System

Abstract

An object of a system according to an embodiment is to enable a reporter to quickly and accurately take appropriate first aid measures in an emergency.SOLUTION: A system includes an information input unit, an analysis unit, and an instruction unit. The information input unit receives information from the reporter. The analysis unit analyzes the information received by the information input unit. The instruction unit advises an appropriate emergency treatment based on the result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for callers to quickly and accurately receive instructions on how to provide appropriate first aid in an emergency.

[0005] The system according to the embodiment aims to enable a caller to quickly and accurately provide appropriate first aid in an emergency. [Means for solving the problem]

[0006] The system according to the embodiment includes an information input unit, an analysis unit, and an instruction unit. The information input unit receives information from a caller. The analysis unit analyzes the information received by the information input unit. The instruction unit advises appropriate emergency measures based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows the caller to quickly and accurately take appropriate first aid measures in the event of an emergency. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In the emergency medical instruction system according to an embodiment of the present invention, a caller informs a generating AI of the patient's condition on-site, and the generating AI advises appropriate first aid. This allows the caller to quickly provide appropriate first aid in an emergency.

[0029] An emergency medical instruction system according to an embodiment includes an information input unit, an analysis unit, and an instruction unit. The information input unit receives information from a caller. For example, the caller inputs, "The patient is unconscious." The information input unit can also receive voice input. For example, the caller inputs, "His breathing is shallow." The information input unit can also receive images and videos. For example, the caller transmits video of the scene using a smartphone camera. The analysis unit analyzes the information received by the information input unit. For example, the generation AI analyzes text information using natural language processing technology. The generation AI can also analyze audio information using speech recognition technology. The generation AI can also analyze video information using image analysis technology. The instruction unit advises appropriate first aid based on the results of the analysis by the analysis unit. For example, the generation AI can instruct, "Check the patient's breathing and secure the airway." The generation AI can also instruct, "Apply pressure to the bleeding area to stop the bleeding." The generating AI can also instruct, "Next, compress the patient's chest. Compress twice per second." This allows the emergency medical instruction system according to the embodiment to enable the caller to quickly provide appropriate first aid in an emergency. For example, if the caller provides first aid according to the generating AI's instructions, the patient's condition can be stabilized. Furthermore, the caller can receive appropriate support even at night or when there is a shortage of doctors.

[0030] The information input unit can automatically complement the information from the caller and generate questions to understand the detailed situation. For example, if the caller inputs "The patient is unconscious," the generation AI automatically generates detailed questions such as "How many minutes ago did the patient lose consciousness?" and "Is the patient breathing?" and presents them to the caller. This makes it possible to complement the information from the caller and generate questions to understand the detailed situation.

[0031] The information input unit converts the caller's voice input into text in real time, and the analysis unit analyzes that text to determine appropriate first aid. For example, if the caller inputs "The patient is unconscious," the information input unit converts that voice into text in real time, and the generation AI automatically generates questions such as "How many minutes ago did the patient lose consciousness?" This allows the caller's voice input to be converted into text in real time, making it possible to determine appropriate first aid.

[0032] The information input unit allows the caller to use their smartphone camera to send footage of the scene to the generation AI, and the analysis unit analyzes the footage to understand the situation. For example, the information input unit allows the caller to take a video of the patient using their smartphone camera and send it to the generation AI. The generation AI analyzes the footage and understands the patient's condition. This allows the caller to send footage of the scene using their smartphone camera and understand the situation.

[0033] The information input unit can link the information entered by the caller with other emergency response applications to provide comprehensive support. For example, the information input unit links the information entered by the caller with the emergency response applications to provide information such as the arrival time of an ambulance and the nearest medical institution. This allows the information entered by the caller to be linked with other emergency response applications to provide comprehensive support.

[0034] The analysis unit can provide individually customized instructions by taking into account the caller's past first aid experience. For example, if the caller has experience performing cardiopulmonary resuscitation in the past, the generation AI will take that experience into account and provide more advanced first aid instructions. This makes it possible to provide individually customized instructions by taking into account the caller's past first aid experience.

