system

Abstract

The system according to the embodiment aims to support the caller in making appropriate decisions in an emergency, and to provide prompt first aid and share information. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a standby support unit, and a coordination unit. The collection unit collects information from callers via camera or chat. The analysis unit analyzes the information collected by the collection unit and details the content of the call. The provision unit provides an automated first aid guide based on the information analyzed by the analysis unit. The standby support unit supports the caller while they wait for the ambulance to arrive. The coordination unit records the information provided by the caller and coordinates with hospitals.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult for the caller to make appropriate decisions in an emergency, and there were issues with insufficient first aid and information sharing.

[0005] The system according to the embodiment aims to support the caller in making appropriate decisions in an emergency, and to provide prompt first aid and share information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a standby support unit, and a collaboration unit. The collection unit collects information from callers via a camera or chat. The analysis unit analyzes the information collected by the collection unit and details the content of the call. The provision unit provides an automated first aid guide based on the information analyzed by the analysis unit. The standby support unit supports the caller while they wait for the ambulance to arrive. The collaboration unit records the information provided by the caller and collaborates with hospitals. [Effects of the Invention]

[0007] The system according to the embodiment can support the caller in making appropriate decisions in an emergency, providing prompt first aid, and sharing information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) An emergency support system according to an embodiment of the present invention supports appropriate decision-making in emergencies. In this emergency support system, a generation AI provides support via a camera or chat. Specifically, when an emergency occurs, a caller provides information via a camera or chat. The generation AI analyzes the provided information and refines the content of the call. The generation AI then provides an automated first aid guide to assist the caller in providing appropriate first aid. Furthermore, the generation AI continues to support the caller while waiting for an ambulance to arrive. For example, it may provide advice on how to deal with the emergency and mental support to reduce the caller's stress. The generation AI also records the information provided by the caller and works with hospitals to support the provision of appropriate medical care. This ensures that the caller receives emergency medical care. For example, the caller provides information via a camera or chat. This information is collected by the generation AI. The generation AI then analyzes the collected information and refines the content of the call. For example, it may analyze the video and text information provided by the caller to understand the emergency situation. The generation AI then provides an automated first aid guide. For example, if the caller needs to perform cardiopulmonary resuscitation, the generating AI will instruct them on specific steps in real time. This allows the caller to provide appropriate first aid. Furthermore, the generating AI continues to support the caller while they wait for the ambulance to arrive. For example, it may provide advice on how to deal with the emergency or provide mental support to reduce the caller's stress. The generating AI also records the information provided by the caller and works with hospitals to support the provision of appropriate medical care. For example, by informing the hospital of the patient's condition in advance based on the information provided by the caller, it can respond quickly. This ensures that the caller is directed to emergency medical care. In this way, the emergency support system can support appropriate decisions in emergencies and guide the caller to emergency medical care.

[0029] An emergency support system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a standby support unit, and a linking unit. The collection unit collects information from a caller via a camera or chat. The collection unit can collect video of the caller using, for example, a smartphone camera. The collection unit can also collect information from the caller via video chat or text chat. For example, the collection unit collects video information provided by the caller via video chat in real time. The collection unit can also collect text information entered by the caller via text chat. The analysis unit analyzes the information collected by the collection unit to refine the content of the call. The analysis unit can analyze the text information using, for example, natural language processing technology to understand the status of the emergency. The analysis unit can also analyze the video information using image analysis technology to understand the details of the emergency. For example, the analysis unit analyzes the video information provided by the caller to identify the location and status of the emergency. The provision unit provides an automated first aid guide based on the information analyzed by the analysis unit. For example, the provision unit can provide instructions on cardiopulmonary resuscitation procedures in real time. The providing unit can also provide specific first aid procedures in the form of text or video. For example, the providing unit may show specific procedures in a video when the caller is performing cardiopulmonary resuscitation. The standby support unit supports the caller while they are waiting for the ambulance to arrive. For example, the standby support unit provides the caller with advice on how to deal with an emergency. The standby support unit can also provide mental support to reduce the caller's stress. For example, the standby support unit provides mental support that helps the caller relax. The linking unit records the information provided by the caller and links with the hospital. For example, the linking unit notifies the hospital of the patient's condition in advance based on the information provided by the caller. This enables the hospital to respond quickly. For example, the linking unit records the information provided by the caller in an electronic medical record and sends it to the hospital. In this way, the emergency support system can support appropriate decisions in an emergency and guide the caller to reliable emergency medical care.

[0030] The collection unit can analyze the reporter's past reporting history and select the optimal information collection method. For example, if the reporter has reported a similar emergency in the past, the collection unit can have the generation AI refer to that history to quickly collect information. The collection unit can also analyze the accuracy of the information provided by the reporter in the past and select the optimal questioning method. For example, the collection unit can prioritize communication methods (camera, chat, etc.) that the reporter has used in the past. This enables efficient information collection by selecting the optimal information collection method based on the reporter's past reporting history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the reporter's past reporting history data into the generation AI and have the generation AI select the optimal information collection method.

[0031] When collecting information, the collection unit can perform filtering based on the caller's current situation or environment. For example, if the caller is outdoors, the generation AI filters the voice input taking into account surrounding noise. Furthermore, if the caller is reporting at night, the collection unit can also have the generation AI collect video using a night vision camera. For example, if the caller is in a group of people, the collection unit can remove background audio to clarify the caller's voice. This enables accurate information collection by filtering according to the caller's situation and environment. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the caller's voice data into the generation AI and have the generation AI perform voice filtering.

