System

Abstract

To provide a system for enabling a user to continuously receive individual support and advice, and for realizing continuous motivation maintenance and a healthy life style.SOLUTION: An input unit configured to input profile information of a user, a storage unit configured to store the input user information, a generation unit configured to generate an advice related to health or lifestyle based on the stored user information, a transmission unit configured to transmit the generated advice to a terminal of the user, a feedback input unit configured to input feedback information of the user, an analysis unit configured to store and analyze the feedback information, a generation unit configured to generate a new advice or an engagement message according to progress of the user based on an analysis result, and a transmission unit configured to transmit the generated new advice or message to the terminal of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, healthy lifestyle habits and efficient scheduling are highly sought after, but achieving them in busy daily lives presents a challenge. Many people need specific advice and support to maintain a healthy lifestyle, but providing personalized support tailored to individual needs is difficult. Furthermore, there is a lack of mechanisms to help users maintain their motivation, which hinders the achievement of health goals. The present invention aims to solve these challenges and provide support for users to live healthy and fulfilling lives. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means. First, the system includes an input means for a user to input profile information. The system includes a storage means for saving this information, and a generation means for generating advice on health and lifestyle habits based on the saved information. The system includes a transmission means for sending the generated advice to the user's terminal, and a feedback input means for inputting user feedback information. The system further includes an analysis means for saving and analyzing the feedback information, and a generation means for generating new advice and encouragement messages according to the user's progress based on the analysis results. Finally, the system includes a transmission means for sending the generated new advice and messages to the user's terminal. This system allows users to receive ongoing individual support and advice, enabling them to maintain sustained motivation and achieve a healthy lifestyle.

[0006] "User" refers to an individual who uses the system to manage and improve their lifestyle and health.

[0007] "Profile information" refers to information about a user's age, sex, weight, height, target weight, and lifestyle habits.

[0008] "Input means" refers to a function that allows a user to input their own profile information and feedback information.

[0009] "Storage means" refers to a function for storing input user information in a database or the like.

[0010] "Generation means" refers to a function that generates advice regarding health and lifestyle habits based on stored user information.

[0011] "Transmission means" refers to a function for transmitting the generated advice or message to the user's terminal.

[0012] "Feedback input means" refers to a function that allows a user to record their actual actions and their results.

[0013] "Analysis means" refers to a function for analyzing feedback information and evaluating the user's progress.

[0014] "Encouragement messages" refer to encouraging messages that maintain and increase the user's motivation.

[0015] "Terminal" refers to the device on which a user operates applications and receives advice and messages. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention relates to a system that uses a generative AI model to provide advice to users to solve their daily problems and issues. This system includes the following various means:

[0038] Collecting user-entered information

[0039] The device provides an interface that allows the user to enter their profile information on an initial setup screen. The user enters information such as age, gender, weight, height, target weight, lifestyle habits, etc. This information is sent from the device to the server.

[0040] Saving and initializing user information

[0041] The server stores the received user information in a database. The stored information includes initial parameters for assessing the user's health status, including calculations of BMI and basal metabolic rate. The server inputs these initial parameters into a generative AI model to generate initial advice for the user.

[0042] Generating and Sending Advice

[0043] The server generates advice such as healthy lifestyle habits, cooking recipes, and exercise plans. This advice is sent to the user as periodic notifications. For example, it provides a "weekly exercise plan" or "today's healthy recipe."

[0044] Collecting feedback on user activities

[0045] The device provides an interface for the user to take action based on the suggested advice and record the results within the app. The user inputs their diet and exercise history, and this information is then sent back to the server.

[0046] Progress data management and analysis

[0047] The server stores and analyzes the received feedback information. This analysis includes evaluating the user's progress, such as the pace of weight loss and exercise status. Based on the analysis results, new advice and encouragement messages are generated based on the user's progress.

[0048] Encouragement and new advice

[0049] The server then sends new advice and encouragement messages based on the progress data to the user's device, allowing the user to receive continuous personalized support and advice.

[0050] Specific examples

[0051] For example, if a user enters "I weigh 70 kg, my goal weight is 65 kg, and I want to exercise five times a week" during initial setup, the server will generate the following advice: jog for 20 minutes every morning and include vegetables in each meal. Furthermore, when the user follows the advice, records their actions, and enters their feedback, the server analyzes the data and generates an encouraging message such as "Great progress! Keep it up!" and sends it to the user's device.

[0052] This system allows users to maintain a healthy lifestyle and sustain their motivation. In this way, the present invention provides a method for effectively supporting users' lifestyle habits and health conditions.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] The user launches the app and enters their profile information (age, gender, weight, height, target weight, lifestyle habits, etc.) on the initial setup screen.

[0056] Step 2:

[0057] The device formats the entered profile information and sends it to the server.

[0058] Step 3:

[0059] The server stores the received user information in a database.

[0060] Step 4:

[0061] The server calculates initial parameters (BMI, basal metabolic rate, etc.) based on the stored user information.

[0062] Step 5:

[0063] The server inputs initial parameters into the generated AI model and generates initial advice for the user (health habits, diet, exercise plan, etc.).

[0064] Step 6:

[0065] The server transmits the generated advice to the user's terminal.

[0066] Step 7:

[0067] The user acts on the suggested advice and enters the results and feedback into the app.

[0068] Step 8:

[0069] The terminal formats the feedback information from the user and sends it to the server.

[0070] Step 9:

[0071] The server receives the feedback information and records it in a database.

[0072] Step 10:

[0073] The server analyzes the recorded feedback information and evaluates the user's progress (weight fluctuations, exercise performance, etc.).

[0074] Step 11:

[0075] The server generates new advice and / or encouragement messages based on the analysis results.

[0076] Step 12:

[0077] The server sends the generated new advice or message to the user's terminal.

[0078] Step 13:

[0079] The user receives new advice and encouragement messages and continues to act accordingly.

[0080] This process allows users to receive ongoing, personalized support and feedback.

[0081] Example 1

[0082] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0083] Conventional health management systems have struggled to effectively utilize user profile information and feedback information to provide individually optimized advice. They also lacked the functionality to generate appropriate encouragement or new advice in real time based on the user's progress. This made it difficult for users to maintain consistent motivation and obtain specific guidance for achieving their goals.

[0084] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0085] In this invention, the server includes input means for inputting user profile information, storage means for storing the input user information, generation means for generating advice regarding health and lifestyle habits based on the stored user information, transmission means for transmitting the generated advice to the user's terminal, feedback input means for inputting user feedback information, analysis means for storing and analyzing the feedback information, generation means for generating new advice and / or encouragement messages according to the user's progress based on the analysis results, transmission means for transmitting the generated new advice and / or messages to the user's terminal, and generation means for analyzing the feedback information using a generative AI model and generating advice based on prompt sentences. This makes it possible to provide advice and encouragement optimized for the user's individual situation in real time.

[0086] "Input means" refers to a device or function that provides an interface for users to input profile information and feedback information.

[0087] The "storage means" is a device or function for safely storing input user information and feedback information.

[0088] The "generation means" is a device or function for generating advice and encouragement messages regarding health and lifestyle habits based on stored user information, feedback information, and analysis results.

[0089] The "transmission means" is a device or function for transmitting the generated advice or message to the user's terminal.

[0090] The "feedback input means" is a device or function that provides an interface for users to input their activity results and progress.

[0091] "Analysis means" is a device or function for storing and analyzing feedback information.

[0092] A "generative AI model" is an artificial intelligence model used to generate optimal advice and encouragement messages based on user information, feedback information, and prompt text.

[0093] A "prompt" is a command entered into a generative AI model to generate specific advice or messages.

[0094] A "user terminal" is a computing device or mobile device that a user uses to enter information or receive advice or messages.

[0095] This invention relates to a system that uses generative AI models to provide advice to users to solve their daily problems and issues. This system is realized by linking a server and a terminal.

[0096] The device provides an interface that allows the user to input profile information on the initial setup screen. Input items include age, gender, weight, height, target weight, and lifestyle habits. The user inputs this information using the device's input means. Once input is complete, the device encrypts the information and sends it to the server.

[0097] The server stores the received user information in a MySQL database. The server then runs a script to calculate BMI and basal metabolic rate, and inputs the calculation results into the generative AI model as initial parameters. For example, a prompt such as "The user is a 35-year-old male, weighs 70 kg, and has a target weight of 65 kg. Please provide appropriate health advice" is input into the generative AI model. The generative AI model generates its initial advice based on this.

[0098] The generated advice is sent to the user's device using the server's transmission means. Specific advice might include "Jogging for 20 minutes every morning and including vegetables in every meal." This advice is displayed as a push notification on the device, allowing the user to check it.

[0099] The device provides a feedback input means for collecting the user's activity results and progress information. The user can input their daily diet and exercise record. For example, they can input information such as "I had a vegetable salad for breakfast and jogged for 20 minutes" and send it to the server.

[0100] The server then stores the feedback information in a MySQL database. The stored data is then analyzed using Python scripts. For example, the server evaluates the user's weight loss rate and exercise status, and generates new advice and encouragement messages based on the user's progress.

[0101] The newly generated advice and encouragement messages are then sent back to the user's device using the server's transmission means, including motivational messages such as "You're only 3kg away from your goal weight! Great progress!"

[0102] In this way, the present invention provides a method for supporting a user's healthy lifestyle and maintaining continuous motivation. Below are specific examples of prompt sentences to be input into the generative AI model.

[0103] Prompt Sentence Examples

[0104] "To support a healthy lifestyle, please generate exercise advice based on the following user information: age 35, gender male, current weight 70kg, goal weight 65kg, desire to exercise 5 times a week."

[0105] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0106] Step 1:

[0107] The terminal provides an interface for the user to input profile information. After the user has input information such as age, gender, weight, height, target weight, and lifestyle habits, the user clicks the "Submit" button. This input information is encrypted and sent from the terminal to the server. The input data includes the user's personal information and health goals. This information becomes the input data. The output is the data sent to the server.

[0108] Step 2:

[0109] The server stores the received user information in a MySQL database. This information includes the user's personal information and health goals. The server calculates BMI and basal metabolic rate based on the stored data. The calculation requires data such as the user's weight, height, age, and gender. The server generates a prompt statement that uses the calculation results as initial parameters and inputs them into the generative AI model. The input data is the user information obtained from the database, and the output is the prompt statement.

[0110] Step 3:

[0111] The server inputs a prompt sentence into the generative AI model. This prompt sentence includes the calculated initial parameters. For example, the prompt sentence might be something like, "The user is a 35-year-old male, weighs 70 kg, and has a target weight of 65 kg. Please provide appropriate health advice." The generative AI model generates advice based on this prompt sentence. The input data is the prompt sentence, and the output is the generated advice.

[0112] Step 4:

[0113] The server sends the generated advice to the user's device. This advice is provided as a concrete guide to action. For example, the advice might be, "Jogging for 20 minutes every morning and including vegetables in every meal." The input data is the generated advice, and the output is the data sent to the user's device.

[0114] Step 5:

[0115] The terminal provides a feedback interface for the user to input the results of their daily activities. The user inputs their dietary and exercise records. Once the input is complete, the user clicks the "Submit" button. The input information is encrypted and sent from the terminal to the server. The input data is the user's daily activity results, and the output is the data sent to the server.

[0116] Step 6:

[0117] The server stores the received feedback information in a MySQL database. It then uses a Python script to analyze the stored data. This analysis includes assessing the user's weight loss rate and exercise status. The input data is the feedback information obtained from the database, and the output is the analysis results.

[0118] Step 7:

[0119] The server generates new advice and / or encouragement messages based on the analysis results, such as "You're 3kg away from your goal weight! Great progress!" The input data are the analysis results, and the output is the generated advice and / or messages.

[0120] Step 8:

[0121] The server then sends the generated new advice and encouragement messages to the user's device, helping the user stay motivated and progress toward their health goals. The input data are the generated advice and messages, and the output is the data sent to the user's device.

[0122] (Application example 1)

[0123] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0124] In recent years, living a healthy lifestyle has become increasingly important, but many people find it difficult to maintain a balanced diet in their busy daily lives. Furthermore, appropriate advice and feedback based on the user's progress are essential for maintaining a healthy diet. However, no consistent system for achieving this has existed. In particular, there is a need for a system that can propose personalized meal menus based on the user's health status and collect feedback to reflect in future proposals.

[0125] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0126] In this invention, the server includes an input means for inputting a user's profile information, a storage means for saving the input user information, a generation means for generating health and lifestyle advice based on the saved user information, a transmission means for sending the generated advice to the user's device, a feedback input means for inputting the user's feedback information, an analysis means for saving and analyzing the feedback information, a generation means for generating new advice and encouragement messages based on the analysis results in accordance with the user's progress, a suggestion means for suggesting health-conscious meal menus through a food delivery service, a feedback collection means for collecting feedback on the suggested meal menus via the user's device, and a reflection means for analyzing the collected feedback information and reflecting it in subsequent advice. This allows users to receive meal suggestions based on their individual health status and goals, and feedback on those suggestions. Furthermore, by utilizing a generative AI model, optimal advice can be provided in real time, supporting the user's long-term health management.

[0127] definition statement

[0128] "User" refers to a person who uses the target system.

[0129] "Profile information" refers to personal data such as a user's age, gender, weight, height, lifestyle habits, and target weight.

[0130] "Input means" refers to the device or interface through which a user inputs profile information and feedback information.

[0131] "Storage means" refers to a device or system for storing input user information and feedback information.

[0132] "Generating means" refers to a device or system for generating advice based on stored user information.

[0133] The "transmission means" refers to a device or system for transmitting the generated advice or message to the user's terminal.

[0134] "Feedback input means" refers to a device or interface that allows a user to input the results of implementing advice and their opinions.

[0135] "Analysis means" refers to a device or system for analyzing input feedback information.

[0136] An "encouragement message" refers to a message intended to encourage the user.

[0137] "Proposal means" refers to a device or system for proposing healthy meal menus to users through food delivery services.

[0138] "Feedback collection means" refers to a device or interface that allows users to collect feedback and experience regarding the proposed meal menu.

[0139] "Reflection means" refers to a device or system that analyzes collected feedback information and uses it to generate next advice.

[0140] A "generative AI model" refers to artificial intelligence that creates advice and messages based on data.

[0141] MODE FOR CARRYING OUT THE INVENTION

[0142] The specific system of this invention is composed of a series of software modules that run on a smartphone application and a cloud server. The system configuration and processing content will be explained below.

[0143] System configuration

[0144] 1. User input and saving:

[0145] The device provides an interface that allows users to enter their profile information on an initial setup screen, including information such as age, gender, weight, height, lifestyle habits, and target weight. This information is sent from the device to a cloud server.

[0146] The server stores the received user information in a database, including initial parameters for assessing the user's health status, such as BMI and basal metabolic rate.

[0147] Advice generation and delivery

[0148] 2. Generation means:

[0149] The server uses the stored user information to generate healthy lifestyle and dietary advice, using generative AI models (e.g., GPT-3) to create personalized exercise plans and meal menus based on the user's profile.

[0150] The generated advice is then periodically sent to the user's smartphone, allowing the user to receive guidelines for consistently maintaining a healthy lifestyle.

[0151] Collecting and analyzing feedback

[0152] 3. Feedback Collection and Analysis:

[0153] The device provides an interface for the user to record the results of their actions based on the suggested advice, such as their diet and exercise record.

[0154] The server receives the collected feedback information and stores it in a database. This information is then analyzed to evaluate the user's progress, including the user's weight fluctuations and exercise performance.

[0155] New Advice and Encouragement

[0156] 4. Next advice generation:

[0157] The server generates new advice and encouragement messages based on feedback information and progress data, again using a generative AI model to provide optimal messages in real time.

[0158] For example, if a user is making good progress towards a goal, a message might be provided saying, "Great progress! Keep it up!"

[0159] Food delivery service

[0160] 5. Healthy meal menu suggestions:

[0161] As a suggestion method, the server proposes meal menus according to the user's health condition through a food delivery service, allowing users to easily incorporate healthy eating into their busy daily lives.

[0162] As a means of collecting feedback, feedback on the provided meal is collected via the user's terminal and reflected in the next advice.

[0163] Hardware and software used

[0164] Smartphone: Use an iOS or Android device.

[0165] Use cloud servers (e.g., AWS EC2, Google Cloud Platform) to store and process data.

[0166] Database: Use a system such as Amazon RDS or Firebase to store user data and feedback information.

[0167] Generative AI models: Use large-scale language models such as GPT-3 to generate advice and messages.

[0168] Examples and prompts:

[0169] The user launches the app and enters their profile information in response to the following prompts:

[0170] "Please tell us your age, gender, weight, height, goal weight and current lifestyle habits."

[0171] Example prompts to input to the generative AI model:

[0172] "User information: Age: 35, Gender: Male, Weight: 70kg, Height: 175cm, Target weight: 65kg, Lifestyle: Mostly desk work. Please suggest a weekly meal menu and exercise plan that is suitable for this user."

[0173] As a result, the present invention realizes health support that is individually customized for each user, and supports continuous health management.

[0174] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0175] Program processing flow

[0176] Step 1:

[0177] User enters profile information

[0178] Users launch the app on their smartphone and enter profile information such as age, gender, weight, height, lifestyle habits, and target weight on the initial setup screen.

[0179] Input: User's personal data (age, gender, weight, height, lifestyle habits, goal weight).

[0180] Output: The entered data is sent from the terminal to the server.

[0181] Step 2:

[0182] Saving user information

[0183] The server stores the user information received from the terminal in a database.

[0184] Input: User profile information sent from the device.

[0185] Output: The user information is stored in the database.

[0186] Step 3:

[0187] Calculating and saving initial parameters

[0188] The server calculates BMI and basal metabolic rate based on the saved user information and stores this in a database.

[0189] Input: Saved user profile information.

[0190] Specific operations: Calculate BMI (weight ÷ (height × height)) and basal metabolic rate.

[0191] Output: The calculated initial parameters are stored in a database.

[0192] Step 4:

[0193] Generating Advice

[0194] The server uses a generative AI model (e.g., GPT-3) to generate personalized advice based on the user's initial parameters.

[0195] Input: User information stored in the database and calculated initial parameters.

[0196] Example prompt: "User information: Age: 35, Gender: Male, Weight: 70kg, Height: 175cm, Target weight: 65kg, Lifestyle: Mostly desk work. Please suggest a weekly meal plan and exercise plan that is appropriate for this user."

[0197] Output: Healthy lifestyle and meal recommendations from a generative AI model.

[0198] Step 5:

[0199] Providing advice

[0200] The server transmits the generated advice to the user's terminal.

[0201] Input: The generated advice.

[0202] Output: The advice is sent to the user's device. Specific implementations include push notifications and in-app notifications.

[0203] Step 6:

[0204] Enter your feedback

[0205] Users take action based on the suggested advice and record the results within the app.

[0206] Input: Feedback information such as dietary details and exercise performance.

[0207] Output: Feedback information is sent from the device to the server.

[0208] Step 7:

[0209] Feedback storage and analysis

[0210] The server stores the feedback information received from the user in a database and analyzes it.

[0211] Input: User feedback information.

[0212] Specific actions: Analyze progress data such as weight fluctuations and exercise status.

[0213] Output: Generation of analytical results based on feedback.

[0214] Step 8:

[0215] Generate new advice and encouragement messages

[0216] The server generates new advice and encouragement messages based on the analysis results, using generative AI models to create personalized messages.

[0217] Input: Parsed feedback data.

[0218] Output: The newly generated advice or encouragement message.

[0219] Step 9:

[0220] Providing new advice and encouragement messages

[0221] The server sends the generated new advice or encouragement message to the user's terminal.

[0222] Input: The new advice or message that is generated.

[0223] Output: A new notification arrives on the user's device.

[0224] Step 10:

[0225] Food delivery service proposal

[0226] The server uses a food delivery service to suggest meal menus that suit the user's health condition.

[0227] Input: User health data and the generative AI model's recommendations.

[0228] Output: The meal menu is displayed on the user's device.

[0229] Step 11:

[0230] Gathering feedback on meal menus

[0231] The user inputs feedback on the proposed meal menu into the terminal and transmits it to the server.

[0232] Input: User's feedback information about the meal menu.

[0233] Output: Feedback information is sent from the device to the server.

[0234] Step 12:

[0235] Reflected in the next proposal

[0236] The server analyzes the collected feedback information and reflects it in the next advice.

[0237] Input: User feedback information.

[0238] Output: New proposals based on the analysis results.

[0239] The above steps realize a system that provides users with personalized health advice and meal suggestions, and supports continuous health management.

[0240] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0241] The present invention relates to a system that uses a generative AI model and an emotion engine to provide advice to users to solve their daily problems and issues, and also provides support according to the user's emotional state. This system is configured to include the following various means.

[0242] Collecting user-entered information

[0243] The device provides an interface that allows the user to enter their profile information on an initial setup screen. The user enters information such as age, gender, weight, height, target weight, lifestyle habits, etc. This information is sent from the device to the server.

[0244] Saving and initializing user information

[0245] The server stores the received user information in a database. The stored information includes initial parameters for assessing the user's health status, including calculations of BMI and basal metabolic rate. The server inputs these initial parameters into a generative AI model to generate initial advice for the user.

[0246] Generating and Sending Advice

[0247] The server generates advice such as healthy lifestyle habits, cooking recipes, and exercise plans. This advice is sent to the user as periodic notifications. For example, it provides a "weekly exercise plan" or "today's healthy recipe."

