System

Abstract

An object of a system according to an embodiment is to enable a hearing-impaired person to recognize a siren sound of an emergency vehicle during driving and to drive safely.SOLUTION: A system according to an embodiment includes a siren sound analysis unit, a sign language video display unit, and an in-vehicle system cooperation unit. The siren sound analysis unit analyzes a siren sound. The sign language animation display part displays the sign language animation on the basis of the information of the siren sound analyzed by the siren sound analysis part. The on-vehicle system cooperation unit transmits information of the sign language animation displayed by the sign language animation display unit to the on-vehicle system.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] With conventional technology, it is difficult for hearing-impaired people to recognize the siren sounds of emergency vehicles while driving, which may hinder safe driving.

[0005] The system according to the embodiment aims to enable hearing-impaired people to recognize the siren sounds of emergency vehicles while driving and drive safely. [Means for solving the problem]

[0006] The system according to the embodiment includes a siren sound analysis unit, a sign language video display unit, and an in-vehicle system linkage unit. The siren sound analysis unit analyzes a siren sound. The sign language video display unit displays a sign language video based on information about the siren sound analyzed by the siren sound analysis unit. The in-vehicle system linkage unit transmits information about the sign language video displayed by the sign language video display unit to the in-vehicle system. [Effects of the Invention]

[0007] The system according to the embodiment enables a hearing-impaired person to recognize the siren sound of an emergency vehicle while driving and drive safely. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The driving assistance system according to an embodiment of the present invention is a system that assists hearing-impaired people in driving automobiles. This driving assistance system uses the ChatGTP smartphone app to analyze siren sounds and, based on the results, displays a sign language video for driving guidance on the head-up display. This allows hearing-impaired people to visually grasp the surrounding situation and drive safely.

[0029] A driving assistance system according to an embodiment includes a siren sound analysis unit, a sign language video display unit, and an in-vehicle system linkage unit. The siren sound analysis unit analyzes siren sounds. For example, the siren sound analysis unit detects siren sounds from an ambulance, police vehicle, or the like, and identifies the type and direction of the siren. The siren sound analysis unit also analyzes siren sounds using a generation AI. For example, the generation AI analyzes the frequency and intensity of the siren sound to evaluate the level of urgency. The sign language video display unit displays a sign language video based on the siren sound information analyzed by the siren sound analysis unit. For example, the sign language video display unit displays a sign language video such as, "An ambulance is approaching from the rear right. Please move to the right and give way." The sign language video display unit also generates content for the sign language video using the generation AI. For example, the generation AI generates appropriate sign language video based on the siren sound information. The in-vehicle system linkage unit transmits information about the sign language video displayed by the sign language video display unit to the in-vehicle system. For example, the in-vehicle system linking unit works in cooperation with Apple CarPlay or Android Auto to display sign language video information on the head-up display. The in-vehicle system linking unit also uses a generation AI to link with the in-vehicle system. For example, the generation AI transmits information analyzed by a smartphone app to the in-vehicle system, and the in-vehicle system displays sign language video based on that information. This allows the driving assistance system according to the embodiment to enable hearing-impaired persons to visually grasp their surroundings and drive safely. For example, the driving assistance system analyzes siren sounds and displays sign language video based on that information, allowing hearing-impaired persons to take prompt and appropriate driving actions. Furthermore, since the driving assistance system works in cooperation with the in-vehicle system, information analyzed by the smartphone app is transmitted to the in-vehicle system and displayed on the head-up display.

[0030] The siren sound analysis unit can evaluate the level of urgency by analyzing not only the type of siren sound but also the intensity and frequency changes of the sound. For example, the siren sound analysis unit not only identifies the type of siren sound using the generation AI, but also analyzes the intensity and frequency changes of the sound to evaluate the level of urgency. For example, if the intensity of the sound increases rapidly, it will determine that the level of urgency is high. The siren sound analysis unit also analyzes the frequency changes of the siren sound to evaluate the level of urgency. For example, if the frequency is high, it will determine that the level of urgency is high and prompt the driver to take immediate action. The siren sound analysis unit also analyzes the intensity and frequency changes of the siren sound in combination to evaluate the level of urgency. For example, if the intensity of the sound is high and the frequency is changing rapidly, it will determine that the level of urgency is very high. This allows for quick determination of high-urgency situations.

