System

Abstract

An object of a system according to an embodiment is to allow a user to easily select a commercially available drug suitable for his / her symptoms and check a combination of drugs.SOLUTION: A system according to an embodiment includes a simple diagnosis unit, a symptom analysis unit, a medicine proposal unit, a purchase support unit, and a combination advice unit. The simple diagnostic unit analyzes symptoms of the user by using the generated AI, and performs simple diagnosis. The symptom analysis unit analyzes the symptom diagnosed by the simple diagnosis unit in detail. The drug proposal unit proposes an optimum commercially available drug based on the symptom analyzed by the symptom analysis unit. The purchase support unit provides a link or information for purchasing the non-prescription drug proposed by the drug proposal unit online. The combination advice unit gives advice on a combination of the drug currently used by the user and the commercially available drug proposed by the drug proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for users to select over-the-counter medicines that suit their symptoms and to check drug interactions.

[0005] The system according to the embodiment aims to enable users to easily select over-the-counter medicines that suit their symptoms and check drug interactions. [Means for solving the problem]

[0006] The system according to the embodiment includes a simple diagnosis unit, a symptom analysis unit, a drug proposal unit, a purchase support unit, and a drug combination advice unit. The simple diagnosis unit uses a generative AI to analyze the user's symptoms and perform a simple diagnosis. The symptom analysis unit performs a detailed analysis of the symptoms diagnosed by the simple diagnosis unit. The drug proposal unit proposes the most suitable over-the-counter drug based on the symptoms analyzed by the symptom analysis unit. The purchase support unit provides links and information for online purchase of the over-the-counter drug proposed by the drug proposal unit. The drug combination advice unit provides advice on the combination of the drug currently being used by the user with the over-the-counter drug proposed by the drug proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily select over-the-counter medicines that suit their symptoms and check drug interactions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The drug recommendation system according to an embodiment of the present invention uses generative AI to analyze a user's symptoms and centrally manages simple diagnosis, symptom analysis, and the recommendation and purchase of optimal drugs. As a result, the drug recommendation system can efficiently and safely recommend and purchase drugs for people who want to receive medical care but cannot, or who are too busy to go to the hospital.

[0029] The drug suggestion system according to the embodiment includes a simple diagnosis unit, a symptom analysis unit, a drug suggestion unit, a purchasing support unit, and a drug combination advice unit. The simple diagnosis unit uses a generation AI to analyze a user's symptoms and perform a simple diagnosis. For example, when a user inputs symptoms such as "I have a headache" or "I have a sore throat," the generation AI analyzes the information and identifies possible illnesses or conditions. The symptom analysis unit performs a detailed analysis of the symptoms diagnosed by the simple diagnosis unit. For example, based on information such as "My headache is continuing" or "My sore throat is getting worse," the generation AI determines the severity and urgency of the symptoms. The drug suggestion unit suggests the most appropriate over-the-counter drug based on the symptoms analyzed by the symptom analysis unit. For example, it suggests specific drug names and usage instructions, such as "This drug is effective for headaches" or "This drug is suitable for sore throats." The purchasing support unit provides links and information for purchasing over-the-counter drugs suggested by the drug suggestion unit online. For example, users can easily purchase drugs by clicking on a link provided by the generation AI. The drug combination advice unit provides advice on interactions between the user's current medications and the over-the-counter drugs suggested by the drug suggestion unit. For example, it can provide specific advice such as, "There is no problem if you take this medicine with this medicine" or "Taking this medicine with this medicine may cause side effects." This allows the drug recommendation system to efficiently and safely recommend and purchase medicines for people who want to receive medical care but cannot, or who are too busy to go to the hospital.

[0030] The simple diagnosis unit analyzes the user's past health data and lifestyle information to perform a more accurate simple diagnosis. For example, the generation AI analyzes the health data and lifestyle information previously entered by the user and compares it with current symptoms to perform a diagnosis. For example, it identifies the cause of specific symptoms by taking into account past dietary habits and exercise habits. The simple diagnosis unit also analyzes the user's past health checkup results and medical history to identify diseases or conditions that may be related to the current symptoms. For example, if a user who has previously been diagnosed with high blood pressure complains of a headache, the generation AI suggests that high blood pressure may be the cause. The simple diagnosis unit also analyzes the user's lifestyle information (e.g., smoking habits and alcohol intake) to evaluate how these factors affect the current symptoms. For example, it suggests that a sore throat in a user who smokes may be caused by smoking. This enables more accurate diagnoses by taking into account the user's past health data and lifestyle information.

[0031] The simple diagnosis unit analyzes the user's voice input and can estimate their health condition from their tone of voice and speaking style. For example, when a user describes their symptoms verbally, the generation AI analyzes their tone of voice and speaking style to estimate their level of stress and fatigue. For example, a hoarse voice may suggest a possible sore throat or cold. The simple diagnosis unit also analyzes the voice input, allowing the generation AI to detect the user's breathing and coughing sounds and identify respiratory problems. For example, coughing sounds may suggest the possibility of bronchitis or pneumonia. The simple diagnosis unit also analyzes the user's voice input and estimates their state of mental stress or anxiety from the trembling or instability of their voice. For example, a trembling voice may suggest the possibility of severe stress or anxiety. This allows for more detailed health condition estimation by analyzing the user's voice input.