[0032] When collecting information, the collection unit can prioritize collecting highly relevant information based on the geographical location information of the caller. For example, if the caller is in a specific area, the collection unit prioritizes collecting information related to an emergency in that area. In addition, if the caller is moving, the collection unit can also collect information related to the caller's current location and destination. For example, if the caller is in a specific building, the collection unit collects information related to the building's structure and evacuation routes. This allows for efficient collection of highly relevant information by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the caller's geographical location data into the generation AI and cause the generation AI to collect highly relevant information.

[0033] When collecting information, the collection unit can analyze the social media activity of the informant and collect related information. For example, if the informant posts about an emergency on social media, the collection unit collects the content of the post. The collection unit can also analyze comments from the informant's social media friends and followers to collect related information. For example, the collection unit can identify the informant's current location based on location information previously shared on social media. This allows for efficient collection of related information by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the informant's social media data into a generation AI and cause the generation AI to collect related information.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis for information with a high level of urgency (e.g., cardiac arrest or severe bleeding). The analysis unit can also perform a brief analysis for information with a low level of urgency (e.g., minor injury). For example, the analysis unit determines the priority of the analysis according to the importance of the information and performs the analysis in order of importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a specialized medical analysis algorithm to medical information. The analysis unit can also apply an environmental analysis algorithm to environmental information. For example, the analysis unit applies a psychological analysis algorithm to information regarding the reporter's psychological state. This enables highly accurate analysis by applying an analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and have the generation AI apply the analysis algorithm.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of the most recent information and responds promptly. The analysis unit can also re-analyze old information as necessary. For example, the analysis unit evaluates the importance of information and determines the priority based on the time of submission. This enables a prompt response by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information submission time data into the generation AI and have the generation AI determine the analysis priority.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information directly related to the emergency situation. The analysis unit can also postpone analysis of less relevant information. For example, the analysis unit dynamically adjusts the order of analysis based on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0038] When providing a first aid guide, the providing unit can adjust the level of detail of the guide based on the importance of the procedure. For example, the providing unit provides detailed guides for procedures with high urgency (e.g., cardiopulmonary resuscitation). The providing unit can also provide concise guides for procedures with low urgency (e.g., treating minor injuries). For example, the providing unit determines the priority of the guide based on the importance of the procedure and provides guides in order of importance. This enables efficient first aid by adjusting the level of detail of the guide based on the importance of the procedure. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the importance of the procedure to the generating AI and cause the generating AI to adjust the level of detail of the guide.

[0039] When providing first aid guides, the providing unit can apply different guide algorithms depending on the category of the procedure. For example, the providing unit applies a medical-specific guide algorithm to medical procedures. The providing unit can also apply an environmental guide algorithm to environmental procedures. For example, the providing unit applies a psychological guide algorithm to procedures related to the caller's psychological state. This enables highly accurate first aid by applying a guide algorithm according to the procedure category. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input procedure category data into the generating AI and cause the generating AI to apply the guide algorithm.

[0040] When providing first aid guides, the providing unit can determine the priority of the guides based on the time of submission of the treatments. For example, the providing unit prioritizes the latest treatment information and provides prompt response. The providing unit can also re-provide guidance for old treatment information as needed. For example, the providing unit evaluates the importance of the treatments and determines the priority based on the time of submission. This enables prompt response by determining the priority of the guides based on the time of submission of the treatments. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input treatment submission time data into the generating AI and have the generating AI determine the priority of the guides.

[0041] When providing a first aid guide, the providing unit can adjust the order of the guide based on the relevance of the procedures. For example, the providing unit prioritizes guidance on procedures directly related to the emergency situation. The providing unit can also postpone guidance on procedures with low relevance. For example, the providing unit dynamically adjusts the order of the guide based on the relevance of the procedures. This enables efficient first aid by adjusting the order of the guide based on the relevance of the procedures. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input procedure relevance data into a generating AI and cause the generating AI to adjust the order of the guide.

[0042] The standby support unit can select an appropriate support method based on the caller's past standby history when providing support while waiting. For example, the standby support unit may provide again a support method that helped the caller relax in the past. The standby support unit may also prioritize providing mental support that was effective for the caller in the past. For example, the standby support unit may analyze the caller's past standby history and select the optimal support method. This enables efficient support by selecting the optimal support method based on the caller's past standby history. Some or all of the above-described processing in the standby support unit may be performed using, for example, AI, or may be performed without using AI. For example, the standby support unit may input the caller's past standby history data into a generation AI and have the generation AI select a support method.

[0043] The standby support unit can customize the support content based on the caller's current situation when providing support while waiting. For example, if the caller is outdoors, the generation AI of the standby support unit can provide support appropriate for the surrounding environment. Also, if the caller is indoors, the generation AI of the standby support unit can provide support appropriate for the indoor environment. For example, if the caller is moving, the generation AI of the standby support unit can provide support appropriate for moving. This enables more appropriate support by providing support content appropriate to the caller's current situation. Some or all of the above-described processing in the standby support unit may be performed using AI, for example, or may be performed without using AI. For example, the standby support unit can input data on the caller's current situation into the generation AI and have the generation AI customize the support content.