[0031] The siren sound analysis unit can simultaneously analyze the siren sound and surrounding environmental sounds to make a comprehensive judgment of the situation. For example, the generation AI analyzes the siren sound as well as surrounding traffic sounds to make a comprehensive judgment of the situation. For example, if there is heavy traffic, the siren sound analysis unit urges the driver to take early action. In addition, the siren sound analysis unit analyzes human voices and other environmental sounds at the same time as analyzing the siren sound to make a judgment of the situation. For example, if there are many human voices, it determines that there are many pedestrians and urges the driver to be careful. The siren sound analysis unit also analyzes the surrounding environmental sounds and evaluates the urgency of the siren sound. For example, if a siren sound is heard in a quiet environment, it determines that the urgency is high. This allows for a more accurate understanding of the surrounding situation.

[0032] The siren sound analysis unit can analyze not only sirens but also other warning sounds. For example, the generation AI may analyze not only sirens but also horn sounds. For example, if horn sounds are heard frequently, the siren sound analysis unit may warn the driver. In addition to analyzing sirens, the siren sound analysis unit may also analyze railroad crossing warning sounds. For example, if a railroad crossing warning sound is heard, the siren sound analysis unit may warn the driver to stop. The siren sound analysis unit may also analyze other warning sounds (for example, emergency bells and fire alarm sounds) and provide appropriate instructions to the driver. For example, if an emergency bell is heard, the siren sound analysis unit may warn the driver to evacuate. This allows the unit to respond to a variety of warning sounds and provide appropriate instructions to the driver.

[0033] The siren sound analysis unit can build a system that shares the analysis results of the siren sound with other vehicles and pedestrians, and notifies them of the surrounding traffic conditions in real time. The siren sound analysis unit, for example, shares the analysis results of the siren sound with other vehicles, and builds a system that notifies them of the surrounding traffic conditions in real time. For example, it notifies other vehicles that an ambulance is approaching. The siren sound analysis unit also shares the analysis results of the siren sound with pedestrians, and builds a system that notifies them of the surrounding traffic conditions in real time. For example, it notifies pedestrians that a police vehicle is approaching. The siren sound analysis unit also shares the analysis results of the siren sound with other vehicles and pedestrians, and builds a system to prevent traffic accidents. For example, it notifies other vehicles and pedestrians of the direction from which a siren sound is heard. This allows the surrounding traffic conditions to be shared in real time, improving safety.

[0034] The sign language video display unit can simultaneously display simple text messages and icons in addition to the sign language video to help understand the information. The sign language video display unit, for example, simultaneously displays simple text messages in addition to the sign language video to help understand the information. For example, it displays a text message such as "An ambulance is approaching from the rear right." The sign language video display unit can also display icons along with the sign language video to help understand the information. For example, it can display an ambulance icon to provide the driver with visual information. The sign language video display unit can also display a combination of sign language video, text messages, and icons to help understand the information. For example, it can display a text message and an ambulance icon along with the sign language video. This helps understand the information and allows the driver to respond more quickly.

[0035] The sign language video display unit can optimize the timing and position of displaying the sign language video based on the driver's gaze tracking data. The sign language video display unit optimizes the timing of displaying the sign language video based on, for example, the driver's gaze tracking data. For example, it displays the sign language video when the driver is looking ahead. The sign language video display unit also uses the gaze tracking data to optimize the display position of the sign language video. For example, it displays the sign language video in a position that is easy for the driver to see. The sign language video display unit also optimizes the timing and position of displaying the sign language video based on the gaze tracking data. For example, it displays the sign language video at the timing when the driver moves their gaze. This makes it easier for the driver to see the sign language video and helps them understand the information.

[0036] The sign language video display unit can display not only sign language video but also other languages ​​and visual instructions. For example, the sign language video display unit displays instructions in other languages ​​in addition to sign language video. For example, instructions in English or Spanish can be displayed to help the driver understand the information. The sign language video display unit also displays visual instructions (e.g., arrows) along with the sign language video. For example, an arrow indicating the direction of travel can be displayed to provide visual information to the driver. The sign language video display unit can also display a combination of sign language video, other languages, and visual instructions to help the driver understand the information. For example, an English text message and arrows can be displayed along with the sign language video. This provides a variety of information to help the driver understand.

[0037] The sign language video display unit can display the driver's health condition on the head-up display and issue a warning if an abnormality is detected. The sign language video display unit, for example, displays the driver's heart rate on the head-up display and issues a warning if an abnormality is detected. For example, if the heart rate is too high, it urges the driver to take a break. The sign language video display unit also displays the driver's blood pressure on the head-up display and issues a warning if an abnormality is detected. For example, if the blood pressure is too high, it urges the driver to see a doctor. The sign language video display unit also displays the driver's health condition (e.g., heart rate or blood pressure) on the head-up display and issues a warning if an abnormality is detected. For example, if the heart rate or blood pressure indicates an abnormal value, it urges the driver to take caution. In this way, safety is improved by monitoring the driver's health condition and issuing a warning if an abnormality occurs.

