System

Abstract

An object of a system according to an embodiment is to share information between automobiles and make an appropriate driving proposal to a driver.SOLUTION: A system according to an embodiment includes an information sharing unit, a speed maintenance suggestion unit, a refueling guide unit, and a congestion guide unit. The information sharing section shares information between the automobiles. The speed maintenance proposal unit proposes speed maintenance based on the information shared by the information sharing unit. The refueling guidance unit proposes a refueling place based on the information shared by the information sharing unit. The congestion guidance unit proposes a congestion avoidance route based on the information shared by the information sharing 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 technology does not allow for sufficient information sharing between vehicles, leaving room for improvement in traffic congestion and the selection of refueling locations.

[0005] The system according to the embodiment aims to share information between vehicles and provide appropriate driving suggestions to the driver. [Means for solving the problem]

[0006] The system according to the embodiment includes an information sharing unit, a speed maintenance suggestion unit, a refueling guide unit, and a traffic congestion information unit. The information sharing unit shares information between vehicles. The speed maintenance suggestion unit suggests maintaining a speed based on information shared by the information sharing unit. The refueling guide unit suggests refueling locations based on information shared by the information sharing unit. The traffic congestion information unit suggests a route to avoid traffic congestion based on information shared by the information sharing unit. [Effects of the Invention]

[0007] The system according to the embodiment can share information between vehicles and provide appropriate driving suggestions to the driver. [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 congestion improvement system according to the embodiment of the present invention is a system in which vehicles share information with each other and provide feedback to drivers on how to improve congestion based on that information. This enables the congestion improvement system to suppress congestion and reduce economic losses.

[0029] A congestion improvement system according to an embodiment includes an information sharing unit, a speed maintenance suggestion unit, a refueling guide unit, and a congestion notification unit. The information sharing unit shares information between vehicles, such as traffic information, weather information, and vehicle status information. The speed maintenance suggestion unit suggests speed maintenance based on the information shared by the information sharing unit. For example, it identifies points where a driver's speed may unconsciously decrease, such as on slopes or sharp curves, and suggests to the driver how to maintain their speed or use appropriate driving techniques before those points. The refueling guide unit suggests refueling locations based on the information shared by the information sharing unit. For example, it monitors the vehicle's remaining gasoline and suggests inexpensive refueling locations along the route. The congestion notification unit suggests a route to avoid congestion based on the information shared by the information sharing unit. For example, it analyzes the real-time movements of other vehicles and suggests to the driver an optimal route to avoid congestion. This allows the congestion improvement system according to an embodiment to improve congestion and reduce economic losses. For example, by following the system's suggestions, the driver can maintain smooth traffic flow and reduce unnecessary stops. Furthermore, selecting an optimal route is expected to improve overall traffic efficiency and reduce economic losses due to congestion.

[0030] The speed maintenance suggestion unit can identify points where speed may unconsciously decrease due to slopes or sharp curves, and suggest to the driver to maintain speed or take appropriate driving measures before the point. The speed maintenance suggestion unit can identify points where speed may unconsciously decrease due to slopes or sharp curves, and suggest to the driver to maintain speed or take appropriate driving measures before the point. For example, the generation AI can give the driver instructions such as "Please maintain your speed on the slope ahead" based on road topography data and past driving data. The generation AI also displays a score indicating how much the driver's following the suggestion contributed to improving congestion. This prevents speed reductions on slopes and sharp curves, and maintains smooth traffic flow.

[0031] The refueling guide unit can monitor the vehicle's remaining gasoline and suggest inexpensive refueling locations along the route. For example, the generation AI can notify the driver, for example, "It would be more economical to refuel at the gas station ahead," based on the vehicle's remaining gasoline data and price information at gas stations along the route. This allows the driver to refuel in advance, preventing extremely eco-friendly driving and avoiding speed reductions that cause traffic jams.

[0032] The congestion guidance unit can analyze the movements of other vehicles in real time and suggest the optimal route to the driver to avoid traffic jams. For example, the congestion guidance unit can analyze the real-time movements of other vehicles and suggest the optimal route to the driver to avoid traffic jams. For example, the generation AI can give instructions such as "Turn right and take a different route to avoid the upcoming traffic jam" based on the position and speed information of other vehicles. Furthermore, if there are many vehicles detouring, it can also provide dynamic guidance such as it being better to continue driving as is. This makes it possible to suggest the optimal route based on the movements of other vehicles and avoid traffic jams.