[0044] The standby support unit can select an appropriate support method based on the geographical location information of the caller when providing support while waiting. For example, if the caller is in a specific area, the standby support unit provides support appropriate to the local environment. Furthermore, if the caller is moving, the standby support unit can also provide support related to the caller's current location and destination. For example, if the caller is in a specific building, the standby support unit provides support related to the building's structure and evacuation routes. This makes it possible to provide the optimal support method by taking geographical location information into consideration. Some or all of the above-described processing in the standby support unit may be performed using, for example, AI, or may be performed without using AI. For example, the standby support unit can input the caller's geographical location data into the generation AI and have the generation AI select a support method.

[0045] The standby support unit can analyze the caller's social media activity and suggest support content during standby support. For example, if the caller posts about an emergency on social media, the standby support unit can provide support based on the content of the post. The standby support unit can also analyze comments from the caller's social media friends and followers to provide relevant support. For example, the standby support unit can provide support appropriate to the current situation based on information the caller previously shared on social media. In this way, relevant support content can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the standby support unit may be performed using, for example, AI, or may be performed without using AI. For example, the standby support unit can input the caller's social media data into a generation AI and have the generation AI execute a support content suggestion.

[0046] The collaboration unit can optimize the collaboration algorithm based on past collaboration data during collaboration. For example, the collaboration unit analyzes past collaboration data and selects an optimal collaboration method. The collaboration unit can also extract and apply an effective collaboration algorithm from the past collaboration data. For example, the collaboration unit determines a collaboration priority based on the past collaboration data. This enables efficient collaboration by optimizing the collaboration algorithm based on the past collaboration data. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input past collaboration data into the generation AI and cause the generation AI to optimize the collaboration algorithm.

[0047] During collaboration, the collaboration unit can weight the linked data based on the time of submission of the information provided by the reporter. For example, the collaboration unit prioritizes collaboration of the latest information and responds quickly. The collaboration unit can also re-link old information as necessary. For example, the collaboration unit evaluates the importance of information based on the time of submission and weights the linked data. This enables a quick response by weighting the linked data based on the time of submission of the information provided. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input data on the time of submission of the information provided by the reporter to the generation AI and have the generation AI weight the linked data.

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

[0049] The collection unit can monitor the caller's health data in real time and predict the occurrence of an emergency. For example, the collection unit can monitor the caller's heart rate and blood pressure, and automatically report an emergency if an abnormality is detected. The collection unit can also track the caller's location information and predict the occurrence of an accident. For example, if the caller makes a sudden stop while driving on a highway, the collection unit can detect the possibility of an accident. This allows the collection unit to predict the occurrence of an emergency based on the caller's health data and location information, enabling a prompt response.

[0050] The analysis unit can analyze the caller's past medical history and identify the cause of the emergency. For example, if the caller has had heart disease in the past, the analysis unit will consider the possibility of a heart attack. In addition, if the caller has had an allergic reaction in the past, the analysis unit can also identify the cause of the allergy. For example, the analysis unit can quickly identify the cause of the emergency based on the caller's past medical history. This allows for a quick and appropriate response by identifying the cause of the emergency based on the caller's past medical history.

[0051] The providing unit can provide a personalized first aid guide based on the caller's health data. For example, the providing unit may instruct appropriate first aid taking into account the caller's allergy information. The providing unit can also customize first aid methods taking into account the caller's chronic illnesses and current medications. For example, if the caller has diabetes, the providing unit may instruct first aid for hypoglycemia. This allows for more appropriate first aid by providing a personalized first aid guide based on the caller's health data.

[0052] The standby support department can analyze the caller's past mental health history and provide optimal mental support. For example, the standby support department can provide relaxation techniques that were effective for the caller in the past again. The standby support department can also provide appropriate mental support by referring to the content of counseling that the caller has received in the past. For example, the standby support department can provide optimal mental support based on the caller's past mental health history. In this way, the caller's stress can be reduced by providing optimal mental support based on the caller's past mental health history.

[0053] The collaboration unit can optimize the collaboration method with hospitals based on the caller's past medical data. For example, the collaboration unit selects an appropriate medical institution based on the treatment the caller has received in the past. The collaboration unit can also provide detailed information to hospitals based on the caller's past medical data. For example, the collaboration unit urges hospitals to take prompt action based on the caller's past medical data. This makes it possible to provide prompt and appropriate medical care by optimizing the collaboration method with hospitals based on past medical data.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The collection unit collects information from the informant via camera or chat. For example, the video of the informant can be collected using a smartphone camera. Information from the informant can also be collected via video chat or text chat. The collection unit collects video information provided by the informant via video chat in real time and collects text information entered by the informant via text chat. Step 2: The analysis unit analyzes the information collected by the collection unit and provides more detail to the report. For example, it uses natural language processing technology to analyze text information and understand the state of the emergency. It also uses image analysis technology to analyze video information and understand the details of the emergency. The analysis unit analyzes the video information provided by the caller and identifies the location and situation of the emergency. Step 3: The providing unit provides an automated first aid guide based on the information analyzed by the analyzing unit. For example, it can provide instructions on cardiopulmonary resuscitation procedures in real time. It also provides specific first aid procedures in text or video. The providing unit shows specific procedures in video as the caller performs cardiopulmonary resuscitation. Step 4: The standby support department supports the caller while they wait for the ambulance to arrive. For example, they provide advice to help the caller deal with the emergency. They also provide mental support to reduce the caller's stress. The standby support department provides mental support to help the caller relax. Step 5: The liaison department records the information provided by the caller and liaises with the hospital. For example, based on the information provided by the caller, the liaison department can notify the hospital of the patient's condition in advance, enabling the hospital to respond quickly. The liaison department records the information provided by the caller in the electronic medical record and sends it to the hospital.

