system

Abstract

Provide a system. 【Solution means】 Means for inputting personal goals and health status and creating individual habit formation plans, Means for monitoring and evaluating progress data in real time, Means for providing feedback to the user based on the evaluation results, Means for generating advice for maintaining the user's motivation, Means for providing the generated advice, Observation means for detecting and recording the user's activities in the house, Automated environmental control means for assisting the user's habit formation using the recorded data, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When an individual tries to form a new habit, there are problems such as difficulty in continuation and failure to achieve the goal. In particular, there is a problem that the motivation of the user decreases due to lack of daily progress confirmation and appropriate feedback. The object of the present invention is to support effective habit formation according to individual goals and health conditions so that the user can continue the habit without difficulty for a long time.

Means for Solving the Problems

[0005] This invention provides a system that includes means for collecting data for inputting an individual's goals and health status, means for creating an individualized habit formation plan based on the collected data, means for monitoring and evaluating progress data in real time, means for providing feedback to the user based on the evaluation results, and means for generating and providing advice to maintain the user's motivation. This system features an intuitive interface and privacy-enhancing functions, effectively supporting individual habit formation.

[0006] "Personal goals" refer to the specific objectives or desires that a user is trying to achieve.

[0007] "Health status" refers to information indicating the user's physical and mental health condition.

[0008] A "habit formation plan" is a plan of actions that a user should take on a daily basis in order to achieve their goals.

[0009] "Progress data" refers to information that shows the current progress based on records of user activities and actions.

[0010] "Real-time monitoring" refers to a state where user data is collected, analyzed, and immediately available for review.

[0011] "Means of evaluation" refer to methods or devices for determining a user's current situation and degree of goal achievement based on collected data.

[0012] "Feedback" refers to information provided to users that shows the results of their actions and areas for improvement.

[0013] "Advice to maintain motivation" refers to suggestions that provide encouragement and specific guidance to help users sustain their goal achievement.

[0014] "Means of delivery" refers to the methods and processes for communicating the generated information to the user. [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] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine.

Embodiments for Carrying Out the Invention

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

[0017] First, the terms used in the following description will be explained.

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

[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] The system of this invention is designed to enable users to effectively form new habits and achieve their goals. This system primarily functions through the exchange of information between a server, a terminal, and the user.

[0037] First, the user enters information about their goals and health status into the device. For example, they record specific details such as the weight they want to lose and their daily exercise time. The device sends this data to the server. Based on the received data, the server generates a personalized habit formation plan. The plan provides specific instructions for daily actions to help the user achieve their goals. For example, if the user aims to jog three times a week, the server will suggest the days and times for jogging.

[0038] Next, users continuously record their daily activities through their devices. For example, they input how far they ran each day, what they ate, and how much time they spent exercising. The devices send this information to a server, which analyzes the progress data. Based on the analysis, the server evaluates the user's current level of achievement and generates feedback.

[0039] The server performs analysis in real time and quickly informs the user of the evaluation results. The terminal receives the feedback and notifies the user in a timely manner. For example, "You have achieved 80% of your goal this week. If you walk for another 10 minutes, you will reach today's goal."

[0040] Furthermore, the server analyzes the user's behavior patterns and generates advice to help the user maintain motivation. This advice includes specific areas for improvement and encouraging messages to help achieve future goals. The server sends this advice to the terminal, which then provides it to the user.

[0041] For example, if a user sets a goal of "sleeping for more than 7 hours every day and doing 30 minutes of stretching in the morning," the server will create a plan for sleep patterns and exercise habit formation and notify the user. The user records the amount of time they slept and the amount of time they stretched each day, and the device relays this information to the server. The server evaluates how close the user is to their goal and sends feedback as needed, such as "try to go to bed a little earlier."

[0042] Thus, the system of the present invention flexibly responds to the needs of individual users and supports them in effectively continuing to form habits.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users input information about their goals and health status using their devices. This includes weight loss goals, daily exercise levels, and details about their diet.

[0046] Step 2:

[0047] The device sends the collected user data to the server. This data includes the user's basic profile information.

[0048] Step 3:

[0049] The server analyzes the received data and generates a personalized habit formation plan. This plan also references data from other users with similar goals and past success stories.

[0050] Step 4:

[0051] The server sends the generated habit formation plan to the device, and the device notifies the user of detailed daily action steps. This allows the user to understand the specific action plan.

[0052] Step 5:

[0053] Users record their daily activity progress on their device. Here, they input information such as the type and duration of exercise, meals, or the type and duration of stretches performed.

[0054] Step 6:

[0055] The device sends user activity data to the server. This transmission is typically done daily or in real time.

[0056] Step 7:

[0057] The server evaluates progress based on the received activity data. This includes analysis of the degree to which weekly goals were achieved and the difference between the plan and actual actions.

[0058] Step 8:

[0059] The server generates feedback messages based on the evaluation results, providing advice to the user. This feedback includes suggestions to help maintain user motivation.

[0060] Step 9:

[0061] The server sends feedback and advice to the device, which then presents it to the user. The user then uses this information to plan their next action.

[0062] Step 10:

[0063] The server collects user behavior data and analyzes patterns. Based on the results of this analysis, future plan updates and new suggestions for users are adjusted.

[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] The challenge lies in providing a support system that enables users to effectively form and maintain habits toward achieving their goals. This aims to support individualized planning, progress-based responses, and continuous motivation.

[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 means for inputting personal goals and health-related information and creating specific habit-forming procedures; means for immediately monitoring and evaluating progress information; and means for providing responses to the user based on the evaluation results. This enables the user to understand their progress toward their goals in real time, adjust their plan as needed, and maintain their motivation.

[0069] "Personal purpose" refers to the individual goals or intentions that the user wishes to achieve.

[0070] "Health-related information" refers to information including the user's health status and health-related data, such as weight, exercise time, and dietary content.

[0071] A "habit-forming procedure" is a detailed outline of the daily action plan and steps that users should take to achieve their goals.

[0072] "Progress information" refers to data that shows the degree to which a user has achieved their goals and details of their activities during that process.

[0073] "Immediate monitoring and evaluation" refers to tracking users' progress in real time, analyzing that data, and evaluating the current situation.

[0074] "Providing a response" refers to communicating feedback or recommendations for the next action to the user.

[0075] "Suggestions to maintain motivation" refers to providing users with appropriate encouragement and advice on areas for improvement to help them continue their efforts toward their goals.

[0076] A "generative AI model" refers to an artificial intelligence model that automatically generates plans and makes suggestions based on given data and prompts.

[0077] A "prompt statement" refers to a command or question that is input to a generative AI model to obtain a specific output.

[0078] The system of this invention supports the achievement of individual goals through communication between the user, terminal, and server. This system is designed to facilitate the formation of specific habits.

[0079] First, users input their goals and health-related information via their device. The device then sends this information to a server. The devices used include smartphones and tablets, and input methods include dedicated applications and web interfaces. Specifically, users set weight loss goals and exercise routines and input the information into forms within the app.

[0080] The server uses AI algorithms based on the received information to generate individual habit-forming procedures. This process utilizes a generative AI model. This model generates plans in response to prompts, providing customized action plans for each user. For example, by entering "Generate an action plan for the user to achieve three exercise sessions per week" as a prompt, the model will suggest an appropriate plan.

[0081] Users record their daily activities on their devices. The devices send this data to a server, which immediately analyzes the progress. Database systems and statistical analysis tools are used for the analysis. The server evaluates the user's progress toward achieving their goals, generates feedback, and sends it to the device. The device notifies the user in real time and provides feedback.

[0082] Furthermore, the server analyzes the user's behavior patterns and generates specific suggestions to maintain motivation. These suggestions and advice are also communicated to the user through the device. As a result, users can adjust and improve their actions toward achieving their goals at their own pace.

[0083] This system utilizes a generated AI model and prompt text to flexibly respond to individual user needs and support effective habit formation.

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

[0085] Step 1:

[0086] Users input their goals and health-related information into their devices. Specifically, they enter information such as "lose 1 kg in a week" or "walk for 30 minutes every day" into input forms within the app. The entered data is then sent from the device to the server. This data, consisting of each individual's goals and health information, forms the basis for subsequent procedures.

[0087] Step 2:

[0088] The server uses an AI algorithm to generate a personalized habit formation plan based on the input data. The generating AI model uses prompts to create the plan. For example, a prompt such as "Create an exercise and meal plan to lose 1kg in one week" might be used, and the output will be a specific action plan aligned with the user's goals.

[0089] Step 3:

[0090] Users record their daily activities on their devices. Specifically, after exercise, users input details of their activity (e.g., walking distance and calories burned) into the app. They also record information about their meals and nutritional intake. This recorded information is then sent back from the device to the server. The entered data forms a daily activity log and serves as material for later analysis.

[0091] Step 4:

[0092] The server receives the daily activity data and analyzes it in real time using statistical analysis tools. Here, the current degree of achievement towards the goal is evaluated. Specifically, the achievement level is compared with exercise and food intake, and the output generates a progress report towards the goal.

[0093] Step 5:

[0094] The server generates feedback based on the analysis results and sends it to the terminal. For example, it might generate a message like, "You've already achieved 70% of your goal this week. Great job!" The outputted feedback is then displayed on the terminal.

[0095] Step 6:

[0096] The device notifies the user of the feedback received from the server. This feedback is communicated via push notifications and app pop-ups. The specific actions taken here are performed in real time through the device's user interface.

[0097] Step 7:

[0098] The server generates advice to maintain the user's long-term motivation. Using the prompt again, the generating AI model creates motivational suggestions. For example, in response to the prompt "Please suggest improvements to achieve future goals," it might generate advice such as "Try increasing your water intake." The generated advice is provided to the device and displayed to the user as suggestions along with feedback.

[0099] (Application Example 1)

[0100] 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."

[0101] In today's busy lifestyle, forming the habits necessary to achieve goals is difficult. Furthermore, manually recording daily activities and receiving feedback is time-consuming and can easily lead to decreased motivation. Therefore, there is a need for a system that more effectively supports users in efficiently forming habits and achieving their goals.

[0102] 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.

[0103] In this invention, the server includes means for inputting an individual's goals and health status and creating an individualized habit formation plan, means for monitoring and evaluating progress data in real time, and observation means for detecting and recording the user's activities in the home. This makes it possible for the user to naturally develop habits in their daily life and easily achieve their goals.

[0104] "Personal goals" refer to the specific objectives or states that a user wishes to achieve.

[0105] "Health status" refers to information regarding the user's physical and mental condition.

[0106] A "habit formation plan" refers to a plan that outlines specific action steps and schedules for a user to achieve their goals.

[0107] "Progress data" refers to information that shows how far a user has progressed towards their goal.

[0108] "Feedback" refers to information about progress and advice provided to users based on their evaluation results.

[0109] "Motivation" refers to the psychological factors that enable users to maintain their willingness and awareness to achieve their goals.

[0110] "Advice" refers to suggestions for improvement or encouraging messages that users need to make to achieve their goals.

[0111] "Observation means" refers to sensors and devices used to detect and record user activity.

[0112] "Environmental control means" refers to a system that automatically adjusts the physical environment to support the user's habit formation.

[0113] This invention is a system for effectively forming habits that enable users to achieve their goals.

[0114] The system includes a terminal that sends data on user-entered goals and health status to a server. This terminal can be a smartphone or tablet, enabling convenient user operation. The server analyzes the received data and generates a personalized habit formation plan tailored to the user. Machine learning frameworks such as Python and Tensorflow® are used for this generation.

[0115] Progress data is sent from the terminal to the server, which monitors and evaluates it in real time. The evaluation references the user's past activity information stored in the database. Based on the evaluation results, feedback and advice are generated and provided to the user via the terminal. The feedback includes information on the degree of goal achievement and areas for improvement.

[0116] The system also includes observation methods to monitor the user's activities within the home. These observation methods include cameras and voice recognition sensors to detect and record the user's actions and speech. This makes habits visible without the user's conscious effort, naturally supporting habit formation. Environmental control mechanisms activate to provide an optimal physical environment tailored to the situation, thereby promoting the user's habit formation.

[0117] For example, if a user sets a goal of "30 minutes of stretching every day," the system will send a reminder at night and adjust the room lighting to create a relaxing atmosphere. The system will monitor the user's movements via camera and provide feedback such as, "Great progress! Let's keep it up tomorrow," depending on the user's progress.

[0118] Examples of prompts for a generative AI model include the following:

[0119] "Design a system that explains how a personal assistant works to support the formation of daily habits."

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

[0121] Step 1:

[0122] Users input data about their goals and health status into a device such as a smartphone or tablet. This data includes specific goals such as the amount of weight they want to lose and the amount of time they exercise each day. The device then formats this input data and prepares it as data packets to be sent to the server.

