system

Abstract

We provide the system. [Solution] A means for inputting user goal information and health status information, Based on the above input goal information and health status information, a means of proposing the most suitable habits for the user using a generative model, A means of receiving and analyzing progress data from users in real time, A means of providing feedback to users based on the above analysis, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Many individuals have difficulty forming and maintaining new habits. The main reasons include the lack of appropriate feedback and continuous motivation to achieve goals. Conventional methods have difficulty providing effective guidance based on individual goals and health conditions, resulting in the problem that habits do not last long. This invention aims to solve these problems by proposing optimal habits for individual goals and providing appropriate feedback to users while evaluating progress in real time.

Means for Solving the Problems

[0005] This invention provides a means for users to input goal information and health status information, enabling them to accurately define their own situation. Based on this, it includes a means for suggesting optimal habits to the user using a generative model. Furthermore, it evaluates the user's current achievement status by receiving and analyzing progress data from the user in real time. Based on this evaluation, it includes a means for providing specific and appropriate feedback, supporting the user in maintaining continuous motivation toward achieving their goals. In addition, it may also include a means for anonymizing data to protect user privacy and a means for visualizing progress.

[0006] "User goal information" refers to information that indicates the specific goals and intentions that the user themselves wants to achieve.

[0007] "Health status information" refers to data about the user's physical condition, including information such as health level and risk of lifestyle-related diseases.

[0008] A "generative model" is an algorithm or AI technology used to analyze past data and patterns to generate new habits or suggestions.

[0009] A "means of suggesting habits" refers to a method or system for presenting users with optimal actions or routines based on the information they input.

[0010] "Progress data" refers to information recorded by users regarding their daily activities and goal achievement, and is used to measure the results and degree of accomplishment of their experiences.

[0011] "Means of analysis" refer to the processes and tools used to analyze received information and evaluate the user's current situation.

[0012] "Means of providing feedback" refers to methods of recommending further actions through advice and evaluations for users.

[0013] "Data anonymization methods" refer to methods and technologies for processing data in a way that does not identify individuals, in order to protect user privacy.

[0014] "Display means" refers to devices or interfaces that provide information to users visually, and plays a role in visually indicating progress. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the language used in the following description will be described.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention provides a system that suggests optimal habits based on the user's individual goals and health status. This system functions through the cooperation of a server, a terminal, and the user.

[0037] First, the user installs the application using their device and enters their goals and health status. The device then receives this information and sends it to the server. After receiving the goal and health status information from the user, the server uses a generative model to generate habits tailored to the user. These generated suggestions cover a wide range of areas, including exercise habits, meal plans, and study schedules.

[0038] Next, the suggestion is sent to the device, and the user confirms it. The user then performs the suggested habit and records their progress on the device. The progress data reflects the user's daily activities and includes steps taken, exercise time, and meals eaten. The device then sends this data back to the server.

[0039] The server analyzes the received progress data in real time and evaluates the user's achievements. Based on this evaluation, the server generates specific feedback for the user. This feedback may include phrases such as, "You're making great progress," or "You'll get even better results if you increase your exercise time." The feedback is sent to the device and presented to the user.

[0040] Furthermore, to protect user privacy, data transmitted and received between the device and the server is anonymized to prevent the identification of individuals. This feature provides a foundation for users to use the system with peace of mind.

[0041] As a concrete example of the program, consider a case where a user sets a goal of "losing 3 kg in one month." The server suggests an optimized diet and exercise plan based on the user's current activity level. The user then monitors their daily activities according to this plan and makes necessary adjustments based on the feedback. Progress is visually displayed on the device, allowing the user to see at a glance their progress towards their goal.

[0042] In this way, the present invention provides a system that supports effective habit formation tailored to individual goals and promotes the user's goal achievement.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user installs the application on their device and creates an account. During this process, the user enters basic information including their name, email address, and goals.

[0046] Step 2:

[0047] The device presents the user with a goal-setting screen, where the user enters information about their desired goals and current health status. This information is then stored in a database by the device.

[0048] Step 3:

[0049] The terminal sends the information entered by the user to the server. Here, data anonymization is performed as needed to prevent the user from being identified.

[0050] Step 4:

[0051] The server launches a generative model to analyze the received goal and health status information. Based on this information, the generative model generates habit suggestions that are best suited to the user.

[0052] Step 5:

[0053] The server generates habit suggestions and sends them to the user's device, where they are displayed on the user's screen. The user reviews these suggestions and selects the habits they wish to implement.

[0054] Step 6:

[0055] The user performs selected habits and records their progress on the device. Here, the user enters detailed information about their daily activities, and the device saves this information chronologically.

[0056] Step 7:

[0057] The device sends daily recorded progress data to the server. This data includes steps taken, calories burned, and tasks completed.

[0058] Step 8:

[0059] The server uses the received progress data to analyze the user's current achievement status. An AI model then uses this data to generate feedback for the user.

[0060] Step 9:

[0061] The server generates feedback and sends it to the terminal, which then notifies the user of its contents. This feedback includes evaluations of progress and suggestions for improvement.

[0062] Step 10:

[0063] Users receive feedback displayed on their devices and use it to guide their future actions. As needed, users can adjust their goals and daily plans to continuously improve their habits.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] Traditional technologies fail to adequately propose effective and appropriate activities to users with individual goals, monitor their progress, and provide feedback. Furthermore, there are privacy concerns regarding the potential leakage of user data to third parties. Therefore, there is a need for a system that supports users in achieving their goals while ensuring the protection of personal information and providing a secure user experience.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes a device for inputting user goal information and health status information, a device for generating an optimal activity plan for the user using a generative AI model, a device for providing feedback based on the analysis results, and means for encrypting communication data. This allows users to receive appropriate activity guidance tailored to their individual goals and to use the system with peace of mind while ensuring their privacy.

[0069] A "user" is an entity that uses this system to receive activity plans and feedback based on individual goals.

[0070] "Goal information" refers to the specific objectives and goals that the user wants to achieve, and is the data entered into this system.

[0071] "Health status information" refers to data that represents the user's current physical and health condition and is entered into the system.

[0072] A "generative AI model" refers to an algorithm or program that generates an optimal activity plan based on the user's input information.

[0073] An "activity plan" refers to the specific actions and initiatives proposed to achieve the user's goals.

[0074] "Device" refers to the technical means or hardware used to perform a specific function or role.

[0075] "Analysis results" refer to information obtained after processing and evaluating data received from users, indicating how much progress the user is making toward their goals.

[0076] "Feedback" refers to information and advice provided to users for improvement, and functions as guidance for activities.

[0077] "Communication methods" refer to the protocols and technologies used to securely send and receive user data.

[0078] This invention is a system that proposes an appropriate activity plan for each user's individual goals and supports the user in achieving those goals. A specific embodiment is shown below.

[0079] First, the user installs a dedicated application using their device. This application runs on typical smartphones and computers and provides an interface where the user can input individual goal information (e.g., "lose 3 kg in one month") and health information (e.g., current weight, height, allergy information, etc.). Once the user inputs and submits the information, the device encrypts this data using security technology (e.g., HTTPS) and sends it to the server.

[0080] The server uses a generative AI model based on the received data to generate an optimal activity plan for the user. This AI model uses historical datasets and common health guidelines to generate the most suitable suggestions for the user. The generated activity plan is presented to the user as options for exercise schedules and meal plans. Specifically, suggestions may include "take a 30-minute walk five times a week" or "limit your daily calorie intake to 1800 kcal." An example of a prompt to the generative AI model is, "Please suggest the most effective health plan to help the user achieve their goals."

[0081] Next, the server sends the generated activity plan to the terminal, which then notifies the user. The user reviews the proposed plan through the terminal's interface and records their daily progress. The recorded data includes steps taken, exercise time, and meal details. This information is periodically sent from the terminal to the server, which analyzes the progress in real time and evaluates the user's performance.

[0082] Based on the evaluation results, the server provides feedback to the user. This feedback may include advice such as, "Great progress! Keep up the current pace," or "To accelerate your goal achievement, we recommend increasing your daily exercise time by another 10 minutes." The feedback is sent to the device and displayed to the user.

[0083] Ultimately, all communications are managed using data anonymization technology to ensure that individuals cannot be identified, and user privacy is strictly protected. This allows users to use the system with peace of mind and effectively progress towards their goals.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] Users install the application using their device and input individual goal information and health status information. This input includes numerical data (e.g., target weight, current weight, height) and text data (e.g., allergies, exercise restrictions). The device encrypts this data and sends it to the server. The input data is structured and stored in the server's database.

[0087] Step 2:

[0088] The server runs a generative AI model on the received user data. The prompt "Please suggest the optimal health plan to help the user achieve their goals" is entered, and the model begins its analysis. The server generates a personalized activity plan, referencing similar past cases and standard health guidelines from the database. The resulting suggestions serve as specific guidelines regarding exercise and diet.

[0089] Step 3:

[0090] The server sends the generated activity plan to the terminal. The terminal receives it and notifies the user. The terminal displays the proposed content in a format that is easy for the user to understand, and visualizes it on the interface, including feedback and advice.

[0091] Step 4:

[0092] Users perform daily activities according to the provided activity plan and record their progress on their device. Input data includes steps taken, exercise time, and meal details, which the device organizes and sends to the server. The device issues alarms and notifications as needed to remind users of their activity progress.

[0093] Step 5:

[0094] The server analyzes the received progress data and evaluates how close the user is to their goal. This evaluation uses basic statistical processing and trend analysis. Based on the evaluation results, the server generates feedback for the user. For example, it might suggest specific content such as, "Your current pace is good. Please continue."

[0095] Step 6:

[0096] The server sends the generated feedback back to the terminal. The terminal presents the feedback to the user and provides motivation for reviewing and continuing future plans. Each data communication is anonymized and secure, so users can enjoy the service without worrying about the leakage of personal information.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] While systems exist that suggest optimal habits based on each user's individual health status and goals, there is a lack of technology to provide users with visual and interactive feedback through more user-friendly automated machines. Therefore, there is a need for effective and motivating feedback methods to help users maintain the suggested habits.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes a device for inputting the user's goal information and health status information, a device for suggesting optimal habits to the user using a generative model, and a device for receiving and analyzing progress data from the user in real time. This enables visual and interactive feedback to the user using a user-friendly humanoid machine.

[0102] "User goal information" refers to information that indicates the specific numerical targets or situations that the user wishes to achieve.

[0103] "Health status information" refers to information about the user's physical health and current fitness level.

[0104] A "generative model" is a data processing and analysis model used to suggest optimal habits based on a user's goal information and health status information.

[0105] A "humanoid machine" is an automated machine that mimics the shape of a human being and is used to interact with users and provide information visually.

[0106] A "data anonymization device" is a device that processes data in order to prevent users' personal information from being identified.

[0107] "Progress data" refers to data that records the progress of a user's activities and indicates the degree of achievement towards their goals.

[0108] "Visualization" is a process or method for presenting information to a user visually.

[0109] This invention is an advanced system that supports users' health management, and specifically has a series of functions to support users in achieving their goals. The system mainly consists of a server, terminals, and a humanoid machine.

[0110] Ecosystem Overview

[0111] server:

[0112] The server receives user goal and health status information and uses a generative AI model to suggest optimal habits. The server analyzes progress data in real time and performs the necessary data processing to provide feedback to the user. Specifically, it is expected to use software such as Python and TENSORFLOW® to perform data analysis and run machine learning models. In addition, data anonymization technology will be used to protect user privacy.

[0113] Terminal:

[0114] The device provides an interface for users to input their goals and health status. The entered data is sent to the server. The device also visually displays feedback and progress received from the server. An interactive UI can be implemented using HTML5 or JavaScript® for the visual display.

[0115] Humanoid machines:

[0116] Humanoid robots provide users with visual and interactive feedback. This can be achieved using home robots such as the NAO robot. Feedback is presented through voice and movement according to the user's progress, making it easier for users to receive feedback intuitively.

[0117] Examples

[0118] As a concrete example, consider a scenario where a user sets a goal of "walking 10,000 steps a day." The server analyzes the user's current walking habits and uses a generation AI model to devise a plan to achieve 10,000 steps. When the user achieves their daily goal, the humanoid robot provides a voice message such as "Great progress!" An example of a prompt message in this case might be an instruction to "Generate an encouraging message appropriate to the user's progress, based on their walking data."

[0119] In this way, each component works together to create an advanced system that supports the user in achieving their goals. The present invention aims to continuously support health management and contribute to the promotion of the user's health.

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] Users input their goal information and health status information using an application on their device. This information includes specific goals the user wants to achieve (e.g., walking 10,000 steps a day) and their current health status (e.g., average number of steps per day). The input information is sent to the server via the network. At this time, the device performs data validation to ensure that the input data is correctly formatted.

[0123] Step 2:

[0124] The server uses a generative AI model to suggest optimal habits based on the user's received goal and health status information. The server uses libraries such as Python and TensorFlow to execute the generative AI model and generate personalized suggestions for the user. This process involves analyzing the user's existing data and performing data calculations to predict exercise patterns. The output includes specific exercise plans and dietary suggestions.