[0248] Emotional state recognition and analysis

[0249] The terminal provides an interface for inputting the user's emotional information. The user can input their emotional state (stress, joy, fatigue, etc.). The emotional information is transmitted from the terminal to the server.

[0250] Analysis using emotion engine

[0251] The server uses an emotion engine to analyze the user's emotion information and generate emotion data, which includes the user's current mood and emotional state.

[0252] Advice adjustment based on sentiment data

[0253] The server uses the generating means to tailor advice and encouragement messages based on the emotion data recognized by the emotion engine, for example, providing special advice including relaxation and coping techniques when the user is experiencing high stress.

[0254] Sending advice and encouragement messages

[0255] The server sends tailored advice and encouragement messages to the user's terminal.

[0256] Collecting feedback on user activities

[0257] The device provides an interface for the user to take action based on the suggested advice and record the results within the app. The user inputs their diet and exercise history, and this information is then sent back to the server.

[0258] Progress data management and analysis

[0259] The server stores and analyzes the received feedback information, including evaluating the user's progress (weight fluctuations, exercise record, etc.), and also includes emotional data collected by the emotion engine to generate more personalized advice.

[0260] Encouragement and new advice

[0261] The server sends new advice and encouragement messages generated based on the progress data and emotion data to the user's terminal.

[0262] Specific examples

[0263] For example, if a user enters "weight 70 kg, target weight 65 kg, and desires to exercise five times a week" during initial setup and indicates that they are currently experiencing high stress, the server will provide advice including relaxation methods and stress management, in addition to daily exercise plans and healthy eating advice. Furthermore, when the user follows the advice, records their actions, and enters their feedback, the server analyzes the data and generates an encouraging message such as "Great progress! Don't forget to relax!" and sends it to the user's device.

[0264] This system allows users to maintain a healthy lifestyle and receive emotional support while maintaining sustained motivation. In this way, the present invention provides a method for effectively supporting users' lifestyle habits, health status, and even emotional well-being.

[0265] The processing flow will be explained below.

[0266] Processing steps of a system that combines emotion engines

[0267] Step 1:

[0268] The user launches the app and enters their profile information (age, gender, weight, height, target weight, lifestyle habits, etc.) and current emotional state (stress level, mood, etc.) on the initial setup screen.

[0269] Step 2:

[0270] The device formats the entered profile information and emotion information and transmits it to the server.

[0271] Step 3:

[0272] The server stores the received user information in a database, along with emotional information.

[0273] Step 4:

[0274] The server calculates initial parameters (BMI, basal metabolic rate, stress level, etc.) based on the stored user information and emotional information.

[0275] Step 5:

[0276] The server inputs initial parameters into the generated AI model and generates initial advice for the user (health habits, diet, exercise plan, and stress management).

[0277] Step 6:

[0278] The server transmits the generated advice to the user's terminal.

[0279] Step 7:

[0280] The user acts on the suggested advice and inputs the results, feedback, and changes in emotional state into the app.

[0281] Step 8:

[0282] The terminal formats the feedback information and emotion information from the user and sends it to the server.

[0283] Step 9:

[0284] The server receives the feedback information and emotion information and records them in a database.

[0285] Step 10:

[0286] The server analyzes the recorded feedback and emotional information, including assessing the user's progress (weight fluctuations, exercise performance, stress levels, etc.).

[0287] Step 11:

[0288] The server generates new advice and reassurance messages based on the analysis results, such as suggesting relaxation techniques or counseling if high stress persists.

[0289] Step 12:

[0290] The server sends the generated new advice or message to the user's terminal.

[0291] Step 13:

[0292] The user receives new advice and encouragement messages and continues to act accordingly.

[0293] Specific examples

[0294] For example, if a user enters the following information during initial setup: "I weigh 70 kg, my goal weight is 65 kg, I want to exercise five times a week, and I'm currently experiencing high stress," the server will generate the following advice:

[0295] Jogging for 20 minutes every morning.

[0296] Include vegetables in every meal.

[0297] I've been under a lot of stress lately, so I'm making sure to set aside time for relaxation every night.

[0298] If the user follows the advice and enters the results into the app, providing feedback such as "I've jogged four days and added vegetables to my diet, but my stress level is still high," the server analyzes the data and generates the following encouragement message:

[0299] "Great progress! Keep jogging. Try deep breathing exercises and yoga to manage stress."

[0300] In this way, users receive ongoing, personalized support and feedback, helping them maintain a healthy lifestyle while also providing the right emotional support.

[0301] Example 2

[0302] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0303] Conventional health management systems are limited to providing advice based on user feedback and lack support that takes into account the user's emotional state. This makes it difficult to maintain health management and poses challenges for users in maintaining their motivation.

[0304] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means for inputting user profile information, a storage means for storing the input user information, a generation means for generating advice on health and lifestyle habits, a transmission means for transmitting the generated advice to the user's terminal, an emotion input means for inputting user emotion information, an emotion analysis means for analyzing the input emotion information, an adjustment means for adjusting advice and encouragement messages based on the emotion analysis results, a feedback input means for inputting user feedback information, an analysis means for storing and analyzing the feedback information, a generation means for generating new advice and encouragement messages according to the user's progress based on the analysis results, and a transmission means for transmitting the generated new advice and messages to the user's terminal. This enables personalized support according to the user's emotional state.

[0305] "Input means" refers to a device or function that provides an interface for users to input profile information, emotional information, etc. into the system.

[0306] "Storage means" refers to devices or functions for recording and storing input user information and feedback information in a database or the like.

[0307] "Generation means" refers to a device or function for creating advice and encouragement messages regarding health and lifestyle habits based on input data.

[0308] The "transmission means" refers to a device or function for transmitting the generated advice or encouragement message to the user's terminal.

[0309] "Emotion input means" refers to a device or function that provides an interface for a user to input their emotional state.

[0310] "Emotion analysis means" refers to a device or function for analyzing input emotion information and generating emotion data of the user.

[0311] The "adjustment means" refers to a device or function for adjusting the generated advice or encouragement message to suit the user's state based on the emotion analysis results.

[0312] The "feedback input means" refers to a device or function that provides an interface for a user to input the results of an action or feedback information.

[0313] The "analysis means" refers to a device or function for analyzing the input feedback information and evaluating the user's progress and goal achievement status.

[0314] The present invention relates to a system that uses a generative AI model and an emotion engine to provide advice to users to solve their daily problems and issues, and also provides support according to the user's emotional state. This system is configured to include the following various means.

[0315] Input Method

[0316] The terminal provides an interface for the user to input their own profile information and emotional information, allowing the user to input information such as age, sex, weight, height, target weight, lifestyle habits, stress, joy, fatigue, etc. This information is transmitted to the server by a transmission means, which will be described later.

[0317] Preservation means

[0318] The server stores the entered user information in a database, including initial parameters for assessing the user's health status, such as BMI and basal metabolic rate.

[0319] generation means

[0320] The server uses the generative AI model to generate advice such as healthy lifestyle habits, cooking recipes, and exercise plans based on the stored user information. For example, it generates a "weekly exercise plan" or "healthy recipe for the day." It also adjusts advice and encouragement messages based on emotional data.

[0321] Transmission method

[0322] The server includes means for transmitting the generated advice and / or encouragement messages to the user's terminal, so that the user can periodically receive appropriate advice.

[0323] Emotion input method

[0324] The terminal provides an interface for inputting the user's emotional information, which includes emotional states such as stress, joy, fatigue, etc. This information is also transmitted to the server by the transmission means.

[0325] Emotion analysis means

[0326] The server uses an emotion engine to analyze the input emotion information, thereby generating emotion data for the user, which includes the user's current mood and emotional state.

[0327] Adjustment means

[0328] The server tailors advice and encouragement messages based on the results of sentiment analysis. For example, if a user is experiencing high stress, it provides special advice including relaxation and coping techniques.

[0329] Feedback Input Method

[0330] The device provides an interface for the user to take action based on the suggested advice and record the results within the app. The user inputs their diet and exercise history, and this information is then sent back to the server.

[0331] Analysis means

[0332] The server stores and analyzes the received feedback information, including the user's progress (e.g., weight fluctuations, exercise history), as well as emotional data collected by the emotion engine, to generate more personalized advice.

[0333] For example, if a user uses the following prompt:

[0334] "I weigh 70kg, my goal weight is 65kg, and I want to exercise five times a week. I'm currently feeling stressed. I'd like some advice on a healthy diet and exercise plan, as well as relaxation techniques."

[0335] The system allows users to maintain a healthy lifestyle, receive emotional support and sustained motivation.

[0336] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0337] Step 1: Enter your user information

[0338] The terminal displays an interface for the user to input their profile information (age, sex, weight, height, target weight, lifestyle habits).

[0339] (Specific action) The user enters information into the form and clicks the "Submit" button.

[0340] Input: Profile information entered by the user.

[0341] Output: User profile information sent to the server.

[0342] Step 2: Submit user information

[0343] The terminal transmits the input user information to the server.

[0344] (Specific operation) The terminal packages user information in JSON format and sends the data to the server using an HTTP request.

[0345] Input: Profile information entered by the user.

[0346] Output: HTTP request for user profile information sent to the server.

[0347] Step 3: Storing user information and initial processing

[0348] The server stores the received user information in a database.

[0349] The server calculates initial parameters such as basal metabolic rate and BMI, and inputs them into a generative AI model to generate initial advice.

[0350] (Specific operation) The server saves the user information in the database using an INSERT query and calculates the BMI by "weight (kg) ÷ height (m)^2".

[0351] Input: User profile information.

[0352] Output: Initial parameters, generated initial advice.

[0353] Step 4: Generating Advice

[0354] The server uses a generative AI model to generate advice such as healthy lifestyle habits, cooking recipes, and exercise plans.

[0355] (Specific operation) The server inputs a prompt sentence into the generative AI model and obtains the generated advice.

[0356] Input: User information and initial parameters.

[0357] Output: The generated advice.

[0358] Step 5: Submitting Advice

[0359] The server transmits the generated advice to the user's terminal.

[0360] (Specific operation) The server sends a notification containing new advice to the terminal via an HTTP request.

[0361] Input: The generated advice.

[0362] Output: Notification to user terminal.

[0363] Step 6: Enter emotional information

[0364] The terminal provides an interface for the user to input emotional information (stress, joy, fatigue, etc.).

[0365] (Specific Action) The user selects an emotional state and clicks the "Send" button.

[0366] Input: A user-selected emotional state.

[0367] Output: Emotion information sent to the server.

[0368] Step 7: Sending Emotional Information

[0369] The terminal transmits the input emotion information to the server.

[0370] (Specific operation) The device packages the emotional information and sends the data to the server using an HTTP request.

[0371] Input: A user-selected emotional state.

[0372] Output: HTTP request for emotion information sent to the server.

[0373] Step 8: Emotional Analysis

[0374] The server analyzes the emotion information using an emotion engine and generates emotion data.

[0375] (Specific operation) The server inputs emotion information into the emotion engine and obtains the analysis results.

[0376] Input: Emotion information.

[0377] Output: The generated emotion data.

[0378] Step 9: Adjusting Advice

[0379] The server tailors advice and encouragement messages based on the emotion data.

[0380] (Specific operation) The server inputs emotional data into the generative AI model and obtains adjusted advice.

[0381] Input: Generated emotion data.

[0382] Output: Tailored advice and / or encouragement messages.

[0383] Step 10: Sending tailored advice

[0384] The server sends tailored advice and encouragement messages to the user's terminal.

[0385] (Specific operation) The server sends a notification to the terminal via an HTTP request.

[0386] Input: Tailored advice and / or encouragement messages.

[0387] Output: Notification to user terminal.

[0388] Step 11: Enter your feedback information

[0389] The terminal provides an interface for the user to take action based on the suggested advice and record the results.

[0390] (Specific action) The user enters detailed feedback and clicks the "Submit" button.

[0391] Input: The result of the user's action.

[0392] Output: Feedback information sent to the server.

[0393] Step 12: Submit your feedback

[0394] The terminal transmits the input feedback information to the server.

[0395] (Specific operation) The terminal packages the feedback information and sends it to the server via an HTTP request.

[0396] Input: Feedback information entered by the user.

[0397] Output: Feedback information sent to the server.

[0398] Step 13: Storing and analyzing feedback information

[0399] The server stores the received feedback information in a database and analyzes it.

[0400] (Specific operation) The server retrieves past feedback data via a query and applies an analysis algorithm.

[0401] Input: User feedback information.

[0402] Output: Analysis results and evaluation data.

[0403] Step 14: Generate new advice

[0404] The server uses a generative AI model to generate new advice and encouragement messages based on the progress data and emotion data.

[0405] (Specific operation) The server inputs the analysis results into the generated AI model and obtains new advice.

[0406] Input: Analysis results and evaluation data.

[0407] Output: New advice or encouragement message.

[0408] Step 15: Submitting a new advice

[0409] The server sends the generated new advice or encouragement message to the user's terminal.

[0410] (Specific operation) The server sends a notification containing new advice to the terminal via an HTTP request.

[0411] Input: A new advice or encouragement message.

[0412] Output: Notification to user terminal.

[0413] The above is the specific flow of the processing of this system. These steps provide personalized support tailored to the user's health and emotional state.

[0414] (Application example 2)

[0415] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0416] Conventional health management and emotional support systems lack personalized support that responds to a user's individual emotional state and in-store behavior. This makes it difficult to provide effective support tailored to a user's specific health goals and emotional state. Furthermore, in-store shopping experiences lack personalized product recommendations that meet the user's needs.

[0417] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0418] In this invention, the server includes input means for inputting user profile information, storage means for storing the input user information, generation means for generating advice on health and lifestyle habits based on the stored user information, transmission means for transmitting the generated advice to the user's device, emotion input means for inputting the user's emotional state, emotion analysis means for analyzing the input emotional information, generation means for adjusting the advice based on the emotion analysis result, generation means for generating product recommendations corresponding to the user's behavior in the store, transmission means for transmitting the generated advice and product recommendations to the user's device, feedback input means for inputting user feedback information, analysis means for storing and analyzing the feedback information, generation means for generating new advice and encouragement messages corresponding to the user's progress based on the analysis result, and transmission means for transmitting the generated new advice and messages to the user's device. This enables personalized health management and emotional support and an improved shopping experience corresponding to the user's individual emotional state and behavior in the physical store.

[0419] "User profile information" refers to information that a user inputs into the system, such as their age, sex, height, weight, target weight, lifestyle habits, etc.

[0420] "Input means" refers to an interface through which a user inputs profile information and emotional state into the system.

[0421] The "storage means" is a function for storing input user information, feedback information, etc. in a database.

[0422] The "generation means" is a function for generating advice on health and lifestyle habits based on stored user information and emotional information, and product recommendations corresponding to in-store behavior.

[0423] The "transmission means" is a function for transmitting the generated advice, encouragement message, and product recommendation to the user's terminal.

[0424] The "emotion input means" is an interface that allows the user to input their own emotional state (stress, joy, fatigue, etc.).

[0425] The "emotion analysis means" is a function for analyzing the user's emotional state based on input emotion information.

[0426] "Product recommendations" refer to products recommended for purchase in-store based on a user's health goals and current emotional state.

[0427] The "feedback input means" is an interface for the user to input the actions taken based on the proposed advice and the results thereof.

[0428] The "analysis means" is a function for performing analysis based on the saved feedback information and evaluating the user's progress.

[0429] "Encouragement messages" are encouraging messages that are generated based on the user's progress and emotional state.

[0430] This invention relates to a system that uses a generative AI model and an emotion engine to provide advice to users to solve their daily problems and issues, and also provides support according to the user's emotional state. This system is configured to include the following multiple means.

[0431] Collecting user-entered information

[0432] The device provides an interface that allows the user to enter their profile information on the initial setup screen. The user enters information such as age, gender, weight, height, lifestyle habits, and target weight. This information is sent from the device to the server.

[0433] Saving and initializing user information

[0434] The server stores the received user information in a database. The stored information includes initial parameters for assessing the user's health status, including calculations of BMI and basal metabolic rate. The server inputs these initial parameters into a generative AI model to generate initial advice for the user.

[0435] Generating and Sending Advice

[0436] The server generates advice such as healthy lifestyle habits, cooking recipes, and exercise plans. This advice is sent to the user as periodic notifications. For example, it provides a "weekly exercise plan" or "today's healthy recipe."

[0437] Emotional state recognition and analysis

[0438] The terminal provides an interface for inputting the user's emotional information. The user can input their emotional state (stress, joy, fatigue, etc.). The emotional information is transmitted from the terminal to the server.

[0439] Analysis using emotion engine

[0440] The server uses an emotion engine to analyze the user's emotion information and generate emotion data, which includes the user's current mood and emotional state.

[0441] Advice adjustment based on sentiment data

[0442] The server uses the generating means to tailor advice and encouragement messages based on the emotion data recognized by the emotion engine, for example, providing special advice including relaxation and coping techniques when the user is experiencing high stress.

[0443] In-store product recommendations

[0444] The server generates product recommendations based on the user's profile information and emotional information, corresponding to their behavior in the physical store. For example, if a user wants a low-calorie meal, the server will recommend low-calorie products.

[0445] Sending advice and encouragement messages

[0446] The server sends tailored advice and encouragement messages and product recommendations to the user's terminal.

[0447] Collecting feedback on user activities

[0448] The device provides an interface for the user to take action based on the suggested advice and record the results within the app. The user inputs their diet and exercise history, and this information is then sent back to the server.

[0449] Progress data management and analysis

[0450] The server stores and analyzes the received feedback information, including evaluating the user's progress (weight fluctuations, exercise record, etc.), and includes emotional data collected by the emotion engine to generate more personalized advice.

[0451] Encouragement and new advice

[0452] The server sends new advice and encouragement messages generated based on the progress data and emotion data to the user's terminal.

[0453] Specific examples

[0454] For example, if a user enters "weight 70 kg, target weight 65 kg, and desires to exercise five times a week" during initial setup and indicates that they are currently experiencing high levels of stress, the server will provide advice including health plans and relaxation methods.Product recommendations based on in-store behavior can suggest directions to shelves with low-calorie foods and relaxation goods.

[0455] Prompt Sentence Examples

[0456] "A user comes to your store feeling very stressed. His health goal is to lose 5kg. Please recommend some products."

[0457] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0458] Step 1: Enter your user information

[0459] The terminal provides an interface on the initial setup screen where the user can enter their profile information (age, gender, weight, height, target weight, lifestyle habits). The user enters this information into the terminal and presses the send button.

[0460] Input: Age, gender, weight, height, target weight, lifestyle habits

[0461] Output: User profile information

[0462] Step 2: Save user information

[0463] The server receives the user profile information sent from the terminal and stores this information in a database using a storage means.

[0464] Input: User profile information

[0465] Output: User information stored in the database

[0466] Step 3: Generate initial advice

[0467] Based on the stored user profile information, the server inputs initial parameters such as BMI and basal metabolic rate into the generative AI model and generates initial health advice for the user.

[0468] Input: User profile information

[0469] Data processing: BMI, basal metabolic rate calculation

[0470] Output: First health advice

[0471] Step 4: Submitting Advice

[0472] The server transmits the generated initial health advice to the user's terminal using the transmission means, and the user receives the advice on the terminal.

[0473] Input: First Health Advice

[0474] Output: Advice sent to the user's terminal

[0475] Step 5: Enter your emotional state

[0476] The terminal provides an interface for the user to input their emotional state (stress, joy, fatigue, etc.) The user inputs the emotional state and transmits it from the terminal to the server.

[0477] Input: Emotional state (stress, joy, fatigue, etc.)

[0478] Output: Emotion information sent to the server

[0479] Step 6: Analyze emotional state

[0480] The server uses an emotion engine to analyze the received emotion information and generate emotion data, which includes the user's current mood and emotional state.

[0481] Input: Emotion information

[0482] Data processing: Sentiment analysis

[0483] Output: Emotion data

[0484] Step 7: Adjusting advice based on sentiment data

[0485] The server uses a generating means to adjust existing advice based on the emotion data and generate new advice and / or encouragement messages.

[0486] Input: Emotion data

[0487] Data calculation:Advice adjustment

[0488] Output: New advice and encouragement messages

[0489] Step 8: Recommend products in-store

[0490] The server generates product recommendations based on the user's profile information and emotional information, corresponding to their behavior in the physical store. For example, if the user wants to eat low-calorie meals, the server will recommend low-calorie products.

[0491] Input: User profile information, emotion information

[0492] Data Computing: Product Recommendation Generation

[0493] Output: Product recommendation

[0494] Step 9: Submitting advice and recommendations

[0495] The server sends tailored advice and product recommendations to the user's device, who receives this information in the store and uses it as a guide for shopping.

[0496] Input: New advice, encouragement message, product recommendation

[0497] Output: Advice and product recommendations sent to the user device

[0498] Step 10: Enter your feedback information

[0499] The terminal provides an interface for the user to take action based on the proposed advice and input the results. The user inputs the details of their meals and exercise history and sends them to the server.

[0500] Input: Meal details, exercise history

[0501] Output: Feedback information sent to the server

[0502] Step 11: Analyze feedback

[0503] The server stores and analyzes the received feedback information, including the user's progress (weight fluctuations, exercise performance, etc.).

[0504] Input: Feedback information

[0505] Data Calculation: Feedback Analysis

[0506] Output: Analysis results

[0507] Step 12: Generate and send new advice

[0508] The server sends new advice and encouragement messages based on the progress data and emotion data to the user's device, and the user receives support to maintain motivation through the device.

