System

Abstract

An object of the system according to the embodiment is to integrally manage a reason for prescribing a drug and a medical history.SOLUTION: A system includes a medication notebook AI, a disease history summary function, and a generation function. The medication notebook function manages information of the medicine. The disease history compiling function compiles a disease history. The generation AI records why the drug was prescribed based on the Medication Diary and Disease History Summary functions.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] With conventional technology, the management of medication prescription reasons and medical history was fragmented, making it difficult to manage them in an integrated manner.

[0005] The system according to the embodiment aims to comprehensively manage the reasons for prescribing medicines and medical history. [Means for solving the problem]

[0006] The system according to the embodiment includes a medicine notebook function, a disease history summary function, and a generation AI. The medicine notebook function manages medicine information. The disease history summary function summarizes the disease history. The generation AI records why medicine was prescribed based on the medicine notebook function and the disease history summary function. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively manage the reasons for prescribing medicines and medical history. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The White Watson system according to an embodiment of the present invention is a system that integrates a medication record function and a disease history summary function into a single app, and uses a generative AI to record the reason for prescribing medication. As a result, the White Watson system can enhance medical support by centrally managing the user's medication information and medical history and recording the reason for prescribing medication.

[0029] The White Watson system according to the embodiment includes a medicine record function, a disease history summary function, and a generation AI. The medicine record function manages information such as the name of the medicine, dosage, and medication history. For example, the user inputs the name of the medicine and the dosage, and the app records that information. The medicine record function also includes a medication reminder function. For example, it sends notifications at a set time to remind the user to take the medicine. The disease history summary function manages information such as the diagnosis, treatment history, and symptom progression. For example, the user inputs the diagnosis and treatment details, and the app records that information. The disease history summary function also includes a function to display symptom progression in a graph. For example, changes in symptoms can be visually confirmed. The generation AI records why a medicine was prescribed based on the medicine record function and the disease history summary function. For example, if a user inputs "I have a headache," the generation AI records "I was prescribed a headache medicine" based on that information. The generation AI can also analyze the user's input and infer the reason for the prescription. For example, if multiple symptoms are entered, the reason for prescribing the corresponding medication will be recorded. This allows the White Watson system to centrally manage the user's medication information and medical history, and record the reason for prescribing medication, thereby enhancing medical support.

[0030] Generative AI can record a user's lifestyle and dietary habits and analyze their relationship to medication effects and side effects. For example, a user can input their daily dietary habits into an app, and the generative AI can use that data to analyze their relationship to medication effects and side effects. For example, it can analyze whether a particular food enhances a medication's effects or causes side effects. Generative AI can also record a user's lifestyle habits and use that data to analyze their relationship to medication effects and side effects. For example, it can analyze the impact of exercise habits and sleep patterns on medication effects. Generative AI can also record a user's smoking and drinking habits and use that data to analyze their relationship to medication effects and side effects. For example, it can analyze whether smoking reduces medication effects. This enables more precise medical support by analyzing medication effects and side effects taking into account the user's lifestyle and dietary habits.

[0031] The generation AI can automatically generate optimal questions based on past data for symptoms entered by the user and collect detailed information. For example, if a user enters "I have a headache," the generation AI can automatically generate questions such as "How often do you have headaches?" and "How severe is your headache?" based on past data and collect detailed information. Similarly, if a user enters "I have a cough," the generation AI can automatically generate questions such as "How long does the cough last?" and "What does the cough sound like?" based on past data and collect detailed information. Similarly, if a user enters "I have a stomachache," the generation AI can automatically generate questions such as "Where is the stomach pain?" and "How severe is the stomach pain?" based on past data and collect detailed information. This automatically generates optimal questions for the user's symptoms and collects detailed information, improving the accuracy of medical support.

[0032] The medicine notebook app has a function that can be accessed by family members and caregivers, allowing multiple people to manage medications. For example, the medicine notebook app can add a function that can be accessed by family members and caregivers, allowing multiple people to manage a user's medications. For example, family members can check the user's medication status and set reminders. The medicine notebook app also allows caregivers to check the user's medication inventory and replenish as needed. For example, the caregiver can check the remaining amount of medication and order more from the pharmacy if it is running low. The medicine notebook app also allows family members and caregivers to check the side effects of the user's medications and consult a doctor as needed. For example, if a severe side effect occurs, the caregiver can contact a doctor and discuss how to deal with it. In this way, adding a function that can be accessed by family members and caregivers enables multiple people to manage medications, thereby strengthening medical support.