[0125] Step 3:

[0126] Suggestions from the server are notified to the terminal, which then visually displays them to the user. The terminal uses HTML5 and JavaScript in its user interface to visualize the suggestions in an easy-to-understand manner. During this process, feedback sounds and pop-ups are also displayed to prompt the user for notification.

[0127] Step 4:

[0128] The user performs the suggested habits and records their progress on the device. The device then connects with activity trackers and other devices to collect the user's steps and exercise time. The collected data is sent back to the server, which then serves as input for further analysis.

[0129] Step 5:

[0130] The server analyzes the submitted progress data in real time and evaluates the user's progress toward achieving their goals. Statistical methods and machine learning algorithms are used for data analysis, examining the user's activity log. The analysis results generate feedback for the user, including specific areas for improvement and advice on their progress.

[0131] Step 6:

[0132] The generated feedback is presented to the user via a terminal and also communicated interactively to the user through a humanoid robot. The humanoid robot uses a control program for robots to determine voice output and movement patterns, helping the user understand the feedback more intuitively. The prompt used in this process is, "Generate an encouraging message appropriate to the user's achievement level based on their walking data."

[0133] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0134] This invention combines an emotional engine with a habit-forming system that supports users in achieving their goals, providing more appropriate habit suggestions and feedback based on both the user's emotional state and physical health status. This system operates around a server, a terminal, and the user.

[0135] First, the user installs the application via their device and inputs their individual goals and health status. This information is sent directly to the server. Based on the received information, the server utilizes a generative model to suggest the most suitable habits for the user. At this point, the emotion engine analyzes the user's facial expressions and tone of voice to understand their emotional state.

[0136] The emotion engine operates using sensors such as cameras and microphones built into the user's device, and analyzes the data obtained from them to recognize the user's emotional state in real time. The recognized emotions are sent to a server and used to adjust habit suggestions according to the user's current mental and physical health. For example, if the user is feeling stressed, habits that promote relaxation may be suggested.

[0137] The user implements the suggested habits and records their progress and impressions on their device. The device sends this data to a server, which analyzes the user's emotional state along with the progress data. Based on the analysis, the server generates appropriate feedback, providing encouragement and new suggestions that take the user's emotions into consideration. This feedback is sent to the device and notified to the user. The content of the feedback is also customized based on the emotional state; for example, if positive feedback is needed, such a message will be provided.

[0138] As a concrete example, consider a case where a user sets "stress management" as their goal. If the emotion engine detects that the user's stress level is high, the server can suggest activities such as "evening walks" or "meditation sessions" to the user. As the user carries out these activities and records their progress, if the emotion engine indicates an increase in positive emotions, it will send a message acknowledging this and encouraging them to continue.

[0139] This invention provides a system that comprehensively understands the user's physical and mental state and promotes the formation of better habits.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The user installs the application on their device and creates an account. The user enters basic information such as their name, goals, and health status. The device sends the entered information to the server.

[0143] Step 2:

[0144] The device prepares to collect the user's emotional state using its camera and microphone. It provides data such as facial expressions and voice tone to the emotion engine through sensors built into the device.

[0145] Step 3:

[0146] The emotion engine analyzes data provided by the device and recognizes the user's emotional state in real time. The recognition results are sent to the server as emotional state data.

[0147] Step 4:

[0148] The server receives goal information, health status information, and emotional state data sent by the user. Based on this, a generative model is used to suggest the most suitable habits for the user.

[0149] Step 5:

[0150] The server sends habit suggestions to the terminal, which then displays the suggestions to the user. The user reviews the suggested habits and selects which ones to implement.

[0151] Step 6:

[0152] The user performs a selected habit and records their progress and feelings on their device. The device then sends the progress data and the user's current emotional state to the server.

[0153] Step 7:

[0154] The server analyzes progress data and emotional state data. Based on the results, it evaluates the user's achievement and generates emotion-based feedback.

[0155] Step 8:

[0156] The server generates feedback and sends it to the device, which then notifies the user of its contents. The feedback is designed to take into account the user's current emotional state.

[0157] Step 9:

[0158] Users can continuously improve their habit formation by reviewing the feedback displayed on their devices and incorporating it into their future actions. Goal settings and habit plans can be adjusted as needed.

[0159] (Example 2)

[0160] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0161] In modern society, it is crucial for users to find appropriate actions to support their goal achievement and to manage their progress. However, customizing these actions to suit individual users' emotional states and physical health is difficult, creating a need for systems that provide appropriate feedback to maximize the effectiveness of self-management. Furthermore, protecting privacy is essential in such systems.

[0162] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0163] In this invention, the server includes means for inputting user goal information and health status information, means for suggesting optimal actions to the user using a generative model, and means for collecting data using an emotion engine to analyze the user's emotional state. This enables personalized action suggestions and feedback to the user, as well as progress management and effective self-management support.

[0164] "Goal information" refers to data related to specific objectives that users set with the aim of achieving them.

[0165] "Health status information" refers to data about the user's current physical health, including information related to their physical condition and lifestyle.

[0166] A "generative model" is an algorithm that generates specific outputs based on input information, creating optimal action suggestions for the user.

[0167] "Emotional state" refers to data that indicates the psychological state a user is experiencing, such as stress, joy, and anxiety.

[0168] An "emotion engine" is software that analyzes a user's facial expressions and tone of voice to recognize their emotional state in real time.

[0169] "Progress data" refers to data that shows the extent to which users are taking the suggested actions, and is used to evaluate the status of goal achievement.

[0170] "Feedback" refers to information or encouraging messages provided to users based on analysis results to support self-management.

[0171] This invention provides a system to support users in achieving their goals. The system consists of a user, a terminal, and a server, and supports the improvement of the user's life through the following operations.

[0172] First, users install a habit-forming application on their device and input their goal information and health status. The device is equipped with sensors such as a camera and microphone, which are used to capture the user's facial expressions and voice tone.

[0173] The device sends the entered goal information and health status information to the server. The server inputs the received information into a generating AI model and suggests the most appropriate actions for the user. This model analyzes the training data using prompts and generates recommendations. An example of a prompt is, "If the user's goal is to maintain health, please suggest appropriate eating habits."

[0174] Furthermore, the server uses an emotion engine to analyze emotional data transmitted from the device. This allows it to understand the user's emotional state and further customize appropriate feedback and suggestions.

[0175] As a concrete example, consider a case where a user sets "weight management" as their goal. If the emotion engine detects a decline in the user's motivation, the server can recommend "light exercise" or "healthy snacks" to the user. Then, if the user does these things and their positive emotions increase, the server will evaluate this and generate messages to support their continued efforts.

[0176] As described above, this system provides a comprehensive solution to support users in achieving their goals.

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] Users launch a habit-forming application via their device and input goal information and health status information. This input data is used as foundational data to customize behavioral suggestions based on the user's individual goals. The input information is sent directly to the server by the device.

[0180] Step 2:

[0181] The server uses a generative AI model to generate appropriate action suggestions based on the user's goal information and health status information received from the terminal. The input here is data sent by the user, and the output is action suggestions optimized for the user. The server feeds prompt sentences into the generative AI model to maximize the generation probability and returns the resulting suggestions to the terminal.

[0182] Step 3:

[0183] The device continuously captures the user's facial expressions and voice using its built-in camera and microphone. The acquired sensor data is analyzed in real time by an emotion engine. The input is sensor data, and the output of this analysis is the user's current emotional state. This emotional state is sent to a server and used to customize feedback.

[0184] Step 4:

[0185] The server combines the user's emotional data and health data to further refine behavioral suggestions and generate feedback. For example, if emotional data indicates stress, the suggested behaviors are updated to be specifically stress-relieving. Feedback based on the analysis results is generated and provided to the user through the device.

[0186] Step 5:

[0187] The user performs actions suggested by the server and records their progress and impressions within the application. The device then sends the user's progress data and emotional changes back to the server. The input progress data is analyzed by the server and forms the basis for subsequent action suggestions and feedback.

[0188] Step 6:

[0189] The server re-analyzes the received progress and sentiment data to customize the feedback for the user. The feedback resulting from the analysis is designed to encourage the next action while maintaining the user's motivation. The terminal then notifies the user of this feedback.

[0190] (Application Example 2)

[0191] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0192] To effectively support users in achieving their goals and maintaining their health in their daily lives, flexible support that takes into account their emotional and physical state is necessary, rather than simply providing information. However, conventional systems have the challenge of being unable to analyze these individual factors in real time and provide optimal habits and feedback.

[0193] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0194] In this invention, the server includes means for acquiring user goal information and health status information, means for suggesting optimal actions to the user using a generative model, and means for analyzing facial expressions and voice to recognize the user's emotional state. This makes it possible to provide optimal habit suggestions and feedback tailored to the user's emotions and health status.

[0195] "User goal information" refers to information about the specific objectives and goals that the user wishes to achieve.

[0196] "Health status information" refers to data about the user's current physical and mental health status.

[0197] A "generative model" is an algorithm used to suggest optimal actions and habits based on user information.

[0198] "Means of recognizing emotional states" refers to technologies that determine a user's emotions through analysis of their facial expressions and voice.

[0199] A "means of presenting habits" refers to a mechanism that suggests optimal actions and lifestyle habits to the user.

[0200] "Progress data" refers to information that records the activities and progress made by the user.

[0201] "Means of providing feedback" refers to the process of communicating encouragement and new suggestions to users in accordance with their behavior and emotional state.

[0202] "Consumer electronic devices" refer to electronic devices and equipment used in ordinary households, and are devices that support or optimize daily life.

[0203] To implement this invention, it is necessary to build a system in which the user, terminal, and server cooperate with each other. The user inputs goal information and health status information through the terminal. The terminal transmits this information to the server. Based on the received information, the server uses a generative AI model to suggest optimal actions and habits to the user. Because this process requires large-scale data processing and analysis, it is suitable for the server to utilize a cloud computing environment.

[0204] The user's device has built-in sensors such as a camera and microphone, which are used to recognize the user's emotional state in real time. This recognition utilizes image processing and voice analysis technologies. The recognized emotional state is sent to a server, which dynamically adjusts habit suggestions based on this information. For example, if stress is detected, the server will offer suggestions to encourage relaxation.

[0205] Furthermore, the progress of habits performed by the user is sent from the device to the server. The server integrates and analyzes this progress data and emotional data to provide feedback to the user. The feedback is tailored to the user's emotional state, and may include messages designed to evoke positive emotions.

[0206] As a concrete example, consider a case where a user sets "stress management" as their goal. When the emotion engine detects that the user's stress level is high, the server runs a program that suggests activities such as "evening walks" or "meditation sessions." When the user performs these activities and records their progress on their device, if the emotion engine detects a positive change, it sends a message acknowledging this and encouraging them to continue.

[0207] Example prompt: "If the user is detected as tired, generate a prompt recommending health-promoting activities."

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] Users input goal information and health status information through their device.

[0211] This input includes specific goals the user wants to achieve and their current health status. The device processes this information as digital data and prepares it for transmission to subsequent processing steps. Processing of the input data includes quantification and categorization.

[0212] Step 2:

[0213] The device transmits the entered goal information and health status information to the server.

[0214] The server stores the received information in an appropriate database for analysis. The data arriving at the server is then used as material for generating optimal actions and habits for the user using a generative AI model.

[0215] Step 3:

[0216] The device uses its built-in camera and microphone to recognize the user's emotional state in real time.

[0217] The device uses video data from the camera and audio data from the microphone as input, and performs image processing and audio analysis using an analysis module within the device. This yields quantified data related to the user's emotional state. This data is treated as a quantitative representation of the user's emotional condition.

[0218] Step 4:

[0219] The device sends the recognized emotional state data to the server.

[0220] The server integrates and analyzes the user's goal information, health status information, and emotional state data. This analysis utilizes statistical methods and machine learning algorithms (including generative AI models) to generate personalized habit suggestions. The output is a list of specific behavioral suggestions.

[0221] Step 5:

[0222] The server sends the generated habit suggestions to the terminal and presents them to the user.

[0223] The terminal displays suggestions received from the server to the user through an interface. Each suggestion is presented at an appropriate time based on the user's environment and circumstances. The user can then choose an action based on the suggestions.

[0224] Step 6:

[0225] The user implements the suggested habits and records their progress on their device.

[0226] The device collects progress information as user feedback and sends it to the server along with newly acquired emotional state data. The input data includes the type and duration of the actions performed.

[0227] Step 7:

[0228] The server re-analyzes the progress data and emotional state data and generates feedback.

[0229] By utilizing generative AI models, the effectiveness of user actions is evaluated, and positive feedback and new suggestions are generated. These results are then reflected in future suggestions and support.

[0230] Step 8:

[0231] The server then sends the generated feedback back to the terminal.

