system

Abstract

The system according to this embodiment aims to identify an emergency and automatically notify the most appropriate agency. [Solution] The system according to the embodiment comprises a shooting unit, an analysis unit, and a notification unit. The shooting unit uses a smartphone to photograph emergencies or infrastructure damage. The analysis unit analyzes the image data captured by the shooting unit and determines the situation. The notification unit automatically notifies the most appropriate agency based on the situation determined by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot 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 an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is impossible to quickly determine to whom to report in case of an emergency.

[0005] The system according to the embodiment aims to discriminate an emergency and automatically report it to an optimal organization.

Means for Solving the Problems

[0006] The system according to the embodiment includes a photographing unit, an analysis unit, and a reporting unit. The photographing unit uses a smartphone to photograph an emergency or infrastructure damage. The analysis unit analyzes the image data photographed by the photographing unit and discriminates the situation. The reporting unit automatically reports to an optimal organization based on the situation discriminated by the analysis unit.

Advantages of the Invention

[0007] The system according to this embodiment can identify an emergency and automatically notify the most appropriate agency. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An emergency notification system according to an embodiment of the present invention is a system that quickly and accurately determines the appropriate recipient and automatically makes a notification in the event of an emergency. This emergency notification system uses a smartphone to photograph emergencies or infrastructure damage, and a generating AI analyzes the image data to determine the situation and automatically notifies the most appropriate agency. This mechanism enables a rapid response and ensures people's safety. For example, if a user photographs a fire using a smartphone, this image data is input into the generating AI. The generating AI uses image analysis technology to identify the scale and location of the fire and automatically notifies the most appropriate agency. Since the notification also includes image data, the receiving agency can accurately grasp the situation. With this mechanism, the user does not need to decide where to report in an emergency, as the generating AI automatically selects the most appropriate recipient, thus preventing delays in the initial response. Furthermore, the receiving agency can take appropriate action by accurately understanding the situation. In addition, this AI agent is targeted at the general public and local crime prevention and disaster prevention activity supporters, and can provide information and services tailored to their needs. Examples include information on evacuation sites and disaster prevention goods during earthquakes and disasters, and methods for installing security cameras. Thus, the AI ​​agent for automatically selecting the recipient of emergency calls is a groundbreaking technology that supports the safety of citizens and the improvement of public services, contributing to a rapid response and increased social contribution. This allows emergency call systems to quickly and accurately determine the recipient and automatically make the call.

[0029] The emergency notification system according to this embodiment comprises a shooting unit, an analysis unit, and a notification unit. The shooting unit uses a smartphone to photograph emergencies and infrastructure damage. For example, the shooting unit can allow a user to photograph the situation of a fire with their smartphone. The shooting unit can also allow a user to photograph the situation of a traffic accident with their smartphone. Furthermore, the shooting unit can also allow a user to photograph the situation of a natural disaster with their smartphone. The analysis unit uses a generation AI to analyze the image data captured by the shooting unit and determine the situation. For example, the analysis unit can use the generation AI to identify the scale and location of a fire using image analysis technology. The analysis unit can also use the generation AI to identify the situation of a traffic accident using image analysis technology. Furthermore, the analysis unit can use the generation AI to identify the situation of a natural disaster using image analysis technology. The notification unit automatically notifies the most appropriate agency based on the situation determined by the analysis unit. For example, the notification unit can notify the fire department in the event of a fire. The notification unit can also notify the police in the event of a traffic accident. Furthermore, the notification unit can also notify the local government in the event of a natural disaster. As a result, the emergency notification system according to this embodiment can quickly and accurately determine the recipient and automatically send a notification.

[0030] The camera unit uses smartphones to film emergencies and infrastructure damage. Specifically, users can use their smartphone cameras to film emergencies such as fires, traffic accidents, and natural disasters. For example, in the event of a fire, the user films the fire with their smartphone camera and sends the footage to the system. The camera unit can utilize the smartphone's high-resolution camera to obtain detailed footage. In the event of a traffic accident, the user films the accident scene with their smartphone and sends the footage to the system. This allows for an accurate assessment of the scale and extent of the damage. Furthermore, in the event of a natural disaster, such as an earthquake or flood, users can film the damage with their smartphone and send the footage to the system. The camera unit can also use the smartphone's GPS function to simultaneously acquire location information of the filming location. This allows for the accurate identification of the location of the emergency by combining the filmed footage with the location information. The camera unit provides an easy-to-use interface and is designed to enable rapid filming even in emergencies. This allows the camera unit to quickly collect detailed footage and location information of emergencies, supporting a swift and accurate response across the entire system.

[0031] The analysis unit uses generative AI to analyze image data captured by the imaging unit and determine the situation. Specifically, the generative AI can use image analysis technology to identify the scale and location of a fire. For example, by analyzing fire footage and detecting the size of the flames and the spread of smoke, it can assess the scale of the fire. It can also identify the location of the fire by analyzing landmarks and building features in the footage. In the case of a traffic accident, the generative AI analyzes the damage to the vehicles involved and the road conditions to determine the scale and cause of the accident. For example, by analyzing the location of damage to the vehicles and the scattering of debris, it can estimate the force and direction of the collision. In the case of a natural disaster, the generative AI analyzes the damage caused by earthquakes and floods to assess the extent and severity of the damage. For example, by analyzing flood footage and identifying the rise in water level and the extent of flooding, it can predict the spread of the damage. The analysis unit processes these analysis results in real time, enabling rapid situational assessment. Furthermore, the analysis unit can utilize past data and statistical information to perform more accurate analyses. For example, by learning fire progression patterns under specific conditions based on past fire data, it is possible to more accurately predict the current fire situation. This allows the analysis unit to quickly and accurately analyze captured video data and accurately grasp the situation in an emergency.