[0123] Step 2:

[0124] The device sends user input data to the server. The server analyzes the received data packets and stores them in a database. Using machine learning algorithms (e.g., TensorFlow) and comparing them with historical data in the database, it generates a personalized habit formation plan. This generated plan includes specific action steps to achieve the user's goals.

[0125] Step 3:

[0126] The server sends the generated habit-forming plan to the device. The device notifies the user of the plan and presents it through a visually understandable interface. Specifically, the device's display shows an overview of the plan and each step, providing information in a format that is easy for the user to accept.

[0127] Step 4:

[0128] The observation system detects and records the user's physical activity in real time. Cameras and voice recognition sensors are used to continuously monitor daily movements. The obtained data is sent to the terminal as progress information, and from there it is transmitted to the server.

[0129] Step 5:

[0130] The server analyzes and evaluates received progress data in real time. The evaluation compares the current performance to past results and standard plans to identify areas for improvement and assess the degree of achievement. Based on the analysis results, feedback is generated for the user, including the percentage of achievement and areas for improvement.

[0131] Step 6:

[0132] The server sends the generated feedback to the device, and the device notifies the user. Furthermore, advice to maintain the user's motivation is also generated and provided to the user through the device. For example, an encouraging message such as, "Let's try exercising a little more this week!" might be displayed.

[0133] Step 7:

[0134] The environmental control system adjusts the physical environment based on the user's activity level. It softens the lighting when the user wants to relax and automatically sets the environment to promote specific habits. This operation is performed in a timely manner according to the user's needs, using data from observation devices.

[0135] 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.

[0136] This invention combines a conventional system that supports user habit formation with an emotion engine to provide feedback and advice tailored to the user's emotional state. This system includes a server, a terminal, and a user, and each component works in coordination.

[0137] First, the user uses their device to input their goals and health status. This creates a rough habit-forming plan. The device sends this information to the server. The server generates a personalized habit-forming plan based on the user's goals and health status. This plan includes specific action steps and outlines what the user should do on a daily basis.

[0138] Next, a newly integrated emotion engine analyzes the user's emotional state in real time. The user's device collects emotional data through voice input, facial recognition, and other means, and sends it to the server. The server evaluates the emotional state based on this data and incorporates it into feedback and advice.

[0139] For example, if the server determines that a user is experiencing stress, it generates advice to help manage that stress. The device then provides this advice to the user and suggests relaxing activities. Furthermore, the emotion engine can accumulate past emotional data and analyze long-term emotional patterns. This allows for the provision of appropriate advice to users whose emotional states fluctuate regularly.

[0140] For example, when a user attempts to exercise according to their schedule, the device analyzes their emotions and, if it detects feelings of anxiety or decreased motivation, the server generates a special motivational message tailored to the user's situation and sends it to the device. This message is displayed to the user, potentially rekindling their motivation to take action.

[0141] The system of this invention considers the impact of the user's emotional state on the success of a plan, enabling more effective habit formation support. Furthermore, by identifying the optimal feedback strategy for the user through long-term analysis of emotional data, it promotes proactive behavioral improvement.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] Users use their devices to input their goals and health status. This includes specific goals (e.g., weight loss) and their current health condition.

[0145] Step 2:

[0146] The device sends user input data to the server. This data is used as source information for generating personalized habit formation plans.

[0147] Step 3:

[0148] The server analyzes the received data and generates a personalized habit formation plan. This plan includes specific action steps and outlines the path to achieving the user's goals.

[0149] Step 4:

[0150] The device displays a habit-forming plan sent from the server to the user. The user reviews the plan and uses it to improve their daily activities.

[0151] Step 5:

[0152] Users record their daily activities and progress on their devices. This includes records of exercise, meals, and sleep duration.

[0153] Step 6:

[0154] The device sends user progress data to the server. The server receives this data in real time and performs analysis.

[0155] Step 7:

[0156] The server uses an emotion engine to analyze the user's emotional data. This emotional data is collected through the user's voice input and facial recognition technology.

[0157] Step 8:

[0158] The server analyzes emotional states and progress data to generate optimal feedback for the user. This feedback includes advice and encouraging messages tailored to the user's emotions.

[0159] Step 9:

[0160] The device notifies the user of feedback received from the server. For example, if the user is feeling stressed, it might display advice such as, "Try listening to some music to relax."

[0161] Step 10:

[0162] The server accumulates user behavior and emotional data over the long term and performs pattern analysis. This allows for regular adjustments to habit formation plans and feedback strategies optimized for each user.

[0163] (Example 2)

[0164] 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".

[0165] Conventional habit-forming support systems only provide plans based on individual goals and health conditions, and fail to offer feedback and advice that takes into account the user's emotional state. As a result, appropriate support tailored to the user's emotional motivation and state is not provided, leading to insufficient habit-forming effectiveness. Furthermore, there is no mechanism for long-term accumulation and analysis of emotional data, making proactive behavioral improvement difficult.

[0166] 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.

[0167] In this invention, the server includes means for analyzing the user's emotional state and generating optimal advice, means for adjusting and providing the generated advice according to the user's emotional state, and means for collecting emotional data and performing long-term analysis. This makes it possible to provide appropriate feedback and advice according to the user's emotional state, thereby realizing more effective support for habit formation.

[0168] "Personal goals" are specific achievement targets that a particular user sets with the aim of improving their own life or health.

[0169] "Health status" refers to information about the user's physical or mental health, including health indicators and conditions entered by the user.

[0170] A "habit formation plan" is a set of specific action steps and schedules designed to help a user achieve their goals.

[0171] "Progress data" refers to information that shows the progress of efforts toward achieving the goals set by the user, and includes daily or weekly action results.

[0172] "Feedback" refers to information that provides comments and suggestions for evaluation and improvement regarding user behavior and progress.

[0173] "Advice" refers to specific guidance and suggestions provided to support users in implementing their habit-forming plans.

[0174] "Emotional state" refers to information that indicates a user's emotional health, and includes classifications of emotions such as stress, joy, and sadness.

[0175] "Emotional data" refers to a collection of information about emotions obtained from users' voices, facial expressions, and behaviors.

[0176] "Long-term analysis" is the process of analyzing users' emotions and behavioral patterns over a long period of time using time-series data to understand their characteristics and trends.

[0177] This invention provides a system that helps users to form habits more effectively. The system consists of a server, a terminal, and a user as its main components, each functioning as follows:

[0178] The server uses a generative AI model to generate a personalized habit formation plan based on the user's individual goals and health status. An example of a prompt given to the AI ​​model is the instruction, "Create a specific schedule for the user to achieve three exercise sessions per week." The server analyzes the user's emotional data transmitted from the device and provides customized feedback and advice based on the results. This process allows for the provision of appropriate advice tailored to the user's emotional state.

[0179] The terminal receives input from the user and transmits it to the server. The terminal can also use a voice input system and camera to collect user emotion data. Emotion recognition software on the terminal analyzes the user's emotions in real time from their voice tone and facial expressions. This collected data is sent to the server and used for emotion analysis.

[0180] Users provide data on their goals and health status using their devices, and receive a plan generated by the server based on this information. For example, the plan is applied when the user inputs specific goals such as "reduce daily stress" or "get regular sleep." Furthermore, users incorporate emotion-based advice and feedback provided by the device into their daily lives, promoting successful habit formation.

[0181] Such a system provides feedback and advice that takes into account the user's emotional state, enabling more flexible and effective habit formation.

[0182] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0183] Step 1:

[0184] Users input their personal goals and health status into the device. Specifically, users enter goals such as "exercise for 30 minutes every day" or "get regular sleep" as text through the device's interface. This input data is saved on the device and immediately sent to the server.

[0185] Step 2:

[0186] The terminal transmits user-entered goal and health status data to the server. Data transfer uses a secure protocol, and data validation is performed to ensure the data format is correct before transmission. The server receives this data and stores it in a database for each user.

[0187] Step 3:

[0188] The server uses a generative AI model to create a personalized habit formation plan based on the user's goals and health status. At this time, the AI ​​model is given a prompt message: "Generate the action steps necessary to achieve the user's goals." The generated plan will include specific actions and schedules, and will be customized according to the user's needs.

[0189] Step 4:

[0190] The device displays a habit-forming plan generated on the server to the user. The user reviews the plan on the device screen and incorporates it into their daily routine. The displayed plan includes specific actions, such as "run for 30 minutes on Mondays, Wednesdays, and Fridays."

[0191] Step 5:

[0192] The device collects user emotional data using voice input and facial recognition technology. Specifically, the built-in microphone and camera are used to record the user's voice tone and facial expression data. The collected data is sent to a server and used to analyze the user's emotional state.

[0193] Step 6:

[0194] The server analyzes collected emotional data to evaluate the user's current emotional state. Text analysis and image recognition algorithms are used for data analysis to identify emotional patterns such as stress and joy. Based on this evaluation, a generative AI model is used to create feedback and advice tailored to the user's emotions.

[0195] Step 7:

[0196] The device notifies the user of emotional feedback and advice generated by the server. These notifications include pop-up messages and announcements via the voice assistant. This allows users to easily see and take action based on their emotional state.

[0197] (Application Example 2)

[0198] 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".

[0199] In modern society, there is a growing need for individualized health management and habit formation, but traditional methods have been insufficient in providing feedback that takes into account the emotional state of the user. Furthermore, it is difficult to maintain user motivation over the long term, which presents challenges in achieving long-term health improvement.

[0200] 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.

[0201] In this invention, the server includes means for inputting an individual's goals and health status and creating an individualized habit formation plan; means for monitoring and evaluating progress data in real time; means for providing feedback to the user based on the evaluation results; means for analyzing emotional data and individually adjusting feedback based on the user's emotional state; and means for analyzing long-term emotional patterns and finding a feedback strategy suitable for the user. This enables effective habit formation support that takes into account the user's emotional state.

[0202] A "goal" refers to a specific situation or state that the user wishes to achieve.

[0203] "Health status" refers to various data and information related to the user's physical and mental condition.

[0204] A "habit-forming plan" refers to a plan that outlines the actions and procedures that should be taken on a daily basis in order to achieve a goal.

[0205] "Progress data" refers to specific information that shows the status of users' activities and the implementation of their plans.

[0206] "Feedback" refers to advice and information provided regarding a user's behavior or condition.

[0207] "Motivation" refers to the internal drive or will that allows users to continue their actions.

[0208] "Advice" refers to specific instructions or suggestions provided to users to encourage appropriate behavior or states.

[0209] "Emotional data" refers to data collected and analyzed for information regarding the emotional state of users.

[0210] "Emotional state" refers to a specific situation that indicates the user's mental state or mood.

[0211] A "feedback strategy" refers to a plan or policy for providing optimal feedback tailored to the user's behavior and emotional state.

[0212] The system implementing this invention consists of three components: a server, a terminal, and a user. The user first uses the terminal to input their goals and health status. Based on this information, the server generates a personalized habit formation plan. This plan includes daily action instructions and outlines specific steps that the user should continuously take.

[0213] The device collects user emotional data using voice input and facial recognition technology. This emotional data is then sent to a server, where an emotion engine analyzes the user's emotional state in real time. Based on the analyzed emotional state, the server generates appropriate feedback and advice, which is then delivered to the user via the device. This enables effective habit formation tailored to the user's emotions.

[0214] For example, if a user has set a goal of "exercising every day," but emotional analysis detects that they are feeling stressed that day, the server will generate feedback such as, "Today, let's start with a short deep breathing session and gradually move on to walking," and present it to the user from their device.

[0215] An example of a prompt message for a generative AI model would be: "His goal is to exercise every day, but he is feeling stressed today. Please create feedback that reflects this situation."

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The user uses a device to input their goals and health status. This input includes data about the goals the user wants to achieve and their current health condition. The device receives this data in digital format and sends it to the server.

[0219] Step 2:

[0220] The server generates a personalized habit formation plan based on the received data. This process creates specific action steps necessary to achieve the goals. The data processing involves analyzing the user's goals and health status to calculate the optimal action sequence. The output is the generated habit formation plan.

[0221] Step 3:

[0222] The device collects user emotion data using voice input and facial recognition. The input consists of the user's voice and facial image, which are converted into a format that can be input into the emotion analysis engine. Data processing involves image processing and voice analysis to generate data that can be used to evaluate the user's emotional state.

[0223] Step 4:

[0224] The collected emotional data is sent to a server for emotional analysis. The server analyzes the input emotional data to evaluate the user's current emotional state. This evaluation uses an emotional analysis algorithm to quantitatively assess the user's stress level and motivation. The output is emotional state data as a result of the analysis.

[0225] Step 5:

[0226] The server generates appropriate feedback and advice based on the user's emotional state. The input consists of analyzed emotional state data and a pre-created habit formation plan. The data calculation selects a feedback strategy appropriate to the emotional state and generates specific advice. The output is a feedback message to be provided to the user.

[0227] Step 6:

[0228] Feedback and advice are sent to the device and provided to the user. The device displays the received messages to the user on the screen and via audio, encouraging them to reflect them in their daily actions. This allows the user to take the most appropriate action in real time based on their emotional state.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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".

