control proposal unit, a monitoring unit, and a maintenance planning unit. The traffic data analysis unit analyzes traffic data, such as traffic volume data, accident data, and speed data. The risk prediction unit predicts the risk of a traffic accident based on the data analyzed by the traffic data analysis unit. For example, the risk is evaluated based on past accident data and driver behavior patterns. The attention warning unit warns the driver based on the risk predicted by the risk prediction unit. For example, the warning is issued using an audio or visual alert. The route guidance unit guides the driver to an appropriate route based on the data analyzed by the traffic data analysis unit. For example, the route is proposed as the shortest route, the shortest time, or a route with minimal traffic volume. The congestion prediction unit predicts congestion and peaks in transportation demand based on the data analyzed by the traffic data analysis unit. For example, the traffic congestion prediction unit predicts the occurrence of congestion based on past data and real-time data. The traffic control proposal unit proposes efficient traffic control based on the data predicted by the congestion prediction unit. For example, the traffic signal control and traffic flow optimization are proposed. The monitoring unit monitors the status of traffic infrastructure and vehicles. For example, it collects data using sensors and performs regular inspections. The maintenance planning unit supports maintenance and inspection planning based on the data collected by the monitoring unit. For example, it proposes preventive maintenance and regular inspection schedules. As a result, the traffic AI mind system according to the embodiment reduces the risk of traffic accidents, realizes efficient traffic control, and enables planned maintenance and inspection of vehicles and traffic infrastructure.

[0030] The risk prediction unit analyzes the driver's driving patterns and behavioral history, and can predict the risk for each individual driver. For example, the risk prediction unit collects the driver's past driving data, and the generation AI analyzes that data. For example, the risk of each individual driver is assessed based on the frequency of sudden braking and abrupt steering, tendency to exceed speeding, etc. This makes it possible to predict the risk for each individual driver.

[0031] The risk prediction unit takes into account the movements of nearby pedestrians and bicycles, enabling more accurate risk prediction. For example, the risk prediction unit detects the movements of nearby pedestrians and bicycles in real time, and the generation AI analyzes that data. For example, it predicts the direction of pedestrians crossing the road or the direction of bicycles traveling, and issues a warning to the driver. This enables more accurate risk prediction.

[0032] The risk prediction unit can also be applied to the operation management of public transportation, and can predict the operational risks of buses and trains. For example, the risk prediction unit collects operational data of public transportation, and the generation AI analyzes that data. For example, the operational risks are evaluated based on the bus and train schedules and number of passengers. This makes it possible to predict the operational risks of public transportation.

[0033] The risk prediction unit can cooperate with insurance companies to use the predicted risk information to dynamically adjust insurance premiums. For example, the risk prediction unit shares traffic accident risk prediction data with insurance companies and uses it to dynamically adjust insurance premiums. For example, it proposes to raise insurance premiums for drivers with high risk. This makes it possible to dynamically adjust insurance premiums.

[0034] The route guidance unit analyzes the driver's past route selection history and can provide route guidance according to individual preferences. For example, the route guidance unit collects the driver's past route selection history, and the generation AI analyzes that data. For example, based on the tendency to frequently select certain routes, it suggests routes according to individual preferences. This makes it possible to provide route guidance according to the driver's preferences.

[0035] The route guidance unit incorporates information on tourist spots and restaurants, and can suggest routes that suit the driver's interests. For example, the route guidance unit collects information on nearby tourist spots and restaurants, and the generation AI analyzes that data. For example, tourist spots and restaurants that suit the driver's interests can be incorporated into the route guidance. This makes it possible to provide route guidance that suits the driver's interests.

[0036] The route guidance unit can also be applied to pedestrians and bicycle users, and can accommodate all modes of transportation. The route guidance unit, for example, builds a system that provides route guidance for pedestrians and bicycle users. For example, it proposes routes that take into account pedestrian-only roads and bicycle-only lanes. This makes it possible to provide route guidance that is compatible with all modes of transportation.