[0033] The disease history summary function allows the user to record details of the treatments and surgeries the user has received, which can be used when consulting a medical institution. The disease history summary function adds a function to record details of the treatments and surgeries the user has received, for example, so that the details can be used when consulting a medical institution. For example, the date, time, and content of the surgery, as well as the progress of the treatment, can be recorded. The disease history summary function also has a function to record the effects and side effects of the treatments the user has received. For example, it can record changes in symptoms after treatment and the presence or absence of side effects. The disease history summary function also has a function to record the results of tests the user has received. For example, the results of blood tests and imaging tests can be recorded and provided to the doctor during the consultation. This allows the user to record details of the treatments and surgeries they have received, which can be used when consulting a medical institution, thereby enhancing medical support.

[0034] Generative AI can analyze the voice input of elderly people and estimate their health condition from the voice. For example, when elderly people explain their symptoms verbally, generative AI adds the function of analyzing that voice and estimating their health condition. For example, it can estimate fatigue and stress levels from the tone of voice and speaking style. Generative AI can also analyze the voice of elderly people reporting their physical condition verbally and estimate their health condition. For example, it can estimate their physical strength from the strength and speed of their voice. Generative AI can also analyze the voice of elderly people expressing their emotions verbally and estimate their health condition. For example, it can estimate their emotional state from the intonation and rhythm of their voice. This will enhance medical support by analyzing the voice input of elderly people and estimating their health condition.

[0035] Generative AI can collect elderly people's walking data, heart rate, and other vital data, and evaluate their health condition based on the vital data. For example, generative AI can collect elderly people's walking data and evaluate their health condition based on that data. For example, it can evaluate their physical strength and muscle power from changes in walking speed and stride length. Generative AI can also collect elderly people's heart rate and evaluate their health condition based on that data. For example, it can evaluate stress and fatigue levels from heart rate fluctuations. Generative AI can also collect elderly people's blood pressure and evaluate their health condition based on that data. For example, it can evaluate the state of their circulatory system from blood pressure fluctuations. This will enhance medical support by collecting elderly people's vital data and evaluating their overall health condition.

[0036] The system can provide comprehensive health support by managing the elderly's medications as well as their diet and exercise. For example, the system can add a function for managing the elderly's diet in addition to managing their medications, thereby providing comprehensive health support. For example, it can record the contents of meals and evaluate nutritional balance. The system can also add a function for managing the elderly's exercise, thereby providing comprehensive health support. For example, it can record the type and frequency of exercise and evaluate the effectiveness of exercise. The system can also manage the elderly's overall lifestyle habits and provide comprehensive health support. For example, it can record sleep patterns and stress levels and provide health advice. In this way, comprehensive health support can be provided by managing the elderly's medications as well as their diet and exercise.

[0037] Medical history support is equipped with a remote medical consultation function, allowing doctors to conduct medical history remotely. Medical history support, for example, adds a remote medical consultation function, allowing doctors to conduct medical history remotely. For example, a doctor may conduct a medical history of an elderly person via video call. Medical history support also includes a chat function, allowing doctors to communicate with elderly people remotely. For example, symptoms and questions may be answered via text message. Medical history support also includes a remote monitoring function, allowing doctors to monitor the health status of elderly people in real time. For example, vital signs data may be collected in real time and provided to doctors. In this way, adding a remote medical consultation function allows doctors to conduct medical history remotely, thereby enhancing medical support.

[0038] Generative AI can analyze prescription drug trends in detail, taking into account regional climate and environmental data. For example, generative AI can collect regional climate data and analyze prescription drug trends based on that data. For example, it can analyze whether cold medicines are more commonly prescribed in cold regions. Generative AI can also collect regional environmental data and analyze prescription drug trends based on that data. For example, it can analyze whether asthma medications are more commonly prescribed in regions with poor air quality. Generative AI can also collect regional temperature data and analyze prescription drug trends based on that data. For example, it can analyze whether heatstroke prevention medications are more commonly prescribed in regions with high temperatures. This allows for detailed analysis of prescription drug trends, taking into account regional climate and environmental data, thereby strengthening medical support.

[0039] Generative AI can perform trend analysis based on demographic data such as the user's age group and gender. For example, generative AI can collect user age group data and analyze prescription drug trends based on that data. For example, it can analyze whether prescriptions for a particular drug are more common in areas with a large elderly population. Generative AI can also collect user gender data and analyze prescription drug trends based on that data. For example, it can analyze whether prescriptions for a particular drug are more common in areas with a large female population. Generative AI can also collect user occupation data and analyze prescription drug trends based on that data. For example, it can analyze whether prescriptions for shoulder pain medication are more common in areas where desk work is common. This allows for more precise trend analysis based on demographic data such as the user's age group and gender, thereby enhancing medical support.

