System

Abstract

An object of a system according to an embodiment is to provide information for performing quick and accurate medical treatment in an emergency.SOLUTION: A system includes a collection unit, an analysis unit, a provision unit, and an advice unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The providing unit provides information based on the result obtained by the analysis unit. The advice unit gives advice based on the information provided by the providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not provide sufficient information to enable prompt and accurate medical response in emergencies, and there is room for improvement.

[0005] The system according to the embodiment aims to provide information for prompt and accurate medical response in an emergency. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and an advice unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides information based on the results obtained by the analysis unit. The advice unit gives advice based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide information for prompt and accurate medical response in an emergency. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A medical response system according to an embodiment of the present invention enables rapid and accurate medical response in emergencies. The medical response system assesses symptoms using photos and videos via AI and provides specific and accurate information to doctors and emergency teams. Furthermore, the AI ​​assesses symptoms and provides appropriate advice and guidelines to participants. This system enables rapid medical response in emergencies, protecting the health and lives of patients. Furthermore, accurate information allows medical professionals to take appropriate countermeasures. For example, the medical response system records a patient's symptoms using photos and videos in emergencies. For example, a device is used to record detailed information about the patient's symptoms. For example, video recording of injuries and breathing patterns is performed. This information is then input into AI. The medical response system then analyzes the input photos and videos to assess symptoms. The AI ​​uses image recognition technology to analyze the patient's symptoms and detect abnormalities. For example, the AI ​​can assess the extent of bleeding and abnormal breathing. This allows specific and accurate information to be provided to doctors and emergency teams. Furthermore, the medical response system provides appropriate advice and guidelines to participants based on the AI's assessment of symptoms. For example, if the bleeding is severe, the system instructs how to apply pressure to stop the bleeding. Also, if breathing is difficult, the system instructs the appropriate position. In this way, participants can respond appropriately with support from AI. This enables the medical response system to provide prompt medical care in emergencies. By recording the patient's symptoms in detail and analyzing them with AI, specific and accurate information can be provided to doctors and emergency responders. Furthermore, with the support of AI, participants can receive appropriate advice and guidelines for action, enabling prompt and appropriate responses. This helps protect the health and lives of patients. For example, at the scene of a traffic accident, a device can take a photo of the injured person's symptoms and have AI analyze them, providing specific information to doctors and emergency responders. First aid can also be administered according to AI advice. This enables prompt medical response and protects the health and lives of patients. This enables the medical response system to provide prompt and accurate medical care in emergencies.For example, by recording detailed patient symptoms and analyzing them with AI, it is possible to provide doctors and emergency responders with specific and accurate information. Furthermore, with the support of AI, participants can receive appropriate advice and guidelines for action, enabling them to respond quickly and appropriately. This will help protect the health and lives of patients.

[0029] The medical response system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and an advice unit. The collection unit collects information. The information includes, but is not limited to, text data, image data, and sensor data. The collection unit can collect information using, for example, a device. Examples of the device include a smartphone and a wearable device. For example, the collection unit may collect information by taking photos or videos using a smartphone camera. The collection unit can also collect biometric data using a sensor in a wearable device. For example, the collection unit collects data such as heart rate and body temperature. The analysis unit analyzes the information collected by the collection unit. Examples of analysis include, but are not limited to, statistical analysis and the use of machine learning algorithms. For example, the analysis unit analyzes the information using image recognition technology. Image recognition technology includes deep learning and computer vision technology. For example, the analysis unit may analyze image data using deep learning to detect abnormalities. The analysis unit may also analyze video data using computer vision technology to determine symptoms. The provision unit provides information based on the results obtained by the analysis unit. Examples of information provision include, but are not limited to, emails, dashboard displays, and the like. For example, the providing unit provides information to doctors and emergency teams. Doctors and emergency teams include hospital doctors, ambulance personnel, and the like. For example, the providing unit provides information to doctors using emails. The providing unit can also provide information to emergency teams using a dashboard. The advising unit gives advice based on the information provided by the providing unit. Examples of advice include, but are not limited to, medical advice, guidelines for action, and the like. For example, the advising unit gives advice to participants. Participants include patients, medical staff, and the like. For example, the advising unit instructs patients on how to stop bleeding by applying pressure. The advising unit can also instruct medical staff on an appropriate position. This enables the medical response system according to the embodiment to provide prompt and accurate medical response in an emergency.For example, by recording detailed patient symptoms and analyzing them with AI, it is possible to provide doctors and emergency responders with specific and accurate information. Furthermore, with the support of AI, participants can receive appropriate advice and guidelines for action, enabling them to respond quickly and appropriately. This will help protect the health and lives of patients.

[0030] The collection unit can collect information using a device. Examples of the device include, but are not limited to, a smartphone and a wearable device. For example, the collection unit can collect information by taking photos or videos using a smartphone camera. The collection unit can also collect biometric data using a sensor in a wearable device. For example, the collection unit collects data such as heart rate and body temperature. This allows detailed information to be collected quickly using the device. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input image data captured by a smartphone camera into a generation AI and cause the generation AI to execute a process of extracting information from the image data.

[0031] The analysis unit can analyze information using image recognition technology. Image recognition technology includes, but is not limited to, deep learning and computer vision technology. For example, the analysis unit can analyze image data and detect abnormalities using deep learning. The analysis unit can also analyze video data and determine symptoms using computer vision technology. For example, the analysis unit can determine the degree of bleeding from the image data using deep learning. The analysis unit can also detect abnormal breathing from the video data using computer vision technology. The analysis unit can also determine the presence or absence of a fracture from the image data using deep learning. This improves the accuracy of information analysis by using image recognition technology. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image data to a generation AI, which analyzes the image data and detects abnormalities.