[0032] The simple diagnosis unit can work in conjunction with the user's smartwatch or fitness tracker to monitor their health status in real time. For example, the generation AI analyzes data acquired from the user's smartwatch or fitness tracker to monitor their health status in real time. For example, it evaluates stress levels and fatigue levels based on heart rate and sleep data. The simple diagnosis unit also works in conjunction with the smartwatch or fitness tracker to analyze the user's exercise and activity levels to assess their health status. For example, it may suggest that a lack of exercise is the cause. The generation AI also notifies the user of changes in their health status in real time based on data acquired from the user's smartwatch or fitness tracker. For example, if it detects a sudden change in heart rate, it will alert the user. This allows for real-time health monitoring by working in conjunction with a smartwatch or fitness tracker.

[0033] The simple diagnostic unit can analyze the user's diet and exercise habits and assess the risk of lifestyle-related diseases. For example, the generation AI analyzes the diet and exercise habits entered by the user to assess the risk of lifestyle-related diseases. For example, a persistent high-fat diet and lack of exercise may suggest a risk of obesity or diabetes. The generation AI also analyzes the user's diet and exercise habits and provides advice on maintaining a healthy lifestyle. For example, it may recommend a balanced diet and regular exercise. The simple diagnostic unit also analyzes the user's diet and exercise habits and calculates a score to assess the risk of lifestyle-related diseases. For example, it provides a health risk score based on the nutritional balance of the diet and the amount of exercise. This allows the risk of lifestyle-related diseases to be assessed by analyzing the user's diet and exercise habits.

[0034] The symptom analysis unit can analyze the user's symptoms over time and identify the symptom progression pattern. For example, the symptom analysis unit analyzes the symptom data entered by the user over time, and the generation AI identifies the symptom progression pattern. For example, it analyzes changes in the frequency and intensity of headaches and evaluates the progression. The symptom analysis unit also analyzes the user's symptom data over time and identifies patterns of symptom worsening or improvement. For example, if a sore throat is gradually getting worse, this indicates the progression pattern. The symptom analysis unit also analyzes the user's symptom data over time, and the generation AI identifies the symptom progression pattern and suggests an appropriate treatment. For example, if symptoms have continued for a certain period of time, it recommends consulting a specialist. In this way, the symptom progression pattern can be identified and an appropriate treatment can be suggested.

[0035] The symptom analysis unit can analyze the user's genetic information and evaluate the impact of genetic factors on symptoms. For example, the symptom analysis unit uses the generation AI to analyze the genetic information provided by the user and evaluate how genetic factors affect current symptoms. For example, it can suggest the risk of a specific disease based on family history. The symptom analysis unit also uses the generation AI to analyze the user's genetic information and evaluate how genetic factors affect the severity and progression of symptoms. For example, if there is a genetically high risk of high blood pressure, it can suggest high blood pressure as a cause of headaches. The symptom analysis unit also analyzes the user's genetic information and uses the generation AI to evaluate the impact of genetic factors on symptoms and propose personalized treatments. For example, if the effectiveness of a drug varies genetically, the generation AI can propose the most appropriate drug based on that information. This makes it possible to propose personalized treatments by analyzing genetic information.

[0036] The symptom analysis unit can compare the symptom analysis results with the user's family history and environmental factors to identify risk factors. For example, the symptom analysis unit uses the generation AI to analyze the user's family history and environmental factors and compare them with the symptom analysis results to identify risk factors. For example, if there are many family members with diabetes, the system may suggest a risk of diabetes. The symptom analysis unit also analyzes the user's family history and environmental factors, and the generation AI identifies the causes of symptoms and risk factors. For example, if the user grew up in a family environment with a smoking habit, the system may suggest a risk of respiratory problems. The symptom analysis unit also uses the generation AI to analyze the user's family history and environmental factors and compare them with the symptom analysis results to identify risk factors, thereby providing personalized advice. For example, the system may evaluate the impact of specific environmental factors on symptoms. This allows risk factors to be identified by taking family history and environmental factors into account.

[0037] The symptom analysis unit can analyze the user's sleep data and evaluate the impact of lack of sleep on symptoms. For example, the symptom analysis unit uses a generation AI to analyze the user's sleep data and evaluate how lack of sleep is affecting current symptoms. For example, it may suggest that lack of sleep is the cause of a headache. The symptom analysis unit also uses a generation AI to analyze the user's sleep data and evaluate how the quality and quantity of sleep affect the severity of symptoms. For example, if the quality of sleep is poor, it may suggest that a sore throat is getting worse. The symptom analysis unit also analyzes the user's sleep data and uses a generation AI to evaluate the impact of lack of sleep on symptoms and provide appropriate advice. For example, it may suggest ways to get good quality sleep. In this way, the impact of lack of sleep on symptoms can be evaluated by analyzing sleep data.

[0038] The drug suggestion unit can analyze the user's allergy information and suggest drugs to avoid allergic reactions. For example, the drug suggestion unit uses a generation AI to analyze allergy information provided by the user and suggest drugs to avoid allergic reactions. For example, if a user is allergic to a specific ingredient, the unit will suggest drugs that do not contain that ingredient. The drug suggestion unit also analyzes the user's allergy information and suggests alternative drugs to avoid allergic reactions. For example, instead of a drug that may cause an allergy, the unit will suggest a different drug with the same effect. The drug suggestion unit also analyzes the user's allergy information and the generation AI suggests drugs to avoid allergic reactions, allowing the user to use drugs safely. For example, the unit will list drugs that do not contain ingredients that cause allergies. This allows the unit to suggest drugs to avoid allergic reactions by analyzing allergy information.

