system

Abstract

We provide the system. [Solution] The information processing device includes means for acquiring user health data, The information processing device provides a means for analyzing health data acquired from a smartwatch, The information processing device includes means for generating health advice for the user based on the analysis results, An information processing device provides means for transmitting generated advice to a user via a data communication medium, The information processing device includes means for generating purchase recommendations based on the user's health data and behavioral patterns, A system that includes means for an information processing device to transmit generated purchase information to a user via a data communication medium.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the modern busy living environment, it is extremely important to timely and efficiently monitor an individual's health status and provide appropriate advice. However, current health management systems and applications often cannot fully reflect the individual lifestyles and health statuses of users, and it is difficult to provide real-time feedback. Therefore, there is a need for a new system that comprehensively manages the health status of users and provides advice and purchase guidance according to individual situations.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides a system having the following features: The information processing device is equipped with means for acquiring and analyzing health data from the user's smartwatch. Based on the analysis results, the information processing device generates appropriate health advice for the user and transmits the advice via a data communication medium. It also generates purchasing guidance based on the user's health data and behavioral patterns and transmits this as well via a data communication medium.

[0006] Furthermore, the information processing device is equipped with means to acquire weather information and generate multiple-choice questions based on that information. This allows the system to send these questions to the user via a data communication medium, receive the answers, and integrate and analyze them with health data. Additionally, the information processing device and the user's smartphone are linked, and the system is equipped with means to periodically acquire health data from a smartwatch and send it to a cloud server for analysis. As a result, data collection and analysis are performed efficiently, creating a system that provides users with optimal advice and purchasing guidance.

[0007] An "information processing device" refers to a computer system that collects and analyzes user data and generates appropriate feedback.

[0008] A "smartwatch" refers to a wearable device that can measure health data (such as heart rate, steps taken, and sleep data) and transmit it to a smartphone or other device.

[0009] "Health data" refers to data used to measure a user's physical condition, and specifically includes heart rate, steps taken, calorie consumption, sleep data, etc.

[0010] "Analysis" refers to calculations and analytical processes used to find meaning and trends based on acquired data.

[0011] "Advice" refers to the behavioral guidelines and health recommendations provided to users based on the analysis results.

[0012] "Data communication medium" refers to technical means for sending and receiving data, including the internet and other communication methods.

[0013] "Purchase guidance" refers to information such as advertisements and coupons for products and services suggested based on the user's health data and behavioral patterns.

[0014] "Weather information" refers to current and future weather data (e.g., temperature, precipitation, wind speed, etc.) for the user's current location or a specified area.

[0015] A "multiple-choice question" refers to a type of question that provides the user with multiple answer options.

[0016] A "cloud server" refers to server equipment that is accessible online and is used for storing, processing, and analyzing data. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0019] First, the terms used in the following description will be described.

[0020] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Further, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units 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), and the like.

[0021] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention realizes a health management system that acquires, analyzes, and provides feedback on user health data. Below, the system's program processing is explained in natural language, and the system's operation is described in detail with specific examples.

[0039] System Configuration

[0040] This system consists of an information processing device (server), a user terminal (smartphone), and a smartwatch that acquires the user's health data.

[0041] Program processing

[0042] 1. The user installs a dedicated health management app on their smartphone. When they open the app, they are presented with an option to link their LINE account, and the user links their LINE account with the app.

[0043] 2. The user pairs the smartwatch with their smartphone and completes the necessary settings. This setting allows the smartwatch to send health data (e.g., heart rate, steps, sleep data) to the app.

[0044] 3. The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. Bluetooth communication is used for this purpose.

[0045] 4. The server uses a weather information API to obtain weather information for the user's location. For example, the server updates the weather information every morning at 6:00 AM.

[0046] 5. The server generates a multiple-choice question for the day based on weather information and the user's past data. For example, if rain is expected, it will create a question such as, "It will rain today, will you take an umbrella with you?"

[0047] 6. The server generates a question and sends it to the user via LINE. This is done using LINE's message sending API.

[0048] 7. The user answers the question via LINE. For example, they tap either the "Yes" or "No" option.

[0049] 8. The smartphone (device) receives the user's response via LINE and passes it to the app, which then sends the response data to the server.

[0050] 9. The server integrates and analyzes the health data and questionnaire responses received via the smartphone. The analysis also takes into account the user's past behavioral patterns and health status.

[0051] 10. The server generates appropriate health advice for the user based on the analysis results. For example, it might generate advice such as, "Your exercise level today was low, so we recommend a short walk when you get home."

[0052] 11. The server sends the generated advice to the user via LINE.

[0053] 12. The server generates purchase recommendations for relevant products based on the user's health data and behavioral patterns. For example, it might create a message such as, "Here is a coupon code for health supplements."

[0054] 13. The server sends the generated purchase information to the user via LINE.

[0055] Specific example

[0056] Example based on morning weather information

[0057] 1. The server calls the weather information API at 6 AM to confirm that rain is expected in the user's area.

[0058] 2. The server generates the question, "It's going to rain today, will you take an umbrella with you?" and sends it to the user via LINE.

[0059] 3. The user answers "Yes".

[0060] 4. The server receives the answers to the questions and health data (e.g., heart rate, steps) obtained from the smartwatch, and analyzes them.

[0061] 5. Based on the analysis results, the server generates advice such as, "You haven't had enough exercise today, so we recommend taking a short walk when you get home," and sends it to the user via LINE.

[0062] 6. The server then generates a purchase guide that says, "Here is the coupon code for health supplements," and sends it to the user via LINE.

[0063] Through the above embodiment, the system can monitor the user's health status in real time and provide advice and purchasing guidance that also takes into account external factors such as weather information.

[0064] The following describes the processing flow.

[0065] Step 1:

[0066] Users install a dedicated app on their smartphone and link it to their LINE account. They then open the app and use the LINE login function to link their account. This synchronizes the app and the LINE account.

[0067] Step 2:

[0068] The user pairs the smartwatch with the smartphone app. The smartwatch searches for the smartphone in its Bluetooth settings and connects. The user completes the smartwatch setup within the app and allows the sharing of health data.

[0069] Step 3:

[0070] The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. For example, it retrieves heart rate and step count data every hour via Bluetooth.

[0071] Step 4:

[0072] The server calls a weather information API to retrieve weather information for the user's location. The server periodically accesses the weather information API to retrieve local weather data based on the user's location. For example, the API might be called every morning at 6:00 AM.

[0073] Step 5:

[0074] The server generates multiple-choice questions based on weather information and historical data. For example, if it's raining, it creates a question from a template such as, "It's going to rain today, will you take an umbrella?"

[0075] Step 6:

[0076] The server generates a question and sends it to the user via LINE. The LINE Message Sending API is used to send the question to the user's LINE account.

[0077] Step 7:

[0078] The system responds to questions received by users via LINE. For example, it selects the appropriate option from "yes" or "no" choices and sends the response.

[0079] Step 8:

[0080] The smartphone (device) receives the user's response via LINE. The smartphone app analyzes the received LINE data and saves the response data within the app.

[0081] Step 9:

[0082] The smartphone (device) sends the user's response data to the server. The response data within the app is sent to the server using internet communication.

[0083] Step 10:

[0084] The server integrates and analyzes health data and user response data sent from smartphones. It executes analysis algorithms using historical data stored in the database as well as current health data.

[0085] Step 11:

[0086] The server generates health advice based on the analysis results. For example, it might create advice such as, "Your exercise level today was low, so we recommend taking a short walk when you get home."

[0087] Step 12:

[0088] The server generates advice and sends it to the user via LINE. The LINE message sending API is used to send health advice to the user.

[0089] Step 13:

[0090] The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might generate a message like, "Here is a coupon code for health supplements."

[0091] Step 14:

[0092] The server generates a purchase guide and sends it to the user via LINE. The LINE message sending API is used to send the purchase guide to the user.

[0093] (Example 1)

[0094] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] Traditional health management systems lack sufficient integration in acquiring and analyzing user health data, resulting in the inability to provide anything more than simple advice based on individual data elements. Furthermore, they struggle to appropriately generate and provide health advice that considers environmental factors such as external weather information, or purchasing guidance based on user behavior patterns. Additionally, limited real-time data collection and analysis capabilities make it difficult to provide optimal feedback to users.

[0096] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0097] In this invention, the server includes: [means for an information processing device to acquire user health data; [means for an information processing device to analyze health data acquired from a wearable device; [means for an information processing device to generate health advice for the user based on the analysis results; [means for an information processing device to transmit the generated advice to the user via a data communication medium]; [means for an information processing device to generate purchase guidance based on the user's health data and behavioral patterns; [means for an information processing device to transmit the generated purchase guidance to the user via a data communication medium]; [means for an information processing device to acquire weather information; [means for generating multiple-choice questions for the user based on the weather information]; [means for transmitting multiple-choice questions to the user via a data communication medium and receiving responses from the user]; and [means for integrating and analyzing the responses received from the user with health data]. This makes it possible to provide real-time feedback that comprehensively considers the user's health status and environmental factors.

[0098] An "information processing device" is a device that acquires and analyzes a user's health data and generates and transmits feedback and purchase recommendations.

[0099] A "wearable device" is a device that is attached to the user's body and collects health data such as heart rate, steps taken, and sleep data.

[0100] "Health data" refers to data that indicates the user's health status, including heart rate, steps taken, and sleep quality.

[0101] "Analysis" is the process of evaluating and diagnosing health status and behavioral patterns using statistical analysis and machine learning techniques based on acquired data.

[0102] "Health advice" refers to messages that, based on analysis results, suggest the most suitable health management strategies for the user.

[0103] A "data communication medium" refers to a communication network such as the internet, and is a means of sending and receiving data.

[0104] A "purchase guide" is a message that contains information designed to encourage the purchase of relevant products based on the user's health data and behavioral patterns.

[0105] "Weather information" refers to data that shows the weather conditions in the area where the user lives.

[0106] A "multiple-choice question" is a type of question in which the user chooses an answer from a set of specific options, and is used to obtain feedback on health status and behavior.

[0107] A "cloud server" is a server located on the internet and used for data collection and analysis.

[0108] "Identifying behavioral patterns" means analyzing a user's past data to understand specific behavioral tendencies and habits.

[0109] A "health advice generation AI model" is an artificial intelligence model that generates appropriate health advice based on the user's health data and behavioral patterns.

[0110] The "Purchase Recommendation Generation AI Model" is an artificial intelligence model that generates purchase recommendations for related products based on the user's health data and behavioral patterns.

[0111] This invention realizes a health management system that acquires, analyzes, and provides feedback on user health data. The details of the system and the program's processing are described below.

[0112] This system consists of an information processing unit (server), a user terminal (smartphone), and a wearable device (smartwatch) that acquires the user's health data.

[0113] System details

[0114] 1. Hardware and software:

[0115] Information Processing Equipment (Server): A cloud-based server that performs high-speed data processing and analysis. It possesses computing resources to run specific AI models.

[0116] User terminal (smartphone): A portable device running iOS or Android®, with a dedicated health management app installed.

[0117] Wearable devices (smartwatches): These are devices equipped with sensors that measure heart rate, steps taken, sleep data, and other information.

[0118] 2. Program processing:

[0119] Users install a dedicated health management app on their smartphones and link it to their LINE account.

[0120] The user pairs the smartwatch with their smartphone and completes the necessary initial setup. This allows the smartwatch to periodically send health data to the smartphone.

[0121] The smartphone (device) retrieves health data from the smartwatch via Bluetooth communication and stores it within the app.

[0122] The server uses a weather information API to retrieve weather information for the user's location. For example, it updates the weather information every morning at 6:00 AM.

[0123] The server generates multiple-choice questions for the day based on weather information and the user's past health data. For example, if rain is expected, it will create a question such as, "It will rain today, will you take an umbrella with you?"

[0124] The server sends the generated question to the user via LINE. It utilizes LINE's message sending API.

[0125] Users answer questions via LINE. For example, they tap either the "yes" or "no" option.

[0126] The smartphone (device) receives the user's responses via LINE and passes them to the health management app, which then sends the response data to a server.

[0127] The server integrates and analyzes health data and questionnaire responses received via smartphones. The analysis also takes into account the user's past behavioral patterns and health status.

[0128] The server generates appropriate health advice for the user based on the analysis results. For example, it might generate advice such as, "Your exercise level today was low, so we recommend a short walk when you get home."

[0129] The server sends the generated advice to the user via LINE.

[0130] The server generates purchase recommendations for relevant products based on the user's health data and behavioral patterns. For example, it might create a message such as, "Here is a coupon code for health supplements."

[0131] The server sends the generated purchase information to the user via LINE.

[0132] 3. Specific examples:

[0133] The server calls the weather information API at 6 AM to confirm that rain is expected in the user's area.

[0134] The server generates the question, "It's going to rain today, will you take an umbrella with you?" and sends it to the user via LINE.

[0135] The user answers "yes".

[0136] The server receives and analyzes the answers to the questions and health data (such as heart rate and steps) obtained from the smartwatch.

[0137] Based on the analysis results, the server generates advice such as, "You haven't had enough exercise today, so we recommend taking a short walk when you get home," and sends it to the user via LINE.

[0138] The server then generates a purchase guide that says, "Here is a coupon code for health supplements," and sends it to the user via LINE.

[0139] As described above, this system monitors the user's health status in real time and provides optimal advice and purchasing guidance to the user, taking into account weather information and lifestyle habits.

[0140] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0141] The program's processing flow is divided into processing steps.

[0142] Step 1:

[0143] Users install a dedicated health management app on their smartphones. This app also offers an option to link with a LINE account, which users can do from the settings screen.

[0144] Specific actions:

[0145] Users download the health management app from the App Store or Google Play (registered trademark) on their smartphones and follow the instructions to link their LINE account. During this process, the linked account information is saved in the app.

[0146] Inputs and outputs:

[0147] Input: User's smartphone, health management app, LINE account information

[0148] Output: Health management app including linked LINE account information

[0149] Step 2:

[0150] The user pairs the smartwatch with their smartphone and completes the necessary initial setup within the app. This allows health data (e.g., heart rate, steps, sleep data) from the smartwatch to be sent to the app.

[0151] Specific actions:

[0152] The user selects the smartwatch on their smartphone's Bluetooth settings screen and presses the pairing button. Afterward, the app is configured to receive and save health data in real time.

[0153] Inputs and outputs:

[0154] Input: User's smartwatch, smartphone, or app settings screen

[0155] Output: Paired smartwatch information and health data received in real time.

[0156] Step 3:

[0157] The smartphone (device) uses Bluetooth communication to periodically retrieve health data from the smartwatch and store it within the app.

[0158] Specific actions:

[0159] The app retrieves data from the smartwatch via Bluetooth communication at specified intervals and stores that data in the app's database.

[0160] Inputs and outputs:

[0161] Input: Health data from smartwatch

[0162] Output: Health data stored within the app (heart rate, steps, sleep data)

[0163] Step 4:

[0164] The server calls a weather information API to retrieve weather information for the user's location. Updates are performed every morning at 6:00 AM.

[0165] Specific actions:

[0166] The server uses weather information APIs such as "OpenWeatherMap" to retrieve the latest weather information for the user's area every morning at 6:00 AM. The retrieved weather information is stored in a database on the server.

[0167] Inputs and outputs:

[0168] Input: Calling the weather information API

[0169] Output: Acquired weather information

[0170] Step 5:

[0171] The server generates multiple-choice questions for the day based on weather information and past health data. For example, if rain is expected, it might create a question like, "It will rain today, will you take an umbrella with you?"

[0172] Specific actions:

[0173] The server retrieves weather information and historical health data, and uses a natural language generation model to generate questions based on this information.

[0174] Inputs and outputs:

[0175] Input: Weather information and historical health data

[0176] Output: Generated multiple-choice questions

[0177] Step 6:

[0178] The server generates questions and sends them to the user via LINE. This is done using LINE's message sending API.

[0179] Specific actions:

[0180] The server sends the generated question to the user via the LINE API. A notification appears in the LINE app, and the user can access the question.

[0181] Inputs and outputs:

[0182] Input: Generated multiple-choice question

[0183] Output: Question sent via LINE

[0184] Step 7:

[0185] The user answers the question via LINE. For example, they tap either the "yes" or "no" option.

[0186] Specific actions:

[0187] Users check the question notification on the LINE app, tap the appropriate option button, and submit their answer.

[0188] Inputs and outputs:

[0189] Input: Question displayed on the LINE app

[0190] Output: Answer ("Yes" / "No")

[0191] Step 8:

[0192] The smartphone (device) receives the user's responses via LINE and passes them to the health management app, which then sends the response data to a server.

[0193] Specific actions:

[0194] The response data received from the LINE app is transferred to the app, and the app then sends that data to the server.

[0195] Inputs and outputs:

[0196] Input: Response data from the LINE app

[0197] Output: Response data sent to the server

[0198] Step 9:

[0199] The server integrates and analyzes health data and questionnaire responses received via smartphones. The analysis also takes into account the user's past behavioral patterns and health status.

[0200] Specific actions:

[0201] The server references past databases and analyzes health data and questionnaire responses using an AI model.

[0202] Inputs and outputs:

[0203] Input: Health data and answers to questions

[0204] Output: Analysis results

[0205] Step 10:

[0206] The server generates appropriate health advice for the user based on the analysis results. For example, it might generate advice such as, "Your exercise level today was low, so we recommend a short walk when you get home."

[0207] Specific actions:

[0208] The server generates health advice using an AI model and sends it to the user via the LINE API.

[0209] Inputs and outputs:

[0210] Input: Analysis results

[0211] Output: Generated health advice

[0212] Step 11:

[0213] The server generates advice and sends it to the user via LINE. This uses the LINE message sending API.

[0214] Specific actions:

[0215] The server sends the generated advice message to the user via the LINE API.

[0216] Inputs and outputs:

[0217] Input: Generated health advice

[0218] Output: Advice sent via LINE

[0219] Step 12:

[0220] The server generates purchase recommendations for relevant products based on the user's health data and behavioral patterns. For example, it might create a message such as, "Here is a coupon code for health supplements."

[0221] Specific actions:

[0222] The server uses an AI model to generate purchase guidance and creates messages with coupon codes for related products.

[0223] Inputs and outputs:

[0224] Input: Health data and behavioral patterns

[0225] Output: Generated purchase guide

[0226] Step 13:

[0227] The server generates a purchase guide and sends it to the user via LINE. This is done using LINE's message sending API.

[0228] Specific actions:

[0229] The server sends the generated purchase guidance message to the user via the LINE API.

[0230] Inputs and outputs:

[0231] Input: Generated purchase guide

[0232] Output: Purchase information sent via LINE

[0233] (Application Example 1)

[0234] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0235] Many conventional health management systems only acquire and analyze user health data, lacking advice and support that takes into account user behavior patterns and external environmental factors. This results in problems such as being unable to provide users with appropriate dietary suggestions or relevant discount coupons. Furthermore, there is no established method for providing personalized health advice based on weather information and user responses, making it difficult to provide more specific and practical support tailored to users' real lives.

[0236] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0237] In this invention, the server includes means for an information processing device to generate appropriate meal suggestions based on the user's health data and transmit them to the user via a data communication medium; means for the information processing device to generate discount coupons related to the suggested meals and transmit them to the user via a data communication medium; and means for the information processing device to acquire weather information, generate multiple-choice questions for the user, transmit the multiple-choice questions to the user via a data communication medium, receive the user's answers, and integrate and analyze them. This makes it possible to provide personalized meal suggestions and discount coupons based not only on the user's health data but also on weather information and the user's answers.

[0238] An "information processing device" is a device that acquires and analyzes a user's health data and generates and transmits various advice and purchasing guidance.

[0239] "Data communication medium" refers to a means of transmitting information, and specifically includes the internet and Bluetooth.

[0240] "Health data" refers to data related to a user's physical health, such as heart rate, steps taken, and sleep data.

[0241] A "smartwatch" is a wearable device that measures and collects health data such as heart rate, steps taken, and sleep data.

[0242] "Health advice" refers to advice on maintaining or improving health that an information processing device provides to the user based on its analysis results.

[0243] "Purchase information" refers to information and coupon codes for products that a user might potentially purchase, generated by an information processing device.

[0244] "Weather information" refers to weather data related to the user's residential area, including temperature, probability of precipitation, and wind speed.

[0245] A "multiple-choice question" is a question generated by an information processing device and sent to a user, designed to elicit an answer from multiple options.

[0246] A "cloud server" is a server that collects and analyzes data via the internet.

[0247] A "discount coupon" is a code or information used to apply a discount to a suggested meal or product.

[0248] "Analysis results" refer to the results calculated by the information processing device based on health data, user responses, weather information, and other factors.

[0249] This invention realizes a health management system that acquires and analyzes users' health data and provides appropriate health advice, dietary suggestions, and related discount coupons.

[0250] System Configuration

[0251] This system consists of an information processing device (server), a user terminal (smartphone), and a smartwatch that acquires the user's health data.

[0252] Program processing

[0253] 1. Users install a dedicated health management app on their smartphones and link it to their LINE account. This allows users to receive health advice, meal suggestions, and coupons via LINE.

[0254] 2. The user pairs the smartwatch with their smartphone. This setup allows the smartwatch to send health data (e.g., heart rate, steps, sleep data) to the app.

[0255] 3. The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. Bluetooth communication is used for this data retrieval.

[0256] 4. The server uses a weather information API to obtain weather information for the user's residential area. Weather information is retrieved every morning at 6:00 AM.

[0257] 5. The server generates a multiple-choice question for the day based on weather information and the user's past health data. For example, on a cold day, it might generate a question like, "How about a warming meal today?"

[0258] 6. The server generates a question and sends it to the user via LINE. This is done using LINE's message sending API.

[0259] 7. The user answers the question via LINE. For example, they can answer by tapping "Yes" or "No" as options.

[0260] 8. The smartphone (device) receives the user's response via LINE and passes it to the app, which then sends the response data to the server.

[0261] 9. The server integrates and analyzes the health data and questionnaire responses received via the smartphone. The analysis also takes into account the user's past behavioral patterns and health status.

[0262] 10. Based on the analysis results, the server generates appropriate meal suggestions and health advice for the user. For example, it might generate advice such as, "Today, we recommend seafood chili soup, which is good for replenishing energy."

[0263] 11. Generate relevant discount coupons along with the advice generated by the server and send them to the user via LINE.

[0264] Hardware and software to be used

[0265] Smartwatch: A wearable device (e.g., Apple Watch, Garmin) that measures and collects health data (e.g., heart rate, steps, sleep data).

[0266] Smartphone: A device used for installing apps, acquiring health data, and displaying analysis results (e.g., iOS devices, Android devices).

[0267] Server: A cloud server that collects and analyzes health data (e.g., AWS®, Azure®).

[0268] Bluetooth communication: A means of communication for sending and receiving data between a smartwatch and a smartphone.

[0269] Weather Information API: An API for obtaining weather information for the user's residential area (e.g., OpenWeatherMap API).

[0270] LINE API: A message sending API for sending health advice, meal suggestions, and coupons via LINE.

[0271] Specific example

[0272] 1. Morning question:

[0273] The server calls a weather information API at 6 AM, and if it's a cold day, it generates the question, "How about a warming meal today?" and sends it to the user via LINE.

[0274] 2. User responses and meal suggestions:

[0275] If the user answers "yes," the server generates advice such as "We recommend seafood chili soup, which is good for replenishing energy," and sends it via LINE.

[0276] 3. Send coupon:

[0277] Together, a "20% discount coupon for seafood chili soup" will also be sent via LINE.