[0245] The system of this invention is designed to enable users to effectively form new habits and achieve their goals. This system primarily functions through the exchange of information between a server, a terminal, and the user.

[0246] First, the user enters information about their goals and health status into the device. For example, they record specific details such as the weight they want to lose and their daily exercise time. The device sends this data to the server. Based on the received data, the server generates a personalized habit formation plan. The plan provides specific instructions for daily actions to help the user achieve their goals. For example, if the user aims to jog three times a week, the server will suggest the days and times for jogging.

[0247] Next, users continuously record their daily activities through their devices. For example, they input how far they ran each day, what they ate, and how much time they spent exercising. The devices send this information to a server, which analyzes the progress data. Based on the analysis, the server evaluates the user's current level of achievement and generates feedback.

[0248] The server performs analysis in real time and quickly informs the user of the evaluation results. The terminal receives the feedback and notifies the user in a timely manner. For example, "You have achieved 80% of your goal this week. If you walk for another 10 minutes, you will reach today's goal."

[0249] Furthermore, the server analyzes the user's behavior patterns and generates advice to help the user maintain motivation. This advice includes specific areas for improvement and encouraging messages to help achieve future goals. The server sends this advice to the terminal, which then provides it to the user.

[0250] For example, if a user sets a goal of "sleeping for more than 7 hours every day and doing 30 minutes of stretching in the morning," the server will create a plan for sleep patterns and exercise habit formation and notify the user. The user records the amount of time they slept and the amount of time they stretched each day, and the device relays this information to the server. The server evaluates how close the user is to their goal and sends feedback as needed, such as "try to go to bed a little earlier."

[0251] Thus, the system of the present invention flexibly responds to the needs of individual users and supports them in effectively continuing to form habits.

[0252] The following describes the processing flow.

[0253] Step 1:

[0254] Users input information about their goals and health status using their devices. This includes weight loss goals, daily exercise levels, and details about their diet.

[0255] Step 2:

[0256] The device sends the collected user data to the server. This data includes the user's basic profile information.

[0257] Step 3:

[0258] The server analyzes the received data and generates a personalized habit formation plan. This plan also references data from other users with similar goals and past success stories.

[0259] Step 4:

[0260] The server sends the generated habit formation plan to the device, and the device notifies the user of detailed daily action steps. This allows the user to understand the specific action plan.

[0261] Step 5:

[0262] Users record their daily activity progress on their device. Here, they input information such as the type and duration of exercise, meals, or the type and duration of stretches performed.

[0263] Step 6:

[0264] The device sends user activity data to the server. This transmission is typically done daily or in real time.

[0265] Step 7:

[0266] The server evaluates progress based on the received activity data. This includes analysis of the degree to which weekly goals were achieved and the difference between the plan and actual actions.

[0267] Step 8:

[0268] The server generates feedback messages based on the evaluation results, providing advice to the user. This feedback includes suggestions to help maintain user motivation.

[0269] Step 9:

[0270] The server sends feedback and advice to the device, which then presents it to the user. The user then uses this information to plan their next action.

[0271] Step 10:

[0272] The server collects user behavior data and analyzes patterns. Based on the results of this analysis, future plan updates and new suggestions for users are adjusted.

[0273] (Example 1)

[0274] 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."

[0275] The challenge lies in providing a support system that enables users to effectively form and maintain habits toward achieving their goals. This aims to support individualized planning, progress-based responses, and continuous motivation.

[0276] 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.

[0277] In this invention, the server includes means for inputting personal goals and health-related information, creating specific habit-forming procedures, immediately monitoring and evaluating progress information, and providing responses to users based on the evaluation results. This enables users to grasp their progress towards their goals in real time, adjust their plans as needed, and continue to maintain motivation.

[0278] "Personal goals" refer to individual goals and intentions that the user wants to achieve.

[0279] "Health-related information" refers to information including the user's health status and health-related data, such as weight, exercise time, diet content, etc.

[0280] "Habit-forming procedures" specifically indicate daily action plans and steps for the user to achieve their goals.

[0281] "Progress information" is data indicating the degree of achievement of the user's goals and the details of activities during the process.

[0282] "Immediately monitoring and evaluating" means tracking the user's progress in real time, analyzing the data, and conducting a current evaluation.

[0283] "Providing a response" means communicating feedback or recommendations for the next action to the user.

[0284] "Proposals for maintaining motivation" is to provide appropriate encouragement and areas for improvement as advice for the user to continue their efforts towards their goals.

[0285] "Generative AI model" refers to an artificial intelligence model that automatically generates plans and makes proposals based on given data and prompts.

[0286] The "prompt sentence" refers to an instruction sentence or a question sentence that is input to a generative AI model to obtain a specific output.

[0287] The system of the present invention supports an individual's goal achievement through communication among a user, a terminal, and a server. This system is designed to promote specific habit formation.

[0288] First, the user inputs their goals and health-related information via the terminal. The terminal transmits this information to the server. The terminals used include smartphones and tablet terminals, and input formats such as dedicated applications and web interfaces are used. Specifically, the user sets a weight loss goal and an exercise routine and inputs the information into a form within the application.

[0289] The server utilizes an AI algorithm based on the received information to generate an individual habit formation procedure. In this process, a generative AI model is utilized. This model generates a plan according to the prompt sentence and provides a customized action plan for each user. For example, by inputting "Please generate an action plan for the user to achieve exercise three times a week" as the prompt sentence, the model proposes an appropriate plan.

[0290] The user records their daily activities on the terminal. The terminal transmits this to the server, and the server immediately analyzes the progress. Database systems and statistical analysis tools are used for the analysis. The server evaluates the progress of the user's goal achievement, generates feedback, and transmits it to the terminal. The terminal notifies the user in real time and provides the feedback.

[0291] Furthermore, the server analyzes the user's behavior patterns and generates specific proposals to maintain motivation. The proposals and advice generated in this way are also communicated to the user through the terminal. As a result, the user can adjust and improve their actions towards goal achievement at their own pace.

[0292] This system utilizes a generated AI model and prompt text to flexibly respond to individual user needs and support effective habit formation.

[0293] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0294] Step 1:

[0295] Users input their goals and health-related information into their devices. Specifically, they enter information such as "lose 1 kg in a week" or "walk for 30 minutes every day" into input forms within the app. The entered data is then sent from the device to the server. This data, consisting of each individual's goals and health information, forms the basis for subsequent procedures.

[0296] Step 2:

[0297] The server uses an AI algorithm to generate a personalized habit formation plan based on the input data. The generating AI model uses prompts to create the plan. For example, a prompt such as "Create an exercise and meal plan to lose 1kg in one week" might be used, and the output will be a specific action plan aligned with the user's goals.

[0298] Step 3:

[0299] Users record their daily activities on their devices. Specifically, after exercise, users input details of their activity (e.g., walking distance and calories burned) into the app. They also record information about their meals and nutritional intake. This recorded information is then sent back from the device to the server. The entered data forms a daily activity log and serves as material for later analysis.

[0300] Step 4:

[0301] The server receives the transmitted daily activity data and analyzes the data in real time using a statistical analysis tool. Here, the current degree of achievement against the target is evaluated. As a specific process, the degree of achievement is compared with the amount of exercise and diet, and the progress status towards the target is generated as output.

[0302] Step 5:

[0303] Based on the results of the analysis, the server creates feedback and sends it to the terminal. For example, a message like "You have already achieved 70% of your goal this week. Great!" is generated. The outputted feedback is displayed on the terminal.

[0304] Step 6:

[0305] The terminal notifies the user of the feedback received from the server. The feedback content is conveyed through push notifications or app pop-ups. The specific operation here is performed in real time through the user interface of the terminal.

[0306] Step 7:

[0307] The server generates advice to maintain the user's long-term motivation. The prompt text is used again, and the generation AI model creates a motivation proposal. For the prompt "Please present improvement plans for future goal achievement", advice like "Please try increasing your water intake" is generated. The generated advice is provided to the terminal and displayed to the user as a proposal along with the feedback.

[0308] (Application Example 1)

[0309] 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".

[0310] In today's busy lifestyle, forming the habits necessary to achieve goals is difficult. Furthermore, manually recording daily activities and receiving feedback is time-consuming and can easily lead to decreased motivation. Therefore, there is a need for a system that more effectively supports users in efficiently forming habits and achieving their goals.

[0311] 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.

[0312] In this invention, the server includes means for inputting an individual's goals and health status and creating an individualized habit formation plan, means for monitoring and evaluating progress data in real time, and observation means for detecting and recording the user's activities in the home. This makes it possible for the user to naturally develop habits in their daily life and easily achieve their goals.

[0313] "Personal goals" refer to the specific objectives or states that a user wishes to achieve.

[0314] "Health status" refers to information regarding the user's physical and mental condition.

[0315] A "habit formation plan" refers to a plan that outlines specific action steps and schedules for a user to achieve their goals.

[0316] "Progress data" refers to information that shows how far a user has progressed towards their goal.

[0317] "Feedback" refers to information about progress and advice provided to users based on their evaluation results.

[0318] "Motivation" refers to the psychological factors that enable users to maintain their willingness and awareness to achieve their goals.

[0319] "Advice" refers to suggestions for improvement or encouraging messages that users need to make to achieve their goals.

[0320] "Observation means" refers to sensors and devices used to detect and record user activity.

[0321] "Environmental control means" refers to a system that automatically adjusts the physical environment to support the user's habit formation.

[0322] This invention is a system for effectively forming habits that enable users to achieve their goals.

[0323] The system includes a terminal that sends data on user-entered goals and health status to a server. This terminal can be a smartphone or tablet, enabling convenient user operation. The server analyzes the received data and generates a personalized habit formation plan tailored to the user. Machine learning frameworks such as Python and TensorFlow are used for this generation.

[0324] Progress data is sent from the terminal to the server, which monitors and evaluates it in real time. The evaluation references the user's past activity information stored in the database. Based on the evaluation results, feedback and advice are generated and provided to the user via the terminal. The feedback includes information on the degree of goal achievement and areas for improvement.

[0325] The system also includes observation methods to monitor the user's activities within the home. These observation methods include cameras and voice recognition sensors to detect and record the user's actions and speech. This makes habits visible without the user's conscious effort, naturally supporting habit formation. Environmental control mechanisms activate to provide an optimal physical environment tailored to the situation, thereby promoting the user's habit formation.

[0326] For example, if a user sets a goal of "30 minutes of stretching every day," the system will send a reminder at night and adjust the room lighting to create a relaxing atmosphere. The system will monitor the user's movements via camera and provide feedback such as, "Great progress! Let's keep it up tomorrow," depending on the user's progress.

[0327] Examples of prompts for a generative AI model include the following:

[0328] "Design a system that explains how a personal assistant works to support the formation of daily habits."

[0329] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0330] Step 1:

[0331] Users input data about their goals and health status into a device such as a smartphone or tablet. This data includes specific goals such as the amount of weight they want to lose and the amount of time they exercise each day. The device then formats this input data and prepares it as data packets to be sent to the server.

[0332] Step 2:

[0333] The device sends user input data to the server. The server analyzes the received data packets and stores them in a database. Using machine learning algorithms (e.g., TensorFlow) and comparing them with historical data in the database, it generates a personalized habit formation plan. This generated plan includes specific action steps to achieve the user's goals.

[0334] Step 3:

[0335] The server sends the generated habit-forming plan to the device. The device notifies the user of the plan and presents it through a visually understandable interface. Specifically, the device's display shows an overview of the plan and each step, providing information in a format that is easy for the user to accept.

[0336] Step 4:

[0337] The observation system detects and records the user's physical activity in real time. Cameras and voice recognition sensors are used to continuously monitor daily movements. The obtained data is sent to the terminal as progress information, and from there it is transmitted to the server.

[0338] Step 5:

[0339] The server analyzes and evaluates received progress data in real time. The evaluation compares the current performance to past results and standard plans to identify areas for improvement and assess the degree of achievement. Based on the analysis results, feedback is generated for the user, including the percentage of achievement and areas for improvement.

[0340] Step 6:

[0341] The server sends the generated feedback to the device, and the device notifies the user. Furthermore, advice to maintain the user's motivation is also generated and provided to the user through the device. For example, an encouraging message such as, "Let's try exercising a little more this week!" might be displayed.

[0342] Step 7:

[0343] The environmental control system adjusts the physical environment based on the user's activity level. It softens the lighting when the user wants to relax and automatically sets the environment to promote specific habits. This operation is performed in a timely manner according to the user's needs, using data from observation devices.

[0344] 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.

[0345] This invention combines a conventional system that supports user habit formation with an emotion engine to provide feedback and advice tailored to the user's emotional state. This system includes a server, a terminal, and a user, and each component works in coordination.