[0039] The drug suggestion unit can suggest the optimal dosage by taking into account individual information such as the user's weight and age. For example, the generation AI analyzes individual information such as the user's weight and age to suggest the optimal dosage. For example, it suggests a smaller dose of medicine for a light-weight user. The drug suggestion unit also considers the user's age and weight to suggest the appropriate dosage. For example, it suggests a smaller dosage than usual for elderly people. The drug suggestion unit also analyzes individual information about the user and the generation AI suggests the optimal dosage, ensuring safe use of medicine. For example, it suggests a dosage according to the user's age for children. This makes it possible to suggest the optimal dosage by taking into account individual information such as weight and age.

[0040] The drug suggestion unit can analyze the user's diet and supplement intake status and suggest drugs that take drug interactions into consideration. For example, the drug suggestion unit uses a generation AI to analyze the diet and supplement intake status entered by the user and suggest drugs that take drug interactions into consideration. For example, it makes a suggestion to avoid drugs that interact with specific supplements. In addition, the drug suggestion unit uses a generation AI to analyze the user's diet and supplement intake status and suggest drugs that take drug interactions into consideration. For example, it makes a suggestion to avoid drugs that interact with specific foods. In addition, the drug suggestion unit analyzes the user's diet and supplement intake status and suggests drugs that take drug interactions into consideration, allowing for safe drug use. For example, it suggests drugs that do not interact with specific supplements. This makes it possible to suggest drugs that avoid interactions by taking into consideration the diet and supplement intake status.

[0041] The drug suggestion unit can analyze the user's exercise habits and suggest drugs that aid in post-exercise recovery. For example, the drug suggestion unit uses a generation AI to analyze the user's exercise habits and suggest drugs that aid in post-exercise recovery. For example, it suggests drugs and supplements that relieve muscle pain. The drug suggestion unit also uses a generation AI to analyze the user's exercise habits and suggest drugs that aid in post-exercise recovery. For example, it suggests drugs and supplements that promote fatigue recovery. The drug suggestion unit also supports effective recovery by analyzing the user's exercise habits and suggesting drugs that aid in post-exercise recovery. For example, it suggests drugs that suppress post-exercise inflammation. In this way, it is possible to suggest drugs to aid in post-exercise recovery by taking exercise habits into consideration.

[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 drug recommendation system can also include an environmental analysis unit that collects data on the user's living environment and evaluates the impact of environmental factors on health. For example, it can analyze air quality data in the user's living environment to evaluate the risk of allergies and respiratory problems. The environmental analysis unit can also analyze data such as the user's work environment and commute time to identify causes of stress and fatigue. Furthermore, the environmental analysis unit can provide advice to reduce health risks based on the user's living environment data. For example, it can suggest using an air purifier or taking appropriate rest. This enables health management that takes the user's living environment into consideration.

[0044] The drug recommendation system may further include a genetic analysis unit that analyzes the user's genetic information and evaluates the impact of genetic factors on health. For example, the genetic information of the user may be analyzed to evaluate the risk of a particular disease. The genetic analysis unit may also propose an individualized health management plan based on the user's genetic information. For example, it may provide appropriate dietary and exercise advice to a user who is genetically at high risk of high blood pressure. Furthermore, the genetic analysis unit may also suggest the most appropriate drug based on the user's genetic information. For example, if a specific drug is genetically effective, it may suggest that drug. This enables individualized health management that takes genetic information into account.

[0045] The drug recommendation system may further include a nutritional analysis unit that analyzes the user's dietary data and evaluates nutritional balance. For example, it may analyze the dietary details entered by the user and evaluate the nutritional balance. The nutritional analysis unit may also propose a healthy meal plan based on the user's nutritional balance. For example, if there is a vitamin or mineral deficiency, it may suggest a method for supplementing. Furthermore, the nutritional analysis unit may evaluate the risk of certain diseases based on the user's dietary data. For example, if a high-fat diet is continued, it may suggest a risk of heart disease. This makes it possible to manage health taking the user's dietary data into consideration.

[0046] The drug recommendation system can also include an exercise analysis unit that analyzes the user's exercise data and evaluates their exercise habits. For example, it analyzes the exercise details entered by the user and evaluates their exercise habits. The exercise analysis unit can also propose a healthy exercise plan based on the user's exercise habits. For example, if a lack of exercise continues, it can suggest an appropriate exercise method. Furthermore, the exercise analysis unit can evaluate the risk of certain diseases based on the user's exercise data. For example, if a lack of exercise increases the risk of obesity or diabetes, it can suggest that risk. This makes it possible to manage health taking the user's exercise data into consideration.