[0038] The in-vehicle system cooperation unit can strengthen cooperation with the in-vehicle system and add a function to automatically adjust the vehicle's operation. For example, the in-vehicle system cooperation unit strengthens cooperation with the in-vehicle system and adds a function to automatically adjust the vehicle's braking operation. For example, automatically applying the brakes in an emergency. The in-vehicle system cooperation unit also strengthens cooperation with the in-vehicle system and adds a function to automatically adjust steering operation. For example, automatically operating the steering wheel in an emergency avoidance. The in-vehicle system cooperation unit also strengthens cooperation with the in-vehicle system and adds a function to automatically adjust the overall vehicle operation. For example, simultaneously operating the brakes and steering wheel in an emergency. This automatically adjusts the vehicle's operation and improves safety.

[0039] The in-vehicle system cooperation unit enables the in-vehicle system to communicate with other vehicles and infrastructure, collecting a wider range of information to provide driving assistance. For example, the in-vehicle system cooperation unit allows the in-vehicle system to communicate with other vehicles and share traffic information. For example, if a vehicle in front suddenly brakes, the in-vehicle system cooperation unit notifies following vehicles of that information. The in-vehicle system also communicates with traffic lights to grasp changes in signals in real time. For example, the in-vehicle system cooperation unit notifies the driver before the signal turns red. The in-vehicle system also communicates with road signs to collect sign information in real time. For example, the in-vehicle system cooperation unit notifies the driver of speed limit sign information. This allows the in-vehicle system to collect a wider range of information and enhance driving assistance.

[0040] The in-vehicle system linking unit can expand the link with the in-vehicle system and link with smart home devices to improve driver comfort. The in-vehicle system linking unit, for example, links the in-vehicle system with smart home devices to improve driver comfort. For example, the in-vehicle system linking unit automatically turns on the lights when the vehicle approaches home. The in-vehicle system linking unit also links the in-vehicle system with the air conditioner to adjust the temperature inside the vehicle before the driver gets into the vehicle. For example, the air conditioner is automatically turned on when the driver approaches the vehicle. The in-vehicle system linking unit also links the in-vehicle system with smart home devices to improve driver comfort. For example, the doors are automatically unlocked when the driver arrives home. This improves driver comfort.

[0041] The in-vehicle system cooperation unit enables the in-vehicle system to learn the driving history and preferences of the driver and provide individually customized driving assistance. For example, the in-vehicle system cooperation unit learns the driving history of the driver and provides individually customized driving assistance. For example, it suggests a navigation route according to the driver's preferences. The in-vehicle system cooperation unit also learns the driver's preferences and provides individually customized driving assistance. For example, it automatically plays music that the driver likes. The in-vehicle system also learns the driver's driving history and preferences and provides individually customized driving assistance. For example, it gives priority to guidance to parking lots that the driver frequently uses. This makes it possible to provide driving assistance according to the driver's preferences.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The driving assistance system can further include a health management unit that monitors the driver's health condition. For example, the health management unit can measure the driver's heart rate and blood pressure in real time and issue a warning if an abnormality is detected. If the driver's heart rate rises suddenly, a message urging the driver to take a break can be displayed. The health management unit can also analyze the driver's stress level and provide relaxing music if the stress level is high. This allows the system to constantly monitor the driver's health condition and support safe driving.

[0044] The driving assistance system can further include an eye-tracking unit that tracks the driver's gaze. The eye-tracking unit analyzes the driver's gaze movements in real time and issues a warning if the driver's attention is distracted. For example, if the driver is not looking ahead, a message urging the driver to return their gaze to the road ahead can be displayed. The eye-tracking unit can also optimize the display position of the sign language video based on the driver's gaze movements. This makes it easier for the driver to view the sign language video and helps them understand the information.

[0045] The driving assistance system can further include a learning unit that learns the driver's driving history. The learning unit analyzes the driver's past driving data and provides individually customized driving assistance. For example, it can prioritize routes that the driver frequently uses. The learning unit can also learn the driver's preferences and automatically adjust music and navigation settings accordingly. This makes it possible to provide a comfortable driving environment that suits the driver's preferences.

[0046] The driving assistance system can also be equipped with a health management unit that monitors the driver's health condition. The health management unit measures the driver's heart rate and blood pressure in real time and issues a warning if an abnormality is detected. For example, if the driver's heart rate rises suddenly, a message urging the driver to take a break can be displayed. The health management unit can also analyze the driver's stress level and provide relaxing music if the stress level is high. This allows the system to constantly monitor the driver's health condition and support safe driving.