[0346] First, the user uses their device to input their goals and health status. This creates a rough habit-forming plan. The device sends this information to the server. The server generates a personalized habit-forming plan based on the user's goals and health status. This plan includes specific action steps and outlines what the user should do on a daily basis.

[0347] Next, a newly integrated emotion engine analyzes the user's emotional state in real time. The user's device collects emotional data through voice input, facial recognition, and other means, and sends it to the server. The server evaluates the emotional state based on this data and incorporates it into feedback and advice.

[0348] For example, if the server determines that a user is experiencing stress, it generates advice to help manage that stress. The device then provides this advice to the user and suggests relaxing activities. Furthermore, the emotion engine can accumulate past emotional data and analyze long-term emotional patterns. This allows for the provision of appropriate advice to users whose emotional states fluctuate regularly.

[0349] For example, when a user attempts to exercise according to their schedule, the device analyzes their emotions and, if it detects feelings of anxiety or decreased motivation, the server generates a special motivational message tailored to the user's situation and sends it to the device. This message is displayed to the user, potentially rekindling their motivation to take action.

[0350] The system of this invention considers the impact of the user's emotional state on the success of a plan, enabling more effective habit formation support. Furthermore, by identifying the optimal feedback strategy for the user through long-term analysis of emotional data, it promotes proactive behavioral improvement.

[0351] The following describes the processing flow.

[0352] Step 1:

[0353] Users use their devices to input their goals and health status. This includes specific goals (e.g., weight loss) and their current health condition.

[0354] Step 2:

[0355] The device sends user input data to the server. This data is used as source information for generating personalized habit formation plans.

[0356] Step 3:

[0357] The server analyzes the received data and generates a personalized habit formation plan. This plan includes specific action steps and outlines the path to achieving the user's goals.

[0358] Step 4:

[0359] The device displays a habit-forming plan sent from the server to the user. The user reviews the plan and uses it to improve their daily activities.

[0360] Step 5:

[0361] Users record their daily activities and progress on their devices. This includes records of exercise, meals, and sleep duration.

[0362] Step 6:

[0363] The device sends user progress data to the server. The server receives this data in real time and performs analysis.

[0364] Step 7:

[0365] The server uses an emotion engine to analyze the user's emotional data. This emotional data is collected through the user's voice input and facial recognition technology.

[0366] Step 8:

[0367] The server analyzes emotional states and progress data to generate optimal feedback for the user. This feedback includes advice and encouraging messages tailored to the user's emotions.

[0368] Step 9:

[0369] The device notifies the user of feedback received from the server. For example, if the user is feeling stressed, it might display advice such as, "Try listening to some music to relax."

[0370] Step 10:

[0371] The server accumulates user behavior and emotional data over the long term and performs pattern analysis. This allows for regular adjustments to habit formation plans and feedback strategies optimized for each user.

[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] Conventional habit-forming support systems only provide plans based on individual goals and health conditions, and fail to offer feedback and advice that takes into account the user's emotional state. As a result, appropriate support tailored to the user's emotional motivation and state is not provided, leading to insufficient habit-forming effectiveness. Furthermore, there is no mechanism for long-term accumulation and analysis of emotional data, making proactive behavioral improvement difficult.

[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 analyzing the user's emotional state and generating optimal advice, means for adjusting and providing the generated advice according to the user's emotional state, and means for collecting emotional data and performing long-term analysis. This makes it possible to provide appropriate feedback and advice according to the user's emotional state, thereby realizing more effective support for habit formation.

[0377] "Personal goals" are specific achievement targets that a particular user sets with the aim of improving their own life or health.

[0378] "Health status" refers to information about the user's physical or mental health, including health indicators and conditions entered by the user.

[0379] A "habit formation plan" is a set of specific action steps and schedules designed to help a user achieve their goals.

[0380] "Progress data" refers to information that shows the progress of efforts toward achieving the goals set by the user, and includes daily or weekly action results.

[0381] "Feedback" refers to information that provides comments and suggestions for evaluation and improvement regarding user behavior and progress.

[0382] "Advice" refers to specific guidance and suggestions provided to support users in implementing their habit-forming plans.

[0383] "Emotional state" refers to information that indicates a user's emotional health, and includes classifications of emotions such as stress, joy, and sadness.

[0384] "Emotional data" refers to a collection of information about emotions obtained from users' voices, facial expressions, and behaviors.

[0385] "Long-term analysis" is the process of analyzing users' emotions and behavioral patterns over a long period of time using time-series data to understand their characteristics and trends.

[0386] This invention provides a system that helps users to form habits more effectively. The system consists of a server, a terminal, and a user as its main components, each functioning as follows:

[0387] The server uses a generative AI model to generate a personalized habit formation plan based on the user's individual goals and health status. An example of a prompt given to the AI ​​model is the instruction, "Create a specific schedule for the user to achieve three exercise sessions per week." The server analyzes the user's emotional data transmitted from the device and provides customized feedback and advice based on the results. This process allows for the provision of appropriate advice tailored to the user's emotional state.

[0388] The terminal receives input from the user and transmits it to the server. The terminal can also use a voice input system and camera to collect user emotion data. Emotion recognition software on the terminal analyzes the user's emotions in real time from their voice tone and facial expressions. This collected data is sent to the server and used for emotion analysis.

[0389] Users provide data on their goals and health status using their devices, and receive a plan generated by the server based on this information. For example, the plan is applied when the user inputs specific goals such as "reduce daily stress" or "get regular sleep." Furthermore, users incorporate emotion-based advice and feedback provided by the device into their daily lives, promoting successful habit formation.

[0390] Such a system provides feedback and advice that takes into account the user's emotional state, enabling more flexible and effective habit formation.

[0391] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0392] Step 1:

[0393] Users input their personal goals and health status into the device. Specifically, users enter goals such as "exercise for 30 minutes every day" or "get regular sleep" as text through the device's interface. This input data is saved on the device and immediately sent to the server.

[0394] Step 2:

[0395] The terminal transmits user-entered goal and health status data to the server. Data transfer uses a secure protocol, and data validation is performed to ensure the data format is correct before transmission. The server receives this data and stores it in a database for each user.

[0396] Step 3:

[0397] The server uses a generative AI model to create a personalized habit formation plan based on the user's goals and health status. At this time, the AI ​​model is given a prompt message: "Generate the action steps necessary to achieve the user's goals." The generated plan will include specific actions and schedules, and will be customized according to the user's needs.

[0398] Step 4:

[0399] The device displays a habit-forming plan generated on the server to the user. The user reviews the plan on the device screen and incorporates it into their daily routine. The displayed plan includes specific actions, such as "run for 30 minutes on Mondays, Wednesdays, and Fridays."

[0400] Step 5:

[0401] The device collects user emotional data using voice input and facial recognition technology. Specifically, the built-in microphone and camera are used to record the user's voice tone and facial expression data. The collected data is sent to a server and used to analyze the user's emotional state.

[0402] Step 6:

[0403] The server analyzes collected emotional data to evaluate the user's current emotional state. Text analysis and image recognition algorithms are used for data analysis to identify emotional patterns such as stress and joy. Based on this evaluation, a generative AI model is used to create feedback and advice tailored to the user's emotions.

[0404] Step 7:

[0405] The device notifies the user of emotional feedback and advice generated by the server. These notifications include pop-up messages and announcements via the voice assistant. This allows users to easily see and take action based on their emotional state.

[0406] (Application Example 2)

[0407] 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."

[0408] In modern society, there is a growing need for individualized health management and habit formation, but traditional methods have been insufficient in providing feedback that takes into account the emotional state of the user. Furthermore, it is difficult to maintain user motivation over the long term, which presents challenges in achieving long-term health improvement.

[0409] 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.

[0410] In this invention, the server includes means for inputting an individual's goals and health status and creating an individualized habit formation plan; means for monitoring and evaluating progress data in real time; means for providing feedback to the user based on the evaluation results; means for analyzing emotional data and individually adjusting feedback based on the user's emotional state; and means for analyzing long-term emotional patterns and finding a feedback strategy suitable for the user. This enables effective habit formation support that takes into account the user's emotional state.

[0411] A "goal" refers to a specific situation or state that the user wishes to achieve.

[0412] "Health status" refers to various data and information related to the user's physical and mental condition.

[0413] A "habit-forming plan" refers to a plan that outlines the actions and procedures that should be taken on a daily basis in order to achieve a goal.

[0414] "Progress data" refers to specific information that shows the status of users' activities and the implementation of their plans.

[0415] "Feedback" refers to advice and information provided regarding a user's behavior or condition.

[0416] "Motivation" refers to the internal drive or will that allows users to continue their actions.

[0417] "Advice" refers to specific instructions or suggestions provided to users to encourage appropriate behavior or states.

[0418] "Emotional data" refers to data collected and analyzed for information regarding the emotional state of users.

[0419] "Emotional state" refers to a specific situation that indicates the user's mental state or mood.

[0420] A "feedback strategy" refers to a plan or policy for providing optimal feedback tailored to the user's behavior and emotional state.

[0421] The system implementing this invention consists of three components: a server, a terminal, and a user. The user first uses the terminal to input their goals and health status. Based on this information, the server generates a personalized habit formation plan. This plan includes daily action instructions and outlines specific steps that the user should continuously take.

[0422] The device collects user emotional data using voice input and facial recognition technology. This emotional data is then sent to a server, where an emotion engine analyzes the user's emotional state in real time. Based on the analyzed emotional state, the server generates appropriate feedback and advice, which is then delivered to the user via the device. This enables effective habit formation tailored to the user's emotions.

[0423] For example, if a user has set a goal of "exercising every day," but emotional analysis detects that they are feeling stressed that day, the server will generate feedback such as, "Today, let's start with a short deep breathing session and gradually move on to walking," and present it to the user from their device.

[0424] An example of a prompt message for a generative AI model would be: "His goal is to exercise every day, but he is feeling stressed today. Please create feedback that reflects this situation."

[0425] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0426] Step 1:

[0427] The user uses a device to input their goals and health status. This input includes data about the goals the user wants to achieve and their current health condition. The device receives this data in digital format and sends it to the server.

[0428] Step 2:

[0429] The server generates a personalized habit formation plan based on the received data. This process creates specific action steps necessary to achieve the goals. The data processing involves analyzing the user's goals and health status to calculate the optimal action sequence. The output is the generated habit formation plan.

[0430] Step 3:

[0431] The device collects user emotion data using voice input and facial recognition. The input consists of the user's voice and facial image, which are converted into a format that can be input into the emotion analysis engine. Data processing involves image processing and voice analysis to generate data that can be used to evaluate the user's emotional state.

[0432] Step 4:

[0433] The collected emotional data is sent to a server for emotional analysis. The server analyzes the input emotional data to evaluate the user's current emotional state. This evaluation uses an emotional analysis algorithm to quantitatively assess the user's stress level and motivation. The output is emotional state data as a result of the analysis.

[0434] Step 5:

[0435] The server generates appropriate feedback and advice based on the user's emotional state. The input consists of analyzed emotional state data and a pre-created habit formation plan. The data calculation selects a feedback strategy appropriate to the emotional state and generates specific advice. The output is a feedback message to be provided to the user.

[0436] Step 6:

[0437] Feedback and advice are sent to the device and provided to the user. The device displays the received messages to the user on the screen and via audio, encouraging them to reflect them in their daily actions. This allows the user to take the most appropriate action in real time based on their emotional state.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] [Third Embodiment]

[0442] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0443] 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.

[0444] 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).

[0445] 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.

[0446] 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.

[0447] 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).

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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".

[0454] The system of this invention is designed to enable users to effectively form new habits and achieve their goals. This system primarily functions through the exchange of information between a server, a terminal, and the user.

[0455] First, the user enters information about their goals and health status into the device. For example, they record specific details such as the weight they want to lose and their daily exercise time. The device sends this data to the server. Based on the received data, the server generates a personalized habit formation plan. The plan provides specific instructions for daily actions to help the user achieve their goals. For example, if the user aims to jog three times a week, the server will suggest the days and times for jogging.

[0456] Next, users continuously record their daily activities through their devices. For example, they input how far they ran each day, what they ate, and how much time they spent exercising. The devices send this information to a server, which analyzes the progress data. Based on the analysis, the server evaluates the user's current level of achievement and generates feedback.

[0457] The server performs analysis in real time and quickly informs the user of the evaluation results. The terminal receives the feedback and notifies the user in a timely manner. For example, "You have achieved 80% of your goal this week. If you walk for another 10 minutes, you will reach today's goal."

[0458] Furthermore, the server analyzes the user's behavior patterns and generates advice to help the user maintain motivation. This advice includes specific areas for improvement and encouraging messages to help achieve future goals. The server sends this advice to the terminal, which then provides it to the user.

