System

Abstract

An object of a system according to an embodiment is to prevent drunk driving and support safe driving.SOLUTION: A system according to an embodiment includes a portable smell sensor, an AI speech recognition technology, a visual / auditory providing unit, a substitute driving arrangement unit, and a public transportation searching unit. The portable odor sensor measures the concentration of alcohol in the driver's breath and displays the result.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 provide sufficient means to prevent drunk driving, and there is room for improvement.

[0005] The system according to the embodiment aims to prevent drunk driving and support safe driving. [Means for solving the problem]

[0006] The system according to the embodiment includes a portable odor sensor, AI voice recognition technology, a visual and auditory information providing unit, a substitute driver dispatching unit, and a public transportation search unit. The portable odor sensor measures the alcohol concentration in the driver's breath and displays the result. [Effects of the Invention]

[0007] The system according to the embodiment can prevent drunk driving and support safe driving. [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 drunk driving prevention system according to an embodiment of the present invention utilizes a portable odor sensor and AI voice recognition technology to check the driver's drinking level and support safe driving. As a result, the drunk driving prevention system can easily check the driver's drinking level and support safe driving. It can also provide visual and audible warnings of drunk driving incidents and suggest alternative measures, such as arranging a substitute driver or searching for public transportation.

[0029] A drunk driving prevention system according to an embodiment includes a portable odor sensor, AI voice recognition technology, a visual and audio information providing unit, a substitute driving dispatching unit, and a public transportation search unit. The portable odor sensor measures the alcohol concentration in the driver's breath and displays the result. For example, when the driver blows into the sensor, the sensor detects the alcohol concentration and displays the result. The AI ​​voice recognition technology recognizes a voice command and starts a drinking level check. For example, it recognizes a voice command such as "Start a drinking level check" and starts a drinking level check. The visual and audio information providing unit provides drunk driving troubles to the user visually and audibly. For example, it issues a warning message such as "Drinking and driving is dangerous. The risk of an accident increases." Visually, it also displays a warning message and a simulated accident video on the smartphone screen. The substitute driving dispatching unit suggests arranging a substitute driving service if drunk driving is detected. For example, it provides a voice message such as "Would you like to arrange a substitute driving service?". If the user answers "Yes," it searches for and arranges a substitute driving service. The public transportation search unit suggests searching for public transportation if drunk driving is detected. For example, a voice message such as "Do you want to search for the nearest bus stop or station?" is provided, and if the user answers "Yes," the system searches for the nearest public transportation and suggests available routes. This allows the drunk driving prevention system according to the embodiment to easily check the driver's drinking status and support safe driving. Furthermore, by preventing trouble caused by drunk driving and suggesting alternative means, safe transportation can be ensured.

[0030] AI voice recognition technology can recognize voice commands and start a sobriety check. For example, AI voice recognition technology recognizes voice commands such as "start a sobriety check" and starts a sobriety check. For example, voice recognition technology analyzes the tone and speed of the user's voice to accurately recognize the voice command. In addition, voice recognition technology supports multiple languages ​​and can accommodate international users. For example, it supports major languages ​​such as English, French, and Chinese. This allows a sobriety check to be started with a voice command.

[0031] The visual and audio provision unit can provide the user with information about the problems of drunk driving through visual and audio means. For example, the visual and audio provision unit can convey a warning message by voice, such as, "Drinking and driving is dangerous. It increases the risk of accidents." Visually, the unit can also display a warning message or a simulated video of an accident on the smartphone screen. For example, the unit can visually simulate the risks of drunk driving and provide the user with a video showing specific dangers. For example, a simulated video of an accident caused by drunk driving can be displayed on the smartphone. This allows the user to be informed of the problems of drunk driving through visual and audio means.

[0032] The substitute driving arrangement unit can suggest arranging a substitute driving service when a drunk state is detected. For example, the substitute driving arrangement unit provides a voice message such as "Would you like to arrange a substitute driving service?", and if the user answers "Yes", searches for a substitute driving service and makes the arrangement. For example, based on evaluation data of substitute driving services, the unit suggests the most suitable substitute driving service to the user. For example, the unit suggests the most suitable service based on the user's current location and destination. This makes it possible to suggest arranging a substitute driving service when a drunk state is detected.

[0033] The public transportation search unit can suggest a search for public transportation when a drinking state is detected. For example, the public transportation search unit provides a voice message such as "Do you want to search for the nearest bus stop or station?", and if the user answers "Yes", it searches for the nearest public transportation and suggests an available route. For example, it monitors the operation status of public transportation in real time and suggests the optimal route. For example, it checks the operation status of buses and trains in real time and suggests the optimal transfer route. This makes it possible to suggest a search for public transportation when a drinking state is detected.