[0047] The driving assistance system can further include an eye-tracking unit that tracks the driver's gaze. The eye-tracking unit analyzes the driver's gaze movements in real time and issues a warning if the driver's attention is distracted. For example, if the driver is not looking ahead, a message urging the driver to return their gaze to the road ahead can be displayed. The eye-tracking unit can also optimize the display position of the sign language video based on the driver's gaze movements. This makes it easier for the driver to view the sign language video and helps them understand the information.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The siren sound analysis unit analyzes the siren sound. For example, the siren sound analysis unit detects the siren sound of an ambulance or police vehicle and identifies its type and direction. The siren sound analysis unit also uses the generation AI to analyze the siren sound. For example, the generation AI analyzes the frequency and intensity of the siren sound and evaluates the level of urgency. Step 2: The sign language video display unit displays a sign language video based on the siren sound information analyzed by the siren sound analysis unit. For example, the sign language video display unit displays a sign language video such as, "An ambulance is approaching from the rear right. Please move to the right and give way." The sign language video display unit also generates the content of the sign language video using a generation AI. For example, the generation AI generates an appropriate sign language video based on the siren sound information. Step 3: The in-vehicle system linking unit transmits the sign language video information displayed by the sign language video display unit to the in-vehicle system. For example, the in-vehicle system linking unit works in conjunction with Apple CarPlay or Android Auto to display the sign language video information on the head-up display. The in-vehicle system linking unit also uses a generation AI to link with the in-vehicle system. For example, the generation AI transmits information analyzed by the smartphone app to the in-vehicle system, and the in-vehicle system displays the sign language video based on that information.

[0050] (Example 2) The driving assistance system according to an embodiment of the present invention is a system that assists hearing-impaired people in driving automobiles. This driving assistance system uses the ChatGTP smartphone app to analyze siren sounds and, based on the results, displays a sign language video for driving guidance on the head-up display. This allows hearing-impaired people to visually grasp the surrounding situation and drive safely.

[0051] A driving assistance system according to an embodiment includes a siren sound analysis unit, a sign language video display unit, and an in-vehicle system linkage unit. The siren sound analysis unit analyzes siren sounds. For example, the siren sound analysis unit detects siren sounds from an ambulance, police vehicle, or the like, and identifies the type and direction of the siren. The siren sound analysis unit also analyzes siren sounds using a generation AI. For example, the generation AI analyzes the frequency and intensity of the siren sound to evaluate the level of urgency. The sign language video display unit displays a sign language video based on the siren sound information analyzed by the siren sound analysis unit. For example, the sign language video display unit displays a sign language video such as, "An ambulance is approaching from the rear right. Please move to the right and give way." The sign language video display unit also generates content for the sign language video using the generation AI. For example, the generation AI generates appropriate sign language video based on the siren sound information. The in-vehicle system linkage unit transmits information about the sign language video displayed by the sign language video display unit to the in-vehicle system. For example, the in-vehicle system linking unit works in cooperation with Apple CarPlay or Android Auto to display sign language video information on the head-up display. The in-vehicle system linking unit also uses a generation AI to link with the in-vehicle system. For example, the generation AI transmits information analyzed by a smartphone app to the in-vehicle system, and the in-vehicle system displays sign language video based on that information. This allows the driving assistance system according to the embodiment to enable hearing-impaired persons to visually grasp their surroundings and drive safely. For example, the driving assistance system analyzes siren sounds and displays sign language video based on that information, allowing hearing-impaired persons to take prompt and appropriate driving actions. Furthermore, since the driving assistance system works in cooperation with the in-vehicle system, information analyzed by the smartphone app is transmitted to the in-vehicle system and displayed on the head-up display.

[0052] The siren sound analysis unit can evaluate the level of urgency by analyzing not only the type of siren sound but also the intensity and frequency changes of the sound. For example, the siren sound analysis unit not only identifies the type of siren sound using the generation AI, but also analyzes the intensity and frequency changes of the sound to evaluate the level of urgency. For example, if the intensity of the sound increases rapidly, it will determine that the level of urgency is high. The siren sound analysis unit also analyzes the frequency changes of the siren sound to evaluate the level of urgency. For example, if the frequency is high, it will determine that the level of urgency is high and prompt the driver to take immediate action. The siren sound analysis unit also analyzes the intensity and frequency changes of the siren sound in combination to evaluate the level of urgency. For example, if the intensity of the sound is high and the frequency is changing rapidly, it will determine that the level of urgency is very high. This allows for quick determination of high-urgency situations.