[0459] For example, if a user sets a goal of "sleeping for more than 7 hours every day and doing 30 minutes of stretching in the morning," the server will create a plan for sleep patterns and exercise habit formation and notify the user. The user records the amount of time they slept and the amount of time they stretched each day, and the device relays this information to the server. The server evaluates how close the user is to their goal and sends feedback as needed, such as "try to go to bed a little earlier."

[0460] Thus, the system of the present invention flexibly responds to the needs of individual users and supports them in effectively continuing to form habits.

[0461] The following describes the processing flow.

[0462] Step 1:

[0463] Users input information about their goals and health status using their devices. This includes weight loss goals, daily exercise levels, and details about their diet.

[0464] Step 2:

[0465] The device sends the collected user data to the server. This data includes the user's basic profile information.

[0466] Step 3:

[0467] The server analyzes the received data and generates a personalized habit formation plan. This plan also references data from other users with similar goals and past success stories.

[0468] Step 4:

[0469] The server sends the generated habit formation plan to the device, and the device notifies the user of detailed daily action steps. This allows the user to understand the specific action plan.

[0470] Step 5:

[0471] Users record their daily activity progress on their device. Here, they input information such as the type and duration of exercise, meals, or the type and duration of stretches performed.

[0472] Step 6:

[0473] The device sends user activity data to the server. This transmission is typically done daily or in real time.

[0474] Step 7:

[0475] The server evaluates progress based on the received activity data. This includes analysis of the degree to which weekly goals were achieved and the difference between the plan and actual actions.

[0476] Step 8:

[0477] The server generates feedback messages based on the evaluation results, providing advice to the user. This feedback includes suggestions to help maintain user motivation.

[0478] Step 9:

[0479] The server sends feedback and advice to the device, which then presents it to the user. The user then uses this information to plan their next action.

[0480] Step 10:

[0481] The server collects user behavior data and analyzes patterns. Based on the results of this analysis, future plan updates and new suggestions for users are adjusted.

[0482] (Example 1)

[0483] 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."

[0484] The challenge lies in providing a support system that enables users to effectively form and maintain habits toward achieving their goals. This aims to support individualized planning, progress-based responses, and continuous motivation.

[0485] 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.

[0486] In this invention, the server includes means for inputting personal goals and health-related information and creating specific habit-forming procedures; means for immediately monitoring and evaluating progress information; and means for providing responses to the user based on the evaluation results. This enables the user to understand their progress toward their goals in real time, adjust their plan as needed, and maintain their motivation.

[0487] "Personal purpose" refers to the individual goals or intentions that the user wishes to achieve.

[0488] "Health-related information" refers to information including the user's health status and health-related data, such as weight, exercise time, and dietary content.

[0489] A "habit-forming procedure" is a detailed outline of the daily action plan and steps that users should take to achieve their goals.

[0490] "Progress information" refers to data that shows the degree to which a user has achieved their goals and details of their activities during that process.

[0491] "Immediate monitoring and evaluation" refers to tracking users' progress in real time, analyzing that data, and evaluating the current situation.

[0492] "Providing a response" refers to communicating feedback or recommendations for the next action to the user.

[0493] "Suggestions to maintain motivation" refers to providing users with appropriate encouragement and advice on areas for improvement to help them continue their efforts toward their goals.

[0494] A "generative AI model" refers to an artificial intelligence model that automatically generates plans and makes suggestions based on given data and prompts.

[0495] A "prompt statement" refers to a command or question that is input to a generative AI model to obtain a specific output.

[0496] The system of this invention supports the achievement of individual goals through communication between the user, terminal, and server. This system is designed to facilitate the formation of specific habits.

[0497] First, users input their goals and health-related information via their device. The device then sends this information to a server. The devices used include smartphones and tablets, and input methods include dedicated applications and web interfaces. Specifically, users set weight loss goals and exercise routines and input the information into forms within the app.

[0498] The server uses AI algorithms based on the received information to generate individual habit-forming procedures. This process utilizes a generative AI model. This model generates plans in response to prompts, providing customized action plans for each user. For example, by entering "Generate an action plan for the user to achieve three exercise sessions per week" as a prompt, the model will suggest an appropriate plan.

[0499] Users record their daily activities on their devices. The devices send this data to a server, which immediately analyzes the progress. Database systems and statistical analysis tools are used for the analysis. The server evaluates the user's progress toward achieving their goals, generates feedback, and sends it to the device. The device notifies the user in real time and provides feedback.

[0500] Furthermore, the server analyzes the user's behavior patterns and generates specific suggestions to maintain motivation. These suggestions and advice are also communicated to the user through the device. As a result, users can adjust and improve their actions toward achieving their goals at their own pace.

[0501] This system utilizes a generated AI model and prompt text to flexibly respond to individual user needs and support effective habit formation.

[0502] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0503] Step 1:

[0504] Users input their goals and health-related information into their devices. Specifically, they enter information such as "lose 1 kg in a week" or "walk for 30 minutes every day" into input forms within the app. The entered data is then sent from the device to the server. This data, consisting of each individual's goals and health information, forms the basis for subsequent procedures.

[0505] Step 2:

[0506] The server uses an AI algorithm to generate a personalized habit formation plan based on the input data. The generating AI model uses prompts to create the plan. For example, a prompt such as "Create an exercise and meal plan to lose 1kg in one week" might be used, and the output will be a specific action plan aligned with the user's goals.

[0507] Step 3:

[0508] Users record their daily activities on their devices. Specifically, after exercise, users input details of their activity (e.g., walking distance and calories burned) into the app. They also record information about their meals and nutritional intake. This recorded information is then sent back from the device to the server. The entered data forms a daily activity log and serves as material for later analysis.

[0509] Step 4:

[0510] The server receives the daily activity data and analyzes it in real time using statistical analysis tools. Here, the current degree of achievement towards the goal is evaluated. Specifically, the achievement level is compared with exercise and food intake, and the output generates a progress report towards the goal.

[0511] Step 5:

[0512] The server generates feedback based on the analysis results and sends it to the terminal. For example, it might generate a message like, "You've already achieved 70% of your goal this week. Great job!" The outputted feedback is then displayed on the terminal.

[0513] Step 6:

[0514] The device notifies the user of the feedback received from the server. This feedback is communicated via push notifications and app pop-ups. The specific actions taken here are performed in real time through the device's user interface.

[0515] Step 7:

[0516] The server generates advice to maintain the user's long-term motivation. Using the prompt again, the generating AI model creates motivational suggestions. For example, in response to the prompt "Please suggest improvements to achieve future goals," it might generate advice such as "Try increasing your water intake." The generated advice is provided to the device and displayed to the user as suggestions along with feedback.

[0517] (Application Example 1)

[0518] 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."

[0519] In today's busy lifestyle, forming the habits necessary to achieve goals is difficult. Furthermore, manually recording daily activities and receiving feedback is time-consuming and can easily lead to decreased motivation. Therefore, there is a need for a system that more effectively supports users in efficiently forming habits and achieving their goals.

[0520] 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.

[0521] In this invention, the server includes means for inputting an individual's goals and health status and creating an individualized habit formation plan, means for monitoring and evaluating progress data in real time, and observation means for detecting and recording the user's activities in the home. This makes it possible for the user to naturally develop habits in their daily life and easily achieve their goals.

[0522] "Personal goals" refer to the specific objectives or states that a user wishes to achieve.

[0523] "Health status" refers to information regarding the user's physical and mental condition.

[0524] A "habit formation plan" refers to a plan that outlines specific action steps and schedules for a user to achieve their goals.

[0525] "Progress data" refers to information that shows how far a user has progressed towards their goal.

[0526] "Feedback" refers to information about progress and advice provided to users based on their evaluation results.

[0527] "Motivation" refers to the psychological factors that enable users to maintain their willingness and awareness to achieve their goals.

[0528] "Advice" refers to suggestions for improvement or encouraging messages that users need to make to achieve their goals.

[0529] "Observation means" refers to sensors and devices used to detect and record user activity.

[0530] "Environmental control means" refers to a system that automatically adjusts the physical environment to support the user's habit formation.

[0531] This invention is a system for effectively forming habits that enable users to achieve their goals.

[0532] The system includes a terminal that sends data on user-entered goals and health status to a server. This terminal can be a smartphone or tablet, enabling convenient user operation. The server analyzes the received data and generates a personalized habit formation plan tailored to the user. Machine learning frameworks such as Python and TensorFlow are used for this generation.

[0533] Progress data is sent from the terminal to the server, which monitors and evaluates it in real time. The evaluation references the user's past activity information stored in the database. Based on the evaluation results, feedback and advice are generated and provided to the user via the terminal. The feedback includes information on the degree of goal achievement and areas for improvement.

[0534] The system also includes observation methods to monitor the user's activities within the home. These observation methods include cameras and voice recognition sensors to detect and record the user's actions and speech. This makes habits visible without the user's conscious effort, naturally supporting habit formation. Environmental control mechanisms activate to provide an optimal physical environment tailored to the situation, thereby promoting the user's habit formation.

[0535] For example, if a user sets a goal of "30 minutes of stretching every day," the system will send a reminder at night and adjust the room lighting to create a relaxing atmosphere. The system will monitor the user's movements via camera and provide feedback such as, "Great progress! Let's keep it up tomorrow," depending on the user's progress.

[0536] Examples of prompts for a generative AI model include the following:

[0537] "Design a system that explains how a personal assistant works to support the formation of daily habits."

[0538] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0539] Step 1:

[0540] Users input data about their goals and health status into a device such as a smartphone or tablet. This data includes specific goals such as the amount of weight they want to lose and the amount of time they exercise each day. The device then formats this input data and prepares it as data packets to be sent to the server.

[0541] Step 2:

[0542] The device sends user input data to the server. The server analyzes the received data packets and stores them in a database. Using machine learning algorithms (e.g., TensorFlow) and comparing them with historical data in the database, it generates a personalized habit formation plan. This generated plan includes specific action steps to achieve the user's goals.

[0543] Step 3:

[0544] The server sends the generated habit-forming plan to the device. The device notifies the user of the plan and presents it through a visually understandable interface. Specifically, the device's display shows an overview of the plan and each step, providing information in a format that is easy for the user to accept.

[0545] Step 4:

[0546] The observation system detects and records the user's physical activity in real time. Cameras and voice recognition sensors are used to continuously monitor daily movements. The obtained data is sent to the terminal as progress information, and from there it is transmitted to the server.

[0547] Step 5:

[0548] The server analyzes and evaluates received progress data in real time. The evaluation compares the current performance to past results and standard plans to identify areas for improvement and assess the degree of achievement. Based on the analysis results, feedback is generated for the user, including the percentage of achievement and areas for improvement.

[0549] Step 6:

[0550] The server sends the generated feedback to the device, and the device notifies the user. Furthermore, advice to maintain the user's motivation is also generated and provided to the user through the device. For example, an encouraging message such as, "Let's try exercising a little more this week!" might be displayed.

[0551] Step 7:

[0552] The environmental control system adjusts the physical environment based on the user's activity level. It softens the lighting when the user wants to relax and automatically sets the environment to promote specific habits. This operation is performed in a timely manner according to the user's needs, using data from observation devices.

[0553] 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.

[0554] This invention combines a conventional system that supports user habit formation with an emotion engine to provide feedback and advice tailored to the user's emotional state. This system includes a server, a terminal, and a user, and each component works in coordination.

[0555] First, the user uses their device to input their goals and health status. This creates a rough habit-forming plan. The device sends this information to the server. The server generates a personalized habit-forming plan based on the user's goals and health status. This plan includes specific action steps and outlines what the user should do on a daily basis.

[0556] Next, a newly integrated emotion engine analyzes the user's emotional state in real time. The user's device collects emotional data through voice input, facial recognition, and other means, and sends it to the server. The server evaluates the emotional state based on this data and incorporates it into feedback and advice.

[0557] For example, if the server determines that a user is experiencing stress, it generates advice to help manage that stress. The device then provides this advice to the user and suggests relaxing activities. Furthermore, the emotion engine can accumulate past emotional data and analyze long-term emotional patterns. This allows for the provision of appropriate advice to users whose emotional states fluctuate regularly.

[0558] For example, when a user attempts to exercise according to their schedule, the device analyzes their emotions and, if it detects feelings of anxiety or decreased motivation, the server generates a special motivational message tailored to the user's situation and sends it to the device. This message is displayed to the user, potentially rekindling their motivation to take action.

[0559] The system of this invention considers the impact of the user's emotional state on the success of a plan, enabling more effective habit formation support. Furthermore, by identifying the optimal feedback strategy for the user through long-term analysis of emotional data, it promotes proactive behavioral improvement.

[0560] The following describes the processing flow.

[0561] Step 1:

[0562] Users use their devices to input their goals and health status. This includes specific goals (e.g., weight loss) and their current health condition.

[0563] Step 2:

[0564] The device sends user input data to the server. This data is used as source information for generating personalized habit formation plans.

[0565] Step 3:

[0566] The server analyzes the received data and generates a personalized habit formation plan. This plan includes specific action steps and outlines the path to achieving the user's goals.

