system

Abstract

An object of a system according to an embodiment is to enhance safety management of children in a school bus.SOLUTION: A system according to an embodiment includes a getting on / off sensor, a non-contact thermometer, a AI camera, a GPS, and a notifying unit. The boarding / alighting sensor checks the number of passengers. A non-contact thermometer confirms body temperature. The AI camera and the GPS identify the kindergarten child / student who has boarded. The AI camera checks the state of the child and whether the child wears a seat belt. The notification unit contacts a designated person in charge by SMS when an abnormality is detected.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately manage the safety of children on school buses, and there is room for improvement.

[0005] The system according to the embodiment aims to strengthen the safety management of children on school buses. [Means for solving the problem]

[0006] The system according to the embodiment comprises a boarding / exiting sensor, a non-contact thermometer, an AI camera, a GPS, and a notification unit. The boarding / exiting sensor checks the number of passengers. The non-contact thermometer checks body temperature. The AI ​​camera and GPS identify the children / students who are boarding. The AI ​​camera checks the children's condition and whether they are wearing seat belts. If the notification unit detects an abnormality, it will contact a designated person via SMS. [Effects of the Invention]

[0007] The system according to the embodiment can enhance the safety management of children on school buses. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The safety confirmation system according to an embodiment of the present invention is a system that ensures the safety and health of children by installing an AI camera, GPS, a non-contact thermometer, and boarding and alighting sensors inside a school bus. As a result, the safety confirmation system can prevent children from being left behind on the school bus, from suffering from heatstroke, or from not wearing seatbelts in the event of an accident, and can check the health status of children and respond quickly if any abnormalities are detected.

[0029] A safety confirmation system according to an embodiment includes a boarding / alighting sensor, a non-contact thermometer, an AI camera, a GPS, and a notification unit. The boarding / alighting sensor checks the number of passengers. For example, the boarding / alighting sensor is installed at the entrance of a bus and automatically counts the number of children boarding. The non-contact thermometer checks body temperature. For example, the non-contact thermometer is installed at the entrance of the bus and measures the temperature of a child by pointing it at their forehead. If an abnormality is detected, an alert is issued. The AI ​​camera and GPS identify the kindergartener / student who boarded the bus. For example, the AI ​​camera recognizes the child's face, and the GPS records their location. The AI ​​camera checks the child's condition and whether or not they are wearing a seat belt. For example, the AI ​​camera monitors the child's movements and checks whether the seat belt is properly fastened. If an abnormality is detected, the notification unit notifies a designated person via SMS. For example, if a child has a high body temperature or a seat belt is not fastened, the notification unit issues an alert and notifies the person via SMS. As a result, the safety confirmation system of the embodiment can prevent children from being left behind on school buses, heatstroke, and not wearing seat belts in the event of an accident, check the health of children, and respond quickly if any abnormalities are found.

[0030] The boarding / alighting sensor can analyze the fluctuation pattern of the number of passengers and detect abnormal patterns. For example, the boarding / alighting sensor collects data from the boarding / alighting sensor and analyzes the past fluctuation pattern of the number of passengers. For example, it analyzes the fluctuation of the number of passengers during a specific time period or day of the week and detects abnormal patterns. This allows abnormal fluctuations in the number of passengers to be detected and responded to quickly.

[0031] The boarding and alighting sensor can transmit data on the number of passengers to the cloud in real time, enabling remote monitoring. For example, the boarding and alighting sensor can transmit data to the cloud, building a system that can monitor the number of passengers in real time from a remote location. For example, the data can be transmitted via the Internet, allowing an administrator to access it from a remote location. This allows the number of passengers to be monitored from a remote location.

[0032] A non-contact thermometer can analyze patterns of body temperature fluctuations and detect abnormal body temperature fluctuations. For example, a non-contact thermometer collects data from the non-contact thermometer and analyzes past patterns of body temperature fluctuations. For example, it analyzes body temperature fluctuations during specific times of the day or seasons to detect abnormal patterns. This allows abnormal body temperature fluctuations to be detected and addressed promptly.