[0032] The reporting unit automatically notifies the most appropriate agency based on the situation determined by the analysis unit. Specifically, it can notify the fire department in the event of a fire. The reporting unit receives information from the analysis unit and automatically notifies the nearest fire station depending on the scale and location of the fire. The notification includes detailed information such as video footage of the fire, location information, and analysis results, allowing the fire station to respond quickly based on this information. In the event of a traffic accident, the reporting unit notifies the police. The notification includes video footage of the accident, location information, and analysis results, allowing the police to rush to the scene and take appropriate action based on this information. In the event of a natural disaster, the reporting unit notifies the local government. The notification includes video footage of the disaster, location information, and analysis results, allowing the local government to take quick countermeasures based on this information. The reporting unit can provide necessary information to the receiving agency quickly and accurately. Furthermore, the reporting unit can reliably transmit information by using multiple communication methods when making a notification. For example, in addition to data transmission via the internet, it uses a combination of voice calls, SMS, and email to ensure that important information is delivered reliably. This allows the reporting department to report emergencies quickly and accurately, supporting the rapid response of relevant agencies.

[0033] The analysis unit can analyze image data using image recognition technology. For example, the analysis unit can identify the situation of a fire from image data using object detection technology. It can also identify the situation of a traffic accident from image data using facial recognition technology. Furthermore, it can identify the situation of a natural disaster from image data using scene analysis technology. This improves the accuracy of image data analysis by utilizing image recognition technology.

[0034] The reporting unit can select the most appropriate recipient based on pre-configured rules and algorithms. For example, in the event of a fire, the reporting unit can prioritize contacting the fire department. Similarly, in the event of a traffic accident, it can prioritize contacting the police. Furthermore, in the event of a natural disaster, it can prioritize contacting the local government. This allows for quick and accurate reporting by selecting the most appropriate recipient based on pre-configured rules and algorithms. Some or all of the above-described processes in the reporting unit may be performed using AI, or not. For example, the reporting unit can input pre-configured rules and algorithms into a generating AI, which can then perform the task of selecting the most appropriate recipient.

[0035] The reporting unit can include image data when reporting to the receiving agency. For example, the reporting unit can include image data when reporting a fire to the fire department. It can also include image data when reporting a traffic accident to the police. Furthermore, it can include image data when reporting a natural disaster to the local government. This allows the receiving agency to accurately understand the situation. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input image data to be sent to the receiving agency into a generating AI and have the generating AI execute the transmission of the image data.

[0036] The analysis unit can determine the situation using machine learning techniques. For example, the analysis unit can identify the situation of a fire from image data using deep learning techniques. The analysis unit can also identify the situation of a traffic accident from image data using support vector machine techniques. Furthermore, the analysis unit can identify the situation of a natural disaster from image data using random forest techniques. This improves the accuracy of situation determination by using machine learning techniques. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input image data into a generative AI and have the generative AI perform the situation determination.

[0037] The camera unit can automatically select the optimal shooting mode according to the type of emergency. For example, in the case of a fire, the AI ​​can select a mode that emphasizes flames and smoke. In the case of a traffic accident, the AI ​​can select a mode that clearly photographs vehicles and victims. Furthermore, in the case of a natural disaster, the AI ​​can select a mode that photographs a wide area. By automatically selecting the optimal shooting mode according to the type of emergency, appropriate photography can be performed according to the situation. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input the type of emergency into a generating AI and have the generating AI select the optimal shooting mode.

[0038] The shooting unit can detect ambient light and automatically adjust the optimal exposure settings. For example, at night, the AI ​​can increase the exposure to take a brighter picture. Conversely, during the day, the AI ​​can decrease the exposure to take a picture at an appropriate brightness. Furthermore, in indoor settings, the AI ​​can adjust the exposure according to the lighting conditions. This allows for shooting at an appropriate brightness by automatically adjusting the optimal exposure settings according to the ambient light. Some or all of the above processing in the shooting unit may be performed using AI, or without AI. For example, the shooting unit can input ambient light data into a generating AI and have the generating AI perform the exposure setting adjustment.

[0039] The camera unit can automatically adjust the optimal shooting angle considering the user's location information. For example, the AI ​​can automatically adjust the optimal angle when the user is moving. The AI ​​can also select the optimal shooting angle when the user is in a fixed position. Furthermore, the AI ​​can adjust the shooting angle to match the direction the user is facing if the user is facing a specific direction. This allows for shooting at the appropriate angle by automatically adjusting the optimal shooting angle based on the user's location information. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input the user's location information into a generating AI and have the generating AI perform the adjustment of the shooting angle.

