System

Abstract

An object of a system according to an embodiment is to provide an appropriate first aid procedure through voice input in an emergency.SOLUTION: A system includes a reception unit, an analysis unit, and a generation unit. The reception unit receives a voice input. The analysis unit analyzes the voice received by the reception unit. The generation unit generates a first aid procedure on the basis of the sound 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 of making it difficult to quickly provide appropriate first aid procedures in an emergency.

[0005] The system according to the embodiment aims to provide appropriate first aid procedures through voice input in the event of an emergency. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives a voice input. The analysis unit analyzes the voice received by the reception unit. The generation unit generates a first aid procedure based on the voice analyzed by the analysis unit. [Effects of the Invention]

[0007] In an emergency, the system according to the embodiment can provide appropriate first aid procedures through voice input. [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) A first aid support system according to an embodiment of the present invention accepts and analyzes voice input and generates first aid procedures. In the first aid support system, a user launches an app and verbally inputs a situation requiring first aid. For example, the user may input specific instructions, such as "Please tell me the first aid procedures." This voice is analyzed by AI. The AI ​​then recognizes the input voice and provides appropriate first aid procedures via voice. For example, specific procedures, such as "Please begin chest compressions. 1, 2, 3, 4...," are given as voice instructions. This allows the user to provide appropriate first aid simply by following the voice instructions. For example, by providing easy-to-understand explanations of emergency procedures, such as cardiopulmonary resuscitation and bleeding control, a prompt and appropriate response is possible. Furthermore, the AI ​​constantly learns the latest medical information, allowing it to provide the latest first aid procedures. This allows the user to always provide first aid based on the latest information. Furthermore, the use of voice recognition technology allows users to follow voice instructions even when their hands are full, making it extremely useful in actual first aid situations. For example, first aid can be provided more quickly and efficiently because there is no need to stop bleeding with one hand while operating a smartphone with the other. This makes the first aid support system easy for anyone to provide first aid. Appropriate first aid can be provided simply by following voice instructions, making it extremely useful in emergencies.

[0029] A first aid support system according to an embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives voice input. For example, a user can input voice using a microphone on a smartphone. The reception unit can also receive voice input via a smartphone application, for example. The analysis unit analyzes the voice received by the reception unit. The analysis unit can convert the voice into text data using, for example, voice recognition technology. The analysis unit can also analyze the content of the voice using natural language processing technology. For example, the analysis unit can convert the voice into text data using voice recognition technology and analyze first aid procedures based on the text data. The generation unit generates first aid procedures based on the voice analyzed by the analysis unit. The generation unit generates first aid procedures, such as cardiopulmonary resuscitation and bleeding control methods. The generation unit can also provide the generated procedures in audio. For example, the generation unit can provide the generated procedures in audio using voice synthesis technology. As a result, the first aid support system according to the embodiment receives and analyzes voice input and generates first aid procedures, allowing the user to provide appropriate first aid.

[0030] The first aid support system includes a providing unit that provides the generated procedure by voice. The providing unit provides the generated procedure by voice. For example, the providing unit provides the generated procedure by voice using voice synthesis technology. The providing unit can also provide the generated procedure by voice using a voice output device. For example, the providing unit provides the generated procedure by voice using a smartphone speaker. By providing the generated procedure by voice, the user can provide first aid according to the voice instructions. 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 inputs the generated procedure into a voice synthesis AI and causes the voice synthesis AI to provide the procedure by voice.

[0031] The first aid support system includes a learning unit that learns the latest medical information. The learning unit learns the latest medical information. For example, the learning unit acquires the latest medical information from a medical database and learns from it. The learning unit can also acquire the latest research papers and learn from them. For example, the learning unit acquires the latest medical information from a medical database on the Internet and learns from it. In this way, by learning the latest medical information, it is possible to always provide the latest first aid procedures. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input information acquired from a medical database into a generation AI and have the generation AI learn the information.

[0032] The reception unit allows the user to input the situation requiring first aid by voice. For example, the reception unit allows the user to input voice using a microphone on a smartphone. The reception unit can also receive voice input, for example, through a smartphone application. This allows the user to input the situation requiring first aid by voice, allowing the system to provide appropriate procedures. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's voice input to a generation AI and have the generation AI analyze it.

[0033] The analysis unit can analyze the voice using voice recognition technology. For example, the analysis unit converts the voice into text data using voice recognition technology. The analysis unit can also analyze the content of the voice using natural language processing technology. For example, the analysis unit converts the voice into text data using voice recognition technology and analyzes first aid procedures based on the text data. In this way, the use of voice recognition technology improves the accuracy of voice analysis. 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 the voice data into a generation AI and have the generation AI analyze it.

[0034] The generation unit can generate first aid procedures based on the analyzed voice. The generation unit generates first aid procedures such as cardiopulmonary resuscitation and bleeding control methods. The generation unit can also provide the generated procedures by voice. For example, the generation unit provides the generated procedures by voice using voice synthesis technology. In this way, appropriate first aid procedures can be provided by generating first aid procedures based on the analyzed voice. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the analyzed voice data to a generation AI and cause the generation AI to generate first aid procedures.

[0035] The reception unit can analyze the user's past voice input history and select the optimal reception method. For example, the reception unit prioritizes reception of voice commands that the user has frequently used in the past. The reception unit can also learn specific phrases and expressions from the user's past voice input history and select the optimal reception method. The reception unit can also analyze time periods during which the user has used voice input in the past and select the optimal reception method for that time period. In this way, the optimal reception method can be selected by analyzing the user's past voice input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past voice input history into a generation AI and cause the generation AI to select the optimal reception method.

[0036] When receiving voice input, the reception unit can perform filtering based on the user's current situation and environmental sounds. For example, when the user is in a noisy environment, the reception unit uses AI to filter environmental sounds and improve the accuracy of the voice input. Furthermore, when the user is in a quiet environment, the reception unit can use AI to increase the sensitivity of the voice input and recognize even subtle sounds. Furthermore, when the user is moving, the reception unit can also use AI to remove background noise and maintain the accuracy of the voice input. This allows for filtering based on the user's current situation and environmental sounds, thereby improving the accuracy of the voice input. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's environmental sound data to a generation AI and have the generation AI perform filtering.

[0037] When receiving voice input, the reception unit can select the optimal reception means depending on the user's input method. For example, if the user selects voice input, the reception unit allows the AI ​​to prioritize voice recognition during reception. Furthermore, if the user selects text input, the reception unit can also allow the AI ​​to perform text analysis and provide appropriate first aid procedures. Furthermore, if the user selects gesture input, the reception unit can also allow the AI ​​to recognize gestures and provide appropriate first aid procedures. By selecting the optimal reception means depending on the user's input method, more appropriate first aid instructions can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal reception means.

