System

Abstract

An object of a system according to an embodiment is to quickly and accurately report to the police when involved in a crime.SOLUTION: A system according to an embodiment includes a video recognition unit, a voice recognition unit, a biological information analysis unit, an urgency determination unit, and an automatic notification unit. The video recognition unit recognizes a video. The voice recognition unit recognizes a voice. The biological information analysis unit analyzes the biological information. The urgency level determination unit determines an urgency level based on analysis results of the video recognition unit, the voice recognition unit, and the biological information analysis unit. The automatic notification unit automatically notifies the police when the degree of urgency determined by the urgency degree determination unit is high.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 and accurately report a crime to the police when involved in it.

[0005] The system according to the embodiment aims to quickly and accurately notify the police when a person is involved in a crime. [Means for solving the problem]

[0006] The system according to the embodiment includes a video recognition unit, a voice recognition unit, a biometric information analysis unit, an urgency determination unit, and an automatic reporting unit. The video recognition unit recognizes video. The voice recognition unit recognizes voice. The biometric information analysis unit analyzes biometric information. The urgency determination unit determines the urgency based on the analysis results of the video recognition unit, the voice recognition unit, and the biometric information analysis unit. The automatic reporting unit automatically reports to the police if the urgency determined by the urgency determination unit is high. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately notify the police when a person is involved in a crime. [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 reporting device according to the embodiment of the present invention is a system that, when a user is involved in a crime, accurately grasps the current situation and automatically reports it to the police. As a result, the automatic reporting device can contribute to crime prevention.

[0029] The automatic reporting device according to the embodiment includes a video recognition unit, a voice recognition unit, a biometric information analysis unit, an emergency level determination unit, and an automatic reporting unit. The video recognition unit recognizes video. For example, the video recognition unit uses a built-in camera to capture video of the surrounding area in real time, and the generation AI analyzes the video. For example, if a person with a knife appears, the generation AI analyzes the video and recognizes the presence of the knife. The generation AI can also identify specific individuals using facial recognition technology. The voice recognition unit recognizes voice. For example, the voice recognition unit collects surrounding voice in real time using a built-in microphone, and the generation AI analyzes the voice. For example, if a command such as "Don't move" is issued, the generation AI analyzes the voice and recognizes that an emergency has occurred. The generation AI can also identify specific instructions using voice command recognition technology. The biometric information analysis unit analyzes biometric information. For example, the biometric information analysis unit measures the user's heart rate, blood pressure, body temperature, etc. in real time, and the generation AI analyzes the data. For example, the generation AI determines that the user is facing an emergency if the user's heart rate suddenly increases. The generation AI can also evaluate the user's stress level by analyzing, for example, skin electrodermal responses. The urgency determination unit determines the level of emergency based on the analysis results of the video recognition unit, the voice recognition unit, and the biometric information analysis unit. For example, the urgency determination unit determines a high level of emergency if a person with a knife commands the user to "stay still" and the user's heart rate suddenly increases. The urgency determination unit can also predict the level of emergency based on similar crime situations by studying past crime data. The automatic reporting unit automatically reports to the police if the urgency determined by the urgency determination unit is high. For example, the automatic reporting unit generates a report including the current situation, the user's location information, and video and audio data, and transmits it to the police. The automatic reporting unit can also generate the report in multiple languages, for example, to enable international response. As a result, the automatic reporting device according to the embodiment automatically reports to the police when a user is involved in a crime, even in situations where it is difficult for the user to report the crime themselves, enabling a rapid response.For example, if a person with a knife appears in front of the user, the device will automatically grasp the situation and notify the police, ensuring the user's safety.

[0030] The video recognition unit analyzes the user's facial expressions and movements to detect expressions of tension or fear, enabling more accurate determination of the level of urgency. For example, the generation AI in the video recognition unit analyzes the user's facial expressions in real time to detect expressions of tension or fear. For example, it analyzes the wrinkles between the eyebrows and the degree of eye openness to determine whether the user is nervous. The generation AI in the video recognition unit also analyzes the user's movements to detect abnormal movements. For example, it analyzes hand tremors and body stiffness to determine whether the user is feeling fear. The generation AI in the video recognition unit also comprehensively analyzes the user's facial expressions and movements to quantify the level of tension or fear. For example, it combines facial expressions and body movements to identify situations with a high level of urgency. This allows the urgency of a criminal situation to be determined with even greater accuracy by analyzing the user's facial expressions and movements.

[0031] The video recognition unit creates 3D models of surrounding objects and the environment based on video data, making it possible to predict the location of a crime and escape routes. For example, the video recognition unit uses a generative AI to analyze video data and create 3D models of surrounding objects and the environment. For example, it can accurately determine the location of buildings and vehicles and pinpoint the location of a crime. The video recognition unit also uses a generative AI to predict a criminal's escape route based on the 3D model. For example, it can analyze the layout of buildings and the structure of roads to identify the most likely escape route. The video recognition unit also uses a generative AI to update the 3D model in real time to understand the progress of a crime. For example, it can track the criminal's movements and predict changes in the escape route. This makes it possible to predict the location of a crime and escape routes by creating 3D models of surrounding objects and the environment.

[0032] The video recognition unit works in conjunction with public surveillance camera systems, enabling real-time monitoring of crime situations over a wide area. In the video recognition unit, for example, the generation AI works in conjunction with public surveillance camera systems to analyze video data over a wide area in real time. For example, it integrates surveillance camera footage from around the city and monitors for crimes. In addition, the video recognition unit uses the generation AI to analyze video data from the surveillance camera system and detect abnormal behavior. For example, it identifies people behaving suspiciously and reports them to the police. In addition, the video recognition unit uses the generation AI to work in conjunction with surveillance camera systems to identify the location of a crime. For example, it integrates footage from multiple cameras and tracks the location of criminals in real time. This allows for real-time monitoring of crime situations over a wide area by working in conjunction with public surveillance camera systems.

[0033] The video recognition unit can work in conjunction with smart devices in the home to enhance safety within the home. For example, the generation AI in the video recognition unit works in conjunction with smart devices in the home to analyze video data in real time. For example, it analyzes video from a smart doorbell to detect the intrusion of a suspicious person. The generation AI in the video recognition unit also analyzes video data from smart devices to detect abnormal behavior. For example, it analyzes video from a security camera to identify abnormal situations within the home. The generation AI in the video recognition unit also works in conjunction with smart devices to enhance safety within the home. For example, it integrates video from smart doorbells and security cameras to monitor abnormalities within the home in real time. This allows for collaboration with smart devices in the home to enhance safety within the home.

