System

Abstract

An object of a system according to an embodiment is to automatically detect a dangerous situation in a vehicle and make an emergency call.SOLUTION: A system according to an embodiment includes an in-vehicle environment monitoring unit, a dangerous situation determination unit, and an emergency notification unit. The in-vehicle environment monitoring unit monitors an in-vehicle environment. The dangerous situation determination unit determines a dangerous situation by analyzing the data collected by the in-vehicle environment monitoring unit. The emergency notification unit makes an emergency notification based on the dangerous situation determined by the dangerous situation determination unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to quickly detect a dangerous situation when a child is left unattended in a vehicle and to respond appropriately.

[0005] The system according to the embodiment aims to automatically detect a dangerous situation inside a vehicle and make an emergency call. [Means for solving the problem]

[0006] The system according to the embodiment includes an in-vehicle environment monitoring unit, a dangerous situation determination unit, and an emergency notification unit. The in-vehicle environment monitoring unit monitors the in-vehicle environment. The dangerous situation determination unit analyzes data collected by the in-vehicle environment monitoring unit to determine a dangerous situation. The emergency notification unit makes an emergency notification based on the dangerous situation determined by the dangerous situation determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically detect dangerous situations inside a vehicle and make an emergency call. [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 automatic emergency call system according to an embodiment of the present invention is a system that automatically makes an emergency call using AI in order to prevent fatal accidents caused by children being left in cars. This system monitors the in-car environment, determines dangerous situations, and makes an emergency call. As a result, the automatic emergency call system can prevent fatal accidents caused by children being left in cars.

[0029] An automatic emergency notification system according to an embodiment includes an in-vehicle environment monitoring unit, a dangerous situation determination unit, and an emergency notification unit. The in-vehicle environment monitoring unit monitors the in-vehicle environment. For example, it collects data such as temperature, humidity, carbon dioxide concentration, voice, and movement inside the vehicle using sensors. The in-vehicle environment monitoring unit also collects this data in real time and converts it into a format that is easy for AI to analyze. The dangerous situation determination unit analyzes the data collected by the in-vehicle environment monitoring unit to determine a dangerous situation. For example, it issues a notification when multiple dangerous situations are met, such as when the temperature inside the vehicle exceeds a certain threshold, when the carbon dioxide concentration becomes high, or when a child does not move for a certain period of time. The emergency notification unit issues an emergency notification based on the dangerous situation determined by the dangerous situation determination unit. For example, the notification destination may be a parent, guardian, police, or emergency service, and the content of the notification may include vehicle location information and the situation inside the vehicle. As a result, the automatic emergency notification system according to an embodiment can prevent fatal accidents caused by children being left in a vehicle.

[0030] The in-vehicle environment monitoring unit can be equipped with sensors that monitor temperature, humidity, carbon dioxide concentration, heart rate, and respiratory rate. For example, sensors installed inside the vehicle monitor temperature, humidity, carbon dioxide concentration, as well as heart rate and respiratory rate in real time. This allows for a detailed understanding of the child's health condition and immediate response if an abnormality occurs. Non-contact vital signs sensors are also installed inside the vehicle to monitor heart rate and respiratory rate. This allows for rapid detection of changes in the child's health condition and, if necessary, makes an emergency call. Furthermore, sensors that monitor heart rate and respiratory rate in addition to temperature, humidity, and carbon dioxide concentration inside the vehicle are integrated, and AI analyzes this data to evaluate the child's health condition. If an abnormality is detected, an emergency call is made immediately. This allows for a more detailed understanding of the child's health condition.

[0031] The in-car environment monitoring unit can analyze audio data and detect crying and abnormal sounds. For example, the in-car environment monitoring unit uses microphones installed in the car to collect audio data, and AI analyzes it for crying and other abnormal sounds. This allows for early detection of dangerous situations and, if necessary, makes an emergency call. In addition, using audio analysis technology, a system is built that detects crying and other abnormal sounds from in-car audio data in real time. If an abnormality is detected, an emergency call is made immediately. Furthermore, in order to analyze in-car audio data, AI learns an algorithm to identify crying and other abnormal sounds. This allows for early detection of dangerous situations and enables rapid response. This allows for early detection of dangerous situations.

[0032] The in-vehicle environment monitoring unit can combine external environmental data to make more accurate danger predictions. For example, the in-vehicle environment monitoring unit integrates external environmental data (weather, temperature, humidity) with data collected by sensors inside the vehicle, and AI analyzes this data to make danger predictions. This enables more accurate danger predictions. In addition, a system will be built that collects external environmental data in real time and combines it with in-vehicle environmental data for analysis. This makes it possible to make danger predictions in response to changes in the external environment. Furthermore, in-vehicle environmental data and external environmental data will be integrated, and AI will analyze this data to make danger predictions. For example, if the outside temperature rises suddenly, the unit will predict a rise in the temperature inside the vehicle and issue a report if necessary. This enables more accurate danger predictions.