[0053] The siren sound analysis unit can simultaneously analyze the siren sound and surrounding environmental sounds to make a comprehensive judgment of the situation. For example, the generation AI analyzes the siren sound as well as surrounding traffic sounds to make a comprehensive judgment of the situation. For example, if there is heavy traffic, the siren sound analysis unit urges the driver to take early action. In addition, the siren sound analysis unit analyzes human voices and other environmental sounds at the same time as analyzing the siren sound to make a judgment of the situation. For example, if there are many human voices, it determines that there are many pedestrians and urges the driver to be careful. The siren sound analysis unit also analyzes the surrounding environmental sounds and evaluates the urgency of the siren sound. For example, if a siren sound is heard in a quiet environment, it determines that the urgency is high. This allows for a more accurate understanding of the surrounding situation.

[0054] The siren sound analysis unit can analyze the driver's stress level using the emotion estimation function and provide more detailed driving instructions if the stress level is high. The siren sound analysis unit can, for example, analyze the driver's stress level using the emotion estimation function and provide more detailed driving instructions if the stress level is high. For example, if the driver is nervous, it can display specific instructions. The siren sound analysis unit can also analyze the driver's stress level in real time and strengthen driving instructions if the stress level is high. For example, if the driver is impatient, it can display a message urging the driver to calm down. The siren sound analysis unit can also evaluate the driver's stress level using the emotion estimation function and strengthen driving assistance if the stress level is high. For example, if the driver is tired, it can display a message urging the driver to take a break. This can reduce the driver's stress and support safe driving.

[0055] The siren sound analysis unit can analyze not only sirens but also other warning sounds. For example, the generation AI may analyze not only sirens but also horn sounds. For example, if horn sounds are heard frequently, the siren sound analysis unit may warn the driver. In addition to analyzing sirens, the siren sound analysis unit may also analyze railroad crossing warning sounds. For example, if a railroad crossing warning sound is heard, the siren sound analysis unit may warn the driver to stop. The siren sound analysis unit may also analyze other warning sounds (for example, emergency bells and fire alarm sounds) and provide appropriate instructions to the driver. For example, if an emergency bell is heard, the siren sound analysis unit may warn the driver to evacuate. This allows the unit to respond to a variety of warning sounds and provide appropriate instructions to the driver.

[0056] The siren sound analysis unit can build a system that shares the analysis results of the siren sound with other vehicles and pedestrians, and notifies them of the surrounding traffic conditions in real time. The siren sound analysis unit, for example, shares the analysis results of the siren sound with other vehicles, and builds a system that notifies them of the surrounding traffic conditions in real time. For example, it notifies other vehicles that an ambulance is approaching. The siren sound analysis unit also shares the analysis results of the siren sound with pedestrians, and builds a system that notifies them of the surrounding traffic conditions in real time. For example, it notifies pedestrians that a police vehicle is approaching. The siren sound analysis unit also shares the analysis results of the siren sound with other vehicles and pedestrians, and builds a system to prevent traffic accidents. For example, it notifies other vehicles and pedestrians of the direction from which a siren sound is heard. This allows the surrounding traffic conditions to be shared in real time, improving safety.

[0057] The siren sound analysis unit can use the emotion estimation function to analyze the emotional response of the driver when he or she hears a siren sound, and provide music or a message to help the driver relax at an appropriate time. The siren sound analysis unit, for example, uses the emotion estimation function to analyze the emotional response of the driver when he or she hears a siren sound, and provide music to help the driver relax. For example, if the driver is nervous, relaxing music is played. The siren sound analysis unit also analyzes the emotional response of the driver, and provides a message to help the driver relax at an appropriate time. For example, if the driver is anxious, a message encouraging the driver to calm down is displayed. The siren sound analysis unit also uses the emotion estimation function to analyze the emotional response of the driver in real time, and provides music or a message to help the driver relax. For example, if the driver is feeling stressed, a relaxing message is displayed. This reduces the driver's stress and supports safe driving.

[0058] The sign language video display unit can simultaneously display simple text messages and icons in addition to the sign language video to help understand the information. The sign language video display unit, for example, simultaneously displays simple text messages in addition to the sign language video to help understand the information. For example, it displays a text message such as "An ambulance is approaching from the rear right." The sign language video display unit can also display icons along with the sign language video to help understand the information. For example, it can display an ambulance icon to provide the driver with visual information. The sign language video display unit can also display a combination of sign language video, text messages, and icons to help understand the information. For example, it can display a text message and an ambulance icon along with the sign language video. This helps understand the information and allows the driver to respond more quickly.

[0059] The sign language video display unit can optimize the timing and position of displaying the sign language video based on the driver's gaze tracking data. The sign language video display unit optimizes the timing of displaying the sign language video based on, for example, the driver's gaze tracking data. For example, it displays the sign language video when the driver is looking ahead. The sign language video display unit also uses the gaze tracking data to optimize the display position of the sign language video. For example, it displays the sign language video in a position that is easy for the driver to see. The sign language video display unit also optimizes the timing and position of displaying the sign language video based on the gaze tracking data. For example, it displays the sign language video at the timing when the driver moves their gaze. This makes it easier for the driver to see the sign language video and helps them understand the information.