[0035] The analysis unit takes into account individual information such as the patient's age and gender and can propose the most appropriate first aid. For example, if the patient is a child, the analysis unit's generation AI will provide first aid instructions suitable for children. For example, it will adjust the depth and number of compressions during CPR. This allows the analysis unit to propose the most appropriate first aid, taking into account individual information such as the patient's age and gender.

[0036] The instruction unit can provide a visual guide to the caller and visually show first aid procedures. For example, the instruction unit uses the generation AI to show cardiopulmonary resuscitation procedures in a visual guide, allowing the caller to visually confirm the procedures. This makes it possible to provide a visual guide to the caller and visually show first aid procedures.

[0037] The instruction unit can provide the caller with a function to simulate first aid procedures, allowing them to practice in advance. For example, the instruction unit allows the generation AI to simulate cardiopulmonary resuscitation procedures, allowing the caller to practice in advance. This provides the caller with a function to simulate first aid procedures, allowing them to practice in advance.

[0038] The instruction unit can provide audio guidance that corresponds to the caller's language or dialect. For example, if the caller speaks English, the instruction unit causes the generation AI to provide audio guidance in English. This makes it possible to provide audio guidance that corresponds to the caller's language or dialect.

[0039] The instruction unit provides real-time feedback to the caller's voice input, urging them to provide accurate first aid. For example, if the caller voice-inputs, "The patient is unconscious," the generation AI will provide real-time feedback, asking, "How many minutes ago did the patient lose consciousness?" This allows the caller to receive real-time feedback on their voice input, urging them to provide accurate first aid.

[0040] The instruction unit provides a text guide to the caller at the same time as the audio guide, allowing instructions to be conveyed both visually and audibly. For example, the instruction unit may have the generation AI provide a text guide to the caller at the same time as the audio guide, allowing the caller to confirm the procedure both visually and audibly. This allows the instruction unit to provide a text guide to the caller at the same time as the audio guide, allowing instructions to be conveyed both visually and audibly.

[0041] The instruction unit can provide a function that allows the caller to record audio guidance and play it back later for review. For example, the instruction unit allows the generation AI to provide audio guidance on cardiopulmonary resuscitation procedures, record the audio, and play it back later. This makes it possible to provide a function that allows the caller to record audio guidance and play it back later for review.

[0042] The analysis unit analyzes information from the caller in real time and can instantly provide instructions according to changes in the situation. For example, if the caller inputs, "The patient's breathing has returned," the generation AI will instantly provide new instructions such as, "Next, check the patient's pulse." This allows the analysis unit to analyze information from the caller in real time and instantly provide instructions according to changes in the situation.

[0043] The analysis unit predicts the next step based on the information input by the caller and can prompt preparations in advance. For example, if the caller inputs, "The patient is unconscious," the generation AI will predict the next step, such as, "Next, check the patient's breathing," and prompt preparations in advance. This makes it possible to predict the next step based on the information input by the caller and prompt preparations in advance.

[0044] The analysis unit can periodically send reminders to the caller to check the situation and encourage continuous information provision. For example, the generation AI can send regular reminders to the caller, such as "Check the patient's breathing," to encourage continuous information provision. This allows the caller to periodically send reminders to check the situation and encourage continuous information provision.

[0045] The analysis unit can share the information input by the caller with other emergency response systems and respond in cooperation. For example, the analysis unit can share the information input by the caller with the emergency response system and provide information on the arrival time of an ambulance and the nearest medical institution. This allows the information input by the caller to be shared with other emergency response systems and respond in cooperation.

[0046] The notification unit can automatically obtain the location information of the caller and notify the nearest emergency contact. The notification unit obtains the location information using, for example, the GPS function of the caller's smartphone and notifies the nearest ambulance. This allows the location information of the caller to be automatically obtained and notified to the nearest emergency contact.

[0047] The notification unit can send a detailed status report to the emergency contact based on the information input by the caller. For example, if the caller inputs "the patient is unconscious," the generation AI will send a detailed status report to the emergency contact based on that information. This allows a detailed status report to be sent to the emergency contact based on the information input by the caller.

[0048] The notification unit can register the caller's emergency contact list in advance to enable prompt notification. For example, the caller registers an emergency contact list in advance, and the generation AI makes prompt notifications based on that list. This allows the caller's emergency contact list to be registered in advance to enable prompt notification.