[0033] The in-vehicle environment monitoring unit can send monitoring data to the cloud and compare it with data from other vehicles and regions to detect abnormalities. For example, the in-vehicle environment monitoring unit builds a system that sends data collected by sensors inside the vehicle to the cloud and compares it with data from other vehicles and regions to detect abnormalities. This allows for immediate reporting if an abnormality is detected. In addition, the in-vehicle monitoring data is analyzed on the cloud and compared with data from other vehicles and regions to detect abnormalities early. For example, an alert is issued if an abnormal temperature rise occurs in a specific region. Furthermore, in-vehicle monitoring data is sent to the cloud, and AI compares it with data from other vehicles and regions to detect abnormalities. This allows for a rapid response if an abnormality is detected. This allows for early detection of abnormalities.

[0034] The dangerous situation judgment unit can learn from past accident data and build a more accurate danger prediction model. For example, the dangerous situation judgment unit has AI learn from past accident data and build a highly accurate danger prediction model when judging dangerous situations. This makes it possible to make highly accurate danger predictions based on past cases. In addition, past accident data is analyzed and an algorithm is developed that the AI ​​uses as a reference when judging dangerous situations. This makes it possible to make dangerous predictions based on past cases. Furthermore, a system is developed in which the AI ​​learns from past accident data and builds a highly accurate danger prediction model when judging dangerous situations. This makes it possible to make highly accurate danger predictions based on past cases. This makes it possible to make highly accurate danger predictions based on past cases.

[0035] The danger situation judgment unit can detect danger from a more multifaceted perspective by adding light change and vibration data. For example, the danger situation judgment unit will collect data on light changes and vibrations inside the vehicle, and an AI will analyze this data to determine danger situations. This will enable danger to be detected from a more multifaceted perspective. In addition, light sensors and vibration sensors will be installed inside the vehicle, and an AI will analyze this data to determine danger situations. For example, an alert will be issued if a sudden change in light or abnormal vibration is detected. Furthermore, a system will be developed in which data on light changes and vibrations inside the vehicle will be added, and an AI will analyze this data to determine danger situations. This will enable danger to be detected from a more multifaceted perspective.

[0036] The danger situation assessment unit can combine operational data to predict danger in specific locations and situations. For example, the danger situation assessment unit will collect vehicle operational data (speed, location, and driving route) and build a system in which AI analyzes this data to determine danger situations. This will make it possible to predict danger in specific locations and situations. We will also develop a system that collects operational data in real time and analyzes it in combination with in-vehicle environmental data. This will enable early prediction of danger on the driving route. Furthermore, vehicle operational data will be integrated, and AI will analyze this data to determine danger situations. For example, a warning will be issued if speeding in a specific location or an abnormal driving route is detected. This will make it possible to predict danger in specific locations and situations.

[0037] The danger situation assessment unit can refer to real-time data from other vehicles and infrastructure and take the surrounding conditions into consideration. For example, the danger situation assessment unit will build a system that collects real-time data from other vehicles and infrastructure, and AI analyzes this data to determine danger situations. This will enable danger prediction that takes the surrounding conditions into consideration. We will also develop a system that collects data from other vehicles and infrastructure in real time and analyzes it in combination with in-vehicle environmental data. This will enable danger prediction that takes the surrounding conditions into consideration. Furthermore, we will integrate real-time data from other vehicles and infrastructure, and AI will analyze this data to determine danger situations. For example, we will issue a warning taking into consideration the surrounding traffic conditions and the state of the infrastructure. This will enable danger prediction that takes the surrounding conditions into consideration.

[0038] The emergency reporting unit can transmit camera footage to the call destination in real time when an emergency call is made. For example, the emergency reporting unit builds a system that transmits footage from a camera installed in a vehicle to the call destination in real time when an emergency call is made. This allows the situation at the scene to be grasped immediately and a prompt response to be made. Furthermore, by transmitting the vehicle's camera footage to the call destination in real time, the call destination can accurately grasp the situation at the scene. For example, the condition of a child or the environment inside the vehicle can be confirmed through the footage. Furthermore, a system is developed that transmits the vehicle's camera footage to the call destination in real time when an emergency call is made. This allows the call destination to immediately grasp the situation at the scene and take appropriate action. This allows the situation at the scene to be grasped immediately.