[0032] The providing unit can provide information to doctors and emergency teams. Doctors and emergency teams include, but are not limited to, hospital doctors and ambulance crews. The providing unit can provide information to doctors, for example, by email. The providing unit can also provide information to emergency teams using a dashboard. For example, the providing unit can inform doctors of the patient's symptoms in detail using email. The providing unit can also provide emergency teams with the patient's location information using the dashboard. The providing unit can also provide doctors with the patient's past medical history using email. This makes it possible to provide doctors and emergency teams with specific and accurate information. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input information obtained by the analysis unit into a generating AI, which can organize the information and provide it to doctors and emergency teams.

[0033] The advising unit can provide advice to participants. Examples of participants include, but are not limited to, patients and medical staff. For example, the advising unit instructs the patient on how to apply pressure to stop bleeding. The advising unit can also instruct the medical staff on an appropriate position. For example, the advising unit instructs the patient on how to apply pressure to stop bleeding when bleeding is severe. The advising unit can also instruct the medical staff on an appropriate position when breathing is difficult. The advising unit can also instruct the patient to rest when a fracture is suspected. This makes it possible to provide appropriate advice and guidelines for action to participants. Some or all of the above-mentioned processing in the advising unit may be performed using, for example, AI, or may be performed without using AI. For example, the advising unit can input information provided by the providing unit into a generating AI, which can generate advice and provide it to participants.

[0034] The collection unit can analyze the patient's past medical history and select the optimal information collection method. For example, the collection unit can refer to the patient's past medical history and prioritize information collection for specific symptoms. The collection unit can also quickly collect necessary information based on the patient's past treatment history. The collection unit can also select information to collect by taking into account the patient's allergy information. This makes it possible to collect optimal information by taking into account the patient's past medical history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's past medical history data into the generation AI, which can then select the optimal information collection method.

[0035] When collecting information, the collection unit can filter the information based on the patient's current health condition and environment. The collection unit filters the information to be collected based on, for example, the patient's current body temperature and blood pressure. The collection unit can also adjust the information to be collected depending on the patient's environment (indoors, outdoors, etc.). The collection unit can also select the information to be collected based on the patient's current activity status (resting, exercising, etc.). This makes it possible to optimize the information to be collected depending on the patient's current health condition and environment. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's health condition data into the generation AI, which then filters the information.

[0036] When collecting information, the collection unit can select the optimal collection means depending on the type of device. For example, when a smartphone is used, the collection unit collects photos and videos using the camera function. Furthermore, when a wearable device is used, the collection unit can also collect sensor information. Furthermore, when a tablet is used, the collection unit can also collect detailed information using the large screen. This enables efficient information collection by selecting the optimal collection means depending on the type of device. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can have the generation AI select the collection means depending on the type of device.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the patient. For example, the collection unit prioritizes collecting information about medical facilities in the area where the patient is located. The collection unit can also adjust the information to be collected by taking into account meteorological information in the area where the patient is located. The collection unit can also select information to be collected based on traffic information in the area where the patient is located. In this way, highly relevant information can be prioritized by taking into account the geographical location information of the patient. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the patient to the generation AI, which can select highly relevant information.

[0038] When collecting information, the collection unit can analyze the patient's social media activities and collect relevant information. For example, the collection unit collects information about health conditions posted by the patient on social media. The collection unit can also collect relevant medical information from the patient's social media activities. The collection unit can also collect relevant information by referring to the activities of the patient's friends on social media. In this way, relevant information can be collected efficiently by analyzing the patient's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's social media data into a generation AI, which then collects relevant information.

[0039] When collecting information, the collection unit can customize the collection method by reflecting the patient's past feedback. For example, the collection unit adjusts the information to be collected based on feedback provided by the patient in the past. The collection unit can also optimize the collection method from the patient's past feedback. The collection unit can also determine the priority of the information to be collected by referring to the patient's past feedback. In this way, the collection method can be optimized by reflecting the patient's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the patient's past feedback data into the generation AI, which can customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a simplified analysis on general information. The analysis unit can also perform a quick analysis on highly urgent information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI, which can then adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies an image recognition algorithm to image data. The analysis unit can also apply a natural language processing algorithm to text data. The analysis unit can also apply a voice recognition algorithm to voice data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to a generation AI, which then applies an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. The analysis unit, for example, corrects the current analysis result based on the patient's past analysis results. The analysis unit can also optimize the analysis algorithm from the patient's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the patient's past analysis results. In this way, the accuracy of the analysis is improved by referring to the patient's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the patient's past analysis result data into the generation AI, which can improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was collected. For example, the analysis unit prioritizes analyzing the latest information. The analysis unit can also postpone analyzing older information. The analysis unit can also prioritize analyzing information with high urgency. This enables efficient analysis by determining the priority of analysis based on the time when the information was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the information was collected into the generation AI, and the generation AI can determine the priority of analysis.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also optimize the order of analysis based on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI, and the generation AI can adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the patient's level of expertise. For example, if the patient has technical expertise, the analysis unit uses detailed technical terminology. Furthermore, if the patient does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can also adjust the way in which the analysis results are expressed according to the patient's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the patient's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input the patient's level of expertise data into the generation AI, which can then adjust the use of technical terminology in the analysis.

[0046] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit provides a detailed explanation for important information. The providing unit can also provide a simplified explanation for general information. The providing unit can also provide information with high urgency quickly. This enables efficient information provision by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information importance data to the generating AI, and the generating AI can adjust the level of detail of the information provided.

