System

Abstract

To provide a system that supports general citizens in quickly performing proper procedures at life-saving emergency sites.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, and an instruction unit. The reception unit receives information from a user. The analysis unit analyzes the information received by the reception unit and proposes a treatment. The instruction unit gives the user instructions based on the treatment proposed by the analysis unit and delivers feedback.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it is difficult for ordinary citizens to quickly provide appropriate treatment at emergency scenes.

[0005] The system according to the embodiment aims to support ordinary citizens in quickly providing appropriate treatment at emergency medical sites. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and an instruction unit. The reception unit receives information from a user. The analysis unit analyzes the information received by the reception unit and proposes a treatment. The instruction unit instructs the user on the treatment proposed by the analysis unit and provides feedback. Effect of the Invention

[0007] The system according to the embodiment can assist ordinary citizens in quickly providing appropriate treatment at emergency scenes. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A lifesaving support system according to an embodiment of the present invention is a system for supporting rapid response in emergency rescue. When a user faces an emergency, this lifesaving support system uses a device to start a lifesaving support app. Next, the app asks the user questions to confirm the patient's condition, and the user answers the questions. For example, questions include "Is the patient conscious?" and "Is the patient breathing?" This information is input into a model trained on emergency rescue. Next, the trained model analyzes the input information and proposes appropriate measures according to the patient's condition. For example, if cardiopulmonary resuscitation is required, the method and rhythm of chest compressions are specifically instructed. Also, if airway management is required, the method of head tilt and chin lift are shown. In this way, the general public is supported to perform appropriate lifesaving measures. Furthermore, the app provides feedback to the user in real time. For example, if the rhythm of chest compressions is not appropriate, the app instructs the user to correct the rhythm. Also, if airway management is insufficient, the app prompts the user to try again. In this way, the app supports the user to perform accurate and effective lifesaving measures. This system will enable ordinary citizens to quickly and appropriately implement life-saving measures in emergency situations, and is expected to improve the survival rate. For example, the success rate of cardiopulmonary resuscitation will improve, increasing the patient's survival rate. In addition, by quickly securing the airway, oxygen supply will be secured and damage to the brain and organs will be reduced. In this way, it is expected that the quality of response in emergency rescue will improve and many lives will be saved. As a result, the life-saving support system can support quick and appropriate life-saving measures.

[0029] The lifesaving support system according to the embodiment includes a reception unit, an analysis unit, and an instruction unit. The reception unit receives information from a user. The information from the user includes, for example, text information, voice information, image information, and the like, but is not limited to such examples. The reception unit can, for example, specify the type and format of information input by the user. For example, symptom information, location information, contact information, and the like can be specified. The analysis unit analyzes the information received by the reception unit using a trained model. The trained model includes, for example, a neural network, a decision tree, and the like, but is not limited to such examples. For example, the analysis unit specifically instructs the method and rhythm of chest compression when cardiopulmonary resuscitation is required. In addition, the analysis unit can indicate the method of head tilt and chin lift when airway management is required. The instruction unit instructs the user on the treatment proposed by the analysis unit and provides feedback. The feedback includes, for example, a text message, a voice instruction, and the like, but is not limited to such examples. The instruction unit can provide feedback in real time, for example. For example, if the rhythm of chest compressions is not appropriate, the system instructs the patient to correct the rhythm. Also, if airway management is insufficient, the system prompts the patient to try again. In this way, the lifesaving support system according to the embodiment can support rapid and appropriate lifesaving measures.

[0030] The reception unit receives information from the user. The information from the user includes, for example, text information, voice information, image information, and the like, but is not limited to such examples. The reception unit can, for example, specify the type and format of information to be input by the user. For example, symptom information, location information, contact information, and the like can be specified. Specifically, it is assumed that the user uses a smartphone or tablet to input information through a dedicated application. The application provides an intuitive interface so that the user can easily input information. For example, when inputting symptom information, a format in which the user selects the relevant symptom from a selection list, or a format in which the user inputs details in a free description format can be considered. Location information is often automatically acquired using a GPS function, but manual input is also possible. For contact information, the user can input the telephone number or email address of a family member or friend as an emergency contact. Furthermore, the reception unit can also allow the user to input information by voice using voice recognition technology. For example, when the user says "my chest hurts," the voice is converted into text and sent to the analysis unit. In addition, for image information, the user can upload photos and videos taken by the user. For example, by sending an image of the injury situation or a video of the situation at the scene to the reception unit, more detailed information can be provided. This allows the receiving unit to receive information in a variety of formats and transmit it to the analyzing unit quickly and accurately.

[0031] The analysis unit uses a trained model to analyze the information received by the reception unit. The trained model includes, but is not limited to, a neural network, a decision tree, and the like. For example, when cardiopulmonary resuscitation is required, the analysis unit specifically instructs the method and rhythm of chest compression. In addition, when airway management is required, the analysis unit can indicate the method of tilting the head back or lifting the chin. Specifically, in the analysis using a neural network, the text information and voice information received from the user are analyzed using natural language processing technology to determine the urgency of the symptoms and the necessary measures. For example, keywords such as "chest pain" and "can't breathe" are detected, and it is determined that cardiopulmonary resuscitation is required. In addition, it is possible to use image recognition technology to analyze images and videos sent by the user to determine the degree of injury and the presence or absence of bleeding. The analysis unit comprehensively judges these pieces of information and proposes the optimal life-saving measures. For example, when cardiopulmonary resuscitation is required, the analysis unit indicates the specific method and rhythm of chest compression. Specifically, the analysis unit instructs in detail the position and depth of chest compression, the number of compressions per minute, and the like. Furthermore, if airway management is required, specific methods for tilting the head back and lifting the chin are shown. This allows the analysis unit to quickly and accurately analyze the information received from the user and propose appropriate life-saving measures. Furthermore, the analysis unit can perform more accurate analysis by utilizing past data and statistical information. For example, by using a model that has learned from past life-saving cases, it becomes possible to make faster and more accurate judgments. This allows the analysis unit to always provide highly accurate analysis based on the latest information and support fast and appropriate life-saving measures.