[0049] The notification unit can automatically send a voice message or a text message to the caller's emergency contact. For example, the generation AI automatically sends a voice message to the caller's emergency contact to report the situation. This makes it possible to automatically send a voice message or a text message to the caller's emergency contact.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The emergency medical instruction system may further include a vital sign measurement unit. The vital sign measurement unit allows the caller to measure the patient's vital signs, such as heart rate and blood pressure, using a smartphone or wearable device, and transmit the data to the analysis unit. For example, if the caller measures the patient's heart rate using a smartwatch and transmits the data to the system, the analysis unit can provide more accurate first aid instructions. The vital sign measurement unit can also measure the patient's body temperature and oxygen saturation. This allows the emergency medical instruction system to grasp the patient's condition in more detail and provide appropriate first aid quickly.

[0052] The emergency medical instruction system can further include a translation unit. When a caller inputs information in a different language, the translation unit automatically translates the information and sends it to the analysis unit. For example, if a caller inputs "the patient is unconscious" in Spanish, the translation unit translates the information into English, and the analysis unit determines the appropriate first aid. The translation unit can also support both voice input and text input. This allows the emergency medical instruction system to support multiple languages ​​and provide appropriate assistance to callers who speak different languages.

[0053] The emergency medical instruction system may further include an environmental sensor unit. The environmental sensor unit acquires environmental information about the scene and transmits it to the analysis unit. For example, if the caller uses the environmental sensor on their smartphone to measure the temperature, humidity, and noise level of the scene and transmits that data to the system, the analysis unit can provide more appropriate first aid instructions. The environmental sensor unit may also measure the illuminance and air quality of the scene. This allows the emergency medical instruction system to quickly provide first aid that takes into account the environmental information about the scene.

[0054] The emergency medical instruction system may further include a history management unit. The history management unit stores a history of first aid procedures previously performed by the caller, and the analysis unit provides appropriate instructions based on that history. For example, if the caller has experience performing cardiopulmonary resuscitation in the past, the history management unit stores that information, and the analysis unit provides instructions taking that experience into consideration. The history management unit may also store a history of first aid training that the caller has previously received. This allows the emergency medical instruction system to provide individually customized instructions that take into consideration the caller's past experience and training history.

[0055] The emergency medical instruction system can further include a prediction unit. The prediction unit predicts future situations based on information from the caller and prompts the caller to make appropriate preparations in advance. For example, if the caller inputs, "The patient is unconscious," the prediction unit may make a prediction such as, "Next, the patient may stop breathing. Please prepare for cardiopulmonary resuscitation," prompting the caller to make preparations in advance. The prediction unit can also predict the possibility that the patient's condition may worsen and provide appropriate first aid procedures in advance. This allows the emergency medical instruction system to support the caller in responding quickly and appropriately.

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

[0057] Step 1: The information input unit receives information from the caller. For example, the caller inputs, "The patient is unconscious." The information input unit can also receive voice input. For example, the caller inputs, "His breathing is shallow." The information input unit can also receive images and videos. For example, the caller sends a video of the scene using the camera on their smartphone. Step 2: The analysis unit analyzes the information received by the information input unit. For example, the generation AI analyzes text information using natural language processing technology. The generation AI can also analyze audio information using voice recognition technology. The generation AI can also analyze video information using image analysis technology. Step 3: The instruction unit advises appropriate first aid based on the results of the analysis by the analysis unit. For example, the generation AI may instruct, "Check the patient's breathing and secure the airway." The generation AI may also instruct, "Apply pressure to the bleeding area to stop the bleeding." The generation AI may also instruct, "Next, compress the patient's chest. Compress twice per second."

[0058] (Example 2) In the emergency medical instruction system according to an embodiment of the present invention, a caller informs a generating AI of the patient's condition on-site, and the generating AI advises appropriate first aid. This allows the caller to quickly provide appropriate first aid in an emergency.