[0056] (Example 2) An emergency support system according to an embodiment of the present invention supports appropriate decision-making in emergencies. In this emergency support system, a generation AI provides support via a camera or chat. Specifically, when an emergency occurs, a caller provides information via a camera or chat. The generation AI analyzes the provided information and refines the content of the call. The generation AI then provides an automated first aid guide to assist the caller in providing appropriate first aid. Furthermore, the generation AI continues to support the caller while waiting for an ambulance to arrive. For example, it may provide advice on how to deal with the emergency and mental support to reduce the caller's stress. The generation AI also records the information provided by the caller and works with hospitals to support the provision of appropriate medical care. This ensures that the caller receives emergency medical care. For example, the caller provides information via a camera or chat. This information is collected by the generation AI. The generation AI then analyzes the collected information and refines the content of the call. For example, it may analyze the video and text information provided by the caller to understand the emergency situation. The generation AI then provides an automated first aid guide. For example, if the caller needs to perform cardiopulmonary resuscitation, the generating AI will instruct them on specific steps in real time. This allows the caller to provide appropriate first aid. Furthermore, the generating AI continues to support the caller while they wait for the ambulance to arrive. For example, it may provide advice on how to deal with the emergency or provide mental support to reduce the caller's stress. The generating AI also records the information provided by the caller and works with hospitals to support the provision of appropriate medical care. For example, by informing the hospital of the patient's condition in advance based on the information provided by the caller, it can respond quickly. This ensures that the caller is directed to emergency medical care. In this way, the emergency support system can support appropriate decisions in emergencies and guide the caller to emergency medical care.

[0057] An emergency support system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a standby support unit, and a linking unit. The collection unit collects information from a caller via a camera or chat. The collection unit can collect video of the caller using, for example, a smartphone camera. The collection unit can also collect information from the caller via video chat or text chat. For example, the collection unit collects video information provided by the caller via video chat in real time. The collection unit can also collect text information entered by the caller via text chat. The analysis unit analyzes the information collected by the collection unit to refine the content of the call. The analysis unit can analyze the text information using, for example, natural language processing technology to understand the status of the emergency. The analysis unit can also analyze the video information using image analysis technology to understand the details of the emergency. For example, the analysis unit analyzes the video information provided by the caller to identify the location and status of the emergency. The provision unit provides an automated first aid guide based on the information analyzed by the analysis unit. For example, the provision unit can provide instructions on cardiopulmonary resuscitation procedures in real time. The providing unit can also provide specific first aid procedures in the form of text or video. For example, the providing unit may show specific procedures in a video when the caller is performing cardiopulmonary resuscitation. The standby support unit supports the caller while they are waiting for the ambulance to arrive. For example, the standby support unit provides the caller with advice on how to deal with an emergency. The standby support unit can also provide mental support to reduce the caller's stress. For example, the standby support unit provides mental support that helps the caller relax. The linking unit records the information provided by the caller and links with the hospital. For example, the linking unit notifies the hospital of the patient's condition in advance based on the information provided by the caller. This enables the hospital to respond quickly. For example, the linking unit records the information provided by the caller in an electronic medical record and sends it to the hospital. In this way, the emergency support system can support appropriate decisions in an emergency and guide the caller to reliable emergency medical care.

[0058] The collection unit can estimate the reporter's emotions and adjust the timing of information collection based on the estimated reporter's emotions. For example, if the reporter is in a panic, the collection unit can have the generation AI temporarily suspend information collection and wait until the reporter calms down. Alternatively, if the reporter is calm, the collection unit can have the generation AI quickly resume information collection and collect detailed information. For example, if the reporter is nervous, the collection unit can have the generation AI ask questions slowly to allow the reporter to relax. This allows for more appropriate information collection by adjusting the timing of information collection according to the reporter's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the reporter's facial expression data into the generation AI and have the generation AI estimate the reporter's emotions.

[0059] The collection unit can analyze the reporter's past reporting history and select the optimal information collection method. For example, if the reporter has reported a similar emergency in the past, the collection unit can have the generation AI refer to that history to quickly collect information. The collection unit can also analyze the accuracy of the information provided by the reporter in the past and select the optimal questioning method. For example, the collection unit can prioritize communication methods (camera, chat, etc.) that the reporter has used in the past. This enables efficient information collection by selecting the optimal information collection method based on the reporter's past reporting history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the reporter's past reporting history data into the generation AI and have the generation AI select the optimal information collection method.

[0060] When collecting information, the collection unit can perform filtering based on the caller's current situation or environment. For example, if the caller is outdoors, the generation AI filters the voice input taking into account surrounding noise. Furthermore, if the caller is reporting at night, the collection unit can also have the generation AI collect video using a night vision camera. For example, if the caller is in a group of people, the collection unit can remove background audio to clarify the caller's voice. This enables accurate information collection by filtering according to the caller's situation and environment. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the caller's voice data into the generation AI and have the generation AI perform voice filtering.

