System

Abstract

An object of the system according to the embodiment is to enable even an elderly person or a person who is inexperienced in AI techniques to receive health support or medical treatment support through voice.SOLUTION: A system according to an embodiment includes a reminder generation unit, a health check unit, a conversation support unit, an emergency handling unit, and a purchase support unit. The reminder generation unit manages a medication schedule and provides a reminder by voice. The health check unit periodically checks the health condition of the user, asks a question by voice, and evaluates the health condition based on the answer. The conversation support unit supports stress relief through a daily conversation with the user. The emergency response unit supports the user in an emergency and automatically contacts the emergency contact destination. The purchase support unit manages the stock of the medicine of the user and supports the user to periodically purchase the medicine.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, elderly people and those with no experience with AI technology had the difficulty of easily managing their health and providing treatment support via voice.

[0005] The system of the embodiment aims to enable elderly people and those with no experience with AI technology to receive health and treatment support through voice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reminder generation unit, a health check unit, a conversation support unit, an emergency response unit, and a purchase support unit. The reminder generation unit manages the user's medication schedule and provides audio reminders. The health check unit periodically checks the user's health condition, asks audio questions, and evaluates the health condition based on the answers. The conversation support unit supports stress relief through everyday conversations with the user. The emergency response unit supports the user in an emergency and automatically contacts emergency contacts. The purchase support unit manages the user's medication inventory and provides support for regular medication purchases. [Effects of the Invention]

[0007] The system according to the embodiment allows even elderly people and those with no experience with AI technology to receive health and treatment support through voice. [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) A health support system according to an embodiment of the present invention provides personalized health and treatment support through voice communication using generative AI. This system is designed to be easy to use, especially for the elderly and those with no experience with AI technology, and provides services that can be operated by voice. This allows users to easily receive health and treatment support through voice.

[0029] A health support system according to an embodiment includes a reminder generation unit, a health check unit, a conversation support unit, an emergency response unit, and a purchase support unit. The reminder generation unit manages a user's medication schedule and provides audio reminders. For example, the reminder generation unit may notify the user, "Good morning. It's time to take your morning medication today," based on the user's medication schedule. The reminder generation unit may also analyze the user's medication history, identify time periods when the user is likely to forget to take their medication, and provide reminders to specifically call attention to those time periods. The reminder generation unit may also learn the user's daily rhythm and dynamically adjust the optimal timing of reminders. The health check unit periodically checks the user's health status, asks audio questions, and evaluates the user's health status based on the user's responses. For example, the health check unit may ask, "How are you feeling today? Are there any symptoms that concern you?" and evaluate the user's health status based on the user's responses. The health check unit may also analyze the user's past health data, predict specific symptoms or patterns, and adjust the questions accordingly. Furthermore, the health check unit can automatically send the health check-in results to medical professionals so that they can receive professional advice. The conversation support unit supports stress relief through everyday conversations with the user. For example, the conversation support unit starts a conversation by asking, "What happened today?" and listens to the user. The conversation support unit can also analyze the user's past conversation history and provide topics based on the user's interests. Furthermore, the conversation support unit can learn the user's tone of voice and speaking style to enable more natural conversations. The emergency response unit supports the user in an emergency and automatically contacts emergency contacts. For example, the emergency response unit can notify the user, "An emergency has occurred. Please call for help immediately," and automatically contact emergency contacts. The emergency response unit can also track the user's location information in real time and contact the nearest medical institution or emergency service in an emergency. Furthermore, the emergency response unit can automatically send the user's past health data to medical institutions in an emergency to support rapid response. The purchasing support unit manages the user's medication inventory and supports regular medication purchases.For example, the purchasing support unit may notify the user, "Your medicine inventory is low. Would you like to order new medicine?" The purchasing support unit may also learn the user's medicine consumption patterns and suggest the optimal timing for purchasing. Furthermore, the purchasing support unit may compare prices at multiple pharmacies and suggest the most cost-effective purchasing source. This allows the health support system according to the embodiment to easily receive health support and treatment support through voice. For example, the user can prevent forgetting to take medicine, check their health status, and enjoy everyday conversation. Furthermore, the system responds quickly to emergencies and supports regular medicine purchases, allowing the user to live a life with peace of mind.

[0030] The reminder generation unit can analyze the user's medication history, identify time periods when users often forget to take their medication, and provide reminders to urge them to pay particular attention during those times. For example, the reminder generation unit uses a generation AI to store the user's past medication history in a database and identify time periods when users often forget to take their medication. For example, if users often forget to take their medication in the morning, the reminder generation unit can set a reminder to urge them to pay particular attention during that time period. This can reduce the number of times the user forgets to take their medication.