[0278] Specific examples of prompt sentences

[0279] When the user needs to choose a meal on a cold day, you generate a question "How about a meal to warm you up today?" and send it via LINE. Then, if the user answers "yes", you propose "Seafood chili soup, which is great for energy replenishment" and also provide a discount coupon for that menu.

[0280] Based on this, this system can provide personalized meal suggestions and health advice based on the user's health data, weather information, and the user's answers, and help the user maintain and promote their health.

[0281] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0282] Step 1:

[0283] The user installs a dedicated health management app on their smartphone and links it to their LINE account. This enables the user to receive health advice, meal suggestions, and coupons via LINE.

[0284] Input: Install a health management app on the smartphone and link it to the LINE account.

[0285] Output: The state of being linked to the user's LINE account.

[0286] Step 2:

[0287] By pairing the user's smartwatch with the smartphone, the smartwatch is set to be able to send health data to the app.

[0288] Input: Setting up the smartwatch to pair with your smartphone.

[0289] Output: The smartwatch is in a state where it can send data to a smartphone.

[0290] Step 3:

[0291] The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. Bluetooth communication is used for sending and receiving data.

[0292] Input: Health data acquired by the smartphone from the smartwatch (e.g., heart rate, steps, sleep data).

[0293] Output: Health data stored within the app.

[0294] Step 4:

[0295] The server uses a weather information API to retrieve weather information for the user's residential area every morning at 6:00 AM.

[0296] Input: Weather information API request.

[0297] Output: The latest weather information for the user's residential area.

[0298] Step 5:

[0299] The server generates a multiple-choice question for the day based on weather information and the user's past health data. For example, on a cold day, it might create a question like, "How about a warming meal today?"

[0300] Input: Weather information, historical health data.

[0301] Output: Generated multiple-choice question.

[0302] Step 6:

[0303] To send the questions generated by the server to the user via LINE, the LINE message sending API is used.

[0304] Input: The generated multiple-choice questions.

[0305] Output: The questions sent to the user via LINE.

[0306] Step 7:

[0307] The user answers the question on LINE. For example, the user answers from options such as "Yes" or "No".

[0308] Input: The questions sent via LINE.

[0309] Output: The user's answer to the question.

[0310] Step 8:

[0311] The smartphone (terminal) passes the user's answer received via LINE to the app, and the app sends the answer data to the server.

[0312] Input: The user's answer to the question.

[0313] Output: The user's answer data sent to the server.

[0314] Step 9:

[0315] The server integrates and analyzes the health data and the answers to the questions received via the smartphone, and obtains the analysis results considering the user's past behavior patterns and health status.

[0316] Input: Health data, the user's answer data.

[0317] Output: Analysis results.

[0318] Step 10:

[0319] Based on the analysis results, the server generates appropriate meal suggestions and health advice for the user. For example, it might generate advice such as, "We recommend seafood chili soup for today's energy boost."

[0320] Input: Analysis results.

[0321] Output: Generated meal suggestions and health advice.

[0322] Step 11:

[0323] The server generates advice and sends related discount coupons to users via LINE.

[0324] Input: Generated meal suggestions, health advice, and related discount coupons.

[0325] Output: Advice and discount coupons sent via LINE.

[0326] These processing steps allow the system to provide personalized meal suggestions and health advice, as well as discount coupons, based on the user's health data, weather information, and user responses.

[0327] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0328] This invention combines a health management system that acquires and analyzes user health data and provides appropriate advice and purchasing guidance with an emotion engine that recognizes user emotions. The system configuration and program processing details are described below.

[0329] System Configuration

[0330] This system consists of an information processing device (server), a user terminal (smartphone), a smartwatch that acquires the user's health data, and an emotion engine that recognizes the user's emotions.

[0331] Program processing

[0332] 1. The user installs the dedicated app on their smartphone and links it with their LINE account. The user opens the app and links their account using the LINE login function.

[0333] 2. The user pairs the smartwatch with the smartphone app. Search for the smartphone in the smartwatch's Bluetooth settings and connect. Complete the smartwatch setup within the app and allow the sharing of health data.

[0334] 3. The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. For example, it retrieves heart rate and step count data every hour via Bluetooth.

[0335] 4. The server calls the weather information API to obtain weather information for the user's area. The server periodically accesses the weather information API to obtain local weather data based on the user's location. For example, the API is called every morning at 6:00 AM.

[0336] 5. The server generates multiple-choice questions based on weather information and historical data. For example, if it is raining, it will create a question from a template such as, "It will rain today, will you take an umbrella?"

[0337] 6. The server sends the generated question to the user via LINE. The LINE Message Sending API is used to send the question to the user's LINE account.

[0338] 7. The user answers the question via LINE. For example, they select the appropriate option from choices such as "yes" or "no" and send it.

[0339] 8. The smartphone (device) receives the user's response via LINE. The smartphone app analyzes the received LINE data and saves the response data within the app.

[0340] 9. The smartphone (device) sends the user's response data to the server. The response data within the app is sent to the server using internet communication.

[0341] 10. The server integrates and analyzes health data and user response data sent from smartphones. It executes an analysis algorithm using historical data stored in the database and current health data.

[0342] 11. The server generates health advice based on the analysis results. For example, it might create advice such as, "Your exercise level today was low, so we recommend taking a short walk when you get home."

[0343] 12. The server sends generated advice to the user via LINE. The LINE message sending API is used to send health advice to the user.

[0344] 13. The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might generate a message such as, "Here is a coupon code for health supplements."

[0345] 14. The server sends the generated purchase information to the user via LINE. The LINE message sending API is used to send the purchase information to the user.

[0346] 15. The server uses an emotion engine to recognize the user's emotions. Specifically, it analyzes the user's voice and text data, and the emotion engine identifies the user's emotional state (e.g., joy, sadness, anger, etc.).

[0347] 16. Customize feedback based on the emotions the server perceives. For example, if the user is feeling stressed, it might generate advice such as, "Try breathing exercises to relax."

[0348] Specific example

[0349] Examples based on user emotions

[0350] 1. Users use the diary function through the app to write down their feelings.

[0351] 2. The server uses an emotion engine to recognize the user's emotions from text data. For example, from the text "Today's work was tough," the emotion engine recognizes stress.

[0352] 3. The server generates a question based on weather information and sends it to the user via LINE. For example, "It's going to rain today, will you take an umbrella with you?"

[0353] 4. The user answers "Yes".

[0354] 5. The server receives the answers to the questions and health data (e.g., heart rate, steps) obtained from the smartwatch, and analyzes them.

[0355] 6. The server integrates and analyzes the user's emotional and health data to generate advice for stress reduction. For example, advice such as, "You are stressed, so try breathing exercises to relax."

[0356] 7. The server sends the generated advice to the user via LINE.

[0357] Through the above embodiment, this system can monitor the user's health status in real time and, by combining it with an emotion engine, can provide advice and purchasing guidance tailored to the user's emotional state.

[0358] The following describes the processing flow.

[0359] Step 1:

[0360] Users install a dedicated app on their smartphone and link it to their LINE account. They open the app and use the LINE login function to link their account.

[0361] Step 2:

[0362] The user pairs the smartwatch with the smartphone app. The smartwatch searches for the smartphone in its Bluetooth settings and connects. The user completes the smartwatch setup within the app and allows the sharing of health data.

[0363] Step 3:

[0364] The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. For example, it retrieves heart rate and step count data every hour via Bluetooth.

[0365] Step 4:

[0366] The server calls a weather information API to retrieve weather information for the user's location. The server periodically accesses the weather information API to retrieve local weather data based on the user's location. For example, the API might be called every morning at 6:00 AM.

[0367] Step 5:

[0368] The server generates multiple-choice questions based on weather information and historical data. For example, if it's raining, it creates a question from a template such as, "It's going to rain today, will you take an umbrella?"

[0369] Step 6:

[0370] The server generates a question and sends it to the user via LINE. The LINE Message Sending API is used to send the question to the user's LINE account.

[0371] Step 7:

[0372] The system responds to questions received by users via LINE. For example, it selects the appropriate option from "yes" or "no" choices and sends the response.

[0373] Step 8:

[0374] The smartphone (device) receives the user's response via LINE. The smartphone app analyzes the received LINE data and saves the response data within the app.

[0375] Step 9:

[0376] The smartphone (device) sends the user's response data to the server. The response data within the app is sent to the server using internet communication.

[0377] Step 10:

[0378] The server integrates and analyzes health data and user response data sent from smartphones. It then executes an analysis algorithm using historical and current health data stored in the database.

[0379] Step 11:

[0380] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's voice and text data to determine their emotional state (e.g., joy, anger, sadness, etc.).

[0381] Step 12:

[0382] The server generates health advice based on the analysis results. For example, if it detects that the user is feeling stressed, it will create advice such as, "Try breathing exercises to relax."

[0383] Step 13:

[0384] The server generates advice and sends it to the user via LINE. The LINE message sending API is used to send health advice to the user.

[0385] Step 14:

[0386] The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might create purchase recommendations that include coupons for products related to stress relief (e.g., aromatherapy oils, health foods).

[0387] Step 15:

[0388] The server generates a purchase guide and sends it to the user via LINE. The LINE message sending API is used to send the purchase guide to the user.

[0389] Specific example

[0390] Specific examples including emotion recognition

[0391] Step 1:

[0392] The user receives a multiple-choice question via LINE based on the morning weather forecast: "It will rain today, will you take an umbrella with you?"

[0393] Step 2:

[0394] The user replies "Yes." The smartphone receives this reply and sends it to the server.

[0395] Step 3:

[0396] The smartphone (device) periodically acquires health data (heart rate, steps, etc.) from the smartwatch and sends it to the server.

[0397] Step 4:

[0398] The server integrates and analyzes user responses and health data. It executes an analysis algorithm based on historical and current data stored in the database.

[0399] Step 5:

[0400] The server uses an emotion engine to recognize the user's emotional state. For example, it can recognize that a user is stressed from a LINE text message.

[0401] Step 6:

[0402] Based on the analysis results and emotional state, the server generates health advice such as "Try breathing exercises to relax" and sends it to the user via LINE.

[0403] Step 7:

[0404] The server then generates and sends to the user a purchase guide that includes coupons for related products (such as aromatherapy oils) to help reduce stress.

[0405] Through the above processing steps, a system is realized that monitors the user's health and emotional state in real time and provides advice and purchasing guidance tailored to their individual needs.

[0406] (Example 2)

[0407] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0408] Traditional health management systems had the functionality to acquire and analyze user health data and provide health advice, but they struggled to provide flexible feedback that considered user emotional data or personalized advice based on weather information. Furthermore, automatically generating purchasing recommendations based on user behavior patterns and emotional states was also a challenge.

[0409] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the information processing device to acquire the user's health data, means for the information processing device to analyze emotional data based on the health data and behavioral patterns, means for the information processing device to transmit health advice to the user via a data communication medium, means for the information processing device to generate purchase guidance based on the user's health data, behavioral patterns, and emotional data, and means for the information processing device to transmit the generated purchase guidance to the user via a data communication medium. This makes it possible to provide more personalized health advice and purchase guidance while taking into account the user's emotional state.

[0410] An "information processing device" is a device that collects, analyzes, and transmits data, and plays a role in acquiring, processing, storing, and transmitting user health data and emotional data.

[0411] "User health data" refers to biometric data such as the user's heart rate, steps taken, and sleep duration, obtained from smartwatches and other sensor devices.

[0412] "Emotional data" refers to data that indicates the emotional state (e.g., joy, sadness, anger, etc.) of a user, analyzed from their voice and text data.

[0413] "Health advice" refers to specific instructions and suggestions aimed at maintaining or improving the user's health, generated based on acquired and analyzed user health and emotional data.

[0414] "Purchase guidance" refers to marketing information such as product and service recommendations and coupon information, generated based on the user's health data, emotional data, and behavioral patterns.

[0415] "Data communication medium" refers to the internet and other communication networks, and includes the infrastructure that enables the sending and receiving of data between users and information processing devices.

[0416] "Weather information" refers to information about current and future weather conditions based on the user's location, obtained from weather data provision services and APIs.

[0417] A "multiple-choice question" is a type of question where the user chooses an answer from a set of options, based on weather information and user sentiment data.

[0418] "User behavior patterns" refer to data that shows the user's lifestyle habits and behavioral tendencies, analyzed from past health data, responses, diary entries, and other sources.

[0419] This invention is a health management system that acquires and analyzes user health data and provides appropriate advice and purchasing guidance, further incorporating an emotion engine that recognizes user emotions. The system configuration and its specific implementation method are described below.

[0420] System Configuration

[0421] This system consists of the following elements:

[0422] 1. Information processing equipment (server)

[0423] 2. User terminal (smartphone)

[0424] 3. Devices (smartwatches) that acquire user health data

[0425] 4. Emotional Engine

[0426] Specific examples of program processing

[0427] Application installation and integration

[0428] Users install a dedicated app on their smartphone, open the app, and link their LINE account. This allows them to send and receive health information and advice through their LINE account.

[0429] Acquisition and storage of health data

[0430] The user sets up the smartwatch to pair with a smartphone app. The smartphone periodically retrieves health data (e.g., heart rate, steps) from the smartwatch via Bluetooth and stores it in the app.

[0431] Obtaining weather information

[0432] The server calls a weather information API to retrieve weather information for the user's region. This information is updated regularly, for example, every morning at 6:00 AM.

[0433] emotion recognition

[0434] The server uses an emotion engine to recognize the user's emotions. It analyzes emotion data from the user's diary and text messages to determine the user's emotional state.

[0435] Generating and sending health advice

[0436] The server generates personalized health advice based on the analyzed health and emotional data. For example, it might create advice such as, "Your exercise level today is low, so we recommend a short walk when you get home," and send it to the user using the LINE message sending API.

[0437] Generating and sending purchase information

[0438] The server generates purchase recommendations based on the user's health data, behavioral patterns, and emotional data. For example, it generates a message such as "Here is a coupon code for health supplements" and sends it to the user via LINE.

[0439] Specific example

[0440] Examples based on user emotions

[0441] 1. Users use the diary function through the app to write down their feelings.

[0442] 2. The server uses an emotion engine to recognize the user's emotions from this text data. For example, from the text "Today's work was tough," the emotion engine recognizes stress.

[0443] 3. The server generates a question based on weather information and sends it to the user via LINE. For example, "It's going to rain today, will you take an umbrella with you?"

[0444] 4. The user answers "Yes".

[0445] 5. The server receives the answers to the questions and health data (e.g., heart rate, steps) obtained from the smartwatch, and analyzes them.

[0446] 6. The server integrates and analyzes the user's emotional and health data to generate advice for stress reduction. For example, advice such as, "You are stressed, so try breathing exercises to relax."

[0447] 7. The server sends the generated advice to the user via LINE.

[0448] Example of a prompt

[0449] The following are specific examples of prompts to input into a generative AI model:

[0450] "Please propose a unique health management system."

[0451] "Please explain how to integrate user emotions and health data to generate advice."

[0452] "Please tell me the steps to generate customized questions based on weather information."

[0453] keyword

[0454] Generative AI model, prompt sentence

[0455] As described above, the system of the present invention can highly personalize the user's health management and provide accurate advice and purchasing guidance tailored to the user's emotional state.

[0456] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0457] System program processing flow

[0458] Processing steps

[0459] Step 1:

[0460] Users install a dedicated app on their smartphone and link it to their LINE account. They open the app and use the LINE login function to link their account.

[0461] Input: Smartphone, LINE account information

[0462] Output: Account linking complete status

[0463] The specific process involves the user first downloading the app, installing it, and then launching it. Upon launching the app, the LINE login screen will appear, and account linking will be completed after logging in.

[0464] Step 2:

[0465] The user pairs the smartwatch with the smartphone app. The smartwatch searches for the smartphone in its Bluetooth settings and connects. The user completes the smartwatch setup within the app and allows the sharing of health data.

[0466] Input: Smartwatch, Smartphone

[0467] Output: Pairing complete status

[0468] The specific steps involve the user opening the Bluetooth settings on their smartwatch, searching for their smartphone, and pairing the devices. They then configure the device settings within the app and enable health data sharing.

[0469] Step 3:

[0470] The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. For example, it retrieves heart rate and step count data every hour via Bluetooth.

[0471] Input: Data from smartwatch

[0472] Output: Health data stored within the app

[0473] Specifically, the smartphone periodically retrieves data from the smartwatch via Bluetooth and saves that data to its internal storage or the cloud.

[0474] Step 4:

[0475] The server calls a weather information API to retrieve weather information for the user's area. The weather information API is accessed periodically to retrieve local weather data based on the user's location. For example, the API might be called every morning at 6:00 AM.

[0476] Input: Location information, weather information API

[0477] Output: Weather data

[0478] Specifically, the server, according to a specified schedule, uses location information to call a weather information API and saves the retrieved weather data to a database.

[0479] Step 5:

[0480] The server generates multiple-choice questions based on weather information and historical data. For example, if it's raining, it creates a question from a template such as, "It's going to rain today, will you take an umbrella?"

[0481] Input: Weather data, historical data

[0482] Output: Question template

[0483] In terms of specific operations, the server analyzes weather data, refers to past data, and selects and generates appropriate questions.

[0484] Step 6:

[0485] The server generates a question and sends it to the user via LINE. The LINE Message Sending API is used to send the question to the user's LINE account.

[0486] Input: Generated question, user's LINE account information

[0487] Output: LINE message

[0488] Specifically, the server calls the LINE message sending API and sends the generated question content to the user.

[0489] Step 7:

[0490] Users answer questions via LINE. For example, they select the appropriate option from "yes" or "no" choices and send their response.

[0491] Input: Question on LINE

[0492] Output: User's response

[0493] In terms of specific actions, the user opens LINE, selects the appropriate answer to the question, and sends it.

[0494] Step 8:

[0495] The smartphone (device) receives the user's response via LINE. The smartphone app analyzes the received LINE data and saves the response data within the app.

[0496] Input: User's response (LINE message)

[0497] Output: Response data stored within the app

[0498] Specifically, the smartphone uses the LINE API to retrieve received data, analyzes its contents, and saves it to a database.

[0499] Step 9:

[0500] The smartphone (device) sends the user's response data to the server. The response data within the app is sent to the server using internet communication.

[0501] Input: Response data

[0502] Output: Data to be transferred to the server

[0503] Specifically, the smartphone uses the internet to transfer the response data to the server.

[0504] Step 10:

[0505] The server integrates and analyzes health data and user response data sent from smartphones. It executes analysis algorithms using historical data stored in the database as well as current health data.

[0506] Input: Health data, user response data

[0507] Output: Analysis results

[0508] In terms of specific operations, the server retrieves the necessary data from the database and performs integrated analysis using an analysis algorithm.

[0509] Step 11:

[0510] The server generates health advice based on the analysis results. For example, it might create advice such as, "Your exercise level today was low, so we recommend taking a short walk when you get home."

[0511] Input: Analysis results

[0512] Output: Health advice

[0513] In terms of specific operations, the server selects and generates an advice template based on the analysis results, and then formats it into a concrete message.

[0514] Step 12:

[0515] The server generates advice and sends it to the user via LINE. The LINE message sending API is used to send health advice to the user.

[0516] Input: Health advice

[0517] Output: LINE message

[0518] Specifically, the server calls the LINE message sending API and sends the advice to the user.

[0519] Step 13:

[0520] The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might generate a message like, "Here is a coupon code for health supplements."

[0521] Input: Health data, behavioral patterns

[0522] Output: Purchase Guide

[0523] Specifically, the server analyzes health data and behavioral patterns, and then selects and generates a template for appropriate purchasing recommendations.

[0524] Step 14:

[0525] The server generates a purchase guide and sends it to the user via LINE. The LINE message sending API is used to send the purchase guide to the user.

[0526] Input: Purchase Information

[0527] Output: LINE message

[0528] Specifically, the server calls the LINE message sending API and sends the purchase information to the user.

[0529] Step 15:

[0530] The server uses an emotion engine to recognize the user's emotions. By analyzing the user's voice and text data, the emotion engine identifies the user's emotional state.

[0531] Input: User's voice data and text data

[0532] Output: Sentiment data

[0533] Specifically, the server executes speech recognition and text analysis algorithms, and the emotion engine identifies the emotional state.

[0534] Step 16:

[0535] The server customizes the feedback based on the emotions it perceives. For example, if the user is feeling stressed, it might generate advice such as, "Try breathing exercises to relax."

[0536] Input: Sentiment data

[0537] Output: Customized feedback

[0538] In terms of specific operations, the server analyzes emotional data, selects and customizes a template to generate appropriate feedback.

[0539] (Application Example 2)

[0540] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0541] In today's world, maintaining and improving user health requires comprehensive management that includes not only health data but also emotions. However, conventional health management systems often fail to consider user emotions, resulting in mechanical and unpersonalized health advice and purchasing guidance. Furthermore, providing users with appropriate health-related products requires integrating and analyzing multiple data sets.

[0542] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0543] In this invention, the server includes means for an information processing device to acquire user health data, means for an information processing device to analyze health data acquired from a smartwatch, means for an information processing device to generate health advice for the user based on the analysis results, means for an information processing device to transmit the generated advice to the user via a data communication medium, means for an information processing device to generate purchase guidance based on the user's health data and behavioral patterns, means for an information processing device to transmit the generated purchase guidance to the user via a data communication medium, means for an information processing device to acquire user emotional data, means for an information processing device to generate appropriate product suggestions for the user based on the emotional data, and means for transmitting the generated product suggestions to the user via a written communication medium. This enables integrated management of the user's health and emotional state, and the provision of personalized health advice and purchase guidance.

[0544] An "information processing device" is an electronic device that acquires, analyzes, and notifies users of their health and emotional data.

[0545] A "user" is an individual who uses this system, and their health data and emotional data are managed by the system.

[0546] "Health data" refers to biometric information such as the user's heart rate, steps taken, and exercise level, which is obtained from smartwatches and other devices.

[0547] A "smartwatch" is an electronic device worn by users to collect health data, and it has the function of acquiring data such as heart rate and step count.

[0548] "Emotional data" refers to information that indicates a user's emotional state, and is obtained through methods such as text analysis and voice analysis.

[0549] "Weather information" refers to data showing the weather conditions in the user's residential area, and is obtained from weather forecasting services.

[0550] "Analysis" is the process of evaluating users' health and emotional states based on collected data, and identifying problems and areas for improvement.

[0551] "Health advice" refers to recommendations based on analysis results that aim to help users maintain or improve their health.

[0552] "Purchase guidance" refers to commercial information such as suggestions for health-related products and coupon codes, generated based on the user's health and emotional data.

[0553] A "data communication medium" refers to a means of sending and receiving digital information, such as the internet or mobile phone networks.

[0554] A "cloud server" is a remote computer server accessible via the internet, where data is collected and analyzed.

[0555] This invention relates to a system that integrates and manages user health data and emotional data to provide personalized health advice and purchasing guidance. The system consists of an information processing device (server), a user terminal (smartphone), and a smartwatch.

[0556] System Configuration

[0557] 1. Information Processing Device (Server): This device analyzes the user's health and emotional data, generates health advice and product suggestions for the user, and transmits them via a data communication medium. The server uses an emotional engine and a weather information API to perform data analysis.

[0558] 2. User terminal (smartphone): This device works in conjunction with the smartwatch to collect health data and transmit it to the server via a data communication medium. It also uses LINE messages to ask questions to the user and receive the user's responses.

