System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting behavioral data of a user; means for collecting biometric data of the user; means for collecting feedback provided by the user; means for transmitting the collected data to a server; means for analyzing the received data to identify patterns of self-control and concentration of the user; means for generating a personalized training plan based on the identified patterns; means for transmitting the generated training plan to a terminal of the user; and means for providing real-time feedback to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, addiction to smartphones and digital media, anxiety, stress, and lack of attention due to multitasking are serious problems. These factors make it difficult for many people to maintain concentration, which in turn makes it difficult to complete tasks and achieve goals. To solve these problems, a support system is needed to improve individuals' self-control and maximize their concentration. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system including the following means: means for collecting user behavioral data, means for collecting user biometric data, means for collecting user-provided feedback, means for transmitting the collected data to a server, means for analyzing the received data and identifying the user's self-control and concentration patterns, means for generating a personalized training plan based on the identified patterns, means for transmitting the generated training plan to the user's device, and means for providing real-time feedback to the user. This system enables the user to improve their self-control and maximize their concentration, thereby enabling them to complete tasks and achieve their goals. Furthermore, the system also includes means for dynamically readjusting the training plan based on the analysis results and means for detecting the user's peak concentration times and optimizing the training plan based on the results, thereby providing more effective support.

[0006] "User behavioral data" refers to data related to the completion of a task, such as the start time, end time, and duration of the task performed by the user.

[0007] "User Biometric Data" means data relating to the physiological state of a user, such as heart rate, electroencephalogram, electromyogram, electrodermal activity, or pupillary reflex.

[0008] "User-provided feedback" refers to the user's own subjective evaluations and emotional fluctuations, as well as their thoughts and opinions about the training and tasks.

[0009] "Means for transmitting collected data to a server" refers to the technology or method for transmitting behavioral data, biometric data, and feedback collected on a user's device to a server via communication means such as the Internet.

[0010] "Means for analyzing received data" refers to algorithms or analytical techniques that the server uses to identify patterns of self-control or concentration of the user based on the collected data.

[0011] "Means for generating personalized training plans" refers to technology that designs and generates training plans that include optimal task management, time management, attention training, and stress management based on the user's identified patterns.

[0012] "Means for transmitting the generated training plan to the user's terminal" refers to the technology or method for distributing the training plan generated by the server to the user's terminal via a communication means such as the Internet.

[0013] "Means for providing real-time feedback to a user" means techniques or methods for providing a user with immediate feedback on their progress or implementation while they are following a training plan.

[0014] "Means for dynamically readjusting training plans based on analysis results" refers to techniques and methods for appropriately updating and optimizing training plans based on user progress data and feedback.

[0015] "Means for detecting the user's peak concentration periods" refers to technology that analyzes the user's behavioral data and biometric data to identify the periods when the user is most focused. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0024] [First embodiment]

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

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

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

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

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

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

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

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

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

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

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0037] System Overview

[0038] This invention is a support system for enhancing an individual's self-control and maximizing concentration. The system utilizes the user's behavioral data, biometric data, and feedback, and generates a personalized training plan based on this data and provides the user with real-time feedback.

[0039] server

[0040] 1. Data Collection:

[0041] The server receives the user's behavioral data, biometric data, and feedback in real time. For example, when a user uses a task management app to record the start and end times of a task, the data is sent from the device to the server. Biometric data includes heart rate, brain waves, and electrodermal activity. This data is collected from the wearable device.

[0042] 2. Data Analysis:

[0043] The server analyzes the collected data to identify patterns of self-control and concentration, for example by cross-referencing heart rate and task time to detect times when concentration is at its peak.

[0044] 3. Generate a training plan:

[0045] The server then generates a personalized training plan based on the analysis results. It uses techniques like the Pomodoro technique to provide plans for task management, attention training, and stress management. For example, it suggests a plan in which the user alternates between 20 minutes of focused work and 5 minutes of rest.

[0046] 4. Providing Feedback:

[0047] The server updates the training plan based on the user's progress and feedback, providing real-time feedback, such as "Your peak concentration time is between 2:00 and 3:00 p.m. Try using this time slot again next week."

[0048] Terminal

[0049] 1. Data recording and transmission:

[0050] The device records the user's behavioral data and biometric data and periodically transmits it to a server. For example, a smartwatch measures heart rate and stores the data on the device. The device then compiles the collected data and transmits it to the server.

[0051] 2. Receive and view training plans:

[0052] The device receives the training plan sent from the server and notifies the user. For example, a notification such as "Please concentrate on your work for the next 25 minutes" will be displayed on the smartphone.

[0053] 3. Displaying and Carrying Out Instructions:

[0054] The device will then prompt the user to start tasks and take rests according to the instructions in the training plan. For example, when it's time to take a break, it will notify the user, "You have 5 minutes left to take a break."

[0055] User

[0056] 1. Implement your training plan:

[0057] The user performs tasks according to the training plan provided by the device, for example, concentrating on the task at the specified time and taking appropriate breaks according to notifications.

[0058] 2. Providing Feedback:

[0059] Users can send feedback about the effectiveness of the training plan to the server via their devices, for example by answering a questionnaire about their sense of accomplishment and satisfaction with the task.

[0060] 3. Self-monitoring:

[0061] Users can use the device to self-monitor and check their own performance based on the data, for example by viewing a graph of their daily concentration fluctuations.

[0062] Through these processes, the system effectively enhances the user's self-control and concentration, helping them complete tasks and achieve goals.

[0063] The processing flow will be explained below.

[0064] Specific processing flow of the program

[0065] Data Collection Phase

[0066] Step 1:

[0067] User

[0068] A user installs a task management app and records the start and end times of a task. For example, they open the "Task Tracker" app and press the start button for the task.

[0069] Step 2:

[0070] Terminal

[0071] The device saves the data recorded by the task management app in its storage, and also saves the end time when the task is completed.

[0072] Step 3:

[0073] Terminal

[0074] The smartwatch periodically records the user's biometric data, such as heart rate, for example, every five minutes and stores it on the device.

[0075] Step 4:

[0076] User

[0077] After completing the training, users provide subjective feedback by completing an in-app survey, answering questions such as, "How focused were you on this task?"

[0078] Data Transfer Phase

[0079] Step 5:

[0080] Terminal

[0081] The collected behavioral data, biometric data, and feedback data package is sent to a server, for example, by converting the collected data into JSON format and uploading it to the server via the internet.

[0082] Data analysis phase

[0083] Step 6:

[0084] server

[0085] Analyzes incoming data, cleansing and normalizing it, and handling outliers and missing data to improve analysis accuracy.

[0086] Step 7:

[0087] server

[0088] The system cross-references the user's heart rate data with the time spent performing a task to detect periods of peak concentration. For example, it identifies periods of sustained low heart rate as "periods of high concentration."

[0089] Step 8:

[0090] server

[0091] The system identifies the user's self-control and concentration patterns and stores them in a database. The user's characteristics are then reflected in a model and used to generate the next plan.

[0092] Training plan generation phase

[0093] Step 9:

[0094] server

[0095] Based on the analysis results, it generates a personalized training plan, for example, suggesting the user to use the Pomodoro technique and setting a cycle of 25 minutes of work and 5 minutes of rest.

[0096] Step 10:

[0097] server

[0098] The generated training plan is sent to the user's device. For example, the generated plan is sent in JSON format and displayed in an app on the device.

[0099] Feedback Phase

[0100] Step 11:

[0101] Terminal

[0102] The device will then notify the user of the training plan and provide instructions, such as "Work hard and stay focused for the next 25 minutes."

[0103] Step 12:

[0104] User

[0105] The user performs tasks according to the training plan and takes a break at the designated time, for example, following the instructions on the device to take a 5-minute break and then resume work.

[0106] Step 13:

[0107] Terminal

[0108] The device monitors the user's progress and sends data to the server in real time, for example, sending the data again when the task is completed.

[0109] Step 14:

[0110] server

[0111] The server dynamically adjusts the training plan based on the user's progress data and feedback. For example, if the user's training plan is shorter than the planned one for the following week, the server receives feedback that the user was unable to maintain concentration.

[0112] Step 15:

[0113] server

[0114] The server generates a weekly progress report and sends it to the user, telling them, for example, "Your peak concentration time this week is between 2:00 and 3:00 PM."

[0115] Step 16:

[0116] User

[0117] Users can check their progress reports and use them to plan their training for the next week, for example by scheduling tasks for the next week based on their peak concentration times.

[0118] Through these processing steps, the system provides effective support to enhance the user's self-control and concentration.

[0119] Example 1

[0120] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0121] In recent years, there has been a growing demand for improving concentration and self-control, and many people are looking for effective training methods. However, it is difficult for a generic training plan to provide optimal improvement measures for each user, and personalized training plans based on individual biometric and behavioral data are needed. Therefore, a method is needed to identify the user's peak concentration times and provide an optimized training plan in real time based on individual feedback.

[0122] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0123] In this invention, the server includes means for collecting user behavioral data, means for collecting user biometric data, means for collecting user-provided feedback, means for transmitting the collected data to the server, means for analyzing the received data and identifying the user's self-control and concentration patterns, means for generating a training plan utilizing the Pomodoro Technique, means for transmitting the generated training plan to the user's device, and means for providing real-time feedback to the user. This allows for the generation of an optimal training plan for each user and the provision of real-time feedback. Furthermore, the system detects the user's peak concentration times and dynamically readjusts the plan based on the detected times, and by linking with a calendar application, the system can efficiently improve the user's self-control and concentration.

[0124] "User behavioral data" refers to information about a user's activities, such as the start time, end time, and type of task performed by the user.

[0125] "User biometric data" means information about a user's physiological state or physical activity, such as heart rate, electrodermal activity, brain waves, and number of steps taken.

[0126] "Feedback" refers to information such as ratings and comments provided by users regarding the effectiveness and satisfaction of their training plans.

[0127] "Server" is a computer system for analyzing collected data, generating training plans, and providing feedback to users.

[0128] A "terminal" is a device that collects user behavioral data and biometric data and transmits it to a server, and includes smartphones and smartwatches.

[0129] "Data analysis" is the process of analyzing collected behavioral and biometric data using statistical methods and time series analysis to identify patterns of the user's concentration and self-control.

[0130] A "personalized training plan" is a training plan that is individually optimized based on the user's behavioral data, biometric data, and feedback.

[0131] "Real-time feedback" refers to advice and notifications provided to users instantly based on their training plan and analysis results.

[0132] The Pomodoro Technique is a time management technique that promotes efficient work by concentrating on a task for a set period of time and then taking a short break.

[0133] "Peak concentration times" are specific times when a user is determined to be able to concentrate and work most efficiently.

[0134] "Calendar application integration" is a mechanism that automatically integrates the generated training plan into the user's schedule management system.

[0135] "Cross-referencing" is a technique for cross-referencing different types of data (for example, behavioral data and biometric data) and analyzing their relationships.

[0136] "Time series analysis" is a statistical method for analyzing fluctuations and trends in data over time.

[0137] System Overview

[0138] This invention is a support system for improving a user's self-control and concentration. The system uses the user's behavioral data, biometric data, and feedback to generate a personalized training plan and provides feedback to the user in real time.

[0139] Server Features

[0140] The server provides the following functions:

[0141] 1. Receiving data:

[0142] The server receives user behavioral data, biometric data, and feedback in real time from devices (e.g., smartphones and smartwatches) via the Internet.

[0143] 2. Data Analysis:

[0144] The server analyzes the received data using data analysis libraries such as Python and R. For example, it uses Python's Pandas or NumPy to format the data and perform time series analysis.

[0145] 3. Generate a training plan:

[0146] The server generates a personalized training plan using the Pomodoro Technique, which can then be integrated into the user's schedule using the Google Calendar API.

[0147] 4. Real-time feedback:

[0148] The server dynamically readjusts the training plan based on the user's performance and feedback, providing real-time feedback.

[0149] Device Features

[0150] The device provides the following features:

[0151] 1. Data collection and transmission:

[0152] A device (such as a smartwatch or smartphone) collects user behavioral and biometric data and sends it to a server at regular intervals. For example, a smartwatch measures a user's heart rate every second and sends the data to a server via an app on the smartphone.

[0153] 2. Receive and view training plans:

[0154] The device receives the training plan sent from the server and notifies the user, for example by displaying a push notification saying, "Please concentrate on your work for the next 20 minutes."

[0155] 3. Enter your feedback:

[0156] The device receives feedback from the user and sends it to the server. For example, after completing a training plan, the device collects information that the user enters in response to the question, "How effective was the training?"

[0157] User Roles

[0158] Users use the system by following the steps below.

[0159] 1. Implement your training plan:

[0160] Users complete tasks according to a training plan provided by the device, for example, starting the task at a specified time and taking regular breaks according to the Pomodoro technique.

[0161] 2. Providing Feedback:

[0162] Users provide feedback on the effectiveness of their training plan, for example by completing a survey about their sense of accomplishment and attention span.

[0163] Specific examples

[0164] Example 1:

[0165] To discover the best times of day during an eight-hour workday, users wear a smartwatch and collect heart rate data and data from a task management app. The device sends this data to a server at specific intervals. The server analyzes the data and determines that peak concentration occurs between 2:00 and 3:00 PM. The server then generates a training plan for tasks requiring the most concentration during this time period. The device notifies the user of this plan, and the user completes the tasks according to the device's notifications. After completing the training, the user submits feedback regarding satisfaction and sense of accomplishment, and the server updates the training plan based on the new data.

[0166] Example prompt sentence:

[0167] "Measure your heart rate every second with a smartwatch, and use Python to send the data from a task management app to a server every five minutes for analysis. Based on the analysis results, create a training plan for your most important tasks between 2:00 and 3:00 PM, and notify the plan on your smartphone."

[0168] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0169] Step 1: Collect data

[0170] Specific behavior:

[0171] The device collects user behavioral and biometric data, such as heart rate, steps, and electrodermal activity, via a smartwatch, and records the start and end times of tasks via a task management app.

[0172] input:

[0173] Data from users' smartwatches and task management apps.

[0174] Data processing / data calculation:

[0175] It collects heart rate data from a smartwatch and task start and end times from a task management app, and formats them on the device.

[0176] output:

[0177] A set of collected biometric and behavioral data.

[0178] Step 2: Sending data

[0179] Specific behavior:

[0180] The device sends the collected data to the server at regular intervals (for example, every 5 minutes). For example, the data collected by a smartwatch is sent to a smartphone, which then uploads the data to the server.

[0181] input:

[0182] Biometric and behavioral data collected.

[0183] Data processing / data calculation:

[0184] The data is batched and encoded into a format that can be sent.

[0185] output:

[0186] The data batch sent to the server.

[0187] Step 3: Receiving the data

[0188] Specific behavior:

[0189] The server receives the data sent from the terminal in real time via the Internet.

[0190] input:

[0191] The data batch sent from the device.

[0192] Data processing / data calculation:

[0193] The received data is then properly organized and saved for storage in a database.

[0194] output:

[0195] Biometric and behavioral data stored in a database.

[0196] Step 4: Analyze the data

[0197] Specific behavior:

[0198] The server analyzes the received data using data analysis tools such as Python and R. Specifically, it uses time series analysis to examine the relationship between heart rate data and task time, and identifies periods of peak concentration.

[0199] input:

[0200] Biometric and behavioral data stored in a database.

[0201] Data processing / data calculation:

[0202] Extract data from the database, preprocess it using Pandas and NumPy, and perform time series analysis and cross-referencing. For example, analyze trends in heart rate data and task execution times to identify times when users are most focused.

[0203] output:

[0204] The user's peak concentration times and self-control patterns.

[0205] Step 5: Generate a training plan

[0206] Specific behavior:

[0207] The server then generates a personalized training plan based on the analysis results, using the Pomodoro technique to specifically create a plan that alternates between 20 minutes of focused work and 5 minutes of rest.

[0208] input:

[0209] Analyzed peak times of concentration and patterns of self-control.

[0210] Data processing / data calculation:

[0211] Based on the analysis results, a Python script is run to generate a training plan consisting of optimal work and rest cycles.

[0212] output:

[0213] The generated training plan.

[0214] Step 6: Submit your training plan

[0215] Specific behavior:

[0216] The server then sends the generated training plan to the user's device, where it can be incorporated into the user's schedule using, for example, the Google Calendar API.

[0217] input:

[0218] Training plan.

[0219] Data processing / data calculation:

[0220] Format your training plan into a sendable format and send the data.

[0221] output:

[0222] Training plans sent to the user's device.

[0223] Step 7: View and follow your training plan

[0224] Specific behavior:

[0225] The device receives the training plan sent from the server and notifies the user. The user checks the notification and performs the tasks according to the plan. For example, a notification saying "Please concentrate on your work for the next 20 minutes" is displayed, and the user starts working according to the instructions.

[0226] input:

[0227] The training plan sent to your device.

[0228] Data processing / data calculation:

[0229] The received training plan will be displayed and notified to the user within the app.

[0230] output:

[0231] Instructions for the training plan that the user follows.

[0232] Step 8: Gather feedback

[0233] Specific behavior:

[0234] After completing the training plan, the device collects feedback from the user, for example by asking them to answer a survey question such as, "Please rate the effectiveness of the training."

[0235] input:

[0236] User feedback.

[0237] Data processing / data calculation:

[0238] The feedback data is organized and formatted for transmission to the server.

[0239] output:

[0240] Feedback data sent to the server.

[0241] Step 9: Re-adjust your training plan

[0242] Specific behavior:

[0243] The server analyzes the user's feedback and dynamically readjusts the training plan as needed, which is then sent back to the user's device.

[0244] input:

[0245] User feedback and initial training plans.

[0246] Data processing / data calculation:

[0247] Analyze the feedback data and make any necessary adjustments to generate a new training plan.

[0248] output:

[0249] A reworked training plan.

[0250] (Application example 1)

[0251] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0252] Conventional factory robots did not have a self-monitoring and feedback system to maximize their operating efficiency, and required human managers to monitor and manually adjust them one by one. This made it difficult to formulate efficient work schedules and improve efficiency in real time, which could result in a decline in overall factory productivity. This created a need for a system that could autonomously optimize the operating efficiency of factory robots and provide feedback in real time.

[0253] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0254] In this invention, the server includes means for analyzing the collected data to analyze the efficiency and error occurrence tendency of the robot, means for generating a training plan dedicated to the robot based on the analysis results, and means for transmitting the generated training plan to the robot, thereby enabling the robot's operating efficiency to be autonomously optimized and feedback to be provided in real time.

[0255] The "means for collecting user behavior data" refers to a device or method for collecting data on the user's behavior, such as the start time, end time, and work content of the user's work.

[0256] A "means for collecting user biometric data" is a device or method for collecting biometric information of a user, such as heart rate, brain waves, or electrodermal activity.

[0257] The "means for collecting user-provided feedback" refers to a device or method for collecting feedback information such as satisfaction and a sense of accomplishment from users.

[0258] The "means for transmitting collected data to a server" refers to a device or method for transmitting collected behavioral data, biometric data, and feedback information to a server.

[0259] "Means for analyzing the received data and identifying patterns of user self-control and concentration" refers to a device or method that analyzes the data sent to the server and identifies characteristics of the user's behavior and concentration.

[0260] The "means for generating a personalized training plan based on the identified patterns" refers to a device or method that creates a training plan specific to the user based on the analysis results.

[0261] "Means for collecting robot operation data and sensor information" refers to a device or method for collecting the robot's work history and sensor data (e.g., motor temperature, battery status).

[0262] "Means for transmitting collected data to a server and analyzing the efficiency of the robot and the tendency for errors to occur" refers to a device or method for transmitting collected information to a server and analyzing the operating efficiency of the robot and the tendency for errors to occur.

[0263] The "means for generating a training plan dedicated to a robot based on the analysis results" refers to a device or method for creating a work schedule or maintenance plan to maximize the efficiency of a robot based on the analysis results.

[0264] The "means for transmitting the generated training plan to the robot" is a device or method for transmitting the generated training plan to the robot.

[0265] The "means for providing real-time feedback to a robot" refers to a device or method that provides optimization instructions or advice for improving efficiency in real time according to the working status of the robot.

[0266] In the system for implementing the present invention, a server, a terminal, and a user work together as follows.

[0267] server

[0268] 1. Data collection

[0269] The server receives the user's behavioral data, biometric data, and feedback information in real time. Specifically, when the user records the start and end times of a task, that data is sent from the device to the server. Biometric data, such as heart rate, brain waves, and electrodermal activity, are collected from the wearable device.

[0270] 2. Data Analysis

[0271] The server analyzes the user's self-control and concentration patterns based on the collected data, for example by cross-referencing behavioral data with heart rate to identify times when concentration is at its best.

[0272] 3. Generate a training plan

[0273] The server then generates a personalized training plan based on the analysis results. It offers plans for task management, attention training, and stress management using techniques such as the Pomodoro Technique. For example, it suggests a plan in which the user alternates between 20 minutes of focused work and 5 minutes of rest.

[0274] 4. Providing Feedback

[0275] The server updates the training plan based on the user's progress and feedback, providing real-time feedback, such as "Your peak concentration is between 2:00 and 3:00 p.m. Try using this time slot again next week."

[0276] Terminal

[0277] 1. Data recording and transmission

[0278] The device records the user's behavioral data and biometric data and periodically transmits it to a server. For example, heart rate data acquired by a smartwatch is stored on the device and then transmitted to a server.

[0279] 2. Receiving and viewing training plans

[0280] The device receives the training plan sent from the server and notifies the user. For example, a notification such as "Please concentrate on your work for the next 25 minutes" will appear on the smartphone.

[0281] 3. Displaying and Carrying Out Instructions

[0282] The device will then instruct the user to start tasks and take rests according to the instructions in the training plan. For example, when it's time to take a break, it will notify the user by saying, "You have 5 minutes left to take a break."

[0283] User

[0284] 1. Implement a training plan

[0285] The user performs tasks according to the training plan provided by the device, for example, concentrating on tasks at designated times and taking appropriate breaks according to notifications.

[0286] 2. Providing Feedback

[0287] Users can send feedback about the effectiveness of the training plan to the server via their devices, for example by answering a questionnaire about their sense of accomplishment and satisfaction with the task.

[0288] 3. Self-monitoring

[0289] Users can use the device to self-monitor and check their own performance based on the data, for example by viewing a graph of their daily concentration fluctuations.

[0290] Specific examples

[0291] Example: A robot's operational data and sensor information are collected and sent to a server. The server analyzes the robot's operational efficiency and error trends, and generates and sends a training plan based on the results. For example, the server provides the robot with feedback such as, "Avoid heavy workloads for the next 30 minutes and perform inspections."

[0292] Prompt Sentence Examples

[0293] "A system that analyzes the operational data of robots in factories and generates optimal work schedules to maximize efficiency."

[0294] "How to generate training plans to improve efficiency in real time based on data collected by a robot"

[0295] This system can optimize the production efficiency and concentration of users and robots in real time, improving their self-control during actual work and tasks.

[0296] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0297] Step 1:

[0298] Data collection

[0299] The user uses a device (e.g., smartphone or smartwatch) to record the start and end times of a task and collect biometric data (e.g., heart rate, electrodermal activity). The device then sends this data to a server. The input is task data and biometric data, and the output is data sent to the server.

[0300] Step 2:

[0301] Receiving data

[0302] The server receives behavioral data and biometric data sent from the device in real time. The input is the data sent from the device, and the output is the storage of the received data. The server stores this data in a database.

[0303] Step 3:

[0304] Data analysis

[0305] The server analyzes the received data to identify patterns of the user's self-control and concentration. For example, it analyzes heart rate fluctuations and task completion times to identify times when concentration is at its peak. The input is the stored data, and the output is the analysis results. Statistical analysis and machine learning models are used to process the data.