[0037] The route guidance unit incorporates public transport operation information and can propose optimal routes that combine multiple modes of transport. For example, the route guidance unit collects real-time public transport operation information, and the generation AI analyzes that data. For example, it proposes optimal routes based on bus and train schedules. This makes it possible to provide optimal route guidance that combines multiple modes of transport.

[0038] The traffic congestion prediction unit makes traffic congestion predictions that take into account specific events and seasonal factors, allowing for more accurate predictions. For example, the traffic congestion prediction unit collects specific event information, and the generation AI analyzes that data. For example, it predicts the occurrence of traffic congestion based on information about concerts and sporting events. This makes it possible to make traffic congestion predictions that take into account specific events and seasonal factors.

[0039] The congestion prediction unit also takes into account the availability of parking spaces in the surrounding area and can make suggestions to avoid congestion in the parking lot. For example, the congestion prediction unit collects information on the availability of parking spaces in the surrounding area, and the generation AI analyzes that data. For example, based on the information on available parking spaces, the unit makes suggestions to avoid congestion in the parking lot. This makes it possible to make suggestions to avoid congestion in the parking lot.

[0040] The congestion prediction unit is provided to logistics companies and can support the optimization of delivery routes. The congestion prediction unit, for example, provides congestion predictions to logistics companies and builds a system to support the optimization of delivery routes. For example, it proposes the optimal delivery route to avoid congestion. This makes it possible for logistics companies to optimize their delivery routes.

[0041] The congestion prediction unit will provide data for urban planning and infrastructure development, which can be useful for long-term traffic improvements. The congestion prediction unit will, for example, build a system that provides congestion prediction information as data for urban planning and infrastructure development. For example, it will propose improvement measures for areas prone to congestion. This will make it possible to provide data for urban planning and infrastructure development.

[0042] In addition to controlling traffic signals, the traffic control proposal unit can provide real-time traffic information using digital signage. For example, the traffic control proposal unit collects traffic signal control data, and the generation AI analyzes that data. For example, it can optimize the timing of signals according to traffic volume. This makes it possible to control traffic signals and provide real-time traffic information using digital signage.

[0043] The traffic control proposal unit can incorporate priority passage for emergency vehicles and optimize traffic flow in emergencies. For example, the traffic control proposal unit collects priority passage data for emergency vehicles, and the generation AI analyzes that data. For example, it can turn the traffic light green when an emergency vehicle is passing through. This makes it possible to optimize traffic flow in emergencies.

[0044] The traffic control proposal unit can also be applied to large-scale transportation hubs such as airports and ports, optimizing overall traffic flow. For example, the traffic control proposal unit collects traffic data from airports and ports, and the generation AI analyzes that data. For example, traffic flow can be optimized based on aircraft and ship schedules. This makes it possible to optimize traffic flow at large-scale transportation hubs such as airports and ports.

[0045] The traffic control proposal unit can be linked with other urban infrastructure as part of a smart city. The traffic control proposal unit, for example, builds a system that links traffic control data with other urban infrastructure. For example, it can link with energy management and water management to achieve efficient traffic control. This makes it possible to link with other urban infrastructure as part of a smart city.

[0046] The monitoring unit can analyze vehicle operation data and propose an optimal inspection schedule for preventive maintenance. For example, the monitoring unit collects vehicle operation data, and the generation AI analyzes that data. For example, it proposes an optimal inspection schedule based on mileage and engine operating hours. This makes it possible to analyze vehicle operation data and propose an optimal inspection schedule for preventive maintenance.

[0047] The monitoring unit can predict the lifespan of parts and notify in advance when it is time to replace them. For example, the monitoring unit collects monitoring data, and the generation AI analyzes that data. For example, it predicts the lifespan of parts based on their usage and deterioration status. This makes it possible to predict the lifespan of parts and notify in advance when it is time to replace them.

[0048] The monitoring unit can also be applied to other modes of transportation, such as railroads and airplanes, to improve overall traffic safety. For example, the monitoring unit collects monitoring data from railroads and airplanes, and the generation AI analyzes that data. For example, it proposes optimal maintenance and inspection schedules based on operational status and component deterioration. This can be applied to other modes of transportation, such as railroads and airplanes, to improve overall traffic safety.