[0039] The emergency call unit can include detailed data obtained from sensors in the call content. For example, the emergency call unit will build a system that includes detailed data obtained from sensors inside the vehicle in the call content when an emergency call is made. This will allow the call recipient to respond quickly. In addition, data obtained from sensors inside the vehicle will be sent to the call recipient in real time and included in the call content. This will allow the call recipient to accurately understand the situation inside the vehicle and respond quickly. Furthermore, a system will be developed that includes detailed data obtained from sensors inside the vehicle in the call content when an emergency call is made. This will allow the call recipient to respond quickly and take appropriate measures. This will allow the call recipient to respond quickly.

[0040] The emergency notification unit can also send alerts to nearby vehicles and pedestrians when an emergency call is made. For example, the emergency notification unit builds a system that sends alerts to nearby vehicles and pedestrians when an emergency call is made. This encourages rapid rescue and ensures the safety of children. In addition, the alert sending function is used to issue warnings to nearby vehicles and pedestrians when an emergency call is made. This allows people in the vicinity to respond quickly and carry out rescue operations. Furthermore, a system is developed that sends alerts to nearby vehicles and pedestrians when an emergency call is made. This encourages rapid rescue and ensures the safety of children. This encourages rapid rescue.

[0041] The emergency reporting department can introduce a system that automatically distributes calls not only to parents and guardians but also to nearby police stations and fire stations. For example, the emergency reporting department will build a system that automatically distributes calls not only to parents and guardians but also to nearby police stations and fire stations in the event of an emergency call. This will enable a quick response. In addition, an algorithm will be developed that automatically distributes calls so that information is sent to the appropriate destination in the event of an emergency call. This will enable a quick response. In addition, a system will be developed that automatically distributes calls not only to parents and guardians but also to nearby police stations and fire stations in the event of an emergency call. This will enable a quick response.

[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 in-vehicle environment monitoring unit can be equipped with sensors that monitor the air quality inside the vehicle. For example, sensors that monitor the concentration of volatile organic compounds (VOCs) and carbon monoxide inside the vehicle in real time can be installed. This allows for immediate response if the air quality inside the vehicle deteriorates. It can also analyze air quality data and issue warnings if there is a possibility of adverse health effects. In addition, the in-vehicle air quality data can be sent to the cloud and compared with data from other vehicles or regions to detect abnormalities. This allows for more accurate air quality management.

[0044] The in-vehicle environment monitoring unit can be equipped with a sensor that monitors the lighting conditions inside the vehicle. For example, a sensor can be installed to detect abnormalities when the lighting conditions inside the vehicle change suddenly. This allows for immediate response when the lighting conditions inside the vehicle are inappropriate. The lighting data can also be analyzed and adjustments can be made to maintain a lighting environment that is comfortable for children. Furthermore, the lighting data inside the vehicle can be sent to the cloud and compared with data from other vehicles or regions to detect abnormalities. This allows for more accurate lighting management.

[0045] The in-vehicle environment monitoring unit can be equipped with a sensor that monitors the acoustic environment inside the vehicle. For example, a sensor can be installed that monitors the noise level inside the vehicle in real time. This can issue a warning if the noise inside the vehicle exceeds a certain threshold. The acoustic data can also be analyzed and adjustments can be made to maintain an acoustic environment that is comfortable for children. Furthermore, the acoustic data inside the vehicle can be sent to the cloud and compared with data from other vehicles or regions to detect abnormalities. This enables more accurate acoustic management.

[0046] The in-car environment monitoring unit can be equipped with a sensor that monitors the vibration state inside the vehicle. For example, a sensor can be installed that detects an abnormality when the vibration inside the vehicle exceeds a certain threshold. This allows immediate action to be taken when the vibration inside the vehicle is inappropriate. In addition, the vibration data can be analyzed and adjustments can be made to maintain a vibration environment that is comfortable for children. Furthermore, the vibration data inside the vehicle can be sent to the cloud and compared with data from other vehicles or regions to detect abnormalities. This allows for more accurate vibration management.

[0047] The in-vehicle environment monitoring unit can be equipped with a sensor that monitors the electromagnetic wave environment inside the vehicle. For example, a sensor can be installed that monitors the electromagnetic wave level inside the vehicle in real time. This makes it possible to issue a warning if the electromagnetic waves inside the vehicle exceed a certain threshold. It can also analyze the electromagnetic wave data and make adjustments to maintain an electromagnetic wave environment where children can stay safe. Furthermore, it can send the electromagnetic wave data inside the vehicle to the cloud and compare it with data from other vehicles or regions to detect abnormalities. This enables more accurate electromagnetic wave management.

