System

Abstract

An object of a system according to an embodiment is to quickly propose and provide an appropriate drug based on a health condition and a symptom of a user.SOLUTION: A system includes an inquiry part, a medicine proposal part, a medicine provision part, and a health data management part. The inquiry unit collects information on a health condition and a symptom of the user. The medicine proposing section analyzes the information collected by the medical interview section and proposes an appropriate medicine. The medicine providing section provides the medicine suggested by the medicine suggesting section. The health data management unit manages health data of a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately suggest and provide appropriate medications quickly based on the user's health condition and symptoms, and there is room for improvement.

[0005] The system according to the embodiment aims to quickly suggest and provide appropriate medicines based on the user's health condition and symptoms. [Means for solving the problem]

[0006] The system according to the embodiment includes a medical interview unit, a medicine suggestion unit, a medicine provision unit, and a health data management unit. The medical interview unit collects information on the user's health condition and symptoms. The medicine suggestion unit analyzes the information collected by the medical interview unit and suggests appropriate medicines. The medicine provision unit provides the medicines suggested by the medicine suggestion unit. The health data management unit manages the user's health data. [Effects of the Invention]

[0007] The system according to the embodiment can quickly suggest and provide appropriate medication based on the user's health condition and symptoms. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The health management system according to an embodiment of the present invention is a system in which a generative AI collects and analyzes information about a user's health condition and symptoms, suggests appropriate medications, and provides them as medicine. This enables the health management system to continuously manage the user's health condition and provide appropriate medications.

[0029] A health management system according to an embodiment includes a medical interview unit, a medicine suggestion unit, a medicine provision unit, and a health data management unit. The medical interview unit collects information about a user's health condition and symptoms. For example, the user can input their health condition through a smartphone app. The medical interview unit can also collect the user's health condition using voice input. The medical interview unit can also evaluate the user's current health condition by referring to the user's past health data. The medicine suggestion unit analyzes the information collected by the medical interview unit to suggest appropriate medicines. For example, the generation AI can suggest painkillers or antibiotics based on the user's symptoms. The medicine suggestion unit can also select medicines taking into account the user's allergy information and past medicine use history. The medicine suggestion unit can also analyze the user's lifestyle data and suggest optimal medicines. The medicine provision unit provides the medicines suggested by the medicine suggestion unit. For example, the medicines can be delivered to the user's home. The medicine provision unit can also arrange for the user to pick up the medicines at a nearby pharmacy. The medicine provision unit can also analyze the user's medicine usage patterns and suggest optimal times to refill medicines. The health data management unit manages the user's health data. For example, the generation AI continuously collects and analyzes the user's health data. The health data management unit can also anonymize the user's health data and perform comparative analysis with other users to provide benchmarks. Furthermore, the health data management unit can create a personalized health plan based on the user's health data. This allows the health management system according to the embodiment to continuously manage the user's health condition and provide appropriate medication. For example, the user can easily undergo a medical interview at home and obtain appropriate medication. Furthermore, the generation AI's continuous management of health data enables more accurate health management.

[0030] The medical interview section can collect data on the user's lifestyle habits and reflect it in the medical interview. For example, the generation AI in the medical interview section analyzes the user's sleep patterns and provides advice on improving sleep if lack of sleep is likely the cause. For example, if the user stays up late, it recommends going to bed earlier and getting up earlier. The medical interview section also analyzes the user's diet and provides advice on improving the diet if the nutritional balance is unbalanced. For example, if the user does not eat many vegetables, it recommends eating more vegetables. The medical interview section also analyzes the user's exercise volume and provides exercise advice if lack of exercise is likely the cause. For example, if the user hardly exercises, it recommends walking 30 minutes every day. This allows the user's lifestyle habits data to be reflected in the medical interview, enabling more precise medical interviews.

[0031] The drug suggestion unit can analyze a user's past drug use history and make suggestions taking into account the drug's effectiveness and side effects. For example, the generation AI in the drug suggestion unit analyzes a user's past drug use history and prioritizes suggesting effective drugs. For example, if a painkiller used in the past was effective, the same drug will be suggested. The drug suggestion unit also analyzes a user's past drug use history and avoids drugs with side effects. For example, if an antibiotic used in the past caused an allergic reaction, that drug will be avoided. The drug suggestion unit also analyzes a user's past drug use history and suggests the optimal drug by considering the balance between effectiveness and side effects. For example, the drug most suitable for the user's condition can be selected from a drug with high effectiveness but strong side effects, and a drug with low effectiveness but few side effects. This makes it possible to suggest more appropriate drugs by taking past drug use history into consideration.

