System

Abstract

A system is provided.SOLUTION: A system including means for collecting action history and activity amount data from a wearable terminal, means for calculating a maximum physical strength value and a remaining amount of a user on the basis of the action history and the activity amount data, means for calculating a physical strength consumption amount in each task and suggesting an optimal task schedule to the user, means for evaluating a stress level of the user and generating a suggestion of counseling or mental care as necessary, and means for optimizing task sharing among family members and displaying suggestion content on a communication platform.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] The present invention aims to solve the problems of household task management and mental care in families experiencing the burden and stress of housework, childcare, and nursing care. Specifically, the complexity of housework, childcare, and the associated tasks, as well as a lack of information, can lead to misunderstandings and unequal burdens among family members. Furthermore, these burdens can cause stress, adversely affecting the overall atmosphere in the household and the mental health of each member, creating problems. [Means for solving the problem]

[0005] This system calculates the user's maximum and remaining physical strength (HP) based on behavioral history and activity data obtained from a wearable device, and generates an optimal schedule for housework, childcare, and nursing care tasks. Specifically, it calculates the amount of physical strength consumed by each task based on the user's physical strength status and proposes efficient task management and prioritization. It also evaluates the user's stress level and suggests counseling or mental care as needed. It also optimizes the division of tasks among family members and proposes fair workload adjustments on a communication platform. In this way, it aims to reduce the burden and stress on the entire household.

[0006] A "wearable device" is an electronic device worn by a user to collect behavioral history and activity data.

[0007] "Behavioral history" is data that records a user's daily activities and movement history.

[0008] "Activity data" refers to data that includes the user's amount of exercise and physiological indicators (e.g., number of steps, heart rate, sleep data, etc.).

[0009] "Maximum stamina" refers to the maximum stamina value that a user can maintain in a healthy state.

[0010] "Remaining" refers to the amount of stamina a user has at a particular time.

[0011] "Tasks" refer to specific tasks and activities that need to be done within the home, such as housework, childcare, and elderly care.

[0012] "Stamina consumption" refers to the amount of stamina consumed when performing a particular task.

[0013] A "task schedule" refers to a plan that shows the tasks a user needs to perform and the order and time in which they should be performed.

[0014] "Stress level" is an indicator that indicates the degree of psychological and physiological stress of the user.

[0015] "Counseling" refers to the provision of consultation and guidance services by professionals to help users resolve psychological issues.

[0016] "Mental care" refers to a range of support to maintain and improve the user's mental health.

[0017] "Task sharing among family members" refers to each member of the household sharing their respective tasks fairly.

[0018] A "communication platform" refers to an electronic infrastructure that allows family members to share information and communicate and coordinate with each other.

[0019] "Load adjustment" refers to optimizing the allocation of tasks within the household to be fair and efficient. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The system of the present invention includes a wearable device, a server, and a user device. These components work together to provide task management and mental care for the user's daily life.

[0042] Data Collection Phase

[0043] Processing performed by the device (wearable device):

[0044] The user wears a wearable device, which collects the user's behavioral history and activity data (e.g., number of steps, heart rate, sleep data). The collected data is sent to a server at regular intervals (e.g., every hour).

[0045] Data Receipt and Confirmation Phase

[0046] The server:

[0047] The server receives the data sent from the terminal and checks the integrity and accuracy of the data, thereby ensuring the authenticity of the data.

[0048] HP calculation phase

[0049] The server:

[0050] The server calculates the user's maximum stamina (HP) based on the received data. It also calculates the user's current remaining HP based on the user's current activity level and saves this data. For example, if a user has walked 7,000 steps and their average heart rate is 80 BPM, their current HP will be calculated as 70 out of 100.

[0051] Task Management Phase

[0052] The server:

[0053] The server uses an algorithm to calculate the HP consumption of each household task. For example, cleaning consumes 10 HP, and cooking consumes 15 HP. Based on this information, the server generates an optimal task schedule that takes into account the user's current remaining HP. This schedule is then sent to the user's device.

[0054] Mental care phase

[0055] The server:

[0056] The server evaluates the user's stress level based on their activity data. For example, if a high stress level is detected from data from the past week, the server will suggest counseling to the user. It will also automatically generate mental care suggestions to alleviate stress (e.g., recommending relaxing yoga).

[0057] Task allocation and communication phase

[0058] The server:

[0059] The server aggregates the home page data of the entire family and proposes fair task allocations, ensuring that the burden within the household is distributed evenly. For example, it may suggest that user A be in charge of cleaning and user B be in charge of laundry. These suggestions are displayed on the family communication platform and can be viewed by each member.

[0060] User Action:

[0061] Users can review the proposed division of labor and communicate with their family members to make adjustments. For example, User A can provide feedback such as, "I'm not feeling well today, so I'd like User B to clean."

[0062] Ongoing Support Phase

[0063] The device:

[0064] The wearable device continuously collects behavioral history and activity data and sends it to a server, which keeps the data up to date.

[0065] The server:

[0066] The server analyzes data in real time, monitors the user's condition, and periodically follows up on mental health care and task management, automatically creating new suggestions and advice and notifying the user.

[0067] Specific examples

[0068] For example, after User A has finished their daily activities, they send data to the server via their wearable device. Based on that data, the server calculates that User A's current HP is 70. It then generates a task schedule for tomorrow, suggesting "cooking in the morning, cleaning in the afternoon." At the same time, it determines that User A's stress level is high based on recent activity data and suggests activities that will help them relax. Furthermore, within the home, a suggestion that User A will be in charge of cleaning and User B will be in charge of laundry is displayed on the communication platform, and User A can check the schedule and make adjustments as necessary.

[0069] As described above, the present invention enables efficient household task management and mental care throughout each phase.

[0070] The processing flow will be explained below.

[0071] Step 1: Wearable device data collection

[0072] Device: The user wears a wearable device that records real-time activity history and activity data, including the number of steps taken, heart rate, and sleep duration.

[0073] Step 2: Sending data

[0074] Terminal: The collected data is sent to the server at regular intervals (for example, every hour). Data communication is carried out using a secure protocol.

[0075] Step 3: Receiving and verifying data

[0076] Server: Receives data sent from the device and checks the data for completeness and accuracy, verifying that there are no outliers or missing data.

[0077] Step 4: Calculate Health (HP)

[0078] Server: Based on the received data, calculate the user's maximum stamina and current remaining stamina. The maximum stamina is calculated based on the user's basic health data and past history data.

[0079] Step 5: Calculate the HP cost of the task

[0080] Server: The HP cost for each task (e.g. cleaning, cooking, laundry) is calculated by an algorithm. For example, cleaning is set to 10 HP, cooking to 15 HP, etc.

[0081] Step 6: Generate a task schedule

[0082] Server: Generates the optimal task schedule for the user, taking into account the current remaining HP and task priority. The generated schedule is sent to the user's device.

[0083] Step 7: Notification of scheduled tasks

[0084] Device: The user device receives the task schedule sent from the server and notifies the user. The schedule includes the specific task order and recommended times.

[0085] Step 8: Assess your stress levels and take care of yourself

[0086] Server: Evaluates the user's stress level based on activity data and generates mental health advice as needed. If a high stress level is detected, counseling or relaxation suggestions are automatically provided.

[0087] Step 9: Optimize task distribution

[0088] Server: Aggregates the home page data of all users in the household and calculates a fair task allocation. For example, it suggests that user A be in charge of cleaning and user B be in charge of laundry.

[0089] Step 10: View the proposal

[0090] Server: Display task sharing proposals on the family communication platform so that all users can view them.

[0091] Step 11: User feedback

[0092] User: Provide feedback on the proposed task distribution, for example, adding a comment like "I'm not feeling well today, so I'd like someone else to clean up."

[0093] Step 12: Recalibrate your sharing proposal

[0094] Server: Based on user feedback, the task sharing proposal is re-adjusted and the updated content is displayed again on the communication platform.

[0095] Step 13: Ongoing data monitoring and follow-up

[0096] Device: Continuously collects behavioral history and activity data and sends it to the server.

[0097] Server: Analyzes data in real time, periodically generates follow-ups and new suggestions tailored to the user's situation, and notifies the user.

[0098] Through this process, household tasks and mental care can be managed efficiently, reducing the burden and stress on the entire household.

[0099] Example 1

[0100] 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."

[0101] The purpose of this invention is to provide a system that efficiently manages daily tasks and provides mental care within a home or community. A particular challenge is how to fairly distribute the burden, taking into account the physical strength and stress level of each user. Furthermore, it is necessary to achieve more flexible and adaptive support by providing real-time data analysis and appropriate feedback.

[0102] 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.

[0103] In this invention, the server includes means for collecting movement records and activity data from the wearable device, means for calculating the user's maximum and current physical strength based on the movement records and activity data, means for calculating the amount of physical strength consumed for each task and proposing an optimal work plan to the user, means for evaluating the user's stress level and generating counseling and mental care suggestions as needed, means for optimizing work allocation within the community and displaying the suggestions on a communication platform, means for aggregating the physical strength data of each member of the community and proposing fair work allocation, means for receiving user feedback and readjusting the work based on the feedback, and means for analyzing data in real time, automatically creating new mental care and work plans, and notifying the user. This enables efficient daily task management and mental care within the home or community, and fair distribution of the burden.

[0104] A "wearable device" is an electronic device that can be worn by a user and is used to record movements and collect activity data.

[0105] "Movement record" refers to historical information about the user's daily physical movements and actions.

[0106] "Activity data" refers to information that quantifies the amount of physical activity a user performs in their daily life.

[0107] "Maximum stamina" refers to the maximum stamina that the user possesses, and is usually a number calculated based on a certain standard.

[0108] "Current amount" is a numerical value that indicates the user's current remaining stamina.

[0109] A "work plan" is a suggested schedule of tasks for a user to perform throughout the day.

[0110] "Stamina consumption" is a numerical value that indicates the amount of stamina consumed when performing each task.

[0111] "Stress level" is a numerical value or evaluation value that indicates the degree of mental and physical stress of the user.

[0112] "Counselling" refers to professional advice and support provided to reduce users' stress levels.

[0113] "Mental care" refers to suggestions and activities offered to maintain or improve the mental health of users.

[0114] A "communications platform" is a digital interface for exchanging information, typically an application or web service over the internet.

[0115] "Equitable division of labor" is the allocation of work within a household or community so that each member shares responsibility equally.

[0116] "Feedback" refers to opinions, impressions, and situational information collected from users.

[0117] "Analyzing data in real time" means processing collected data in real time and analyzing it to obtain immediate results.