[0040] The shooting unit can analyze the user's past shooting history and suggest optimal shooting settings. For example, the shooting unit can suggest optimal settings based on the settings used for images the user has taken in the past. It can also analyze the user's preferred settings from their past shooting history and suggest them. Furthermore, the shooting unit can prioritize suggesting settings the user has used in the past. In this way, by analyzing the user's past shooting history, it can suggest optimal shooting settings. Some or all of the above processing in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input the user's past shooting history data into a generating AI and have the generating AI suggest optimal shooting settings.

[0041] The analysis unit can apply different analysis algorithms depending on the resolution of the image data. For example, the analysis unit can apply a detailed analysis algorithm to high-resolution image data. It can also apply a simpler analysis algorithm to low-resolution image data. Furthermore, it can apply a balanced analysis algorithm to medium-resolution image data. By applying different analysis algorithms depending on the resolution of the image data, appropriate analysis can be performed. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the resolution of the image data to the generative AI and have the generative AI select the analysis algorithm to apply.

[0042] The analysis unit can apply different analysis methods depending on the type of emergency. For example, in the case of a fire, the analysis unit can apply analysis methods for flames and smoke. In the case of a traffic accident, the analysis unit can also apply analysis methods for vehicles and victims. Furthermore, in the case of a natural disaster, the analysis unit can apply a wide range of analysis methods. This allows for appropriate analysis by applying different analysis methods depending on the type of emergency. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the type of emergency into the generating AI and have the generating AI select the analysis method to apply.

[0043] The analysis unit can determine the priority of analysis based on the time the image data was captured. For example, the analysis unit can prioritize the analysis of the most recent image data. It can also postpone the analysis of older image data. Furthermore, the analysis unit can prioritize the analysis of image data captured during a specific time period. By determining the priority of analysis based on the time the image data was captured, the analysis can be performed in an appropriate order. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the time the image data was captured into a generating AI and have the generating AI determine the priority of analysis.

[0044] The analysis unit can improve the accuracy of the analysis by referring to relevant information of the image data. For example, the analysis unit can perform a detailed analysis of the conditions of a specific location based on location information. The analysis unit can also perform an analysis considering the effects of weather based on weather information. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to other relevant information. In this way, the accuracy of the analysis is improved by referring to relevant information of the image data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input relevant information of the image data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0045] The reporting unit can select the most suitable reporting destination based on the response time of the receiving organization. For example, the reporting unit can prioritize organizations with short response times. It can also postpone reporting to organizations with long response times. Furthermore, the reporting unit can prioritize organizations with short response times during specific time periods. This enables a rapid response by selecting the most suitable reporting destination based on the response time of the receiving organization. Some or all of the above processing in the reporting unit may be performed using, for example, a generating AI, or without a generating AI. For example, the reporting unit can input response time data of the receiving organizations into a generating AI and have the generating AI select the most suitable reporting destination.

[0046] The reporting unit can apply different reporting formats depending on the content of the report. For example, in the case of a fire, the reporting unit can apply a reporting format specifically for fires. It can also apply a reporting format specifically for traffic accidents. Furthermore, it can apply a reporting format specifically for natural disasters. This allows for appropriate reporting by applying different reporting formats depending on the content of the report. Some or all of the above-described processing in the reporting unit may be performed using, for example, a generation AI, or without a generation AI. For example, the reporting unit can input the report content into a generation AI and have the generation AI select the reporting format to apply.

[0047] The reporting unit can select the most suitable reporting destination by referring to the past response history of the receiving organization. For example, the reporting unit can prioritize organizations that have responded quickly in the past. It can also prioritize organizations that have responded slowly in the past. Furthermore, the reporting unit can prioritize organizations that have responded quickly within a specific time period. This enables a quick response by referring to the past response history of the receiving organization. Some or all of the above processing in the reporting unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the reporting unit can input past response history data of the receiving organization into a generating AI and have the generating AI select the most suitable reporting destination.

[0048] The reporting unit can select a recipient for reporting by considering the current status of the receiving organization. For example, the reporting unit can prioritize organizations that are currently operational. It can also select the nearest organization based on location information. Furthermore, the reporting unit can select the most suitable recipient by considering the current situation. This allows for prompt and appropriate reporting by considering the current status of the receiving organization. Some or all of the above processing in the reporting unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the reporting unit can input data on the current status of the receiving organization into a generating AI and have the generating AI select the most suitable recipient.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The camera unit can start recording based on the user's voice commands. For example, if the user says "start recording," the camera unit recognizes the command and begins recording. Similarly, if the user says "stop," the camera unit can stop recording. Furthermore, if the user describes a specific situation by voice, the camera unit can select the appropriate recording mode based on that description. This allows users to control recording hands-free using their voice, enabling quick responses in emergencies.

[0051] The reporting department can select the most suitable reporting destination based on the response capabilities of the receiving agency. For example, it can evaluate whether the receiving agency is able to handle the current situation and prioritize selecting agencies with high response capabilities. It can also prioritize selecting agencies that have a track record of responding quickly to similar situations in the past. Furthermore, it can select the most suitable reporting destination by considering the current operational status of the receiving agency. By selecting the most suitable reporting destination based on the response capabilities of the receiving agency, a swift and appropriate response becomes possible.

