system

Abstract

The system according to this embodiment aims to support the interpretation and explanation of preliminary data and to suggest appropriate medical interview questions. [Solution] The system according to the embodiment comprises an interpretation unit, an explanation unit, a question suggestion unit, and an information provision unit. The interpretation unit interprets the patient's preliminary values. The explanation unit explains the results interpreted by the interpretation unit. The question suggestion unit proposes appropriate medical questions based on the results explained by the explanation unit. The information provision unit provides additional information based on the questions proposed by the question suggestion unit.

Description

Technical Field

[0004] ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that in the medical interview after receiving a preliminary value, the reliability of the explanation and judgment by the doctor's skill is lacking.

[0005] The system according to the embodiment aims to assist in the interpretation and explanation of the preliminary value and propose appropriate medical interview questions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an interpretation unit, an explanation unit, a question suggestion unit, and an information provision unit. The interpretation unit interprets the patient's preliminary values. The explanation unit explains the results interpreted by the interpretation unit. The question suggestion unit proposes appropriate medical history questions based on the results explained by the explanation unit. The information provision unit provides additional information based on the questions proposed by the question suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can assist in the interpretation and explanation of preliminary data and can suggest appropriate medical interview questions. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The health checkup support system according to an embodiment of the present invention is a system that uses AI to support the explanation of preliminary health checkup results and medical interviews. The health checkup support system solves the lack of reliability in explanations and judgments based on the doctor's skill by having the AI ​​act as an assistant to the doctor, interpreting and explaining preliminary results, suggesting appropriate medical interview questions, and providing additional information. For example, the health checkup support system interprets the patient's preliminary results (such as test results) and generates a document that explains the results in an easy-to-understand manner. For example, if the blood test results indicate high blood pressure, the health checkup support system generates an explanation based on the results such as "If high blood pressure persists, the risk of heart disease increases." Next, the health checkup support system suggests appropriate medical interview questions. For example, to a patient with high blood pressure, it suggests a question such as "Please tell me about your recent diet and exercise habits," assisting the doctor in effectively collecting information related to the preliminary results. Furthermore, the health checkup support system provides background information and general explanations related to the preliminary results. For example, to a patient with high blood pressure, it provides information such as "Possible causes of high blood pressure include excessive salt intake and stress," making it easier for the patient to understand their own health condition. This system allows doctors to provide reliable explanations and make informed decisions to patients with the support of AI, leading to a deeper understanding of the patients' situations. It also reduces the burden on doctors and enables more efficient patient interviews. As a result, the health checkup support system addresses the lack of reliability in explanations and decisions based on the doctor's skills, providing patients with highly reliable explanations and decisions.

[0029] The health checkup support system according to this embodiment comprises an interpretation unit, an explanation unit, a question suggestion unit, and an information provision unit. The interpretation unit interprets the patient's preliminary values. The interpretation unit interprets specific health indicators such as blood pressure, heart rate, and blood glucose levels. The interpretation unit can perform interpretations using statistical methods or machine learning algorithms. For example, the interpretation unit uses a machine learning algorithm to analyze the patient's blood pressure data and assess the risk of hypertension. The explanation unit explains the results interpreted by the interpretation unit. The explanation unit explains the interpretation results in an easy-to-understand manner, for example, using text generation or speech synthesis. For example, the explanation unit uses text generation technology to explain to the patient that "a persistently high blood pressure increases the risk of heart disease." The question suggestion unit proposes appropriate medical questions based on the results explained by the explanation unit. The question suggestion unit proposes questions that are appropriate to the patient's health condition. For example, the question suggestion unit proposes a question to a patient with high blood pressure such as "Please tell me about your recent diet and exercise habits." The information provision unit provides additional information based on the questions proposed by the question suggestion unit. The information provision unit provides patients with information on the causes and prevention methods of hypertension, for example. For example, the information provision unit provides information such as, "Possible causes of hypertension include excessive salt intake and stress." In this way, the health checkup support system according to the embodiment can interpret and explain the patient's preliminary results, suggest appropriate medical questions, and provide additional information, thereby resolving the lack of reliability in explanations and judgments based on the doctor's skills.

[0030] The interpretation unit interprets patient data. For example, it interprets specific health indicators such as blood pressure, heart rate, and blood glucose levels. Specifically, the interpretation unit analyzes data collected from patients in real time to quickly detect abnormal values ​​and signs of risk. For example, with blood pressure data, it determines whether the collected data falls within the normal range and evaluates the degree and frequency of any abnormalities. Regarding heart rate data, it analyzes fluctuations in resting and exercise heart rates to detect abnormal patterns and signs of arrhythmias. For blood glucose data, it analyzes fluctuations in fasting and postprandial blood glucose levels to assess the risk of diabetes. The interpretation unit can perform interpretations using statistical methods and machine learning algorithms. For example, it uses machine learning algorithms to analyze a patient's blood pressure data and assess the risk of hypertension. Specifically, it builds a model based on past data and applies newly collected data to that model to assess the risk. Furthermore, the interpretation unit analyzes correlations between different health indicators and can determine a higher risk if multiple indicators show abnormalities simultaneously. This allows the interpreter to comprehensively evaluate the patient's health status and detect abnormalities early.

[0031] The explanatory unit explains the results interpreted by the interpreting unit. The explanatory unit uses methods such as text generation and speech synthesis to explain the interpretation clearly. Specifically, based on the data provided by the interpreting unit, the explanatory unit generates text to provide appropriate explanations to the patient. For example, when explaining that persistently high blood pressure increases the risk of heart disease, it uses simple language, avoiding technical jargon, depending on the patient's level of understanding and background knowledge. Furthermore, speech synthesis technology can be used to play the generated text aloud, providing information to patients who have difficulty reading text, such as the visually impaired and the elderly. The explanatory unit can also monitor the patient's reactions and understanding in real time and adjust the explanation as needed. For example, if a patient has difficulty understanding a particular part, it will either explain that part in more detail or try explaining it in different terms. The explanatory unit can also suggest specific advice and next steps based on the interpretation results. For example, it might suggest specific lifestyle improvements, such as, "Since your blood pressure is high, you should reduce your salt intake." This allows the explanatory unit to support patients in accurately understanding their health condition and taking appropriate action.