[0118] "Notifying the user" means that work plans and mental care suggestions are sent from the server to the user's device and displayed.

[0119] The system of the present invention combines a wearable device, a server, and a user terminal to provide task management and mental care for a user in their daily life. Detailed embodiments of the system are described below.

[0120] Hardware Configuration

[0121] Wearable devices:

[0122] Wearable devices can be worn by users to record movement and collect activity data. They include an accelerometer, heart rate monitor, and GPS sensor, and record data such as steps taken, heart rate, and location information in real time.

[0123] server:

[0124] The server receives and analyzes the data sent from the wearable device, and also performs processes such as calculating maximum stamina and current physical capacity, proposing work plans and mental care, and adjusting work allocation.

[0125] User device:

[0126] The user terminal is a device that receives notifications sent from the server, such as a smartphone or tablet, and allows interaction with the user through an interface.

[0127] Software Configuration

[0128] Data collection modules:

[0129] A software module installed in a wearable device collects data from various sensors, such as the number of steps taken from an accelerometer and the heart rate from a heart rate monitor.

[0130] Data Analysis Module:

[0131] A software module installed on the server analyzes the collected data, calculates the maximum stamina and current stock, and calculates the amount of stamina consumed for each task to generate an optimal work plan.

[0132] Stress Assessment Module:

[0133] The software module installed on the server evaluates the user's stress level from collected activity data and automatically generates counseling and mental care suggestions as needed.

[0134] Work allocation adjustment module:

[0135] This module aggregates the physical fitness data of each member of a family or community and proposes fair division of labor. It displays the proposals using a communication platform and can also readjust based on feedback.

[0136] Specific examples

[0137] For example, after completing a day's activities, User A sends data to a server via a wearable device. Based on that data, the server calculates that User A's current available physical energy is 70. It then generates a work plan for the next day and notifies the user's device with suggestions such as "cook in the morning and clean in the afternoon." At the same time, if the server determines that the user's stress level is high based on recent activity data, it also makes mental care suggestions such as "do 30 minutes of relaxing yoga." Furthermore, a suggestion for dividing household tasks, with User A doing the cleaning and User B doing the laundry, is displayed on the communication platform.

[0138] Prompt Sentence Examples

[0139] Example prompt:

[0140] "Please specifically design a system that uses wearable devices and a server to manage the user's daily tasks and provide mental care. In particular, please provide a detailed explanation of each phase: data collection, HP calculation, task management, and mental care."

[0141] As described above, close cooperation between the user, server, and wearable device makes it possible to achieve efficient daily task management and mental care.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1: Data collection

[0144] Processing performed by the device (wearable device):

[0145] Wearable devices use built-in sensors (e.g., accelerometer, heart rate monitor, GPS) to record and collect user movement and activity data. Specifically, when a user walks, the accelerometer counts the number of steps, and the heart rate monitor measures the heart rate. The collected data (e.g., number of steps: 5000, heart rate: 75 BPM) is temporarily stored in the device.

[0146] Input: User's physical activity (e.g., walking, exercising)

[0147] Output: Collected motion records and activity data (e.g., steps, heart rate)

[0148] Step 2: Data Transfer

[0149] The device:

[0150] The wearable device transmits the collected motion records and activity data to a server at regular intervals (e.g., every hour). Data transmission uses a secure communication protocol (e.g., HTTPS).

[0151] Input: Collected motion and activity data (e.g., steps, heart rate)

[0152] Output: Data sent to the server

[0153] Step 3: Data reception and confirmation

[0154] The server:

[0155] The server receives the data sent by the terminal and checks the data for completeness and accuracy using a checksum algorithm. If there is a problem with any part of the received data, it requests a retransmission.

[0156] Input: Data sent from the terminal

[0157] Output: Verified data (e.g., clean step and heart rate data)

[0158] Step 4: HP Calculation

[0159] The server:

[0160] The server calculates the user's maximum stamina and current stamina based on the confirmed data. For example, it runs an algorithm to calculate current stamina based on the number of steps and heart rate data for that day. For example, if the maximum stamina is 100, the current stamina is calculated to be 80 based on the number of steps 5000 and heart rate 75 BPM.

[0161] Input: Verified activity data (e.g., steps, heart rate)

[0162] Output: User's current stamina (e.g. 80 / 100)

[0163] Step 5: Task Management

[0164] The server:

[0165] The server calculates the amount of stamina consumed for each task. For example, cleaning requires 10 HP, cooking requires 15 HP, and generates an optimal task plan based on the user's current stamina and sends it to the user's device.

[0166] Input: Current stamina (e.g. 80 / 100), task list and consumption (e.g. cleaning 10 HP, cooking 15 HP)

[0167] Output: Work plan (e.g. cooking in the morning, cleaning in the afternoon)

[0168] Step 6: Stress assessment and mental health advice

[0169] The server:

[0170] The server analyzes the collected activity data and evaluates the user's stress level. For example, if it determines that the stress level is high based on data from the past week, it will automatically generate mental care suggestions, such as a relaxing 30-minute yoga session, and send them to the user's device.

[0171] Input: Activity data (e.g., number of steps taken in the past week, heart rate)

[0172] Output: Mental care suggestions (e.g. yoga for relaxation)

[0173] Step 7: Assign tasks and communicate

[0174] The server:

[0175] The server collects the physical fitness data of each household member and proposes fair division of labor. The proposals are displayed on the communication platform and can be confirmed by the user and the other members. For example, it may be suggested that User A be in charge of cleaning and User B be in charge of laundry.

[0176] Input: Physical fitness data of household members

[0177] Output: Fair work sharing proposal (e.g., User A cleans, User B does laundry)

[0178] Step 8: Gather feedback and refine

[0179] User Action:

[0180] Users can send feedback on the provided work assignments through the communication platform, for example, sending a message such as, "I'm not feeling well today, so I'd like you to do the cleaning for me."

[0181] Input: User feedback

[0182] Output: Server receiving feedback

[0183] The server:

[0184] The server re-adjusts the division of labor based on the collected feedback and displays the updated proposal again on the communication platform.

[0185] Input: User feedback

[0186] Output: Rebalanced work assignments (e.g., User B now cleans)

[0187] Step 9: Ongoing support

[0188] The device:

[0189] The wearable device continuously collects motion records and activity data and periodically transmits them to a server.

[0190] Input: User's ongoing activity

[0191] Output: Latest collected motion records and activity data

[0192] The server:

[0193] The server analyzes the data in real time, keeping it up to date and providing appropriate follow-up care and task management.

[0194] Input: Latest activity data

[0195] Output: Real-time suggestions and notifications (e.g. break suggestions, task update notifications)

[0196] (Application example 1)

[0197] 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."

[0198] In conventional factories, it has been difficult to grasp the workload and stress levels of workers and efficiently manage work tasks based on this information. Furthermore, optimization of work tasks within factories and mental care for workers are often insufficient, resulting in decreased productivity and increased mental strain on workers. To solve these problems, the present invention provides a comprehensive task management and mental care system based on worker activity data.

[0199] 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.

[0200] In this invention, the server includes means for collecting behavioral history and activity data from the wearable device, means for calculating the user's maximum and remaining physical strength based on the behavioral history and activity data, means for calculating the physical strength consumption for each task and proposing an optimal task schedule to the user, means for evaluating the user's stress level and generating counseling and mental care suggestions as needed, means for generating an optimal task schedule for factory robots based on the worker's activity data and allocating tasks, and means for analyzing the worker's stress level and providing mental care suggestions as needed. This enables efficient task management that takes into account the physical strength and mental health of workers, improving productivity, and reducing the mental burden on workers.

[0201] A "wearable device" is a device worn on the body that constantly monitors and collects the user's behavioral history and activity data.

[0202] "Behavioral history" refers to a record of various actions taken by a user, such as the number of steps taken, distance traveled, and daily activity patterns.

[0203] "Activity data" is data that quantitatively indicates the user's physical activity, and specific examples include the number of steps taken, heart rate, and calorie consumption.

[0204] "Maximum stamina" is a numerical representation of the user's total stamina, and is an indicator of the maximum amount of activity possible in a day.

[0205] "Remaining physical strength" is a numerical value that indicates the user's current physical strength, and indicates the current level of fatigue and the remaining amount of activity that can be performed.

[0206] A "task schedule" is a plan that optimally arranges each task, taking into account the user's physical strength, stress level, and priority.

[0207] "Stress level" indicates the user's mental stress and fatigue level using numerical values ​​and indicators to evaluate the user's mental state.

[0208] "Counseling" refers to suggestions and interventions to provide psychological support and consultation to users experiencing high stress levels.

[0209] "Mental care" refers to specific activities and suggestions for maintaining and improving the user's mental health, including stress reduction and relaxation methods.

[0210] "Worker" refers to an employee or worker who works in a factory or business.

[0211] A "factory robot" is a machine or automated device used to automate work in a factory, performing tasks in place of humans.

[0212] "Task sharing" means allocating multiple tasks fairly to each member, with the aim of efficiently dividing up work.

[0213] The present invention provides a system for improving the efficiency of task management and mental care within a factory based on worker activity data. The system includes a wearable device, a server, and a user terminal.

[0214] Hardware and Software Use

[0215] Wearable devices

[0216] Wearable devices are devices that collect workers' behavioral history and activity data in real time. These devices have built-in sensors such as pedometers, heart rate monitors, and sleep monitors, and periodically send data to a server. Possible devices used include smartwatches and fitness trackers.

[0217] server

[0218] The server receives the data sent from the wearable device and checks its completeness and accuracy. Based on the collected data, it calculates the worker's HP (maximum and remaining stamina) and evaluates their stress level. Furthermore, it calculates the stamina consumption for each task based on the worker's current HP and generates an optimal task schedule. It also makes mental health care suggestions as needed. Cloud services such as Amazon Web Services (AWS) and Google Cloud Platform can be used for server-side processing.

[0219] User terminal

[0220] User devices, such as smartphones, tablets, and PCs, are used by workers and managers to check task schedules and mental health advice and provide feedback.

[0221] Data processing and calculation

[0222] After receiving the data transmitted from the wearable device, the server performs the following processing.

[0223] HP Calculation: Calculates the maximum stamina and current remaining stamina based on data such as the worker's steps, heart rate, sleep time, etc. For example, calculate the HP of a worker who has walked 7,000 steps and save that data.

[0224] Task Schedule Generation: Based on the current HP of the workers, calculate the HP required for each task and generate an optimal task schedule. For example, assign a worker 20 HP of packaging work, 15 HP of inspection work, and 30 HP of assembly work.