[0034] The voice recognition unit analyzes the tone and intensity of the voice and can prioritize the detection of voices with a high level of urgency. For example, the generation AI analyzes voice data and detects voices with a high level of urgency based on the tone and intensity. For example, it prioritizes the recognition of screaming and crying. The voice recognition unit also quantifies the tone and intensity of the voice and determines the level of urgency based on that numerical value. For example, if the intensity of the voice is high, it sets the level of urgency high. The voice recognition unit also analyzes the voice data in real time using the generation AI and prioritizes the detection of voices with a high level of urgency. For example, if it detects screaming or crying, it immediately notifies the police. This allows the voice tone and intensity to be analyzed and the detection of voices with a high level of urgency to be prioritized.

[0035] The voice recognition unit works in conjunction with smart speakers and voice assistants to automatically detect emergencies within the home. For example, the generation AI in the voice recognition unit works in conjunction with smart speakers and voice assistants to analyze voice data in real time. For example, it detects screams and crying within the home. The generation AI in the voice recognition unit analyzes voice data from smart speakers and voice assistants to automatically detect emergencies. For example, it recognizes emergency calls such as "help." The generation AI in the voice recognition unit works in conjunction with smart speakers and voice assistants to detect emergencies based on voice data within the home. For example, if it detects an abnormal voice pattern, it will immediately notify the police. This makes it possible to automatically detect emergencies within the home by working in conjunction with smart speakers and voice assistants.

[0036] The voice recognition unit works in conjunction with microphone systems installed in public places, enabling wide-area emergency monitoring. For example, the generation AI works in conjunction with microphone systems installed in public places to analyze voice data in real time. For example, it detects screams and cries at train stations and shopping malls. The generation AI also analyzes voice data from microphone systems in public places and automatically detects emergencies. For example, it recognizes emergency calls such as "help." The generation AI also works in conjunction with microphone systems installed in public places to monitor emergencies based on wide-area voice data. For example, if it detects an abnormal voice pattern, it immediately notifies the police. This allows wide-area emergency monitoring by working in conjunction with microphone systems installed in public places.

[0037] The biometric information analysis unit can analyze the user's electrodermal response and detect the level of stress or tension. For example, the generation AI in the biometric information analysis unit measures the user's electrodermal response in real time and analyzes the level of stress or tension. For example, the stress level is determined based on changes in the skin's electrical resistance. The generation AI in the biometric information analysis unit quantifies the electrodermal response data and evaluates the level of stress or tension based on that numerical value. For example, if a sudden change in electrical resistance is detected, a high stress level is set. The generation AI in the biometric information analysis unit also integrates the electrodermal response data with other biometric information (heart rate, blood pressure, etc.) to comprehensively determine the level of stress or tension. For example, the stress level is evaluated by combining electrodermal response and heart rate data. This allows the user's electrodermal response to be analyzed and the level of stress or tension to be detected.

[0038] The biometric information analysis unit can analyze the user's breathing pattern and detect abnormal breathing. For example, the generation AI in the biometric information analysis unit measures the user's breathing pattern in real time and analyzes abnormal breathing. For example, it detects hyperventilation or dyspnea based on the rhythm and depth of breathing. The generation AI in the biometric information analysis unit also digitizes breathing pattern data and evaluates abnormal breathing based on that numerical value. For example, a sudden change in breathing rhythm is determined to be abnormal breathing. The generation AI in the biometric information analysis unit also integrates breathing pattern data with other biometric information (heart rate, blood pressure, etc.) to comprehensively determine abnormal breathing. For example, it combines breathing pattern and heart rate data to evaluate hyperventilation or dyspnea. This allows the user's breathing pattern to be analyzed and abnormal breathing to be detected.

[0039] The biometric information analysis unit works in conjunction with a wearable device to enable both daily health management and emergency detection. For example, the generation AI works in conjunction with a wearable device to analyze the user's biometric information in real time. For example, it analyzes heart rate and blood pressure data from a smartwatch to monitor health status. The generation AI also works in conjunction with a wearable device to perform daily health management and emergency detection based on the biometric information from the wearable device. For example, it issues an alert if it detects abnormal changes in heart rate or blood pressure. The generation AI also works in conjunction with a wearable device to comprehensively analyze the user's biometric information. For example, it integrates data from a smartwatch and fitness tracker to simultaneously monitor health status and emergencies. This allows the system to work in conjunction with a wearable device to enable both daily health management and emergency detection.

[0040] The biometric information analysis unit works in conjunction with the in-vehicle system to automatically detect emergencies while driving. For example, the generation AI works in conjunction with the in-vehicle system to analyze the user's biometric information in real time while driving. For example, it detects sudden illness based on heart rate and blood pressure data. The generation AI also automatically detects emergencies while driving based on biometric information from the in-vehicle system. For example, it issues a warning if it detects an abnormal heart rate or breathing pattern. The generation AI also works in conjunction with the in-vehicle system to comprehensively analyze the user's biometric information while driving. For example, it integrates heart rate and blood pressure data to evaluate the risk of sudden illness or an accident. This allows the generation AI to work in conjunction with the in-vehicle system to automatically detect emergencies while driving.

[0041] The urgency determination unit analyzes the user's behavior history and can detect deviations from normal behavior patterns. For example, the generation AI analyzes the user's behavior history and detects deviations from normal behavior patterns. For example, an abnormality is detected if the user behaves differently from usual. The urgency determination unit also evaluates abnormal behavior based on the user's behavior history. For example, if abnormal behavior is detected compared to normal behavior patterns, the urgency determination unit sets a high level of urgency. The generation AI also analyzes the user's behavior history in real time and detects deviations from normal behavior patterns. For example, an abnormality is detected if the user is in a different location than usual. This makes it possible to analyze the user's behavior history and detect deviations from normal behavior patterns.

[0042] The urgency determination unit can work in conjunction with a smart home system to automatically detect abnormal situations within the home. For example, the generation AI in the urgency determination unit works in conjunction with the smart home system to detect abnormal situations within the home in real time. For example, it analyzes data from smart sensors and detects abnormalities. The generation AI in the urgency determination unit also evaluates the level of urgency based on data from the smart home system. For example, if it detects an abnormal temperature change or a door opening or closing, it sets the level of urgency high. The generation AI in the urgency determination unit also works in conjunction with the smart home system to comprehensively analyze abnormal situations within the home. For example, it integrates data from smart sensors and cameras to detect abnormalities. This allows the unit to automatically detect abnormal situations within the home by working in conjunction with the smart home system.