[0031] The reminder generation unit can learn the user's daily rhythm and dynamically adjust the timing of reminders. For example, the reminder generation unit uses a generation AI to learn the user's daily rhythm and adjust the timing of reminders to match wake-up and bedtime. For example, on days when the user wakes up early, the reminder is sent earlier. This allows the system to provide reminders that fit the user's daily rhythm.

[0032] The reminder generation unit can link reminder notification methods not only with voice but also with vibration notifications from a smartwatch or smartphone. For example, the reminder generation unit can link reminder notification methods not only with voice but also with vibration notifications from a smartwatch. For example, the reminder generation unit can set the smartwatch to vibrate simultaneously with a voice reminder. This makes it possible to send reminder notifications using multiple devices.

[0033] The reminder generation unit can add a function to share reminders with family members and caregivers, enabling them to check whether the user has taken their medicine. For example, the reminder generation unit can add a function to share reminders with family members and caregivers, enabling them to check whether the user has taken their medicine. For example, the reminder generation unit can be set so that notifications are sent to family members and caregivers when a reminder is sent. This allows family members and caregivers to check whether the user has taken their medicine.

[0034] The health check unit can analyze the user's past health data, predict specific symptoms and patterns, and adjust the questions. For example, the health check unit uses a generative AI to analyze the user's past health data and predict specific symptoms and patterns. For example, the questions can be adjusted based on symptoms that have frequently occurred in the past. This makes it possible to provide questions based on the user's health condition.

[0035] The health check unit can automatically transmit the results of the health check-in to a medical professional so that the user can receive professional advice. The health check unit, for example, builds a system that automatically transmits the results of the health check-in to a medical professional. For example, the health check unit transmits the user's answer data to a medical professional so that the user can receive professional advice. This allows the user to receive professional advice.

[0036] The health check unit can customize the content of the health check-in questions to suit the age and gender of the user. For example, the health check unit customizes the content of the health check-in questions to suit the age of the user. For example, the health check unit sets questions aimed at elderly people. This allows the health check unit to provide questions that are appropriate for the user's age and gender.

[0037] The health check unit can visualize the results of the health check-in in graphs and charts, allowing the user to intuitively understand their health condition. The health check unit, for example, builds a system that visualizes the results of the health check-in in graphs and charts. For example, it displays changes in health condition in a line graph. This allows the user to intuitively understand their health condition.

[0038] The conversation support unit can analyze the user's past conversation history and provide topics based on the user's interests and concerns. For example, the conversation support unit constructs a system in which a generation AI analyzes the user's past conversation history and provides topics based on the user's interests and concerns. For example, it can provide conversations about the user's favorite hobbies and topics. This makes it possible to provide topics based on the user's interests and concerns.

[0039] The conversation support unit learns the user's tone of voice and speaking style, enabling more natural conversations. For example, the conversation support unit constructs a system in which a generation AI learns the user's tone of voice and speaking style, enabling more natural conversations. For example, it performs voice synthesis that matches the user's speaking style. This enables more natural conversations with the user.

[0040] The conversation support unit can save the contents of everyday conversations as text so that the user can review them later. The conversation support unit, for example, builds a system that saves the contents of everyday conversations as text so that the user can review them later. For example, the conversation history is saved as a text file. This allows the user to review the contents of everyday conversations later.

[0041] The conversation support unit can suggest events and activities based on the user's hobbies and interests. For example, the conversation support unit builds a system in which a generation AI suggests events and activities based on the user's hobbies and interests. For example, it suggests music events or sports activities that the user likes. This makes it possible to suggest events and activities based on the user's hobbies and interests.

[0042] The emergency response department can track the user's location information in real time and contact the nearest medical institution or emergency service in the event of an emergency. For example, the emergency response department will build a system in which the generative AI tracks the user's location information in real time and automatically contacts the nearest medical institution in the event of an emergency. For example, if the user collapses, the nearest hospital will be contacted. This allows for a quick response based on the user's location information.

[0043] The emergency response department can automatically send a user's past health data to a medical institution in the event of an emergency, supporting a rapid response. For example, the emergency response department will build a system in which the generation AI automatically sends a user's past health data to a medical institution in the event of an emergency. For example, when a user is taken to the hospital by ambulance, the past health data is sent to the hospital. This enables a rapid response based on the user's health data.

[0044] The emergency response department can add a function to automatically contact the user's family and friends in the event of an emergency. For example, the emergency response department will build a system in which the generation AI automatically contacts the user's family and friends in the event of an emergency. For example, if the user collapses, the system will notify the family and friends. This will allow the user's family and friends to be contacted quickly in the event of an emergency.