[0061] The collection unit can estimate the caller's emotions and prioritize the information to be collected based on the estimated caller's emotions. For example, if the caller is in a panic, the collection unit causes the generation AI to prioritize collecting the most important information (e.g., current location and type of emergency). Alternatively, if the caller is calm, the collection unit can cause the generation AI to collect detailed information (e.g., the progression of symptoms and the surrounding situation). For example, if the caller is nervous, the collection unit causes the generation AI to start with simple questions and gradually collect more detailed information. This allows important information to be prioritized by prioritizing information according to the caller's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using AI, or without AI. For example, the collection unit can input the caller's emotion data into the generation AI and have the generation AI determine the priority of information.

[0062] When collecting information, the collection unit can prioritize collecting highly relevant information based on the geographical location information of the caller. For example, if the caller is in a specific area, the collection unit prioritizes collecting information related to an emergency in that area. In addition, if the caller is moving, the collection unit can also collect information related to the caller's current location and destination. For example, if the caller is in a specific building, the collection unit collects information related to the building's structure and evacuation routes. This allows for efficient collection of highly relevant information by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the caller's geographical location data into the generation AI and cause the generation AI to collect highly relevant information.

[0063] When collecting information, the collection unit can analyze the social media activity of the informant and collect related information. For example, if the informant posts about an emergency on social media, the collection unit collects the content of the post. The collection unit can also analyze comments from the informant's social media friends and followers to collect related information. For example, the collection unit can identify the informant's current location based on location information previously shared on social media. This allows for efficient collection of related information by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the informant's social media data into a generation AI and cause the generation AI to collect related information.

[0064] The analysis unit can estimate the reporter's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the reporter is in a panicked state, the analysis unit can have the generation AI provide the analysis results using concise and clear expressions. Furthermore, if the reporter is calm, the analysis unit can have the generation AI provide detailed analysis results. For example, if the reporter is nervous, the analysis unit can have the generation AI provide the analysis results using expressions that help the reporter feel relaxed. This allows the analysis results to be adjusted according to the reporter's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the reporter's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0065] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis for information with a high level of urgency (e.g., cardiac arrest or severe bleeding). The analysis unit can also perform a brief analysis for information with a low level of urgency (e.g., minor injury). For example, the analysis unit determines the priority of the analysis according to the importance of the information and performs the analysis in order of importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0066] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a specialized medical analysis algorithm to medical information. The analysis unit can also apply an environmental analysis algorithm to environmental information. For example, the analysis unit applies a psychological analysis algorithm to information regarding the reporter's psychological state. This enables highly accurate analysis by applying an analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and have the generation AI apply the analysis algorithm.

[0067] The analysis unit can estimate the caller's emotions and adjust the length of the analysis based on the estimated caller's emotions. For example, if the caller is in a panic, the analysis unit can have the generation AI provide a short, concise analysis result. Alternatively, if the caller is calm, the analysis unit can have the generation AI provide a detailed analysis result. For example, if the caller is nervous, the analysis unit can have the generation AI provide an analysis result using expressions that help the caller relax. This allows the length of the analysis to be adjusted according to the caller's emotions, thereby providing a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the caller's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0068] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of the most recent information and responds promptly. The analysis unit can also re-analyze old information as necessary. For example, the analysis unit evaluates the importance of information and determines the priority based on the time of submission. This enables a prompt response by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information submission time data into the generation AI and have the generation AI determine the analysis priority.

[0069] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information directly related to the emergency situation. The analysis unit can also postpone analysis of less relevant information. For example, the analysis unit dynamically adjusts the order of analysis based on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0070] The providing unit can estimate the caller's emotions and adjust the way the first aid guide is presented based on the estimated emotions of the caller. For example, if the caller is in a panic, the providing unit causes the generation AI to provide the first aid guide using concise and clear expressions. Furthermore, if the caller is calm, the providing unit can also cause the generation AI to provide a detailed first aid guide. For example, if the caller is nervous, the providing unit causes the generation AI to provide the first aid guide using expressions that help the caller relax. This allows the first aid guide to be presented in a more appropriate manner by adjusting the way the first aid guide is presented according to the caller's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the caller's emotion data into the generation AI and cause the generation AI to adjust the way the first aid guide is presented.

[0071] When providing a first aid guide, the providing unit can adjust the level of detail of the guide based on the importance of the procedure. For example, the providing unit provides detailed guides for procedures with high urgency (e.g., cardiopulmonary resuscitation). The providing unit can also provide concise guides for procedures with low urgency (e.g., treating minor injuries). For example, the providing unit determines the priority of the guide based on the importance of the procedure and provides guides in order of importance. This enables efficient first aid by adjusting the level of detail of the guide based on the importance of the procedure. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the importance of the procedure to the generating AI and cause the generating AI to adjust the level of detail of the guide.

[0072] When providing first aid guides, the providing unit can apply different guide algorithms depending on the category of the procedure. For example, the providing unit applies a medical-specific guide algorithm to medical procedures. The providing unit can also apply an environmental guide algorithm to environmental procedures. For example, the providing unit applies a psychological guide algorithm to procedures related to the caller's psychological state. This enables highly accurate first aid by applying a guide algorithm according to the procedure category. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input procedure category data into the generating AI and cause the generating AI to apply the guide algorithm.