[0043] The urgency judgment unit works in conjunction with a company's security system to automatically detect emergencies within the office. For example, the generation AI works in conjunction with a company's security system to detect emergencies within the office in real time. For example, it analyzes data from security cameras and sensors to detect abnormalities. The urgency judgment unit also evaluates the level of urgency based on data from the company's security system. For example, if abnormal movement or sound is detected, it sets the level of urgency high. The urgency judgment unit also works in conjunction with a company's security system to comprehensively analyze emergencies within the office. For example, it integrates data from security cameras and sensors to detect abnormalities. This makes it possible to automatically detect emergencies within the office by working in conjunction with a company's security system.

[0044] The automatic reporting unit can generate report content in multiple languages, enabling international response. For example, the generation AI of the automatic reporting unit generates report content in multiple languages, enabling international response. For example, report content is created in multiple languages ​​such as English, French, and Chinese. The generation AI of the automatic reporting unit also automatically translates the report content, enabling international response. For example, the report content is translated in real time and reported to the police in each country in the appropriate language. The generation AI of the automatic reporting unit also builds a multilingual reporting system, enabling international response. For example, the report content is generated in multiple languages ​​and reported to international police agencies. In this way, generating report content in multiple languages ​​enables international response.

[0045] The automatic reporting unit can provide detailed map information of the location of the crime in addition to the report content. For example, the generation AI of the automatic reporting unit provides detailed map information of the location of the crime in addition to the report content. For example, the report content includes GPS data to provide accurate location information to the police. The automatic reporting unit also integrates map information into the report content to show the location of the crime in detail. For example, the report content includes a screenshot or link to the map. The automatic reporting unit also generates detailed map information of the location of the crime in real time in addition to the report content. For example, the report content includes a map based on the current location information. This allows the police to respond quickly by providing detailed map information of the location of the crime in addition to the report content.

[0046] The automatic reporting unit can also respond to medical emergencies by linking with the medical institution's emergency reporting system. For example, the generation AI can link the automatic reporting function with the medical institution's emergency reporting system to respond to medical emergencies. For example, if an abnormality in heart rate or blood pressure is detected, it will notify the medical institution. The automatic reporting unit can also link with the medical institution's emergency reporting system to automatically detect medical emergencies. For example, if abnormal biological information is detected, it will notify the medical institution. The generation AI can also integrate the automatic reporting function with the medical institution's system to respond quickly to medical emergencies. For example, it can analyze abnormal biological information in real time and notify the medical institution. This allows it to respond to medical emergencies by linking with the medical institution's emergency reporting system.

[0047] The automatic reporting unit can work in conjunction with the school's security system to respond to emergencies within the school. For example, the generation AI can link the automatic reporting function with the school's security system to respond to emergencies within the school. For example, if it detects abnormal behavior or sound, it will notify school security. The automatic reporting unit can also work in conjunction with the school's security system to automatically detect emergencies within the school. For example, if it detects abnormal behavior or sound, it will notify school security. The generation AI can also integrate the automatic reporting function with the school's system to respond quickly to emergencies within the school. For example, it can analyze abnormal behavior or sound in real time and notify school security. This allows it to work in conjunction with the school's security system to respond to emergencies within the school.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The automatic reporting device can also analyze the user's behavioral history and detect deviations from normal behavioral patterns. For example, if the user behaves differently than usual, an anomaly is detected. The generation AI also evaluates abnormal behavior based on the user's behavioral history. For example, if abnormal behavior is detected compared to normal behavioral patterns, a high level of urgency is set. The generation AI also analyzes the user's behavioral history in real time and detects deviations from normal behavioral patterns. For example, an anomaly is detected if the user is in a different location than usual. This makes it possible to analyze the user's behavioral history and detect deviations from normal behavioral patterns.

[0050] The automatic reporting device can also link with a smart home system to automatically detect abnormal situations within the home. For example, the generating AI can link with the smart home system to detect abnormal situations within the home in real time. For example, it can analyze data from smart sensors and detect abnormalities. The generating AI can also evaluate the level of urgency based on the data from the smart home system. For example, if it detects an abnormal temperature change or a door opening or closing, it can set the level of urgency to a high level. The generating AI can also link with the smart home system to comprehensively analyze abnormal situations within the home. For example, it can integrate data from smart sensors and cameras to detect abnormalities. In this way, by linking with the smart home system, it can automatically detect abnormal situations within the home.

[0051] The automatic reporting device can also link with a company's security system to automatically detect emergencies within the office. For example, the generating AI can link with a company's security system to detect emergencies within the office in real time. For example, it can analyze data from security cameras and sensors to detect anomalies. The generating AI can also evaluate the level of urgency based on the data from the company's security system. For example, if it detects abnormal movement or sound, it can set the level of urgency high. The generating AI can also link with the company's security system to comprehensively analyze emergencies within the office. For example, it can integrate data from security cameras and sensors to detect anomalies. This makes it possible to automatically detect emergencies within the office by linking with the company's security system.

[0052] The automatic reporting device can also generate report content in multiple languages, enabling international response. For example, the generation AI can generate report content in multiple languages, enabling international response. For example, report content can be created in multiple languages ​​such as English, French, and Chinese. The generation AI can also automatically translate the report content, enabling international response. For example, the report content can be translated in real time and reported to the police in each country in the appropriate language. The generation AI can also build a multilingual reporting system, enabling international response. For example, the report content can be generated in multiple languages ​​and reported to an international police agency. In this way, generating report content in multiple languages ​​enables international response.

[0053] The automatic reporting device can also provide detailed map information of the location of the crime in addition to the report content. For example, the generation AI provides detailed map information of the location of the crime in addition to the report content. For example, the report content may include GPS data to provide the police with precise location information. The generation AI may also integrate map information into the report content to show the location of the crime in detail. For example, the report content may include a screenshot or link to the map. The generation AI may also generate detailed map information of the location of the crime in real time in addition to the report content. For example, the report content may include a map based on the current location information. This allows the police to respond quickly by providing detailed map information of the location of the crime in addition to the report content.

[0054] The automatic reporting device can also be linked to a medical institution's emergency reporting system to respond to medical emergencies. For example, the generating AI can link the automatic reporting function with a medical institution's emergency reporting system to respond to medical emergencies. For example, if it detects an abnormality in heart rate or blood pressure, it will notify the medical institution. The generating AI can also link with a medical institution's emergency reporting system to automatically detect medical emergencies. For example, if it detects abnormal biological information, it will notify the medical institution. The generating AI can also integrate the automatic reporting function with the medical institution's system to respond quickly to medical emergencies. For example, it can analyze abnormal biological information in real time and notify the medical institution. This allows it to respond to medical emergencies by linking with the medical institution's emergency reporting system.