[0559] 3. Smartwatch: This device acquires health data such as heart rate and steps and transmits it to a smartphone. It pairs with the user's smartphone using Bluetooth.

[0560] Hardware and software to be used

[0561] Health data acquisition and analysis: Smartwatch and smartphone. The smartphone pairs with the smartwatch using Bluetooth and periodically sends health data to the server.

[0562] Emotional data acquisition and analysis: On the server, an Emotion Recognition Engine is used to analyze user emotional data from text and audio data.

[0563] Data communication: Use the LINE Messaging API to send health advice and product suggestions to users.

[0564] Specific example

[0565] Let's say a user uses the diary function and enters, "Work was tough today." The server uses an emotion engine to recognize stress from this text and also analyzes health data (high heart rate) obtained from a smartwatch. As a result, the server generates health advice for the user, such as "Try buying a relaxing beverage to reduce stress," and simultaneously sends it to the user via LINE message along with a coupon code for health-related products.

[0566] Example prompts for a generative AI model

[0567] The following prompt statements can be used to generate optimal product suggestions for the user.

[0568] Generate personalized product recommendations based on the user's health and emotional data. The following data is available:

[0569] Health data: Low activity level, normal heart rate

[0570] Emotional data: High stress levels

[0571] Please generate recommended products.

[0572] Thus, the system of this invention can comprehensively manage the user's health and emotional state and provide personalized health advice and product recommendations in real time.

[0573] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0574] Step 1:

[0575] The user installs a dedicated app on their smartphone and creates an account. During this process, a registration screen appears on the smartphone, and the user enters the required information. The entered information is sent to the server, and a new account is created.

[0576] Input: User registration information (name, email address, etc.)

[0577] Output: Account information is saved on the server.

[0578] Step 2:

[0579] The user pairs the smartwatch with their smartphone. The user searches for the smartphone in the smartwatch's settings screen and connects via Bluetooth. This allows the smartwatch to periodically send health data (heart rate, steps, etc.) to the smartphone.

[0580] Input: Smartwatch pairing information

[0581] Output: Smartwatch and smartphone connection complete.

[0582] Step 3:

[0583] The smartphone periodically acquires health data from the smartwatch. Specifically, the smartphone app communicates with the smartwatch via Bluetooth, acquiring data such as heart rate and steps taken every hour.

[0584] Input: Health data from smartwatch

[0585] Output: Health data acquired on the smartphone

[0586] Step 4:

[0587] The server calls a weather information API to obtain weather information for the user's area. The server periodically accesses the weather information API to obtain local weather data based on the user's location.

[0588] Input: User's location information

[0589] Output: Acquired weather information

[0590] Step 5:

[0591] The server generates multiple-choice questions based on health data and weather information. For example, if it's raining, it might create a question from a template such as, "It's raining today, will you take an umbrella with you?"

[0592] Input: Health data, weather information

[0593] Output: Generated multiple-choice questions

[0594] Step 6:

[0595] The server generates a question and sends it to the user using the LINE API. The question is then displayed on the user's smartphone using the LINE Message Sending API.

[0596] Input: Generated Question

[0597] Output: Question sent to the user's LINE account

[0598] Step 7:

[0599] Users answer questions via LINE. For example, they select the appropriate option from "yes" or "no" choices and send it to the server via LINE message.

[0600] Input: User's response

[0601] Output: User responses received by the server

[0602] Step 8:

[0603] The smartphone receives the user's response via LINE and sends the analysis data to the server. The smartphone app analyzes the received data from LINE and uploads the response data to the server.

[0604] Input: User's response received via LINE

[0605] Output: Analyzed response data

[0606] Step 9:

[0607] The server integrates and analyzes health data and user response data sent from smartphones. This involves running an analysis algorithm using historical and current health data stored in a database.

[0608] Input: Health data, user response data

[0609] Output: Integrated analysis results

[0610] Step 10:

[0611] The server generates health advice based on the analysis results. For example, it might create advice such as, "Your exercise level today was low, so we recommend taking a short walk when you get home."

[0612] Input: Analysis results

[0613] Output: Generated health advice

[0614] Step 11:

[0615] The server generates advice and sends it to the user via LINE. The LINE Message Sending API is used to send the health advice to the user's LINE account.

[0616] Input: Generated health advice

[0617] Output: Advice sent to the user's LINE account

[0618] Step 12:

[0619] The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might create a message like, "Here is a coupon code for health supplements."

[0620] Input: Health data, behavioral patterns

[0621] Output: Generated purchase guide

[0622] Step 13:

[0623] The server generates a purchase guide and sends it to the user via LINE. The purchase guide is sent to the user's LINE account using the LINE Message Sending API.

[0624] Input: Generated purchase guide

[0625] Output: Purchase information sent to the user's LINE account

[0626] Step 14:

[0627] The server uses an emotion engine to recognize the user's emotions. Specifically, it analyzes the user's voice and text data, and the emotion engine identifies the user's emotional state (joy, sadness, anger, etc.).

[0628] Input: Audio data, text data

[0629] Output: User's emotional state

[0630] Step 15:

[0631] The server customizes the feedback based on the emotions it perceives. For example, if the user is feeling stressed, it might generate advice such as, "Try breathing exercises to relax."

[0632] Input: User's emotional state

[0633] Output: Customized feedback content

[0634] In this manner, this invention can comprehensively manage the user's health and emotional state and provide personalized health advice and purchasing guidance.

[0635] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0636] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0637] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0638] [Second Embodiment]

[0639] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0640] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0641] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0642] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0643] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0644] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0645] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0646] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0647] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0648] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0649] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0650] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0651] This invention realizes a health management system that acquires, analyzes, and provides feedback on user health data. Below, the system's program processing is explained in natural language, and the system's operation is described in detail with specific examples.

[0652] System Configuration

[0653] This system consists of an information processing device (server), a user terminal (smartphone), and a smartwatch that acquires the user's health data.

[0654] Program processing

[0655] 1. The user installs a dedicated health management app on their smartphone. When they open the app, they are presented with an option to link their LINE account, and the user links their LINE account with the app.

[0656] 2. The user pairs the smartwatch with their smartphone and completes the necessary settings. This setting allows the smartwatch to send health data (e.g., heart rate, steps, sleep data) to the app.

[0657] 3. The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. Bluetooth communication is used for this purpose.

[0658] 4. The server uses a weather information API to obtain weather information for the user's location. For example, the server updates the weather information every morning at 6:00 AM.

[0659] 5. The server generates a multiple-choice question for the day based on weather information and the user's past data. For example, if rain is expected, it will create a question such as, "It will rain today, will you take an umbrella with you?"

[0660] 6. The server generates a question and sends it to the user via LINE. This is done using LINE's message sending API.

[0661] 7. The user answers the question via LINE. For example, they tap either the "Yes" or "No" option.

[0662] 8. The smartphone (device) receives the user's response via LINE and passes it to the app, which then sends the response data to the server.

[0663] 9. The server integrates and analyzes the health data and questionnaire responses received via the smartphone. The analysis also takes into account the user's past behavioral patterns and health status.

[0664] 10. The server generates appropriate health advice for the user based on the analysis results. For example, it might generate advice such as, "Your exercise level today was low, so we recommend a short walk when you get home."

[0665] 11. The server sends the generated advice to the user via LINE.

[0666] 12. The server generates purchase recommendations for relevant products based on the user's health data and behavioral patterns. For example, it might create a message such as, "Here is a coupon code for health supplements."

[0667] 13. The server sends the generated purchase information to the user via LINE.

[0668] Specific example

[0669] Example based on morning weather information

[0670] 1. The server calls the weather information API at 6 AM to confirm that rain is expected in the user's area.

[0671] 2. The server generates the question, "It's going to rain today, will you take an umbrella with you?" and sends it to the user via LINE.

[0672] 3. The user answers "Yes".

[0673] 4. The server receives the answers to the questions and health data (e.g., heart rate, steps) obtained from the smartwatch, and analyzes them.

[0674] 5. Based on the analysis results, the server generates advice such as, "You haven't had enough exercise today, so we recommend taking a short walk when you get home," and sends it to the user via LINE.

[0675] 6. The server then generates a purchase guide that says, "Here is the coupon code for health supplements," and sends it to the user via LINE.

[0676] Through the above embodiment, the system can monitor the user's health status in real time and provide advice and purchasing guidance that also takes into account external factors such as weather information.

[0677] The following describes the processing flow.

[0678] Step 1:

[0679] Users install a dedicated app on their smartphone and link it to their LINE account. They then open the app and use the LINE login function to link their account. This synchronizes the app and the LINE account.

[0680] Step 2:

[0681] The user pairs the smartwatch with the smartphone app. The smartwatch searches for the smartphone in its Bluetooth settings and connects. The user completes the smartwatch setup within the app and allows the sharing of health data.

[0682] Step 3:

[0683] The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. For example, it retrieves heart rate and step count data every hour via Bluetooth.

[0684] Step 4:

[0685] The server calls a weather information API to retrieve weather information for the user's location. The server periodically accesses the weather information API to retrieve local weather data based on the user's location. For example, the API might be called every morning at 6:00 AM.

[0686] Step 5:

[0687] The server generates multiple-choice questions based on weather information and historical data. For example, if it's raining, it creates a question from a template such as, "It's going to rain today, will you take an umbrella?"

[0688] Step 6:

[0689] The server generates a question and sends it to the user via LINE. The LINE Message Sending API is used to send the question to the user's LINE account.

[0690] Step 7:

[0691] The system responds to questions received by users via LINE. For example, it selects the appropriate option from "yes" or "no" choices and sends the response.

[0692] Step 8:

[0693] The smartphone (device) receives the user's response via LINE. The smartphone app analyzes the received LINE data and saves the response data within the app.

[0694] Step 9:

[0695] The smartphone (device) sends the user's response data to the server. The response data within the app is sent to the server using internet communication.

[0696] Step 10:

[0697] The server integrates and analyzes health data and user response data sent from smartphones. It executes analysis algorithms using historical data stored in the database as well as current health data.

[0698] Step 11:

[0699] The server generates health advice based on the analysis results. For example, it might create advice such as, "Your exercise level today was low, so we recommend taking a short walk when you get home."

[0700] Step 12:

[0701] The server generates advice and sends it to the user via LINE. The LINE message sending API is used to send health advice to the user.

[0702] Step 13:

[0703] The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might generate a message like, "Here is a coupon code for health supplements."

[0704] Step 14:

[0705] The server generates a purchase guide and sends it to the user via LINE. The LINE message sending API is used to send the purchase guide to the user.

[0706] (Example 1)

[0707] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0708] Traditional health management systems lack sufficient integration in acquiring and analyzing user health data, resulting in the inability to provide anything more than simplistic advice based on individual data elements. Furthermore, they struggle to appropriately generate and provide health advice that considers environmental factors such as external weather information, or purchasing guidance based on user behavior patterns. Additionally, limited real-time data collection and analysis capabilities make it difficult to provide optimal feedback to users.

[0709] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0710] In this invention, the server includes: [means for an information processing device to acquire user health data; [means for an information processing device to analyze health data acquired from a wearable device; [means for an information processing device to generate health advice for the user based on the analysis results; [means for an information processing device to transmit the generated advice to the user via a data communication medium]; [means for an information processing device to generate purchase guidance based on the user's health data and behavioral patterns; [means for an information processing device to transmit the generated purchase guidance to the user via a data communication medium]; [means for an information processing device to acquire weather information; [means for generating multiple-choice questions for the user based on the weather information]; [means for transmitting multiple-choice questions to the user via a data communication medium and receiving responses from the user]; and [means for integrating and analyzing the responses received from the user with health data]. This makes it possible to provide real-time feedback that comprehensively considers the user's health status and environmental factors.

[0711] An "information processing device" is a device that acquires and analyzes a user's health data and generates and transmits feedback and purchase recommendations.

[0712] A "wearable device" is a device that is attached to the user's body and collects health data such as heart rate, steps taken, and sleep data.

[0713] "Health data" refers to data that indicates the user's health status, including heart rate, steps taken, and sleep quality.

[0714] "Analysis" is the process of evaluating and diagnosing health status and behavioral patterns using statistical analysis and machine learning techniques based on acquired data.

[0715] "Health advice" refers to messages that, based on analysis results, suggest the most suitable health management strategies for the user.

[0716] A "data communication medium" refers to a communication network such as the internet, and is a means of sending and receiving data.

[0717] A "purchase guide" is a message that contains information designed to encourage the purchase of relevant products based on the user's health data and behavioral patterns.

[0718] "Weather information" refers to data that shows the weather conditions in the area where the user lives.

[0719] A "multiple-choice question" is a type of question in which the user chooses an answer from a set of specific options, and is used to obtain feedback on health status and behavior.

[0720] A "cloud server" is a server located on the internet and used for data collection and analysis.

[0721] "Identifying behavioral patterns" means analyzing a user's past data to understand specific behavioral tendencies and habits.

[0722] A "health advice generation AI model" is an artificial intelligence model that generates appropriate health advice based on a user's health data and behavioral patterns.

[0723] The "Purchase Recommendation Generation AI Model" is an artificial intelligence model that generates purchase recommendations for related products based on the user's health data and behavioral patterns.

[0724] This invention realizes a health management system that acquires, analyzes, and provides feedback on user health data. The details of the system and the program's processing are described below.

[0725] This system consists of an information processing unit (server), a user terminal (smartphone), and a wearable device (smartwatch) that acquires the user's health data.

[0726] System details

[0727] 1. Hardware and software:

[0728] Information Processing System (Server): A cloud-based server that performs high-speed data processing and analysis. It possesses computing resources to run specific AI models.

[0729] User terminal (smartphone): A portable device running iOS or Android, with a dedicated health management app installed.

[0730] Wearable devices (smartwatches): These are devices equipped with sensors that measure heart rate, steps taken, sleep data, and other information.

[0731] 2. Program processing:

[0732] Users install a dedicated health management app on their smartphones and link it to their LINE account.

[0733] The user pairs the smartwatch with their smartphone and completes the necessary initial setup. This allows the smartwatch to periodically send health data to the smartphone.

[0734] The smartphone (device) retrieves health data from the smartwatch via Bluetooth communication and stores it within the app.

[0735] The server uses a weather information API to retrieve weather information for the user's location. For example, it updates the weather information every morning at 6:00 AM.

[0736] The server generates multiple-choice questions for the day based on weather information and the user's past health data. For example, if rain is expected, it will create a question such as, "It will rain today, will you take an umbrella with you?"

[0737] The server sends the generated question to the user via LINE. It utilizes LINE's message sending API.

[0738] Users answer questions via LINE. For example, they tap either the "yes" or "no" option.

[0739] The smartphone (device) receives the user's responses via LINE and passes them to the health management app, which then sends the response data to a server.

[0740] The server integrates and analyzes health data and questionnaire responses received via smartphones. The analysis also takes into account the user's past behavioral patterns and health status.

[0741] The server generates appropriate health advice for the user based on the analysis results. For example, it might generate advice such as, "Your exercise level today was low, so we recommend a short walk when you get home."

[0742] The server sends the generated advice to the user via LINE.

[0743] The server generates purchase recommendations for relevant products based on the user's health data and behavioral patterns. For example, it might create a message such as, "Here is a coupon code for health supplements."

[0744] The server sends the generated purchase information to the user via LINE.

[0745] 3. Specific examples:

[0746] The server calls the weather information API at 6 AM to confirm that rain is expected in the user's area.

[0747] The server generates the question, "It's going to rain today, will you take an umbrella with you?" and sends it to the user via LINE.

[0748] The user answers "yes".

[0749] The server receives and analyzes the answers to the questions and health data (such as heart rate and steps) obtained from the smartwatch.

[0750] Based on the analysis results, the server generates advice such as, "You haven't had enough exercise today, so we recommend taking a short walk when you get home," and sends it to the user via LINE.

[0751] The server then generates a purchase guide that says, "Here is a coupon code for health supplements," and sends it to the user via LINE.

[0752] As described above, this system monitors the user's health status in real time and provides optimal advice and purchasing guidance to the user, taking into account weather information and lifestyle habits.

[0753] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0754] The program's processing flow is divided into processing steps.

[0755] Step 1:

[0756] Users install a dedicated health management app on their smartphones. This app also offers an option to link with a LINE account, which users can do from the settings screen.

[0757] Specific actions:

[0758] Users download the health management app from the App Store or Google Play on their smartphones and follow the instructions to link their LINE account. During this process, the linked account information is saved in the app.

[0759] Inputs and outputs:

[0760] Input: User's smartphone, health management app, LINE account information

[0761] Output: Health management app including linked LINE account information

[0762] Step 2:

[0763] The user pairs the smartwatch with their smartphone and completes the necessary initial setup within the app. This allows health data (e.g., heart rate, steps, sleep data) from the smartwatch to be sent to the app.

[0764] Specific actions:

[0765] The user selects the smartwatch on their smartphone's Bluetooth settings screen and presses the pairing button. Afterward, the app is configured to receive and save health data in real time.

[0766] Inputs and outputs:

[0767] Input: User's smartwatch, smartphone, or app settings screen

[0768] Output: Paired smartwatch information and health data received in real time.

[0769] Step 3:

[0770] The smartphone (device) uses Bluetooth communication to periodically retrieve health data from the smartwatch and store it within the app.

[0771] Specific actions:

[0772] The app retrieves data from the smartwatch via Bluetooth communication at specified intervals and stores that data in the app's database.

[0773] Inputs and outputs:

[0774] Input: Health data from smartwatch

[0775] Output: Health data stored within the app (heart rate, steps, sleep data)

[0776] Step 4:

[0777] The server calls a weather information API to retrieve weather information for the user's location. Updates are performed every morning at 6:00 AM.

[0778] Specific actions:

[0779] The server uses weather information APIs such as "OpenWeatherMap" to retrieve the latest weather information for the user's area every morning at 6:00 AM. The retrieved weather information is stored in a database on the server.

[0780] Inputs and outputs:

[0781] Input: Calling the weather information API

[0782] Output: Acquired weather information

[0783] Step 5:

[0784] The server generates multiple-choice questions for the day based on weather information and past health data. For example, if rain is expected, it might create a question like, "It will rain today, will you take an umbrella with you?"

[0785] Specific actions:

[0786] The server retrieves weather information and historical health data, and uses a natural language generation model to generate questions based on this information.

[0787] Inputs and outputs:

[0788] Input: Weather information and historical health data

[0789] Output: Generated multiple-choice questions

[0790] Step 6:

[0791] The server generates questions and sends them to the user via LINE. This is done using LINE's message sending API.

[0792] Specific actions:

[0793] The server sends the generated question to the user via the LINE API. A notification appears in the LINE app, and the user can access the question.

[0794] Inputs and outputs:

[0795] Input: Generated multiple-choice question

[0796] Output: Question sent via LINE

[0797] Step 7:

[0798] The user answers the question via LINE. For example, they tap either the "yes" or "no" option.

[0799] Specific actions:

[0800] Users check the question notification on the LINE app, tap the appropriate option button, and submit their answer.

[0801] Inputs and outputs:

[0802] Input: Question displayed on the LINE app

[0803] Output: Answer ("Yes" / "No")

[0804] Step 8:

[0805] The smartphone (device) receives the user's responses via LINE and passes them to the health management app, which then sends the response data to a server.

[0806] Specific actions:

[0807] The response data received from the LINE app is transferred to the app, and the app then sends that data to the server.

[0808] Inputs and outputs:

[0809] Input: Response data from the LINE app

[0810] Output: Response data sent to the server

[0811] Step 9:

[0812] The server integrates and analyzes health data and questionnaire responses received via smartphones. The analysis also takes into account the user's past behavioral patterns and health status.

[0813] Specific actions:

[0814] The server references past databases and analyzes health data and questionnaire responses using an AI model.

[0815] Inputs and outputs:

[0816] Input: Health data and answers to questions

[0817] Output: Analysis results

[0818] Step 10:

[0819] The server generates appropriate health advice for the user based on the analysis results. For example, it might generate advice such as, "Your exercise level today was low, so we recommend a short walk when you get home."

[0820] Specific actions:

[0821] The server generates health advice using an AI model and sends it to the user via the LINE API.

[0822] Inputs and outputs:

[0823] Input: Analysis results

[0824] Output: Generated health advice

[0825] Step 11:

[0826] The server generates advice and sends it to the user via LINE. This uses the LINE message sending API.

[0827] Specific actions:

[0828] The server sends the generated advice message to the user via the LINE API.

[0829] Inputs and outputs:

[0830] Input: Generated health advice

[0831] Output: Advice sent via LINE

[0832] Step 12:

[0833] The server generates purchase recommendations for relevant products based on the user's health data and behavioral patterns. For example, it might create a message such as, "Here is a coupon code for health supplements."

[0834] Specific actions:

[0835] The server uses an AI model to generate purchase guidance and creates messages with coupon codes for related products.

[0836] Inputs and outputs:

[0837] Input: Health data and behavioral patterns

[0838] Output: Generated purchase guide

[0839] Step 13:

[0840] The server generates a purchase guide and sends it to the user via LINE. This is done using LINE's message sending API.

[0841] Specific actions:

[0842] The server sends the generated purchase guidance message to the user via the LINE API.

[0843] Inputs and outputs:

[0844] Input: Generated purchase guide

[0845] Output: Purchase information sent via LINE

[0846] (Application Example 1)

[0847] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0848] Many conventional health management systems only acquire and analyze user health data, lacking advice and support that takes into account user behavior patterns and external environmental factors. This results in problems such as being unable to provide users with appropriate dietary suggestions or relevant discount coupons. Furthermore, there is no established method for providing personalized health advice based on weather information and user responses, making it difficult to provide more specific and practical support tailored to users' real lives.

[0849] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0850] In this invention, the server includes means for an information processing device to generate appropriate meal suggestions based on the user's health data and transmit them to the user via a data communication medium; means for the information processing device to generate discount coupons related to the suggested meals and transmit them to the user via a data communication medium; and means for the information processing device to acquire weather information, generate multiple-choice questions for the user, transmit the multiple-choice questions to the user via a data communication medium, receive the user's answers, and integrate and analyze them. This makes it possible to provide personalized meal suggestions and discount coupons based not only on the user's health data but also on weather information and the user's answers.

[0851] An "information processing device" is a device that acquires and analyzes a user's health data and generates and transmits various advice and purchasing guidance.

[0852] "Data communication medium" refers to a means of transmitting information, and specifically includes the internet and Bluetooth.

[0853] "Health data" refers to data related to a user's physical health, such as heart rate, steps taken, and sleep data.

[0854] A "smartwatch" is a wearable device that measures and collects health data such as heart rate, steps taken, and sleep data.

[0855] "Health advice" refers to advice on maintaining or improving health that an information processing device provides to the user based on its analysis results.

[0856] "Purchase information" refers to information and coupon codes for products that a user might potentially purchase, generated by an information processing device.

[0857] "Weather information" refers to weather data related to the user's residential area, including temperature, probability of precipitation, and wind speed.

[0858] A "multiple-choice question" is a question generated by an information processing device and sent to a user, designed to elicit an answer from multiple options.

[0859] A "cloud server" is a server that collects and analyzes data via the internet.

[0860] A "discount coupon" is a code or information used to apply a discount to a suggested meal or product.

[0861] "Analysis results" refer to the results calculated by the information processing device based on health data, user responses, weather information, and other factors.

[0862] This invention realizes a health management system that acquires and analyzes users' health data and provides appropriate health advice, dietary suggestions, and related discount coupons.