[0034] The portable odor sensor can learn the user's past drinking history based on the detected alcohol concentration and predict individual drinking patterns. For example, the portable odor sensor sends the detected alcohol concentration data to the cloud and compares it with the user's past drinking history. This allows the sensor to learn the user's drinking pattern and predict the next drinking time. For example, for a user who tends to drink on weekends, a warning is displayed before the weekend. This allows the sensor to learn the user's past drinking history and predict individual drinking patterns.

[0035] The portable odor sensor transmits the detected alcohol concentration to the cloud in real time and can monitor the drinking level in cooperation with other devices. The portable odor sensor, for example, transmits the detected alcohol concentration to the cloud in real time and monitors the drinking level in cooperation with a smartphone or smartwatch. For example, if the drinking level is detected, a warning is displayed on the smartwatch. This allows the data to be transmitted to the cloud in real time and can be monitored in cooperation with other devices.

[0036] The portable odor sensor can be incorporated into the air conditioning system to detect the alcohol concentration in the air inside the vehicle. For example, a portable odor sensor can be incorporated into the vehicle's air conditioning system to develop a system that detects the alcohol concentration in the air inside the vehicle. For example, the sensor can be installed in the air conditioning intake and the air inside the vehicle can be sampled periodically. This makes it possible to detect the alcohol concentration in the air inside the vehicle.

[0037] Portable odor sensors can be integrated into smartwatches or fitness trackers to simultaneously monitor exercise status and alcohol consumption. For example, a portable odor sensor can be integrated into a smartwatch to develop a system that simultaneously monitors exercise status and alcohol consumption. For example, the smartwatch measures the user's heart rate and number of steps, while simultaneously detecting alcohol concentration. This allows for simultaneous monitoring of exercise status and alcohol consumption.

[0038] AI voice recognition technology can learn the history of voice commands, predict the user's operation patterns, and suggest the next operation. AI voice recognition technology, for example, will develop a system that learns the history of voice commands and predicts the user's operation patterns. For example, it will suggest the next operation based on past voice commands. This will allow it to learn the history of voice commands, predict the user's operation patterns, and suggest the next operation.

[0039] AI speech recognition technology can support multiple languages ​​and accommodate international users. For example, AI speech recognition technology uses speech recognition technology to develop a system that supports multiple languages. For example, it supports major languages ​​such as English, French, and Chinese. This makes it possible to support multiple languages ​​and accommodate international users.

[0040] AI voice recognition technology can be integrated into home devices to support in-home alcohol intake checks. For example, AI voice recognition technology can be integrated into smart speakers to develop systems that support in-home alcohol intake checks. For example, a smart speaker can recognize voice commands for alcohol intake checks and confirm the intoxication status. This allows AI voice recognition technology to be integrated into home devices to support in-home alcohol intake checks.

[0041] The visual and audio provision unit can visually simulate the risks of drunk driving and provide the user with images showing specific dangers. For example, the visual and audio provision unit can develop a system that visually simulates the risks of drunk driving and provide the user with images showing specific dangers. For example, a simulated video of an accident caused by drunk driving can be displayed on a smartphone. This allows the risks of drunk driving to be visually simulated and the user to be provided with images showing specific dangers.

[0042] The visual and audio provision unit can perform an individual risk assessment for the user based on data on past accidents caused by drunk driving. The visual and audio provision unit will develop a system that performs an individual risk assessment for the user based on data on past accidents caused by drunk driving. For example, the unit will compare the user's drinking history with accident data and perform a risk assessment. This allows an individual risk assessment for the user to be performed based on data on past accidents caused by drunk driving.

[0043] The visual and audio providing unit can simulate the risks of drunk driving in a game format, allowing users to learn interactively. For example, the visual and audio providing unit develops a system that simulates the risks of drunk driving in a game format, allowing users to learn interactively. For example, it provides a simulation game of an accident caused by drunk driving to a smartphone. This allows users to learn interactively by simulating the risks of drunk driving in a game format.

[0044] The visual and audio provision unit can superimpose the risks of drunk driving onto real scenery using AR technology. The visual and audio provision unit will develop a system that uses AR technology to superimpose the risks of drunk driving onto real scenery, for example. For example, the risks of drunk driving can be displayed onto real scenery using a smartphone camera. This allows the risks of drunk driving to be superimposed onto real scenery using AR technology.

[0045] The substitute driving arrangement unit can propose the optimal substitute driving service to the user based on the evaluation data of the substitute driving service. The substitute driving arrangement unit develops a system that proposes the optimal substitute driving service to the user based on, for example, the evaluation data of the substitute driving service. For example, the optimal service is proposed based on the user's current location and destination. This makes it possible to propose the optimal substitute driving service to the user based on the evaluation data of the substitute driving service.