[0055] The automatic reporting device can also be linked to the school's security system to respond to emergencies within the school. For example, the generation AI can link the automatic reporting function with the school's security system to respond to emergencies within the school. For example, if it detects abnormal behavior or sound, it will notify school security. The generation AI can also link with the school's security system to automatically detect emergencies within the school. For example, if it detects abnormal behavior or sound, it will notify school security. The generation AI can also integrate the automatic reporting function with the school's system to respond quickly to emergencies within the school. For example, it can analyze abnormal behavior or sound in real time and notify school security. This allows it to respond to emergencies within the school by linking with the school's security system.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The image recognition unit recognizes the image. For example, the built-in camera can be used to capture real-time images of the surrounding area, and the generation AI can analyze the images. If a person holding a knife appears, the generation AI can analyze the image and recognize the presence of the knife. It can also use facial recognition technology to identify specific people. Step 2: The voice recognition unit recognizes voices. For example, it uses a built-in microphone to collect surrounding sounds in real time, and the generation AI analyzes the voices. If a command such as "Don't move" is given, the generation AI analyzes the voice and recognizes that it is an emergency. It can also identify specific instructions using voice command recognition technology. Step 3: The biometric analysis unit analyzes the biometric information. For example, it measures the user's heart rate, blood pressure, body temperature, etc. in real time, and the generation AI analyzes the data. If the heart rate rises suddenly, the generation AI determines that the user is facing an emergency. It can also analyze the user's electrodermal response to assess the user's stress level. Step 4: The urgency determination unit determines the level of urgency based on the analysis results of the video recognition unit, voice recognition unit, and biometric information analysis unit. For example, if a person with a knife commands the user to "stay still" and the user's heart rate rises sharply, the unit determines the level of urgency to be high. It can also learn from past crime data and predict the level of urgency based on similar crime situations. Step 5: The automatic reporting unit automatically reports to the police if the emergency level determined by the emergency level determination unit is high. For example, it generates a report including the current situation, the user's location information, and video and audio data, and sends it to the police. It can also generate report content in multiple languages, enabling international response.

[0058] (Example 2) The automatic reporting device according to the embodiment of the present invention is a system that, when a user is involved in a crime, accurately grasps the current situation and automatically reports it to the police. As a result, the automatic reporting device can contribute to crime prevention.

[0059] The automatic reporting device according to the embodiment includes a video recognition unit, a voice recognition unit, a biometric information analysis unit, an emergency level determination unit, and an automatic reporting unit. The video recognition unit recognizes video. For example, the video recognition unit uses a built-in camera to capture video of the surrounding area in real time, and the generation AI analyzes the video. For example, if a person with a knife appears, the generation AI analyzes the video and recognizes the presence of the knife. The generation AI can also identify specific individuals using facial recognition technology. The voice recognition unit recognizes voice. For example, the voice recognition unit collects surrounding voice in real time using a built-in microphone, and the generation AI analyzes the voice. For example, if a command such as "Don't move" is issued, the generation AI analyzes the voice and recognizes that an emergency has occurred. The generation AI can also identify specific instructions using voice command recognition technology. The biometric information analysis unit analyzes biometric information. For example, the biometric information analysis unit measures the user's heart rate, blood pressure, body temperature, etc. in real time, and the generation AI analyzes the data. For example, the generation AI determines that the user is facing an emergency if the user's heart rate suddenly increases. The generation AI can also evaluate the user's stress level by analyzing, for example, skin electrodermal responses. The urgency determination unit determines the level of emergency based on the analysis results of the video recognition unit, the voice recognition unit, and the biometric information analysis unit. For example, the urgency determination unit determines a high level of emergency if a person with a knife commands the user to "stay still" and the user's heart rate suddenly increases. The urgency determination unit can also predict the level of emergency based on similar crime situations by studying past crime data. The automatic reporting unit automatically reports to the police if the urgency determined by the urgency determination unit is high. For example, the automatic reporting unit generates a report including the current situation, the user's location information, and video and audio data, and transmits it to the police. The automatic reporting unit can also generate the report in multiple languages, for example, to enable international response. As a result, the automatic reporting device according to the embodiment automatically reports to the police when a user is involved in a crime, even in situations where it is difficult for the user to report the crime themselves, enabling a rapid response.For example, if a person with a knife appears in front of the user, the device will automatically grasp the situation and notify the police, ensuring the user's safety.

[0060] The video recognition unit analyzes the user's facial expressions and movements to detect expressions of tension or fear, enabling more accurate determination of the level of urgency. For example, the generation AI in the video recognition unit analyzes the user's facial expressions in real time to detect expressions of tension or fear. For example, it analyzes the wrinkles between the eyebrows and the degree of eye openness to determine whether the user is nervous. The generation AI in the video recognition unit also analyzes the user's movements to detect abnormal movements. For example, it analyzes hand tremors and body stiffness to determine whether the user is feeling fear. The generation AI in the video recognition unit also comprehensively analyzes the user's facial expressions and movements to quantify the level of tension or fear. For example, it combines facial expressions and body movements to identify situations with a high level of urgency. This allows the urgency of a criminal situation to be determined with even greater accuracy by analyzing the user's facial expressions and movements.

[0061] The video recognition unit creates 3D models of surrounding objects and the environment based on video data, making it possible to predict the location of a crime and escape routes. For example, the video recognition unit uses a generative AI to analyze video data and create 3D models of surrounding objects and the environment. For example, it can accurately determine the location of buildings and vehicles and pinpoint the location of a crime. The video recognition unit also uses a generative AI to predict a criminal's escape route based on the 3D model. For example, it can analyze the layout of buildings and the structure of roads to identify the most likely escape route. The video recognition unit also uses a generative AI to update the 3D model in real time to understand the progress of a crime. For example, it can track the criminal's movements and predict changes in the escape route. This makes it possible to predict the location of a crime and escape routes by creating 3D models of surrounding objects and the environment.

[0062] The video recognition unit uses an emotion estimation function to estimate emotions from the user's facial expressions and can determine the level of urgency based on those emotions. For example, the generation AI in the video recognition unit analyzes the user's facial expressions to estimate emotions. For example, it analyzes smiling and angry expressions to determine the user's emotional state. The generation AI in the video recognition unit also quantifies the user's emotions and determines the level of urgency based on those values. For example, if the emotion of fear is strong, the urgency level is set high. The generation AI in the video recognition unit also integrates the user's emotion estimation results with other data (video, audio, biometric information) to comprehensively determine the level of urgency. For example, it combines the emotion estimation results with an increase in heart rate to evaluate the level of urgency. This makes it possible to estimate emotions from the user's facial expressions and determine the urgency of the crime situation based on those emotions.