[0052] The analysis unit can adjust the accuracy of the analysis based on the location where the image data was taken. For example, image data taken at a specific location will be given an analysis algorithm specific to that location. The accuracy of the analysis can also be adjusted by considering environmental information of the location where the image was taken. Furthermore, the accuracy of the analysis can be improved by referring to past data of the location where the image was taken. This allows for more accurate analysis by adjusting the accuracy of the analysis based on the location where the image data was taken.

[0053] The camera unit can automatically select the optimal shooting mode by considering the user's location. For example, if the user is outdoors, the unit will select a mode that captures a wide area. If the user is indoors, the unit can select a mode suitable for indoor conditions. Furthermore, if the user is in a specific location, it can select a shooting mode tailored to that location. This allows for appropriate shooting by automatically selecting the optimal shooting mode based on the user's location.

[0054] The reporting department can prioritize reports by considering the current operational status of the receiving organization. For example, it can prioritize organizations that are currently operational to ensure a quick response. Organizations whose operational status is unknown can be given lower priority. Furthermore, it can prioritize organizations whose operational status is stable during specific time periods. This allows for prompt and appropriate reporting by considering the current operational status of the receiving organization.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The filming team uses smartphones to film emergencies and infrastructure damage. For example, users can use their smartphones to film fires, traffic accidents, and natural disasters. Step 2: The analysis unit uses a generation AI to analyze the image data captured by the shooting unit and determine the situation. For example, the generation AI can use image analysis technology to identify the scale and location of a fire, the circumstances of a traffic accident, or the circumstances of a natural disaster. Step 3: The reporting unit automatically notifies the most appropriate agency based on the situation determined by the analysis unit. For example, it can notify the fire department in the event of a fire, the police in the event of a traffic accident, and the local government in the event of a natural disaster.

[0057] (Example of form 2) An emergency notification system according to an embodiment of the present invention is a system that quickly and accurately determines the appropriate recipient and automatically makes a notification in the event of an emergency. This emergency notification system uses a smartphone to photograph emergencies or infrastructure damage, and a generating AI analyzes the image data to determine the situation and automatically notifies the most appropriate agency. This mechanism enables a rapid response and ensures people's safety. For example, if a user photographs a fire using a smartphone, this image data is input into the generating AI. The generating AI uses image analysis technology to identify the scale and location of the fire and automatically notifies the most appropriate agency. Since the notification also includes image data, the receiving agency can accurately grasp the situation. With this mechanism, the user does not need to decide where to report in an emergency, as the generating AI automatically selects the most appropriate recipient, thus preventing delays in the initial response. Furthermore, the receiving agency can take appropriate action by accurately understanding the situation. In addition, this AI agent is targeted at the general public and local crime prevention and disaster prevention activity supporters, and can provide information and services tailored to their needs. Examples include information on evacuation sites and disaster prevention goods during earthquakes and disasters, and methods for installing security cameras. Thus, the AI ​​agent for automatically selecting the recipient of emergency calls is a groundbreaking technology that supports the safety of citizens and the improvement of public services, contributing to a rapid response and increased social contribution. This allows emergency call systems to quickly and accurately determine the recipient and automatically make the call.

[0058] The emergency notification system according to this embodiment comprises a shooting unit, an analysis unit, and a notification unit. The shooting unit uses a smartphone to photograph emergencies and infrastructure damage. For example, the shooting unit can allow a user to photograph the situation of a fire with their smartphone. The shooting unit can also allow a user to photograph the situation of a traffic accident with their smartphone. Furthermore, the shooting unit can also allow a user to photograph the situation of a natural disaster with their smartphone. The analysis unit uses a generation AI to analyze the image data captured by the shooting unit and determine the situation. For example, the analysis unit can use the generation AI to identify the scale and location of a fire using image analysis technology. The analysis unit can also use the generation AI to identify the situation of a traffic accident using image analysis technology. Furthermore, the analysis unit can use the generation AI to identify the situation of a natural disaster using image analysis technology. The notification unit automatically notifies the most appropriate agency based on the situation determined by the analysis unit. For example, the notification unit can notify the fire department in the event of a fire. The notification unit can also notify the police in the event of a traffic accident. Furthermore, the notification unit can also notify the local government in the event of a natural disaster. As a result, the emergency notification system according to this embodiment can quickly and accurately determine the recipient and automatically send a notification.

[0059] The camera unit uses smartphones to film emergencies and infrastructure damage. Specifically, users can use their smartphone cameras to film emergencies such as fires, traffic accidents, and natural disasters. For example, in the event of a fire, the user films the fire with their smartphone camera and sends the footage to the system. The camera unit can utilize the smartphone's high-resolution camera to obtain detailed footage. In the event of a traffic accident, the user films the accident scene with their smartphone and sends the footage to the system. This allows for an accurate assessment of the scale and extent of the damage. Furthermore, in the event of a natural disaster, such as an earthquake or flood, users can film the damage with their smartphone and send the footage to the system. The camera unit can also use the smartphone's GPS function to simultaneously acquire location information of the filming location. This allows for the accurate identification of the location of the emergency by combining the filmed footage with the location information. The camera unit provides an easy-to-use interface and is designed to enable rapid filming even in emergencies. This allows the camera unit to quickly collect detailed footage and location information of emergencies, supporting a swift and accurate response across the entire system.