[0040] The system can provide pharmaceutical companies with regional prescription drug trends in real time, thereby supporting the rapid development of marketing strategies. For example, the system collects regional prescription drug trends in real time and provides the data to pharmaceutical companies. For example, it can instantly provide data on regions where prescriptions for a particular drug are rapidly increasing. The system can also analyze regional prescription drug trends in real time and provide the results to pharmaceutical companies. For example, it can provide data on regions where demand for a particular drug is increasing in real time. The system can also visualize regional prescription drug trends in real time and provide the information to pharmaceutical companies. For example, it can provide the data visually using graphs and charts. In this way, by providing pharmaceutical companies with regional prescription drug trends in real time, it can support the rapid development of marketing strategies.

[0041] The system can also provide trend data to medical institutions and pharmacies, which can help improve medical services in each region. For example, the system can provide regional prescription drug trend data to medical institutions and pharmacies, which can help improve medical services in each region. For example, inventory management can be optimized in areas where there is high demand for specific drugs. The system can also provide regional prescription drug trend data to medical institutions and pharmacies, which can help optimize medical treatment processes. For example, prescription patterns for drugs for specific symptoms can be analyzed to improve treatment guidelines. The system can also provide regional prescription drug trend data to medical institutions and pharmacies, which can help introduce patient education programs. For example, educational materials can be provided on how to use specific drugs and their side effects. In this way, providing trend data to medical institutions and pharmacies can help improve medical services in each region.

[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 White Watson system can enhance medical support by centrally managing a user's medication information and medical history and recording the reasons for medication prescriptions. Furthermore, the system can record the user's lifestyle and dietary habits and analyze their relationship to medication effectiveness and side effects. For example, by having the user enter their daily dietary habits into the app, the system can analyze the relationship between medication effectiveness and side effects based on that data. It can analyze whether specific foods enhance medication effectiveness or cause side effects. It can also record the user's lifestyle and analyze the relationship between medication effectiveness and side effects based on that data. It can also analyze the impact of exercise habits and sleep patterns on medication effectiveness. It can also record the user's smoking and drinking habits and analyze the relationship between medication effectiveness and side effects based on that data. It can analyze whether smoking reduces medication effectiveness. This enables more precise medical support by analyzing medication effectiveness and side effects by taking into account the user's lifestyle and dietary habits.

[0044] The White Watson system can automatically generate optimal questions based on past data for symptoms entered by the user and collect detailed information. For example, if a user enters "I have a headache," questions such as "How often do you have headaches?" and "How severe is your headache?" are automatically generated based on past data, and detailed information is collected. Similarly, if a user enters "I have a cough," questions such as "How long does the cough last?" and "What does the cough sound like?" are automatically generated based on past data, and detailed information can be collected. Furthermore, if a user enters "I have a stomachache," questions such as "Where is the stomach pain?" and "How severe is the stomach pain?" are automatically generated based on past data, and detailed information is collected. This automatically generates optimal questions for the user's symptoms and collects detailed information, improving the accuracy of medical support.

[0045] The White Watson system is equipped with a function that can be accessed by family members and caregivers, allowing multiple people to manage medications. For example, adding a function that can be accessed by family members and caregivers allows multiple people to manage a user's medications. Family members can check the user's medication status and set reminders. Caregivers can also check the user's medication inventory and replenish as needed. Caregivers can check the remaining amount of medication and place an order with the pharmacy if it is low. Family members and caregivers can also check the side effects of the user's medications and consult a doctor if necessary. If a severe side effect occurs, they can contact a doctor and discuss how to deal with it. By adding a function that can be accessed by family members and caregivers, medications can be managed by multiple people, thereby strengthening medical support.

[0046] The White Watson system can record details of the treatments and surgeries a user has received, which can be used during consultations at medical institutions. For example, a function can be added to record details of the treatments and surgeries a user has received, making them available during consultations at medical institutions. The system records the date, time, and content of the surgery, as well as the progress of the treatment. It also has a function to record the effectiveness and side effects of the treatment the user has received. It can record changes in symptoms after treatment and the presence or absence of side effects. It also has a function to record the results of tests the user has received. The results of blood tests and imaging tests can be recorded and provided to the doctor during consultations. This allows users to record details of the treatments and surgeries they have received, which can be used during consultations at medical institutions, enhancing medical support.

[0047] The White Watson system can analyze the voice input of elderly people and estimate their health condition from the voice. For example, when elderly people explain their symptoms verbally, the system adds a function to analyze the voice and estimate their health condition. Fatigue and stress levels can be estimated from the tone of voice and speaking style. In addition, when elderly people report their physical condition verbally, the voice can be analyzed to estimate their health condition. The physical strength and speed of the voice can be estimated. Furthermore, when elderly people express their emotions verbally, the voice can be analyzed to estimate their health condition. The emotional state can be estimated from the intonation and rhythm of the voice. This will enhance medical support by analyzing the voice input of elderly people and estimating their health condition.