[0225] Stress level assessment: Based on the collected data, the stress level of the worker is assessed and mental health care recommendations are made as necessary. For example, if stress is determined to be high based on heart rate data from the past week, counseling is recommended.

[0226] Optimizing task sharing: Optimizing task sharing among family members and adjusting the workload fairly, which will improve the efficiency of work within the factory.

[0227] Specific examples

[0228] In an actual use case, Worker A at a factory wears a wearable device while working. The device collects data such as the number of steps taken each day, heart rate, and sleep time, and sends it to the server. After receiving this data, the server calculates Worker A's current HP and generates a task schedule for the next day based on that. For example, it may make adjustments such as "allocating 20 HP to packaging, 15 HP to inspection, and 30 HP to assembly." It may also determine from recent data that Worker A's stress level is rising and suggest relaxing activities.

[0229] Prompt Sentence Examples

[0230] "Please input the following end-user data into the deep learning model to generate an optimal factory task schedule.

[0231] Number of steps: 7000

[0232] Heart rate: 75 BPM

[0233] Sleep time: 6 hours

[0234] Task suggestions:

[0235] Packaging work (20HP)

[0236] Inspection work (15HP)

[0237] Assembly work (30HP)

[0238] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0239] Step 1:

[0240] Data Collection Phase

[0241] The wearable device collects the worker's behavioral history and activity data. This data includes the number of steps, heart rate, sleep time, etc. The collected data is sent to a server at regular intervals (e.g., every hour). The input is the worker's daily activity data, and the output is the data sent to the server.

[0242] Step 2:

[0243] Data Receipt and Confirmation Phase

[0244] The server receives the data sent from the wearable device and performs a data verification process to verify the completeness and accuracy of the received data. The input is the data from the wearable device and the output is the verified worker activity data.

[0245] Step 3:

[0246] HP calculation phase

[0247] The server calculates the worker's maximum stamina (HP) and current remaining stamina based on the confirmed data. For example, the server calculates the worker's current HP by subtracting the worker's maximum stamina from 100 based on the number of steps, heart rate, and sleep time. The input is the confirmed activity data, and the output is the calculated remaining HP.

[0248] Step 4:

[0249] Task schedule generation phase

[0250] The server calculates the stamina consumption required for each task based on the worker's current HP and generates an optimal task schedule. The server inputs the stamina consumption for each task and adjusts and calculates the schedule based on the worker's HP. The input is the worker's remaining stamina and task data, and the output is the optimal task schedule.

[0251] Step 5:

[0252] Task Schedule Notification Phase

[0253] The server sends the generated task schedule to the user terminal. Workers and managers can check this schedule using the user terminal and provide feedback as needed. The input is the optimal task schedule, and the output is a schedule notification to the user terminal.

[0254] Step 6:

[0255] Stress Level Assessment Phase

[0256] The server evaluates the worker's stress level from the collected activity data. For example, it determines whether stress is increasing from trends in heart rate and sleep time, and makes recommendations for counseling or mental care as necessary. The input is activity data, and the output is the evaluated stress level and recommendations.

[0257] Step 7:

[0258] Feedback reflection phase

[0259] The server receives feedback from users and adjusts task schedules and mental health care suggestions based on that feedback. For example, if a worker provides feedback such as "I'm not feeling well, so I'd like to reduce my workload," the server takes that into account when generating a new schedule. The input is the feedback data, and the output is the adjusted task schedule.

[0260] Step 8:

[0261] Data update and save phase

[0262] The server continuously receives new data, analyzes and stores it, and always manages based on the latest data. The input is new activity data collected in real time, and the output is an updated database and continuous recommendations.

[0263] 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.

[0264] The system of the present invention includes a wearable terminal, a server, a user terminal, and an emotion engine. These components work together to provide task management, mental care, and emotion recognition in the user's daily life.

[0265] Data Collection Phase

[0266] Processing performed by the device (wearable device):

[0267] The user wears a wearable device, which records real-time activity data (e.g., number of steps, heart rate, sleep data) and emotional data (e.g., facial expressions, voice tone). The collected data is sent to a server at regular intervals (e.g., every hour).

[0268] Data Receipt and Confirmation Phase

[0269] The server:

[0270] The server receives the data sent from the device and checks its completeness and accuracy, verifying that there are no outliers or missing data.

[0271] HP calculation phase

[0272] The server:

[0273] The server calculates the user's maximum HP and current remaining HP based on the received data. The maximum HP is calculated based on the user's basic health data and past history data.

[0274] Emotion Recognition Phase

[0275] The server:

[0276] The server uses an emotion engine to analyze the collected emotion data and recognize the user's emotional state. For example, if the user expresses anxiety, it will recognize it as "anxiety."

[0277] Task Management Phase

[0278] The server:

[0279] The server uses an algorithm to calculate the HP consumption of each household task. For example, cleaning consumes 10 HP, and cooking consumes 15 HP. Furthermore, based on the emotional data recognized by the emotion engine, the server generates an optimal task schedule that takes into account the user's current remaining HP and emotional state. This schedule is then sent to the user's device.

[0280] Task Schedule Notifications

[0281] Device:

[0282] The user's device receives the task schedule sent from the server and notifies the user. The schedule includes the specific task order and recommended time. For example, if the user's HP is 70 and the emotion engine recognizes "stress," it will prioritize tasks that will reduce stress, such as suggesting "light stretching" or "relaxing housework."

[0283] Stress level assessment and mental health care

[0284] The server:

[0285] The server evaluates the user's stress level based on activity data and emotional data. If a high stress level is detected, it generates suggestions for counseling or mental care. Specifically, if the emotion engine recognizes "stress," it suggests relaxation methods or counseling.

[0286] Task allocation and communication phase

[0287] The server:

[0288] The server aggregates the HP and emotional data of all users in the household and proposes fair task sharing. These proposals are displayed on the family communication platform. For example, if user A is feeling "anxious," user B will be suggested to take on a larger share of the important tasks for the day.

[0289] User Action:

[0290] Users can provide feedback on the proposed task allocation. For example, User A can add a comment saying, "I'm not feeling well today, so I'd like User B to clean up."

[0291] Realigning the proposed allocation

[0292] The server:

[0293] Based on user feedback, the task sharing proposal will be re-adjusted and the updated content will be displayed again on the communication platform.

[0294] Ongoing data monitoring and follow-up

[0295] The device:

[0296] The wearable device continuously collects behavioral history, activity data, and emotional data and transmits them to a server.

[0297] The server:

[0298] The server analyzes the data in real time and periodically generates follow-ups and new suggestions tailored to the user's situation and notifies them. For example, if the user has recently been feeling "fatigue," it will suggest measures to reduce the user's stress.

[0299] Specific examples

[0300] For example, after User A has finished their daily activities, they send data to the server via their wearable device. Based on that data, the server calculates that User A's current HP is 70. Furthermore, if the emotion engine recognizes "stress," it will prioritize stress-reducing tasks such as "light stretching" and "easy cooking" in the task schedule for the next day. At home, a suggestion is displayed on the communication platform that User B should take on more of the important tasks, and User A can check the content and make adjustments as necessary.

[0301] In this way, the present invention can perform household task management, mental care, and emotion recognition in an integrated manner throughout each phase.

[0302] The processing flow will be explained below.

[0303] Step 1: Wearable device data collection

[0304] Device: The user wears a wearable device, which collects real-time behavioral history and activity data (e.g., steps, heart rate, sleep data) and emotional data (e.g., facial expressions, voice tone).

[0305] Step 2: Sending data

[0306] Terminal: The collected data is sent to the server at regular intervals (for example, every hour). Data communication is carried out using a secure protocol.

[0307] Step 3: Receiving and verifying data

[0308] Server: Receives data sent from the device and checks the data for completeness and accuracy, verifying that there are no outliers or missing data.

[0309] Step 4: Calculate Health (HP)

[0310] Server: Based on the received data, calculate the user's maximum stamina and current remaining stamina. The maximum stamina is calculated based on the user's basic health data and past history data.

[0311] Step 5: Emotion Recognition

[0312] Server: The server uses an emotion engine to analyze the collected emotion data and recognize the user's emotional state, for example, "anxiety" or "joy" based on the user's facial expressions and voice tone.

[0313] Step 6: Calculate the HP cost of the task

[0314] Server: The HP cost for each household task is calculated by an algorithm, for example, cleaning is set to 10 HP, cooking is set to 15 HP, etc.

[0315] Step 7: Generate a task schedule

[0316] Server: Generates an optimal task schedule for the user, taking into account the user's current remaining HP and the emotional state provided by the emotion engine. For example, if the user's HP is 70 and the emotion engine detects "stress," it prioritizes tasks that reduce stress. The generated schedule is sent to the user's device.

[0317] Step 8: Notification of scheduled tasks

[0318] Device: The user device receives the task schedule sent from the server and notifies the user. The schedule includes the specific task order and recommended times.

[0319] Step 9: Stress level assessment and mental health recommendations

[0320] Server: Evaluates the user's stress level based on activity data and emotional data, and generates mental health advice as needed. For example, if the emotion engine detects "stress," it will automatically suggest counseling or relaxation techniques.

[0321] Step 10: Optimize task distribution

[0322] Server: Aggregates HP data and emotional data from all users in the household and proposes fair task sharing. For example, if user A is feeling anxious, user B will be suggested to share more of the important tasks for that day. The proposal is displayed on the family communication platform.

[0323] Step 11: View the proposal

[0324] Server: Display the task sharing proposal on the family communication platform so that all users can view the contents.

[0325] Step 12: User feedback

[0326] Users: Provide feedback on the proposed task distribution. For example, User A can add a comment saying, "I'm not feeling well today, so I'd like User B to clean up."

[0327] Step 13: Recalibrate your sharing proposal

[0328] Server: Based on user feedback, the task sharing proposal is re-adjusted and the updated content is displayed again on the communication platform.

[0329] Step 14: Ongoing data monitoring and follow-up

[0330] Device: Continuously collects behavioral history, activity data, and emotional data, and sends them to the server.

[0331] Server: Analyzes data in real time, periodically generates follow-ups and new suggestions based on the user's situation, and notifies the user. For example, if the user has recently been feeling "tired," the server will suggest measures to reduce the user's stress.

[0332] Through this process, household task management, mental care, and emotional awareness can be integrated, reducing the burden and stress on the entire household.

[0333] Example 2

[0334] 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."

[0335] Conventional home task management systems often provide a uniform task schedule without fully considering the user's physical condition or emotional state. This can result in excessive burden on users and problems such as insufficient mental care and stress management. Furthermore, because the allocation of tasks within the home is done without taking into account each individual's physical condition or emotional state, it can easily lead to a sense of unfairness. By solving these issues, we aim to realize a healthier and stress-free home life.