[0509] Input: Progress data, emotion data

[0510] Output: New advice and encouragement messages

[0511] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0512] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0513] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0514] [Second embodiment]

[0515] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0516] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0517] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0518] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0519] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0520] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0521] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0522] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0523] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0524] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0525] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0526] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0527] The present invention relates to a system that uses a generative AI model to provide advice to users to solve their daily problems and issues. This system includes the following various means:

[0528] Collecting user-entered information

[0529] The device provides an interface that allows the user to enter their profile information on an initial setup screen. The user enters information such as age, gender, weight, height, target weight, lifestyle habits, etc. This information is sent from the device to the server.

[0530] Saving and initializing user information

[0531] The server stores the received user information in a database. The stored information includes initial parameters for assessing the user's health status, including calculations of BMI and basal metabolic rate. The server inputs these initial parameters into a generative AI model to generate initial advice for the user.

[0532] Generating and Sending Advice

[0533] The server generates advice such as healthy lifestyle habits, cooking recipes, and exercise plans. This advice is sent to the user as periodic notifications. For example, it provides a "weekly exercise plan" or "today's healthy recipe."

[0534] Collecting feedback on user activities

[0535] The device provides an interface for the user to take action based on the suggested advice and record the results within the app. The user inputs their diet and exercise history, and this information is then sent back to the server.

[0536] Progress data management and analysis

[0537] The server stores and analyzes the received feedback information. This analysis includes evaluating the user's progress, such as the pace of weight loss and exercise status. Based on the analysis results, new advice and encouragement messages are generated based on the user's progress.

[0538] Encouragement and new advice

[0539] The server then sends new advice and encouragement messages based on the progress data to the user's device, allowing the user to receive continuous personalized support and advice.

[0540] Specific examples

[0541] For example, if a user enters "I weigh 70 kg, my goal weight is 65 kg, and I want to exercise five times a week" during initial setup, the server will generate the following advice: jog for 20 minutes every morning and include vegetables in each meal. Furthermore, when the user follows the advice, records their actions, and enters their feedback, the server analyzes the data and generates an encouraging message such as "Great progress! Keep it up!" and sends it to the user's device.

[0542] This system allows users to maintain a healthy lifestyle and sustain their motivation. In this way, the present invention provides a method for effectively supporting users' lifestyle habits and health conditions.

[0543] The processing flow will be explained below.

[0544] Step 1:

[0545] The user launches the app and enters their profile information (age, gender, weight, height, target weight, lifestyle habits, etc.) on the initial setup screen.

[0546] Step 2:

[0547] The device formats the entered profile information and sends it to the server.

[0548] Step 3:

[0549] The server stores the received user information in a database.

[0550] Step 4:

[0551] The server calculates initial parameters (BMI, basal metabolic rate, etc.) based on the stored user information.

[0552] Step 5:

[0553] The server inputs initial parameters into the generated AI model and generates initial advice for the user (health habits, diet, exercise plan, etc.).

[0554] Step 6:

[0555] The server transmits the generated advice to the user's terminal.

[0556] Step 7:

[0557] The user acts on the suggested advice and enters the results and feedback into the app.

[0558] Step 8:

[0559] The terminal formats the feedback information from the user and sends it to the server.

[0560] Step 9:

[0561] The server receives the feedback information and records it in a database.

[0562] Step 10:

[0563] The server analyzes the recorded feedback information and evaluates the user's progress (weight fluctuations, exercise performance, etc.).

[0564] Step 11:

[0565] The server generates new advice and / or encouragement messages based on the analysis results.

[0566] Step 12:

[0567] The server sends the generated new advice or message to the user's terminal.

[0568] Step 13:

[0569] The user receives new advice and encouragement messages and continues to act accordingly.

[0570] This process allows users to receive ongoing, personalized support and feedback.

[0571] Example 1

[0572] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0573] Conventional health management systems have struggled to effectively utilize user profile information and feedback information to provide individually optimized advice. They also lacked the functionality to generate appropriate encouragement or new advice in real time based on the user's progress. This made it difficult for users to maintain consistent motivation and obtain specific guidance for achieving their goals.

[0574] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0575] In this invention, the server includes input means for inputting user profile information, storage means for storing the input user information, generation means for generating advice regarding health and lifestyle habits based on the stored user information, transmission means for transmitting the generated advice to the user's terminal, feedback input means for inputting user feedback information, analysis means for storing and analyzing the feedback information, generation means for generating new advice and / or encouragement messages according to the user's progress based on the analysis results, transmission means for transmitting the generated new advice and / or messages to the user's terminal, and generation means for analyzing the feedback information using a generative AI model and generating advice based on prompt sentences. This makes it possible to provide advice and encouragement optimized for the user's individual situation in real time.

[0576] "Input means" refers to a device or function that provides an interface for users to input profile information and feedback information.

[0577] The "storage means" is a device or function for safely storing input user information and feedback information.

[0578] The "generation means" is a device or function for generating advice and encouragement messages regarding health and lifestyle habits based on stored user information, feedback information, and analysis results.

[0579] The "transmission means" is a device or function for transmitting the generated advice or message to the user's terminal.

[0580] The "feedback input means" is a device or function that provides an interface for users to input their activity results and progress.

[0581] "Analysis means" is a device or function for storing and analyzing feedback information.

[0582] A "generative AI model" is an artificial intelligence model used to generate optimal advice and encouragement messages based on user information, feedback information, and prompt text.

[0583] A "prompt" is a command entered into a generative AI model to generate specific advice or messages.

[0584] A "user terminal" is a computing device or mobile device that a user uses to enter information or receive advice or messages.

[0585] This invention relates to a system that uses generative AI models to provide advice to users to solve their daily problems and issues. This system is realized by linking a server and a terminal.

[0586] The device provides an interface that allows the user to input profile information on the initial setup screen. Input items include age, gender, weight, height, target weight, and lifestyle habits. The user inputs this information using the device's input means. Once input is complete, the device encrypts the information and sends it to the server.

[0587] The server stores the received user information in a MySQL database. The server then runs a script to calculate BMI and basal metabolic rate, and inputs the calculation results into the generative AI model as initial parameters. For example, a prompt such as "The user is a 35-year-old male, weighs 70 kg, and has a target weight of 65 kg. Please provide appropriate health advice" is input into the generative AI model. The generative AI model generates its initial advice based on this.

[0588] The generated advice is sent to the user's device using the server's transmission means. Specific advice might include "Jogging for 20 minutes every morning and including vegetables in every meal." This advice is displayed as a push notification on the device, allowing the user to check it.

[0589] The device provides a feedback input means for collecting the user's activity results and progress information. The user can input their daily diet and exercise record. For example, they can input information such as "I had a vegetable salad for breakfast and jogged for 20 minutes" and send it to the server.

[0590] The server then stores the feedback information in a MySQL database. The stored data is then analyzed using Python scripts. For example, the server evaluates the user's weight loss rate and exercise status, and generates new advice and encouragement messages based on the user's progress.

[0591] The newly generated advice and encouragement messages are then sent back to the user's device using the server's transmission means, including motivational messages such as "You're only 3kg away from your goal weight! Great progress!"

[0592] In this way, the present invention provides a method for supporting a user's healthy lifestyle and maintaining continuous motivation. Below are specific examples of prompt sentences to be input into the generative AI model.

[0593] Prompt Sentence Examples

[0594] "To support a healthy lifestyle, please generate exercise advice based on the following user information: age 35, gender male, current weight 70kg, goal weight 65kg, desire to exercise 5 times a week."

[0595] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0596] Step 1:

[0597] The terminal provides an interface for the user to input profile information. After the user has input information such as age, gender, weight, height, target weight, and lifestyle habits, the user clicks the "Submit" button. This input information is encrypted and sent from the terminal to the server. The input data includes the user's personal information and health goals. This information becomes the input data. The output is the data sent to the server.

[0598] Step 2:

[0599] The server stores the received user information in a MySQL database. This information includes the user's personal information and health goals. The server calculates BMI and basal metabolic rate based on the stored data. The calculation requires data such as the user's weight, height, age, and gender. The server generates a prompt statement that uses the calculation results as initial parameters and inputs them into the generative AI model. The input data is the user information obtained from the database, and the output is the prompt statement.

[0600] Step 3:

[0601] The server inputs a prompt sentence into the generative AI model. This prompt sentence includes the calculated initial parameters. For example, the prompt sentence might be something like, "The user is a 35-year-old male, weighs 70 kg, and has a target weight of 65 kg. Please provide appropriate health advice." The generative AI model generates advice based on this prompt sentence. The input data is the prompt sentence, and the output is the generated advice.

[0602] Step 4:

[0603] The server sends the generated advice to the user's device. This advice is provided as a concrete guide to action. For example, the advice might be, "Jogging for 20 minutes every morning and including vegetables in every meal." The input data is the generated advice, and the output is the data sent to the user's device.

[0604] Step 5:

[0605] The terminal provides a feedback interface for the user to input the results of their daily activities. The user inputs their dietary and exercise records. Once the input is complete, the user clicks the "Submit" button. The input information is encrypted and sent from the terminal to the server. The input data is the user's daily activity results, and the output is the data sent to the server.

[0606] Step 6:

[0607] The server stores the received feedback information in a MySQL database. It then uses a Python script to analyze the stored data. This analysis includes assessing the user's weight loss rate and exercise status. The input data is the feedback information obtained from the database, and the output is the analysis results.

[0608] Step 7:

[0609] The server generates new advice and / or encouragement messages based on the analysis results, such as "You're 3kg away from your goal weight! Great progress!" The input data are the analysis results, and the output is the generated advice and / or messages.

[0610] Step 8:

[0611] The server then sends the generated new advice and encouragement messages to the user's device, helping the user stay motivated and progress toward their health goals. The input data are the generated advice and messages, and the output is the data sent to the user's device.

[0612] (Application example 1)

[0613] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0614] In recent years, living a healthy lifestyle has become increasingly important, but many people find it difficult to maintain a balanced diet in their busy daily lives. Furthermore, appropriate advice and feedback based on the user's progress are essential for maintaining a healthy diet. However, no consistent system for achieving this has existed. In particular, there is a need for a system that can propose personalized meal menus based on the user's health status and collect feedback to reflect in future proposals.

[0615] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0616] In this invention, the server includes an input means for inputting a user's profile information, a storage means for saving the input user information, a generation means for generating health and lifestyle advice based on the saved user information, a transmission means for sending the generated advice to the user's device, a feedback input means for inputting the user's feedback information, an analysis means for saving and analyzing the feedback information, a generation means for generating new advice and encouragement messages based on the analysis results in accordance with the user's progress, a suggestion means for suggesting health-conscious meal menus through a food delivery service, a feedback collection means for collecting feedback on the suggested meal menus via the user's device, and a reflection means for analyzing the collected feedback information and reflecting it in subsequent advice. This allows users to receive meal suggestions based on their individual health status and goals, and feedback on those suggestions. Furthermore, by utilizing a generative AI model, optimal advice can be provided in real time, supporting the user's long-term health management.

[0617] definition statement

[0618] "User" refers to a person who uses the target system.

[0619] "Profile information" refers to personal data such as a user's age, gender, weight, height, lifestyle habits, and target weight.

[0620] "Input means" refers to the device or interface through which a user inputs profile information and feedback information.

[0621] "Storage means" refers to a device or system for storing input user information and feedback information.

[0622] "Generating means" refers to a device or system for generating advice based on stored user information.

[0623] The "transmission means" refers to a device or system for transmitting the generated advice or message to the user's terminal.

[0624] "Feedback input means" refers to a device or interface that allows a user to input the results of implementing advice and their opinions.

[0625] "Analysis means" refers to a device or system for analyzing input feedback information.

[0626] An "encouragement message" refers to a message intended to encourage the user.

[0627] "Proposal means" refers to a device or system for proposing healthy meal menus to users through food delivery services.

[0628] "Feedback collection means" refers to a device or interface that allows users to collect feedback and experience regarding the proposed meal menu.

[0629] "Reflection means" refers to a device or system that analyzes collected feedback information and uses it to generate next advice.

[0630] A "generative AI model" refers to artificial intelligence that creates advice and messages based on data.

[0631] MODE FOR CARRYING OUT THE INVENTION

[0632] The specific system of this invention is composed of a series of software modules that run on a smartphone application and a cloud server. The system configuration and processing content will be explained below.

[0633] System configuration

[0634] 1. User input and saving:

[0635] The device provides an interface that allows users to enter their profile information on an initial setup screen, including information such as age, gender, weight, height, lifestyle habits, and target weight. This information is sent from the device to a cloud server.

[0636] The server stores the received user information in a database, including initial parameters for assessing the user's health status, such as BMI and basal metabolic rate.

[0637] Advice generation and delivery

[0638] 2. Generation means:

[0639] The server uses the stored user information to generate healthy lifestyle and dietary advice, using generative AI models (e.g., GPT-3) to create personalized exercise plans and meal menus based on the user's profile.

[0640] The generated advice is then periodically sent to the user's smartphone, allowing the user to receive guidelines for consistently maintaining a healthy lifestyle.

[0641] Collecting and analyzing feedback

[0642] 3. Feedback Collection and Analysis:

[0643] The device provides an interface for the user to record the results of their actions based on the suggested advice, such as their diet and exercise record.

[0644] The server receives the collected feedback information and stores it in a database. This information is then analyzed to evaluate the user's progress, including the user's weight fluctuations and exercise performance.

[0645] New Advice and Encouragement

[0646] 4. Next advice generation:

[0647] The server generates new advice and encouragement messages based on feedback information and progress data, again using a generative AI model to provide optimal messages in real time.

[0648] For example, if a user is making good progress towards a goal, a message might be provided saying, "Great progress! Keep it up!"

[0649] Food delivery service

[0650] 5. Healthy meal menu suggestions:

[0651] As a suggestion method, the server proposes meal menus according to the user's health condition through a food delivery service, allowing users to easily incorporate healthy eating into their busy daily lives.

[0652] As a means of collecting feedback, feedback on the provided meal is collected via the user's terminal and reflected in the next advice.

[0653] Hardware and software used

[0654] Smartphone: Use an iOS or Android device.

[0655] Use cloud servers (e.g., AWS EC2, Google Cloud Platform) to store and process data.

[0656] Database: Use a system such as Amazon RDS or Firebase to store user data and feedback information.

[0657] Generative AI models: Use large-scale language models such as GPT-3 to generate advice and messages.

[0658] Examples and prompts:

[0659] The user launches the app and enters their profile information in response to the following prompts:

[0660] "Please tell us your age, gender, weight, height, goal weight and current lifestyle habits."

[0661] Example prompts to input to the generative AI model:

[0662] "User information: Age: 35, Gender: Male, Weight: 70kg, Height: 175cm, Target weight: 65kg, Lifestyle: Mostly desk work. Please suggest a weekly meal menu and exercise plan that is suitable for this user."

[0663] As a result, the present invention realizes health support that is individually customized for each user, and supports continuous health management.

[0664] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0665] Program processing flow

[0666] Step 1:

[0667] User enters profile information

[0668] Users launch the app on their smartphone and enter profile information such as age, gender, weight, height, lifestyle habits, and target weight on the initial setup screen.

[0669] Input: User's personal data (age, gender, weight, height, lifestyle habits, goal weight).

[0670] Output: The entered data is sent from the terminal to the server.

[0671] Step 2:

[0672] Saving user information

[0673] The server stores the user information received from the terminal in a database.

[0674] Input: User profile information sent from the device.

[0675] Output: The user information is stored in the database.

[0676] Step 3:

[0677] Calculating and saving initial parameters

[0678] The server calculates BMI and basal metabolic rate based on the saved user information and stores this in a database.

[0679] Input: Saved user profile information.

[0680] Specific operations: Calculate BMI (weight ÷ (height × height)) and basal metabolic rate.

[0681] Output: The calculated initial parameters are stored in a database.

[0682] Step 4:

[0683] Generating Advice

[0684] The server uses a generative AI model (e.g., GPT-3) to generate personalized advice based on the user's initial parameters.

[0685] Input: User information stored in the database and calculated initial parameters.

[0686] Example prompt: "User information: Age: 35, Gender: Male, Weight: 70kg, Height: 175cm, Target weight: 65kg, Lifestyle: Mostly desk work. Please suggest a weekly meal plan and exercise plan that is appropriate for this user."

[0687] Output: Healthy lifestyle and meal recommendations from a generative AI model.

[0688] Step 5:

[0689] Providing advice

[0690] The server transmits the generated advice to the user's terminal.

[0691] Input: The generated advice.

[0692] Output: The advice is sent to the user's device. Specific implementations include push notifications and in-app notifications.

[0693] Step 6:

[0694] Enter your feedback

[0695] Users take action based on the suggested advice and record the results within the app.

[0696] Input: Feedback information such as dietary details and exercise performance.

[0697] Output: Feedback information is sent from the device to the server.

[0698] Step 7:

[0699] Feedback storage and analysis

[0700] The server stores the feedback information received from the user in a database and analyzes it.

[0701] Input: User feedback information.

[0702] Specific actions: Analyze progress data such as weight fluctuations and exercise status.

[0703] Output: Generation of analytical results based on feedback.

[0704] Step 8:

[0705] Generate new advice and encouragement messages

[0706] The server generates new advice and encouragement messages based on the analysis results, using generative AI models to create personalized messages.

[0707] Input: Parsed feedback data.

[0708] Output: The newly generated advice or encouragement message.

[0709] Step 9:

[0710] Providing new advice and encouragement messages

[0711] The server sends the generated new advice or encouragement message to the user's terminal.

[0712] Input: The new advice or message that is generated.

[0713] Output: A new notification arrives on the user's device.

[0714] Step 10:

[0715] Food delivery service proposal

[0716] The server uses a food delivery service to suggest meal menus that suit the user's health condition.

[0717] Input: User health data and the generative AI model's recommendations.

[0718] Output: The meal menu is displayed on the user's device.

[0719] Step 11:

[0720] Gathering feedback on meal menus

[0721] The user inputs feedback on the proposed meal menu into the terminal and transmits it to the server.

[0722] Input: User's feedback information about the meal menu.

[0723] Output: Feedback information is sent from the device to the server.

[0724] Step 12:

[0725] Reflected in the next proposal

[0726] The server analyzes the collected feedback information and reflects it in the next advice.

[0727] Input: User feedback information.

[0728] Output: New proposals based on the analysis results.

[0729] The above steps realize a system that provides users with personalized health advice and meal suggestions, and supports continuous health management.

[0730] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0731] The present invention relates to a system that uses a generative AI model and an emotion engine to provide advice to users to solve their daily problems and issues, and also provides support according to the user's emotional state. This system is configured to include the following various means.

[0732] Collecting user-entered information

[0733] The device provides an interface that allows the user to enter their profile information on an initial setup screen. The user enters information such as age, gender, weight, height, target weight, lifestyle habits, etc. This information is sent from the device to the server.

[0734] Saving and initializing user information

[0735] The server stores the received user information in a database. The stored information includes initial parameters for assessing the user's health status, including calculations of BMI and basal metabolic rate. The server inputs these initial parameters into a generative AI model to generate initial advice for the user.

[0736] Generating and Sending Advice

[0737] The server generates advice such as healthy lifestyle habits, cooking recipes, and exercise plans. This advice is sent to the user as periodic notifications. For example, it provides a "weekly exercise plan" or "today's healthy recipe."

[0738] Emotional state recognition and analysis

[0739] The terminal provides an interface for inputting the user's emotional information. The user can input their emotional state (stress, joy, fatigue, etc.). The emotional information is transmitted from the terminal to the server.

[0740] Analysis using emotion engine

[0741] The server uses an emotion engine to analyze the user's emotion information and generate emotion data, which includes the user's current mood and emotional state.

[0742] Advice adjustment based on sentiment data

[0743] The server uses the generating means to tailor advice and encouragement messages based on the emotion data recognized by the emotion engine, for example, providing special advice including relaxation and coping techniques when the user is experiencing high stress.

[0744] Sending advice and encouragement messages

[0745] The server sends tailored advice and encouragement messages to the user's terminal.

[0746] Collecting feedback on user activities

[0747] The device provides an interface for the user to take action based on the suggested advice and record the results within the app. The user inputs their diet and exercise history, and this information is then sent back to the server.

[0748] Progress data management and analysis

[0749] The server stores and analyzes the received feedback information, including evaluating the user's progress (weight fluctuations, exercise record, etc.), and also includes emotional data collected by the emotion engine to generate more personalized advice.

[0750] Encouragement and new advice

[0751] The server sends new advice and encouragement messages generated based on the progress data and emotion data to the user's terminal.

[0752] Specific examples

[0753] For example, if a user enters "weight 70 kg, target weight 65 kg, and desires to exercise five times a week" during initial setup and indicates that they are currently experiencing high stress, the server will provide advice including relaxation methods and stress management, in addition to daily exercise plans and healthy eating advice. Furthermore, when the user follows the advice, records their actions, and enters their feedback, the server analyzes the data and generates an encouraging message such as "Great progress! Don't forget to relax!" and sends it to the user's device.

[0754] This system allows users to maintain a healthy lifestyle and receive emotional support while maintaining sustained motivation. In this way, the present invention provides a method for effectively supporting users' lifestyle habits, health status, and even emotional well-being.

[0755] The processing flow will be explained below.

[0756] Processing steps of a system that combines emotion engines

[0757] Step 1:

[0758] The user launches the app and enters their profile information (age, gender, weight, height, target weight, lifestyle habits, etc.) and current emotional state (stress level, mood, etc.) on the initial setup screen.