[0048] The White Watson system can collect elderly people's walking data, heart rate, and other vital data, and evaluate their health condition based on the vital data. For example, it can collect elderly people's walking data and evaluate their health condition based on that data. It can evaluate their physical strength and muscle power from changes in walking speed and stride length. It can also collect elderly people's heart rate and evaluate their health condition based on that data. It can evaluate stress and fatigue levels from heart rate fluctuations. It can also collect elderly people's blood pressure and evaluate their health condition based on that data. It can evaluate the state of their circulatory system from blood pressure fluctuations. This will enhance medical support by collecting elderly people's vital data and evaluating their overall health condition.

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

[0050] Step 1: The medicine notebook function manages information such as the name of the medicine, dosage, and medication history. For example, the user can enter the name of the medicine and dosage, and the app will record that information. The medicine notebook function also has a function to remind users when to take their medicine. For example, it can send notifications at a set time to encourage them to take their medicine. Step 2: The disease history summary function manages information such as diagnosis, treatment history, and symptom progression. For example, the user can enter the diagnosis and treatment details, and the app will record that information. The disease history summary function also has a function to display symptom progression in a graph. For example, changes in symptoms can be visually confirmed. Step 3: The generating AI records why the medication was prescribed based on the medication record function and illness history summary function. For example, if the user inputs "I have a headache," the generating AI will record "I was prescribed a headache medication" based on that information. The generating AI can also analyze the user's input and infer the reason for the medication prescription. For example, if multiple symptoms are input, the reason for the prescription of the medication corresponding to each symptom will be recorded.

[0051] (Example 2) The White Watson system according to an embodiment of the present invention is a system that integrates a medication record function and a disease history summary function into a single app, and uses a generative AI to record the reason for prescribing medication. As a result, the White Watson system can enhance medical support by centrally managing the user's medication information and medical history and recording the reason for prescribing medication.

[0052] The White Watson system according to the embodiment includes a medicine record function, a disease history summary function, and a generation AI. The medicine record function manages information such as the name of the medicine, dosage, and medication history. For example, the user inputs the name of the medicine and the dosage, and the app records that information. The medicine record function also includes a medication reminder function. For example, it sends notifications at a set time to remind the user to take the medicine. The disease history summary function manages information such as the diagnosis, treatment history, and symptom progression. For example, the user inputs the diagnosis and treatment details, and the app records that information. The disease history summary function also includes a function to display symptom progression in a graph. For example, changes in symptoms can be visually confirmed. The generation AI records why a medicine was prescribed based on the medicine record function and the disease history summary function. For example, if a user inputs "I have a headache," the generation AI records "I was prescribed a headache medicine" based on that information. The generation AI can also analyze the user's input and infer the reason for the prescription. For example, if multiple symptoms are entered, the reason for prescribing the corresponding medication will be recorded. This allows the White Watson system to centrally manage the user's medication information and medical history, and record the reason for prescribing medication, thereby enhancing medical support.

[0053] Generative AI can record a user's lifestyle and dietary habits and analyze their relationship to medication effects and side effects. For example, a user can input their daily dietary habits into an app, and the generative AI can use that data to analyze their relationship to medication effects and side effects. For example, it can analyze whether a particular food enhances a medication's effects or causes side effects. Generative AI can also record a user's lifestyle habits and use that data to analyze their relationship to medication effects and side effects. For example, it can analyze the impact of exercise habits and sleep patterns on medication effects. Generative AI can also record a user's smoking and drinking habits and use that data to analyze their relationship to medication effects and side effects. For example, it can analyze whether smoking reduces medication effects. This enables more precise medical support by analyzing medication effects and side effects taking into account the user's lifestyle and dietary habits.

[0054] The generation AI can automatically generate optimal questions based on past data for symptoms entered by the user and collect detailed information. For example, if a user enters "I have a headache," the generation AI can automatically generate questions such as "How often do you have headaches?" and "How severe is your headache?" based on past data and collect detailed information. Similarly, if a user enters "I have a cough," the generation AI can automatically generate questions such as "How long does the cough last?" and "What does the cough sound like?" based on past data and collect detailed information. Similarly, if a user enters "I have a stomachache," the generation AI can automatically generate questions such as "Where is the stomach pain?" and "How severe is the stomach pain?" based on past data and collect detailed information. This automatically generates optimal questions for the user's symptoms and collects detailed information, improving the accuracy of medical support.

[0055] The generation AI can use its emotion estimation function to analyze the user's emotions at the time of input and record the impact of stress and anxiety on the effectiveness of medication. For example, when a user inputs information about medication effects, the generation AI uses the emotion estimation function to analyze the user's emotions at that time and record the impact of stress and anxiety on the medication's effectiveness. For example, it can analyze whether medication effectiveness decreases when stress is high. Furthermore, when a user inputs information about medication side effects, the generation AI uses the emotion estimation function to analyze the user's emotions at that time and record the impact of stress and anxiety on the side effects. For example, it can analyze whether side effects are more pronounced when anxiety is high. Furthermore, when a user inputs information about medication taking, the generation AI uses the emotion estimation function to analyze the user's emotions at that time and record the impact of stress and anxiety on the user's willingness to take the medication. For example, it can analyze whether users are more likely to forget to take medication when stress is high. This enables more precise medical support by analyzing the user's emotional state and recording the impact of stress and anxiety on medication effectiveness.