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

[0049] Step 1: The in-vehicle environment monitoring unit monitors the in-vehicle environment. For example, it collects data such as temperature, humidity, carbon dioxide concentration, voice, and movement inside the vehicle through sensors. It also collects this data in real time and converts it into a format that is easy for AI to analyze. Step 2: The danger situation determination unit analyzes the data collected by the in-vehicle environment monitoring unit and determines the danger situation. For example, if the temperature inside the vehicle exceeds a certain threshold, if the carbon dioxide concentration becomes high, or if the child does not move for a certain period of time, or if multiple danger situations are met, a report is sent. Step 3: The emergency notification unit makes an emergency call based on the dangerous situation determined by the dangerous situation determination unit. For example, the call may be made to a parent or guardian, the police, an emergency service, etc., and the call content may include the vehicle's location information and the situation inside the vehicle.

[0050] (Example 2) The automatic emergency call system according to an embodiment of the present invention is a system that automatically makes an emergency call using AI in order to prevent fatal accidents caused by children being left in cars. This system monitors the in-car environment, determines dangerous situations, and makes an emergency call. As a result, the automatic emergency call system can prevent fatal accidents caused by children being left in cars.

[0051] An automatic emergency notification system according to an embodiment includes an in-vehicle environment monitoring unit, a dangerous situation determination unit, and an emergency notification unit. The in-vehicle environment monitoring unit monitors the in-vehicle environment. For example, it collects data such as temperature, humidity, carbon dioxide concentration, voice, and movement inside the vehicle using sensors. The in-vehicle environment monitoring unit also collects this data in real time and converts it into a format that is easy for AI to analyze. The dangerous situation determination unit analyzes the data collected by the in-vehicle environment monitoring unit to determine a dangerous situation. For example, it issues a notification when multiple dangerous situations are met, such as when the temperature inside the vehicle exceeds a certain threshold, when the carbon dioxide concentration becomes high, or when a child does not move for a certain period of time. The emergency notification unit issues an emergency notification based on the dangerous situation determined by the dangerous situation determination unit. For example, the notification destination may be a parent, guardian, police, or emergency service, and the content of the notification may include vehicle location information and the situation inside the vehicle. As a result, the automatic emergency notification system according to an embodiment can prevent fatal accidents caused by children being left in a vehicle.

[0052] The in-vehicle environment monitoring unit can be equipped with sensors that monitor temperature, humidity, carbon dioxide concentration, heart rate, and respiratory rate. For example, sensors installed inside the vehicle monitor temperature, humidity, carbon dioxide concentration, as well as heart rate and respiratory rate in real time. This allows for a detailed understanding of the child's health condition and immediate response if an abnormality occurs. Non-contact vital signs sensors are also installed inside the vehicle to monitor heart rate and respiratory rate. This allows for rapid detection of changes in the child's health condition and, if necessary, makes an emergency call. Furthermore, sensors that monitor heart rate and respiratory rate in addition to temperature, humidity, and carbon dioxide concentration inside the vehicle are integrated, and AI analyzes this data to evaluate the child's health condition. If an abnormality is detected, an emergency call is made immediately. This allows for a more detailed understanding of the child's health condition.

[0053] The in-car environment monitoring unit can analyze audio data and detect crying and abnormal sounds. For example, the in-car environment monitoring unit uses microphones installed in the car to collect audio data, and AI analyzes it for crying and other abnormal sounds. This allows for early detection of dangerous situations and, if necessary, makes an emergency call. In addition, using audio analysis technology, a system is built that detects crying and other abnormal sounds from in-car audio data in real time. If an abnormality is detected, an emergency call is made immediately. Furthermore, in order to analyze in-car audio data, AI learns an algorithm to identify crying and other abnormal sounds. This allows for early detection of dangerous situations and enables rapid response. This allows for early detection of dangerous situations.

[0054] The in-car environment monitoring unit can use an emotion estimation function to estimate emotions from voice and facial expressions and detect signs of stress and anxiety. For example, the in-car environment monitoring unit uses cameras and microphones installed inside the vehicle to collect a child's voice and facial expressions, and AI estimates emotions. This allows it to detect signs of stress or anxiety and, if necessary, make an emergency call. In addition, an emotion estimation algorithm is used to build a system that analyzes emotions from a child's voice and facial expressions in real time. If an abnormal emotional state is detected, an emergency call is made immediately. Furthermore, the AI ​​learns the emotion estimation function to analyze a child's voice and facial expressions. This enables early detection of signs of stress and anxiety and enables a prompt response. This makes it possible to detect signs of stress and anxiety.