[0759] Step 2:

[0760] The device formats the entered profile information and emotion information and transmits it to the server.

[0761] Step 3:

[0762] The server stores the received user information in a database, along with emotional information.

[0763] Step 4:

[0764] The server calculates initial parameters (BMI, basal metabolic rate, stress level, etc.) based on the stored user information and emotional information.

[0765] Step 5:

[0766] The server inputs initial parameters into the generated AI model and generates initial advice for the user (health habits, diet, exercise plan, and stress management).

[0767] Step 6:

[0768] The server transmits the generated advice to the user's terminal.

[0769] Step 7:

[0770] The user acts on the suggested advice and inputs the results, feedback, and changes in emotional state into the app.

[0771] Step 8:

[0772] The terminal formats the feedback information and emotion information from the user and sends it to the server.

[0773] Step 9:

[0774] The server receives the feedback information and emotion information and records them in a database.

[0775] Step 10:

[0776] The server analyzes the recorded feedback and emotional information, including assessing the user's progress (weight fluctuations, exercise performance, stress levels, etc.).

[0777] Step 11:

[0778] The server generates new advice and reassurance messages based on the analysis results, such as suggesting relaxation techniques or counseling if high stress persists.

[0779] Step 12:

[0780] The server sends the generated new advice or message to the user's terminal.

[0781] Step 13:

[0782] The user receives new advice and encouragement messages and continues to act accordingly.

[0783] Specific examples

[0784] For example, if a user enters the following information during initial setup: "I weigh 70 kg, my goal weight is 65 kg, I want to exercise five times a week, and I'm currently experiencing high stress," the server will generate the following advice:

[0785] Jogging for 20 minutes every morning.

[0786] Include vegetables in every meal.

[0787] I've been under a lot of stress lately, so I'm making sure to set aside time for relaxation every night.

[0788] If the user follows the advice and enters the results into the app, providing feedback such as "I've jogged four days and added vegetables to my diet, but my stress level is still high," the server analyzes the data and generates the following encouragement message:

[0789] "Great progress! Keep jogging. Try deep breathing exercises and yoga to manage stress."

[0790] In this way, users receive ongoing, personalized support and feedback, helping them maintain a healthy lifestyle while also providing the right emotional support.

[0791] Example 2

[0792] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0793] Conventional health management systems are limited to providing advice based on user feedback and lack support that takes into account the user's emotional state. This makes it difficult to maintain health management and poses challenges for users in maintaining their motivation.

[0794] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means for inputting user profile information, a storage means for storing the input user information, a generation means for generating advice on health and lifestyle habits, a transmission means for transmitting the generated advice to the user's terminal, an emotion input means for inputting user emotion information, an emotion analysis means for analyzing the input emotion information, an adjustment means for adjusting advice and encouragement messages based on the emotion analysis results, a feedback input means for inputting user feedback information, an analysis means for storing and analyzing the feedback information, a generation means for generating new advice and encouragement messages according to the user's progress based on the analysis results, and a transmission means for transmitting the generated new advice and messages to the user's terminal. This enables personalized support according to the user's emotional state.

[0795] "Input means" refers to a device or function that provides an interface for users to input profile information, emotional information, etc. into the system.

[0796] "Storage means" refers to devices or functions for recording and storing input user information and feedback information in a database or the like.

[0797] "Generation means" refers to a device or function for creating advice and encouragement messages regarding health and lifestyle habits based on input data.

[0798] The "transmission means" refers to a device or function for transmitting the generated advice or encouragement message to the user's terminal.

[0799] "Emotion input means" refers to a device or function that provides an interface for a user to input their emotional state.

[0800] "Emotion analysis means" refers to a device or function for analyzing input emotion information and generating emotion data of the user.

[0801] The "adjustment means" refers to a device or function for adjusting the generated advice or encouragement message to suit the user's state based on the emotion analysis results.

[0802] The "feedback input means" refers to a device or function that provides an interface for a user to input the results of an action or feedback information.

[0803] The "analysis means" refers to a device or function for analyzing the input feedback information and evaluating the user's progress and goal achievement status.

[0804] The present invention relates to a system that uses a generative AI model and an emotion engine to provide advice to users to solve their daily problems and issues, and also provides support according to the user's emotional state. This system is configured to include the following various means.

[0805] Input Method

[0806] The terminal provides an interface for the user to input their own profile information and emotional information, allowing the user to input information such as age, sex, weight, height, target weight, lifestyle habits, stress, joy, fatigue, etc. This information is transmitted to the server by a transmission means, which will be described later.

[0807] Preservation means

[0808] The server stores the entered user information in a database, including initial parameters for assessing the user's health status, such as BMI and basal metabolic rate.

[0809] generation means

[0810] The server uses the generative AI model to generate advice such as healthy lifestyle habits, cooking recipes, and exercise plans based on the stored user information. For example, it generates a "weekly exercise plan" or "healthy recipe for the day." It also adjusts advice and encouragement messages based on emotional data.

[0811] Transmission method

[0812] The server includes means for transmitting the generated advice and / or encouragement messages to the user's terminal, so that the user can periodically receive appropriate advice.

[0813] Emotion input method

[0814] The terminal provides an interface for inputting the user's emotional information, which includes emotional states such as stress, joy, fatigue, etc. This information is also transmitted to the server by the transmission means.

[0815] Emotion analysis means

[0816] The server uses an emotion engine to analyze the input emotion information, thereby generating emotion data for the user, which includes the user's current mood and emotional state.

[0817] Adjustment means

[0818] The server tailors advice and encouragement messages based on the results of sentiment analysis. For example, if a user is experiencing high stress, it provides special advice including relaxation and coping techniques.

[0819] Feedback Input Method

[0820] The device provides an interface for the user to take action based on the suggested advice and record the results within the app. The user inputs their diet and exercise history, and this information is then sent back to the server.

[0821] Analysis means

[0822] The server stores and analyzes the received feedback information, including the user's progress (e.g., weight fluctuations, exercise history), as well as emotional data collected by the emotion engine, to generate more personalized advice.

[0823] For example, if a user uses the following prompt:

[0824] "I weigh 70kg, my goal weight is 65kg, and I want to exercise five times a week. I'm currently feeling stressed. I'd like some advice on a healthy diet and exercise plan, as well as relaxation techniques."

[0825] The system allows users to maintain a healthy lifestyle, receive emotional support and sustained motivation.

[0826] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0827] Step 1: Enter your user information

[0828] The terminal displays an interface for the user to input their profile information (age, sex, weight, height, target weight, lifestyle habits).

[0829] (Specific action) The user enters information into the form and clicks the "Submit" button.

[0830] Input: Profile information entered by the user.

[0831] Output: User profile information sent to the server.

[0832] Step 2: Submit user information

[0833] The terminal transmits the input user information to the server.

[0834] (Specific operation) The terminal packages user information in JSON format and sends the data to the server using an HTTP request.

[0835] Input: Profile information entered by the user.

[0836] Output: HTTP request for user profile information sent to the server.

[0837] Step 3: Storing user information and initial processing

[0838] The server stores the received user information in a database.

[0839] The server calculates initial parameters such as basal metabolic rate and BMI, and inputs them into a generative AI model to generate initial advice.

[0840] (Specific operation) The server saves the user information in the database using an INSERT query and calculates the BMI by "weight (kg) ÷ height (m)^2".

[0841] Input: User profile information.

[0842] Output: Initial parameters, generated initial advice.

[0843] Step 4: Generating Advice

[0844] The server uses a generative AI model to generate advice such as healthy lifestyle habits, cooking recipes, and exercise plans.

[0845] (Specific operation) The server inputs a prompt sentence into the generative AI model and obtains the generated advice.

[0846] Input: User information and initial parameters.

[0847] Output: The generated advice.

[0848] Step 5: Submitting Advice

[0849] The server transmits the generated advice to the user's terminal.

[0850] (Specific operation) The server sends a notification containing new advice to the terminal via an HTTP request.

[0851] Input: The generated advice.

[0852] Output: Notification to user terminal.

[0853] Step 6: Enter emotional information

[0854] The terminal provides an interface for the user to input emotional information (stress, joy, fatigue, etc.).

[0855] (Specific Action) The user selects an emotional state and clicks the "Send" button.

[0856] Input: A user-selected emotional state.

[0857] Output: Emotion information sent to the server.

[0858] Step 7: Sending Emotional Information

[0859] The terminal transmits the input emotion information to the server.

[0860] (Specific operation) The device packages the emotional information and sends the data to the server using an HTTP request.

[0861] Input: A user-selected emotional state.

[0862] Output: HTTP request for emotion information sent to the server.

[0863] Step 8: Emotional Analysis

[0864] The server analyzes the emotion information using an emotion engine and generates emotion data.

[0865] (Specific operation) The server inputs emotion information into the emotion engine and obtains the analysis results.

[0866] Input: Emotion information.

[0867] Output: The generated emotion data.

[0868] Step 9: Adjusting Advice

[0869] The server tailors advice and encouragement messages based on the emotion data.

[0870] (Specific operation) The server inputs emotional data into the generative AI model and obtains adjusted advice.

[0871] Input: Generated emotion data.

[0872] Output: Tailored advice and / or encouragement messages.

[0873] Step 10: Sending tailored advice

[0874] The server sends tailored advice and encouragement messages to the user's terminal.

[0875] (Specific operation) The server sends a notification to the terminal via an HTTP request.

[0876] Input: Tailored advice and / or encouragement messages.

[0877] Output: Notification to user terminal.

[0878] Step 11: Enter your feedback information

[0879] The terminal provides an interface for the user to take action based on the suggested advice and record the results.

[0880] (Specific action) The user enters detailed feedback and clicks the "Submit" button.

[0881] Input: The result of the user's action.

[0882] Output: Feedback information sent to the server.

[0883] Step 12: Submit your feedback

[0884] The terminal transmits the input feedback information to the server.

[0885] (Specific operation) The terminal packages the feedback information and sends it to the server via an HTTP request.

[0886] Input: Feedback information entered by the user.

[0887] Output: Feedback information sent to the server.

[0888] Step 13: Storing and analyzing feedback information

[0889] The server stores the received feedback information in a database and analyzes it.

[0890] (Specific operation) The server retrieves past feedback data via a query and applies an analysis algorithm.

[0891] Input: User feedback information.

[0892] Output: Analysis results and evaluation data.

[0893] Step 14: Generate new advice

[0894] The server uses a generative AI model to generate new advice and encouragement messages based on the progress data and emotion data.

[0895] (Specific operation) The server inputs the analysis results into the generated AI model and obtains new advice.

[0896] Input: Analysis results and evaluation data.

[0897] Output: New advice or encouragement message.

[0898] Step 15: Submitting a new advice

[0899] The server sends the generated new advice or encouragement message to the user's terminal.

[0900] (Specific operation) The server sends a notification containing new advice to the terminal via an HTTP request.

[0901] Input: A new advice or encouragement message.

[0902] Output: Notification to user terminal.

[0903] The above is the specific flow of the processing of this system. These steps provide personalized support tailored to the user's health and emotional state.

[0904] (Application example 2)

[0905] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0906] Conventional health management and emotional support systems lack personalized support that responds to a user's individual emotional state and in-store behavior. This makes it difficult to provide effective support tailored to a user's specific health goals and emotional state. Furthermore, in-store shopping experiences lack personalized product recommendations that meet the user's needs.

[0907] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0908] In this invention, the server includes input means for inputting user profile information, storage means for storing the input user information, generation means for generating advice on health and lifestyle habits based on the stored user information, transmission means for transmitting the generated advice to the user's device, emotion input means for inputting the user's emotional state, emotion analysis means for analyzing the input emotional information, generation means for adjusting the advice based on the emotion analysis result, generation means for generating product recommendations corresponding to the user's behavior in the store, transmission means for transmitting the generated advice and product recommendations to the user's device, feedback input means for inputting user feedback information, analysis means for storing and analyzing the feedback information, generation means for generating new advice and encouragement messages corresponding to the user's progress based on the analysis result, and transmission means for transmitting the generated new advice and messages to the user's device. This enables personalized health management and emotional support and an improved shopping experience corresponding to the user's individual emotional state and behavior in the physical store.

[0909] "User profile information" refers to information that a user inputs into the system, such as their age, sex, height, weight, target weight, lifestyle habits, etc.

[0910] "Input means" refers to an interface through which a user inputs profile information and emotional state into the system.

[0911] The "storage means" is a function for storing input user information, feedback information, etc. in a database.

[0912] The "generation means" is a function for generating advice on health and lifestyle habits based on stored user information and emotional information, and product recommendations corresponding to in-store behavior.

[0913] The "transmission means" is a function for transmitting the generated advice, encouragement message, and product recommendation to the user's terminal.

[0914] The "emotion input means" is an interface that allows the user to input their own emotional state (stress, joy, fatigue, etc.).

[0915] The "emotion analysis means" is a function for analyzing the user's emotional state based on input emotion information.

[0916] "Product recommendations" refer to products recommended for purchase in-store based on a user's health goals and current emotional state.

[0917] The "feedback input means" is an interface for the user to input the actions taken based on the proposed advice and the results thereof.

[0918] The "analysis means" is a function for performing analysis based on the saved feedback information and evaluating the user's progress.

[0919] "Encouragement messages" are encouraging messages that are generated based on the user's progress and emotional state.

[0920] This invention relates to a system that uses a generative AI model and an emotion engine to provide advice to users to solve their daily problems and issues, and also provides support according to the user's emotional state. This system is configured to include the following multiple means.

[0921] Collecting user-entered information

[0922] The device provides an interface that allows the user to enter their profile information on the initial setup screen. The user enters information such as age, gender, weight, height, lifestyle habits, and target weight. This information is sent from the device to the server.

[0923] Saving and initializing user information

[0924] The server stores the received user information in a database. The stored information includes initial parameters for assessing the user's health status, including calculations of BMI and basal metabolic rate. The server inputs these initial parameters into a generative AI model to generate initial advice for the user.

[0925] Generating and Sending Advice

[0926] The server generates advice such as healthy lifestyle habits, cooking recipes, and exercise plans. This advice is sent to the user as periodic notifications. For example, it provides a "weekly exercise plan" or "today's healthy recipe."

[0927] Emotional state recognition and analysis

[0928] The terminal provides an interface for inputting the user's emotional information. The user can input their emotional state (stress, joy, fatigue, etc.). The emotional information is transmitted from the terminal to the server.

[0929] Analysis using emotion engine

[0930] The server uses an emotion engine to analyze the user's emotion information and generate emotion data, which includes the user's current mood and emotional state.

[0931] Advice adjustment based on sentiment data

[0932] The server uses the generating means to tailor advice and encouragement messages based on the emotion data recognized by the emotion engine, for example, providing special advice including relaxation and coping techniques when the user is experiencing high stress.

[0933] In-store product recommendations

[0934] The server generates product recommendations based on the user's profile information and emotional information, corresponding to their behavior in the physical store. For example, if a user wants a low-calorie meal, the server will recommend low-calorie products.

[0935] Sending advice and encouragement messages

[0936] The server sends tailored advice and encouragement messages and product recommendations to the user's terminal.

[0937] Collecting feedback on user activities

[0938] The device provides an interface for the user to take action based on the suggested advice and record the results within the app. The user inputs their diet and exercise history, and this information is then sent back to the server.

[0939] Progress data management and analysis

[0940] The server stores and analyzes the received feedback information, including evaluating the user's progress (weight fluctuations, exercise record, etc.), and includes emotional data collected by the emotion engine to generate more personalized advice.

[0941] Encouragement and new advice

[0942] The server sends new advice and encouragement messages generated based on the progress data and emotion data to the user's terminal.

[0943] Specific examples

[0944] For example, if a user enters "weight 70 kg, target weight 65 kg, and desires to exercise five times a week" during initial setup and indicates that they are currently experiencing high levels of stress, the server will provide advice including health plans and relaxation methods.Product recommendations based on in-store behavior can suggest directions to shelves with low-calorie foods and relaxation goods.

[0945] Prompt Sentence Examples

[0946] "A user comes to your store feeling very stressed. His health goal is to lose 5kg. Please recommend some products."

[0947] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0948] Step 1: Enter your user information

[0949] The terminal provides an interface on the initial setup screen where the user can enter their profile information (age, gender, weight, height, target weight, lifestyle habits). The user enters this information into the terminal and presses the send button.

[0950] Input: Age, gender, weight, height, target weight, lifestyle habits

[0951] Output: User profile information

[0952] Step 2: Save user information

[0953] The server receives the user profile information sent from the terminal and stores this information in a database using a storage means.

[0954] Input: User profile information

[0955] Output: User information stored in the database

[0956] Step 3: Generate initial advice

[0957] Based on the stored user profile information, the server inputs initial parameters such as BMI and basal metabolic rate into the generative AI model and generates initial health advice for the user.

[0958] Input: User profile information

[0959] Data processing: BMI, basal metabolic rate calculation

[0960] Output: First health advice

[0961] Step 4: Submitting Advice

[0962] The server transmits the generated initial health advice to the user's terminal using the transmission means, and the user receives the advice on the terminal.

[0963] Input: First Health Advice

[0964] Output: Advice sent to the user's terminal

[0965] Step 5: Enter your emotional state

[0966] The terminal provides an interface for the user to input their emotional state (stress, joy, fatigue, etc.) The user inputs the emotional state and transmits it from the terminal to the server.

[0967] Input: Emotional state (stress, joy, fatigue, etc.)

[0968] Output: Emotion information sent to the server

[0969] Step 6: Analyze emotional state

[0970] The server uses an emotion engine to analyze the received emotion information and generate emotion data, which includes the user's current mood and emotional state.

[0971] Input: Emotion information

[0972] Data processing: Sentiment analysis

[0973] Output: Emotion data

[0974] Step 7: Adjusting advice based on sentiment data

[0975] The server uses a generating means to adjust existing advice based on the emotion data and generate new advice and / or encouragement messages.

[0976] Input: Emotion data

[0977] Data calculation:Advice adjustment

[0978] Output: New advice and encouragement messages

[0979] Step 8: Recommend products in-store

[0980] The server generates product recommendations based on the user's profile information and emotional information, corresponding to their behavior in the physical store. For example, if the user wants to eat low-calorie meals, the server will recommend low-calorie products.

[0981] Input: User profile information, emotion information

[0982] Data Computing: Product Recommendation Generation

[0983] Output: Product recommendation

[0984] Step 9: Submitting advice and recommendations

[0985] The server sends tailored advice and product recommendations to the user's device, who receives this information in the store and uses it as a guide for shopping.

[0986] Input: New advice, encouragement message, product recommendation

[0987] Output: Advice and product recommendations sent to the user device

[0988] Step 10: Enter your feedback information

[0989] The terminal provides an interface for the user to take action based on the proposed advice and input the results. The user inputs the details of their meals and exercise history and sends them to the server.

[0990] Input: Meal details, exercise history

[0991] Output: Feedback information sent to the server

[0992] Step 11: Analyze feedback

[0993] The server stores and analyzes the received feedback information, including the user's progress (weight fluctuations, exercise performance, etc.).

[0994] Input: Feedback information

[0995] Data Calculation: Feedback Analysis

[0996] Output: Analysis results

[0997] Step 12: Generate and send new advice

[0998] The server sends new advice and encouragement messages based on the progress data and emotion data to the user's device, and the user receives support to maintain motivation through the device.

[0999] Input: Progress data, emotion data

[1000] Output: New advice and encouragement messages

[1001] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1002] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1003] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1004] [Third embodiment]

[1005] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1006] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1007] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1008] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1009] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1010] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1011] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1012] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1013] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1014] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1015] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1016] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1017] The present invention relates to a system that uses a generative AI model to provide advice to users to solve their daily problems and issues. This system includes the following various means:

[1018] Collecting user-entered information

[1019] The device provides an interface that allows the user to enter their profile information on an initial setup screen. The user enters information such as age, gender, weight, height, target weight, lifestyle habits, etc. This information is sent from the device to the server.

[1020] Saving and initializing user information

[1021] The server stores the received user information in a database. The stored information includes initial parameters for assessing the user's health status, including calculations of BMI and basal metabolic rate. The server inputs these initial parameters into a generative AI model to generate initial advice for the user.

[1022] Generating and Sending Advice

[1023] The server generates advice such as healthy lifestyle habits, cooking recipes, and exercise plans. This advice is sent to the user as periodic notifications. For example, it provides a "weekly exercise plan" or "today's healthy recipe."

[1024] Collecting feedback on user activities

[1025] The device provides an interface for the user to take action based on the suggested advice and record the results within the app. The user inputs their diet and exercise history, and this information is then sent back to the server.

[1026] Progress data management and analysis

[1027] The server stores and analyzes the received feedback information. This analysis includes evaluating the user's progress, such as the pace of weight loss and exercise status. Based on the analysis results, new advice and encouragement messages are generated based on the user's progress.

[1028] Encouragement and new advice

[1029] The server then sends new advice and encouragement messages based on the progress data to the user's device, allowing the user to receive continuous personalized support and advice.

[1030] Specific examples

[1031] For example, if a user enters "I weigh 70 kg, my goal weight is 65 kg, and I want to exercise five times a week" during initial setup, the server will generate the following advice: jog for 20 minutes every morning and include vegetables in each meal. Furthermore, when the user follows the advice, records their actions, and enters their feedback, the server analyzes the data and generates an encouraging message such as "Great progress! Keep it up!" and sends it to the user's device.