[0047] The drug recommendation system may further include a sleep analysis unit that analyzes the user's sleep data and evaluates the quality of their sleep. For example, it may analyze the sleep data entered by the user and evaluate the quality of their sleep. The sleep analysis unit may also propose a healthy sleep plan based on the user's sleep data. For example, if the user has been experiencing sleep deprivation, it may suggest an appropriate sleeping method. Furthermore, the sleep analysis unit may evaluate the risk of certain diseases based on the user's sleep data. For example, if stress or fatigue has accumulated due to lack of sleep, it may suggest the risk. This enables health management that takes the user's sleep data into consideration.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The simple diagnosis unit uses the generation AI to analyze the user's symptoms and perform a simple diagnosis. For example, if the user inputs symptoms such as "I have a headache" or "I have a sore throat," the generation AI analyzes that information and identifies possible illnesses and conditions. Step 2: The symptom analysis unit performs a detailed analysis of the symptoms diagnosed by the simple diagnosis unit. For example, based on information such as "the headache continues" or "the sore throat is getting worse," the generation AI determines the severity and urgency of the symptoms. Step 3: The drug suggestion unit suggests the most suitable over-the-counter drug based on the symptoms analyzed by the symptom analysis unit. For example, it suggests specific drug names and usage instructions, such as "This drug is effective for headaches" or "This drug is suitable for sore throats." Step 4: The purchasing support department provides links and information for online purchase of over-the-counter drugs suggested by the drug suggestion department. For example, the user can easily purchase the drug by clicking on the link provided by the generation AI. Step 5: The drug interaction advice unit provides advice on interactions between the drugs the user is currently taking and the over-the-counter drugs suggested by the drug suggestion unit. For example, it provides specific advice such as "There is no problem if you take this drug and this drug together" or "Taking this drug and this drug together may cause side effects."

[0050] (Example 2) The drug recommendation system according to an embodiment of the present invention uses generative AI to analyze a user's symptoms and centrally manages simple diagnosis, symptom analysis, and the recommendation and purchase of optimal drugs. As a result, the drug recommendation system can efficiently and safely recommend and purchase drugs for people who want to receive medical care but cannot, or who are too busy to go to the hospital.

[0051] The drug suggestion system according to the embodiment includes a simple diagnosis unit, a symptom analysis unit, a drug suggestion unit, a purchasing support unit, and a drug combination advice unit. The simple diagnosis unit uses a generation AI to analyze a user's symptoms and perform a simple diagnosis. For example, when a user inputs symptoms such as "I have a headache" or "I have a sore throat," the generation AI analyzes the information and identifies possible illnesses or conditions. The symptom analysis unit performs a detailed analysis of the symptoms diagnosed by the simple diagnosis unit. For example, based on information such as "My headache is continuing" or "My sore throat is getting worse," the generation AI determines the severity and urgency of the symptoms. The drug suggestion unit suggests the most appropriate over-the-counter drug based on the symptoms analyzed by the symptom analysis unit. For example, it suggests specific drug names and usage instructions, such as "This drug is effective for headaches" or "This drug is suitable for sore throats." The purchasing support unit provides links and information for purchasing over-the-counter drugs suggested by the drug suggestion unit online. For example, users can easily purchase drugs by clicking on a link provided by the generation AI. The drug combination advice unit provides advice on interactions between the user's current medications and the over-the-counter drugs suggested by the drug suggestion unit. For example, it can provide specific advice such as, "There is no problem if you take this medicine with this medicine" or "Taking this medicine with this medicine may cause side effects." This allows the drug recommendation system to efficiently and safely recommend and purchase medicines for people who want to receive medical care but cannot, or who are too busy to go to the hospital.

[0052] The simple diagnosis unit analyzes the user's past health data and lifestyle information to perform a more accurate simple diagnosis. For example, the generation AI analyzes the health data and lifestyle information previously entered by the user and compares it with current symptoms to perform a diagnosis. For example, it identifies the cause of specific symptoms by taking into account past dietary habits and exercise habits. The simple diagnosis unit also analyzes the user's past health checkup results and medical history to identify diseases or conditions that may be related to the current symptoms. For example, if a user who has previously been diagnosed with high blood pressure complains of a headache, the generation AI suggests that high blood pressure may be the cause. The simple diagnosis unit also analyzes the user's lifestyle information (e.g., smoking habits and alcohol intake) to evaluate how these factors affect the current symptoms. For example, it suggests that a sore throat in a user who smokes may be caused by smoking. This enables more accurate diagnoses by taking into account the user's past health data and lifestyle information.

[0053] The simple diagnosis unit analyzes the user's voice input and can estimate their health condition from their tone of voice and speaking style. For example, when a user describes their symptoms verbally, the generation AI analyzes their tone of voice and speaking style to estimate their level of stress and fatigue. For example, a hoarse voice may suggest a possible sore throat or cold. The simple diagnosis unit also analyzes the voice input, allowing the generation AI to detect the user's breathing and coughing sounds and identify respiratory problems. For example, coughing sounds may suggest the possibility of bronchitis or pneumonia. The simple diagnosis unit also analyzes the user's voice input and estimates their state of mental stress or anxiety from the trembling or instability of their voice. For example, a trembling voice may suggest the possibility of severe stress or anxiety. This allows for more detailed health condition estimation by analyzing the user's voice input.