[0032] The drug suggestion unit can check the user's allergy information and drug interactions in real time and select the most appropriate drug. For example, the generation AI in the drug suggestion unit analyzes the user's allergy information and selects drugs that do not cause allergic reactions. For example, if the user is allergic to a specific ingredient, the generation AI will suggest drugs that do not contain that ingredient. The drug suggestion unit also analyzes the user's drug interactions and selects drugs that do not interact. For example, it will avoid drugs that interact with drugs currently being taken. The drug suggestion unit also analyzes the user's allergy information and drug interactions in combination and selects the most appropriate drug. For example, it will suggest drugs that do not cause allergic reactions and do not have any interactions. This makes it possible to suggest safer drugs by checking allergy information and drug interactions in real time.

[0033] The medicine provision unit can analyze the user's medicine usage pattern and suggest refilling medicine at the optimal time. For example, the generation AI in the medicine provision unit analyzes the user's medicine usage pattern and suggests refilling medicine before the medicine runs out. For example, the medicine provision unit calculates the timing of refilling based on the frequency of medicine use. The generation AI in the medicine provision unit also analyzes the user's medicine usage pattern and suggests refilling earlier if the amount of medicine used is increasing. For example, if the pain is getting worse, the generation AI in the medicine provision unit also delays the suggestion of refilling if the amount of medicine used is decreasing. For example, if the symptoms are improving, the timing of refilling is delayed. In this way, by analyzing the medicine usage pattern, it is possible to suggest refilling medicine at the optimal time.

[0034] The health data management unit can analyze the user's health data, predict long-term health trends, and provide feedback. In the health data management unit, for example, the generation AI analyzes the user's health data and predicts long-term health trends. For example, it predicts future health risks based on past data. In addition, the generation AI analyzes the user's health data and detects changes in health trends. For example, it analyzes weight gain or loss and blood pressure fluctuations. In addition, the generation AI analyzes the user's health data and provides feedback based on health trends. For example, if health risks are increasing, it suggests improving lifestyle habits. This makes it possible to predict long-term health trends and provide appropriate feedback to the user.

[0035] The health data management unit anonymizes the user's health data and performs comparative analysis with other users to provide a benchmark. In the health data management unit, for example, the generation AI anonymizes the user's health data and performs comparative analysis with other users. For example, it compares the health status with users of the same age. In addition, the health data management unit anonymizes the user's health data and compares the health status by region. For example, it compares the health status with users living in the same region. In addition, the generation AI anonymizes the user's health data and provides a benchmark. For example, it evaluates the user's health status based on the average value and standard deviation. This makes it possible to use the anonymized health data to perform comparative analysis with other users and provide a benchmark.

[0036] The health data management unit can create a personalized health plan based on the user's health data. In the health data management unit, for example, the generation AI analyzes the user's health data and creates a personalized health plan. For example, it provides a meal and exercise plan based on the user's health condition. In addition, the health data management unit analyzes the user's health data and creates a preventive plan based on health risks. For example, if the risk of heart disease is high, it suggests heart-healthy meals and exercise. In addition, the health data management unit analyzes the user's health data and creates a plan based on health goals. For example, if the user wants to lose weight, it provides a calorie restriction and exercise plan. This makes it possible to provide a personalized health plan based on the user's health data.

[0037] The health data management unit can share the user's health data with other medical institutions to support comprehensive health management. For example, the generation AI shares the user's health data with other medical institutions to support comprehensive health management. For example, a doctor makes a diagnosis based on the user's health data. The health data management unit also allows the generation AI to share the user's health data with other medical institutions to create a treatment plan. For example, multiple medical institutions work together to provide a treatment plan. The health data management unit also allows the generation AI to share the user's health data with other medical institutions to support emergency response. For example, health data is provided so that doctors can respond quickly in an emergency. In this way, sharing health data with other medical institutions enables comprehensive health management.