[0049] The monitoring unit can cooperate with insurance companies and use the data to dynamically adjust insurance premiums. The monitoring unit, for example, shares monitoring data with insurance companies and uses the data to dynamically adjust insurance premiums. For example, the monitoring unit proposes raising insurance premiums for high-risk drivers. This makes it possible to cooperate with insurance companies and use the data to dynamically adjust insurance premiums.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The Traffic AI Mind System also includes an energy consumption optimization unit. Based on the data analyzed by the traffic data analysis unit, the energy consumption optimization unit can make suggestions to optimize the vehicle's energy consumption. For example, it can suggest routes with low energy consumption and provide advice on eco-driving. This allows drivers to improve fuel efficiency and reduce environmental impact. The energy consumption optimization unit can also provide location information of electric vehicle charging stations and suggest optimal charging times. This allows electric vehicle drivers to charge their vehicles efficiently.

[0052] The Traffic AI Mind System also has an emergency response unit. Based on the data analyzed by the traffic data analysis unit, the emergency response unit can propose countermeasures in the event of an emergency. For example, in the event of an accident or disaster, it can propose the optimal evacuation route. It can also support emergency vehicles in giving priority to traffic, enabling a rapid response. This enables a rapid and appropriate response in an emergency. Furthermore, the emergency response unit can provide information on nearby medical institutions and evacuation shelters, ensuring the safety of drivers and pedestrians.

[0053] The Traffic AI Mind System also has a weather data linkage unit. The weather data linkage unit collects weather data and analyzes it in cooperation with the traffic data analysis unit. For example, it can predict traffic risks in bad weather such as rain or snow and warn drivers, thereby reducing the risk of traffic accidents in bad weather. The weather data linkage unit can also suggest optimal routes depending on weather conditions. For example, it can suggest routes that avoid snowy roads or routes that are less affected by wind. This allows drivers to travel safely and comfortably.

[0054] The Traffic AI Mind System also includes a health monitoring unit. This unit monitors the driver's health condition in real time and can issue a warning if an abnormality is detected. For example, it can monitor heart rate and blood pressure and prompt the driver to stop driving if an abnormality is detected. It can also detect fatigue caused by long periods of driving and suggest taking a break. This allows the system to constantly monitor the driver's health condition and support safe driving.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The traffic data analysis unit analyzes traffic data, such as traffic volume data, accident data, and speed data. Step 2: The risk prediction unit predicts the risk of traffic accidents based on the data analyzed by the traffic data analysis unit. For example, the risk is assessed based on past accident data and driver behavior patterns. Step 3: The attention alerting unit alerts the driver based on the risk predicted by the risk prediction unit, for example, by using an audio or visual alert. Step 4: The route guidance unit guides the driver to an appropriate route based on the data analyzed by the traffic data analysis unit. For example, it suggests the shortest route, the shortest time, or the least traffic volume route. Step 5: The traffic congestion prediction unit predicts traffic congestion and peaks in transportation demand based on the data analyzed by the traffic data analysis unit. For example, it predicts the occurrence of traffic congestion based on past data and real-time data. Step 6: The traffic control proposal unit proposes efficient traffic control based on the data predicted by the congestion prediction unit, for example, signal control and optimization of traffic flow. Step 7: The monitoring unit monitors the status of the transportation infrastructure and vehicles, for example by collecting data using sensors and conducting regular inspections. Step 8: The Maintenance Planning Department assists in planning maintenance and inspections based on the data collected by the Monitoring Department, for example, by proposing preventive maintenance and periodic inspection schedules.

[0057] (Example 2) The Traffic AI Mind System according to an embodiment of the present invention is a system that analyzes traffic data, predicts the risk of traffic accidents, warns drivers and provides appropriate route guidance, predicts congestion and peak transport demand, supports efficient traffic control, monitors the status of automobiles and transportation infrastructure, and supports the planned implementation of maintenance and inspection. As a result, the Traffic AI Mind System reduces the risk of traffic accidents, realizes efficient traffic control, and enables planned maintenance and inspection of automobiles and transportation infrastructure.