[0073] The providing unit can estimate the caller's emotions and adjust the length of the first aid guide based on the estimated emotions of the caller. For example, if the caller is in a panic, the providing unit can cause the generation AI to provide a short, concise first aid guide. Furthermore, if the caller is calm, the providing unit can cause the generation AI to provide a detailed first aid guide. For example, if the caller is nervous, the providing unit can provide the first aid guide using expressions that help the caller relax. This allows the length of the first aid guide to be adjusted according to the caller's emotions, thereby providing a more appropriate guide. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the caller's emotion data into the generation AI and cause the generation AI to adjust the length of the first aid guide.

[0074] When providing first aid guides, the providing unit can determine the priority of the guides based on the time of submission of the treatments. For example, the providing unit prioritizes the latest treatment information and provides prompt response. The providing unit can also re-provide guidance for old treatment information as needed. For example, the providing unit evaluates the importance of the treatments and determines the priority based on the time of submission. This enables prompt response by determining the priority of the guides based on the time of submission of the treatments. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input treatment submission time data into the generating AI and have the generating AI determine the priority of the guides.

[0075] When providing a first aid guide, the providing unit can adjust the order of the guide based on the relevance of the procedures. For example, the providing unit prioritizes guidance on procedures directly related to the emergency situation. The providing unit can also postpone guidance on procedures with low relevance. For example, the providing unit dynamically adjusts the order of the guide based on the relevance of the procedures. This enables efficient first aid by adjusting the order of the guide based on the relevance of the procedures. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input procedure relevance data into a generating AI and cause the generating AI to adjust the order of the guide.

[0076] The standby support unit can estimate the caller's emotions and adjust the support content during standby based on the estimated emotions. For example, if the caller is in a panic, the standby support unit can have the generation AI provide mental support to help the caller relax. Furthermore, if the caller is calm, the standby support unit can have the generation AI provide detailed advice. For example, if the caller is nervous, the standby support unit can provide support using expressions that help the caller relax. This allows the support content during standby to be adjusted according to the caller's emotions, thereby providing more appropriate support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the standby support unit can be performed using AI, for example, or without AI. For example, the standby support unit can input the caller's emotion data into the generation AI and have the generation AI adjust the support content.

[0077] The standby support unit can select an appropriate support method based on the caller's past standby history when providing support while waiting. For example, the standby support unit may provide again a support method that helped the caller relax in the past. The standby support unit may also prioritize providing mental support that was effective for the caller in the past. For example, the standby support unit may analyze the caller's past standby history and select the optimal support method. This enables efficient support by selecting the optimal support method based on the caller's past standby history. Some or all of the above-described processing in the standby support unit may be performed using, for example, AI, or may be performed without using AI. For example, the standby support unit may input the caller's past standby history data into a generation AI and have the generation AI select a support method.

[0078] The standby support unit can customize the support content based on the caller's current situation when providing support while waiting. For example, if the caller is outdoors, the generation AI of the standby support unit can provide support appropriate for the surrounding environment. Also, if the caller is indoors, the generation AI of the standby support unit can provide support appropriate for the indoor environment. For example, if the caller is moving, the generation AI of the standby support unit can provide support appropriate for moving. This enables more appropriate support by providing support content appropriate to the caller's current situation. Some or all of the above-described processing in the standby support unit may be performed using AI, for example, or may be performed without using AI. For example, the standby support unit can input data on the caller's current situation into the generation AI and have the generation AI customize the support content.

[0079] The standby support unit can estimate the caller's emotions and determine the priority of support during standby based on the estimated caller's emotions. For example, if the caller is in a panic, the generation AI can prioritize providing the most important support. Furthermore, if the caller is calm, the generation AI can provide detailed support. For example, if the caller is nervous, the standby support unit can prioritize providing support that helps the caller relax. This allows for prioritized support based on the caller's emotions, thereby providing important support. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the standby support unit can be performed using, for example, AI, or without AI. For example, the standby support unit can input the caller's emotion data into the generation AI and have the generation AI determine the support priority.

[0080] The standby support unit can select an appropriate support method based on the geographical location information of the caller when providing support while waiting. For example, if the caller is in a specific area, the standby support unit provides support appropriate to the local environment. Furthermore, if the caller is moving, the standby support unit can also provide support related to the caller's current location and destination. For example, if the caller is in a specific building, the standby support unit provides support related to the building's structure and evacuation routes. This makes it possible to provide the optimal support method by taking geographical location information into consideration. Some or all of the above-described processing in the standby support unit may be performed using, for example, AI, or may be performed without using AI. For example, the standby support unit can input the caller's geographical location data into the generation AI and have the generation AI select a support method.

[0081] The standby support unit can analyze the caller's social media activity and suggest support content during standby support. For example, if the caller posts about an emergency on social media, the standby support unit can provide support based on the content of the post. The standby support unit can also analyze comments from the caller's social media friends and followers to provide relevant support. For example, the standby support unit can provide support appropriate to the current situation based on information the caller previously shared on social media. In this way, relevant support content can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the standby support unit may be performed using, for example, AI, or may be performed without using AI. For example, the standby support unit can input the caller's social media data into a generation AI and have the generation AI execute a support content suggestion.