[0033] Non-contact thermometers can transmit body temperature data to the cloud, making it possible to monitor body temperature from remote locations. For example, non-contact thermometers can transmit data from the non-contact thermometer to the cloud, creating a system that can monitor body temperature in real time from remote locations. For example, data can be transmitted via the Internet, allowing administrators to access the data from remote locations. This makes it possible to monitor body temperature from remote locations.

[0034] AI cameras can analyze children's movements and postures and detect abnormal movements. For example, AI cameras can be used to capture images of children's movements and postures while in the car, and algorithms can be developed to detect abnormal movements. For example, an alert can be issued if a child unbuckles their seat belt or makes an abnormal movement. This allows for the detection of abnormal movements and a quick response.

[0035] When a warning occurs and a notification unit notifies the user by SMS, the notification unit can analyze the cause of the warning and automatically propose measures to prevent recurrence. The notification unit, for example, builds a system that analyzes the cause of the warning and automatically proposes measures to prevent recurrence. For example, it identifies the cause based on past data and proposes measures. This makes it possible to analyze the cause of the warning and propose measures to prevent recurrence.

[0036] The notification unit can send warning data to the cloud, enabling monitoring from a remote location. The notification unit, for example, sends warning data to the cloud, building a system that allows real-time monitoring from a remote location. For example, the data can be sent via the Internet, allowing an administrator to access it from a remote location. This allows warnings to be monitored from a remote location.

[0037] The system can be applied to other public transportation systems to ensure passenger safety. For example, the system can be installed in train cars as a warning system to monitor passenger safety. For example, if an abnormal condition is detected, the system notifies the driver or manager. This can ensure passenger safety in other public transportation systems as well.

[0038] The notification unit can use the warning data to automatically notify a medical institution when an abnormality is detected. The notification unit, for example, analyzes the warning data and builds a system that automatically notifies a medical institution when an abnormality is detected. For example, an alert is sent to a medical institution when an abnormal state is detected. This allows automatic notification to a medical institution when an abnormality is detected.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The safety confirmation system can further include a voice recognition unit. The voice recognition unit can analyze the child's voice and detect abnormal vocalizations or cries for help. For example, if a child utters words such as "help me" or "it hurts," the voice recognition unit will detect this and immediately issue an alert. This allows for the rapid detection and response of abnormalities expressed by children.

[0041] The safety confirmation system can also be equipped with a vibration sensor. The vibration sensor detects abnormal shaking or impact of the bus and can detect signs of an accident early. For example, it detects abnormal vibrations during sudden braking or sharp turns and issues a warning to the driver. This reduces the risk of accidents and ensures the safety of children.

[0042] The safety confirmation system can also be equipped with an air quality sensor. The air quality sensor monitors the carbon dioxide concentration and levels of harmful substances inside the bus and issues an alert if an abnormality is detected. For example, if the carbon dioxide concentration becomes high, an alert is issued to prompt ventilation. This helps maintain the air quality inside the bus and protect the health of children.

[0043] The safety confirmation system can also be equipped with an ultraviolet sensor. The ultraviolet sensor monitors the level of ultraviolet light entering the bus and issues an alert if excessive ultraviolet light is detected. For example, if strong ultraviolet light is entering through the windows, an alert will be issued to prompt the driver to close the curtains. This will help protect children's skin from ultraviolet light.