[0032] The question suggestion unit proposes appropriate medical interview questions based on the results explained by the explanation unit. For example, the question suggestion unit proposes questions tailored to the patient's health condition. Specifically, based on the information provided by the interpretation and explanation units, the question suggestion unit generates questions related to the patient's current health condition and lifestyle. For example, for a patient with high blood pressure, it might suggest a question such as, "Please tell me about your recent diet and exercise habits." Furthermore, the question suggestion unit can analyze the patient's responses in real time and dynamically generate additional questions. For example, if a patient answers, "I haven't been exercising much lately," it might suggest additional specific questions such as, "How often do you exercise?" or "Are there any obstacles to starting exercise?" The question suggestion unit can also accumulate patient response data and generate personalized questions based on past response history. This allows the question suggestion unit to conduct appropriate interviews tailored to the patient's individual situation and collect more detailed information. In addition, based on the collected information, the question suggestion unit can also propose appropriate questions to doctors and other healthcare professionals, supporting improvements in the accuracy of diagnosis and treatment. This allows the question-and-suggestion department to facilitate communication between patients and medical professionals, thereby improving the quality of diagnosis and treatment.

[0033] The Information Provision Department provides additional information based on questions proposed by the Question Proposal Department. For example, the Information Provision Department provides patients with information on the causes and prevention methods of hypertension. Specifically, the Information Provision Department provides relevant medical information and advice for lifestyle improvements according to the patient's responses and health condition. For example, when providing information such as, "Excessive salt intake and stress are possible causes of hypertension," the Information Provision Department will also provide detailed explanations of specific salt intake guidelines and stress management methods. Furthermore, the Information Provision Department obtains the latest research results and guidelines from reliable medical information sources to ensure the accuracy and reliability of the information provided to patients. In addition, the Information Provision Department can customize the method of information provision according to the patient's level of understanding and interest. For example, it can provide visually easy-to-understand explanations using diagrams and videos in addition to text information. The Information Provision Department also monitors how well patients understand and implement the information provided, and provides additional information and support as needed. For example, it checks whether patients are implementing advice such as "reduce salt intake," and if it is difficult to implement, it proposes specific methods or alternatives. This allows the information provision department to support patients in effectively managing their own health and promote improvements in their health status.

[0034] The interpretation unit can analyze the patient's past health data to improve the accuracy of the interpretation. For example, the interpretation unit can analyze the patient's past blood pressure data and interpret it in comparison to the current blood pressure result. For example, the interpretation unit can refer to the patient's past diagnostic results and interpret their relationship with the current preliminary value. For example, the interpretation unit can interpret the patient's current health status based on the patient's past lifestyle data. This improves the accuracy of the interpretation by analyzing past health data. Some or all of the above processing in the interpretation unit may be performed using AI, for example, or without AI. For example, the interpretation unit can input the patient's past health data into a generating AI and have the generating AI perform the task of improving the accuracy of the interpretation.

[0035] The interpretation unit can interpret preliminary results by considering the patient's lifestyle and medical history. For example, the interpretation unit may consider the patient's diet and exercise habits when interpreting blood pressure results. For example, the interpretation unit may refer to the patient's medical history when interpreting the current health status. For example, the interpretation unit may consider the patient's stress level when interpreting the health status. This allows for a more accurate interpretation by considering lifestyle and medical history. Some or all of the above processing in the interpretation unit may be performed using AI, for example, or without AI. For example, the interpretation unit may input data on the patient's lifestyle and medical history into a generating AI and have the generating AI perform the interpretation.

[0036] The interpretation unit can reflect region-specific health risks by considering the patient's geographical location information when interpreting preliminary data. For example, if the patient lives at high altitude, the interpretation unit will consider the health risks specific to high altitude when interpreting the data. For example, if the patient lives in an urban area, the interpretation unit will consider the health risks specific to urban areas when interpreting the data. For example, if the patient lives in a rural area, the interpretation unit will consider the health risks specific to rural areas when interpreting the data. This makes it possible to interpret data that reflects region-specific health risks by considering geographical location information. Some or all of the above processing in the interpretation unit may be performed using AI, for example, or without AI. For example, the interpretation unit can input the patient's geographical location information into a generating AI and have the generating AI perform an interpretation that reflects region-specific health risks.

[0037] The interpretation unit can analyze the patient's social media activity and obtain relevant health information when interpreting preliminary data. For example, the interpretation unit can perform interpretations based on dietary information shared by the patient on social media. For example, the interpretation unit can perform interpretations based on exercise habit information shared by the patient on social media. For example, the interpretation unit can perform interpretations based on stress level information shared by the patient on social media. In this way, by analyzing social media activity, relevant health information can be obtained and reflected in the interpretation. Some or all of the above processing in the interpretation unit may be performed using AI, for example, or without AI. For example, the interpretation unit can input the patient's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant health information.

[0038] The explanation unit can adjust the level of detail in its explanations based on the patient's level of understanding. For example, if the patient has medical knowledge, the explanation unit will provide a detailed explanation using specialized terminology. If the patient does not have medical knowledge, the explanation unit will provide a clear and easy-to-understand explanation using simple language. If the patient only partially understands, the explanation unit will provide supplementary information. In this way, by adjusting the level of detail in the explanation according to the patient's level of understanding, appropriate information can be provided to the patient. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input patient understanding data into a generating AI and have the generating AI perform the adjustment of the level of detail in the explanation.

[0039] The explanation unit can apply different explanation algorithms depending on the patient's health literacy during explanation. For example, if the patient has high health literacy, the explanation unit provides detailed data and statistical information. If the patient has low health literacy, the explanation unit uses simple graphs and diagrams for explanation. If the patient has moderate health literacy, the explanation unit provides information with an appropriate level of detail. This allows the explanation unit to provide appropriate explanations to patients by applying explanation algorithms according to their health literacy. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the patient's health literacy data into a generating AI and have the generating AI perform the application of the explanation algorithm.

[0040] The explanation unit can determine the priority of explanations based on the patient's submission timing. For example, if a patient submits an urgent diagnosis, the explanation unit will give it the highest priority. If a patient submits the results of a routine health checkup, the explanation unit will give it the normal priority. If a patient resubmits past diagnosis results, the explanation unit will compare them with the past results. This allows for the priority provision of highly urgent information by determining the priority of explanations based on the submission timing. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input patient submission timing data into a generating AI and have the generating AI determine the priority of explanations.

[0041] The explanation unit can adjust the order of explanations based on the patient's relevance during the explanation. For example, the explanation unit might first explain the information most relevant to the patient's current health condition. Next, it might explain information related to the patient's past diagnoses. Finally, it might explain information related to the patient's lifestyle. By adjusting the order of explanations based on relevance, the explanation unit can appropriately provide the patient with the most relevant information. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input patient relevance data into a generating AI and have the generating AI perform the adjustment of the explanation order.

[0042] The question suggestion unit can analyze the patient's past response history to select the most appropriate question when suggesting questions. For example, the question suggestion unit can suggest new, relevant questions based on questions the patient has answered in the past. For example, the question suggestion unit can prioritize suggesting unanswered questions from the patient's past response history. For example, the question suggestion unit can analyze the patient's past response history to select the most effective question. In this way, the optimal question can be selected by analyzing the past response history. Some or all of the above processes in the question suggestion unit may be performed using AI, for example, or without AI. For example, the question suggestion unit can input the patient's past response history data into a generating AI and have the generating AI select the optimal question.