[0567] Step 4:

[0568] The device displays a habit-forming plan sent from the server to the user. The user reviews the plan and uses it to improve their daily activities.

[0569] Step 5:

[0570] Users record their daily activities and progress on their devices. This includes records of exercise, meals, and sleep duration.

[0571] Step 6:

[0572] The device sends user progress data to the server. The server receives this data in real time and performs analysis.

[0573] Step 7:

[0574] The server uses an emotion engine to analyze the user's emotional data. This emotional data is collected through the user's voice input and facial recognition technology.

[0575] Step 8:

[0576] The server analyzes emotional states and progress data to generate optimal feedback for the user. This feedback includes advice and encouraging messages tailored to the user's emotions.

[0577] Step 9:

[0578] The device notifies the user of feedback received from the server. For example, if the user is feeling stressed, it might display advice such as, "Try listening to some music to relax."

[0579] Step 10:

[0580] The server accumulates user behavior and emotional data over the long term and performs pattern analysis. This allows for regular adjustments to habit formation plans and feedback strategies optimized for each user.

[0581] (Example 2)

[0582] 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."

[0583] Conventional habit-forming support systems only provide plans based on individual goals and health conditions, and fail to offer feedback and advice that takes into account the user's emotional state. As a result, appropriate support tailored to the user's emotional motivation and state is not provided, leading to insufficient habit-forming effectiveness. Furthermore, there is no mechanism for long-term accumulation and analysis of emotional data, making proactive behavioral improvement difficult.

[0584] 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.

[0585] In this invention, the server includes means for analyzing the user's emotional state and generating optimal advice, means for adjusting and providing the generated advice according to the user's emotional state, and means for collecting emotional data and performing long-term analysis. This makes it possible to provide appropriate feedback and advice according to the user's emotional state, thereby realizing more effective support for habit formation.

[0586] "Personal goals" are specific achievement targets that a particular user sets with the aim of improving their own life or health.

[0587] "Health status" refers to information about the user's physical or mental health, including health indicators and conditions entered by the user.

[0588] A "habit formation plan" is a set of specific action steps and schedules designed to help a user achieve their goals.

[0589] "Progress data" refers to information that shows the progress of efforts toward achieving the goals set by the user, and includes daily or weekly action results.

[0590] "Feedback" refers to information that provides comments and suggestions for evaluation and improvement regarding user behavior and progress.

[0591] "Advice" refers to specific guidance and suggestions provided to support users in implementing their habit-forming plans.

[0592] "Emotional state" refers to information that indicates a user's emotional health, and includes classifications of emotions such as stress, joy, and sadness.

[0593] "Emotional data" refers to a collection of information about emotions obtained from users' voices, facial expressions, and behaviors.

[0594] "Long-term analysis" is the process of analyzing users' emotions and behavioral patterns over a long period of time using time-series data to understand their characteristics and trends.

[0595] This invention provides a system that helps users to form habits more effectively. The system consists of a server, a terminal, and a user as its main components, each functioning as follows:

[0596] The server uses a generative AI model to generate a personalized habit formation plan based on the user's individual goals and health status. An example of a prompt given to the AI ​​model is the instruction, "Create a specific schedule for the user to achieve three exercise sessions per week." The server analyzes the user's emotional data transmitted from the device and provides customized feedback and advice based on the results. This process allows for the provision of appropriate advice tailored to the user's emotional state.

[0597] The terminal receives input from the user and transmits it to the server. The terminal can also use a voice input system and camera to collect user emotion data. Emotion recognition software on the terminal analyzes the user's emotions in real time from their voice tone and facial expressions. This collected data is sent to the server and used for emotion analysis.

[0598] Users provide data on their goals and health status using their devices, and receive a plan generated by the server based on this information. For example, the plan is applied when the user inputs specific goals such as "reduce daily stress" or "get regular sleep." Furthermore, users incorporate emotion-based advice and feedback provided by the device into their daily lives, promoting successful habit formation.

[0599] Such a system provides feedback and advice that takes into account the user's emotional state, enabling more flexible and effective habit formation.

[0600] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0601] Step 1:

[0602] Users input their personal goals and health status into the device. Specifically, users enter goals such as "exercise for 30 minutes every day" or "get regular sleep" as text through the device's interface. This input data is saved on the device and immediately sent to the server.

[0603] Step 2:

[0604] The terminal transmits user-entered goal and health status data to the server. Data transfer uses a secure protocol, and data validation is performed to ensure the data format is correct before transmission. The server receives this data and stores it in a database for each user.

[0605] Step 3:

[0606] The server uses a generative AI model to create a personalized habit formation plan based on the user's goals and health status. At this time, the AI ​​model is given a prompt message: "Generate the action steps necessary to achieve the user's goals." The generated plan will include specific actions and schedules, and will be customized according to the user's needs.

[0607] Step 4:

[0608] The device displays a habit-forming plan generated on the server to the user. The user reviews the plan on the device screen and incorporates it into their daily routine. The displayed plan includes specific actions, such as "run for 30 minutes on Mondays, Wednesdays, and Fridays."

[0609] Step 5:

[0610] The device collects user emotional data using voice input and facial recognition technology. Specifically, the built-in microphone and camera are used to record the user's voice tone and facial expression data. The collected data is sent to a server and used to analyze the user's emotional state.

[0611] Step 6:

[0612] The server analyzes collected emotional data to evaluate the user's current emotional state. Text analysis and image recognition algorithms are used for data analysis to identify emotional patterns such as stress and joy. Based on this evaluation, a generative AI model is used to create feedback and advice tailored to the user's emotions.

[0613] Step 7:

[0614] The device notifies the user of emotional feedback and advice generated by the server. These notifications include pop-up messages and announcements via the voice assistant. This allows users to easily see and take action based on their emotional state.

[0615] (Application Example 2)

[0616] 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."

[0617] In modern society, there is a growing need for individualized health management and habit formation, but traditional methods have been insufficient in providing feedback that takes into account the emotional state of the user. Furthermore, it is difficult to maintain user motivation over the long term, which presents challenges in achieving long-term health improvement.

[0618] 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.

[0619] In this invention, the server includes means for inputting an individual's goals and health status and creating an individualized habit formation plan; means for monitoring and evaluating progress data in real time; means for providing feedback to the user based on the evaluation results; means for analyzing emotional data and individually adjusting feedback based on the user's emotional state; and means for analyzing long-term emotional patterns and finding a feedback strategy suitable for the user. This enables effective habit formation support that takes into account the user's emotional state.

[0620] A "goal" refers to a specific situation or state that the user wishes to achieve.

[0621] "Health status" refers to various data and information related to the user's physical and mental condition.

[0622] A "habit-forming plan" refers to a plan that outlines the actions and procedures that should be taken on a daily basis in order to achieve a goal.

[0623] "Progress data" refers to specific information that shows the status of users' activities and the implementation of their plans.

[0624] "Feedback" refers to advice and information provided regarding a user's behavior or condition.

[0625] "Motivation" refers to the internal drive or will that allows users to continue their actions.

[0626] "Advice" refers to specific instructions or suggestions provided to users to encourage appropriate behavior or states.

[0627] "Emotional data" refers to data collected and analyzed for information regarding the emotional state of users.

[0628] "Emotional state" refers to a specific situation that indicates the user's mental state or mood.

[0629] A "feedback strategy" refers to a plan or policy for providing optimal feedback tailored to the user's behavior and emotional state.

[0630] The system implementing this invention consists of three components: a server, a terminal, and a user. The user first uses the terminal to input their goals and health status. Based on this information, the server generates a personalized habit formation plan. This plan includes daily action instructions and outlines specific steps that the user should continuously take.

[0631] The device collects user emotional data using voice input and facial recognition technology. This emotional data is then sent to a server, where an emotion engine analyzes the user's emotional state in real time. Based on the analyzed emotional state, the server generates appropriate feedback and advice, which is then delivered to the user via the device. This enables effective habit formation tailored to the user's emotions.

[0632] For example, if a user has set a goal of "exercising every day," but emotional analysis detects that they are feeling stressed that day, the server will generate feedback such as, "Today, let's start with a short deep breathing session and gradually move on to walking," and present it to the user from their device.

[0633] An example of a prompt message for a generative AI model would be: "His goal is to exercise every day, but he is feeling stressed today. Please create feedback that reflects this situation."

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

[0635] Step 1:

[0636] The user uses a device to input their goals and health status. This input includes data about the goals the user wants to achieve and their current health condition. The device receives this data in digital format and sends it to the server.

[0637] Step 2:

[0638] The server generates a personalized habit formation plan based on the received data. This process creates specific action steps necessary to achieve the goals. The data processing involves analyzing the user's goals and health status to calculate the optimal action sequence. The output is the generated habit formation plan.

[0639] Step 3:

[0640] The device collects user emotion data using voice input and facial recognition. The input consists of the user's voice and facial image, which are converted into a format that can be input into the emotion analysis engine. Data processing involves image processing and voice analysis to generate data that can be used to evaluate the user's emotional state.

[0641] Step 4:

[0642] The collected emotional data is sent to a server for emotional analysis. The server analyzes the input emotional data to evaluate the user's current emotional state. This evaluation uses an emotional analysis algorithm to quantitatively assess the user's stress level and motivation. The output is emotional state data as a result of the analysis.

[0643] Step 5:

[0644] The server generates appropriate feedback and advice based on the user's emotional state. The input consists of analyzed emotional state data and a pre-created habit formation plan. The data calculation selects a feedback strategy appropriate to the emotional state and generates specific advice. The output is a feedback message to be provided to the user.

[0645] Step 6:

[0646] Feedback and advice are sent to the device and provided to the user. The device displays the received messages to the user on the screen and via audio, encouraging them to reflect them in their daily actions. This allows the user to take the most appropriate action in real time based on their emotional state.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] [Fourth Embodiment]

[0651] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0652] 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.

[0653] 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).

[0654] 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.

[0655] 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.

[0656] 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).

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] 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.

[0663] 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".

[0664] The system of this invention is designed to enable users to effectively form new habits and achieve their goals. This system primarily functions through the exchange of information between a server, a terminal, and the user.

[0665] First, the user enters information about their goals and health status into the device. For example, they record specific details such as the weight they want to lose and their daily exercise time. The device sends this data to the server. Based on the received data, the server generates a personalized habit formation plan. The plan provides specific instructions for daily actions to help the user achieve their goals. For example, if the user aims to jog three times a week, the server will suggest the days and times for jogging.

[0666] Next, users continuously record their daily activities through their devices. For example, they input how far they ran each day, what they ate, and how much time they spent exercising. The devices send this information to a server, which analyzes the progress data. Based on the analysis, the server evaluates the user's current level of achievement and generates feedback.

[0667] The server performs analysis in real time and quickly informs the user of the evaluation results. The terminal receives the feedback and notifies the user in a timely manner. For example, "You have achieved 80% of your goal this week. If you walk for another 10 minutes, you will reach today's goal."

[0668] Furthermore, the server analyzes the user's behavior patterns and generates advice to help the user maintain motivation. This advice includes specific areas for improvement and encouraging messages to help achieve future goals. The server sends this advice to the terminal, which then provides it to the user.

[0669] For example, if a user sets a goal of "sleeping for more than 7 hours every day and doing 30 minutes of stretching in the morning," the server will create a plan for sleep patterns and exercise habit formation and notify the user. The user records the amount of time they slept and the amount of time they stretched each day, and the device relays this information to the server. The server evaluates how close the user is to their goal and sends feedback as needed, such as "try to go to bed a little earlier."

[0670] Thus, the system of the present invention flexibly responds to the needs of individual users and supports them in effectively continuing to form habits.

[0671] The following describes the processing flow.

[0672] Step 1:

[0673] Users input information about their goals and health status using their devices. This includes weight loss goals, daily exercise levels, and details about their diet.

[0674] Step 2:

[0675] The device sends the collected user data to the server. This data includes the user's basic profile information.

[0676] Step 3:

[0677] The server analyzes the received data and generates a personalized habit formation plan. This plan also references data from other users with similar goals and past success stories.

[0678] Step 4:

[0679] The server sends the generated habit formation plan to the device, and the device notifies the user of detailed daily action steps. This allows the user to understand the specific action plan.

[0680] Step 5:

[0681] Users record their daily activity progress on their device. Here, they input information such as the type and duration of exercise, meals, or the type and duration of stretches performed.

[0682] Step 6:

[0683] The device sends user activity data to the server. This transmission is typically done daily or in real time.

[0684] Step 7:

[0685] The server evaluates progress based on the received activity data. This includes analysis of the degree to which weekly goals were achieved and the difference between the plan and actual actions.

[0686] Step 8:

[0687] The server generates feedback messages based on the evaluation results, providing advice to the user. This feedback includes suggestions to help maintain user motivation.