[0058] The traffic AI mind system according to the embodiment includes a traffic data analysis unit, a risk prediction unit, an attention warning unit, a route guidance unit, a congestion prediction unit, a traffic control proposal unit, a monitoring unit, and a maintenance planning unit. The traffic data analysis unit analyzes traffic data, such as traffic volume data, accident data, and speed data. The risk prediction unit predicts the risk of a traffic accident based on the data analyzed by the traffic data analysis unit. For example, the risk is evaluated based on past accident data and driver behavior patterns. The attention warning unit warns the driver based on the risk predicted by the risk prediction unit. For example, the warning is issued using an audio or visual alert. The route guidance unit guides the driver to an appropriate route based on the data analyzed by the traffic data analysis unit. For example, the route is proposed as the shortest route, the shortest time, or a route with minimal traffic volume. The congestion prediction unit predicts congestion and peaks in transportation demand based on the data analyzed by the traffic data analysis unit. For example, the traffic congestion prediction unit predicts the occurrence of congestion based on past data and real-time data. The traffic control proposal unit proposes efficient traffic control based on the data predicted by the congestion prediction unit. For example, the traffic signal control and traffic flow optimization are proposed. The monitoring unit monitors the status of traffic infrastructure and vehicles. For example, it collects data using sensors and performs regular inspections. The maintenance planning unit supports maintenance and inspection planning based on the data collected by the monitoring unit. For example, it proposes preventive maintenance and regular inspection schedules. As a result, the traffic AI mind system according to the embodiment reduces the risk of traffic accidents, realizes efficient traffic control, and enables planned maintenance and inspection of vehicles and traffic infrastructure.

[0059] The risk prediction unit analyzes the driver's driving patterns and behavioral history, and can predict the risk for each individual driver. For example, the risk prediction unit collects the driver's past driving data, and the generation AI analyzes that data. For example, the risk of each individual driver is assessed based on the frequency of sudden braking and abrupt steering, tendency to exceed speeding, etc. This makes it possible to predict the risk for each individual driver.

[0060] The risk prediction unit takes into account the movements of nearby pedestrians and bicycles, enabling more accurate risk prediction. For example, the risk prediction unit detects the movements of nearby pedestrians and bicycles in real time, and the generation AI analyzes that data. For example, it predicts the direction of pedestrians crossing the road or the direction of bicycles traveling, and issues a warning to the driver. This enables more accurate risk prediction.

[0061] The risk prediction unit can evaluate the driver's stress level and fatigue level in real time and issue a warning when the risk increases. The risk prediction unit, for example, analyzes the driver's facial expressions and voice to evaluate the stress level and fatigue level in real time. For example, it calculates an emotion score based on facial tension and voice tone and issues a warning when the risk increases. This makes it possible to evaluate the driver's stress level and fatigue level in real time and issue a warning when the risk increases.

[0062] The risk prediction unit can also be applied to the operation management of public transportation, and can predict the operational risks of buses and trains. For example, the risk prediction unit collects operational data of public transportation, and the generation AI analyzes that data. For example, the operational risks are evaluated based on the bus and train schedules and number of passengers. This makes it possible to predict the operational risks of public transportation.

[0063] The risk prediction unit can cooperate with insurance companies to use the predicted risk information to dynamically adjust insurance premiums. For example, the risk prediction unit shares traffic accident risk prediction data with insurance companies and uses it to dynamically adjust insurance premiums. For example, it proposes to raise insurance premiums for drivers with high risk. This makes it possible to dynamically adjust insurance premiums.

[0064] The risk prediction unit customizes the warning message according to the emotional state of the driver, enabling more effective warning. The risk prediction unit, for example, uses an emotion estimation function to analyze the emotional state of the driver in real time and customizes the warning message based on the results. For example, a message encouraging a driver who is highly stressed to relax is displayed. This makes it possible to warn the driver according to their emotional state.