[0038] When receiving a voice input, the reception unit can prioritize receiving highly relevant input by taking into account the user's geographical location information. For example, when the user is near a hospital, the reception unit causes the AI ​​to prioritize receiving medical-related voice input. Furthermore, when the user is at home, the reception unit can also cause the AI ​​to prioritize receiving voice input related to first aid at home. Furthermore, when the user is in a public place, the reception unit can also cause the AI ​​to prioritize receiving voice input related to first aid in public places. In this way, by taking the user's geographical location information into account, highly relevant voice input can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant input.

[0039] When receiving a voice input, the reception unit can analyze the user's social media activity and receive related input. For example, if the user posts about first aid on social media, the reception unit can prioritize the AI ​​reception of the related voice input. The reception unit can also infer the need for first aid from the user's social media activity and receive related voice input. The reception unit can also analyze posts by the user's friends on social media and receive related voice input. In this way, by analyzing the user's social media activity, related voice input can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to select related input.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving voice input. For example, if the user has provided feedback on the accuracy of the voice input in the past, the reception unit adjusts the reception method by reflecting that feedback. Furthermore, if the user has previously preferred a particular voice command, the reception unit can cause the AI ​​to preferentially receive that command. Furthermore, the reception unit can customize the optimal reception method by the AI ​​based on the feedback provided by the user in the past. In this way, the optimal reception method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input user feedback data into the generation AI and cause the generation AI to customize the reception method.

[0041] During voice analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input voice. For example, in the case of a voice input with a high level of urgency, the analysis unit has the AI ​​perform a detailed analysis and respond quickly. In addition, in the case of a voice input with a low level of urgency, the analysis unit can also have the AI ​​perform a normal analysis. In addition, the analysis unit can adjust the level of detail of the analysis based on the content of the voice input and provide appropriate first aid procedures. In this way, by adjusting the level of detail of the analysis based on the importance of the input voice, appropriate first aid procedures can be provided. 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 voice data to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] When analyzing voice, the analysis unit can apply different analysis algorithms depending on the category of voice. For example, in the case of voice input related to cardiopulmonary resuscitation, the analysis unit has the AI ​​apply a dedicated analysis algorithm. Furthermore, in the case of voice input related to bleeding control methods, the analysis unit can also have the AI ​​apply a different analysis algorithm. Furthermore, in the case of voice input related to other first aid, the analysis unit can have the AI ​​select an appropriate analysis algorithm and perform analysis. In this way, by applying different analysis algorithms depending on the category of voice, more appropriate analysis results can be obtained. 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 voice data to a generation AI and have the generation AI select an analysis algorithm.

[0043] During voice analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit uses AI to improve the accuracy of the analysis based on the analysis results of voice input provided by the user in the past. The analysis unit can also learn specific patterns from the user's past analysis results and improve the accuracy of the analysis. The analysis unit can also use AI to select the optimal analysis method by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] During voice analysis, the analysis unit can determine the priority of analysis based on the time when the input voice was submitted. For example, in the case of a voice input with a high level of urgency, the analysis unit causes the AI ​​to analyze that voice with the highest priority. In addition, in the case of a voice input with a low level of urgency, the analysis unit can also cause the AI ​​to analyze with a normal priority. In addition, the analysis unit can adjust the priority of analysis depending on the time when the voice input was submitted and provide appropriate first aid procedures. In this way, by determining the priority of analysis based on the time when the input voice was submitted, appropriate first aid procedures can be provided. 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 voice data to a generation AI and have the generation AI determine the priority of analysis.

[0045] During voice analysis, the analysis unit can adjust the order of analysis based on the relevance of the voice. For example, in the case of a voice input related to first aid procedures, the analysis unit causes the AI ​​to analyze the voice preferentially. In addition, in the case of a voice input not related to first aid procedures, the analysis unit can also cause the AI ​​to analyze in the normal order. In addition, the analysis unit can also cause the AI ​​to adjust the order of analysis according to the relevance of the voice and provide appropriate first aid procedures. In this way, by adjusting the order of analysis based on the relevance of the voice, appropriate first aid procedures can be provided. 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 voice data to a generation AI and cause the generation AI to adjust the order of analysis.

[0046] During voice analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is a medical professional, the analysis unit can provide analysis results using technical terminology through the AI. Furthermore, if the user is a layperson, the analysis unit can also provide analysis results using the AI ​​while avoiding technical terminology through the AI. Furthermore, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise and provide appropriate first aid procedures. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, appropriate first aid procedures can be provided. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0047] The generation unit can adjust the level of detail of the procedure based on the importance of the first aid when generating the procedure. For example, in the case of a first aid with a high level of urgency, the generation unit can have the AI ​​generate a detailed procedure. In addition, in the case of a first aid with a low level of urgency, the generation unit can also have the AI ​​generate a concise procedure. In addition, the generation unit can also adjust the level of detail of the procedure based on the importance of the first aid and provide an appropriate procedure. In this way, by adjusting the level of detail of the procedure based on the importance of the first aid, an appropriate procedure can be provided. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input first aid importance data to the generation AI and cause the generation AI to adjust the level of detail of the procedure.

[0048] When generating a procedure, the generation unit can apply different generation algorithms depending on the first aid category. For example, in the case of a procedure related to cardiopulmonary resuscitation, the AI ​​can apply a dedicated generation algorithm. In addition, in the case of a procedure related to a bleeding control method, the generation unit can also apply a different generation algorithm. In addition, in the case of a procedure related to other first aid, the AI ​​can select an appropriate generation algorithm to generate a procedure. In this way, by applying different generation algorithms depending on the first aid category, more appropriate procedures can be provided. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input first aid category data into the generation AI and cause the generation AI to select a generation algorithm.

[0049] When generating a procedure, the generation unit can improve the accuracy of the generation by referring to the user's past procedure generation results. For example, the generation unit uses AI to improve the generation accuracy based on procedure generation results provided by the user in the past. The generation unit can also improve the generation accuracy by learning specific patterns from the user's past procedure generation results. The generation unit can also use AI to select the optimal generation method by referring to the user's past procedure generation results. In this way, the generation accuracy can be improved by referring to the user's past procedure generation results. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input past procedure generation result data into the generation AI and cause the generation AI to improve the generation accuracy.

[0050] When generating procedures, the generation unit can determine the priority of procedures based on the timing of first aid submission. For example, in the case of a first aid procedure with a high level of urgency, the generation unit causes the AI ​​to generate that procedure with the highest priority. In addition, in the case of a first aid procedure with a low level of urgency, the generation unit can also cause the AI ​​to generate the procedure with a normal priority. In addition, the generation unit can cause the AI ​​to adjust the priority of procedures according to the timing of first aid submission and provide an appropriate procedure. In this way, by determining the priority of procedures based on the timing of first aid submission, an appropriate procedure can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input first aid submission timing data into the generation AI and cause the generation AI to determine the priority of procedures.