[0232] The device will send notifications to the user to provide feedback. This feedback will include inspiring messages and suggestions for the next steps to boost user motivation.

[0233] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0234] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0235] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0239] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0240] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0241] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0242] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0243] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0244] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0245] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0246] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0247] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0248] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0249] This invention provides a system that suggests optimal habits based on the user's individual goals and health status. This system functions through the cooperation of a server, a terminal, and the user.

[0250] First, the user installs the application using their device and enters their goals and health status. The device then receives this information and sends it to the server. After receiving the goal and health status information from the user, the server uses a generative model to generate habits tailored to the user. These generated suggestions cover a wide range of areas, including exercise habits, meal plans, and study schedules.

[0251] Next, the suggestion is sent to the device, and the user confirms it. The user then performs the suggested habit and records their progress on the device. The progress data reflects the user's daily activities and includes steps taken, exercise time, and meals eaten. The device then sends this data back to the server.

[0252] The server analyzes the received progress data in real time and evaluates the user's achievements. Based on this evaluation, the server generates specific feedback for the user. This feedback may include phrases such as, "You're making great progress," or "You'll get even better results if you increase your exercise time." The feedback is sent to the device and presented to the user.

[0253] Furthermore, to protect user privacy, data transmitted and received between the device and the server is anonymized to prevent the identification of individuals. This feature provides a foundation for users to use the system with peace of mind.

[0254] As a concrete example of the program, consider a case where a user sets a goal of "losing 3 kg in one month." The server suggests an optimized diet and exercise plan based on the user's current activity level. The user then monitors their daily activities according to this plan and makes necessary adjustments based on the feedback. Progress is visually displayed on the device, allowing the user to see at a glance their progress towards their goal.

[0255] In this way, the present invention provides a system that supports effective habit formation tailored to individual goals and promotes the user's goal achievement.

[0256] The following describes the processing flow.

[0257] Step 1:

[0258] The user installs the application on their device and creates an account. During this process, the user enters basic information including their name, email address, and goals.

[0259] Step 2:

[0260] The device presents the user with a goal-setting screen, where the user enters information about their desired goals and current health status. This information is then stored in a database by the device.

[0261] Step 3:

[0262] The terminal sends the information entered by the user to the server. Here, data anonymization is performed as needed to prevent the user from being identified.

[0263] Step 4:

[0264] The server launches a generative model to analyze the received goal and health status information. Based on this information, the generative model generates habit suggestions that are best suited to the user.

[0265] Step 5:

[0266] The server generates habit suggestions and sends them to the user's device, where they are displayed on the user's screen. The user reviews these suggestions and selects the habits they wish to implement.

[0267] Step 6:

[0268] The user performs selected habits and records their progress on the device. Here, the user enters detailed information about their daily activities, and the device saves this information chronologically.

[0269] Step 7:

[0270] The device sends daily recorded progress data to the server. This data includes steps taken, calories burned, and tasks completed.

[0271] Step 8:

[0272] The server uses the received progress data to analyze the user's current achievement status. An AI model then uses this data to generate feedback for the user.

[0273] Step 9:

[0274] The server generates feedback and sends it to the terminal, which then notifies the user of its contents. This feedback includes evaluations of progress and suggestions for improvement.

[0275] Step 10:

[0276] Users receive feedback displayed on their devices and use it to guide their future actions. As needed, users can adjust their goals and daily plans to continuously improve their habits.

[0277] (Example 1)

[0278] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] Traditional technologies fail to adequately propose effective and appropriate activities to users with individual goals, monitor their progress, and provide feedback. Furthermore, there are privacy concerns regarding the potential leakage of user data to third parties. Therefore, there is a need for a system that supports users in achieving their goals while ensuring the protection of personal information and providing a secure user experience.

[0280] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0281] In this invention, the server includes a device for inputting the user's target information and health status information, a device for generating an optimal activity plan for the user using a generation AI model, a device for providing feedback based on the analysis results, and means for encrypting communication data. As a result, the user can receive appropriate activity guidance tailored to individual goals and can use the system with confidence while ensuring privacy.

[0282] The "user" is the entity that receives an activity plan and feedback based on individual goals using this system.

[0283] The "target information" refers to the specific purposes or goals that the user wishes to achieve and is the data input into this system.

[0284] The "health status information" is information representing the user's current physical and health conditions and is the data input into the system.

[0285] The "generation AI model" refers to an algorithm or program for generating an optimal activity plan based on the input user information.

[0286] The "activity plan" refers to the specific actions or efforts proposed to achieve the user's goals.

[0287] The "device" refers to the technical means or hardware used to perform a specific function or role.

[0288] The "analysis result" is information indicating how far the user has progressed towards the goal, obtained after processing and evaluating the data received from the user.

[0289] The "feedback" refers to the information or advice for improvement provided to the user and functions as guidance for activities.

[0290] "Communication methods" refer to the protocols and technologies used to securely send and receive user data.

[0291] This invention is a system that proposes an appropriate activity plan for each user's individual goals and supports the user in achieving those goals. A specific embodiment is shown below.

[0292] First, the user installs a dedicated application using their device. This application runs on typical smartphones and computers and provides an interface where the user can input individual goal information (e.g., "lose 3 kg in one month") and health information (e.g., current weight, height, allergy information, etc.). Once the user inputs and submits the information, the device encrypts this data using security technology (e.g., HTTPS) and sends it to the server.

[0293] The server uses a generative AI model based on the received data to generate an optimal activity plan for the user. This AI model uses historical datasets and common health guidelines to generate the most suitable suggestions for the user. The generated activity plan is presented to the user as options for exercise schedules and meal plans. Specifically, suggestions may include "take a 30-minute walk five times a week" or "limit your daily calorie intake to 1800 kcal." An example of a prompt to the generative AI model is, "Please suggest the most effective health plan to help the user achieve their goals."

[0294] Next, the server sends the generated activity plan to the terminal, which then notifies the user. The user reviews the proposed plan through the terminal's interface and records their daily progress. The recorded data includes steps taken, exercise time, and meal details. This information is periodically sent from the terminal to the server, which analyzes the progress in real time and evaluates the user's performance.

[0295] Based on the evaluation results, the server provides feedback to the user. This feedback may include advice such as, "Great progress! Keep up the current pace," or "To accelerate your goal achievement, we recommend increasing your daily exercise time by another 10 minutes." The feedback is sent to the device and displayed to the user.

[0296] Ultimately, all communications are managed using data anonymization technology to ensure that individuals cannot be identified, and user privacy is strictly protected. This allows users to use the system with peace of mind and effectively progress towards their goals.

[0297] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0298] Step 1:

[0299] Users install the application using their device and input individual goal information and health status information. This input includes numerical data (e.g., target weight, current weight, height) and text data (e.g., allergies, exercise restrictions). The device encrypts this data and sends it to the server. The input data is structured and stored in the server's database.

[0300] Step 2:

[0301] The server runs a generative AI model on the received user data. The prompt "Please suggest the optimal health plan to help the user achieve their goals" is entered, and the model begins its analysis. The server generates a personalized activity plan, referencing similar past cases and standard health guidelines from the database. The resulting suggestions serve as specific guidelines regarding exercise and diet.

[0302] Step 3:

[0303] The server sends the generated activity plan to the terminal. The terminal receives this and notifies the user. The terminal displays the proposed content in a user-friendly format and visualizes it on the interface, including feedback and advice.

[0304] Step 4:

[0305] The user conducts daily activities according to the provided activity plan and records the progress on the terminal. The input data includes the number of steps, exercise time, meal content, etc., and the terminal organizes this and sends it to the server. The terminal issues alarms and notifications for reminding the activity progress as needed.

[0306] Step 5:

[0307] The server analyzes the received progress data and evaluates how close the user is to the goal. Basic statistical processing and trend analysis are used for this evaluation. Based on the evaluation result, the server generates feedback to the user. For example, it proposes specific content such as "Your current pace is good. Please continue like this."

[0308] Step 6:

[0309] The server sends the generated feedback to the terminal again. The terminal presents the feedback to the user and provides motivation for reviewing and continuing the future plan. Since each data communication is anonymized and security is ensured, the user can enjoy the service without worrying about the leakage of personal information.

[0310] (Application Example 1)

[0311] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0312] While systems exist that suggest optimal habits based on each user's individual health status and goals, there is a lack of technology to provide users with visual and interactive feedback through more user-friendly automated machines. Therefore, there is a need for effective and motivating feedback methods to help users maintain the suggested habits.

[0313] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0314] In this invention, the server includes a device for inputting the user's goal information and health status information, a device for suggesting optimal habits to the user using a generative model, and a device for receiving and analyzing progress data from the user in real time. This enables visual and interactive feedback to the user using a user-friendly humanoid machine.

[0315] "User goal information" refers to information that indicates the specific numerical targets or situations that the user wishes to achieve.

[0316] "Health status information" refers to information about the user's physical health and current fitness level.

[0317] A "generative model" is a data processing and analysis model used to suggest optimal habits based on a user's goal information and health status information.

[0318] A "humanoid machine" is an automated machine that mimics the shape of a human being and is used to interact with users and provide information visually.

[0319] A "data anonymization device" is a device that processes data in order to prevent users' personal information from being identified.

[0320] "Progress data" refers to data that records the progress of a user's activities and indicates the degree of achievement towards their goals.

[0321] "Visualization" is a process or method for presenting information to a user visually.

[0322] This invention is an advanced system that supports users' health management, and specifically has a series of functions to support users in achieving their goals. The system mainly consists of a server, terminals, and a humanoid machine.

[0323] Ecosystem Overview

[0324] server:

[0325] The server receives user goal and health status information and uses a generative AI model to suggest optimal habits based on this information. The server analyzes progress data in real time and performs the necessary data processing to provide feedback to the user. Specifically, it is expected to use software such as Python and TensorFlow for data analysis and machine learning model execution. In addition, data anonymization technology will be used to protect user privacy.

[0326] Terminal:

[0327] The device provides an interface for users to input their goals and health status. The entered data is sent to the server. The device also visually displays feedback and progress received from the server. An interactive UI can be implemented using HTML5 or JavaScript for the visual display.

[0328] Humanoid machines:

[0329] Humanoid robots provide users with visual and interactive feedback. This can be achieved using home robots such as the NAO robot. Feedback is presented through voice and movement according to the user's progress, making it easier for users to receive feedback intuitively.

[0330] Examples

[0331] As a concrete example, consider a scenario where a user sets a goal of "walking 10,000 steps a day." The server analyzes the user's current walking habits and uses a generation AI model to devise a plan to achieve 10,000 steps. When the user achieves their daily goal, the humanoid robot provides a voice message such as "Great progress!" An example of a prompt message in this case might be an instruction to "Generate an encouraging message appropriate to the user's progress, based on their walking data."

[0332] In this way, each component works together to create an advanced system that supports the user in achieving their goals. The present invention aims to continuously support health management and contribute to the promotion of the user's health.

[0333] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0334] Step 1:

[0335] Users input their goal information and health status information using an application on their device. This information includes specific goals the user wants to achieve (e.g., walking 10,000 steps a day) and their current health status (e.g., average number of steps per day). The input information is sent to the server via the network. At this time, the device performs data validation to ensure that the input data is correctly formatted.

[0336] Step 2:

[0337] The server uses a generative AI model to suggest optimal habits based on the user's received goal and health status information. The server uses libraries such as Python and TensorFlow to execute the generative AI model and generate personalized suggestions for the user. This process involves analyzing the user's existing data and performing data calculations to predict exercise patterns. The output includes specific exercise plans and dietary suggestions.

[0338] Step 3:

[0339] Suggestions from the server are notified to the terminal, which then visually displays them to the user. The terminal uses HTML5 and JavaScript in its user interface to visualize the suggestions in an easy-to-understand manner. During this process, feedback sounds and pop-ups are also displayed to prompt the user for notification.

[0340] Step 4:

[0341] The user performs the suggested habits and records their progress on the device. The device then connects with activity trackers and other devices to collect the user's steps and exercise time. The collected data is sent back to the server, which then serves as input for further analysis.

[0342] Step 5:

[0343] The server analyzes the submitted progress data in real time and evaluates the user's progress toward achieving their goals. Statistical methods and machine learning algorithms are used for data analysis, examining the user's activity log. The analysis results generate feedback for the user, including specific areas for improvement and advice on their progress.

[0344] Step 6:

[0345] The generated feedback is presented to the user via a terminal and also communicated interactively to the user through a humanoid robot. The humanoid robot uses a control program for robots to determine voice output and movement patterns, helping the user understand the feedback more intuitively. The prompt used in this process is, "Generate an encouraging message appropriate to the user's achievement level based on their walking data."

[0346] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0347] This invention combines an emotional engine with a habit-forming system that supports users in achieving their goals, providing more appropriate habit suggestions and feedback based on both the user's emotional state and physical health status. This system operates around a server, a terminal, and the user.