[0055] The in-vehicle environment monitoring unit can combine external environmental data to make more accurate danger predictions. For example, the in-vehicle environment monitoring unit integrates external environmental data (weather, temperature, humidity) with data collected by sensors inside the vehicle, and AI analyzes this data to make danger predictions. This enables more accurate danger predictions. In addition, a system will be built that collects external environmental data in real time and combines it with in-vehicle environmental data for analysis. This makes it possible to make danger predictions in response to changes in the external environment. Furthermore, in-vehicle environmental data and external environmental data will be integrated, and AI will analyze this data to make danger predictions. For example, if the outside temperature rises suddenly, the unit will predict a rise in the temperature inside the vehicle and issue a report if necessary. This enables more accurate danger predictions.

[0056] The in-vehicle environment monitoring unit can send monitoring data to the cloud and compare it with data from other vehicles and regions to detect abnormalities. For example, the in-vehicle environment monitoring unit builds a system that sends data collected by sensors inside the vehicle to the cloud and compares it with data from other vehicles and regions to detect abnormalities. This allows for immediate reporting if an abnormality is detected. In addition, the in-vehicle monitoring data is analyzed on the cloud and compared with data from other vehicles and regions to detect abnormalities early. For example, an alert is issued if an abnormal temperature rise occurs in a specific region. Furthermore, in-vehicle monitoring data is sent to the cloud, and AI compares it with data from other vehicles and regions to detect abnormalities. This allows for a rapid response if an abnormality is detected. This allows for early detection of abnormalities.

[0057] The in-car environment monitoring unit can use the emotion estimation function to notify parents and guardians of their emotional state in real time. For example, the in-car environment monitoring unit uses cameras and microphones installed in the car to collect the child's voice and facial expression, and AI performs emotion estimation. This notifies parents and guardians of the child's emotional state in real time. In addition, an emotion estimation algorithm is used to build a system that analyzes emotions from the child's voice and facial expression in real time. This allows parents and guardians to understand their child's emotional state and feel reassured. Furthermore, AI learns the emotion estimation function to analyze the child's voice and facial expression. This notifies parents and guardians of the child's emotional state in real time, providing a sense of security. This provides parents and guardians with a sense of security.

[0058] The dangerous situation judgment unit can learn from past accident data and build a more accurate danger prediction model. For example, the dangerous situation judgment unit has AI learn from past accident data and build a highly accurate danger prediction model when judging dangerous situations. This makes it possible to make highly accurate danger predictions based on past cases. In addition, past accident data is analyzed and an algorithm is developed that the AI ​​uses as a reference when judging dangerous situations. This makes it possible to make dangerous predictions based on past cases. Furthermore, a system is developed in which the AI ​​learns from past accident data and builds a highly accurate danger prediction model when judging dangerous situations. This makes it possible to make highly accurate danger predictions based on past cases. This makes it possible to make highly accurate danger predictions based on past cases.

[0059] The danger situation judgment unit can detect danger from a more multifaceted perspective by adding light change and vibration data. For example, the danger situation judgment unit will collect data on light changes and vibrations inside the vehicle, and an AI will analyze this data to determine danger situations. This will enable danger to be detected from a more multifaceted perspective. In addition, light sensors and vibration sensors will be installed inside the vehicle, and an AI will analyze this data to determine danger situations. For example, an alert will be issued if a sudden change in light or abnormal vibration is detected. Furthermore, a system will be developed in which data on light changes and vibrations inside the vehicle will be added, and an AI will analyze this data to determine danger situations. This will enable danger to be detected from a more multifaceted perspective.

[0060] The dangerous situation judgment unit uses an emotion estimation function to take into account the emotional state and can judge that a situation is dangerous if the emotionally unstable state continues. For example, the dangerous situation judgment unit uses a camera and microphone installed inside the vehicle to collect the child's voice and facial expression, and AI estimates the child's emotion. If the emotionally unstable state continues, it judges that a situation is dangerous and makes an emergency call if necessary. In addition, a system is built that uses an emotion estimation algorithm to analyze emotions from the child's voice and facial expression in real time. If the emotionally unstable state continues, it judges that a situation is dangerous and makes an immediate call. Furthermore, the AI ​​learns the emotion estimation function to analyze the child's voice and facial expression. If the emotionally unstable state continues, it judges that a situation is dangerous, enabling a rapid response. This makes it possible to judge that a situation is dangerous if the emotionally unstable state continues.