[0306] Step 4:

[0307] Generate a training plan

[0308] The server generates a personalized training plan for the user based on the analysis results. For example, it suggests 20 minutes of focused work followed by a 5-minute break based on the Pomodoro technique. The input is the analysis results, and the output is the training plan. A generative AI model is used to generate a personalized plan.

[0309] Step 5:

[0310] Submit a training plan

[0311] The server sends the generated training plan to the device. The input is the training plan, and the output is the transmission of the plan to the device. Feedback messages are also included.

[0312] Step 6:

[0313] View your training plan

[0314] The device displays the received training plan to the user. For example, it may notify the user, "Please concentrate on your work for the next 25 minutes." The input is the plan from the server, and the output is the notification to the user.

[0315] Step 7:

[0316] Executing a task

[0317] The user performs tasks according to the training plan provided by the device. The input is instructions from the device, and the output is the completion of the task. For example, the user can concentrate on the work at a specified time and take appropriate breaks according to notifications.

[0318] Step 8:

[0319] Providing Feedback

[0320] The user sends feedback about the effectiveness of the training plan to the server via their device. The input is the user's feedback, and the output is the transmission of feedback to the server. This includes a questionnaire about the sense of accomplishment and satisfaction with the task.

[0321] Step 9:

[0322] Analyzing feedback and updating plans

[0323] The server analyzes user feedback and dynamically readjusts the training plan. The input is user feedback and the output is an updated training plan. The server regenerates the personalized plan and resubmits it if necessary.

[0324] This series of steps optimizes the self-control and concentration of users and robots in real time, improving production efficiency.

[0325] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0326] System Overview

[0327] This invention combines a support system for enhancing an individual's self-control and maximizing concentration with an emotional engine. The system utilizes the user's behavioral data, biometric data, feedback, and even emotional state, and generates a personalized training plan based on this data and provides the user with real-time feedback.

[0328] server

[0329] 1. Data Collection:

[0330] The server receives the user's behavioral data, biometric data, feedback, and emotional data in real time. For example, if a user is using an emotion recognition application, emotional data from the application will be sent to the server via the device. This data includes behavioral data (such as the start and end times of a task) and biometric data (such as heart rate and brain waves), as well as the user's current emotional state.

[0331] 2. Data Analysis:

[0332] The server analyzes the received data to identify patterns of the user's self-control, focus, and emotions. For example, it cross-references heart rate data with emotional data to identify times when they are most focused or stressed.

[0333] 3. Role of the Emotion Engine:

[0334] The emotion engine analyzes the user's facial expressions, tone of voice, input text, etc. to identify the user's emotional state in real time, for example, it can identify different emotional states such as whether the user is stressed or relaxed.

[0335] 4. Generate a training plan:

[0336] The server then generates a personalized training plan based on the analysis results, taking into account the user's emotional state and offering plans incorporating the Pomodoro technique, specific breathing exercises, and relaxation techniques. For example, if the user is feeling stressed, the server will suggest a plan that includes a short period of relaxation.

[0337] 5. Providing Feedback:

[0338] The server dynamically updates the training plan based on the user's progress, feedback, and emotional data, providing real-time feedback, such as "Taking five minutes of relaxation time into account, given your current emotional state."

[0339] Terminal

[0340] 1. Data recording and transmission:

[0341] The device records the user's behavioral data, biometric data, feedback, and emotional data and periodically transmits them to a server. For example, a smartwatch records the user's heart rate and emotional state and transmits the data to a server.

[0342] 2. Receive and view training plans:

[0343] The device notifies the user of the training plan sent from the server. For example, a notification such as "Work with focus for the next 25 minutes. You appear to be in a stressful emotional state, so take 5 minutes of relaxation afterwards" will appear on the smartphone.

[0344] 3. Displaying and Carrying Out Instructions:

[0345] The device will prompt the user to start tasks and take rests according to the instructions in the training plan. For example, when it's time for a break, it will notify the user, "You have 5 minutes left to take a break." If emotions are negative, it will give instructions such as, "Take a deep breath and relax."

[0346] User

[0347] 1. Implement your training plan:

[0348] The user performs tasks according to the training plan provided by the device, for example, concentrating on a task at a specified time and taking appropriate breaks or relaxation according to notifications.

[0349] 2. Providing Feedback:

[0350] Users send feedback about the effectiveness of the training plan to the server via their device, for example by answering a questionnaire about their sense of accomplishment, satisfaction, and emotional fluctuations.

[0351] 3. Self-monitoring:

[0352] Users can use the device to self-monitor and check their performance and emotional state based on the data, for example by viewing graphs of their daily concentration and emotional fluctuations.

[0353] Through these processes, the system effectively supports users in self-control, concentration and emotional management.

[0354] The processing flow will be explained below.

[0355] Specific processing flow of the program

[0356] Data Collection Phase

[0357] Step 1:

[0358] User

[0359] A user installs a task management app and records the start and end times of a task. For example, they open the "Task Tracker" app and press the start button for the task.

[0360] Step 2:

[0361] Terminal

[0362] The device saves the data recorded by the task management app in its storage, and also saves the end time when the task is completed.

[0363] Step 3:

[0364] Terminal

[0365] The smartwatch periodically records the user's biometric data, such as heart rate, for example, every five minutes and stores it on the device.

[0366] Step 4:

[0367] Terminal

[0368] The emotion recognition app analyzes the user's facial expressions and voice to collect real-time emotional data, which is also stored on the device.

[0369] Step 5:

[0370] User

[0371] After completing the training, users complete an in-app survey to provide subjective feedback, such as, "How focused were you on this task?"

[0372] Data Transfer Phase

[0373] Step 6:

[0374] Terminal

[0375] The collected behavioral data, biometric data, emotional data, and feedback data packages are sent to a server, for example, by converting the collected data into JSON format and uploading it to the server via the Internet.

[0376] Data analysis phase

[0377] Step 7:

[0378] server

[0379] Analyzes incoming data, cleansing and normalizing it, and handling outliers and missing data to improve analysis accuracy.

[0380] Step 8:

[0381] server

[0382] By cross-referencing the user's heart rate data with their emotional data, the system identifies times when they are most likely to concentrate or feel stressed. For example, it identifies "times when the heart rate is stable and positive emotions are high" as times when they are most likely to concentrate.

[0383] Step 9:

[0384] server

[0385] It identifies patterns in the user's self-control, focus, and emotions, and stores the results in a database. For example, the user's characteristics can be reflected in a model and used to generate a plan for the next time.

[0386] Training plan generation phase

[0387] Step 10:

[0388] server

[0389] Based on the analysis results, the system generates a personalized training plan that takes into account the user's emotional state. For example, it suggests the user follow the Pomodoro technique (25 minutes of work + 5 minutes of rest) and also provides a plan that includes relaxation.

[0390] Step 11:

[0391] server

[0392] The generated training plan is sent to the user's device. For example, the generated plan is sent in JSON format and displayed in an app on the device.

[0393] Feedback Phase

[0394] Step 12:

[0395] Terminal

[0396] The device will then notify the user of the training plan and display instructions, such as "Work with focus for the next 25 minutes, after which you will experience emotional stress, and then take 5 minutes of relaxation."

[0397] Step 13:

[0398] User

[0399] The user performs tasks according to the training plan, taking breaks and relaxation at appropriate times, for example, following the instructions on the device to take a five-minute break and then resume work.

[0400] Step 14:

[0401] Terminal

[0402] The device monitors the user's progress and real-time emotional data and continuously transmits the data to the server, for example, after completing a task or when the user's emotional state changes.

[0403] Step 15:

[0404] server

[0405] The server dynamically readjusts the training plan based on the user's progress data and feedback. For example, if the user tells the server that they were losing focus, the server will create a plan that includes shorter sessions and more frequent breaks.

[0406] Step 16:

[0407] server

[0408] The server generates a weekly progress report and sends it to the user, notifying them, for example, that their peak concentration time this week is between 2:00 and 3:00 PM. It also provides advice such as, "Based on your emotional data, it would be beneficial to spend more time relaxing."

[0409] Step 17:

[0410] User

[0411] Users can check their progress reports and use them to plan their training for the next week, for example by scheduling tasks for the next week based on their peak concentration times.

[0412] Through these processing steps, the system effectively supports the user's self-control, concentration and emotional management.

[0413] Example 2

[0414] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0415] A user's self-control, concentration, and emotional management need to be appropriately supported based on their individual state and situation. However, conventional systems collect users' biometric and behavioral data, but do not consider their emotional data, making it difficult to provide training plans tailored to individual emotional states. Therefore, a system that provides more personalized support by taking into account the user's emotional state is needed.

[0416] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0417] In this invention, the server includes means for collecting user behavioral data, means for collecting user biometric data, means for collecting user-provided feedback, means for collecting user emotional data, means for transmitting the collected data to the server, means for analyzing the received data and identifying patterns of the user's self-control, concentration, and emotions, means for generating a personalized training plan based on the identified patterns, means for transmitting the generated training plan to the user's device, and means for providing real-time feedback to the user, thereby making it possible to provide a training plan that also takes the user's emotional state into consideration.

[0418] "User behavioral data" refers to data that records the specific movements and actions of a user in their daily life and work.

[0419] "User's biometric data" refers to data that indicates the user's physical condition, including physiological indicators such as heart rate and brain waves.

[0420] "Feedback" is data that records opinions, ratings, and reactions provided by users.

[0421] "Emotional data" refers to data that indicates a user's emotional state, and includes information such as facial expressions, voice tone, and text input obtained using emotion recognition technology.

[0422] A "server" is a central processing unit that receives data sent from the user's device and performs analysis, generates training plans, etc.

[0423] A "terminal" is a device that is directly operated by a user, including smartphones and smartwatches.

[0424] "Self-control" refers to the user's ability to appropriately manage their own behavior and emotions and maintain a desired state.

[0425] "Concentration" refers to a user's ability to focus their mind on a particular task or activity.

[0426] "Training Plan" means a personalized plan or instruction to improve a User's self-control and focus.

[0427] "Real-time feedback" refers to instant feedback and advice based on the actions a user takes and the data they provide.

[0428] "Analysis" refers to the data processing process that uses collected data to identify patterns of user behavior and emotions.

[0429] An "emotion engine" refers to software or hardware processing that analyzes a user's facial expressions, voice tone, text, etc. to identify their emotional state.

[0430] "Personalization" refers to customizing training plans and feedback to suit each individual user's specific needs and circumstances.

[0431] "Peak hours" refer to times when users are most focused and productive.

[0432] "Stressful times" refer to the times when users are most likely to feel stressed.

[0433] System Overview

[0434] This invention combines an emotional engine with a support system for enhancing an individual's self-control and maximizing concentration. The system utilizes the user's behavioral data, biometric data, feedback, and even emotional data to generate a personalized training plan and provide real-time feedback to the user.

[0435] Hardware and Software

[0436] Hardware: smartwatches (e.g., devices for measuring heart rate), smartphones (e.g., used as a user interface)

[0437] Software: emotion recognition apps (e.g., tools for analyzing user emotions in real time), self-control support apps (e.g., apps that provide notifications and collect feedback)

[0438] System program processing flow

[0439] server

[0440] 1. Data Collection:

[0441] The server receives users' behavioral data, biometric data, feedback, and emotional data in real time. For example, a smartwatch records heart rate and activity data and sends the data to the server. An emotion recognition app also collects users' emotional data and sends it to the server.

[0442] 2. Data Analysis:

[0443] The server analyzes the received data to identify patterns of self-control, concentration, and emotions of the user. Specifically, it cross-references heart rate data with emotional data to identify times of high concentration and times of stress.

[0444] 3. Role of the Emotion Engine:

[0445] The emotion engine analyzes the user's facial expressions, voice tone, and input text to identify the user's emotional state in real time. For example, if the user inputs "I'm very tired today," the engine will identify fatigue from the text. The facial recognition camera also detects the user's smile and determines that the user is relaxed.

[0446] 4. Generate a training plan:

[0447] The server then generates a personalized training plan based on the analysis results. For example, if a user's concentration decreases and stress increases at 3 p.m., the server will suggest a plan that includes relaxation time before and after 3 p.m.

[0448] 5. Providing Feedback:

[0449] The server dynamically updates the training plan based on the user's progress, feedback, and emotional data, providing real-time feedback, such as notifications like "Take five minutes of relaxation time to consider your current emotional state."

[0450] Terminal

[0451] 1. Data recording and transmission:

[0452] The device records the user's behavioral data, biometric data, feedback, and emotional data and periodically transmits them to a server. For example, a smartwatch collects heart rate data every hour and transmits it to a server.

[0453] 2. Receive and view training plans:

[0454] The device receives the training plan sent from the server in real time and notifies the user, for example, a message such as "Work hard for the next 25 minutes, then take 5 minutes of relaxation" will be displayed on the smartphone.

[0455] 3. Displaying and Carrying Out Instructions:

[0456] The device will follow the instructions of the training plan and prompt the user to start a task or take a rest. For example, the smartwatch will notify the user that it's time to take a break in five minutes. If the user is feeling negative, it will display instructions such as "Take a deep breath and relax."

[0457] User

[0458] 1. Implement your training plan:

[0459] The device provides users with a training plan to help them accomplish tasks, such as working during designated periods of focus and then using breaks to practice relaxation.

[0460] 2. Providing Feedback:

[0461] Users can send feedback about the effectiveness of their training plan to a server via their device, for example by answering a questionnaire after completing a task, asking questions such as "Was your concentration high while working?" or "Did your stress level decrease?"

[0462] 3. Self-monitoring:

[0463] Users can use the device to self-monitor and check their own performance and emotional state based on the data. For example, the smartphone app can visualize daily fluctuations in concentration and emotions in graphs to check the effectiveness of their training.

[0464] Example prompt

[0465] 1. The user puts on the smartwatch

[0466] "Wear your smartwatch and enter your self-assessment at each moment of exercise and rest throughout the day."

[0467] 2. Applying the Pomodoro Technique

[0468] "You'll have 25 minutes of focused work time, after which you'll receive a notification to take a 5-minute break."

[0469] 3. Entering Emotion Data

[0470] "Enter your current mood on a scale of 1 to 5. The emotion recognition app will analyze your facial expressions and collect emotional data."

[0471] This allows the system to effectively support users' self-control, concentration and emotional management.

[0472] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0473] System processing steps

[0474] Step 1:

[0475] The server receives the user's behavioral data, biometric data, feedback, and emotional data in real time. Specifically, the device (smartwatch) records heart rate and activity data and sends the data to the server. The emotion recognition app also collects the user's emotional data and sends it to the server. The input data are behavioral data, biometric data, feedback, and emotional data, which are integrated and efficiently sent to the server. The output is the integrated data sent to the server.

[0476] Step 2:

[0477] The server analyzes the received data. Specifically, it cross-references the heart rate data with the emotional data to identify the user's concentration and stress patterns. The input data is the integrated data sent to the server. Data analysis algorithms are used to identify the user's peak concentration and stress times. The output is data that identifies the user's concentration and stress patterns.

[0478] Step 3:

[0479] The emotion engine on the server analyzes the user's facial expression, voice tone, input text, etc. to identify the user's emotional state in real time. The input data is facial expression data, voice tone data, and text data. The emotion recognition algorithm is used to analyze the user's emotional state in real time. The output is the identified emotional state data.

[0480] Step 4:

[0481] The server generates a personalized training plan based on the analysis results. For example, for a user whose concentration decreases and stress increases around 3:00 p.m., it suggests a plan that includes relaxation time around that time. The input data is analysis data on the user's concentration, stress, and emotional state. An algorithm is used to generate a plan that incorporates the Pomodoro technique, specific breathing techniques, etc. The output is a personalized training plan.

[0482] Step 5:

[0483] The server dynamically updates the training plan based on the user's progress, feedback, and emotional data, and provides real-time feedback. For example, it sends a notification such as, "Taking five minutes of relaxation time into account, given your current emotional state." The input data are the user's progress data, feedback data, and emotional data. The dynamic plan update algorithm generates real-time feedback. The output is the updated training plan and feedback.

[0484] Step 6:

[0485] The device records the user's behavioral data, biometric data, feedback, and emotional data and periodically transmits them to a server. Specifically, the smartwatch collects heart rate data every hour and transmits it to the server. The input data are the user's behavioral data, biometric data, feedback, and emotional data. A data collection and transmission algorithm is used. The output is the recorded data sent to the server.

[0486] Step 7:

[0487] The device receives the training plan sent from the server and notifies the user. For example, a message such as "Work concentratively for the next 25 minutes, then take 5 minutes of relaxation" is displayed on the smartphone. The input data is the training plan sent from the server. The notification function is used. The output is the notification displayed to the user.

[0488] Step 8:

[0489] The device prompts the user to start a task or take a rest according to the instructions in the training plan. For example, the smartwatch may send a notification saying, "Take a break in 5 minutes," or, if emotions are negative, it may display instructions such as, "Take a deep breath and relax." The input data are instructions based on the training plan. Task management and notification algorithms are used. The output is instructions displayed to the user.

[0490] Step 9:

[0491] The user performs tasks according to a training plan provided by the device. For example, the user works during a designated concentration period and then uses the subsequent break time to practice relaxation. The input data is the training plan provided by the device. A task execution algorithm is used. The output is the completed task.

[0492] Step 10:

[0493] The user sends feedback about the effectiveness of the training plan to the server via their device. For example, after completing a task, they answer a questionnaire such as "Was your concentration high while working?" or "Did your stress level decrease?" The input data is the user's feedback about the effectiveness of the training plan. A feedback collection algorithm is used. The output is the feedback sent to the server.

[0494] Step 11:

[0495] Users use the device to self-monitor and check their own performance and emotional state based on the data. For example, a smartphone app can visualize daily fluctuations in concentration and emotions in graphs to check the effectiveness of training. The input data is past behavioral data, biological data, and emotional data. A data visualization algorithm is used. The output is data visualized as a graph.

[0496] (Application example 2)

[0497] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0498] Many existing systems for improving self-control and concentration generate training plans based on biometric and behavioral data. However, few systems take the user's emotional state into account. Therefore, there is a need for a system that can grasp the user's stress and relaxation levels in real time and provide feedback and content recommendations that adapt to those conditions. Furthermore, there has yet to be a fully developed system that can comprehensively analyze this data and provide dynamically personalized plans.

[0499] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric data of the user, means for collecting the user's emotional state, and means for analyzing the received data and identifying the user's self-control, concentration, and emotional patterns. This makes it possible to recommend personalized training plans and content adapted to the user's stress and relaxation state based on the analysis results. This allows the user to receive support that allows them to maintain concentration more effectively and comfortably and manage their emotional state.

[0500] definition statement

[0501] "User behavior data" refers to information about user behavior, specifically data including viewing time, frequency of clicks and swipes, etc.

[0502] "User biometric data" refers to information about the user's physiological state, including, for example, heart rate and eye tracking data.

[0503] "Feedback provided by users" refers to information such as ratings, impressions, and survey responses provided by users to the system.

[0504] "User's emotional state" refers to information about the user's real-time emotions analyzed by the emotion engine, including stress levels, relaxed states, etc.

[0505] "Server" refers to the computer system that receives and analyzes data, generates personalized training plans and content recommendations, and transmits them to the User's device.

[0506] "Analysis" refers to the process of using collected user behavioral data, biometric data, feedback, and emotional state to identify patterns of self-control, focus, and emotions.

[0507] "Personalized training plan" refers to an individually optimized training plan generated based on data for each individual user.

[0508] "Content recommendation" refers to the act of recommending optimal content (videos, music, articles, etc.) based on the user's current state and patterns.

[0509] "Real-time feedback" refers to advice or instructions provided immediately in response to a user's actions or state.

[0510] "Dynamic readjustment" refers to the process of updating and changing training plans and content recommendations on the fly based on analysis results.

[0511] MODE FOR CARRYING OUT THE INVENTION

[0512] System Overview

[0513] This invention is a support system for improving an individual's self-control and concentration, and combines an emotion engine. The system utilizes the user's behavioral data, biometric data, feedback, and emotional state, and generates personalized training plans and content recommendations based on this data, providing real-time feedback to the user.

[0514] server

[0515] 1. Data Collection:

[0516] The server receives user behavioral data, biometric data, feedback, and emotional data in real time. For example, the server receives data sent from smartphones, wearable devices, etc. This data includes browsing time, click / swipe frequency, heart rate, facial expressions, voice tone, and input text.

[0517] 2. Data Analysis:

[0518] The server analyzes the received data to identify patterns of the user's self-control, concentration, and emotions. For example, it cross-references behavioral data with biometric data to identify times of high concentration and times of stress. The emotion engine analyzes facial expressions, voice tone, text, etc. to identify emotional states.

[0519] 3. Generating training plans and content recommendations:

[0520] The server generates personalized training plans and content recommendations based on the analysis results. Taking into account the user's emotional state, the server offers plans incorporating the Pomodoro technique, specific breathing exercises, and relaxation techniques. For users feeling stressed, the server recommends relaxation music and meditation videos.

[0521] 4. Providing Feedback:

[0522] The server dynamically updates training plans and content recommendations based on the user's progress, feedback, and emotional data, providing real-time feedback, such as "Taking five minutes of relaxation time into account, given your current emotional state."

[0523] Terminal

[0524] 1. Data recording and transmission:

[0525] The device records the user's behavioral data, biometric data, feedback, and emotional data and periodically transmits them to a server. For example, a smartphone records browsing data, and a smartwatch records heart rate and emotional state and transmits them to a server.

[0526] 2. Receive and view training plans and content recommendations:

[0527] The device will notify the user of training plans and content recommendations sent from the server, such as "Work hard for the next 25 minutes, then listen to 5 minutes of relaxation music."

[0528] 3. Displaying and Carrying Out Instructions:

[0529] The device will prompt the user to start tasks or take a rest according to the instructions in the training plan. For example, when it's time for a break, it will notify the user, "You have 5 minutes left to take a break," and if it detects negative emotions, it will instruct the user to "take a deep breath and relax."

[0530] User

[0531] 1. Implement your training plan:

[0532] The user performs tasks according to the training plan provided by the device, for example, concentrating on a task at a specified time and taking appropriate breaks or relaxation according to notifications.

[0533] 2. Providing Feedback:

[0534] Users can send feedback about the effectiveness of their training plan to the server via their device, for example by answering a questionnaire about their emotional state.

[0535] 3. Self-monitoring:

[0536] Users can use the device to self-monitor and check their performance and emotional state based on the data, for example by viewing graphs of their daily concentration and emotional fluctuations.

[0537] Hardware and software used

[0538] Hardware:

[0539] Smartphones, smartwatches, heart rate monitors, facial recognition cameras

[0540] software:

[0541] Python, emotion recognition API (Emotion API, etc.), data analysis library (Pandas, etc.)

[0542] Specific examples / prompt sentence examples

[0543] Examples:

[0544] If a user watches a documentary for 30 minutes and their heart rate increases and they click and swipe more, the app will determine that they are in a high-stress state and notify them, "It's time to take a short break. We recommend watching a relaxing meditation video next."

[0545] Example prompt for a generative AI model:

[0546] "Generate a Python program to recommend optimal content and break timing using user behavioral data, heart rate, biometric data, and real-time analyzed emotional state."

[0547] "If a user is in a high-stress state, design your app to recommend relaxing content and encourage them to take a break."

[0548] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0549] Program processing steps

[0550] Step 1: Collect data

[0551] The server collects user behavioral data, biometric data, feedback and emotional data in real time.

[0552] Input: Data sent from smartphones, smartwatches, heart rate monitors, and facial recognition cameras.

[0553] Data processing: All data is converted into a unified format and stored in a database.

[0554] Output: A consolidated user dataset.