[0065] The route guidance unit analyzes the driver's past route selection history and can provide route guidance according to individual preferences. For example, the route guidance unit collects the driver's past route selection history, and the generation AI analyzes that data. For example, based on the tendency to frequently select certain routes, it suggests routes according to individual preferences. This makes it possible to provide route guidance according to the driver's preferences.

[0066] The route guidance unit incorporates information on tourist spots and restaurants, and can suggest routes that suit the driver's interests. For example, the route guidance unit collects information on nearby tourist spots and restaurants, and the generation AI analyzes that data. For example, tourist spots and restaurants that suit the driver's interests can be incorporated into the route guidance. This makes it possible to provide route guidance that suits the driver's interests.

[0067] The route guidance unit can suggest a relaxing route or a scenic route according to the emotional state of the driver. For example, the route guidance unit uses an emotion estimation function to analyze the driver's current emotional state in real time and suggest a relaxing route based on the results. For example, the route guidance unit may suggest a route rich in nature or a route with beautiful scenery. This makes it possible to provide a relaxing route according to the driver's emotional state.

[0068] The route guidance unit can also be applied to pedestrians and bicycle users, and can accommodate all modes of transportation. The route guidance unit, for example, builds a system that provides route guidance for pedestrians and bicycle users. For example, it proposes routes that take into account pedestrian-only roads and bicycle-only lanes. This makes it possible to provide route guidance that is compatible with all modes of transportation.

[0069] The route guidance unit incorporates public transport operation information and can propose optimal routes that combine multiple modes of transport. For example, the route guidance unit collects real-time public transport operation information, and the generation AI analyzes that data. For example, it proposes optimal routes based on bus and train schedules. This makes it possible to provide optimal route guidance that combines multiple modes of transport.

[0070] The route guidance unit can support a comfortable drive by suggesting music or podcasts to play according to the driver's emotional state. For example, the route guidance unit uses an emotion estimation function to analyze the driver's emotional state in real time and suggests music or podcasts to play based on the results. For example, it can suggest relaxing music or interesting podcasts. This makes it possible to play music or podcasts according to the driver's emotional state.

[0071] The traffic congestion prediction unit makes traffic congestion predictions that take into account specific events and seasonal factors, allowing for more accurate predictions. For example, the traffic congestion prediction unit collects specific event information, and the generation AI analyzes that data. For example, it predicts the occurrence of traffic congestion based on information about concerts and sporting events. This makes it possible to make traffic congestion predictions that take into account specific events and seasonal factors.

[0072] The congestion prediction unit also takes into account the availability of parking spaces in the surrounding area and can make suggestions to avoid congestion in the parking lot. For example, the congestion prediction unit collects information on the availability of parking spaces in the surrounding area, and the generation AI analyzes that data. For example, based on the information on available parking spaces, the unit makes suggestions to avoid congestion in the parking lot. This makes it possible to make suggestions to avoid congestion in the parking lot.

[0073] The congestion prediction unit can evaluate the driver's stress level and make suggestions to avoid time periods and routes where stress levels are high. The congestion prediction unit, for example, uses an emotion estimation function to evaluate the driver's stress level in real time and, based on the results, makes suggestions to avoid time periods and routes where stress levels are high. For example, it suggests a route that avoids congested time periods. This makes it possible to evaluate the driver's stress level and make suggestions to avoid time periods and routes where stress levels are high.

[0074] The congestion prediction unit is provided to logistics companies and can support the optimization of delivery routes. The congestion prediction unit, for example, provides congestion predictions to logistics companies and builds a system to support the optimization of delivery routes. For example, it proposes the optimal delivery route to avoid congestion. This makes it possible for logistics companies to optimize their delivery routes.

[0075] The congestion prediction unit will provide data for urban planning and infrastructure development, which can be useful for long-term traffic improvements. The congestion prediction unit will, for example, build a system that provides congestion prediction information as data for urban planning and infrastructure development. For example, it will propose improvement measures for areas prone to congestion. This will make it possible to provide data for urban planning and infrastructure development.