[0051] The generation unit can adjust the order of steps based on first aid relevance when generating procedures. For example, if a procedure is related to first aid, the generation unit causes the AI ​​to generate that procedure preferentially. Furthermore, if a procedure is not related to first aid, the generation unit can also cause the AI ​​to generate the procedure in the normal order. Furthermore, the generation unit can also cause the AI ​​to adjust the order of steps according to first aid relevance and provide an appropriate procedure. In this way, by adjusting the order of steps based on first aid relevance, an appropriate procedure can be provided. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input first aid relevance data to the generation AI and cause the generation AI to adjust the order of the steps.

[0052] When generating a procedure, the generation unit can adjust the use of technical terminology in the procedure according to the user's level of expertise. For example, if the user is a medical professional, the generation unit can have the AI ​​generate the procedure using technical terminology. Furthermore, if the user is a layperson, the generation unit can also have the AI ​​generate the procedure while avoiding technical terminology. Furthermore, the generation unit can adjust the use of technical terminology in the procedure according to the user's level of expertise and provide an appropriate procedure. This allows an appropriate procedure to be provided by adjusting the use of technical terminology in the procedure according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0053] When providing procedures, the providing unit can adjust the level of detail of the provided procedures based on the importance of the first aid. For example, in the case of first aid with a high level of urgency, the providing unit has the AI ​​provide detailed procedures. Furthermore, in the case of first aid with a low level of urgency, the providing unit can also have the AI ​​provide concise procedures. Furthermore, the providing unit can have the AI ​​adjust the level of detail of the provided procedures according to the importance of the first aid and provide appropriate procedures. In this way, appropriate procedures can be provided by adjusting the level of detail of the provided procedures based on the importance of the first aid. 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 first aid importance data to the generating AI and cause the generating AI to adjust the level of detail of the provided procedures.

[0054] When providing procedures, the providing unit can apply different provision algorithms depending on the first aid category. For example, in the case of procedures related to cardiopulmonary resuscitation, the AI ​​can apply a dedicated provision algorithm. In addition, in the case of procedures related to hemostasis, the providing unit can also apply a different provision algorithm. In addition, in the case of procedures related to other first aid, the AI ​​can select an appropriate provision algorithm and provide the procedures. In this way, by applying different provision algorithms depending on the first aid category, more appropriate procedures can be provided. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input first aid category data to the generation AI and cause the generation AI to select a provision algorithm.

[0055] When providing procedures, the providing unit can determine the priority of provision based on the timing of first aid submission. For example, in the case of a first aid procedure with a high level of urgency, the providing unit causes the AI ​​to provide that procedure with the highest priority. In addition, in the case of a first aid procedure with a low level of urgency, the providing unit can cause the AI ​​to provide the procedure with a normal priority. In addition, the providing unit can cause the AI ​​to adjust the priority of provision according to the timing of first aid submission and provide an appropriate procedure. In this way, by determining the priority of provision based on the timing of first aid submission, it is possible to provide an appropriate procedure. 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 first aid submission timing data to the generating AI and cause the generating AI to determine the priority of provision.

[0056] When providing procedures, the providing unit can adjust the order of provision based on the relevance of first aid. For example, if the providing unit is related to a first aid procedure, the AI ​​can provide that procedure preferentially. Furthermore, if the providing unit is not related to a first aid procedure, the AI ​​can provide the procedure in the normal order. Furthermore, the providing unit can adjust the order of provision based on the relevance of first aid and provide appropriate procedures. In this way, appropriate procedures can be provided by adjusting the order of provision based on the relevance of first aid. 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 first aid relevance data to the generation AI and cause the generation AI to adjust the order of provision.

[0057] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit selects an optimal learning algorithm using AI based on past learning data. The learning unit can also learn specific patterns from past learning data and optimize the learning algorithm. The learning unit can also improve the accuracy of the learning algorithm using AI by referring to past learning data. In this way, by referring to past learning data, the learning algorithm can be optimized and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input past learning data into the generation AI and cause the generation AI to optimize the learning algorithm.

[0058] The learning unit can update the learning data by reflecting user feedback during learning. In the learning unit, for example, the AI ​​updates the learning data based on feedback provided by the user. The learning unit can also learn specific patterns from user feedback and update the learning data. The learning unit can also select optimal learning data by reflecting user feedback. In this way, the learning data can be updated by reflecting user feedback, allowing for more appropriate learning. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input user feedback data into the generation AI and cause the generation AI to update the learning data.

[0059] During learning, the learning unit can weight the learning data based on the timing of first aid submission. For example, in the case of first aid with high urgency, the learning unit causes the AI ​​to weight the learning data highly. In addition, in the case of first aid with low urgency, the learning unit can also cause the AI ​​to perform learning with normal weighting. In addition, the learning unit can cause the AI ​​to adjust the weighting of the learning data according to the timing of first aid submission and perform appropriate learning. In this way, appropriate learning can be performed by weighting the learning data based on the timing of first aid submission. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data on the timing of first aid submission to the generation AI and cause the generation AI to weight the learning data.

[0060] During learning, the learning unit can integrate information from different data sources to expand the training data. For example, the learning unit integrates data provided by medical institutions, and the AI ​​expands the training data. The learning unit can also integrate feedback provided by users, and the AI ​​can expand the training data. The learning unit can also integrate publicly available medical information, and the AI ​​can expand the training data. In this way, by integrating information from different data sources, the training data can be expanded and more appropriate learning can be performed. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into the generation AI and cause the generation AI to integrate the training data.

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

[0062] When accepting a voice input from a user, the acceptance unit can refer to the user's past behavior history and select the optimal acceptance method. For example, it can preferentially accept voice commands that the user has frequently used in the past. The acceptance unit can also learn specific phrases and expressions from the user's past voice input history and select the optimal acceptance method. Furthermore, the acceptance unit can analyze the time period in which the user has used voice input in the past and select the optimal acceptance method for that time period. In this way, the optimal acceptance method can be selected by referring to the user's past behavior history.

[0063] When providing the generated instructions by voice, the providing unit can perform filtering based on the user's current situation and environmental sounds. For example, if the user is in a noisy environment, the AI ​​filters out environmental sounds to improve the accuracy of the voice input. In addition, if the user is in a quiet environment, the providing unit can increase the sensitivity of the voice input to recognize even subtle sounds. Furthermore, if the user is moving, the providing unit can also remove background noise to maintain the accuracy of the voice input. This makes it possible to improve the accuracy of the voice input by filtering based on the user's current situation and environmental sounds.

[0064] When learning the latest medical information, the learning unit can integrate information from different data sources to expand the learning data. For example, data provided by medical institutions can be integrated, and the AI ​​can expand the learning data. The learning unit can also integrate feedback provided by users, and the AI ​​can expand the learning data. Furthermore, the learning unit can integrate publicly available medical information, and the AI ​​can expand the learning data. In this way, by integrating information from different data sources, the learning data can be expanded and more appropriate learning can be performed.