[0056] The medicine notebook app has a function that can be accessed by family members and caregivers, allowing multiple people to manage medications. For example, the medicine notebook app can add a function that can be accessed by family members and caregivers, allowing multiple people to manage a user's medications. For example, family members can check the user's medication status and set reminders. The medicine notebook app also allows caregivers to check the user's medication inventory and replenish as needed. For example, the caregiver can check the remaining amount of medication and order more from the pharmacy if it is running low. The medicine notebook app also allows family members and caregivers to check the side effects of the user's medications and consult a doctor as needed. For example, if a severe side effect occurs, the caregiver can contact a doctor and discuss how to deal with it. In this way, adding a function that can be accessed by family members and caregivers enables multiple people to manage medications, thereby strengthening medical support.

[0057] The disease history summary function allows the user to record details of the treatments and surgeries the user has received, which can be used when consulting a medical institution. The disease history summary function adds a function to record details of the treatments and surgeries the user has received, for example, so that the details can be used when consulting a medical institution. For example, the date, time, and content of the surgery, as well as the progress of the treatment, can be recorded. The disease history summary function also has a function to record the effects and side effects of the treatments the user has received. For example, it can record changes in symptoms after treatment and the presence or absence of side effects. The disease history summary function also has a function to record the results of tests the user has received. For example, the results of blood tests and imaging tests can be recorded and provided to the doctor during the consultation. This allows the user to record details of the treatments and surgeries they have received, which can be used when consulting a medical institution, thereby enhancing medical support.

[0058] The emotion estimation function can record the emotions a user feels when taking medicine and provide a reminder to elicit positive emotions. For example, the emotion estimation function can record the emotions a user feels when taking medicine and provide a reminder to elicit positive emotions. For example, it can display an encouraging message before taking medicine. The emotion estimation function can also analyze the emotions a user feels when taking medicine and play music to elicit positive emotions. For example, it can play music with a relaxing effect. The emotion estimation function can also record the emotions a user feels when taking medicine and display an image to elicit positive emotions. For example, it can display an image of a natural landscape. In this way, the user can record the emotions a user feels when taking medicine and provide a reminder to elicit positive emotions, thereby encouraging them to take their medicine.

[0059] Generative AI can analyze the voice input of elderly people and estimate their health condition from the voice. For example, when elderly people explain their symptoms verbally, generative AI adds the function of analyzing that voice and estimating their health condition. For example, it can estimate fatigue and stress levels from the tone of voice and speaking style. Generative AI can also analyze the voice of elderly people reporting their physical condition verbally and estimate their health condition. For example, it can estimate their physical strength from the strength and speed of their voice. Generative AI can also analyze the voice of elderly people expressing their emotions verbally and estimate their health condition. For example, it can estimate their emotional state from the intonation and rhythm of their voice. This will enhance medical support by analyzing the voice input of elderly people and estimating their health condition.

[0060] Generative AI can collect elderly people's walking data, heart rate, and other vital data, and evaluate their health condition based on the vital data. For example, generative AI can collect elderly people's walking data and evaluate their health condition based on that data. For example, it can evaluate their physical strength and muscle power from changes in walking speed and stride length. Generative AI can also collect elderly people's heart rate and evaluate their health condition based on that data. For example, it can evaluate stress and fatigue levels from heart rate fluctuations. Generative AI can also collect elderly people's blood pressure and evaluate their health condition based on that data. For example, it can evaluate the state of their circulatory system from blood pressure fluctuations. This will enhance medical support by collecting elderly people's vital data and evaluating their overall health condition.

[0061] The system can provide comprehensive health support by managing the elderly's medications as well as their diet and exercise. For example, the system can add a function for managing the elderly's diet in addition to managing their medications, thereby providing comprehensive health support. For example, it can record the contents of meals and evaluate nutritional balance. The system can also add a function for managing the elderly's exercise, thereby providing comprehensive health support. For example, it can record the type and frequency of exercise and evaluate the effectiveness of exercise. The system can also manage the elderly's overall lifestyle habits and provide comprehensive health support. For example, it can record sleep patterns and stress levels and provide health advice. In this way, comprehensive health support can be provided by managing the elderly's medications as well as their diet and exercise.