[0336] 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.

[0337] In this invention, the server includes: means for collecting behavior history and activity amount data from the wearable device;

[0338] A means for calculating the user's maximum stamina and remaining stamina based on the behavior history and activity amount data;

[0339] A means of emotion recognition by analyzing the user's facial expressions and vocal tone;

[0340] A method for calculating the amount of energy consumed for each task using an algorithm and proposing an optimal task schedule taking into account the emotional state and remaining energy;

[0341] a means for notifying a user terminal of the proposed task schedule;

[0342] A means for evaluating a user's stress level based on their activity data and emotional data, and generating suggestions for counseling and mental care;

[0343] The system also includes a means for optimizing task allocation among family members and displaying the proposed results on the communication platform. This enables optimal task scheduling and fair task allocation that takes into account the user's physical condition and emotional state. Furthermore, by providing appropriate mental care and stress management, a healthy and stress-free family life can be achieved.

[0344] A "wearable device" is a small electronic device that can be worn by the user and has built-in sensors that collect behavioral history, activity data, emotional data, and other information.

[0345] "Behavioral history" refers to the history of various activities and movements undertaken by a user, including detailed information such as time and location.

[0346] "Activity data" is data that quantifies the physical movements and amount of exercise a user performs in their daily life, and examples include the number of steps taken, heart rate, calories burned, and sleep data.

[0347] "Maximum stamina" is the theoretical maximum stamina of a user, calculated by an algorithm based on the user's basic health status and past history data.

[0348] "Remaining physical strength" is a numerical value that indicates the user's current physical strength, and is calculated in real time based on behavioral history and activity data.

[0349] "Facial expressions" refers to the movement of a user's facial muscles and the movements of the eyes, mouth, eyebrows, etc., and are used as data for analyzing emotional states.

[0350] "Voice tone" refers to characteristics such as pitch, volume, and rhythm of the user's speaking voice, and is used as data for analyzing emotional state.

[0351] "Emotion recognition" is a technology that analyzes collected data such as facial expressions and voice tone to identify a user's emotional state.

[0352] A "task" refers to a specific task or activity that a user must perform in their daily life, such as cleaning, cooking, or laundry.

[0353] A "task schedule" is a planning table that suggests the optimal order and start time of tasks, taking into account the user's remaining physical energy and emotional state.

[0354] "Notifications" refers to the form of alerts or push notifications generated by the server to inform users of task schedules, mental health suggestions, etc.

[0355] "Stress level" is a numerical representation of the degree of stress a user feels, and is evaluated based on activity data and emotional data.

[0356] "Mental care" refers to counseling and relaxation suggestions provided to maintain the user's psychological health.

[0357] "Task sharing" refers to the fair allocation of tasks that are jointly performed by multiple household members, with the aim of optimizing the burden among family members.

[0358] "Communication platform" refers to a digital environment or application for sharing, coordinating, and managing tasks and other information among family members.

[0359] The system of the present invention supports task management, mental care, and emotion recognition in a user's daily life. This system includes a wearable device, a server, a user device, and an emotion engine, and these components function in cooperation with each other.

[0360] Data Collection Phase

[0361] The wearable device:

[0362] The wearable device worn by the user has built-in sensors, cameras, and microphones. This hardware is used to record the user's behavioral history (e.g., number of steps, distance traveled) and activity data (e.g., heart rate, sleep data) in real time. It also collects emotional data such as facial expressions and vocal tone. This data is sent to a server at regular intervals (e.g., every hour) via Bluetooth or Wi-Fi.

[0363] Data Receipt and Confirmation Phase

[0364] The server:

[0365] The server receives the data sent from the wearable device and checks its completeness and accuracy before storing it in the database, verifies whether there are any outliers or missing data, and requests a retransmission if an anomaly is detected.

[0366] HP calculation phase

[0367] The server:

[0368] The server calculates the user's maximum stamina and current remaining stamina based on the data collected from the wearable device. This uses the user's basic health data and past history data. The calculation is performed using a specific algorithm, and the user's remaining stamina is updated in real time.

[0369] Emotion Recognition Phase

[0370] The server:

[0371] The emotion engine in the server analyzes the user's facial expression and voice tone to recognize their emotion. For example, if the facial expression indicates sadness, it will be recognized as "sadness." Voice tone is also analyzed in the same way, and the overall emotional state is determined.

[0372] Task Management Phase

[0373] The server:

[0374] The server uses an algorithm to calculate the amount of stamina consumed for each task and generates an optimal task schedule based on the user's remaining stamina and emotional state. For example, cleaning is set to consume 10 HP, and cooking is set to consume 15 HP. The generated task schedule is sent to the user's device.

[0375] Task Schedule Notifications

[0376] User device processes:

[0377] The system receives task schedules sent to the user's device and notifies the user using push notifications and alerts. The notifications include the specific task execution order and recommended time. For example, if the user's remaining stamina is 70 and the emotion engine recognizes "stress," it will suggest "light stretching" or "relaxing housework" to reduce stress.

[0378] Stress level assessment and mental health care

[0379] The server:

[0380] The server evaluates the user's stress level based on activity and emotional data. If a high stress level is detected, counseling and relaxation methods are offered. For example, specific advice such as "Try 10 minutes of meditation" is provided.

[0381] Task allocation and communication phase

[0382] The server:

[0383] The server aggregates the physical strength and emotional data of all users in the household and proposes fair task sharing. The task sharing among users is displayed and shared on the communication platform. For example, if user A has low physical strength and feels "anxious," a suggestion is made that user B should share more of the important tasks for the day.

[0384] User Action:

[0385] Users can provide feedback on the proposed task allocation. For example, they can comment, "I'm not feeling well today, so I'd like to ask User B to clean up."

[0386] Realigning the proposed allocation

[0387] The server:

[0388] Based on the user's feedback, the server readjusts the task sharing proposal and displays the updated content again on the communication platform.

[0389] Ongoing data monitoring and follow-up

[0390] The wearable device:

[0391] The wearable device continuously collects behavioral history, activity data, and emotional data and transmits them to a server.

[0392] The server:

[0393] The server analyzes the received data in real time and generates follow-ups and new suggestions based on the user's situation. For example, if the user's recent data shows that they frequently feel "tired," the server will make new suggestions to reduce the burden, such as notifying them to "reduce their activity today and take a rest."

[0394] Specific examples

[0395] For example, after User A has finished his or her daily activities, he or she sends data to the server via a wearable device. The server analyzes the data and calculates that User A's current remaining stamina is 70. Furthermore, if the emotion engine recognizes "stress," it will suggest "light stretching" or "easy cooking" in the task schedule for the next day. At home, suggestions are displayed on the communication platform for User B to share many important tasks.

[0396] Prompt Sentence Examples

[0397] "I'm feeling stressed today. My current stamina is at 70. Please suggest a task schedule for tomorrow."

[0398] As described above, the system of the present invention can perform household task management, mental care, and emotion recognition in an integrated manner throughout each phase.

[0399] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0400] Step 1:

[0401] Data Collection Phase

[0402] Processing performed by the device (wearable device):

[0403] Input: The wearable device collects the user's behavioral history (e.g., steps taken, distance traveled), activity data (e.g., heart rate, sleep data), and emotional data (e.g., facial expressions, voice tone) in real time.

[0404] Data processing or calculation: Various biometric data is recorded using the built-in sensors, camera, and microphone.

[0405] Output: The collected data is encoded at regular intervals (e.g., every hour) and sent to a server via Bluetooth or Wi-Fi.

[0406] Specific operation: After data collection, the wearable device's communication function is activated and the data is uploaded to the server using a secure protocol.

[0407] Step 2:

[0408] Data Receipt and Confirmation Phase

[0409] The server:

[0410] Input: Behavioral history, activity data, and emotion data sent from the wearable device.

[0411] Data processing or data calculations performed: Checking the completeness and accuracy of received data, verifying that there are no outliers or missing data.

[0412] Output: The data whose accuracy has been confirmed is stored in a database, and if there is an abnormality, a resend request is made.

[0413] What happens: After receiving the data, the server's validation algorithm checks the integrity of the data and, if necessary, requests that the data be resent.

[0414] Step 3:

[0415] HP calculation phase

[0416] The server:

[0417] Input: Verified activity data and behavioral history data.

[0418] Data processing or calculation: The server's algorithm calculates the maximum stamina and current remaining stamina based on the user's basic health data (age, gender, height, weight, etc.) and past history data.

[0419] Output: Calculated maximum health and current health remaining data.

[0420] Specific operation: The server periodically evaluates the user's physical strength status and updates the remaining physical strength data based on that.

[0421] Step 4:

[0422] Emotion Recognition Phase

[0423] The server:

[0424] Input: Emotion data (facial expressions, vocal tones) sent from the wearable device.

[0425] Data processing or data calculation performed: Using an emotion engine, the user's emotional state is analyzed using facial recognition technology and voice analysis technology.

[0426] Output: Recognized user emotional state data (e.g., "happiness", "anxiety", "anger", "stress").

[0427] What it does: The server's emotion engine analyzes facial expressions and vocal tone, and then uses the resulting data to label emotions.

[0428] Step 5:

[0429] Task Management Phase

[0430] The server:

[0431] Input: User's remaining energy data, emotional state data, and energy consumption data for each task.

[0432] Data processing or calculation: The amount of energy consumed by a task is calculated using an algorithm, and an optimal task schedule is generated based on the user's current remaining energy and emotional state.

[0433] Output: The generated task schedule data.

[0434] Specific operation: The server predicts the amount of energy required for each task and dynamically generates a schedule based on the user's energy and emotional state.

[0435] Step 6:

[0436] Task Schedule Notifications

[0437] Processing performed by the terminal (user terminal):

[0438] Input: Task schedule data sent from the server.

[0439] Data processing or data calculation to be performed: Converting task schedule information into a format for notifying the user.

[0440] Output: Task schedule information as push notifications and alerts.

[0441] Specific operation: The user terminal notifies the user visually or audibly based on the schedule data received from the server. The notification includes the specific task content, execution time, and order.

[0442] Step 7:

[0443] Stress level assessment and mental health care

[0444] The server:

[0445] Input: Activity data and emotion data.

[0446] Data processing or data calculation to be performed: Based on the collected data, the user's stress level is evaluated, and if a high stress level is detected, suggestions for counseling or relaxation methods are generated.

[0447] Output: Stress level assessment results and mental care recommendations.