[0063] The video recognition unit works in conjunction with public surveillance camera systems, enabling real-time monitoring of crime situations over a wide area. In the video recognition unit, for example, the generation AI works in conjunction with public surveillance camera systems to analyze video data over a wide area in real time. For example, it integrates surveillance camera footage from around the city and monitors for crimes. In addition, the video recognition unit uses the generation AI to analyze video data from the surveillance camera system and detect abnormal behavior. For example, it identifies people behaving suspiciously and reports them to the police. In addition, the video recognition unit uses the generation AI to work in conjunction with surveillance camera systems to identify the location of a crime. For example, it integrates footage from multiple cameras and tracks the location of criminals in real time. This allows for real-time monitoring of crime situations over a wide area by working in conjunction with public surveillance camera systems.

[0064] The video recognition unit can work in conjunction with smart devices in the home to enhance safety within the home. For example, the generation AI in the video recognition unit works in conjunction with smart devices in the home to analyze video data in real time. For example, it analyzes video from a smart doorbell to detect the intrusion of a suspicious person. The generation AI in the video recognition unit also analyzes video data from smart devices to detect abnormal behavior. For example, it analyzes video from a security camera to identify abnormal situations within the home. The generation AI in the video recognition unit also works in conjunction with smart devices to enhance safety within the home. For example, it integrates video from smart doorbells and security cameras to monitor abnormalities within the home in real time. This allows for collaboration with smart devices in the home to enhance safety within the home.

[0065] The video recognition unit uses the emotion estimation function to analyze the user's emotions in real time and issue alerts and notifications to elicit positive emotions. For example, the generation AI in the video recognition unit analyzes the user's facial expressions and estimates their emotions in real time. For example, it analyzes smiling and surprised expressions to determine the user's emotional state. The video recognition unit also issues alerts and notifications to elicit positive emotions based on the user's emotion estimation results. For example, if the user is nervous, it displays a message to relax. The video recognition unit also integrates the user's emotion estimation results with other data (video, audio, biometric information) and issues alerts and notifications to elicit overall positive emotions. For example, it combines the emotion estimation results with heart rate data to display an appropriate alert. This allows the user's emotions to be analyzed in real time and alerts and notifications to elicit positive emotions.

[0066] The voice recognition unit analyzes the tone and intensity of the voice and can prioritize the detection of voices with a high level of urgency. For example, the generation AI analyzes voice data and detects voices with a high level of urgency based on the tone and intensity. For example, it prioritizes the recognition of screaming and crying. The voice recognition unit also quantifies the tone and intensity of the voice and determines the level of urgency based on that numerical value. For example, if the intensity of the voice is high, it sets the level of urgency high. The voice recognition unit also analyzes the voice data in real time using the generation AI and prioritizes the detection of voices with a high level of urgency. For example, if it detects screaming or crying, it immediately notifies the police. This allows the voice tone and intensity to be analyzed and the detection of voices with a high level of urgency to be prioritized.

[0067] The speech recognition unit uses an emotion estimation function to estimate emotions from speech and can determine the level of urgency based on those emotions. In the speech recognition unit, for example, the generation AI analyzes speech data and estimates emotions. For example, it analyzes the tone and intensity of the voice to determine the user's emotional state. The speech recognition unit also quantifies the emotions estimated from speech by the generation AI and determines the level of urgency based on that numerical value. For example, if the emotion of fear is strong, the urgency level is set high. The speech recognition unit also analyzes speech data in real time using the generation AI and determines the level of urgency of the criminal situation based on the emotion estimation results. For example, if the emotion of fear is strong, the police will be notified immediately. This makes it possible to estimate emotions from speech and determine the level of urgency of the criminal situation based on those emotions.

[0068] The voice recognition unit works in conjunction with smart speakers and voice assistants to automatically detect emergencies within the home. For example, the generation AI in the voice recognition unit works in conjunction with smart speakers and voice assistants to analyze voice data in real time. For example, it detects screams and crying within the home. The generation AI in the voice recognition unit analyzes voice data from smart speakers and voice assistants to automatically detect emergencies. For example, it recognizes emergency calls such as "help." The generation AI in the voice recognition unit works in conjunction with smart speakers and voice assistants to detect emergencies based on voice data within the home. For example, if it detects an abnormal voice pattern, it will immediately notify the police. This makes it possible to automatically detect emergencies within the home by working in conjunction with smart speakers and voice assistants.

[0069] The voice recognition unit works in conjunction with microphone systems installed in public places, enabling wide-area emergency monitoring. For example, the generation AI works in conjunction with microphone systems installed in public places to analyze voice data in real time. For example, it detects screams and cries at train stations and shopping malls. The generation AI also analyzes voice data from microphone systems in public places and automatically detects emergencies. For example, it recognizes emergency calls such as "help." The generation AI also works in conjunction with microphone systems installed in public places to monitor emergencies based on wide-area voice data. For example, if it detects an abnormal voice pattern, it immediately notifies the police. This allows wide-area emergency monitoring by working in conjunction with microphone systems installed in public places.

[0070] The voice recognition unit uses an emotion estimation function to analyze the user's emotions in real time during voice recognition and can provide voice feedback to elicit positive emotions. In the voice recognition unit, for example, a generation AI analyzes voice data and estimates emotions in real time. For example, the voice tone and intensity are analyzed to determine the user's emotional state. The voice recognition unit also provides voice feedback to elicit positive emotions based on the emotions estimated from the voice by the generation AI. For example, if the user is nervous, a message to relax is played. The voice recognition unit also analyzes voice data in real time using the generation AI and provides voice feedback to elicit positive emotions based on the emotion estimation results. For example, the emotion estimation results and voice data are combined to provide appropriate feedback. This makes it possible to analyze the user's emotions in real time and provide voice feedback to elicit positive emotions.

[0071] The biometric information analysis unit can analyze the user's electrodermal response and detect the level of stress or tension. For example, the generation AI in the biometric information analysis unit measures the user's electrodermal response in real time and analyzes the level of stress or tension. For example, the stress level is determined based on changes in the skin's electrical resistance. The generation AI in the biometric information analysis unit quantifies the electrodermal response data and evaluates the level of stress or tension based on that numerical value. For example, if a sudden change in electrical resistance is detected, a high stress level is set. The generation AI in the biometric information analysis unit also integrates the electrodermal response data with other biometric information (heart rate, blood pressure, etc.) to comprehensively determine the level of stress or tension. For example, the stress level is evaluated by combining electrodermal response and heart rate data. This allows the user's electrodermal response to be analyzed and the level of stress or tension to be detected.