[0045] The emergency response department can automatically provide a user's medical history and allergy information to a medical institution in an emergency. The emergency response department will build a system in which, for example, the generation AI will automatically provide a user's medical history and allergy information to a medical institution in an emergency. For example, when a user is taken to the hospital by ambulance, the medical history will be sent to the hospital. This will allow a user's medical history and allergy information to be provided quickly in an emergency.

[0046] The purchasing support unit can learn the user's drug consumption patterns and suggest the optimal timing for purchase. For example, the purchasing support unit will build a system in which a generative AI learns the user's drug consumption patterns and suggests the optimal timing for purchase. For example, it will send a reminder before the user runs out of medicine. This will allow the optimal timing for purchase to be suggested based on the user's drug consumption patterns.

[0047] The purchasing support department can compare prices from multiple pharmacies and suggest the most cost-effective purchasing source. For example, the purchasing support department will build a system in which generative AI collects price data from multiple pharmacies and suggests the most cost-effective purchasing source. For example, it will compare prices of a drug specified by the user and suggest the lowest price. This will allow the department to suggest the most cost-effective purchasing source to the user.

[0048] The purchasing support department can expand the regular drug purchase support to purchasing support for other health-related products. For example, the purchasing support department builds a system that expands the regular drug purchase support to purchasing support for other health-related products. For example, it provides purchasing support for supplements and health foods. This allows users to receive purchasing support for other health-related products as well.

[0049] The purchasing support department can add a function that monitors the user's drug inventory in real time and automatically reorders as needed. For example, the purchasing support department will build a system in which the generative AI monitors the user's drug inventory in real time and automatically reorders as needed. For example, an order will be placed automatically when inventory is low. This allows the user's drug inventory to be managed in real time and automatically reordered as needed.

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

[0051] The health support system can further include a meal recording unit that supports the user's dietary management. The meal recording unit records the meals the user has eaten by voice and analyzes the nutritional balance. For example, if the user says, "Today's breakfast was toast and eggs," the meal recording unit saves this information in a database and evaluates the nutritional balance. The meal recording unit can also make meal suggestions to supplement any nutritional deficiencies based on the user's dietary history. Furthermore, the meal recording unit can create a meal plan tailored to the user's health goals and make audio suggestions.

[0052] The health support system can further include an exercise management unit. The exercise management unit records the user's exercise history and provides advice to prevent lack of exercise. For example, if a user reports, "I walked for 30 minutes today," the exercise management unit records this information and evaluates the amount of exercise. The exercise management unit can also suggest an appropriate exercise plan based on the user's exercise history. Furthermore, the exercise management unit can create an exercise program tailored to the user's health goals and provide audio instructions.

[0053] The health support system may further include a sleep management unit. The sleep management unit records the user's sleep patterns and evaluates the quality of sleep. For example, if a user reports, "I slept seven hours last night," the sleep management unit records this information and evaluates the quality of sleep. The sleep management unit can also provide advice to improve the quality of sleep based on the user's sleep history. Furthermore, the sleep management unit can also suggest an optimal sleep schedule that matches the user's lifestyle.

[0054] The health support system may further include a community support unit to support the user's social connections. The community support unit provides a platform where users can interact with other users and share health information and experiences. For example, if a user says, "I want to discuss today's exercise," the community support unit can set up an online meeting with other users. The community support unit can also suggest groups based on the user's interests to increase opportunities for interaction. Furthermore, the community support unit can suggest events and activities to strengthen the user's social connections.

[0055] The health support system can further include a mental health support unit for supporting the user's mental health. The mental health support unit evaluates the user's mental health state and suggests professional counseling if necessary. For example, if a user reports that they have been feeling depressed recently, the mental health support unit records this information and suggests professional counseling. The mental health support unit can also suggest relaxation and stress relief methods according to the user's mental health state. Furthermore, the mental health support unit can regularly check the user's mental health state and detect problems early.