[0046] The substitute driving arrangement unit can automatically detect the user's current location and destination when arranging a substitute driving service, and propose the optimal route. For example, the substitute driving arrangement unit will develop a system that automatically detects the user's current location and destination when arranging a substitute driving service. For example, the current location and destination can be automatically set based on GPS data. This allows the user's current location and destination to be automatically detected when arranging a substitute driving service, and the optimal route to be proposed.

[0047] The substitute driving arrangement unit can partner with substitute driving services to add a function that allows users to make reservations directly within the app. For example, the substitute driving arrangement unit can partner with substitute driving services to develop a system that adds a function that allows users to make reservations directly within the app. For example, it can make it possible to complete reservations for substitute driving services within the app. This allows the unit to partner with substitute driving services to add a function that allows users to make reservations directly within the app.

[0048] The substitute driving arrangement unit can also make it possible to arrange for substitute driving from a smart home device. The substitute driving arrangement unit develops a system that, for example, makes it possible to arrange for substitute driving from a smart home device. For example, a substitute driving service can be arranged by inputting a voice command into a smart speaker. This makes it possible to arrange for substitute driving from a smart home device.

[0049] The public transportation search unit can monitor the operation status of public transportation in real time and propose the optimal route. For example, the public transportation search unit can develop a system that monitors the operation status of public transportation in real time and propose the optimal route. For example, it can check the operation status of buses and trains in real time and propose the optimal transfer route. This makes it possible to monitor the operation status of public transportation in real time and propose the optimal route.

[0050] The public transportation search unit can learn the user's past travel history and suggest the most convenient public transportation. The public transportation search unit, for example, develops a system that learns the user's past travel history and suggests the most convenient public transportation. For example, it suggests the optimal bus or train route based on the past travel history. This makes it possible to learn the user's past travel history and suggest the most convenient public transportation.

[0051] The public transportation search unit can use AR technology to display search results for public transportation overlaid on real scenery. The public transportation search unit will develop a system that uses AR technology to display search results for public transportation overlaid on real scenery, for example. For example, the nearest bus stop or station can be displayed on real scenery through a smartphone camera. This allows the search results for public transportation to be displayed overlaid on real scenery using AR technology.

[0052] The public transportation search unit can enable searches for public transportation from smartwatches and fitness trackers. The public transportation search unit will develop a system that enables searches for public transportation from smartwatches, for example. For example, a user can input a voice command into the smartwatch to search for the nearest bus stop or station. This allows searches for public transportation to be performed from smartwatches and fitness trackers.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The drunk driving prevention system can further include a health management unit that monitors the user's health condition. For example, it measures the user's heart rate and blood pressure and provides real-time feedback on the impact of drinking on health. Furthermore, the health management unit can learn from the user's past health data and predict the health risks that drinking poses. For example, it can predict the impact of drinking on the heart based on past data and display a warning. This allows the user to understand the impact of drinking on their health and live a healthier life.

[0055] The drunk driving prevention system may further include a driving evaluation unit that evaluates the user's driving skills. For example, it may monitor the user's driving behavior and detect dangerous driving behaviors such as sudden braking or sudden acceleration. Furthermore, the driving evaluation unit may evaluate the user's driving skills and provide feedback on areas for improvement. For example, it may provide the user with advice on safe driving based on data obtained while driving. This allows the user to improve their driving skills and achieve safe driving.

[0056] The drunk driving prevention system can further include a driving history learning unit that learns the user's driving history and predicts individual driving patterns. For example, if a user has a tendency to drink and drive at a specific time or location based on past driving data, a warning is displayed before the user drinks and drives. Furthermore, the driving history learning unit can learn the user's driving patterns and predict the next driving risk. For example, if a user has a tendency to drink and drive on weekends, a warning is displayed before the weekend. This allows the user to understand their own driving patterns and prevent drunk driving.

[0057] The drunk driving prevention system can further include a maintenance management unit that monitors the user's vehicle maintenance status. For example, it can periodically check the vehicle's oil level and tire pressure and notify the user when maintenance is required. Furthermore, the maintenance management unit can learn the vehicle's past maintenance history and predict when the next maintenance is due. For example, it can predict when an oil change is required based on past data and notify the user. This allows the user to always be aware of the vehicle's maintenance status and maintain safe driving.