[0059] An emergency medical instruction system according to an embodiment includes an information input unit, an analysis unit, and an instruction unit. The information input unit receives information from a caller. For example, the caller inputs, "The patient is unconscious." The information input unit can also receive voice input. For example, the caller inputs, "His breathing is shallow." The information input unit can also receive images and videos. For example, the caller transmits video of the scene using a smartphone camera. The analysis unit analyzes the information received by the information input unit. For example, the generation AI analyzes text information using natural language processing technology. The generation AI can also analyze audio information using speech recognition technology. The generation AI can also analyze video information using image analysis technology. The instruction unit advises appropriate first aid based on the results of the analysis by the analysis unit. For example, the generation AI can instruct, "Check the patient's breathing and secure the airway." The generation AI can also instruct, "Apply pressure to the bleeding area to stop the bleeding." The generating AI can also instruct, "Next, compress the patient's chest. Compress twice per second." This allows the emergency medical instruction system according to the embodiment to enable the caller to quickly provide appropriate first aid in an emergency. For example, if the caller provides first aid according to the generating AI's instructions, the patient's condition can be stabilized. Furthermore, the caller can receive appropriate support even at night or when there is a shortage of doctors.

[0060] The information input unit can automatically complement the information from the caller and generate questions to understand the detailed situation. For example, if the caller inputs "The patient is unconscious," the generation AI automatically generates detailed questions such as "How many minutes ago did the patient lose consciousness?" and "Is the patient breathing?" and presents them to the caller. This makes it possible to complement the information from the caller and generate questions to understand the detailed situation.

[0061] The information input unit converts the caller's voice input into text in real time, and the analysis unit analyzes that text to determine appropriate first aid. For example, if the caller inputs "The patient is unconscious," the information input unit converts that voice into text in real time, and the generation AI automatically generates questions such as "How many minutes ago did the patient lose consciousness?" This allows the caller's voice input to be converted into text in real time, making it possible to determine appropriate first aid.

[0062] The information input unit can use the emotion estimation function to detect tension or impatience in the caller and provide guidance to calm them down. For example, if the caller is nervous, the generation AI will provide guidance such as "Take a deep breath. Stay calm and tell us the situation." This makes it possible to detect tension or impatience in the caller and provide guidance to calm them down.

[0063] The information input unit allows the caller to use their smartphone camera to send footage of the scene to the generation AI, and the analysis unit analyzes the footage to understand the situation. For example, the information input unit allows the caller to take a video of the patient using their smartphone camera and send it to the generation AI. The generation AI analyzes the footage and understands the patient's condition. This allows the caller to send footage of the scene using their smartphone camera and understand the situation.

[0064] The information input unit can link the information entered by the caller with other emergency response applications to provide comprehensive support. For example, the information input unit links the information entered by the caller with the emergency response applications to provide information such as the arrival time of an ambulance and the nearest medical institution. This allows the information entered by the caller to be linked with other emergency response applications to provide comprehensive support.

[0065] The information input unit can use the emotion estimation function to automatically adjust the interface according to the caller's emotional state, improving usability. For example, if the caller is nervous, the generation AI will simplify the interface and change it to a more subdued color scheme. This automatically adjusts the interface according to the caller's emotional state, improving usability.

[0066] The analysis unit can provide individually customized instructions by taking into account the caller's past first aid experience. For example, if the caller has experience performing cardiopulmonary resuscitation in the past, the generation AI will take that experience into account and provide more advanced first aid instructions. This makes it possible to provide individually customized instructions by taking into account the caller's past first aid experience.

[0067] The analysis unit takes into account individual information such as the patient's age and gender and can propose the most appropriate first aid. For example, if the patient is a child, the analysis unit's generation AI will provide first aid instructions suitable for children. For example, it will adjust the depth and number of compressions during CPR. This allows the analysis unit to propose the most appropriate first aid, taking into account individual information such as the patient's age and gender.

[0068] The analysis unit can use the emotion estimation function to adjust the tone and content of instructions according to the caller's emotional state. For example, if the caller is nervous, the generation AI will provide instructions in a calm tone to reassure the caller. This allows the analysis unit to adjust the tone and content of instructions according to the caller's emotional state.

[0069] The instruction unit can provide a visual guide to the caller and visually show first aid procedures. For example, the instruction unit uses the generation AI to show cardiopulmonary resuscitation procedures in a visual guide, allowing the caller to visually confirm the procedures. This makes it possible to provide a visual guide to the caller and visually show first aid procedures.