[0032] The instruction unit instructs the user on the treatment proposed by the analysis unit and provides feedback. The feedback includes, for example, a text message, a voice instruction, and the like, but is not limited to such examples. The instruction unit can provide feedback in real time, for example. For example, if the rhythm of chest compression is not appropriate, the instruction unit instructs the user to correct the rhythm. Also, if the airway is not sufficiently secured, the instruction unit prompts the user to try again. Specifically, the user can receive instructions in real time through a smartphone or tablet. For example, if the rhythm of chest compression is too fast, a voice instruction such as "Please compress a little more slowly" is provided. Also, if the airway is not sufficiently secured, a specific instruction such as "Please tilt your head back a little more" is provided. Furthermore, the instruction unit can receive feedback from the user and appropriately modify the instruction content. For example, if the user feels that "chest compression is difficult," a simpler method can be suggested based on the feedback. Also, the instruction unit can monitor whether the user is performing appropriate treatment and provide additional instructions as necessary. For example, if the depth of chest compression is insufficient, an instruction such as "Please compress a little deeper" is provided. This allows the instruction unit to provide the user with prompt and appropriate feedback, maximizing the effectiveness of the life-saving measures. Furthermore, the instruction unit can reliably transmit information using multiple communication means. For example, important information can be reliably delivered by using not only smartphone notifications, but also voice calls, SMS, emails, and the like. This allows the instruction unit to provide the user with prompt and reliable instructions for actions, maximizing the effectiveness of the life-saving measures.

[0033] The reception unit can specify the type of information to be input by the user. The reception unit, for example, specifies the type of information to be input by the user. For example, symptom information, location information, contact information, etc. can be specified. This improves the consistency and accuracy of the information by specifying the type and format of the information to be input by the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the type of information to be input by the user to the AI, and the AI ​​can specify the type of information.

[0034] The analysis unit can analyze information using a trained model. The analysis unit, for example, analyzes information using a trained model. Trained models include, for example, neural networks, decision trees, and the like, but are not limited to such examples. For example, a neural network has learned a large amount of data and has advanced analytical capabilities. A decision tree hierarchically classifies data and derives an analysis result. As a result, by using the trained model, the accuracy of analyzing information is improved. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs a trained model into a generation AI, and the generation AI can analyze information.

[0035] The instruction unit can provide feedback. The instruction unit provides, for example, feedback. The feedback includes, for example, a text message, a voice instruction, and the like, but is not limited to such examples. For example, the instruction unit can provide feedback in real time. For example, if the rhythm of chest compressions is not appropriate, the instruction unit instructs the user to correct the rhythm. Also, if airway maintenance is insufficient, the instruction unit prompts the user to try again. In this way, by providing feedback in real time, the user is assisted in carrying out appropriate life-saving measures. A part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the content of the feedback to AI, and the AI ​​can provide the feedback.

[0036] The analysis unit can instruct the method and rhythm of chest compression when cardiopulmonary resuscitation is necessary. For example, the analysis unit instructs the method and rhythm of chest compression when cardiopulmonary resuscitation is necessary. The method and rhythm of chest compression include, for example, the depth of compression and the speed of compression, but are not limited to such examples. For example, the analysis unit instructs the depth of compression to be about 5 cm. The analysis unit can also instruct the speed of compression to be 100 to 120 times per minute. This supports appropriate life-saving measures by providing specific instructions when cardiopulmonary resuscitation is necessary. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the method and rhythm of cardiopulmonary resuscitation to the generation AI, and the generation AI can provide specific instructions.

[0037] The analysis unit can instruct the method of tilting the head back or lifting the chin when airway management is required. For example, the analysis unit instructs the method of tilting the head back or lifting the chin when airway management is required. The method of tilting the head back or lifting the chin includes, for example, the position of the hand and the way of applying force, but is not limited to such examples. For example, the analysis unit instructs the user to place the hand on the forehead and lift the chin when tilting the head back. The analysis unit can also instruct the user to appropriately adjust the force of lifting the chin when lifting the chin. This supports appropriate life-saving measures by showing a specific method when airway management is required. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the method of airway management to the generation AI, and the generation AI can provide specific instructions.

[0038] The reception unit can analyze the user's past emergency response history and select the optimal question format. The reception unit, for example, analyzes the user's past emergency response history and selects the optimal question format. The emergency response history includes, for example, past response records, response results, etc., but is not limited to such examples. For example, if the user has experience performing cardiopulmonary resuscitation in the past, the reception unit asks questions based on that experience. In addition, if the user has never faced an emergency in the past, the reception unit can start with basic questions. Furthermore, if the user has a history of responding to a specific emergency in the past, the reception unit can ask questions based on that history. As a result, the accuracy of the questions is improved by selecting the optimal question format based on the user's past emergency response history. Part or all of the above-mentioned 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 emergency response history into AI, and the AI ​​can select the optimal question format.

[0039] The reception unit can analyze the user's current environmental sound and select an appropriate question when asking a question. The reception unit, for example, analyzes the user's current environmental sound and selects an appropriate question when asking a question. The environmental sound includes, for example, a noise level, detection of a specific sound, and the like, but is not limited to such examples. For example, when the surroundings are noisy, the reception unit asks a concise and clear question. Also, when the surroundings are quiet, the reception unit can ask a detailed question. Furthermore, when the surrounding sound indicates an emergency, the reception unit can ask a question based on the sound. As a result, by selecting an appropriate question based on the user's current environmental sound, the accuracy of the question is improved. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's current environmental sound into AI, and the AI ​​can select an appropriate question.

[0040] The reception unit can ask a question corresponding to an emergency specific to the region, taking into consideration the geographical location information of the user, when asking a question. For example, the reception unit asks a question corresponding to an emergency specific to the region, taking into consideration the geographical location information of the user, when asking a question. The geographical location information includes, for example, GPS data, location information services, and the like, but is not limited to such examples. For example, the reception unit asks a question about drowning when the user is at the beach. Also, the reception unit can ask a question about falls and slips when the user is in the mountains. Furthermore, the reception unit can ask a question about traffic accidents when the user is in an urban area. This improves the accuracy of the question by asking a question corresponding to an emergency specific to the region based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit inputs the geographical location information of the user to AI, and the AI ​​can ask a question corresponding to an emergency specific to the region.

[0041] The reception unit can analyze the social media activity of the user and acquire related information when a question is asked. The reception unit, for example, analyzes the social media activity of the user and acquires related information when a question is asked. The social media activity includes, for example, the content of the post, the number of likes, and the like, but is not limited to such examples. For example, the reception unit infers the current situation from the content recently posted by the user and asks a question. In addition, the reception unit can suggest emergency contacts from the user's social media friend list. Furthermore, the reception unit can infer the user's experience with an emergency from the content of the user's past posts and ask a question. As a result, the accuracy of the question is improved by acquiring related information based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI, and the AI ​​can acquire related information.

[0042] The analysis unit can improve the accuracy of the analysis by referring to past emergency data during the analysis. The analysis unit can improve the accuracy of the analysis by referring to past emergency data during the analysis. The emergency data includes, for example, past emergency records, response results, etc., but is not limited to such examples. For example, the analysis unit performs the analysis by referring to past cardiopulmonary resuscitation data. The analysis unit can also perform the analysis by referring to past airway management data. Furthermore, the analysis unit can perform the analysis by referring to past emergency response data. As a result, the accuracy of the analysis is improved by referring to the past emergency data. A part 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 past emergency data into AI, and the AI ​​can improve the accuracy of the analysis.