[0062] Medical history support is equipped with a remote medical consultation function, allowing doctors to conduct medical history remotely. Medical history support, for example, adds a remote medical consultation function, allowing doctors to conduct medical history remotely. For example, a doctor may conduct a medical history of an elderly person via video call. Medical history support also includes a chat function, allowing doctors to communicate with elderly people remotely. For example, symptoms and questions may be answered via text message. Medical history support also includes a remote monitoring function, allowing doctors to monitor the health status of elderly people in real time. For example, vital signs data may be collected in real time and provided to doctors. In this way, adding a remote medical consultation function allows doctors to conduct medical history remotely, thereby enhancing medical support.

[0063] The emotion estimation function can provide relaxing music and images when an elderly person is undergoing a medical interview. For example, the emotion estimation function analyzes the emotional state of the elderly person at the time of the medical interview and provides relaxing music. For example, if the elderly person is under high stress, it can play relaxing classical music. The emotion estimation function can also analyze the emotional state of the elderly person at the time of the medical interview and provide relaxing images. For example, it can play images of natural scenery. The emotion estimation function can also analyze the emotional state of the elderly person at the time of the medical interview and provide relaxing scents. For example, it can provide relaxing scents using an aroma diffuser. This enhances medical support by providing relaxing music and images so that the elderly can undergo the medical interview with peace of mind.

[0064] Generative AI can analyze prescription drug trends in detail, taking into account regional climate and environmental data. For example, generative AI can collect regional climate data and analyze prescription drug trends based on that data. For example, it can analyze whether cold medicines are more commonly prescribed in cold regions. Generative AI can also collect regional environmental data and analyze prescription drug trends based on that data. For example, it can analyze whether asthma medications are more commonly prescribed in regions with poor air quality. Generative AI can also collect regional temperature data and analyze prescription drug trends based on that data. For example, it can analyze whether heatstroke prevention medications are more commonly prescribed in regions with high temperatures. This allows for detailed analysis of prescription drug trends, taking into account regional climate and environmental data, thereby strengthening medical support.

[0065] Generative AI can perform trend analysis based on demographic data such as the user's age group and gender. For example, generative AI can collect user age group data and analyze prescription drug trends based on that data. For example, it can analyze whether prescriptions for a particular drug are more common in areas with a large elderly population. Generative AI can also collect user gender data and analyze prescription drug trends based on that data. For example, it can analyze whether prescriptions for a particular drug are more common in areas with a large female population. Generative AI can also collect user occupation data and analyze prescription drug trends based on that data. For example, it can analyze whether prescriptions for shoulder pain medication are more common in areas where desk work is common. This allows for more precise trend analysis based on demographic data such as the user's age group and gender, thereby enhancing medical support.

[0066] The emotion estimation function can analyze the emotional state of users by region and clarify the relationship between emotions and prescription drugs. The emotion estimation function, for example, analyzes the emotional state of users by region and analyzes prescription drug trends based on the data. For example, it analyzes whether prescriptions for a particular drug are more common in regions with high stress. The emotion estimation function can also analyze the emotional state of users by region and evaluate the effectiveness of prescription drugs based on the data. For example, it analyzes whether the effectiveness of a drug is reduced in regions with high stress. The emotion estimation function can also analyze the emotional state of users by region and evaluate the side effects of prescription drugs based on the data. For example, it analyzes whether side effects are more severe in regions with high stress. In this way, medical support can be enhanced by analyzing the emotional state of users by region and clarifying the relationship between emotions and prescription drugs.

[0067] The system can provide pharmaceutical companies with regional prescription drug trends in real time, thereby supporting the rapid development of marketing strategies. For example, the system collects regional prescription drug trends in real time and provides the data to pharmaceutical companies. For example, it can instantly provide data on regions where prescriptions for a particular drug are rapidly increasing. The system can also analyze regional prescription drug trends in real time and provide the results to pharmaceutical companies. For example, it can provide data on regions where demand for a particular drug is increasing in real time. The system can also visualize regional prescription drug trends in real time and provide the information to pharmaceutical companies. For example, it can provide the data visually using graphs and charts. In this way, by providing pharmaceutical companies with regional prescription drug trends in real time, it can support the rapid development of marketing strategies.

[0068] The system can also provide trend data to medical institutions and pharmacies, which can help improve medical services in each region. For example, the system can provide regional prescription drug trend data to medical institutions and pharmacies, which can help improve medical services in each region. For example, inventory management can be optimized in areas where there is high demand for specific drugs. The system can also provide regional prescription drug trend data to medical institutions and pharmacies, which can help optimize medical treatment processes. For example, prescription patterns for drugs for specific symptoms can be analyzed to improve treatment guidelines. The system can also provide regional prescription drug trend data to medical institutions and pharmacies, which can help introduce patient education programs. For example, educational materials can be provided on how to use specific drugs and their side effects. In this way, providing trend data to medical institutions and pharmacies can help improve medical services in each region.