[0070] The instruction unit can provide the caller with a function to simulate first aid procedures, allowing them to practice in advance. For example, the instruction unit allows the generation AI to simulate cardiopulmonary resuscitation procedures, allowing the caller to practice in advance. This provides the caller with a function to simulate first aid procedures, allowing them to practice in advance.

[0071] The instruction unit can automatically adjust the priority of first aid measures according to the caller's emotional state using the emotion estimation function. For example, if the caller is nervous, the generation AI will provide instructions starting with the first aid measures with the highest priority, reassuring the caller. This makes it possible to automatically adjust the priority of first aid measures according to the caller's emotional state.

[0072] The instruction unit can provide audio guidance that corresponds to the caller's language or dialect. For example, if the caller speaks English, the instruction unit causes the generation AI to provide audio guidance in English. This makes it possible to provide audio guidance that corresponds to the caller's language or dialect.

[0073] The instruction unit provides real-time feedback to the caller's voice input, urging them to provide accurate first aid. For example, if the caller voice-inputs, "The patient is unconscious," the generation AI will provide real-time feedback, asking, "How many minutes ago did the patient lose consciousness?" This allows the caller to receive real-time feedback on their voice input, urging them to provide accurate first aid.

[0074] The instruction unit uses the emotion estimation function to adjust the voice tone according to the caller's emotional state, giving them a sense of security. For example, if the caller is nervous, the generation AI will provide voice guidance in a calm tone to reassure the caller. This allows the voice tone to be adjusted according to the caller's emotional state, giving them a sense of security.

[0075] The instruction unit provides a text guide to the caller at the same time as the audio guide, allowing instructions to be conveyed both visually and audibly. For example, the instruction unit may have the generation AI provide a text guide to the caller at the same time as the audio guide, allowing the caller to confirm the procedure both visually and audibly. This allows the instruction unit to provide a text guide to the caller at the same time as the audio guide, allowing instructions to be conveyed both visually and audibly.

[0076] The instruction unit can provide a function that allows the caller to record audio guidance and play it back later for review. For example, the instruction unit allows the generation AI to provide audio guidance on cardiopulmonary resuscitation procedures, record the audio, and play it back later. This makes it possible to provide a function that allows the caller to record audio guidance and play it back later for review.

[0077] The instruction unit can automatically adjust the content of the voice guidance according to the caller's emotional state using the emotion estimation function. For example, if the caller is nervous, the instruction unit can have the generation AI provide voice guidance in a calm tone to reassure the caller. This makes it possible to automatically adjust the content of the voice guidance according to the caller's emotional state.

[0078] The analysis unit analyzes information from the caller in real time and can instantly provide instructions according to changes in the situation. For example, if the caller inputs, "The patient's breathing has returned," the generation AI will instantly provide new instructions such as, "Next, check the patient's pulse." This allows the analysis unit to analyze information from the caller in real time and instantly provide instructions according to changes in the situation.

[0079] The analysis unit predicts the next step based on the information input by the caller and can prompt preparations in advance. For example, if the caller inputs, "The patient is unconscious," the generation AI will predict the next step, such as, "Next, check the patient's breathing," and prompt preparations in advance. This makes it possible to predict the next step based on the information input by the caller and prompt preparations in advance.

[0080] The analysis unit can use the emotion estimation function to adjust the frequency and content of situation checks according to the caller's emotional state. For example, if the caller is nervous, the generation AI will reduce the frequency of situation checks and check in a calmer tone. This makes it possible to adjust the frequency and content of situation checks according to the caller's emotional state.

[0081] The analysis unit can periodically send reminders to the caller to check the situation and encourage continuous information provision. For example, the generation AI can send regular reminders to the caller, such as "Check the patient's breathing," to encourage continuous information provision. This allows the caller to periodically send reminders to check the situation and encourage continuous information provision.

[0082] The analysis unit can share the information input by the caller with other emergency response systems and respond in cooperation. For example, the analysis unit can share the information input by the caller with the emergency response system and provide information on the arrival time of an ambulance and the nearest medical institution. This allows the information input by the caller to be shared with other emergency response systems and respond in cooperation.

[0083] The analysis unit can use the emotion estimation function to automatically adjust the method of situation confirmation according to the caller's emotional state. For example, if the caller is nervous, the generation AI will simplify the method of situation confirmation and confirm in a calm tone. This makes it possible to automatically adjust the method of situation confirmation according to the caller's emotional state.