[0065] When accepting a user's voice input, the reception unit can prioritize accepting highly relevant inputs by taking into account the user's geographical location information. For example, if the user is near a hospital, the AI ​​can prioritize accepting medical-related voice inputs. In addition, if the user is at home, the reception unit can also prioritize accepting voice inputs related to first aid at home. Furthermore, if the user is in a public place, the reception unit can also prioritize accepting voice inputs related to first aid in public places. In this way, by taking into account the user's geographical location information, it is possible to prioritize accepting highly relevant voice inputs.

[0066] During voice analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input voice. For example, in the case of a voice input with a high level of urgency, the AI ​​will perform a detailed analysis and respond quickly. In addition, in the case of a voice input with a low level of urgency, the analysis unit can also have the AI ​​perform a normal analysis. Furthermore, the analysis unit can also have the AI ​​adjust the level of detail of the analysis according to the content of the voice input and provide appropriate first aid procedures. In this way, by adjusting the level of detail of the analysis based on the importance of the input voice, it is possible to provide appropriate first aid procedures.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit receives voice input. For example, a user can input voice using a microphone on a smartphone. Alternatively, the reception unit can receive voice via an application on the smartphone as the voice input format. Step 2: The analysis unit analyzes the voice received by the reception unit. The analysis unit can convert the voice into text data using voice recognition technology and further analyze the content of the voice using natural language processing technology. For example, the analysis unit can convert the voice into text data using voice recognition technology and analyze first aid procedures based on the text data. Step 3: The generator generates first aid procedures based on the speech analyzed by the analyzer. The generator generates first aid procedures such as cardiopulmonary resuscitation and hemostasis, and can also provide the generated procedures by voice using speech synthesis technology.

[0069] (Example 2) A first aid support system according to an embodiment of the present invention accepts and analyzes voice input and generates first aid procedures. In the first aid support system, a user launches an app and verbally inputs a situation requiring first aid. For example, the user may input specific instructions, such as "Please tell me the first aid procedures." This voice is analyzed by AI. The AI ​​then recognizes the input voice and provides appropriate first aid procedures via voice. For example, specific procedures, such as "Please begin chest compressions. 1, 2, 3, 4...," are given as voice instructions. This allows the user to provide appropriate first aid simply by following the voice instructions. For example, by providing easy-to-understand explanations of emergency procedures, such as cardiopulmonary resuscitation and bleeding control, a prompt and appropriate response is possible. Furthermore, the AI ​​constantly learns the latest medical information, allowing it to provide the latest first aid procedures. This allows the user to always provide first aid based on the latest information. Furthermore, the use of voice recognition technology allows users to follow voice instructions even when their hands are full, making it extremely useful in actual first aid situations. For example, first aid can be provided more quickly and efficiently because there is no need to stop bleeding with one hand while operating a smartphone with the other. This makes the first aid support system easy for anyone to provide first aid. Appropriate first aid can be provided simply by following voice instructions, making it extremely useful in emergencies.

[0070] A first aid support system according to an embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives voice input. For example, a user can input voice using a microphone on a smartphone. The reception unit can also receive voice input via a smartphone application, for example. The analysis unit analyzes the voice received by the reception unit. The analysis unit can convert the voice into text data using, for example, voice recognition technology. The analysis unit can also analyze the content of the voice using natural language processing technology. For example, the analysis unit can convert the voice into text data using voice recognition technology and analyze first aid procedures based on the text data. The generation unit generates first aid procedures based on the voice analyzed by the analysis unit. The generation unit generates first aid procedures, such as cardiopulmonary resuscitation and bleeding control methods. The generation unit can also provide the generated procedures in audio. For example, the generation unit can provide the generated procedures in audio using voice synthesis technology. As a result, the first aid support system according to the embodiment receives and analyzes voice input and generates first aid procedures, allowing the user to provide appropriate first aid.

[0071] The first aid support system includes a providing unit that provides the generated procedure by voice. The providing unit provides the generated procedure by voice. For example, the providing unit provides the generated procedure by voice using voice synthesis technology. The providing unit can also provide the generated procedure by voice using a voice output device. For example, the providing unit provides the generated procedure by voice using a smartphone speaker. By providing the generated procedure by voice, the user can provide first aid according to the voice instructions. 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 inputs the generated procedure into a voice synthesis AI and causes the voice synthesis AI to provide the procedure by voice.

[0072] The first aid support system includes a learning unit that learns the latest medical information. The learning unit learns the latest medical information. For example, the learning unit acquires the latest medical information from a medical database and learns from it. The learning unit can also acquire the latest research papers and learn from them. For example, the learning unit acquires the latest medical information from a medical database on the Internet and learns from it. In this way, by learning the latest medical information, it is possible to always provide the latest first aid procedures. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input information acquired from a medical database into a generation AI and have the generation AI learn the information.

[0073] The reception unit allows the user to input the situation requiring first aid by voice. For example, the reception unit allows the user to input voice using a microphone on a smartphone. The reception unit can also receive voice input, for example, through a smartphone application. This allows the user to input the situation requiring first aid by voice, allowing the system to provide appropriate procedures. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's voice input to a generation AI and have the generation AI analyze it.

[0074] The analysis unit can analyze the voice using voice recognition technology. For example, the analysis unit converts the voice into text data using voice recognition technology. The analysis unit can also analyze the content of the voice using natural language processing technology. For example, the analysis unit converts the voice into text data using voice recognition technology and analyzes first aid procedures based on the text data. In this way, the use of voice recognition technology improves the accuracy of voice analysis. 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 the voice data into a generation AI and have the generation AI analyze it.

[0075] The generation unit can generate first aid procedures based on the analyzed voice. The generation unit generates first aid procedures such as cardiopulmonary resuscitation and bleeding control methods. The generation unit can also provide the generated procedures by voice. For example, the generation unit provides the generated procedures by voice using voice synthesis technology. In this way, appropriate first aid procedures can be provided by generating first aid procedures based on the analyzed voice. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the analyzed voice data to a generation AI and cause the generation AI to generate first aid procedures.

[0076] The reception unit can estimate the user's emotions and adjust the timing of voice input reception based on the estimated user emotions. For example, if the user is nervous, the reception unit can delay the timing of voice input reception by the AI ​​and wait until the user calms down. Furthermore, if the user is anxious, the reception unit can accelerate the timing of voice input reception and quickly start providing first aid instructions. Furthermore, if the user is relaxed, the reception unit can set the timing of voice input reception to the normal level and provide first aid instructions in a natural flow. By adjusting the timing of voice input reception according to the user's emotions, more appropriate first aid instructions can be provided. The emotion estimation is realized 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 such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's voice data into the generation AI and have the generation AI estimate emotions.