[0043] The analysis unit can acquire the biological information of the patient in real time during the analysis and reflect it in the analysis. The analysis unit can acquire the biological information of the patient in real time during the analysis and reflect it in the analysis. The biological information includes, for example, heart rate, blood pressure, body temperature, etc., but is not limited to such examples. For example, the analysis unit can acquire the heart rate of the patient in real time and reflect it in the analysis. In addition, the analysis unit can acquire the respiratory rate of the patient in real time and reflect it in the analysis. Furthermore, the analysis unit can acquire the blood pressure of the patient in real time and reflect it in the analysis. Thereby, by acquiring the biological information of the patient in real time, the accuracy of the analysis is improved. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the biological information of the patient to AI, and the AI ​​can reflect it in the analysis.

[0044] The analysis unit can perform an analysis corresponding to an emergency specific to the region, taking into account the geographical location information of the patient during the analysis. The analysis unit, for example, performs an analysis corresponding to an emergency specific to the region, taking into account the geographical location information of the patient during the analysis. The geographical location information includes, for example, GPS data, location information services, and the like, but is not limited to such examples. For example, the analysis unit performs an analysis related to drowning when the patient is at the beach. Furthermore, the analysis unit can perform an analysis related to falls and slips when the patient is in the mountains. Furthermore, the analysis unit can perform an analysis related to traffic accidents when the patient is in an urban area. This improves the accuracy of the analysis by performing an analysis corresponding to an emergency specific to the region based on the geographical location information of the patient. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the patient to AI, and the AI ​​can perform an analysis corresponding to an emergency specific to the region.

[0045] The analysis unit can improve the accuracy of the analysis by referring to the patient's past medical history during the analysis. The analysis unit can improve the accuracy of the analysis by referring to the patient's past medical history during the analysis, for example. The medical history includes, for example, past diagnosis results, treatment history, etc., but is not limited to such examples. For example, the analysis unit performs the analysis by referring to the patient's past cardiopulmonary resuscitation history. In addition, the analysis unit can perform the analysis by referring to the patient's past airway management history. Furthermore, the analysis unit can perform the analysis by referring to the patient's past emergency response history. As a result, the accuracy of the analysis is improved by referring to the patient's past medical history. Part 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 patient's past medical history into AI, and the AI ​​can improve the accuracy of the analysis.

[0046] The instruction unit can select the optimal instruction method by referring to the user's past emergency response history when giving an instruction. For example, the instruction unit selects the optimal instruction method by referring to the user's past emergency response history when giving an instruction. The emergency response history includes, for example, past response records, response results, etc., but is not limited to such examples. For example, if the user has experience performing cardiopulmonary resuscitation in the past, the instruction unit gives instructions based on that experience. In addition, if the user has never faced an emergency in the past, the instruction unit can start with basic instructions. Furthermore, if the user has a history of responding to a specific emergency in the past, the instruction unit can give instructions based on that history. As a result, the accuracy of the instructions is improved by selecting the optimal instruction method based on the user's past emergency response history. Part or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input the user's past emergency response history into AI, and the AI ​​can select the optimal instruction method.

[0047] The instruction unit can analyze the user's current environmental sound at the time of instruction and select an appropriate instruction. The instruction unit can analyze the user's current environmental sound at the time of instruction and select an appropriate instruction. The environmental sound includes, for example, a noise level, detection of a specific sound, and the like, but is not limited to such examples. For example, the instruction unit can provide a concise and clear instruction when the surroundings are noisy. Also, the instruction unit can provide a detailed instruction when the surroundings are quiet. Furthermore, when the surrounding sound indicates an emergency, the instruction unit can provide an instruction based on the sound. This improves the accuracy of the instruction by selecting an appropriate instruction based on the user's current environmental sound. Part or all of the above-mentioned processing in the instruction unit can be performed, for example, using AI or can be performed without using AI. For example, the instruction unit can input the user's current environmental sound to AI, and the AI ​​can select an appropriate instruction.

[0048] The instruction unit can give an instruction to respond to an emergency situation specific to the region, taking into consideration the geographical location information of the user when giving an instruction. The instruction unit, for example, gives an instruction to respond to an emergency situation specific to the region, taking into consideration the geographical location information of the user when giving an instruction. The geographical location information includes, for example, GPS data, location information services, and the like, but is not limited to such examples. For example, the instruction unit gives an instruction regarding drowning when the user is at the beach. Also, the instruction unit can give an instruction regarding a fall or a slip when the user is in the mountains. Furthermore, the instruction unit can give an instruction regarding a traffic accident when the user is in an urban area. As a result, by giving an instruction to respond to an emergency situation specific to the region based on the geographical location information of the user, the accuracy of the instruction is improved. A part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the geographical location information of the user to AI, and the AI ​​can give an instruction to respond to an emergency situation specific to the region.

[0049] The instruction unit can analyze the social media activity of the user at the time of instruction and provide related information. The instruction unit can, for example, analyze the social media activity of the user at the time of instruction and provide related information. Social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to such examples. For example, the instruction unit infers the current situation from the content recently posted by the user and provides instructions. In addition, the instruction unit can suggest emergency contacts from the user's social media friend list. Furthermore, the instruction unit can infer the user's experience with an emergency from the content of past posts and provide instructions. This improves the accuracy of instructions by providing related information based on the user's social media activity. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the user's social media activity into AI, and the AI ​​can provide related information.

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

[0051] The reception unit can learn the user's behavioral patterns in emergency situations and adjust the optimal timing of questions. For example, the reception unit can analyze the timing at which the user can respond most calmly from past data and ask questions at that timing. The reception unit can also optimize the order of questions based on how the user responded to specific questions in the past. Furthermore, the reception unit can learn how the user acted in emergency situations in the past and adjust the content of questions based on the actions. This can help the user provide information most effectively.

[0052] The analysis unit monitors the patient's vital signs in real time, and can immediately issue an alert if an abnormality is detected. For example, an alert is issued if the heart rate drops suddenly or blood pressure becomes abnormally high. An alert can also be issued if the respiratory rate falls outside the normal range. Furthermore, an alert can also be issued if there is a sudden change in body temperature. This allows for a rapid response when the patient's condition suddenly changes.

[0053] The instruction unit can select an optimal instruction method based on the user's past emergency response history. For example, if the user has experience performing cardiopulmonary resuscitation in the past, instructions can be given based on that experience. Also, if the user has never faced an emergency in the past, basic instructions can be given first. Furthermore, if the user has a history of responding to a specific emergency in the past, instructions can be given based on that history. This improves the accuracy of instructions by selecting an optimal instruction method based on the user's past emergency response history.