[0054] The simple diagnosis unit uses the emotion estimation function to reflect the user's emotional state in the diagnosis and consider the impact of stress and anxiety on symptoms. For example, when a user inputs their symptoms, the simple diagnosis unit uses the emotion estimation function to analyze the user's emotional state and evaluate the impact of stress and anxiety on the symptoms. For example, it suggests that severe stress may be the cause of a headache. The simple diagnosis unit also uses the emotion estimation function to monitor the user's emotional state in real time and reflect this in the diagnosis results. For example, for users with severe anxiety, it provides advice on relaxation methods and stress management. The simple diagnosis unit also analyzes the user's emotional state and evaluates how stress and anxiety affect the severity of symptoms. For example, if stress is worsening a sore throat, it notifies the user. This allows for a more accurate diagnosis by taking the user's emotional state into consideration.

[0055] The simple diagnosis unit can work in conjunction with the user's smartwatch or fitness tracker to monitor their health status in real time. For example, the generation AI analyzes data acquired from the user's smartwatch or fitness tracker to monitor their health status in real time. For example, it evaluates stress levels and fatigue levels based on heart rate and sleep data. The simple diagnosis unit also works in conjunction with the smartwatch or fitness tracker to analyze the user's exercise and activity levels to assess their health status. For example, it may suggest that a lack of exercise is the cause. The generation AI also notifies the user of changes in their health status in real time based on data acquired from the user's smartwatch or fitness tracker. For example, if it detects a sudden change in heart rate, it will alert the user. This allows for real-time health monitoring by working in conjunction with a smartwatch or fitness tracker.

[0056] The simple diagnostic unit can analyze the user's diet and exercise habits and assess the risk of lifestyle-related diseases. For example, the generation AI analyzes the diet and exercise habits entered by the user to assess the risk of lifestyle-related diseases. For example, a persistent high-fat diet and lack of exercise may suggest a risk of obesity or diabetes. The generation AI also analyzes the user's diet and exercise habits and provides advice on maintaining a healthy lifestyle. For example, it may recommend a balanced diet and regular exercise. The simple diagnostic unit also analyzes the user's diet and exercise habits and calculates a score to assess the risk of lifestyle-related diseases. For example, it provides a health risk score based on the nutritional balance of the diet and the amount of exercise. This allows the risk of lifestyle-related diseases to be assessed by analyzing the user's diet and exercise habits.

[0057] The simple diagnosis unit uses the emotion estimation function to analyze how the user feels about the diagnosis results and suggests appropriate follow-up actions. For example, the simple diagnosis unit uses a generation AI to analyze the user's emotions about the diagnosis results and suggests follow-up actions to elicit positive emotions. For example, it provides encouraging messages and success stories. The simple diagnosis unit also analyzes the user's emotions about the diagnosis results in real time and provides relaxation methods and stress management advice to users who have negative emotions. The simple diagnosis unit also uses the emotion estimation function to analyze how the user feels about the diagnosis results and suggests appropriate follow-up actions. For example, it recommends that a user who is feeling anxious consult a specialist. This allows the unit to suggest follow-up actions that take the user's emotions into consideration, thereby increasing the user's sense of security.

[0058] The symptom analysis unit can analyze the user's symptoms over time and identify the symptom progression pattern. For example, the symptom analysis unit analyzes the symptom data entered by the user over time, and the generation AI identifies the symptom progression pattern. For example, it analyzes changes in the frequency and intensity of headaches and evaluates the progression. The symptom analysis unit also analyzes the user's symptom data over time and identifies patterns of symptom worsening or improvement. For example, if a sore throat is gradually getting worse, this indicates the progression pattern. The symptom analysis unit also analyzes the user's symptom data over time, and the generation AI identifies the symptom progression pattern and suggests an appropriate treatment. For example, if symptoms have continued for a certain period of time, it recommends consulting a specialist. In this way, the symptom progression pattern can be identified and an appropriate treatment can be suggested.

[0059] The symptom analysis unit can analyze the user's genetic information and evaluate the impact of genetic factors on symptoms. For example, the symptom analysis unit uses the generation AI to analyze the genetic information provided by the user and evaluate how genetic factors affect current symptoms. For example, it can suggest the risk of a specific disease based on family history. The symptom analysis unit also uses the generation AI to analyze the user's genetic information and evaluate how genetic factors affect the severity and progression of symptoms. For example, if there is a genetically high risk of high blood pressure, it can suggest high blood pressure as a cause of headaches. The symptom analysis unit also analyzes the user's genetic information and uses the generation AI to evaluate the impact of genetic factors on symptoms and propose personalized treatments. For example, if the effectiveness of a drug varies genetically, the generation AI can propose the most appropriate drug based on that information. This makes it possible to propose personalized treatments by analyzing genetic information.

[0060] The symptom analysis unit can use the emotion estimation function to analyze the impact of the user's emotional state on the severity of symptoms. For example, the generative AI in the symptom analysis unit uses the emotion estimation function to analyze how the user's emotional state affects the severity of symptoms. For example, it suggests that high stress may be increasing the severity of a headache. The symptom analysis unit also analyzes the user's emotional state in real time, and the generative AI evaluates the impact of the emotional state on the progression of symptoms. For example, if anxiety is high, it suggests that a sore throat may be worsening. The symptom analysis unit also uses the emotion estimation function to analyze the impact of the user's emotional state on the severity of symptoms and provide appropriate advice. For example, it provides advice on relaxation methods and stress management. This allows for a more accurate assessment of the severity of symptoms by taking the user's emotional state into account.