[0060] The analysis unit uses generative AI to analyze image data captured by the imaging unit and determine the situation. Specifically, the generative AI can use image analysis technology to identify the scale and location of a fire. For example, by analyzing fire footage and detecting the size of the flames and the spread of smoke, it can assess the scale of the fire. It can also identify the location of the fire by analyzing landmarks and building features in the footage. In the case of a traffic accident, the generative AI analyzes the damage to the vehicles involved and the road conditions to determine the scale and cause of the accident. For example, by analyzing the location of damage to the vehicles and the scattering of debris, it can estimate the force and direction of the collision. In the case of a natural disaster, the generative AI analyzes the damage caused by earthquakes and floods to assess the extent and severity of the damage. For example, by analyzing flood footage and identifying the rise in water level and the extent of flooding, it can predict the spread of the damage. The analysis unit processes these analysis results in real time, enabling rapid situational assessment. Furthermore, the analysis unit can utilize past data and statistical information to perform more accurate analyses. For example, by learning fire progression patterns under specific conditions based on past fire data, it is possible to more accurately predict the current fire situation. This allows the analysis unit to quickly and accurately analyze captured video data and accurately grasp the situation in an emergency.

[0061] The reporting unit automatically notifies the most appropriate agency based on the situation determined by the analysis unit. Specifically, it can notify the fire department in the event of a fire. The reporting unit receives information from the analysis unit and automatically notifies the nearest fire station depending on the scale and location of the fire. The notification includes detailed information such as video footage of the fire, location information, and analysis results, allowing the fire station to respond quickly based on this information. In the event of a traffic accident, the reporting unit notifies the police. The notification includes video footage of the accident, location information, and analysis results, allowing the police to rush to the scene and take appropriate action based on this information. In the event of a natural disaster, the reporting unit notifies the local government. The notification includes video footage of the disaster, location information, and analysis results, allowing the local government to take quick countermeasures based on this information. The reporting unit can provide necessary information to the receiving agency quickly and accurately. Furthermore, the reporting unit can reliably transmit information by using multiple communication methods when making a notification. For example, in addition to data transmission via the internet, it uses a combination of voice calls, SMS, and email to ensure that important information is delivered reliably. This allows the reporting department to report emergencies quickly and accurately, supporting the rapid response of relevant agencies.

[0062] The analysis unit can analyze image data using image recognition technology. For example, the analysis unit can identify the situation of a fire from image data using object detection technology. It can also identify the situation of a traffic accident from image data using facial recognition technology. Furthermore, it can identify the situation of a natural disaster from image data using scene analysis technology. This improves the accuracy of image data analysis by utilizing image recognition technology.

[0063] The reporting unit can select the most appropriate recipient based on pre-configured rules and algorithms. For example, in the event of a fire, the reporting unit can prioritize contacting the fire department. Similarly, in the event of a traffic accident, it can prioritize contacting the police. Furthermore, in the event of a natural disaster, it can prioritize contacting the local government. This allows for quick and accurate reporting by selecting the most appropriate recipient based on pre-configured rules and algorithms. Some or all of the above-described processes in the reporting unit may be performed using AI, or not. For example, the reporting unit can input pre-configured rules and algorithms into a generating AI, which can then perform the task of selecting the most appropriate recipient.

[0064] The reporting unit can include image data when reporting to the receiving agency. For example, the reporting unit can include image data when reporting a fire to the fire department. It can also include image data when reporting a traffic accident to the police. Furthermore, it can include image data when reporting a natural disaster to the local government. This allows the receiving agency to accurately understand the situation. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input image data to be sent to the receiving agency into a generating AI and have the generating AI execute the transmission of the image data.

[0065] The analysis unit can determine the situation using machine learning techniques. For example, the analysis unit can identify the situation of a fire from image data using deep learning techniques. The analysis unit can also identify the situation of a traffic accident from image data using support vector machine techniques. Furthermore, the analysis unit can identify the situation of a natural disaster from image data using random forest techniques. This improves the accuracy of situation determination by using machine learning techniques. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input image data into a generative AI and have the generative AI perform the situation determination.

[0066] The camera unit can estimate the user's emotions and adjust the timing of the shot based on the estimated emotions. For example, if the user is nervous, the AI ​​can automatically start shooting at the optimal time. The camera unit can also follow the user's instructions when the user is relaxed. Furthermore, if the user is in a hurry, the AI ​​can quickly start shooting. By adjusting the timing of the shot according to the user's emotions, the camera can be taken at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI or not using AI. For example, the camera unit can input user emotion data into the generative AI and have the generative AI adjust the timing of the shot.

[0067] The camera unit can automatically select the optimal shooting mode according to the type of emergency. For example, in the case of a fire, the AI ​​can select a mode that emphasizes flames and smoke. In the case of a traffic accident, the AI ​​can select a mode that clearly photographs vehicles and victims. Furthermore, in the case of a natural disaster, the AI ​​can select a mode that photographs a wide area. By automatically selecting the optimal shooting mode according to the type of emergency, appropriate photography can be performed according to the situation. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input the type of emergency into a generating AI and have the generating AI select the optimal shooting mode.