[0348] First, the user installs the application via their device and inputs their individual goals and health status. This information is sent directly to the server. Based on the received information, the server utilizes a generative model to suggest the most suitable habits for the user. At this point, the emotion engine analyzes the user's facial expressions and tone of voice to understand their emotional state.

[0349] The emotion engine operates using sensors such as cameras and microphones built into the user's device, and analyzes the data obtained from them to recognize the user's emotional state in real time. The recognized emotions are sent to a server and used to adjust habit suggestions according to the user's current mental and physical health. For example, if the user is feeling stressed, habits that promote relaxation may be suggested.

[0350] The user implements the suggested habits and records their progress and impressions on their device. The device sends this data to a server, which analyzes the user's emotional state along with the progress data. Based on the analysis, the server generates appropriate feedback, providing encouragement and new suggestions that take the user's emotions into consideration. This feedback is sent to the device and notified to the user. The content of the feedback is also customized based on the emotional state; for example, if positive feedback is needed, such a message will be provided.

[0351] As a concrete example, consider a case where a user sets "stress management" as their goal. If the emotion engine detects that the user's stress level is high, the server can suggest activities such as "evening walks" or "meditation sessions" to the user. As the user carries out these activities and records their progress, if the emotion engine indicates an increase in positive emotions, it will send a message acknowledging this and encouraging them to continue.

[0352] This invention provides a system that comprehensively understands the user's physical and mental state and promotes the formation of better habits.

[0353] The following describes the processing flow.

[0354] Step 1:

[0355] The user installs the application on their device and creates an account. The user enters basic information such as their name, goals, and health status. The device sends the entered information to the server.

[0356] Step 2:

[0357] The device prepares to collect the user's emotional state using its camera and microphone. It provides data such as facial expressions and voice tone to the emotion engine through sensors built into the device.

[0358] Step 3:

[0359] The emotion engine analyzes data provided by the device and recognizes the user's emotional state in real time. The recognition results are sent to the server as emotional state data.

[0360] Step 4:

[0361] The server receives goal information, health status information, and emotional state data sent by the user. Based on this, a generative model is used to suggest the most suitable habits for the user.

[0362] Step 5:

[0363] The server sends habit suggestions to the terminal, which then displays the suggestions to the user. The user reviews the suggested habits and selects which ones to implement.

[0364] Step 6:

[0365] The user performs a selected habit and records their progress and feelings on their device. The device then sends the progress data and the user's current emotional state to the server.

[0366] Step 7:

[0367] The server analyzes progress data and emotional state data. Based on the results, it evaluates the user's achievement and generates emotion-based feedback.

[0368] Step 8:

[0369] The server generates feedback and sends it to the device, which then notifies the user of its contents. The feedback is designed to take into account the user's current emotional state.

[0370] Step 9:

[0371] Users can continuously improve their habit formation by reviewing the feedback displayed on their devices and incorporating it into their future actions. Goal settings and habit plans can be adjusted as needed.

[0372] (Example 2)

[0373] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0374] In modern society, it is crucial for users to find appropriate actions to support their goal achievement and to manage their progress. However, customizing these actions to suit individual users' emotional states and physical health is difficult, creating a need for systems that provide appropriate feedback to maximize the effectiveness of self-management. Furthermore, protecting privacy is essential in such systems.

[0375] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0376] In this invention, the server includes means for inputting user goal information and health status information, means for suggesting optimal actions to the user using a generative model, and means for collecting data using an emotion engine to analyze the user's emotional state. This enables personalized action suggestions and feedback to the user, as well as progress management and effective self-management support.

[0377] "Goal information" refers to data related to specific objectives that users set with the aim of achieving them.

[0378] "Health status information" refers to data about the user's current physical health, including information related to their physical condition and lifestyle.

[0379] A "generative model" is an algorithm that generates specific outputs based on input information, creating optimal action suggestions for the user.

[0380] "Emotional state" refers to data that indicates the psychological state a user is experiencing, such as stress, joy, and anxiety.

[0381] An "emotion engine" is software that analyzes a user's facial expressions and tone of voice to recognize their emotional state in real time.

[0382] "Progress data" refers to data that shows the extent to which users are taking the suggested actions, and is used to evaluate the status of goal achievement.

[0383] "Feedback" refers to information or encouraging messages provided to users based on analysis results to support self-management.

[0384] This invention provides a system to support users in achieving their goals. The system consists of a user, a terminal, and a server, and supports the improvement of the user's life through the following operations.

[0385] First, users install a habit-forming application on their device and input their goal information and health status. The device is equipped with sensors such as a camera and microphone, which are used to capture the user's facial expressions and voice tone.

[0386] The device sends the entered goal information and health status information to the server. The server inputs the received information into a generating AI model and suggests the most appropriate actions for the user. This model analyzes the training data using prompts and generates recommendations. An example of a prompt is, "If the user's goal is to maintain health, please suggest appropriate eating habits."

[0387] Furthermore, the server uses an emotion engine to analyze emotional data transmitted from the device. This allows it to understand the user's emotional state and further customize appropriate feedback and suggestions.

[0388] As a concrete example, consider a case where a user sets "weight management" as their goal. If the emotion engine detects a decline in the user's motivation, the server can recommend "light exercise" or "healthy snacks" to the user. Then, if the user does these things and their positive emotions increase, the server will evaluate this and generate messages to support their continued efforts.

[0389] As described above, this system provides a comprehensive solution to support users in achieving their goals.

[0390] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0391] Step 1:

[0392] Users launch a habit-forming application via their device and input goal information and health status information. This input data is used as foundational data to customize behavioral suggestions based on the user's individual goals. The input information is sent directly to the server by the device.

[0393] Step 2:

[0394] The server uses a generative AI model to generate appropriate action suggestions based on the user's goal information and health status information received from the terminal. The input here is data sent by the user, and the output is action suggestions optimized for the user. The server feeds prompt sentences into the generative AI model to maximize the generation probability and returns the resulting suggestions to the terminal.

[0395] Step 3:

[0396] The device continuously captures the user's facial expressions and voice using its built-in camera and microphone. The acquired sensor data is analyzed in real time by an emotion engine. The input is sensor data, and the output of this analysis is the user's current emotional state. This emotional state is sent to a server and used to customize feedback.

[0397] Step 4:

[0398] The server combines the user's emotional data and health data to further refine behavioral suggestions and generate feedback. For example, if emotional data indicates stress, the suggested behaviors are updated to be specifically stress-relieving. Feedback based on the analysis results is generated and provided to the user through the device.

[0399] Step 5:

[0400] The user performs actions suggested by the server and records their progress and impressions within the application. The device then sends the user's progress data and emotional changes back to the server. The input progress data is analyzed by the server and forms the basis for subsequent action suggestions and feedback.

[0401] Step 6:

[0402] The server re-analyzes the received progress and sentiment data to customize the feedback for the user. The feedback resulting from the analysis is designed to encourage the next action while maintaining the user's motivation. The terminal then notifies the user of this feedback.

[0403] (Application Example 2)

[0404] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0405] To effectively support users in achieving their goals and maintaining their health in their daily lives, flexible support that takes into account their emotional and physical state is necessary, rather than simply providing information. However, conventional systems have the challenge of being unable to analyze these individual factors in real time and provide optimal habits and feedback.

[0406] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0407] In this invention, the server includes means for acquiring user goal information and health status information, means for suggesting optimal actions to the user using a generative model, and means for analyzing facial expressions and voice to recognize the user's emotional state. This makes it possible to provide optimal habit suggestions and feedback tailored to the user's emotions and health status.

[0408] "User goal information" refers to information about the specific objectives and goals that the user wishes to achieve.

[0409] "Health status information" refers to data about the user's current physical and mental health status.

[0410] A "generative model" is an algorithm used to suggest optimal actions and habits based on user information.

[0411] "Means of recognizing emotional states" refers to technologies that determine a user's emotions through analysis of their facial expressions and voice.

[0412] A "means of presenting habits" refers to a mechanism that suggests optimal actions and lifestyle habits to the user.

[0413] "Progress data" refers to information that records the activities and progress made by the user.

[0414] "Means of providing feedback" refers to the process of communicating encouragement and new suggestions to users in accordance with their behavior and emotional state.

[0415] "Consumer electronic devices" refer to electronic devices and equipment used in ordinary households, and are devices that support or optimize daily life.

[0416] To implement this invention, it is necessary to build a system in which the user, terminal, and server cooperate with each other. The user inputs goal information and health status information through the terminal. The terminal transmits this information to the server. Based on the received information, the server uses a generative AI model to suggest optimal actions and habits to the user. Because this process requires large-scale data processing and analysis, it is suitable for the server to utilize a cloud computing environment.

[0417] The user's device has built-in sensors such as a camera and microphone, which are used to recognize the user's emotional state in real time. This recognition utilizes image processing and voice analysis technologies. The recognized emotional state is sent to a server, which dynamically adjusts habit suggestions based on this information. For example, if stress is detected, the server will offer suggestions to encourage relaxation.

[0418] Furthermore, the progress of habits performed by the user is sent from the device to the server. The server integrates and analyzes this progress data and emotional data to provide feedback to the user. The feedback is tailored to the user's emotional state, and may include messages designed to evoke positive emotions.

[0419] As a concrete example, consider a case where a user sets "stress management" as their goal. When the emotion engine detects that the user's stress level is high, the server runs a program that suggests activities such as "evening walks" or "meditation sessions." When the user performs these activities and records their progress on their device, if the emotion engine detects a positive change, it sends a message acknowledging this and encouraging them to continue.

[0420] Example prompt: "If the user is detected as tired, generate a prompt recommending health-promoting activities."

[0421] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0422] Step 1:

[0423] Users input goal information and health status information through their device.

[0424] This input includes specific goals the user wants to achieve and their current health status. The device processes this information as digital data and prepares it for transmission to subsequent processing steps. Processing of the input data includes quantification and categorization.

[0425] Step 2:

[0426] The device transmits the entered goal information and health status information to the server.

[0427] The server stores the received information in an appropriate database for analysis. The data arriving at the server is then used as material for generating optimal actions and habits for the user using a generative AI model.

[0428] Step 3:

[0429] The device uses its built-in camera and microphone to recognize the user's emotional state in real time.

[0430] The device uses video data from the camera and audio data from the microphone as input, and performs image processing and audio analysis using an analysis module within the device. This yields quantified data related to the user's emotional state. This data is treated as a quantitative representation of the user's emotional condition.

[0431] Step 4:

[0432] The device sends the recognized emotional state data to the server.

[0433] The server integrates and analyzes the user's goal information, health status information, and emotional state data. This analysis utilizes statistical methods and machine learning algorithms (including generative AI models) to generate personalized habit suggestions. The output is a list of specific behavioral suggestions.

[0434] Step 5:

[0435] The server sends the generated habit suggestions to the terminal and presents them to the user.

[0436] The terminal displays suggestions received from the server to the user through an interface. Each suggestion is presented at an appropriate time based on the user's environment and circumstances. The user can then choose an action based on the suggestions.

[0437] Step 6:

[0438] The user implements the suggested habits and records their progress on their device.

[0439] The device collects progress information as user feedback and sends it to the server along with newly acquired emotional state data. The input data includes the type and duration of the actions performed.

[0440] Step 7:

[0441] The server re-analyzes the progress data and emotional state data and generates feedback.

[0442] By utilizing generative AI models, the effectiveness of user actions is evaluated, and positive feedback and new suggestions are generated. These results are then reflected in future suggestions and support.

[0443] Step 8:

[0444] The server then sends the generated feedback back to the terminal.

[0445] The device will send notifications to the user to provide feedback. This feedback will include inspiring messages and suggestions for the next steps to boost user motivation.

[0446] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0447] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0448] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0449] [Third Embodiment]

[0450] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0451] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0452] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0453] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0454] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0455] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0456] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0457] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0458] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0459] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0460] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0461] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0462] This invention provides a system that suggests optimal habits based on the user's individual goals and health status. This system functions through the cooperation of a server, a terminal, and the user.

[0463] First, the user installs the application using their device and enters their goals and health status. The device then receives this information and sends it to the server. After receiving the goal and health status information from the user, the server uses a generative model to generate habits tailored to the user. These generated suggestions cover a wide range of areas, including exercise habits, meal plans, and study schedules.

[0464] Next, the suggestion is sent to the device, and the user confirms it. The user then performs the suggested habit and records their progress on the device. The progress data reflects the user's daily activities and includes steps taken, exercise time, and meals eaten. The device then sends this data back to the server.

[0465] The server analyzes the received progress data in real time and evaluates the user's achievements. Based on this evaluation, the server generates specific feedback for the user. This feedback may include phrases such as, "You're making great progress," or "You'll get even better results if you increase your exercise time." The feedback is sent to the device and presented to the user.

[0466] Furthermore, to protect user privacy, data transmitted and received between the device and the server is anonymized to prevent the identification of individuals. This feature provides a foundation for users to use the system with peace of mind.