[0072] The biometric information analysis unit can analyze the user's breathing pattern and detect abnormal breathing. For example, the generation AI in the biometric information analysis unit measures the user's breathing pattern in real time and analyzes abnormal breathing. For example, it detects hyperventilation or dyspnea based on the rhythm and depth of breathing. The generation AI in the biometric information analysis unit also digitizes breathing pattern data and evaluates abnormal breathing based on that numerical value. For example, a sudden change in breathing rhythm is determined to be abnormal breathing. The generation AI in the biometric information analysis unit also integrates breathing pattern data with other biometric information (heart rate, blood pressure, etc.) to comprehensively determine abnormal breathing. For example, it combines breathing pattern and heart rate data to evaluate hyperventilation or dyspnea. This allows the user's breathing pattern to be analyzed and abnormal breathing to be detected.

[0073] The biometric information analysis unit can use the emotion estimation function to estimate emotions from biometric information and determine the level of urgency based on those emotions. For example, the generation AI in the biometric information analysis unit analyzes the user's biometric information (heart rate, blood pressure, skin galvanic response, etc.) and estimates emotions. For example, the user's emotional state is determined based on an increase in heart rate or changes in skin galvanic response. The biometric information analysis unit also quantifies the emotions estimated by the generation AI from the biometric information and determines the level of urgency based on those numerical values. For example, if the emotion of fear is strong, the urgency level is set high. The biometric information analysis unit also analyzes the biometric information in real time and determines the level of urgency of the criminal situation based on the emotion estimation results. For example, if the emotion of fear is strong, the police are immediately notified. This makes it possible to estimate emotions from biometric information and determine the level of urgency of the criminal situation based on those emotions.

[0074] The biometric information analysis unit works in conjunction with a wearable device to enable both daily health management and emergency detection. For example, the generation AI works in conjunction with a wearable device to analyze the user's biometric information in real time. For example, it analyzes heart rate and blood pressure data from a smartwatch to monitor health status. The generation AI also works in conjunction with a wearable device to perform daily health management and emergency detection based on the biometric information from the wearable device. For example, it issues an alert if it detects abnormal changes in heart rate or blood pressure. The generation AI also works in conjunction with a wearable device to comprehensively analyze the user's biometric information. For example, it integrates data from a smartwatch and fitness tracker to simultaneously monitor health status and emergencies. This allows the system to work in conjunction with a wearable device to enable both daily health management and emergency detection.

[0075] The biometric information analysis unit works in conjunction with the in-vehicle system to automatically detect emergencies while driving. For example, the generation AI works in conjunction with the in-vehicle system to analyze the user's biometric information in real time while driving. For example, it detects sudden illness based on heart rate and blood pressure data. The generation AI also automatically detects emergencies while driving based on biometric information from the in-vehicle system. For example, it issues a warning if it detects an abnormal heart rate or breathing pattern. The generation AI also works in conjunction with the in-vehicle system to comprehensively analyze the user's biometric information while driving. For example, it integrates heart rate and blood pressure data to evaluate the risk of sudden illness or an accident. This allows the generation AI to work in conjunction with the in-vehicle system to automatically detect emergencies while driving.

[0076] The biometric information analysis unit uses the emotion estimation function to analyze the user's emotions in real time during biometric information analysis and can provide feedback to elicit positive emotions. In the biometric information analysis unit, for example, the generation AI analyzes the user's biometric information and estimates emotions in real time. For example, the user's emotional state is determined based on heart rate and skin electrical response. The biometric information analysis unit also provides feedback to elicit positive emotions based on the emotions estimated by the generation AI from the biometric information. For example, if the user is nervous, a message to relax is displayed. The biometric information analysis unit also analyzes the biometric information in real time using the generation AI and provides feedback to elicit positive emotions based on the emotion estimation results. For example, the emotion estimation results and biometric information are combined to provide appropriate feedback. This makes it possible to analyze the user's emotions in real time and provide feedback to elicit positive emotions.

[0077] The urgency determination unit analyzes the user's behavior history and can detect deviations from normal behavior patterns. For example, the generation AI analyzes the user's behavior history and detects deviations from normal behavior patterns. For example, an abnormality is detected if the user behaves differently from usual. The urgency determination unit also evaluates abnormal behavior based on the user's behavior history. For example, if abnormal behavior is detected compared to normal behavior patterns, the urgency determination unit sets a high level of urgency. The generation AI also analyzes the user's behavior history in real time and detects deviations from normal behavior patterns. For example, an abnormality is detected if the user is in a different location than usual. This makes it possible to analyze the user's behavior history and detect deviations from normal behavior patterns.

[0078] The urgency determination unit can analyze the user's emotions using the emotion estimation function and determine the level of urgency based on those emotions. For example, the generation AI analyzes the user's emotions and determines the level of urgency based on those emotions. For example, if the emotion of fear is strong, the urgency determination unit sets a high level of urgency. The generation AI also quantifies the emotion estimation result and evaluates the level of urgency based on that numerical value. For example, if the emotion of fear is strong, the police are immediately notified. The generation AI also integrates the emotion estimation result with other data (video, audio, biometric information) to comprehensively determine the level of urgency. For example, the emotion estimation result is combined with heart rate data to evaluate the level of urgency. This allows the generation AI to analyze the user's emotions and determine the level of urgency based on those emotions.

[0079] The urgency determination unit can work in conjunction with a smart home system to automatically detect abnormal situations within the home. For example, the generation AI in the urgency determination unit works in conjunction with the smart home system to detect abnormal situations within the home in real time. For example, it analyzes data from smart sensors and detects abnormalities. The generation AI in the urgency determination unit also evaluates the level of urgency based on data from the smart home system. For example, if it detects an abnormal temperature change or a door opening or closing, it sets the level of urgency high. The generation AI in the urgency determination unit also works in conjunction with the smart home system to comprehensively analyze abnormal situations within the home. For example, it integrates data from smart sensors and cameras to detect abnormalities. This allows the unit to automatically detect abnormal situations within the home by working in conjunction with the smart home system.

[0080] The urgency judgment unit works in conjunction with a company's security system to automatically detect emergencies within the office. For example, the generation AI works in conjunction with a company's security system to detect emergencies within the office in real time. For example, it analyzes data from security cameras and sensors to detect abnormalities. The urgency judgment unit also evaluates the level of urgency based on data from the company's security system. For example, if abnormal movement or sound is detected, it sets the level of urgency high. The urgency judgment unit also works in conjunction with a company's security system to comprehensively analyze emergencies within the office. For example, it integrates data from security cameras and sensors to detect abnormalities. This makes it possible to automatically detect emergencies within the office by working in conjunction with a company's security system.