[0061] The symptom analysis unit can compare the symptom analysis results with the user's family history and environmental factors to identify risk factors. For example, the symptom analysis unit uses the generation AI to analyze the user's family history and environmental factors and compare them with the symptom analysis results to identify risk factors. For example, if there are many family members with diabetes, the system may suggest a risk of diabetes. The symptom analysis unit also analyzes the user's family history and environmental factors, and the generation AI identifies the causes of symptoms and risk factors. For example, if the user grew up in a family environment with a smoking habit, the system may suggest a risk of respiratory problems. The symptom analysis unit also uses the generation AI to analyze the user's family history and environmental factors and compare them with the symptom analysis results to identify risk factors, thereby providing personalized advice. For example, the system may evaluate the impact of specific environmental factors on symptoms. This allows risk factors to be identified by taking family history and environmental factors into account.

[0062] The symptom analysis unit can analyze the user's sleep data and evaluate the impact of lack of sleep on symptoms. For example, the symptom analysis unit uses a generation AI to analyze the user's sleep data and evaluate how lack of sleep is affecting current symptoms. For example, it may suggest that lack of sleep is the cause of a headache. The symptom analysis unit also uses a generation AI to analyze the user's sleep data and evaluate how the quality and quantity of sleep affect the severity of symptoms. For example, if the quality of sleep is poor, it may suggest that a sore throat is getting worse. The symptom analysis unit also analyzes the user's sleep data and uses a generation AI to evaluate the impact of lack of sleep on symptoms and provide appropriate advice. For example, it may suggest ways to get good quality sleep. In this way, the impact of lack of sleep on symptoms can be evaluated by analyzing sleep data.

[0063] The symptom analysis unit uses the emotion estimation function to analyze how the user feels about the symptoms and can suggest psychological support. For example, the symptom analysis unit uses the emotion estimation function to analyze how the user feels about the symptoms and suggest psychological support. For example, the symptom analysis unit suggests relaxation methods for a user who is feeling anxious. The symptom analysis unit also analyzes the user's emotional state in real time, and the generation AI evaluates their emotions about the symptoms. For example, if the user is feeling very stressed, it provides advice on stress management. The symptom analysis unit also uses the emotion estimation function to analyze how the user feels about the symptoms and suggest appropriate psychological support. For example, it recommends counseling to reduce anxiety about the symptoms. This makes it possible to suggest psychological support that takes the user's emotions into consideration, thereby increasing the user's sense of security.

[0064] The drug suggestion unit can analyze the user's allergy information and suggest drugs to avoid allergic reactions. For example, the drug suggestion unit uses a generation AI to analyze allergy information provided by the user and suggest drugs to avoid allergic reactions. For example, if a user is allergic to a specific ingredient, the unit will suggest drugs that do not contain that ingredient. The drug suggestion unit also analyzes the user's allergy information and suggests alternative drugs to avoid allergic reactions. For example, instead of a drug that may cause an allergy, the unit will suggest a different drug with the same effect. The drug suggestion unit also analyzes the user's allergy information and the generation AI suggests drugs to avoid allergic reactions, allowing the user to use drugs safely. For example, the unit will list drugs that do not contain ingredients that cause allergies. This allows the unit to suggest drugs to avoid allergic reactions by analyzing allergy information.

[0065] The drug suggestion unit can suggest the optimal dosage by taking into account individual information such as the user's weight and age. For example, the generation AI analyzes individual information such as the user's weight and age to suggest the optimal dosage. For example, it suggests a smaller dose of medicine for a light-weight user. The drug suggestion unit also considers the user's age and weight to suggest the appropriate dosage. For example, it suggests a smaller dosage than usual for elderly people. The drug suggestion unit also analyzes individual information about the user and the generation AI suggests the optimal dosage, ensuring safe use of medicine. For example, it suggests a dosage according to the user's age for children. This makes it possible to suggest the optimal dosage by taking into account individual information such as weight and age.

[0066] The drug suggestion unit uses the emotion estimation function to analyze how the user feels about the proposed drug and make suggestions that give a sense of security. For example, the generation AI in the drug suggestion unit uses the emotion estimation function to analyze how the user feels about the proposed drug and make suggestions that give a sense of security. For example, for a user who is feeling anxious, the generation AI provides detailed explanations about the effects and safety of the drug. The drug suggestion unit also analyzes the user's emotional state in real time, and the generation AI evaluates their emotions about the proposed drug. For example, to elicit positive emotions, it provides success stories and user reviews. The drug suggestion unit also uses the emotion estimation function to analyze how the user feels about the proposed drug and suggest appropriate follow-up measures. For example, it recommends that a user who is feeling anxious consult a specialist. This allows the system to provide suggestions that take the user's emotions into consideration and give them a sense of security.

[0067] The drug suggestion unit can analyze the user's diet and supplement intake status and suggest drugs that take drug interactions into consideration. For example, the drug suggestion unit uses a generation AI to analyze the diet and supplement intake status entered by the user and suggest drugs that take drug interactions into consideration. For example, it makes a suggestion to avoid drugs that interact with specific supplements. In addition, the drug suggestion unit uses a generation AI to analyze the user's diet and supplement intake status and suggest drugs that take drug interactions into consideration. For example, it makes a suggestion to avoid drugs that interact with specific foods. In addition, the drug suggestion unit analyzes the user's diet and supplement intake status and suggests drugs that take drug interactions into consideration, allowing for safe drug use. For example, it suggests drugs that do not interact with specific supplements. This makes it possible to suggest drugs that avoid interactions by taking into consideration the diet and supplement intake status.