[0448] Specific operation: The server periodically analyzes data, and if any abnormalities are found in the stress level, it launches a suggestion function to notify the user of countermeasures.

[0449] Step 8:

[0450] Task allocation and communication phase

[0451] The server:

[0452] Input: Physical and emotional data of all users in the household.

[0453] Data processing or data calculation: Aggregate each user's data, calculate a fair task share, and generate a proposal.

[0454] Output: Fairly allocated task sharing proposal data.

[0455] Specific operation: The server processes the data of all household members, calculates a fair task distribution, and then creates a proposal to display on the communication platform.

[0456] User Action:

[0457] Input: Task sharing proposal data received from the server.

[0458] Data processing or calculation to be performed: Enter feedback on the proposal.

[0459] Output: Updated feedback data.

[0460] Specific behavior: Users can send comments and correction requests to the proposal on the communication platform.

[0461] Step 9:

[0462] Realigning the proposed allocation

[0463] The server:

[0464] Input: Feedback data from users.

[0465] Data processing or data calculations performed: Readjust task allocation proposals based on feedback.

[0466] Output: Updated task sharing proposal data.

[0467] Specific operation: The server collects user feedback, reapplies the task allocation calculation algorithm, and displays the updated proposal again on the communication platform.

[0468] Step 10:

[0469] Ongoing data monitoring and follow-up

[0470] Processing performed by the device (wearable device):

[0471] Input: User behavior history, activity data, and emotion data.

[0472] Data processing or calculation: Data is collected in real time and periodically sent to the server.

[0473] Output: Latest behavioral history, activity data, and emotion data.

[0474] Specific operation: The wearable device stores the collected data in a buffer and transmits the data to the server at regular intervals.

[0475] The server:

[0476] Input: Data sent from the wearable device.

[0477] Data processing or calculation: Analyzing data in real time and generating follow-ups or new suggestions according to the user's situation.

[0478] Output: Follow-up and new suggestion data for the user.

[0479] Specific operation: Based on the data analysis results, the server generates the follow-up that is most appropriate for the user's situation and notifies the user's device. For example, if the user frequently feels tired, the server will make new suggestions to reduce the user's fatigue.

[0480] (Application example 2)

[0481] 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."

[0482] In modern society, stress and fatigue affect many people's lives, resulting in an increasing number of health problems. Furthermore, it is difficult to suggest meals that are tailored to the user's health condition and manage appropriate task schedules in daily life. Furthermore, while efficient and fair management of household task allocation is required, it is difficult to adjust this.

[0483] 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 behavioral history and activity amount data from the wearable device, means for calculating the user's maximum and remaining physical strength based on the behavioral history and activity amount data, means for calculating the physical strength consumption for each task and proposing an optimal task schedule to the user, means for assessing the user's stress level and generating counseling and mental care suggestions as needed, means for optimizing task allocation among family members and displaying the suggestions on a communication platform, and means for proposing optimal food menus and delivery times based on the user's physical strength and emotional state and placing orders in cooperation with a food delivery service. This allows the user to receive optimal meal suggestions and manage tasks based on their own health condition, and enables efficient and fair allocation of household tasks.

[0484] A "wearable device" is a device that can collect behavioral history, activity data, and emotional data in real time when worn by the user.

[0485] "Behavioral history data" refers to data related to the user's daily activities, including the number of steps taken, distance traveled, and the duration of specific activities.

[0486] "Activity data" refers to data related to the user's physical activity, including, for example, heart rate, calories burned, exercise amount, and sleep patterns.

[0487] "Maximum stamina" is the theoretical maximum stamina value that a user can have based on past activity data and health data.

[0488] "Remaining physical strength" is a value indicating the user's current physical strength, and is a value indicating the remaining physical strength calculated from the activity amount data and the behavior history data.

[0489] The "Task Schedule" is a plan that suggests tasks such as daily activities and household chores to the user in an optimized order and time based on the user's physical strength and emotional state.

[0490] "Stress level" is an indicator that shows the user's current mental burden and degree of stress.

[0491] "Mental care" refers to methods and suggestions to support the user's mental health, including relaxation techniques and counseling.

[0492] "Task sharing" refers to the efficient allocation of tasks such as housework among multiple users within a household.

[0493] A "communication platform" is a digital environment where multiple users can share information and stay in touch.

[0494] A "food menu" is a meal suggestion that is optimized for a user based on their health and emotional state.

[0495] "Delivery timing" refers to the time or date set so that food is delivered to the user at the optimal time.

[0496] A "food delivery service" is a service that delivers food selected by a user to a specified location.

[0497] "Placing an order" means purchasing food based on the user's selections and arranging delivery through a delivery service.

[0498] The present invention is implemented by a system including a wearable terminal, a server, a user terminal, and an emotion engine, which provides task management, mental care, emotion recognition, and food delivery optimization in a user's daily life in an integrated manner.

[0499] First, the user wears a wearable device. This device collects behavioral history data, activity data, and emotional data in real time and transmits them to a server at regular intervals (e.g., every hour). The wearable device is equipped with a heart rate monitor, accelerometer, gyro sensor, microphone, and camera.

[0500] The server receives the data sent from the wearable device, checks its completeness and accuracy, verifies the data for any outliers or missing data, and then calculates the user's maximum and remaining stamina. This calculation is performed by the HP calculation module and is based on the user's heart rate, activity level, sleep data, etc.

[0501] The server then uses an emotion engine to analyze the collected emotion data and recognize the user's emotional state. For example, if the user expresses anxiety, it can identify this as "anxiety." This emotional state is integrated with HP data to calculate the energy consumption for each task and propose an optimal task schedule for the user. This schedule is then sent to the user's device, along with the specific task order and recommended time.

[0502] The server also evaluates the user's stress level and generates counseling and mental care suggestions as needed. By using an emotion engine to recognize emotions and assessing stress levels based on activity data, the server determines relaxation methods and the need for counseling.

[0503] One of the features of this system is its integration with food delivery services. The server suggests the optimal food menu and delivery timing based on the user's physical strength and emotional state. The suggested menu is then automatically ordered in collaboration with the food delivery service. For example, if the user is tired, it will suggest an easy-to-digest meal or a menu with a relaxing effect. A notification will appear on the user's smartphone saying, "A menu with a relaxing effect has been ordered."

[0504] When it comes to dividing up household tasks, the server aggregates the HP and emotional data of all users in the household and proposes fair task division. These proposals are displayed on the communication platform, and each user can review them and provide feedback. For example, User A can add a comment saying, "I'm not feeling well today, so I'd like User B to clean up." Based on this feedback, the server readjusts the task division proposal and displays the updated content again on the communication platform.

[0505] For example, if User A is very tired and the emotion engine recognizes "stress," the system will suggest a "menu with a relaxing effect" (for example, herbal tea and an easy-to-digest seafood salad) and immediately order it through a delivery service. A notification will appear on the user's smartphone saying, "You have ordered a menu with a relaxing effect."

[0506] In addition, the following are specific examples of prompts that the emotion engine uses to analyze emotion data using a generative AI model:

[0507] Example prompt sentence:

[0508] "If a user is feeling tired or stressed, suggest a relaxing menu and order it through a delivery service. For example, a herbal tea and a digestive seafood salad."

[0509] This system allows users to receive optimal meal suggestions and manage tasks based on their own health status, and also allows for efficient and fair division of household tasks.

[0510] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0511] Step 1:

[0512] A user puts on a wearable device and starts their daily activities. The wearable device collects behavioral history and activity data such as heart rate, number of steps, sleep data, and emotional data (e.g., facial expressions, voice tone) in real time. The input is the user's biometric and behavioral data, and the output is that these data are temporarily stored within the device.

[0513] Step 2:

[0514] The device sends the collected data to a cloud server at regular intervals (e.g., every hour). The input here is the data stored on the device, and the output is a series of biometric and behavioral data sent to the server.

[0515] Step 3:

[0516] The server receives the data sent from the device and checks the completeness and accuracy of the data. It verifies whether there are any outliers or missing data. The input is the collected data, and the output is the accurate data with any outliers or missing data corrected.

[0517] Step 4:

[0518] The server calculates the user's maximum stamina (HP) and current remaining stamina based on the received data. Using the HP calculation module, it calculates stamina based on heart rate, activity level, sleep data, etc. The input is the processed data stored on the server, and the output is the user's maximum stamina and remaining stamina.

[0519] Step 5:

[0520] The server uses an emotion engine to analyze the collected emotion data and recognize the user's emotional state. It identifies emotions based on prompt sentences using a generative AI model. The input is the emotion data stored on the server, and the output is the user's emotional state (e.g., "anxiety" or "stress").

[0521] Step 6:

[0522] The server uses an algorithm to calculate the energy consumption for each task and proposes an optimal task schedule to the user. The input is the calculated energy data and emotional state, and the output is a task schedule. This schedule is sent to the user's device.

[0523] Step 7:

[0524] The user terminal receives the task schedule sent from the server and notifies the user. The schedule includes the specific task order and recommended time. The input is the task schedule received from the server, and the output is task notification information.

[0525] Step 8:

[0526] The server evaluates the user's stress level based on the activity data and emotion data, and generates counseling and mental care suggestions as needed. The input is activity data and emotion data, and the output is counseling and mental care suggestions.

[0527] Step 9:

[0528] The server aggregates the HP and emotional data of all users in the home and proposes fair task sharing. This proposal is displayed on the communication platform. The input is the HP and emotional data of multiple users, and the output is a task sharing proposal.

[0529] Step 10:

[0530] Users provide feedback on the proposed task distribution. For example, User A can add a comment saying, "I'm not feeling well today, so I'd like User B to clean up." The input is the user feedback, and the output is the adjustment data sent to the server.

[0531] Step 11:

[0532] The server readjusts the task allocation proposal based on the user feedback and displays the updated content on the communication platform again. The input is the user feedback, and the output is the readjusted task allocation proposal.

[0533] Step 12:

[0534] In cooperation with the food delivery service, the server proposes the optimal food menu and delivery timing based on the user's physical strength and emotional state. The proposed menu is then linked to the food delivery service, and an order is placed. The input is the user's physical strength and emotional state, and the output is the food delivery order information.

[0535] Step 13:

[0536] The server continuously monitors the user's condition and periodically generates follow-ups and new suggestions, including suggestions for reducing stress based on the latest emotional and activity data. The input is new data collected in real time, and the output is the latest suggestion information.

[0537] 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.

[0538] 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.

[0539] 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.

[0540] [Second embodiment]

[0541] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0542] 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.

[0543] 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).

[0544] 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.