[0033] The information sharing unit monitors the vehicle's maintenance status and can suggest appropriate driving methods for vehicles that require maintenance. For example, the information sharing unit monitors the degree of tire wear on a vehicle and suggests safe driving methods if the wear is severe. For example, the generation AI makes suggestions such as "Please drive carefully to avoid sudden braking" based on tire wear data. This makes it possible to suggest appropriate driving methods based on the vehicle's maintenance status and improve safety.

[0034] The information sharing unit can make driving suggestions for overloading or underloading, including the number of passengers or load information of the vehicle. For example, the information sharing unit monitors the number of passengers in the vehicle and suggests safe driving methods in the event of overloading. For example, the generation AI makes suggestions such as "Please drive carefully to avoid sharp turns and sudden braking" based on the passenger number data. This makes it possible to suggest appropriate driving methods based on the number of passengers and load information of the vehicle, thereby improving safety.

[0035] The information sharing unit reflects weather information or road conditions in real time and can suggest optimal driving methods to the driver. For example, the information sharing unit obtains weather information in real time and makes driving suggestions for slippery roads during rainy weather. For example, the generation AI makes suggestions such as "Please reduce your speed" based on weather data. It also obtains road conditions in real time and suggests detour routes when traffic is heavy. This makes it possible to suggest optimal driving methods based on weather information and road conditions, improving safety and efficiency.

[0036] The information sharing unit can grasp the overall traffic situation, including information on other modes of transportation. For example, the information sharing unit obtains bicycle location information and suggests safe driving methods in areas with many bicycles. For example, the generation AI can make suggestions such as "Please drive in a way that avoids bicycle lanes" based on bicycle location data. This allows the system to grasp the overall traffic situation, including information on other modes of transportation, and suggest the optimal driving method to the driver.

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

[0038] The congestion improvement system can further include an eco-driving suggestion unit. The eco-driving suggestion unit analyzes the driver's driving style and suggests driving methods to improve fuel efficiency. For example, it can instruct the driver to avoid sudden acceleration and braking. The eco-driving suggestion unit can also provide feedback on the results of the driver's eco-driving and display the degree of improvement in fuel efficiency. This allows the driver to drive with an awareness of fuel efficiency and reduce fuel consumption.

[0039] The congestion improvement system can further include a health management unit that monitors the driver's health condition. The health management unit monitors the driver's heart rate and blood pressure, and suggests taking a break if any abnormalities are detected. For example, if fatigue accumulates due to long driving hours, a message such as "Please take a break at the next service area" can be displayed. This makes it possible to manage the driver's health condition and promote safe driving.

[0040] The congestion improvement system can also be equipped with a driving skill improvement unit that analyzes the driver's driving history and provides feedback to improve their driving skills. Based on past driving data, the driving skill improvement unit identifies weaknesses in the driver's driving skills and suggests areas for improvement. For example, it can provide specific advice such as, "You brake suddenly too often, so try braking earlier." This allows the driver to improve their driving skills and achieve safer driving.

[0041] The congestion improvement system can further include a driving style improvement unit that analyzes the driver's driving style and makes suggestions for improving the driver's driving style. The driving style improvement unit identifies weaknesses in the driver's driving style based on past driving data and suggests areas for improvement. For example, it can provide specific advice such as, "You tend to accelerate suddenly, so try to accelerate more smoothly." This allows the driver to improve their driving style and achieve safer and more efficient driving.

[0042] The congestion improvement system can further include a driving route optimization unit that analyzes the driver's driving history and proposes optimized driving routes to the driver. The driving route optimization unit proposes the optimal route from among the routes frequently used by the driver based on past driving data. For example, it can provide specific advice such as, "This route is prone to congestion at this time of day, so please choose a different route." This allows the driver to select the optimal route and avoid congestion.