[0555] Step 2: Analyze the data

[0556] The server analyzes the user's self-control, concentration and emotional patterns based on the collected data.

[0557] Input: The consolidated user dataset.

[0558] Data calculations: Cross-reference behavioral and biological data to identify periods of high concentration and stress. Analyze emotional states using an emotion engine.

[0559] Output: User's self-control, concentration, and emotional patterns.

[0560] Step 3: Generate training plans and content recommendations

[0561] The server generates personalized training plans and content recommendations based on the analysis results.

[0562] Input: The user's self-control, focus, and emotional patterns.

[0563] Data processing: Based on the user's identified patterns, training plans including the Pomodoro technique and breathing exercises, as well as content such as relaxation music and meditation videos are determined.

[0564] Output: Personalized training plans and recommended content.

[0565] Step 4: Submit your training plan and content recommendations

[0566] The server transmits the generated training plan and content recommendations to the user's terminal.

[0567] Input: Personalized training plans and recommended content.

[0568] Data processing: Convert the training plan and recommended content into notification format and send it to your device.

[0569] Output: The notification that appears on the user's device.

[0570] Step 5: Provide real-time feedback

[0571] The device notifies the user of the feedback sent from the server in real time.

[0572] Input: Notification data from the server.

[0573] Data processing: Display notification content in a format that is easy for users to understand.

[0574] Output: Real-time instructions and advice displayed on the user's device.

[0575] Step 6: User feedback

[0576] Users send feedback about training plans and content recommendations to the server via their devices.

[0577] Input: User ratings, comments, and survey responses.

[0578] Data processing: The feedback data is converted into a format that can be sent to the server.

[0579] Output: The feedback data received by the server.

[0580] Step 7: Dynamically adjust training plans and content recommendations

[0581] The server dynamically readjusts training plans and content recommendations based on user feedback and progress data.

[0582] Input: Feedback data, progress data.

[0583] Data Calculation: Analyzes feedback and progress data to update training plans and content recommendations.

[0584] Output: Dynamically adjusted training plans and recommended content.

[0585] Step 8: Provide self-monitoring data

[0586] The device provides the user with self-monitoring data.

[0587] Input: Analysis results of user data sent from the server.

[0588] Data processing: Converting data into graphs and charts that are easy for users to understand.

[0589] Output: Self-monitoring data displayed on the terminal.

[0590] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0591] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0592] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0593] [Second embodiment]

[0594] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0595] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0596] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0597] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0598] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0599] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0600] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0601] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0602] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0603] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0604] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0605] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0606] System Overview

[0607] This invention is a support system for enhancing an individual's self-control and maximizing concentration. The system utilizes the user's behavioral data, biometric data, and feedback, and generates a personalized training plan based on this data and provides the user with real-time feedback.

[0608] server

[0609] 1. Data Collection:

[0610] The server receives the user's behavioral data, biometric data, and feedback in real time. For example, when a user uses a task management app to record the start and end times of a task, the data is sent from the device to the server. Biometric data includes heart rate, brain waves, and electrodermal activity. This data is collected from the wearable device.

[0611] 2. Data Analysis:

[0612] The server analyzes the collected data to identify patterns of self-control and concentration, for example by cross-referencing heart rate and task time to detect times when concentration is at its peak.

[0613] 3. Generate a training plan:

[0614] The server then generates a personalized training plan based on the analysis results. It uses techniques like the Pomodoro technique to provide plans for task management, attention training, and stress management. For example, it suggests a plan in which the user alternates between 20 minutes of focused work and 5 minutes of rest.

[0615] 4. Providing Feedback:

[0616] The server updates the training plan based on the user's progress and feedback, providing real-time feedback, such as "Your peak concentration time is between 2:00 and 3:00 p.m. Try using this time slot again next week."

[0617] Terminal

[0618] 1. Data recording and transmission:

[0619] The device records the user's behavioral data and biometric data and periodically transmits it to a server. For example, a smartwatch measures heart rate and stores the data on the device. The device then compiles the collected data and transmits it to the server.

[0620] 2. Receive and view training plans:

[0621] The device receives the training plan sent from the server and notifies the user. For example, a notification such as "Please concentrate on your work for the next 25 minutes" will be displayed on the smartphone.

[0622] 3. Displaying and Carrying Out Instructions:

[0623] The device will then prompt the user to start tasks and take rests according to the instructions in the training plan. For example, when it's time to take a break, it will notify the user, "You have 5 minutes left to take a break."

[0624] User

[0625] 1. Implement your training plan:

[0626] The user performs tasks according to the training plan provided by the device, for example, concentrating on the task at the specified time and taking appropriate breaks according to notifications.

[0627] 2. Providing Feedback:

[0628] Users can send feedback about the effectiveness of the training plan to the server via their devices, for example by answering a questionnaire about their sense of accomplishment and satisfaction with the task.

[0629] 3. Self-monitoring:

[0630] Users can use the device to self-monitor and check their own performance based on the data, for example by viewing a graph of their daily concentration fluctuations.

[0631] Through these processes, the system effectively enhances the user's self-control and concentration, helping them complete tasks and achieve goals.

[0632] The processing flow will be explained below.

[0633] Specific processing flow of the program

[0634] Data Collection Phase

[0635] Step 1:

[0636] User

[0637] A user installs a task management app and records the start and end times of a task. For example, they open the "Task Tracker" app and press the start button for the task.

[0638] Step 2:

[0639] Terminal

[0640] The device saves the data recorded by the task management app in its storage, and also saves the end time when the task is completed.

[0641] Step 3:

[0642] Terminal

[0643] The smartwatch periodically records the user's biometric data, such as heart rate, for example, every five minutes and stores it on the device.

[0644] Step 4:

[0645] User

[0646] After completing the training, users provide subjective feedback by completing an in-app survey, answering questions such as, "How focused were you on this task?"

[0647] Data Transfer Phase

[0648] Step 5:

[0649] Terminal

[0650] The collected behavioral data, biometric data, and feedback data package is sent to a server, for example, by converting the collected data into JSON format and uploading it to the server via the internet.

[0651] Data analysis phase

[0652] Step 6:

[0653] server

[0654] Analyzes incoming data, cleansing and normalizing it, and handling outliers and missing data to improve analysis accuracy.

[0655] Step 7:

[0656] server

[0657] The system cross-references the user's heart rate data with the time spent performing a task to detect periods of peak concentration. For example, it identifies periods of sustained low heart rate as "periods of high concentration."

[0658] Step 8:

[0659] server

[0660] The system identifies the user's self-control and concentration patterns and stores them in a database. The user's characteristics are then reflected in a model and used to generate the next plan.

[0661] Training plan generation phase

[0662] Step 9:

[0663] server

[0664] Based on the analysis results, it generates a personalized training plan, for example, suggesting the user to use the Pomodoro technique and setting a cycle of 25 minutes of work and 5 minutes of rest.

[0665] Step 10:

[0666] server

[0667] The generated training plan is sent to the user's device. For example, the generated plan is sent in JSON format and displayed in an app on the device.

[0668] Feedback Phase

[0669] Step 11:

[0670] Terminal

[0671] The device will then notify the user of the training plan and provide instructions, such as "Work hard and stay focused for the next 25 minutes."

[0672] Step 12:

[0673] User

[0674] The user performs tasks according to the training plan and takes a break at the designated time, for example, following the instructions on the device to take a 5-minute break and then resume work.

[0675] Step 13:

[0676] Terminal

[0677] The device monitors the user's progress and sends data to the server in real time, for example, sending the data again when the task is completed.

[0678] Step 14:

[0679] server

[0680] The server dynamically adjusts the training plan based on the user's progress data and feedback. For example, if the user's training plan is shorter than the planned one for the following week, the server receives feedback that the user was unable to maintain concentration.

[0681] Step 15:

[0682] server

[0683] The server generates a weekly progress report and sends it to the user, telling them, for example, "Your peak concentration time this week is between 2:00 and 3:00 PM."

[0684] Step 16:

[0685] User

[0686] Users can check their progress reports and use them to plan their training for the next week, for example by scheduling tasks for the next week based on their peak concentration times.

[0687] Through these processing steps, the system provides effective support to enhance the user's self-control and concentration.

[0688] Example 1

[0689] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0690] In recent years, there has been a growing demand for improving concentration and self-control, and many people are looking for effective training methods. However, it is difficult for a generic training plan to provide optimal improvement measures for each user, and personalized training plans based on individual biometric and behavioral data are needed. Therefore, a method is needed to identify the user's peak concentration times and provide an optimized training plan in real time based on individual feedback.

[0691] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0692] In this invention, the server includes means for collecting user behavioral data, means for collecting user biometric data, means for collecting user-provided feedback, means for transmitting the collected data to the server, means for analyzing the received data and identifying the user's self-control and concentration patterns, means for generating a training plan utilizing the Pomodoro Technique, means for transmitting the generated training plan to the user's device, and means for providing real-time feedback to the user. This allows for the generation of an optimal training plan for each user and the provision of real-time feedback. Furthermore, the system detects the user's peak concentration times and dynamically readjusts the plan based on the detected times, and by linking with a calendar application, the system can efficiently improve the user's self-control and concentration.

[0693] "User behavioral data" refers to information about a user's activities, such as the start time, end time, and type of task performed by the user.

[0694] "User biometric data" means information about a user's physiological state or physical activity, such as heart rate, electrodermal activity, brain waves, and number of steps taken.

[0695] "Feedback" refers to information such as ratings and comments provided by users regarding the effectiveness and satisfaction of their training plans.

[0696] "Server" is a computer system for analyzing collected data, generating training plans, and providing feedback to users.

[0697] A "terminal" is a device that collects user behavioral data and biometric data and transmits it to a server, and includes smartphones and smartwatches.

[0698] "Data analysis" is the process of analyzing collected behavioral and biometric data using statistical methods and time series analysis to identify patterns of the user's concentration and self-control.

[0699] A "personalized training plan" is a training plan that is individually optimized based on the user's behavioral data, biometric data, and feedback.

[0700] "Real-time feedback" refers to advice and notifications provided to users instantly based on their training plan and analysis results.

[0701] The Pomodoro Technique is a time management technique that promotes efficient work by concentrating on a task for a set period of time and then taking a short break.

[0702] "Peak concentration times" are specific times when a user is determined to be able to concentrate and work most efficiently.

[0703] "Calendar application integration" is a mechanism that automatically integrates the generated training plan into the user's schedule management system.

[0704] "Cross-referencing" is a technique for cross-referencing different types of data (for example, behavioral data and biometric data) and analyzing their relationships.

[0705] "Time series analysis" is a statistical method for analyzing fluctuations and trends in data over time.

[0706] System Overview

[0707] This invention is a support system for improving a user's self-control and concentration. The system uses the user's behavioral data, biometric data, and feedback to generate a personalized training plan and provides feedback to the user in real time.

[0708] Server Features

[0709] The server provides the following functions:

[0710] 1. Receiving data:

[0711] The server receives user behavioral data, biometric data, and feedback in real time from devices (e.g., smartphones and smartwatches) via the Internet.

[0712] 2. Data Analysis:

[0713] The server analyzes the received data using data analysis libraries such as Python and R. For example, it uses Python's Pandas or NumPy to format the data and perform time series analysis.

[0714] 3. Generate a training plan:

[0715] The server generates a personalized training plan using the Pomodoro Technique, which can then be integrated into the user's schedule using the Google Calendar API.

[0716] 4. Real-time feedback:

[0717] The server dynamically readjusts the training plan based on the user's performance and feedback, providing real-time feedback.

[0718] Device Features

[0719] The device provides the following features:

[0720] 1. Data collection and transmission:

[0721] A device (such as a smartwatch or smartphone) collects user behavioral and biometric data and sends it to a server at regular intervals. For example, a smartwatch measures a user's heart rate every second and sends the data to a server via an app on the smartphone.

[0722] 2. Receive and view training plans:

[0723] The device receives the training plan sent from the server and notifies the user, for example by displaying a push notification saying, "Please concentrate on your work for the next 20 minutes."

[0724] 3. Enter your feedback:

[0725] The device receives feedback from the user and sends it to the server. For example, after completing a training plan, the device collects information that the user enters in response to the question, "How effective was the training?"

[0726] User Roles

[0727] Users use the system by following the steps below.

[0728] 1. Implement your training plan:

[0729] Users complete tasks according to a training plan provided by the device, for example, starting the task at a specified time and taking regular breaks according to the Pomodoro technique.

[0730] 2. Providing Feedback:

[0731] Users provide feedback on the effectiveness of their training plan, for example by completing a survey about their sense of accomplishment and attention span.

[0732] Specific examples

[0733] Example 1:

[0734] To discover the best times of day during an eight-hour workday, users wear a smartwatch and collect heart rate data and data from a task management app. The device sends this data to a server at specific intervals. The server analyzes the data and determines that peak concentration occurs between 2:00 and 3:00 PM. The server then generates a training plan for tasks requiring the most concentration during this time period. The device notifies the user of this plan, and the user completes the tasks according to the device's notifications. After completing the training, the user submits feedback regarding satisfaction and sense of accomplishment, and the server updates the training plan based on the new data.

[0735] Example prompt sentence:

[0736] "Measure your heart rate every second with a smartwatch, and use Python to send the data from a task management app to a server every five minutes for analysis. Based on the analysis results, create a training plan for your most important tasks between 2:00 and 3:00 PM, and notify the plan on your smartphone."

[0737] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0738] Step 1: Collect data

[0739] Specific behavior:

[0740] The device collects user behavioral and biometric data, such as heart rate, steps, and electrodermal activity, via a smartwatch, and records the start and end times of tasks via a task management app.

[0741] input:

[0742] Data from users' smartwatches and task management apps.

[0743] Data processing / data calculation:

[0744] It collects heart rate data from a smartwatch and task start and end times from a task management app, and formats them on the device.

[0745] output:

[0746] A set of collected biometric and behavioral data.

[0747] Step 2: Sending data

[0748] Specific behavior:

[0749] The device sends the collected data to the server at regular intervals (for example, every 5 minutes). For example, the data collected by a smartwatch is sent to a smartphone, which then uploads the data to the server.

[0750] input:

[0751] Biometric and behavioral data collected.

[0752] Data processing / data calculation:

[0753] The data is batched and encoded into a format that can be sent.

[0754] output:

[0755] The data batch sent to the server.

[0756] Step 3: Receiving the data

[0757] Specific behavior:

[0758] The server receives the data sent from the terminal in real time via the Internet.

[0759] input:

[0760] The data batch sent from the device.

[0761] Data processing / data calculation:

[0762] The received data is then properly organized and saved for storage in a database.

[0763] output:

[0764] Biometric and behavioral data stored in a database.

[0765] Step 4: Analyze the data

[0766] Specific behavior:

[0767] The server analyzes the received data using data analysis tools such as Python and R. Specifically, it uses time series analysis to examine the relationship between heart rate data and task time, and identifies periods of peak concentration.

[0768] input:

[0769] Biometric and behavioral data stored in a database.

[0770] Data processing / data calculation:

[0771] Extract data from the database, preprocess it using Pandas and NumPy, and perform time series analysis and cross-referencing. For example, analyze trends in heart rate data and task execution times to identify times when users are most focused.

[0772] output:

[0773] The user's peak concentration times and self-control patterns.

[0774] Step 5: Generate a training plan

[0775] Specific behavior:

[0776] The server then generates a personalized training plan based on the analysis results, using the Pomodoro technique to specifically create a plan that alternates between 20 minutes of focused work and 5 minutes of rest.

[0777] input:

[0778] Analyzed peak times of concentration and patterns of self-control.

[0779] Data processing / data calculation:

[0780] Based on the analysis results, a Python script is run to generate a training plan consisting of optimal work and rest cycles.

[0781] output:

[0782] The generated training plan.

[0783] Step 6: Submit your training plan

[0784] Specific behavior:

[0785] The server then sends the generated training plan to the user's device, where it can be incorporated into the user's schedule using, for example, the Google Calendar API.

[0786] input:

[0787] Training plan.

[0788] Data processing / data calculation:

[0789] Format your training plan into a sendable format and send the data.

[0790] output:

[0791] Training plans sent to the user's device.

[0792] Step 7: View and follow your training plan

[0793] Specific behavior:

[0794] The device receives the training plan sent from the server and notifies the user. The user checks the notification and performs the tasks according to the plan. For example, a notification saying "Please concentrate on your work for the next 20 minutes" is displayed, and the user starts working according to the instructions.

[0795] input:

[0796] The training plan sent to your device.

[0797] Data processing / data calculation:

[0798] The received training plan will be displayed and notified to the user within the app.

[0799] output:

[0800] Instructions for the training plan that the user follows.

[0801] Step 8: Gather feedback

[0802] Specific behavior:

[0803] After completing the training plan, the device collects feedback from the user, for example by asking them to answer a survey question such as, "Please rate the effectiveness of the training."

[0804] input:

[0805] User feedback.

[0806] Data processing / data calculation:

[0807] The feedback data is organized and formatted for transmission to the server.

[0808] output:

[0809] Feedback data sent to the server.

[0810] Step 9: Re-adjust your training plan

[0811] Specific behavior:

[0812] The server analyzes the user's feedback and dynamically readjusts the training plan as needed, which is then sent back to the user's device.

[0813] input:

[0814] User feedback and initial training plans.

[0815] Data processing / data calculation:

[0816] Analyze the feedback data and make any necessary adjustments to generate a new training plan.

[0817] output:

[0818] A reworked training plan.

[0819] (Application example 1)

[0820] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0821] Conventional factory robots did not have a self-monitoring and feedback system to maximize their operating efficiency, and required human managers to monitor and manually adjust them one by one. This made it difficult to formulate efficient work schedules and improve efficiency in real time, which could result in a decline in overall factory productivity. This created a need for a system that could autonomously optimize the operating efficiency of factory robots and provide feedback in real time.

[0822] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0823] In this invention, the server includes means for analyzing the collected data to analyze the efficiency and error occurrence tendency of the robot, means for generating a training plan dedicated to the robot based on the analysis results, and means for transmitting the generated training plan to the robot, thereby enabling the robot's operating efficiency to be autonomously optimized and feedback to be provided in real time.

[0824] The "means for collecting user behavior data" refers to a device or method for collecting data on the user's behavior, such as the start time, end time, and work content of the user's work.

[0825] A "means for collecting user biometric data" is a device or method for collecting biometric information of a user, such as heart rate, brain waves, or electrodermal activity.

[0826] The "means for collecting user-provided feedback" refers to a device or method for collecting feedback information such as satisfaction and a sense of accomplishment from users.

[0827] The "means for transmitting collected data to a server" refers to a device or method for transmitting collected behavioral data, biometric data, and feedback information to a server.

[0828] "Means for analyzing the received data and identifying patterns of user self-control and concentration" refers to a device or method that analyzes the data sent to the server and identifies characteristics of the user's behavior and concentration.

[0829] The "means for generating a personalized training plan based on the identified patterns" refers to a device or method that creates a training plan specific to the user based on the analysis results.

[0830] "Means for collecting robot operation data and sensor information" refers to a device or method for collecting the robot's work history and sensor data (e.g., motor temperature, battery status).

[0831] "Means for transmitting collected data to a server and analyzing the efficiency of the robot and the tendency for errors to occur" refers to a device or method for transmitting collected information to a server and analyzing the operating efficiency of the robot and the tendency for errors to occur.

[0832] The "means for generating a training plan dedicated to a robot based on the analysis results" refers to a device or method for creating a work schedule or maintenance plan to maximize the efficiency of a robot based on the analysis results.

[0833] The "means for transmitting the generated training plan to the robot" is a device or method for transmitting the generated training plan to the robot.

[0834] The "means for providing real-time feedback to a robot" refers to a device or method that provides optimization instructions or advice for improving efficiency in real time according to the working status of the robot.

[0835] In the system for implementing the present invention, a server, a terminal, and a user work together as follows.

[0836] server

[0837] 1. Data collection

[0838] The server receives the user's behavioral data, biometric data, and feedback information in real time. Specifically, when the user records the start and end times of a task, that data is sent from the device to the server. Biometric data, such as heart rate, brain waves, and electrodermal activity, are collected from the wearable device.

[0839] 2. Data Analysis

[0840] The server analyzes the user's self-control and concentration patterns based on the collected data, for example by cross-referencing behavioral data with heart rate to identify times when concentration is at its best.

[0841] 3. Generate a training plan

[0842] The server then generates a personalized training plan based on the analysis results. It offers plans for task management, attention training, and stress management using techniques such as the Pomodoro Technique. For example, it suggests a plan in which the user alternates between 20 minutes of focused work and 5 minutes of rest.

[0843] 4. Providing Feedback

[0844] The server updates the training plan based on the user's progress and feedback, providing real-time feedback, such as "Your peak concentration is between 2:00 and 3:00 p.m. Try using this time slot again next week."

[0845] Terminal

[0846] 1. Data recording and transmission

[0847] The device records the user's behavioral data and biometric data and periodically transmits it to a server. For example, heart rate data acquired by a smartwatch is stored on the device and then transmitted to a server.

[0848] 2. Receiving and viewing training plans

[0849] The device receives the training plan sent from the server and notifies the user. For example, a notification such as "Please concentrate on your work for the next 25 minutes" will appear on the smartphone.

[0850] 3. Displaying and Carrying Out Instructions

[0851] The device will then instruct the user to start tasks and take rests according to the instructions in the training plan. For example, when it's time to take a break, it will notify the user by saying, "You have 5 minutes left to take a break."

[0852] User

[0853] 1. Implement a training plan

[0854] The user performs tasks according to the training plan provided by the device, for example, concentrating on tasks at designated times and taking appropriate breaks according to notifications.

[0855] 2. Providing Feedback

[0856] Users can send feedback about the effectiveness of the training plan to the server via their devices, for example by answering a questionnaire about their sense of accomplishment and satisfaction with the task.

[0857] 3. Self-monitoring

[0858] Users can use the device to self-monitor and check their own performance based on the data, for example by viewing a graph of their daily concentration fluctuations.

[0859] Specific examples

[0860] Example: A robot's operational data and sensor information are collected and sent to a server. The server analyzes the robot's operational efficiency and error trends, and generates and sends a training plan based on the results. For example, the server provides the robot with feedback such as, "Avoid heavy workloads for the next 30 minutes and perform inspections."

[0861] Prompt Sentence Examples

[0862] "A system that analyzes the operational data of robots in factories and generates optimal work schedules to maximize efficiency."

[0863] "How to generate training plans to improve efficiency in real time based on data collected by a robot"

[0864] This system can optimize the production efficiency and concentration of users and robots in real time, improving their self-control during actual work and tasks.

[0865] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0866] Step 1:

[0867] Data collection

[0868] The user uses a device (e.g., smartphone or smartwatch) to record the start and end times of a task and collect biometric data (e.g., heart rate, electrodermal activity). The device then sends this data to a server. The input is task data and biometric data, and the output is data sent to the server.

[0869] Step 2:

[0870] Receiving data

[0871] The server receives behavioral data and biometric data sent from the device in real time. The input is the data sent from the device, and the output is the storage of the received data. The server stores this data in a database.

[0872] Step 3:

[0873] Data analysis

[0874] The server analyzes the received data to identify patterns of the user's self-control and concentration. For example, it analyzes heart rate fluctuations and task completion times to identify times when concentration is at its peak. The input is the stored data, and the output is the analysis results. Statistical analysis and machine learning models are used to process the data.

[0875] Step 4:

[0876] Generate a training plan

[0877] The server generates a personalized training plan for the user based on the analysis results. For example, it suggests 20 minutes of focused work followed by a 5-minute break based on the Pomodoro technique. The input is the analysis results, and the output is the training plan. A generative AI model is used to generate a personalized plan.