[0467] As a concrete example of the program, consider a case where a user sets a goal of "losing 3 kg in one month." The server suggests an optimized diet and exercise plan based on the user's current activity level. The user then monitors their daily activities according to this plan and makes necessary adjustments based on the feedback. Progress is visually displayed on the device, allowing the user to see at a glance their progress towards their goal.

[0468] In this way, the present invention provides a system that supports effective habit formation tailored to individual goals and promotes the user's goal achievement.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] The user installs the application on their device and creates an account. During this process, the user enters basic information including their name, email address, and goals.

[0472] Step 2:

[0473] The device presents the user with a goal-setting screen, where the user enters information about their desired goals and current health status. This information is then stored in a database by the device.

[0474] Step 3:

[0475] The terminal sends the information entered by the user to the server. Here, data anonymization is performed as needed to prevent the user from being identified.

[0476] Step 4:

[0477] The server launches a generative model to analyze the received goal and health status information. Based on this information, the generative model generates habit suggestions that are best suited to the user.

[0478] Step 5:

[0479] The server generates habit suggestions and sends them to the user's device, where they are displayed on the user's screen. The user reviews these suggestions and selects the habits they wish to implement.

[0480] Step 6:

[0481] The user performs selected habits and records their progress on the device. Here, the user enters detailed information about their daily activities, and the device saves this information chronologically.

[0482] Step 7:

[0483] The device sends daily recorded progress data to the server. This data includes steps taken, calories burned, and tasks completed.

[0484] Step 8:

[0485] The server uses the received progress data to analyze the user's current achievement status. An AI model then uses this data to generate feedback for the user.

[0486] Step 9:

[0487] The server generates feedback and sends it to the terminal, which then notifies the user of its contents. This feedback includes evaluations of progress and suggestions for improvement.

[0488] Step 10:

[0489] Users receive feedback displayed on their devices and use it to guide their future actions. As needed, users can adjust their goals and daily plans to continuously improve their habits.

[0490] (Example 1)

[0491] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0492] Traditional technologies fail to adequately propose effective and appropriate activities to users with individual goals, monitor their progress, and provide feedback. Furthermore, there are privacy concerns regarding the potential leakage of user data to third parties. Therefore, there is a need for a system that supports users in achieving their goals while ensuring the protection of personal information and providing a secure user experience.

[0493] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0494] In this invention, the server includes a device for inputting user goal information and health status information, a device for generating an optimal activity plan for the user using a generative AI model, a device for providing feedback based on the analysis results, and means for encrypting communication data. This allows users to receive appropriate activity guidance tailored to their individual goals and to use the system with peace of mind while ensuring their privacy.

[0495] A "user" is an entity that uses this system to receive activity plans and feedback based on individual goals.

[0496] "Goal information" refers to the specific objectives and goals that the user wants to achieve, and is the data entered into this system.

[0497] "Health status information" refers to data that represents the user's current physical and health condition and is entered into the system.

[0498] A "generative AI model" refers to an algorithm or program that generates an optimal activity plan based on the user's input information.

[0499] An "activity plan" refers to the specific actions and initiatives proposed to achieve the user's goals.

[0500] "Device" refers to the technical means or hardware used to perform a specific function or role.

[0501] "Analysis results" refer to information obtained after processing and evaluating data received from users, indicating how much progress the user is making toward their goals.

[0502] "Feedback" refers to information and advice provided to users for improvement, and functions as guidance for activities.

[0503] "Communication methods" refer to the protocols and technologies used to securely send and receive user data.

[0504] This invention is a system that proposes an appropriate activity plan for each user's individual goals and supports the user in achieving those goals. A specific embodiment is shown below.

[0505] First, the user installs a dedicated application using their device. This application runs on typical smartphones and computers and provides an interface where the user can input individual goal information (e.g., "lose 3 kg in one month") and health information (e.g., current weight, height, allergy information, etc.). Once the user inputs and submits the information, the device encrypts this data using security technology (e.g., HTTPS) and sends it to the server.

[0506] The server uses a generative AI model based on the received data to generate an optimal activity plan for the user. This AI model uses historical datasets and common health guidelines to generate the most suitable suggestions for the user. The generated activity plan is presented to the user as options for exercise schedules and meal plans. Specifically, suggestions may include "take a 30-minute walk five times a week" or "limit your daily calorie intake to 1800 kcal." An example of a prompt to the generative AI model is, "Please suggest the most effective health plan to help the user achieve their goals."

[0507] Next, the server sends the generated activity plan to the terminal, which then notifies the user. The user reviews the proposed plan through the terminal's interface and records their daily progress. The recorded data includes steps taken, exercise time, and meal details. This information is periodically sent from the terminal to the server, which analyzes the progress in real time and evaluates the user's performance.

[0508] Based on the evaluation results, the server provides feedback to the user. This feedback may include advice such as, "Great progress! Keep up the current pace," or "To accelerate your goal achievement, we recommend increasing your daily exercise time by another 10 minutes." The feedback is sent to the device and displayed to the user.

[0509] Ultimately, all communications are managed using data anonymization technology to ensure that individuals cannot be identified, and user privacy is strictly protected. This allows users to use the system with peace of mind and effectively progress towards their goals.

[0510] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0511] Step 1:

[0512] Users install the application using their device and input individual goal information and health status information. This input includes numerical data (e.g., target weight, current weight, height) and text data (e.g., allergies, exercise restrictions). The device encrypts this data and sends it to the server. The input data is structured and stored in the server's database.

[0513] Step 2:

[0514] The server runs a generative AI model on the received user data. The prompt "Please suggest the optimal health plan to help the user achieve their goals" is entered, and the model begins its analysis. The server generates a personalized activity plan, referencing similar past cases and standard health guidelines from the database. The resulting suggestions serve as specific guidelines regarding exercise and diet.

[0515] Step 3:

[0516] The server sends the generated activity plan to the terminal. The terminal receives it and notifies the user. The terminal displays the proposed content in a format that is easy for the user to understand, and visualizes it on the interface, including feedback and advice.

[0517] Step 4:

[0518] Users perform daily activities according to the provided activity plan and record their progress on their device. Input data includes steps taken, exercise time, and meal details, which the device organizes and sends to the server. The device issues alarms and notifications as needed to remind users of their activity progress.

[0519] Step 5:

[0520] The server analyzes the received progress data and evaluates how close the user is to their goal. This evaluation uses basic statistical processing and trend analysis. Based on the evaluation results, the server generates feedback for the user. For example, it might suggest specific content such as, "Your current pace is good. Please continue."

[0521] Step 6:

[0522] The server sends the generated feedback back to the terminal. The terminal presents the feedback to the user and provides motivation for reviewing and continuing future plans. Each data communication is anonymized and secure, so users can enjoy the service without worrying about the leakage of personal information.

[0523] (Application Example 1)

[0524] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0525] While systems exist that suggest optimal habits based on each user's individual health status and goals, there is a lack of technology to provide users with visual and interactive feedback through more user-friendly automated machines. Therefore, there is a need for effective and motivating feedback methods to help users maintain the suggested habits.

[0526] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0527] In this invention, the server includes a device for inputting the user's goal information and health status information, a device for suggesting optimal habits to the user using a generative model, and a device for receiving and analyzing progress data from the user in real time. This enables visual and interactive feedback to the user using a user-friendly humanoid machine.

[0528] "User goal information" refers to information that indicates the specific numerical targets or situations that the user wishes to achieve.

[0529] "Health status information" refers to information about the user's physical health and current fitness level.

[0530] A "generative model" is a data processing and analysis model used to suggest optimal habits based on a user's goal information and health status information.

[0531] A "humanoid machine" is an automated machine that mimics the shape of a human being and is used to interact with users and provide information visually.

[0532] A "data anonymization device" is a device that processes data in order to prevent users' personal information from being identified.

[0533] "Progress data" refers to data that records the progress of a user's activities and indicates the degree of achievement towards their goals.

[0534] "Visualization" is a process or method for presenting information to a user visually.

[0535] This invention is an advanced system that supports users' health management, and specifically has a series of functions to support users in achieving their goals. The system mainly consists of a server, terminals, and a humanoid machine.

[0536] Ecosystem Overview

[0537] server:

[0538] The server receives user goal and health status information and uses a generative AI model to suggest optimal habits based on this information. The server analyzes progress data in real time and performs the necessary data processing to provide feedback to the user. Specifically, it is expected to use software such as Python and TensorFlow for data analysis and machine learning model execution. In addition, data anonymization technology will be used to protect user privacy.

[0539] Terminal:

[0540] The device provides an interface for users to input their goals and health status. The entered data is sent to the server. The device also visually displays feedback and progress received from the server. An interactive UI can be implemented using HTML5 or JavaScript for the visual display.

[0541] Humanoid machines:

[0542] Humanoid robots provide users with visual and interactive feedback. This can be achieved using home robots such as the NAO robot. Feedback is presented through voice and movement according to the user's progress, making it easier for users to receive feedback intuitively.

[0543] Examples

[0544] As a concrete example, consider a scenario where a user sets a goal of "walking 10,000 steps a day." The server analyzes the user's current walking habits and uses a generation AI model to devise a plan to achieve 10,000 steps. When the user achieves their daily goal, the humanoid robot provides a voice message such as "Great progress!" An example of a prompt message in this case might be an instruction to "Generate an encouraging message appropriate to the user's progress, based on their walking data."

[0545] In this way, each component works together to create an advanced system that supports the user in achieving their goals. The present invention aims to continuously support health management and contribute to the promotion of the user's health.

[0546] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0547] Step 1:

[0548] Users input their goal information and health status information using an application on their device. This information includes specific goals the user wants to achieve (e.g., walking 10,000 steps a day) and their current health status (e.g., average number of steps per day). The input information is sent to the server via the network. At this time, the device performs data validation to ensure that the input data is correctly formatted.

[0549] Step 2:

[0550] The server uses a generative AI model to suggest optimal habits based on the user's received goal and health status information. The server uses libraries such as Python and TensorFlow to execute the generative AI model and generate personalized suggestions for the user. This process involves analyzing the user's existing data and performing data calculations to predict exercise patterns. The output includes specific exercise plans and dietary suggestions.

[0551] Step 3:

[0552] Suggestions from the server are notified to the terminal, which then visually displays them to the user. The terminal uses HTML5 and JavaScript in its user interface to visualize the suggestions in an easy-to-understand manner. During this process, feedback sounds and pop-ups are also displayed to prompt the user for notification.

[0553] Step 4:

[0554] The user performs the suggested habits and records their progress on the device. The device then connects with activity trackers and other devices to collect the user's steps and exercise time. The collected data is sent back to the server, which then serves as input for further analysis.

[0555] Step 5:

[0556] The server analyzes the submitted progress data in real time and evaluates the user's progress toward achieving their goals. Statistical methods and machine learning algorithms are used for data analysis, examining the user's activity log. The analysis results generate feedback for the user, including specific areas for improvement and advice on their progress.

[0557] Step 6:

[0558] The generated feedback is presented to the user via a terminal and also communicated interactively to the user through a humanoid robot. The humanoid robot uses a control program for robots to determine voice output and movement patterns, helping the user understand the feedback more intuitively. The prompt used in this process is, "Generate an encouraging message appropriate to the user's achievement level based on their walking data."

[0559] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0560] This invention combines an emotional engine with a habit-forming system that supports users in achieving their goals, providing more appropriate habit suggestions and feedback based on both the user's emotional state and physical health status. This system operates around a server, a terminal, and the user.

[0561] First, the user installs the application via their device and inputs their individual goals and health status. This information is sent directly to the server. Based on the received information, the server utilizes a generative model to suggest the most suitable habits for the user. At this point, the emotion engine analyzes the user's facial expressions and tone of voice to understand their emotional state.

[0562] The emotion engine operates using sensors such as cameras and microphones built into the user's device, and analyzes the data obtained from them to recognize the user's emotional state in real time. The recognized emotions are sent to a server and used to adjust habit suggestions according to the user's current mental and physical health. For example, if the user is feeling stressed, habits that promote relaxation may be suggested.

[0563] The user implements the suggested habits and records their progress and impressions on their device. The device sends this data to a server, which analyzes the user's emotional state along with the progress data. Based on the analysis, the server generates appropriate feedback, providing encouragement and new suggestions that take the user's emotions into consideration. This feedback is sent to the device and notified to the user. The content of the feedback is also customized based on the emotional state; for example, if positive feedback is needed, such a message will be provided.

[0564] As a concrete example, consider a case where a user sets "stress management" as their goal. If the emotion engine detects that the user's stress level is high, the server can suggest activities such as "evening walks" or "meditation sessions" to the user. As the user carries out these activities and records their progress, if the emotion engine indicates an increase in positive emotions, it will send a message acknowledging this and encouraging them to continue.

[0565] This invention provides a system that comprehensively understands the user's physical and mental state and promotes the formation of better habits.

[0566] The following describes the processing flow.

[0567] Step 1:

[0568] The user installs the application on their device and creates an account. The user enters basic information such as their name, goals, and health status. The device sends the entered information to the server.