[1032] This system allows users to maintain a healthy lifestyle and sustain their motivation. In this way, the present invention provides a method for effectively supporting users' lifestyle habits and health conditions.

[1033] The processing flow will be explained below.

[1034] Step 1:

[1035] The user launches the app and enters their profile information (age, gender, weight, height, target weight, lifestyle habits, etc.) on the initial setup screen.

[1036] Step 2:

[1037] The device formats the entered profile information and sends it to the server.

[1038] Step 3:

[1039] The server stores the received user information in a database.

[1040] Step 4:

[1041] The server calculates initial parameters (BMI, basal metabolic rate, etc.) based on the stored user information.

[1042] Step 5:

[1043] The server inputs initial parameters into the generated AI model and generates initial advice for the user (health habits, diet, exercise plan, etc.).

[1044] Step 6:

[1045] The server transmits the generated advice to the user's terminal.

[1046] Step 7:

[1047] The user acts on the suggested advice and enters the results and feedback into the app.

[1048] Step 8:

[1049] The terminal formats the feedback information from the user and sends it to the server.

[1050] Step 9:

[1051] The server receives the feedback information and records it in a database.

[1052] Step 10:

[1053] The server analyzes the recorded feedback information and evaluates the user's progress (weight fluctuations, exercise performance, etc.).

[1054] Step 11:

[1055] The server generates new advice and / or encouragement messages based on the analysis results.

[1056] Step 12:

[1057] The server sends the generated new advice or message to the user's terminal.

[1058] Step 13:

[1059] The user receives new advice and encouragement messages and continues to act accordingly.

[1060] This process allows users to receive ongoing, personalized support and feedback.

[1061] Example 1

[1062] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1063] Conventional health management systems have struggled to effectively utilize user profile information and feedback information to provide individually optimized advice. They also lacked the functionality to generate appropriate encouragement or new advice in real time based on the user's progress. This made it difficult for users to maintain consistent motivation and obtain specific guidance for achieving their goals.

[1064] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1065] In this invention, the server includes input means for inputting user profile information, storage means for storing the input user information, generation means for generating advice regarding health and lifestyle habits based on the stored user information, transmission means for transmitting the generated advice to the user's terminal, feedback input means for inputting user feedback information, analysis means for storing and analyzing the feedback information, generation means for generating new advice and / or encouragement messages according to the user's progress based on the analysis results, transmission means for transmitting the generated new advice and / or messages to the user's terminal, and generation means for analyzing the feedback information using a generative AI model and generating advice based on prompt sentences. This makes it possible to provide advice and encouragement optimized for the user's individual situation in real time.

[1066] "Input means" refers to a device or function that provides an interface for users to input profile information and feedback information.

[1067] The "storage means" is a device or function for safely storing input user information and feedback information.

[1068] The "generation means" is a device or function for generating advice and encouragement messages regarding health and lifestyle habits based on stored user information, feedback information, and analysis results.

[1069] The "transmission means" is a device or function for transmitting the generated advice or message to the user's terminal.

[1070] The "feedback input means" is a device or function that provides an interface for users to input their activity results and progress.

[1071] "Analysis means" is a device or function for storing and analyzing feedback information.

[1072] A "generative AI model" is an artificial intelligence model used to generate optimal advice and encouragement messages based on user information, feedback information, and prompt text.

[1073] A "prompt" is a command entered into a generative AI model to generate specific advice or messages.

[1074] A "user terminal" is a computing device or mobile device that a user uses to enter information or receive advice or messages.

[1075] This invention relates to a system that uses generative AI models to provide advice to users to solve their daily problems and issues. This system is realized by linking a server and a terminal.

[1076] The device provides an interface that allows the user to input profile information on the initial setup screen. Input items include age, gender, weight, height, target weight, and lifestyle habits. The user inputs this information using the device's input means. Once input is complete, the device encrypts the information and sends it to the server.

[1077] The server stores the received user information in a MySQL database. The server then runs a script to calculate BMI and basal metabolic rate, and inputs the calculation results into the generative AI model as initial parameters. For example, a prompt such as "The user is a 35-year-old male, weighs 70 kg, and has a target weight of 65 kg. Please provide appropriate health advice" is input into the generative AI model. The generative AI model generates its initial advice based on this.

[1078] The generated advice is sent to the user's device using the server's transmission means. Specific advice might include "Jogging for 20 minutes every morning and including vegetables in every meal." This advice is displayed as a push notification on the device, allowing the user to check it.

[1079] The device provides a feedback input means for collecting the user's activity results and progress information. The user can input their daily diet and exercise record. For example, they can input information such as "I had a vegetable salad for breakfast and jogged for 20 minutes" and send it to the server.

[1080] The server then stores the feedback information in a MySQL database. The stored data is then analyzed using Python scripts. For example, the server evaluates the user's weight loss rate and exercise status, and generates new advice and encouragement messages based on the user's progress.

[1081] The newly generated advice and encouragement messages are then sent back to the user's device using the server's transmission means, including motivational messages such as "You're only 3kg away from your goal weight! Great progress!"

[1082] In this way, the present invention provides a method for supporting a user's healthy lifestyle and maintaining continuous motivation. Below are specific examples of prompt sentences to be input into the generative AI model.

[1083] Prompt Sentence Examples

[1084] "To support a healthy lifestyle, please generate exercise advice based on the following user information: age 35, gender male, current weight 70kg, goal weight 65kg, desire to exercise 5 times a week."

[1085] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1086] Step 1:

[1087] The terminal provides an interface for the user to input profile information. After the user has input information such as age, gender, weight, height, target weight, and lifestyle habits, the user clicks the "Submit" button. This input information is encrypted and sent from the terminal to the server. The input data includes the user's personal information and health goals. This information becomes the input data. The output is the data sent to the server.

[1088] Step 2:

[1089] The server stores the received user information in a MySQL database. This information includes the user's personal information and health goals. The server calculates BMI and basal metabolic rate based on the stored data. The calculation requires data such as the user's weight, height, age, and gender. The server generates a prompt statement that uses the calculation results as initial parameters and inputs them into the generative AI model. The input data is the user information obtained from the database, and the output is the prompt statement.

[1090] Step 3:

[1091] The server inputs a prompt sentence into the generative AI model. This prompt sentence includes the calculated initial parameters. For example, the prompt sentence might be something like, "The user is a 35-year-old male, weighs 70 kg, and has a target weight of 65 kg. Please provide appropriate health advice." The generative AI model generates advice based on this prompt sentence. The input data is the prompt sentence, and the output is the generated advice.

[1092] Step 4:

[1093] The server sends the generated advice to the user's device. This advice is provided as a concrete guide to action. For example, the advice might be, "Jogging for 20 minutes every morning and including vegetables in every meal." The input data is the generated advice, and the output is the data sent to the user's device.

[1094] Step 5:

[1095] The terminal provides a feedback interface for the user to input the results of their daily activities. The user inputs their dietary and exercise records. Once the input is complete, the user clicks the "Submit" button. The input information is encrypted and sent from the terminal to the server. The input data is the user's daily activity results, and the output is the data sent to the server.

[1096] Step 6:

[1097] The server stores the received feedback information in a MySQL database. It then uses a Python script to analyze the stored data. This analysis includes assessing the user's weight loss rate and exercise status. The input data is the feedback information obtained from the database, and the output is the analysis results.

[1098] Step 7:

[1099] The server generates new advice and / or encouragement messages based on the analysis results, such as "You're 3kg away from your goal weight! Great progress!" The input data are the analysis results, and the output is the generated advice and / or messages.

[1100] Step 8:

[1101] The server then sends the generated new advice and encouragement messages to the user's device, helping the user stay motivated and progress toward their health goals. The input data are the generated advice and messages, and the output is the data sent to the user's device.

[1102] (Application example 1)

[1103] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1104] In recent years, living a healthy lifestyle has become increasingly important, but many people find it difficult to maintain a balanced diet in their busy daily lives. Furthermore, appropriate advice and feedback based on the user's progress are essential for maintaining a healthy diet. However, no consistent system for achieving this has existed. In particular, there is a need for a system that can propose personalized meal menus based on the user's health status and collect feedback to reflect in future proposals.

[1105] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1106] In this invention, the server includes an input means for inputting a user's profile information, a storage means for saving the input user information, a generation means for generating health and lifestyle advice based on the saved user information, a transmission means for sending the generated advice to the user's device, a feedback input means for inputting the user's feedback information, an analysis means for saving and analyzing the feedback information, a generation means for generating new advice and encouragement messages based on the analysis results in accordance with the user's progress, a suggestion means for suggesting health-conscious meal menus through a food delivery service, a feedback collection means for collecting feedback on the suggested meal menus via the user's device, and a reflection means for analyzing the collected feedback information and reflecting it in subsequent advice. This allows users to receive meal suggestions based on their individual health status and goals, and feedback on those suggestions. Furthermore, by utilizing a generative AI model, optimal advice can be provided in real time, supporting the user's long-term health management.

[1107] definition statement

[1108] "User" refers to a person who uses the target system.

[1109] "Profile information" refers to personal data such as a user's age, gender, weight, height, lifestyle habits, and target weight.

[1110] "Input means" refers to the device or interface through which a user inputs profile information and feedback information.

[1111] "Storage means" refers to a device or system for storing input user information and feedback information.

[1112] "Generating means" refers to a device or system for generating advice based on stored user information.

[1113] The "transmission means" refers to a device or system for transmitting the generated advice or message to the user's terminal.

[1114] "Feedback input means" refers to a device or interface that allows a user to input the results of implementing advice and their opinions.

[1115] "Analysis means" refers to a device or system for analyzing input feedback information.

[1116] An "encouragement message" refers to a message intended to encourage the user.

[1117] "Proposal means" refers to a device or system for proposing healthy meal menus to users through food delivery services.

[1118] "Feedback collection means" refers to a device or interface that allows users to collect feedback and experience regarding the proposed meal menu.

[1119] "Reflection means" refers to a device or system that analyzes collected feedback information and uses it to generate next advice.

[1120] A "generative AI model" refers to artificial intelligence that creates advice and messages based on data.

[1121] MODE FOR CARRYING OUT THE INVENTION

[1122] The specific system of this invention is composed of a series of software modules that run on a smartphone application and a cloud server. The system configuration and processing content will be explained below.

[1123] System configuration

[1124] 1. User input and saving:

[1125] The device provides an interface that allows users to enter their profile information on an initial setup screen, including information such as age, gender, weight, height, lifestyle habits, and target weight. This information is sent from the device to a cloud server.

[1126] The server stores the received user information in a database, including initial parameters for assessing the user's health status, such as BMI and basal metabolic rate.

[1127] Advice generation and delivery

[1128] 2. Generation means:

[1129] The server uses the stored user information to generate healthy lifestyle and dietary advice, using generative AI models (e.g., GPT-3) to create personalized exercise plans and meal menus based on the user's profile.

[1130] The generated advice is then periodically sent to the user's smartphone, allowing the user to receive guidelines for consistently maintaining a healthy lifestyle.

[1131] Collecting and analyzing feedback

[1132] 3. Feedback Collection and Analysis:

[1133] The device provides an interface for the user to record the results of their actions based on the suggested advice, such as their diet and exercise record.

[1134] The server receives the collected feedback information and stores it in a database. This information is then analyzed to evaluate the user's progress, including the user's weight fluctuations and exercise performance.

[1135] New Advice and Encouragement

[1136] 4. Next advice generation:

[1137] The server generates new advice and encouragement messages based on feedback information and progress data, again using a generative AI model to provide optimal messages in real time.

[1138] For example, if a user is making good progress towards a goal, a message might be provided saying, "Great progress! Keep it up!"

[1139] Food delivery service

[1140] 5. Healthy meal menu suggestions:

[1141] As a suggestion method, the server proposes meal menus according to the user's health condition through a food delivery service, allowing users to easily incorporate healthy eating into their busy daily lives.

[1142] As a means of collecting feedback, feedback on the provided meal is collected via the user's terminal and reflected in the next advice.

[1143] Hardware and software used

[1144] Smartphone: Use an iOS or Android device.

[1145] Use cloud servers (e.g., AWS EC2, Google Cloud Platform) to store and process data.

[1146] Database: Use a system such as Amazon RDS or Firebase to store user data and feedback information.

[1147] Generative AI models: Use large-scale language models such as GPT-3 to generate advice and messages.

[1148] Examples and prompts:

[1149] The user launches the app and enters their profile information in response to the following prompts:

[1150] "Please tell us your age, gender, weight, height, goal weight and current lifestyle habits."

[1151] Example prompts to input to the generative AI model:

[1152] "User information: Age: 35, Gender: Male, Weight: 70kg, Height: 175cm, Target weight: 65kg, Lifestyle: Mostly desk work. Please suggest a weekly meal menu and exercise plan that is suitable for this user."

[1153] As a result, the present invention realizes health support that is individually customized for each user, and supports continuous health management.

[1154] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1155] Program processing flow

[1156] Step 1:

[1157] User enters profile information

[1158] Users launch the app on their smartphone and enter profile information such as age, gender, weight, height, lifestyle habits, and target weight on the initial setup screen.

[1159] Input: User's personal data (age, gender, weight, height, lifestyle habits, goal weight).

[1160] Output: The entered data is sent from the terminal to the server.

[1161] Step 2:

[1162] Saving user information

[1163] The server stores the user information received from the terminal in a database.

[1164] Input: User profile information sent from the device.

[1165] Output: The user information is stored in the database.

[1166] Step 3:

[1167] Calculating and saving initial parameters

[1168] The server calculates BMI and basal metabolic rate based on the saved user information and stores this in a database.

[1169] Input: Saved user profile information.

[1170] Specific operations: Calculate BMI (weight ÷ (height × height)) and basal metabolic rate.

[1171] Output: The calculated initial parameters are stored in a database.

[1172] Step 4:

[1173] Generating Advice

[1174] The server uses a generative AI model (e.g., GPT-3) to generate personalized advice based on the user's initial parameters.

[1175] Input: User information stored in the database and calculated initial parameters.

[1176] Example prompt: "User information: Age: 35, Gender: Male, Weight: 70kg, Height: 175cm, Target weight: 65kg, Lifestyle: Mostly desk work. Please suggest a weekly meal plan and exercise plan that is appropriate for this user."

[1177] Output: Healthy lifestyle and meal recommendations from a generative AI model.

[1178] Step 5:

[1179] Providing advice

[1180] The server transmits the generated advice to the user's terminal.

[1181] Input: The generated advice.

[1182] Output: The advice is sent to the user's device. Specific implementations include push notifications and in-app notifications.

[1183] Step 6:

[1184] Enter your feedback

[1185] Users take action based on the suggested advice and record the results within the app.

[1186] Input: Feedback information such as dietary details and exercise performance.

[1187] Output: Feedback information is sent from the device to the server.

[1188] Step 7:

[1189] Feedback storage and analysis

[1190] The server stores the feedback information received from the user in a database and analyzes it.

[1191] Input: User feedback information.

[1192] Specific actions: Analyze progress data such as weight fluctuations and exercise status.

[1193] Output: Generation of analytical results based on feedback.

[1194] Step 8:

[1195] Generate new advice and encouragement messages

[1196] The server generates new advice and encouragement messages based on the analysis results, using generative AI models to create personalized messages.

[1197] Input: Parsed feedback data.

[1198] Output: The newly generated advice or encouragement message.

[1199] Step 9:

[1200] Providing new advice and encouragement messages

[1201] The server sends the generated new advice or encouragement message to the user's terminal.

[1202] Input: The new advice or message that is generated.

[1203] Output: A new notification arrives on the user's device.

[1204] Step 10:

[1205] Food delivery service proposal

[1206] The server uses a food delivery service to suggest meal menus that suit the user's health condition.

[1207] Input: User health data and the generative AI model's recommendations.

[1208] Output: The meal menu is displayed on the user's device.

[1209] Step 11:

[1210] Gathering feedback on meal menus

[1211] The user inputs feedback on the proposed meal menu into the terminal and transmits it to the server.

[1212] Input: User's feedback information about the meal menu.

[1213] Output: Feedback information is sent from the device to the server.

[1214] Step 12:

[1215] Reflected in the next proposal

[1216] The server analyzes the collected feedback information and reflects it in the next advice.

[1217] Input: User feedback information.

[1218] Output: New proposals based on the analysis results.

[1219] The above steps realize a system that provides users with personalized health advice and meal suggestions, and supports continuous health management.

[1220] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1221] The present invention relates to a system that uses a generative AI model and an emotion engine to provide advice to users to solve their daily problems and issues, and also provides support according to the user's emotional state. This system is configured to include the following various means.

[1222] Collecting user-entered information

[1223] The device provides an interface that allows the user to enter their profile information on an initial setup screen. The user enters information such as age, gender, weight, height, target weight, lifestyle habits, etc. This information is sent from the device to the server.

[1224] Saving and initializing user information

[1225] The server stores the received user information in a database. The stored information includes initial parameters for assessing the user's health status, including calculations of BMI and basal metabolic rate. The server inputs these initial parameters into a generative AI model to generate initial advice for the user.

[1226] Generating and Sending Advice

[1227] The server generates advice such as healthy lifestyle habits, cooking recipes, and exercise plans. This advice is sent to the user as periodic notifications. For example, it provides a "weekly exercise plan" or "today's healthy recipe."

[1228] Emotional state recognition and analysis

[1229] The terminal provides an interface for inputting the user's emotional information. The user can input their emotional state (stress, joy, fatigue, etc.). The emotional information is transmitted from the terminal to the server.

[1230] Analysis using emotion engine

[1231] The server uses an emotion engine to analyze the user's emotion information and generate emotion data, which includes the user's current mood and emotional state.

[1232] Advice adjustment based on sentiment data

[1233] The server uses the generating means to tailor advice and encouragement messages based on the emotion data recognized by the emotion engine, for example, providing special advice including relaxation and coping techniques when the user is experiencing high stress.

[1234] Sending advice and encouragement messages

[1235] The server sends tailored advice and encouragement messages to the user's terminal.

[1236] Collecting feedback on user activities

[1237] The device provides an interface for the user to take action based on the suggested advice and record the results within the app. The user inputs their diet and exercise history, and this information is then sent back to the server.

[1238] Progress data management and analysis

[1239] The server stores and analyzes the received feedback information, including evaluating the user's progress (weight fluctuations, exercise record, etc.), and also includes emotional data collected by the emotion engine to generate more personalized advice.

[1240] Encouragement and new advice

[1241] The server sends new advice and encouragement messages generated based on the progress data and emotion data to the user's terminal.

[1242] Specific examples

[1243] For example, if a user enters "weight 70 kg, target weight 65 kg, and desires to exercise five times a week" during initial setup and indicates that they are currently experiencing high stress, the server will provide advice including relaxation methods and stress management, in addition to daily exercise plans and healthy eating advice. Furthermore, when the user follows the advice, records their actions, and enters their feedback, the server analyzes the data and generates an encouraging message such as "Great progress! Don't forget to relax!" and sends it to the user's device.

[1244] This system allows users to maintain a healthy lifestyle and receive emotional support while maintaining sustained motivation. In this way, the present invention provides a method for effectively supporting users' lifestyle habits, health status, and even emotional well-being.

[1245] The processing flow will be explained below.

[1246] Processing steps of a system that combines emotion engines

[1247] Step 1:

[1248] The user launches the app and enters their profile information (age, gender, weight, height, target weight, lifestyle habits, etc.) and current emotional state (stress level, mood, etc.) on the initial setup screen.

[1249] Step 2:

[1250] The device formats the entered profile information and emotion information and transmits it to the server.

[1251] Step 3:

[1252] The server stores the received user information in a database, along with emotional information.

[1253] Step 4:

[1254] The server calculates initial parameters (BMI, basal metabolic rate, stress level, etc.) based on the stored user information and emotional information.

[1255] Step 5:

[1256] The server inputs initial parameters into the generated AI model and generates initial advice for the user (health habits, diet, exercise plan, and stress management).

[1257] Step 6:

[1258] The server transmits the generated advice to the user's terminal.

[1259] Step 7:

[1260] The user acts on the suggested advice and inputs the results, feedback, and changes in emotional state into the app.

[1261] Step 8:

[1262] The terminal formats the feedback information and emotion information from the user and sends it to the server.

[1263] Step 9:

[1264] The server receives the feedback information and emotion information and records them in a database.

[1265] Step 10:

[1266] The server analyzes the recorded feedback and emotional information, including assessing the user's progress (weight fluctuations, exercise performance, stress levels, etc.).

[1267] Step 11:

[1268] The server generates new advice and reassurance messages based on the analysis results, such as suggesting relaxation techniques or counseling if high stress persists.

[1269] Step 12:

[1270] The server sends the generated new advice or message to the user's terminal.

[1271] Step 13:

[1272] The user receives new advice and encouragement messages and continues to act accordingly.

[1273] Specific examples

[1274] For example, if a user enters the following information during initial setup: "I weigh 70 kg, my goal weight is 65 kg, I want to exercise five times a week, and I'm currently experiencing high stress," the server will generate the following advice:

[1275] Jogging for 20 minutes every morning.

[1276] Include vegetables in every meal.

[1277] I've been under a lot of stress lately, so I'm making sure to set aside time for relaxation every night.

[1278] If the user follows the advice and enters the results into the app, providing feedback such as "I've jogged four days and added vegetables to my diet, but my stress level is still high," the server analyzes the data and generates the following encouragement message:

[1279] "Great progress! Keep jogging. Try deep breathing exercises and yoga to manage stress."