[0878] Step 5:

[0879] Submit a training plan

[0880] The server sends the generated training plan to the device. The input is the training plan, and the output is the transmission of the plan to the device. Feedback messages are also included.

[0881] Step 6:

[0882] View your training plan

[0883] The device displays the received training plan to the user. For example, it may notify the user, "Please concentrate on your work for the next 25 minutes." The input is the plan from the server, and the output is the notification to the user.

[0884] Step 7:

[0885] Executing a task

[0886] The user performs tasks according to the training plan provided by the device. The input is instructions from the device, and the output is the completion of the task. For example, the user can concentrate on the work at a specified time and take appropriate breaks according to notifications.

[0887] Step 8:

[0888] Providing Feedback

[0889] The user sends feedback about the effectiveness of the training plan to the server via their device. The input is the user's feedback, and the output is the transmission of feedback to the server. This includes a questionnaire about the sense of accomplishment and satisfaction with the task.

[0890] Step 9:

[0891] Analyzing feedback and updating plans

[0892] The server analyzes user feedback and dynamically readjusts the training plan. The input is user feedback and the output is an updated training plan. The server regenerates the personalized plan and resubmits it if necessary.

[0893] This series of steps optimizes the self-control and concentration of users and robots in real time, improving production efficiency.

[0894] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0895] System Overview

[0896] This invention combines a support system for enhancing an individual's self-control and maximizing concentration with an emotional engine. The system utilizes the user's behavioral data, biometric data, feedback, and even emotional state, and generates a personalized training plan based on this data and provides the user with real-time feedback.

[0897] server

[0898] 1. Data Collection:

[0899] The server receives the user's behavioral data, biometric data, feedback, and emotional data in real time. For example, if a user is using an emotion recognition application, emotional data from the application will be sent to the server via the device. This data includes behavioral data (such as the start and end times of a task) and biometric data (such as heart rate and brain waves), as well as the user's current emotional state.

[0900] 2. Data Analysis:

[0901] The server analyzes the received data to identify patterns of the user's self-control, focus, and emotions. For example, it cross-references heart rate data with emotional data to identify times when they are most focused or stressed.

[0902] 3. Role of the Emotion Engine:

[0903] The emotion engine analyzes the user's facial expressions, tone of voice, input text, etc. to identify the user's emotional state in real time, for example, it can identify different emotional states such as whether the user is stressed or relaxed.

[0904] 4. Generate a training plan:

[0905] The server then generates a personalized training plan based on the analysis results, taking into account the user's emotional state and offering plans incorporating the Pomodoro technique, specific breathing exercises, and relaxation techniques. For example, if the user is feeling stressed, the server will suggest a plan that includes a short period of relaxation.

[0906] 5. Providing Feedback:

[0907] The server dynamically updates the training plan based on the user's progress, feedback, and emotional data, providing real-time feedback, such as "Taking five minutes of relaxation time into account, given your current emotional state."

[0908] Terminal

[0909] 1. Data recording and transmission:

[0910] The device records the user's behavioral data, biometric data, feedback, and emotional data and periodically transmits them to a server. For example, a smartwatch records the user's heart rate and emotional state and transmits the data to a server.

[0911] 2. Receive and view training plans:

[0912] The device notifies the user of the training plan sent from the server. For example, a notification such as "Work with focus for the next 25 minutes. You appear to be in a stressful emotional state, so take 5 minutes of relaxation afterwards" will appear on the smartphone.

[0913] 3. Displaying and Carrying Out Instructions:

[0914] The device will prompt the user to start tasks and take rests according to the instructions in the training plan. For example, when it's time for a break, it will notify the user, "You have 5 minutes left to take a break." If emotions are negative, it will give instructions such as, "Take a deep breath and relax."

[0915] User

[0916] 1. Implement your training plan:

[0917] The user performs tasks according to the training plan provided by the device, for example, concentrating on a task at a specified time and taking appropriate breaks or relaxation according to notifications.

[0918] 2. Providing Feedback:

[0919] Users send feedback about the effectiveness of the training plan to the server via their device, for example by answering a questionnaire about their sense of accomplishment, satisfaction, and emotional fluctuations.

[0920] 3. Self-monitoring:

[0921] Users can use the device to self-monitor and check their performance and emotional state based on the data, for example by viewing graphs of their daily concentration and emotional fluctuations.

[0922] Through these processes, the system effectively supports users in self-control, concentration and emotional management.

[0923] The processing flow will be explained below.

[0924] Specific processing flow of the program

[0925] Data Collection Phase

[0926] Step 1:

[0927] User

[0928] A user installs a task management app and records the start and end times of a task. For example, they open the "Task Tracker" app and press the start button for the task.

[0929] Step 2:

[0930] Terminal

[0931] The device saves the data recorded by the task management app in its storage, and also saves the end time when the task is completed.

[0932] Step 3:

[0933] Terminal

[0934] The smartwatch periodically records the user's biometric data, such as heart rate, for example, every five minutes and stores it on the device.

[0935] Step 4:

[0936] Terminal

[0937] The emotion recognition app analyzes the user's facial expressions and voice to collect real-time emotional data, which is also stored on the device.

[0938] Step 5:

[0939] User

[0940] After completing the training, users complete an in-app survey to provide subjective feedback, such as, "How focused were you on this task?"

[0941] Data Transfer Phase

[0942] Step 6:

[0943] Terminal

[0944] The collected behavioral data, biometric data, emotional data, and feedback data packages are sent to a server, for example, by converting the collected data into JSON format and uploading it to the server via the Internet.

[0945] Data analysis phase

[0946] Step 7:

[0947] server

[0948] Analyzes incoming data, cleansing and normalizing it, and handling outliers and missing data to improve analysis accuracy.

[0949] Step 8:

[0950] server

[0951] By cross-referencing the user's heart rate data with their emotional data, the system identifies times when they are most likely to concentrate or feel stressed. For example, it identifies "times when the heart rate is stable and positive emotions are high" as times when they are most likely to concentrate.

[0952] Step 9:

[0953] server

[0954] It identifies patterns in the user's self-control, focus, and emotions, and stores the results in a database. For example, the user's characteristics can be reflected in a model and used to generate a plan for the next time.

[0955] Training plan generation phase

[0956] Step 10:

[0957] server

[0958] Based on the analysis results, the system generates a personalized training plan that takes into account the user's emotional state. For example, it suggests the user follow the Pomodoro technique (25 minutes of work + 5 minutes of rest) and also provides a plan that includes relaxation.

[0959] Step 11:

[0960] server

[0961] The generated training plan is sent to the user's device. For example, the generated plan is sent in JSON format and displayed in an app on the device.

[0962] Feedback Phase

[0963] Step 12:

[0964] Terminal

[0965] The device will then notify the user of the training plan and display instructions, such as "Work with focus for the next 25 minutes, after which you will experience emotional stress, and then take 5 minutes of relaxation."

[0966] Step 13:

[0967] User

[0968] The user performs tasks according to the training plan, taking breaks and relaxation at appropriate times, for example, following the instructions on the device to take a five-minute break and then resume work.

[0969] Step 14:

[0970] Terminal

[0971] The device monitors the user's progress and real-time emotional data and continuously transmits the data to the server, for example, after completing a task or when the user's emotional state changes.

[0972] Step 15:

[0973] server

[0974] The server dynamically readjusts the training plan based on the user's progress data and feedback. For example, if the user tells the server that they were losing focus, the server will create a plan that includes shorter sessions and more frequent breaks.

[0975] Step 16:

[0976] server

[0977] The server generates a weekly progress report and sends it to the user, notifying them, for example, that their peak concentration time this week is between 2:00 and 3:00 PM. It also provides advice such as, "Based on your emotional data, it would be beneficial to spend more time relaxing."

[0978] Step 17:

[0979] User

[0980] Users can check their progress reports and use them to plan their training for the next week, for example by scheduling tasks for the next week based on their peak concentration times.

[0981] Through these processing steps, the system effectively supports the user's self-control, concentration and emotional management.

[0982] Example 2

[0983] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0984] A user's self-control, concentration, and emotional management need to be appropriately supported based on their individual state and situation. However, conventional systems collect users' biometric and behavioral data, but do not consider their emotional data, making it difficult to provide training plans tailored to individual emotional states. Therefore, a system that provides more personalized support by taking into account the user's emotional state is needed.

[0985] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0986] In this invention, the server includes means for collecting user behavioral data, means for collecting user biometric data, means for collecting user-provided feedback, means for collecting user emotional data, means for transmitting the collected data to the server, means for analyzing the received data and identifying patterns of the user's self-control, concentration, and emotions, means for generating a personalized training plan based on the identified patterns, means for transmitting the generated training plan to the user's device, and means for providing real-time feedback to the user, thereby making it possible to provide a training plan that also takes the user's emotional state into consideration.

[0987] "User behavioral data" refers to data that records the specific movements and actions of a user in their daily life and work.

[0988] "User's biometric data" refers to data that indicates the user's physical condition, including physiological indicators such as heart rate and brain waves.

[0989] "Feedback" is data that records opinions, ratings, and reactions provided by users.

[0990] "Emotional data" refers to data that indicates a user's emotional state, and includes information such as facial expressions, voice tone, and text input obtained using emotion recognition technology.

[0991] A "server" is a central processing unit that receives data sent from the user's device and performs analysis, generates training plans, etc.

[0992] A "terminal" is a device that is directly operated by a user, including smartphones and smartwatches.

[0993] "Self-control" refers to the user's ability to appropriately manage their own behavior and emotions and maintain a desired state.

[0994] "Concentration" refers to a user's ability to focus their mind on a particular task or activity.

[0995] "Training Plan" means a personalized plan or instruction to improve a User's self-control and focus.

[0996] "Real-time feedback" refers to instant feedback and advice based on the actions a user takes and the data they provide.

[0997] "Analysis" refers to the data processing process that uses collected data to identify patterns of user behavior and emotions.

[0998] An "emotion engine" refers to software or hardware processing that analyzes a user's facial expressions, voice tone, text, etc. to identify their emotional state.

[0999] "Personalization" refers to customizing training plans and feedback to suit each individual user's specific needs and circumstances.

[1000] "Peak hours" refer to times when users are most focused and productive.

[1001] "Stressful times" refer to the times when users are most likely to feel stressed.

[1002] System Overview

[1003] This invention combines an emotional engine with a support system for enhancing an individual's self-control and maximizing concentration. The system utilizes the user's behavioral data, biometric data, feedback, and even emotional data to generate a personalized training plan and provide real-time feedback to the user.

[1004] Hardware and Software

[1005] Hardware: smartwatches (e.g., devices for measuring heart rate), smartphones (e.g., used as a user interface)

[1006] Software: emotion recognition apps (e.g., tools for analyzing user emotions in real time), self-control support apps (e.g., apps that provide notifications and collect feedback)

[1007] System program processing flow

[1008] server

[1009] 1. Data Collection:

[1010] The server receives users' behavioral data, biometric data, feedback, and emotional data in real time. For example, a smartwatch records heart rate and activity data and sends the data to the server. An emotion recognition app also collects users' emotional data and sends it to the server.

[1011] 2. Data Analysis:

[1012] The server analyzes the received data to identify patterns of self-control, concentration, and emotions of the user. Specifically, it cross-references heart rate data with emotional data to identify times of high concentration and times of stress.

[1013] 3. Role of the Emotion Engine:

[1014] The emotion engine analyzes the user's facial expressions, voice tone, and input text to identify the user's emotional state in real time. For example, if the user inputs "I'm very tired today," the engine will identify fatigue from the text. The facial recognition camera also detects the user's smile and determines that the user is relaxed.

[1015] 4. Generate a training plan:

[1016] The server then generates a personalized training plan based on the analysis results. For example, if a user's concentration decreases and stress increases at 3 p.m., the server will suggest a plan that includes relaxation time before and after 3 p.m.

[1017] 5. Providing Feedback:

[1018] The server dynamically updates the training plan based on the user's progress, feedback, and emotional data, providing real-time feedback, such as notifications like "Take five minutes of relaxation time to consider your current emotional state."

[1019] Terminal

[1020] 1. Data recording and transmission:

[1021] The device records the user's behavioral data, biometric data, feedback, and emotional data and periodically transmits them to a server. For example, a smartwatch collects heart rate data every hour and transmits it to a server.

[1022] 2. Receive and view training plans:

[1023] The device receives the training plan sent from the server in real time and notifies the user, for example, a message such as "Work hard for the next 25 minutes, then take 5 minutes of relaxation" will be displayed on the smartphone.

[1024] 3. Displaying and Carrying Out Instructions:

[1025] The device will follow the instructions of the training plan and prompt the user to start a task or take a rest. For example, the smartwatch will notify the user that it's time to take a break in five minutes. If the user is feeling negative, it will display instructions such as "Take a deep breath and relax."

[1026] User

[1027] 1. Implement your training plan:

[1028] The device provides users with a training plan to help them accomplish tasks, such as working during designated periods of focus and then using breaks to practice relaxation.

[1029] 2. Providing Feedback:

[1030] Users can send feedback about the effectiveness of their training plan to a server via their device, for example by answering a questionnaire after completing a task, asking questions such as "Was your concentration high while working?" or "Did your stress level decrease?"

[1031] 3. Self-monitoring:

[1032] Users can use the device to self-monitor and check their own performance and emotional state based on the data. For example, the smartphone app can visualize daily fluctuations in concentration and emotions in graphs to check the effectiveness of their training.

[1033] Example prompt

[1034] 1. The user puts on the smartwatch

[1035] "Wear your smartwatch and enter your self-assessment at each moment of exercise and rest throughout the day."

[1036] 2. Applying the Pomodoro Technique

[1037] "You'll have 25 minutes of focused work time, after which you'll receive a notification to take a 5-minute break."

[1038] 3. Entering Emotion Data

[1039] "Enter your current mood on a scale of 1 to 5. The emotion recognition app will analyze your facial expressions and collect emotional data."

[1040] This allows the system to effectively support users' self-control, concentration and emotional management.

[1041] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1042] System processing steps

[1043] Step 1:

[1044] The server receives the user's behavioral data, biometric data, feedback, and emotional data in real time. Specifically, the device (smartwatch) records heart rate and activity data and sends the data to the server. The emotion recognition app also collects the user's emotional data and sends it to the server. The input data are behavioral data, biometric data, feedback, and emotional data, which are integrated and efficiently sent to the server. The output is the integrated data sent to the server.

[1045] Step 2:

[1046] The server analyzes the received data. Specifically, it cross-references the heart rate data with the emotional data to identify the user's concentration and stress patterns. The input data is the integrated data sent to the server. Data analysis algorithms are used to identify the user's peak concentration and stress times. The output is data that identifies the user's concentration and stress patterns.

[1047] Step 3:

[1048] The emotion engine on the server analyzes the user's facial expression, voice tone, input text, etc. to identify the user's emotional state in real time. The input data is facial expression data, voice tone data, and text data. The emotion recognition algorithm is used to analyze the user's emotional state in real time. The output is the identified emotional state data.

[1049] Step 4:

[1050] The server generates a personalized training plan based on the analysis results. For example, for a user whose concentration decreases and stress increases around 3:00 p.m., it suggests a plan that includes relaxation time around that time. The input data is analysis data on the user's concentration, stress, and emotional state. An algorithm is used to generate a plan that incorporates the Pomodoro technique, specific breathing techniques, etc. The output is a personalized training plan.

[1051] Step 5:

[1052] The server dynamically updates the training plan based on the user's progress, feedback, and emotional data, and provides real-time feedback. For example, it sends a notification such as, "Taking five minutes of relaxation time into account, given your current emotional state." The input data are the user's progress data, feedback data, and emotional data. The dynamic plan update algorithm generates real-time feedback. The output is the updated training plan and feedback.

[1053] Step 6:

[1054] The device records the user's behavioral data, biometric data, feedback, and emotional data and periodically transmits them to a server. Specifically, the smartwatch collects heart rate data every hour and transmits it to the server. The input data are the user's behavioral data, biometric data, feedback, and emotional data. A data collection and transmission algorithm is used. The output is the recorded data sent to the server.

[1055] Step 7:

[1056] The device receives the training plan sent from the server and notifies the user. For example, a message such as "Work concentratively for the next 25 minutes, then take 5 minutes of relaxation" is displayed on the smartphone. The input data is the training plan sent from the server. The notification function is used. The output is the notification displayed to the user.

[1057] Step 8:

[1058] The device prompts the user to start a task or take a rest according to the instructions in the training plan. For example, the smartwatch may send a notification saying, "Take a break in 5 minutes," or, if emotions are negative, it may display instructions such as, "Take a deep breath and relax." The input data are instructions based on the training plan. Task management and notification algorithms are used. The output is instructions displayed to the user.

[1059] Step 9:

[1060] The user performs tasks according to a training plan provided by the device. For example, the user works during a designated concentration period and then uses the subsequent break time to practice relaxation. The input data is the training plan provided by the device. A task execution algorithm is used. The output is the completed task.

[1061] Step 10:

[1062] The user sends feedback about the effectiveness of the training plan to the server via their device. For example, after completing a task, they answer a questionnaire such as "Was your concentration high while working?" or "Did your stress level decrease?" The input data is the user's feedback about the effectiveness of the training plan. A feedback collection algorithm is used. The output is the feedback sent to the server.

[1063] Step 11:

[1064] Users use the device to self-monitor and check their own performance and emotional state based on the data. For example, a smartphone app can visualize daily fluctuations in concentration and emotions in graphs to check the effectiveness of training. The input data is past behavioral data, biological data, and emotional data. A data visualization algorithm is used. The output is data visualized as a graph.

[1065] (Application example 2)

[1066] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1067] Many existing systems for improving self-control and concentration generate training plans based on biometric and behavioral data. However, few systems take the user's emotional state into account. Therefore, there is a need for a system that can grasp the user's stress and relaxation levels in real time and provide feedback and content recommendations that adapt to those conditions. Furthermore, there has yet to be a fully developed system that can comprehensively analyze this data and provide dynamically personalized plans.

[1068] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric data of the user, means for collecting the user's emotional state, and means for analyzing the received data and identifying the user's self-control, concentration, and emotional patterns. This makes it possible to recommend personalized training plans and content adapted to the user's stress and relaxation state based on the analysis results. This allows the user to receive support that allows them to maintain concentration more effectively and comfortably and manage their emotional state.

[1069] definition statement

[1070] "User behavior data" refers to information about user behavior, specifically data including viewing time, frequency of clicks and swipes, etc.

[1071] "User biometric data" refers to information about the user's physiological state, including, for example, heart rate and eye tracking data.

[1072] "Feedback provided by users" refers to information such as ratings, impressions, and survey responses provided by users to the system.

[1073] "User's emotional state" refers to information about the user's real-time emotions analyzed by the emotion engine, including stress levels, relaxed states, etc.

[1074] "Server" refers to the computer system that receives and analyzes data, generates personalized training plans and content recommendations, and transmits them to the User's device.

[1075] "Analysis" refers to the process of using collected user behavioral data, biometric data, feedback, and emotional state to identify patterns of self-control, focus, and emotions.

[1076] "Personalized training plan" refers to an individually optimized training plan generated based on data for each individual user.

[1077] "Content recommendation" refers to the act of recommending optimal content (videos, music, articles, etc.) based on the user's current state and patterns.

[1078] "Real-time feedback" refers to advice or instructions provided immediately in response to a user's actions or state.

[1079] "Dynamic readjustment" refers to the process of updating and changing training plans and content recommendations on the fly based on analysis results.

[1080] MODE FOR CARRYING OUT THE INVENTION

[1081] System Overview

[1082] This invention is a support system for improving an individual's self-control and concentration, and combines an emotion engine. The system utilizes the user's behavioral data, biometric data, feedback, and emotional state, and generates personalized training plans and content recommendations based on this data, providing real-time feedback to the user.

[1083] server

[1084] 1. Data Collection:

[1085] The server receives user behavioral data, biometric data, feedback, and emotional data in real time. For example, the server receives data sent from smartphones, wearable devices, etc. This data includes browsing time, click / swipe frequency, heart rate, facial expressions, voice tone, and input text.

[1086] 2. Data Analysis:

[1087] The server analyzes the received data to identify patterns of the user's self-control, concentration, and emotions. For example, it cross-references behavioral data with biometric data to identify times of high concentration and times of stress. The emotion engine analyzes facial expressions, voice tone, text, etc. to identify emotional states.

[1088] 3. Generating training plans and content recommendations:

[1089] The server generates personalized training plans and content recommendations based on the analysis results. Taking into account the user's emotional state, the server offers plans incorporating the Pomodoro technique, specific breathing exercises, and relaxation techniques. For users feeling stressed, the server recommends relaxation music and meditation videos.

[1090] 4. Providing Feedback:

[1091] The server dynamically updates training plans and content recommendations based on the user's progress, feedback, and emotional data, providing real-time feedback, such as "Taking five minutes of relaxation time into account, given your current emotional state."

[1092] Terminal

[1093] 1. Data recording and transmission:

[1094] The device records the user's behavioral data, biometric data, feedback, and emotional data and periodically transmits them to a server. For example, a smartphone records browsing data, and a smartwatch records heart rate and emotional state and transmits them to a server.

[1095] 2. Receive and view training plans and content recommendations:

[1096] The device will notify the user of training plans and content recommendations sent from the server, such as "Work hard for the next 25 minutes, then listen to 5 minutes of relaxation music."

[1097] 3. Displaying and Carrying Out Instructions:

[1098] The device will prompt the user to start tasks or take a rest according to the instructions in the training plan. For example, when it's time for a break, it will notify the user, "You have 5 minutes left to take a break," and if it detects negative emotions, it will instruct the user to "take a deep breath and relax."

[1099] User

[1100] 1. Implement your training plan:

[1101] The user performs tasks according to the training plan provided by the device, for example, concentrating on a task at a specified time and taking appropriate breaks or relaxation according to notifications.

[1102] 2. Providing Feedback:

[1103] Users can send feedback about the effectiveness of their training plan to the server via their device, for example by answering a questionnaire about their emotional state.

[1104] 3. Self-monitoring:

[1105] Users can use the device to self-monitor and check their performance and emotional state based on the data, for example by viewing graphs of their daily concentration and emotional fluctuations.

[1106] Hardware and software used

[1107] Hardware:

[1108] Smartphones, smartwatches, heart rate monitors, facial recognition cameras

[1109] software:

[1110] Python, emotion recognition API (Emotion API, etc.), data analysis library (Pandas, etc.)

[1111] Specific examples / prompt sentence examples

[1112] Examples:

[1113] If a user watches a documentary for 30 minutes and their heart rate increases and they click and swipe more, the app will determine that they are in a high-stress state and notify them, "It's time to take a short break. We recommend watching a relaxing meditation video next."

[1114] Example prompt for a generative AI model:

[1115] "Generate a Python program to recommend optimal content and break timing using user behavioral data, heart rate, biometric data, and real-time analyzed emotional state."

[1116] "If a user is in a high-stress state, design your app to recommend relaxing content and encourage them to take a break."

[1117] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1118] Program processing steps

[1119] Step 1: Collect data

[1120] The server collects user behavioral data, biometric data, feedback and emotional data in real time.

[1121] Input: Data sent from smartphones, smartwatches, heart rate monitors, and facial recognition cameras.

[1122] Data processing: All data is converted into a unified format and stored in a database.

[1123] Output: A consolidated user dataset.

[1124] Step 2: Analyze the data

[1125] The server analyzes the user's self-control, concentration and emotional patterns based on the collected data.

[1126] Input: The consolidated user dataset.

[1127] Data calculations: Cross-reference behavioral and biological data to identify periods of high concentration and stress. Analyze emotional states using an emotion engine.

[1128] Output: User's self-control, concentration, and emotional patterns.

[1129] Step 3: Generate training plans and content recommendations

[1130] The server generates personalized training plans and content recommendations based on the analysis results.