[0061] The danger situation assessment unit can combine operational data to predict danger in specific locations and situations. For example, the danger situation assessment unit will collect vehicle operational data (speed, location, and driving route) and build a system in which AI analyzes this data to determine danger situations. This will make it possible to predict danger in specific locations and situations. We will also develop a system that collects operational data in real time and analyzes it in combination with in-vehicle environmental data. This will enable early prediction of danger on the driving route. Furthermore, vehicle operational data will be integrated, and AI will analyze this data to determine danger situations. For example, a warning will be issued if speeding in a specific location or an abnormal driving route is detected. This will make it possible to predict danger in specific locations and situations.

[0062] The danger situation assessment unit can refer to real-time data from other vehicles and infrastructure and take the surrounding conditions into consideration. For example, the danger situation assessment unit will build a system that collects real-time data from other vehicles and infrastructure, and AI analyzes this data to determine danger situations. This will enable danger prediction that takes the surrounding conditions into consideration. We will also develop a system that collects data from other vehicles and infrastructure in real time and analyzes it in combination with in-vehicle environmental data. This will enable danger prediction that takes the surrounding conditions into consideration. Furthermore, we will integrate real-time data from other vehicles and infrastructure, and AI will analyze this data to determine danger situations. For example, we will issue a warning taking into consideration the surrounding traffic conditions and the state of the infrastructure. This will enable danger prediction that takes the surrounding conditions into consideration.

[0063] The dangerous situation judgment unit can use the emotion estimation function to dynamically adjust the criteria for judging dangerous situations based on the emotional state. For example, the dangerous situation judgment unit uses a camera and microphone installed inside the vehicle to collect the child's voice and facial expression, and AI estimates the child's emotion. This allows the criteria for judging dangerous situations to be dynamically adjusted based on the child's emotional state. In addition, an emotion estimation algorithm is used to build a system that analyzes emotions from the child's voice and facial expression in real time. This makes it possible to judge dangerous situations based on the child's emotional state. Furthermore, the AI ​​learns the emotion estimation function to analyze the child's voice and facial expression. This allows the criteria for judging dangerous situations to be dynamically adjusted based on the child's emotional state, enabling a rapid response. This allows the criteria for judging dangerous situations to be dynamically adjusted based on the child's emotional state.

[0064] The emergency reporting unit can transmit camera footage to the call destination in real time when an emergency call is made. For example, the emergency reporting unit builds a system that transmits footage from a camera installed in a vehicle to the call destination in real time when an emergency call is made. This allows the situation at the scene to be grasped immediately and a prompt response to be made. Furthermore, by transmitting the vehicle's camera footage to the call destination in real time, the call destination can accurately grasp the situation at the scene. For example, the condition of a child or the environment inside the vehicle can be confirmed through the footage. Furthermore, a system is developed that transmits the vehicle's camera footage to the call destination in real time when an emergency call is made. This allows the call destination to immediately grasp the situation at the scene and take appropriate action. This allows the situation at the scene to be grasped immediately.

[0065] The emergency call unit can include detailed data obtained from sensors in the call content. For example, the emergency call unit will build a system that includes detailed data obtained from sensors inside the vehicle in the call content when an emergency call is made. This will allow the call recipient to respond quickly. In addition, data obtained from sensors inside the vehicle will be sent to the call recipient in real time and included in the call content. This will allow the call recipient to accurately understand the situation inside the vehicle and respond quickly. Furthermore, a system will be developed that includes detailed data obtained from sensors inside the vehicle in the call content when an emergency call is made. This will allow the call recipient to respond quickly and take appropriate measures. This will allow the call recipient to respond quickly.

[0066] The emergency call unit can use the emotion estimation function to report the emotional state at the time of the call, and use it as information for determining the priority of the emergency response. For example, the emergency call unit builds a system that uses the emotion estimation function to report the emotional state of a child at the time of an emergency call. This allows the call recipient to use it as information for determining the priority of the emergency response. In addition, an emotion estimation algorithm is used to analyze the emotional state of the child in real time at the time of the call and include it in the call content. This provides information for the call recipient to take an appropriate response. Furthermore, a system is developed that uses the emotion estimation function to report the emotional state of a child at the time of an emergency call. This allows the call recipient to determine the priority of the emergency response and make a quick response. This allows it to use it as information for determining the priority of the emergency response.

[0067] The emergency notification unit can also send alerts to nearby vehicles and pedestrians when an emergency call is made. For example, the emergency notification unit builds a system that sends alerts to nearby vehicles and pedestrians when an emergency call is made. This encourages rapid rescue and ensures the safety of children. In addition, the alert sending function is used to issue warnings to nearby vehicles and pedestrians when an emergency call is made. This allows people in the vicinity to respond quickly and carry out rescue operations. Furthermore, a system is developed that sends alerts to nearby vehicles and pedestrians when an emergency call is made. This encourages rapid rescue and ensures the safety of children. This encourages rapid rescue.