[0060] The sign language video display unit can use the emotion estimation function to adjust the content and expression of the sign language video according to the emotional state of the driver. The sign language video display unit, for example, uses the emotion estimation function to adjust the content of the sign language video according to the emotional state of the driver. For example, if the driver is nervous, it displays a sign language video that is calming. The sign language video display unit also analyzes the emotional state of the driver and adjusts the expression of the sign language video. For example, if the driver is impatient, it displays a slow sign language video. The sign language video display unit also uses the emotion estimation function to adjust the content and expression of the sign language video according to the emotional state of the driver. For example, if the driver is feeling stressed, it displays a sign language video that is relaxing. This makes it possible to provide appropriate information according to the emotional state of the driver.

[0061] The sign language video display unit can display not only sign language video but also other languages ​​and visual instructions. For example, the sign language video display unit displays instructions in other languages ​​in addition to sign language video. For example, instructions in English or Spanish can be displayed to help the driver understand the information. The sign language video display unit also displays visual instructions (e.g., arrows) along with the sign language video. For example, an arrow indicating the direction of travel can be displayed to provide visual information to the driver. The sign language video display unit can also display a combination of sign language video, other languages, and visual instructions to help the driver understand the information. For example, an English text message and arrows can be displayed along with the sign language video. This provides a variety of information to help the driver understand.

[0062] The sign language video display unit can display the driver's health condition on the head-up display and issue a warning if an abnormality is detected. The sign language video display unit, for example, displays the driver's heart rate on the head-up display and issues a warning if an abnormality is detected. For example, if the heart rate is too high, it urges the driver to take a break. The sign language video display unit also displays the driver's blood pressure on the head-up display and issues a warning if an abnormality is detected. For example, if the blood pressure is too high, it urges the driver to see a doctor. The sign language video display unit also displays the driver's health condition (e.g., heart rate or blood pressure) on the head-up display and issues a warning if an abnormality is detected. For example, if the heart rate or blood pressure indicates an abnormal value, it urges the driver to take caution. In this way, safety is improved by monitoring the driver's health condition and issuing a warning if an abnormality occurs.

[0063] The sign language video display unit can use the emotion estimation function to analyze the emotional response of the driver when watching the sign language video and adjust the content of the video in real time. The sign language video display unit, for example, uses the emotion estimation function to analyze the emotional response of the driver when watching the sign language video and adjust the content of the video in real time. For example, if the driver is nervous, the content is changed to something that will help them relax. The sign language video display unit also analyzes the driver's emotional response in real time and adjusts the content of the sign language video. For example, if the driver is impatient, the content is changed to something that will slow them down. The sign language video display unit also uses the emotion estimation function to analyze the driver's emotional response and adjust the content of the sign language video in real time. For example, if the driver is feeling stressed, the content is changed to something that will help them relax. This makes it possible to provide appropriate information according to the driver's emotional response.

[0064] The in-vehicle system cooperation unit can strengthen cooperation with the in-vehicle system and add a function to automatically adjust the vehicle's operation. For example, the in-vehicle system cooperation unit strengthens cooperation with the in-vehicle system and adds a function to automatically adjust the vehicle's braking operation. For example, automatically applying the brakes in an emergency. The in-vehicle system cooperation unit also strengthens cooperation with the in-vehicle system and adds a function to automatically adjust steering operation. For example, automatically operating the steering wheel in an emergency avoidance. The in-vehicle system cooperation unit also strengthens cooperation with the in-vehicle system and adds a function to automatically adjust the overall vehicle operation. For example, simultaneously operating the brakes and steering wheel in an emergency. This automatically adjusts the vehicle's operation and improves safety.

[0065] The in-vehicle system cooperation unit enables the in-vehicle system to communicate with other vehicles and infrastructure, collecting a wider range of information to provide driving assistance. For example, the in-vehicle system cooperation unit allows the in-vehicle system to communicate with other vehicles and share traffic information. For example, if a vehicle in front suddenly brakes, the in-vehicle system cooperation unit notifies following vehicles of that information. The in-vehicle system also communicates with traffic lights to grasp changes in signals in real time. For example, the in-vehicle system cooperation unit notifies the driver before the signal turns red. The in-vehicle system also communicates with road signs to collect sign information in real time. For example, the in-vehicle system cooperation unit notifies the driver of speed limit sign information. This allows the in-vehicle system to collect a wider range of information and enhance driving assistance.