[1131] Input: The user's self-control, focus, and emotional patterns.

[1132] Data processing: Based on the user's identified patterns, training plans including the Pomodoro technique and breathing exercises, as well as content such as relaxation music and meditation videos are determined.

[1133] Output: Personalized training plans and recommended content.

[1134] Step 4: Submit your training plan and content recommendations

[1135] The server transmits the generated training plan and content recommendations to the user's terminal.

[1136] Input: Personalized training plans and recommended content.

[1137] Data processing: Convert the training plan and recommended content into notification format and send it to your device.

[1138] Output: The notification that appears on the user's device.

[1139] Step 5: Provide real-time feedback

[1140] The device notifies the user of the feedback sent from the server in real time.

[1141] Input: Notification data from the server.

[1142] Data processing: Display notification content in a format that is easy for users to understand.

[1143] Output: Real-time instructions and advice displayed on the user's device.

[1144] Step 6: User feedback

[1145] Users send feedback about training plans and content recommendations to the server via their devices.

[1146] Input: User ratings, comments, and survey responses.

[1147] Data processing: The feedback data is converted into a format that can be sent to the server.

[1148] Output: The feedback data received by the server.

[1149] Step 7: Dynamically adjust training plans and content recommendations

[1150] The server dynamically readjusts training plans and content recommendations based on user feedback and progress data.

[1151] Input: Feedback data, progress data.

[1152] Data Calculation: Analyzes feedback and progress data to update training plans and content recommendations.

[1153] Output: Dynamically adjusted training plans and recommended content.

[1154] Step 8: Provide self-monitoring data

[1155] The device provides the user with self-monitoring data.

[1156] Input: Analysis results of user data sent from the server.

[1157] Data processing: Converting data into graphs and charts that are easy for users to understand.

[1158] Output: Self-monitoring data displayed on the terminal.

[1159] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1160] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1161] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1162] [Third embodiment]

[1163] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1164] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1165] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1166] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1167] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1169] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1170] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1171] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1172] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1173] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1174] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1175] System Overview

[1176] This invention is a support system for enhancing an individual's self-control and maximizing concentration. The system utilizes the user's behavioral data, biometric data, and feedback, and generates a personalized training plan based on this data and provides the user with real-time feedback.

[1177] server

[1178] 1. Data Collection:

[1179] The server receives the user's behavioral data, biometric data, and feedback in real time. For example, when a user uses a task management app to record the start and end times of a task, the data is sent from the device to the server. Biometric data includes heart rate, brain waves, and electrodermal activity. This data is collected from the wearable device.

[1180] 2. Data Analysis:

[1181] The server analyzes the collected data to identify patterns of self-control and concentration, for example by cross-referencing heart rate and task time to detect times when concentration is at its peak.

[1182] 3. Generate a training plan:

[1183] The server then generates a personalized training plan based on the analysis results. It uses techniques like the Pomodoro technique to provide plans for task management, attention training, and stress management. For example, it suggests a plan in which the user alternates between 20 minutes of focused work and 5 minutes of rest.

[1184] 4. Providing Feedback:

[1185] The server updates the training plan based on the user's progress and feedback, providing real-time feedback, such as "Your peak concentration time is between 2:00 and 3:00 p.m. Try using this time slot again next week."

[1186] Terminal

[1187] 1. Data recording and transmission:

[1188] The device records the user's behavioral data and biometric data and periodically transmits it to a server. For example, a smartwatch measures heart rate and stores the data on the device. The device then compiles the collected data and transmits it to the server.

[1189] 2. Receive and view training plans:

[1190] The device receives the training plan sent from the server and notifies the user. For example, a notification such as "Please concentrate on your work for the next 25 minutes" will be displayed on the smartphone.

[1191] 3. Displaying and Carrying Out Instructions:

[1192] The device will then prompt the user to start tasks and take rests according to the instructions in the training plan. For example, when it's time to take a break, it will notify the user, "You have 5 minutes left to take a break."

[1193] User

[1194] 1. Implement your training plan:

[1195] The user performs tasks according to the training plan provided by the device, for example, concentrating on the task at the specified time and taking appropriate breaks according to notifications.

[1196] 2. Providing Feedback:

[1197] Users can send feedback about the effectiveness of the training plan to the server via their devices, for example by answering a questionnaire about their sense of accomplishment and satisfaction with the task.

[1198] 3. Self-monitoring:

[1199] Users can use the device to self-monitor and check their own performance based on the data, for example by viewing a graph of their daily concentration fluctuations.

[1200] Through these processes, the system effectively enhances the user's self-control and concentration, helping them complete tasks and achieve goals.

[1201] The processing flow will be explained below.

[1202] Specific processing flow of the program

[1203] Data Collection Phase

[1204] Step 1:

[1205] User

[1206] A user installs a task management app and records the start and end times of a task. For example, they open the "Task Tracker" app and press the start button for the task.

[1207] Step 2:

[1208] Terminal

[1209] The device saves the data recorded by the task management app in its storage, and also saves the end time when the task is completed.

[1210] Step 3:

[1211] Terminal

[1212] The smartwatch periodically records the user's biometric data, such as heart rate, for example, every five minutes and stores it on the device.

[1213] Step 4:

[1214] User

[1215] After completing the training, users provide subjective feedback by completing an in-app survey, answering questions such as, "How focused were you on this task?"

[1216] Data Transfer Phase

[1217] Step 5:

[1218] Terminal

[1219] The collected behavioral data, biometric data, and feedback data package is sent to a server, for example, by converting the collected data into JSON format and uploading it to the server via the internet.

[1220] Data analysis phase

[1221] Step 6:

[1222] server

[1223] Analyzes incoming data, cleansing and normalizing it, and handling outliers and missing data to improve analysis accuracy.

[1224] Step 7:

[1225] server

[1226] The system cross-references the user's heart rate data with the time spent performing a task to detect periods of peak concentration. For example, it identifies periods of sustained low heart rate as "periods of high concentration."

[1227] Step 8:

[1228] server

[1229] The system identifies the user's self-control and concentration patterns and stores them in a database. The user's characteristics are then reflected in a model and used to generate the next plan.

[1230] Training plan generation phase

[1231] Step 9:

[1232] server

[1233] Based on the analysis results, it generates a personalized training plan, for example, suggesting the user to use the Pomodoro technique and setting a cycle of 25 minutes of work and 5 minutes of rest.

[1234] Step 10:

[1235] server

[1236] The generated training plan is sent to the user's device. For example, the generated plan is sent in JSON format and displayed in an app on the device.

[1237] Feedback Phase

[1238] Step 11:

[1239] Terminal

[1240] The device will then notify the user of the training plan and provide instructions, such as "Work hard and stay focused for the next 25 minutes."

[1241] Step 12:

[1242] User

[1243] The user performs tasks according to the training plan and takes a break at the designated time, for example, following the instructions on the device to take a 5-minute break and then resume work.

[1244] Step 13:

[1245] Terminal

[1246] The device monitors the user's progress and sends data to the server in real time, for example, sending the data again when the task is completed.

[1247] Step 14:

[1248] server

[1249] The server dynamically adjusts the training plan based on the user's progress data and feedback. For example, if the user's training plan is shorter than the planned one for the following week, the server receives feedback that the user was unable to maintain concentration.

[1250] Step 15:

[1251] server

[1252] The server generates a weekly progress report and sends it to the user, telling them, for example, "Your peak concentration time this week is between 2:00 and 3:00 PM."

[1253] Step 16:

[1254] User

[1255] Users can check their progress reports and use them to plan their training for the next week, for example by scheduling tasks for the next week based on their peak concentration times.

[1256] Through these processing steps, the system provides effective support to enhance the user's self-control and concentration.

[1257] Example 1

[1258] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1259] In recent years, there has been a growing demand for improving concentration and self-control, and many people are looking for effective training methods. However, it is difficult for a generic training plan to provide optimal improvement measures for each user, and personalized training plans based on individual biometric and behavioral data are needed. Therefore, a method is needed to identify the user's peak concentration times and provide an optimized training plan in real time based on individual feedback.

[1260] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1261] In this invention, the server includes means for collecting user behavioral data, means for collecting user biometric data, means for collecting user-provided feedback, means for transmitting the collected data to the server, means for analyzing the received data and identifying the user's self-control and concentration patterns, means for generating a training plan utilizing the Pomodoro Technique, means for transmitting the generated training plan to the user's device, and means for providing real-time feedback to the user. This allows for the generation of an optimal training plan for each user and the provision of real-time feedback. Furthermore, the system detects the user's peak concentration times and dynamically readjusts the plan based on the detected times, and by linking with a calendar application, the system can efficiently improve the user's self-control and concentration.

[1262] "User behavioral data" refers to information about a user's activities, such as the start time, end time, and type of task performed by the user.

[1263] "User biometric data" means information about a user's physiological state or physical activity, such as heart rate, electrodermal activity, brain waves, and number of steps taken.

[1264] "Feedback" refers to information such as ratings and comments provided by users regarding the effectiveness and satisfaction of their training plans.

[1265] "Server" is a computer system for analyzing collected data, generating training plans, and providing feedback to users.

[1266] A "terminal" is a device that collects user behavioral data and biometric data and transmits it to a server, and includes smartphones and smartwatches.

[1267] "Data analysis" is the process of analyzing collected behavioral and biometric data using statistical methods and time series analysis to identify patterns of the user's concentration and self-control.

[1268] A "personalized training plan" is a training plan that is individually optimized based on the user's behavioral data, biometric data, and feedback.

[1269] "Real-time feedback" refers to advice and notifications provided to users instantly based on their training plan and analysis results.

[1270] The Pomodoro Technique is a time management technique that promotes efficient work by concentrating on a task for a set period of time and then taking a short break.

[1271] "Peak concentration times" are specific times when a user is determined to be able to concentrate and work most efficiently.

[1272] "Calendar application integration" is a mechanism that automatically integrates the generated training plan into the user's schedule management system.

[1273] "Cross-referencing" is a technique for cross-referencing different types of data (for example, behavioral data and biometric data) and analyzing their relationships.

[1274] "Time series analysis" is a statistical method for analyzing fluctuations and trends in data over time.

[1275] System Overview

[1276] This invention is a support system for improving a user's self-control and concentration. The system uses the user's behavioral data, biometric data, and feedback to generate a personalized training plan and provides feedback to the user in real time.

[1277] Server Features

[1278] The server provides the following functions:

[1279] 1. Receiving data:

[1280] The server receives user behavioral data, biometric data, and feedback in real time from devices (e.g., smartphones and smartwatches) via the Internet.

[1281] 2. Data Analysis:

[1282] The server analyzes the received data using data analysis libraries such as Python and R. For example, it uses Python's Pandas or NumPy to format the data and perform time series analysis.

[1283] 3. Generate a training plan:

[1284] The server generates a personalized training plan using the Pomodoro Technique, which can then be integrated into the user's schedule using the Google Calendar API.

[1285] 4. Real-time feedback:

[1286] The server dynamically readjusts the training plan based on the user's performance and feedback, providing real-time feedback.

[1287] Device Features

[1288] The device provides the following features:

[1289] 1. Data collection and transmission:

[1290] A device (such as a smartwatch or smartphone) collects user behavioral and biometric data and sends it to a server at regular intervals. For example, a smartwatch measures a user's heart rate every second and sends the data to a server via an app on the smartphone.

[1291] 2. Receive and view training plans:

[1292] The device receives the training plan sent from the server and notifies the user, for example by displaying a push notification saying, "Please concentrate on your work for the next 20 minutes."

[1293] 3. Enter your feedback:

[1294] The device receives feedback from the user and sends it to the server. For example, after completing a training plan, the device collects information that the user enters in response to the question, "How effective was the training?"

[1295] User Roles

[1296] Users use the system by following the steps below.

[1297] 1. Implement your training plan:

[1298] Users complete tasks according to a training plan provided by the device, for example, starting the task at a specified time and taking regular breaks according to the Pomodoro technique.

[1299] 2. Providing Feedback:

[1300] Users provide feedback on the effectiveness of their training plan, for example by completing a survey about their sense of accomplishment and attention span.

[1301] Specific examples

[1302] Example 1:

[1303] To discover the best times of day during an eight-hour workday, users wear a smartwatch and collect heart rate data and data from a task management app. The device sends this data to a server at specific intervals. The server analyzes the data and determines that peak concentration occurs between 2:00 and 3:00 PM. The server then generates a training plan for tasks requiring the most concentration during this time period. The device notifies the user of this plan, and the user completes the tasks according to the device's notifications. After completing the training, the user submits feedback regarding satisfaction and sense of accomplishment, and the server updates the training plan based on the new data.

[1304] Example prompt sentence:

[1305] "Measure your heart rate every second with a smartwatch, and use Python to send the data from a task management app to a server every five minutes for analysis. Based on the analysis results, create a training plan for your most important tasks between 2:00 and 3:00 PM, and notify the plan on your smartphone."

[1306] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1307] Step 1: Collect data

[1308] Specific behavior:

[1309] The device collects user behavioral and biometric data, such as heart rate, steps, and electrodermal activity, via a smartwatch, and records the start and end times of tasks via a task management app.

[1310] input:

[1311] Data from users' smartwatches and task management apps.

[1312] Data processing / data calculation:

[1313] It collects heart rate data from a smartwatch and task start and end times from a task management app, and formats them on the device.

[1314] output:

[1315] A set of collected biometric and behavioral data.

[1316] Step 2: Sending data

[1317] Specific behavior:

[1318] The device sends the collected data to the server at regular intervals (for example, every 5 minutes). For example, the data collected by a smartwatch is sent to a smartphone, which then uploads the data to the server.

[1319] input:

[1320] Biometric and behavioral data collected.

[1321] Data processing / data calculation:

[1322] The data is batched and encoded into a format that can be sent.

[1323] output:

[1324] The data batch sent to the server.

[1325] Step 3: Receiving the data

[1326] Specific behavior:

[1327] The server receives the data sent from the terminal in real time via the Internet.

[1328] input:

[1329] The data batch sent from the device.

[1330] Data processing / data calculation:

[1331] The received data is then properly organized and saved for storage in a database.

[1332] output:

[1333] Biometric and behavioral data stored in a database.

[1334] Step 4: Analyze the data

[1335] Specific behavior:

[1336] The server analyzes the received data using data analysis tools such as Python and R. Specifically, it uses time series analysis to examine the relationship between heart rate data and task time, and identifies periods of peak concentration.

[1337] input:

[1338] Biometric and behavioral data stored in a database.

[1339] Data processing / data calculation:

[1340] Extract data from the database, preprocess it using Pandas and NumPy, and perform time series analysis and cross-referencing. For example, analyze trends in heart rate data and task execution times to identify times when users are most focused.

[1341] output:

[1342] The user's peak concentration times and self-control patterns.

[1343] Step 5: Generate a training plan

[1344] Specific behavior:

[1345] The server then generates a personalized training plan based on the analysis results, using the Pomodoro technique to specifically create a plan that alternates between 20 minutes of focused work and 5 minutes of rest.

[1346] input:

[1347] Analyzed peak times of concentration and patterns of self-control.

[1348] Data processing / data calculation:

[1349] Based on the analysis results, a Python script is run to generate a training plan consisting of optimal work and rest cycles.

[1350] output:

[1351] The generated training plan.

[1352] Step 6: Submit your training plan

[1353] Specific behavior:

[1354] The server then sends the generated training plan to the user's device, where it can be incorporated into the user's schedule using, for example, the Google Calendar API.

[1355] input:

[1356] Training plan.

[1357] Data processing / data calculation:

[1358] Format your training plan into a sendable format and send the data.

[1359] output:

[1360] Training plans sent to the user's device.

[1361] Step 7: View and follow your training plan

[1362] Specific behavior:

[1363] The device receives the training plan sent from the server and notifies the user. The user checks the notification and performs the tasks according to the plan. For example, a notification saying "Please concentrate on your work for the next 20 minutes" is displayed, and the user starts working according to the instructions.

[1364] input:

[1365] The training plan sent to your device.

[1366] Data processing / data calculation:

[1367] The received training plan will be displayed and notified to the user within the app.

[1368] output:

[1369] Instructions for the training plan that the user follows.

[1370] Step 8: Gather feedback

[1371] Specific behavior:

[1372] After completing the training plan, the device collects feedback from the user, for example by asking them to answer a survey question such as, "Please rate the effectiveness of the training."

[1373] input:

[1374] User feedback.

[1375] Data processing / data calculation:

[1376] The feedback data is organized and formatted for transmission to the server.

[1377] output:

[1378] Feedback data sent to the server.

[1379] Step 9: Re-adjust your training plan

[1380] Specific behavior:

[1381] The server analyzes the user's feedback and dynamically readjusts the training plan as needed, which is then sent back to the user's device.

[1382] input:

[1383] User feedback and initial training plans.

[1384] Data processing / data calculation:

[1385] Analyze the feedback data and make any necessary adjustments to generate a new training plan.

[1386] output:

[1387] A reworked training plan.

[1388] (Application example 1)

[1389] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1390] Conventional factory robots did not have a self-monitoring and feedback system to maximize their operating efficiency, and required human managers to monitor and manually adjust them one by one. This made it difficult to formulate efficient work schedules and improve efficiency in real time, which could result in a decline in overall factory productivity. This created a need for a system that could autonomously optimize the operating efficiency of factory robots and provide feedback in real time.

[1391] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1392] In this invention, the server includes means for analyzing the collected data to analyze the efficiency and error occurrence tendency of the robot, means for generating a training plan dedicated to the robot based on the analysis results, and means for transmitting the generated training plan to the robot, thereby enabling the robot's operating efficiency to be autonomously optimized and feedback to be provided in real time.

[1393] The "means for collecting user behavior data" refers to a device or method for collecting data on the user's behavior, such as the start time, end time, and work content of the user's work.

[1394] A "means for collecting user biometric data" is a device or method for collecting biometric information of a user, such as heart rate, brain waves, or electrodermal activity.

[1395] The "means for collecting user-provided feedback" refers to a device or method for collecting feedback information such as satisfaction and a sense of accomplishment from users.

[1396] The "means for transmitting collected data to a server" refers to a device or method for transmitting collected behavioral data, biometric data, and feedback information to a server.

[1397] "Means for analyzing the received data and identifying patterns of user self-control and concentration" refers to a device or method that analyzes the data sent to the server and identifies characteristics of the user's behavior and concentration.

[1398] The "means for generating a personalized training plan based on the identified patterns" refers to a device or method that creates a training plan specific to the user based on the analysis results.

[1399] "Means for collecting robot operation data and sensor information" refers to a device or method for collecting the robot's work history and sensor data (e.g., motor temperature, battery status).

[1400] "Means for transmitting collected data to a server and analyzing the efficiency of the robot and the tendency for errors to occur" refers to a device or method for transmitting collected information to a server and analyzing the operating efficiency of the robot and the tendency for errors to occur.

[1401] The "means for generating a training plan dedicated to a robot based on the analysis results" refers to a device or method for creating a work schedule or maintenance plan to maximize the efficiency of a robot based on the analysis results.

[1402] The "means for transmitting the generated training plan to the robot" is a device or method for transmitting the generated training plan to the robot.

[1403] The "means for providing real-time feedback to a robot" refers to a device or method that provides optimization instructions or advice for improving efficiency in real time according to the working status of the robot.

[1404] In the system for implementing the present invention, a server, a terminal, and a user work together as follows.

[1405] server

[1406] 1. Data collection

[1407] The server receives the user's behavioral data, biometric data, and feedback information in real time. Specifically, when the user records the start and end times of a task, that data is sent from the device to the server. Biometric data, such as heart rate, brain waves, and electrodermal activity, are collected from the wearable device.

[1408] 2. Data Analysis

[1409] The server analyzes the user's self-control and concentration patterns based on the collected data, for example by cross-referencing behavioral data with heart rate to identify times when concentration is at its best.

[1410] 3. Generate a training plan

[1411] The server then generates a personalized training plan based on the analysis results. It offers plans for task management, attention training, and stress management using techniques such as the Pomodoro Technique. For example, it suggests a plan in which the user alternates between 20 minutes of focused work and 5 minutes of rest.

[1412] 4. Providing Feedback

[1413] The server updates the training plan based on the user's progress and feedback, providing real-time feedback, such as "Your peak concentration is between 2:00 and 3:00 p.m. Try using this time slot again next week."

[1414] Terminal

[1415] 1. Data recording and transmission

[1416] The device records the user's behavioral data and biometric data and periodically transmits it to a server. For example, heart rate data acquired by a smartwatch is stored on the device and then transmitted to a server.

[1417] 2. Receiving and viewing training plans

[1418] The device receives the training plan sent from the server and notifies the user. For example, a notification such as "Please concentrate on your work for the next 25 minutes" will appear on the smartphone.

[1419] 3. Displaying and Carrying Out Instructions

[1420] The device will then instruct the user to start tasks and take rests according to the instructions in the training plan. For example, when it's time to take a break, it will notify the user by saying, "You have 5 minutes left to take a break."

[1421] User

[1422] 1. Implement a training plan

[1423] The user performs tasks according to the training plan provided by the device, for example, concentrating on tasks at designated times and taking appropriate breaks according to notifications.

[1424] 2. Providing Feedback

[1425] Users can send feedback about the effectiveness of the training plan to the server via their devices, for example by answering a questionnaire about their sense of accomplishment and satisfaction with the task.

[1426] 3. Self-monitoring

[1427] Users can use the device to self-monitor and check their own performance based on the data, for example by viewing a graph of their daily concentration fluctuations.

[1428] Specific examples

[1429] Example: A robot's operational data and sensor information are collected and sent to a server. The server analyzes the robot's operational efficiency and error trends, and generates and sends a training plan based on the results. For example, the server provides the robot with feedback such as, "Avoid heavy workloads for the next 30 minutes and perform inspections."

[1430] Prompt Sentence Examples

[1431] "A system that analyzes the operational data of robots in factories and generates optimal work schedules to maximize efficiency."

[1432] "How to generate training plans to improve efficiency in real time based on data collected by a robot"

[1433] This system can optimize the production efficiency and concentration of users and robots in real time, improving their self-control during actual work and tasks.

[1434] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1435] Step 1:

[1436] Data collection

[1437] The user uses a device (e.g., smartphone or smartwatch) to record the start and end times of a task and collect biometric data (e.g., heart rate, electrodermal activity). The device then sends this data to a server. The input is task data and biometric data, and the output is data sent to the server.

[1438] Step 2:

[1439] Receiving data

[1440] The server receives behavioral data and biometric data sent from the device in real time. The input is the data sent from the device, and the output is the storage of the received data. The server stores this data in a database.

[1441] Step 3:

[1442] Data analysis

[1443] The server analyzes the received data to identify patterns of the user's self-control and concentration. For example, it analyzes heart rate fluctuations and task completion times to identify times when concentration is at its peak. The input is the stored data, and the output is the analysis results. Statistical analysis and machine learning models are used to process the data.

[1444] Step 4:

[1445] Generate a training plan

[1446] The server generates a personalized training plan for the user based on the analysis results. For example, it suggests 20 minutes of focused work followed by a 5-minute break based on the Pomodoro technique. The input is the analysis results, and the output is the training plan. A generative AI model is used to generate a personalized plan.

[1447] Step 5:

[1448] Submit a training plan

[1449] The server sends the generated training plan to the device. The input is the training plan, and the output is the transmission of the plan to the device. Feedback messages are also included.

[1450] Step 6:

[1451] View your training plan

[1452] The device displays the received training plan to the user. For example, it may notify the user, "Please concentrate on your work for the next 25 minutes." The input is the plan from the server, and the output is the notification to the user.