[0054] The reception unit can analyze the user's current environmental sounds and select an appropriate question. For example, if the surroundings are noisy, a simple and clear question can be asked. If the surroundings are quiet, a detailed question can be asked. Furthermore, if the surrounding sounds indicate an emergency, a question based on the sound can be asked. This improves the accuracy of the questions by selecting an appropriate question based on the user's current environmental sounds.

[0055] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past medical history. For example, the analysis can be performed by referring to the patient's past cardiopulmonary resuscitation history. The analysis can also be performed by referring to the patient's past airway management history. Furthermore, the analysis can be performed by referring to the patient's past emergency response history. In this way, the accuracy of the analysis can be improved by referring to the patient's past medical history.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The reception unit receives information from a user. The information from the user includes, for example, text information, voice information, image information, etc. The reception unit can specify the type and format of information to be input by the user, and can specify, for example, symptom information, location information, contact information, etc. Step 2: The analysis unit uses the trained model to analyze the information received by the reception unit. The trained model includes, for example, a neural network, a decision tree, etc. For example, the analysis unit can provide specific instructions on the method and rhythm of chest compressions when cardiopulmonary resuscitation is required, and can provide instructions on how to tilt the head back and lift the chin when airway management is required. Step 3: The instruction unit instructs the user on the action suggested by the analysis unit and provides feedback to the user, such as by text message, voice instruction, etc. The instruction unit provides real-time feedback, such as instructing the user to correct the rhythm if the chest compression rhythm is not appropriate, and encouraging the user to try again if the airway is not sufficiently secured.

[0058] (Example 2) A lifesaving support system according to an embodiment of the present invention is a system for supporting rapid response in emergency rescue. When a user faces an emergency, this lifesaving support system uses a device to start a lifesaving support app. Next, the app asks the user questions to confirm the patient's condition, and the user answers the questions. For example, questions include "Is the patient conscious?" and "Is the patient breathing?" This information is input into a model trained on emergency rescue. Next, the trained model analyzes the input information and proposes appropriate measures according to the patient's condition. For example, if cardiopulmonary resuscitation is required, the method and rhythm of chest compressions are specifically instructed. Also, if airway management is required, the method of head tilt and chin lift are shown. In this way, the general public is supported to perform appropriate lifesaving measures. Furthermore, the app provides feedback to the user in real time. For example, if the rhythm of chest compressions is not appropriate, the app instructs the user to correct the rhythm. Also, if airway management is insufficient, the app prompts the user to try again. In this way, the app supports the user to perform accurate and effective lifesaving measures. This system will enable ordinary citizens to quickly and appropriately implement life-saving measures in emergency situations, and is expected to improve the survival rate. For example, the success rate of cardiopulmonary resuscitation will improve, increasing the patient's survival rate. In addition, by quickly securing the airway, oxygen supply will be secured and damage to the brain and organs will be reduced. In this way, it is expected that the quality of response in emergency rescue will improve and many lives will be saved. As a result, the life-saving support system can support quick and appropriate life-saving measures.

[0059] The lifesaving support system according to the embodiment includes a reception unit, an analysis unit, and an instruction unit. The reception unit receives information from a user. The information from the user includes, for example, text information, voice information, image information, and the like, but is not limited to such examples. The reception unit can, for example, specify the type and format of information input by the user. For example, symptom information, location information, contact information, and the like can be specified. The analysis unit analyzes the information received by the reception unit using a trained model. The trained model includes, for example, a neural network, a decision tree, and the like, but is not limited to such examples. For example, the analysis unit specifically instructs the method and rhythm of chest compression when cardiopulmonary resuscitation is required. In addition, the analysis unit can indicate the method of head tilt and chin lift when airway management is required. The instruction unit instructs the user on the treatment proposed by the analysis unit and provides feedback. The feedback includes, for example, a text message, a voice instruction, and the like, but is not limited to such examples. The instruction unit can provide feedback in real time, for example. For example, if the rhythm of chest compressions is not appropriate, the system instructs the patient to correct the rhythm. Also, if airway management is insufficient, the system prompts the patient to try again. In this way, the lifesaving support system according to the embodiment can support rapid and appropriate lifesaving measures.

[0060] The reception unit receives information from the user. The information from the user includes, for example, text information, voice information, image information, and the like, but is not limited to such examples. The reception unit can, for example, specify the type and format of information to be input by the user. For example, symptom information, location information, contact information, and the like can be specified. Specifically, it is assumed that the user uses a smartphone or tablet to input information through a dedicated application. The application provides an intuitive interface so that the user can easily input information. For example, when inputting symptom information, a format in which the user selects the relevant symptom from a selection list, or a format in which the user inputs details in a free description format can be considered. Location information is often automatically acquired using a GPS function, but manual input is also possible. For contact information, the user can input the telephone number or email address of a family member or friend as an emergency contact. Furthermore, the reception unit can also allow the user to input information by voice using voice recognition technology. For example, when the user says "my chest hurts," the voice is converted into text and sent to the analysis unit. In addition, for image information, the user can upload photos and videos taken by the user. For example, by sending an image of the injury situation or a video of the situation at the scene to the reception unit, more detailed information can be provided. This allows the receiving unit to receive information in a variety of formats and transmit it to the analyzing unit quickly and accurately.

[0061] The analysis unit uses a trained model to analyze the information received by the reception unit. The trained model includes, but is not limited to, a neural network, a decision tree, and the like. For example, when cardiopulmonary resuscitation is required, the analysis unit specifically instructs the method and rhythm of chest compression. In addition, when airway management is required, the analysis unit can indicate the method of tilting the head back or lifting the chin. Specifically, in the analysis using a neural network, the text information and voice information received from the user are analyzed using natural language processing technology to determine the urgency of the symptoms and the necessary measures. For example, keywords such as "chest pain" and "can't breathe" are detected, and it is determined that cardiopulmonary resuscitation is required. In addition, it is possible to use image recognition technology to analyze images and videos sent by the user to determine the degree of injury and the presence or absence of bleeding. The analysis unit comprehensively judges these pieces of information and proposes the optimal life-saving measures. For example, when cardiopulmonary resuscitation is required, the analysis unit indicates the specific method and rhythm of chest compression. Specifically, the analysis unit instructs in detail the position and depth of chest compression, the number of compressions per minute, and the like. Furthermore, if airway management is required, specific methods for tilting the head back and lifting the chin are shown. This allows the analysis unit to quickly and accurately analyze the information received from the user and propose appropriate life-saving measures. Furthermore, the analysis unit can perform more accurate analysis by utilizing past data and statistical information. For example, by using a model that has learned from past life-saving cases, it becomes possible to make faster and more accurate judgments. This allows the analysis unit to always provide highly accurate analysis based on the latest information and support fast and appropriate life-saving measures.