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

[0044] Step 1: The information sharing unit shares information between vehicles, such as traffic information, weather information, and vehicle status information. Step 2: The speed maintenance suggestion unit suggests maintaining a certain speed based on the information shared by the information sharing unit. For example, it identifies points where the driver's speed tends to drop unconsciously, such as on slopes or sharp curves, and suggests maintaining a certain speed or using appropriate driving methods to the driver before those points. Step 3: The fuel guide unit suggests fueling locations based on the information shared by the information sharing unit. For example, it monitors the remaining gasoline in the vehicle and suggests inexpensive fueling locations along the route. Step 4: The congestion guidance unit proposes a route to avoid congestion based on the information shared by the information sharing unit. For example, it analyzes the real-time movements of other vehicles and proposes the optimal route to the driver to avoid congestion.

[0045] (Example 2) The congestion improvement system according to the embodiment of the present invention is a system in which vehicles share information with each other and provide feedback to drivers on how to improve congestion based on that information. This enables the congestion improvement system to suppress congestion and reduce economic losses.

[0046] A congestion improvement system according to an embodiment includes an information sharing unit, a speed maintenance suggestion unit, a refueling guide unit, and a congestion notification unit. The information sharing unit shares information between vehicles, such as traffic information, weather information, and vehicle status information. The speed maintenance suggestion unit suggests speed maintenance based on the information shared by the information sharing unit. For example, it identifies points where a driver's speed may unconsciously decrease, such as on slopes or sharp curves, and suggests to the driver how to maintain their speed or use appropriate driving techniques before those points. The refueling guide unit suggests refueling locations based on the information shared by the information sharing unit. For example, it monitors the vehicle's remaining gasoline and suggests inexpensive refueling locations along the route. The congestion notification unit suggests a route to avoid congestion based on the information shared by the information sharing unit. For example, it analyzes the real-time movements of other vehicles and suggests to the driver an optimal route to avoid congestion. This allows the congestion improvement system according to an embodiment to improve congestion and reduce economic losses. For example, by following the system's suggestions, the driver can maintain smooth traffic flow and reduce unnecessary stops. Furthermore, selecting an optimal route is expected to improve overall traffic efficiency and reduce economic losses due to congestion.

[0047] The speed maintenance suggestion unit can identify points where speed may unconsciously decrease due to slopes or sharp curves, and suggest to the driver to maintain speed or take appropriate driving measures before the point. The speed maintenance suggestion unit can identify points where speed may unconsciously decrease due to slopes or sharp curves, and suggest to the driver to maintain speed or take appropriate driving measures before the point. For example, the generation AI can give the driver instructions such as "Please maintain your speed on the slope ahead" based on road topography data and past driving data. The generation AI also displays a score indicating how much the driver's following the suggestion contributed to improving congestion. This prevents speed reductions on slopes and sharp curves, and maintains smooth traffic flow.

[0048] The refueling guide unit can monitor the vehicle's remaining gasoline and suggest inexpensive refueling locations along the route. For example, the generation AI can notify the driver, for example, "It would be more economical to refuel at the gas station ahead," based on the vehicle's remaining gasoline data and price information at gas stations along the route. This allows the driver to refuel in advance, preventing extremely eco-friendly driving and avoiding speed reductions that cause traffic jams.

[0049] The congestion guidance unit can analyze the movements of other vehicles in real time and suggest the optimal route to the driver to avoid traffic jams. For example, the congestion guidance unit can analyze the real-time movements of other vehicles and suggest the optimal route to the driver to avoid traffic jams. For example, the generation AI can give instructions such as "Turn right and take a different route to avoid the upcoming traffic jam" based on the position and speed information of other vehicles. Furthermore, if there are many vehicles detouring, it can also provide dynamic guidance such as it being better to continue driving as is. This makes it possible to suggest the optimal route based on the movements of other vehicles and avoid traffic jams.

[0050] The information sharing unit can estimate the emotional state of the driver and make driving suggestions to help drivers who are highly stressed to relax. The information sharing unit, for example, analyzes the driver's facial expressions and voice to estimate the emotional state in real time. For example, the generation AI measures the driver's stress level using a camera or microphone, and makes driving suggestions to help the driver relax if the driver is highly stressed. For example, it displays a message such as "Take a deep breath and relax." This makes it possible to make driving suggestions to help the driver relax based on the driver's emotional state and reduce stress.