[0069] The emotion estimation function can provide health advice based on the emotions of users in each region and promote preventive medicine. For example, the emotion estimation function can analyze the emotional state of users in each region and provide health advice based on the data. For example, it can suggest relaxation methods in high-stress regions. The emotion estimation function can also analyze the emotional state of users in each region and make suggestions for improving lifestyle habits based on the data. For example, it can suggest reviewing exercise habits or improving diet. The emotion estimation function can also analyze the emotional state of users in each region and make suggestions for preventive measures based on the data. For example, it can recommend regular health checkups or vaccinations. In this way, preventive medicine can be promoted by using the emotion estimation function to provide health advice based on the emotions of users in each region.

[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 White Watson system can enhance medical support by centrally managing a user's medication information and medical history and recording the reasons for medication prescriptions. Furthermore, the system can record the user's lifestyle and dietary habits and analyze their relationship to medication effectiveness and side effects. For example, by having the user enter their daily dietary habits into the app, the system can analyze the relationship between medication effectiveness and side effects based on that data. It can analyze whether specific foods enhance medication effectiveness or cause side effects. It can also record the user's lifestyle and analyze the relationship between medication effectiveness and side effects based on that data. It can also analyze the impact of exercise habits and sleep patterns on medication effectiveness. It can also record the user's smoking and drinking habits and analyze the relationship between medication effectiveness and side effects based on that data. It can analyze whether smoking reduces medication effectiveness. This enables more precise medical support by analyzing medication effectiveness and side effects by taking into account the user's lifestyle and dietary habits.

[0072] The White Watson system can automatically generate optimal questions based on past data for symptoms entered by the user and collect detailed information. For example, if a user enters "I have a headache," questions such as "How often do you have headaches?" and "How severe is your headache?" are automatically generated based on past data, and detailed information is collected. Similarly, if a user enters "I have a cough," questions such as "How long does the cough last?" and "What does the cough sound like?" are automatically generated based on past data, and detailed information can be collected. Furthermore, if a user enters "I have a stomachache," questions such as "Where is the stomach pain?" and "How severe is the stomach pain?" are automatically generated based on past data, and detailed information is collected. This automatically generates optimal questions for the user's symptoms and collects detailed information, improving the accuracy of medical support.

[0073] The White Watson system uses its emotion estimation function to analyze the user's emotions at the time of input and record the impact of stress and anxiety on the effectiveness of medication. For example, when a user inputs information about medication effects, the emotion estimation function is used to analyze the user's emotions at that time and record the impact of stress and anxiety on the medication's effectiveness. This analyzes whether medication effectiveness decreases when stress is high. Furthermore, when a user inputs information about medication side effects, the emotion estimation function is used to analyze the user's emotions at that time and record the impact of stress and anxiety on the side effects. This can analyze whether side effects are more pronounced when anxiety is high. Furthermore, when a user inputs information about taking medication, the emotion estimation function is used to analyze the user's emotions at that time and record the impact of stress and anxiety on the user's willingness to take the medication. This analyzes whether users are more likely to forget to take medication when stress is high. This allows for more precise medical support by analyzing the user's emotional state and recording the impact of stress and anxiety on medication effectiveness.

[0074] The White Watson system is equipped with a function that can be accessed by family members and caregivers, allowing multiple people to manage medications. For example, adding a function that can be accessed by family members and caregivers allows multiple people to manage a user's medications. Family members can check the user's medication status and set reminders. Caregivers can also check the user's medication inventory and replenish as needed. Caregivers can check the remaining amount of medication and place an order with the pharmacy if it is low. Family members and caregivers can also check the side effects of the user's medications and consult a doctor if necessary. If a severe side effect occurs, they can contact a doctor and discuss how to deal with it. By adding a function that can be accessed by family members and caregivers, medications can be managed by multiple people, thereby strengthening medical support.

[0075] The White Watson system can record details of the treatments and surgeries a user has received, which can be used during consultations at medical institutions. For example, a function can be added to record details of the treatments and surgeries a user has received, making them available during consultations at medical institutions. The system records the date, time, and content of the surgery, as well as the progress of the treatment. It also has a function to record the effectiveness and side effects of the treatment the user has received. It can record changes in symptoms after treatment and the presence or absence of side effects. It also has a function to record the results of tests the user has received. The results of blood tests and imaging tests can be recorded and provided to the doctor during consultations. This allows users to record details of the treatments and surgeries they have received, which can be used during consultations at medical institutions, enhancing medical support.

[0076] The White Watson system can use its emotion estimation function to record the emotions a user feels when taking their medicine and provide reminders to elicit positive emotions. For example, it can record the emotions a user feels when taking their medicine and provide reminders to elicit positive emotions. For example, it can display an encouraging message before taking the medicine. It can also analyze the emotions a user feels when taking their medicine and play music to elicit positive emotions. For example, it can play music with a relaxing effect. It can also record the emotions a user feels when taking their medicine and display images to elicit positive emotions. For example, it can display images of natural scenery. This allows the user to record the emotions a user feels when taking their medicine and provide reminders to elicit positive emotions, thereby encouraging them to take their medicine.