[0058] The drunk driving prevention system may further include an environmental sound management unit that monitors the ambient sounds while the user is driving and provides appropriate music or voice messages. For example, it may measure the noise level while driving and play relaxing music. Furthermore, the environmental sound management unit may estimate the user's stress level while driving and provide advice on how to relax. For example, it may provide the user with a voice message encouraging them to take deep breaths based on the data obtained while driving. This allows the user to reduce stress while driving and achieve safer driving.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The portable odor sensor measures the alcohol concentration in the driver's breath and displays the result. For example, the driver blows into the sensor, which detects the alcohol concentration and displays the result. Step 2: The AI ​​voice recognition technology recognizes the voice command and starts the alcohol consumption check. For example, it recognizes the voice command "Start alcohol consumption check" and starts the alcohol consumption check. Step 3: The visual and audio information provider provides the user with information about drunk driving issues through visual and audio channels. For example, it may provide a warning message such as, "Drinking and driving is dangerous. It increases the risk of accidents." It also visually displays warning messages and simulated accident images on the smartphone screen. Step 4: The substitute driving arrangement unit suggests arranging a substitute driving service if the user is found to be drunk. For example, it provides a voice message such as "Would you like to arrange a substitute driving service?" If the user answers "Yes," it searches for a substitute driving service and makes the arrangement. Step 5: The public transport search unit suggests searching for public transport if the user is drunk. For example, it provides a voice message such as "Do you want to search for the nearest bus stop or station?" If the user answers "yes," it searches for the nearest public transport and suggests available routes.

[0061] (Example 2) The drunk driving prevention system according to an embodiment of the present invention utilizes a portable odor sensor and AI voice recognition technology to check the driver's drinking level and support safe driving. As a result, the drunk driving prevention system can easily check the driver's drinking level and support safe driving. It can also provide visual and audible warnings of drunk driving incidents and suggest alternative measures, such as arranging a substitute driver or searching for public transportation.

[0062] A drunk driving prevention system according to an embodiment includes a portable odor sensor, AI voice recognition technology, a visual and audio information providing unit, a substitute driving dispatching unit, and a public transportation search unit. The portable odor sensor measures the alcohol concentration in the driver's breath and displays the result. For example, when the driver blows into the sensor, the sensor detects the alcohol concentration and displays the result. The AI ​​voice recognition technology recognizes a voice command and starts a drinking level check. For example, it recognizes a voice command such as "Start a drinking level check" and starts a drinking level check. The visual and audio information providing unit provides drunk driving troubles to the user visually and audibly. For example, it issues a warning message such as "Drinking and driving is dangerous. The risk of an accident increases." Visually, it also displays a warning message and a simulated accident video on the smartphone screen. The substitute driving dispatching unit suggests arranging a substitute driving service if drunk driving is detected. For example, it provides a voice message such as "Would you like to arrange a substitute driving service?". If the user answers "Yes," it searches for and arranges a substitute driving service. The public transportation search unit suggests searching for public transportation if drunk driving is detected. For example, a voice message such as "Do you want to search for the nearest bus stop or station?" is provided, and if the user answers "Yes," the system searches for the nearest public transportation and suggests available routes. This allows the drunk driving prevention system according to the embodiment to easily check the driver's drinking status and support safe driving. Furthermore, by preventing trouble caused by drunk driving and suggesting alternative means, safe transportation can be ensured.

[0063] AI voice recognition technology can recognize voice commands and start a sobriety check. For example, AI voice recognition technology recognizes voice commands such as "start a sobriety check" and starts a sobriety check. For example, voice recognition technology analyzes the tone and speed of the user's voice to accurately recognize the voice command. In addition, voice recognition technology supports multiple languages ​​and can accommodate international users. For example, it supports major languages ​​such as English, French, and Chinese. This allows a sobriety check to be started with a voice command.

[0064] The visual and audio provision unit can provide the user with information about the problems of drunk driving through visual and audio means. For example, the visual and audio provision unit can convey a warning message by voice, such as, "Drinking and driving is dangerous. It increases the risk of accidents." Visually, the unit can also display a warning message or a simulated video of an accident on the smartphone screen. For example, the unit can visually simulate the risks of drunk driving and provide the user with a video showing specific dangers. For example, a simulated video of an accident caused by drunk driving can be displayed on the smartphone. This allows the user to be informed of the problems of drunk driving through visual and audio means.

[0065] The substitute driving arrangement unit can suggest arranging a substitute driving service when a drunk state is detected. For example, the substitute driving arrangement unit provides a voice message such as "Would you like to arrange a substitute driving service?", and if the user answers "Yes", searches for a substitute driving service and makes the arrangement. For example, based on evaluation data of substitute driving services, the unit suggests the most suitable substitute driving service to the user. For example, the unit suggests the most suitable service based on the user's current location and destination. This makes it possible to suggest arranging a substitute driving service when a drunk state is detected.