[0688] Step 9:

[0689] The server sends feedback and advice to the device, which then presents it to the user. The user then uses this information to plan their next action.

[0690] Step 10:

[0691] The server collects user behavior data and analyzes patterns. Based on the results of this analysis, future plan updates and new suggestions for users are adjusted.

[0692] (Example 1)

[0693] 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".

[0694] The challenge lies in providing a support system that enables users to effectively form and maintain habits toward achieving their goals. This aims to support individualized planning, progress-based responses, and continuous motivation.

[0695] 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.

[0696] In this invention, the server includes means for inputting personal goals and health-related information and creating specific habit-forming procedures; means for immediately monitoring and evaluating progress information; and means for providing responses to the user based on the evaluation results. This enables the user to understand their progress toward their goals in real time, adjust their plan as needed, and maintain their motivation.

[0697] "Personal purpose" refers to the individual goals or intentions that the user wishes to achieve.

[0698] "Health-related information" refers to information including the user's health status and health-related data, such as weight, exercise time, and dietary content.

[0699] A "habit-forming procedure" is a detailed outline of the daily action plan and steps that users should take to achieve their goals.

[0700] "Progress information" refers to data that shows the degree to which a user has achieved their goals and details of their activities during that process.

[0701] "Immediate monitoring and evaluation" refers to tracking users' progress in real time, analyzing that data, and evaluating the current situation.

[0702] "Providing a response" refers to communicating feedback or recommendations for the next action to the user.

[0703] "Suggestions to maintain motivation" refers to providing users with appropriate encouragement and advice on areas for improvement to help them continue their efforts toward their goals.

[0704] A "generative AI model" refers to an artificial intelligence model that automatically generates plans and makes suggestions based on given data and prompts.

[0705] A "prompt statement" refers to a command or question that is input to a generative AI model to obtain a specific output.

[0706] The system of this invention supports the achievement of individual goals through communication between the user, terminal, and server. This system is designed to facilitate the formation of specific habits.

[0707] First, users input their goals and health-related information via their device. The device then sends this information to a server. The devices used include smartphones and tablets, and input methods include dedicated applications and web interfaces. Specifically, users set weight loss goals and exercise routines and input the information into forms within the app.

[0708] The server uses AI algorithms based on the received information to generate individual habit-forming procedures. This process utilizes a generative AI model. This model generates plans in response to prompts, providing customized action plans for each user. For example, by entering "Generate an action plan for the user to achieve three exercise sessions per week" as a prompt, the model will suggest an appropriate plan.

[0709] Users record their daily activities on their devices. The devices send this data to a server, which immediately analyzes the progress. Database systems and statistical analysis tools are used for the analysis. The server evaluates the user's progress toward achieving their goals, generates feedback, and sends it to the device. The device notifies the user in real time and provides feedback.

[0710] Furthermore, the server analyzes the user's behavior patterns and generates specific suggestions to maintain motivation. These suggestions and advice are also communicated to the user through the device. As a result, users can adjust and improve their actions toward achieving their goals at their own pace.

[0711] This system utilizes a generated AI model and prompt text to flexibly respond to individual user needs and support effective habit formation.

[0712] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0713] Step 1:

[0714] Users input their goals and health-related information into their devices. Specifically, they enter information such as "lose 1 kg in a week" or "walk for 30 minutes every day" into input forms within the app. The entered data is then sent from the device to the server. This data, consisting of each individual's goals and health information, forms the basis for subsequent procedures.

[0715] Step 2:

[0716] The server uses an AI algorithm to generate a personalized habit formation plan based on the input data. The generating AI model uses prompts to create the plan. For example, a prompt such as "Create an exercise and meal plan to lose 1kg in one week" might be used, and the output will be a specific action plan aligned with the user's goals.

[0717] Step 3:

[0718] Users record their daily activities on their devices. Specifically, after exercise, users input details of their activity (e.g., walking distance and calories burned) into the app. They also record information about their meals and nutritional intake. This recorded information is then sent back from the device to the server. The entered data forms a daily activity log and serves as material for later analysis.

[0719] Step 4:

[0720] The server receives the daily activity data and analyzes it in real time using statistical analysis tools. Here, the current degree of achievement towards the goal is evaluated. Specifically, the achievement level is compared with exercise and food intake, and the output generates a progress report towards the goal.

[0721] Step 5:

[0722] The server generates feedback based on the analysis results and sends it to the terminal. For example, it might generate a message like, "You've already achieved 70% of your goal this week. Great job!" The outputted feedback is then displayed on the terminal.

[0723] Step 6:

[0724] The device notifies the user of the feedback received from the server. This feedback is communicated via push notifications and app pop-ups. The specific actions taken here are performed in real time through the device's user interface.

[0725] Step 7:

[0726] The server generates advice to maintain the user's long-term motivation. Using the prompt again, the generating AI model creates motivational suggestions. For example, in response to the prompt "Please suggest improvements to achieve future goals," it might generate advice such as "Try increasing your water intake." The generated advice is provided to the device and displayed to the user as suggestions along with feedback.

[0727] (Application Example 1)

[0728] 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".

[0729] In today's busy lifestyle, forming the habits necessary to achieve goals is difficult. Furthermore, manually recording daily activities and receiving feedback is time-consuming and can easily lead to decreased motivation. Therefore, there is a need for a system that more effectively supports users in efficiently forming habits and achieving their goals.

[0730] 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.

[0731] In this invention, the server includes means for inputting an individual's goals and health status and creating an individualized habit formation plan, means for monitoring and evaluating progress data in real time, and observation means for detecting and recording the user's activities in the home. This makes it possible for the user to naturally develop habits in their daily life and easily achieve their goals.

[0732] "Personal goals" refer to the specific objectives or states that a user wishes to achieve.

[0733] "Health status" refers to information regarding the user's physical and mental condition.

[0734] A "habit formation plan" refers to a plan that outlines specific action steps and schedules for a user to achieve their goals.

[0735] "Progress data" refers to information that shows how far a user has progressed towards their goal.

[0736] "Feedback" refers to information about progress and advice provided to users based on their evaluation results.

[0737] "Motivation" refers to the psychological factors that enable users to maintain their willingness and awareness to achieve their goals.

[0738] "Advice" refers to suggestions for improvement or encouraging messages that users need to make to achieve their goals.

[0739] "Observation means" refers to sensors and devices used to detect and record user activity.

[0740] "Environmental control means" refers to a system that automatically adjusts the physical environment to support the user's habit formation.

[0741] This invention is a system for effectively forming habits that enable users to achieve their goals.

[0742] The system includes a terminal that sends data on user-entered goals and health status to a server. This terminal can be a smartphone or tablet, enabling convenient user operation. The server analyzes the received data and generates a personalized habit formation plan tailored to the user. Machine learning frameworks such as Python and TensorFlow are used for this generation.

[0743] Progress data is sent from the terminal to the server, which monitors and evaluates it in real time. The evaluation references the user's past activity information stored in the database. Based on the evaluation results, feedback and advice are generated and provided to the user via the terminal. The feedback includes information on the degree of goal achievement and areas for improvement.

[0744] The system also includes observation methods to monitor the user's activities within the home. These observation methods include cameras and voice recognition sensors to detect and record the user's actions and speech. This makes habits visible without the user's conscious effort, naturally supporting habit formation. Environmental control mechanisms activate to provide an optimal physical environment tailored to the situation, thereby promoting the user's habit formation.

[0745] For example, if a user sets a goal of "30 minutes of stretching every day," the system will send a reminder at night and adjust the room lighting to create a relaxing atmosphere. The system will monitor the user's movements via camera and provide feedback such as, "Great progress! Let's keep it up tomorrow," depending on the user's progress.

[0746] Examples of prompts for a generative AI model include the following:

[0747] "Design a system that explains how a personal assistant works to support the formation of daily habits."

[0748] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0749] Step 1:

[0750] Users input data about their goals and health status into a device such as a smartphone or tablet. This data includes specific goals such as the amount of weight they want to lose and the amount of time they exercise each day. The device then formats this input data and prepares it as data packets to be sent to the server.

[0751] Step 2:

[0752] The device sends user input data to the server. The server analyzes the received data packets and stores them in a database. Using machine learning algorithms (e.g., TensorFlow) and comparing them with historical data in the database, it generates a personalized habit formation plan. This generated plan includes specific action steps to achieve the user's goals.

[0753] Step 3:

[0754] The server sends the generated habit-forming plan to the device. The device notifies the user of the plan and presents it through a visually understandable interface. Specifically, the device's display shows an overview of the plan and each step, providing information in a format that is easy for the user to accept.

[0755] Step 4:

[0756] The observation system detects and records the user's physical activity in real time. Cameras and voice recognition sensors are used to continuously monitor daily movements. The obtained data is sent to the terminal as progress information, and from there it is transmitted to the server.

[0757] Step 5:

[0758] The server analyzes and evaluates received progress data in real time. The evaluation compares the current performance to past results and standard plans to identify areas for improvement and assess the degree of achievement. Based on the analysis results, feedback is generated for the user, including the percentage of achievement and areas for improvement.

[0759] Step 6:

[0760] The server sends the generated feedback to the device, and the device notifies the user. Furthermore, advice to maintain the user's motivation is also generated and provided to the user through the device. For example, an encouraging message such as, "Let's try exercising a little more this week!" might be displayed.

[0761] Step 7:

[0762] The environmental control system adjusts the physical environment based on the user's activity level. It softens the lighting when the user wants to relax and automatically sets the environment to promote specific habits. This operation is performed in a timely manner according to the user's needs, using data from observation devices.

[0763] 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.

[0764] This invention combines a conventional system that supports user habit formation with an emotion engine to provide feedback and advice tailored to the user's emotional state. This system includes a server, a terminal, and a user, and each component works in coordination.

[0765] First, the user uses their device to input their goals and health status. This creates a rough habit-forming plan. The device sends this information to the server. The server generates a personalized habit-forming plan based on the user's goals and health status. This plan includes specific action steps and outlines what the user should do on a daily basis.

[0766] Next, a newly integrated emotion engine analyzes the user's emotional state in real time. The user's device collects emotional data through voice input, facial recognition, and other means, and sends it to the server. The server evaluates the emotional state based on this data and incorporates it into feedback and advice.

[0767] For example, if the server determines that a user is experiencing stress, it generates advice to help manage that stress. The device then provides this advice to the user and suggests relaxing activities. Furthermore, the emotion engine can accumulate past emotional data and analyze long-term emotional patterns. This allows for the provision of appropriate advice to users whose emotional states fluctuate regularly.

[0768] For example, when a user attempts to exercise according to their schedule, the device analyzes their emotions and, if it detects feelings of anxiety or decreased motivation, the server generates a special motivational message tailored to the user's situation and sends it to the device. This message is displayed to the user, potentially rekindling their motivation to take action.

[0769] The system of this invention considers the impact of the user's emotional state on the success of a plan, enabling more effective habit formation support. Furthermore, by identifying the optimal feedback strategy for the user through long-term analysis of emotional data, it promotes proactive behavioral improvement.

[0770] The following describes the processing flow.

[0771] Step 1:

[0772] Users use their devices to input their goals and health status. This includes specific goals (e.g., weight loss) and their current health condition.

[0773] Step 2:

[0774] The device sends user input data to the server. This data is used as source information for generating personalized habit formation plans.

[0775] Step 3:

[0776] The server analyzes the received data and generates a personalized habit formation plan. This plan includes specific action steps and outlines the path to achieving the user's goals.

[0777] Step 4:

[0778] The device displays a habit-forming plan sent from the server to the user. The user reviews the plan and uses it to improve their daily activities.

[0779] Step 5:

[0780] Users record their daily activities and progress on their devices. This includes records of exercise, meals, and sleep duration.

[0781] Step 6:

[0782] The device sends user progress data to the server. The server receives this data in real time and performs analysis.

[0783] Step 7:

[0784] The server uses an emotion engine to analyze the user's emotional data. This emotional data is collected through the user's voice input and facial recognition technology.

[0785] Step 8:

[0786] The server analyzes emotional states and progress data to generate optimal feedback for the user. This feedback includes advice and encouraging messages tailored to the user's emotions.

[0787] Step 9:

[0788] The device notifies the user of feedback received from the server. For example, if the user is feeling stressed, it might display advice such as, "Try listening to some music to relax."

[0789] Step 10:

[0790] The server accumulates user behavior and emotional data over the long term and performs pattern analysis. This allows for regular adjustments to habit formation plans and feedback strategies optimized for each user.

[0791] (Example 2)

[0792] 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".

[0793] Conventional habit-forming support systems only provide plans based on individual goals and health conditions, and fail to offer feedback and advice that takes into account the user's emotional state. As a result, appropriate support tailored to the user's emotional motivation and state is not provided, leading to insufficient habit-forming effectiveness. Furthermore, there is no mechanism for long-term accumulation and analysis of emotional data, making proactive behavioral improvement difficult.