[1280] In this way, users receive ongoing, personalized support and feedback, helping them maintain a healthy lifestyle while also providing the right emotional support.

[1281] Example 2

[1282] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1283] Conventional health management systems are limited to providing advice based on user feedback and lack support that takes into account the user's emotional state. This makes it difficult to maintain health management and poses challenges for users in maintaining their motivation.

[1284] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means for inputting user profile information, a storage means for storing the input user information, a generation means for generating advice on health and lifestyle habits, a transmission means for transmitting the generated advice to the user's terminal, an emotion input means for inputting user emotion information, an emotion analysis means for analyzing the input emotion information, an adjustment means for adjusting advice and encouragement messages based on the emotion analysis results, a feedback input means for inputting user feedback information, an analysis means for storing and analyzing the feedback information, a generation means for generating new advice and encouragement messages according to the user's progress based on the analysis results, and a transmission means for transmitting the generated new advice and messages to the user's terminal. This enables personalized support according to the user's emotional state.

[1285] "Input means" refers to a device or function that provides an interface for users to input profile information, emotional information, etc. into the system.

[1286] "Storage means" refers to devices or functions for recording and storing input user information and feedback information in a database or the like.

[1287] "Generation means" refers to a device or function for creating advice and encouragement messages regarding health and lifestyle habits based on input data.

[1288] The "transmission means" refers to a device or function for transmitting the generated advice or encouragement message to the user's terminal.

[1289] "Emotion input means" refers to a device or function that provides an interface for a user to input their emotional state.

[1290] "Emotion analysis means" refers to a device or function for analyzing input emotion information and generating emotion data of the user.

[1291] The "adjustment means" refers to a device or function for adjusting the generated advice or encouragement message to suit the user's state based on the emotion analysis results.

[1292] The "feedback input means" refers to a device or function that provides an interface for a user to input the results of an action or feedback information.

[1293] The "analysis means" refers to a device or function for analyzing the input feedback information and evaluating the user's progress and goal achievement status.

[1294] The present invention relates to a system that uses a generative AI model and an emotion engine to provide advice to users to solve their daily problems and issues, and also provides support according to the user's emotional state. This system is configured to include the following various means.

[1295] Input Method

[1296] The terminal provides an interface for the user to input their own profile information and emotional information, allowing the user to input information such as age, sex, weight, height, target weight, lifestyle habits, stress, joy, fatigue, etc. This information is transmitted to the server by a transmission means, which will be described later.

[1297] Preservation means

[1298] The server stores the entered user information in a database, including initial parameters for assessing the user's health status, such as BMI and basal metabolic rate.

[1299] generation means

[1300] The server uses the generative AI model to generate advice such as healthy lifestyle habits, cooking recipes, and exercise plans based on the stored user information. For example, it generates a "weekly exercise plan" or "healthy recipe for the day." It also adjusts advice and encouragement messages based on emotional data.

[1301] Transmission method

[1302] The server includes means for transmitting the generated advice and / or encouragement messages to the user's terminal, so that the user can periodically receive appropriate advice.

[1303] Emotion input method

[1304] The terminal provides an interface for inputting the user's emotional information, which includes emotional states such as stress, joy, fatigue, etc. This information is also transmitted to the server by the transmission means.

[1305] Emotion analysis means

[1306] The server uses an emotion engine to analyze the input emotion information, thereby generating emotion data for the user, which includes the user's current mood and emotional state.

[1307] Adjustment means

[1308] The server tailors advice and encouragement messages based on the results of sentiment analysis. For example, if a user is experiencing high stress, it provides special advice including relaxation and coping techniques.

[1309] Feedback Input Method

[1310] The device provides an interface for the user to take action based on the suggested advice and record the results within the app. The user inputs their diet and exercise history, and this information is then sent back to the server.

[1311] Analysis means

[1312] The server stores and analyzes the received feedback information, including the user's progress (e.g., weight fluctuations, exercise history), as well as emotional data collected by the emotion engine, to generate more personalized advice.

[1313] For example, if a user uses the following prompt:

[1314] "I weigh 70kg, my goal weight is 65kg, and I want to exercise five times a week. I'm currently feeling stressed. I'd like some advice on a healthy diet and exercise plan, as well as relaxation techniques."

[1315] The system allows users to maintain a healthy lifestyle, receive emotional support and sustained motivation.

[1316] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1317] Step 1: Enter your user information

[1318] The terminal displays an interface for the user to input their profile information (age, sex, weight, height, target weight, lifestyle habits).

[1319] (Specific action) The user enters information into the form and clicks the "Submit" button.

[1320] Input: Profile information entered by the user.

[1321] Output: User profile information sent to the server.

[1322] Step 2: Submit user information

[1323] The terminal transmits the input user information to the server.

[1324] (Specific operation) The terminal packages user information in JSON format and sends the data to the server using an HTTP request.

[1325] Input: Profile information entered by the user.

[1326] Output: HTTP request for user profile information sent to the server.

[1327] Step 3: Storing user information and initial processing

[1328] The server stores the received user information in a database.

[1329] The server calculates initial parameters such as basal metabolic rate and BMI, and inputs them into a generative AI model to generate initial advice.

[1330] (Specific operation) The server saves the user information in the database using an INSERT query and calculates the BMI by "weight (kg) ÷ height (m)^2".

[1331] Input: User profile information.

[1332] Output: Initial parameters, generated initial advice.

[1333] Step 4: Generating Advice

[1334] The server uses a generative AI model to generate advice such as healthy lifestyle habits, cooking recipes, and exercise plans.

[1335] (Specific operation) The server inputs a prompt sentence into the generative AI model and obtains the generated advice.

[1336] Input: User information and initial parameters.

[1337] Output: The generated advice.

[1338] Step 5: Submitting Advice

[1339] The server transmits the generated advice to the user's terminal.

[1340] (Specific operation) The server sends a notification containing new advice to the terminal via an HTTP request.

[1341] Input: The generated advice.

[1342] Output: Notification to user terminal.

[1343] Step 6: Enter emotional information

[1344] The terminal provides an interface for the user to input emotional information (stress, joy, fatigue, etc.).

[1345] (Specific Action) The user selects an emotional state and clicks the "Send" button.

[1346] Input: A user-selected emotional state.

[1347] Output: Emotion information sent to the server.

[1348] Step 7: Sending Emotional Information

[1349] The terminal transmits the input emotion information to the server.

[1350] (Specific operation) The device packages the emotional information and sends the data to the server using an HTTP request.

[1351] Input: A user-selected emotional state.

[1352] Output: HTTP request for emotion information sent to the server.

[1353] Step 8: Emotional Analysis

[1354] The server analyzes the emotion information using an emotion engine and generates emotion data.

[1355] (Specific operation) The server inputs emotion information into the emotion engine and obtains the analysis results.

[1356] Input: Emotion information.

[1357] Output: The generated emotion data.

[1358] Step 9: Adjusting Advice

[1359] The server tailors advice and encouragement messages based on the emotion data.

[1360] (Specific operation) The server inputs emotional data into the generative AI model and obtains adjusted advice.

[1361] Input: Generated emotion data.

[1362] Output: Tailored advice and / or encouragement messages.

[1363] Step 10: Sending tailored advice

[1364] The server sends tailored advice and encouragement messages to the user's terminal.

[1365] (Specific operation) The server sends a notification to the terminal via an HTTP request.

[1366] Input: Tailored advice and / or encouragement messages.

[1367] Output: Notification to user terminal.

[1368] Step 11: Enter your feedback information

[1369] The terminal provides an interface for the user to take action based on the suggested advice and record the results.

[1370] (Specific action) The user enters detailed feedback and clicks the "Submit" button.

[1371] Input: The result of the user's action.

[1372] Output: Feedback information sent to the server.

[1373] Step 12: Submit your feedback

[1374] The terminal transmits the input feedback information to the server.

[1375] (Specific operation) The terminal packages the feedback information and sends it to the server via an HTTP request.

[1376] Input: Feedback information entered by the user.

[1377] Output: Feedback information sent to the server.

[1378] Step 13: Storing and analyzing feedback information

[1379] The server stores the received feedback information in a database and analyzes it.

[1380] (Specific operation) The server retrieves past feedback data via a query and applies an analysis algorithm.

[1381] Input: User feedback information.

[1382] Output: Analysis results and evaluation data.

[1383] Step 14: Generate new advice

[1384] The server uses a generative AI model to generate new advice and encouragement messages based on the progress data and emotion data.

[1385] (Specific operation) The server inputs the analysis results into the generated AI model and obtains new advice.

[1386] Input: Analysis results and evaluation data.

[1387] Output: New advice or encouragement message.

[1388] Step 15: Submitting a new advice

[1389] The server sends the generated new advice or encouragement message to the user's terminal.

[1390] (Specific operation) The server sends a notification containing new advice to the terminal via an HTTP request.

[1391] Input: A new advice or encouragement message.

[1392] Output: Notification to user terminal.

[1393] The above is the specific flow of the processing of this system. These steps provide personalized support tailored to the user's health and emotional state.

[1394] (Application example 2)

[1395] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1396] Conventional health management and emotional support systems lack personalized support that responds to a user's individual emotional state and in-store behavior. This makes it difficult to provide effective support tailored to a user's specific health goals and emotional state. Furthermore, in-store shopping experiences lack personalized product recommendations that meet the user's needs.

[1397] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1398] In this invention, the server includes input means for inputting user profile information, storage means for storing the input user information, generation means for generating advice on health and lifestyle habits based on the stored user information, transmission means for transmitting the generated advice to the user's device, emotion input means for inputting the user's emotional state, emotion analysis means for analyzing the input emotional information, generation means for adjusting the advice based on the emotion analysis result, generation means for generating product recommendations corresponding to the user's behavior in the store, transmission means for transmitting the generated advice and product recommendations to the user's device, feedback input means for inputting user feedback information, analysis means for storing and analyzing the feedback information, generation means for generating new advice and encouragement messages corresponding to the user's progress based on the analysis result, and transmission means for transmitting the generated new advice and messages to the user's device. This enables personalized health management and emotional support and an improved shopping experience corresponding to the user's individual emotional state and behavior in the physical store.

[1399] "User profile information" refers to information that a user inputs into the system, such as their age, sex, height, weight, target weight, lifestyle habits, etc.

[1400] "Input means" refers to an interface through which a user inputs profile information and emotional state into the system.

[1401] The "storage means" is a function for storing input user information, feedback information, etc. in a database.

[1402] The "generation means" is a function for generating advice on health and lifestyle habits based on stored user information and emotional information, and product recommendations corresponding to in-store behavior.

[1403] The "transmission means" is a function for transmitting the generated advice, encouragement message, and product recommendation to the user's terminal.

[1404] The "emotion input means" is an interface that allows the user to input their own emotional state (stress, joy, fatigue, etc.).

[1405] The "emotion analysis means" is a function for analyzing the user's emotional state based on input emotion information.

[1406] "Product recommendations" refer to products recommended for purchase in-store based on a user's health goals and current emotional state.

[1407] The "feedback input means" is an interface for the user to input the actions taken based on the proposed advice and the results thereof.

[1408] The "analysis means" is a function for performing analysis based on the saved feedback information and evaluating the user's progress.

[1409] "Encouragement messages" are encouraging messages that are generated based on the user's progress and emotional state.

[1410] This invention relates to a system that uses a generative AI model and an emotion engine to provide advice to users to solve their daily problems and issues, and also provides support according to the user's emotional state. This system is configured to include the following multiple means.

[1411] Collecting user-entered information

[1412] The device provides an interface that allows the user to enter their profile information on the initial setup screen. The user enters information such as age, gender, weight, height, lifestyle habits, and target weight. This information is sent from the device to the server.

[1413] Saving and initializing user information

[1414] The server stores the received user information in a database. The stored information includes initial parameters for assessing the user's health status, including calculations of BMI and basal metabolic rate. The server inputs these initial parameters into a generative AI model to generate initial advice for the user.

[1415] Generating and Sending Advice

[1416] The server generates advice such as healthy lifestyle habits, cooking recipes, and exercise plans. This advice is sent to the user as periodic notifications. For example, it provides a "weekly exercise plan" or "today's healthy recipe."

[1417] Emotional state recognition and analysis

[1418] The terminal provides an interface for inputting the user's emotional information. The user can input their emotional state (stress, joy, fatigue, etc.). The emotional information is transmitted from the terminal to the server.

[1419] Analysis using emotion engine

[1420] The server uses an emotion engine to analyze the user's emotion information and generate emotion data, which includes the user's current mood and emotional state.

[1421] Advice adjustment based on sentiment data

[1422] The server uses the generating means to tailor advice and encouragement messages based on the emotion data recognized by the emotion engine, for example, providing special advice including relaxation and coping techniques when the user is experiencing high stress.

[1423] In-store product recommendations

[1424] The server generates product recommendations based on the user's profile information and emotional information, corresponding to their behavior in the physical store. For example, if a user wants a low-calorie meal, the server will recommend low-calorie products.

[1425] Sending advice and encouragement messages

[1426] The server sends tailored advice and encouragement messages and product recommendations to the user's terminal.

[1427] Collecting feedback on user activities

[1428] The device provides an interface for the user to take action based on the suggested advice and record the results within the app. The user inputs their diet and exercise history, and this information is then sent back to the server.

[1429] Progress data management and analysis

[1430] The server stores and analyzes the received feedback information, including evaluating the user's progress (weight fluctuations, exercise record, etc.), and includes emotional data collected by the emotion engine to generate more personalized advice.

[1431] Encouragement and new advice

[1432] The server sends new advice and encouragement messages generated based on the progress data and emotion data to the user's terminal.

[1433] Specific examples

[1434] For example, if a user enters "weight 70 kg, target weight 65 kg, and desires to exercise five times a week" during initial setup and indicates that they are currently experiencing high levels of stress, the server will provide advice including health plans and relaxation methods.Product recommendations based on in-store behavior can suggest directions to shelves with low-calorie foods and relaxation goods.

[1435] Prompt Sentence Examples

[1436] "A user comes to your store feeling very stressed. His health goal is to lose 5kg. Please recommend some products."

[1437] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1438] Step 1: Enter your user information

[1439] The terminal provides an interface on the initial setup screen where the user can enter their profile information (age, gender, weight, height, target weight, lifestyle habits). The user enters this information into the terminal and presses the send button.

[1440] Input: Age, gender, weight, height, target weight, lifestyle habits

[1441] Output: User profile information

[1442] Step 2: Save user information

[1443] The server receives the user profile information sent from the terminal and stores this information in a database using a storage means.

[1444] Input: User profile information

[1445] Output: User information stored in the database

[1446] Step 3: Generate initial advice

[1447] Based on the stored user profile information, the server inputs initial parameters such as BMI and basal metabolic rate into the generative AI model and generates initial health advice for the user.

[1448] Input: User profile information

[1449] Data processing: BMI, basal metabolic rate calculation

[1450] Output: First health advice

[1451] Step 4: Submitting Advice

[1452] The server transmits the generated initial health advice to the user's terminal using the transmission means, and the user receives the advice on the terminal.

[1453] Input: First Health Advice

[1454] Output: Advice sent to the user's terminal

[1455] Step 5: Enter your emotional state

[1456] The terminal provides an interface for the user to input their emotional state (stress, joy, fatigue, etc.) The user inputs the emotional state and transmits it from the terminal to the server.

[1457] Input: Emotional state (stress, joy, fatigue, etc.)

[1458] Output: Emotion information sent to the server

[1459] Step 6: Analyze emotional state

[1460] The server uses an emotion engine to analyze the received emotion information and generate emotion data, which includes the user's current mood and emotional state.

[1461] Input: Emotion information

[1462] Data processing: Sentiment analysis

[1463] Output: Emotion data

[1464] Step 7: Adjusting advice based on sentiment data

[1465] The server uses a generating means to adjust existing advice based on the emotion data and generate new advice and / or encouragement messages.

[1466] Input: Emotion data

[1467] Data calculation:Advice adjustment

[1468] Output: New advice and encouragement messages

[1469] Step 8: Recommend products in-store

[1470] The server generates product recommendations based on the user's profile information and emotional information, corresponding to their behavior in the physical store. For example, if the user wants to eat low-calorie meals, the server will recommend low-calorie products.

[1471] Input: User profile information, emotion information

[1472] Data Computing: Product Recommendation Generation

[1473] Output: Product recommendation

[1474] Step 9: Submitting advice and recommendations

[1475] The server sends tailored advice and product recommendations to the user's device, who receives this information in the store and uses it as a guide for shopping.

[1476] Input: New advice, encouragement message, product recommendation

[1477] Output: Advice and product recommendations sent to the user device

[1478] Step 10: Enter your feedback information

[1479] The terminal provides an interface for the user to take action based on the proposed advice and input the results. The user inputs the details of their meals and exercise history and sends them to the server.

[1480] Input: Meal details, exercise history

[1481] Output: Feedback information sent to the server

[1482] Step 11: Analyze feedback

[1483] The server stores and analyzes the received feedback information, including the user's progress (weight fluctuations, exercise performance, etc.).

[1484] Input: Feedback information

[1485] Data Calculation: Feedback Analysis

[1486] Output: Analysis results

[1487] Step 12: Generate and send new advice

[1488] The server sends new advice and encouragement messages based on the progress data and emotion data to the user's device, and the user receives support to maintain motivation through the device.

[1489] Input: Progress data, emotion data

[1490] Output: New advice and encouragement messages

[1491] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1492] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1493] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1494] [Fourth embodiment]

[1495] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1496] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1497] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1498] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1499] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1500] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1501] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1502] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1503] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1504] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1505] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1506] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1507] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1508] The present invention relates to a system that uses a generative AI model to provide advice to users to solve their daily problems and issues. This system includes the following various means:

[1509] Collecting user-entered information

[1510] The device provides an interface that allows the user to enter their profile information on an initial setup screen. The user enters information such as age, gender, weight, height, target weight, lifestyle habits, etc. This information is sent from the device to the server.

[1511] Saving and initializing user information

[1512] The server stores the received user information in a database. The stored information includes initial parameters for assessing the user's health status, including calculations of BMI and basal metabolic rate. The server inputs these initial parameters into a generative AI model to generate initial advice for the user.

[1513] Generating and Sending Advice

[1514] The server generates advice such as healthy lifestyle habits, cooking recipes, and exercise plans. This advice is sent to the user as periodic notifications. For example, it provides a "weekly exercise plan" or "today's healthy recipe."

[1515] Collecting feedback on user activities

[1516] The device provides an interface for the user to take action based on the suggested advice and record the results within the app. The user inputs their diet and exercise history, and this information is then sent back to the server.

[1517] Progress data management and analysis

[1518] The server stores and analyzes the received feedback information. This analysis includes evaluating the user's progress, such as the pace of weight loss and exercise status. Based on the analysis results, new advice and encouragement messages are generated based on the user's progress.

[1519] Encouragement and new advice

[1520] The server then sends new advice and encouragement messages based on the progress data to the user's device, allowing the user to receive continuous personalized support and advice.

[1521] Specific examples

[1522] For example, if a user enters "I weigh 70 kg, my goal weight is 65 kg, and I want to exercise five times a week" during initial setup, the server will generate the following advice: jog for 20 minutes every morning and include vegetables in each meal. Furthermore, when the user follows the advice, records their actions, and enters their feedback, the server analyzes the data and generates an encouraging message such as "Great progress! Keep it up!" and sends it to the user's device.

[1523] This system allows users to maintain a healthy lifestyle and sustain their motivation. In this way, the present invention provides a method for effectively supporting users' lifestyle habits and health conditions.

[1524] The processing flow will be explained below.

[1525] Step 1:

[1526] The user launches the app and enters their profile information (age, gender, weight, height, target weight, lifestyle habits, etc.) on the initial setup screen.

[1527] Step 2:

[1528] The device formats the entered profile information and sends it to the server.

[1529] Step 3:

[1530] The server stores the received user information in a database.

[1531] Step 4:

[1532] The server calculates initial parameters (BMI, basal metabolic rate, etc.) based on the stored user information.

[1533] Step 5:

[1534] The server inputs initial parameters into the generated AI model and generates initial advice for the user (health habits, diet, exercise plan, etc.).

[1535] Step 6:

[1536] The server transmits the generated advice to the user's terminal.

[1537] Step 7:

[1538] The user acts on the suggested advice and enters the results and feedback into the app.

[1539] Step 8:

[1540] The terminal formats the feedback information from the user and sends it to the server.

[1541] Step 9:

[1542] The server receives the feedback information and records it in a database.

[1543] Step 10:

[1544] The server analyzes the recorded feedback information and evaluates the user's progress (weight fluctuations, exercise performance, etc.).

[1545] Step 11:

[1546] The server generates new advice and / or encouragement messages based on the analysis results.

[1547] Step 12:

[1548] The server sends the generated new advice or message to the user's terminal.

[1549] Step 13:

[1550] The user receives new advice and encouragement messages and continues to act accordingly.

[1551] This process allows users to receive ongoing, personalized support and feedback.

[1552] Example 1

[1553] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1554] Conventional health management systems have struggled to effectively utilize user profile information and feedback information to provide individually optimized advice. They also lacked the functionality to generate appropriate encouragement or new advice in real time based on the user's progress. This made it difficult for users to maintain consistent motivation and obtain specific guidance for achieving their goals.