[0076] The traffic congestion prediction unit can propose a route that avoids traffic congestion according to the emotional state of the driver, thereby providing a comfortable driving environment. The traffic congestion prediction unit, for example, uses an emotion estimation function to analyze the emotional state of the driver in real time and proposes a route that avoids traffic congestion based on the results. For example, a route that avoids traffic congestion can be proposed for a driver who is highly stressed. This makes it possible to propose a route that avoids traffic congestion according to the emotional state of the driver.

[0077] In addition to controlling traffic signals, the traffic control proposal unit can provide real-time traffic information using digital signage. For example, the traffic control proposal unit collects traffic signal control data, and the generation AI analyzes that data. For example, it can optimize the timing of signals according to traffic volume. This makes it possible to control traffic signals and provide real-time traffic information using digital signage.

[0078] The traffic control proposal unit can incorporate priority passage for emergency vehicles and optimize traffic flow in emergencies. For example, the traffic control proposal unit collects priority passage data for emergency vehicles, and the generation AI analyzes that data. For example, it can turn the traffic light green when an emergency vehicle is passing through. This makes it possible to optimize traffic flow in emergencies.

[0079] The traffic control proposal unit adjusts the timing of traffic signals according to the emotional state of the driver, thereby reducing stress. The traffic control proposal unit, for example, uses an emotion estimation function to analyze the emotional state of the driver in real time and adjusts the timing of traffic signals based on the results. For example, the traffic signal may be turned green earlier for a driver with high stress. This makes it possible to adjust the timing of traffic signals according to the emotional state of the driver.

[0080] The traffic control proposal unit can also be applied to large-scale transportation hubs such as airports and ports, optimizing overall traffic flow. For example, the traffic control proposal unit collects traffic data from airports and ports, and the generation AI analyzes that data. For example, traffic flow can be optimized based on aircraft and ship schedules. This makes it possible to optimize traffic flow at large-scale transportation hubs such as airports and ports.

[0081] The traffic control proposal unit can be linked with other urban infrastructure as part of a smart city. The traffic control proposal unit, for example, builds a system that links traffic control data with other urban infrastructure. For example, it can link with energy management and water management to achieve efficient traffic control. This makes it possible to link with other urban infrastructure as part of a smart city.

[0082] The traffic control proposal unit provides traffic information according to the emotional state of the driver, thereby reducing the driver's stress. The traffic control proposal unit, for example, uses an emotion estimation function to analyze the driver's emotional state in real time and provides traffic information based on the results. For example, it provides information that helps a driver who is highly stressed to relax. This makes it possible to provide traffic information according to the driver's emotional state.

[0083] The monitoring unit can analyze vehicle operation data and propose an optimal inspection schedule for preventive maintenance. For example, the monitoring unit collects vehicle operation data, and the generation AI analyzes that data. For example, it proposes an optimal inspection schedule based on mileage and engine operating hours. This makes it possible to analyze vehicle operation data and propose an optimal inspection schedule for preventive maintenance.

[0084] The monitoring unit can predict the lifespan of parts and notify in advance when it is time to replace them. For example, the monitoring unit collects monitoring data, and the generation AI analyzes that data. For example, it predicts the lifespan of parts based on their usage and deterioration status. This makes it possible to predict the lifespan of parts and notify in advance when it is time to replace them.

[0085] The monitoring unit can suggest maintenance and inspection timing according to the driver's emotional state, increasing the driver's sense of security. For example, the monitoring unit can use an emotion estimation function to analyze the driver's emotional state in real time and suggest maintenance and inspection timing based on the results. For example, it can suggest earlier inspections for drivers with high stress levels. This makes it possible to suggest maintenance and inspection timing according to the driver's emotional state.

[0086] The monitoring unit can also be applied to other modes of transportation, such as railroads and airplanes, to improve overall traffic safety. For example, the monitoring unit collects monitoring data from railroads and airplanes, and the generation AI analyzes that data. For example, it proposes optimal maintenance and inspection schedules based on operational status and component deterioration. This can be applied to other modes of transportation, such as railroads and airplanes, to improve overall traffic safety.