[0051] The information sharing unit monitors the vehicle's maintenance status and can suggest appropriate driving methods for vehicles that require maintenance. For example, the information sharing unit monitors the degree of tire wear on a vehicle and suggests safe driving methods if the wear is severe. For example, the generation AI makes suggestions such as "Please drive carefully to avoid sudden braking" based on tire wear data. This makes it possible to suggest appropriate driving methods based on the vehicle's maintenance status and improve safety.

[0052] The information sharing unit can make driving suggestions for overloading or underloading, including the number of passengers or load information of the vehicle. For example, the information sharing unit monitors the number of passengers in the vehicle and suggests safe driving methods in the event of overloading. For example, the generation AI makes suggestions such as "Please drive carefully to avoid sharp turns and sudden braking" based on the passenger number data. This makes it possible to suggest appropriate driving methods based on the number of passengers and load information of the vehicle, thereby improving safety.

[0053] The information sharing unit reflects weather information or road conditions in real time and can suggest optimal driving methods to the driver. For example, the information sharing unit obtains weather information in real time and makes driving suggestions for slippery roads during rainy weather. For example, the generation AI makes suggestions such as "Please reduce your speed" based on weather data. It also obtains road conditions in real time and suggests detour routes when traffic is heavy. This makes it possible to suggest optimal driving methods based on weather information and road conditions, improving safety and efficiency.

[0054] The information sharing unit can grasp the overall traffic situation, including information on other modes of transportation. For example, the information sharing unit obtains bicycle location information and suggests safe driving methods in areas with many bicycles. For example, the generation AI can make suggestions such as "Please drive in a way that avoids bicycle lanes" based on bicycle location data. This allows the system to grasp the overall traffic situation, including information on other modes of transportation, and suggest the optimal driving method to the driver.

[0055] The information sharing unit can use the driver's emotion estimation function to make driving suggestions to promote emotionally stable driving. For example, the information sharing unit analyzes the driver's emotional state in real time and makes driving suggestions to help the driver relax if they are under high stress. For example, the generation AI can estimate the driver's emotional state using facial recognition technology and voice analysis technology and display a message such as "Take a deep breath and relax." This makes it possible to make suggestions to promote stable driving based on the driver's emotional state, thereby improving safety.

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

[0057] The congestion improvement system can further include an eco-driving suggestion unit. The eco-driving suggestion unit analyzes the driver's driving style and suggests driving methods to improve fuel efficiency. For example, it can instruct the driver to avoid sudden acceleration and braking. The eco-driving suggestion unit can also provide feedback on the results of the driver's eco-driving and display the degree of improvement in fuel efficiency. This allows the driver to drive with an awareness of fuel efficiency and reduce fuel consumption.

[0058] The congestion improvement system can further include a music suggestion unit. The music suggestion unit estimates the emotional state of the driver and suggests music that matches that emotion. For example, if the driver is feeling stressed, it can suggest relaxing music, and conversely, if the driver is feeling drowsy, it can suggest music that has an awakening effect. This allows the system to provide music that matches the driver's emotional state and adjust the driver's mood while driving.

[0059] The congestion improvement system can further include an in-vehicle environment adjustment unit. The in-vehicle environment adjustment unit estimates the emotional state of the driver and adjusts the temperature and lighting inside the vehicle according to that emotion. For example, if the driver is feeling stressed, the in-vehicle temperature can be adjusted to a comfortable range and the lighting can be softened. This provides an in-vehicle environment that corresponds to the driver's emotional state, supporting comfortable driving.

[0060] The congestion improvement system can further include a health management unit that monitors the driver's health condition. The health management unit monitors the driver's heart rate and blood pressure, and suggests taking a break if any abnormalities are detected. For example, if fatigue accumulates due to long driving hours, a message such as "Please take a break at the next service area" can be displayed. This makes it possible to manage the driver's health condition and promote safe driving.

[0061] The congestion improvement system can also estimate the driver's emotional state and display messages of praise or encouragement to the driver based on that emotion. For example, if the driver is feeling stressed, an encouraging message such as "You're doing a great job" can be displayed to ease the driver's mood. This allows the system to provide messages that correspond to the driver's emotional state and reduce stress while driving.