[0863] System Configuration

[0864] This system consists of an information processing device (server), a user terminal (smartphone), and a smartwatch that acquires the user's health data.

[0865] Program processing

[0866] 1. Users install a dedicated health management app on their smartphones and link it to their LINE account. This allows users to receive health advice, meal suggestions, and coupons via LINE.

[0867] 2. The user pairs the smartwatch with their smartphone. This setup allows the smartwatch to send health data (e.g., heart rate, steps, sleep data) to the app.

[0868] 3. The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. Bluetooth communication is used for this data retrieval.

[0869] 4. The server uses a weather information API to obtain weather information for the user's residential area. Weather information is retrieved every morning at 6:00 AM.

[0870] 5. The server generates a multiple-choice question for the day based on weather information and the user's past health data. For example, on a cold day, it might generate a question like, "How about a warming meal today?"

[0871] 6. The server generates a question and sends it to the user via LINE. This is done using LINE's message sending API.

[0872] 7. The user answers the question via LINE. For example, they can answer by tapping "Yes" or "No" as options.

[0873] 8. The smartphone (device) receives the user's response via LINE and passes it to the app, which then sends the response data to the server.

[0874] 9. The server integrates and analyzes the health data and questionnaire responses received via the smartphone. The analysis also takes into account the user's past behavioral patterns and health status.

[0875] 10. Based on the analysis results, the server generates appropriate meal suggestions and health advice for the user. For example, it might generate advice such as, "Today, we recommend seafood chili soup, which is good for replenishing energy."

[0876] 11. Generate relevant discount coupons along with the advice generated by the server and send them to the user via LINE.

[0877] Hardware and software to be used

[0878] Smartwatch: A wearable device (e.g., Apple Watch, Garmin) that measures and collects health data (e.g., heart rate, steps, sleep data).

[0879] Smartphone: A device used for installing apps, acquiring health data, and displaying analysis results (e.g., iOS devices, Android devices).

[0880] Server: A cloud server (e.g., AWS, Azure) that collects and analyzes health data.

[0881] Bluetooth communication: A means of communication for sending and receiving data between a smartwatch and a smartphone.

[0882] Weather Information API: An API for obtaining weather information for the user's residential area (e.g., OpenWeatherMap API).

[0883] LINE API: A message sending API for sending health advice, meal suggestions, and coupons via LINE.

[0884] Specific example

[0885] 1. Morning question:

[0886] The server calls a weather information API at 6 AM, and if it's a cold day, it generates the question, "How about a warming meal today?" and sends it to the user via LINE.

[0887] 2. User responses and meal suggestions:

[0888] If the user answers "yes," the server generates advice such as "We recommend seafood chili soup, which is good for replenishing energy," and sends it via LINE.

[0889] 3. Send coupon:

[0890] A 20% discount coupon for seafood chili soup will also be sent via LINE.

[0891] Examples of prompt statements

[0892] If a user needs to choose a meal on a cold day, you generate a question like, "How about a warming meal today?" and send it via LINE. If the user replies "yes," you then suggest, "I recommend the seafood chili soup, which is great for replenishing energy," and also offer a discount coupon for that menu item.

[0893] This system can use users' health data to provide personalized meal suggestions and health advice based on weather information and user responses, helping users maintain and improve their health.

[0894] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0895] Step 1:

[0896] Users install a dedicated health management app on their smartphones and link it to their LINE account. This allows them to receive health advice, meal suggestions, and coupons via LINE.

[0897] Input: Install a health management app on your smartphone and link it to your LINE account.

[0898] Output: Linked to the user's LINE account.

[0899] Step 2:

[0900] By pairing the smartwatch with their smartphone, the user can configure it to send health data from the smartwatch to the app.

[0901] Input: Setting up the smartwatch to pair with your smartphone.

[0902] Output: The smartwatch is in a state where it can send data to a smartphone.

[0903] Step 3:

[0904] The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. Bluetooth communication is used for sending and receiving data.

[0905] Input: Health data acquired by the smartphone from the smartwatch (e.g., heart rate, steps, sleep data).

[0906] Output: Health data stored within the app.

[0907] Step 4:

[0908] The server uses a weather information API to retrieve weather information for the user's residential area every morning at 6:00 AM.

[0909] Input: Weather information API request.

[0910] Output: The latest weather information for the user's residential area.

[0911] Step 5:

[0912] The server generates a multiple-choice question for the day based on weather information and the user's past health data. For example, on a cold day, it might create a question like, "How about a warming meal today?"

[0913] Input: Weather information, historical health data.

[0914] Output: Generated multiple-choice question.

[0915] Step 6:

[0916] The server generates questions and sends them to users via LINE, utilizing LINE's message sending API.

[0917] Input: A generated multiple-choice question.

[0918] Output: Questions sent to the user via LINE.

[0919] Step 7:

[0920] Users answer questions via LINE. For example, they can choose to answer from "yes" or "no" options.

[0921] Input: Question sent via LINE.

[0922] Output: User's response to the question.

[0923] Step 8:

[0924] The smartphone (device) receives the user's response via LINE and passes it to the app, which then sends that response data to the server.

[0925] Input: The user's response to the question.

[0926] Output: User response data sent to the server.

[0927] Step 9:

[0928] The server integrates and analyzes health data and answers to questions received via smartphone, taking into account the user's past behavioral patterns and health status to obtain analysis results.

[0929] Input: Health data, user response data.

[0930] Output: Analysis results.

[0931] Step 10:

[0932] Based on the analysis results, the server generates appropriate meal suggestions and health advice for the user. For example, it might generate advice such as, "We recommend seafood chili soup for today's energy boost."

[0933] Input: Analysis results.

[0934] Output: Generated meal suggestions and health advice.

[0935] Step 11:

[0936] The server generates advice and sends related discount coupons to users via LINE.

[0937] Input: Generated meal suggestions, health advice, and related discount coupons.

[0938] Output: Advice and discount coupons sent via LINE.

[0939] These processing steps allow the system to provide personalized meal suggestions and health advice, as well as discount coupons, based on the user's health data, weather information, and user responses.

[0940] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0941] This invention combines a health management system that acquires and analyzes user health data and provides appropriate advice and purchasing guidance with an emotion engine that recognizes user emotions. The system configuration and program processing details are described below.

[0942] System Configuration

[0943] This system consists of an information processing device (server), a user terminal (smartphone), a smartwatch that acquires the user's health data, and an emotion engine that recognizes the user's emotions.

[0944] Program processing

[0945] 1. The user installs the dedicated app on their smartphone and links it with their LINE account. The user opens the app and links their account using the LINE login function.

[0946] 2. The user pairs the smartwatch with the smartphone app. Search for the smartphone in the smartwatch's Bluetooth settings and connect. Complete the smartwatch setup within the app and allow the sharing of health data.

[0947] 3. The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. For example, it retrieves heart rate and step count data every hour via Bluetooth.

[0948] 4. The server calls the weather information API to obtain weather information for the user's area. The server periodically accesses the weather information API to obtain local weather data based on the user's location. For example, the API is called every morning at 6:00 AM.

[0949] 5. The server generates multiple-choice questions based on weather information and historical data. For example, if it is raining, it will create a question from a template such as, "It will rain today, will you take an umbrella?"

[0950] 6. The server sends the generated question to the user via LINE. The LINE Message Sending API is used to send the question to the user's LINE account.

[0951] 7. The user answers the question via LINE. For example, they select the appropriate option from "yes" or "no" choices and send it.

[0952] 8. The smartphone (device) receives the user's response via LINE. The smartphone app analyzes the received LINE data and saves the response data within the app.

[0953] 9. The smartphone (device) sends the user's response data to the server. The response data within the app is sent to the server using internet communication.

[0954] 10. The server integrates and analyzes health data and user response data sent from smartphones. It executes an analysis algorithm using historical data stored in the database and current health data.

[0955] 11. The server generates health advice based on the analysis results. For example, it might create advice such as, "Your exercise level today was low, so we recommend taking a short walk when you get home."

[0956] 12. The server sends generated advice to the user via LINE. The LINE message sending API is used to send health advice to the user.

[0957] 13. The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might generate a message such as, "Here is a coupon code for health supplements."

[0958] 14. The server sends the generated purchase information to the user via LINE. The LINE message sending API is used to send the purchase information to the user.

[0959] 15. The server uses an emotion engine to recognize the user's emotions. Specifically, it analyzes the user's voice and text data, and the emotion engine identifies the user's emotional state (e.g., joy, sadness, anger, etc.).

[0960] 16. Customize feedback based on the emotions the server perceives. For example, if the user is feeling stressed, it might generate advice such as, "Try breathing exercises to relax."

[0961] Specific example

[0962] Examples based on user emotions

[0963] 1. Users use the diary function through the app to write down their feelings.

[0964] 2. The server uses an emotion engine to recognize the user's emotions from text data. For example, from the text "Today's work was tough," the emotion engine recognizes stress.

[0965] 3. The server generates a question based on weather information and sends it to the user via LINE. For example, "It's going to rain today, will you take an umbrella with you?"

[0966] 4. The user answers "Yes".

[0967] 5. The server receives the answers to the questions and health data (e.g., heart rate, steps) obtained from the smartwatch, and analyzes them.

[0968] 6. The server integrates and analyzes the user's emotional and health data to generate advice for stress reduction. For example, advice such as, "You are stressed, so try breathing exercises to relax."

[0969] 7. The server sends the generated advice to the user via LINE.

[0970] Through the above embodiment, this system can monitor the user's health status in real time and, by combining it with an emotion engine, can provide advice and purchasing guidance tailored to the user's emotional state.

[0971] The following describes the processing flow.

[0972] Step 1:

[0973] Users install a dedicated app on their smartphone and link it to their LINE account. They open the app and use the LINE login function to link their account.

[0974] Step 2:

[0975] The user pairs the smartwatch with the smartphone app. The smartwatch searches for the smartphone in its Bluetooth settings and connects. The user completes the smartwatch setup within the app and allows the sharing of health data.

[0976] Step 3:

[0977] The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. For example, it retrieves heart rate and step count data every hour via Bluetooth.

[0978] Step 4:

[0979] The server calls a weather information API to retrieve weather information for the user's location. The server periodically accesses the weather information API to retrieve local weather data based on the user's location. For example, the API might be called every morning at 6:00 AM.

[0980] Step 5:

[0981] The server generates multiple-choice questions based on weather information and historical data. For example, if it's raining, it creates a question from a template such as, "It's going to rain today, will you take an umbrella?"

[0982] Step 6:

[0983] The server generates a question and sends it to the user via LINE. The LINE Message Sending API is used to send the question to the user's LINE account.

[0984] Step 7:

[0985] The system responds to questions received by users via LINE. For example, it selects the appropriate option from "yes" or "no" choices and sends the response.

[0986] Step 8:

[0987] The smartphone (device) receives the user's response via LINE. The smartphone app analyzes the received LINE data and saves the response data within the app.

[0988] Step 9:

[0989] The smartphone (device) sends the user's response data to the server. The response data within the app is sent to the server using internet communication.

[0990] Step 10:

[0991] The server integrates and analyzes health data and user response data sent from smartphones. It then executes an analysis algorithm using historical and current health data stored in the database.

[0992] Step 11:

[0993] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's voice and text data to determine their emotional state (e.g., joy, anger, sadness, etc.).

[0994] Step 12:

[0995] The server generates health advice based on the analysis results. For example, if it detects that the user is feeling stressed, it will create advice such as, "Try breathing exercises to relax."

[0996] Step 13:

[0997] The server generates advice and sends it to the user via LINE. The LINE message sending API is used to send health advice to the user.

[0998] Step 14:

[0999] The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might create purchase recommendations that include coupons for products related to stress relief (e.g., aromatherapy oils, health foods).

[1000] Step 15:

[1001] The server generates a purchase guide and sends it to the user via LINE. The LINE message sending API is used to send the purchase guide to the user.

[1002] Specific example

[1003] Specific examples including emotion recognition

[1004] Step 1:

[1005] The user receives a multiple-choice question via LINE based on the morning weather forecast: "It will rain today, will you take an umbrella with you?"

[1006] Step 2:

[1007] The user replies "Yes." The smartphone receives this reply and sends it to the server.

[1008] Step 3:

[1009] The smartphone (device) periodically acquires health data (heart rate, steps, etc.) from the smartwatch and sends it to the server.

[1010] Step 4:

[1011] The server integrates and analyzes user responses and health data. It executes an analysis algorithm based on historical and current data stored in the database.

[1012] Step 5:

[1013] The server uses an emotion engine to recognize the user's emotional state. For example, it can recognize that a user is stressed from a LINE text message.

[1014] Step 6:

[1015] Based on the analysis results and emotional state, the server generates health advice such as "Try breathing exercises to relax" and sends it to the user via LINE.

[1016] Step 7:

[1017] The server then generates and sends to the user a purchase guide that includes coupons for related products (such as aromatherapy oils) to help reduce stress.

[1018] Through the above processing steps, a system is realized that monitors the user's health and emotional state in real time and provides advice and purchasing guidance tailored to their individual needs.

[1019] (Example 2)

[1020] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[1021] Traditional health management systems had the functionality to acquire and analyze user health data and provide health advice, but they struggled to provide flexible feedback that took into account user emotional data, or personalized advice based on weather information. Furthermore, automatically generating purchasing recommendations based on user behavior patterns and emotional states was also a challenge.

[1022] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the information processing device to acquire the user's health data, means for the information processing device to analyze emotional data based on the health data and behavioral patterns, means for the information processing device to transmit health advice to the user via a data communication medium, means for the information processing device to generate purchase guidance based on the user's health data, behavioral patterns, and emotional data, and means for the information processing device to transmit the generated purchase guidance to the user via a data communication medium. This makes it possible to provide more personalized health advice and purchase guidance while taking into account the user's emotional state.

[1023] An "information processing device" is a device that collects, analyzes, and transmits data, and plays a role in acquiring, processing, storing, and transmitting user health data and emotional data.

[1024] "User health data" refers to biometric data such as the user's heart rate, steps taken, and sleep duration, obtained from smartwatches and other sensor devices.

[1025] "Emotional data" refers to data that indicates the emotional state (e.g., joy, sadness, anger, etc.) of a user, analyzed from their voice and text data.

[1026] "Health advice" refers to specific instructions and suggestions aimed at maintaining or improving the user's health, generated based on acquired and analyzed user health and emotional data.

[1027] "Purchase guidance" refers to marketing information such as product and service recommendations and coupon information, generated based on the user's health data, emotional data, and behavioral patterns.

[1028] "Data communication medium" refers to the internet and other communication networks, and includes the infrastructure that enables the sending and receiving of data between users and information processing devices.

[1029] "Weather information" refers to information about current and future weather conditions based on the user's location, obtained from weather data provision services and APIs.

[1030] A "multiple-choice question" is a type of question where the user chooses an answer from a set of options, based on weather information and user sentiment data.

[1031] "User behavior patterns" refer to data that shows the user's lifestyle habits and behavioral tendencies, analyzed from past health data, responses, diary entries, and other sources.

[1032] This invention is a health management system that acquires and analyzes user health data and provides appropriate advice and purchasing guidance, further incorporating an emotion engine that recognizes user emotions. The system configuration and its specific implementation method are described below.

[1033] System Configuration

[1034] This system consists of the following elements:

[1035] 1. Information processing equipment (server)

[1036] 2. User terminal (smartphone)

[1037] 3. Devices (smartwatches) that acquire user health data

[1038] 4. Emotional Engine

[1039] Specific examples of program processing

[1040] Application installation and integration

[1041] Users install a dedicated app on their smartphone, open the app, and link their LINE account. This allows them to send and receive health information and advice through their LINE account.

[1042] Acquisition and storage of health data

[1043] The user sets up the smartwatch to pair with a smartphone app. The smartphone periodically retrieves health data (e.g., heart rate, steps) from the smartwatch via Bluetooth and stores it in the app.

[1044] Obtaining weather information

[1045] The server calls a weather information API to retrieve weather information for the user's region. This information is updated regularly, for example, every morning at 6:00 AM.

[1046] emotion recognition

[1047] The server uses an emotion engine to recognize the user's emotions. It analyzes emotion data from the user's diary and text messages to determine the user's emotional state.

[1048] Generating and sending health advice

[1049] The server generates personalized health advice based on the analyzed health and emotional data. For example, it might create advice such as, "Your exercise level today is low, so we recommend a short walk when you get home," and send it to the user using the LINE message sending API.

[1050] Generating and sending purchase information

[1051] The server generates purchase recommendations based on the user's health data, behavioral patterns, and emotional data. For example, it generates a message such as "Here is a coupon code for health supplements" and sends it to the user via LINE.

[1052] Specific example

[1053] Examples based on user emotions

[1054] 1. Users use the diary function through the app to write down their feelings.

[1055] 2. The server uses an emotion engine to recognize the user's emotions from this text data. For example, from the text "Today's work was tough," the emotion engine recognizes stress.

[1056] 3. The server generates a question based on weather information and sends it to the user via LINE. For example, "It's going to rain today, will you take an umbrella with you?"

[1057] 4. The user answers "Yes".

[1058] 5. The server receives the answers to the questions and health data (e.g., heart rate, steps) obtained from the smartwatch, and analyzes them.

[1059] 6. The server integrates and analyzes the user's emotional and health data to generate advice for stress reduction. For example, advice such as, "You are stressed, so try breathing exercises to relax."

[1060] 7. The server sends the generated advice to the user via LINE.

[1061] Example of a prompt

[1062] The following are specific examples of prompts to input into a generative AI model:

[1063] "Please propose a unique health management system."

[1064] "Please explain how to integrate user emotions and health data to generate advice."

[1065] "Please tell me the steps to generate customized questions based on weather information."

[1066] keyword

[1067] Generative AI model, prompt sentence

[1068] As described above, the system of the present invention can highly personalize the user's health management and provide accurate advice and purchasing guidance tailored to the user's emotional state.

[1069] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1070] System program processing flow

[1071] Processing steps

[1072] Step 1:

[1073] Users install a dedicated app on their smartphone and link it to their LINE account. They open the app and use the LINE login function to link their account.

[1074] Input: Smartphone, LINE account information

[1075] Output: Account linking complete status

[1076] The specific process involves the user first downloading the app, installing it, and then launching it. Upon launching the app, the LINE login screen will appear, and account linking will be completed after logging in.

[1077] Step 2:

[1078] The user pairs the smartwatch with the smartphone app. The smartwatch searches for the smartphone in its Bluetooth settings and connects. The user completes the smartwatch setup within the app and allows the sharing of health data.

[1079] Input: Smartwatch, Smartphone

[1080] Output: Pairing complete status

[1081] The specific steps involve the user opening the Bluetooth settings on their smartwatch, searching for their smartphone, and pairing the devices. They then configure the device settings within the app and enable health data sharing.

[1082] Step 3:

[1083] The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. For example, it retrieves heart rate and step count data every hour via Bluetooth.

[1084] Input: Data from smartwatch

[1085] Output: Health data stored within the app

[1086] Specifically, the smartphone periodically retrieves data from the smartwatch via Bluetooth and saves that data to its internal storage or the cloud.

[1087] Step 4:

[1088] The server calls a weather information API to retrieve weather information for the user's area. The weather information API is accessed periodically to retrieve local weather data based on the user's location. For example, the API might be called every morning at 6:00 AM.

[1089] Input: Location information, weather information API

[1090] Output: Weather data

[1091] Specifically, the server, according to a specified schedule, uses location information to call a weather information API and saves the retrieved weather data to a database.

[1092] Step 5:

[1093] The server generates multiple-choice questions based on weather information and historical data. For example, if it's raining, it creates a question from a template such as, "It's going to rain today, will you take an umbrella?"

[1094] Input: Weather data, historical data

[1095] Output: Question template

[1096] In terms of specific operations, the server analyzes weather data, refers to past data, and selects and generates appropriate questions.

[1097] Step 6:

[1098] The server generates a question and sends it to the user via LINE. The LINE Message Sending API is used to send the question to the user's LINE account.

[1099] Input: Generated question, user's LINE account information

[1100] Output: LINE message

[1101] Specifically, the server calls the LINE message sending API and sends the generated question content to the user.

[1102] Step 7:

[1103] Users answer questions via LINE. For example, they select the appropriate option from "yes" or "no" choices and send their response.

[1104] Input: Question on LINE

[1105] Output: User's response

[1106] In terms of specific actions, the user opens LINE, selects the appropriate answer to the question, and sends it.

[1107] Step 8:

[1108] The smartphone (device) receives the user's response via LINE. The smartphone app analyzes the received LINE data and saves the response data within the app.

[1109] Input: User's response (LINE message)

[1110] Output: Response data stored within the app

[1111] Specifically, the smartphone uses the LINE API to retrieve received data, analyzes its contents, and saves it to a database.

[1112] Step 9:

[1113] The smartphone (device) sends the user's response data to the server. The response data within the app is sent to the server using internet communication.

[1114] Input: Response data

[1115] Output: Data to be transferred to the server

[1116] Specifically, the smartphone uses the internet to transfer the response data to the server.

[1117] Step 10:

[1118] The server integrates and analyzes health data and user response data sent from smartphones. It executes analysis algorithms using historical data stored in the database as well as current health data.

[1119] Input: Health data, user response data

[1120] Output: Analysis results

[1121] In terms of specific operations, the server retrieves the necessary data from the database and performs integrated analysis using an analysis algorithm.

[1122] Step 11:

[1123] The server generates health advice based on the analysis results. For example, it might create advice such as, "Your exercise level today was low, so we recommend taking a short walk when you get home."

[1124] Input: Analysis results

[1125] Output: Health advice

[1126] In terms of specific operations, the server selects and generates an advice template based on the analysis results, and then formats it into a concrete message.

[1127] Step 12:

[1128] The server generates advice and sends it to the user via LINE. The LINE message sending API is used to send health advice to the user.

[1129] Input: Health advice

[1130] Output: LINE message

[1131] Specifically, the server calls the LINE message sending API and sends the advice to the user.

[1132] Step 13:

[1133] The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might generate a message like, "Here is a coupon code for health supplements."

[1134] Input: Health data, behavioral patterns

[1135] Output: Purchase Guide

[1136] Specifically, the server analyzes health data and behavioral patterns, and then selects and generates a template for appropriate purchasing recommendations.

[1137] Step 14:

[1138] The server generates a purchase guide and sends it to the user via LINE. The LINE message sending API is used to send the purchase guide to the user.

[1139] Input: Purchase Information

[1140] Output: LINE message

[1141] Specifically, the server calls the LINE message sending API and sends the purchase information to the user.

[1142] Step 15:

[1143] The server uses an emotion engine to recognize the user's emotions. By analyzing the user's voice and text data, the emotion engine identifies the user's emotional state.

[1144] Input: User's voice data and text data

[1145] Output: Sentiment data

[1146] Specifically, the server executes speech recognition and text analysis algorithms, and the emotion engine identifies the emotional state.

[1147] Step 16:

[1148] The server customizes the feedback based on the emotions it perceives. For example, if the user is feeling stressed, it might generate advice such as, "Try breathing exercises to relax."

[1149] Input: Sentiment data

[1150] Output: Customized feedback

[1151] In terms of specific operations, the server analyzes emotional data, selects and customizes a template to generate appropriate feedback.

[1152] (Application Example 2)

[1153] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1154] In today's world, maintaining and improving user health requires comprehensive management that includes not only health data but also emotions. However, conventional health management systems often fail to consider user emotions, resulting in mechanical and unpersonalized health advice and purchasing guidance. Furthermore, providing users with appropriate health-related products requires integrating and analyzing multiple data sets.