[0062] The instruction unit instructs the user on the treatment proposed by the analysis unit and provides feedback. The feedback includes, for example, a text message, a voice instruction, and the like, but is not limited to such examples. The instruction unit can provide feedback in real time, for example. For example, if the rhythm of chest compression is not appropriate, the instruction unit instructs the user to correct the rhythm. Also, if the airway is not sufficiently secured, the instruction unit prompts the user to try again. Specifically, the user can receive instructions in real time through a smartphone or tablet. For example, if the rhythm of chest compression is too fast, a voice instruction such as "Please compress a little more slowly" is provided. Also, if the airway is not sufficiently secured, a specific instruction such as "Please tilt your head back a little more" is provided. Furthermore, the instruction unit can receive feedback from the user and appropriately modify the instruction content. For example, if the user feels that "chest compression is difficult," a simpler method can be suggested based on the feedback. Also, the instruction unit can monitor whether the user is performing appropriate treatment and provide additional instructions as necessary. For example, if the depth of chest compression is insufficient, an instruction such as "Please compress a little deeper" is provided. This allows the instruction unit to provide the user with prompt and appropriate feedback, maximizing the effectiveness of the life-saving measures. Furthermore, the instruction unit can reliably transmit information using multiple communication means. For example, important information can be reliably delivered by using not only smartphone notifications, but also voice calls, SMS, emails, and the like. This allows the instruction unit to provide the user with prompt and reliable instructions for actions, maximizing the effectiveness of the life-saving measures.

[0063] The reception unit can specify the type of information to be input by the user. The reception unit, for example, specifies the type of information to be input by the user. For example, symptom information, location information, contact information, etc. can be specified. This improves the consistency and accuracy of the information by specifying the type and format of the information to be input by the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the type of information to be input by the user to the AI, and the AI ​​can specify the type of information.

[0064] The analysis unit can analyze information using a trained model. The analysis unit, for example, analyzes information using a trained model. Trained models include, for example, neural networks, decision trees, and the like, but are not limited to such examples. For example, a neural network has learned a large amount of data and has advanced analytical capabilities. A decision tree hierarchically classifies data and derives an analysis result. As a result, by using the trained model, the accuracy of analyzing information is improved. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs a trained model into a generation AI, and the generation AI can analyze information.

[0065] The instruction unit can provide feedback. The instruction unit provides, for example, feedback. The feedback includes, for example, a text message, a voice instruction, and the like, but is not limited to such examples. For example, the instruction unit can provide feedback in real time. For example, if the rhythm of chest compressions is not appropriate, the instruction unit instructs the user to correct the rhythm. Also, if airway maintenance is insufficient, the instruction unit prompts the user to try again. In this way, by providing feedback in real time, the user is assisted in carrying out appropriate life-saving measures. A part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the content of the feedback to AI, and the AI ​​can provide the feedback.

[0066] The analysis unit can instruct the method and rhythm of chest compression when cardiopulmonary resuscitation is necessary. For example, the analysis unit instructs the method and rhythm of chest compression when cardiopulmonary resuscitation is necessary. The method and rhythm of chest compression include, for example, the depth of compression and the speed of compression, but are not limited to such examples. For example, the analysis unit instructs the depth of compression to be about 5 cm. The analysis unit can also instruct the speed of compression to be 100 to 120 times per minute. This supports appropriate life-saving measures by providing specific instructions when cardiopulmonary resuscitation is necessary. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the method and rhythm of cardiopulmonary resuscitation to the generation AI, and the generation AI can provide specific instructions.

[0067] The analysis unit can instruct the method of tilting the head back or lifting the chin when airway management is required. For example, the analysis unit instructs the method of tilting the head back or lifting the chin when airway management is required. The method of tilting the head back or lifting the chin includes, for example, the position of the hand and the way of applying force, but is not limited to such examples. For example, the analysis unit instructs the user to place the hand on the forehead and lift the chin when tilting the head back. The analysis unit can also instruct the user to appropriately adjust the force of lifting the chin when lifting the chin. This supports appropriate life-saving measures by showing a specific method when airway management is required. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the method of airway management to the generation AI, and the generation AI can provide specific instructions.

[0068] The reception unit can estimate the user's emotion and adjust the order and contents of questions based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the order and contents of questions based on the estimated user's emotion. For example, facial expression recognition, voice analysis, text analysis, and other technologies are used for emotion estimation, but the examples are not limited to these. For example, when the user is in a panicked state, the reception unit starts with simple questions to calm the user. Also, when the user is calm, the reception unit can quickly ask detailed questions. Furthermore, when the user is nervous, the reception unit can proceed while asking questions to relax the user. This helps the user to answer appropriately by adjusting the order and contents of questions according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the examples are not limited to these. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotions into the AI, which can then adjust the order and content of the questions.

[0069] The reception unit can analyze the user's past emergency response history and select the optimal question format. The reception unit, for example, analyzes the user's past emergency response history and selects the optimal question format. The emergency response history includes, for example, past response records, response results, etc., but is not limited to such examples. For example, if the user has experience performing cardiopulmonary resuscitation in the past, the reception unit asks questions based on that experience. In addition, if the user has never faced an emergency in the past, the reception unit can start with basic questions. Furthermore, if the user has a history of responding to a specific emergency in the past, the reception unit can ask questions based on that history. As a result, the accuracy of the questions is improved by selecting the optimal question format based on the user's past emergency response history. Part or all of the above-mentioned 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 emergency response history into AI, and the AI ​​can select the optimal question format.

[0070] The reception unit can analyze the user's current environmental sound and select an appropriate question when asking a question. The reception unit, for example, analyzes the user's current environmental sound and selects an appropriate question when asking a question. The environmental sound includes, for example, a noise level, detection of a specific sound, and the like, but is not limited to such examples. For example, when the surroundings are noisy, the reception unit asks a concise and clear question. Also, when the surroundings are quiet, the reception unit can ask a detailed question. Furthermore, when the surrounding sound indicates an emergency, the reception unit can ask a question based on the sound. As a result, by selecting an appropriate question based on the user's current environmental sound, the accuracy of the question is improved. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's current environmental sound into AI, and the AI ​​can select an appropriate question.