[0545] 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.

[0546] 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).

[0547] 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.

[0548] 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.

[0549] 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.

[0550] 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.

[0551] 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.

[0552] 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."

[0553] The system of the present invention includes a wearable device, a server, and a user device. These components work together to provide task management and mental care for the user's daily life.

[0554] Data Collection Phase

[0555] Processing performed by the device (wearable device):

[0556] The user wears a wearable device, which collects the user's behavioral history and activity data (e.g., number of steps, heart rate, sleep data). The collected data is sent to a server at regular intervals (e.g., every hour).

[0557] Data Receipt and Confirmation Phase

[0558] The server:

[0559] The server receives the data sent from the terminal and checks the integrity and accuracy of the data, thereby ensuring the authenticity of the data.

[0560] HP calculation phase

[0561] The server:

[0562] The server calculates the user's maximum stamina (HP) based on the received data. It also calculates the user's current remaining HP based on the user's current activity level and saves this data. For example, if a user has walked 7,000 steps and their average heart rate is 80 BPM, their current HP will be calculated as 70 out of 100.

[0563] Task Management Phase

[0564] The server:

[0565] The server uses an algorithm to calculate the HP consumption of each household task. For example, cleaning consumes 10 HP, and cooking consumes 15 HP. Based on this information, the server generates an optimal task schedule that takes into account the user's current remaining HP. This schedule is then sent to the user's device.

[0566] Mental care phase

[0567] The server:

[0568] The server evaluates the user's stress level based on their activity data. For example, if a high stress level is detected from data from the past week, the server will suggest counseling to the user. It will also automatically generate mental care suggestions to alleviate stress (e.g., recommending relaxing yoga).

[0569] Task allocation and communication phase

[0570] The server:

[0571] The server aggregates the home page data of the entire family and proposes fair task allocations, ensuring that the burden within the household is distributed evenly. For example, it may suggest that user A be in charge of cleaning and user B be in charge of laundry. These suggestions are displayed on the family communication platform and can be viewed by each member.

[0572] User Action:

[0573] Users can review the proposed division of labor and communicate with their family members to make adjustments. For example, User A can provide feedback such as, "I'm not feeling well today, so I'd like User B to clean."

[0574] Ongoing Support Phase

[0575] The device:

[0576] The wearable device continuously collects behavioral history and activity data and sends it to a server, which keeps the data up to date.

[0577] The server:

[0578] The server analyzes data in real time, monitors the user's condition, and periodically follows up on mental health care and task management, automatically creating new suggestions and advice and notifying the user.

[0579] Specific examples

[0580] For example, after User A has finished their daily activities, they send data to the server via their wearable device. Based on that data, the server calculates that User A's current HP is 70. It then generates a task schedule for tomorrow, suggesting "cooking in the morning, cleaning in the afternoon." At the same time, it determines that User A's stress level is high based on recent activity data and suggests activities that will help them relax. Furthermore, within the home, a suggestion that User A will be in charge of cleaning and User B will be in charge of laundry is displayed on the communication platform, and User A can check the schedule and make adjustments as necessary.

[0581] As described above, the present invention enables efficient household task management and mental care throughout each phase.

[0582] The processing flow will be explained below.

[0583] Step 1: Wearable device data collection

[0584] Device: The user wears a wearable device that records real-time activity history and activity data, including the number of steps taken, heart rate, and sleep duration.

[0585] Step 2: Sending data

[0586] Terminal: The collected data is sent to the server at regular intervals (for example, every hour). Data communication is carried out using a secure protocol.

[0587] Step 3: Receiving and verifying data

[0588] Server: Receives data sent from the device and checks the data for completeness and accuracy, verifying that there are no outliers or missing data.

[0589] Step 4: Calculate Health (HP)

[0590] Server: Based on the received data, calculate the user's maximum stamina and current remaining stamina. The maximum stamina is calculated based on the user's basic health data and past history data.

[0591] Step 5: Calculate the HP cost of the task

[0592] Server: The HP cost for each task (e.g. cleaning, cooking, laundry) is calculated by an algorithm. For example, cleaning is set to 10 HP, cooking to 15 HP, etc.

[0593] Step 6: Generate a task schedule

[0594] Server: Generates the optimal task schedule for the user, taking into account the current remaining HP and task priority. The generated schedule is sent to the user's device.

[0595] Step 7: Notification of scheduled tasks

[0596] Device: The user device receives the task schedule sent from the server and notifies the user. The schedule includes the specific task order and recommended times.

[0597] Step 8: Assess your stress levels and take care of yourself

[0598] Server: Evaluates the user's stress level based on activity data and generates mental health advice as needed. If a high stress level is detected, counseling or relaxation suggestions are automatically provided.

[0599] Step 9: Optimize task distribution

[0600] Server: Aggregates the home page data of all users in the household and calculates a fair task allocation. For example, it suggests that user A be in charge of cleaning and user B be in charge of laundry.

[0601] Step 10: View the proposal

[0602] Server: Display task sharing proposals on the family communication platform so that all users can view them.

[0603] Step 11: User feedback

[0604] User: Provide feedback on the proposed task distribution, for example, adding a comment like "I'm not feeling well today, so I'd like someone else to clean up."

[0605] Step 12: Recalibrate your sharing proposal

[0606] Server: Based on user feedback, the task sharing proposal is re-adjusted and the updated content is displayed again on the communication platform.

[0607] Step 13: Ongoing data monitoring and follow-up

[0608] Device: Continuously collects behavioral history and activity data and sends it to the server.

[0609] Server: Analyzes data in real time, periodically generates follow-ups and new suggestions tailored to the user's situation, and notifies the user.

[0610] Through this process, household tasks and mental care can be managed efficiently, reducing the burden and stress on the entire household.

[0611] Example 1

[0612] 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."

[0613] The purpose of this invention is to provide a system that efficiently manages daily tasks and provides mental care within a home or community. A particular challenge is how to fairly distribute the burden, taking into account the physical strength and stress level of each user. Furthermore, it is necessary to achieve more flexible and adaptive support by providing real-time data analysis and appropriate feedback.

[0614] 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.

[0615] In this invention, the server includes means for collecting movement records and activity data from the wearable device, means for calculating the user's maximum and current physical strength based on the movement records and activity data, means for calculating the amount of physical strength consumed for each task and proposing an optimal work plan to the user, means for evaluating the user's stress level and generating counseling and mental care suggestions as needed, means for optimizing work allocation within the community and displaying the suggestions on a communication platform, means for aggregating the physical strength data of each member of the community and proposing fair work allocation, means for receiving user feedback and readjusting the work based on the feedback, and means for analyzing data in real time, automatically creating new mental care and work plans, and notifying the user. This enables efficient daily task management and mental care within the home or community, and fair distribution of the burden.

[0616] A "wearable device" is an electronic device that can be worn by a user and is used to record movements and collect activity data.

[0617] "Movement record" refers to historical information about the user's daily physical movements and actions.

[0618] "Activity data" refers to information that quantifies the amount of physical activity a user performs in their daily life.

[0619] "Maximum stamina" refers to the maximum stamina that the user possesses, and is usually a number calculated based on a certain standard.

[0620] "Current amount" is a numerical value that indicates the user's current remaining stamina.

[0621] A "work plan" is a suggested schedule of tasks for a user to perform throughout the day.

[0622] "Stamina consumption" is a numerical value that indicates the amount of stamina consumed when performing each task.

[0623] "Stress level" is a numerical value or evaluation value that indicates the degree of mental and physical stress of the user.

[0624] "Counselling" refers to professional advice and support provided to reduce users' stress levels.

[0625] "Mental care" refers to suggestions and activities offered to maintain or improve the mental health of users.

[0626] A "communications platform" is a digital interface for exchanging information, typically an application or web service over the internet.

[0627] "Equitable division of labor" is the allocation of work within a household or community so that each member shares responsibility equally.

[0628] "Feedback" refers to opinions, impressions, and situational information collected from users.

[0629] "Analyzing data in real time" means processing collected data in real time and analyzing it to obtain immediate results.

[0630] "Notifying the user" means that work plans and mental care suggestions are sent from the server to the user's device and displayed.

[0631] The system of the present invention combines a wearable device, a server, and a user terminal to provide task management and mental care for a user in their daily life. Detailed embodiments of the system are described below.

[0632] Hardware Configuration

[0633] Wearable devices:

[0634] Wearable devices can be worn by users to record movement and collect activity data. They include an accelerometer, heart rate monitor, and GPS sensor, and record data such as steps taken, heart rate, and location information in real time.

[0635] server:

[0636] The server receives and analyzes the data sent from the wearable device, and also performs processes such as calculating maximum stamina and current physical capacity, proposing work plans and mental care, and adjusting work allocation.

[0637] User device:

[0638] The user terminal is a device that receives notifications sent from the server, such as a smartphone or tablet, and allows interaction with the user through an interface.

[0639] Software Configuration

[0640] Data collection modules:

[0641] A software module installed in a wearable device collects data from various sensors, such as the number of steps taken from an accelerometer and the heart rate from a heart rate monitor.

[0642] Data Analysis Module:

[0643] A software module installed on the server analyzes the collected data, calculates the maximum stamina and current stock, and calculates the amount of stamina consumed for each task to generate an optimal work plan.

[0644] Stress Assessment Module:

[0645] The software module installed on the server evaluates the user's stress level from collected activity data and automatically generates counseling and mental care suggestions as needed.

[0646] Work allocation adjustment module:

[0647] This module aggregates the physical fitness data of each member of a family or community and proposes fair division of labor. It displays the proposals using a communication platform and can also readjust based on feedback.

[0648] Specific examples

[0649] For example, after completing a day's activities, User A sends data to a server via a wearable device. Based on that data, the server calculates that User A's current available physical energy is 70. It then generates a work plan for the next day and notifies the user's device with suggestions such as "cook in the morning and clean in the afternoon." At the same time, if the server determines that the user's stress level is high based on recent activity data, it also makes mental care suggestions such as "do 30 minutes of relaxing yoga." Furthermore, a suggestion for dividing household tasks, with User A doing the cleaning and User B doing the laundry, is displayed on the communication platform.

[0650] Prompt Sentence Examples

[0651] Example prompt:

[0652] "Please specifically design a system that uses wearable devices and a server to manage the user's daily tasks and provide mental care. In particular, please provide a detailed explanation of each phase: data collection, HP calculation, task management, and mental care."

[0653] As described above, close cooperation between the user, server, and wearable device makes it possible to achieve efficient daily task management and mental care.