[0043] The question suggestion unit can propose questions while considering the patient's lifestyle and medical history. For example, the question suggestion unit can consider the patient's eating habits and propose questions about diet. For example, the question suggestion unit can consider the patient's exercise habits and propose questions about exercise. For example, the question suggestion unit can consider the patient's medical history and propose questions related to past illnesses. In this way, appropriate questions can be proposed by considering lifestyle and medical history. Some or all of the above processing in the question suggestion unit may be performed using AI, for example, or without AI. For example, the question suggestion unit can input data on the patient's lifestyle and medical history into a generating AI and have the generating AI execute the question suggestion.

[0044] The question suggestion unit can prioritize suggesting highly relevant questions by considering the patient's geographical location when suggesting questions. For example, if the patient lives at high altitude, the question suggestion unit will suggest questions about health risks specific to high altitude. For example, if the patient lives in an urban area, the question suggestion unit will suggest questions about health risks specific to urban areas. For example, if the patient lives in a rural area, the question suggestion unit will suggest questions about health risks specific to rural areas. In this way, highly relevant questions can be suggested by considering geographical location. Some or all of the above processing in the question suggestion unit may be performed using AI, for example, or without AI. For example, the question suggestion unit can input the patient's geographical location information into a generating AI and have the generating AI suggest highly relevant questions.

[0045] The question suggestion unit can analyze the patient's social media activity and suggest relevant questions when suggesting questions. For example, the question suggestion unit can suggest questions based on dietary information shared by the patient on social media. For example, the question suggestion unit can suggest questions based on exercise habit information shared by the patient on social media. For example, the question suggestion unit can suggest questions based on stress level information shared by the patient on social media. In this way, relevant questions can be suggested by analyzing social media activity. Some or all of the above processing in the question suggestion unit may be performed using AI, for example, or without AI. For example, the question suggestion unit can input the patient's social media activity data into a generating AI and have the generating AI suggest relevant questions.

[0046] The information provision unit can analyze the patient's past health data to select the most relevant information when providing information. For example, the information provision unit can provide information on hypertension based on the patient's past blood pressure data. For example, the information provision unit can refer to the patient's past diagnostic results to provide relevant health information. For example, the information provision unit can provide health advice based on the patient's past lifestyle data. In this way, by analyzing past health data, the most relevant information can be provided. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the patient's past health data into a generating AI and have the generating AI select the most relevant information.

[0047] The information provision unit can provide information while considering the patient's lifestyle and medical history. For example, the information provision unit can consider the patient's eating habits and provide health information related to diet. For example, the information provision unit can consider the patient's exercise habits and provide health information related to exercise. For example, the information provision unit can consider the patient's medical history and provide health information related to past illnesses. In this way, appropriate information can be provided by considering lifestyle and medical history. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input data on the patient's lifestyle and medical history into a generating AI and have the generating AI perform the information provision.

[0048] The information provision unit can reflect region-specific health risks by considering the patient's geographical location when providing information. For example, if the patient lives at high altitude, the information provision unit will provide information on health risks specific to high altitude. For example, if the patient lives in an urban area, the information provision unit will provide information on health risks specific to urban areas. For example, if the patient lives in a rural area, the information provision unit will provide information on health risks specific to rural areas. In this way, by considering geographical location, it is possible to provide information that reflects region-specific health risks. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the patient's geographical location information into a generating AI and have the generating AI perform the task of providing information that reflects region-specific health risks.

[0049] The information provision unit can analyze the patient's social media activity and provide relevant health information when providing information. For example, the information provision unit can provide health information based on dietary information shared by the patient on social media. For example, the information provision unit can provide health information based on exercise habit information shared by the patient on social media. For example, the information provision unit can provide health information based on stress level information shared by the patient on social media. In this way, relevant health information can be provided by analyzing social media activity. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the patient's social media activity data into a generating AI and have the generating AI perform the provision of relevant health information.

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

[0051] The health checkup support system can also include a genetic information analysis unit that analyzes the patient's genetic information. The genetic information analysis unit analyzes the patient's genetic data and assesses genetic risks. For example, the genetic information analysis unit assesses the genetic risk for a specific disease based on the patient's genetic data and provides the results to the interpretation unit. The interpretation unit can then interpret the patient's health status in more detail based on the genetic risk information provided by the genetic information analysis unit. For example, if the genetic information analysis unit assesses the genetic risk of hypertension from the patient's genetic data, the interpretation unit can interpret the blood pressure results based on that information. This makes it possible to interpret health checkup results more accurately by considering genetic information.

[0052] The health checkup support system may also include an environmental data collection unit that collects data on the patient's living environment. The environmental data collection unit, for example, collects data on the patient's living and working environment and provides it to the interpretation unit. The interpretation unit interprets the patient's health status based on the data provided by the environmental data collection unit. For example, the environmental data collection unit could collect data on the patient's living environment, and the interpretation unit could use that data to assess allergy risk. This would enable the interpretation of health checkup results that takes the living environment into account.

[0053] The health checkup support system may also include an exercise data collection unit that collects patient exercise data. The exercise data collection unit, for example, collects data on the patient's exercise volume and exercise habits and provides it to the interpretation unit. The interpretation unit interprets the patient's health status based on the data provided by the exercise data collection unit. For example, the exercise data collection unit can collect patient exercise volume data, and the interpretation unit can use that data to evaluate cardiopulmonary function. This makes it possible to interpret health checkup results while taking exercise data into consideration.

[0054] The health checkup support system may also include a dietary data collection unit that collects patient dietary data. The dietary data collection unit, for example, collects data on the patient's diet and eating habits and provides it to the interpretation unit. The interpretation unit interprets the patient's health status based on the data provided by the dietary data collection unit. For example, the dietary data collection unit can collect data on the patient's diet, and the interpretation unit can evaluate nutritional balance based on that data. This makes it possible to interpret health checkup results while taking dietary data into consideration.