[0071] The reception unit can estimate the user's emotion and adjust the difficulty of the question based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the difficulty of the question based on the estimated user's emotion. For example, facial expression recognition, voice analysis, text analysis, and other technologies are used for estimating the emotion, but the present invention is not limited to such examples. For example, when the user is in a panicked state, the reception unit starts with simple questions. Also, when the user is calm, the reception unit can quickly ask detailed questions. Furthermore, when the user is nervous, the reception unit can proceed by inserting questions to relax the user. This adjusts the difficulty of the question according to the user's emotion, thereby supporting the user to answer appropriately. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the present invention is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion to an AI, and the AI ​​can adjust the difficulty of the question.

[0072] The reception unit can ask a question corresponding to an emergency specific to the region, taking into consideration the geographical location information of the user, when asking a question. For example, the reception unit asks a question corresponding to an emergency specific to the region, taking into consideration the geographical location information of the user, when asking a question. The geographical location information includes, for example, GPS data, location information services, and the like, but is not limited to such examples. For example, the reception unit asks a question about drowning when the user is at the beach. Also, the reception unit can ask a question about falls and slips when the user is in the mountains. Furthermore, the reception unit can ask a question about traffic accidents when the user is in an urban area. This improves the accuracy of the question by asking a question corresponding to an emergency specific to the region based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit inputs the geographical location information of the user to AI, and the AI ​​can ask a question corresponding to an emergency specific to the region.

[0073] The reception unit can analyze the social media activity of the user and acquire related information when a question is asked. The reception unit, for example, analyzes the social media activity of the user and acquires related information when a question is asked. The social media activity includes, for example, the content of the post, the number of likes, and the like, but is not limited to such examples. For example, the reception unit infers the current situation from the content recently posted by the user and asks a question. In addition, the reception unit can suggest emergency contacts from the user's social media friend list. Furthermore, the reception unit can infer the user's experience with an emergency from the content of the user's past posts and ask a question. As a result, the accuracy of the question is improved by acquiring related information based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI, and the AI ​​can acquire related information.

[0074] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the analysis algorithm based on the estimated user's emotion. For example, facial expression recognition, voice analysis, text analysis, and other technologies are used to estimate the emotion, but the examples are not limited to these. For example, the analysis unit performs a concise and quick analysis when the user is in a panicked state. Furthermore, the analysis unit can perform a detailed analysis when the user is calm. Furthermore, the analysis unit can perform an analysis to relax the user when the user is nervous. This improves the accuracy of the analysis by adjusting the analysis algorithm according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to these examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion into the AI, and the AI ​​can adjust the analysis algorithm.

[0075] The analysis unit can improve the accuracy of the analysis by referring to past emergency data during the analysis. The analysis unit can improve the accuracy of the analysis by referring to past emergency data during the analysis. The emergency data includes, for example, past emergency records, response results, etc., but is not limited to such examples. For example, the analysis unit performs the analysis by referring to past cardiopulmonary resuscitation data. The analysis unit can also perform the analysis by referring to past airway management data. Furthermore, the analysis unit can perform the analysis by referring to past emergency response data. As a result, the accuracy of the analysis is improved by referring to the past emergency data. A part 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 past emergency data into AI, and the AI ​​can improve the accuracy of the analysis.

[0076] The analysis unit can acquire the biological information of the patient in real time during the analysis and reflect it in the analysis. The analysis unit can acquire the biological information of the patient in real time during the analysis and reflect it in the analysis. The biological information includes, for example, heart rate, blood pressure, body temperature, etc., but is not limited to such examples. For example, the analysis unit can acquire the heart rate of the patient in real time and reflect it in the analysis. In addition, the analysis unit can acquire the respiratory rate of the patient in real time and reflect it in the analysis. Furthermore, the analysis unit can acquire the blood pressure of the patient in real time and reflect it in the analysis. Thereby, by acquiring the biological information of the patient in real time, the accuracy of the analysis is improved. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the biological information of the patient to AI, and the AI ​​can reflect it in the analysis.

[0077] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. For example, facial expression recognition, voice analysis, text analysis, and other technologies are used for emotion estimation, but the examples are not limited to these. For example, when the user is in a panicked state, the analysis unit provides a simple and highly visible display method. Also, when the user is calm, the analysis unit can provide a display method including detailed information. Furthermore, when the user is nervous, the analysis unit can provide a display method for relaxing the user. This helps the user to properly understand by adjusting the display method of the analysis result according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the examples are not limited to these. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotions into the AI, which can then adjust how the analysis results are displayed.

[0078] The analysis unit can perform an analysis corresponding to an emergency specific to the region, taking into account the geographical location information of the patient during the analysis. The analysis unit, for example, performs an analysis corresponding to an emergency specific to the region, taking into account the geographical location information of the patient during the analysis. The geographical location information includes, for example, GPS data, location information services, and the like, but is not limited to such examples. For example, the analysis unit performs an analysis related to drowning when the patient is at the beach. Furthermore, the analysis unit can perform an analysis related to falls and slips when the patient is in the mountains. Furthermore, the analysis unit can perform an analysis related to traffic accidents when the patient is in an urban area. This improves the accuracy of the analysis by performing an analysis corresponding to an emergency specific to the region based on the geographical location information of the patient. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the patient to AI, and the AI ​​can perform an analysis corresponding to an emergency specific to the region.

[0079] The analysis unit can improve the accuracy of the analysis by referring to the patient's past medical history during the analysis. The analysis unit can improve the accuracy of the analysis by referring to the patient's past medical history during the analysis, for example. The medical history includes, for example, past diagnosis results, treatment history, etc., but is not limited to such examples. For example, the analysis unit performs the analysis by referring to the patient's past cardiopulmonary resuscitation history. In addition, the analysis unit can perform the analysis by referring to the patient's past airway management history. Furthermore, the analysis unit can perform the analysis by referring to the patient's past emergency response history. As a result, the accuracy of the analysis is improved by referring to the patient's past medical history. Part 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 patient's past medical history into AI, and the AI ​​can improve the accuracy of the analysis.

[0080] The instruction unit can estimate the user's emotion and adjust the way of expressing the instruction based on the estimated user's emotion. The instruction unit, for example, estimates the user's emotion and adjusts the way of expressing the instruction based on the estimated user's emotion. For example, facial expression recognition, voice analysis, text analysis, and other technologies are used for estimating the emotion, but the examples are not limited to these. For example, when the user is in a panicked state, the instruction unit can provide a concise and clear instruction. Furthermore, when the user is calm, the instruction unit can provide a detailed instruction. Furthermore, when the user is nervous, the instruction unit can provide an instruction to relax the user. This adjusts the way of expressing the instruction according to the user's emotion, thereby supporting the user to be able to properly understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the examples are not limited to these. A part or all of the above-mentioned processing in the instruction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the instruction unit can input the user's emotions into the AI, which can then adjust the way the instructions are expressed.