[0569] Step 2:

[0570] The device prepares to collect the user's emotional state using its camera and microphone. It provides data such as facial expressions and voice tone to the emotion engine through sensors built into the device.

[0571] Step 3:

[0572] The emotion engine analyzes data provided by the device and recognizes the user's emotional state in real time. The recognition results are sent to the server as emotional state data.

[0573] Step 4:

[0574] The server receives goal information, health status information, and emotional state data sent by the user. Based on this, a generative model is used to suggest the most suitable habits for the user.

[0575] Step 5:

[0576] The server sends habit suggestions to the terminal, which then displays the suggestions to the user. The user reviews the suggested habits and selects which ones to implement.

[0577] Step 6:

[0578] The user performs a selected habit and records their progress and feelings on their device. The device then sends the progress data and the user's current emotional state to the server.

[0579] Step 7:

[0580] The server analyzes progress data and emotional state data. Based on the results, it evaluates the user's achievement and generates emotion-based feedback.

[0581] Step 8:

[0582] The server generates feedback and sends it to the device, which then notifies the user of its contents. The feedback is designed to take into account the user's current emotional state.

[0583] Step 9:

[0584] Users can continuously improve their habit formation by reviewing the feedback displayed on their devices and incorporating it into their future actions. Goal settings and habit plans can be adjusted as needed.

[0585] (Example 2)

[0586] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0587] In modern society, it is crucial for users to find appropriate actions to support their goal achievement and to manage their progress. However, customizing these actions to suit individual users' emotional states and physical health is difficult, creating a need for systems that provide appropriate feedback to maximize the effectiveness of self-management. Furthermore, protecting privacy is essential in such systems.

[0588] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0589] In this invention, the server includes means for inputting user goal information and health status information, means for suggesting optimal actions to the user using a generative model, and means for collecting data using an emotion engine to analyze the user's emotional state. This enables personalized action suggestions and feedback to the user, as well as progress management and effective self-management support.

[0590] "Goal information" refers to data related to specific objectives that users set with the aim of achieving them.

[0591] "Health status information" refers to data about the user's current physical health, including information related to their physical condition and lifestyle.

[0592] A "generative model" is an algorithm that generates specific outputs based on input information, creating optimal action suggestions for the user.

[0593] "Emotional state" refers to data that indicates the psychological state a user is experiencing, such as stress, joy, and anxiety.

[0594] An "emotion engine" is software that analyzes a user's facial expressions and tone of voice to recognize their emotional state in real time.

[0595] "Progress data" refers to data that shows the extent to which users are taking the suggested actions, and is used to evaluate the status of goal achievement.

[0596] "Feedback" refers to information or encouraging messages provided to users based on analysis results to support self-management.

[0597] This invention provides a system to support users in achieving their goals. The system consists of a user, a terminal, and a server, and supports the improvement of the user's life through the following operations.

[0598] First, users install a habit-forming application on their device and input their goal information and health status. The device is equipped with sensors such as a camera and microphone, which are used to capture the user's facial expressions and voice tone.

[0599] The device sends the entered goal information and health status information to the server. The server inputs the received information into a generating AI model and suggests the most appropriate actions for the user. This model analyzes the training data using prompts and generates recommendations. An example of a prompt is, "If the user's goal is to maintain health, please suggest appropriate eating habits."

[0600] Furthermore, the server uses an emotion engine to analyze emotional data transmitted from the device. This allows it to understand the user's emotional state and further customize appropriate feedback and suggestions.

[0601] As a concrete example, consider a case where a user sets "weight management" as their goal. If the emotion engine detects a decline in the user's motivation, the server can recommend "light exercise" or "healthy snacks" to the user. Then, if the user does these things and their positive emotions increase, the server will evaluate this and generate messages to support their continued efforts.

[0602] As described above, this system provides a comprehensive solution to support users in achieving their goals.

[0603] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0604] Step 1:

[0605] Users launch a habit-forming application via their device and input goal information and health status information. This input data is used as foundational data to customize behavioral suggestions based on the user's individual goals. The input information is sent directly to the server by the device.

[0606] Step 2:

[0607] The server uses a generative AI model to generate appropriate action suggestions based on the user's goal information and health status information received from the terminal. The input here is data sent by the user, and the output is action suggestions optimized for the user. The server feeds prompt sentences into the generative AI model to maximize the generation probability and returns the resulting suggestions to the terminal.

[0608] Step 3:

[0609] The device continuously captures the user's facial expressions and voice using its built-in camera and microphone. The acquired sensor data is analyzed in real time by an emotion engine. The input is sensor data, and the output of this analysis is the user's current emotional state. This emotional state is sent to a server and used to customize feedback.

[0610] Step 4:

[0611] The server combines the user's emotional data and health data to further refine behavioral suggestions and generate feedback. For example, if emotional data indicates stress, the suggested behaviors are updated to be specifically stress-relieving. Feedback based on the analysis results is generated and provided to the user through the device.

[0612] Step 5:

[0613] The user performs actions suggested by the server and records their progress and impressions within the application. The device then sends the user's progress data and emotional changes back to the server. The input progress data is analyzed by the server and forms the basis for subsequent action suggestions and feedback.

[0614] Step 6:

[0615] The server re-analyzes the received progress and sentiment data to customize the feedback for the user. The feedback resulting from the analysis is designed to encourage the next action while maintaining the user's motivation. The terminal then notifies the user of this feedback.

[0616] (Application Example 2)

[0617] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0618] To effectively support users in achieving their goals and maintaining their health in their daily lives, flexible support that takes into account their emotional and physical state is necessary, rather than simply providing information. However, conventional systems have the challenge of being unable to analyze these individual factors in real time and provide optimal habits and feedback.

[0619] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0620] In this invention, the server includes means for acquiring user goal information and health status information, means for suggesting optimal actions to the user using a generative model, and means for analyzing facial expressions and voice to recognize the user's emotional state. This makes it possible to provide optimal habit suggestions and feedback tailored to the user's emotions and health status.

[0621] "User goal information" refers to information about the specific objectives and goals that the user wishes to achieve.

[0622] "Health status information" refers to data about the user's current physical and mental health status.

[0623] A "generative model" is an algorithm used to suggest optimal actions and habits based on user information.

[0624] "Means of recognizing emotional states" refers to technologies that determine a user's emotions through analysis of their facial expressions and voice.

[0625] A "means of presenting habits" refers to a mechanism that suggests optimal actions and lifestyle habits to the user.

[0626] "Progress data" refers to information that records the activities and progress made by the user.

[0627] "Means of providing feedback" refers to the process of communicating encouragement and new suggestions to users in accordance with their behavior and emotional state.

[0628] "Consumer electronic devices" refer to electronic devices and equipment used in ordinary households, and are devices that support or optimize daily life.

[0629] To implement this invention, it is necessary to build a system in which the user, terminal, and server cooperate with each other. The user inputs goal information and health status information through the terminal. The terminal transmits this information to the server. Based on the received information, the server uses a generative AI model to suggest optimal actions and habits to the user. Because this process requires large-scale data processing and analysis, it is suitable for the server to utilize a cloud computing environment.

[0630] The user's device has built-in sensors such as a camera and microphone, which are used to recognize the user's emotional state in real time. This recognition utilizes image processing and voice analysis technologies. The recognized emotional state is sent to a server, which dynamically adjusts habit suggestions based on this information. For example, if stress is detected, the server will offer suggestions to encourage relaxation.

[0631] Furthermore, the progress of habits performed by the user is sent from the device to the server. The server integrates and analyzes this progress data and emotional data to provide feedback to the user. The feedback is tailored to the user's emotional state, and may include messages designed to evoke positive emotions.

[0632] As a concrete example, consider a case where a user sets "stress management" as their goal. When the emotion engine detects that the user's stress level is high, the server runs a program that suggests activities such as "evening walks" or "meditation sessions." When the user performs these activities and records their progress on their device, if the emotion engine detects a positive change, it sends a message acknowledging this and encouraging them to continue.

[0633] Example prompt: "If the user is detected as tired, generate a prompt recommending health-promoting activities."

[0634] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0635] Step 1:

[0636] Users input goal information and health status information through their device.

[0637] This input includes specific goals the user wants to achieve and their current health status. The device processes this information as digital data and prepares it for transmission to subsequent processing steps. Processing of the input data includes quantification and categorization.

[0638] Step 2:

[0639] The device transmits the entered goal information and health status information to the server.

[0640] The server stores the received information in an appropriate database for analysis. The data arriving at the server is then used as material for generating optimal actions and habits for the user using a generative AI model.

[0641] Step 3:

[0642] The device uses its built-in camera and microphone to recognize the user's emotional state in real time.

[0643] The device uses video data from the camera and audio data from the microphone as input, and performs image processing and audio analysis using an analysis module within the device. This yields quantified data related to the user's emotional state. This data is treated as a quantitative representation of the user's emotional condition.

[0644] Step 4:

[0645] The device sends the recognized emotional state data to the server.

[0646] The server integrates and analyzes the user's goal information, health status information, and emotional state data. This analysis utilizes statistical methods and machine learning algorithms (including generative AI models) to generate personalized habit suggestions. The output is a list of specific behavioral suggestions.

[0647] Step 5:

[0648] The server sends the generated habit suggestions to the terminal and presents them to the user.

[0649] The terminal displays suggestions received from the server to the user through an interface. Each suggestion is presented at an appropriate time based on the user's environment and circumstances. The user can then choose an action based on the suggestions.

[0650] Step 6:

[0651] The user implements the suggested habits and records their progress on their device.

[0652] The device collects progress information as user feedback and sends it to the server along with newly acquired emotional state data. The input data includes the type and duration of the actions performed.

[0653] Step 7:

[0654] The server re-analyzes the progress data and emotional state data and generates feedback.

[0655] By utilizing generative AI models, the effectiveness of user actions is evaluated, and positive feedback and new suggestions are generated. These results are then reflected in future suggestions and support.

[0656] Step 8:

[0657] The server then sends the generated feedback back to the terminal.

[0658] The device will send notifications to the user to provide feedback. This feedback will include inspiring messages and suggestions for the next steps to boost user motivation.

[0659] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0660] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0661] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0662] [Fourth Embodiment]

[0663] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0664] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0665] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0666] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0667] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0668] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0669] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0670] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0671] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0672] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0673] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0674] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0675] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0676] This invention provides a system that suggests optimal habits based on the user's individual goals and health status. This system functions through the cooperation of a server, a terminal, and the user.

[0677] First, the user installs the application using their device and enters their goals and health status. The device then receives this information and sends it to the server. After receiving the goal and health status information from the user, the server uses a generative model to generate habits tailored to the user. These generated suggestions cover a wide range of areas, including exercise habits, meal plans, and study schedules.

[0678] Next, the suggestion is sent to the device, and the user confirms it. The user then performs the suggested habit and records their progress on the device. The progress data reflects the user's daily activities and includes steps taken, exercise time, and meals eaten. The device then sends this data back to the server.

[0679] The server analyzes the received progress data in real time and evaluates the user's achievements. Based on this evaluation, the server generates specific feedback for the user. This feedback may include phrases such as, "You're making great progress," or "You'll get even better results if you increase your exercise time." The feedback is sent to the device and presented to the user.

[0680] Furthermore, to protect user privacy, data transmitted and received between the device and the server is anonymized to prevent the identification of individuals. This feature provides a foundation for users to use the system with peace of mind.

[0681] As a concrete example of the program, consider a case where a user sets a goal of "losing 3 kg in one month." The server suggests an optimized diet and exercise plan based on the user's current activity level. The user then monitors their daily activities according to this plan and makes necessary adjustments based on the feedback. Progress is visually displayed on the device, allowing the user to see at a glance their progress towards their goal.

[0682] In this way, the present invention provides a system that supports effective habit formation tailored to individual goals and promotes the user's goal achievement.

[0683] The following describes the processing flow.

[0684] Step 1:

[0685] The user installs the application on their device and creates an account. During this process, the user enters basic information including their name, email address, and goals.

[0686] Step 2:

[0687] The device presents the user with a goal-setting screen, where the user enters information about their desired goals and current health status. This information is then stored in a database by the device.

[0688] Step 3:

[0689] The terminal sends the information entered by the user to the server. Here, data anonymization is performed as needed to prevent the user from being identified.

[0690] Step 4:

[0691] The server launches a generative model to analyze the received goal and health status information. Based on this information, the generative model generates habit suggestions that are best suited to the user.

[0692] Step 5:

[0693] The server generates habit suggestions and sends them to the user's device, where they are displayed on the user's screen. The user reviews these suggestions and selects the habits they wish to implement.

[0694] Step 6:

[0695] The user performs selected habits and records their progress on the device. Here, the user enters detailed information about their daily activities, and the device saves this information chronologically.

[0696] Step 7:

[0697] The device sends daily recorded progress data to the server. This data includes steps taken, calories burned, and tasks completed.