[0654] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0655] Step 1: Data collection

[0656] Processing performed by the device (wearable device):

[0657] Wearable devices use built-in sensors (e.g., accelerometer, heart rate monitor, GPS) to record and collect user movement and activity data. Specifically, when a user walks, the accelerometer counts the number of steps, and the heart rate monitor measures the heart rate. The collected data (e.g., number of steps: 5000, heart rate: 75 BPM) is temporarily stored in the device.

[0658] Input: User's physical activity (e.g., walking, exercising)

[0659] Output: Collected motion records and activity data (e.g., steps, heart rate)

[0660] Step 2: Data Transfer

[0661] The device:

[0662] The wearable device transmits the collected motion records and activity data to a server at regular intervals (e.g., every hour). Data transmission uses a secure communication protocol (e.g., HTTPS).

[0663] Input: Collected motion and activity data (e.g., steps, heart rate)

[0664] Output: Data sent to the server

[0665] Step 3: Data reception and confirmation

[0666] The server:

[0667] The server receives the data sent by the terminal and checks the data for completeness and accuracy using a checksum algorithm. If there is a problem with any part of the received data, it requests a retransmission.

[0668] Input: Data sent from the terminal

[0669] Output: Verified data (e.g., clean step and heart rate data)

[0670] Step 4: HP Calculation

[0671] The server:

[0672] The server calculates the user's maximum stamina and current stamina based on the confirmed data. For example, it runs an algorithm to calculate current stamina based on the number of steps and heart rate data for that day. For example, if the maximum stamina is 100, the current stamina is calculated to be 80 based on the number of steps 5000 and heart rate 75 BPM.

[0673] Input: Verified activity data (e.g., steps, heart rate)

[0674] Output: User's current stamina (e.g. 80 / 100)

[0675] Step 5: Task Management

[0676] The server:

[0677] The server calculates the amount of stamina consumed for each task. For example, cleaning requires 10 HP, cooking requires 15 HP, and generates an optimal task plan based on the user's current stamina and sends it to the user's device.

[0678] Input: Current stamina (e.g. 80 / 100), task list and consumption (e.g. cleaning 10 HP, cooking 15 HP)

[0679] Output: Work plan (e.g. cooking in the morning, cleaning in the afternoon)

[0680] Step 6: Stress assessment and mental health advice

[0681] The server:

[0682] The server analyzes the collected activity data and evaluates the user's stress level. For example, if it determines that the stress level is high based on data from the past week, it will automatically generate mental care suggestions, such as a relaxing 30-minute yoga session, and send them to the user's device.

[0683] Input: Activity data (e.g., number of steps taken in the past week, heart rate)

[0684] Output: Mental care suggestions (e.g. yoga for relaxation)

[0685] Step 7: Assign tasks and communicate

[0686] The server:

[0687] The server collects the physical fitness data of each household member and proposes fair division of labor. The proposals are displayed on the communication platform and can be confirmed by the user and the other members. For example, it may be suggested that User A be in charge of cleaning and User B be in charge of laundry.

[0688] Input: Physical fitness data of household members

[0689] Output: Fair work sharing proposal (e.g., User A cleans, User B does laundry)

[0690] Step 8: Gather feedback and refine

[0691] User Action:

[0692] Users can send feedback on the provided work assignments through the communication platform, for example, sending a message such as, "I'm not feeling well today, so I'd like you to do the cleaning for me."

[0693] Input: User feedback

[0694] Output: Server receiving feedback

[0695] The server:

[0696] The server re-adjusts the division of labor based on the collected feedback and displays the updated proposal again on the communication platform.

[0697] Input: User feedback

[0698] Output: Rebalanced work assignments (e.g., User B now cleans)

[0699] Step 9: Ongoing support

[0700] The device:

[0701] The wearable device continuously collects motion records and activity data and periodically transmits them to a server.

[0702] Input: User's ongoing activity

[0703] Output: Latest collected motion records and activity data

[0704] The server:

[0705] The server analyzes the data in real time, keeping it up to date and providing appropriate follow-up care and task management.

[0706] Input: Latest activity data

[0707] Output: Real-time suggestions and notifications (e.g. break suggestions, task update notifications)

[0708] (Application example 1)

[0709] 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."

[0710] In conventional factories, it has been difficult to grasp the workload and stress levels of workers and efficiently manage work tasks based on this information. Furthermore, optimization of work tasks within factories and mental care for workers are often insufficient, resulting in decreased productivity and increased mental strain on workers. To solve these problems, the present invention provides a comprehensive task management and mental care system based on worker activity data.

[0711] 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.

[0712] In this invention, the server includes means for collecting behavioral history and activity data from the wearable device, means for calculating the user's maximum and remaining physical strength based on the behavioral history and activity data, means for calculating the physical strength consumption for each task and proposing an optimal task schedule to the user, means for evaluating the user's stress level and generating counseling and mental care suggestions as needed, means for generating an optimal task schedule for factory robots based on the worker's activity data and allocating tasks, and means for analyzing the worker's stress level and providing mental care suggestions as needed. This enables efficient task management that takes into account the physical strength and mental health of workers, improving productivity, and reducing the mental burden on workers.

[0713] A "wearable device" is a device worn on the body that constantly monitors and collects the user's behavioral history and activity data.

[0714] "Behavioral history" refers to a record of various actions taken by a user, such as the number of steps taken, distance traveled, and daily activity patterns.

[0715] "Activity data" is data that quantitatively indicates the user's physical activity, and specific examples include the number of steps taken, heart rate, and calorie consumption.

[0716] "Maximum stamina" is a numerical representation of the user's total stamina, and is an indicator of the maximum amount of activity possible in a day.

[0717] "Remaining physical strength" is a numerical value that indicates the user's current physical strength, and indicates the current level of fatigue and the remaining amount of activity that can be performed.

[0718] A "task schedule" is a plan that optimally arranges each task, taking into account the user's physical strength, stress level, and priority.

[0719] "Stress level" indicates the user's mental stress and fatigue level using numerical values ​​and indicators to evaluate the user's mental state.

[0720] "Counseling" refers to suggestions and interventions to provide psychological support and consultation to users experiencing high stress levels.

[0721] "Mental care" refers to specific activities and suggestions for maintaining and improving the user's mental health, including stress reduction and relaxation methods.

[0722] "Worker" refers to an employee or worker who works in a factory or business.

[0723] A "factory robot" is a machine or automated device used to automate work in a factory, performing tasks in place of humans.

[0724] "Task sharing" means allocating multiple tasks fairly to each member, with the aim of efficiently dividing up work.

[0725] The present invention provides a system for improving the efficiency of task management and mental care within a factory based on worker activity data. The system includes a wearable device, a server, and a user terminal.

[0726] Hardware and Software Use

[0727] Wearable devices

[0728] Wearable devices are devices that collect workers' behavioral history and activity data in real time. These devices have built-in sensors such as pedometers, heart rate monitors, and sleep monitors, and periodically send data to a server. Possible devices used include smartwatches and fitness trackers.

[0729] server

[0730] The server receives the data sent from the wearable device and checks its completeness and accuracy. Based on the collected data, it calculates the worker's HP (maximum and remaining stamina) and evaluates their stress level. Furthermore, it calculates the stamina consumption for each task based on the worker's current HP and generates an optimal task schedule. It also makes mental health care suggestions as needed. Cloud services such as Amazon Web Services (AWS) and Google Cloud Platform can be used for server-side processing.

[0731] User terminal

[0732] User devices, such as smartphones, tablets, and PCs, are used by workers and managers to check task schedules and mental health advice and provide feedback.

[0733] Data processing and calculation

[0734] After receiving the data transmitted from the wearable device, the server performs the following processing.

[0735] HP Calculation: Calculates the maximum stamina and current remaining stamina based on data such as the worker's steps, heart rate, sleep time, etc. For example, calculate the HP of a worker who has walked 7,000 steps and save that data.

[0736] Task Schedule Generation: Based on the current HP of the workers, calculate the HP required for each task and generate an optimal task schedule. For example, assign a worker 20 HP of packaging work, 15 HP of inspection work, and 30 HP of assembly work.

[0737] Stress level assessment: Based on the collected data, the stress level of the worker is assessed and mental health care recommendations are made as necessary. For example, if stress is determined to be high based on heart rate data from the past week, counseling is recommended.

[0738] Optimizing task sharing: Optimizing task sharing among family members and adjusting the workload fairly, which will improve the efficiency of work within the factory.

[0739] Specific examples

[0740] In an actual use case, Worker A at a factory wears a wearable device while working. The device collects data such as the number of steps taken each day, heart rate, and sleep time, and sends it to the server. After receiving this data, the server calculates Worker A's current HP and generates a task schedule for the next day based on that. For example, it may make adjustments such as "allocating 20 HP to packaging, 15 HP to inspection, and 30 HP to assembly." It may also determine from recent data that Worker A's stress level is rising and suggest relaxing activities.

[0741] Prompt Sentence Examples

[0742] "Please input the following end-user data into the deep learning model to generate an optimal factory task schedule.

[0743] Number of steps: 7000

[0744] Heart rate: 75 BPM

[0745] Sleep time: 6 hours

[0746] Task suggestions:

[0747] Packaging work (20HP)

[0748] Inspection work (15HP)

[0749] Assembly work (30HP)

[0750] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0751] Step 1:

[0752] Data Collection Phase

[0753] The wearable device collects the worker's behavioral history and activity data. This data includes the number of steps, heart rate, sleep time, etc. The collected data is sent to a server at regular intervals (e.g., every hour). The input is the worker's daily activity data, and the output is the data sent to the server.

[0754] Step 2:

[0755] Data Receipt and Confirmation Phase

[0756] The server receives the data sent from the wearable device and performs a data verification process to verify the completeness and accuracy of the received data. The input is the data from the wearable device and the output is the verified worker activity data.

[0757] Step 3:

[0758] HP calculation phase

[0759] The server calculates the worker's maximum stamina (HP) and current remaining stamina based on the confirmed data. For example, the server calculates the worker's current HP by subtracting the worker's maximum stamina from 100 based on the number of steps, heart rate, and sleep time. The input is the confirmed activity data, and the output is the calculated remaining HP.

[0760] Step 4:

[0761] Task schedule generation phase

[0762] The server calculates the stamina consumption required for each task based on the worker's current HP and generates an optimal task schedule. The server inputs the stamina consumption for each task and adjusts and calculates the schedule based on the worker's HP. The input is the worker's remaining stamina and task data, and the output is the optimal task schedule.