[0055] The health checkup support system may also include a sleep data collection unit that collects patient sleep data. The sleep data collection unit, for example, collects data on the patient's sleep duration and sleep quality, and provides it to the interpretation unit. The interpretation unit interprets the patient's health status based on the data provided by the sleep data collection unit. For example, the sleep data collection unit can collect the patient's sleep duration data, and the interpretation unit can use that data to assess the risk of sleep deprivation. This makes it possible to interpret health checkup results while taking sleep data into consideration.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The interpreter interprets the patient's preliminary data. The interpreter interprets specific health indicators such as blood pressure, heart rate, and blood glucose levels. The interpreter can perform interpretations using statistical methods or machine learning algorithms. For example, the interpreter can use machine learning algorithms to analyze the patient's blood pressure data and assess the risk of hypertension. Step 2: The explanatory section explains the results interpreted by the interpreting section. The explanatory section explains the interpretation results in an easy-to-understand manner, for example, by using text generation or speech synthesis. For example, the explanatory section uses text generation technology to explain to the patient that "a persistently high blood pressure increases the risk of heart disease." Step 3: The question suggestion unit proposes appropriate medical questions based on the results explained by the explanation unit. For example, the question suggestion unit proposes questions tailored to the patient's health condition. For example, the question suggestion unit might suggest to a patient with high blood pressure, "Please tell me about your recent eating habits and exercise routine." Step 4: The Information Provision Department provides additional information based on the questions proposed by the Question Proposal Department. For example, the Information Provision Department provides patients with information on the causes and prevention methods of hypertension. For example, the Information Provision Department provides information such as, "Possible causes of hypertension include excessive salt intake and stress."

[0058] (Example of form 2) The health checkup support system according to an embodiment of the present invention is a system that uses AI to support the explanation of preliminary health checkup results and medical interviews. The health checkup support system solves the lack of reliability in explanations and judgments based on the doctor's skill by having the AI ​​act as an assistant to the doctor, interpreting and explaining preliminary results, suggesting appropriate medical interview questions, and providing additional information. For example, the health checkup support system interprets the patient's preliminary results (such as test results) and generates a document that explains the results in an easy-to-understand manner. For example, if the blood test results indicate high blood pressure, the health checkup support system generates an explanation based on the results such as "If high blood pressure persists, the risk of heart disease increases." Next, the health checkup support system suggests appropriate medical interview questions. For example, to a patient with high blood pressure, it suggests a question such as "Please tell me about your recent diet and exercise habits," assisting the doctor in effectively collecting information related to the preliminary results. Furthermore, the health checkup support system provides background information and general explanations related to the preliminary results. For example, to a patient with high blood pressure, it provides information such as "Possible causes of high blood pressure include excessive salt intake and stress," making it easier for the patient to understand their own health condition. This system allows doctors to provide reliable explanations and make informed decisions to patients with the support of AI, leading to a deeper understanding of the patients' situations. It also reduces the burden on doctors and enables more efficient patient interviews. As a result, the health checkup support system addresses the lack of reliability in explanations and decisions based on the doctor's skills, providing patients with highly reliable explanations and decisions.

[0059] The health checkup support system according to this embodiment comprises an interpretation unit, an explanation unit, a question suggestion unit, and an information provision unit. The interpretation unit interprets the patient's preliminary values. The interpretation unit interprets specific health indicators such as blood pressure, heart rate, and blood glucose levels. The interpretation unit can perform interpretations using statistical methods or machine learning algorithms. For example, the interpretation unit uses a machine learning algorithm to analyze the patient's blood pressure data and assess the risk of hypertension. The explanation unit explains the results interpreted by the interpretation unit. The explanation unit explains the interpretation results in an easy-to-understand manner, for example, using text generation or speech synthesis. For example, the explanation unit uses text generation technology to explain to the patient that "a persistently high blood pressure increases the risk of heart disease." The question suggestion unit proposes appropriate medical questions based on the results explained by the explanation unit. The question suggestion unit proposes questions that are appropriate to the patient's health condition. For example, the question suggestion unit proposes a question to a patient with high blood pressure such as "Please tell me about your recent diet and exercise habits." The information provision unit provides additional information based on the questions proposed by the question suggestion unit. The information provision unit provides patients with information on the causes and prevention methods of hypertension, for example. For example, the information provision unit provides information such as, "Possible causes of hypertension include excessive salt intake and stress." In this way, the health checkup support system according to the embodiment can interpret and explain the patient's preliminary results, suggest appropriate medical questions, and provide additional information, thereby resolving the lack of reliability in explanations and judgments based on the doctor's skills.

[0060] The interpretation unit interprets patient data. For example, it interprets specific health indicators such as blood pressure, heart rate, and blood glucose levels. Specifically, the interpretation unit analyzes data collected from patients in real time to quickly detect abnormal values ​​and signs of risk. For example, with blood pressure data, it determines whether the collected data falls within the normal range and evaluates the degree and frequency of any abnormalities. Regarding heart rate data, it analyzes fluctuations in resting and exercise heart rates to detect abnormal patterns and signs of arrhythmias. For blood glucose data, it analyzes fluctuations in fasting and postprandial blood glucose levels to assess the risk of diabetes. The interpretation unit can perform interpretations using statistical methods and machine learning algorithms. For example, it uses machine learning algorithms to analyze a patient's blood pressure data and assess the risk of hypertension. Specifically, it builds a model based on past data and applies newly collected data to that model to assess the risk. Furthermore, the interpretation unit analyzes correlations between different health indicators and can determine a higher risk if multiple indicators show abnormalities simultaneously. This allows the interpreter to comprehensively evaluate the patient's health status and detect abnormalities early.

[0061] The explanatory unit explains the results interpreted by the interpreting unit. The explanatory unit uses methods such as text generation and speech synthesis to explain the interpretation clearly. Specifically, based on the data provided by the interpreting unit, the explanatory unit generates text to provide appropriate explanations to the patient. For example, when explaining that persistently high blood pressure increases the risk of heart disease, it uses simple language, avoiding technical jargon, depending on the patient's level of understanding and background knowledge. Furthermore, speech synthesis technology can be used to play the generated text aloud, providing information to patients who have difficulty reading text, such as the visually impaired and the elderly. The explanatory unit can also monitor the patient's reactions and understanding in real time and adjust the explanation as needed. For example, if a patient has difficulty understanding a particular part, it will either explain that part in more detail or try explaining it in different terms. The explanatory unit can also suggest specific advice and next steps based on the interpretation results. For example, it might suggest specific lifestyle improvements, such as, "Since your blood pressure is high, you should reduce your salt intake." This allows the explanatory unit to support patients in accurately understanding their health condition and taking appropriate action.

[0062] The question suggestion unit proposes appropriate medical interview questions based on the results explained by the explanation unit. For example, the question suggestion unit proposes questions tailored to the patient's health condition. Specifically, based on the information provided by the interpretation and explanation units, the question suggestion unit generates questions related to the patient's current health condition and lifestyle. For example, for a patient with high blood pressure, it might suggest a question such as, "Please tell me about your recent diet and exercise habits." Furthermore, the question suggestion unit can analyze the patient's responses in real time and dynamically generate additional questions. For example, if a patient answers, "I haven't been exercising much lately," it might suggest additional specific questions such as, "How often do you exercise?" or "Are there any obstacles to starting exercise?" The question suggestion unit can also accumulate patient response data and generate personalized questions based on past response history. This allows the question suggestion unit to conduct appropriate interviews tailored to the patient's individual situation and collect more detailed information. In addition, based on the collected information, the question suggestion unit can also propose appropriate questions to doctors and other healthcare professionals, supporting improvements in the accuracy of diagnosis and treatment. This allows the question-and-suggestion department to facilitate communication between patients and medical professionals, thereby improving the quality of diagnosis and treatment.