[0038] The user interface unit can analyze the user's usage patterns and suggest customization of the interface. For example, the generation AI in the user interface unit analyzes the user's usage patterns and suggests customization of the interface. For example, it may prioritize displaying functions that the user uses frequently. The generation AI in the user interface unit also analyzes the user's usage patterns and suggests an easy-to-use layout. For example, it may change the button placement or menu configuration. The generation AI in the user interface unit also analyzes the user's usage patterns and suggests adding necessary functions. For example, it may add functions that the user uses frequently. In this way, usability is improved by customizing the interface based on the user's usage patterns.

[0039] The user interface unit can collect user feedback in real time and improve the interface. For example, the generation AI in the user interface unit collects user feedback in real time and improves the interface. For example, it changes the button layout based on user opinions. The generation AI in the user interface unit also collects user feedback in real time and makes improvements to improve usability. For example, it improves usability and fixes bugs. The generation AI in the user interface unit also collects user feedback in real time and suggests adding new features. For example, it adds new features in response to user requests. In this way, feedback is collected in real time and the interface is improved, thereby increasing user satisfaction.

[0040] The user interface unit can provide the optimal interface depending on the user's device environment. For example, the generation AI in the user interface unit analyzes the user's device environment and provides the optimal interface. For example, different layouts may be proposed for smartphones and tablets. The generation AI in the user interface unit also analyzes the user's device environment and optimizes functions depending on the device. For example, simple operations may be possible on smartphones, and detailed operations may be possible on PCs. The generation AI in the user interface unit also analyzes the user's device environment and optimizes the display depending on the device. For example, the display content may be adjusted depending on the screen size. This improves usability by providing the optimal interface depending on the user's device environment.

[0041] The user interface unit can localize the interface according to the user's language and culture. In the user interface unit, for example, the generation AI analyzes the user's language and localizes the interface. For example, it provides a display according to the language used by the user. In addition, the generation AI analyzes the user's culture and localizes the interface according to the culture. For example, it provides a display according to local customs and cultural background. In addition, the generation AI analyzes the user's language and culture and provides the optimal interface. For example, it performs language translation and cultural adaptation. This improves usability by localizing the interface according to the user's language and culture.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The health management system can also include a sensor unit that monitors the user's health condition in real time. For example, a wearable device can continuously measure the user's heart rate and blood pressure and send an alert if an abnormality is detected. The sensor unit can also measure the user's body temperature and blood sugar level and notify a doctor if an abnormal value is detected. Furthermore, the sensor unit can monitor the user's exercise level and sleep patterns and grasp changes in the user's health condition in real time. This makes it possible to manage the user's health condition in more detail and detect abnormalities early.

[0044] The health management system can also include a nutrition management unit that collects the user's dietary data and evaluates nutritional balance. For example, when the user enters their dietary information into the app, the generative AI analyzes nutrient intake and proposes a balanced meal plan. The nutrition management unit can also provide meal plans that enhance specific nutrients based on the user's health status and goals. Furthermore, the nutrition management unit provides feedback on areas for improvement based on the user's dietary data, supporting a healthy diet. This allows the user to practice a nutritionally balanced diet and maintain good health.

[0045] The health management system can also include an exercise management unit that collects the user's exercise data and proposes an exercise plan. For example, the generative AI analyzes the user's exercise volume and type and provides an exercise plan tailored to the user's individual health condition and goals. The exercise management unit can also evaluate the effectiveness of the exercise based on the user's exercise data and adjust the plan as necessary. Furthermore, the exercise management unit can compare the user's exercise data with other users and provide feedback to increase motivation. This allows the user to implement an effective exercise plan and improve their health.

[0046] The health management system can also include a preventive medicine section that supports preventive medicine based on the user's health data. For example, the generative AI analyzes the user's health data and predicts future health risks. If the risk is high, it suggests preventive measures. The preventive medicine section can also suggest schedules for regular health checks and examinations based on the user's health data. Furthermore, the preventive medicine section can share the user's health data with other medical institutions to create a comprehensive preventive medicine plan. This allows the user to reduce future health risks and maintain their health.

[0047] The health management system can further include an education module that provides personalized health education content based on the user's health data. For example, the generative AI analyzes the user's health data and provides educational content tailored to the user's individual health status and goals. The education module can also provide information on specific health risks and preventative measures based on the user's health data. Furthermore, the education module can compare the user's health data with other users and share best practices. This allows users to deepen their health knowledge and practice more effective health management.