[0066] The public transportation search unit can suggest a search for public transportation when a drinking state is detected. For example, the public transportation search unit provides a voice message such as "Do you want to search for the nearest bus stop or station?", and if the user answers "Yes", it searches for the nearest public transportation and suggests an available route. For example, it monitors the operation status of public transportation in real time and suggests the optimal route. For example, it checks the operation status of buses and trains in real time and suggests the optimal transfer route. This makes it possible to suggest a search for public transportation when a drinking state is detected.

[0067] The portable odor sensor can learn the user's past drinking history based on the detected alcohol concentration and predict individual drinking patterns. For example, the portable odor sensor sends the detected alcohol concentration data to the cloud and compares it with the user's past drinking history. This allows the sensor to learn the user's drinking pattern and predict the next drinking time. For example, for a user who tends to drink on weekends, a warning is displayed before the weekend. This allows the sensor to learn the user's past drinking history and predict individual drinking patterns.

[0068] The portable odor sensor transmits the detected alcohol concentration to the cloud in real time and can monitor the drinking level in cooperation with other devices. The portable odor sensor, for example, transmits the detected alcohol concentration to the cloud in real time and monitors the drinking level in cooperation with a smartphone or smartwatch. For example, if the drinking level is detected, a warning is displayed on the smartwatch. This allows the data to be transmitted to the cloud in real time and can be monitored in cooperation with other devices.

[0069] The portable odor sensor can use its emotion estimation function to analyze the user's emotions when checking their drinking level and provide advice to reduce stress and anxiety. The portable odor sensor adds a function to analyze the user's emotional state along with the detected alcohol concentration, for example. For example, if the user is feeling stressed when their drinking level is detected, advice to relax is provided. This makes it possible to analyze the user's emotions when checking their drinking level and provide advice to reduce stress and anxiety.

[0070] The portable odor sensor can be incorporated into the air conditioning system to detect the alcohol concentration in the air inside the vehicle. For example, a portable odor sensor can be incorporated into the vehicle's air conditioning system to develop a system that detects the alcohol concentration in the air inside the vehicle. For example, the sensor can be installed in the air conditioning intake and the air inside the vehicle can be sampled periodically. This makes it possible to detect the alcohol concentration in the air inside the vehicle.

[0071] Portable odor sensors can be integrated into smartwatches or fitness trackers to simultaneously monitor exercise status and alcohol consumption. For example, a portable odor sensor can be integrated into a smartwatch to develop a system that simultaneously monitors exercise status and alcohol consumption. For example, the smartwatch measures the user's heart rate and number of steps, while simultaneously detecting alcohol concentration. This allows for simultaneous monitoring of exercise status and alcohol consumption.

[0072] A portable odor sensor can use its emotion estimation function to monitor a user's emotions in real time when checking their drinking level and provide positive feedback. A portable odor sensor can use its emotion estimation function to develop a system that monitors a user's emotions in real time when checking their drinking level and provides positive feedback. For example, when drinking is detected, a message that helps the user relax is displayed. This allows a user's emotions to be monitored in real time when checking their drinking level and provide positive feedback.

[0073] AI speech recognition technology can infer emotions from the tone and speed of a user's voice and provide appropriate feedback. For example, AI speech recognition technology will be used to develop a system that infers emotions from the tone and speed of a user's voice. For example, if a user is feeling stressed, advice on how to relax will be provided. This will enable the system to infer emotions from the tone and speed of a user's voice and provide appropriate feedback.

[0074] AI voice recognition technology can learn the history of voice commands, predict the user's operation patterns, and suggest the next operation. AI voice recognition technology, for example, will develop a system that learns the history of voice commands and predicts the user's operation patterns. For example, it will suggest the next operation based on past voice commands. This will allow it to learn the history of voice commands, predict the user's operation patterns, and suggest the next operation.

[0075] AI voice recognition technology can use its emotion estimation function to provide music or messages to help users relax when they are feeling stressed. AI voice recognition technology, for example, can use its emotion estimation function to develop a system that provides music to help users relax when they are feeling stressed. For example, it can monitor the user's emotional state in real time and play appropriate music. This makes it possible to provide music or messages to help users relax when they are feeling stressed.

[0076] AI speech recognition technology can support multiple languages ​​and accommodate international users. For example, AI speech recognition technology uses speech recognition technology to develop a system that supports multiple languages. For example, it supports major languages ​​such as English, French, and Chinese. This makes it possible to support multiple languages ​​and accommodate international users.

[0077] AI voice recognition technology can be integrated into home devices to support in-home alcohol intake checks. For example, AI voice recognition technology can be integrated into smart speakers to develop systems that support in-home alcohol intake checks. For example, a smart speaker can recognize voice commands for alcohol intake checks and confirm the intoxication status. This allows AI voice recognition technology to be integrated into home devices to support in-home alcohol intake checks.