[1453] Step 7:

[1454] Executing a task

[1455] The user performs tasks according to the training plan provided by the device. The input is instructions from the device, and the output is the completion of the task. For example, the user can concentrate on the work at a specified time and take appropriate breaks according to notifications.

[1456] Step 8:

[1457] Providing Feedback

[1458] The user sends feedback about the effectiveness of the training plan to the server via their device. The input is the user's feedback, and the output is the transmission of feedback to the server. This includes a questionnaire about the sense of accomplishment and satisfaction with the task.

[1459] Step 9:

[1460] Analyzing feedback and updating plans

[1461] The server analyzes user feedback and dynamically readjusts the training plan. The input is user feedback and the output is an updated training plan. The server regenerates the personalized plan and resubmits it if necessary.

[1462] This series of steps optimizes the self-control and concentration of users and robots in real time, improving production efficiency.

[1463] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1464] System Overview

[1465] This invention combines a support system for enhancing an individual's self-control and maximizing concentration with an emotional engine. The system utilizes the user's behavioral data, biometric data, feedback, and even emotional state, and generates a personalized training plan based on this data and provides the user with real-time feedback.

[1466] server

[1467] 1. Data Collection:

[1468] The server receives the user's behavioral data, biometric data, feedback, and emotional data in real time. For example, if a user is using an emotion recognition application, emotional data from the application will be sent to the server via the device. This data includes behavioral data (such as the start and end times of a task) and biometric data (such as heart rate and brain waves), as well as the user's current emotional state.

[1469] 2. Data Analysis:

[1470] The server analyzes the received data to identify patterns of the user's self-control, focus, and emotions. For example, it cross-references heart rate data with emotional data to identify times when they are most focused or stressed.

[1471] 3. Role of the Emotion Engine:

[1472] The emotion engine analyzes the user's facial expressions, tone of voice, input text, etc. to identify the user's emotional state in real time, for example, it can identify different emotional states such as whether the user is stressed or relaxed.

[1473] 4. Generate a training plan:

[1474] The server then generates a personalized training plan based on the analysis results, taking into account the user's emotional state and offering plans incorporating the Pomodoro technique, specific breathing exercises, and relaxation techniques. For example, if the user is feeling stressed, the server will suggest a plan that includes a short period of relaxation.

[1475] 5. Providing Feedback:

[1476] The server dynamically updates the training plan based on the user's progress, feedback, and emotional data, providing real-time feedback, such as "Taking five minutes of relaxation time into account, given your current emotional state."

[1477] Terminal

[1478] 1. Data recording and transmission:

[1479] The device records the user's behavioral data, biometric data, feedback, and emotional data and periodically transmits them to a server. For example, a smartwatch records the user's heart rate and emotional state and transmits the data to a server.

[1480] 2. Receive and view training plans:

[1481] The device notifies the user of the training plan sent from the server. For example, a notification such as "Work with focus for the next 25 minutes. You appear to be in a stressful emotional state, so take 5 minutes of relaxation afterwards" will appear on the smartphone.

[1482] 3. Displaying and Carrying Out Instructions:

[1483] The device will prompt the user to start tasks and take rests according to the instructions in the training plan. For example, when it's time for a break, it will notify the user, "You have 5 minutes left to take a break." If emotions are negative, it will give instructions such as, "Take a deep breath and relax."

[1484] User

[1485] 1. Implement your training plan:

[1486] The user performs tasks according to the training plan provided by the device, for example, concentrating on a task at a specified time and taking appropriate breaks or relaxation according to notifications.

[1487] 2. Providing Feedback:

[1488] Users send feedback about the effectiveness of the training plan to the server via their device, for example by answering a questionnaire about their sense of accomplishment, satisfaction, and emotional fluctuations.

[1489] 3. Self-monitoring:

[1490] Users can use the device to self-monitor and check their performance and emotional state based on the data, for example by viewing graphs of their daily concentration and emotional fluctuations.

[1491] Through these processes, the system effectively supports users in self-control, concentration and emotional management.

[1492] The processing flow will be explained below.

[1493] Specific processing flow of the program

[1494] Data Collection Phase

[1495] Step 1:

[1496] User

[1497] A user installs a task management app and records the start and end times of a task. For example, they open the "Task Tracker" app and press the start button for the task.

[1498] Step 2:

[1499] Terminal

[1500] The device saves the data recorded by the task management app in its storage, and also saves the end time when the task is completed.

[1501] Step 3:

[1502] Terminal

[1503] The smartwatch periodically records the user's biometric data, such as heart rate, for example, every five minutes and stores it on the device.

[1504] Step 4:

[1505] Terminal

[1506] The emotion recognition app analyzes the user's facial expressions and voice to collect real-time emotional data, which is also stored on the device.

[1507] Step 5:

[1508] User

[1509] After completing the training, users complete an in-app survey to provide subjective feedback, such as, "How focused were you on this task?"

[1510] Data Transfer Phase

[1511] Step 6:

[1512] Terminal

[1513] The collected behavioral data, biometric data, emotional data, and feedback data packages are sent to a server, for example, by converting the collected data into JSON format and uploading it to the server via the Internet.

[1514] Data analysis phase

[1515] Step 7:

[1516] server

[1517] Analyzes incoming data, cleansing and normalizing it, and handling outliers and missing data to improve analysis accuracy.

[1518] Step 8:

[1519] server

[1520] By cross-referencing the user's heart rate data with their emotional data, the system identifies times when they are most likely to concentrate or feel stressed. For example, it identifies "times when the heart rate is stable and positive emotions are high" as times when they are most likely to concentrate.

[1521] Step 9:

[1522] server

[1523] It identifies patterns in the user's self-control, focus, and emotions, and stores the results in a database. For example, the user's characteristics can be reflected in a model and used to generate a plan for the next time.

[1524] Training plan generation phase

[1525] Step 10:

[1526] server

[1527] Based on the analysis results, the system generates a personalized training plan that takes into account the user's emotional state. For example, it suggests the user follow the Pomodoro technique (25 minutes of work + 5 minutes of rest) and also provides a plan that includes relaxation.

[1528] Step 11:

[1529] server

[1530] The generated training plan is sent to the user's device. For example, the generated plan is sent in JSON format and displayed in an app on the device.

[1531] Feedback Phase

[1532] Step 12:

[1533] Terminal

[1534] The device will then notify the user of the training plan and display instructions, such as "Work with focus for the next 25 minutes, after which you will experience emotional stress, and then take 5 minutes of relaxation."

[1535] Step 13:

[1536] User

[1537] The user performs tasks according to the training plan, taking breaks and relaxation at appropriate times, for example, following the instructions on the device to take a five-minute break and then resume work.

[1538] Step 14:

[1539] Terminal

[1540] The device monitors the user's progress and real-time emotional data and continuously transmits the data to the server, for example, after completing a task or when the user's emotional state changes.

[1541] Step 15:

[1542] server

[1543] The server dynamically readjusts the training plan based on the user's progress data and feedback. For example, if the user tells the server that they were losing focus, the server will create a plan that includes shorter sessions and more frequent breaks.

[1544] Step 16:

[1545] server

[1546] The server generates a weekly progress report and sends it to the user, notifying them, for example, that their peak concentration time this week is between 2:00 and 3:00 PM. It also provides advice such as, "Based on your emotional data, it would be beneficial to spend more time relaxing."

[1547] Step 17:

[1548] User

[1549] Users can check their progress reports and use them to plan their training for the next week, for example by scheduling tasks for the next week based on their peak concentration times.

[1550] Through these processing steps, the system effectively supports the user's self-control, concentration and emotional management.

[1551] Example 2

[1552] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1553] A user's self-control, concentration, and emotional management need to be appropriately supported based on their individual state and situation. However, conventional systems collect users' biometric and behavioral data, but do not consider their emotional data, making it difficult to provide training plans tailored to individual emotional states. Therefore, a system that provides more personalized support by taking into account the user's emotional state is needed.

[1554] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1555] In this invention, the server includes means for collecting user behavioral data, means for collecting user biometric data, means for collecting user-provided feedback, means for collecting user emotional data, means for transmitting the collected data to the server, means for analyzing the received data and identifying patterns of the user's self-control, concentration, and emotions, means for generating a personalized training plan based on the identified patterns, means for transmitting the generated training plan to the user's device, and means for providing real-time feedback to the user, thereby making it possible to provide a training plan that also takes the user's emotional state into consideration.

[1556] "User behavioral data" refers to data that records the specific movements and actions of a user in their daily life and work.

[1557] "User's biometric data" refers to data that indicates the user's physical condition, including physiological indicators such as heart rate and brain waves.

[1558] "Feedback" is data that records opinions, ratings, and reactions provided by users.

[1559] "Emotional data" refers to data that indicates a user's emotional state, and includes information such as facial expressions, voice tone, and text input obtained using emotion recognition technology.

[1560] A "server" is a central processing unit that receives data sent from the user's device and performs analysis, generates training plans, etc.

[1561] A "terminal" is a device that is directly operated by a user, including smartphones and smartwatches.

[1562] "Self-control" refers to the user's ability to appropriately manage their own behavior and emotions and maintain a desired state.

[1563] "Concentration" refers to a user's ability to focus their mind on a particular task or activity.

[1564] "Training Plan" means a personalized plan or instruction to improve a User's self-control and focus.

[1565] "Real-time feedback" refers to instant feedback and advice based on the actions a user takes and the data they provide.

[1566] "Analysis" refers to the data processing process that uses collected data to identify patterns of user behavior and emotions.

[1567] An "emotion engine" refers to software or hardware processing that analyzes a user's facial expressions, voice tone, text, etc. to identify their emotional state.

[1568] "Personalization" refers to customizing training plans and feedback to suit each individual user's specific needs and circumstances.

[1569] "Peak hours" refer to times when users are most focused and productive.

[1570] "Stressful times" refer to the times when users are most likely to feel stressed.

[1571] System Overview

[1572] This invention combines an emotional engine with a support system for enhancing an individual's self-control and maximizing concentration. The system utilizes the user's behavioral data, biometric data, feedback, and even emotional data to generate a personalized training plan and provide real-time feedback to the user.

[1573] Hardware and Software

[1574] Hardware: smartwatches (e.g., devices for measuring heart rate), smartphones (e.g., used as a user interface)

[1575] Software: emotion recognition apps (e.g., tools for analyzing user emotions in real time), self-control support apps (e.g., apps that provide notifications and collect feedback)

[1576] System program processing flow

[1577] server

[1578] 1. Data Collection:

[1579] The server receives users' behavioral data, biometric data, feedback, and emotional data in real time. For example, a smartwatch records heart rate and activity data and sends the data to the server. An emotion recognition app also collects users' emotional data and sends it to the server.

[1580] 2. Data Analysis:

[1581] The server analyzes the received data to identify patterns of self-control, concentration, and emotions of the user. Specifically, it cross-references heart rate data with emotional data to identify times of high concentration and times of stress.

[1582] 3. Role of the Emotion Engine:

[1583] The emotion engine analyzes the user's facial expressions, voice tone, and input text to identify the user's emotional state in real time. For example, if the user inputs "I'm very tired today," the engine will identify fatigue from the text. The facial recognition camera also detects the user's smile and determines that the user is relaxed.

[1584] 4. Generate a training plan:

[1585] The server then generates a personalized training plan based on the analysis results. For example, if a user's concentration decreases and stress increases at 3 p.m., the server will suggest a plan that includes relaxation time before and after 3 p.m.

[1586] 5. Providing Feedback:

[1587] The server dynamically updates the training plan based on the user's progress, feedback, and emotional data, providing real-time feedback, such as notifications like "Take five minutes of relaxation time to consider your current emotional state."

[1588] Terminal

[1589] 1. Data recording and transmission:

[1590] The device records the user's behavioral data, biometric data, feedback, and emotional data and periodically transmits them to a server. For example, a smartwatch collects heart rate data every hour and transmits it to a server.

[1591] 2. Receive and view training plans:

[1592] The device receives the training plan sent from the server in real time and notifies the user, for example, a message such as "Work hard for the next 25 minutes, then take 5 minutes of relaxation" will be displayed on the smartphone.

[1593] 3. Displaying and Carrying Out Instructions:

[1594] The device will follow the instructions of the training plan and prompt the user to start a task or take a rest. For example, the smartwatch will notify the user that it's time to take a break in five minutes. If the user is feeling negative, it will display instructions such as "Take a deep breath and relax."

[1595] User

[1596] 1. Implement your training plan:

[1597] The device provides users with a training plan to help them accomplish tasks, such as working during designated periods of focus and then using breaks to practice relaxation.

[1598] 2. Providing Feedback:

[1599] Users can send feedback about the effectiveness of their training plan to a server via their device, for example by answering a questionnaire after completing a task, asking questions such as "Was your concentration high while working?" or "Did your stress level decrease?"

[1600] 3. Self-monitoring:

[1601] Users can use the device to self-monitor and check their own performance and emotional state based on the data. For example, the smartphone app can visualize daily fluctuations in concentration and emotions in graphs to check the effectiveness of their training.

[1602] Example prompt

[1603] 1. The user puts on the smartwatch

[1604] "Wear your smartwatch and enter your self-assessment at each moment of exercise and rest throughout the day."

[1605] 2. Applying the Pomodoro Technique

[1606] "You'll have 25 minutes of focused work time, after which you'll receive a notification to take a 5-minute break."

[1607] 3. Entering Emotion Data

[1608] "Enter your current mood on a scale of 1 to 5. The emotion recognition app will analyze your facial expressions and collect emotional data."

[1609] This allows the system to effectively support users' self-control, concentration and emotional management.

[1610] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1611] System processing steps

[1612] Step 1:

[1613] The server receives the user's behavioral data, biometric data, feedback, and emotional data in real time. Specifically, the device (smartwatch) records heart rate and activity data and sends the data to the server. The emotion recognition app also collects the user's emotional data and sends it to the server. The input data are behavioral data, biometric data, feedback, and emotional data, which are integrated and efficiently sent to the server. The output is the integrated data sent to the server.

[1614] Step 2:

[1615] The server analyzes the received data. Specifically, it cross-references the heart rate data with the emotional data to identify the user's concentration and stress patterns. The input data is the integrated data sent to the server. Data analysis algorithms are used to identify the user's peak concentration and stress times. The output is data that identifies the user's concentration and stress patterns.

[1616] Step 3:

[1617] The emotion engine on the server analyzes the user's facial expression, voice tone, input text, etc. to identify the user's emotional state in real time. The input data is facial expression data, voice tone data, and text data. The emotion recognition algorithm is used to analyze the user's emotional state in real time. The output is the identified emotional state data.

[1618] Step 4:

[1619] The server generates a personalized training plan based on the analysis results. For example, for a user whose concentration decreases and stress increases around 3:00 p.m., it suggests a plan that includes relaxation time around that time. The input data is analysis data on the user's concentration, stress, and emotional state. An algorithm is used to generate a plan that incorporates the Pomodoro technique, specific breathing techniques, etc. The output is a personalized training plan.

[1620] Step 5:

[1621] The server dynamically updates the training plan based on the user's progress, feedback, and emotional data, and provides real-time feedback. For example, it sends a notification such as, "Taking five minutes of relaxation time into account, given your current emotional state." The input data are the user's progress data, feedback data, and emotional data. The dynamic plan update algorithm generates real-time feedback. The output is the updated training plan and feedback.

[1622] Step 6:

[1623] The device records the user's behavioral data, biometric data, feedback, and emotional data and periodically transmits them to a server. Specifically, the smartwatch collects heart rate data every hour and transmits it to the server. The input data are the user's behavioral data, biometric data, feedback, and emotional data. A data collection and transmission algorithm is used. The output is the recorded data sent to the server.

[1624] Step 7:

[1625] The device receives the training plan sent from the server and notifies the user. For example, a message such as "Work concentratively for the next 25 minutes, then take 5 minutes of relaxation" is displayed on the smartphone. The input data is the training plan sent from the server. The notification function is used. The output is the notification displayed to the user.

[1626] Step 8:

[1627] The device prompts the user to start a task or take a rest according to the instructions in the training plan. For example, the smartwatch may send a notification saying, "Take a break in 5 minutes," or, if emotions are negative, it may display instructions such as, "Take a deep breath and relax." The input data are instructions based on the training plan. Task management and notification algorithms are used. The output is instructions displayed to the user.

[1628] Step 9:

[1629] The user performs tasks according to a training plan provided by the device. For example, the user works during a designated concentration period and then uses the subsequent break time to practice relaxation. The input data is the training plan provided by the device. A task execution algorithm is used. The output is the completed task.

[1630] Step 10:

[1631] The user sends feedback about the effectiveness of the training plan to the server via their device. For example, after completing a task, they answer a questionnaire such as "Was your concentration high while working?" or "Did your stress level decrease?" The input data is the user's feedback about the effectiveness of the training plan. A feedback collection algorithm is used. The output is the feedback sent to the server.

[1632] Step 11:

[1633] Users use the device to self-monitor and check their own performance and emotional state based on the data. For example, a smartphone app can visualize daily fluctuations in concentration and emotions in graphs to check the effectiveness of training. The input data is past behavioral data, biological data, and emotional data. A data visualization algorithm is used. The output is data visualized as a graph.

[1634] (Application example 2)

[1635] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1636] Many existing systems for improving self-control and concentration generate training plans based on biometric and behavioral data. However, few systems take the user's emotional state into account. Therefore, there is a need for a system that can grasp the user's stress and relaxation levels in real time and provide feedback and content recommendations that adapt to those conditions. Furthermore, there has yet to be a fully developed system that can comprehensively analyze this data and provide dynamically personalized plans.

[1637] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric data of the user, means for collecting the user's emotional state, and means for analyzing the received data and identifying the user's self-control, concentration, and emotional patterns. This makes it possible to recommend personalized training plans and content adapted to the user's stress and relaxation state based on the analysis results. This allows the user to receive support that allows them to maintain concentration more effectively and comfortably and manage their emotional state.

[1638] definition statement

[1639] "User behavior data" refers to information about user behavior, specifically data including viewing time, frequency of clicks and swipes, etc.

[1640] "User biometric data" refers to information about the user's physiological state, including, for example, heart rate and eye tracking data.

[1641] "Feedback provided by users" refers to information such as ratings, impressions, and survey responses provided by users to the system.

[1642] "User's emotional state" refers to information about the user's real-time emotions analyzed by the emotion engine, including stress levels, relaxed states, etc.

[1643] "Server" refers to the computer system that receives and analyzes data, generates personalized training plans and content recommendations, and transmits them to the User's device.

[1644] "Analysis" refers to the process of using collected user behavioral data, biometric data, feedback, and emotional state to identify patterns of self-control, focus, and emotions.

[1645] "Personalized training plan" refers to an individually optimized training plan generated based on data for each individual user.

[1646] "Content recommendation" refers to the act of recommending optimal content (videos, music, articles, etc.) based on the user's current state and patterns.

[1647] "Real-time feedback" refers to advice or instructions provided immediately in response to a user's actions or state.

[1648] "Dynamic readjustment" refers to the process of updating and changing training plans and content recommendations on the fly based on analysis results.

[1649] MODE FOR CARRYING OUT THE INVENTION

[1650] System Overview

[1651] This invention is a support system for improving an individual's self-control and concentration, and combines an emotion engine. The system utilizes the user's behavioral data, biometric data, feedback, and emotional state, and generates personalized training plans and content recommendations based on this data, providing real-time feedback to the user.

[1652] server

[1653] 1. Data Collection:

[1654] The server receives user behavioral data, biometric data, feedback, and emotional data in real time. For example, the server receives data sent from smartphones, wearable devices, etc. This data includes browsing time, click / swipe frequency, heart rate, facial expressions, voice tone, and input text.

[1655] 2. Data Analysis:

[1656] The server analyzes the received data to identify patterns of the user's self-control, concentration, and emotions. For example, it cross-references behavioral data with biometric data to identify times of high concentration and times of stress. The emotion engine analyzes facial expressions, voice tone, text, etc. to identify emotional states.

[1657] 3. Generating training plans and content recommendations:

[1658] The server generates personalized training plans and content recommendations based on the analysis results. Taking into account the user's emotional state, the server offers plans incorporating the Pomodoro technique, specific breathing exercises, and relaxation techniques. For users feeling stressed, the server recommends relaxation music and meditation videos.

[1659] 4. Providing Feedback:

[1660] The server dynamically updates training plans and content recommendations based on the user's progress, feedback, and emotional data, providing real-time feedback, such as "Taking five minutes of relaxation time into account, given your current emotional state."

[1661] Terminal

[1662] 1. Data recording and transmission:

[1663] The device records the user's behavioral data, biometric data, feedback, and emotional data and periodically transmits them to a server. For example, a smartphone records browsing data, and a smartwatch records heart rate and emotional state and transmits them to a server.

[1664] 2. Receive and view training plans and content recommendations:

[1665] The device will notify the user of training plans and content recommendations sent from the server, such as "Work hard for the next 25 minutes, then listen to 5 minutes of relaxation music."

[1666] 3. Displaying and Carrying Out Instructions:

[1667] The device will prompt the user to start tasks or take a rest according to the instructions in the training plan. For example, when it's time for a break, it will notify the user, "You have 5 minutes left to take a break," and if it detects negative emotions, it will instruct the user to "take a deep breath and relax."

[1668] User

[1669] 1. Implement your training plan:

[1670] The user performs tasks according to the training plan provided by the device, for example, concentrating on a task at a specified time and taking appropriate breaks or relaxation according to notifications.

[1671] 2. Providing Feedback:

[1672] Users can send feedback about the effectiveness of their training plan to the server via their device, for example by answering a questionnaire about their emotional state.

[1673] 3. Self-monitoring:

[1674] Users can use the device to self-monitor and check their performance and emotional state based on the data, for example by viewing graphs of their daily concentration and emotional fluctuations.

[1675] Hardware and software used

[1676] Hardware:

[1677] Smartphones, smartwatches, heart rate monitors, facial recognition cameras

[1678] software:

[1679] Python, emotion recognition API (Emotion API, etc.), data analysis library (Pandas, etc.)

[1680] Specific examples / prompt sentence examples

[1681] Examples:

[1682] If a user watches a documentary for 30 minutes and their heart rate increases and they click and swipe more, the app will determine that they are in a high-stress state and notify them, "It's time to take a short break. We recommend watching a relaxing meditation video next."

[1683] Example prompt for a generative AI model:

[1684] "Generate a Python program to recommend optimal content and break timing using user behavioral data, heart rate, biometric data, and real-time analyzed emotional state."

[1685] "If a user is in a high-stress state, design your app to recommend relaxing content and encourage them to take a break."

[1686] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1687] Program processing steps

[1688] Step 1: Collect data

[1689] The server collects user behavioral data, biometric data, feedback and emotional data in real time.

[1690] Input: Data sent from smartphones, smartwatches, heart rate monitors, and facial recognition cameras.

[1691] Data processing: All data is converted into a unified format and stored in a database.

[1692] Output: A consolidated user dataset.

[1693] Step 2: Analyze the data

[1694] The server analyzes the user's self-control, concentration and emotional patterns based on the collected data.

[1695] Input: The consolidated user dataset.

[1696] Data calculations: Cross-reference behavioral and biological data to identify periods of high concentration and stress. Analyze emotional states using an emotion engine.

[1697] Output: User's self-control, concentration, and emotional patterns.

[1698] Step 3: Generate training plans and content recommendations

[1699] The server generates personalized training plans and content recommendations based on the analysis results.

[1700] Input: The user's self-control, focus, and emotional patterns.

[1701] Data processing: Based on the user's identified patterns, training plans including the Pomodoro technique and breathing exercises, as well as content such as relaxation music and meditation videos are determined.

[1702] Output: Personalized training plans and recommended content.

[1703] Step 4: Submit your training plan and content recommendations

[1704] The server transmits the generated training plan and content recommendations to the user's terminal.