[0048] The health management system can also include a community section that provides community functions based on the user's health data. For example, the generative AI analyzes the user's health data and matches users with the same health goals. The community section can also provide a platform where users can share health information and experiences. Furthermore, the community section can set group challenges and joint goals based on the user's health data and plan events to increase motivation. This allows users to manage their health while collaborating with other users and maintain their motivation.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The medical interview unit collects information about the user's health condition and symptoms. For example, the user can input their health condition through a smartphone app. The medical interview unit can also collect the user's health condition using voice input. Furthermore, the medical interview unit can refer to the user's past health data to evaluate their current health condition. Step 2: The drug suggestion unit analyzes the information collected by the interview unit and suggests appropriate medications. For example, the generation AI can suggest painkillers or antibiotics based on the user's symptoms. The drug suggestion unit can also select medications taking into account the user's allergy information and past drug use history. Furthermore, the drug suggestion unit can also analyze the user's lifestyle data and suggest the most appropriate medication. Step 3: The medicine providing unit provides the medicine suggested by the medicine suggestion unit. For example, the medicine can be delivered to the user's home. The medicine providing unit can also arrange for the medicine to be picked up at a nearby pharmacy. Furthermore, the medicine providing unit can analyze the user's medicine usage pattern and suggest optimal timing for refilling the medicine. Step 4: The health data management unit manages the user's health data. For example, the generative AI continuously collects and analyzes the user's health data. The health data management unit can also anonymize the user's health data and perform comparative analysis with other users to provide benchmarks. Furthermore, the health data management unit can create a personalized health plan based on the user's health data.

[0051] (Example 2) The health management system according to an embodiment of the present invention is a system in which a generative AI collects and analyzes information about a user's health condition and symptoms, suggests appropriate medications, and provides them as medicine. This enables the health management system to continuously manage the user's health condition and provide appropriate medications.

[0052] A health management system according to an embodiment includes a medical interview unit, a medicine suggestion unit, a medicine provision unit, and a health data management unit. The medical interview unit collects information about a user's health condition and symptoms. For example, the user can input their health condition through a smartphone app. The medical interview unit can also collect the user's health condition using voice input. The medical interview unit can also evaluate the user's current health condition by referring to the user's past health data. The medicine suggestion unit analyzes the information collected by the medical interview unit to suggest appropriate medicines. For example, the generation AI can suggest painkillers or antibiotics based on the user's symptoms. The medicine suggestion unit can also select medicines taking into account the user's allergy information and past medicine use history. The medicine suggestion unit can also analyze the user's lifestyle data and suggest optimal medicines. The medicine provision unit provides the medicines suggested by the medicine suggestion unit. For example, the medicines can be delivered to the user's home. The medicine provision unit can also arrange for the user to pick up the medicines at a nearby pharmacy. The medicine provision unit can also analyze the user's medicine usage patterns and suggest optimal times to refill medicines. The health data management unit manages the user's health data. For example, the generation AI continuously collects and analyzes the user's health data. The health data management unit can also anonymize the user's health data and perform comparative analysis with other users to provide benchmarks. Furthermore, the health data management unit can create a personalized health plan based on the user's health data. This allows the health management system according to the embodiment to continuously manage the user's health condition and provide appropriate medication. For example, the user can easily undergo a medical interview at home and obtain appropriate medication. Furthermore, the generation AI's continuous management of health data enables more accurate health management.

[0053] The medical interview unit can analyze the user's voice tone and speaking patterns to estimate their emotional state and adjust the content of the interview accordingly. For example, the generation AI in the medical interview unit analyzes the user's voice tone and, if they are feeling stressed or anxious, provides advice on how to relax. For example, if the user is nervous, the generation AI speaks in a gentle tone. The medical interview unit also analyzes the user's speaking patterns to estimate their emotional state. For example, if the user is speaking quickly, the generation AI encourages them to speak more slowly. The medical interview unit also analyzes the user's voice tone and speaking patterns in combination to estimate a more accurate emotional state. For example, if the user has a low voice tone and speaks slowly, the generation AI determines that the user is relaxed. This allows the content of the interview to be adjusted according to the user's emotional state, enabling more appropriate medical interviews.