[0078] AI voice recognition technology can use an emotion estimation function to provide customized voice feedback according to the user's emotions. For example, AI voice recognition technology will develop a system that uses the emotion estimation function to provide customized voice feedback according to the user's emotions. For example, if the user is feeling stressed, a voice message to help them relax can be provided. This makes it possible to provide customized voice feedback according to the user's emotions.

[0079] The visual and audio provision unit can visually simulate the risks of drunk driving and provide the user with images showing specific dangers. For example, the visual and audio provision unit can develop a system that visually simulates the risks of drunk driving and provide the user with images showing specific dangers. For example, a simulated video of an accident caused by drunk driving can be displayed on a smartphone. This allows the risks of drunk driving to be visually simulated and the user to be provided with images showing specific dangers.

[0080] The visual and audio provision unit can perform an individual risk assessment for the user based on data on past accidents caused by drunk driving. The visual and audio provision unit will develop a system that performs an individual risk assessment for the user based on data on past accidents caused by drunk driving. For example, the unit will compare the user's drinking history with accident data and perform a risk assessment. This allows an individual risk assessment for the user to be performed based on data on past accidents caused by drunk driving.

[0081] The visual and auditory information providing unit uses the emotion estimation function to analyze the emotions felt when a user receives a warning message and can propose more effective warning methods. The visual and auditory information providing unit will develop a system that uses the emotion estimation function to analyze the emotions felt when a user receives a warning message. For example, it will analyze the facial expressions and voice of the user when they receive a warning message and estimate their emotions. This will allow it to analyze the emotions felt when a user receives a warning message and propose more effective warning methods.

[0082] The visual and audio providing unit can simulate the risks of drunk driving in a game format, allowing users to learn interactively. For example, the visual and audio providing unit develops a system that simulates the risks of drunk driving in a game format, allowing users to learn interactively. For example, it provides a simulation game of an accident caused by drunk driving to a smartphone. This allows users to learn interactively by simulating the risks of drunk driving in a game format.

[0083] The visual and audio provision unit can superimpose the risks of drunk driving onto real scenery using AR technology. The visual and audio provision unit will develop a system that uses AR technology to superimpose the risks of drunk driving onto real scenery, for example. For example, the risks of drunk driving can be displayed onto real scenery using a smartphone camera. This allows the risks of drunk driving to be superimposed onto real scenery using AR technology.

[0084] The visual and auditory feedback unit uses the emotion estimation function to monitor the emotions of users when they receive a warning message in real time and provide appropriate feedback. The visual and auditory feedback unit will develop a system that uses the emotion estimation function to monitor the emotions of users when they receive a warning message in real time. For example, it will analyze the user's facial expressions and voice to estimate their emotions. This will enable the visual and auditory feedback unit to monitor the emotions of users when they receive a warning message in real time and provide appropriate feedback.

[0085] The substitute driving arrangement unit can propose the optimal substitute driving service to the user based on the evaluation data of the substitute driving service. The substitute driving arrangement unit develops a system that proposes the optimal substitute driving service to the user based on, for example, the evaluation data of the substitute driving service. For example, the optimal service is proposed based on the user's current location and destination. This makes it possible to propose the optimal substitute driving service to the user based on the evaluation data of the substitute driving service.

[0086] The substitute driving arrangement unit can automatically detect the user's current location and destination when arranging a substitute driving service, and propose the optimal route. For example, the substitute driving arrangement unit will develop a system that automatically detects the user's current location and destination when arranging a substitute driving service. For example, the current location and destination can be automatically set based on GPS data. This allows the user's current location and destination to be automatically detected when arranging a substitute driving service, and the optimal route to be proposed.

[0087] The substitute driving arrangement unit uses the emotion estimation function to analyze the emotions of the user when arranging for substitute driving and can provide a message that gives a sense of security. The substitute driving arrangement unit develops a system that uses the emotion estimation function to analyze the emotions of the user when arranging for substitute driving. For example, the emotion is estimated by analyzing the user's facial expression and voice. This makes it possible to analyze the emotions of the user when arranging for substitute driving and provide a message that gives a sense of security.

[0088] The substitute driving arrangement unit can partner with substitute driving services to add a function that allows users to make reservations directly within the app. For example, the substitute driving arrangement unit can partner with substitute driving services to develop a system that adds a function that allows users to make reservations directly within the app. For example, it can make it possible to complete reservations for substitute driving services within the app. This allows the unit to partner with substitute driving services to add a function that allows users to make reservations directly within the app.

[0089] The substitute driving arrangement unit can also make it possible to arrange for substitute driving from a smart home device. The substitute driving arrangement unit develops a system that, for example, makes it possible to arrange for substitute driving from a smart home device. For example, a substitute driving service can be arranged by inputting a voice command into a smart speaker. This makes it possible to arrange for substitute driving from a smart home device.