[0056] The health support system may further include a data visualization unit for visualizing the user's health data. The data visualization unit displays the user's health data in graphs and charts, allowing the user to intuitively understand their health condition. For example, the data visualization unit may display the user's weight change in a line graph, allowing the user to visually confirm changes in their health condition. The data visualization unit may also compare the user's health data and clarify areas for improvement. Furthermore, the data visualization unit may visually display the user's progress toward their health goals, thereby increasing motivation.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reminder generation unit manages the medication schedule and provides voice reminders. For example, the reminder generation unit may notify the user, "Good morning. It's time to take your morning medication today," based on the user's medication schedule. The reminder generation unit may also analyze the user's medication history, identify time periods when medication is often forgotten, and provide reminders to call special attention to those time periods. Furthermore, the reminder generation unit may learn the user's daily rhythm and dynamically adjust the optimal reminder timing. Step 2: The health check module periodically checks the user's health condition, asks voice questions, and evaluates the user's health condition based on the user's answers. For example, the health check module may ask, "How are you feeling today? Are there any symptoms that concern you?" and evaluate the user's health condition based on the user's answers. The health check module can also analyze the user's past health data, predict specific symptoms or patterns, and adjust the questions it asks. Furthermore, the health check module can automatically send the results of the health check-in to a medical professional so that the user can receive professional advice. Step 3: The conversation support unit supports stress relief through everyday conversations with the user. For example, the conversation support unit starts a conversation by asking, "What happened today?" and listens to what the user has to say. The conversation support unit can also analyze the user's past conversation history and provide topics based on the user's interests and concerns. Furthermore, the conversation support unit can learn the user's tone of voice and speaking style to achieve more natural conversations. Step 4: The emergency response department supports the user in an emergency and automatically contacts emergency contacts. For example, the emergency response department may notify the user, "An emergency has occurred. We will call for help immediately," and automatically contact emergency contacts. The emergency response department can also track the user's location in real time and contact the nearest medical institution or emergency service in an emergency. Furthermore, the emergency response department can automatically send the user's past health data to medical institutions in an emergency to support a rapid response. Step 5: The purchasing support department manages the user's drug inventory and provides support for regular drug purchases. For example, the purchasing support department may notify the user, "Your drug inventory is low. Would you like to order new drugs?" The purchasing support department can also learn the user's drug consumption patterns and suggest optimal drug purchase timing. Furthermore, the purchasing support department can compare prices from multiple pharmacies and suggest the most cost-effective purchasing source.

[0059] (Example 2) A health support system according to an embodiment of the present invention provides personalized health and treatment support through voice communication using generative AI. This system is designed to be easy to use, especially for the elderly and those with no experience with AI technology, and provides services that can be operated by voice. This allows users to easily receive health and treatment support through voice.

[0060] A health support system according to an embodiment includes a reminder generation unit, a health check unit, a conversation support unit, an emergency response unit, and a purchase support unit. The reminder generation unit manages a user's medication schedule and provides audio reminders. For example, the reminder generation unit may notify the user, "Good morning. It's time to take your morning medication today," based on the user's medication schedule. The reminder generation unit may also analyze the user's medication history, identify time periods when the user is likely to forget to take their medication, and provide reminders to specifically call attention to those time periods. The reminder generation unit may also learn the user's daily rhythm and dynamically adjust the optimal timing of reminders. The health check unit periodically checks the user's health status, asks audio questions, and evaluates the user's health status based on the user's responses. For example, the health check unit may ask, "How are you feeling today? Are there any symptoms that concern you?" and evaluate the user's health status based on the user's responses. The health check unit may also analyze the user's past health data, predict specific symptoms or patterns, and adjust the questions accordingly. Furthermore, the health check unit can automatically send the health check-in results to medical professionals so that they can receive professional advice. The conversation support unit supports stress relief through everyday conversations with the user. For example, the conversation support unit starts a conversation by asking, "What happened today?" and listens to the user. The conversation support unit can also analyze the user's past conversation history and provide topics based on the user's interests. Furthermore, the conversation support unit can learn the user's tone of voice and speaking style to enable more natural conversations. The emergency response unit supports the user in an emergency and automatically contacts emergency contacts. For example, the emergency response unit can notify the user, "An emergency has occurred. Please call for help immediately," and automatically contact emergency contacts. The emergency response unit can also track the user's location information in real time and contact the nearest medical institution or emergency service in an emergency. Furthermore, the emergency response unit can automatically send the user's past health data to medical institutions in an emergency to support rapid response. The purchasing support unit manages the user's medication inventory and supports regular medication purchases.For example, the purchasing support unit may notify the user, "Your medicine inventory is low. Would you like to order new medicine?" The purchasing support unit may also learn the user's medicine consumption patterns and suggest the optimal timing for purchasing. Furthermore, the purchasing support unit may compare prices at multiple pharmacies and suggest the most cost-effective purchasing source. This allows the health support system according to the embodiment to easily receive health support and treatment support through voice. For example, the user can prevent forgetting to take medicine, check their health status, and enjoy everyday conversation. Furthermore, the system responds quickly to emergencies and supports regular medicine purchases, allowing the user to live a life with peace of mind.