[0063] The Information Provision Department provides additional information based on questions proposed by the Question Proposal Department. For example, the Information Provision Department provides patients with information on the causes and prevention methods of hypertension. Specifically, the Information Provision Department provides relevant medical information and advice for lifestyle improvements according to the patient's responses and health condition. For example, when providing information such as, "Excessive salt intake and stress are possible causes of hypertension," the Information Provision Department will also provide detailed explanations of specific salt intake guidelines and stress management methods. Furthermore, the Information Provision Department obtains the latest research results and guidelines from reliable medical information sources to ensure the accuracy and reliability of the information provided to patients. In addition, the Information Provision Department can customize the method of information provision according to the patient's level of understanding and interest. For example, it can provide visually easy-to-understand explanations using diagrams and videos in addition to text information. The Information Provision Department also monitors how well patients understand and implement the information provided, and provides additional information and support as needed. For example, it checks whether patients are implementing advice such as "reduce salt intake," and if it is difficult to implement, it proposes specific methods or alternatives. This allows the information provision department to support patients in effectively managing their own health and promote improvements in their health status.

[0064] The interpretation unit can estimate the patient's emotions and adjust the interpretation method of the preliminary results based on the estimated patient emotions. For example, if the patient is feeling anxious, the interpretation unit provides the interpretation result using reassuring language. For example, if the patient is relaxed, the interpretation unit provides a detailed interpretation that is easy for the patient to understand. For example, if the patient is in a hurry, the interpretation unit provides a concise and to-the-point interpretation. In this way, by adjusting the interpretation method according to the patient's emotions, more appropriate interpretation results can be provided to the patient. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is 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 processing in the interpretation unit may be performed using AI, for example, or not using AI. For example, the interpretation unit can input the patient's emotion data into the generative AI and have the generative AI perform an adjustment of the interpretation method based on the emotions.

[0065] The interpretation unit can analyze the patient's past health data to improve the accuracy of the interpretation. For example, the interpretation unit can analyze the patient's past blood pressure data and interpret it in comparison to the current blood pressure result. For example, the interpretation unit can refer to the patient's past diagnostic results and interpret their relationship with the current preliminary value. For example, the interpretation unit can interpret the patient's current health status based on the patient's past lifestyle data. This improves the accuracy of the interpretation by analyzing past health data. Some or all of the above processing in the interpretation unit may be performed using AI, for example, or without AI. For example, the interpretation unit can input the patient's past health data into a generating AI and have the generating AI perform the task of improving the accuracy of the interpretation.

[0066] The interpretation unit can interpret preliminary results by considering the patient's lifestyle and medical history. For example, the interpretation unit may consider the patient's diet and exercise habits when interpreting blood pressure results. For example, the interpretation unit may refer to the patient's medical history when interpreting the current health status. For example, the interpretation unit may consider the patient's stress level when interpreting the health status. This allows for a more accurate interpretation by considering lifestyle and medical history. Some or all of the above processing in the interpretation unit may be performed using AI, for example, or without AI. For example, the interpretation unit may input data on the patient's lifestyle and medical history into a generating AI and have the generating AI perform the interpretation.

[0067] The interpretation unit can estimate the patient's emotions and determine the priority of interpretation results based on the estimated emotions. For example, if the patient is feeling anxious, the interpretation unit will provide the most important information first. If the patient is relaxed, the interpretation unit will provide detailed information sequentially. If the patient is in a hurry, the interpretation unit will provide concise information summarizing the key points. This allows the interpretation unit to appropriately provide the patient with the most important information by determining the priority of interpretation results according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interpretation unit may be performed using AI, for example, or without AI. For example, the interpretation unit can input patient emotion data into a generative AI and have the generative AI determine the priority of interpretation results.

[0068] The interpretation unit can reflect region-specific health risks by considering the patient's geographical location information when interpreting preliminary data. For example, if the patient lives at high altitude, the interpretation unit will consider the health risks specific to high altitude when interpreting the data. For example, if the patient lives in an urban area, the interpretation unit will consider the health risks specific to urban areas when interpreting the data. For example, if the patient lives in a rural area, the interpretation unit will consider the health risks specific to rural areas when interpreting the data. This makes it possible to interpret data that reflects region-specific health risks by considering geographical location information. Some or all of the above processing in the interpretation unit may be performed using AI, for example, or without AI. For example, the interpretation unit can input the patient's geographical location information into a generating AI and have the generating AI perform an interpretation that reflects region-specific health risks.

[0069] The interpretation unit can analyze the patient's social media activity and obtain relevant health information when interpreting preliminary data. For example, the interpretation unit can perform interpretations based on dietary information shared by the patient on social media. For example, the interpretation unit can perform interpretations based on exercise habit information shared by the patient on social media. For example, the interpretation unit can perform interpretations based on stress level information shared by the patient on social media. In this way, by analyzing social media activity, relevant health information can be obtained and reflected in the interpretation. Some or all of the above processing in the interpretation unit may be performed using AI, for example, or without AI. For example, the interpretation unit can input the patient's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant health information.

[0070] The explanation unit can estimate the patient's emotions and adjust the way the explanation is presented based on the estimated emotions. For example, if the patient is feeling anxious, the explanation unit will use reassuring language. If the patient is relaxed, the explanation unit will provide a detailed explanation that is easy for the patient to understand. If the patient is in a hurry, the explanation unit will provide a concise and to-the-point explanation. In this way, by adjusting the way the explanation is presented according to the patient's emotions, a more appropriate explanation can be provided to the patient. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the explanation unit may be performed using AI, for example, or not using AI. For example, the explanation unit can input patient emotion data into the generative AI and have the generative AI adjust the way the explanation is presented.

[0071] The explanation unit can adjust the level of detail in its explanations based on the patient's level of understanding. For example, if the patient has medical knowledge, the explanation unit will provide a detailed explanation using specialized terminology. If the patient does not have medical knowledge, the explanation unit will provide a clear and easy-to-understand explanation using simple language. If the patient only partially understands, the explanation unit will provide supplementary information. In this way, by adjusting the level of detail in the explanation according to the patient's level of understanding, appropriate information can be provided to the patient. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input patient understanding data into a generating AI and have the generating AI perform the adjustment of the level of detail in the explanation.