[0068] The shooting unit can detect ambient light and automatically adjust the optimal exposure settings. For example, at night, the AI ​​can increase the exposure to take a brighter picture. Conversely, during the day, the AI ​​can decrease the exposure to take a picture at an appropriate brightness. Furthermore, in indoor settings, the AI ​​can adjust the exposure according to the lighting conditions. This allows for shooting at an appropriate brightness by automatically adjusting the optimal exposure settings according to the ambient light. Some or all of the above processing in the shooting unit may be performed using AI, or without AI. For example, the shooting unit can input ambient light data into a generating AI and have the generating AI perform the exposure setting adjustment.

[0069] The camera unit can estimate the user's emotions and determine the priority of images to capture based on the estimated emotions. For example, if the user is nervous, the camera unit can prioritize capturing important parts. If the user is relaxed, the camera unit can capture a balanced overall image. Furthermore, if the user is in a hurry, the camera unit can quickly capture the most important parts. This allows for prioritizing the capture of important parts by determining the priority of images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI or not. For example, the camera unit can input user emotion data into a generative AI and have the generative AI determine the priority of images to capture.

[0070] The camera unit can automatically adjust the optimal shooting angle considering the user's location information. For example, the AI ​​can automatically adjust the optimal angle when the user is moving. The AI ​​can also select the optimal shooting angle when the user is in a fixed position. Furthermore, the AI ​​can adjust the shooting angle to match the direction the user is facing if the user is facing a specific direction. This allows for shooting at the appropriate angle by automatically adjusting the optimal shooting angle based on the user's location information. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input the user's location information into a generating AI and have the generating AI perform the adjustment of the shooting angle.

[0071] The shooting unit can analyze the user's past shooting history and suggest optimal shooting settings. For example, the shooting unit can suggest optimal settings based on the settings used for images the user has taken in the past. It can also analyze the user's preferred settings from their past shooting history and suggest them. Furthermore, the shooting unit can prioritize suggesting settings the user has used in the past. In this way, by analyzing the user's past shooting history, it can suggest optimal shooting settings. Some or all of the above processing in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input the user's past shooting history data into a generating AI and have the generating AI suggest optimal shooting settings.

[0072] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, the analysis unit can improve the accuracy of the analysis when the user is nervous. It can also perform the analysis with normal accuracy when the user is relaxed. Furthermore, the analysis unit can adjust the accuracy to perform the analysis quickly when the user is in a hurry. By adjusting the accuracy of the analysis according to the user's emotions, a more appropriate analysis can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the accuracy of the analysis.

[0073] The analysis unit can apply different analysis algorithms depending on the resolution of the image data. For example, the analysis unit can apply a detailed analysis algorithm to high-resolution image data. It can also apply a simpler analysis algorithm to low-resolution image data. Furthermore, it can apply a balanced analysis algorithm to medium-resolution image data. By applying different analysis algorithms depending on the resolution of the image data, appropriate analysis can be performed. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the resolution of the image data to the generative AI and have the generative AI select the analysis algorithm to apply.

[0074] The analysis unit can apply different analysis methods depending on the type of emergency. For example, in the case of a fire, the analysis unit can apply analysis methods for flames and smoke. In the case of a traffic accident, the analysis unit can also apply analysis methods for vehicles and victims. Furthermore, in the case of a natural disaster, the analysis unit can apply a wide range of analysis methods. This allows for appropriate analysis by applying different analysis methods depending on the type of emergency. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the type of emergency into the generating AI and have the generating AI select the analysis method to apply.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can provide a simple and highly visible display method when the user is tense. It can also provide a display method that includes detailed information when the user is relaxed. Furthermore, it can provide a concise display method when the user is in a hurry. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0076] The analysis unit can determine the priority of analysis based on the time the image data was captured. For example, the analysis unit can prioritize the analysis of the most recent image data. It can also postpone the analysis of older image data. Furthermore, the analysis unit can prioritize the analysis of image data captured during a specific time period. By determining the priority of analysis based on the time the image data was captured, the analysis can be performed in an appropriate order. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the time the image data was captured into a generating AI and have the generating AI determine the priority of analysis.

[0077] The analysis unit can improve the accuracy of the analysis by referring to relevant information of the image data. For example, the analysis unit can perform a detailed analysis of the conditions of a specific location based on location information. The analysis unit can also perform an analysis considering the effects of weather based on weather information. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to other relevant information. In this way, the accuracy of the analysis is improved by referring to relevant information of the image data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input relevant information of the image data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0078] The reporting unit can estimate the user's emotions and adjust the urgency of the report based on the estimated emotions. For example, the reporting unit can increase the urgency of the report if the user is stressed. It can also report with a normal level of urgency if the user is relaxed. Furthermore, it can adjust the urgency to report quickly if the user is in a hurry. This allows for more appropriate reporting by adjusting the urgency of the report according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI adjust the urgency of the report.

[0079] The reporting unit can select the most suitable reporting destination based on the response time of the receiving organization. For example, the reporting unit can prioritize organizations with short response times. It can also postpone reporting to organizations with long response times. Furthermore, the reporting unit can prioritize organizations with short response times during specific time periods. This enables a rapid response by selecting the most suitable reporting destination based on the response time of the receiving organization. Some or all of the above processing in the reporting unit may be performed using, for example, a generating AI, or without a generating AI. For example, the reporting unit can input response time data of the receiving organizations into a generating AI and have the generating AI select the most suitable reporting destination.