[1155] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1156] In this invention, the server includes means for an information processing device to acquire user health data, means for an information processing device to analyze health data acquired from a smartwatch, means for an information processing device to generate health advice for the user based on the analysis results, means for an information processing device to transmit the generated advice to the user via a data communication medium, means for an information processing device to generate purchase guidance based on the user's health data and behavioral patterns, means for an information processing device to transmit the generated purchase guidance to the user via a data communication medium, means for an information processing device to acquire user emotional data, means for an information processing device to generate appropriate product suggestions for the user based on the emotional data, and means for transmitting the generated product suggestions to the user via a written communication medium. This enables integrated management of the user's health and emotional state, and the provision of personalized health advice and purchase guidance.

[1157] An "information processing device" is an electronic device that acquires, analyzes, and notifies users of their health and emotional data.

[1158] A "user" is an individual who uses this system, and their health data and emotional data are managed by the system.

[1159] "Health data" refers to biometric information such as the user's heart rate, steps taken, and exercise level, which is obtained from smartwatches and other devices.

[1160] A "smartwatch" is an electronic device worn by users to collect health data, and it has the function of acquiring data such as heart rate and step count.

[1161] "Emotional data" refers to information that indicates a user's emotional state, and is obtained through methods such as text analysis and voice analysis.

[1162] "Weather information" refers to data showing the weather conditions in the user's residential area, and is obtained from weather forecasting services.

[1163] "Analysis" is the process of evaluating users' health and emotional states based on collected data, and identifying problems and areas for improvement.

[1164] "Health advice" refers to recommendations based on analysis results that aim to help users maintain or improve their health.

[1165] "Purchase guidance" refers to commercial information such as suggestions for health-related products and coupon codes, generated based on the user's health and emotional data.

[1166] A "data communication medium" refers to a means of sending and receiving digital information, such as the internet or mobile phone networks.

[1167] A "cloud server" is a remote computer server accessible via the internet, where data is collected and analyzed.

[1168] This invention relates to a system that integrates and manages user health data and emotional data to provide personalized health advice and purchasing guidance. The system consists of an information processing device (server), a user terminal (smartphone), and a smartwatch.

[1169] System Configuration

[1170] 1. Information Processing Device (Server): This device analyzes the user's health and emotional data, generates health advice and product suggestions for the user, and transmits them via a data communication medium. The server uses an emotional engine and a weather information API to perform data analysis.

[1171] 2. User terminal (smartphone): This device works in conjunction with the smartwatch to collect health data and transmit it to the server via a data communication medium. It also uses LINE messages to ask questions to the user and receive the user's responses.

[1172] 3. Smartwatch: This device acquires health data such as heart rate and steps and transmits it to a smartphone. It pairs with the user's smartphone using Bluetooth.

[1173] Hardware and software to be used

[1174] Health data acquisition and analysis: Smartwatch and smartphone. The smartphone pairs with the smartwatch using Bluetooth and periodically sends health data to the server.

[1175] Emotional data acquisition and analysis: On the server, an Emotion Recognition Engine is used to analyze user emotional data from text and audio data.

[1176] Data communication: Use the LINE Messaging API to send health advice and product suggestions to users.

[1177] Specific example

[1178] Let's say a user uses the diary function and enters, "Work was tough today." The server uses an emotion engine to recognize stress from this text and also analyzes health data (high heart rate) obtained from a smartwatch. As a result, the server generates health advice for the user, such as "Try buying a relaxing beverage to reduce stress," and simultaneously sends it to the user via LINE message along with a coupon code for health-related products.

[1179] Example prompts for a generative AI model

[1180] The following prompt statements can be used to generate optimal product suggestions for the user.

[1181] Generate personalized product recommendations based on the user's health and emotional data. The following data is available:

[1182] Health data: Low activity level, normal heart rate

[1183] Emotional data: High stress levels

[1184] Please generate recommended products.

[1185] Thus, the system of this invention can comprehensively manage the user's health and emotional state and provide personalized health advice and product recommendations in real time.

[1186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1187] Step 1:

[1188] The user installs a dedicated app on their smartphone and creates an account. During this process, a registration screen appears on the smartphone, and the user enters the required information. The entered information is sent to the server, and a new account is created.

[1189] Input: User registration information (name, email address, etc.)

[1190] Output: Account information is saved on the server.

[1191] Step 2:

[1192] The user pairs the smartwatch with their smartphone. The user searches for the smartphone in the smartwatch's settings screen and connects via Bluetooth. This allows the smartwatch to periodically send health data (heart rate, steps, etc.) to the smartphone.

[1193] Input: Smartwatch pairing information

[1194] Output: Smartwatch and smartphone connection complete.

[1195] Step 3:

[1196] The smartphone periodically acquires health data from the smartwatch. Specifically, the smartphone app communicates with the smartwatch via Bluetooth, acquiring data such as heart rate and steps taken every hour.

[1197] Input: Health data from smartwatch

[1198] Output: Health data acquired on the smartphone

[1199] Step 4:

[1200] The server calls a weather information API to obtain weather information for the user's area. The server periodically accesses the weather information API to obtain local weather data based on the user's location.

[1201] Input: User's location information

[1202] Output: Acquired weather information

[1203] Step 5:

[1204] The server generates multiple-choice questions based on health data and weather information. For example, if it's raining, it might create a question from a template such as, "It's raining today, will you take an umbrella with you?"

[1205] Input: Health data, weather information

[1206] Output: Generated multiple-choice questions

[1207] Step 6:

[1208] The server generates a question and sends it to the user using the LINE API. The question is then displayed on the user's smartphone using the LINE Message Sending API.

[1209] Input: Generated Question

[1210] Output: Question sent to the user's LINE account

[1211] Step 7:

[1212] Users answer questions via LINE. For example, they select the appropriate option from "yes" or "no" choices and send it to the server via LINE message.

[1213] Input: User's response

[1214] Output: User responses received by the server

[1215] Step 8:

[1216] The smartphone receives the user's response via LINE and sends the analysis data to the server. The smartphone app analyzes the received data from LINE and uploads the response data to the server.

[1217] Input: User's response received via LINE

[1218] Output: Analyzed response data

[1219] Step 9:

[1220] The server integrates and analyzes health data and user response data sent from smartphones. This involves running an analysis algorithm using historical and current health data stored in a database.

[1221] Input: Health data, user response data

[1222] Output: Integrated analysis results

[1223] Step 10:

[1224] The server generates health advice based on the analysis results. For example, it might create advice such as, "Your exercise level today was low, so we recommend taking a short walk when you get home."

[1225] Input: Analysis results

[1226] Output: Generated health advice

[1227] Step 11:

[1228] The server generates advice and sends it to the user via LINE. The LINE Message Sending API is used to send the health advice to the user's LINE account.

[1229] Input: Generated health advice

[1230] Output: Advice sent to the user's LINE account

[1231] Step 12:

[1232] The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might create a message like, "Here is a coupon code for health supplements."

[1233] Input: Health data, behavioral patterns

[1234] Output: Generated purchase guide

[1235] Step 13:

[1236] The server generates a purchase guide and sends it to the user via LINE. The purchase guide is sent to the user's LINE account using the LINE Message Sending API.

[1237] Input: Generated purchase guide

[1238] Output: Purchase information sent to the user's LINE account

[1239] Step 14:

[1240] The server uses an emotion engine to recognize the user's emotions. Specifically, it analyzes the user's voice and text data, and the emotion engine identifies the user's emotional state (joy, sadness, anger, etc.).

[1241] Input: Audio data, text data

[1242] Output: User's emotional state

[1243] Step 15:

[1244] The server customizes the feedback based on the emotions it perceives. For example, if the user is feeling stressed, it might generate advice such as, "Try breathing exercises to relax."

[1245] Input: User's emotional state

[1246] Output: Customized feedback content

[1247] In this manner, this invention can comprehensively manage the user's health and emotional state and provide personalized health advice and purchasing guidance.

[1248] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1249] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1250] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1251] [Third Embodiment]

[1252] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1253] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1254] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1255] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1256] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1257] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1258] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1259] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1260] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1261] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1262] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1263] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1264] This invention realizes a health management system that acquires, analyzes, and provides feedback on user health data. Below, the system's program processing is explained in natural language, and the system's operation is described in detail with specific examples.

[1265] System Configuration

[1266] This system consists of an information processing device (server), a user terminal (smartphone), and a smartwatch that acquires the user's health data.

[1267] Program processing

[1268] 1. The user installs a dedicated health management app on their smartphone. When they open the app, they are presented with an option to link their LINE account, and the user links their LINE account with the app.

[1269] 2. The user pairs the smartwatch with their smartphone and completes the necessary settings. This setting allows the smartwatch to send health data (e.g., heart rate, steps, sleep data) to the app.

[1270] 3. The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. Bluetooth communication is used for this purpose.

[1271] 4. The server uses a weather information API to obtain weather information for the user's location. For example, the server updates the weather information every morning at 6:00 AM.

[1272] 5. The server generates a multiple-choice question for the day based on weather information and the user's past data. For example, if rain is expected, it will create a question such as, "It will rain today, will you take an umbrella with you?"

[1273] 6. The server generates a question and sends it to the user via LINE. This is done using LINE's message sending API.

[1274] 7. The user answers the question via LINE. For example, they tap either the "Yes" or "No" option.

[1275] 8. The smartphone (device) receives the user's response via LINE and passes it to the app, which then sends the response data to the server.

[1276] 9. The server integrates and analyzes the health data and questionnaire responses received via the smartphone. The analysis also takes into account the user's past behavioral patterns and health status.

[1277] 10. The server generates appropriate health advice for the user based on the analysis results. For example, it might generate advice such as, "Your exercise level today was low, so we recommend a short walk when you get home."

[1278] 11. The server sends the generated advice to the user via LINE.

[1279] 12. The server generates purchase recommendations for relevant products based on the user's health data and behavioral patterns. For example, it might create a message such as, "Here is a coupon code for health supplements."

[1280] 13. The server sends the generated purchase information to the user via LINE.

[1281] Specific example

[1282] Example based on morning weather information

[1283] 1. The server calls the weather information API at 6 AM to confirm that rain is expected in the user's area.

[1284] 2. The server generates the question, "It's going to rain today, will you take an umbrella with you?" and sends it to the user via LINE.

[1285] 3. The user answers "Yes".

[1286] 4. The server receives the answers to the questions and health data (e.g., heart rate, steps) obtained from the smartwatch, and analyzes them.

[1287] 5. Based on the analysis results, the server generates advice such as, "You haven't had enough exercise today, so we recommend taking a short walk when you get home," and sends it to the user via LINE.

[1288] 6. The server then generates a purchase guide that says, "Here is the coupon code for health supplements," and sends it to the user via LINE.

[1289] Through the above embodiment, the system can monitor the user's health status in real time and provide advice and purchasing guidance that also takes into account external factors such as weather information.

[1290] The following describes the processing flow.

[1291] Step 1:

[1292] Users install a dedicated app on their smartphone and link it to their LINE account. They then open the app and use the LINE login function to link their account. This synchronizes the app and the LINE account.

[1293] Step 2:

[1294] The user pairs the smartwatch with the smartphone app. The smartwatch searches for the smartphone in its Bluetooth settings and connects. The user completes the smartwatch setup within the app and allows the sharing of health data.

[1295] Step 3:

[1296] The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. For example, it retrieves heart rate and step count data every hour via Bluetooth.

[1297] Step 4:

[1298] The server calls a weather information API to retrieve weather information for the user's location. The server periodically accesses the weather information API to retrieve local weather data based on the user's location. For example, the API might be called every morning at 6:00 AM.

[1299] Step 5:

[1300] The server generates multiple-choice questions based on weather information and historical data. For example, if it's raining, it creates a question from a template such as, "It's going to rain today, will you take an umbrella?"

[1301] Step 6:

[1302] The server generates a question and sends it to the user via LINE. The LINE Message Sending API is used to send the question to the user's LINE account.

[1303] Step 7:

[1304] The system responds to questions received by users via LINE. For example, it selects the appropriate option from "yes" or "no" choices and sends the response.

[1305] Step 8:

[1306] The smartphone (device) receives the user's response via LINE. The smartphone app analyzes the received LINE data and saves the response data within the app.

[1307] Step 9:

[1308] The smartphone (device) sends the user's response data to the server. The response data within the app is sent to the server using internet communication.

[1309] Step 10:

[1310] The server integrates and analyzes health data and user response data sent from smartphones. It executes analysis algorithms using historical data stored in the database as well as current health data.

[1311] Step 11:

[1312] The server generates health advice based on the analysis results. For example, it might create advice such as, "Your exercise level today was low, so we recommend taking a short walk when you get home."

[1313] Step 12:

[1314] The server generates advice and sends it to the user via LINE. The LINE message sending API is used to send health advice to the user.

[1315] Step 13:

[1316] The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might generate a message like, "Here is a coupon code for health supplements."

[1317] Step 14:

[1318] The server generates a purchase guide and sends it to the user via LINE. The LINE message sending API is used to send the purchase guide to the user.

[1319] (Example 1)

[1320] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1321] Traditional health management systems lack sufficient integration in acquiring and analyzing user health data, resulting in the inability to provide anything more than simplistic advice based on individual data elements. Furthermore, they struggle to appropriately generate and provide health advice that considers environmental factors such as external weather information, or purchasing guidance based on user behavior patterns. Additionally, limited real-time data collection and analysis capabilities make it difficult to provide optimal feedback to users.

[1322] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1323] In this invention, the server includes: [means for an information processing device to acquire user health data; [means for an information processing device to analyze health data acquired from a wearable device; [means for an information processing device to generate health advice for the user based on the analysis results; [means for an information processing device to transmit the generated advice to the user via a data communication medium]; [means for an information processing device to generate purchase guidance based on the user's health data and behavioral patterns; [means for an information processing device to transmit the generated purchase guidance to the user via a data communication medium]; [means for an information processing device to acquire weather information; [means for generating multiple-choice questions for the user based on the weather information]; [means for transmitting multiple-choice questions to the user via a data communication medium and receiving responses from the user]; and [means for integrating and analyzing the responses received from the user with health data]. This makes it possible to provide real-time feedback that comprehensively considers the user's health status and environmental factors.

[1324] An "information processing device" is a device that acquires and analyzes a user's health data and generates and transmits feedback and purchase recommendations.

[1325] A "wearable device" is a device that is attached to the user's body and collects health data such as heart rate, steps taken, and sleep data.

[1326] "Health data" refers to data that indicates the user's health status, including heart rate, steps taken, and sleep quality.

[1327] "Analysis" is the process of evaluating and diagnosing health status and behavioral patterns using statistical analysis and machine learning techniques based on acquired data.

[1328] "Health advice" refers to messages that, based on analysis results, suggest the most suitable health management strategies for the user.

[1329] A "data communication medium" refers to a communication network such as the internet, and is a means of sending and receiving data.

[1330] A "purchase guide" is a message that contains information designed to encourage the purchase of relevant products based on the user's health data and behavioral patterns.

[1331] "Weather information" refers to data that shows the weather conditions in the area where the user lives.

[1332] A "multiple-choice question" is a type of question in which the user chooses an answer from a set of specific options, and is used to obtain feedback on health status and behavior.

[1333] A "cloud server" is a server located on the internet and used for data collection and analysis.

[1334] "Identifying behavioral patterns" means analyzing a user's past data to understand specific behavioral tendencies and habits.

[1335] A "health advice generation AI model" is an artificial intelligence model that generates appropriate health advice based on the user's health data and behavioral patterns.

[1336] The "Purchase Recommendation Generation AI Model" is an artificial intelligence model that generates purchase recommendations for related products based on the user's health data and behavioral patterns.

[1337] This invention realizes a health management system that acquires, analyzes, and provides feedback on user health data. The details of the system and the program's processing are described below.

[1338] This system consists of an information processing unit (server), a user terminal (smartphone), and a wearable device (smartwatch) that acquires the user's health data.

[1339] System details

[1340] 1. Hardware and software:

[1341] Information Processing Equipment (Server): A cloud-based server that performs high-speed data processing and analysis. It possesses computing resources to run specific AI models.

[1342] User terminal (smartphone): A portable device running iOS or Android, with a dedicated health management app installed.

[1343] Wearable devices (smartwatches): These are devices equipped with sensors that measure heart rate, steps taken, sleep data, and other information.

[1344] 2. Program processing:

[1345] Users install a dedicated health management app on their smartphones and link it to their LINE account.

[1346] The user pairs the smartwatch with their smartphone and completes the necessary initial setup. This allows the smartwatch to periodically send health data to the smartphone.

[1347] The smartphone (device) retrieves health data from the smartwatch via Bluetooth communication and stores it within the app.

[1348] The server uses a weather information API to retrieve weather information for the user's location. For example, it updates the weather information every morning at 6:00 AM.

[1349] The server generates multiple-choice questions for the day based on weather information and the user's past health data. For example, if rain is expected, it will create a question such as, "It will rain today, will you take an umbrella with you?"

[1350] The server sends the generated question to the user via LINE. It utilizes LINE's message sending API.

[1351] Users answer questions via LINE. For example, they tap either the "yes" or "no" option.

[1352] The smartphone (device) receives the user's responses via LINE and passes them to the health management app, which then sends the response data to a server.

[1353] The server integrates and analyzes health data and questionnaire responses received via smartphones. The analysis also takes into account the user's past behavioral patterns and health status.

[1354] The server generates appropriate health advice for the user based on the analysis results. For example, it might generate advice such as, "Your exercise level today was low, so we recommend a short walk when you get home."

[1355] The server sends the generated advice to the user via LINE.

[1356] The server generates purchase recommendations for relevant products based on the user's health data and behavioral patterns. For example, it might create a message such as, "Here is a coupon code for health supplements."

[1357] The server sends the generated purchase information to the user via LINE.

[1358] 3. Specific examples:

[1359] The server calls the weather information API at 6 AM to confirm that rain is expected in the user's area.

[1360] The server generates the question, "It's going to rain today, will you take an umbrella with you?" and sends it to the user via LINE.

[1361] The user answers "yes".

[1362] The server receives and analyzes the answers to the questions and health data (such as heart rate and steps) obtained from the smartwatch.

[1363] Based on the analysis results, the server generates advice such as, "You haven't had enough exercise today, so we recommend taking a short walk when you get home," and sends it to the user via LINE.

[1364] The server then generates a purchase guide that says, "Here is a coupon code for health supplements," and sends it to the user via LINE.

[1365] As described above, this system monitors the user's health status in real time and provides optimal advice and purchasing guidance to the user, taking into account weather information and lifestyle habits.

[1366] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1367] The program's processing flow is divided into processing steps.

[1368] Step 1:

[1369] Users install a dedicated health management app on their smartphones. This app also offers an option to link with a LINE account, which users can do from the settings screen.

[1370] Specific actions:

[1371] Users download the health management app from the App Store or Google Play on their smartphones and follow the instructions to link their LINE account. During this process, the linked account information is saved in the app.

[1372] Inputs and outputs:

[1373] Input: User's smartphone, health management app, LINE account information

[1374] Output: Health management app including linked LINE account information

[1375] Step 2:

[1376] The user pairs the smartwatch with their smartphone and completes the necessary initial setup within the app. This allows health data (e.g., heart rate, steps, sleep data) from the smartwatch to be sent to the app.

[1377] Specific actions:

[1378] The user selects the smartwatch on their smartphone's Bluetooth settings screen and presses the pairing button. Afterward, the app is configured to receive and save health data in real time.

[1379] Inputs and outputs:

[1380] Input: User's smartwatch, smartphone, or app settings screen

[1381] Output: Paired smartwatch information and health data received in real time.

[1382] Step 3:

[1383] The smartphone (device) uses Bluetooth communication to periodically retrieve health data from the smartwatch and store it within the app.

[1384] Specific actions:

[1385] The app retrieves data from the smartwatch via Bluetooth communication at specified intervals and stores that data in the app's database.

[1386] Inputs and outputs:

[1387] Input: Health data from smartwatch

[1388] Output: Health data stored within the app (heart rate, steps, sleep data)

[1389] Step 4:

[1390] The server calls a weather information API to retrieve weather information for the user's location. Updates are performed every morning at 6:00 AM.

[1391] Specific actions:

[1392] The server uses weather information APIs such as "OpenWeatherMap" to retrieve the latest weather information for the user's area every morning at 6:00 AM. The retrieved weather information is stored in a database on the server.

[1393] Inputs and outputs:

[1394] Input: Calling the weather information API

[1395] Output: Acquired weather information

[1396] Step 5:

[1397] The server generates multiple-choice questions for the day based on weather information and past health data. For example, if rain is expected, it might create a question like, "It will rain today, will you take an umbrella with you?"

[1398] Specific actions:

[1399] The server retrieves weather information and historical health data, and uses a natural language generation model to generate questions based on this information.

[1400] Inputs and outputs:

[1401] Input: Weather information and historical health data

[1402] Output: Generated multiple-choice questions

[1403] Step 6:

[1404] The server generates questions and sends them to the user via LINE. This is done using LINE's message sending API.

[1405] Specific actions:

[1406] The server sends the generated question to the user via the LINE API. A notification appears in the LINE app, and the user can access the question.

[1407] Inputs and outputs:

[1408] Input: Generated multiple-choice question

[1409] Output: Question sent via LINE

[1410] Step 7:

[1411] The user answers the question via LINE. For example, they tap either the "yes" or "no" option.

[1412] Specific actions:

[1413] Users check the question notification on the LINE app, tap the appropriate option button, and submit their answer.

[1414] Inputs and outputs:

[1415] Input: Question displayed on the LINE app

[1416] Output: Answer ("Yes" / "No")

[1417] Step 8:

[1418] The smartphone (device) receives the user's responses via LINE and passes them to the health management app, which then sends the response data to a server.

[1419] Specific actions:

[1420] The response data received from the LINE app is transferred to the app, and the app then sends that data to the server.

[1421] Inputs and outputs:

[1422] Input: Response data from the LINE app

[1423] Output: Response data sent to the server

[1424] Step 9:

[1425] The server integrates and analyzes health data and questionnaire responses received via smartphones. The analysis also takes into account the user's past behavioral patterns and health status.

[1426] Specific actions:

[1427] The server references past databases and analyzes health data and questionnaire responses using an AI model.

[1428] Inputs and outputs:

[1429] Input: Health data and answers to questions

[1430] Output: Analysis results

[1431] Step 10:

[1432] The server generates appropriate health advice for the user based on the analysis results. For example, it might generate advice such as, "Your exercise level today was low, so we recommend a short walk when you get home."

[1433] Specific actions:

[1434] The server generates health advice using an AI model and sends it to the user via the LINE API.

[1435] Inputs and outputs:

[1436] Input: Analysis results

[1437] Output: Generated health advice

[1438] Step 11:

[1439] The server generates advice and sends it to the user via LINE. This uses the LINE message sending API.

[1440] Specific actions:

[1441] The server sends the generated advice message to the user via the LINE API.

[1442] Inputs and outputs:

[1443] Input: Generated health advice

[1444] Output: Advice sent via LINE

[1445] Step 12:

[1446] The server generates purchase recommendations for relevant products based on the user's health data and behavioral patterns. For example, it might create a message such as, "Here is a coupon code for health supplements."

[1447] Specific actions:

[1448] The server uses an AI model to generate purchase guidance and creates messages with coupon codes for related products.