[0072] The explanation unit can apply different explanation algorithms depending on the patient's health literacy during explanation. For example, if the patient has high health literacy, the explanation unit provides detailed data and statistical information. If the patient has low health literacy, the explanation unit uses simple graphs and diagrams for explanation. If the patient has moderate health literacy, the explanation unit provides information with an appropriate level of detail. This allows the explanation unit to provide appropriate explanations to patients by applying explanation algorithms according to their health literacy. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the patient's health literacy data into a generating AI and have the generating AI perform the application of the explanation algorithm.

[0073] The explanation unit can estimate the patient's emotions and adjust the length of the explanation based on the estimated emotions. For example, if the patient is feeling anxious, the explanation unit will provide a short, concise explanation. If the patient is relaxed, the explanation unit will provide a detailed explanation. If the patient is in a hurry, the explanation unit will provide a brief and quick explanation. This allows the explanation unit to provide the patient with appropriate information by adjusting the length of the explanation according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the explanation unit may be performed using AI or not using AI. For example, the explanation unit can input patient emotion data into the generative AI and have the generative AI adjust the length of the explanation.

[0074] The explanation unit can determine the priority of explanations based on the patient's submission timing. For example, if a patient submits an urgent diagnosis, the explanation unit will give it the highest priority. If a patient submits the results of a routine health checkup, the explanation unit will give it the normal priority. If a patient resubmits past diagnosis results, the explanation unit will compare them with the past results. This allows for the priority provision of highly urgent information by determining the priority of explanations based on the submission timing. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input patient submission timing data into a generating AI and have the generating AI determine the priority of explanations.

[0075] The explanation unit can adjust the order of explanations based on the patient's relevance during the explanation. For example, the explanation unit might first explain the information most relevant to the patient's current health condition. Next, it might explain information related to the patient's past diagnoses. Finally, it might explain information related to the patient's lifestyle. By adjusting the order of explanations based on relevance, the explanation unit can appropriately provide the patient with the most relevant information. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input patient relevance data into a generating AI and have the generating AI perform the adjustment of the explanation order.

[0076] The question suggestion unit can estimate the patient's emotions and adjust the wording of questions based on the estimated emotions. For example, if the patient is feeling anxious, the question suggestion unit will ask questions in gentle language. For example, if the patient is relaxed, the question suggestion unit will ask detailed questions. For example, if the patient is in a hurry, the question suggestion unit will ask concise and to-the-point questions. In this way, by adjusting the wording of questions according to the patient's emotions, appropriate questions can be provided to the patient. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question suggestion unit may be performed using AI or not using AI. For example, the question suggestion unit can input patient emotion data into the generative AI and have the generative AI adjust the wording of questions.

[0077] The question suggestion unit can analyze the patient's past response history to select the most appropriate question when suggesting questions. For example, the question suggestion unit can suggest new, relevant questions based on questions the patient has answered in the past. For example, the question suggestion unit can prioritize suggesting unanswered questions from the patient's past response history. For example, the question suggestion unit can analyze the patient's past response history to select the most effective question. In this way, the optimal question can be selected by analyzing the past response history. Some or all of the above processes in the question suggestion unit may be performed using AI, for example, or without AI. For example, the question suggestion unit can input the patient's past response history data into a generating AI and have the generating AI select the optimal question.

[0078] The question suggestion unit can propose questions while considering the patient's lifestyle and medical history. For example, the question suggestion unit can consider the patient's eating habits and propose questions about diet. For example, the question suggestion unit can consider the patient's exercise habits and propose questions about exercise. For example, the question suggestion unit can consider the patient's medical history and propose questions related to past illnesses. In this way, appropriate questions can be proposed by considering lifestyle and medical history. Some or all of the above processing in the question suggestion unit may be performed using AI, for example, or without AI. For example, the question suggestion unit can input data on the patient's lifestyle and medical history into a generating AI and have the generating AI execute the question suggestion.

[0079] The question suggestion unit can estimate the patient's emotions and prioritize questions based on those emotions. For example, if the patient is anxious, the question suggestion unit will suggest the most important questions first. If the patient is relaxed, the question suggestion unit will suggest detailed questions in sequence. If the patient is in a hurry, the question suggestion unit will suggest concise questions that get straight to the point. This allows the system to appropriately provide the patient with the most important questions by prioritizing questions according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the question suggestion unit may be performed using AI or not. For example, the question suggestion unit can input patient emotion data into a generative AI and have the generative AI determine the priority of questions.

[0080] The question suggestion unit can prioritize suggesting highly relevant questions by considering the patient's geographical location when suggesting questions. For example, if the patient lives at high altitude, the question suggestion unit will suggest questions about health risks specific to high altitude. For example, if the patient lives in an urban area, the question suggestion unit will suggest questions about health risks specific to urban areas. For example, if the patient lives in a rural area, the question suggestion unit will suggest questions about health risks specific to rural areas. In this way, highly relevant questions can be suggested by considering geographical location. Some or all of the above processing in the question suggestion unit may be performed using AI, for example, or without AI. For example, the question suggestion unit can input the patient's geographical location information into a generating AI and have the generating AI suggest highly relevant questions.

[0081] The question suggestion unit can analyze the patient's social media activity and suggest relevant questions when suggesting questions. For example, the question suggestion unit can suggest questions based on dietary information shared by the patient on social media. For example, the question suggestion unit can suggest questions based on exercise habit information shared by the patient on social media. For example, the question suggestion unit can suggest questions based on stress level information shared by the patient on social media. In this way, relevant questions can be suggested by analyzing social media activity. Some or all of the above processing in the question suggestion unit may be performed using AI, for example, or without AI. For example, the question suggestion unit can input the patient's social media activity data into a generating AI and have the generating AI suggest relevant questions.

[0082] The information provision unit can estimate the patient's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the patient is feeling anxious, the information provision unit will provide information using reassuring language. For example, if the patient is relaxed, the information provision unit will provide detailed information in a way that is easy for the patient to understand. For example, if the patient is in a hurry, the information provision unit will provide concise and to-the-point information. In this way, by adjusting the way the information is presented according to the patient's emotions, appropriate information can be provided to the patient. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can input patient emotion data into a generative AI and have the generative AI adjust the way the information is presented.

[0083] The information provision unit can analyze the patient's past health data to select the most relevant information when providing information. For example, the information provision unit can provide information on hypertension based on the patient's past blood pressure data. For example, the information provision unit can refer to the patient's past diagnostic results to provide relevant health information. For example, the information provision unit can provide health advice based on the patient's past lifestyle data. In this way, by analyzing past health data, the most relevant information can be provided. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the patient's past health data into a generating AI and have the generating AI select the most relevant information.

[0084] The information provision unit can provide information while considering the patient's lifestyle and medical history. For example, the information provision unit can consider the patient's eating habits and provide health information related to diet. For example, the information provision unit can consider the patient's exercise habits and provide health information related to exercise. For example, the information provision unit can consider the patient's medical history and provide health information related to past illnesses. In this way, appropriate information can be provided by considering lifestyle and medical history. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input data on the patient's lifestyle and medical history into a generating AI and have the generating AI perform the information provision.