[0090] The substitute driving arrangement unit can use the emotion estimation function to monitor the user's emotions in real time when arranging a substitute driving service and provide appropriate feedback. The substitute driving arrangement unit, for example, uses the emotion estimation function to develop a system that monitors the user's emotions in real time when arranging a substitute driving service. For example, the emotion is estimated by analyzing the user's facial expressions and voice. This allows the user's emotions in real time when arranging a substitute driving service to be monitored and appropriate feedback to be provided.

[0091] The public transportation search unit can monitor the operation status of public transportation in real time and propose the optimal route. For example, the public transportation search unit can develop a system that monitors the operation status of public transportation in real time and propose the optimal route. For example, it can check the operation status of buses and trains in real time and propose the optimal transfer route. This makes it possible to monitor the operation status of public transportation in real time and propose the optimal route.

[0092] The public transportation search unit can learn the user's past travel history and suggest the most convenient public transportation. The public transportation search unit, for example, develops a system that learns the user's past travel history and suggests the most convenient public transportation. For example, it suggests the optimal bus or train route based on the past travel history. This makes it possible to learn the user's past travel history and suggest the most convenient public transportation.

[0093] The public transportation search unit uses the emotion estimation function to analyze the emotion a user feels when searching for public transportation and can make suggestions to reduce stress. The public transportation search unit develops a system that uses the emotion estimation function to analyze the emotion a user feels when searching for public transportation. For example, the system analyzes the user's facial expression and voice to estimate the emotion. This allows the system to analyze the emotion a user feels when searching for public transportation and make suggestions to reduce stress.

[0094] The public transportation search unit can use AR technology to display search results for public transportation overlaid on real scenery. The public transportation search unit will develop a system that uses AR technology to display search results for public transportation overlaid on real scenery, for example. For example, the nearest bus stop or station can be displayed on real scenery through a smartphone camera. This allows the search results for public transportation to be displayed overlaid on real scenery using AR technology.

[0095] The public transportation search unit can enable searches for public transportation from smartwatches and fitness trackers. The public transportation search unit will develop a system that enables searches for public transportation from smartwatches, for example. For example, a user can input a voice command into the smartwatch to search for the nearest bus stop or station. This allows searches for public transportation to be performed from smartwatches and fitness trackers.

[0096] The public transportation search unit uses the emotion estimation function to monitor the user's emotions in real time when searching for public transportation and provide appropriate feedback. The public transportation search unit develops a system that uses the emotion estimation function to monitor the user's emotions in real time when searching for public transportation. For example, the system analyzes the user's facial expressions and voice to estimate emotions. This allows the user's emotions in real time when searching for public transportation to be monitored and appropriate feedback to be provided.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The drunk driving prevention system can further include a health management unit that monitors the user's health condition. For example, it measures the user's heart rate and blood pressure and provides real-time feedback on the impact of drinking on health. Furthermore, the health management unit can learn from the user's past health data and predict the health risks that drinking poses. For example, it can predict the impact of drinking on the heart based on past data and display a warning. This allows the user to understand the impact of drinking on their health and live a healthier life.

[0099] The drunk driving prevention system may further include a driving evaluation unit that evaluates the user's driving skills. For example, it may monitor the user's driving behavior and detect dangerous driving behaviors such as sudden braking or sudden acceleration. Furthermore, the driving evaluation unit may evaluate the user's driving skills and provide feedback on areas for improvement. For example, it may provide the user with advice on safe driving based on data obtained while driving. This allows the user to improve their driving skills and achieve safe driving.

[0100] The drunk driving prevention system can further include a driving history learning unit that learns the user's driving history and predicts individual driving patterns. For example, if a user has a tendency to drink and drive at a specific time or location based on past driving data, a warning is displayed before the user drinks and drives. Furthermore, the driving history learning unit can learn the user's driving patterns and predict the next driving risk. For example, if a user has a tendency to drink and drive on weekends, a warning is displayed before the weekend. This allows the user to understand their own driving patterns and prevent drunk driving.

[0101] The drunk driving prevention system can further include a maintenance management unit that monitors the user's vehicle maintenance status. For example, it can periodically check the vehicle's oil level and tire pressure and notify the user when maintenance is required. Furthermore, the maintenance management unit can learn the vehicle's past maintenance history and predict when the next maintenance is due. For example, it can predict when an oil change is required based on past data and notify the user. This allows the user to always be aware of the vehicle's maintenance status and maintain safe driving.