[0794] 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.

[0795] In this invention, the server includes means for analyzing the user's emotional state and generating optimal advice, means for adjusting and providing the generated advice according to the user's emotional state, and means for collecting emotional data and performing long-term analysis. This makes it possible to provide appropriate feedback and advice according to the user's emotional state, thereby realizing more effective support for habit formation.

[0796] "Personal goals" are specific achievement targets that a particular user sets with the aim of improving their own life or health.

[0797] "Health status" refers to information about the user's physical or mental health, including health indicators and conditions entered by the user.

[0798] A "habit formation plan" is a set of specific action steps and schedules designed to help a user achieve their goals.

[0799] "Progress data" refers to information that shows the progress of efforts toward achieving the goals set by the user, and includes daily or weekly action results.

[0800] "Feedback" refers to information that provides comments and suggestions for evaluation and improvement regarding user behavior and progress.

[0801] "Advice" refers to specific guidance and suggestions provided to support users in implementing their habit-forming plans.

[0802] "Emotional state" refers to information that indicates a user's emotional health, and includes classifications of emotions such as stress, joy, and sadness.

[0803] "Emotional data" refers to a collection of information about emotions obtained from users' voices, facial expressions, and behaviors.

[0804] "Long-term analysis" is the process of analyzing users' emotions and behavioral patterns over a long period of time using time-series data to understand their characteristics and trends.

[0805] This invention provides a system that helps users to form habits more effectively. The system consists of a server, a terminal, and a user as its main components, each functioning as follows:

[0806] The server uses a generative AI model to generate a personalized habit formation plan based on the user's individual goals and health status. An example of a prompt given to the AI ​​model is the instruction, "Create a specific schedule for the user to achieve three exercise sessions per week." The server analyzes the user's emotional data transmitted from the device and provides customized feedback and advice based on the results. This process allows for the provision of appropriate advice tailored to the user's emotional state.

[0807] The terminal receives input from the user and transmits it to the server. The terminal can also use a voice input system and camera to collect user emotion data. Emotion recognition software on the terminal analyzes the user's emotions in real time from their voice tone and facial expressions. This collected data is sent to the server and used for emotion analysis.

[0808] Users provide data on their goals and health status using their devices, and receive a plan generated by the server based on this information. For example, the plan is applied when the user inputs specific goals such as "reduce daily stress" or "get regular sleep." Furthermore, users incorporate emotion-based advice and feedback provided by the device into their daily lives, promoting successful habit formation.

[0809] Such a system provides feedback and advice that takes into account the user's emotional state, enabling more flexible and effective habit formation.

[0810] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0811] Step 1:

[0812] Users input their personal goals and health status into the device. Specifically, users enter goals such as "exercise for 30 minutes every day" or "get regular sleep" as text through the device's interface. This input data is saved on the device and immediately sent to the server.

[0813] Step 2:

[0814] The terminal transmits user-entered goal and health status data to the server. Data transfer uses a secure protocol, and data validation is performed to ensure the data format is correct before transmission. The server receives this data and stores it in a database for each user.

[0815] Step 3:

[0816] The server uses a generative AI model to create a personalized habit formation plan based on the user's goals and health status. At this time, the AI ​​model is given a prompt message: "Generate the action steps necessary to achieve the user's goals." The generated plan will include specific actions and schedules, and will be customized according to the user's needs.

[0817] Step 4:

[0818] The device displays a habit-forming plan generated on the server to the user. The user reviews the plan on the device screen and incorporates it into their daily routine. The displayed plan includes specific actions, such as "run for 30 minutes on Mondays, Wednesdays, and Fridays."

[0819] Step 5:

[0820] The device collects user emotional data using voice input and facial recognition technology. Specifically, the built-in microphone and camera are used to record the user's voice tone and facial expression data. The collected data is sent to a server and used to analyze the user's emotional state.

[0821] Step 6:

[0822] The server analyzes collected emotional data to evaluate the user's current emotional state. Text analysis and image recognition algorithms are used for data analysis to identify emotional patterns such as stress and joy. Based on this evaluation, a generative AI model is used to create feedback and advice tailored to the user's emotions.

[0823] Step 7:

[0824] The device notifies the user of emotional feedback and advice generated by the server. These notifications include pop-up messages and announcements via the voice assistant. This allows users to easily see and take action based on their emotional state.

[0825] (Application Example 2)

[0826] 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".

[0827] In modern society, there is a growing need for individualized health management and habit formation, but traditional methods have been insufficient in providing feedback that takes into account the emotional state of the user. Furthermore, it is difficult to maintain user motivation over the long term, which presents challenges in achieving long-term health improvement.

[0828] 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.

[0829] In this invention, the server includes means for inputting an individual's goals and health status and creating an individualized habit formation plan; means for monitoring and evaluating progress data in real time; means for providing feedback to the user based on the evaluation results; means for analyzing emotional data and individually adjusting feedback based on the user's emotional state; and means for analyzing long-term emotional patterns and finding a feedback strategy suitable for the user. This enables effective habit formation support that takes into account the user's emotional state.

[0830] A "goal" refers to a specific situation or state that the user wishes to achieve.

[0831] "Health status" refers to various data and information related to the user's physical and mental condition.

[0832] A "habit-forming plan" refers to a plan that outlines the actions and procedures that should be taken on a daily basis in order to achieve a goal.

[0833] "Progress data" refers to specific information that shows the status of users' activities and the implementation of their plans.

[0834] "Feedback" refers to advice and information provided regarding a user's behavior or condition.

[0835] "Motivation" refers to the internal drive or will that allows users to continue their actions.

[0836] "Advice" refers to specific instructions or suggestions provided to users to encourage appropriate behavior or states.

[0837] "Emotional data" refers to data collected and analyzed for information regarding the emotional state of users.

[0838] "Emotional state" refers to a specific situation that indicates the user's mental state or mood.

[0839] A "feedback strategy" refers to a plan or policy for providing optimal feedback tailored to the user's behavior and emotional state.

[0840] The system implementing this invention consists of three components: a server, a terminal, and a user. The user first uses the terminal to input their goals and health status. Based on this information, the server generates a personalized habit formation plan. This plan includes daily action instructions and outlines specific steps that the user should continuously take.

[0841] The device collects user emotional data using voice input and facial recognition technology. This emotional data is then sent to a server, where an emotion engine analyzes the user's emotional state in real time. Based on the analyzed emotional state, the server generates appropriate feedback and advice, which is then delivered to the user via the device. This enables effective habit formation tailored to the user's emotions.

[0842] For example, if a user has set a goal of "exercising every day," but emotional analysis detects that they are feeling stressed that day, the server will generate feedback such as, "Today, let's start with a short deep breathing session and gradually move on to walking," and present it to the user from their device.

[0843] An example of a prompt message for a generative AI model would be: "His goal is to exercise every day, but he is feeling stressed today. Please create feedback that reflects this situation."

[0844] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0845] Step 1:

[0846] The user uses a device to input their goals and health status. This input includes data about the goals the user wants to achieve and their current health condition. The device receives this data in digital format and sends it to the server.

[0847] Step 2:

[0848] The server generates a personalized habit formation plan based on the received data. This process creates specific action steps necessary to achieve the goals. The data processing involves analyzing the user's goals and health status to calculate the optimal action sequence. The output is the generated habit formation plan.

[0849] Step 3:

[0850] The device collects user emotion data using voice input and facial recognition. The input consists of the user's voice and facial image, which are converted into a format that can be input into the emotion analysis engine. Data processing involves image processing and voice analysis to generate data that can be used to evaluate the user's emotional state.

[0851] Step 4:

[0852] The collected emotional data is sent to a server for emotional analysis. The server analyzes the input emotional data to evaluate the user's current emotional state. This evaluation uses an emotional analysis algorithm to quantitatively assess the user's stress level and motivation. The output is emotional state data as a result of the analysis.

[0853] Step 5:

[0854] The server generates appropriate feedback and advice based on the user's emotional state. The input consists of analyzed emotional state data and a pre-created habit formation plan. The data calculation selects a feedback strategy appropriate to the emotional state and generates specific advice. The output is a feedback message to be provided to the user.

[0855] Step 6:

[0856] Feedback and advice are sent to the device and provided to the user. The device displays the received messages to the user on the screen and via audio, encouraging them to reflect them in their daily actions. This allows the user to take the most appropriate action in real time based on their emotional state.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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."

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] The following is further disclosed regarding the embodiments described above.

[0879] (Claim 1)

[0880] A means of inputting personal goals and health status to create an individualized habit formation plan,

[0881] A means of monitoring and evaluating progress data in real time,

[0882] A means of providing feedback to users based on evaluation results,

[0883] A means of generating advice to maintain user motivation,

[0884] Means for providing generated advice,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, comprising means for analyzing daily activity information entered by the user and automatically updating the appropriateness of the proposed plan.

[0888] (Claim 3)

[0889] The system according to claim 1, comprising means for analyzing user behavior patterns and predicting future behavior.

[0890] "Example 1"

[0891] (Claim 1)

[0892] A device that allows users to input their personal goals and health-related information and create specific habit formation procedures,

[0893] A device that instantly monitors and evaluates progress information,

[0894] A device that provides a response to the user based on the evaluation results,

[0895] A device that generates suggestions to maintain the user's motivation,

[0896] A device that provides the generated proposals,

[0897] A generative AI model that analyzes daily activity records and generates optimized procedures,

[0898] A device equipped with a generative AI model that uses prompt statements for procedure generation,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, comprising a device that analyzes daily activity records entered by the user and automatically updates the appropriateness of the suggested procedures.

[0902] (Claim 3)

[0903] The system according to claim 1, comprising a device that analyzes the user's behavioral patterns and predicts future behavior.

[0904] "Application Example 1"

[0905] (Claim 1)

[0906] A means of inputting personal goals and health status to create an individualized habit formation plan,

[0907] A means of monitoring and evaluating progress data in real time,

[0908] A means of providing feedback to users based on evaluation results,

[0909] A means of generating advice to maintain user motivation,

[0910] Means for providing generated advice,

[0911] An observation method for detecting and recording user activity in a residential setting,

[0912] An automated environmental control system that uses recorded data to support the formation of user habits,

[0913] A system that includes this.

[0914] (Claim 2)

[0915] The system according to claim 1, comprising means for analyzing daily activity information entered by the user and automatically updating the appropriateness of the proposed plan, and for identifying the user's behavior in real time by observation means.

[0916] (Claim 3)

[0917] The system according to claim 1, comprising means for analyzing user behavior patterns and predicting future behavior, and means for optimizing feedback in conjunction with observation means.

[0918] "Example 2 of combining an emotion engine"

[0919] (Claim 1)

[0920] A means of inputting personal goals and health status to create an individualized habit formation plan,

[0921] A means of monitoring and evaluating progress data in real time,

[0922] A means of providing feedback to users based on the evaluation results,

[0923] A means of analyzing the emotional state of users and generating optimal advice,

[0924] A means of adjusting and providing the generated advice according to the user's emotional state,

[0925] A means of collecting emotional data and conducting long-term analysis,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, comprising means for analyzing activity information entered by the user and automatically updating the appropriateness of the proposed plan.

[0929] (Claim 3)

[0930] The system according to claim 1, comprising means for analyzing user behavioral trends and predicting future behavior.

[0931] "Application example 2 when combining with an emotional engine"

[0932] (Claim 1)

[0933] A means of inputting personal goals and health status to create an individualized habit formation plan,

[0934] A means of monitoring and evaluating progress data in real time,

[0935] A means of providing feedback to users based on the evaluation results,

[0936] A means of generating advice to maintain user motivation,

[0937] Means for providing generated advice,

[0938] A means of analyzing emotional data and individually adjusting feedback based on the user's emotional state,

[0939] A means to analyze long-term emotional patterns and find a feedback strategy that is appropriate for the user,

[0940] An information processing system that includes this.

[0941] (Claim 2)

[0942] The information processing system according to claim 1, comprising means for analyzing daily activity information entered by the user and automatically updating the appropriateness of the proposed plan.

[0943] (Claim 3)

[0944] The information processing system according to claim 1, comprising means for analyzing user behavior patterns and predicting future behavior. [Explanation of symbols]

[0945] 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 of inputting personal goals and health status to create an individualized habit formation plan, A means of monitoring and evaluating progress data in real time, A means of providing feedback to users based on evaluation results, A means of generating advice to maintain user motivation, Means for providing generated advice, An observation method for detecting and recording user activity in a residential setting, An automated environmental control system that uses recorded data to support the formation of user habits, A system that includes this.

2. The system according to claim 1, comprising means for analyzing daily activity information entered by the user and automatically updating the appropriateness of the proposed plan, and for identifying the user's behavior in real time by observation means.

3. The system according to claim 1, comprising means for analyzing user behavior patterns and predicting future behavior, and means for optimizing feedback in conjunction with observation means.

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