[1555] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1556] In this invention, the server includes input means for inputting user profile information, storage means for storing the input user information, generation means for generating advice regarding health and lifestyle habits based on the stored user information, transmission means for transmitting the generated advice to the user's terminal, feedback input means for inputting user feedback information, analysis means for storing and analyzing the feedback information, generation means for generating new advice and / or encouragement messages according to the user's progress based on the analysis results, transmission means for transmitting the generated new advice and / or messages to the user's terminal, and generation means for analyzing the feedback information using a generative AI model and generating advice based on prompt sentences. This makes it possible to provide advice and encouragement optimized for the user's individual situation in real time.

[1557] "Input means" refers to a device or function that provides an interface for users to input profile information and feedback information.

[1558] The "storage means" is a device or function for safely storing input user information and feedback information.

[1559] The "generation means" is a device or function for generating advice and encouragement messages regarding health and lifestyle habits based on stored user information, feedback information, and analysis results.

[1560] The "transmission means" is a device or function for transmitting the generated advice or message to the user's terminal.

[1561] The "feedback input means" is a device or function that provides an interface for users to input their activity results and progress.

[1562] "Analysis means" is a device or function for storing and analyzing feedback information.

[1563] A "generative AI model" is an artificial intelligence model used to generate optimal advice and encouragement messages based on user information, feedback information, and prompt text.

[1564] A "prompt" is a command entered into a generative AI model to generate specific advice or messages.

[1565] A "user terminal" is a computing device or mobile device that a user uses to enter information or receive advice or messages.

[1566] This invention relates to a system that uses generative AI models to provide advice to users to solve their daily problems and issues. This system is realized by linking a server and a terminal.

[1567] The device provides an interface that allows the user to input profile information on the initial setup screen. Input items include age, gender, weight, height, target weight, and lifestyle habits. The user inputs this information using the device's input means. Once input is complete, the device encrypts the information and sends it to the server.

[1568] The server stores the received user information in a MySQL database. The server then runs a script to calculate BMI and basal metabolic rate, and inputs the calculation results into the generative AI model as initial parameters. For example, a prompt such as "The user is a 35-year-old male, weighs 70 kg, and has a target weight of 65 kg. Please provide appropriate health advice" is input into the generative AI model. The generative AI model generates its initial advice based on this.

[1569] The generated advice is sent to the user's device using the server's transmission means. Specific advice might include "Jogging for 20 minutes every morning and including vegetables in every meal." This advice is displayed as a push notification on the device, allowing the user to check it.

[1570] The device provides a feedback input means for collecting the user's activity results and progress information. The user can input their daily diet and exercise record. For example, they can input information such as "I had a vegetable salad for breakfast and jogged for 20 minutes" and send it to the server.

[1571] The server then stores the feedback information in a MySQL database. The stored data is then analyzed using Python scripts. For example, the server evaluates the user's weight loss rate and exercise status, and generates new advice and encouragement messages based on the user's progress.

[1572] The newly generated advice and encouragement messages are then sent back to the user's device using the server's transmission means, including motivational messages such as "You're only 3kg away from your goal weight! Great progress!"

[1573] In this way, the present invention provides a method for supporting a user's healthy lifestyle and maintaining continuous motivation. Below are specific examples of prompt sentences to be input into the generative AI model.

[1574] Prompt Sentence Examples

[1575] "To support a healthy lifestyle, please generate exercise advice based on the following user information: age 35, gender male, current weight 70kg, goal weight 65kg, desire to exercise 5 times a week."

[1576] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1577] Step 1:

[1578] The terminal provides an interface for the user to input profile information. After the user has input information such as age, gender, weight, height, target weight, and lifestyle habits, the user clicks the "Submit" button. This input information is encrypted and sent from the terminal to the server. The input data includes the user's personal information and health goals. This information becomes the input data. The output is the data sent to the server.

[1579] Step 2:

[1580] The server stores the received user information in a MySQL database. This information includes the user's personal information and health goals. The server calculates BMI and basal metabolic rate based on the stored data. The calculation requires data such as the user's weight, height, age, and gender. The server generates a prompt statement that uses the calculation results as initial parameters and inputs them into the generative AI model. The input data is the user information obtained from the database, and the output is the prompt statement.

[1581] Step 3:

[1582] The server inputs a prompt sentence into the generative AI model. This prompt sentence includes the calculated initial parameters. For example, the prompt sentence might be something like, "The user is a 35-year-old male, weighs 70 kg, and has a target weight of 65 kg. Please provide appropriate health advice." The generative AI model generates advice based on this prompt sentence. The input data is the prompt sentence, and the output is the generated advice.

[1583] Step 4:

[1584] The server sends the generated advice to the user's device. This advice is provided as a concrete guide to action. For example, the advice might be, "Jogging for 20 minutes every morning and including vegetables in every meal." The input data is the generated advice, and the output is the data sent to the user's device.

[1585] Step 5:

[1586] The terminal provides a feedback interface for the user to input the results of their daily activities. The user inputs their dietary and exercise records. Once the input is complete, the user clicks the "Submit" button. The input information is encrypted and sent from the terminal to the server. The input data is the user's daily activity results, and the output is the data sent to the server.

[1587] Step 6:

[1588] The server stores the received feedback information in a MySQL database. It then uses a Python script to analyze the stored data. This analysis includes assessing the user's weight loss rate and exercise status. The input data is the feedback information obtained from the database, and the output is the analysis results.

[1589] Step 7:

[1590] The server generates new advice and / or encouragement messages based on the analysis results, such as "You're 3kg away from your goal weight! Great progress!" The input data are the analysis results, and the output is the generated advice and / or messages.

[1591] Step 8:

[1592] The server then sends the generated new advice and encouragement messages to the user's device, helping the user stay motivated and progress toward their health goals. The input data are the generated advice and messages, and the output is the data sent to the user's device.

[1593] (Application example 1)

[1594] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1595] In recent years, living a healthy lifestyle has become increasingly important, but many people find it difficult to maintain a balanced diet in their busy daily lives. Furthermore, appropriate advice and feedback based on the user's progress are essential for maintaining a healthy diet. However, no consistent system for achieving this has existed. In particular, there is a need for a system that can propose personalized meal menus based on the user's health status and collect feedback to reflect in future proposals.

[1596] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1597] In this invention, the server includes an input means for inputting a user's profile information, a storage means for saving the input user information, a generation means for generating health and lifestyle advice based on the saved user information, a transmission means for sending the generated advice to the user's device, a feedback input means for inputting the user's feedback information, an analysis means for saving and analyzing the feedback information, a generation means for generating new advice and encouragement messages based on the analysis results in accordance with the user's progress, a suggestion means for suggesting health-conscious meal menus through a food delivery service, a feedback collection means for collecting feedback on the suggested meal menus via the user's device, and a reflection means for analyzing the collected feedback information and reflecting it in subsequent advice. This allows users to receive meal suggestions based on their individual health status and goals, and feedback on those suggestions. Furthermore, by utilizing a generative AI model, optimal advice can be provided in real time, supporting the user's long-term health management.

[1598] definition statement

[1599] "User" refers to a person who uses the target system.

[1600] "Profile information" refers to personal data such as a user's age, gender, weight, height, lifestyle habits, and target weight.

[1601] "Input means" refers to the device or interface through which a user inputs profile information and feedback information.

[1602] "Storage means" refers to a device or system for storing input user information and feedback information.

[1603] "Generating means" refers to a device or system for generating advice based on stored user information.

[1604] The "transmission means" refers to a device or system for transmitting the generated advice or message to the user's terminal.

[1605] "Feedback input means" refers to a device or interface that allows a user to input the results of implementing advice and their opinions.

[1606] "Analysis means" refers to a device or system for analyzing input feedback information.

[1607] An "encouragement message" refers to a message intended to encourage the user.

[1608] "Proposal means" refers to a device or system for proposing healthy meal menus to users through food delivery services.

[1609] "Feedback collection means" refers to a device or interface that allows users to collect feedback and experience regarding the proposed meal menu.

[1610] "Reflection means" refers to a device or system that analyzes collected feedback information and uses it to generate next advice.

[1611] A "generative AI model" refers to artificial intelligence that creates advice and messages based on data.

[1612] MODE FOR CARRYING OUT THE INVENTION

[1613] The specific system of this invention is composed of a series of software modules that run on a smartphone application and a cloud server. The system configuration and processing content will be explained below.

[1614] System configuration

[1615] 1. User input and saving:

[1616] The device provides an interface that allows users to enter their profile information on an initial setup screen, including information such as age, gender, weight, height, lifestyle habits, and target weight. This information is sent from the device to a cloud server.

[1617] The server stores the received user information in a database, including initial parameters for assessing the user's health status, such as BMI and basal metabolic rate.

[1618] Advice generation and delivery

[1619] 2. Generation means:

[1620] The server uses the stored user information to generate healthy lifestyle and dietary advice, using generative AI models (e.g., GPT-3) to create personalized exercise plans and meal menus based on the user's profile.

[1621] The generated advice is then periodically sent to the user's smartphone, allowing the user to receive guidelines for consistently maintaining a healthy lifestyle.

[1622] Collecting and analyzing feedback

[1623] 3. Feedback Collection and Analysis:

[1624] The device provides an interface for the user to record the results of their actions based on the suggested advice, such as their diet and exercise record.

[1625] The server receives the collected feedback information and stores it in a database. This information is then analyzed to evaluate the user's progress, including the user's weight fluctuations and exercise performance.

[1626] New Advice and Encouragement

[1627] 4. Next advice generation:

[1628] The server generates new advice and encouragement messages based on feedback information and progress data, again using a generative AI model to provide optimal messages in real time.

[1629] For example, if a user is making good progress towards a goal, a message might be provided saying, "Great progress! Keep it up!"

[1630] Food delivery service

[1631] 5. Healthy meal menu suggestions:

[1632] As a suggestion method, the server proposes meal menus according to the user's health condition through a food delivery service, allowing users to easily incorporate healthy eating into their busy daily lives.

[1633] As a means of collecting feedback, feedback on the provided meal is collected via the user's terminal and reflected in the next advice.

[1634] Hardware and software used

[1635] Smartphone: Use an iOS or Android device.

[1636] Use cloud servers (e.g., AWS EC2, Google Cloud Platform) to store and process data.

[1637] Database: Use a system such as Amazon RDS or Firebase to store user data and feedback information.

[1638] Generative AI models: Use large-scale language models such as GPT-3 to generate advice and messages.

[1639] Examples and prompts:

[1640] The user launches the app and enters their profile information in response to the following prompts:

[1641] "Please tell us your age, gender, weight, height, goal weight and current lifestyle habits."

[1642] Example prompts to input to the generative AI model:

[1643] "User information: Age: 35, Gender: Male, Weight: 70kg, Height: 175cm, Target weight: 65kg, Lifestyle: Mostly desk work. Please suggest a weekly meal menu and exercise plan that is suitable for this user."

[1644] As a result, the present invention realizes health support that is individually customized for each user, and supports continuous health management.

[1645] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1646] Program processing flow

[1647] Step 1:

[1648] User enters profile information

[1649] Users launch the app on their smartphone and enter profile information such as age, gender, weight, height, lifestyle habits, and target weight on the initial setup screen.

[1650] Input: User's personal data (age, gender, weight, height, lifestyle habits, goal weight).

[1651] Output: The entered data is sent from the terminal to the server.

[1652] Step 2:

[1653] Saving user information

[1654] The server stores the user information received from the terminal in a database.

[1655] Input: User profile information sent from the device.

[1656] Output: The user information is stored in the database.

[1657] Step 3:

[1658] Calculating and saving initial parameters

[1659] The server calculates BMI and basal metabolic rate based on the saved user information and stores this in a database.

[1660] Input: Saved user profile information.

[1661] Specific operations: Calculate BMI (weight ÷ (height × height)) and basal metabolic rate.

[1662] Output: The calculated initial parameters are stored in a database.

[1663] Step 4:

[1664] Generating Advice

[1665] The server uses a generative AI model (e.g., GPT-3) to generate personalized advice based on the user's initial parameters.

[1666] Input: User information stored in the database and calculated initial parameters.

[1667] Example prompt: "User information: Age: 35, Gender: Male, Weight: 70kg, Height: 175cm, Target weight: 65kg, Lifestyle: Mostly desk work. Please suggest a weekly meal plan and exercise plan that is appropriate for this user."

[1668] Output: Healthy lifestyle and meal recommendations from a generative AI model.

[1669] Step 5:

[1670] Providing advice

[1671] The server transmits the generated advice to the user's terminal.

[1672] Input: The generated advice.

[1673] Output: The advice is sent to the user's device. Specific implementations include push notifications and in-app notifications.

[1674] Step 6:

[1675] Enter your feedback

[1676] Users take action based on the suggested advice and record the results within the app.

[1677] Input: Feedback information such as dietary details and exercise performance.

[1678] Output: Feedback information is sent from the device to the server.

[1679] Step 7:

[1680] Feedback storage and analysis

[1681] The server stores the feedback information received from the user in a database and analyzes it.

[1682] Input: User feedback information.

[1683] Specific actions: Analyze progress data such as weight fluctuations and exercise status.

[1684] Output: Generation of analytical results based on feedback.

[1685] Step 8:

[1686] Generate new advice and encouragement messages

[1687] The server generates new advice and encouragement messages based on the analysis results, using generative AI models to create personalized messages.

[1688] Input: Parsed feedback data.

[1689] Output: The newly generated advice or encouragement message.

[1690] Step 9:

[1691] Providing new advice and encouragement messages

[1692] The server sends the generated new advice or encouragement message to the user's terminal.

[1693] Input: The new advice or message that is generated.

[1694] Output: A new notification arrives on the user's device.

[1695] Step 10:

[1696] Food delivery service proposal

[1697] The server uses a food delivery service to suggest meal menus that suit the user's health condition.

[1698] Input: User health data and the generative AI model's recommendations.

[1699] Output: The meal menu is displayed on the user's device.

[1700] Step 11:

[1701] Gathering feedback on meal menus

[1702] The user inputs feedback on the proposed meal menu into the terminal and transmits it to the server.

[1703] Input: User's feedback information about the meal menu.

[1704] Output: Feedback information is sent from the device to the server.

[1705] Step 12:

[1706] Reflected in the next proposal

[1707] The server analyzes the collected feedback information and reflects it in the next advice.

[1708] Input: User feedback information.

[1709] Output: New proposals based on the analysis results.

[1710] The above steps realize a system that provides users with personalized health advice and meal suggestions, and supports continuous health management.

[1711] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1712] The present invention relates to a system that uses a generative AI model and an emotion engine to provide advice to users to solve their daily problems and issues, and also provides support according to the user's emotional state. This system is configured to include the following various means.

[1713] Collecting user-entered information

[1714] The device provides an interface that allows the user to enter their profile information on an initial setup screen. The user enters information such as age, gender, weight, height, target weight, lifestyle habits, etc. This information is sent from the device to the server.

[1715] Saving and initializing user information

[1716] The server stores the received user information in a database. The stored information includes initial parameters for assessing the user's health status, including calculations of BMI and basal metabolic rate. The server inputs these initial parameters into a generative AI model to generate initial advice for the user.

[1717] Generating and Sending Advice

[1718] The server generates advice such as healthy lifestyle habits, cooking recipes, and exercise plans. This advice is sent to the user as periodic notifications. For example, it provides a "weekly exercise plan" or "today's healthy recipe."

[1719] Emotional state recognition and analysis

[1720] The terminal provides an interface for inputting the user's emotional information. The user can input their emotional state (stress, joy, fatigue, etc.). The emotional information is transmitted from the terminal to the server.

[1721] Analysis using emotion engine

[1722] The server uses an emotion engine to analyze the user's emotion information and generate emotion data, which includes the user's current mood and emotional state.

[1723] Advice adjustment based on sentiment data

[1724] The server uses the generating means to tailor advice and encouragement messages based on the emotion data recognized by the emotion engine, for example, providing special advice including relaxation and coping techniques when the user is experiencing high stress.

[1725] Sending advice and encouragement messages

[1726] The server sends tailored advice and encouragement messages to the user's terminal.

[1727] Collecting feedback on user activities

[1728] The device provides an interface for the user to take action based on the suggested advice and record the results within the app. The user inputs their diet and exercise history, and this information is then sent back to the server.

[1729] Progress data management and analysis

[1730] The server stores and analyzes the received feedback information, including evaluating the user's progress (weight fluctuations, exercise record, etc.), and also includes emotional data collected by the emotion engine to generate more personalized advice.

[1731] Encouragement and new advice

[1732] The server sends new advice and encouragement messages generated based on the progress data and emotion data to the user's terminal.

[1733] Specific examples

[1734] For example, if a user enters "weight 70 kg, target weight 65 kg, and desires to exercise five times a week" during initial setup and indicates that they are currently experiencing high stress, the server will provide advice including relaxation methods and stress management, in addition to daily exercise plans and healthy eating advice. Furthermore, when the user follows the advice, records their actions, and enters their feedback, the server analyzes the data and generates an encouraging message such as "Great progress! Don't forget to relax!" and sends it to the user's device.

[1735] This system allows users to maintain a healthy lifestyle and receive emotional support while maintaining sustained motivation. In this way, the present invention provides a method for effectively supporting users' lifestyle habits, health status, and even emotional well-being.

[1736] The processing flow will be explained below.

[1737] Processing steps of a system that combines emotion engines

[1738] Step 1:

[1739] The user launches the app and enters their profile information (age, gender, weight, height, target weight, lifestyle habits, etc.) and current emotional state (stress level, mood, etc.) on the initial setup screen.

[1740] Step 2:

[1741] The device formats the entered profile information and emotion information and transmits it to the server.

[1742] Step 3:

[1743] The server stores the received user information in a database, along with emotional information.

[1744] Step 4:

[1745] The server calculates initial parameters (BMI, basal metabolic rate, stress level, etc.) based on the stored user information and emotional information.

[1746] Step 5:

[1747] The server inputs initial parameters into the generated AI model and generates initial advice for the user (health habits, diet, exercise plan, and stress management).

[1748] Step 6:

[1749] The server transmits the generated advice to the user's terminal.

[1750] Step 7:

[1751] The user acts on the suggested advice and inputs the results, feedback, and changes in emotional state into the app.

[1752] Step 8:

[1753] The terminal formats the feedback information and emotion information from the user and sends it to the server.

[1754] Step 9:

[1755] The server receives the feedback information and emotion information and records them in a database.

[1756] Step 10:

[1757] The server analyzes the recorded feedback and emotional information, including assessing the user's progress (weight fluctuations, exercise performance, stress levels, etc.).

[1758] Step 11:

[1759] The server generates new advice and reassurance messages based on the analysis results, such as suggesting relaxation techniques or counseling if high stress persists.

[1760] Step 12:

[1761] The server sends the generated new advice or message to the user's terminal.

[1762] Step 13:

[1763] The user receives new advice and encouragement messages and continues to act accordingly.

[1764] Specific examples

[1765] For example, if a user enters the following information during initial setup: "I weigh 70 kg, my goal weight is 65 kg, I want to exercise five times a week, and I'm currently experiencing high stress," the server will generate the following advice:

[1766] Jogging for 20 minutes every morning.

[1767] Include vegetables in every meal.

[1768] I've been under a lot of stress lately, so I'm making sure to set aside time for relaxation every night.

[1769] If the user follows the advice and enters the results into the app, providing feedback such as "I've jogged four days and added vegetables to my diet, but my stress level is still high," the server analyzes the data and generates the following encouragement message:

[1770] "Great progress! Keep jogging. Try deep breathing exercises and yoga to manage stress."

[1771] In this way, users receive ongoing, personalized support and feedback, helping them maintain a healthy lifestyle while also providing the right emotional support.

[1772] Example 2

[1773] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1774] Conventional health management systems are limited to providing advice based on user feedback and lack support that takes into account the user's emotional state. This makes it difficult to maintain health management and poses challenges for users in maintaining their motivation.

[1775] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means for inputting user profile information, a storage means for storing the input user information, a generation means for generating advice on health and lifestyle habits, a transmission means for transmitting the generated advice to the user's terminal, an emotion input means for inputting user emotion information, an emotion analysis means for analyzing the input emotion information, an adjustment means for adjusting advice and encouragement messages based on the emotion analysis results, a feedback input means for inputting user feedback information, an analysis means for storing and analyzing the feedback information, a generation means for generating new advice and encouragement messages according to the user's progress based on the analysis results, and a transmission means for transmitting the generated new advice and messages to the user's terminal. This enables personalized support according to the user's emotional state.

[1776] "Input means" refers to a device or function that provides an interface for users to input profile information, emotional information, etc. into the system.

[1777] "Storage means" refers to devices or functions for recording and storing input user information and feedback information in a database or the like.

[1778] "Generation means" refers to a device or function for creating advice and encouragement messages regarding health and lifestyle habits based on input data.

[1779] The "transmission means" refers to a device or function for transmitting the generated advice or encouragement message to the user's terminal.

[1780] "Emotion input means" refers to a device or function that provides an interface for a user to input their emotional state.

[1781] "Emotion analysis means" refers to a device or function for analyzing input emotion information and generating emotion data of the user.

[1782] The "adjustment means" refers to a device or function for adjusting the generated advice or encouragement message to suit the user's state based on the emotion analysis results.

[1783] The "feedback input means" refers to a device or function that provides an interface for a user to input the results of an action or feedback information.

[1784] The "analysis means" refers to a device or function for analyzing the input feedback information and evaluating the user's progress and goal achievement status.

[1785] The present invention relates to a system that uses a generative AI model and an emotion engine to provide advice to users to solve their daily problems and issues, and also provides support according to the user's emotional state. This system is configured to include the following various means.