[0080] The reporting unit can apply different reporting formats depending on the content of the report. For example, in the case of a fire, the reporting unit can apply a reporting format specifically for fires. It can also apply a reporting format specifically for traffic accidents. Furthermore, it can apply a reporting format specifically for natural disasters. This allows for appropriate reporting by applying different reporting formats depending on the content of the report. Some or all of the above-described processing in the reporting unit may be performed using, for example, a generation AI, or without a generation AI. For example, the reporting unit can input the report content into a generation AI and have the generation AI select the reporting format to apply.

[0081] The reporting unit can estimate the user's emotions and adjust the level of detail in the report based on the estimated emotions. For example, the reporting unit can provide detailed information when the user is stressed. It can also provide standard information when the user is relaxed. Furthermore, it can provide concise information when the user is in a hurry. By adjusting the level of detail in the report according to the user's emotions, more appropriate reports can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI adjust the level of detail in the report.

[0082] The reporting unit can select the most suitable reporting destination by referring to the past response history of the receiving organization. For example, the reporting unit can prioritize organizations that have responded quickly in the past. It can also prioritize organizations that have responded slowly in the past. Furthermore, the reporting unit can prioritize organizations that have responded quickly within a specific time period. This enables a quick response by referring to the past response history of the receiving organization. Some or all of the above processing in the reporting unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the reporting unit can input past response history data of the receiving organization into a generating AI and have the generating AI select the most suitable reporting destination.

[0083] The reporting unit can select a recipient for reporting by considering the current status of the receiving organization. For example, the reporting unit can prioritize organizations that are currently operational. It can also select the nearest organization based on location information. Furthermore, the reporting unit can select the most suitable recipient by considering the current situation. This allows for prompt and appropriate reporting by considering the current status of the receiving organization. Some or all of the above processing in the reporting unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the reporting unit can input data on the current status of the receiving organization into a generating AI and have the generating AI select the most suitable recipient.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The camera unit can start recording based on the user's voice commands. For example, if the user says "start recording," the camera unit recognizes the command and begins recording. Similarly, if the user says "stop," the camera unit can stop recording. Furthermore, if the user describes a specific situation by voice, the camera unit can select the appropriate recording mode based on that description. This allows users to control recording hands-free using their voice, enabling quick responses in emergencies.

[0086] The analysis unit can estimate the user's emotions and determine the analysis priority based on those emotions. For example, if the user is very nervous, the analysis unit will prioritize analyzing that image data. If the user is relaxed, the analysis can proceed with the normal priority. Furthermore, if the user is in a hurry, the priority can be adjusted to perform the analysis quickly. By adjusting the analysis priority according to the user's emotions, more appropriate analysis can be performed.

[0087] The reporting department can select the most suitable reporting destination based on the response capabilities of the receiving agency. For example, it can evaluate whether the receiving agency is able to handle the current situation and prioritize selecting agencies with high response capabilities. It can also prioritize selecting agencies that have a track record of responding quickly to similar situations in the past. Furthermore, it can select the most suitable reporting destination by considering the current operational status of the receiving agency. By selecting the most suitable reporting destination based on the response capabilities of the receiving agency, a swift and appropriate response becomes possible.

[0088] The camera unit can estimate the user's emotions and adjust the frame rate based on those emotions. For example, if the user is nervous, the camera unit will shoot at a high frame rate to record detailed footage. If the user is relaxed, it can shoot at a normal frame rate. Furthermore, if the user is in a hurry, the camera unit can adjust the frame rate to shoot quickly. By adjusting the frame rate according to the user's emotions, more appropriate video recording can be achieved.

[0089] The analysis unit can adjust the accuracy of the analysis based on the location where the image data was taken. For example, image data taken at a specific location will be given an analysis algorithm specific to that location. The accuracy of the analysis can also be adjusted by considering environmental information of the location where the image was taken. Furthermore, the accuracy of the analysis can be improved by referring to past data of the location where the image was taken. This allows for more accurate analysis by adjusting the accuracy of the analysis based on the location where the image data was taken.

[0090] The reporting system can estimate the user's emotions and prioritize reports based on those emotions. For example, if a user is very stressed, the system will prioritize that report. If the user is relaxed, the report can be processed with the normal priority. Furthermore, if the user is in a hurry, the priority can be adjusted to process the report quickly. This allows for more appropriate reporting by adjusting the priority of reports according to the user's emotions.

[0091] The camera unit can automatically select the optimal shooting mode by considering the user's location. For example, if the user is outdoors, the unit will select a mode that captures a wide area. If the user is indoors, the unit can select a mode suitable for indoor conditions. Furthermore, if the user is in a specific location, it can select a shooting mode tailored to that location. This allows for appropriate shooting by automatically selecting the optimal shooting mode based on the user's location.

[0092] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible notification method. If the user is relaxed, it can provide a notification method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a notification method that gets straight to the point. In this way, by adjusting the notification method of the analysis results according to the user's emotions, more appropriate notifications can be provided.