[0047] When providing information, the providing unit can apply different providing algorithms depending on the category of information. For example, the providing unit provides image data in a visually easy-to-understand manner. The providing unit can also provide text data in an easy-to-read format. The providing unit can also provide audio data in an easy-to-listen-to manner. This improves the accuracy of information provision by applying an appropriate providing algorithm depending on the category of information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information category data to a generation AI, which then applies an appropriate providing algorithm.

[0048] When providing information, the providing unit can improve the accuracy of the information provision by referring to the patient's past provision results. The providing unit, for example, corrects the current provision method based on the patient's past provision results. The providing unit can also optimize the provision algorithm from the patient's past provision results. The providing unit can also improve the accuracy of the information provision by referring to the patient's past provision results. In this way, the accuracy of the information provision is improved by referring to the patient's past provision results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the patient's past provision result data into the generating AI, which can improve the accuracy of the information provision.

[0049] When providing information, the providing unit can determine the priority of provision based on the time when the information was collected. For example, the providing unit can provide the latest information preferentially. The providing unit can also postpone providing older information. The providing unit can also provide information with higher urgency preferentially. This enables efficient information provision by determining the priority of provision based on the time when the information was collected. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information collection time data to the generating AI, and the generating AI can determine the priority of provision.

[0050] The providing unit can adjust the order of information provision based on the relevance of the information when providing the information. For example, the providing unit can provide highly relevant information preferentially. The providing unit can also provide less relevant information later. The providing unit can also optimize the order of information provision based on the relevance of the information. This enables efficient information provision by adjusting the order of information provision based on the relevance of the information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information relevance data to a generating AI, and the generating AI can adjust the order of information provision.

[0051] When providing information, the providing unit can adjust the use of technical terminology provided according to the patient's level of expertise. For example, if the patient has technical expertise, the providing unit can use detailed technical terminology. Also, if the patient does not have technical expertise, the providing unit can provide information in simple language. The providing unit can also adjust the way information is expressed according to the patient's level of expertise. This allows more appropriate information to be provided by adjusting the use of technical terminology provided according to the patient's level of expertise. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the patient's level of expertise data into the generating AI, which can then adjust the use of technical terminology provided.

[0052] When giving advice, the advice unit can adjust the level of detail of the advice based on the importance of the information. For example, the advice unit provides detailed advice for important information. The advice unit can also provide simplified advice for general information. The advice unit can also provide quick advice for highly urgent information. In this way, adjusting the level of detail of the advice based on the importance of the information enables efficient advice. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input information importance data to a generation AI, and the generation AI can adjust the level of detail of the advice.

[0053] When giving advice, the advice unit can apply different advice algorithms depending on the category of information. For example, the advice unit provides visually easy-to-understand advice for image data. The advice unit can also provide advice in an easy-to-read format for text data. The advice unit can also provide advice in an easy-to-listen-to manner for audio data. This improves the accuracy of advice by applying an appropriate advice algorithm depending on the category of information. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input information category data to a generation AI, which then applies an appropriate advice algorithm.

[0054] When providing advice, the advice unit can improve the accuracy of the advice by referring to past advice results for the patient. The advice unit, for example, corrects the current advice method based on the past advice results for the patient. The advice unit can also optimize the advice algorithm from the past advice results for the patient. The advice unit can also improve the accuracy of the advice by referring to the past advice results for the patient. In this way, the accuracy of the advice is improved by referring to the past advice results for the patient. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input past advice result data for the patient into the generation AI, which can improve the accuracy of the advice.

[0055] When giving advice, the advice unit can determine the priority of the advice based on the time when the information was collected. For example, the advice unit gives priority to the latest information. The advice unit can also give advice on older information later. The advice unit can also give priority to information with high urgency. In this way, by determining the priority of advice based on the time when the information was collected, efficient advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input data on the time when the information was collected into the generation AI, and the generation AI can determine the priority of the advice.

[0056] When giving advice, the advice unit can adjust the order of advice based on the relevance of the information. For example, the advice unit gives priority to advice on highly relevant information. The advice unit can also postpone advice on less relevant information. The advice unit can also optimize the order of advice based on the relevance of the information. This enables efficient advice by adjusting the order of advice based on the relevance of the information. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input information relevance data to a generation AI, and the generation AI can adjust the order of advice.

[0057] When providing advice, the advice unit can adjust the use of technical terms in the advice depending on the patient's level of expertise. For example, if the patient has technical expertise, the advice unit can use detailed technical terms. Also, if the patient does not have technical expertise, the advice unit can provide advice in simple language. The advice unit can also adjust the way the advice is expressed depending on the patient's level of expertise. This allows more appropriate advice to be provided by adjusting the use of technical terms in the advice depending on the patient's level of expertise. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input patient's level of expertise data into the generation AI, which can then adjust the use of technical terms in the advice.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The collection unit collects audio data from around the patient, and the analysis unit analyzes the audio data to detect signs of an emergency. For example, the collection unit collects abnormal sounds such as screams and crashes occurring around the patient. The collection unit can also collect the patient's breathing and heartbeat sounds, and the analysis unit can analyze the audio data to detect abnormalities. Furthermore, the collection unit can collect environmental sounds from around the patient, and the analysis unit can analyze the environmental sounds to detect signs of an emergency. This makes it possible to detect emergencies more quickly and accurately by using audio data.

[0060] The analysis unit can refer to the patient's past medical data and compare it with current symptoms to detect abnormalities. For example, the analysis unit can refer to the patient's past electrocardiogram data and compare it with current electrocardiogram data to detect abnormalities. The analysis unit can also refer to the patient's past blood test data and compare it with current blood test data to detect abnormalities. Furthermore, the analysis unit can refer to the patient's past image data and compare it with current image data to detect abnormalities. In this way, by utilizing past medical data, more accurate abnormality detection is possible.