[1786] Input Method

[1787] The terminal provides an interface for the user to input their own profile information and emotional information, allowing the user to input information such as age, sex, weight, height, target weight, lifestyle habits, stress, joy, fatigue, etc. This information is transmitted to the server by a transmission means, which will be described later.

[1788] Preservation means

[1789] The server stores the entered user information in a database, including initial parameters for assessing the user's health status, such as BMI and basal metabolic rate.

[1790] generation means

[1791] The server uses the generative AI model to generate advice such as healthy lifestyle habits, cooking recipes, and exercise plans based on the stored user information. For example, it generates a "weekly exercise plan" or "healthy recipe for the day." It also adjusts advice and encouragement messages based on emotional data.

[1792] Transmission method

[1793] The server includes means for transmitting the generated advice and / or encouragement messages to the user's terminal, so that the user can periodically receive appropriate advice.

[1794] Emotion input method

[1795] The terminal provides an interface for inputting the user's emotional information, which includes emotional states such as stress, joy, fatigue, etc. This information is also transmitted to the server by the transmission means.

[1796] Emotion analysis means

[1797] The server uses an emotion engine to analyze the input emotion information, thereby generating emotion data for the user, which includes the user's current mood and emotional state.

[1798] Adjustment means

[1799] The server tailors advice and encouragement messages based on the results of sentiment analysis. For example, if a user is experiencing high stress, it provides special advice including relaxation and coping techniques.

[1800] Feedback Input Method

[1801] The device provides an interface for the user to take action based on the suggested advice and record the results within the app. The user inputs their diet and exercise history, and this information is then sent back to the server.

[1802] Analysis means

[1803] The server stores and analyzes the received feedback information, including the user's progress (e.g., weight fluctuations, exercise history), as well as emotional data collected by the emotion engine, to generate more personalized advice.

[1804] For example, if a user uses the following prompt:

[1805] "I weigh 70kg, my goal weight is 65kg, and I want to exercise five times a week. I'm currently feeling stressed. I'd like some advice on a healthy diet and exercise plan, as well as relaxation techniques."

[1806] The system allows users to maintain a healthy lifestyle, receive emotional support and sustained motivation.

[1807] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1808] Step 1: Enter your user information

[1809] The terminal displays an interface for the user to input their profile information (age, sex, weight, height, target weight, lifestyle habits).

[1810] (Specific action) The user enters information into the form and clicks the "Submit" button.

[1811] Input: Profile information entered by the user.

[1812] Output: User profile information sent to the server.

[1813] Step 2: Submit user information

[1814] The terminal transmits the input user information to the server.

[1815] (Specific operation) The terminal packages user information in JSON format and sends the data to the server using an HTTP request.

[1816] Input: Profile information entered by the user.

[1817] Output: HTTP request for user profile information sent to the server.

[1818] Step 3: Storing user information and initial processing

[1819] The server stores the received user information in a database.

[1820] The server calculates initial parameters such as basal metabolic rate and BMI, and inputs them into a generative AI model to generate initial advice.

[1821] (Specific operation) The server saves the user information in the database using an INSERT query and calculates the BMI by "weight (kg) ÷ height (m)^2".

[1822] Input: User profile information.

[1823] Output: Initial parameters, generated initial advice.

[1824] Step 4: Generating Advice

[1825] The server uses a generative AI model to generate advice such as healthy lifestyle habits, cooking recipes, and exercise plans.

[1826] (Specific operation) The server inputs a prompt sentence into the generative AI model and obtains the generated advice.

[1827] Input: User information and initial parameters.

[1828] Output: The generated advice.

[1829] Step 5: Submitting Advice

[1830] The server transmits the generated advice to the user's terminal.

[1831] (Specific operation) The server sends a notification containing new advice to the terminal via an HTTP request.

[1832] Input: The generated advice.

[1833] Output: Notification to user terminal.

[1834] Step 6: Enter emotional information

[1835] The terminal provides an interface for the user to input emotional information (stress, joy, fatigue, etc.).

[1836] (Specific Action) The user selects an emotional state and clicks the "Send" button.

[1837] Input: A user-selected emotional state.

[1838] Output: Emotion information sent to the server.

[1839] Step 7: Sending Emotional Information

[1840] The terminal transmits the input emotion information to the server.

[1841] (Specific operation) The device packages the emotional information and sends the data to the server using an HTTP request.

[1842] Input: A user-selected emotional state.

[1843] Output: HTTP request for emotion information sent to the server.

[1844] Step 8: Emotional Analysis

[1845] The server analyzes the emotion information using an emotion engine and generates emotion data.

[1846] (Specific operation) The server inputs emotion information into the emotion engine and obtains the analysis results.

[1847] Input: Emotion information.

[1848] Output: The generated emotion data.

[1849] Step 9: Adjusting Advice

[1850] The server tailors advice and encouragement messages based on the emotion data.

[1851] (Specific operation) The server inputs emotional data into the generative AI model and obtains adjusted advice.

[1852] Input: Generated emotion data.

[1853] Output: Tailored advice and / or encouragement messages.

[1854] Step 10: Sending tailored advice

[1855] The server sends tailored advice and encouragement messages to the user's terminal.

[1856] (Specific operation) The server sends a notification to the terminal via an HTTP request.

[1857] Input: Tailored advice and / or encouragement messages.

[1858] Output: Notification to user terminal.

[1859] Step 11: Enter your feedback information

[1860] The terminal provides an interface for the user to take action based on the suggested advice and record the results.

[1861] (Specific action) The user enters detailed feedback and clicks the "Submit" button.

[1862] Input: The result of the user's action.

[1863] Output: Feedback information sent to the server.

[1864] Step 12: Submit your feedback

[1865] The terminal transmits the input feedback information to the server.

[1866] (Specific operation) The terminal packages the feedback information and sends it to the server via an HTTP request.

[1867] Input: Feedback information entered by the user.

[1868] Output: Feedback information sent to the server.

[1869] Step 13: Storing and analyzing feedback information

[1870] The server stores the received feedback information in a database and analyzes it.

[1871] (Specific operation) The server retrieves past feedback data via a query and applies an analysis algorithm.

[1872] Input: User feedback information.

[1873] Output: Analysis results and evaluation data.

[1874] Step 14: Generate new advice

[1875] The server uses a generative AI model to generate new advice and encouragement messages based on the progress data and emotion data.

[1876] (Specific operation) The server inputs the analysis results into the generated AI model and obtains new advice.

[1877] Input: Analysis results and evaluation data.

[1878] Output: New advice or encouragement message.

[1879] Step 15: Submitting a new advice

[1880] The server sends the generated new advice or encouragement message to the user's terminal.

[1881] (Specific operation) The server sends a notification containing new advice to the terminal via an HTTP request.

[1882] Input: A new advice or encouragement message.

[1883] Output: Notification to user terminal.

[1884] The above is the specific flow of the processing of this system. These steps provide personalized support tailored to the user's health and emotional state.

[1885] (Application example 2)

[1886] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1887] Conventional health management and emotional support systems lack personalized support that responds to a user's individual emotional state and in-store behavior. This makes it difficult to provide effective support tailored to a user's specific health goals and emotional state. Furthermore, in-store shopping experiences lack personalized product recommendations that meet the user's needs.

[1888] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1889] In this invention, the server includes input means for inputting user profile information, storage means for storing the input user information, generation means for generating advice on health and lifestyle habits based on the stored user information, transmission means for transmitting the generated advice to the user's device, emotion input means for inputting the user's emotional state, emotion analysis means for analyzing the input emotional information, generation means for adjusting the advice based on the emotion analysis result, generation means for generating product recommendations corresponding to the user's behavior in the store, transmission means for transmitting the generated advice and product recommendations to the user's device, feedback input means for inputting user feedback information, analysis means for storing and analyzing the feedback information, generation means for generating new advice and encouragement messages corresponding to the user's progress based on the analysis result, and transmission means for transmitting the generated new advice and messages to the user's device. This enables personalized health management and emotional support and an improved shopping experience corresponding to the user's individual emotional state and behavior in the physical store.

[1890] "User profile information" refers to information that a user inputs into the system, such as their age, sex, height, weight, target weight, lifestyle habits, etc.

[1891] "Input means" refers to an interface through which a user inputs profile information and emotional state into the system.

[1892] The "storage means" is a function for storing input user information, feedback information, etc. in a database.

[1893] The "generation means" is a function for generating advice on health and lifestyle habits based on stored user information and emotional information, and product recommendations corresponding to in-store behavior.

[1894] The "transmission means" is a function for transmitting the generated advice, encouragement message, and product recommendation to the user's terminal.

[1895] The "emotion input means" is an interface that allows the user to input their own emotional state (stress, joy, fatigue, etc.).

[1896] The "emotion analysis means" is a function for analyzing the user's emotional state based on input emotion information.

[1897] "Product recommendations" refer to products recommended for purchase in-store based on a user's health goals and current emotional state.

[1898] The "feedback input means" is an interface for the user to input the actions taken based on the proposed advice and the results thereof.

[1899] The "analysis means" is a function for performing analysis based on the saved feedback information and evaluating the user's progress.

[1900] "Encouragement messages" are encouraging messages that are generated based on the user's progress and emotional state.

[1901] This invention relates to a system that uses a generative AI model and an emotion engine to provide advice to users to solve their daily problems and issues, and also provides support according to the user's emotional state. This system is configured to include the following multiple means.

[1902] Collecting user-entered information

[1903] The device provides an interface that allows the user to enter their profile information on the initial setup screen. The user enters information such as age, gender, weight, height, lifestyle habits, and target weight. This information is sent from the device to the server.

[1904] Saving and initializing user information

[1905] The server stores the received user information in a database. The stored information includes initial parameters for assessing the user's health status, including calculations of BMI and basal metabolic rate. The server inputs these initial parameters into a generative AI model to generate initial advice for the user.

[1906] Generating and Sending Advice

[1907] The server generates advice such as healthy lifestyle habits, cooking recipes, and exercise plans. This advice is sent to the user as periodic notifications. For example, it provides a "weekly exercise plan" or "today's healthy recipe."

[1908] Emotional state recognition and analysis

[1909] The terminal provides an interface for inputting the user's emotional information. The user can input their emotional state (stress, joy, fatigue, etc.). The emotional information is transmitted from the terminal to the server.

[1910] Analysis using emotion engine

[1911] The server uses an emotion engine to analyze the user's emotion information and generate emotion data, which includes the user's current mood and emotional state.

[1912] Advice adjustment based on sentiment data

[1913] The server uses the generating means to tailor advice and encouragement messages based on the emotion data recognized by the emotion engine, for example, providing special advice including relaxation and coping techniques when the user is experiencing high stress.

[1914] In-store product recommendations

[1915] The server generates product recommendations based on the user's profile information and emotional information, corresponding to their behavior in the physical store. For example, if a user wants a low-calorie meal, the server will recommend low-calorie products.

[1916] Sending advice and encouragement messages

[1917] The server sends tailored advice and encouragement messages and product recommendations to the user's terminal.

[1918] Collecting feedback on user activities

[1919] The device provides an interface for the user to take action based on the suggested advice and record the results within the app. The user inputs their diet and exercise history, and this information is then sent back to the server.

[1920] Progress data management and analysis

[1921] The server stores and analyzes the received feedback information, including evaluating the user's progress (weight fluctuations, exercise record, etc.), and includes emotional data collected by the emotion engine to generate more personalized advice.

[1922] Encouragement and new advice

[1923] The server sends new advice and encouragement messages generated based on the progress data and emotion data to the user's terminal.

[1924] Specific examples

[1925] For example, if a user enters "weight 70 kg, target weight 65 kg, and desires to exercise five times a week" during initial setup and indicates that they are currently experiencing high levels of stress, the server will provide advice including health plans and relaxation methods.Product recommendations based on in-store behavior can suggest directions to shelves with low-calorie foods and relaxation goods.

[1926] Prompt Sentence Examples

[1927] "A user comes to your store feeling very stressed. His health goal is to lose 5kg. Please recommend some products."

[1928] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1929] Step 1: Enter your user information

[1930] The terminal provides an interface on the initial setup screen where the user can enter their profile information (age, gender, weight, height, target weight, lifestyle habits). The user enters this information into the terminal and presses the send button.

[1931] Input: Age, gender, weight, height, target weight, lifestyle habits

[1932] Output: User profile information

[1933] Step 2: Save user information

[1934] The server receives the user profile information sent from the terminal and stores this information in a database using a storage means.

[1935] Input: User profile information

[1936] Output: User information stored in the database

[1937] Step 3: Generate initial advice

[1938] Based on the stored user profile information, the server inputs initial parameters such as BMI and basal metabolic rate into the generative AI model and generates initial health advice for the user.

[1939] Input: User profile information

[1940] Data processing: BMI, basal metabolic rate calculation

[1941] Output: First health advice

[1942] Step 4: Submitting Advice

[1943] The server transmits the generated initial health advice to the user's terminal using the transmission means, and the user receives the advice on the terminal.

[1944] Input: First Health Advice

[1945] Output: Advice sent to the user's terminal

[1946] Step 5: Enter your emotional state

[1947] The terminal provides an interface for the user to input their emotional state (stress, joy, fatigue, etc.) The user inputs the emotional state and transmits it from the terminal to the server.

[1948] Input: Emotional state (stress, joy, fatigue, etc.)

[1949] Output: Emotion information sent to the server

[1950] Step 6: Analyze emotional state

[1951] The server uses an emotion engine to analyze the received emotion information and generate emotion data, which includes the user's current mood and emotional state.

[1952] Input: Emotion information

[1953] Data processing: Sentiment analysis

[1954] Output: Emotion data

[1955] Step 7: Adjusting advice based on sentiment data

[1956] The server uses a generating means to adjust existing advice based on the emotion data and generate new advice and / or encouragement messages.

[1957] Input: Emotion data

[1958] Data calculation:Advice adjustment

[1959] Output: New advice and encouragement messages

[1960] Step 8: Recommend products in-store

[1961] The server generates product recommendations based on the user's profile information and emotional information, corresponding to their behavior in the physical store. For example, if the user wants to eat low-calorie meals, the server will recommend low-calorie products.

[1962] Input: User profile information, emotion information

[1963] Data Computing: Product Recommendation Generation

[1964] Output: Product recommendation

[1965] Step 9: Submitting advice and recommendations

[1966] The server sends tailored advice and product recommendations to the user's device, who receives this information in the store and uses it as a guide for shopping.

[1967] Input: New advice, encouragement message, product recommendation

[1968] Output: Advice and product recommendations sent to the user device

[1969] Step 10: Enter your feedback information

[1970] The terminal provides an interface for the user to take action based on the proposed advice and input the results. The user inputs the details of their meals and exercise history and sends them to the server.

[1971] Input: Meal details, exercise history

[1972] Output: Feedback information sent to the server

[1973] Step 11: Analyze feedback

[1974] The server stores and analyzes the received feedback information, including the user's progress (weight fluctuations, exercise performance, etc.).

[1975] Input: Feedback information

[1976] Data Calculation: Feedback Analysis

[1977] Output: Analysis results

[1978] Step 12: Generate and send new advice

[1979] The server sends new advice and encouragement messages based on the progress data and emotion data to the user's device, and the user receives support to maintain motivation through the device.

[1980] Input: Progress data, emotion data

[1981] Output: New advice and encouragement messages

[1982] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1983] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1984] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1985] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1986] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1987] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1988] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1989] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1990] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1991] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1992] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1993] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1994] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1995] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1996] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1997] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1998] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1999] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2000] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2001] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2002] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2003] The following is further disclosed regarding the above embodiment.

[2004] (Claim 1)

[2005] input means for inputting user profile information;

[2006] a storage means for storing the input user information;

[2007] generating means for generating advice on health and lifestyle habits based on the stored user information;

[2008] a transmitting means for transmitting the generated advice to a user terminal;

[2009] a feedback input means for inputting user feedback information;

[2010] analysis means for storing and analyzing the feedback information;

[2011] a generating means for generating new advice and / or encouragement messages according to the user's progress based on the analysis results;

[2012] a transmitting means for transmitting the generated new advice or message to the user's terminal;

[2013] A system including:

[2014] (Claim 2)

[2015] 2. The system according to claim 1, wherein the analysis means includes evaluation means for evaluating the user's progress data and determining the progress of the user's goal.

[2016] (Claim 3)

[2017] 2. The system according to claim 1, wherein the generating means generates the advice in a notification format according to a specific condition of the user.

[2018] "Example 1"

[2019] (Claim 1)

[2020] input means for inputting user profile information;

[2021] a storage means for storing the input user information;

[2022] generating means for generating advice on health and lifestyle habits based on the stored user information;

[2023] a transmitting means for transmitting the generated advice to a user terminal;

[2024] a feedback input means for inputting user feedback information;

[2025] analysis means for storing and analyzing the feedback information;

[2026] a generating means for generating new advice and / or encouragement messages according to the user's progress based on the analysis results;

[2027] a transmitting means for transmitting the generated new advice or message to the user's terminal;

[2028] a generating means for analyzing the feedback information using a generative AI model and generating advice based on the prompt sentence;

[2029] A system including:

[2030] (Claim 2)

[2031] 2. The system according to claim 1, wherein the analysis means includes evaluation means for evaluating the user's progress data and determining the progress of the user's goal.

[2032] (Claim 3)

[2033] 2. The system of claim 1, wherein the generating means uses a generative AI model to generate advice in the form of a notification according to a user's specific conditions.

[2034] "Application Example 1"

[2035] (Claim 1)

[2036] input means for inputting user profile information;

[2037] a storage means for storing the input user information;

[2038] generating means for generating advice on health and lifestyle habits based on the stored user information;

[2039] a transmitting means for transmitting the generated advice to a user terminal;

[2040] a feedback input means for inputting user feedback information;

[2041] analysis means for storing and analyzing the feedback information;

[2042] a generating means for generating new advice and / or encouragement messages according to the user's progress based on the analysis results;

[2043] A proposal method for proposing healthy meal menus through food delivery services, and

[2044] a feedback collection means for collecting feedback on the proposed meal menu via a user's terminal;

[2045] A means for analyzing the collected feedback information and reflecting it in the next advice;

[2046] A system including:

[2047] (Claim 2)

[2048] 2. The system according to claim 1, wherein the analysis means includes evaluation means for evaluating the user's progress data and determining the progress of the user's goal.

[2049] (Claim 3)

[2050] 2. The system according to claim 1, wherein the generating means generates the advice in a notification format according to a specific condition of the user.

[2051] "Example 2: Combining Emotion Engines"

[2052] (Claim 1)

[2053] input means for inputting user profile information;

[2054] a storage means for storing the input user information;

[2055] generating means for generating advice on health and lifestyle habits based on the stored user information;

[2056] a transmitting means for transmitting the generated advice to a user terminal;

[2057] emotion input means for inputting emotion information of a user;

[2058] emotion analysis means for analyzing input emotion information;

[2059] an adjustment means for adjusting advice and encouragement messages based on the sentiment analysis results;

[2060] a feedback input means for inputting user feedback information;

[2061] analysis means for storing and analyzing the feedback information;

[2062] a generating means for generating new advice and / or encouragement messages according to the user's progress based on the analysis results;

[2063] a transmitting means for transmitting the generated new advice or message to the user's terminal;

[2064] A system including:

[2065] (Claim 2)

[2066] 2. The system of claim 1, wherein the analysis means includes evaluation means for evaluating the user's progress data and determining goal achievement status.

[2067] (Claim 3)

[2068] 2. The system according to claim 1, wherein the generating means generates the advice in a notification format according to a specific condition of the user.

[2069] "Application example 2 when combining emotion engines"

[2070] (Claim 1)

[2071] input means for inputting user profile information;

[2072] a storage means for storing the input user information;

[2073] generating means for generating advice on health and lifestyle habits based on the stored user information;

[2074] a transmitting means for transmitting the generated advice to a user terminal;

[2075] emotion input means for inputting an emotional state of a user;

[2076] emotion analysis means for performing analysis based on input emotion information;

[2077] generating means for tailoring advice based on sentiment analysis results;

[2078] A generating means for generating product recommendations corresponding to the user's behavior in the store;

[2079] a transmitting means for transmitting the generated advice or product recommendation to a user's terminal;

[2080] a feedback input means for inputting user feedback information;

[2081] analysis means for storing and analyzing the feedback information;

[2082] a generating means for generating new advice and / or encouragement messages according to the user's progress based on the analysis results;

[2083] a transmitting means for transmitting the generated new advice or message to the user's terminal;

[2084] A system including:

[2085] (Claim 2)

[2086] 2. The system according to claim 1, wherein the analysis means includes evaluation means for evaluating the user's progress data and determining the progress of the user's goal.

[2087] (Claim 3)

[2088] 2. The system according to claim 1, wherein the generating means generates the advice in a notification format according to a specific condition of the user. [Explanation of symbols]

[2089] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. input means for inputting user profile information; a storage means for storing the input user information; generating means for generating advice on health and lifestyle habits based on the stored user information; a transmitting means for transmitting the generated advice to a user terminal; a feedback input means for inputting user feedback information; analysis means for storing and analyzing the feedback information; a generating means for generating new advice and / or encouragement messages according to the user's progress based on the analysis results; a transmitting means for transmitting the generated new advice or message to the user's terminal; A system including:

2. 2. The system according to claim 1, wherein the analysis means includes evaluation means for evaluating the user's progress data and determining the progress of the user's goals.

3. 2. The system according to claim 1, wherein the generating means generates the advice in a notification format according to a specific condition of the user.

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