[0087] The monitoring unit can cooperate with insurance companies and use the data to dynamically adjust insurance premiums. The monitoring unit, for example, shares monitoring data with insurance companies and uses the data to dynamically adjust insurance premiums. For example, the monitoring unit proposes raising insurance premiums for high-risk drivers. This makes it possible to cooperate with insurance companies and use the data to dynamically adjust insurance premiums.

[0088] The monitoring unit notifies the driver of maintenance and inspections according to the driver's emotional state, thereby increasing the driver's sense of security. For example, the monitoring unit uses an emotion estimation function to analyze the driver's emotional state in real time and notifies the driver of maintenance and inspections based on the results. For example, a driver who is highly stressed may be notified of an earlier inspection. This makes it possible to notify the driver of maintenance and inspections according to their emotional state.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The Traffic AI Mind System also includes an energy consumption optimization unit. Based on the data analyzed by the traffic data analysis unit, the energy consumption optimization unit can make suggestions to optimize the vehicle's energy consumption. For example, it can suggest routes with low energy consumption and provide advice on eco-driving. This allows drivers to improve fuel efficiency and reduce environmental impact. The energy consumption optimization unit can also provide location information of electric vehicle charging stations and suggest optimal charging times. This allows electric vehicle drivers to charge their vehicles efficiently.

[0091] The Traffic AI Mind System also has an emergency response unit. Based on the data analyzed by the traffic data analysis unit, the emergency response unit can propose countermeasures in the event of an emergency. For example, in the event of an accident or disaster, it can propose the optimal evacuation route. It can also support emergency vehicles in giving priority to traffic, enabling a rapid response. This enables a rapid and appropriate response in an emergency. Furthermore, the emergency response unit can provide information on nearby medical institutions and evacuation shelters, ensuring the safety of drivers and pedestrians.

[0092] The Traffic AI Mind System also has a weather data linkage unit. The weather data linkage unit collects weather data and analyzes it in cooperation with the traffic data analysis unit. For example, it can predict traffic risks in bad weather such as rain or snow and warn drivers, thereby reducing the risk of traffic accidents in bad weather. The weather data linkage unit can also suggest optimal routes depending on weather conditions. For example, it can suggest routes that avoid snowy roads or routes that are less affected by wind. This allows drivers to travel safely and comfortably.

[0093] The Traffic AI Mind System also includes an entertainment provider. The entertainment provider can estimate the driver's emotional state and provide entertainment content appropriate to that state. For example, if the driver is feeling stressed, it can suggest relaxing music or podcasts. If the driver is bored, it can provide interesting audiobooks or news. This makes it possible to provide entertainment content appropriate to the driver's emotional state, supporting a more comfortable drive.

[0094] The Traffic AI Mind System also includes a health monitoring unit. This unit monitors the driver's health condition in real time and can issue a warning if an abnormality is detected. For example, it can monitor heart rate and blood pressure and prompt the driver to stop driving if an abnormality is detected. It can also detect fatigue caused by long periods of driving and suggest taking a break. This allows the system to constantly monitor the driver's health condition and support safe driving.

[0095] The Traffic AI Mind System also includes an advertisement provision unit based on the driver's emotional state. The advertisement provision unit can estimate the driver's emotional state and provide advertisements according to that state. For example, if the driver is relaxed, advertisements for relaxing travel destinations are displayed. If the driver is stressed, advertisements for stress relief products are displayed. This makes it possible to provide advertisements according to the driver's emotional state, maximizing the effectiveness of the advertisements.

[0096] The Traffic AI Mind System also includes a driving assistance unit that is based on the driver's emotional state. The driving assistance unit estimates the driver's emotional state and provides driving assistance according to that state. For example, if the driver is tense, assistance will be provided to encourage relaxation. If the driver is tired, a break will be suggested. This makes it possible to provide driving assistance according to the driver's emotional state, supporting safe driving.

[0097] The Traffic AI Mind System also has a navigation voice customization function based on the driver's emotional state. The navigation voice customization function estimates the driver's emotional state and provides navigation voices that correspond to that state. For example, if the driver is relaxed, guidance will be provided in a calm voice. On the other hand, if the driver is tense, guidance will be provided in a calm voice. This makes it possible to customize the navigation voice according to the driver's emotional state, supporting a more comfortable drive.