[0054] The medical interview section can collect data on the user's lifestyle habits and reflect it in the medical interview. For example, the generation AI in the medical interview section analyzes the user's sleep patterns and provides advice on improving sleep if lack of sleep is likely the cause. For example, if the user stays up late, it recommends going to bed earlier and getting up earlier. The medical interview section also analyzes the user's diet and provides advice on improving the diet if the nutritional balance is unbalanced. For example, if the user does not eat many vegetables, it recommends eating more vegetables. The medical interview section also analyzes the user's exercise volume and provides exercise advice if lack of exercise is likely the cause. For example, if the user hardly exercises, it recommends walking 30 minutes every day. This allows the user's lifestyle habits data to be reflected in the medical interview, enabling more precise medical interviews.

[0055] The drug suggestion unit can analyze a user's past drug use history and make suggestions taking into account the drug's effectiveness and side effects. For example, the generation AI in the drug suggestion unit analyzes a user's past drug use history and prioritizes suggesting effective drugs. For example, if a painkiller used in the past was effective, the same drug will be suggested. The drug suggestion unit also analyzes a user's past drug use history and avoids drugs with side effects. For example, if an antibiotic used in the past caused an allergic reaction, that drug will be avoided. The drug suggestion unit also analyzes a user's past drug use history and suggests the optimal drug by considering the balance between effectiveness and side effects. For example, the drug most suitable for the user's condition can be selected from a drug with high effectiveness but strong side effects, and a drug with low effectiveness but few side effects. This makes it possible to suggest more appropriate drugs by taking past drug use history into consideration.

[0056] The drug suggestion unit can check the user's allergy information and drug interactions in real time and select the most appropriate drug. For example, the generation AI in the drug suggestion unit analyzes the user's allergy information and selects drugs that do not cause allergic reactions. For example, if the user is allergic to a specific ingredient, the generation AI will suggest drugs that do not contain that ingredient. The drug suggestion unit also analyzes the user's drug interactions and selects drugs that do not interact. For example, it will avoid drugs that interact with drugs currently being taken. The drug suggestion unit also analyzes the user's allergy information and drug interactions in combination and selects the most appropriate drug. For example, it will suggest drugs that do not cause allergic reactions and do not have any interactions. This makes it possible to suggest safer drugs by checking allergy information and drug interactions in real time.

[0057] The medicine provision unit can analyze the user's medicine usage pattern and suggest refilling medicine at the optimal time. For example, the generation AI in the medicine provision unit analyzes the user's medicine usage pattern and suggests refilling medicine before the medicine runs out. For example, the medicine provision unit calculates the timing of refilling based on the frequency of medicine use. The generation AI in the medicine provision unit also analyzes the user's medicine usage pattern and suggests refilling earlier if the amount of medicine used is increasing. For example, if the pain is getting worse, the generation AI in the medicine provision unit also delays the suggestion of refilling if the amount of medicine used is decreasing. For example, if the symptoms are improving, the timing of refilling is delayed. In this way, by analyzing the medicine usage pattern, it is possible to suggest refilling medicine at the optimal time.

[0058] The health data management unit can analyze the user's health data, predict long-term health trends, and provide feedback. In the health data management unit, for example, the generation AI analyzes the user's health data and predicts long-term health trends. For example, it predicts future health risks based on past data. In addition, the generation AI analyzes the user's health data and detects changes in health trends. For example, it analyzes weight gain or loss and blood pressure fluctuations. In addition, the generation AI analyzes the user's health data and provides feedback based on health trends. For example, if health risks are increasing, it suggests improving lifestyle habits. This makes it possible to predict long-term health trends and provide appropriate feedback to the user.

[0059] The health data management unit anonymizes the user's health data and performs comparative analysis with other users to provide a benchmark. In the health data management unit, for example, the generation AI anonymizes the user's health data and performs comparative analysis with other users. For example, it compares the health status with users of the same age. In addition, the health data management unit anonymizes the user's health data and compares the health status by region. For example, it compares the health status with users living in the same region. In addition, the generation AI anonymizes the user's health data and provides a benchmark. For example, it evaluates the user's health status based on the average value and standard deviation. This makes it possible to use the anonymized health data to perform comparative analysis with other users and provide a benchmark.