[0082] The linking unit can estimate the reporter's emotions and select information to be linked based on the estimated reporter's emotions. For example, if the reporter is in a panic, the linking unit allows the generation AI to prioritize linking the most important information. Furthermore, if the reporter is calm, the linking unit can also allow the generation AI to link detailed information. For example, if the reporter is nervous, the linking unit prioritizes linking information that will help the reporter relax. This allows important information to be prioritized by selecting information to be linked based on the reporter's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or without AI. For example, the linking unit can input the reporter's emotion data into the generation AI and have the generation AI select the information to be linked.

[0083] The collaboration unit can optimize the collaboration algorithm based on past collaboration data during collaboration. For example, the collaboration unit analyzes past collaboration data and selects an optimal collaboration method. The collaboration unit can also extract and apply an effective collaboration algorithm from the past collaboration data. For example, the collaboration unit determines a collaboration priority based on the past collaboration data. This enables efficient collaboration by optimizing the collaboration algorithm based on the past collaboration data. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input past collaboration data into the generation AI and cause the generation AI to optimize the collaboration algorithm.

[0084] The collaboration unit can estimate the reporter's emotions and adjust the frequency of collaboration based on the estimated emotions. For example, if the reporter is in a panicked state, the collaboration unit can cause the generation AI to collaborate frequently to provide a prompt response. Furthermore, if the reporter is calm, the collaboration unit can also cause the generation AI to collaborate as needed. For example, if the reporter is nervous, the collaboration unit can cause the generation AI to collaborate at an appropriate frequency to help the reporter relax. This allows for a prompt response by adjusting the frequency of collaboration according to the reporter's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collaboration unit can be performed using, for example, an AI, or without an AI. For example, the collaboration unit can input the reporter's emotion data into the generation AI and have the generation AI adjust the frequency of collaboration.

[0085] During collaboration, the collaboration unit can weight the linked data based on the time of submission of the information provided by the reporter. For example, the collaboration unit prioritizes collaboration of the latest information and responds quickly. The collaboration unit can also re-link old information as necessary. For example, the collaboration unit evaluates the importance of information based on the time of submission and weights the linked data. This enables a quick response by weighting the linked data based on the time of submission of the information provided. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input data on the time of submission of the information provided by the reporter to the generation AI and have the generation AI weight the linked data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, waiting support unit, and linkage unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information from the caller using the camera 42 or chat function of the smart device 14. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and details the content of the call. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides first aid guidance. The waiting support unit, realized, for example, by the control unit 46A of the smart device 14, supports the caller. The linkage unit, realized, for example, by the specific processing unit 290 of the data processing device 12, cooperates with hospitals to support the provision of appropriate medical care. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, waiting support unit, and collaboration unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information from the caller using the camera 42 or chat function of the smart glasses 214. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and refines the content of the call. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides first aid guidance. The waiting support unit, realized, for example, by the control unit 46A of the smart glasses 214, supports the caller. The collaboration unit, realized, for example, by the specific processing unit 290 of the data processing device 12, collaborates with hospitals to support the provision of appropriate medical care. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, provision unit, waiting support unit, and linking unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects information from the caller using the camera 42 or chat function of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and details the content of the call. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides a first aid guide. The waiting support unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and supports the caller. The linking unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and cooperates with hospitals to support the provision of appropriate medical care. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, provision unit, waiting support unit, and collaboration unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information from the caller using the camera 42 or chat function of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and details the content of the call. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides first aid guidance. The waiting support unit is realized, for example, by the control unit 46A of the robot 414, and supports the caller. The collaboration unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collaborates with hospitals to support the provision of appropriate medical care.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The collection unit can monitor the caller's health data in real time and predict the occurrence of an emergency. For example, the collection unit can monitor the caller's heart rate and blood pressure, and automatically report an emergency if an abnormality is detected. The collection unit can also track the caller's location information and predict the occurrence of an accident. For example, if the caller makes a sudden stop while driving on a highway, the collection unit can detect the possibility of an accident. This allows the collection unit to predict the occurrence of an emergency based on the caller's health data and location information, enabling a prompt response.

[0088] The collection unit can estimate the reporter's emotions and customize the information collection method based on the estimated reporter's emotions. For example, if the reporter is in a panic, the generation AI will ask questions in a gentle tone to help the reporter relax. The collection unit can also allow the generation AI to collect information quickly and efficiently if the reporter is calm. For example, if the reporter is nervous, the collection unit will have the generation AI collect information while playing relaxing music. This allows the information collection method to be customized according to the reporter's emotions, making it possible to collect more appropriate information.

[0089] The analysis unit can analyze the caller's past medical history and identify the cause of the emergency. For example, if the caller has had heart disease in the past, the analysis unit will consider the possibility of a heart attack. In addition, if the caller has had an allergic reaction in the past, the analysis unit can also identify the cause of the allergy. For example, the analysis unit can quickly identify the cause of the emergency based on the caller's past medical history. This allows for a quick and appropriate response by identifying the cause of the emergency based on the caller's past medical history.

[0090] The analysis unit can estimate the caller's emotions and determine the analysis priority based on the estimated caller's emotions. For example, if the caller is in a panic, the generation AI will prioritize analyzing the most important information. The analysis unit can also allow the generation AI to analyze detailed information if the caller is calm. For example, if the caller is nervous, the analysis unit will provide analysis results using expressions that will help the generation AI relax. This allows the analysis priority to be determined according to the caller's emotions, making it possible to prioritize the analysis of important information.