[0061] The providing unit can also provide information to the patient's family and emergency contacts. For example, the providing unit can provide information to the patient's family by email or SMS. The providing unit can also provide information by calling the patient's emergency contact. Furthermore, the providing unit can provide information to the patient's family and emergency contacts through a dedicated app. This allows the patient's family and emergency contacts to receive prompt and accurate information.

[0062] The advice unit can provide advice based on the patient's current activity status. For example, if the patient is exercising, the advice unit instructs the patient to stop exercising. Also, if the patient is resting, the advice unit can instruct the patient to maintain an appropriate posture. Furthermore, if the patient is eating, the advice unit can instruct the patient to stop eating. In this way, appropriate advice can be provided according to the patient's current activity status.

[0063] The collection unit can identify the most suitable medical facility based on the patient's current location information and transmit that information to the provision unit. For example, the collection unit collects location information of medical facilities in the patient's area and identifies the nearest medical facility. The collection unit can also identify the most suitable medical facility based on the patient's current location information, taking into account traffic conditions. Furthermore, the collection unit can identify the most suitable medical facility based on the patient's current location information, taking into account the congestion status of the medical facility. This makes it possible to quickly identify an appropriate medical facility by utilizing the patient's current location information.

[0064] The analysis unit can monitor the patient's current health condition in real time and issue an alert if an abnormality is detected. For example, the analysis unit can monitor the patient's heart rate and blood pressure in real time and issue an alert if an abnormality is detected. The analysis unit can also monitor the patient's respiratory condition in real time and issue an alert if an abnormality is detected. Furthermore, the analysis unit can monitor the patient's body temperature in real time and issue an alert if an abnormality is detected. This makes it possible to monitor the patient's health condition and detect abnormalities in real time.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects information. This information includes text data, image data, sensor data, etc. The collection unit collects information using a smartphone or wearable device. For example, the collection unit collects information by taking photos or videos using the smartphone camera. The collection unit also collects biometric data such as heart rate and body temperature using the sensors in the wearable device. Step 2: The analysis unit analyzes the information collected by the collection unit. This analysis can involve the use of statistical analysis and machine learning algorithms. For example, deep learning can be used to analyze image data and detect abnormalities. Computer vision technology can also be used to analyze video data and determine symptoms. Step 3: The providing unit provides information based on the results obtained by the analysis unit. The information provision includes email and dashboard display. For example, the providing unit provides information to doctors via email and to emergency teams via a dashboard. Step 4: The advice unit provides advice based on the information provided by the provision unit. The advice includes medical advice and guidelines for action. For example, the advice unit instructs the patient on how to apply pressure to stop bleeding and instructs medical staff on the appropriate positioning.

[0067] (Example 2) A medical response system according to an embodiment of the present invention enables rapid and accurate medical response in emergencies. The medical response system assesses symptoms using photos and videos via AI and provides specific and accurate information to doctors and emergency teams. Furthermore, the AI ​​assesses symptoms and provides appropriate advice and guidelines to participants. This system enables rapid medical response in emergencies, protecting the health and lives of patients. Furthermore, accurate information allows medical professionals to take appropriate countermeasures. For example, the medical response system records a patient's symptoms using photos and videos in emergencies. For example, a device is used to record detailed information about the patient's symptoms. For example, video recording of injuries and breathing patterns is performed. This information is then input into AI. The medical response system then analyzes the input photos and videos to assess symptoms. The AI ​​uses image recognition technology to analyze the patient's symptoms and detect abnormalities. For example, the AI ​​can assess the extent of bleeding and abnormal breathing. This allows specific and accurate information to be provided to doctors and emergency teams. Furthermore, the medical response system provides appropriate advice and guidelines to participants based on the AI's assessment of symptoms. For example, if the bleeding is severe, the system instructs how to apply pressure to stop the bleeding. Also, if breathing is difficult, the system instructs the appropriate position. In this way, participants can respond appropriately with support from AI. This enables the medical response system to provide prompt medical care in emergencies. By recording the patient's symptoms in detail and analyzing them with AI, specific and accurate information can be provided to doctors and emergency responders. Furthermore, with the support of AI, participants can receive appropriate advice and guidelines for action, enabling prompt and appropriate responses. This helps protect the health and lives of patients. For example, at the scene of a traffic accident, a device can take a photo of the injured person's symptoms and have AI analyze them, providing specific information to doctors and emergency responders. First aid can also be administered according to AI advice. This enables prompt medical response and protects the health and lives of patients. This enables the medical response system to provide prompt and accurate medical care in emergencies.For example, by recording detailed patient symptoms and analyzing them with AI, it is possible to provide doctors and emergency responders with specific and accurate information. Furthermore, with the support of AI, participants can receive appropriate advice and guidelines for action, enabling them to respond quickly and appropriately. This will help protect the health and lives of patients.