[0060] The health data management unit can create a personalized health plan based on the user's health data. In the health data management unit, for example, the generation AI analyzes the user's health data and creates a personalized health plan. For example, it provides a meal and exercise plan based on the user's health condition. In addition, the health data management unit analyzes the user's health data and creates a preventive plan based on health risks. For example, if the risk of heart disease is high, it suggests heart-healthy meals and exercise. In addition, the health data management unit analyzes the user's health data and creates a plan based on health goals. For example, if the user wants to lose weight, it provides a calorie restriction and exercise plan. This makes it possible to provide a personalized health plan based on the user's health data.

[0061] The health data management unit can share the user's health data with other medical institutions to support comprehensive health management. For example, the generation AI shares the user's health data with other medical institutions to support comprehensive health management. For example, a doctor makes a diagnosis based on the user's health data. The health data management unit also allows the generation AI to share the user's health data with other medical institutions to create a treatment plan. For example, multiple medical institutions work together to provide a treatment plan. The health data management unit also allows the generation AI to share the user's health data with other medical institutions to support emergency response. For example, health data is provided so that doctors can respond quickly in an emergency. In this way, sharing health data with other medical institutions enables comprehensive health management.

[0062] The health data management unit can use the emotion estimation function to provide advice to reduce the anxiety the user feels about health data feedback. For example, the health data management unit may have the generating AI analyze the user's emotional state and provide reassuring advice if the user feels anxious about the health data feedback. For example, the health data management unit may provide detailed explanations about the interpretation of the health data. The health data management unit may also have the generating AI analyze the user's emotional state and suggest relaxation methods to reduce anxiety. For example, the health data management unit may introduce deep breathing or meditation techniques. The health data management unit may also have the generating AI analyze the user's emotional state and provide support to reduce anxiety. For example, the health data management unit may introduce counseling services. This may make the user more receptive to health data feedback by providing advice to reduce anxiety.

[0063] The health data management unit can use the emotion estimation function to provide messages to reinforce the positive emotions the user feels in response to the health data feedback. For example, the health data management unit may have the generation AI analyze the user's emotional state, and if the user feels positive emotions in response to the health data feedback, provide a message to reinforce those emotions. For example, it may introduce success stories about improving health data. The health data management unit may also have the generation AI analyze the user's emotional state and provide encouraging words to reinforce the positive emotions. For example, it may send a message praising the achievement of a health goal. The health data management unit may also have the generation AI analyze the user's emotional state and share successful experiences to reinforce the positive emotions. For example, it may introduce success stories of other users. In this way, by providing messages that reinforce positive emotions, it is possible to increase the user's motivation to manage their health.

[0064] The user interface unit can analyze the user's usage patterns and suggest customization of the interface. For example, the generation AI in the user interface unit analyzes the user's usage patterns and suggests customization of the interface. For example, it may prioritize displaying functions that the user uses frequently. The generation AI in the user interface unit also analyzes the user's usage patterns and suggests an easy-to-use layout. For example, it may change the button placement or menu configuration. The generation AI in the user interface unit also analyzes the user's usage patterns and suggests adding necessary functions. For example, it may add functions that the user uses frequently. In this way, usability is improved by customizing the interface based on the user's usage patterns.

[0065] The user interface unit can collect user feedback in real time and improve the interface. For example, the generation AI in the user interface unit collects user feedback in real time and improves the interface. For example, it changes the button layout based on user opinions. The generation AI in the user interface unit also collects user feedback in real time and makes improvements to improve usability. For example, it improves usability and fixes bugs. The generation AI in the user interface unit also collects user feedback in real time and suggests adding new features. For example, it adds new features in response to user requests. In this way, feedback is collected in real time and the interface is improved, thereby increasing user satisfaction.

[0066] The user interface unit can use the emotion estimation function to suggest design changes to reduce the dissatisfaction the user feels with the interface. For example, the generation AI in the user interface unit analyzes the user's emotional state and suggests design changes if the user is dissatisfied with the interface. For example, it improves parts that the user finds difficult to use. The generation AI in the user interface unit also analyzes the user's emotional state and suggests color changes to reduce dissatisfaction. For example, if the user is feeling stressed, it changes the color to one that is relaxing. The generation AI in the user interface unit also analyzes the user's emotional state and suggests layout adjustments to reduce dissatisfaction. For example, it changes the button placement to make it easier for the user to operate. In this way, the usability of the interface is improved by suggesting design changes to reduce user dissatisfaction.