[0091] The providing unit can provide a personalized first aid guide based on the caller's health data. For example, the providing unit may instruct appropriate first aid taking into account the caller's allergy information. The providing unit can also customize first aid methods taking into account the caller's chronic illnesses and current medications. For example, if the caller has diabetes, the providing unit may instruct first aid for hypoglycemia. This allows for more appropriate first aid by providing a personalized first aid guide based on the caller's health data.

[0092] The provision unit can estimate the caller's emotions and adjust the way the first aid guide is provided based on the estimated emotions of the caller. For example, if the caller is in a panic, the generation AI provides concise and clear instructions. The provision unit can also have the generation AI provide detailed procedures if the caller is calm. For example, if the caller is nervous, the provision unit can have the generation AI provide the first aid guide using expressions that will help the caller relax. This makes it possible to provide a more appropriate guide by adjusting the way the first aid guide is provided according to the caller's emotions.

[0093] The standby support department can analyze the caller's past mental health history and provide optimal mental support. For example, the standby support department can provide relaxation techniques that were effective for the caller in the past again. The standby support department can also provide appropriate mental support by referring to the content of counseling that the caller has received in the past. For example, the standby support department can provide optimal mental support based on the caller's past mental health history. In this way, the caller's stress can be reduced by providing optimal mental support based on the caller's past mental health history.

[0094] The standby support unit can estimate the caller's emotions and adjust the frequency of standby support based on the estimated caller's emotions. For example, if the caller is in a panic, the generation AI will provide frequent mental support. The standby support unit can also provide support as needed if the caller is calm. For example, if the caller is nervous, the generation AI will provide support at an appropriate frequency. This allows for more appropriate support to be provided by adjusting the frequency of standby support according to the caller's emotions.

[0095] The collaboration unit can optimize the collaboration method with hospitals based on the caller's past medical data. For example, the collaboration unit selects an appropriate medical institution based on the treatment the caller has received in the past. The collaboration unit can also provide detailed information to hospitals based on the caller's past medical data. For example, the collaboration unit urges hospitals to take prompt action based on the caller's past medical data. This makes it possible to provide prompt and appropriate medical care by optimizing the collaboration method with hospitals based on past medical data.

[0096] The linking unit can estimate the reporter's emotions and adjust the level of detail of the information to be linked based on the estimated reporter's emotions. For example, if the reporter is in a panic, the generation AI will link concise, to-the-point information. The linking unit can also have the generation AI link detailed information if the reporter is calm. For example, if the reporter is nervous, the linking unit will link information that will help the reporter relax. This allows important information to be linked appropriately by adjusting the level of detail of the information to be linked according to the reporter's emotions.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The collection unit collects information from the informant via camera or chat. For example, the video of the informant can be collected using a smartphone camera. Information from the informant can also be collected via video chat or text chat. The collection unit collects video information provided by the informant via video chat in real time and collects text information entered by the informant via text chat. Step 2: The analysis unit analyzes the information collected by the collection unit and provides more detail to the report. For example, it uses natural language processing technology to analyze text information and understand the state of the emergency. It also uses image analysis technology to analyze video information and understand the details of the emergency. The analysis unit analyzes the video information provided by the caller and identifies the location and situation of the emergency. Step 3: The providing unit provides an automated first aid guide based on the information analyzed by the analyzing unit. For example, it can provide instructions on cardiopulmonary resuscitation procedures in real time. It also provides specific first aid procedures in text or video. The providing unit shows specific procedures in video as the caller performs cardiopulmonary resuscitation. Step 4: The standby support department supports the caller while they wait for the ambulance to arrive. For example, they provide advice to help the caller deal with the emergency. They also provide mental support to reduce the caller's stress. The standby support department provides mental support to help the caller relax. Step 5: The liaison department records the information provided by the caller and liaises with the hospital. For example, based on the information provided by the caller, the liaison department can notify the hospital of the patient's condition in advance, enabling the hospital to respond quickly. The liaison department records the information provided by the caller in the electronic medical record and sends it to the hospital.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0101] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0170] [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects information from informants via camera or chat; an analysis unit that analyzes the information collected by the collection unit and details the content of the report; a providing unit that provides an automated first aid guide based on the information analyzed by the analyzing unit; A standby support department that supports callers while they wait for the ambulance to arrive; A coordination department that records information provided by the informant and coordinates with the hospital. A system characterized by:

2. The collecting unit Estimate the whistleblower's feelings and adjust the timing of information gathering based on the estimated whistleblower's feelings. The system of claim 1 .

3. The collecting unit Analyze the whistleblower's past reporting history and select the appropriate information gathering method The system of claim 1 .

4. The collecting unit When collecting information, filter it based on the reporter's current situation or environment. The system of claim 1 .

5. The collecting unit Estimate the whistleblower's feelings and prioritize the information to be collected based on the estimated whistleblower's feelings. The system of claim 1 .

6. The collecting unit When collecting information, prioritize the collection of relevant information based on the reporter's geographic location. The system of claim 1 .

7. The collecting unit When collecting information, we analyze the whistleblower's social media activity and collect relevant information. The system of claim 1 .

8. The analysis unit Estimate the reporter's emotions and adjust the analysis presentation based on the estimated emotions. The system of claim 1 .

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