[0098] The Traffic AI Mind System also includes an in-car environment adjustment unit that adjusts the in-car environment based on the driver's emotional state. The in-car environment adjustment unit estimates the driver's emotional state and adjusts the in-car environment accordingly. For example, if the driver is feeling stressed, the lighting inside the car will be softened. If the driver is relaxed, the temperature will be adjusted to a comfortable level. This makes it possible to adjust the in-car environment according to the driver's emotional state, supporting a comfortable drive.

[0099] The Traffic AI Mind System also has a function that suggests driving styles based on the driver's emotional state. The driving style suggestion function estimates the driver's emotional state and suggests driving styles that correspond to that state. For example, if the driver is relaxed, it will suggest eco-driving. If the driver is nervous, it will make suggestions to encourage safe driving. This makes it possible to suggest driving styles that correspond to the driver's emotional state, supporting safe and efficient driving.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The traffic data analysis unit analyzes traffic data, such as traffic volume data, accident data, and speed data. Step 2: The risk prediction unit predicts the risk of traffic accidents based on the data analyzed by the traffic data analysis unit. For example, the risk is assessed based on past accident data and driver behavior patterns. Step 3: The attention alerting unit alerts the driver based on the risk predicted by the risk prediction unit, for example, by using an audio or visual alert. Step 4: The route guidance unit guides the driver to an appropriate route based on the data analyzed by the traffic data analysis unit. For example, it suggests the shortest route, the shortest time, or the least traffic volume route. Step 5: The traffic congestion prediction unit predicts traffic congestion and peaks in transportation demand based on the data analyzed by the traffic data analysis unit. For example, it predicts the occurrence of traffic congestion based on past data and real-time data. Step 6: The traffic control proposal unit proposes efficient traffic control based on the data predicted by the congestion prediction unit, for example, signal control and optimization of traffic flow. Step 7: The monitoring unit monitors the status of the transportation infrastructure and vehicles, for example by collecting data using sensors and conducting regular inspections. Step 8: The Maintenance Planning Department assists in planning maintenance and inspections based on the data collected by the Monitoring Department, for example, by proposing preventive maintenance and periodic inspection schedules.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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, in order to avoid confusion and to 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.

[0168] 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]

[0169] 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 traffic data analysis unit that analyzes traffic data; a risk prediction unit that predicts the risk of a traffic accident based on the data analyzed by the traffic data analysis unit; an attention calling unit that calls the driver's attention based on the risk predicted by the risk prediction unit; a route guidance unit that guides a driver to an appropriate route based on the data analyzed by the traffic data analysis unit; a traffic congestion prediction unit that predicts traffic congestion and peaks in transportation demand based on the data analyzed by the traffic data analysis unit; a traffic control proposal unit that proposes efficient traffic control based on the data predicted by the congestion prediction unit; A monitoring department that monitors the status of transportation infrastructure and vehicles; a maintenance planning unit that supports maintenance and inspection planning based on the data collected by the monitoring unit. A system characterized by:

2. The risk prediction unit Taking into account the movements of pedestrians and cyclists in the vicinity, we can make more accurate risk predictions.

2. The system of claim 1.

3. The risk prediction unit The attention-calling message is customized according to the emotional state of the driver, thereby making the attention more effective.

2. The system of claim 1.

4. The route guidance unit Suggesting relaxing or scenic routes according to the driver's emotional state 2. The system of claim 1.

5. The congestion prediction unit Evaluate the driver's stress level and make suggestions to avoid times of high stress and routes.

2. The system of claim 1.

6. The traffic control proposal unit The timing of traffic signals is adjusted according to the driver's emotional state to reduce stress.

2. The system of claim 1.

7. The monitoring unit Proposing the timing of the maintenance and inspection according to the emotional state of the driver, thereby increasing the sense of security of the driver 2. The system of claim 1.

8. The monitoring unit Providing a notice of the maintenance / inspection in accordance with the emotional state of the driver, thereby increasing the sense of security of the driver 2. The system of claim 1.

Images