[0068] The drug suggestion unit can analyze the user's exercise habits and suggest drugs that aid in post-exercise recovery. For example, the drug suggestion unit uses a generation AI to analyze the user's exercise habits and suggest drugs that aid in post-exercise recovery. For example, it suggests drugs and supplements that relieve muscle pain. The drug suggestion unit also uses a generation AI to analyze the user's exercise habits and suggest drugs that aid in post-exercise recovery. For example, it suggests drugs and supplements that promote fatigue recovery. The drug suggestion unit also supports effective recovery by analyzing the user's exercise habits and suggesting drugs that aid in post-exercise recovery. For example, it suggests drugs that suppress post-exercise inflammation. In this way, it is possible to suggest drugs to aid in post-exercise recovery by taking exercise habits into consideration.

[0069] The drug suggestion unit uses the emotion estimation function to analyze how the user feels about the drug suggestion and can make suggestions that elicit positive emotions. For example, the generation AI in the drug suggestion unit uses the emotion estimation function to analyze how the user feels about the drug suggestion and make suggestions that elicit positive emotions. For example, the effectiveness and safety of the drug may be explained in detail. The drug suggestion unit also analyzes the user's emotional state in real time, and the generation AI evaluates the user's emotions about the drug suggestion. For example, to elicit positive emotions, it provides success stories and user reviews. The drug suggestion unit also uses the emotion estimation function to analyze how the user feels about the drug suggestion and suggests appropriate follow-up measures. For example, it may recommend that a user who is feeling anxious consult a specialist. This makes it possible to elicit positive emotions by making suggestions that take the user's emotions into consideration.

[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 drug recommendation system can also include an environmental analysis unit that collects data on the user's living environment and evaluates the impact of environmental factors on health. For example, it can analyze air quality data in the user's living environment to evaluate the risk of allergies and respiratory problems. The environmental analysis unit can also analyze data such as the user's work environment and commute time to identify causes of stress and fatigue. Furthermore, the environmental analysis unit can provide advice to reduce health risks based on the user's living environment data. For example, it can suggest using an air purifier or taking appropriate rest. This enables health management that takes the user's living environment into consideration.

[0072] The drug recommendation system may further include a genetic analysis unit that analyzes the user's genetic information and evaluates the impact of genetic factors on health. For example, the genetic information of the user may be analyzed to evaluate the risk of a particular disease. The genetic analysis unit may also propose an individualized health management plan based on the user's genetic information. For example, it may provide appropriate dietary and exercise advice to a user who is genetically at high risk of high blood pressure. Furthermore, the genetic analysis unit may also suggest the most appropriate drug based on the user's genetic information. For example, if a specific drug is genetically effective, it may suggest that drug. This enables individualized health management that takes genetic information into account.

[0073] The drug recommendation system may further include a nutritional analysis unit that analyzes the user's dietary data and evaluates nutritional balance. For example, it may analyze the dietary details entered by the user and evaluate the nutritional balance. The nutritional analysis unit may also propose a healthy meal plan based on the user's nutritional balance. For example, if there is a vitamin or mineral deficiency, it may suggest a method for supplementing. Furthermore, the nutritional analysis unit may evaluate the risk of certain diseases based on the user's dietary data. For example, if a high-fat diet is continued, it may suggest a risk of heart disease. This makes it possible to manage health taking the user's dietary data into consideration.

[0074] The drug recommendation system can also include an exercise analysis unit that analyzes the user's exercise data and evaluates their exercise habits. For example, it analyzes the exercise details entered by the user and evaluates their exercise habits. The exercise analysis unit can also propose a healthy exercise plan based on the user's exercise habits. For example, if a lack of exercise continues, it can suggest an appropriate exercise method. Furthermore, the exercise analysis unit can evaluate the risk of certain diseases based on the user's exercise data. For example, if a lack of exercise increases the risk of obesity or diabetes, it can suggest that risk. This makes it possible to manage health taking the user's exercise data into consideration.

[0075] The drug recommendation system may further include a sleep analysis unit that analyzes the user's sleep data and evaluates the quality of their sleep. For example, it may analyze the sleep data entered by the user and evaluate the quality of their sleep. The sleep analysis unit may also propose a healthy sleep plan based on the user's sleep data. For example, if the user has been experiencing sleep deprivation, it may suggest an appropriate sleeping method. Furthermore, the sleep analysis unit may evaluate the risk of certain diseases based on the user's sleep data. For example, if stress or fatigue has accumulated due to lack of sleep, it may suggest the risk. This enables health management that takes the user's sleep data into consideration.

[0076] The medicine recommendation system may further include an emotion analysis unit that analyzes the user's emotional state and evaluates the impact of the emotional state on health. For example, it may analyze emotional data entered by the user and evaluate the emotional state. The emotion analysis unit may also suggest stress management and relaxation methods based on the user's emotional state. For example, if stress is accumulating, it may suggest relaxation methods. Furthermore, the emotion analysis unit may evaluate the risk of specific diseases based on the user's emotional data. For example, if headaches or stomachaches are caused by severe stress, it may suggest the risk. This enables health management that takes the user's emotional state into consideration.