[0068] The emergency reporting department can introduce a system that automatically distributes calls not only to parents and guardians but also to nearby police stations and fire stations. For example, the emergency reporting department will build a system that automatically distributes calls not only to parents and guardians but also to nearby police stations and fire stations in the event of an emergency call. This will enable a quick response. In addition, an algorithm will be developed that automatically distributes calls so that information is sent to the appropriate destination in the event of an emergency call. This will enable a quick response. In addition, a system will be developed that automatically distributes calls not only to parents and guardians but also to nearby police stations and fire stations in the event of an emergency call. This will enable a quick response.

[0069] The emergency call unit can use the emotion estimation function to select the optimal call destination based on the emotional state at the time of the call. For example, the emergency call unit uses the emotion estimation function to analyze the emotional state of the child at the time of the emergency call and builds a system to select the optimal call destination. This enables a rapid response. In addition, an emotion estimation algorithm is used to analyze the emotional state of the child in real time at the time of the call and select the optimal call destination. This enables the call destination to respond quickly. Furthermore, a system is developed that uses the emotion estimation function to select the optimal call destination based on the emotional state of the child at the time of the emergency call. This enables a rapid response. This enables a rapid response.

[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 in-vehicle environment monitoring unit can be equipped with sensors that monitor the air quality inside the vehicle. For example, sensors that monitor the concentration of volatile organic compounds (VOCs) and carbon monoxide inside the vehicle in real time can be installed. This allows for immediate response if the air quality inside the vehicle deteriorates. It can also analyze air quality data and issue warnings if there is a possibility of adverse health effects. In addition, the in-vehicle air quality data can be sent to the cloud and compared with data from other vehicles or regions to detect abnormalities. This allows for more accurate air quality management.

[0072] The in-vehicle environment monitoring unit can be equipped with a sensor that monitors the lighting conditions inside the vehicle. For example, a sensor can be installed to detect abnormalities when the lighting conditions inside the vehicle change suddenly. This allows for immediate response when the lighting conditions inside the vehicle are inappropriate. The lighting data can also be analyzed and adjustments can be made to maintain a lighting environment that is comfortable for children. Furthermore, the lighting data inside the vehicle can be sent to the cloud and compared with data from other vehicles or regions to detect abnormalities. This allows for more accurate lighting management.

[0073] The in-vehicle environment monitoring unit can be equipped with a sensor that monitors the acoustic environment inside the vehicle. For example, a sensor can be installed that monitors the noise level inside the vehicle in real time. This can issue a warning if the noise inside the vehicle exceeds a certain threshold. The acoustic data can also be analyzed and adjustments can be made to maintain an acoustic environment that is comfortable for children. Furthermore, the acoustic data inside the vehicle can be sent to the cloud and compared with data from other vehicles or regions to detect abnormalities. This enables more accurate acoustic management.

[0074] The in-car environment monitoring unit can be equipped with a sensor that monitors the vibration state inside the vehicle. For example, a sensor can be installed that detects an abnormality when the vibration inside the vehicle exceeds a certain threshold. This allows immediate action to be taken when the vibration inside the vehicle is inappropriate. In addition, the vibration data can be analyzed and adjustments can be made to maintain a vibration environment that is comfortable for children. Furthermore, the vibration data inside the vehicle can be sent to the cloud and compared with data from other vehicles or regions to detect abnormalities. This allows for more accurate vibration management.

[0075] The in-vehicle environment monitoring unit can be equipped with a sensor that monitors the electromagnetic wave environment inside the vehicle. For example, a sensor can be installed that monitors the electromagnetic wave level inside the vehicle in real time. This makes it possible to issue a warning if the electromagnetic waves inside the vehicle exceed a certain threshold. It can also analyze the electromagnetic wave data and make adjustments to maintain an electromagnetic wave environment where children can stay safe. Furthermore, it can send the electromagnetic wave data inside the vehicle to the cloud and compare it with data from other vehicles or regions to detect abnormalities. This enables more accurate electromagnetic wave management.

[0076] The dangerous situation judgment unit can estimate the user's emotions and dynamically adjust the criteria for judging dangerous situations based on the estimated user's emotions. For example, if a child's emotional state is unstable, the criteria for judging dangerous situations can be made stricter. This enables flexible judgment of dangerous situations according to the emotional state. In addition, a system can be constructed that uses an emotion estimation algorithm to analyze emotions from a child's voice and facial expressions in real time. This enables rapid response according to the emotional state. Furthermore, the emotion estimation function can be trained and the criteria for judging dangerous situations can be dynamically adjusted based on the emotional state. This enables flexible judgment of dangerous situations according to the emotional state.