[0066] The in-vehicle system cooperation unit can customize the interface and notification method of the in-vehicle system according to the emotional state of the driver using the emotion estimation function. The in-vehicle system cooperation unit, for example, uses the emotion estimation function to customize the interface of the in-vehicle system according to the emotional state of the driver. For example, if the driver is nervous, a simple interface is displayed. The in-vehicle system cooperation unit also analyzes the emotional state of the driver and customizes the notification method. For example, if the driver is impatient, a calming voice notification is provided. The in-vehicle system cooperation unit also uses the emotion estimation function to customize the interface and notification method of the in-vehicle system according to the emotional state of the driver. For example, if the driver is feeling stressed, an interface and notification method that allows the driver to relax are provided. This makes it possible to provide appropriate information according to the emotional state of the driver.

[0067] The in-vehicle system linking unit can expand the link with the in-vehicle system and link with smart home devices to improve driver comfort. The in-vehicle system linking unit, for example, links the in-vehicle system with smart home devices to improve driver comfort. For example, the in-vehicle system linking unit automatically turns on the lights when the vehicle approaches home. The in-vehicle system linking unit also links the in-vehicle system with the air conditioner to adjust the temperature inside the vehicle before the driver gets into the vehicle. For example, the air conditioner is automatically turned on when the driver approaches the vehicle. The in-vehicle system linking unit also links the in-vehicle system with smart home devices to improve driver comfort. For example, the doors are automatically unlocked when the driver arrives home. This improves driver comfort.

[0068] The in-vehicle system cooperation unit enables the in-vehicle system to learn the driving history and preferences of the driver and provide individually customized driving assistance. For example, the in-vehicle system cooperation unit learns the driving history of the driver and provides individually customized driving assistance. For example, it suggests a navigation route according to the driver's preferences. The in-vehicle system cooperation unit also learns the driver's preferences and provides individually customized driving assistance. For example, it automatically plays music that the driver likes. The in-vehicle system also learns the driver's driving history and preferences and provides individually customized driving assistance. For example, it gives priority to guidance to parking lots that the driver frequently uses. This makes it possible to provide driving assistance according to the driver's preferences.

[0069] The in-vehicle system cooperation unit can automatically adjust the in-vehicle environment based on the emotional state of the driver using the emotion estimation function. For example, the in-vehicle system cooperation unit automatically adjusts the in-vehicle temperature based on the emotional state of the driver using the emotion estimation function. For example, if the driver is feeling stressed, the temperature is set to a comfortable level. The in-vehicle system cooperation unit also analyzes the emotional state of the driver and automatically adjusts the music in the vehicle. For example, if the driver wants to relax, relaxing music is played. The in-vehicle system cooperation unit also automatically adjusts the in-vehicle environment based on the emotional state of the driver using the emotion estimation function. For example, if the driver is tired, a refreshing environment is provided. This makes it possible to provide a comfortable in-vehicle environment according to the emotional state of the driver.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The driving assistance system can further include a health management unit that monitors the driver's health condition. For example, the health management unit can measure the driver's heart rate and blood pressure in real time and issue a warning if an abnormality is detected. If the driver's heart rate rises suddenly, a message urging the driver to take a break can be displayed. The health management unit can also analyze the driver's stress level and provide relaxing music if the stress level is high. This allows the system to constantly monitor the driver's health condition and support safe driving.

[0072] The driving assistance system can further include an eye-tracking unit that tracks the driver's gaze. The eye-tracking unit analyzes the driver's gaze movements in real time and issues a warning if the driver's attention is distracted. For example, if the driver is not looking ahead, a message urging the driver to return their gaze to the road ahead can be displayed. The eye-tracking unit can also optimize the display position of the sign language video based on the driver's gaze movements. This makes it easier for the driver to view the sign language video and helps them understand the information.

[0073] The driving assistance system can further include a learning unit that learns the driver's driving history. The learning unit analyzes the driver's past driving data and provides individually customized driving assistance. For example, it can prioritize routes that the driver frequently uses. The learning unit can also learn the driver's preferences and automatically adjust music and navigation settings accordingly. This makes it possible to provide a comfortable driving environment that suits the driver's preferences.

[0074] The driving assistance system may further include an environmental adjustment unit that automatically adjusts the in-vehicle environment based on the emotional state of the driver. The environmental adjustment unit analyzes the emotional state of the driver and automatically adjusts the temperature and music inside the vehicle. For example, if the driver is feeling stressed, the temperature can be set to a comfortable level and relaxing music can be played. Also, if the driver is tired, a refreshing environment can be provided. This makes it possible to provide a comfortable in-vehicle environment according to the emotional state of the driver.