[0044] The safety confirmation system can also include a humidity sensor. The humidity sensor monitors the humidity level inside the bus and issues an alert if an abnormality is detected. For example, if the humidity is too high, an alert is issued to prompt dehumidification. This helps maintain a comfortable environment inside the bus and protects the health of children.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The boarding and alighting sensor checks the number of passengers. For example, the boarding and alighting sensor is installed at the entrance of the bus and automatically counts the number of children boarding. Step 2: A non-contact thermometer checks the body temperature. For example, a non-contact thermometer could be installed at the entrance of the bus, point it at the child's forehead to measure their temperature, and issue an alert if there is an abnormality. Step 3: The AI ​​camera and GPS identify the children / students who boarded the bus. For example, the AI ​​camera recognizes the children's faces, and the GPS records their location. Step 4: The AI ​​camera checks the child's condition and whether they are wearing a seat belt. For example, the AI ​​camera can monitor the child's movements and check whether the seat belt is properly fastened. Step 5: If the notification unit detects an abnormality, it will contact a designated person in charge via SMS. For example, if the person has a high body temperature or the seat belt is not fastened, the notification unit will issue an alert and notify the person in charge via SMS.

[0047] (Example 2) The safety confirmation system according to an embodiment of the present invention is a system that ensures the safety and health of children by installing an AI camera, GPS, a non-contact thermometer, and boarding and alighting sensors inside a school bus. As a result, the safety confirmation system can prevent children from being left behind on the school bus, from suffering from heatstroke, or from not wearing seatbelts in the event of an accident, and can check the health status of children and respond quickly if any abnormalities are detected.

[0048] A safety confirmation system according to an embodiment includes a boarding / alighting sensor, a non-contact thermometer, an AI camera, a GPS, and a notification unit. The boarding / alighting sensor checks the number of passengers. For example, the boarding / alighting sensor is installed at the entrance of a bus and automatically counts the number of children boarding. The non-contact thermometer checks body temperature. For example, the non-contact thermometer is installed at the entrance of the bus and measures the temperature of a child by pointing it at their forehead. If an abnormality is detected, an alert is issued. The AI ​​camera and GPS identify the kindergartener / student who boarded the bus. For example, the AI ​​camera recognizes the child's face, and the GPS records their location. The AI ​​camera checks the child's condition and whether or not they are wearing a seat belt. For example, the AI ​​camera monitors the child's movements and checks whether the seat belt is properly fastened. If an abnormality is detected, the notification unit notifies a designated person via SMS. For example, if a child has a high body temperature or a seat belt is not fastened, the notification unit issues an alert and notifies the person via SMS. As a result, the safety confirmation system of the embodiment can prevent children from being left behind on school buses, heatstroke, and not wearing seat belts in the event of an accident, check the health of children, and respond quickly if any abnormalities are found.

[0049] The boarding / alighting sensor can analyze the fluctuation pattern of the number of passengers and detect abnormal patterns. For example, the boarding / alighting sensor collects data from the boarding / alighting sensor and analyzes the past fluctuation pattern of the number of passengers. For example, it analyzes the fluctuation of the number of passengers during a specific time period or day of the week and detects abnormal patterns. This allows abnormal fluctuations in the number of passengers to be detected and responded to quickly.

[0050] The boarding and alighting sensor can transmit data on the number of passengers to the cloud in real time, enabling remote monitoring. For example, the boarding and alighting sensor can transmit data to the cloud, building a system that can monitor the number of passengers in real time from a remote location. For example, the data can be transmitted via the Internet, allowing an administrator to access it from a remote location. This allows the number of passengers to be monitored from a remote location.

[0051] A non-contact thermometer can analyze patterns of body temperature fluctuations and detect abnormal body temperature fluctuations. For example, a non-contact thermometer collects data from the non-contact thermometer and analyzes past patterns of body temperature fluctuations. For example, it analyzes body temperature fluctuations during specific times of the day or seasons to detect abnormal patterns. This allows abnormal body temperature fluctuations to be detected and addressed promptly.

[0052] Non-contact thermometers can transmit body temperature data to the cloud, making it possible to monitor body temperature from remote locations. For example, non-contact thermometers can transmit data from the non-contact thermometer to the cloud, creating a system that can monitor body temperature in real time from remote locations. For example, data can be transmitted via the Internet, allowing administrators to access the data from remote locations. This makes it possible to monitor body temperature from remote locations.