[0068] The medical response system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and an advice unit. The collection unit collects information. The information includes, but is not limited to, text data, image data, and sensor data. The collection unit can collect information using, for example, a device. Examples of the device include a smartphone and a wearable device. For example, the collection unit may collect information by taking photos or videos using a smartphone camera. The collection unit can also collect biometric data using a sensor in a wearable device. For example, the collection unit collects data such as heart rate and body temperature. The analysis unit analyzes the information collected by the collection unit. Examples of analysis include, but are not limited to, statistical analysis and the use of machine learning algorithms. For example, the analysis unit analyzes the information using image recognition technology. Image recognition technology includes deep learning and computer vision technology. For example, the analysis unit may analyze image data using deep learning to detect abnormalities. The analysis unit may also analyze video data using computer vision technology to determine symptoms. The provision unit provides information based on the results obtained by the analysis unit. Examples of information provision include, but are not limited to, emails, dashboard displays, and the like. For example, the providing unit provides information to doctors and emergency teams. Doctors and emergency teams include hospital doctors, ambulance personnel, and the like. For example, the providing unit provides information to doctors using emails. The providing unit can also provide information to emergency teams using a dashboard. The advising unit gives advice based on the information provided by the providing unit. Examples of advice include, but are not limited to, medical advice, guidelines for action, and the like. For example, the advising unit gives advice to participants. Participants include patients, medical staff, and the like. For example, the advising unit instructs patients on how to stop bleeding by applying pressure. The advising unit can also instruct medical staff on an appropriate position. This enables the medical response system according to the embodiment to provide prompt and accurate medical response in an emergency.For example, by recording detailed patient symptoms and analyzing them with AI, it is possible to provide doctors and emergency responders with specific and accurate information. Furthermore, with the support of AI, participants can receive appropriate advice and guidelines for action, enabling them to respond quickly and appropriately. This will help protect the health and lives of patients.

[0069] The collection unit can collect information using a device. Examples of the device include, but are not limited to, a smartphone and a wearable device. For example, the collection unit can collect information by taking photos or videos using a smartphone camera. The collection unit can also collect biometric data using a sensor in a wearable device. For example, the collection unit collects data such as heart rate and body temperature. This allows detailed information to be collected quickly using the device. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input image data captured by a smartphone camera into a generation AI and cause the generation AI to execute a process of extracting information from the image data.

[0070] The analysis unit can analyze information using image recognition technology. Image recognition technology includes, but is not limited to, deep learning and computer vision technology. For example, the analysis unit can analyze image data and detect abnormalities using deep learning. The analysis unit can also analyze video data and determine symptoms using computer vision technology. For example, the analysis unit can determine the degree of bleeding from the image data using deep learning. The analysis unit can also detect abnormal breathing from the video data using computer vision technology. The analysis unit can also determine the presence or absence of a fracture from the image data using deep learning. This improves the accuracy of information analysis by using image recognition technology. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image data to a generation AI, which analyzes the image data and detects abnormalities.

[0071] The providing unit can provide information to doctors and emergency teams. Doctors and emergency teams include, but are not limited to, hospital doctors and ambulance crews. The providing unit can provide information to doctors, for example, by email. The providing unit can also provide information to emergency teams using a dashboard. For example, the providing unit can inform doctors of the patient's symptoms in detail using email. The providing unit can also provide emergency teams with the patient's location information using the dashboard. The providing unit can also provide doctors with the patient's past medical history using email. This makes it possible to provide doctors and emergency teams with specific and accurate information. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input information obtained by the analysis unit into a generating AI, which can organize the information and provide it to doctors and emergency teams.

[0072] The advising unit can provide advice to participants. Examples of participants include, but are not limited to, patients and medical staff. For example, the advising unit instructs the patient on how to apply pressure to stop bleeding. The advising unit can also instruct the medical staff on an appropriate position. For example, the advising unit instructs the patient on how to apply pressure to stop bleeding when bleeding is severe. The advising unit can also instruct the medical staff on an appropriate position when breathing is difficult. The advising unit can also instruct the patient to rest when a fracture is suspected. This makes it possible to provide appropriate advice and guidelines for action to participants. Some or all of the above-mentioned processing in the advising unit may be performed using, for example, AI, or may be performed without using AI. For example, the advising unit can input information provided by the providing unit into a generating AI, which can generate advice and provide it to participants.

[0073] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, when the user is nervous, the collection unit can quickly collect information and provide it to medical professionals. Furthermore, when the user is relaxed, the collection unit can also collect detailed information and provide data for more accurate analysis. Furthermore, when the user is confused, the collection unit can issue simple instructions and quickly collect the minimum amount of information necessary. This allows for more appropriate information to be collected by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into a generation AI, which can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions.

[0074] The collection unit can analyze the patient's past medical history and select the optimal information collection method. For example, the collection unit can refer to the patient's past medical history and prioritize information collection for specific symptoms. The collection unit can also quickly collect necessary information based on the patient's past treatment history. The collection unit can also select information to collect by taking into account the patient's allergy information. This makes it possible to collect optimal information by taking into account the patient's past medical history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's past medical history data into the generation AI, which can then select the optimal information collection method.

[0075] When collecting information, the collection unit can filter the information based on the patient's current health condition and environment. The collection unit filters the information to be collected based on, for example, the patient's current body temperature and blood pressure. The collection unit can also adjust the information to be collected depending on the patient's environment (indoors, outdoors, etc.). The collection unit can also select the information to be collected based on the patient's current activity status (resting, exercising, etc.). This makes it possible to optimize the information to be collected depending on the patient's current health condition and environment. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's health condition data into the generation AI, which then filters the information.

[0076] When collecting information, the collection unit can select the optimal collection means depending on the type of device. For example, when a smartphone is used, the collection unit collects photos and videos using the camera function. Furthermore, when a wearable device is used, the collection unit can also collect sensor information. Furthermore, when a tablet is used, the collection unit can also collect detailed information using the large screen. This enables efficient information collection by selecting the optimal collection means depending on the type of device. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can have the generation AI select the collection means depending on the type of device.

[0077] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is nervous, the collection unit prioritizes collecting important information. Furthermore, when the user is relaxed, the collection unit can also prioritize collecting detailed information. Furthermore, when the user is confused, the collection unit can prioritize collecting simple information. Thus, by determining the priority of information to be collected according to the user's emotions, important information can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI, which then prioritizes the information.