[0075] The driving assistance system may further include an instruction adjustment unit that adjusts driving instructions based on the emotional state of the driver. The instruction adjustment unit analyzes the emotional state of the driver and provides appropriate driving instructions. For example, if the driver is nervous, specific instructions may be displayed, and if the driver is impatient, a message urging the driver to calm down may be displayed. Also, if the driver is tired, a message urging the driver to take a break may be displayed. In this way, appropriate driving instructions can be provided according to the emotional state of the driver.

[0076] The driving assistance system can further include a video adjustment unit that adjusts the content and expression of the sign language video based on the emotional state of the driver. The video adjustment unit analyzes the emotional state of the driver and adjusts the content and expression of the sign language video in real time. For example, if the driver is nervous, a calming sign language video can be displayed, and if the driver is impatient, a slower sign language video can be displayed. Also, if the driver is feeling stressed, a relaxing sign language video can be displayed. This makes it possible to provide appropriate information according to the driver's emotional state.

[0077] The driving assistance system can further include an interface adjustment unit that customizes the interface and notification method of the in-vehicle system based on the emotional state of the driver. The interface adjustment unit analyzes the emotional state of the driver and provides a simple interface and calming voice notification. For example, if the driver is nervous, a simple interface can be displayed, and if the driver is impatient, a calming voice notification can be provided. Also, if the driver is feeling stressed, a relaxing interface and notification method can be provided. This makes it possible to provide appropriate information according to the driver's emotional state.

[0078] The driving assistance system may further include an assistance enhancement unit that enhances driving assistance based on the driver's emotional state. The assistance enhancement unit analyzes the driver's emotional state and provides detailed driving instructions if the driver is under high stress. For example, if the driver is nervous, specific instructions may be displayed, and if the driver is impatient, a message urging the driver to calm down may be displayed. Also, if the driver is tired, a message urging the driver to take a break may be displayed. This reduces the driver's stress and supports safe driving.

[0079] The driving assistance system can also be equipped with a health management unit that monitors the driver's health condition. The health management unit measures the driver's heart rate and blood pressure in real time and issues a warning if an abnormality is detected. For example, if the driver's heart rate rises suddenly, a message urging the driver to take a break can be displayed. The health management unit can also analyze the driver's stress level and provide relaxing music if the stress level is high. This allows the system to constantly monitor the driver's health condition and support safe driving.

[0080] The driving assistance system can further include an eye-tracking unit that tracks the driver's gaze. The eye-tracking unit analyzes the driver's gaze movements in real time and issues a warning if the driver's attention is distracted. For example, if the driver is not looking ahead, a message urging the driver to return their gaze to the road ahead can be displayed. The eye-tracking unit can also optimize the display position of the sign language video based on the driver's gaze movements. This makes it easier for the driver to view the sign language video and helps them understand the information.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The siren sound analysis unit analyzes the siren sound. For example, the siren sound analysis unit detects the siren sound of an ambulance or police vehicle and identifies its type and direction. The siren sound analysis unit also uses the generation AI to analyze the siren sound. For example, the generation AI analyzes the frequency and intensity of the siren sound and evaluates the level of urgency. Step 2: The sign language video display unit displays a sign language video based on the siren sound information analyzed by the siren sound analysis unit. For example, the sign language video display unit displays a sign language video such as, "An ambulance is approaching from the rear right. Please move to the right and give way." The sign language video display unit also generates the content of the sign language video using a generation AI. For example, the generation AI generates an appropriate sign language video based on the siren sound information. Step 3: The in-vehicle system linking unit transmits the sign language video information displayed by the sign language video display unit to the in-vehicle system. For example, the in-vehicle system linking unit works in conjunction with Apple CarPlay or Android Auto to display the sign language video information on the head-up display. The in-vehicle system linking unit also uses a generation AI to link with the in-vehicle system. For example, the generation AI transmits information analyzed by the smartphone app to the in-vehicle system, and the in-vehicle system displays the sign language video based on that information.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 siren sound analysis unit that analyzes a siren sound; a sign language video display unit that displays a sign language video based on the information about the siren sound analyzed by the siren sound analysis unit; an in-vehicle system linking unit that transmits information about the sign language video displayed by the sign language video display unit to an in-vehicle system; A system characterized by:

2. The siren sound analysis unit Not only the type of siren sound, but also the intensity and frequency changes of the sound are analyzed to assess the urgency.

2. The system of claim 1.

3. The siren sound analysis unit In addition to analyzing the siren sound, the system also analyzes the surrounding environmental sounds to make a comprehensive judgment of the situation.

2. The system of claim 1.

4. The siren sound analysis unit Analyzes the driver's stress level and provides more detailed driving instructions if stress levels are high 2. The system of claim 1.

5. The siren sound analysis unit In addition to the siren sound, other warning sounds are also included in the analysis.

2. The system of claim 1.

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