[1705] Input: Personalized training plans and recommended content.

[1706] Data processing: Convert the training plan and recommended content into notification format and send it to your device.

[1707] Output: The notification that appears on the user's device.

[1708] Step 5: Provide real-time feedback

[1709] The device notifies the user of the feedback sent from the server in real time.

[1710] Input: Notification data from the server.

[1711] Data processing: Display notification content in a format that is easy for users to understand.

[1712] Output: Real-time instructions and advice displayed on the user's device.

[1713] Step 6: User feedback

[1714] Users send feedback about training plans and content recommendations to the server via their devices.

[1715] Input: User ratings, comments, and survey responses.

[1716] Data processing: The feedback data is converted into a format that can be sent to the server.

[1717] Output: The feedback data received by the server.

[1718] Step 7: Dynamically adjust training plans and content recommendations

[1719] The server dynamically readjusts training plans and content recommendations based on user feedback and progress data.

[1720] Input: Feedback data, progress data.

[1721] Data Calculation: Analyzes feedback and progress data to update training plans and content recommendations.

[1722] Output: Dynamically adjusted training plans and recommended content.

[1723] Step 8: Provide self-monitoring data

[1724] The device provides the user with self-monitoring data.

[1725] Input: Analysis results of user data sent from the server.

[1726] Data processing: Converting data into graphs and charts that are easy for users to understand.

[1727] Output: Self-monitoring data displayed on the terminal.

[1728] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1729] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1730] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1731] [Fourth embodiment]

[1732] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1733] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1734] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1735] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1736] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1737] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1738] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1739] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1740] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1741] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1742] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1743] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1744] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1745] System Overview

[1746] This invention is a support system for enhancing an individual's self-control and maximizing concentration. The system utilizes the user's behavioral data, biometric data, and feedback, and generates a personalized training plan based on this data and provides the user with real-time feedback.

[1747] server

[1748] 1. Data Collection:

[1749] The server receives the user's behavioral data, biometric data, and feedback in real time. For example, when a user uses a task management app to record the start and end times of a task, the data is sent from the device to the server. Biometric data includes heart rate, brain waves, and electrodermal activity. This data is collected from the wearable device.

[1750] 2. Data Analysis:

[1751] The server analyzes the collected data to identify patterns of self-control and concentration, for example by cross-referencing heart rate and task time to detect times when concentration is at its peak.

[1752] 3. Generate a training plan:

[1753] The server then generates a personalized training plan based on the analysis results. It uses techniques like the Pomodoro technique to provide plans for task management, attention training, and stress management. For example, it suggests a plan in which the user alternates between 20 minutes of focused work and 5 minutes of rest.

[1754] 4. Providing Feedback:

[1755] The server updates the training plan based on the user's progress and feedback, providing real-time feedback, such as "Your peak concentration time is between 2:00 and 3:00 p.m. Try using this time slot again next week."

[1756] Terminal

[1757] 1. Data recording and transmission:

[1758] The device records the user's behavioral data and biometric data and periodically transmits it to a server. For example, a smartwatch measures heart rate and stores the data on the device. The device then compiles the collected data and transmits it to the server.

[1759] 2. Receive and view training plans:

[1760] The device receives the training plan sent from the server and notifies the user. For example, a notification such as "Please concentrate on your work for the next 25 minutes" will be displayed on the smartphone.

[1761] 3. Displaying and Carrying Out Instructions:

[1762] The device will then prompt the user to start tasks and take rests according to the instructions in the training plan. For example, when it's time to take a break, it will notify the user, "You have 5 minutes left to take a break."

[1763] User

[1764] 1. Implement your training plan:

[1765] The user performs tasks according to the training plan provided by the device, for example, concentrating on the task at the specified time and taking appropriate breaks according to notifications.

[1766] 2. Providing Feedback:

[1767] Users can send feedback about the effectiveness of the training plan to the server via their devices, for example by answering a questionnaire about their sense of accomplishment and satisfaction with the task.

[1768] 3. Self-monitoring:

[1769] Users can use the device to self-monitor and check their own performance based on the data, for example by viewing a graph of their daily concentration fluctuations.

[1770] Through these processes, the system effectively enhances the user's self-control and concentration, helping them complete tasks and achieve goals.

[1771] The processing flow will be explained below.

[1772] Specific processing flow of the program

[1773] Data Collection Phase

[1774] Step 1:

[1775] User

[1776] A user installs a task management app and records the start and end times of a task. For example, they open the "Task Tracker" app and press the start button for the task.

[1777] Step 2:

[1778] Terminal

[1779] The device saves the data recorded by the task management app in its storage, and also saves the end time when the task is completed.

[1780] Step 3:

[1781] Terminal

[1782] The smartwatch periodically records the user's biometric data, such as heart rate, for example, every five minutes and stores it on the device.

[1783] Step 4:

[1784] User

[1785] After completing the training, users provide subjective feedback by completing an in-app survey, answering questions such as, "How focused were you on this task?"

[1786] Data Transfer Phase

[1787] Step 5:

[1788] Terminal

[1789] The collected behavioral data, biometric data, and feedback data package is sent to a server, for example, by converting the collected data into JSON format and uploading it to the server via the internet.

[1790] Data analysis phase

[1791] Step 6:

[1792] server

[1793] Analyzes incoming data, cleansing and normalizing it, and handling outliers and missing data to improve analysis accuracy.

[1794] Step 7:

[1795] server

[1796] The system cross-references the user's heart rate data with the time spent performing a task to detect periods of peak concentration. For example, it identifies periods of sustained low heart rate as "periods of high concentration."

[1797] Step 8:

[1798] server

[1799] The system identifies the user's self-control and concentration patterns and stores them in a database. The user's characteristics are then reflected in a model and used to generate the next plan.

[1800] Training plan generation phase

[1801] Step 9:

[1802] server

[1803] Based on the analysis results, it generates a personalized training plan, for example, suggesting the user to use the Pomodoro technique and setting a cycle of 25 minutes of work and 5 minutes of rest.

[1804] Step 10:

[1805] server

[1806] The generated training plan is sent to the user's device. For example, the generated plan is sent in JSON format and displayed in an app on the device.

[1807] Feedback Phase

[1808] Step 11:

[1809] Terminal

[1810] The device will then notify the user of the training plan and provide instructions, such as "Work hard and stay focused for the next 25 minutes."

[1811] Step 12:

[1812] User

[1813] The user performs tasks according to the training plan and takes a break at the designated time, for example, following the instructions on the device to take a 5-minute break and then resume work.

[1814] Step 13:

[1815] Terminal

[1816] The device monitors the user's progress and sends data to the server in real time, for example, sending the data again when the task is completed.

[1817] Step 14:

[1818] server

[1819] The server dynamically adjusts the training plan based on the user's progress data and feedback. For example, if the user's training plan is shorter than the planned one for the following week, the server receives feedback that the user was unable to maintain concentration.

[1820] Step 15:

[1821] server

[1822] The server generates a weekly progress report and sends it to the user, telling them, for example, "Your peak concentration time this week is between 2:00 and 3:00 PM."

[1823] Step 16:

[1824] User

[1825] Users can check their progress reports and use them to plan their training for the next week, for example by scheduling tasks for the next week based on their peak concentration times.

[1826] Through these processing steps, the system provides effective support to enhance the user's self-control and concentration.

[1827] Example 1

[1828] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1829] In recent years, there has been a growing demand for improving concentration and self-control, and many people are looking for effective training methods. However, it is difficult for a generic training plan to provide optimal improvement measures for each user, and personalized training plans based on individual biometric and behavioral data are needed. Therefore, a method is needed to identify the user's peak concentration times and provide an optimized training plan in real time based on individual feedback.

[1830] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1831] In this invention, the server includes means for collecting user behavioral data, means for collecting user biometric data, means for collecting user-provided feedback, means for transmitting the collected data to the server, means for analyzing the received data and identifying the user's self-control and concentration patterns, means for generating a training plan utilizing the Pomodoro Technique, means for transmitting the generated training plan to the user's device, and means for providing real-time feedback to the user. This allows for the generation of an optimal training plan for each user and the provision of real-time feedback. Furthermore, the system detects the user's peak concentration times and dynamically readjusts the plan based on the detected times, and by linking with a calendar application, the system can efficiently improve the user's self-control and concentration.

[1832] "User behavioral data" refers to information about a user's activities, such as the start time, end time, and type of task performed by the user.

[1833] "User biometric data" means information about a user's physiological state or physical activity, such as heart rate, electrodermal activity, brain waves, and number of steps taken.

[1834] "Feedback" refers to information such as ratings and comments provided by users regarding the effectiveness and satisfaction of their training plans.

[1835] "Server" is a computer system for analyzing collected data, generating training plans, and providing feedback to users.

[1836] A "terminal" is a device that collects user behavioral data and biometric data and transmits it to a server, and includes smartphones and smartwatches.

[1837] "Data analysis" is the process of analyzing collected behavioral and biometric data using statistical methods and time series analysis to identify patterns of the user's concentration and self-control.

[1838] A "personalized training plan" is a training plan that is individually optimized based on the user's behavioral data, biometric data, and feedback.

[1839] "Real-time feedback" refers to advice and notifications provided to users instantly based on their training plan and analysis results.

[1840] The Pomodoro Technique is a time management technique that promotes efficient work by concentrating on a task for a set period of time and then taking a short break.

[1841] "Peak concentration times" are specific times when a user is determined to be able to concentrate and work most efficiently.

[1842] "Calendar application integration" is a mechanism that automatically integrates the generated training plan into the user's schedule management system.

[1843] "Cross-referencing" is a technique for cross-referencing different types of data (for example, behavioral data and biometric data) and analyzing their relationships.

[1844] "Time series analysis" is a statistical method for analyzing fluctuations and trends in data over time.

[1845] System Overview

[1846] This invention is a support system for improving a user's self-control and concentration. The system uses the user's behavioral data, biometric data, and feedback to generate a personalized training plan and provides feedback to the user in real time.

[1847] Server Features

[1848] The server provides the following functions:

[1849] 1. Receiving data:

[1850] The server receives user behavioral data, biometric data, and feedback in real time from devices (e.g., smartphones and smartwatches) via the Internet.

[1851] 2. Data Analysis:

[1852] The server analyzes the received data using data analysis libraries such as Python and R. For example, it uses Python's Pandas or NumPy to format the data and perform time series analysis.

[1853] 3. Generate a training plan:

[1854] The server generates a personalized training plan using the Pomodoro Technique, which can then be integrated into the user's schedule using the Google Calendar API.

[1855] 4. Real-time feedback:

[1856] The server dynamically readjusts the training plan based on the user's performance and feedback, providing real-time feedback.

[1857] Device Features

[1858] The device provides the following features:

[1859] 1. Data collection and transmission:

[1860] A device (such as a smartwatch or smartphone) collects user behavioral and biometric data and sends it to a server at regular intervals. For example, a smartwatch measures a user's heart rate every second and sends the data to a server via an app on the smartphone.

[1861] 2. Receive and view training plans:

[1862] The device receives the training plan sent from the server and notifies the user, for example by displaying a push notification saying, "Please concentrate on your work for the next 20 minutes."

[1863] 3. Enter your feedback:

[1864] The device receives feedback from the user and sends it to the server. For example, after completing a training plan, the device collects information that the user enters in response to the question, "How effective was the training?"

[1865] User Roles

[1866] Users use the system by following the steps below.

[1867] 1. Implement your training plan:

[1868] Users complete tasks according to a training plan provided by the device, for example, starting the task at a specified time and taking regular breaks according to the Pomodoro technique.

[1869] 2. Providing Feedback:

[1870] Users provide feedback on the effectiveness of their training plan, for example by completing a survey about their sense of accomplishment and attention span.

[1871] Specific examples

[1872] Example 1:

[1873] To discover the best times of day during an eight-hour workday, users wear a smartwatch and collect heart rate data and data from a task management app. The device sends this data to a server at specific intervals. The server analyzes the data and determines that peak concentration occurs between 2:00 and 3:00 PM. The server then generates a training plan for tasks requiring the most concentration during this time period. The device notifies the user of this plan, and the user completes the tasks according to the device's notifications. After completing the training, the user submits feedback regarding satisfaction and sense of accomplishment, and the server updates the training plan based on the new data.

[1874] Example prompt sentence:

[1875] "Measure your heart rate every second with a smartwatch, and use Python to send the data from a task management app to a server every five minutes for analysis. Based on the analysis results, create a training plan for your most important tasks between 2:00 and 3:00 PM, and notify the plan on your smartphone."

[1876] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1877] Step 1: Collect data

[1878] Specific behavior:

[1879] The device collects user behavioral and biometric data, such as heart rate, steps, and electrodermal activity, via a smartwatch, and records the start and end times of tasks via a task management app.

[1880] input:

[1881] Data from users' smartwatches and task management apps.

[1882] Data processing / data calculation:

[1883] It collects heart rate data from a smartwatch and task start and end times from a task management app, and formats them on the device.

[1884] output:

[1885] A set of collected biometric and behavioral data.

[1886] Step 2: Sending data

[1887] Specific behavior:

[1888] The device sends the collected data to the server at regular intervals (for example, every 5 minutes). For example, the data collected by a smartwatch is sent to a smartphone, which then uploads the data to the server.

[1889] input:

[1890] Biometric and behavioral data collected.

[1891] Data processing / data calculation:

[1892] The data is batched and encoded into a format that can be sent.

[1893] output:

[1894] The data batch sent to the server.

[1895] Step 3: Receiving the data

[1896] Specific behavior:

[1897] The server receives the data sent from the terminal in real time via the Internet.

[1898] input:

[1899] The data batch sent from the device.

[1900] Data processing / data calculation:

[1901] The received data is then properly organized and saved for storage in a database.

[1902] output:

[1903] Biometric and behavioral data stored in a database.

[1904] Step 4: Analyze the data

[1905] Specific behavior:

[1906] The server analyzes the received data using data analysis tools such as Python and R. Specifically, it uses time series analysis to examine the relationship between heart rate data and task time, and identifies periods of peak concentration.

[1907] input:

[1908] Biometric and behavioral data stored in a database.

[1909] Data processing / data calculation:

[1910] Extract data from the database, preprocess it using Pandas and NumPy, and perform time series analysis and cross-referencing. For example, analyze trends in heart rate data and task execution times to identify times when users are most focused.

[1911] output:

[1912] The user's peak concentration times and self-control patterns.

[1913] Step 5: Generate a training plan

[1914] Specific behavior:

[1915] The server then generates a personalized training plan based on the analysis results, using the Pomodoro technique to specifically create a plan that alternates between 20 minutes of focused work and 5 minutes of rest.

[1916] input:

[1917] Analyzed peak times of concentration and patterns of self-control.

[1918] Data processing / data calculation:

[1919] Based on the analysis results, a Python script is run to generate a training plan consisting of optimal work and rest cycles.

[1920] output:

[1921] The generated training plan.

[1922] Step 6: Submit your training plan

[1923] Specific behavior:

[1924] The server then sends the generated training plan to the user's device, where it can be incorporated into the user's schedule using, for example, the Google Calendar API.

[1925] input:

[1926] Training plan.

[1927] Data processing / data calculation:

[1928] Format your training plan into a sendable format and send the data.

[1929] output:

[1930] Training plans sent to the user's device.

[1931] Step 7: View and follow your training plan

[1932] Specific behavior:

[1933] The device receives the training plan sent from the server and notifies the user. The user checks the notification and performs the tasks according to the plan. For example, a notification saying "Please concentrate on your work for the next 20 minutes" is displayed, and the user starts working according to the instructions.

[1934] input:

[1935] The training plan sent to your device.

[1936] Data processing / data calculation:

[1937] The received training plan will be displayed and notified to the user within the app.

[1938] output:

[1939] Instructions for the training plan that the user follows.

[1940] Step 8: Gather feedback

[1941] Specific behavior:

[1942] After completing the training plan, the device collects feedback from the user, for example by asking them to answer a survey question such as, "Please rate the effectiveness of the training."

[1943] input:

[1944] User feedback.

[1945] Data processing / data calculation:

[1946] The feedback data is organized and formatted for transmission to the server.

[1947] output:

[1948] Feedback data sent to the server.

[1949] Step 9: Re-adjust your training plan

[1950] Specific behavior:

[1951] The server analyzes the user's feedback and dynamically readjusts the training plan as needed, which is then sent back to the user's device.

[1952] input:

[1953] User feedback and initial training plans.

[1954] Data processing / data calculation:

[1955] Analyze the feedback data and make any necessary adjustments to generate a new training plan.

[1956] output:

[1957] A reworked training plan.

[1958] (Application example 1)

[1959] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1960] Conventional factory robots did not have a self-monitoring and feedback system to maximize their operating efficiency, and required human managers to monitor and manually adjust them one by one. This made it difficult to formulate efficient work schedules and improve efficiency in real time, which could result in a decline in overall factory productivity. This created a need for a system that could autonomously optimize the operating efficiency of factory robots and provide feedback in real time.

[1961] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1962] In this invention, the server includes means for analyzing the collected data to analyze the efficiency and error occurrence tendency of the robot, means for generating a training plan dedicated to the robot based on the analysis results, and means for transmitting the generated training plan to the robot, thereby enabling the robot's operating efficiency to be autonomously optimized and feedback to be provided in real time.

[1963] The "means for collecting user behavior data" refers to a device or method for collecting data on the user's behavior, such as the start time, end time, and work content of the user's work.

[1964] A "means for collecting user biometric data" is a device or method for collecting biometric information of a user, such as heart rate, brain waves, or electrodermal activity.

[1965] The "means for collecting user-provided feedback" refers to a device or method for collecting feedback information such as satisfaction and a sense of accomplishment from users.

[1966] The "means for transmitting collected data to a server" refers to a device or method for transmitting collected behavioral data, biometric data, and feedback information to a server.

[1967] "Means for analyzing the received data and identifying patterns of user self-control and concentration" refers to a device or method that analyzes the data sent to the server and identifies characteristics of the user's behavior and concentration.

[1968] The "means for generating a personalized training plan based on the identified patterns" refers to a device or method that creates a training plan specific to the user based on the analysis results.

[1969] "Means for collecting robot operation data and sensor information" refers to a device or method for collecting the robot's work history and sensor data (e.g., motor temperature, battery status).

[1970] "Means for transmitting collected data to a server and analyzing the efficiency of the robot and the tendency for errors to occur" refers to a device or method for transmitting collected information to a server and analyzing the operating efficiency of the robot and the tendency for errors to occur.

[1971] The "means for generating a training plan dedicated to a robot based on the analysis results" refers to a device or method for creating a work schedule or maintenance plan to maximize the efficiency of a robot based on the analysis results.

[1972] The "means for transmitting the generated training plan to the robot" is a device or method for transmitting the generated training plan to the robot.

[1973] The "means for providing real-time feedback to a robot" refers to a device or method that provides optimization instructions or advice for improving efficiency in real time according to the working status of the robot.

[1974] In the system for implementing the present invention, a server, a terminal, and a user work together as follows.

[1975] server

[1976] 1. Data collection

[1977] The server receives the user's behavioral data, biometric data, and feedback information in real time. Specifically, when the user records the start and end times of a task, that data is sent from the device to the server. Biometric data, such as heart rate, brain waves, and electrodermal activity, are collected from the wearable device.

[1978] 2. Data Analysis

[1979] The server analyzes the user's self-control and concentration patterns based on the collected data, for example by cross-referencing behavioral data with heart rate to identify times when concentration is at its best.

[1980] 3. Generate a training plan

[1981] The server then generates a personalized training plan based on the analysis results. It offers plans for task management, attention training, and stress management using techniques such as the Pomodoro Technique. For example, it suggests a plan in which the user alternates between 20 minutes of focused work and 5 minutes of rest.

[1982] 4. Providing Feedback

[1983] The server updates the training plan based on the user's progress and feedback, providing real-time feedback, such as "Your peak concentration is between 2:00 and 3:00 p.m. Try using this time slot again next week."

[1984] Terminal

[1985] 1. Data recording and transmission

[1986] The device records the user's behavioral data and biometric data and periodically transmits it to a server. For example, heart rate data acquired by a smartwatch is stored on the device and then transmitted to a server.

[1987] 2. Receiving and viewing training plans

[1988] The device receives the training plan sent from the server and notifies the user. For example, a notification such as "Please concentrate on your work for the next 25 minutes" will appear on the smartphone.

[1989] 3. Displaying and Carrying Out Instructions

[1990] The device will then instruct the user to start tasks and take rests according to the instructions in the training plan. For example, when it's time to take a break, it will notify the user by saying, "You have 5 minutes left to take a break."

[1991] User

[1992] 1. Implement a training plan

[1993] The user performs tasks according to the training plan provided by the device, for example, concentrating on tasks at designated times and taking appropriate breaks according to notifications.

[1994] 2. Providing Feedback

[1995] Users can send feedback about the effectiveness of the training plan to the server via their devices, for example by answering a questionnaire about their sense of accomplishment and satisfaction with the task.

[1996] 3. Self-monitoring

[1997] Users can use the device to self-monitor and check their own performance based on the data, for example by viewing a graph of their daily concentration fluctuations.

[1998] Specific examples

[1999] Example: A robot's operational data and sensor information are collected and sent to a server. The server analyzes the robot's operational efficiency and error trends, and generates and sends a training plan based on the results. For example, the server provides the robot with feedback such as, "Avoid heavy workloads for the next 30 minutes and perform inspections."

[2000] Prompt Sentence Examples

[2001] "A system that analyzes the operational data of robots in factories and generates optimal work schedules to maximize efficiency."

[2002] "How to generate training plans to improve efficiency in real time based on data collected by a robot"

[2003] This system can optimize the production efficiency and concentration of users and robots in real time, improving their self-control during actual work and tasks.

[2004] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2005] Step 1:

[2006] Data collection

[2007] The user uses a device (e.g., smartphone or smartwatch) to record the start and end times of a task and collect biometric data (e.g., heart rate, electrodermal activity). The device then sends this data to a server. The input is task data and biometric data, and the output is data sent to the server.

[2008] Step 2:

[2009] Receiving data

[2010] The server receives behavioral data and biometric data sent from the device in real time. The input is the data sent from the device, and the output is the storage of the received data. The server stores this data in a database.

[2011] Step 3:

[2012] Data analysis

[2013] The server analyzes the received data to identify patterns of the user's self-control and concentration. For example, it analyzes heart rate fluctuations and task completion times to identify times when concentration is at its peak. The input is the stored data, and the output is the analysis results. Statistical analysis and machine learning models are used to process the data.

[2014] Step 4:

[2015] Generate a training plan

[2016] The server generates a personalized training plan for the user based on the analysis results. For example, it suggests 20 minutes of focused work followed by a 5-minute break based on the Pomodoro technique. The input is the analysis results, and the output is the training plan. A generative AI model is used to generate a personalized plan.

[2017] Step 5:

[2018] Submit a training plan

[2019] The server sends the generated training plan to the device. The input is the training plan, and the output is the transmission of the plan to the device. Feedback messages are also included.

[2020] Step 6:

[2021] View your training plan

[2022] The device displays the received training plan to the user. For example, it may notify the user, "Please concentrate on your work for the next 25 minutes." The input is the plan from the server, and the output is the notification to the user.

[2023] Step 7:

[2024] Executing a task

[2025] The user performs tasks according to the training plan provided by the device. The input is instructions from the device, and the output is the completion of the task. For example, the user can concentrate on the work at a specified time and take appropriate breaks according to notifications.

[2026] Step 8:

[2027] Providing Feedback

[2028] The user sends feedback about the effectiveness of the training plan to the server via their device. The input is the user's feedback, and the output is the transmission of feedback to the server. This includes a questionnaire about the sense of accomplishment and satisfaction with the task.