[0077] The reception unit can analyze the user's past voice input history and select the optimal reception method. For example, the reception unit prioritizes reception of voice commands that the user has frequently used in the past. The reception unit can also learn specific phrases and expressions from the user's past voice input history and select the optimal reception method. The reception unit can also analyze time periods during which the user has used voice input in the past and select the optimal reception method for that time period. In this way, the optimal reception method can be selected by analyzing the user's past voice input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past voice input history into a generation AI and cause the generation AI to select the optimal reception method.

[0078] When receiving voice input, the reception unit can perform filtering based on the user's current situation and environmental sounds. For example, when the user is in a noisy environment, the reception unit uses AI to filter environmental sounds and improve the accuracy of the voice input. Furthermore, when the user is in a quiet environment, the reception unit can use AI to increase the sensitivity of the voice input and recognize even subtle sounds. Furthermore, when the user is moving, the reception unit can also use AI to remove background noise and maintain the accuracy of the voice input. This allows for filtering based on the user's current situation and environmental sounds, thereby improving the accuracy of the voice input. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's environmental sound data to a generation AI and have the generation AI perform filtering.

[0079] When receiving voice input, the reception unit can select the optimal reception means depending on the user's input method. For example, if the user selects voice input, the reception unit allows the AI ​​to prioritize voice recognition during reception. Furthermore, if the user selects text input, the reception unit can also allow the AI ​​to perform text analysis and provide appropriate first aid procedures. Furthermore, if the user selects gesture input, the reception unit can also allow the AI ​​to recognize gestures and provide appropriate first aid procedures. By selecting the optimal reception means depending on the user's input method, more appropriate first aid instructions can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal reception means.

[0080] The reception unit can estimate the user's emotions and determine the priority of the voice inputs to be received based on the estimated user emotions. For example, if the user is facing an emergency, the reception unit allows the AI ​​to receive the voice input with the highest priority. Furthermore, if the user is calm, the reception unit can also allow the AI ​​to receive the voice input with the same priority as other voice inputs. Furthermore, if the user is panicked, the reception unit can also allow the AI ​​to receive the voice input with a high priority and respond quickly. By determining the priority of the voice inputs according to the user's emotions, more appropriate first aid instructions can be provided. The emotion estimation is realized using an emotion estimation function, for example, 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 reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0081] When receiving a voice input, the reception unit can prioritize receiving highly relevant input by taking into account the user's geographical location information. For example, when the user is near a hospital, the reception unit causes the AI ​​to prioritize receiving medical-related voice input. Furthermore, when the user is at home, the reception unit can also cause the AI ​​to prioritize receiving voice input related to first aid at home. Furthermore, when the user is in a public place, the reception unit can also cause the AI ​​to prioritize receiving voice input related to first aid in public places. In this way, by taking the user's geographical location information into account, highly relevant voice input can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant input.

[0082] When receiving a voice input, the reception unit can analyze the user's social media activity and receive related input. For example, if the user posts about first aid on social media, the reception unit can prioritize the AI ​​reception of the related voice input. The reception unit can also infer the need for first aid from the user's social media activity and receive related voice input. The reception unit can also analyze posts by the user's friends on social media and receive related voice input. In this way, by analyzing the user's social media activity, related voice input can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to select related input.

[0083] The reception unit can customize the reception method by reflecting the user's past feedback when receiving voice input. For example, if the user has provided feedback on the accuracy of the voice input in the past, the reception unit adjusts the reception method by reflecting that feedback. Furthermore, if the user has previously preferred a particular voice command, the reception unit can cause the AI ​​to preferentially receive that command. Furthermore, the reception unit can customize the optimal reception method by the AI ​​based on the feedback provided by the user in the past. In this way, the optimal reception method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input user feedback data into the generation AI and cause the generation AI to customize the reception method.

[0084] The analysis unit can estimate the user's emotions and adjust the accuracy of the voice analysis based on the estimated user emotions. For example, when the user is nervous, the analysis unit uses AI to increase the accuracy of the voice analysis and prevent misrecognition. Furthermore, when the user is relaxed, the analysis unit can also use AI to perform analysis with normal voice analysis accuracy. Furthermore, when the user is impatient, the analysis unit can also maximize the accuracy of the voice analysis and perform analysis quickly. This allows for more accurate analysis results to be obtained by adjusting the accuracy of the voice analysis according to the user'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 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 may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0085] During voice analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input voice. For example, in the case of a voice input with a high level of urgency, the analysis unit has the AI ​​perform a detailed analysis and respond quickly. In addition, in the case of a voice input with a low level of urgency, the analysis unit can also have the AI ​​perform a normal analysis. In addition, the analysis unit can adjust the level of detail of the analysis based on the content of the voice input and provide appropriate first aid procedures. In this way, by adjusting the level of detail of the analysis based on the importance of the input voice, appropriate first aid procedures can be provided. 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 voice data to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0086] When analyzing voice, the analysis unit can apply different analysis algorithms depending on the category of voice. For example, in the case of voice input related to cardiopulmonary resuscitation, the analysis unit has the AI ​​apply a dedicated analysis algorithm. Furthermore, in the case of voice input related to bleeding control methods, the analysis unit can also have the AI ​​apply a different analysis algorithm. Furthermore, in the case of voice input related to other first aid, the analysis unit can have the AI ​​select an appropriate analysis algorithm and perform analysis. In this way, by applying different analysis algorithms depending on the category of voice, more appropriate analysis results can be obtained. 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 voice data to a generation AI and have the generation AI select an analysis algorithm.

[0087] During voice analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit uses AI to improve the accuracy of the analysis based on the analysis results of voice input provided by the user in the past. The analysis unit can also learn specific patterns from the user's past analysis results and improve the accuracy of the analysis. The analysis unit can also use AI to select the optimal analysis method by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0088] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is facing an emergency, the analysis unit allows the AI ​​to analyze the user's voice input with the highest priority. Furthermore, if the user is calm, the analysis unit can also allow the AI ​​to analyze the user's voice input with the same priority as other voice inputs. Furthermore, if the user is panicked, the analysis unit can allow the AI ​​to analyze the user's voice input with a high priority and respond quickly. This allows for more appropriate first aid procedures to be provided by determining the analysis priority according to the user'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 analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0089] During voice analysis, the analysis unit can determine the priority of analysis based on the time when the input voice was submitted. For example, in the case of a voice input with a high level of urgency, the analysis unit causes the AI ​​to analyze that voice with the highest priority. In addition, in the case of a voice input with a low level of urgency, the analysis unit can also cause the AI ​​to analyze with a normal priority. In addition, the analysis unit can adjust the priority of analysis depending on the time when the voice input was submitted and provide appropriate first aid procedures. In this way, by determining the priority of analysis based on the time when the input voice was submitted, appropriate first aid procedures can be provided. 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 voice data to a generation AI and have the generation AI determine the priority of analysis.