[0061] The reminder generation unit can analyze the user's medication history, identify time periods when users often forget to take their medication, and provide reminders to urge them to pay particular attention during those times. For example, the reminder generation unit uses a generation AI to store the user's past medication history in a database and identify time periods when users often forget to take their medication. For example, if users often forget to take their medication in the morning, the reminder generation unit can set a reminder to urge them to pay particular attention during that time period. This can reduce the number of times the user forgets to take their medication.

[0062] The reminder generation unit can learn the user's daily rhythm and dynamically adjust the timing of reminders. For example, the reminder generation unit uses a generation AI to learn the user's daily rhythm and adjust the timing of reminders to match wake-up and bedtime. For example, on days when the user wakes up early, the reminder is sent earlier. This allows the system to provide reminders that fit the user's daily rhythm.

[0063] The reminder generation unit can use the emotion estimation function to generate a voice tone and message content that will cause the user to have a positive reaction to the reminder. For example, the reminder generation unit uses the emotion estimation function to generate a voice tone that will cause the user to have a positive reaction to the reminder. For example, the reminder generation unit provides a reminder in a gentle voice or an encouraging tone. This allows the user to have a positive reaction to the reminder.

[0064] The reminder generation unit can link reminder notification methods not only with voice but also with vibration notifications from a smartwatch or smartphone. For example, the reminder generation unit can link reminder notification methods not only with voice but also with vibration notifications from a smartwatch. For example, the reminder generation unit can set the smartwatch to vibrate simultaneously with a voice reminder. This makes it possible to send reminder notifications using multiple devices.

[0065] The reminder generation unit can add a function to share reminders with family members and caregivers, enabling them to check whether the user has taken their medicine. For example, the reminder generation unit can add a function to share reminders with family members and caregivers, enabling them to check whether the user has taken their medicine. For example, the reminder generation unit can be set so that notifications are sent to family members and caregivers when a reminder is sent. This allows family members and caregivers to check whether the user has taken their medicine.

[0066] The reminder generation unit can use the emotion estimation function to customize the content of the reminder to match the user's mood and provide a more friendly message. For example, the reminder generation unit uses the emotion estimation function to customize the content of the reminder to match the user's mood. For example, if the user is tired, an encouraging message is provided. This allows the user to feel that the reminder is friendly.

[0067] The health check unit can analyze the user's past health data, predict specific symptoms and patterns, and adjust the questions. For example, the health check unit uses a generative AI to analyze the user's past health data and predict specific symptoms and patterns. For example, the questions can be adjusted based on symptoms that have frequently occurred in the past. This makes it possible to provide questions based on the user's health condition.

[0068] The health check unit can automatically transmit the results of the health check-in to a medical professional so that the user can receive professional advice. The health check unit, for example, builds a system that automatically transmits the results of the health check-in to a medical professional. For example, the health check unit transmits the user's answer data to a medical professional so that the user can receive professional advice. This allows the user to receive professional advice.

[0069] The health check unit can use the emotion estimation function to evaluate the user's emotional state and provide advice on how to relax if stress or anxiety is high. The health check unit, for example, uses the emotion estimation function to build a system that evaluates the user's emotional state. For example, it analyzes the user's facial expressions and voice to measure the user's stress and anxiety levels. This can reduce the user's stress and anxiety.

[0070] The health check unit can customize the content of the health check-in questions to suit the age and gender of the user. For example, the health check unit customizes the content of the health check-in questions to suit the age of the user. For example, the health check unit sets questions aimed at elderly people. This allows the health check unit to provide questions that are appropriate for the user's age and gender.

[0071] The health check unit can visualize the results of the health check-in in graphs and charts, allowing the user to intuitively understand their health condition. The health check unit, for example, builds a system that visualizes the results of the health check-in in graphs and charts. For example, it displays changes in health condition in a line graph. This allows the user to intuitively understand their health condition.

[0072] The health check unit uses the emotion estimation function to provide health advice according to the user's emotional state, thereby eliciting positive emotions. The health check unit, for example, uses the emotion estimation function to build a system that provides health advice according to the user's emotional state. For example, if the user is feeling stressed, the health check unit provides advice on how to relax. This makes it possible to elicit positive emotions from the user.

[0073] The conversation support unit can analyze the user's past conversation history and provide topics based on the user's interests and concerns. For example, the conversation support unit constructs a system in which a generation AI analyzes the user's past conversation history and provides topics based on the user's interests and concerns. For example, it can provide conversations about the user's favorite hobbies and topics. This makes it possible to provide topics based on the user's interests and concerns.