[0698] Step 8:

[0699] The server uses the received progress data to analyze the user's current achievement status. An AI model then uses this data to generate feedback for the user.

[0700] Step 9:

[0701] The server generates feedback and sends it to the terminal, which then notifies the user of its contents. This feedback includes evaluations of progress and suggestions for improvement.

[0702] Step 10:

[0703] Users receive feedback displayed on their devices and use it to guide their future actions. As needed, users can adjust their goals and daily plans to continuously improve their habits.

[0704] (Example 1)

[0705] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0706] Traditional technologies fail to adequately propose effective and appropriate activities to users with individual goals, monitor their progress, and provide feedback. Furthermore, there are privacy concerns regarding the potential leakage of user data to third parties. Therefore, there is a need for a system that supports users in achieving their goals while ensuring the protection of personal information and providing a secure user experience.

[0707] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0708] In this invention, the server includes a device for inputting user goal information and health status information, a device for generating an optimal activity plan for the user using a generative AI model, a device for providing feedback based on the analysis results, and means for encrypting communication data. This allows users to receive appropriate activity guidance tailored to their individual goals and to use the system with peace of mind while ensuring their privacy.

[0709] A "user" is an entity that uses this system to receive activity plans and feedback based on individual goals.

[0710] "Goal information" refers to the specific objectives and goals that the user wants to achieve, and is the data entered into this system.

[0711] "Health status information" refers to data that represents the user's current physical and health condition and is entered into the system.

[0712] A "generative AI model" refers to an algorithm or program that generates an optimal activity plan based on the user's input information.

[0713] An "activity plan" refers to the specific actions and initiatives proposed to achieve the user's goals.

[0714] "Device" refers to the technical means or hardware used to perform a specific function or role.

[0715] "Analysis results" refer to information obtained after processing and evaluating data received from users, indicating how much progress the user is making toward their goals.

[0716] "Feedback" refers to information and advice provided to users for improvement, and functions as guidance for activities.

[0717] "Communication methods" refer to the protocols and technologies used to securely send and receive user data.

[0718] This invention is a system that proposes an appropriate activity plan for each user's individual goals and supports the user in achieving those goals. A specific embodiment is shown below.

[0719] First, the user installs a dedicated application using their device. This application runs on typical smartphones and computers and provides an interface where the user can input individual goal information (e.g., "lose 3 kg in one month") and health information (e.g., current weight, height, allergy information, etc.). Once the user inputs and submits the information, the device encrypts this data using security technology (e.g., HTTPS) and sends it to the server.

[0720] The server uses a generative AI model based on the received data to generate an optimal activity plan for the user. This AI model uses historical datasets and common health guidelines to generate the most suitable suggestions for the user. The generated activity plan is presented to the user as options for exercise schedules and meal plans. Specifically, suggestions may include "take a 30-minute walk five times a week" or "limit your daily calorie intake to 1800 kcal." An example of a prompt to the generative AI model is, "Please suggest the most effective health plan to help the user achieve their goals."

[0721] Next, the server sends the generated activity plan to the terminal, which then notifies the user. The user reviews the proposed plan through the terminal's interface and records their daily progress. The recorded data includes steps taken, exercise time, and meal details. This information is periodically sent from the terminal to the server, which analyzes the progress in real time and evaluates the user's performance.

[0722] Based on the evaluation results, the server provides feedback to the user. This feedback may include advice such as, "Great progress! Keep up the current pace," or "To accelerate your goal achievement, we recommend increasing your daily exercise time by another 10 minutes." The feedback is sent to the device and displayed to the user.

[0723] Ultimately, all communications are managed using data anonymization technology to ensure that individuals cannot be identified, and user privacy is strictly protected. This allows users to use the system with peace of mind and effectively progress towards their goals.

[0724] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0725] Step 1:

[0726] Users install the application using their device and input individual goal information and health status information. This input includes numerical data (e.g., target weight, current weight, height) and text data (e.g., allergies, exercise restrictions). The device encrypts this data and sends it to the server. The input data is structured and stored in the server's database.

[0727] Step 2:

[0728] The server runs a generative AI model on the received user data. The prompt "Please suggest the optimal health plan to help the user achieve their goals" is entered, and the model begins its analysis. The server generates a personalized activity plan, referencing similar past cases and standard health guidelines from the database. The resulting suggestions serve as specific guidelines regarding exercise and diet.

[0729] Step 3:

[0730] The server sends the generated activity plan to the terminal. The terminal receives it and notifies the user. The terminal displays the proposed content in a format that is easy for the user to understand, and visualizes it on the interface, including feedback and advice.

[0731] Step 4:

[0732] Users perform daily activities according to the provided activity plan and record their progress on their device. Input data includes steps taken, exercise time, and meal details, which the device organizes and sends to the server. The device issues alarms and notifications as needed to remind users of their activity progress.

[0733] Step 5:

[0734] The server analyzes the received progress data and evaluates how close the user is to their goal. This evaluation uses basic statistical processing and trend analysis. Based on the evaluation results, the server generates feedback for the user. For example, it might suggest specific content such as, "Your current pace is good. Please continue."

[0735] Step 6:

[0736] The server sends the generated feedback back to the terminal. The terminal presents the feedback to the user and provides motivation for reviewing and continuing future plans. Each data communication is anonymized and secure, so users can enjoy the service without worrying about the leakage of personal information.

[0737] (Application Example 1)

[0738] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0739] While systems exist that suggest optimal habits based on each user's individual health status and goals, there is a lack of technology to provide users with visual and interactive feedback through more user-friendly automated machines. Therefore, there is a need for effective and motivating feedback methods to help users maintain the suggested habits.

[0740] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0741] In this invention, the server includes a device for inputting the user's goal information and health status information, a device for suggesting optimal habits to the user using a generative model, and a device for receiving and analyzing progress data from the user in real time. This enables visual and interactive feedback to the user using a user-friendly humanoid machine.

[0742] "User goal information" refers to information that indicates the specific numerical targets or situations that the user wishes to achieve.

[0743] "Health status information" refers to information about the user's physical health and current fitness level.

[0744] A "generative model" is a data processing and analysis model used to suggest optimal habits based on a user's goal information and health status information.

[0745] A "humanoid machine" is an automated machine that mimics the shape of a human being and is used to interact with users and provide information visually.

[0746] A "data anonymization device" is a device that processes data in order to prevent users' personal information from being identified.

[0747] "Progress data" refers to data that records the progress of a user's activities and indicates the degree of achievement towards their goals.

[0748] "Visualization" is a process or method for presenting information to a user visually.

[0749] This invention is an advanced system that supports users' health management, and specifically has a series of functions to support users in achieving their goals. The system mainly consists of a server, terminals, and a humanoid machine.

[0750] Ecosystem Overview

[0751] server:

[0752] The server receives user goal and health status information and uses a generative AI model to suggest optimal habits based on this information. The server analyzes progress data in real time and performs the necessary data processing to provide feedback to the user. Specifically, it is expected to use software such as Python and TensorFlow for data analysis and machine learning model execution. In addition, data anonymization technology will be used to protect user privacy.

[0753] Terminal:

[0754] The device provides an interface for users to input their goals and health status. The entered data is sent to the server. The device also visually displays feedback and progress received from the server. An interactive UI can be implemented using HTML5 or JavaScript for the visual display.

[0755] Humanoid machines:

[0756] Humanoid robots provide users with visual and interactive feedback. This can be achieved using home robots such as the NAO robot. Feedback is presented through voice and movement according to the user's progress, making it easier for users to receive feedback intuitively.

[0757] Examples

[0758] As a concrete example, consider a scenario where a user sets a goal of "walking 10,000 steps a day." The server analyzes the user's current walking habits and uses a generation AI model to devise a plan to achieve 10,000 steps. When the user achieves their daily goal, the humanoid robot provides a voice message such as "Great progress!" An example of a prompt message in this case might be an instruction to "Generate an encouraging message appropriate to the user's progress, based on their walking data."

[0759] In this way, each component works together to create an advanced system that supports the user in achieving their goals. The present invention aims to continuously support health management and contribute to the promotion of the user's health.

[0760] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0761] Step 1:

[0762] Users input their goal information and health status information using an application on their device. This information includes specific goals the user wants to achieve (e.g., walking 10,000 steps a day) and their current health status (e.g., average number of steps per day). The input information is sent to the server via the network. At this time, the device performs data validation to ensure that the input data is correctly formatted.

[0763] Step 2:

[0764] The server uses a generative AI model to suggest optimal habits based on the user's received goal and health status information. The server uses libraries such as Python and TensorFlow to execute the generative AI model and generate personalized suggestions for the user. This process involves analyzing the user's existing data and performing data calculations to predict exercise patterns. The output includes specific exercise plans and dietary suggestions.

[0765] Step 3:

[0766] Suggestions from the server are notified to the terminal, which then visually displays them to the user. The terminal uses HTML5 and JavaScript in its user interface to visualize the suggestions in an easy-to-understand manner. During this process, feedback sounds and pop-ups are also displayed to prompt the user for notification.

[0767] Step 4:

[0768] The user performs the suggested habits and records their progress on the device. The device then connects with activity trackers and other devices to collect the user's steps and exercise time. The collected data is sent back to the server, which then serves as input for further analysis.

[0769] Step 5:

[0770] The server analyzes the submitted progress data in real time and evaluates the user's progress toward achieving their goals. Statistical methods and machine learning algorithms are used for data analysis, examining the user's activity log. The analysis results generate feedback for the user, including specific areas for improvement and advice on their progress.

[0771] Step 6:

[0772] The generated feedback is presented to the user via a terminal and also communicated interactively to the user through a humanoid robot. The humanoid robot uses a control program for robots to determine voice output and movement patterns, helping the user understand the feedback more intuitively. The prompt used in this process is, "Generate an encouraging message appropriate to the user's achievement level based on their walking data."

[0773] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0774] This invention combines an emotional engine with a habit-forming system that supports users in achieving their goals, providing more appropriate habit suggestions and feedback based on both the user's emotional state and physical health status. This system operates around a server, a terminal, and the user.

[0775] First, the user installs the application via their device and inputs their individual goals and health status. This information is sent directly to the server. Based on the received information, the server utilizes a generative model to suggest the most suitable habits for the user. At this point, the emotion engine analyzes the user's facial expressions and tone of voice to understand their emotional state.

[0776] The emotion engine operates using sensors such as cameras and microphones built into the user's device, and analyzes the data obtained from them to recognize the user's emotional state in real time. The recognized emotions are sent to a server and used to adjust habit suggestions according to the user's current mental and physical health. For example, if the user is feeling stressed, habits that promote relaxation may be suggested.

[0777] The user implements the suggested habits and records their progress and impressions on their device. The device sends this data to a server, which analyzes the user's emotional state along with the progress data. Based on the analysis, the server generates appropriate feedback, providing encouragement and new suggestions that take the user's emotions into consideration. This feedback is sent to the device and notified to the user. The content of the feedback is also customized based on the emotional state; for example, if positive feedback is needed, such a message will be provided.

[0778] As a concrete example, consider a case where a user sets "stress management" as their goal. If the emotion engine detects that the user's stress level is high, the server can suggest activities such as "evening walks" or "meditation sessions" to the user. As the user carries out these activities and records their progress, if the emotion engine indicates an increase in positive emotions, it will send a message acknowledging this and encouraging them to continue.

[0779] This invention provides a system that comprehensively understands the user's physical and mental state and promotes the formation of better habits.

[0780] The following describes the processing flow.

[0781] Step 1:

[0782] The user installs the application on their device and creates an account. The user enters basic information such as their name, goals, and health status. The device sends the entered information to the server.

[0783] Step 2:

[0784] The device prepares to collect the user's emotional state using its camera and microphone. It provides data such as facial expressions and voice tone to the emotion engine through sensors built into the device.

[0785] Step 3:

[0786] The emotion engine analyzes data provided by the device and recognizes the user's emotional state in real time. The recognition results are sent to the server as emotional state data.

[0787] Step 4:

[0788] The server receives goal information, health status information, and emotional state data sent by the user. Based on this, a generative model is used to suggest the most suitable habits for the user.

[0789] Step 5:

[0790] The server sends habit suggestions to the terminal, which then displays the suggestions to the user. The user reviews the suggested habits and selects which ones to implement.

[0791] Step 6:

[0792] The user performs a selected habit and records their progress and feelings on their device. The device then sends the progress data and the user's current emotional state to the server.

[0793] Step 7:

[0794] The server analyzes progress data and emotional state data. Based on the results, it evaluates the user's achievement and generates emotion-based feedback.

[0795] Step 8:

[0796] The server generates feedback and sends it to the device, which then notifies the user of its contents. The feedback is designed to take into account the user's current emotional state.

[0797] Step 9:

[0798] Users can continuously improve their habit formation by reviewing the feedback displayed on their devices and incorporating it into their future actions. Goal settings and habit plans can be adjusted as needed.