[0085] The information provision unit can estimate the patient's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the patient is feeling anxious, the information provision unit will provide the most important information first. For example, if the patient is relaxed, the information provision unit will provide detailed information sequentially. For example, if the patient is in a hurry, the information provision unit will provide concise information summarizing the key points. This ensures that the most important information is appropriately provided by prioritizing information according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can input patient emotion data into a generative AI and have the generative AI perform the determination of information priority.

[0086] The information provision unit can reflect region-specific health risks by considering the patient's geographical location when providing information. For example, if the patient lives at high altitude, the information provision unit will provide information on health risks specific to high altitude. For example, if the patient lives in an urban area, the information provision unit will provide information on health risks specific to urban areas. For example, if the patient lives in a rural area, the information provision unit will provide information on health risks specific to rural areas. In this way, by considering geographical location, it is possible to provide information that reflects region-specific health risks. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the patient's geographical location information into a generating AI and have the generating AI perform the task of providing information that reflects region-specific health risks.

[0087] The information provision unit can analyze the patient's social media activity and provide relevant health information when providing information. For example, the information provision unit can provide health information based on dietary information shared by the patient on social media. For example, the information provision unit can provide health information based on exercise habit information shared by the patient on social media. For example, the information provision unit can provide health information based on stress level information shared by the patient on social media. In this way, relevant health information can be provided by analyzing social media activity. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the patient's social media activity data into a generating AI and have the generating AI perform the provision of relevant health information.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The health checkup support system can also include a genetic information analysis unit that analyzes the patient's genetic information. The genetic information analysis unit analyzes the patient's genetic data and assesses genetic risks. For example, the genetic information analysis unit assesses the genetic risk for a specific disease based on the patient's genetic data and provides the results to the interpretation unit. The interpretation unit can then interpret the patient's health status in more detail based on the genetic risk information provided by the genetic information analysis unit. For example, if the genetic information analysis unit assesses the genetic risk of hypertension from the patient's genetic data, the interpretation unit can interpret the blood pressure results based on that information. This makes it possible to interpret health checkup results more accurately by considering genetic information.

[0090] The health checkup support system can further include an advice-providing unit that estimates the patient's emotions and provides health advice based on those emotions. For example, if the patient is feeling anxious, the advice-providing unit will provide advice on how to relax. If the patient is feeling stressed, the advice-providing unit will suggest specific ways to reduce stress. If the patient is feeling motivated, the advice-providing unit will provide proactive advice on maintaining health. In this way, by providing health advice tailored to the patient's emotions, the system can support the patient's health management.

[0091] The health checkup support system may also include an environmental data collection unit that collects data on the patient's living environment. The environmental data collection unit, for example, collects data on the patient's living and working environment and provides it to the interpretation unit. The interpretation unit interprets the patient's health status based on the data provided by the environmental data collection unit. For example, the environmental data collection unit could collect data on the patient's living environment, and the interpretation unit could use that data to assess allergy risk. This would enable the interpretation of health checkup results that takes the living environment into account.

[0092] The health checkup support system may also include a notification adjustment unit that estimates the patient's emotions and adjusts the method of notifying the patient of the diagnosis results based on those emotions. For example, if the patient is feeling anxious, the notification adjustment unit may notify the patient of the diagnosis results in gentle language. If the patient is relaxed, the notification adjustment unit may notify the patient of detailed diagnosis results. If the patient is in a hurry, the notification adjustment unit may notify the patient of concise and to-the-point diagnosis results. This allows for a deeper understanding of the patient's condition by providing an appropriate notification method that matches their emotions.

[0093] The health checkup support system may also include an exercise data collection unit that collects patient exercise data. The exercise data collection unit, for example, collects data on the patient's exercise volume and exercise habits and provides it to the interpretation unit. The interpretation unit interprets the patient's health status based on the data provided by the exercise data collection unit. For example, the exercise data collection unit can collect patient exercise volume data, and the interpretation unit can use that data to evaluate cardiopulmonary function. This makes it possible to interpret health checkup results while taking exercise data into consideration.

[0094] The health checkup support system may further include a feedback adjustment unit that estimates the patient's emotions and adjusts the feedback on the diagnostic results based on the estimated emotions. For example, if the patient is feeling anxious, the feedback adjustment unit provides reassuring feedback. For example, if the patient is relaxed, the feedback adjustment unit provides detailed feedback. For example, if the patient is in a hurry, the feedback adjustment unit provides concise and to-the-point feedback. This allows for a deeper understanding of the patient by providing appropriate feedback tailored to their emotions.

[0095] The health checkup support system may also include a dietary data collection unit that collects patient dietary data. The dietary data collection unit, for example, collects data on the patient's diet and eating habits and provides it to the interpretation unit. The interpretation unit interprets the patient's health status based on the data provided by the dietary data collection unit. For example, the dietary data collection unit can collect data on the patient's diet, and the interpretation unit can evaluate nutritional balance based on that data. This makes it possible to interpret health checkup results while taking dietary data into consideration.

[0096] The health checkup support system may further include a goal-setting unit that estimates the patient's emotions and sets health goals based on those estimated emotions. For example, if the patient is feeling anxious, the goal-setting unit will set easily achievable health goals. For example, if the patient is relaxed, the goal-setting unit will set challenging health goals. For example, if the patient is motivated, the goal-setting unit will set long-term health goals. This allows the system to support the patient's health management by setting health goals that are appropriate to the patient's emotions.

[0097] The health checkup support system may also include a sleep data collection unit that collects patient sleep data. The sleep data collection unit, for example, collects data on the patient's sleep duration and sleep quality, and provides it to the interpretation unit. The interpretation unit interprets the patient's health status based on the data provided by the sleep data collection unit. For example, the sleep data collection unit can collect the patient's sleep duration data, and the interpretation unit can use that data to assess the risk of sleep deprivation. This makes it possible to interpret health checkup results while taking sleep data into consideration.