[0090] During voice analysis, the analysis unit can adjust the order of analysis based on the relevance of the voice. For example, in the case of a voice input related to first aid procedures, the analysis unit causes the AI ​​to analyze the voice preferentially. In addition, in the case of a voice input not related to first aid procedures, the analysis unit can also cause the AI ​​to analyze in the normal order. In addition, the analysis unit can also cause the AI ​​to adjust the order of analysis according to the relevance of the voice and provide appropriate first aid procedures. In this way, by adjusting the order of analysis based on the relevance of the voice, appropriate first aid procedures can be provided. 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 voice data to a generation AI and cause the generation AI to adjust the order of analysis.

[0091] During voice analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is a medical professional, the analysis unit can provide analysis results using technical terminology through the AI. Furthermore, if the user is a layperson, the analysis unit can also provide analysis results using the AI ​​while avoiding technical terminology through the AI. Furthermore, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise and provide appropriate first aid procedures. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, appropriate first aid procedures can be provided. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0092] The generation unit can estimate the user's emotions and adjust the method for generating first aid procedures based on the estimated user emotions. For example, if the user is nervous, the AI ​​can generate concise and easy-to-understand procedures. Furthermore, if the user is relaxed, the generation unit can generate detailed procedures. Furthermore, if the user is impatient, the AI ​​can generate procedures that can be quickly executed. This allows the method for generating first aid procedures to be adjusted according to the user's emotions, thereby providing more appropriate procedures. 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 generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method for generating procedures.

[0093] The generation unit can adjust the level of detail of the procedure based on the importance of the first aid when generating the procedure. For example, in the case of a first aid with a high level of urgency, the generation unit can have the AI ​​generate a detailed procedure. In addition, in the case of a first aid with a low level of urgency, the generation unit can also have the AI ​​generate a concise procedure. In addition, the generation unit can also adjust the level of detail of the procedure based on the importance of the first aid and provide an appropriate procedure. In this way, by adjusting the level of detail of the procedure based on the importance of the first aid, an appropriate procedure can be provided. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input first aid importance data to the generation AI and cause the generation AI to adjust the level of detail of the procedure.

[0094] When generating a procedure, the generation unit can apply different generation algorithms depending on the first aid category. For example, in the case of a procedure related to cardiopulmonary resuscitation, the AI ​​can apply a dedicated generation algorithm. In addition, in the case of a procedure related to a bleeding control method, the generation unit can also apply a different generation algorithm. In addition, in the case of a procedure related to other first aid, the AI ​​can select an appropriate generation algorithm to generate a procedure. In this way, by applying different generation algorithms depending on the first aid category, more appropriate procedures can be provided. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input first aid category data into the generation AI and cause the generation AI to select a generation algorithm.

[0095] When generating a procedure, the generation unit can improve the accuracy of the generation by referring to the user's past procedure generation results. For example, the generation unit uses AI to improve the generation accuracy based on procedure generation results provided by the user in the past. The generation unit can also improve the generation accuracy by learning specific patterns from the user's past procedure generation results. The generation unit can also use AI to select the optimal generation method by referring to the user's past procedure generation results. In this way, the generation accuracy can be improved by referring to the user's past procedure generation results. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input past procedure generation result data into the generation AI and cause the generation AI to improve the generation accuracy.

[0096] The generation unit can estimate the user's emotions and prioritize procedures based on the estimated user emotions. For example, if the user is facing an emergency, the AI ​​generates that procedure with the highest priority. Furthermore, if the user is calm, the generation unit can also generate the procedure with the same priority as other procedures. Furthermore, if the user is panicking, the generation unit can also generate the procedure with the highest priority and respond quickly. This allows for more appropriate first aid procedures to be provided by prioritizing procedures according to the user'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 generation unit can be performed using, for example, an AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI determine the priority of procedures.

[0097] When generating procedures, the generation unit can determine the priority of procedures based on the timing of first aid submission. For example, in the case of a first aid procedure with a high level of urgency, the generation unit causes the AI ​​to generate that procedure with the highest priority. In addition, in the case of a first aid procedure with a low level of urgency, the generation unit can also cause the AI ​​to generate the procedure with a normal priority. In addition, the generation unit can cause the AI ​​to adjust the priority of procedures according to the timing of first aid submission and provide an appropriate procedure. In this way, by determining the priority of procedures based on the timing of first aid submission, an appropriate procedure can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input first aid submission timing data into the generation AI and cause the generation AI to determine the priority of procedures.

[0098] The generation unit can adjust the order of steps based on first aid relevance when generating procedures. For example, if a procedure is related to first aid, the generation unit causes the AI ​​to generate that procedure preferentially. Furthermore, if a procedure is not related to first aid, the generation unit can also cause the AI ​​to generate the procedure in the normal order. Furthermore, the generation unit can also cause the AI ​​to adjust the order of steps according to first aid relevance and provide an appropriate procedure. In this way, by adjusting the order of steps based on first aid relevance, an appropriate procedure can be provided. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input first aid relevance data to the generation AI and cause the generation AI to adjust the order of the steps.

[0099] When generating a procedure, the generation unit can adjust the use of technical terminology in the procedure according to the user's level of expertise. For example, if the user is a medical professional, the generation unit can have the AI ​​generate the procedure using technical terminology. Furthermore, if the user is a layperson, the generation unit can also have the AI ​​generate the procedure while avoiding technical terminology. Furthermore, the generation unit can adjust the use of technical terminology in the procedure according to the user's level of expertise and provide an appropriate procedure. This allows an appropriate procedure to be provided by adjusting the use of technical terminology in the procedure according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0100] The providing unit can estimate the user's emotions and adjust the method of providing instructions based on the estimated user's emotions. For example, if the user is nervous, the providing unit can have the AI ​​provide instructions in a calm voice. Furthermore, if the user is relaxed, the providing unit can have the AI ​​provide instructions in a cheerful voice. Furthermore, if the user is impatient, the providing unit can have the AI ​​provide quick and concise instructions. This allows for more appropriate first aid instructions to be provided by adjusting the method of providing instructions according to the user's emotions. 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, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method of providing instructions.

[0101] When providing procedures, the providing unit can adjust the level of detail of the provided procedures based on the importance of the first aid. For example, in the case of first aid with a high level of urgency, the providing unit has the AI ​​provide detailed procedures. Furthermore, in the case of first aid with a low level of urgency, the providing unit can also have the AI ​​provide concise procedures. Furthermore, the providing unit can have the AI ​​adjust the level of detail of the provided procedures according to the importance of the first aid and provide appropriate procedures. In this way, appropriate procedures can be provided by adjusting the level of detail of the provided procedures based on the importance of the first aid. 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 first aid importance data to the generating AI and cause the generating AI to adjust the level of detail of the provided procedures.