[0081] The urgency determination unit uses the emotion estimation function to analyze the user's emotions in real time when determining the urgency level, and can provide a notification to elicit positive emotions. For example, the generation AI in the urgency determination unit analyzes the user's emotions in real time and provides a notification to elicit positive emotions when determining the urgency level. For example, if the user is nervous, a message to relax is displayed. The urgency determination unit also provides a notification to elicit positive emotions based on the emotion estimation results of the generation AI. For example, if the user is feeling scared, a message to reassure is displayed. The urgency determination unit also integrates the emotion estimation results with other data (video, audio, biometric information) and provides a notification to comprehensively elicit positive emotions. For example, the emotion estimation results are combined with heart rate data to provide an appropriate notification. This allows the user's emotions to be analyzed in real time, and a notification to elicit positive emotions to be provided.

[0082] The automatic reporting unit can generate report content in multiple languages, enabling international response. For example, the generation AI of the automatic reporting unit generates report content in multiple languages, enabling international response. For example, report content is created in multiple languages ​​such as English, French, and Chinese. The generation AI of the automatic reporting unit also automatically translates the report content, enabling international response. For example, the report content is translated in real time and reported to the police in each country in the appropriate language. The generation AI of the automatic reporting unit also builds a multilingual reporting system, enabling international response. For example, the report content is generated in multiple languages ​​and reported to international police agencies. In this way, generating report content in multiple languages ​​enables international response.

[0083] The automatic reporting unit can provide detailed map information of the location of the crime in addition to the report content. For example, the generation AI of the automatic reporting unit provides detailed map information of the location of the crime in addition to the report content. For example, the report content includes GPS data to provide accurate location information to the police. The automatic reporting unit also integrates map information into the report content to show the location of the crime in detail. For example, the report content includes a screenshot or link to the map. The automatic reporting unit also generates detailed map information of the location of the crime in real time in addition to the report content. For example, the report content includes a map based on the current location information. This allows the police to respond quickly by providing detailed map information of the location of the crime in addition to the report content.

[0084] The automatic reporting unit can use the emotion estimation function to include the user's emotional state in the report content, allowing the police to grasp the situation more accurately. For example, the automatic reporting unit allows the generation AI to include the user's emotional state in the report content, allowing the police to grasp the situation more accurately. For example, the generation AI may include the user's degree of fear or tension in the report content. The automatic reporting unit also allows the generation AI to integrate the emotion estimation results into the report content, allowing the police to grasp the situation more accurately. For example, the generation AI may include the user's emotion score in the report content. The automatic reporting unit also allows the generation AI to reflect the user's emotional state in the report content in real time, allowing the police to grasp the situation more accurately. For example, the generation AI may add the user's emotion estimation results to the report content. In this way, the inclusion of the user's emotional state in the report content allows the police to grasp the situation more accurately.

[0085] The automatic reporting unit can also respond to medical emergencies by linking with the medical institution's emergency reporting system. For example, the generation AI can link the automatic reporting function with the medical institution's emergency reporting system to respond to medical emergencies. For example, if an abnormality in heart rate or blood pressure is detected, it will notify the medical institution. The automatic reporting unit can also link with the medical institution's emergency reporting system to automatically detect medical emergencies. For example, if abnormal biological information is detected, it will notify the medical institution. The generation AI can also integrate the automatic reporting function with the medical institution's system to respond quickly to medical emergencies. For example, it can analyze abnormal biological information in real time and notify the medical institution. This allows it to respond to medical emergencies by linking with the medical institution's emergency reporting system.

[0086] The automatic reporting unit can work in conjunction with the school's security system to respond to emergencies within the school. For example, the generation AI can link the automatic reporting function with the school's security system to respond to emergencies within the school. For example, if it detects abnormal behavior or sound, it will notify school security. The automatic reporting unit can also work in conjunction with the school's security system to automatically detect emergencies within the school. For example, if it detects abnormal behavior or sound, it will notify school security. The generation AI can also integrate the automatic reporting function with the school's system to respond quickly to emergencies within the school. For example, it can analyze abnormal behavior or sound in real time and notify school security. This allows it to work in conjunction with the school's security system to respond to emergencies within the school.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The automatic reporting device can also analyze the user's behavioral history and detect deviations from normal behavioral patterns. For example, if the user behaves differently than usual, an anomaly is detected. The generation AI also evaluates abnormal behavior based on the user's behavioral history. For example, if abnormal behavior is detected compared to normal behavioral patterns, a high level of urgency is set. The generation AI also analyzes the user's behavioral history in real time and detects deviations from normal behavioral patterns. For example, an anomaly is detected if the user is in a different location than usual. This makes it possible to analyze the user's behavioral history and detect deviations from normal behavioral patterns.

[0089] The automatic reporting device can also use an emotion estimation function to analyze the user's emotions and determine the level of urgency based on those emotions. For example, the generation AI analyzes the user's emotions and determines the level of urgency based on those emotions. For example, if the emotion of fear is strong, the level of urgency is set high. The generation AI also quantifies the emotion estimation results and evaluates the level of urgency based on that number. For example, if the emotion of fear is strong, the police will be notified immediately. The generation AI also integrates the emotion estimation results with other data (video, audio, biometric information) to comprehensively determine the level of urgency. For example, the emotion estimation results are combined with heart rate data to evaluate the level of urgency. This allows the user's emotions to be analyzed and the level of urgency to be determined based on those emotions.

[0090] The automatic reporting device can also link with a smart home system to automatically detect abnormal situations within the home. For example, the generating AI can link with the smart home system to detect abnormal situations within the home in real time. For example, it can analyze data from smart sensors and detect abnormalities. The generating AI can also evaluate the level of urgency based on the data from the smart home system. For example, if it detects an abnormal temperature change or a door opening or closing, it can set the level of urgency to a high level. The generating AI can also link with the smart home system to comprehensively analyze abnormal situations within the home. For example, it can integrate data from smart sensors and cameras to detect abnormalities. In this way, by linking with the smart home system, it can automatically detect abnormal situations within the home.

[0091] The automatic reporting device can also link with a company's security system to automatically detect emergencies within the office. For example, the generating AI can link with a company's security system to detect emergencies within the office in real time. For example, it can analyze data from security cameras and sensors to detect anomalies. The generating AI can also evaluate the level of urgency based on the data from the company's security system. For example, if it detects abnormal movement or sound, it can set the level of urgency high. The generating AI can also link with the company's security system to comprehensively analyze emergencies within the office. For example, it can integrate data from security cameras and sensors to detect anomalies. This makes it possible to automatically detect emergencies within the office by linking with the company's security system.