[0098] The health checkup support system can also include an education delivery unit that estimates the patient's emotions and provides health education based on those estimated emotions. For example, if the patient is feeling anxious, the education delivery unit will provide reassuring health education. For example, if the patient is relaxed, the education delivery unit will provide detailed health education. For example, if the patient is in a hurry, the education delivery unit will provide concise and to-the-point health education. This allows for a deeper understanding of the patient by providing appropriate health education tailored to their emotions.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The interpreter interprets the patient's preliminary data. The interpreter interprets specific health indicators such as blood pressure, heart rate, and blood glucose levels. The interpreter can perform interpretations using statistical methods or machine learning algorithms. For example, the interpreter can use machine learning algorithms to analyze the patient's blood pressure data and assess the risk of hypertension. Step 2: The explanatory section explains the results interpreted by the interpreting section. The explanatory section explains the interpretation results in an easy-to-understand manner, for example, by using text generation or speech synthesis. For example, the explanatory section uses text generation technology to explain to the patient that "a persistently high blood pressure increases the risk of heart disease." Step 3: The question suggestion unit proposes appropriate medical questions based on the results explained by the explanation unit. For example, the question suggestion unit proposes questions tailored to the patient's health condition. For example, the question suggestion unit might suggest to a patient with high blood pressure, "Please tell me about your recent eating habits and exercise routine." Step 4: The Information Provision Department provides additional information based on the questions proposed by the Question Proposal Department. For example, the Information Provision Department provides patients with information on the causes and prevention methods of hypertension. For example, the Information Provision Department provides information such as, "Possible causes of hypertension include excessive salt intake and stress."

[0101] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0104] Each of the multiple elements described above, including the interpretation unit, explanation unit, question suggestion unit, and information provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the interpretation unit is implemented by the processor 46 of the smart device 14 and analyzes the patient's preliminary values. The explanation unit is implemented by the specific processing unit 290 of the data processing device 12 and provides an easy-to-understand explanation of the interpretation results. The question suggestion unit is implemented by the control unit 46A of the smart device 14 and suggests appropriate medical questions. The information provision unit is implemented by the specific processing unit 290 of the data processing device 12 and provides additional information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the interpretation unit, explanation unit, question suggestion unit, and information provision unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the interpretation unit is implemented by the processor 46 of the smart glasses 214 and analyzes the patient's preliminary values. The explanation unit is implemented by the specific processing unit 290 of the data processing device 12 and provides an easy-to-understand explanation of the interpretation results. The question suggestion unit is implemented by the control unit 46A of the smart glasses 214 and suggests appropriate medical questions. The information provision unit is implemented by the specific processing unit 290 of the data processing device 12 and provides additional information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the interpretation unit, explanation unit, question suggestion unit, and information provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the interpretation unit is implemented by the processor 46 of the headset terminal 314 and analyzes the patient's preliminary values. The explanation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides an easy-to-understand explanation of the interpretation results. The question suggestion unit is implemented by the control unit 46A of the headset terminal 314 and suggests appropriate medical questions. The information provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides additional information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 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 a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the interpretation unit, explanation unit, question suggestion unit, and information provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the interpretation unit is implemented by the processor 46 of the robot 414 and analyzes the patient's preliminary values. The explanation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides an easy-to-understand explanation of the interpretation results. The question suggestion unit is implemented by the control unit 46A of the robot 414 and suggests appropriate medical questions. The information provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides additional information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) An interpretation unit that interprets the patient's preliminary values, An explanatory section that explains the results interpreted by the aforementioned interpreting section, A question suggestion unit proposes appropriate medical interview questions based on the results explained by the explanation unit, The system includes an information providing unit that provides additional information based on the questions proposed by the question proposal unit. A system characterized by the following features. (Note 2) The aforementioned interpretation section is: We estimate the patient's emotions and adjust the interpretation method of the preliminary results based on the estimated patient emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned interpretation section is: Analyzing patients' past health data to improve the accuracy of interpretation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned interpretation section is: When interpreting preliminary results, the patient's lifestyle and medical history should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned interpretation section is: The system estimates the patient's emotions and prioritizes the interpretation results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned interpretation section is: When interpreting preliminary figures, consider the patients' geographical location to reflect region-specific health risks. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned interpretation section is: When interpreting preliminary data, we analyze patients' social media activity and obtain relevant health information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The above explanatory section is, The system estimates the patient's emotions and adjusts the way explanations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The above explanatory section is, During explanations, adjust the level of detail based on the patient's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 10) The above explanatory section is, During explanations, different explanation algorithms are applied depending on the patient's health literacy. The system described in Appendix 1, characterized by the features described herein. (Note 11) The above explanatory section is, The system estimates the patient's emotions and adjusts the length of the explanation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The above explanatory section is, During the explanation, prioritize the explanation based on when the patient submitted their information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The above explanatory section is, During the explanation, adjust the order of explanations based on the patient's relevance. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned question proposal section, The system estimates the patient's emotions and adjusts the wording of questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned question proposal section, When proposing questions, the system analyzes the patient's past response history to select the most appropriate questions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned question proposal section, When proposing questions, take into consideration the patient's lifestyle and medical history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned question proposal section, The system estimates the patient's emotions and prioritizes questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned question proposal section, When proposing questions, the system prioritizes suggesting highly relevant questions by considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned question proposal section, When proposing questions, analyze the patient's social media activity and suggest relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned information provision unit, We estimate the patient's emotions and adjust the way we present information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned information provision unit, When providing information, the system analyzes the patient's past health data to select the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned information provision unit, When providing information, we will take into consideration the patient's lifestyle and medical history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned information provision unit, The system estimates the patient's emotions and prioritizes the information to be provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned information provision unit, When providing information, consider the patient's geographical location to reflect region-specific health risks. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned information provision unit, When providing information, we analyze the patient's social media activity and provide relevant health information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An interpretation unit that interprets the patient's preliminary values, An explanatory section that explains the results interpreted by the aforementioned interpreting section, A question suggestion unit proposes appropriate medical interview questions based on the results explained by the explanation unit, The system includes an information providing unit that provides additional information based on the questions proposed by the question proposal unit. A system characterized by the following features.

2. The aforementioned interpretation section is: We estimate the patient's emotions and adjust the interpretation method of the preliminary results based on the estimated patient emotions. The system according to feature 1.

3. The aforementioned interpretation section is: Analyzing patients' past health data to improve the accuracy of interpretation. The system according to feature 1.

4. The aforementioned interpretation section is: When interpreting preliminary results, the patient's lifestyle and medical history should be taken into consideration. The system according to feature 1.

5. The aforementioned interpretation section is: The system estimates the patient's emotions and prioritizes the interpretation results based on the estimated emotions. The system according to feature 1.

6. The aforementioned interpretation section is: When interpreting preliminary figures, consider the patients' geographical location to reflect region-specific health risks. The system according to feature 1.

7. The aforementioned interpretation section is: When interpreting preliminary data, we analyze patients' social media activity and obtain relevant health information. The system according to feature 1.

8. The above explanatory section is, The system estimates the patient's emotions and adjusts the way explanations are presented based on those estimated emotions. The system according to feature 1.

9. The above explanatory section is, During explanations, adjust the level of detail based on the patient's level of understanding. The system according to feature 1.

10. The above explanatory section is, During explanations, different explanation algorithms are applied depending on the patient's health literacy. The system according to feature 1.

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