[0081] The instruction unit can select the optimal instruction method by referring to the user's past emergency response history when giving an instruction. For example, the instruction unit selects the optimal instruction method by referring to the user's past emergency response history when giving an instruction. The emergency response history includes, for example, past response records, response results, etc., but is not limited to such examples. For example, if the user has experience performing cardiopulmonary resuscitation in the past, the instruction unit gives instructions based on that experience. In addition, if the user has never faced an emergency in the past, the instruction unit can start with basic instructions. Furthermore, if the user has a history of responding to a specific emergency in the past, the instruction unit can give instructions based on that history. As a result, the accuracy of the instructions is improved by selecting the optimal instruction method based on the user's past emergency response history. Part or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input the user's past emergency response history into AI, and the AI ​​can select the optimal instruction method.

[0082] The instruction unit can analyze the user's current environmental sound at the time of instruction and select an appropriate instruction. The instruction unit can analyze the user's current environmental sound at the time of instruction and select an appropriate instruction. The environmental sound includes, for example, a noise level, detection of a specific sound, and the like, but is not limited to such examples. For example, the instruction unit can provide a concise and clear instruction when the surroundings are noisy. Also, the instruction unit can provide a detailed instruction when the surroundings are quiet. Furthermore, when the surrounding sound indicates an emergency, the instruction unit can provide an instruction based on the sound. This improves the accuracy of the instruction by selecting an appropriate instruction based on the user's current environmental sound. Part or all of the above-mentioned processing in the instruction unit can be performed, for example, using AI or can be performed without using AI. For example, the instruction unit can input the user's current environmental sound to AI, and the AI ​​can select an appropriate instruction.

[0083] The instruction unit can estimate the user's emotion and adjust the level of detail of the instruction based on the estimated user's emotion. The instruction unit can estimate the user's emotion and adjust the level of detail of the instruction based on the estimated user's emotion, for example. For estimating the emotion, for example, facial expression recognition, voice analysis, text analysis, and other technologies are used, but the examples are not limited to these. For example, when the user is in a panicked state, the instruction unit can provide concise and clear instructions. Furthermore, when the user is calm, the instruction unit can provide detailed instructions. Furthermore, when the user is nervous, the instruction unit can provide instructions to relax the user. This adjusts the level of detail of the instruction according to the user's emotion, thereby supporting the user to be able to properly understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the examples are not limited to these. A part or all of the above-mentioned processing in the instruction unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the instruction unit can input the user's emotion to an AI, and the AI ​​can adjust the level of detail of the instruction.

[0084] The instruction unit can give an instruction to respond to an emergency situation specific to the region, taking into consideration the geographical location information of the user when giving an instruction. The instruction unit, for example, gives an instruction to respond to an emergency situation specific to the region, taking into consideration the geographical location information of the user when giving an instruction. The geographical location information includes, for example, GPS data, location information services, and the like, but is not limited to such examples. For example, the instruction unit gives an instruction regarding drowning when the user is at the beach. Also, the instruction unit can give an instruction regarding a fall or a slip when the user is in the mountains. Furthermore, the instruction unit can give an instruction regarding a traffic accident when the user is in an urban area. As a result, by giving an instruction to respond to an emergency situation specific to the region based on the geographical location information of the user, the accuracy of the instruction is improved. A part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the geographical location information of the user to AI, and the AI ​​can give an instruction to respond to an emergency situation specific to the region.

[0085] The instruction unit can analyze the social media activity of the user at the time of instruction and provide related information. The instruction unit can, for example, analyze the social media activity of the user at the time of instruction and provide related information. Social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to such examples. For example, the instruction unit infers the current situation from the content recently posted by the user and provides instructions. In addition, the instruction unit can suggest emergency contacts from the user's social media friend list. Furthermore, the instruction unit can infer the user's experience with an emergency from the content of past posts and provide instructions. This improves the accuracy of instructions by providing related information based on the user's social media activity. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the user's social media activity into AI, and the AI ​​can provide related information.

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

[0087] The reception unit can learn the user's behavioral patterns in emergency situations and adjust the optimal timing of questions. For example, the reception unit can analyze the timing at which the user can respond most calmly from past data and ask questions at that timing. The reception unit can also optimize the order of questions based on how the user responded to specific questions in the past. Furthermore, the reception unit can learn how the user acted in emergency situations in the past and adjust the content of questions based on the actions. This can help the user provide information most effectively.

[0088] The analysis unit monitors the patient's vital signs in real time, and can immediately issue an alert if an abnormality is detected. For example, an alert is issued if the heart rate drops suddenly or blood pressure becomes abnormally high. An alert can also be issued if the respiratory rate falls outside the normal range. Furthermore, an alert can also be issued if there is a sudden change in body temperature. This allows for a rapid response when the patient's condition suddenly changes.

[0089] The instruction unit can select an optimal instruction method based on the user's past emergency response history. For example, if the user has experience performing cardiopulmonary resuscitation in the past, instructions can be given based on that experience. Also, if the user has never faced an emergency in the past, basic instructions can be given first. Furthermore, if the user has a history of responding to a specific emergency in the past, instructions can be given based on that history. This improves the accuracy of instructions by selecting an optimal instruction method based on the user's past emergency response history.

[0090] The reception unit can analyze the user's current environmental sounds and select an appropriate question. For example, if the surroundings are noisy, a simple and clear question can be asked. If the surroundings are quiet, a detailed question can be asked. Furthermore, if the surrounding sounds indicate an emergency, a question based on the sound can be asked. This improves the accuracy of the questions by selecting an appropriate question based on the user's current environmental sounds.

[0091] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past medical history. For example, the analysis can be performed by referring to the patient's past cardiopulmonary resuscitation history. The analysis can also be performed by referring to the patient's past airway management history. Furthermore, the analysis can be performed by referring to the patient's past emergency response history. In this way, the accuracy of the analysis can be improved by referring to the patient's past medical history.

[0092] The reception unit can estimate the user's emotions and adjust the order and content of questions based on the estimated user's emotions. For example, if the user is in a panic, the reception unit can start with simple questions to calm the user down. If the user is calm, detailed questions can be asked quickly. If the user is nervous, questions to relax the user can be interspersed as the conversation proceeds. In this way, the reception unit can assist the user in providing appropriate answers by adjusting the order and content of questions according to the user's emotions.

[0093] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. For example, if the user is in a panicked state, a concise and quick analysis can be performed. If the user is calm, a detailed analysis can be performed. Furthermore, if the user is nervous, an analysis for relaxing the user can be performed. In this way, the analysis algorithm can be adjusted according to the user's emotions, thereby improving the accuracy of the analysis.