[0093] The reporting department can prioritize reports by considering the current operational status of the receiving organization. For example, it can prioritize organizations that are currently operational to ensure a quick response. Organizations whose operational status is unknown can be given lower priority. Furthermore, it can prioritize organizations whose operational status is stable during specific time periods. This allows for prompt and appropriate reporting by considering the current operational status of the receiving organization.

[0094] The camera unit can estimate the user's emotions and adjust the exposure settings based on those emotions. For example, if the user is nervous, the unit will increase the exposure to take a brighter picture. If the user is relaxed, it can take a picture with normal exposure settings. Furthermore, if the user is in a hurry, the exposure settings can be adjusted to take a picture quickly. By adjusting the exposure settings according to the user's emotions, more appropriate photos can be taken.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The filming team uses smartphones to film emergencies and infrastructure damage. For example, users can use their smartphones to film fires, traffic accidents, and natural disasters. Step 2: The analysis unit uses a generation AI to analyze the image data captured by the shooting unit and determine the situation. For example, the generation AI can use image analysis technology to identify the scale and location of a fire, the circumstances of a traffic accident, or the circumstances of a natural disaster. Step 3: The reporting unit automatically notifies the most appropriate agency based on the situation determined by the analysis unit. For example, it can notify the fire department in the event of a fire, the police in the event of a traffic accident, and the local government in the event of a natural disaster.

[0097] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0100] Each of the multiple elements described above, including the imaging unit, analysis unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the imaging unit uses the camera 42 of the smart device 14 to photograph emergencies or infrastructure damage. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the captured image data using generated AI to determine the situation. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12, which automatically notifies the appropriate agency based on the situation determined by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 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.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the imaging unit, analysis unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the imaging unit uses the camera 42 of the smart glasses 214 to photograph emergencies or infrastructure damage. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which analyzes the captured image data using generated AI to determine the situation. The notification unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which automatically notifies the appropriate agency based on the situation determined by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the imaging unit, analysis unit, and notification unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the imaging unit uses the camera 42 of the headset terminal 314 to photograph emergencies or infrastructure damage. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which uses generated AI to analyze the captured image data and determine the situation. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12, which automatically notifies the appropriate agency based on the situation determined by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0134] As shown in Figure 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 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the imaging unit, analysis unit, and notification unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the imaging unit uses the camera 42 of the robot 414 to photograph emergencies or infrastructure damage. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the captured image data using generated AI to determine the situation. The notification unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and automatically notifies the most appropriate agency based on the situation determined by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) A filming team that uses smartphones to film emergencies and infrastructure damage, An analysis unit analyzes the image data captured by the aforementioned imaging unit and determines the situation, The system includes a notification unit that automatically notifies the most appropriate organization based on the situation determined by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Image data is analyzed using image recognition technology. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reporting unit, The system selects the optimal reporting destination based on pre-configured rules and algorithms. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reporting unit, Report the image data to the relevant agency. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Use machine learning techniques to determine the situation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the photo shoot based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned imaging unit is The system automatically selects the optimal shooting mode depending on the type of emergency. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned imaging unit is It detects ambient light and automatically adjusts the exposure settings to the optimal level. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned imaging unit is It estimates the user's emotions and determines the priority of images to capture based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned imaging unit is The system automatically adjusts the optimal shooting angle, taking into account the user's location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned imaging unit is It analyzes the user's past shooting history and suggests the optimal shooting settings. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, Apply different analysis algorithms depending on the resolution of the image data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Apply different analytical methods depending on the type of emergency. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The priority of analysis is determined based on the time the image data was captured. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Improve the accuracy of the analysis by referring to related information in the image data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reporting unit, The system estimates the user's emotions and adjusts the urgency of the report based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reporting unit, The most suitable reporting destination is selected based on the response time of the reporting agency. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reporting unit, Apply different reporting formats depending on the content of the report. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reporting unit, The system estimates the user's emotions and adjusts the level of detail in the report based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reporting unit, Select the most suitable reporting agency by referring to their past response history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reporting unit, Select a reporting destination considering the current situation of the agency to which you are reporting. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A filming team that uses smartphones to photograph emergencies and infrastructure damage, An analysis unit analyzes the image data captured by the aforementioned imaging unit and determines the situation, The system includes a notification unit that automatically notifies the most appropriate organization based on the situation determined by the analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, Image data is analyzed using image recognition technology. The system according to feature 1.

3. The aforementioned reporting unit, The system selects the optimal reporting destination based on pre-configured rules and algorithms. The system according to feature 1.

4. The aforementioned reporting unit, Report the image data to the relevant agency. The system according to feature 1.

5. The aforementioned analysis unit, Use machine learning techniques to determine the situation. The system according to feature 1.

6. The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the photo shoot based on those emotions. The system according to feature 1.

7. The aforementioned imaging unit is The system automatically selects the optimal shooting mode depending on the type of emergency. The system according to feature 1.

8. The aforementioned imaging unit is It detects ambient light and automatically adjusts the exposure settings to the optimal level. The system according to feature 1.

9. The aforementioned imaging unit is It estimates the user's emotions and determines the priority of images to capture based on the estimated emotions. The system according to feature 1.

10. The aforementioned imaging unit is The system automatically adjusts the optimal shooting angle, taking into account the user's location. The system according to feature 1.

Images