[1449] Inputs and outputs:

[1450] Input: Health data and behavioral patterns

[1451] Output: Generated purchase guide

[1452] Step 13:

[1453] The server generates a purchase guide and sends it to the user via LINE. This is done using LINE's message sending API.

[1454] Specific actions:

[1455] The server sends the generated purchase guidance message to the user via the LINE API.

[1456] Inputs and outputs:

[1457] Input: Generated purchase guide

[1458] Output: Purchase information sent via LINE

[1459] (Application Example 1)

[1460] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1461] Many conventional health management systems only acquire and analyze user health data, lacking advice and support that takes into account user behavior patterns and external environmental factors. This results in problems such as being unable to provide users with appropriate dietary suggestions or relevant discount coupons. Furthermore, there is no established method for providing personalized health advice based on weather information and user responses, making it difficult to provide more specific and practical support tailored to users' real lives.

[1462] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1463] In this invention, the server includes means for an information processing device to generate appropriate meal suggestions based on the user's health data and transmit them to the user via a data communication medium; means for the information processing device to generate discount coupons related to the suggested meals and transmit them to the user via a data communication medium; and means for the information processing device to acquire weather information, generate multiple-choice questions for the user, transmit the multiple-choice questions to the user via a data communication medium, receive the user's answers, and integrate and analyze them. This makes it possible to provide personalized meal suggestions and discount coupons based not only on the user's health data but also on weather information and the user's answers.

[1464] An "information processing device" is a device that acquires and analyzes a user's health data and generates and transmits various advice and purchasing guidance.

[1465] "Data communication medium" refers to a means of transmitting information, and specifically includes the internet and Bluetooth.

[1466] "Health data" refers to data related to a user's physical health, such as heart rate, steps taken, and sleep data.

[1467] A "smartwatch" is a wearable device that measures and collects health data such as heart rate, steps taken, and sleep data.

[1468] "Health advice" refers to advice on maintaining or improving health that an information processing device provides to the user based on its analysis results.

[1469] "Purchase information" refers to information and coupon codes for products that a user might potentially purchase, generated by an information processing device.

[1470] "Weather information" refers to weather data related to the user's residential area, including temperature, probability of precipitation, and wind speed.

[1471] A "multiple-choice question" is a question generated by an information processing device and sent to a user, designed to elicit an answer from multiple options.

[1472] A "cloud server" is a server that collects and analyzes data via the internet.

[1473] A "discount coupon" is a code or information used to apply a discount to a suggested meal or product.

[1474] "Analysis results" refer to the results calculated by the information processing device based on health data, user responses, weather information, and other factors.

[1475] This invention realizes a health management system that acquires and analyzes users' health data and provides appropriate health advice, dietary suggestions, and related discount coupons.

[1476] System Configuration

[1477] This system consists of an information processing device (server), a user terminal (smartphone), and a smartwatch that acquires the user's health data.

[1478] Program processing

[1479] 1. Users install a dedicated health management app on their smartphones and link it to their LINE account. This allows users to receive health advice, meal suggestions, and coupons via LINE.

[1480] 2. The user pairs the smartwatch with their smartphone. This setup allows the smartwatch to send health data (e.g., heart rate, steps, sleep data) to the app.

[1481] 3. The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. Bluetooth communication is used for this data retrieval.

[1482] 4. The server uses a weather information API to obtain weather information for the user's residential area. Weather information is retrieved every morning at 6:00 AM.

[1483] 5. The server generates a multiple-choice question for the day based on weather information and the user's past health data. For example, on a cold day, it might generate a question like, "How about a warming meal today?"

[1484] 6. The server generates a question and sends it to the user via LINE. This is done using LINE's message sending API.

[1485] 7. The user answers the question via LINE. For example, they can answer by tapping "Yes" or "No" as options.

[1486] 8. The smartphone (device) receives the user's response via LINE and passes it to the app, which then sends the response data to the server.

[1487] 9. The server integrates and analyzes the health data and questionnaire responses received via the smartphone. The analysis also takes into account the user's past behavioral patterns and health status.

[1488] 10. Based on the analysis results, the server generates appropriate meal suggestions and health advice for the user. For example, it might generate advice such as, "Today, we recommend seafood chili soup, which is good for replenishing energy."

[1489] 11. Generate relevant discount coupons along with the advice generated by the server and send them to the user via LINE.

[1490] Hardware and software to be used

[1491] Smartwatch: A wearable device (e.g., Apple Watch, Garmin) that measures and collects health data (e.g., heart rate, steps, sleep data).

[1492] Smartphone: A device used for installing apps, acquiring health data, and displaying analysis results (e.g., iOS devices, Android devices).

[1493] Server: A cloud server (e.g., AWS, Azure) that collects and analyzes health data.

[1494] Bluetooth communication: A means of communication for sending and receiving data between a smartwatch and a smartphone.

[1495] Weather Information API: An API for obtaining weather information for the user's residential area (e.g., OpenWeatherMap API).

[1496] LINE API: A message sending API for sending health advice, meal suggestions, and coupons via LINE.

[1497] Specific example

[1498] 1. Morning question:

[1499] The server calls a weather information API at 6 AM, and if it's a cold day, it generates the question, "How about a warming meal today?" and sends it to the user via LINE.

[1500] 2. User responses and meal suggestions:

[1501] If the user answers "yes," the server generates advice such as "We recommend seafood chili soup, which is good for replenishing energy," and sends it via LINE.

[1502] 3. Send coupon:

[1503] A 20% discount coupon for seafood chili soup will also be sent via LINE.

[1504] Examples of prompt statements

[1505] If a user needs to choose a meal on a cold day, you generate a question like, "How about a warming meal today?" and send it via LINE. If the user replies "yes," you then suggest, "I recommend the seafood chili soup, which is great for replenishing energy," and also offer a discount coupon for that menu item.

[1506] This system can use users' health data to provide personalized meal suggestions and health advice based on weather information and user responses, helping users maintain and improve their health.

[1507] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1508] Step 1:

[1509] Users install a dedicated health management app on their smartphones and link it to their LINE account. This allows them to receive health advice, meal suggestions, and coupons via LINE.

[1510] Input: Install a health management app on your smartphone and link it to your LINE account.

[1511] Output: Linked to the user's LINE account.

[1512] Step 2:

[1513] By pairing the smartwatch with their smartphone, the user can configure it to send health data from the smartwatch to the app.

[1514] Input: Setting up the smartwatch to pair with your smartphone.

[1515] Output: The smartwatch is in a state where it can send data to a smartphone.

[1516] Step 3:

[1517] The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. Bluetooth communication is used for sending and receiving data.

[1518] Input: Health data acquired by the smartphone from the smartwatch (e.g., heart rate, steps, sleep data).

[1519] Output: Health data stored within the app.

[1520] Step 4:

[1521] The server uses a weather information API to retrieve weather information for the user's residential area every morning at 6:00 AM.

[1522] Input: Weather information API request.

[1523] Output: The latest weather information for the user's residential area.

[1524] Step 5:

[1525] The server generates a multiple-choice question for the day based on weather information and the user's past health data. For example, on a cold day, it might create a question like, "How about a warming meal today?"

[1526] Input: Weather information, historical health data.

[1527] Output: Generated multiple-choice question.

[1528] Step 6:

[1529] The server generates questions and sends them to users via LINE, utilizing LINE's message sending API.

[1530] Input: A generated multiple-choice question.

[1531] Output: Questions sent to the user via LINE.

[1532] Step 7:

[1533] Users answer questions via LINE. For example, they can choose to answer from "yes" or "no" options.

[1534] Input: Question sent via LINE.

[1535] Output: User's response to the question.

[1536] Step 8:

[1537] The smartphone (device) receives the user's response via LINE and passes it to the app, which then sends that response data to the server.

[1538] Input: User's response to a question.

[1539] Output: User response data sent to the server.

[1540] Step 9:

[1541] The server integrates and analyzes health data and answers to questions received via smartphone, taking into account the user's past behavioral patterns and health status to obtain analysis results.

[1542] Input: Health data, user response data.

[1543] Output: Analysis results.

[1544] Step 10:

[1545] Based on the analysis results, the server generates appropriate meal suggestions and health advice for the user. For example, it might generate advice such as, "We recommend seafood chili soup for today's energy boost."

[1546] Input: Analysis results.

[1547] Output: Generated meal suggestions and health advice.

[1548] Step 11:

[1549] The server generates advice and sends related discount coupons to users via LINE.

[1550] Input: Generated meal suggestions, health advice, and related discount coupons.

[1551] Output: Advice and discount coupons sent via LINE.

[1552] These processing steps allow the system to provide personalized meal suggestions and health advice, as well as discount coupons, based on the user's health data, weather information, and user responses.

[1553] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1554] This invention combines a health management system that acquires and analyzes user health data and provides appropriate advice and purchasing guidance with an emotion engine that recognizes user emotions. The system configuration and program processing details are described below.

[1555] System Configuration

[1556] This system consists of an information processing device (server), a user terminal (smartphone), a smartwatch that acquires the user's health data, and an emotion engine that recognizes the user's emotions.

[1557] Program processing

[1558] 1. The user installs the dedicated app on their smartphone and links it with their LINE account. The user opens the app and links their account using the LINE login function.

[1559] 2. The user pairs the smartwatch with the smartphone app. Search for the smartphone in the smartwatch's Bluetooth settings and connect. Complete the smartwatch setup within the app and allow the sharing of health data.

[1560] 3. The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. For example, it retrieves heart rate and step count data every hour via Bluetooth.

[1561] 4. The server calls the weather information API to obtain weather information for the user's area. The server periodically accesses the weather information API to obtain local weather data based on the user's location. For example, the API is called every morning at 6:00 AM.

[1562] 5. The server generates multiple-choice questions based on weather information and historical data. For example, if it is raining, it will create a question from a template such as, "It will rain today, will you take an umbrella?"

[1563] 6. The server sends the generated question to the user via LINE. The LINE Message Sending API is used to send the question to the user's LINE account.

[1564] 7. The user answers the question via LINE. For example, they select the appropriate option from choices such as "yes" or "no" and send it.

[1565] 8. The smartphone (device) receives the user's response via LINE. The smartphone app analyzes the received LINE data and saves the response data within the app.

[1566] 9. The smartphone (device) sends the user's response data to the server. The response data within the app is sent to the server using internet communication.

[1567] 10. The server integrates and analyzes health data and user response data sent from smartphones. It executes an analysis algorithm using historical data stored in the database and current health data.

[1568] 11. The server generates health advice based on the analysis results. For example, it might create advice such as, "Your exercise level today was low, so we recommend taking a short walk when you get home."

[1569] 12. The server sends generated advice to the user via LINE. The LINE message sending API is used to send health advice to the user.

[1570] 13. The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might generate a message such as, "Here is a coupon code for health supplements."

[1571] 14. The server sends the generated purchase information to the user via LINE. The LINE message sending API is used to send the purchase information to the user.

[1572] 15. The server uses an emotion engine to recognize the user's emotions. Specifically, it analyzes the user's voice and text data, and the emotion engine identifies the user's emotional state (e.g., joy, sadness, anger, etc.).

[1573] 16. Customize feedback based on the emotions the server perceives. For example, if the user is feeling stressed, it might generate advice such as, "Try breathing exercises to relax."

[1574] Specific example

[1575] Examples based on user emotions

[1576] 1. Users use the diary function through the app to write down their feelings.

[1577] 2. The server uses an emotion engine to recognize the user's emotions from text data. For example, from the text "Today's work was tough," the emotion engine recognizes stress.

[1578] 3. The server generates a question based on weather information and sends it to the user via LINE. For example, "It's going to rain today, will you take an umbrella with you?"

[1579] 4. The user answers "Yes".

[1580] 5. The server receives the answers to the questions and health data (e.g., heart rate, steps) obtained from the smartwatch, and analyzes them.

[1581] 6. The server integrates and analyzes the user's emotional and health data to generate advice for stress reduction. For example, advice such as, "You are stressed, so try breathing exercises to relax."

[1582] 7. The server sends the generated advice to the user via LINE.

[1583] Through the above embodiment, this system can monitor the user's health status in real time and, by combining it with an emotion engine, can provide advice and purchasing guidance tailored to the user's emotional state.

[1584] The following describes the processing flow.

[1585] Step 1:

[1586] Users install a dedicated app on their smartphone and link it to their LINE account. They open the app and use the LINE login function to link their account.

[1587] Step 2:

[1588] The user pairs the smartwatch with the smartphone app. The smartwatch searches for the smartphone in its Bluetooth settings and connects. The user completes the smartwatch setup within the app and allows the sharing of health data.

[1589] Step 3:

[1590] The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. For example, it retrieves heart rate and step count data every hour via Bluetooth.

[1591] Step 4:

[1592] The server calls a weather information API to retrieve weather information for the user's location. The server periodically accesses the weather information API to retrieve local weather data based on the user's location. For example, the API might be called every morning at 6:00 AM.

[1593] Step 5:

[1594] The server generates multiple-choice questions based on weather information and historical data. For example, if it's raining, it creates a question from a template such as, "It's going to rain today, will you take an umbrella?"

[1595] Step 6:

[1596] The server generates a question and sends it to the user via LINE. The LINE Message Sending API is used to send the question to the user's LINE account.

[1597] Step 7:

[1598] The system responds to questions received by users via LINE. For example, it selects the appropriate option from "yes" or "no" choices and sends the response.

[1599] Step 8:

[1600] The smartphone (device) receives the user's response via LINE. The smartphone app analyzes the received LINE data and saves the response data within the app.

[1601] Step 9:

[1602] The smartphone (device) sends the user's response data to the server. The response data within the app is sent to the server using internet communication.

[1603] Step 10:

[1604] The server integrates and analyzes health data and user response data sent from smartphones. It then executes an analysis algorithm using historical and current health data stored in the database.

[1605] Step 11:

[1606] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's voice and text data to determine their emotional state (e.g., joy, anger, sadness, etc.).

[1607] Step 12:

[1608] The server generates health advice based on the analysis results. For example, if it detects that the user is feeling stressed, it will create advice such as, "Try breathing exercises to relax."

[1609] Step 13:

[1610] The server generates advice and sends it to the user via LINE. The LINE message sending API is used to send health advice to the user.

[1611] Step 14:

[1612] The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might create purchase recommendations that include coupons for products related to stress relief (e.g., aromatherapy oils, health foods).

[1613] Step 15:

[1614] The server generates a purchase guide and sends it to the user via LINE. The LINE message sending API is used to send the purchase guide to the user.

[1615] Specific example

[1616] Specific examples including emotion recognition

[1617] Step 1:

[1618] The user receives a multiple-choice question via LINE based on the morning weather forecast: "It will rain today, will you take an umbrella with you?"

[1619] Step 2:

[1620] The user replies "Yes." The smartphone receives this reply and sends it to the server.

[1621] Step 3:

[1622] The smartphone (device) periodically acquires health data (heart rate, steps, etc.) from the smartwatch and sends it to the server.

[1623] Step 4:

[1624] The server integrates and analyzes user responses and health data. It executes an analysis algorithm based on historical and current data stored in the database.

[1625] Step 5:

[1626] The server uses an emotion engine to recognize the user's emotional state. For example, it can recognize that a user is stressed from a LINE text message.

[1627] Step 6:

[1628] Based on the analysis results and emotional state, the server generates health advice such as "Try breathing exercises to relax" and sends it to the user via LINE.

[1629] Step 7:

[1630] The server then generates and sends to the user a purchase guide that includes coupons for related products (such as aromatherapy oils) to help reduce stress.

[1631] Through the above processing steps, a system is realized that monitors the user's health and emotional state in real time and provides advice and purchasing guidance tailored to their individual needs.

[1632] (Example 2)

[1633] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1634] Traditional health management systems had the functionality to acquire and analyze user health data and provide health advice, but they struggled to provide flexible feedback that took into account user emotional data, or personalized advice based on weather information. Furthermore, automatically generating purchasing recommendations based on user behavior patterns and emotional states was also a challenge.

[1635] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the information processing device to acquire the user's health data, means for the information processing device to analyze emotional data based on the health data and behavioral patterns, means for the information processing device to transmit health advice to the user via a data communication medium, means for the information processing device to generate purchase guidance based on the user's health data, behavioral patterns, and emotional data, and means for the information processing device to transmit the generated purchase guidance to the user via a data communication medium. This makes it possible to provide more personalized health advice and purchase guidance while taking into account the user's emotional state.

[1636] An "information processing device" is a device that collects, analyzes, and transmits data, and plays a role in acquiring, processing, storing, and transmitting user health data and emotional data.

[1637] "User health data" refers to biometric data such as the user's heart rate, steps taken, and sleep duration, obtained from smartwatches and other sensor devices.

[1638] "Emotional data" refers to data that indicates the emotional state (e.g., joy, sadness, anger, etc.) of a user, analyzed from their voice and text data.

[1639] "Health advice" refers to specific instructions and suggestions aimed at maintaining or improving the user's health, generated based on acquired and analyzed user health and emotional data.

[1640] "Purchase guidance" refers to marketing information such as product and service recommendations and coupon information, generated based on the user's health data, emotional data, and behavioral patterns.

[1641] "Data communication medium" refers to the internet and other communication networks, and includes the infrastructure that enables the sending and receiving of data between users and information processing devices.

[1642] "Weather information" refers to information about current and future weather conditions based on the user's location, obtained from weather data provision services and APIs.

[1643] A "multiple-choice question" is a type of question where the user chooses an answer from a set of options, based on weather information and user sentiment data.

[1644] "User behavior patterns" refer to data that shows the user's lifestyle habits and behavioral tendencies, analyzed from past health data, responses, diary entries, and other sources.

[1645] This invention is a health management system that acquires and analyzes user health data and provides appropriate advice and purchasing guidance, further incorporating an emotion engine that recognizes user emotions. The system configuration and its specific implementation method are described below.

[1646] System Configuration

[1647] This system consists of the following elements:

[1648] 1. Information processing equipment (server)

[1649] 2. User terminal (smartphone)

[1650] 3. Devices (smartwatches) that acquire user health data

[1651] 4. Emotional Engine

[1652] Specific examples of program processing

[1653] Application installation and integration

[1654] Users install a dedicated app on their smartphone, open the app, and link their LINE account. This allows them to send and receive health information and advice through their LINE account.

[1655] Acquisition and storage of health data

[1656] The user sets up the smartwatch to pair with a smartphone app. The smartphone periodically retrieves health data (e.g., heart rate, steps) from the smartwatch via Bluetooth and stores it in the app.

[1657] Obtaining weather information

[1658] The server calls a weather information API to retrieve weather information for the user's region. This information is updated regularly, for example, every morning at 6:00 AM.

[1659] emotion recognition

[1660] The server uses an emotion engine to recognize the user's emotions. It analyzes emotion data from the user's diary and text messages to determine the user's emotional state.

[1661] Generating and sending health advice

[1662] The server generates personalized health advice based on the analyzed health and emotional data. For example, it might create advice such as, "Your exercise level today is low, so we recommend a short walk when you get home," and send it to the user using the LINE message sending API.

[1663] Generating and sending purchase information

[1664] The server generates purchase recommendations based on the user's health data, behavioral patterns, and emotional data. For example, it generates a message such as "Here is a coupon code for health supplements" and sends it to the user via LINE.

[1665] Specific example

[1666] Examples based on user emotions

[1667] 1. Users use the diary function through the app to write down their feelings.

[1668] 2. The server uses an emotion engine to recognize the user's emotions from this text data. For example, from the text "Today's work was tough," the emotion engine recognizes stress.

[1669] 3. The server generates a question based on weather information and sends it to the user via LINE. For example, "It's going to rain today, will you take an umbrella with you?"

[1670] 4. The user answers "Yes".

[1671] 5. The server receives the answers to the questions and health data (e.g., heart rate, steps) obtained from the smartwatch, and analyzes them.

[1672] 6. The server integrates and analyzes the user's emotional and health data to generate advice for stress reduction. For example, advice such as, "You are stressed, so try breathing exercises to relax."

[1673] 7. The server sends the generated advice to the user via LINE.

[1674] Example of a prompt

[1675] The following are specific examples of prompts to input into a generative AI model:

[1676] "Please propose a unique health management system."

[1677] "Please explain how to integrate user emotions and health data to generate advice."

[1678] "Please tell me the steps to generate customized questions based on weather information."

[1679] keyword

[1680] Generative AI model, prompt sentence

[1681] As described above, the system of the present invention can highly personalize the user's health management and provide accurate advice and purchasing guidance tailored to the user's emotional state.

[1682] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1683] System program processing flow

[1684] Processing steps

[1685] Step 1:

[1686] Users install a dedicated app on their smartphone and link it to their LINE account. They open the app and use the LINE login function to link their account.

[1687] Input: Smartphone, LINE account information

[1688] Output: Account linking complete status

[1689] The specific process involves the user first downloading the app, installing it, and then launching it. Upon launching the app, the LINE login screen will appear, and account linking will be completed after logging in.

[1690] Step 2:

[1691] The user pairs the smartwatch with the smartphone app. The smartwatch searches for the smartphone in its Bluetooth settings and connects. The user completes the smartwatch setup within the app and allows the sharing of health data.

[1692] Input: Smartwatch, Smartphone

[1693] Output: Pairing complete status

[1694] The specific steps involve the user opening the Bluetooth settings on their smartwatch, searching for their smartphone, and pairing the devices. They then configure the device settings within the app and enable health data sharing.

[1695] Step 3:

[1696] The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. For example, it retrieves heart rate and step count data every hour via Bluetooth.

[1697] Input: Data from smartwatch

[1698] Output: Health data stored within the app

[1699] Specifically, the smartphone periodically retrieves data from the smartwatch via Bluetooth and saves that data to its internal storage or the cloud.

[1700] Step 4:

[1701] The server calls a weather information API to retrieve weather information for the user's area. The weather information API is accessed periodically to retrieve local weather data based on the user's location. For example, the API might be called every morning at 6:00 AM.

[1702] Input: Location information, weather information API

[1703] Output: Weather data

[1704] Specifically, the server, according to a specified schedule, uses location information to call a weather information API and saves the retrieved weather data to a database.

[1705] Step 5:

[1706] The server generates multiple-choice questions based on weather information and historical data. For example, if it's raining, it creates a question from a template such as, "It's going to rain today, will you take an umbrella?"

[1707] Input: Weather data, historical data

[1708] Output: Question template

[1709] In terms of specific operations, the server analyzes weather data, refers to past data, and selects and generates appropriate questions.

[1710] Step 6:

[1711] The server generates a question and sends it to the user via LINE. The LINE Message Sending API is used to send the question to the user's LINE account.

[1712] Input: Generated question, user's LINE account information

[1713] Output: LINE message

[1714] Specifically, the server calls the LINE message sending API and sends the generated question content to the user.

[1715] Step 7:

[1716] Users answer questions via LINE. For example, they select the appropriate option from "yes" or "no" choices and send their response.

[1717] Input: Question on LINE

[1718] Output: User's response

[1719] In terms of specific actions, the user opens LINE, selects the appropriate answer to the question, and sends it.

[1720] Step 8:

[1721] The smartphone (device) receives the user's response via LINE. The smartphone app analyzes the received LINE data and saves the response data within the app.

[1722] Input: User's response (LINE message)

[1723] Output: Response data stored within the app

[1724] Specifically, the smartphone uses the LINE API to retrieve received data, analyzes its contents, and saves it to a database.

[1725] Step 9:

[1726] The smartphone (device) sends the user's response data to the server. The response data within the app is sent to the server using internet communication.