[0094] The instruction unit can estimate the user's emotions and adjust the way instructions are expressed based on the estimated user's emotions. For example, if the user is in a panic, the instruction unit can provide concise and clear instructions. If the user is calm, the instruction unit can provide detailed instructions. If the user is nervous, the instruction unit can provide instructions to relax the user. In this way, the instruction unit can support the user in understanding appropriately by adjusting the way instructions are expressed depending on the user's emotions.

[0095] The instruction unit can estimate the user's emotion and adjust the level of detail of the instruction based on the estimated user's emotion. For example, if the user is in a panic, the instruction unit can provide concise and clear instructions. If the user is calm, the instruction unit can provide detailed instructions. If the user is nervous, the instruction unit can provide instructions to relax the user. In this way, the level of detail of the instruction can be adjusted according to the user's emotion, thereby supporting the user to appropriately understand the instruction.

[0096] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is in a panicked state, a simple and highly visible display method can be provided. If the user is calm, a display method including detailed information can be provided. Furthermore, if the user is nervous, a display method for relaxing the user can be provided. In this way, the display method of the analysis results can be adjusted according to the user's emotions, thereby supporting the user in understanding appropriately.

[0097] The process flow of the second embodiment will be briefly described below.

[0098] Step 1: The reception unit receives information from a user. The information from the user includes, for example, text information, voice information, image information, etc. The reception unit can specify the type and format of information to be input by the user, and can specify, for example, symptom information, location information, contact information, etc. Step 2: The analysis unit uses the trained model to analyze the information received by the reception unit. The trained model includes, for example, a neural network, a decision tree, etc. For example, the analysis unit can provide specific instructions on the method and rhythm of chest compressions when cardiopulmonary resuscitation is required, and can provide instructions on how to tilt the head back and lift the chin when airway management is required. Step 3: The instruction unit instructs the user on the action suggested by the analysis unit and provides feedback to the user, such as by text message, voice instruction, etc. The instruction unit provides real-time feedback, such as instructing the user to correct the rhythm if the chest compression rhythm is not appropriate, and encouraging the user to try again if the airway is not sufficiently secured.

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

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

[0101] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and instruction 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 is realized by the control unit 46A of the smart device 14 and receives information from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information using a trained model. The instruction unit is realized, for example, by the control unit 46A of the smart device 14 and provides instructions to the user based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

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

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

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

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

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

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

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

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

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

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

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

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

[0118] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and instruction 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 is realized by the control unit 46A of the smart glasses 214 and receives information from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information using a trained model. The instruction unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides instructions to the user based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

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

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

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

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

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

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

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

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

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

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

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

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

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

[0134] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and instruction 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 is realized by the control unit 46A of the headset type terminal 314 and receives information from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information using a trained model. The instruction unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides instructions to the user based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

[0146] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

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

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

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

[0151] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and instruction unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives information from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information using a trained model. The instruction unit is realized, for example, by the control unit 46A of the robot 414 and provides instructions to the user based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0153] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

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

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

[0156] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

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

[0158] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

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

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

[0161] In addition, 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 upon request from the data processing device 12.

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

[0163] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

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

[0165] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

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

[0168] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

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

[0170] (Appendix 1) A reception unit that receives information from a user; an analysis unit that analyzes the information received by the reception unit and proposes a treatment; An instruction unit that instructs a user on the action proposed by the analysis unit and provides feedback to the user. A system characterized by: (Appendix 2) The reception unit is Specify the type of information the user must enter 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze information using a trained model 2. The system of claim 1, (Appendix 4) The instruction unit is Provide feedback 2. The system of claim 1, (Appendix 5) The analysis unit is If CPR is required, instruct the technique and rhythm of chest compressions 2. The system of claim 1, (Appendix 6) The analysis unit is If airway management is required, instruct patient on head tilt and chin lift techniques 2. The system of claim 1, (Appendix 7) The reception unit is Infer user sentiment and adjust question order and content based on the inferred sentiment 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past emergency response history and select the most appropriate question format 2. The system of claim 1, (Appendix 9) The reception unit is When asking a question, the system analyzes the user's current ambient sound and selects the appropriate question. 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the user's emotions and adjust the difficulty of questions based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The reception unit is When asking questions, the system takes into account the user's geographic location to ask questions that address local emergencies 2. The system of claim 1, (Appendix 12) The reception unit is When you ask a question, analyze your social media activity to get relevant information 2. The system of claim 1, (Appendix 13) The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, past emergency data is referenced to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, the patient's biological information is acquired in real time and reflected in the analysis. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, the geographic location of the patient is taken into account to provide analysis that responds to localized emergencies. 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, the patient's past medical history is referenced to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 19) The instruction unit is Estimating a user's emotion and adjusting the way instructions are expressed based on the estimated user's emotion 2. The system of claim 1, (Appendix 20) The instruction unit is When issuing instructions, the system selects the most appropriate instruction method by referring to the user's past emergency response history. 2. The system of claim 1, (Appendix 21) The instruction unit is When an instruction is given, the system analyzes the user's current ambient sound and selects an appropriate instruction. 2. The system of claim 1, (Appendix 22) The instruction unit is To estimate a user's emotion and adjust the level of detail of an instruction based on the estimated user's emotion. 2. The system of claim 1, (Appendix 23) The instruction unit is When issuing instructions, the system takes into account the user's geographic location to provide instructions to respond to local emergency situations. 2. The system of claim 1, (Appendix 24) The instruction unit is When instructed, analyze your social media activity and provide you with relevant information 2. The system of claim 1, [Explanation of symbols]

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

Claims

1. A reception unit that receives information from a user; an analysis unit that analyzes the information received by the reception unit and proposes a treatment; An instruction unit that instructs a user on the action proposed by the analysis unit and provides feedback to the user. A system characterized by:

2. The reception unit is Specify the type of information the user must enter 2. The system of claim 1 .

3. The analysis unit is Analyze information using a trained model 2. The system of claim 1 .

4. The instruction unit is Provide feedback 2. The system of claim 1 .

5. The analysis unit is If CPR is required, instruct the technique and rhythm of chest compressions 2. The system of claim 1 .

6. The analysis unit is If airway management is required, instruct patient on head tilt and chin lift techniques 2. The system of claim 1 .

7. The reception unit is Infer user sentiment and adjust question order and content based on the inferred sentiment 2. The system of claim 1 .

8. The reception unit is Analyze the user's past emergency response history and select the most appropriate question format 2. The system of claim 1 .

9. The reception unit is When asking a question, the system analyzes the user's current ambient sound and selects the appropriate question.

2. The system of claim 1 .

10. The reception unit is Estimate the user's emotions and adjust the difficulty of questions based on the estimated user emotions 2. The system of claim 1 .

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