[0799] (Example 2)

[0800] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0801] In modern society, it is crucial for users to find appropriate actions to support their goal achievement and to manage their progress. However, customizing these actions to suit individual users' emotional states and physical health is difficult, creating a need for systems that provide appropriate feedback to maximize the effectiveness of self-management. Furthermore, protecting privacy is essential in such systems.

[0802] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0803] In this invention, the server includes means for inputting user goal information and health status information, means for suggesting optimal actions to the user using a generative model, and means for collecting data using an emotion engine to analyze the user's emotional state. This enables personalized action suggestions and feedback to the user, as well as progress management and effective self-management support.

[0804] "Goal information" refers to data related to specific objectives that users set with the aim of achieving them.

[0805] "Health status information" refers to data about the user's current physical health, including information related to their physical condition and lifestyle.

[0806] A "generative model" is an algorithm that generates specific outputs based on input information, creating optimal action suggestions for the user.

[0807] "Emotional state" refers to data that indicates the psychological state a user is experiencing, such as stress, joy, and anxiety.

[0808] An "emotion engine" is software that analyzes a user's facial expressions and tone of voice to recognize their emotional state in real time.

[0809] "Progress data" refers to data that shows the extent to which users are taking the suggested actions, and is used to evaluate the status of goal achievement.

[0810] "Feedback" refers to information or encouraging messages provided to users based on analysis results to support self-management.

[0811] This invention provides a system to support users in achieving their goals. The system consists of a user, a terminal, and a server, and supports the improvement of the user's life through the following operations.

[0812] First, users install a habit-forming application on their device and input their goal information and health status. The device is equipped with sensors such as a camera and microphone, which are used to capture the user's facial expressions and voice tone.

[0813] The device sends the entered goal information and health status information to the server. The server inputs the received information into a generating AI model and suggests the most appropriate actions for the user. This model analyzes the training data using prompts and generates recommendations. An example of a prompt is, "If the user's goal is to maintain health, please suggest appropriate eating habits."

[0814] Furthermore, the server uses an emotion engine to analyze emotional data transmitted from the device. This allows it to understand the user's emotional state and further customize appropriate feedback and suggestions.

[0815] As a concrete example, consider a case where a user sets "weight management" as their goal. If the emotion engine detects a decline in the user's motivation, the server can recommend "light exercise" or "healthy snacks" to the user. Then, if the user does these things and their positive emotions increase, the server will evaluate this and generate messages to support their continued efforts.

[0816] As described above, this system provides a comprehensive solution to support users in achieving their goals.

[0817] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0818] Step 1:

[0819] Users launch a habit-forming application via their device and input goal information and health status information. This input data is used as foundational data to customize behavioral suggestions based on the user's individual goals. The input information is sent directly to the server by the device.

[0820] Step 2:

[0821] The server uses a generative AI model to generate appropriate action suggestions based on the user's goal information and health status information received from the terminal. The input here is data sent by the user, and the output is action suggestions optimized for the user. The server feeds prompt sentences into the generative AI model to maximize the generation probability and returns the resulting suggestions to the terminal.

[0822] Step 3:

[0823] The device continuously captures the user's facial expressions and voice using its built-in camera and microphone. The acquired sensor data is analyzed in real time by an emotion engine. The input is sensor data, and the output of this analysis is the user's current emotional state. This emotional state is sent to a server and used to customize feedback.

[0824] Step 4:

[0825] The server combines the user's emotional data and health data to further refine behavioral suggestions and generate feedback. For example, if emotional data indicates stress, the suggested behaviors are updated to be specifically stress-relieving. Feedback based on the analysis results is generated and provided to the user through the device.

[0826] Step 5:

[0827] The user performs actions suggested by the server and records their progress and impressions within the application. The device then sends the user's progress data and emotional changes back to the server. The input progress data is analyzed by the server and forms the basis for subsequent action suggestions and feedback.

[0828] Step 6:

[0829] The server re-analyzes the received progress and sentiment data to customize the feedback for the user. The feedback resulting from the analysis is designed to encourage the next action while maintaining the user's motivation. The terminal then notifies the user of this feedback.

[0830] (Application Example 2)

[0831] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0832] To effectively support users in achieving their goals and maintaining their health in their daily lives, flexible support that takes into account their emotional and physical state is necessary, rather than simply providing information. However, conventional systems have the challenge of being unable to analyze these individual factors in real time and provide optimal habits and feedback.

[0833] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0834] In this invention, the server includes means for acquiring user goal information and health status information, means for suggesting optimal actions to the user using a generative model, and means for analyzing facial expressions and voice to recognize the user's emotional state. This makes it possible to provide optimal habit suggestions and feedback tailored to the user's emotions and health status.

[0835] "User goal information" refers to information about the specific objectives and goals that the user wishes to achieve.

[0836] "Health status information" refers to data about the user's current physical and mental health status.

[0837] A "generative model" is an algorithm used to suggest optimal actions and habits based on user information.

[0838] "Means of recognizing emotional states" refers to technologies that determine a user's emotions through analysis of their facial expressions and voice.

[0839] A "means of presenting habits" refers to a mechanism that suggests optimal actions and lifestyle habits to the user.

[0840] "Progress data" refers to information that records the activities and progress made by the user.

[0841] "Means of providing feedback" refers to the process of communicating encouragement and new suggestions to users in accordance with their behavior and emotional state.

[0842] "Consumer electronic devices" refer to electronic devices and equipment used in ordinary households, and are devices that support or optimize daily life.

[0843] To implement this invention, it is necessary to build a system in which the user, terminal, and server cooperate with each other. The user inputs goal information and health status information through the terminal. The terminal transmits this information to the server. Based on the received information, the server uses a generative AI model to suggest optimal actions and habits to the user. Because this process requires large-scale data processing and analysis, it is suitable for the server to utilize a cloud computing environment.

[0844] The user's device has built-in sensors such as a camera and microphone, which are used to recognize the user's emotional state in real time. This recognition utilizes image processing and voice analysis technologies. The recognized emotional state is sent to a server, which dynamically adjusts habit suggestions based on this information. For example, if stress is detected, the server will offer suggestions to encourage relaxation.

[0845] Furthermore, the progress of habits performed by the user is sent from the device to the server. The server integrates and analyzes this progress data and emotional data to provide feedback to the user. The feedback is tailored to the user's emotional state, and may include messages designed to evoke positive emotions.

[0846] As a concrete example, consider a case where a user sets "stress management" as their goal. When the emotion engine detects that the user's stress level is high, the server runs a program that suggests activities such as "evening walks" or "meditation sessions." When the user performs these activities and records their progress on their device, if the emotion engine detects a positive change, it sends a message acknowledging this and encouraging them to continue.

[0847] Example prompt: "If the user is detected as tired, generate a prompt recommending health-promoting activities."

[0848] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0849] Step 1:

[0850] Users input goal information and health status information through their device.

[0851] This input includes specific goals the user wants to achieve and their current health status. The device processes this information as digital data and prepares it for transmission to subsequent processing steps. Processing of the input data includes quantification and categorization.

[0852] Step 2:

[0853] The device transmits the entered goal information and health status information to the server.

[0854] The server stores the received information in an appropriate database for analysis. The data arriving at the server is then used as material for generating optimal actions and habits for the user using a generative AI model.

[0855] Step 3:

[0856] The device uses its built-in camera and microphone to recognize the user's emotional state in real time.

[0857] The device uses video data from the camera and audio data from the microphone as input, and performs image processing and audio analysis using an analysis module within the device. This yields quantified data related to the user's emotional state. This data is treated as a quantitative representation of the user's emotional condition.

[0858] Step 4:

[0859] The device sends the recognized emotional state data to the server.

[0860] The server integrates and analyzes the user's goal information, health status information, and emotional state data. This analysis utilizes statistical methods and machine learning algorithms (including generative AI models) to generate personalized habit suggestions. The output is a list of specific behavioral suggestions.

[0861] Step 5:

[0862] The server sends the generated habit suggestions to the terminal and presents them to the user.

[0863] The terminal displays suggestions received from the server to the user through an interface. Each suggestion is presented at an appropriate time based on the user's environment and circumstances. The user can then choose an action based on the suggestions.

[0864] Step 6:

[0865] The user implements the suggested habits and records their progress on their device.

[0866] The device collects progress information as user feedback and sends it to the server along with newly acquired emotional state data. The input data includes the type and duration of the actions performed.

[0867] Step 7:

[0868] The server re-analyzes the progress data and emotional state data and generates feedback.

[0869] By utilizing generative AI models, the effectiveness of user actions is evaluated, and positive feedback and new suggestions are generated. These results are then reflected in future suggestions and support.

[0870] Step 8:

[0871] The server then sends the generated feedback back to the terminal.

[0872] The device will send notifications to the user to provide feedback. This feedback will include inspiring messages and suggestions for the next steps to boost user motivation.

[0873] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0874] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0875] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0876] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0877] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0878] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0879] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0880] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0881] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0882] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0883] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0884] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0885] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0886] 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.

[0887] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0888] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0889] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0890] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0891] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0892] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0893] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0894] The following is further disclosed regarding the embodiments described above.

[0895] (Claim 1)

[0896] A means for inputting user goal information and health status information,

[0897] Based on the above input goal information and health status information, a means of proposing the most suitable habits for the user using a generative model,

[0898] A means of receiving and analyzing progress data from users in real time,

[0899] A means of providing feedback to users based on the above analysis,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, comprising means for anonymizing data to protect user privacy.

[0903] (Claim 3)

[0904] The system according to claim 1, comprising a display means for visualizing the user's progress.

[0905] "Example 1"

[0906] (Claim 1)

[0907] A device for inputting user goal information and health status information,

[0908] A device that generates an optimal activity plan for the user using a generated AI model based on the input goal information and health status information described above.

[0909] A device that receives and analyzes progress data from users in real time,

[0910] A device that generates advice to provide feedback to the user and encourage action based on the analysis results,

[0911] A device equipped with an interface for displaying the above feedback,

[0912] A means of communication to encrypt user data and protect privacy,

[0913] A system that includes this.

[0914] (Claim 2)

[0915] The system according to claim 1, which uses a generative model to generate and propose a health plan customized to the individual goals of a user.

[0916] (Claim 3)

[0917] The system according to claim 1, which provides a display function to visually display the user's progress and support monitoring.

[0918] "Application Example 1"

[0919] (Claim 1)

[0920] A device for inputting user goal information and health status information,

[0921] Based on the input goal information and health status information, the device uses a generative model to propose the most suitable habits for the user.

[0922] A device that receives and analyzes progress data from users in real time,

[0923] A device that provides feedback to the user based on the above analysis,

[0924] A device using a humanoid machine to visually provide the above feedback to the user,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, comprising a data anonymization device for protecting user privacy.

[0928] (Claim 3)

[0929] The system according to claim 1, further comprising a display device for visualizing the user's progress and a humanoid machine for providing guidance and explanations.

[0930] "Example 2 of combining an emotion engine"

[0931] (Claim 1)

[0932] A means for inputting user goal information and health status information,

[0933] Based on the input goal information and health status information described above, a means of proposing the optimal action for the user using a generative model,

[0934] A means of collecting data using an emotion engine to analyze the emotional state of users,

[0935] A means of adjusting the proposed content based on the analysis of the above emotional state,

[0936] A means of receiving and analyzing progress data from users in real time,

[0937] Based on the above analysis, a means of providing feedback to users and customizing that feedback while considering the user's emotions,

[0938] A system that includes this.

[0939] (Claim 2)

[0940] The system according to claim 1, comprising means for anonymizing data to protect user privacy.

[0941] (Claim 3)

[0942] The system according to claim 1, comprising a display means for visualizing the user's progress.

[0943] "Application example 2 when combining with an emotional engine"

[0944] (Claim 1)

[0945] Means for obtaining user goal information and health status information,

[0946] Based on the acquired goal information and health status information, a means of presenting the optimal action to the user using a generative model,

[0947] A means of analyzing facial expressions and voice to recognize the user's emotional state,

[0948] A means of suggesting habits optimized for the user based on the emotional state analyzed above,

[0949] A means of receiving and analyzing progress data from users in real time,

[0950] Based on the above analysis, a means of providing feedback to the user and sending suggestions and encouragement that take their emotional state into consideration,

[0951] Consumer electronic devices that support user activities and optimize daily life,

[0952] A system that includes this.

[0953] (Claim 2)

[0954] The system according to claim 1, comprising means for anonymizing data to protect user privacy.

[0955] (Claim 3)

[0956] The system according to claim 1, comprising a display means for visualizing the user's progress. [Explanation of symbols]

[0957] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting user goal information and health status information, Based on the above input goal information and health status information, a means of proposing the most suitable habits for the user using a generative model, A means of receiving and analyzing progress data from users in real time, A means of providing feedback to users based on the above analysis, A system that includes this.

2. The system according to claim 1, comprising means for anonymizing data to protect user privacy.

3. The system according to claim 1, comprising a display means for visualizing the user's progress.

Images