[0078] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the patient. For example, the collection unit prioritizes collecting information about medical facilities in the area where the patient is located. The collection unit can also adjust the information to be collected by taking into account meteorological information in the area where the patient is located. The collection unit can also select information to be collected based on traffic information in the area where the patient is located. In this way, highly relevant information can be prioritized by taking into account the geographical location information of the patient. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the patient to the generation AI, which can select highly relevant information.

[0079] When collecting information, the collection unit can analyze the patient's social media activities and collect relevant information. For example, the collection unit collects information about health conditions posted by the patient on social media. The collection unit can also collect relevant medical information from the patient's social media activities. The collection unit can also collect relevant information by referring to the activities of the patient's friends on social media. In this way, relevant information can be collected efficiently by analyzing the patient's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's social media data into a generation AI, which then collects relevant information.

[0080] When collecting information, the collection unit can customize the collection method by reflecting the patient's past feedback. For example, the collection unit adjusts the information to be collected based on feedback provided by the patient in the past. The collection unit can also optimize the collection method from the patient's past feedback. The collection unit can also determine the priority of the information to be collected by referring to the patient's past feedback. In this way, the collection method can be optimized by reflecting the patient's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the patient's past feedback data into the generation AI, which can customize the collection method.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is confused, the analysis unit can provide a summary analysis result. By adjusting the presentation method of the analysis according to the user's emotions, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the presentation method of the analysis results.

[0082] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a simplified analysis on general information. The analysis unit can also perform a quick analysis on highly urgent information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI, which can then adjust the level of detail of the analysis.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies an image recognition algorithm to image data. The analysis unit can also apply a natural language processing algorithm to text data. The analysis unit can also apply a voice recognition algorithm to voice data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to a generation AI, which then applies an appropriate analysis algorithm.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. The analysis unit, for example, corrects the current analysis result based on the patient's past analysis results. The analysis unit can also optimize the analysis algorithm from the patient's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the patient's past analysis results. In this way, the accuracy of the analysis is improved by referring to the patient's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the patient's past analysis result data into the generation AI, which can improve the accuracy of the analysis.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is confused, the analysis unit can provide a concise analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the length of the analysis.

[0086] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was collected. For example, the analysis unit prioritizes analyzing the latest information. The analysis unit can also postpone analyzing older information. The analysis unit can also prioritize analyzing information with high urgency. This enables efficient analysis by determining the priority of analysis based on the time when the information was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the information was collected into the generation AI, and the generation AI can determine the priority of analysis.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also optimize the order of analysis based on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI, and the generation AI can adjust the order of analysis.

[0088] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the patient's level of expertise. For example, if the patient has technical expertise, the analysis unit uses detailed technical terminology. Furthermore, if the patient does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can also adjust the way in which the analysis results are expressed according to the patient's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the patient's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input the patient's level of expertise data into the generation AI, which can then adjust the use of technical terminology in the analysis.

[0089] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide information in a simple, highly visible manner. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is confused, the providing unit can provide information that focuses on the main points. This allows the method of providing information to be adjusted according to the user's emotions, thereby providing more appropriate information. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then adjust the method of providing information.

[0090] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit provides a detailed explanation for important information. The providing unit can also provide a simplified explanation for general information. The providing unit can also provide information with high urgency quickly. This enables efficient information provision by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information importance data to the generating AI, and the generating AI can adjust the level of detail of the information provided.

[0091] When providing information, the providing unit can apply different providing algorithms depending on the category of information. For example, the providing unit provides image data in a visually easy-to-understand manner. The providing unit can also provide text data in an easy-to-read format. The providing unit can also provide audio data in an easy-to-listen-to manner. This improves the accuracy of information provision by applying an appropriate providing algorithm depending on the category of information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information category data to a generation AI, which then applies an appropriate providing algorithm.

[0092] When providing information, the providing unit can improve the accuracy of the information provision by referring to the patient's past provision results. The providing unit, for example, corrects the current provision method based on the patient's past provision results. The providing unit can also optimize the provision algorithm from the patient's past provision results. The providing unit can also improve the accuracy of the information provision by referring to the patient's past provision results. In this way, the accuracy of the information provision is improved by referring to the patient's past provision results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the patient's past provision result data into the generating AI, which can improve the accuracy of the information provision.

[0093] The providing unit can estimate the user's emotions and adjust the length of information provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide short, to-the-point information. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is confused, the providing unit can provide concise information. This allows for adjusting the length of information provided according to the user's emotions, thereby providing more appropriate information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then adjust the length of the information provided.

[0094] When providing information, the providing unit can determine the priority of provision based on the time when the information was collected. For example, the providing unit can provide the latest information preferentially. The providing unit can also postpone providing older information. The providing unit can also provide information with higher urgency preferentially. This enables efficient information provision by determining the priority of provision based on the time when the information was collected. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information collection time data to the generating AI, and the generating AI can determine the priority of provision.

[0095] The providing unit can adjust the order of information provision based on the relevance of the information when providing the information. For example, the providing unit can provide highly relevant information preferentially. The providing unit can also provide less relevant information later. The providing unit can also optimize the order of information provision based on the relevance of the information. This enables efficient information provision by adjusting the order of information provision based on the relevance of the information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information relevance data to a generating AI, and the generating AI can adjust the order of information provision.

[0096] When providing information, the providing unit can adjust the use of technical terminology provided according to the patient's level of expertise. For example, if the patient has technical expertise, the providing unit can use detailed technical terminology. Also, if the patient does not have technical expertise, the providing unit can provide information in simple language. The providing unit can also adjust the way information is expressed according to the patient's level of expertise. This allows more appropriate information to be provided by adjusting the use of technical terminology provided according to the patient's level of expertise. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the patient's level of expertise data into the generating AI, which can then adjust the use of technical terminology provided.