[0084] The notification unit can automatically obtain the location information of the caller and notify the nearest emergency contact. The notification unit obtains the location information using, for example, the GPS function of the caller's smartphone and notifies the nearest ambulance. This allows the location information of the caller to be automatically obtained and notified to the nearest emergency contact.

[0085] The notification unit can send a detailed status report to the emergency contact based on the information input by the caller. For example, if the caller inputs "the patient is unconscious," the generation AI will send a detailed status report to the emergency contact based on that information. This allows a detailed status report to be sent to the emergency contact based on the information input by the caller.

[0086] The notification unit can use the emotion estimation function to adjust the content of the notification to emergency contacts according to the caller's emotional state. For example, if the caller is nervous, the generation AI will adjust the content of the notification to emergency contacts in a calm tone. This makes it possible to adjust the content of the notification to emergency contacts according to the caller's emotional state.

[0087] The notification unit can register the caller's emergency contact list in advance to enable prompt notification. For example, the caller registers an emergency contact list in advance, and the generation AI makes prompt notifications based on that list. This allows the caller's emergency contact list to be registered in advance to enable prompt notification.

[0088] The notification unit can automatically send a voice message or a text message to the caller's emergency contact. For example, the generation AI automatically sends a voice message to the caller's emergency contact to report the situation. This makes it possible to automatically send a voice message or a text message to the caller's emergency contact.

[0089] The notification unit can automatically adjust the notification method to emergency contacts according to the caller's emotional state using the emotion estimation function. For example, if the caller is nervous, the generation AI will notify the emergency contacts in a calm tone. This makes it possible to automatically adjust the notification method to emergency contacts according to the caller's emotional state.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The emergency medical instruction system may further include a vital sign measurement unit. The vital sign measurement unit allows the caller to measure the patient's vital signs, such as heart rate and blood pressure, using a smartphone or wearable device, and transmit the data to the analysis unit. For example, if the caller measures the patient's heart rate using a smartwatch and transmits the data to the system, the analysis unit can provide more accurate first aid instructions. The vital sign measurement unit can also measure the patient's body temperature and oxygen saturation. This allows the emergency medical instruction system to grasp the patient's condition in more detail and provide appropriate first aid quickly.

[0092] The emergency medical instruction system can further include a translation unit. When a caller inputs information in a different language, the translation unit automatically translates the information and sends it to the analysis unit. For example, if a caller inputs "the patient is unconscious" in Spanish, the translation unit translates the information into English, and the analysis unit determines the appropriate first aid. The translation unit can also support both voice input and text input. This allows the emergency medical instruction system to support multiple languages ​​and provide appropriate assistance to callers who speak different languages.

[0093] The emergency medical instruction system may further include an environmental sensor unit. The environmental sensor unit acquires environmental information about the scene and transmits it to the analysis unit. For example, if the caller uses the environmental sensor on their smartphone to measure the temperature, humidity, and noise level of the scene and transmits that data to the system, the analysis unit can provide more appropriate first aid instructions. The environmental sensor unit may also measure the illuminance and air quality of the scene. This allows the emergency medical instruction system to quickly provide first aid that takes into account the environmental information about the scene.

[0094] The emergency medical instruction system may further include a history management unit. The history management unit stores a history of first aid procedures previously performed by the caller, and the analysis unit provides appropriate instructions based on that history. For example, if the caller has experience performing cardiopulmonary resuscitation in the past, the history management unit stores that information, and the analysis unit provides instructions taking that experience into consideration. The history management unit may also store a history of first aid training that the caller has previously received. This allows the emergency medical instruction system to provide individually customized instructions that take into consideration the caller's past experience and training history.

[0095] The emergency medical instruction system can further include a prediction unit. The prediction unit predicts future situations based on information from the caller and prompts the caller to make appropriate preparations in advance. For example, if the caller inputs, "The patient is unconscious," the prediction unit may make a prediction such as, "Next, the patient may stop breathing. Please prepare for cardiopulmonary resuscitation," prompting the caller to make preparations in advance. The prediction unit can also predict the possibility that the patient's condition may worsen and provide appropriate first aid procedures in advance. This allows the emergency medical instruction system to support the caller in responding quickly and appropriately.