[1727] Input: Response data

[1728] Output: Data to be transferred to the server

[1729] Specifically, the smartphone uses the internet to transfer the response data to the server.

[1730] Step 10:

[1731] The server integrates and analyzes health data and user response data sent from smartphones. It executes analysis algorithms using historical data stored in the database as well as current health data.

[1732] Input: Health data, user response data

[1733] Output: Analysis results

[1734] In terms of specific operations, the server retrieves the necessary data from the database and performs integrated analysis using an analysis algorithm.

[1735] Step 11:

[1736] The server generates health advice based on the analysis results. For example, it might create advice such as, "Your exercise level today was low, so we recommend taking a short walk when you get home."

[1737] Input: Analysis results

[1738] Output: Health advice

[1739] In terms of specific operations, the server selects and generates an advice template based on the analysis results, and then formats it into a concrete message.

[1740] Step 12:

[1741] The server generates advice and sends it to the user via LINE. The LINE message sending API is used to send health advice to the user.

[1742] Input: Health advice

[1743] Output: LINE message

[1744] Specifically, the server calls the LINE message sending API and sends the advice to the user.

[1745] Step 13:

[1746] The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might generate a message like, "Here is a coupon code for health supplements."

[1747] Input: Health data, behavioral patterns

[1748] Output: Purchase Guide

[1749] Specifically, the server analyzes health data and behavioral patterns, and then selects and generates a template for appropriate purchasing recommendations.

[1750] Step 14:

[1751] The server generates a purchase guide and sends it to the user via LINE. The LINE message sending API is used to send the purchase guide to the user.

[1752] Input: Purchase Information

[1753] Output: LINE message

[1754] Specifically, the server calls the LINE message sending API and sends the purchase information to the user.

[1755] Step 15:

[1756] The server uses an emotion engine to recognize the user's emotions. By analyzing the user's voice and text data, the emotion engine identifies the user's emotional state.

[1757] Input: User's voice data and text data

[1758] Output: Sentiment data

[1759] Specifically, the server executes speech recognition and text analysis algorithms, and the emotion engine identifies the emotional state.

[1760] Step 16:

[1761] The server customizes the feedback based on the emotions it perceives. For example, if the user is feeling stressed, it might generate advice such as, "Try breathing exercises to relax."

[1762] Input: Sentiment data

[1763] Output: Customized feedback

[1764] In terms of specific operations, the server analyzes emotional data, selects and customizes a template to generate appropriate feedback.

[1765] (Application Example 2)

[1766] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1767] In today's world, maintaining and improving user health requires comprehensive management that includes not only health data but also emotions. However, conventional health management systems often fail to consider user emotions, resulting in mechanical and unpersonalized health advice and purchasing guidance. Furthermore, providing users with appropriate health-related products requires integrating and analyzing multiple data sets.

[1768] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1769] In this invention, the server includes means for an information processing device to acquire user health data, means for an information processing device to analyze health data acquired from a smartwatch, means for an information processing device to generate health advice for the user based on the analysis results, means for an information processing device to transmit the generated advice to the user via a data communication medium, means for an information processing device to generate purchase guidance based on the user's health data and behavioral patterns, means for an information processing device to transmit the generated purchase guidance to the user via a data communication medium, means for an information processing device to acquire user emotional data, means for an information processing device to generate appropriate product suggestions for the user based on the emotional data, and means for transmitting the generated product suggestions to the user via a written communication medium. This enables integrated management of the user's health and emotional state, and the provision of personalized health advice and purchase guidance.

[1770] An "information processing device" is an electronic device that acquires, analyzes, and notifies users of their health and emotional data.

[1771] A "user" is an individual who uses this system, and their health data and emotional data are managed by the system.

[1772] "Health data" refers to biometric information such as the user's heart rate, steps taken, and exercise level, which is obtained from smartwatches and other devices.

[1773] A "smartwatch" is an electronic device worn by users to collect health data, and it has the function of acquiring data such as heart rate and step count.

[1774] "Emotional data" refers to information that indicates a user's emotional state, and is obtained through methods such as text analysis and voice analysis.

[1775] "Weather information" refers to data showing the weather conditions in the user's residential area, and is obtained from weather forecasting services.

[1776] "Analysis" is the process of evaluating users' health and emotional states based on collected data, and identifying problems and areas for improvement.

[1777] "Health advice" refers to recommendations based on analysis results that aim to help users maintain or improve their health.

[1778] "Purchase guidance" refers to commercial information such as suggestions for health-related products and coupon codes, generated based on the user's health and emotional data.

[1779] A "data communication medium" refers to a means of sending and receiving digital information, such as the internet or mobile phone networks.

[1780] A "cloud server" is a remote computer server accessible via the internet, where data is collected and analyzed.

[1781] This invention relates to a system that integrates and manages user health data and emotional data to provide personalized health advice and purchasing guidance. The system consists of an information processing device (server), a user terminal (smartphone), and a smartwatch.

[1782] System Configuration

[1783] 1. Information Processing Device (Server): This device analyzes the user's health and emotional data, generates health advice and product suggestions for the user, and transmits them via a data communication medium. The server uses an emotional engine and a weather information API to perform data analysis.

[1784] 2. User terminal (smartphone): This device works in conjunction with the smartwatch to collect health data and transmit it to the server via a data communication medium. It also uses LINE messages to ask questions to the user and receive the user's responses.

[1785] 3. Smartwatch: This device acquires health data such as heart rate and steps and transmits it to a smartphone. It pairs with the user's smartphone using Bluetooth.

[1786] Hardware and software to be used

[1787] Health data acquisition and analysis: Smartwatch and smartphone. The smartphone pairs with the smartwatch using Bluetooth and periodically sends health data to the server.

[1788] Emotional data acquisition and analysis: On the server, an Emotion Recognition Engine is used to analyze user emotional data from text and audio data.

[1789] Data communication: Use the LINE Messaging API to send health advice and product suggestions to users.

[1790] Specific example

[1791] Let's say a user uses the diary function and enters, "Work was tough today." The server uses an emotion engine to recognize stress from this text and also analyzes health data (high heart rate) obtained from a smartwatch. As a result, the server generates health advice for the user, such as "Try buying a relaxing beverage to reduce stress," and simultaneously sends it to the user via LINE message along with a coupon code for health-related products.

[1792] Example prompts for a generative AI model

[1793] The following prompt statements can be used to generate optimal product suggestions for the user.

[1794] Generate personalized product recommendations based on the user's health and emotional data. The following data is available:

[1795] Health data: Low activity level, normal heart rate

[1796] Emotional data: High stress levels

[1797] Please generate recommended products.

[1798] Thus, the system of this invention can comprehensively manage the user's health and emotional state and provide personalized health advice and product recommendations in real time.

[1799] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1800] Step 1:

[1801] The user installs a dedicated app on their smartphone and creates an account. During this process, a registration screen appears on the smartphone, and the user enters the required information. The entered information is sent to the server, and a new account is created.

[1802] Input: User registration information (name, email address, etc.)

[1803] Output: Account information is saved on the server.

[1804] Step 2:

[1805] The user pairs the smartwatch with their smartphone. The user searches for the smartphone in the smartwatch's settings screen and connects via Bluetooth. This allows the smartwatch to periodically send health data (heart rate, steps, etc.) to the smartphone.

[1806] Input: Smartwatch pairing information

[1807] Output: Smartwatch and smartphone connection complete.

[1808] Step 3:

[1809] The smartphone periodically acquires health data from the smartwatch. Specifically, the smartphone app communicates with the smartwatch via Bluetooth, acquiring data such as heart rate and steps taken every hour.

[1810] Input: Health data from smartwatch

[1811] Output: Health data acquired on the smartphone

[1812] Step 4:

[1813] The server calls a weather information API to obtain weather information for the user's area. The server periodically accesses the weather information API to obtain local weather data based on the user's location.

[1814] Input: User's location information

[1815] Output: Acquired weather information

[1816] Step 5:

[1817] The server generates multiple-choice questions based on health data and weather information. For example, if it's raining, it might create a question from a template such as, "It's raining today, will you take an umbrella with you?"

[1818] Input: Health data, weather information

[1819] Output: Generated multiple-choice questions

[1820] Step 6:

[1821] The server generates a question and sends it to the user using the LINE API. The question is then displayed on the user's smartphone using the LINE Message Sending API.

[1822] Input: Generated Question

[1823] Output: Question sent to the user's LINE account

[1824] Step 7:

[1825] Users answer questions via LINE. For example, they select the appropriate option from "yes" or "no" choices and send it to the server via LINE message.

[1826] Input: User's response

[1827] Output: User responses received by the server

[1828] Step 8:

[1829] The smartphone receives the user's response via LINE and sends the analysis data to the server. The smartphone app analyzes the received data from LINE and uploads the response data to the server.

[1830] Input: User's response received via LINE

[1831] Output: Analyzed response data

[1832] Step 9:

[1833] The server integrates and analyzes health data and user response data sent from smartphones. This involves running an analysis algorithm using historical and current health data stored in a database.

[1834] Input: Health data, user response data

[1835] Output: Integrated analysis results

[1836] Step 10:

[1837] The server generates health advice based on the analysis results. For example, it might create advice such as, "Your exercise level today was low, so we recommend taking a short walk when you get home."

[1838] Input: Analysis results

[1839] Output: Generated health advice

[1840] Step 11:

[1841] The server generates advice and sends it to the user via LINE. The LINE Message Sending API is used to send the health advice to the user's LINE account.

[1842] Input: Generated health advice

[1843] Output: Advice sent to the user's LINE account

[1844] Step 12:

[1845] The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might create a message like, "Here is a coupon code for health supplements."

[1846] Input: Health data, behavioral patterns

[1847] Output: Generated purchase guide

[1848] Step 13:

[1849] The server generates a purchase guide and sends it to the user via LINE. The purchase guide is sent to the user's LINE account using the LINE Message Sending API.

[1850] Input: Generated purchase guide

[1851] Output: Purchase information sent to the user's LINE account

[1852] Step 14:

[1853] The server uses an emotion engine to recognize the user's emotions. Specifically, it analyzes the user's voice and text data, and the emotion engine identifies the user's emotional state (joy, sadness, anger, etc.).

[1854] Input: Audio data, text data

[1855] Output: User's emotional state

[1856] Step 15:

[1857] The server customizes the feedback based on the emotions it perceives. For example, if the user is feeling stressed, it might generate advice such as, "Try breathing exercises to relax."

[1858] Input: User's emotional state

[1859] Output: Customized feedback content

[1860] In this manner, this invention can comprehensively manage the user's health and emotional state and provide personalized health advice and purchasing guidance.

[1861] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1862] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1863] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1864] [Fourth Embodiment]

[1865] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1866] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1867] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1868] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1869] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1870] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1871] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1872] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1873] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1874] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1875] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1876] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1877] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1878] This invention realizes a health management system that acquires, analyzes, and provides feedback on user health data. Below, the system's program processing is explained in natural language, and the system's operation is described in detail with specific examples.

[1879] System Configuration

[1880] This system consists of an information processing device (server), a user terminal (smartphone), and a smartwatch that acquires the user's health data.

[1881] Program processing

[1882] 1. The user installs a dedicated health management app on their smartphone. When they open the app, they are presented with an option to link their LINE account, and the user links their LINE account with the app.

[1883] 2. The user pairs the smartwatch with their smartphone and completes the necessary settings. This setting allows the smartwatch to send health data (e.g., heart rate, steps, sleep data) to the app.

[1884] 3. The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. Bluetooth communication is used for this purpose.

[1885] 4. The server uses a weather information API to obtain weather information for the user's location. For example, the server updates the weather information every morning at 6:00 AM.

[1886] 5. The server generates a multiple-choice question for the day based on weather information and the user's past data. For example, if rain is expected, it will create a question such as, "It will rain today, will you take an umbrella with you?"

[1887] 6. The server generates a question and sends it to the user via LINE. This is done using LINE's message sending API.

[1888] 7. The user answers the question via LINE. For example, they tap either the "Yes" or "No" option.

[1889] 8. The smartphone (device) receives the user's response via LINE and passes it to the app, which then sends the response data to the server.

[1890] 9. The server integrates and analyzes the health data and questionnaire responses received via the smartphone. The analysis also takes into account the user's past behavioral patterns and health status.

[1891] 10. The server generates appropriate health advice for the user based on the analysis results. For example, it might generate advice such as, "Your exercise level today was low, so we recommend a short walk when you get home."

[1892] 11. The server sends the generated advice to the user via LINE.

[1893] 12. The server generates purchase recommendations for relevant products based on the user's health data and behavioral patterns. For example, it might create a message such as, "Here is a coupon code for health supplements."

[1894] 13. The server sends the generated purchase information to the user via LINE.

[1895] Specific example

[1896] Example based on morning weather information

[1897] 1. The server calls the weather information API at 6 AM to confirm that rain is expected in the user's area.

[1898] 2. The server generates the question, "It's going to rain today, will you take an umbrella with you?" and sends it to the user via LINE.

[1899] 3. The user answers "Yes".

[1900] 4. The server receives the answers to the questions and health data (e.g., heart rate, steps) obtained from the smartwatch, and analyzes them.

[1901] 5. Based on the analysis results, the server generates advice such as, "You haven't had enough exercise today, so we recommend taking a short walk when you get home," and sends it to the user via LINE.

[1902] 6. The server then generates a purchase guide that says, "Here is the coupon code for health supplements," and sends it to the user via LINE.

[1903] Through the above embodiment, the system can monitor the user's health status in real time and provide advice and purchasing guidance that also takes into account external factors such as weather information.

[1904] The following describes the processing flow.

[1905] Step 1:

[1906] Users install a dedicated app on their smartphone and link it to their LINE account. They then open the app and use the LINE login function to link their account. This synchronizes the app and the LINE account.

[1907] Step 2:

[1908] The user pairs the smartwatch with the smartphone app. The smartwatch searches for the smartphone in its Bluetooth settings and connects. The user completes the smartwatch setup within the app and allows the sharing of health data.

[1909] Step 3:

[1910] The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. For example, it retrieves heart rate and step count data every hour via Bluetooth.

[1911] Step 4:

[1912] The server calls a weather information API to retrieve weather information for the user's location. The server periodically accesses the weather information API to retrieve local weather data based on the user's location. For example, the API might be called every morning at 6:00 AM.

[1913] Step 5:

[1914] The server generates multiple-choice questions based on weather information and historical data. For example, if it's raining, it creates a question from a template such as, "It's going to rain today, will you take an umbrella?"

[1915] Step 6:

[1916] The server generates a question and sends it to the user via LINE. The LINE Message Sending API is used to send the question to the user's LINE account.

[1917] Step 7:

[1918] The system responds to questions received by users via LINE. For example, it selects the appropriate option from "yes" or "no" choices and sends the response.

[1919] Step 8:

[1920] The smartphone (device) receives the user's response via LINE. The smartphone app analyzes the received LINE data and saves the response data within the app.

[1921] Step 9:

[1922] The smartphone (device) sends the user's response data to the server. The response data within the app is sent to the server using internet communication.

[1923] Step 10:

[1924] The server integrates and analyzes health data and user response data sent from smartphones. It executes analysis algorithms using historical data stored in the database as well as current health data.

[1925] Step 11:

[1926] The server generates health advice based on the analysis results. For example, it might create advice such as, "Your exercise level today was low, so we recommend taking a short walk when you get home."

[1927] Step 12:

[1928] The server generates advice and sends it to the user via LINE. The LINE message sending API is used to send health advice to the user.

[1929] Step 13:

[1930] The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might generate a message like, "Here is a coupon code for health supplements."

[1931] Step 14:

[1932] The server generates a purchase guide and sends it to the user via LINE. The LINE message sending API is used to send the purchase guide to the user.

[1933] (Example 1)

[1934] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1935] Traditional health management systems lack sufficient integration in acquiring and analyzing user health data, resulting in the inability to provide anything more than simplistic advice based on individual data elements. Furthermore, they struggle to appropriately generate and provide health advice that considers environmental factors such as external weather information, or purchasing guidance based on user behavior patterns. Additionally, limited real-time data collection and analysis capabilities make it difficult to provide optimal feedback to users.

[1936] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1937] In this invention, the server includes: [means for an information processing device to acquire user health data; [means for an information processing device to analyze health data acquired from a wearable device; [means for an information processing device to generate health advice for the user based on the analysis results; [means for an information processing device to transmit the generated advice to the user via a data communication medium]; [means for an information processing device to generate purchase guidance based on the user's health data and behavioral patterns; [means for an information processing device to transmit the generated purchase guidance to the user via a data communication medium]; [means for an information processing device to acquire weather information; [means for generating multiple-choice questions for the user based on the weather information]; [means for transmitting multiple-choice questions to the user via a data communication medium and receiving responses from the user]; and [means for integrating and analyzing the responses received from the user with health data]. This makes it possible to provide real-time feedback that comprehensively considers the user's health status and environmental factors.

[1938] An "information processing device" is a device that acquires and analyzes a user's health data and generates and transmits feedback and purchase recommendations.

[1939] A "wearable device" is a device that is attached to the user's body and collects health data such as heart rate, steps taken, and sleep data.

[1940] "Health data" refers to data that indicates the user's health status, including heart rate, steps taken, and sleep quality.

[1941] "Analysis" is the process of evaluating and diagnosing health status and behavioral patterns using statistical analysis and machine learning techniques based on acquired data.

[1942] "Health advice" refers to messages that, based on analysis results, suggest the most suitable health management strategies for the user.

[1943] A "data communication medium" refers to a communication network such as the internet, and is a means of sending and receiving data.

[1944] A "purchase guide" is a message that contains information designed to encourage the purchase of relevant products based on the user's health data and behavioral patterns.

[1945] "Weather information" refers to data that shows the weather conditions in the area where the user lives.

[1946] A "multiple-choice question" is a type of question in which the user chooses an answer from a set of specific options, and is used to obtain feedback on health status and behavior.

[1947] A "cloud server" is a server located on the internet and used for data collection and analysis.

[1948] "Identifying behavioral patterns" means analyzing a user's past data to understand specific behavioral tendencies and habits.

[1949] A "health advice generation AI model" is an artificial intelligence model that generates appropriate health advice based on the user's health data and behavioral patterns.

[1950] The "Purchase Recommendation Generation AI Model" is an artificial intelligence model that generates purchase recommendations for related products based on the user's health data and behavioral patterns.

[1951] This invention realizes a health management system that acquires, analyzes, and provides feedback on user health data. The details of the system and the program's processing are described below.

[1952] This system consists of an information processing unit (server), a user terminal (smartphone), and a wearable device (smartwatch) that acquires the user's health data.

[1953] System details

[1954] 1. Hardware and software:

[1955] Information Processing Equipment (Server): A cloud-based server that performs high-speed data processing and analysis. It possesses computing resources to run specific AI models.

[1956] User terminal (smartphone): A portable device running iOS or Android, with a dedicated health management app installed.

[1957] Wearable devices (smartwatches): These are devices equipped with sensors that measure heart rate, steps taken, sleep data, and other information.

[1958] 2. Program processing:

[1959] Users install a dedicated health management app on their smartphones and link it to their LINE account.

[1960] The user pairs the smartwatch with their smartphone and completes the necessary initial setup. This allows the smartwatch to periodically send health data to the smartphone.

[1961] The smartphone (device) retrieves health data from the smartwatch via Bluetooth communication and stores it within the app.

[1962] The server uses a weather information API to retrieve weather information for the user's location. For example, it updates the weather information every morning at 6:00 AM.

[1963] The server generates multiple-choice questions for the day based on weather information and the user's past health data. For example, if rain is expected, it will create a question such as, "It will rain today, will you take an umbrella with you?"

[1964] The server sends the generated question to the user via LINE. It utilizes LINE's message sending API.

[1965] Users answer questions via LINE. For example, they tap either the "yes" or "no" option.

[1966] The smartphone (device) receives the user's responses via LINE and passes them to the health management app, which then sends the response data to a server.

[1967] The server integrates and analyzes health data and questionnaire responses received via smartphones. The analysis also takes into account the user's past behavioral patterns and health status.

[1968] The server generates appropriate health advice for the user based on the analysis results. For example, it might generate advice such as, "Your exercise level today was low, so we recommend a short walk when you get home."

[1969] The server sends the generated advice to the user via LINE.

[1970] The server generates purchase recommendations for relevant products based on the user's health data and behavioral patterns. For example, it might create a message such as, "Here is a coupon code for health supplements."

[1971] The server sends the generated purchase information to the user via LINE.

[1972] 3. Specific examples:

[1973] The server calls the weather information API at 6 AM to confirm that rain is expected in the user's area.

[1974] The server generates the question, "It's going to rain today, will you take an umbrella with you?" and sends it to the user via LINE.

[1975] The user answers "yes".

[1976] The server receives and analyzes the answers to the questions and health data (such as heart rate and steps) obtained from the smartwatch.

[1977] Based on the analysis results, the server generates advice such as, "You haven't had enough exercise today, so we recommend taking a short walk when you get home," and sends it to the user via LINE.

[1978] The server then generates a purchase guide that says, "Here is a coupon code for health supplements," and sends it to the user via LINE.

[1979] As described above, this system monitors the user's health status in real time and provides optimal advice and purchasing guidance to the user, taking into account weather information and lifestyle habits.

[1980] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1981] The program's processing flow is divided into processing steps.

[1982] Step 1:

[1983] Users install a dedicated health management app on their smartphones. This app also offers an option to link with a LINE account, which users can do from the settings screen.

[1984] Specific actions:

[1985] Users download the health management app from the App Store or Google Play on their smartphones and follow the instructions to link their LINE account. During this process, the linked account information is saved in the app.

[1986] Inputs and outputs:

[1987] Input: User's smartphone, health management app, LINE account information

[1988] Output: Health management app including linked LINE account information

[1989] Step 2:

[1990] The user pairs the smartwatch with their smartphone and completes the necessary initial setup within the app. This allows health data (e.g., heart rate, steps, sleep data) from the smartwatch to be sent to the app.

[1991] Specific actions:

[1992] The user selects the smartwatch on their smartphone's Bluetooth settings screen and presses the pairing button. Afterward, the app is configured to receive and save health data in real time.

[1993] Inputs and outputs:

[1994] Input: User's smartwatch, smartphone, or app settings screen

[1995] Output: Paired smartwatch information and health data received in real time.

[1996] Step 3:

[1997] The smartphone (device) uses Bluetooth communication to periodically retrieve health data from the smartwatch and store it within the app.

[1998] Specific actions:

[1999] The app retrieves data from the smartwatch via Bluetooth communication at specified intervals and stores that data in the app's database.

[2000] Inputs and outputs:

[2001] Input: Health data from smartwatch

[2002] Output: Health data stored within the app (heart rate, steps, sleep data)

[2003] Step 4:

[2004] The server calls a weather information API to retrieve weather information for the user's location. Updates are performed every morning at 6:00 AM.

[2005] Specific actions:

[2006] The server uses weather information APIs such as "OpenWeatherMap" to retrieve the latest weather information for the user's area every morning at 6:00 AM. The retrieved weather information is stored in a database on the server.

[2007] Inputs and outputs:

[2008] Input: Calling the weather information API

[2009] Output: Acquired weather information

[2010] Step 5:

[2011] The server generates multiple-choice questions for the day based on weather information and past health data. For example, if rain is expected, it might create a question like, "It will rain today, will you take an umbrella with you?"

[2012] Specific actions:

[2013] The server retrieves weather information and historical health data, and uses a natural language generation model to generate questions based on this information.