[0077] The danger situation determination unit can estimate the user's emotions and dynamically adjust the priority of emergency calls based on the estimated user's emotions. For example, if a child's emotional state is very unstable, the priority of the emergency call can be increased. This enables a prompt response according to the emotional state. In addition, an emotion estimation algorithm is used to build a system that analyzes emotions from a child's voice and facial expressions in real time. This enables a prompt response according to the emotional state. Furthermore, the emotion estimation function can be trained to dynamically adjust the priority of emergency calls based on the emotional state. This enables a prompt response according to the emotional state.

[0078] The danger situation judgment unit can estimate the user's emotions and select an appropriate reporting destination based on the estimated user's emotions. For example, if a child's emotional state is very unstable, it can not only notify the parent or guardian but also a medical institution. This allows for an appropriate response according to the emotional state. In addition, an emotion estimation algorithm is used to build a system that analyzes emotions from a child's voice and facial expressions in real time. This allows for a quick response according to the emotional state. Furthermore, the emotion estimation function can be trained to select an appropriate reporting destination based on the emotional state. This allows for an appropriate response according to the emotional state.

[0079] The danger situation assessment unit can estimate the user's emotions and dynamically adjust the content of the emergency call based on the estimated user's emotions. For example, if a child's emotional state is very unstable, the call content can include details of the child's emotional state. This allows the call recipient to respond quickly. In addition, an emotion estimation algorithm is used to build a system that analyzes the child's emotions from their voice and facial expressions in real time. This allows for a quick response based on the child's emotional state. Furthermore, the emotion estimation function can be trained to dynamically adjust the content of the emergency call based on the child's emotional state. This allows the call recipient to respond quickly.

[0080] The danger situation determination unit can estimate the user's emotions and dynamically adjust the timing of an emergency call based on the estimated user's emotions. For example, if a child's emotional state is very unstable, an emergency call can be made early. This enables a prompt response according to the emotional state. In addition, an emotion estimation algorithm is used to build a system that analyzes emotions from a child's voice and facial expressions in real time. This enables a prompt response according to the emotional state. Furthermore, the emotion estimation function can be trained to dynamically adjust the timing of an emergency call based on the emotional state. This enables a prompt response according to the emotional state.

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

[0082] Step 1: The in-vehicle environment monitoring unit monitors the in-vehicle environment. For example, it collects data such as temperature, humidity, carbon dioxide concentration, voice, and movement inside the vehicle through sensors. It also collects this data in real time and converts it into a format that is easy for AI to analyze. Step 2: The danger situation determination unit analyzes the data collected by the in-vehicle environment monitoring unit and determines the danger situation. For example, if the temperature inside the vehicle exceeds a certain threshold, if the carbon dioxide concentration becomes high, or if the child does not move for a certain period of time, or if multiple danger situations are met, a report is sent. Step 3: The emergency notification unit makes an emergency call based on the dangerous situation determined by the dangerous situation determination unit. For example, the call may be made to a parent or guardian, the police, an emergency service, etc., and the call content may include the vehicle's location information and the situation inside the vehicle.

[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 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. an in-vehicle environment monitoring unit that monitors the in-vehicle environment; a dangerous situation determination unit that analyzes the data collected by the in-vehicle environment monitoring unit and determines a dangerous situation; an emergency notification unit that issues an emergency notification based on the dangerous situation determined by the dangerous situation determination unit; A system characterized by:

2. The in-vehicle environment monitoring unit Equipped with sensors to monitor temperature, humidity, carbon dioxide concentration, heart rate, and respiratory rate 2. The system of claim 1.

3. The in-vehicle environment monitoring unit Analyzing the audio data to detect crying and abnormal sounds 2. The system of claim 1.

4. The in-vehicle environment monitoring unit Inferring emotions from the voice and facial expressions and detecting signs of stress and anxiety 2. The system of claim 1.

5. The in-vehicle environment monitoring unit Combining external environmental data to make more accurate risk predictions 2. The system of claim 1.

6. The in-vehicle environment monitoring unit Monitoring data is sent to the cloud and compared with data from other vehicles and regions to detect anomalies.

2. The system of claim 1.

7. The in-vehicle environment monitoring unit Notifying said parents and guardians of their emotional state in real time 2. The system of claim 1.

8. The dangerous situation determination unit Learning from past accident data to build a more accurate risk prediction model 2. The system of claim 1.

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