[0067] The user interface unit can provide the optimal interface depending on the user's device environment. For example, the generation AI in the user interface unit analyzes the user's device environment and provides the optimal interface. For example, different layouts may be proposed for smartphones and tablets. The generation AI in the user interface unit also analyzes the user's device environment and optimizes functions depending on the device. For example, simple operations may be possible on smartphones, and detailed operations may be possible on PCs. The generation AI in the user interface unit also analyzes the user's device environment and optimizes the display depending on the device. For example, the display content may be adjusted depending on the screen size. This improves usability by providing the optimal interface depending on the user's device environment.

[0068] The user interface unit can localize the interface according to the user's language and culture. In the user interface unit, for example, the generation AI analyzes the user's language and localizes the interface. For example, it provides a display according to the language used by the user. In addition, the generation AI analyzes the user's culture and localizes the interface according to the culture. For example, it provides a display according to local customs and cultural background. In addition, the generation AI analyzes the user's language and culture and provides the optimal interface. For example, it performs language translation and cultural adaptation. This improves usability by localizing the interface according to the user's language and culture.

[0069] The user interface unit can use the emotion estimation function to provide feedback to reinforce the positive emotions the user feels toward the interface. For example, the generation AI analyzes the user's emotional state, and if the user feels positive emotions toward the interface, the user interface unit provides feedback to reinforce those emotions. For example, the generation AI emphasizes the parts that the user finds easy to use. The user interface unit also analyzes the user's emotional state and provides encouraging words to reinforce the positive emotions. For example, if the user successfully performs an operation, the generation AI sends a message of praise. The user interface unit also analyzes the user's emotional state and shares successful experiences to reinforce the positive emotions. For example, the generation AI introduces success stories of other users. This provides feedback that reinforces positive emotions, thereby improving user satisfaction.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The health management system can also include a sensor unit that monitors the user's health condition in real time. For example, a wearable device can continuously measure the user's heart rate and blood pressure and send an alert if an abnormality is detected. The sensor unit can also measure the user's body temperature and blood sugar level and notify a doctor if an abnormal value is detected. Furthermore, the sensor unit can monitor the user's exercise level and sleep patterns and grasp changes in the user's health condition in real time. This makes it possible to manage the user's health condition in more detail and detect abnormalities early.

[0072] The health management system can also include a stress management module that estimates the user's emotional state and supports stress management. For example, the generative AI analyzes the user's tone of voice and facial expressions to estimate their stress level. If the stress level is high, it suggests relaxation methods and activities to relieve stress. The stress management module can also provide relaxing music or guided meditations at appropriate times depending on the user's stress level. Furthermore, the stress management module can continuously monitor the user's stress level and create a long-term stress management plan. This can reduce the user's stress and improve their overall health.

[0073] The health management system can also include a nutrition management unit that collects the user's dietary data and evaluates nutritional balance. For example, when the user enters their dietary information into the app, the generative AI analyzes nutrient intake and proposes a balanced meal plan. The nutrition management unit can also provide meal plans that enhance specific nutrients based on the user's health status and goals. Furthermore, the nutrition management unit provides feedback on areas for improvement based on the user's dietary data, supporting a healthy diet. This allows the user to practice a nutritionally balanced diet and maintain good health.

[0074] The health management system can also include an exercise management unit that collects the user's exercise data and proposes an exercise plan. For example, the generative AI analyzes the user's exercise volume and type and provides an exercise plan tailored to the user's individual health condition and goals. The exercise management unit can also evaluate the effectiveness of the exercise based on the user's exercise data and adjust the plan as necessary. Furthermore, the exercise management unit can compare the user's exercise data with other users and provide feedback to increase motivation. This allows the user to implement an effective exercise plan and improve their health.

[0075] The health management system can further include a feedback unit that estimates the user's emotional state and personalizes health data feedback. For example, the generative AI can analyze the user's emotional state and provide feedback emphasizing successes if the user is feeling positive emotions. Alternatively, if the user is feeling negative emotions, the system can provide encouraging messages or feedback that specifically suggests areas for improvement. Furthermore, the feedback unit can adjust the frequency and content of feedback according to the user's emotional state. This makes the user more receptive to health data feedback and increases their motivation to manage their health.

[0076] The health management system can also include a preventive medicine section that supports preventive medicine based on the user's health data. For example, the generative AI analyzes the user's health data and predicts future health risks. If the risk is high, it suggests preventive measures. The preventive medicine section can also suggest schedules for regular health checks and examinations based on the user's health data. Furthermore, the preventive medicine section can share the user's health data with other medical institutions to create a comprehensive preventive medicine plan. This allows the user to reduce future health risks and maintain their health.