[0102] The drunk driving prevention system may further include an environmental sound management unit that monitors the ambient sounds while the user is driving and provides appropriate music or voice messages. For example, it may measure the noise level while driving and play relaxing music. Furthermore, the environmental sound management unit may estimate the user's stress level while driving and provide advice on how to relax. For example, it may provide the user with a voice message encouraging them to take deep breaths based on the data obtained while driving. This allows the user to reduce stress while driving and achieve safer driving.

[0103] The drunk driving prevention system may further include an emotion management unit that estimates the user's emotions and provides advice to reduce stress while driving based on the estimated emotions. For example, if the user is feeling stressed, the emotion management unit may play relaxing music. Furthermore, the emotion management unit may monitor the user's emotional state in real time and provide appropriate feedback. For example, if the user is feeling anxious, the emotion management unit may provide a reassuring voice message. This allows the user to reduce stress while driving and achieve safe driving.

[0104] The drunk driving prevention system may further include an attention management unit that estimates the user's emotions and provides advice to improve attention while driving based on the estimated emotions. For example, if the user is tired, music to improve attention may be played. Furthermore, the attention management unit may monitor the user's emotional state in real time and provide appropriate feedback. For example, if the user is not concentrating, a voice message to improve concentration may be provided. This allows the user to improve their attention while driving and achieve safe driving.

[0105] The drunk driving prevention system may further include a risk assessment unit that estimates the user's emotions and assesses the risks involved while driving based on the estimated emotions. For example, if the user is feeling angry, the driving risk increases, and the system provides advice to reduce the risks. Furthermore, the risk assessment unit may monitor the user's emotional state in real time and provide appropriate feedback. For example, if the user is feeling impatient, the system provides a voice message to calm down. This allows the user to reduce risks while driving and achieve safe driving.

[0106] The drunk driving prevention system may further include a performance management unit that estimates the user's emotions and provides advice to improve driving performance based on the estimated emotions. For example, if the user feels tired, music to improve performance may be played. Furthermore, the performance management unit may monitor the user's emotional state in real time and provide appropriate feedback. For example, if the user is lacking concentration, a voice message to improve concentration may be provided. This allows the user to improve their driving performance and achieve safe driving.

[0107] The drunk driving prevention system may further include a relaxation management unit that estimates the user's emotions and provides advice to promote relaxation while driving based on the estimated emotions. For example, if the user feels tense, the relaxation management unit may play relaxing music. Furthermore, the relaxation management unit may monitor the user's emotional state in real time and provide appropriate feedback. For example, if the user feels anxious, the relaxation management unit may provide a reassuring voice message. This may promote relaxation while driving and enable the user to drive safely.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The portable odor sensor measures the alcohol concentration in the driver's breath and displays the result. For example, the driver blows into the sensor, which detects the alcohol concentration and displays the result. Step 2: The AI ​​voice recognition technology recognizes the voice command and starts the alcohol consumption check. For example, it recognizes the voice command "Start alcohol consumption check" and starts the alcohol consumption check. Step 3: The visual and audio information provider provides the user with information about drunk driving issues through visual and audio channels. For example, it may provide a warning message such as, "Drinking and driving is dangerous. It increases the risk of accidents." It also visually displays warning messages and simulated accident images on the smartphone screen. Step 4: The substitute driving arrangement unit suggests arranging a substitute driving service if the user is found to be drunk. For example, it provides a voice message such as "Would you like to arrange a substitute driving service?" If the user answers "Yes," it searches for a substitute driving service and makes the arrangement. Step 5: The public transport search unit suggests searching for public transport if the user is drunk. For example, it provides a voice message such as "Do you want to search for the nearest bus stop or station?" If the user answers "yes," it searches for the nearest public transport and suggests available routes.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] 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]

[0177] 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 portable odor sensor, AI voice recognition technology and The Visual and Hearing Services Department and The substitute driving arrangement department, a public transportation search unit; The portable odor sensor comprises: Measures the alcohol concentration in the driver's breath and displays the results A system characterized by:

2. The portable odor sensor comprises: It is incorporated into the air conditioning system and detects the alcohol concentration in the air inside the vehicle.

2. The system of claim 1.

3. The AI ​​speech recognition technology is Inferring the user's emotions from the tone and speed of their voice and providing appropriate feedback 2. The system of claim 1.

4. The visual and audio providing unit Visually simulates the risks of drunk driving and provides users with images that specifically show the dangers 2. The system of claim 1.

5. The substitute driving arrangement unit Based on evaluation data of the substitute driving service, the system proposes the most suitable substitute driving service to the user.

2. The system of claim 1.

6. The public transportation search unit Real-time monitoring of public transport operation status and suggesting optimal routes 2. The system of claim 1.

7. The portable odor sensor comprises: Analyzes the user's emotions when checking their sobriety and provides advice to reduce stress and anxiety 2. The system of claim 1.

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