[0062] The congestion improvement system can also be equipped with a driving skill improvement unit that analyzes the driver's driving history and provides feedback to improve their driving skills. Based on past driving data, the driving skill improvement unit identifies weaknesses in the driver's driving skills and suggests areas for improvement. For example, it can provide specific advice such as, "You brake suddenly too often, so try braking earlier." This allows the driver to improve their driving skills and achieve safer driving.

[0063] The congestion improvement system can also estimate the driver's emotional state and suggest relaxation methods to the driver based on that emotion. For example, if the driver is feeling stressed, it can display a message such as "Take a deep breath and relax" and suggest relaxation methods. This allows the system to provide relaxation methods that correspond to the driver's emotional state and reduce stress while driving.

[0064] The congestion improvement system can further include a driving style improvement unit that analyzes the driver's driving style and makes suggestions for improving the driver's driving style. The driving style improvement unit identifies weaknesses in the driver's driving style based on past driving data and suggests areas for improvement. For example, it can provide specific advice such as, "You tend to accelerate suddenly, so try to accelerate more smoothly." This allows the driver to improve their driving style and achieve safer and more efficient driving.

[0065] The congestion improvement system can also estimate the driver's emotional state and suggest rest breaks to the driver based on that emotion. For example, if the driver feels tired, it can display a message such as "Please take a break at the next service area" to encourage the driver to take a break. This makes it possible to provide rest suggestions according to the driver's emotional state and reduce fatigue while driving.

[0066] The congestion improvement system can further include a driving route optimization unit that analyzes the driver's driving history and proposes optimized driving routes to the driver. The driving route optimization unit proposes the optimal route from among the routes frequently used by the driver based on past driving data. For example, it can provide specific advice such as, "This route is prone to congestion at this time of day, so please choose a different route." This allows the driver to select the optimal route and avoid congestion.

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

[0068] Step 1: The information sharing unit shares information between vehicles, such as traffic information, weather information, and vehicle status information. Step 2: The speed maintenance suggestion unit suggests maintaining a certain speed based on the information shared by the information sharing unit. For example, it identifies points where the driver's speed tends to drop unconsciously, such as on slopes or sharp curves, and suggests maintaining a certain speed or using appropriate driving methods to the driver before those points. Step 3: The fuel guide unit suggests fueling locations based on the information shared by the information sharing unit. For example, it monitors the remaining gasoline in the vehicle and suggests inexpensive fueling locations along the route. Step 4: The congestion guidance unit proposes a route to avoid congestion based on the information shared by the information sharing unit. For example, it analyzes the real-time movements of other vehicles and proposes the optimal route to the driver to avoid congestion.

[0069] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0070] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0071] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0074] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0075] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0076] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0077] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0078] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0079] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0080] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0081] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0082] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0083] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0103] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0110] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0119] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0120] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0121] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0122] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0123] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0124] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0125] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0126] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0127] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0128] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

[0130] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0131] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0132] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0133] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0134] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, 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.

[0135] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0136] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An information sharing unit that shares information between vehicles; a speed maintenance suggestion unit that suggests maintaining a speed based on the information shared by the information sharing unit; a fuel supply guide unit that suggests a fuel supply location based on the information shared by the information sharing unit; a congestion guidance unit that proposes a congestion avoidance route based on the information shared by the information sharing unit. A system characterized by:

2. The speed maintenance suggestion unit Identifies areas where the driver unconsciously slows down on slopes or sharp curves, and suggests maintaining speed or appropriate driving methods before those areas.

2. The system of claim 1.

3. The oil supply guide portion is Monitors vehicle gasoline levels and suggests low-cost refueling stops along the route 2. The system of claim 1.

4. The congestion information unit Analyzes the movements of other vehicles in real time and suggests optimal routes to drivers to avoid traffic jams 2. The system of claim 1.

5. The information sharing unit Reflects weather information or road conditions in real time and suggests optimal driving methods to drivers 2. The system of claim 1.

6. The information sharing unit Get a comprehensive view of traffic conditions, including information on other modes of transportation 2. The system of claim 1.

7. The information sharing unit Providing driving suggestions to promote emotionally stable driving 2. The system of claim 1.

8. The information sharing unit Estimate the emotional state of the driver and provide driving suggestions to relax the driver who is under high stress.

2. The system of claim 1.

Images