[0074] The conversation support unit learns the user's tone of voice and speaking style, enabling more natural conversations. For example, the conversation support unit constructs a system in which a generation AI learns the user's tone of voice and speaking style, enabling more natural conversations. For example, it performs voice synthesis that matches the user's speaking style. This enables more natural conversations with the user.

[0075] The conversation support unit can use the emotion estimation function to provide relaxing music or meditation guides that correspond to the user's emotional state. For example, the conversation support unit uses the emotion estimation function to build a system that provides relaxing music that corresponds to the user's emotional state. For example, if the user is feeling stressed, relaxing music is played. This makes it possible to provide a relaxation method that corresponds to the user's emotional state.

[0076] The conversation support unit can save the contents of everyday conversations as text so that the user can review them later. The conversation support unit, for example, builds a system that saves the contents of everyday conversations as text so that the user can review them later. For example, the conversation history is saved as a text file. This allows the user to review the contents of everyday conversations later.

[0077] The conversation support unit can suggest events and activities based on the user's hobbies and interests. For example, the conversation support unit builds a system in which a generation AI suggests events and activities based on the user's hobbies and interests. For example, it suggests music events or sports activities that the user likes. This makes it possible to suggest events and activities based on the user's hobbies and interests.

[0078] The conversation support unit can use the emotion estimation function to provide jokes and positive topics that correspond to the user's emotional state, thereby improving the user's mood. For example, the conversation support unit uses the emotion estimation function to build a system that provides jokes that correspond to the user's emotional state. For example, if the user is feeling down, the conversation support unit can provide jokes to lift the user's spirits. This can improve the user's mood.

[0079] The emergency response department can track the user's location information in real time and contact the nearest medical institution or emergency service in the event of an emergency. For example, the emergency response department will build a system in which the generative AI tracks the user's location information in real time and automatically contacts the nearest medical institution in the event of an emergency. For example, if the user collapses, the nearest hospital will be contacted. This allows for a quick response based on the user's location information.

[0080] The emergency response department can automatically send a user's past health data to a medical institution in the event of an emergency, supporting a rapid response. For example, the emergency response department will build a system in which the generation AI automatically sends a user's past health data to a medical institution in the event of an emergency. For example, when a user is taken to the hospital by ambulance, the past health data is sent to the hospital. This enables a rapid response based on the user's health data.

[0081] The emergency response unit can use the emotion estimation function to provide a calm voice message to reduce the user's anxiety in an emergency. The emergency response unit, for example, uses the emotion estimation function to build a system that provides a calm voice message to reduce the user's anxiety in an emergency. For example, if the user is in a panic, a reassuring message is provided in a calm voice. This can reduce the user's anxiety in an emergency.

[0082] The emergency response department can add a function to automatically contact the user's family and friends in the event of an emergency. For example, the emergency response department will build a system in which the generation AI automatically contacts the user's family and friends in the event of an emergency. For example, if the user collapses, the system will notify the family and friends. This will allow the user's family and friends to be contacted quickly in the event of an emergency.

[0083] The emergency response department can automatically provide a user's medical history and allergy information to a medical institution in an emergency. The emergency response department will build a system in which, for example, the generation AI will automatically provide a user's medical history and allergy information to a medical institution in an emergency. For example, when a user is taken to the hospital by ambulance, the medical history will be sent to the hospital. This will allow a user's medical history and allergy information to be provided quickly in an emergency.

[0084] The emergency response unit can use the emotion estimation function to monitor the emotional state of the user in real time in an emergency and provide appropriate support. The emergency response unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of the user in real time in an emergency. For example, if the user is in a panic state, the emotional state is monitored and appropriate support is provided. This makes it possible to monitor the emotional state of the user in an emergency and provide appropriate support.

[0085] The purchasing support unit can learn the user's drug consumption patterns and suggest the optimal timing for purchase. For example, the purchasing support unit will build a system in which a generative AI learns the user's drug consumption patterns and suggests the optimal timing for purchase. For example, it will send a reminder before the user runs out of medicine. This will allow the optimal timing for purchase to be suggested based on the user's drug consumption patterns.

[0086] The purchasing support department can compare prices from multiple pharmacies and suggest the most cost-effective purchasing source. For example, the purchasing support department will build a system in which generative AI collects price data from multiple pharmacies and suggests the most cost-effective purchasing source. For example, it will compare prices of a drug specified by the user and suggest the lowest price. This will allow the department to suggest the most cost-effective purchasing source to the user.