[0053] AI cameras can analyze children's movements and postures and detect abnormal movements. For example, AI cameras can be used to capture images of children's movements and postures while in the car, and algorithms can be developed to detect abnormal movements. For example, an alert can be issued if a child unbuckles their seat belt or makes an abnormal movement. This allows for the detection of abnormal movements and a quick response.

[0054] AI cameras can estimate a child's emotions and identify children who are experiencing stress or anxiety. For example, AI cameras can analyze a child's facial expressions while riding using emotion estimation algorithms linked to the AI ​​camera. For example, facial recognition technology can be used to analyze a child's facial expressions and detect signs of stress or anxiety. This allows for the identification of children who are experiencing stress or anxiety and for quick response.

[0055] When a warning occurs and a notification unit notifies the user by SMS, the notification unit can analyze the cause of the warning and automatically propose measures to prevent recurrence. The notification unit, for example, builds a system that analyzes the cause of the warning and automatically proposes measures to prevent recurrence. For example, it identifies the cause based on past data and proposes measures. This makes it possible to analyze the cause of the warning and propose measures to prevent recurrence.

[0056] The notification unit can send warning data to the cloud, enabling monitoring from a remote location. The notification unit, for example, sends warning data to the cloud, building a system that allows real-time monitoring from a remote location. For example, the data can be sent via the Internet, allowing an administrator to access it from a remote location. This allows warnings to be monitored from a remote location.

[0057] The notification unit can estimate a child's emotions when a warning is issued and identify children who are feeling stressed or anxious. For example, when a warning is issued, the notification unit uses an emotion estimation algorithm linked to an AI camera to analyze the child's facial expression. For example, it can use facial recognition technology to analyze the child's facial expression and detect signs of stress or anxiety. This makes it possible to identify children who are feeling stressed or anxious when a warning is issued and respond quickly.

[0058] The system can be applied to other public transportation systems to ensure passenger safety. For example, the system can be installed in train cars as a warning system to monitor passenger safety. For example, if an abnormal condition is detected, the system notifies the driver or manager. This can ensure passenger safety in other public transportation systems as well.

[0059] The notification unit can use the warning data to automatically notify a medical institution when an abnormality is detected. The notification unit, for example, analyzes the warning data and builds a system that automatically notifies a medical institution when an abnormality is detected. For example, an alert is sent to a medical institution when an abnormal state is detected. This allows automatic notification to a medical institution when an abnormality is detected.

[0060] The notification unit can analyze the child's emotions when a warning is issued and provide music or images to help them relax. For example, the notification unit can analyze the child's facial expression using an emotion estimation algorithm linked to an AI camera when a warning is issued. For example, the notification unit can analyze the child's facial expression using facial recognition technology and play music to help them relax. This makes it possible to provide music or images to help the child relax when a warning is issued.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The safety confirmation system can further include a voice recognition unit. The voice recognition unit can analyze the child's voice and detect abnormal vocalizations or cries for help. For example, if a child utters words such as "help me" or "it hurts," the voice recognition unit will detect this and immediately issue an alert. This allows for the rapid detection and response of abnormalities expressed by children.

[0063] The safety confirmation system can also be equipped with a vibration sensor. The vibration sensor detects abnormal shaking or impact of the bus and can detect signs of an accident early. For example, it detects abnormal vibrations during sudden braking or sharp turns and issues a warning to the driver. This reduces the risk of accidents and ensures the safety of children.

[0064] The safety confirmation system can also be equipped with an air quality sensor. The air quality sensor monitors the carbon dioxide concentration and levels of harmful substances inside the bus and issues an alert if an abnormality is detected. For example, if the carbon dioxide concentration becomes high, an alert is issued to prompt ventilation. This helps maintain the air quality inside the bus and protect the health of children.