[0092] The automatic reporting device can also use its emotion estimation function to analyze the user's emotions in real time when determining the level of urgency and issue a notification designed to elicit positive emotions. For example, the generation AI can analyze the user's emotions in real time and issue a notification designed to elicit positive emotions when determining the level of urgency. For example, if the user is nervous, it can display a message to encourage relaxation. The generation AI can also issue a notification designed to elicit positive emotions based on the emotion estimation results. For example, if the user is feeling fear, it can display a message to reassure them. The generation AI can also integrate the emotion estimation results with other data (video, audio, biometric information) and issue a notification designed to elicit overall positive emotions. For example, it can combine the emotion estimation results with heart rate data to issue an appropriate notification. This makes it possible to analyze the user's emotions in real time and issue a notification designed to elicit positive emotions.

[0093] The automatic reporting device can also generate report content in multiple languages, enabling international response. For example, the generation AI can generate report content in multiple languages, enabling international response. For example, report content can be created in multiple languages ​​such as English, French, and Chinese. The generation AI can also automatically translate the report content, enabling international response. For example, the report content can be translated in real time and reported to the police in each country in the appropriate language. The generation AI can also build a multilingual reporting system, enabling international response. For example, the report content can be generated in multiple languages ​​and reported to an international police agency. In this way, generating report content in multiple languages ​​enables international response.

[0094] The automatic reporting device can also provide detailed map information of the location of the crime in addition to the report content. For example, the generation AI provides detailed map information of the location of the crime in addition to the report content. For example, the report content may include GPS data to provide the police with precise location information. The generation AI may also integrate map information into the report content to show the location of the crime in detail. For example, the report content may include a screenshot or link to the map. The generation AI may also generate detailed map information of the location of the crime in real time in addition to the report content. For example, the report content may include a map based on the current location information. This allows the police to respond quickly by providing detailed map information of the location of the crime in addition to the report content.

[0095] The automatic reporting device can also use an emotion estimation function to include the user's emotional state in the report, allowing the police to understand the situation more accurately. For example, the generation AI can include the user's emotional state in the report, allowing the police to understand the situation more accurately. For example, the user's level of fear or tension can be noted in the report. The generation AI can also integrate the emotion estimation results into the report, allowing the police to understand the situation more accurately. For example, the user's emotion score can be included in the report. The generation AI can also reflect the user's emotional state in real time in the report, allowing the police to understand the situation more accurately. For example, the user's emotion estimation results can be added to the report. By including the user's emotional state in the report, the police can understand the situation more accurately.

[0096] The automatic reporting device can also be linked to a medical institution's emergency reporting system to respond to medical emergencies. For example, the generating AI can link the automatic reporting function with a medical institution's emergency reporting system to respond to medical emergencies. For example, if it detects an abnormality in heart rate or blood pressure, it will notify the medical institution. The generating AI can also link with a medical institution's emergency reporting system to automatically detect medical emergencies. For example, if it detects abnormal biological information, it will notify the medical institution. The generating AI can also integrate the automatic reporting function with the medical institution's system to respond quickly to medical emergencies. For example, it can analyze abnormal biological information in real time and notify the medical institution. This allows it to respond to medical emergencies by linking with the medical institution's emergency reporting system.

[0097] The automatic reporting device can also be linked to the school's security system to respond to emergencies within the school. For example, the generation AI can link the automatic reporting function with the school's security system to respond to emergencies within the school. For example, if it detects abnormal behavior or sound, it will notify school security. The generation AI can also link with the school's security system to automatically detect emergencies within the school. For example, if it detects abnormal behavior or sound, it will notify school security. The generation AI can also integrate the automatic reporting function with the school's system to respond quickly to emergencies within the school. For example, it can analyze abnormal behavior or sound in real time and notify school security. This allows it to respond to emergencies within the school by linking with the school's security system.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The image recognition unit recognizes the image. For example, the built-in camera can be used to capture real-time images of the surrounding area, and the generation AI can analyze the images. If a person holding a knife appears, the generation AI can analyze the image and recognize the presence of the knife. It can also use facial recognition technology to identify specific people. Step 2: The voice recognition unit recognizes voices. For example, it uses a built-in microphone to collect surrounding sounds in real time, and the generation AI analyzes the voices. If a command such as "Don't move" is given, the generation AI analyzes the voice and recognizes that it is an emergency. It can also identify specific instructions using voice command recognition technology. Step 3: The biometric analysis unit analyzes the biometric information. For example, it measures the user's heart rate, blood pressure, body temperature, etc. in real time, and the generation AI analyzes the data. If the heart rate rises suddenly, the generation AI determines that the user is facing an emergency. It can also analyze the user's electrodermal response to assess the user's stress level. Step 4: The urgency determination unit determines the level of urgency based on the analysis results of the video recognition unit, voice recognition unit, and biometric information analysis unit. For example, if a person with a knife commands the user to "stay still" and the user's heart rate rises sharply, the unit determines the level of urgency to be high. It can also learn from past crime data and predict the level of urgency based on similar crime situations. Step 5: The automatic reporting unit automatically reports to the police if the emergency level determined by the emergency level determination unit is high. For example, it generates a report including the current situation, the user's location information, and video and audio data, and sends it to the police. It can also generate report content in multiple languages, enabling international response.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

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

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

[0113] 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.

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

[0115] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0117] The data processing system 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 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 image recognition unit that recognizes an image; a speech recognition unit that recognizes speech; a biometric information analysis unit that analyzes biometric information; an urgency determination unit that determines an urgency level based on the analysis results of the video recognition unit, the voice recognition unit, and the biological information analysis unit; an automatic reporting unit that automatically reports to the police when the level of emergency determined by the level of emergency determination unit is high; A system characterized by:

2. The image recognition unit By analyzing the user's facial expressions and movements and detecting facial expressions of tension or fear, the urgency level can be determined with even greater accuracy.

2. The system of claim 1.

3. The image recognition unit The 3D modeling of surrounding objects and environments is based on video data, and crime locations and escape routes are predicted.

2. The system of claim 1.

4. The image recognition unit The emotion of the user is estimated from the facial expression, and the urgency is determined based on the emotion.

2. The system of claim 1.

5. The image recognition unit Linking with public surveillance camera systems to monitor crime situations over a wide area in real time 2. The system of claim 1.

6. The image recognition unit Linking with smart devices in the home to enhance safety within the home 2. The system of claim 1.

Images