[0102] When providing procedures, the providing unit can apply different provision algorithms depending on the first aid category. For example, in the case of procedures related to cardiopulmonary resuscitation, the AI ​​can apply a dedicated provision algorithm. In addition, in the case of procedures related to hemostasis, the providing unit can also apply a different provision algorithm. In addition, in the case of procedures related to other first aid, the AI ​​can select an appropriate provision algorithm and provide the procedures. In this way, by applying different provision algorithms depending on the first aid category, more appropriate procedures can be provided. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input first aid category data to the generation AI and cause the generation AI to select a provision algorithm.

[0103] The providing unit can estimate the user's emotions and determine the order in which the procedures are provided based on the estimated user emotions. For example, if the user is facing an emergency, the providing unit can have the AI ​​provide that procedure with the highest priority. Furthermore, if the user is calm, the providing unit can also have the AI ​​provide that procedure with the same priority as other procedures. Furthermore, if the user is panicking, the providing unit can have the AI ​​provide that procedure with a high priority and respond quickly. This allows for more appropriate first aid instructions to be provided by determining the order in which the procedures are provided based on the user's emotions. 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, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the order in which the procedures are provided.

[0104] When providing procedures, the providing unit can determine the priority of provision based on the timing of first aid submission. For example, in the case of a first aid procedure with a high level of urgency, the providing unit causes the AI ​​to provide that procedure with the highest priority. In addition, in the case of a first aid procedure with a low level of urgency, the providing unit can cause the AI ​​to provide the procedure with a normal priority. In addition, the providing unit can cause the AI ​​to adjust the priority of provision according to the timing of first aid submission and provide an appropriate procedure. In this way, by determining the priority of provision based on the timing of first aid submission, it is possible to provide an appropriate procedure. 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 first aid submission timing data to the generating AI and cause the generating AI to determine the priority of provision.

[0105] When providing procedures, the providing unit can adjust the order of provision based on the relevance of first aid. For example, if the providing unit is related to a first aid procedure, the AI ​​can provide that procedure preferentially. Furthermore, if the providing unit is not related to a first aid procedure, the AI ​​can provide the procedure in the normal order. Furthermore, the providing unit can adjust the order of provision based on the relevance of first aid and provide appropriate procedures. In this way, appropriate procedures can be provided by adjusting the order of provision based on the relevance of first aid. 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 first aid relevance data to the generation AI and cause the generation AI to adjust the order of provision.

[0106] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is nervous, the learning unit causes the AI ​​to prioritize selecting training data related to emergency situations. Furthermore, if the user is relaxed, the learning unit can also cause the AI ​​to select normal training data. Furthermore, if the user is anxious, the learning unit can also select training data that the AI ​​can respond to quickly. This allows the AI ​​to select more appropriate training data by selecting training data according to the user'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 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 learning unit can be performed using, for example, an AI, or without an AI. For example, the learning unit can input the user's emotion data into the generation AI and have the generation AI select the training data.

[0107] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit selects an optimal learning algorithm using AI based on past learning data. The learning unit can also learn specific patterns from past learning data and optimize the learning algorithm. The learning unit can also improve the accuracy of the learning algorithm using AI by referring to past learning data. In this way, by referring to past learning data, the learning algorithm can be optimized and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input past learning data into the generation AI and cause the generation AI to optimize the learning algorithm.

[0108] The learning unit can update the learning data by reflecting user feedback during learning. In the learning unit, for example, the AI ​​updates the learning data based on feedback provided by the user. The learning unit can also learn specific patterns from user feedback and update the learning data. The learning unit can also select optimal learning data by reflecting user feedback. In this way, the learning data can be updated by reflecting user feedback, allowing for more appropriate learning. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input user feedback data into the generation AI and cause the generation AI to update the learning data.

[0109] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user's emotions. For example, if the user is nervous, the learning unit can increase the learning frequency of the AI ​​so that it can respond quickly. Furthermore, if the user is relaxed, the learning unit can also cause the AI ​​to learn at a normal learning frequency. Furthermore, if the user is impatient, the learning unit can also maximize the learning frequency so that it can respond quickly. This allows for more appropriate learning by adjusting the learning frequency according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the learning frequency.

[0110] During learning, the learning unit can weight the learning data based on the timing of first aid submission. For example, in the case of first aid with high urgency, the learning unit causes the AI ​​to weight the learning data highly. In addition, in the case of first aid with low urgency, the learning unit can also cause the AI ​​to perform learning with normal weighting. In addition, the learning unit can cause the AI ​​to adjust the weighting of the learning data according to the timing of first aid submission and perform appropriate learning. In this way, appropriate learning can be performed by weighting the learning data based on the timing of first aid submission. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data on the timing of first aid submission to the generation AI and cause the generation AI to weight the learning data.

[0111] During learning, the learning unit can integrate information from different data sources to expand the training data. For example, the learning unit integrates data provided by medical institutions, and the AI ​​expands the training data. The learning unit can also integrate feedback provided by users, and the AI ​​can expand the training data. The learning unit can also integrate publicly available medical information, and the AI ​​can expand the training data. In this way, by integrating information from different data sources, the training data can be expanded and more appropriate learning can be performed. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into the generation AI and cause the generation AI to integrate the training data. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, provision unit, and learning unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive voice input using the microphone 38B of the smart device 14. For example, the reception unit can receive voice input using the microphone 38B of the smart device 14. The analysis unit can analyze the voice and convert it into text data using the specific processing unit 290 of the data processing device 12. The generation unit can generate first aid procedures using the specific processing unit 290 of the data processing device 12, and the provision unit can provide the generated procedures by voice using the speaker 40B of the smart device 14. The learning unit can learn the latest medical information using the specific processing unit 290 of the data processing device 12 and always provide the latest first aid procedures. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, provision unit, and learning unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive voice input using the microphone 238 of the smart glasses 214. The analysis unit can analyze the voice and convert it into text data using the specific processing unit 290 of the data processing device 12. The generation unit can generate first aid procedures using the specific processing unit 290 of the data processing device 12, and the provision unit can provide the generated procedures by voice using the speaker 240 of the smart glasses 214. The learning unit can learn the latest medical information using the specific processing unit 290 of the data processing device 12 and always provide the latest first aid procedures. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and learning unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive voice input using the microphone 238 of the headset-type terminal 314. The analysis unit can analyze the voice and convert it into text data using the specific processing unit 290 of the data processing device 12. The generation unit can generate first aid procedures using the specific processing unit 290 of the data processing device 12, and the provision unit can provide the generated procedures by voice using the speaker 240 of the headset-type terminal 314. The learning unit can learn the latest medical information using the specific processing unit 290 of the data processing device 12 and always provide the latest first aid procedures. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and learning unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive voice input using the microphone 238 of the robot 414. The analysis unit can analyze the voice and convert it into text data using the specific processing unit 290 of the data processing device 12. The generation unit can generate first aid procedures using the specific processing unit 290 of the data processing device 12, and the provision unit can provide the generated procedures by voice using the speaker 240 of the robot 414. The learning unit can learn the latest medical information using the specific processing unit 290 of the data processing device 12 and always provide the latest first aid procedures.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] When accepting a voice input from a user, the acceptance unit can refer to the user's past behavior history and select the optimal acceptance method. For example, it can preferentially accept voice commands that the user has frequently used in the past. The acceptance unit can also learn specific phrases and expressions from the user's past voice input history and select the optimal acceptance method. Furthermore, the acceptance unit can analyze the time period in which the user has used voice input in the past and select the optimal acceptance method for that time period. In this way, the optimal acceptance method can be selected by referring to the user's past behavior history.