[0065] The safety confirmation system can also be equipped with an ultraviolet sensor. The ultraviolet sensor monitors the level of ultraviolet light entering the bus and issues an alert if excessive ultraviolet light is detected. For example, if strong ultraviolet light is entering through the windows, an alert will be issued to prompt the driver to close the curtains. This will help protect children's skin from ultraviolet light.

[0066] The safety confirmation system can also include a humidity sensor. The humidity sensor monitors the humidity level inside the bus and issues an alert if an abnormality is detected. For example, if the humidity is too high, an alert is issued to prompt dehumidification. This helps maintain a comfortable environment inside the bus and protects the health of children.

[0067] The determination unit can estimate the user's emotions and provide a relaxing environment for the child based on the estimated user's emotions. For example, if the child is feeling anxious, the determination unit detects this and plays relaxing music, allowing the child to ride the bus in peace.

[0068] The determination unit can estimate the user's emotion and provide an activity that the child can enjoy based on the estimated user's emotion. For example, if the child is bored, the determination unit can detect this and provide a fun game or quiz, allowing the child to have a fun time on the bus.

[0069] The determination unit can estimate the user's emotions and provide a message that reassures the child based on the estimated user's emotions. For example, if a child is feeling anxious, the determination unit detects this and displays a reassuring message from a parent or teacher. This allows the child to ride the bus with peace of mind.

[0070] The determination unit can estimate the user's emotions and provide a scent that helps the child relax based on the estimated user's emotions. For example, if the child is feeling stressed, the determination unit detects this and emits a relaxing aroma, allowing the child to relax while riding the bus.

[0071] The determination unit can estimate the user's emotions and provide images that make the child feel safe based on the estimated user's emotions. For example, if the child is feeling anxious, the determination unit detects this and plays reassuring animations or images. This allows the child to ride the bus with peace of mind.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The boarding and alighting sensor checks the number of passengers. For example, the boarding and alighting sensor is installed at the entrance of the bus and automatically counts the number of children boarding. Step 2: A non-contact thermometer checks the body temperature. For example, a non-contact thermometer could be installed at the entrance of the bus, point it at the child's forehead to measure their temperature, and issue an alert if there is an abnormality. Step 3: The AI ​​camera and GPS identify the children / students who boarded the bus. For example, the AI ​​camera recognizes the children's faces, and the GPS records their location. Step 4: The AI ​​camera checks the child's condition and whether they are wearing a seat belt. For example, the AI ​​camera can monitor the child's movements and check whether the seat belt is properly fastened. Step 5: If the notification unit detects an abnormality, it will contact a designated person in charge via SMS. For example, if the person has a high body temperature or the seat belt is not fastened, the notification unit will issue an alert and notify the person in charge via SMS.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0087] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0140] 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]

[0141] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A boarding and alighting sensor that checks the number of passengers; A non-contact thermometer to check body temperature, AI cameras and GPS to identify the children / students riding in the bus, AI cameras check the child's condition and whether they are wearing a seat belt, A notification unit that notifies a designated person in charge by SMS when an abnormality is detected. A system characterized by:

2. The boarding / exiting sensor Analyzing the change pattern of the number of passengers and detecting abnormal patterns 2. The system of claim 1.

3. The non-contact thermometer The temperature fluctuation pattern is analyzed to detect abnormal temperature fluctuations.

2. The system of claim 1.

4. The AI ​​camera is Analyzing the child's movements and posture to detect abnormal movements 2. The system of claim 1.

5. The notification unit When a warning occurs, the cause of the warning is analyzed and measures to prevent recurrence are automatically proposed when contacting the customer via SMS.

2. The system of claim 1.

6. The boarding / exiting sensor The passenger count data is sent to the cloud in real time, enabling remote monitoring.

2. The system of claim 1.

7. The non-contact thermometer Body temperature data is sent to the cloud, allowing for remote monitoring of body temperature.

2. The system of claim 1.

8. The AI ​​camera is Estimate the child's emotions and identify children who are experiencing stress or anxiety 2. The system of claim 1.

Images