[0097] The advice unit can estimate the user's emotions and adjust the method of advice based on the estimated user emotions. For example, if the user is nervous, the advice unit can provide simple, highly visible advice. Furthermore, if the user is relaxed, the advice unit can also provide detailed advice. Furthermore, if the user is confused, the advice unit can also provide advice that focuses on the main points. By adjusting the method of advice according to the user's emotions, more appropriate advice can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the advice unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the advice unit can input the user's emotion data into the generation AI, which can then adjust the method of advice.

[0098] When giving advice, the advice unit can adjust the level of detail of the advice based on the importance of the information. For example, the advice unit provides detailed advice for important information. The advice unit can also provide simplified advice for general information. The advice unit can also provide quick advice for highly urgent information. In this way, adjusting the level of detail of the advice based on the importance of the information enables efficient advice. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input information importance data to a generation AI, and the generation AI can adjust the level of detail of the advice.

[0099] When giving advice, the advice unit can apply different advice algorithms depending on the category of information. For example, the advice unit provides visually easy-to-understand advice for image data. The advice unit can also provide advice in an easy-to-read format for text data. The advice unit can also provide advice in an easy-to-listen-to manner for audio data. This improves the accuracy of advice by applying an appropriate advice algorithm depending on the category of information. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input information category data to a generation AI, which then applies an appropriate advice algorithm.

[0100] When providing advice, the advice unit can improve the accuracy of the advice by referring to past advice results for the patient. The advice unit, for example, corrects the current advice method based on the past advice results for the patient. The advice unit can also optimize the advice algorithm from the past advice results for the patient. The advice unit can also improve the accuracy of the advice by referring to the past advice results for the patient. In this way, the accuracy of the advice is improved by referring to the past advice results for the patient. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input past advice result data for the patient into the generation AI, which can improve the accuracy of the advice.

[0101] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. For example, if the user is nervous, the advice unit can provide short, to-the-point advice. Furthermore, if the user is relaxed, the advice unit can provide detailed advice. Furthermore, if the user is confused, the advice unit can provide concise advice. By adjusting the length of advice according to the user's emotions, more appropriate advice can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the advice unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the advice unit can input the user's emotion data into the generation AI, which can then adjust the length of the advice.

[0102] When giving advice, the advice unit can determine the priority of the advice based on the time when the information was collected. For example, the advice unit gives priority to the latest information. The advice unit can also give advice on older information later. The advice unit can also give priority to information with high urgency. In this way, by determining the priority of advice based on the time when the information was collected, efficient advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input data on the time when the information was collected into the generation AI, and the generation AI can determine the priority of the advice.

[0103] When giving advice, the advice unit can adjust the order of advice based on the relevance of the information. For example, the advice unit gives priority to advice on highly relevant information. The advice unit can also postpone advice on less relevant information. The advice unit can also optimize the order of advice based on the relevance of the information. This enables efficient advice by adjusting the order of advice based on the relevance of the information. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input information relevance data to a generation AI, and the generation AI can adjust the order of advice.

[0104] When providing advice, the advice unit can adjust the use of technical terms in the advice depending on the patient's level of expertise. For example, if the patient has technical expertise, the advice unit can use detailed technical terms. Also, if the patient does not have technical expertise, the advice unit can provide advice in simple language. The advice unit can also adjust the way the advice is expressed depending on the patient's level of expertise. This allows more appropriate advice to be provided by adjusting the use of technical terms in the advice depending on the patient's level of expertise. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input patient's level of expertise data into the generation AI, which can then adjust the use of technical terms in the advice. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and advice unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect information using the camera 42 or a sensor of the smart device 14. For example, the camera 42 of the smart device 14 is used to take photos or videos and collect information. The collection unit is also realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12. For example, it analyzes image data using deep learning to detect abnormalities. The provision unit provides information based on results obtained by the specific processing unit 290 of the data processing device 12. For example, it provides information to a doctor via email. The advice unit provides advice based on information provided by the control unit 46A of the smart device 14. For example, it instructs a patient on how to perform compression hemostasis. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and advice unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect information using the camera 42 or a sensor of the smart glasses 214. For example, the camera 42 of the smart glasses 214 is used to take photos or videos and collect information. The collection unit is also realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes information collected by the specific processing unit 290 of the data processing device 12. For example, it analyzes image data using deep learning to detect abnormalities. The provision unit provides information based on results obtained by the specific processing unit 290 of the data processing device 12. For example, it provides information to a doctor via email. The advice unit provides advice based on information provided by the control unit 46A of the smart glasses 214. For example, it instructs a patient on how to perform pressure hemostasis. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and advice unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect information using the camera 42 or a sensor of the headset-type terminal 314. For example, the camera 42 of the headset-type terminal 314 is used to take photos or videos and collect information. The collection unit is also realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes image data using deep learning to detect abnormalities. The provision unit provides information based on results obtained by the specific processing unit 290 of the data processing device 12. For example, the provision unit provides information to a doctor via email. The advice unit provides advice based on information provided by the control unit 46A of the headset-type terminal 314. For example, the advice unit instructs the patient on how to perform compression hemostasis. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and advice unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect information using the camera 42 or a sensor of the robot 414. For example, the camera 42 of the robot 414 is used to take photos or videos and collect information. The collection unit is also realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12. For example, it analyzes image data using deep learning and detects abnormalities. The provision unit provides information based on results obtained by the specific processing unit 290 of the data processing device 12. For example, it provides information to a doctor via email. The advice unit provides advice based on information provided by the control unit 46A of the robot 414. For example, it instructs the patient on how to perform pressure hemostasis.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The collection unit collects audio data from around the patient, and the analysis unit analyzes the audio data to detect signs of an emergency. For example, the collection unit collects abnormal sounds such as screams and crashes occurring around the patient. The collection unit can also collect the patient's breathing and heartbeat sounds, and the analysis unit can analyze the audio data to detect abnormalities. Furthermore, the collection unit can collect environmental sounds from around the patient, and the analysis unit can analyze the environmental sounds to detect signs of an emergency. This makes it possible to detect emergencies more quickly and accurately by using audio data.