[0114] When providing the generated instructions by voice, the providing unit can perform filtering based on the user's current situation and environmental sounds. For example, if the user is in a noisy environment, the AI ​​filters out environmental sounds to improve the accuracy of the voice input. In addition, if the user is in a quiet environment, the providing unit can increase the sensitivity of the voice input to recognize even subtle sounds. Furthermore, if the user is moving, the providing unit can also remove background noise to maintain the accuracy of the voice input. This makes it possible to improve the accuracy of the voice input by filtering based on the user's current situation and environmental sounds.

[0115] When learning the latest medical information, the learning unit can integrate information from different data sources to expand the learning data. For example, data provided by medical institutions can be integrated, and the AI ​​can expand the learning data. The learning unit can also integrate feedback provided by users, and the AI ​​can expand the learning data. Furthermore, the learning unit can integrate publicly available medical information, and the AI ​​can expand the learning data. In this way, by integrating information from different data sources, the learning data can be expanded and more appropriate learning can be performed.

[0116] The reception unit can estimate the user's emotions and adjust the timing of voice input reception based on the estimated user emotions. For example, if the user is nervous, the AI ​​can delay the timing of voice input reception and wait until the user calms down. Also, if the user is impatient, the reception unit can accelerate the timing of voice input reception and quickly start giving first aid instructions. Furthermore, if the user is relaxed, the reception unit can set the timing of voice input reception to the normal level and give first aid instructions in a natural flow. In this way, by adjusting the timing of voice input reception according to the user's emotions, more appropriate first aid instructions can be provided.

[0117] When analyzing voice using voice recognition technology, the analysis unit can estimate the user's emotions and adjust the accuracy of the voice analysis based on the estimated user emotions. For example, if the user is nervous, the AI ​​will increase the accuracy of the voice analysis to prevent misrecognition. In addition, if the user is relaxed, the analysis unit can also perform analysis with normal voice analysis accuracy. Furthermore, if the user is impatient, the AI ​​can maximize the accuracy of the voice analysis to perform analysis quickly. In this way, by adjusting the accuracy of the voice analysis according to the user's emotions, more accurate analysis results can be obtained.

[0118] When generating first aid procedures based on the analyzed voice, the generation unit can estimate the user's emotions and adjust the method for generating the procedures based on the estimated user's emotions. For example, if the user is nervous, the AI ​​generates concise and easy-to-understand procedures. The generation unit can also generate detailed procedures if the user is relaxed. Furthermore, if the user is impatient, the generation unit can generate procedures that the AI ​​can execute quickly. This makes it possible to provide more appropriate procedures by adjusting the method for generating first aid procedures according to the user's emotions.

[0119] When providing the generated instructions by voice, the providing unit can estimate the user's emotions and adjust the way the instructions are provided based on the estimated user's emotions. For example, if the user is nervous, the AI ​​can provide the instructions in a calm voice. Also, if the user is relaxed, the providing unit can provide the instructions in a cheerful voice. Furthermore, if the user is impatient, the providing unit can provide the AI ​​with quick and concise instructions. In this way, by adjusting the way the instructions are provided according to the user's emotions, more appropriate first aid instructions can be provided.

[0120] When learning the latest medical information, the learning unit can estimate the user's emotions and select learning data based on the estimated user emotions. For example, if the user is nervous, the AI ​​will prioritize selecting learning data related to emergencies. The learning unit can also select normal learning data if the user is relaxed. Furthermore, if the user is anxious, the learning unit can select learning data that the AI ​​can respond to quickly. This allows the AI ​​to use more appropriate learning data by selecting learning data according to the user's emotions.

[0121] When accepting a user's voice input, the reception unit can prioritize accepting highly relevant inputs by taking into account the user's geographical location information. For example, if the user is near a hospital, the AI ​​can prioritize accepting medical-related voice inputs. In addition, if the user is at home, the reception unit can also prioritize accepting voice inputs related to first aid at home. Furthermore, if the user is in a public place, the reception unit can also prioritize accepting voice inputs related to first aid in public places. In this way, by taking into account the user's geographical location information, it is possible to prioritize accepting highly relevant voice inputs.

[0122] During voice analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input voice. For example, in the case of a voice input with a high level of urgency, the AI ​​will perform a detailed analysis and respond quickly. In addition, in the case of a voice input with a low level of urgency, the analysis unit can also have the AI ​​perform a normal analysis. Furthermore, the analysis unit can also have the AI ​​adjust the level of detail of the analysis according to the content of the voice input and provide appropriate first aid procedures. In this way, by adjusting the level of detail of the analysis based on the importance of the input voice, it is possible to provide appropriate first aid procedures.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The reception unit receives voice input. For example, a user can input voice using a microphone on a smartphone. Alternatively, the reception unit can receive voice via an application on the smartphone as the voice input format. Step 2: The analysis unit analyzes the voice received by the reception unit. The analysis unit can convert the voice into text data using voice recognition technology and further analyze the content of the voice using natural language processing technology. For example, the analysis unit can convert the voice into text data using voice recognition technology and analyze first aid procedures based on the text data. Step 3: The generator generates first aid procedures based on the speech analyzed by the analyzer. The generator generates first aid procedures such as cardiopulmonary resuscitation and hemostasis, and can also provide the generated procedures by voice using speech synthesis technology.

[0125] 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.

[0126] 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 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] [Explanation of symbols]

[0197] 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 reception unit that receives voice input; an analysis unit that analyzes the voice received by the reception unit; a generation unit that generates a first aid procedure based on the voice analyzed by the analysis unit; Equipped with A system characterized by:

2. A provision unit is provided to provide the generated procedure by voice.

2. The system of claim 1.

3. Equipping a learning department to study the latest medical information 2. The system of claim 1.

4. The reception unit The user speaks out the situation requiring first aid 2. The system of claim 1.

5. The analysis unit Analyze voice using voice recognition technology 2. The system of claim 1.

6. The generation unit Generate first aid instructions based on analyzed speech 2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past voice input history and select the optimal reception method 2. The system of claim 1.

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