[0077] The health management system can further include a support unit that estimates the user's emotional state and reduces anxiety about health data feedback. For example, the generative AI can analyze the user's emotional state and provide information to reassure them if they feel anxious. The support unit can also suggest relaxation methods or counseling services based on the user's emotional state. Furthermore, the support unit can continuously monitor the user's emotional state and adjust the support content as needed. This reduces the user's anxiety about health data feedback and enables more effective health management.

[0078] The health management system can further include an education module that provides personalized health education content based on the user's health data. For example, the generative AI analyzes the user's health data and provides educational content tailored to the user's individual health status and goals. The education module can also provide information on specific health risks and preventative measures based on the user's health data. Furthermore, the education module can compare the user's health data with other users and share best practices. This allows users to deepen their health knowledge and practice more effective health management.

[0079] The health management system can further include a motivation unit that estimates the user's emotional state and reinforces positive emotions in response to health data feedback. For example, the generative AI analyzes the user's emotional state and, if the user is feeling positive emotions, provides a message emphasizing successful experiences. The motivation unit can also provide encouraging words or messages of praise depending on the user's emotional state. Furthermore, the motivation unit can continuously monitor the user's emotional state and provide feedback to maintain positive emotions. This increases the user's motivation for health management and enables them to practice more effective health management.

[0080] The health management system can also include a community section that provides community functions based on the user's health data. For example, the generative AI analyzes the user's health data and matches users with the same health goals. The community section can also provide a platform where users can share health information and experiences. Furthermore, the community section can set group challenges and joint goals based on the user's health data and plan events to increase motivation. This allows users to manage their health while collaborating with other users and maintain their motivation.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The medical interview unit collects information about the user's health condition and symptoms. For example, the user can input their health condition through a smartphone app. The medical interview unit can also collect the user's health condition using voice input. Furthermore, the medical interview unit can refer to the user's past health data to evaluate their current health condition. Step 2: The drug suggestion unit analyzes the information collected by the interview unit and suggests appropriate medications. For example, the generation AI can suggest painkillers or antibiotics based on the user's symptoms. The drug suggestion unit can also select medications taking into account the user's allergy information and past drug use history. Furthermore, the drug suggestion unit can also analyze the user's lifestyle data and suggest the most appropriate medication. Step 3: The medicine providing unit provides the medicine suggested by the medicine suggestion unit. For example, the medicine can be delivered to the user's home. The medicine providing unit can also arrange for the medicine to be picked up at a nearby pharmacy. Furthermore, the medicine providing unit can analyze the user's medicine usage pattern and suggest optimal timing for refilling the medicine. Step 4: The health data management unit manages the user's health data. For example, the generative AI continuously collects and analyzes the user's health data. The health data management unit can also anonymize the user's health data and perform comparative analysis with other users to provide benchmarks. Furthermore, the health data management unit can create a personalized health plan based on the user's health data.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] 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.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a medical interview section that collects information about the user's health condition and symptoms; a medicine suggestion unit that analyzes the information collected by the medical interview unit and suggests an appropriate medicine; a medicine providing unit that provides medicines suggested by the medicine suggestion unit; a health data management unit that manages the user's health data; A system characterized by:

2. The interview unit Analyzing the user's tone of voice and speaking patterns, inferring their emotional state, and adjusting the content of the medical interview 2. The system of claim 1.

3. The medicine suggestion unit Analyze the user's past drug use history and make suggestions taking into account the drug's effects and side effects.

2. The system of claim 1.

4. The medicine providing unit includes: Analyze the user's medication usage patterns and suggest optimal timing for refilling medication.

2. The system of claim 1.

5. The health data management unit Analyzing the user's health data to predict long-term health trends and provide feedback 2. The system of claim 1.

6. The health data management unit Using an emotion estimation function, advice is provided to reduce the anxiety the user feels about the feedback of the health data.

2. The system of claim 1.

7. The user interface section is Analyzing the user's usage patterns and suggesting interface customizations 2. The system of claim 1.

8. The user interface section is Using emotion estimation, we propose design changes to reduce the dissatisfaction the user feels with the interface.

2. The system of claim 1.

Images