[0107] The analysis unit can refer to the patient's past medical data and compare it with current symptoms to detect abnormalities. For example, the analysis unit can refer to the patient's past electrocardiogram data and compare it with current electrocardiogram data to detect abnormalities. The analysis unit can also refer to the patient's past blood test data and compare it with current blood test data to detect abnormalities. Furthermore, the analysis unit can refer to the patient's past image data and compare it with current image data to detect abnormalities. In this way, by utilizing past medical data, more accurate abnormality detection is possible.

[0108] The providing unit can also provide information to the patient's family and emergency contacts. For example, the providing unit can provide information to the patient's family by email or SMS. The providing unit can also provide information by calling the patient's emergency contact. Furthermore, the providing unit can provide information to the patient's family and emergency contacts through a dedicated app. This allows the patient's family and emergency contacts to receive prompt and accurate information.

[0109] The advice unit can provide advice based on the patient's current activity status. For example, if the patient is exercising, the advice unit instructs the patient to stop exercising. Also, if the patient is resting, the advice unit can instruct the patient to maintain an appropriate posture. Furthermore, if the patient is eating, the advice unit can instruct the patient to stop eating. In this way, appropriate advice can be provided according to the patient's current activity status.

[0110] The collection unit can estimate the user's emotions and adjust the method of collecting information based on the estimated user's emotions. For example, if the user is nervous, the collection unit can collect information using simple questions. If the user is relaxed, the collection unit can also collect information using detailed questions. Furthermore, if the user is confused, the collection unit can present options to collect information. This makes it possible to collect appropriate information according to the user's emotions.

[0111] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit prioritizes analyzing important information. Also, if the user is relaxed, the analysis unit can analyze detailed information. Furthermore, if the user is confused, the analysis unit can prioritize analyzing simple information. This enables appropriate analysis according to the user's emotions.

[0112] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the estimated user's emotions. For example, the providing unit can provide information quickly when the user is nervous. The providing unit can also provide detailed information when the user is relaxed. Furthermore, the providing unit can also provide simple information when the user is confused. This makes it possible to provide appropriate information according to the user's emotions.

[0113] The advice unit can estimate the user's emotions and adjust the content of the advice based on the estimated user's emotions. For example, if the user is nervous, the advice unit can provide simple, highly visible advice. If the user is relaxed, the advice unit can also provide detailed advice. Furthermore, if the user is confused, the advice unit can also provide advice that focuses on the main points. This makes it possible to provide appropriate advice according to the user's emotions.

[0114] The collection unit can identify the most suitable medical facility based on the patient's current location information and transmit that information to the provision unit. For example, the collection unit collects location information of medical facilities in the patient's area and identifies the nearest medical facility. The collection unit can also identify the most suitable medical facility based on the patient's current location information, taking into account traffic conditions. Furthermore, the collection unit can identify the most suitable medical facility based on the patient's current location information, taking into account the congestion status of the medical facility. This makes it possible to quickly identify an appropriate medical facility by utilizing the patient's current location information.

[0115] The analysis unit can monitor the patient's current health condition in real time and issue an alert if an abnormality is detected. For example, the analysis unit can monitor the patient's heart rate and blood pressure in real time and issue an alert if an abnormality is detected. The analysis unit can also monitor the patient's respiratory condition in real time and issue an alert if an abnormality is detected. Furthermore, the analysis unit can monitor the patient's body temperature in real time and issue an alert if an abnormality is detected. This makes it possible to monitor the patient's health condition and detect abnormalities in real time.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection unit collects information. This information includes text data, image data, sensor data, etc. The collection unit collects information using a smartphone or wearable device. For example, the collection unit collects information by taking photos or videos using the smartphone camera. The collection unit also collects biometric data such as heart rate and body temperature using the sensors in the wearable device. Step 2: The analysis unit analyzes the information collected by the collection unit. This analysis can involve the use of statistical analysis and machine learning algorithms. For example, deep learning can be used to analyze image data and detect abnormalities. Computer vision technology can also be used to analyze video data and determine symptoms. Step 3: The providing unit provides information based on the results obtained by the analysis unit. The information provision includes email and dashboard display. For example, the providing unit provides information to doctors via email and to emergency teams via a dashboard. Step 4: The advice unit provides advice based on the information provided by the provision unit. The advice includes medical advice and guidelines for action. For example, the advice unit instructs the patient on how to apply pressure to stop bleeding and instructs medical staff on the appropriate positioning.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] 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.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] 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.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0181] 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.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information; an analysis unit that analyzes the information collected by the collection unit; a providing unit that provides information based on the results obtained by the analyzing unit; an advice unit that provides advice based on the information provided by the provision unit; A system characterized by:

2. The collecting unit Collecting information using devices The system of claim 1 .

3. The analysis unit Analyze information using image recognition technology The system of claim 1 .

4. The providing unit Providing information to doctors and emergency responders The system of claim 1 .

5. The advice unit Providing advice to participants The system of claim 1 .

6. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The system of claim 1 .

7. The collecting unit Analyze the patient's past medical history and select the most appropriate method of collecting information The system of claim 1 .

8. The collecting unit When collecting information, filter it based on the patient's current health status and environment. The system of claim 1 .

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