[0077] The drug recommendation system can also analyze the user's emotional state and evaluate the impact of the emotional state on the effectiveness of the drug. For example, it can analyze the emotional data entered by the user and evaluate the emotional state. The emotion analysis unit can also provide advice to maximize the effectiveness of the drug based on the user's emotional state. For example, if the user is under high stress, it can enhance the effectiveness of the drug by suggesting relaxation methods. Furthermore, the emotion analysis unit can evaluate the risk of side effects of the drug based on the user's emotional data. For example, if side effects are likely to occur due to high stress, it will suggest the risk. This makes it possible to recommend drugs that take the user's emotional state into consideration.

[0078] The drug recommendation system can also analyze the user's emotional state and evaluate the impact of the emotional state on drug selection. For example, it can analyze the emotional data entered by the user and evaluate the emotional state. The emotion analysis unit can also provide advice to optimize drug selection based on the user's emotional state. For example, if the user is highly anxious, it can suggest a drug with a relaxing effect. Furthermore, the emotion analysis unit can evaluate the risks involved in drug selection based on the user's emotional data. For example, if a particular drug is less effective due to high stress, it can suggest the risks. This makes it possible to select drugs that take the user's emotional state into consideration.

[0079] The drug recommendation system can also analyze the user's emotional state and evaluate the impact of the emotional state on the use of the drug. For example, it can analyze the emotional data entered by the user and evaluate the emotional state. The emotion analysis unit can also provide advice to optimize the use of the drug based on the user's emotional state. For example, if the user is under high stress, it can suggest relaxation methods to improve the effectiveness of the drug. Furthermore, the emotion analysis unit can evaluate the risks of using the drug based on the user's emotional data. For example, if strong stress makes side effects more likely to occur, it can suggest those risks. This makes it possible to use drugs that take the user's emotional state into consideration.

[0080] The drug recommendation system can also analyze the user's emotional state and evaluate the impact of the emotional state on drug recommendations. For example, it can analyze the emotional data entered by the user and evaluate the emotional state. The emotion analysis unit can also provide advice to optimize drug recommendations based on the user's emotional state. For example, if the user is highly anxious, it can recommend a drug with a relaxing effect. Furthermore, the emotion analysis unit can evaluate the risks involved in drug recommendations based on the user's emotional data. For example, if a particular drug is less effective due to high stress, it can suggest the risks. This makes it possible to recommend drugs that take the user's emotional state into consideration.

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

[0082] Step 1: The simple diagnosis unit uses the generation AI to analyze the user's symptoms and perform a simple diagnosis. For example, if the user inputs symptoms such as "I have a headache" or "I have a sore throat," the generation AI analyzes that information and identifies possible illnesses and conditions. Step 2: The symptom analysis unit performs a detailed analysis of the symptoms diagnosed by the simple diagnosis unit. For example, based on information such as "the headache continues" or "the sore throat is getting worse," the generation AI determines the severity and urgency of the symptoms. Step 3: The drug suggestion unit suggests the most suitable over-the-counter drug based on the symptoms analyzed by the symptom analysis unit. For example, it suggests specific drug names and usage instructions, such as "This drug is effective for headaches" or "This drug is suitable for sore throats." Step 4: The purchasing support department provides links and information for online purchase of over-the-counter drugs suggested by the drug suggestion department. For example, the user can easily purchase the drug by clicking on the link provided by the generation AI. Step 5: The drug interaction advice unit provides advice on interactions between the drugs the user is currently taking and the over-the-counter drugs suggested by the drug suggestion unit. For example, it provides specific advice such as "There is no problem if you take this drug and this drug together" or "Taking this drug and this drug together may cause side effects."

[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 the 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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. Using generative AI, a simple diagnosis unit that analyzes the symptoms of a user and performs a simple diagnosis; a symptom analysis unit that analyzes in detail the symptoms diagnosed by the simple diagnosis unit; a drug suggestion unit that suggests an optimal over-the-counter drug based on the symptoms analyzed by the symptom analysis unit; a purchasing support unit that provides links and information for online purchase of the over-the-counter drugs suggested by the drug suggestion unit; and a drug combination advice unit that provides advice on drug combinations between the drug currently being used by the user and the over-the-counter drug suggested by the drug suggestion unit. A system characterized by:

2. The simple diagnosis unit Analyze the user's past health data and lifestyle information to perform a more accurate simple diagnosis 2. The system of claim 1.

3. The simple diagnosis unit Analyzing the user's voice input and estimating the user's health condition from the tone of voice and speaking style 2. The system of claim 1.

4. The simple diagnosis unit The user's emotional state is reflected in the diagnosis, and the impact of stress and anxiety on the symptoms is taken into account.

2. The system of claim 1.

5. The simple diagnosis unit Link with the user's smartwatch or fitness tracker to monitor their health in real time 2. The system of claim 1.

6. The simple diagnosis unit Analyzing the user's diet and exercise habits and assessing the risk of lifestyle-related diseases 2. The system of claim 1.

7. The simple diagnosis unit Analyze how the user feels about the diagnosis results and suggest appropriate follow-up measures 2. The system of claim 1.

8. The symptom analysis unit Analyzing the symptoms of the user over time and identifying a progression pattern of the symptoms.

2. The system of claim 1.

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