[2014] Inputs and outputs:

[2015] Input: Weather information and historical health data

[2016] Output: Generated multiple-choice questions

[2017] Step 6:

[2018] The server generates questions and sends them to the user via LINE. This is done using LINE's message sending API.

[2019] Specific actions:

[2020] The server sends the generated question to the user via the LINE API. A notification appears in the LINE app, and the user can access the question.

[2021] Inputs and outputs:

[2022] Input: Generated multiple-choice question

[2023] Output: Question sent via LINE

[2024] Step 7:

[2025] The user answers the question via LINE. For example, they tap either the "yes" or "no" option.

[2026] Specific actions:

[2027] Users check the question notification on the LINE app, tap the appropriate option button, and submit their answer.

[2028] Inputs and outputs:

[2029] Input: Question displayed on the LINE app

[2030] Output: Answer ("Yes" / "No")

[2031] Step 8:

[2032] The smartphone (device) receives the user's responses via LINE and passes them to the health management app, which then sends the response data to a server.

[2033] Specific actions:

[2034] The response data received from the LINE app is transferred to the app, and the app then sends that data to the server.

[2035] Inputs and outputs:

[2036] Input: Response data from the LINE app

[2037] Output: Response data sent to the server

[2038] Step 9:

[2039] The server integrates and analyzes health data and questionnaire responses received via smartphones. The analysis also takes into account the user's past behavioral patterns and health status.

[2040] Specific actions:

[2041] The server references past databases and analyzes health data and questionnaire responses using an AI model.

[2042] Inputs and outputs:

[2043] Input: Health data and answers to questions

[2044] Output: Analysis results

[2045] Step 10:

[2046] The server generates appropriate health advice for the user based on the analysis results. For example, it might generate advice such as, "Your exercise level today was low, so we recommend a short walk when you get home."

[2047] Specific actions:

[2048] The server generates health advice using an AI model and sends it to the user via the LINE API.

[2049] Inputs and outputs:

[2050] Input: Analysis results

[2051] Output: Generated health advice

[2052] Step 11:

[2053] The server generates advice and sends it to the user via LINE. This uses the LINE message sending API.

[2054] Specific actions:

[2055] The server sends the generated advice message to the user via the LINE API.

[2056] Inputs and outputs:

[2057] Input: Generated health advice

[2058] Output: Advice sent via LINE

[2059] Step 12:

[2060] The server generates purchase recommendations for relevant products based on the user's health data and behavioral patterns. For example, it might create a message such as, "Here is a coupon code for health supplements."

[2061] Specific actions:

[2062] The server uses an AI model to generate purchase guidance and creates messages with coupon codes for related products.

[2063] Inputs and outputs:

[2064] Input: Health data and behavioral patterns

[2065] Output: Generated purchase guide

[2066] Step 13:

[2067] The server generates a purchase guide and sends it to the user via LINE. This is done using LINE's message sending API.

[2068] Specific actions:

[2069] The server sends the generated purchase guidance message to the user via the LINE API.

[2070] Inputs and outputs:

[2071] Input: Generated purchase guide

[2072] Output: Purchase information sent via LINE

[2073] (Application Example 1)

[2074] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2075] Many conventional health management systems only acquire and analyze user health data, lacking advice and support that takes into account user behavior patterns and external environmental factors. This results in problems such as being unable to provide users with appropriate dietary suggestions or relevant discount coupons. Furthermore, there is no established method for providing personalized health advice based on weather information and user responses, making it difficult to provide more specific and practical support tailored to users' real lives.

[2076] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[2077] In this invention, the server includes means for an information processing device to generate appropriate meal suggestions based on the user's health data and transmit them to the user via a data communication medium; means for the information processing device to generate discount coupons related to the suggested meals and transmit them to the user via a data communication medium; and means for the information processing device to acquire weather information, generate multiple-choice questions for the user, transmit the multiple-choice questions to the user via a data communication medium, receive the user's answers, and integrate and analyze them. This makes it possible to provide personalized meal suggestions and discount coupons based not only on the user's health data but also on weather information and the user's answers.

[2078] An "information processing device" is a device that acquires and analyzes a user's health data and generates and transmits various advice and purchasing guidance.

[2079] "Data communication medium" refers to a means of transmitting information, and specifically includes the internet and Bluetooth.

[2080] "Health data" refers to data related to a user's physical health, such as heart rate, steps taken, and sleep data.

[2081] A "smartwatch" is a wearable device that measures and collects health data such as heart rate, steps taken, and sleep data.

[2082] "Health advice" refers to advice on maintaining or improving health that an information processing device provides to the user based on its analysis results.

[2083] "Purchase information" refers to information and coupon codes for products that a user might potentially purchase, generated by an information processing device.

[2084] "Weather information" refers to weather data related to the user's residential area, including temperature, probability of precipitation, and wind speed.

[2085] A "multiple-choice question" is a question generated by an information processing device and sent to a user, designed to elicit an answer from multiple options.

[2086] A "cloud server" is a server that collects and analyzes data via the internet.

[2087] A "discount coupon" is a code or information used to apply a discount to a suggested meal or product.

[2088] "Analysis results" refer to the results calculated by the information processing device based on health data, user responses, weather information, and other factors.

[2089] This invention realizes a health management system that acquires and analyzes users' health data and provides appropriate health advice, dietary suggestions, and related discount coupons.

[2090] System Configuration

[2091] This system consists of an information processing device (server), a user terminal (smartphone), and a smartwatch that acquires the user's health data.

[2092] Program processing

[2093] 1. Users install a dedicated health management app on their smartphones and link it to their LINE account. This allows users to receive health advice, meal suggestions, and coupons via LINE.

[2094] 2. The user pairs the smartwatch with their smartphone. This setup allows the smartwatch to send health data (e.g., heart rate, steps, sleep data) to the app.

[2095] 3. The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. Bluetooth communication is used for this data retrieval.

[2096] 4. The server uses a weather information API to obtain weather information for the user's residential area. Weather information is retrieved every morning at 6:00 AM.

[2097] 5. The server generates a multiple-choice question for the day based on weather information and the user's past health data. For example, on a cold day, it might generate a question like, "How about a warming meal today?"

[2098] 6. The server generates a question and sends it to the user via LINE. This is done using LINE's message sending API.

[2099] 7. The user answers the question via LINE. For example, they can answer by tapping "Yes" or "No" as options.

[2100] 8. The smartphone (device) receives the user's response via LINE and passes it to the app, which then sends the response data to the server.

[2101] 9. The server integrates and analyzes the health data and questionnaire responses received via the smartphone. The analysis also takes into account the user's past behavioral patterns and health status.

[2102] 10. Based on the analysis results, the server generates appropriate meal suggestions and health advice for the user. For example, it might generate advice such as, "Today, we recommend seafood chili soup, which is good for replenishing energy."

[2103] 11. Generate relevant discount coupons along with the advice generated by the server and send them to the user via LINE.

[2104] Hardware and software to be used

[2105] Smartwatch: A wearable device (e.g., Apple Watch, Garmin) that measures and collects health data (e.g., heart rate, steps, sleep data).

[2106] Smartphone: A device used for installing apps, acquiring health data, and displaying analysis results (e.g., iOS devices, Android devices).

[2107] Server: A cloud server (e.g., AWS, Azure) that collects and analyzes health data.

[2108] Bluetooth communication: A means of communication for sending and receiving data between a smartwatch and a smartphone.

[2109] Weather Information API: An API for obtaining weather information for the user's residential area (e.g., OpenWeatherMap API).

[2110] LINE API: A message sending API for sending health advice, meal suggestions, and coupons via LINE.

[2111] Specific example

[2112] 1. Morning question:

[2113] The server calls a weather information API at 6 AM, and if it's a cold day, it generates the question, "How about a warming meal today?" and sends it to the user via LINE.

[2114] 2. User responses and meal suggestions:

[2115] If the user answers "yes," the server generates advice such as "We recommend seafood chili soup, which is good for replenishing energy," and sends it via LINE.

[2116] 3. Send coupon:

[2117] A 20% discount coupon for seafood chili soup will also be sent via LINE.

[2118] Examples of prompt statements

[2119] If a user needs to choose a meal on a cold day, you generate a question like, "How about a warming meal today?" and send it via LINE. If the user replies "yes," you then suggest, "I recommend the seafood chili soup, which is great for replenishing energy," and also offer a discount coupon for that menu item.

[2120] This system can use users' health data to provide personalized meal suggestions and health advice based on weather information and user responses, helping users maintain and improve their health.

[2121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[2122] Step 1:

[2123] Users install a dedicated health management app on their smartphones and link it to their LINE account. This allows them to receive health advice, meal suggestions, and coupons via LINE.

[2124] Input: Install a health management app on your smartphone and link it to your LINE account.

[2125] Output: Linked to the user's LINE account.

[2126] Step 2:

[2127] By pairing the smartwatch with their smartphone, the user can configure it to send health data from the smartwatch to the app.

[2128] Input: Setting up the smartwatch to pair with your smartphone.

[2129] Output: The smartwatch is in a state where it can send data to a smartphone.

[2130] Step 3:

[2131] The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. Bluetooth communication is used for sending and receiving data.

[2132] Input: Health data acquired by the smartphone from the smartwatch (e.g., heart rate, steps, sleep data).

[2133] Output: Health data stored within the app.

[2134] Step 4:

[2135] The server uses a weather information API to retrieve weather information for the user's residential area every morning at 6:00 AM.

[2136] Input: Weather information API request.

[2137] Output: The latest weather information for the user's residential area.

[2138] Step 5:

[2139] The server generates a multiple-choice question for the day based on weather information and the user's past health data. For example, on a cold day, it might create a question like, "How about a warming meal today?"

[2140] Input: Weather information, historical health data.

[2141] Output: Generated multiple-choice question.

[2142] Step 6:

[2143] The server generates questions and sends them to users via LINE, utilizing LINE's message sending API.

[2144] Input: A generated multiple-choice question.

[2145] Output: Questions sent to the user via LINE.

[2146] Step 7:

[2147] Users answer questions via LINE. For example, they can choose to answer from "yes" or "no" options.

[2148] Input: Question sent via LINE.

[2149] Output: User's response to the question.

[2150] Step 8:

[2151] The smartphone (device) receives the user's response via LINE and passes it to the app, which then sends that response data to the server.

[2152] Input: User's response to a question.

[2153] Output: User response data sent to the server.

[2154] Step 9:

[2155] The server integrates and analyzes health data and answers to questions received via smartphone, taking into account the user's past behavioral patterns and health status to obtain analysis results.

[2156] Input: Health data, user response data.

[2157] Output: Analysis results.

[2158] Step 10:

[2159] Based on the analysis results, the server generates appropriate meal suggestions and health advice for the user. For example, it might generate advice such as, "We recommend seafood chili soup for today's energy boost."

[2160] Input: Analysis results.

[2161] Output: Generated meal suggestions and health advice.

[2162] Step 11:

[2163] The server generates advice and sends related discount coupons to users via LINE.

[2164] Input: Generated meal suggestions, health advice, and related discount coupons.

[2165] Output: Advice and discount coupons sent via LINE.

[2166] These processing steps allow the system to provide personalized meal suggestions and health advice, as well as discount coupons, based on the user's health data, weather information, and user responses.

[2167] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[2168] This invention combines a health management system that acquires and analyzes user health data and provides appropriate advice and purchasing guidance with an emotion engine that recognizes user emotions. The system configuration and program processing details are described below.

[2169] System Configuration

[2170] This system consists of an information processing device (server), a user terminal (smartphone), a smartwatch that acquires the user's health data, and an emotion engine that recognizes the user's emotions.

[2171] Program processing

[2172] 1. The user installs the dedicated app on their smartphone and links it with their LINE account. The user opens the app and links their account using the LINE login function.

[2173] 2. The user pairs the smartwatch with the smartphone app. Search for the smartphone in the smartwatch's Bluetooth settings and connect. Complete the smartwatch setup within the app and allow the sharing of health data.

[2174] 3. The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. For example, it retrieves heart rate and step count data every hour via Bluetooth.

[2175] 4. The server calls the weather information API to obtain weather information for the user's area. The server periodically accesses the weather information API to obtain local weather data based on the user's location. For example, the API is called every morning at 6:00 AM.

[2176] 5. The server generates multiple-choice questions based on weather information and historical data. For example, if it is raining, it will create a question from a template such as, "It will rain today, will you take an umbrella?"

[2177] 6. The server sends the generated question to the user via LINE. The LINE Message Sending API is used to send the question to the user's LINE account.

[2178] 7. The user answers the question via LINE. For example, they select the appropriate option from choices such as "yes" or "no" and send it.

[2179] 8. The smartphone (device) receives the user's response via LINE. The smartphone app analyzes the received LINE data and saves the response data within the app.

[2180] 9. The smartphone (device) sends the user's response data to the server. The response data within the app is sent to the server using internet communication.

[2181] 10. The server integrates and analyzes health data and user response data sent from smartphones. It executes an analysis algorithm using historical data stored in the database and current health data.

[2182] 11. The server generates health advice based on the analysis results. For example, it might create advice such as, "Your exercise level today was low, so we recommend taking a short walk when you get home."

[2183] 12. The server sends generated advice to the user via LINE. The LINE message sending API is used to send health advice to the user.

[2184] 13. The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might generate a message such as, "Here is a coupon code for health supplements."

[2185] 14. The server sends the generated purchase information to the user via LINE. The LINE message sending API is used to send the purchase information to the user.

[2186] 15. The server uses an emotion engine to recognize the user's emotions. Specifically, it analyzes the user's voice and text data, and the emotion engine identifies the user's emotional state (e.g., joy, sadness, anger, etc.).

[2187] 16. Customize feedback based on the emotions the server perceives. For example, if the user is feeling stressed, it might generate advice such as, "Try breathing exercises to relax."

[2188] Specific example

[2189] Examples based on user emotions

[2190] 1. Users use the diary function through the app to write down their feelings.

[2191] 2. The server uses an emotion engine to recognize the user's emotions from text data. For example, from the text "Today's work was tough," the emotion engine recognizes stress.

[2192] 3. The server generates a question based on weather information and sends it to the user via LINE. For example, "It's going to rain today, will you take an umbrella with you?"

[2193] 4. The user answers "Yes".

[2194] 5. The server receives the answers to the questions and health data (e.g., heart rate, steps) obtained from the smartwatch, and analyzes them.

[2195] 6. The server integrates and analyzes the user's emotional and health data to generate advice for stress reduction. For example, advice such as, "You are stressed, so try breathing exercises to relax."

[2196] 7. The server sends the generated advice to the user via LINE.

[2197] Through the above embodiment, this system can monitor the user's health status in real time and, by combining it with an emotion engine, can provide advice and purchasing guidance tailored to the user's emotional state.

[2198] The following describes the processing flow.

[2199] Step 1:

[2200] Users install a dedicated app on their smartphone and link it to their LINE account. They open the app and use the LINE login function to link their account.

[2201] Step 2:

[2202] The user pairs the smartwatch with the smartphone app. The smartwatch searches for the smartphone in its Bluetooth settings and connects. The user completes the smartwatch setup within the app and allows the sharing of health data.

[2203] Step 3:

[2204] The smartphone (device) periodically retrieves health data from the smartwatch and stores it within the app. For example, it retrieves heart rate and step count data every hour via Bluetooth.

[2205] Step 4:

[2206] The server calls a weather information API to retrieve weather information for the user's location. The server periodically accesses the weather information API to retrieve local weather data based on the user's location. For example, the API might be called every morning at 6:00 AM.

[2207] Step 5:

[2208] The server generates multiple-choice questions based on weather information and historical data. For example, if it's raining, it creates a question from a template such as, "It's going to rain today, will you take an umbrella?"

[2209] Step 6:

[2210] The server generates a question and sends it to the user via LINE. The LINE Message Sending API is used to send the question to the user's LINE account.

[2211] Step 7:

[2212] The system responds to questions received by users via LINE. For example, it selects the appropriate option from "yes" or "no" choices and sends the response.

[2213] Step 8:

[2214] The smartphone (device) receives the user's response via LINE. The smartphone app analyzes the received LINE data and saves the response data within the app.

[2215] Step 9:

[2216] The smartphone (device) sends the user's response data to the server. The response data within the app is sent to the server using internet communication.

[2217] Step 10:

[2218] The server integrates and analyzes health data and user response data sent from smartphones. It then executes an analysis algorithm using historical and current health data stored in the database.

[2219] Step 11:

[2220] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's voice and text data to determine their emotional state (e.g., joy, anger, sadness, etc.).

[2221] Step 12:

[2222] The server generates health advice based on the analysis results. For example, if it detects that the user is feeling stressed, it will create advice such as, "Try breathing exercises to relax."

[2223] Step 13:

[2224] The server generates advice and sends it to the user via LINE. The LINE message sending API is used to send health advice to the user.

[2225] Step 14:

[2226] The server generates purchase recommendations based on the user's health data and behavioral patterns. For example, it might create purchase recommendations that include coupons for products related to stress relief (e.g., aromatherapy oils, health foods).

[2227] Step 15:

[2228] The server generates a purchase guide and sends it to the user via LINE. The LINE message sending API is used to send the purchase guide to the user.

[2229] Specific example

[2230] Specific examples including emotion recognition

[2231] Step 1:

[2232] The user receives a multiple-choice question via LINE based on the morning weather forecast: "It will rain today, will you take an umbrella with you?"

[2233] Step 2:

[2234] The user replies "Yes." The smartphone receives this reply and sends it to the server.

[2235] Step 3:

[2236] The smartphone (device) periodically acquires health data (heart rate, steps, etc.) from the smartwatch and sends it to the server.

[2237] Step 4:

[2238] The server integrates and analyzes user responses and health data. It executes an analysis algorithm based on historical and current data stored in the database.

[2239] Step 5:

[2240] The server uses an emotion engine to recognize the user's emotional state. For example, it can recognize that a user is stressed from a LINE text message.

[2241] Step 6:

[2242] Based on the analysis results and emotional state, the server generates health advice such as "Try breathing exercises to relax" and sends it to the user via LINE.

[2243] Step 7:

[2244] The server then generates and sends to the user a purchase guide that includes coupons for related products (such as aromatherapy oils) to help reduce stress.

[2245] Through the above processing steps, a system is realized that monitors the user's health and emotional state in real time and provides advice and purchasing guidance tailored to their individual needs.

[2246] (Example 2)

[2247] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2248] Traditional health management systems had the functionality to acquire and analyze user health data and provide health advice, but they struggled to provide flexible feedback that considered user emotional data or personalized advice based on weather information. Furthermore, automatically generating purchasing recommendations based on user behavior patterns and emotional states was also a challenge.

[2249] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the information processing device to acquire the user's health data, means for the information processing device to analyze emotional data based on the health data and behavioral patterns, means for the information processing device to transmit health advice to the user via a data communication medium, means for the information processing device to generate purchase guidance based on the user's health data, behavioral patterns, and emotional data, and means for the information processing device to transmit the generated purchase guidance to the user via a data communication medium. This makes it possible to provide more personalized health advice and purchase guidance while taking into account the user's emotional state.

[2250] An "information processing device" is a device that collects, analyzes, and transmits data, and plays a role in acquiring, processing, storing, and transmitting user health data and emotional data.

[2251] "User health data" refers to biometric data such as the user's heart rate, steps taken, and sleep duration, obtained from smartwatches and other sensor devices.

[2252] "Emotional data" refers to data that indicates the emotional state (e.g., joy, sadness, anger, etc.) of a user, analyzed from their voice and text data.

[2253] "Health advice" refers to specific instructions and suggestions aimed at maintaining or improving the user's health, generated based on acquired and analyzed user health and emotional data.

[2254] "Purchase guidance" refers to marketing information such as product and service recommendations and coupon information, generated based on the user's health data, emotional data, and behavioral patterns.

[2255] "Data communication medium" refers to the internet and other communication networks, and includes the infrastructure that enables the sending and receiving of data between users and information processing devices.

[2256] "Weather information" refers to information about current and future weather conditions based on the user's location, obtained from weather data provision services and APIs.

[2257] A "multiple-choice question" is a type of question where the user chooses an answer from a set of options, based on weather information and user sentiment data.

[2258] "User behavior patterns" refer to data that shows the user's lifestyle habits and behavioral tendencies, analyzed from past health data, responses, diary entries, and other sources.

[2259] This invention is a health management system that acquires and analyzes user health data and provides appropriate advice and purchasing guidance, further incorporating an emotion engine that recognizes user emotions. The system configuration and its specific implementation method are described below.

[2260] System Configuration

[2261] This system consists of the following elements:

[2262] 1. Information processing equipment (server)

[2263] 2. User terminal (smartphone)

[2264] 3. Devices (smartwatches) that acquire user health data

[2265] 4. Emotional Engine

[2266] Specific examples of program processing

[2267] Application installation and integration

[2268] Users install a dedicated app on their smartphone, open the app, and link their LINE account. This allows them to send and receive health information and advice through their LINE account.

[2269] Acquisition and storage of health data

[2270] The user sets up the smartwatch to pair with a smartphone app. The smartphone periodically retrieves health data (e.g., heart rate, steps) from the smartwatch via Bluetooth and stores it in the app.

[2271] Obtaining weather information

[2272] The server calls a weather information API to retrieve weather information for the user's region. This information is updated regularly, for example, every morning at 6:00 AM.

[2273] emotion recognition

[2274] The server uses an emotion engine to recognize the user's emotions. It analyzes emotion data from the user's diary and text messages to determine the user's emotional state.

[2275] Generating and sending health advice

[2276] The server generates personalized health advice based on the analyzed health and emotional data. For example, it might create advice such as, "Your exercise level today is low, so we recommend a short walk when you get home," and send it to the user using the LINE message sending API.

[2277] Generating and sending purchase information

[2278] The server generates purchase recommendations based on the user's health data, behavioral patterns, and emotional data. For example, it generates a message such as "Here is a coupon code for health supplements" and sends it to the user via LINE.

[2279] Specific example

[2280] Examples based on user emotions

[2281] 1. Users use the diary function through the app to write down their feelings.

[2282] 2. The server uses an emotion engine to recognize the user's emotions from this text data. For example, from the text "Today's work was tough," the emotion engine recognizes stress.

[2283] 3. The server generates a question based on weather information and sends it to the user via LINE. For example, "It's going to rain today, will you take an umbrella with you?"

[2284] 4. The user answers "Yes".

[2285] 5. The server receives the answers to the questions and health data (e.g., heart rate, steps) obtained from the smartwatch, and analyzes them.

[2286] 6. The server integrates and analyzes the user's emo...

Claims

1. The information processing device provides a means for acquiring user health data, The information processing device provides a means for analyzing health data acquired from a smartwatch, The information processing device includes means for generating health advice for the user based on the analysis results, An information processing device provides means for transmitting generated advice to a user via a data communication medium, The information processing device includes means for generating purchase recommendations based on the user's health data and behavioral patterns, A system that includes means for an information processing device to transmit generated purchase information to a user via a data communication medium.

2. The aforementioned information processing device includes means for acquiring weather information, A means for generating multiple-choice questions for the user based on weather information, A means for sending multiple-choice questions to a user via a data communication medium and receiving responses from the user, The system according to claim 1, further comprising means for integrating and analyzing responses received from users with health data.

3. The aforementioned information processing device includes means for periodically acquiring health data from a smartwatch in conjunction with the user's smartphone, A means of sending health data to a cloud server via the internet, The system according to claim 1, further comprising means for collecting and analyzing data on a cloud server.

Images