[0087] The purchasing support unit can use the emotion estimation function to provide a message that makes the user feel positive about purchasing medicine. The purchasing support unit, for example, uses the emotion estimation function to build a system that provides a message that makes the user feel positive about purchasing medicine. For example, a message that emphasizes the benefits of the purchase is provided. This allows the user to feel positive about purchasing medicine.

[0088] The purchasing support department can expand the regular drug purchase support to purchasing support for other health-related products. For example, the purchasing support department builds a system that expands the regular drug purchase support to purchasing support for other health-related products. For example, it provides purchasing support for supplements and health foods. This allows users to receive purchasing support for other health-related products as well.

[0089] The purchasing support department can add a function that monitors the user's drug inventory in real time and automatically reorders as needed. For example, the purchasing support department will build a system in which the generative AI monitors the user's drug inventory in real time and automatically reorders as needed. For example, an order will be placed automatically when inventory is low. This allows the user's drug inventory to be managed in real time and automatically reordered as needed.

[0090] The purchasing support unit can use the emotion estimation function to provide appropriate support and information when a user has anxiety or questions about purchasing medicine. The purchasing support unit, for example, uses the emotion estimation function to build a system that provides appropriate support when a user has anxiety or questions about purchasing medicine. For example, when a user feels anxious, information that gives a sense of security is provided. This makes it possible to provide appropriate support and information when a user has anxiety or questions about purchasing medicine.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The health support system can further include a meal recording unit that supports the user's dietary management. The meal recording unit records the meals the user has eaten by voice and analyzes the nutritional balance. For example, if the user says, "Today's breakfast was toast and eggs," the meal recording unit saves this information in a database and evaluates the nutritional balance. The meal recording unit can also make meal suggestions to supplement any nutritional deficiencies based on the user's dietary history. Furthermore, the meal recording unit can create a meal plan tailored to the user's health goals and make audio suggestions.

[0093] The health support system can further include an exercise management unit. The exercise management unit records the user's exercise history and provides advice to prevent lack of exercise. For example, if a user reports, "I walked for 30 minutes today," the exercise management unit records this information and evaluates the amount of exercise. The exercise management unit can also suggest an appropriate exercise plan based on the user's exercise history. Furthermore, the exercise management unit can create an exercise program tailored to the user's health goals and provide audio instructions.

[0094] The health support system may further include a sleep management unit. The sleep management unit records the user's sleep patterns and evaluates the quality of sleep. For example, if a user reports, "I slept seven hours last night," the sleep management unit records this information and evaluates the quality of sleep. The sleep management unit can also provide advice to improve the quality of sleep based on the user's sleep history. Furthermore, the sleep management unit can also suggest an optimal sleep schedule that matches the user's lifestyle.

[0095] The health support system can further include a relaxation support unit that uses the emotion estimation function to suggest relaxation methods according to the user's emotional state. The relaxation support unit evaluates the user's emotional state and provides relaxation advice if the user is experiencing high levels of stress or anxiety. For example, if the user is feeling stressed, the relaxation support unit can provide relaxing music or a meditation guide. The relaxation support unit can also customize relaxation methods according to the user's emotional state and suggest more effective relaxation methods.

[0096] The health support system can further use the emotion estimation function to propose an exercise plan according to the user's emotional state. The exercise management unit evaluates the user's emotional state and suggests exercises with a relaxing effect if the user is experiencing high levels of stress or anxiety. For example, if the user is feeling stressed, the exercise management unit suggests exercises with a relaxing effect, such as yoga or stretching. The exercise management unit can also customize an exercise plan according to the user's emotional state and suggest more effective exercise methods.

[0097] The health support system can further use the emotion estimation function to suggest meals according to the user's emotional state. The meal recording unit evaluates the user's emotional state and suggests meals with a relaxing effect if the user is experiencing high levels of stress or anxiety. For example, if the user is feeling stressed, the system can suggest relaxing herbal tea or a nutritionally balanced meal. The meal recording unit can also customize a meal plan according to the user's emotional state and suggest more effective eating methods.

[0098] The health support system can further use its emotion estimation function to provide sleep advice according to the user's emotional state. The sleep management unit evaluates the user's emotional state and suggests a relaxing sleep environment if the user is experiencing high levels of stress or anxiety. For example, if the user is feeling stressed, the sleep management unit suggests a sleep environment using relaxing music or aromas. The sleep management unit can also customize sleep advice according to the user's emotional state and suggest more effective sleep methods.

[0099] The health support system may further include a community support unit to support the user's social connections. The community support unit provides a platform where users can interact with other users and share health information and experiences. For example, if a user says, "I want to discuss today's exercise," the community support unit can set up an online meeting with other users. The community support unit can also suggest groups based on the user's interests to increase opportunities for interaction. Furthermore, the community support unit can suggest events and activities to strengthen the user's social connections.