[0096] The emergency medical instruction system can further use emotion estimation to provide first aid procedures that correspond to the caller's emotional state. For example, if the caller is nervous, the generation AI can provide guidance such as "Take a deep breath. Stay calm and explain the situation," followed by step-by-step instructions on first aid procedures. Alternatively, if the caller is impatient, the generation AI can provide instructions such as "Proceed slowly and steadily." This allows the emergency medical instruction system to provide first aid procedures that correspond to the caller's emotional state, helping the caller respond calmly.

[0097] The emergency medical instruction system can further use emotion estimation to automatically adjust the interface according to the caller's emotional state. For example, if the caller is nervous, the generation AI can simplify the interface and change to a more subdued color scheme. Alternatively, if the caller is anxious, the generation AI can simplify the interface operation and highlight important information. This allows the emergency medical instruction system to automatically adjust the interface according to the caller's emotional state, improving usability.

[0098] The emergency medical instruction system can further use emotion estimation to provide voice guidance that corresponds to the caller's emotional state. For example, if the caller is nervous, the generation AI can provide voice guidance in a calm tone to reassure the caller. Alternatively, if the caller is panicking, the generation AI can provide voice guidance at a slower pace to help the caller respond calmly. This allows the emergency medical instruction system to provide voice guidance that corresponds to the caller's emotional state and support the caller in providing appropriate first aid.

[0099] The emergency medical instruction system can further use emotion estimation to automatically adjust the priority of first aid procedures according to the caller's emotional state. For example, if the caller is nervous, the generation AI can provide instructions for first aid procedures with the highest priority, reassuring the caller. Alternatively, if the caller is in a hurry, the generation AI can highlight important steps to help the caller ensure they are carried out correctly. This allows the emergency medical instruction system to automatically adjust the priority of first aid procedures according to the caller's emotional state, helping the caller respond calmly.

[0100] The emergency medical instruction system can also use emotion estimation to adjust the frequency and content of situation checks according to the caller's emotional state. For example, if the caller is nervous, the generation AI can reduce the frequency of situation checks and check in a calmer tone. Also, if the caller is anxious, the generation AI can simplify the content of situation checks and emphasize important information. This allows the emergency medical instruction system to adjust the frequency and content of situation checks according to the caller's emotional state, helping the caller respond calmly.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The information input unit receives information from the caller. For example, the caller inputs, "The patient is unconscious." The information input unit can also receive voice input. For example, the caller inputs, "His breathing is shallow." The information input unit can also receive images and videos. For example, the caller sends a video of the scene using the camera on their smartphone. Step 2: The analysis unit analyzes the information received by the information input unit. For example, the generation AI analyzes text information using natural language processing technology. The generation AI can also analyze audio information using voice recognition technology. The generation AI can also analyze video information using image analysis technology. Step 3: The instruction unit advises appropriate first aid based on the results of the analysis by the analysis unit. For example, the generation AI may instruct, "Check the patient's breathing and secure the airway." The generation AI may also instruct, "Apply pressure to the bleeding area to stop the bleeding." The generation AI may also instruct, "Next, compress the patient's chest. Compress twice per second."

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[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, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[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. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information input unit that receives information from a reporter; an analysis unit that analyzes the information received by the information input unit; an instruction unit that advises appropriate emergency measures based on the results of the analysis by the analysis unit. A system characterized by:

2. The information input unit The caller uses a smartphone camera to send footage of the scene to the AI ​​generator, and the analysis unit analyzes the footage to understand the situation.

2. The system of claim 1.

3. The analysis unit Provide personalized instructions that take into account the caller's past first aid experience 2. The system of claim 1.

4. The instruction unit Provide the caller with a visual guide to visually demonstrate first aid procedures 2. The system of claim 1.

5. The analysis unit Analyze information from the caller in real time and provide immediate instructions according to changes in the situation.

2. The system of claim 1.

6. The notification section The location information of the caller is automatically acquired and notified to the nearest emergency contact.

2. The system of claim 1.

7. The information input unit Detecting tension or impatience in the caller and providing guidance to calm them down 2. The system of claim 1.

8. The analysis unit Adjust the tone and content of instructions according to the caller's emotional state 2. The system of claim 1.

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