[2029] Step 9:

[2030] Analyzing feedback and updating plans

[2031] The server analyzes user feedback and dynamically readjusts the training plan. The input is user feedback and the output is an updated training plan. The server regenerates the personalized plan and resubmits it if necessary.

[2032] This series of steps optimizes the self-control and concentration of users and robots in real time, improving production efficiency.

[2033] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2034] System Overview

[2035] This invention combines a support system for enhancing an individual's self-control and maximizing concentration with an emotional engine. The system utilizes the user's behavioral data, biometric data, feedback, and even emotional state, and generates a personalized training plan based on this data and provides the user with real-time feedback.

[2036] server

[2037] 1. Data Collection:

[2038] The server receives the user's behavioral data, biometric data, feedback, and emotional data in real time. For example, if a user is using an emotion recognition application, emotional data from the application will be sent to the server via the device. This data includes behavioral data (such as the start and end times of a task) and biometric data (such as heart rate and brain waves), as well as the user's current emotional state.

[2039] 2. Data Analysis:

[2040] The server analyzes the received data to identify patterns of the user's self-control, focus, and emotions. For example, it cross-references heart rate data with emotional data to identify times when they are most focused or stressed.

[2041] 3. Role of the Emotion Engine:

[2042] The emotion engine analyzes the user's facial expressions, tone of voice, input text, etc. to identify the user's emotional state in real time, for example, it can identify different emotional states such as whether the user is stressed or relaxed.

[2043] 4. Generate a training plan:

[2044] The server then generates a personalized training plan based on the analysis results, taking into account the user's emotional state and offering plans incorporating the Pomodoro technique, specific breathing exercises, and relaxation techniques. For example, if the user is feeling stressed, the server will suggest a plan that includes a short period of relaxation.

[2045] 5. Providing Feedback:

[2046] The server dynamically updates the training plan based on the user's progress, feedback, and emotional data, providing real-time feedback, such as "Taking five minutes of relaxation time into account, given your current emotional state."

[2047] Terminal

[2048] 1. Data recording and transmission:

[2049] The device records the user's behavioral data, biometric data, feedback, and emotional data and periodically transmits them to a server. For example, a smartwatch records the user's heart rate and emotional state and transmits the data to a server.

[2050] 2. Receive and view training plans:

[2051] The device notifies the user of the training plan sent from the server. For example, a notification such as "Work with focus for the next 25 minutes. You appear to be in a stressful emotional state, so take 5 minutes of relaxation afterwards" will appear on the smartphone.

[2052] 3. Displaying and Carrying Out Instructions:

[2053] The device will prompt the user to start tasks and take rests according to the instructions in the training plan. For example, when it's time for a break, it will notify the user, "You have 5 minutes left to take a break." If emotions are negative, it will give instructions such as, "Take a deep breath and relax."

[2054] User

[2055] 1. Implement your training plan:

[2056] The user performs tasks according to the training plan provided by the device, for example, concentrating on a task at a specified time and taking appropriate breaks or relaxation according to notifications.

[2057] 2. Providing Feedback:

[2058] Users send feedback about the effectiveness of the training plan to the server via their device, for example by answering a questionnaire about their sense of accomplishment, satisfaction, and emotional fluctuations.

[2059] 3. Self-monitoring:

[2060] Users can use the device to self-monitor and check their performance and emotional state based on the data, for example by viewing graphs of their daily concentration and emotional fluctuations.

[2061] Through these processes, the system effectively supports users in self-control, concentration and emotional management.

[2062] The processing flow will be explained below.

[2063] Specific processing flow of the program

[2064] Data Collection Phase

[2065] Step 1:

[2066] User

[2067] A user installs a task management app and records the start and end times of a task. For example, they open the "Task Tracker" app and press the start button for the task.

[2068] Step 2:

[2069] Terminal

[2070] The device saves the data recorded by the task management app in its storage, and also saves the end time when the task is completed.

[2071] Step 3:

[2072] Terminal

[2073] The smartwatch periodically records the user's biometric data, such as heart rate, for example, every five minutes and stores it on the device.

[2074] Step 4:

[2075] Terminal

[2076] The emotion recognition app analyzes the user's facial expressions and voice to collect real-time emotional data, which is also stored on the device.

[2077] Step 5:

[2078] User

[2079] After completing the training, users complete an in-app survey to provide subjective feedback, such as, "How focused were you on this task?"

[2080] Data Transfer Phase

[2081] Step 6:

[2082] Terminal

[2083] The collected behavioral data, biometric data, emotional data, and feedback data packages are sent to a server, for example, by converting the collected data into JSON format and uploading it to the server via the Internet.

[2084] Data analysis phase

[2085] Step 7:

[2086] server

[2087] Analyzes incoming data, cleansing and normalizing it, and handling outliers and missing data to improve analysis accuracy.

[2088] Step 8:

[2089] server

[2090] By cross-referencing the user's heart rate data with their emotional data, the system identifies times when they are most likely to concentrate or feel stressed. For example, it identifies "times when the heart rate is stable and positive emotions are high" as times when they are most likely to concentrate.

[2091] Step 9:

[2092] server

[2093] It identifies patterns in the user's self-control, focus, and emotions, and stores the results in a database. For example, the user's characteristics can be reflected in a model and used to generate a plan for the next time.

[2094] Training plan generation phase

[2095] Step 10:

[2096] server

[2097] Based on the analysis results, the system generates a personalized training plan that takes into account the user's emotional state. For example, it suggests the user follow the Pomodoro technique (25 minutes of work + 5 minutes of rest) and also provides a plan that includes relaxation.

[2098] Step 11:

[2099] server

[2100] The generated training plan is sent to the user's device. For example, the generated plan is sent in JSON format and displayed in an app on the device.

[2101] Feedback Phase

[2102] Step 12:

[2103] Terminal

[2104] The device will then notify the user of the training plan and display instructions, such as "Work with focus for the next 25 minutes, after which you will experience emotional stress, and then take 5 minutes of relaxation."

[2105] Step 13:

[2106] User

[2107] The user performs tasks according to the training plan, taking breaks and relaxation at appropriate times, for example, following the instructions on the device to take a five-minute break and then resume work.

[2108] Step 14:

[2109] Terminal

[2110] The device monitors the user's progress and real-time emotional data and continuously transmits the data to the server, for example, after completing a task or when the user's emotional state changes.

[2111] Step 15:

[2112] server

[2113] The server dynamically readjusts the training plan based on the user's progress data and feedback. For example, if the user tells the server that they were losing focus, the server will create a plan that includes shorter sessions and more frequent breaks.

[2114] Step 16:

[2115] server

[2116] The server generates a weekly progress report and sends it to the user, notifying them, for example, that their peak concentration time this week is between 2:00 and 3:00 PM. It also provides advice such as, "Based on your emotional data, it would be beneficial to spend more time relaxing."

[2117] Step 17:

[2118] User

[2119] Users can check their progress reports and use them to plan their training for the next week, for example by scheduling tasks for the next week based on their peak concentration times.

[2120] Through these processing steps, the system effectively supports the user's self-control, concentration and emotional management.

[2121] Example 2

[2122] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2123] A user's self-control, concentration, and emotional management need to be appropriately supported based on their individual state and situation. However, conventional systems collect users' biometric and behavioral data, but do not consider their emotional data, making it difficult to provide training plans tailored to individual emotional states. Therefore, a system that provides more personalized support by taking into account the user's emotional state is needed.

[2124] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2125] In this invention, the server includes means for collecting user behavioral data, means for collecting user biometric data, means for collecting user-provided feedback, means for collecting user emotional data, means for transmitting the collected data to the server, means for analyzing the received data and identifying patterns of the user's self-control, concentration, and emotions, means for generating a personalized training plan based on the identified patterns, means for transmitting the generated training plan to the user's device, and means for providing real-time feedback to the user, thereby making it possible to provide a training plan that also takes the user's emotional state into consideration.

[2126] "User behavioral data" refers to data that records the specific movements and actions of a user in their daily life and work.

[2127] "User's biometric data" refers to data that indicates the user's physical condition, including physiological indicators such as heart rate and brain waves.

[2128] "Feedback" is data that records opinions, ratings, and reactions provided by users.

[2129] "Emotional data" refers to data that indicates a user's emotional state, and includes information such as facial expressions, voice tone, and text input obtained using emotion recognition technology.

[2130] A "server" is a central processing unit that receives data sent from the user's device and performs analysis, generates training plans, etc.

[2131] A "terminal" is a device that is directly operated by a user, including smartphones and smartwatches.

[2132] "Self-control" refers to the user's ability to appropriately manage their own behavior and emotions and maintain a desired state.

[2133] "Concentration" refers to a user's ability to focus their mind on a particular task or activity.

[2134] "Training Plan" means a personalized plan or instruction to improve a User's self-control and focus.

[2135] "Real-time feedback" refers to instant feedback and advice based on the actions a user takes and the data they provide.

[2136] "Analysis" refers to the data processing process that uses collected data to identify patterns of user behavior and emotions.

[2137] An "emotion engine" refers to software or hardware processing that analyzes a user's facial expressions, voice tone, text, etc. to identify their emotional state.

[2138] "Personalization" refers to customizing training plans and feedback to suit each individual user's specific needs and circumstances.

[2139] "Peak hours" refer to times when users are most focused and productive.

[2140] "Stressful times" refer to the times when users are most likely to feel stressed.

[2141] System Overview

[2142] This invention combines an emotional engine with a support system for enhancing an individual's self-control and maximizing concentration. The system utilizes the user's behavioral data, biometric data, feedback, and even emotional data to generate a personalized training plan and provide real-time feedback to the user.

[2143] Hardware and Software

[2144] Hardware: smartwatches (e.g., devices for measuring heart rate), smartphones (e.g., used as a user interface)

[2145] Software: emotion recognition apps (e.g., tools for analyzing user emotions in real time), self-control support apps (e.g., apps that provide notifications and collect feedback)

[2146] System program processing flow

[2147] server

[2148] 1. Data Collection:

[2149] The server receives users' behavioral data, biometric data, feedback, and emotional data in real time. For example, a smartwatch records heart rate and activity data and sends the data to the server. An emotion recognition app also collects users' emotional data and sends it to the server.

[2150] 2. Data Analysis:

[2151] The server analyzes the received data to identify patterns of self-control, concentration, and emotions of the user. Specifically, it cross-references heart rate data with emotional data to identify times of high concentration and times of stress.

[2152] 3. Role of the Emotion Engine:

[2153] The emotion engine analyzes the user's facial expressions, voice tone, and input text to identify the user's emotional state in real time. For example, if the user inputs "I'm very tired today," the engine will identify fatigue from the text. The facial recognition camera also detects the user's smile and determines that the user is relaxed.

[2154] 4. Generate a training plan:

[2155] The server then generates a personalized training plan based on the analysis results. For example, if a user's concentration decreases and stress increases at 3 p.m., the server will suggest a plan that includes relaxation time before and after 3 p.m.

[2156] 5. Providing Feedback:

[2157] The server dynamically updates the training plan based on the user's progress, feedback, and emotional data, providing real-time feedback, such as notifications like "Take five minutes of relaxation time to consider your current emotional state."

[2158] Terminal

[2159] 1. Data recording and transmission:

[2160] The device records the user's behavioral data, biometric data, feedback, and emotional data and periodically transmits them to a server. For example, a smartwatch collects heart rate data every hour and transmits it to a server.

[2161] 2. Receive and view training plans:

[2162] The device receives the training plan sent from the server in real time and notifies the user, for example, a message such as "Work hard for the next 25 minutes, then take 5 minutes of relaxation" will be displayed on the smartphone.

[2163] 3. Displaying and Carrying Out Instructions:

[2164] The device will follow the instructions of the training plan and prompt the user to start a task or take a rest. For example, the smartwatch will notify the user that it's time to take a break in five minutes. If the user is feeling negative, it will display instructions such as "Take a deep breath and relax."

[2165] User

[2166] 1. Implement your training plan:

[2167] The device provides users with a training plan to help them accomplish tasks, such as working during designated periods of focus and then using breaks to practice relaxation.

[2168] 2. Providing Feedback:

[2169] Users can send feedback about the effectiveness of their training plan to a server via their device, for example by answering a questionnaire after completing a task, asking questions such as "Was your concentration high while working?" or "Did your stress level decrease?"

[2170] 3. Self-monitoring:

[2171] Users can use the device to self-monitor and check their own performance and emotional state based on the data. For example, the smartphone app can visualize daily fluctuations in concentration and emotions in graphs to check the effectiveness of their training.

[2172] Example prompt

[2173] 1. The user puts on the smartwatch

[2174] "Wear your smartwatch and enter your self-assessment at each moment of exercise and rest throughout the day."

[2175] 2. Applying the Pomodoro Technique

[2176] "You'll have 25 minutes of focused work time, after which you'll receive a notification to take a 5-minute break."

[2177] 3. Entering Emotion Data

[2178] "Enter your current mood on a scale of 1 to 5. The emotion recognition app will analyze your facial expressions and collect emotional data."

[2179] This allows the system to effectively support users' self-control, concentration and emotional management.

[2180] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2181] System processing steps

[2182] Step 1:

[2183] The server receives the user's behavioral data, biometric data, feedback, and emotional data in real time. Specifically, the device (smartwatch) records heart rate and activity data and sends the data to the server. The emotion recognition app also collects the user's emotional data and sends it to the server. The input data are behavioral data, biometric data, feedback, and emotional data, which are integrated and efficiently sent to the server. The output is the integrated data sent to the server.

[2184] Step 2:

[2185] The server analyzes the received data. Specifically, it cross-references the heart rate data with the emotional data to identify the user's concentration and stress patterns. The input data is the integrated data sent to the server. Data analysis algorithms are used to identify the user's peak concentration and stress times. The output is data that identifies the user's concentration and stress patterns.

[2186] Step 3:

[2187] The emotion engine on the server analyzes the user's facial expression, voice tone, input text, etc. to identify the user's emotional state in real time. The input data is facial expression data, voice tone data, and text data. The emotion recognition algorithm is used to analyze the user's emotional state in real time. The output is the identified emotional state data.

[2188] Step 4:

[2189] The server generates a personalized training plan based on the analysis results. For example, for a user whose concentration decreases and stress increases around 3:00 p.m., it suggests a plan that includes relaxation time around that time. The input data is analysis data on the user's concentration, stress, and emotional state. An algorithm is used to generate a plan that incorporates the Pomodoro technique, specific breathing techniques, etc. The output is a personalized training plan.

[2190] Step 5:

[2191] The server dynamically updates the training plan based on the user's progress, feedback, and emotional data, and provides real-time feedback. For example, it sends a notification such as, "Taking five minutes of relaxation time into account, given your current emotional state." The input data are the user's progress data, feedback data, and emotional data. The dynamic plan update algorithm generates real-time feedback. The output is the updated training plan and feedback.

[2192] Step 6:

[2193] The device records the user's behavioral data, biometric data, feedback, and emotional data and periodically transmits them to a server. Specifically, the smartwatch collects heart rate data every hour and transmits it to the server. The input data are the user's behavioral data, biometric data, feedback, and emotional data. A data collection and transmission algorithm is used. The output is the recorded data sent to the server.

[2194] Step 7:

[2195] The device receives the training plan sent from the server and notifies the user. For example, a message such as "Work concentratively for the next 25 minutes, then take 5 minutes of relaxation" is displayed on the smartphone. The input data is the training plan sent from the server. The notification function is used. The output is the notification displayed to the user.

[2196] Step 8:

[2197] The device prompts the user to start a task or take a rest according to the instructions in the training plan. For example, the smartwatch may send a notification saying, "Take a break in 5 minutes," or, if emotions are negative, it may display instructions such as, "Take a deep breath and relax." The input data are instructions based on the training plan. Task management and notification algorithms are used. The output is instructions displayed to the user.

[2198] Step 9:

[2199] The user performs tasks according to a training plan provided by the device. For example, the user works during a designated concentration period and then uses the subsequent break time to practice relaxation. The input data is the training plan provided by the device. A task execution algorithm is used. The output is the completed task.

[2200] Step 10:

[2201] The user sends feedback about the effectiveness of the training plan to the server via their device. For example, after completing a task, they answer a questionnaire such as "Was your concentration high while working?" or "Did your stress level decrease?" The input data is the user's feedback about the effectiveness of the training plan. A feedback collection algorithm is used. The output is the feedback sent to the server.

[2202] Step 11:

[2203] Users use the device to self-monitor and check their own performance and emotional state based on the data. For example, a smartphone app can visualize daily fluctuations in concentration and emotions in graphs to check the effectiveness of training. The input data is past behavioral data, biological data, and emotional data. A data visualization algorithm is used. The output is data visualized as a graph.

[2204] (Application example 2)

[2205] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2206] Many existing systems for improving self-control and concentration generate training plans based on biometric and behavioral data. However, few systems take the user's emotional state into account. Therefore, there is a need for a system that can grasp the user's stress and relaxation levels in real time and provide feedback and content recommendations that adapt to those conditions. Furthermore, there has yet to be a fully developed system that can comprehensively analyze this data and provide dynamically personalized plans.

[2207] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric data of the user, means for collecting the user's emotional state, and means for analyzing the received data and identifying the user's self-control, concentration, and emotional patterns. This makes it possible to recommend personalized training plans and content adapted to the user's stress and relaxation state based on the analysis results. This allows the user to receive support that allows them to maintain concentration more effectively and comfortably and manage their emotional state.

[2208] definition statement

[2209] "User behavior data" refers to information about user behavior, specifically data including viewing time, frequency of clicks and swipes, etc.

[2210] "User biometric data" refers to information about the user's physiological state, including, for example, heart rate and eye tracking data.

[2211] "Feedback provided by users" refers to information such as ratings, impressions, and survey responses provided by users to the system.

[2212] "User's emotional state" refers to information about the user's real-time emotions analyzed by the emotion engine, including stress levels, relaxed states, etc.

[2213] "Server" refers to the computer system that receives and analyzes data, generates personalized training plans and content recommendations, and transmits them to the User's device.

[2214] "Analysis" refers to the process of using collected user behavioral data, biometric data, feedback, and emotional state to identify patterns of self-control, focus, and emotions.

[2215] "Personalized training plan" refers to an individually optimized training plan generated based on data for each individual user.

[2216] "Content recommendation" refers to the act of recommending optimal content (videos, music, articles, etc.) based on the user's current state and patterns.

[2217] "Real-time feedback" refers to advice or instructions provided immediately in response to a user's actions or state.

[2218] "Dynamic readjustment" refers to the process of updating and changing training plans and content recommendations on the fly based on analysis results.

[2219] MODE FOR CARRYING OUT THE INVENTION

[2220] System Overview

[2221] This invention is a support system for improving an individual's self-control and concentration, and combines an emotion engine. The system utilizes the user's behavioral data, biometric data, feedback, and emotional state, and generates personalized training plans and content recommendations based on this data, providing real-time feedback to the user.

[2222] server

[2223] 1. Data Collection:

[2224] The server receives user behavioral data, biometric data, feedback, and emotional data in real time. For example, the server receives data sent from smartphones, wearable devices, etc. This data includes browsing time, click / swipe frequency, heart rate, facial expressions, voice tone, and input text.

[2225] 2. Data Analysis:

[2226] The server analyzes the received data to identify patterns of the user's self-control, concentration, and emotions. For example, it cross-references behavioral data with biometric data to identify times of high concentration and times of stress. The emotion engine analyzes facial expressions, voice tone, text, etc. to identify emotional states.

[2227] 3. Generating training plans and content recommendations:

[2228] The server generates personalized training plans and content recommendations based on the analysis results. Taking into account the user's emotional state, the server offers plans incorporating the Pomodoro technique, specific breathing exercises, and relaxation techniques. For users feeling stressed, the server recommends relaxation music and meditation videos.

[2229] 4. Providing Feedback:

[2230] The server dynamically updates training plans and content recommendations based on the user's progress, feedback, and emotional data, providing real-time feedback, such as "Taking five minutes of relaxation time into account, given your current emotional state."

[2231] Terminal

[2232] 1. Data recording and transmission:

[2233] The device records the user's behavioral data, biometric data, feedback, and emotional data and periodically transmits them to a server. For example, a smartphone records browsing data, and a smartwatch records heart rate and emotional state and transmits them to a server.

[2234] 2. Receive and view training plans and content recommendations:

[2235] The device will notify the user of training plans and content recommendations sent from the server, such as "Work hard for the next 25 minutes, then listen to 5 minutes of relaxation music."

[2236] 3. Displaying and Carrying Out Instructions:

[2237] The device will prompt the user to start tasks or take a rest according to the instructions in the training plan. For example, when it's time for a break, it will notify the user, "You have 5 minutes left to take a break," and if it detects negative emotions, it will instruct the user to "take a deep breath and relax."

[2238] User

[2239] 1. Implement your training plan:

[2240] The user performs tasks according to the training plan provided by the device, for example, concentrating on a task at a specified time and taking appropriate breaks or relaxation according to notifications.

[2241] 2. Providing Feedback:

[2242] Users can send feedback about the effectiveness of their training plan to the server via their device, for example by answering a questionnaire about their emotional state.

[2243] 3. Self-monitoring:

[2244] Users can use the device to self-monitor and check their performance and emotional state based on the data, for example by viewing graphs of their daily concentration and emotional fluctuations.

[2245] Hardware and software used

[2246] Hardware:

[2247] Smartphones, smartwatches, heart rate monitors, facial recognition cameras

[2248] software:

[2249] Python, emotion recognition API (Emotion API, etc.), data analysis library (Pandas, etc.)

[2250] Specific examples / prompt sentence examples

[2251] Examples:

[2252] If a user watches a documentary for 30 minutes and their heart rate increases and they click and swipe more, the app will determine that they are in a high-stress state and notify them, "It's time to take a short break. We recommend watching a relaxing meditation video next."

[2253] Example prompt for a generative AI model:

[2254] "Generate a Python program to recommend optimal content and break timing using user behavioral data, heart rate, biometric data, and real-time analyzed emotional state."

[2255] "If a user is in a high-stress state, design your app to recommend relaxing content and encourage them to take a break."

[2256] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2257] Program processing steps

[2258] Step 1: Collect data

[2259] The server collects user behavioral data, biometric data, feedback and emotional data in real time.

[2260] Input: Data sent from smartphones, smartwatches, heart rate monitors, and facial recognition cameras.

[2261] Data processing: All data is converted into a unified format and stored in a database.

[2262] Output: A consolidated user dataset.

[2263] Step 2: Analyze the data

[2264] The server analyzes the user's self-control, concentration and emotional patterns based on the collected data.

[2265] Input: The consolidated user dataset.

[2266] Data calculations: Cross-reference behavioral and biological data to identify periods of high concentration and stress. Analyze emotional states using an emotion engine.

[2267] Output: User's self-control, concentration, and emotional patterns.

[2268] Step 3: Generate training plans and content recommendations

[2269] The server generates personalized training plans and content recommendations based on the analysis results.

[2270] Input: The user's self-control, focus, and emotional patterns.

[2271] Data processing: Based on the user's identified patterns, training plans including the Pomodoro technique and breathing exercises, as well as content such as relaxation music a...

Claims

1. means of collecting user behavior data; a means for collecting biometric data of a user; a means of collecting user-provided feedback; means for transmitting the collected data to a server; a means for analyzing the received data to identify patterns of self-control and concentration of the user; and means for generating a personalized training plan based on the identified patterns; A means for transmitting the generated training plan to the user's device; a means of providing real-time feedback to users; A system including:

2. The system of claim 1 , further comprising means for dynamically readjusting the training plan based on the analysis results.

3. 10. The system of claim 1, further comprising means for detecting a user's peak concentration times and optimizing a training plan based thereon.

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