[0100] The health support system can further include a mental health support unit for supporting the user's mental health. The mental health support unit evaluates the user's mental health state and suggests professional counseling if necessary. For example, if a user reports that they have been feeling depressed recently, the mental health support unit records this information and suggests professional counseling. The mental health support unit can also suggest relaxation and stress relief methods according to the user's mental health state. Furthermore, the mental health support unit can regularly check the user's mental health state and detect problems early.

[0101] The health support system may further include a data visualization unit for visualizing the user's health data. The data visualization unit displays the user's health data in graphs and charts, allowing the user to intuitively understand their health condition. For example, the data visualization unit may display the user's weight change in a line graph, allowing the user to visually confirm changes in their health condition. The data visualization unit may also compare the user's health data and clarify areas for improvement. Furthermore, the data visualization unit may visually display the user's progress toward their health goals, thereby increasing motivation.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The reminder generation unit manages the medication schedule and provides voice reminders. For example, the reminder generation unit may notify the user, "Good morning. It's time to take your morning medication today," based on the user's medication schedule. The reminder generation unit may also analyze the user's medication history, identify time periods when medication is often forgotten, and provide reminders to call special attention to those time periods. Furthermore, the reminder generation unit may learn the user's daily rhythm and dynamically adjust the optimal reminder timing. Step 2: The health check module periodically checks the user's health condition, asks voice questions, and evaluates the user's health condition based on the user's answers. For example, the health check module may ask, "How are you feeling today? Are there any symptoms that concern you?" and evaluate the user's health condition based on the user's answers. The health check module can also analyze the user's past health data, predict specific symptoms or patterns, and adjust the questions it asks. Furthermore, the health check module can automatically send the results of the health check-in to a medical professional so that the user can receive professional advice. Step 3: The conversation support unit supports stress relief through everyday conversations with the user. For example, the conversation support unit starts a conversation by asking, "What happened today?" and listens to what the user has to say. The conversation support unit can also analyze the user's past conversation history and provide topics based on the user's interests and concerns. Furthermore, the conversation support unit can learn the user's tone of voice and speaking style to achieve more natural conversations. Step 4: The emergency response department supports the user in an emergency and automatically contacts emergency contacts. For example, the emergency response department may notify the user, "An emergency has occurred. We will call for help immediately," and automatically contact emergency contacts. The emergency response department can also track the user's location in real time and contact the nearest medical institution or emergency service in an emergency. Furthermore, the emergency response department can automatically send the user's past health data to medical institutions in an emergency to support a rapid response. Step 5: The purchasing support department manages the user's drug inventory and provides support for regular drug purchases. For example, the purchasing support department may notify the user, "Your drug inventory is low. Would you like to order new drugs?" The purchasing support department can also learn the user's drug consumption patterns and suggest optimal drug purchase timing. Furthermore, the purchasing support department can compare prices from multiple pharmacies and suggest the most cost-effective purchasing source.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

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

[0125] The 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.

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

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

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

[0129] Fig. 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.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] 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.

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

[0134] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0136] 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.

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

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

[0139] The data processing device 12 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] 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.

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

[0150] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A voice communication system using a generative AI, a reminder generation unit that manages medication schedules and provides voice reminders; a health check unit that periodically checks the user's health condition, asks questions by voice, and evaluates the user's health condition based on the answers; a conversation support unit that supports stress relief through daily conversations with the user; An emergency response department that supports users in emergencies and automatically contacts emergency contacts; A purchasing support unit that manages the user's drug inventory and provides support for regular drug purchases. A system characterized by:

2. The reminder generation unit Learn the user's daily rhythm and dynamically adjust the timing of the reminder 2. The system of claim 1.

3. The health check unit Analyze the user's past health data to predict specific symptoms and patterns and tailor questions accordingly 2. The system of claim 1.

4. The conversation support unit Analyzing the user's past conversation history and providing topics based on the user's interests 2. The system of claim 1.

5. The emergency response department: Tracking the user's location in real time and contacting the nearest medical facility or emergency services in the event of an emergency.

2. The system of claim 1.

6. The purchasing support department Compare prices from multiple pharmacies and recommend the most cost-effective option 2. The system of claim 1.

7. The reminder generation unit Generate a tone of voice or message content that encourages the user to respond positively to the reminder.

2. The system of claim 1.

8. The health check unit Assessing the user's emotional state and providing relaxation advice if stress or anxiety is high 2. The system of claim 1.

Images