[0077] The White Watson system can analyze the voice input of elderly people and estimate their health condition from the voice. For example, when elderly people explain their symptoms verbally, the system adds a function to analyze the voice and estimate their health condition. Fatigue and stress levels can be estimated from the tone of voice and speaking style. In addition, when elderly people report their physical condition verbally, the voice can be analyzed to estimate their health condition. The physical strength and speed of the voice can be estimated. Furthermore, when elderly people express their emotions verbally, the voice can be analyzed to estimate their health condition. The emotional state can be estimated from the intonation and rhythm of the voice. This will enhance medical support by analyzing the voice input of elderly people and estimating their health condition.

[0078] The White Watson system can collect elderly people's walking data, heart rate, and other vital data, and evaluate their health condition based on the vital data. For example, it can collect elderly people's walking data and evaluate their health condition based on that data. It can evaluate their physical strength and muscle power from changes in walking speed and stride length. It can also collect elderly people's heart rate and evaluate their health condition based on that data. It can evaluate stress and fatigue levels from heart rate fluctuations. It can also collect elderly people's blood pressure and evaluate their health condition based on that data. It can evaluate the state of their circulatory system from blood pressure fluctuations. This will enhance medical support by collecting elderly people's vital data and evaluating their overall health condition.

[0079] The White Watson system uses its emotion estimation function to analyze the emotional state of users by region and clarify the relationship between emotions and prescribed medications. For example, it can analyze the emotional state of users by region and use that data to analyze prescription medication trends. It can analyze whether specific medications are prescribed more frequently in regions with high stress. It can also analyze the emotional state of users by region and use that data to evaluate the effectiveness of prescription medications. It can analyze whether medication effectiveness is reduced in regions with high stress. It can also analyze the emotional state of users by region and use that data to evaluate the side effects of prescription medications. It can analyze whether side effects are more pronounced in regions with high stress. This can enhance medical support by analyzing the emotional state of users by region and clarifying the relationship between emotions and prescribed medications.

[0080] The White Watson system can use its emotion estimation function to provide health advice based on the emotions of users in each region, promoting preventive medicine. For example, it can analyze the emotional state of users in each region and provide health advice based on that data. It can suggest relaxation methods in high-stress regions. It can also analyze the emotional state of users in each region and make suggestions for improving lifestyle habits based on that data. It can suggest reviewing exercise habits and improving diet. It can also analyze the emotional state of users in each region and make suggestions for preventive measures based on that data. It can recommend regular health checkups and vaccinations. In this way, it can promote preventive medicine by using the emotion estimation function to provide health advice based on the emotions of users in each region.

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

[0082] Step 1: The medicine notebook function manages information such as the name of the medicine, dosage, and medication history. For example, the user can enter the name of the medicine and dosage, and the app will record that information. The medicine notebook function also has a function to remind users when to take their medicine. For example, it can send notifications at a set time to encourage them to take their medicine. Step 2: The disease history summary function manages information such as diagnosis, treatment history, and symptom progression. For example, the user can enter the diagnosis and treatment details, and the app will record that information. The disease history summary function also has a function to display symptom progression in a graph. For example, changes in symptoms can be visually confirmed. Step 3: The generating AI records why the medication was prescribed based on the medication record function and illness history summary function. For example, if the user inputs "I have a headache," the generating AI will record "I was prescribed a headache medication" based on that information. The generating AI can also analyze the user's input and infer the reason for the medication prescription. For example, if multiple symptoms are input, the reason for the prescription of the medication corresponding to each symptom will be recorded.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

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

Claims

1. Medicine notebook function, Illness history summary function, and a generation AI that records why a medicine was prescribed based on the medicine notebook function and the disease history summary function. A system characterized by:

2. The generated AI is Record the user's lifestyle and dietary habits and analyze the correlation with the effects and side effects of the drug 2. The system of claim 1.

3. The medicine notebook app is Equipped with a function that allows family members and caregivers to access the medication, allowing multiple people to manage the medication 2. The system of claim 1.

4. The generated AI is Analyzing elderly people's voice input and estimating their health status from the voice 2. The system of claim 1.

5. The generated AI is Analyzes the user's emotions as they type and records how stress and anxiety affect the effectiveness of the drug 2. The system of claim 1.

6. The emotion estimation function Recording the user's feelings when taking the medicine and providing reminders to elicit positive feelings 2. The system of claim 1.

7. The emotion estimation function Analyzing the emotional state of the elderly person and generating interview content according to said emotional state 2. The system of claim 1.

8. The emotion estimation function Analyzing the emotional state of users by region and identifying the relationship between emotions and prescription drugs 2. The system of claim 1.

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