[0763] Step 5:

[0764] Task Schedule Notification Phase

[0765] The server sends the generated task schedule to the user terminal. Workers and managers can check this schedule using the user terminal and provide feedback as needed. The input is the optimal task schedule, and the output is a schedule notification to the user terminal.

[0766] Step 6:

[0767] Stress Level Assessment Phase

[0768] The server evaluates the worker's stress level from the collected activity data. For example, it determines whether stress is increasing from trends in heart rate and sleep time, and makes recommendations for counseling or mental care as necessary. The input is activity data, and the output is the evaluated stress level and recommendations.

[0769] Step 7:

[0770] Feedback reflection phase

[0771] The server receives feedback from users and adjusts task schedules and mental health care suggestions based on that feedback. For example, if a worker provides feedback such as "I'm not feeling well, so I'd like to reduce my workload," the server takes that into account when generating a new schedule. The input is the feedback data, and the output is the adjusted task schedule.

[0772] Step 8:

[0773] Data update and save phase

[0774] The server continuously receives new data, analyzes and stores it, and always manages based on the latest data. The input is new activity data collected in real time, and the output is an updated database and continuous recommendations.

[0775] 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.

[0776] The system of the present invention includes a wearable terminal, a server, a user terminal, and an emotion engine. These components work together to provide task management, mental care, and emotion recognition in the user's daily life.

[0777] Data Collection Phase

[0778] Processing performed by the device (wearable device):

[0779] The user wears a wearable device, which records real-time activity data (e.g., number of steps, heart rate, sleep data) and emotional data (e.g., facial expressions, voice tone). The collected data is sent to a server at regular intervals (e.g., every hour).

[0780] Data Receipt and Confirmation Phase

[0781] The server:

[0782] The server receives the data sent from the device and checks its completeness and accuracy, verifying that there are no outliers or missing data.

[0783] HP calculation phase

[0784] The server:

[0785] The server calculates the user's maximum HP and current remaining HP based on the received data. The maximum HP is calculated based on the user's basic health data and past history data.

[0786] Emotion Recognition Phase

[0787] The server:

[0788] The server uses an emotion engine to analyze the collected emotion data and recognize the user's emotional state. For example, if the user expresses anxiety, it will recognize it as "anxiety."

[0789] Task Management Phase

[0790] The server:

[0791] The server uses an algorithm to calculate the HP consumption of each household task. For example, cleaning consumes 10 HP, and cooking consumes 15 HP. Furthermore, based on the emotional data recognized by the emotion engine, the server generates an optimal task schedule that takes into account the user's current remaining HP and emotional state. This schedule is then sent to the user's device.

[0792] Task Schedule Notifications

[0793] Device:

[0794] The user's device receives the task schedule sent from the server and notifies the user. The schedule includes the specific task order and recommended time. For example, if the user's HP is 70 and the emotion engine recognizes "stress," it will prioritize tasks that will reduce stress, such as suggesting "light stretching" or "relaxing housework."

[0795] Stress level assessment and mental health care

[0796] The server:

[0797] The server evaluates the user's stress level based on activity data and emotional data. If a high stress level is detected, it generates suggestions for counseling or mental care. Specifically, if the emotion engine recognizes "stress," it suggests relaxation methods or counseling.

[0798] Task allocation and communication phase

[0799] The server:

[0800] The server aggregates the HP and emotional data of all users in the household and proposes fair task sharing. These proposals are displayed on the family communication platform. For example, if user A is feeling "anxious," user B will be suggested to take on a larger share of the important tasks for the day.

[0801] User Action:

[0802] Users can provide feedback on the proposed task allocation. For example, User A can add a comment saying, "I'm not feeling well today, so I'd like User B to clean up."

[0803] Realigning the proposed allocation

[0804] The server:

[0805] Based on user feedback, the task sharing proposal will be re-adjusted and the updated content will be displayed again on the communication platform.

[0806] Ongoing data monitoring and follow-up

[0807] The device:

[0808] The wearable device continuously collects behavioral history, activity data, and emotional data and transmits them to a server.

[0809] The server:

[0810] The server analyzes the data in real time and periodically generates follow-ups and new suggestions tailored to the user's situation and notifies them. For example, if the user has recently been feeling "fatigue," it will suggest measures to reduce the user's stress.

[0811] Specific examples

[0812] For example, after User A has finished their daily activities, they send data to the server via their wearable device. Based on that data, the server calculates that User A's current HP is 70. Furthermore, if the emotion engine recognizes "stress," it will prioritize stress-reducing tasks such as "light stretching" and "easy cooking" in the task schedule for the next day. At home, a suggestion is displayed on the communication platform that User B should take on more of the important tasks, and User A can check the content and make adjustments as necessary.

[0813] In this way, the present invention can perform household task management, mental care, and emotion recognition in an integrated manner throughout each phase.

[0814] The processing flow will be explained below.

[0815] Step 1: Wearable device data collection

[0816] Device: The user wears a wearable device, which collects real-time behavioral history and activity data (e.g., steps, heart rate, sleep data) and emotional data (e.g., facial expressions, voice tone).

[0817] Step 2: Sending data

[0818] Terminal: The collected data is sent to the server at regular intervals (for example, every hour). Data communication is carried out using a secure protocol.

[0819] Step 3: Receiving and verifying data

[0820] Server: Receives data sent from the device and checks the data for completeness and accuracy, verifying that there are no outliers or missing data.

[0821] Step 4: Calculate Health (HP)

[0822] Server: Based on the received data, calculate the user's maximum stamina and current remaining stamina. The maximum stamina is calculated based on the user's basic health data and past history data.

[0823] Step 5: Emotion Recognition

[0824] Server: The server uses an emotion engine to analyze the collected emotion data and recognize the user's emotional state, for example, "anxiety" or "joy" based on the user's facial expressions and voice tone.

[0825] Step 6: Calculate the HP cost of the task

[0826] Server: The HP cost for each household task is calculated by an algorithm, for example, cleaning is set to 10 HP, cooking is set to 15 HP, etc.

[0827] Step 7: Generate a task schedule

[0828] Server: Generates an optimal task schedule for the user, taking into account the user's current remaining HP and the emotional state provided by the emotion engine. For example, if the user's HP is 70 and the emotion engine detects "stress," it prioritizes tasks that reduce stress. The generated schedule is sent to the user's device.

[0829] Step 8: Notification of scheduled tasks

[0830] Device: The user device receives the task schedule sent from the server and notifies the user. The schedule includes the specific task order and recommended times.

[0831] Step 9: Stress level assessment and mental health recommendations

[0832] Server: Evaluates the user's stress level based on activity data and emotional data, and generates mental health advice as needed. For example, if the emotion engine detects "stress," it will automatically suggest counseling or relaxation techniques.

[0833] Step 10: Optimize task distribution

[0834] Server: Aggregates HP data and emotional data from all users in the household and proposes fair task sharing. For example, if user A is feeling anxious, user B will be suggested to share more of the important tasks for that day. The proposal is displayed on the family communication platform.

[0835] Step 11: View the proposal

[0836] Server: Display the task sharing proposal on the family communication platform so that all users can view the contents.

[0837] Step 12: User feedback

[0838] Users: Provide feedback on the proposed task distribution. For example, User A can add a comment saying, "I'm not feeling well today, so I'd like User B to clean up."

[0839] Step 13: Recalibrate your sharing proposal

[0840] Server: Based on user feedback, the task sharing proposal is re-adjusted and the updated content is displayed again on the communication platform.

[0841] Step 14: Ongoing data monitoring and follow-up

[0842] Device: Continuously collects behavioral history, activity data, and emotional data, and sends them to the server.

[0843] Server: Analyzes data in real time, periodically generates follow-ups and new suggestions based on the user's situation, and notifies the user. For example, if the user has recently been feeling "tired," the server will suggest measures to reduce the user's stress.

[0844] Through this process, household task management, mental care, and emotional awareness can be integrated, reducing the burden and stress on the entire household.

[0845] Example 2

[0846] 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."

[0847] Conventional home task management systems often provide a uniform task schedule without fully considering the user's physical condition or emotional state. This can result in excessive burden on users and problems such as insufficient mental care and stress management. Furthermore, because the allocation of tasks within the home is done without taking into account each individual's physical condition or emotional state, it can easily lead to a sense of unfairness. By solving these issues, we aim to realize a healthier and stress-free home life.

[0848] 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.

[0849] In this invention, the server includes: means for collecting behavior history and activity amount data from the wearable device;

[0850] A means for calculating the user's maximum stamina and remaining stamina based on the behavior history and activity amount data;

[0851] A means of emotion recognition by analyzing the user's facial expressions and vocal tone;

[0852] A method for calculating the amount of energy consumed for each task using an algorithm and proposing an optimal task schedule taking into account the emotional state and remaining energy;

[0853] a means for notifying a user terminal of the proposed task schedule;

[0854] A means for evaluating a user's stress level based on their activity data and emotional data, and generating suggestions for counseling and mental care;

[0855] The system also includes a means for optimizing task allocation among family members and displaying the proposed results on the communication platform. This enables optimal task scheduling and fair task allocation that takes into account the user's physical condition and emotional state. Furthermore, by providing appropriate mental care and stress management, a healthy and stress-free family life can be achieved.

[0856] A "wearable device" is a small electronic device that can be worn by the user and has built-in sensors that collect behavioral history, activity data, emotional data, and other information.

[0857] "Behavioral history" refers to the history of various activities and movements undertaken by a user, including detailed information such as time and location.

[0858] "Activity data" is data that quantifies the physical movements and amount of exercise a user performs in their daily life, and examples include the number of steps taken, heart rate, calories burned, and sleep data.

[0859] "Maximum stamina" is the theoretical maximum stamina of a user, calculated by an algorithm based on the user's basic health status and past history data.

[0860] "Remaining physical strength" is a numerical value that indicates the user's current physical strength, and is calculated in real time based on behavioral history and activity data.

[0861] "Facial expressions" refers to the movement of a user's facial muscles and the movements of the eyes, mouth, eyebrows, etc., and are used as data for analyzing emotional states.

[0862] "Voice tone" refers to characteristics such as pitch, volume, and rhythm of the user's speaking voice, and is used as data for analyzing emotional state.

[0863] "Emotion recognition" is a technology that analyzes collected data such as facial expressions and voice tone to identify a user's emotional state.

[0864] A "task" refers to a specific task or activity that a user must perform in their daily life, such as cleaning, cooking, or laundry.

[0865] A "task schedule" is a planning table that suggests the optimal order and start time of tasks, taking into account the user's remaining physical energy and emotional state.

[0866] "Notifications" refers to the form of alerts or push notifications generated by the server to inform users of task schedules, mental health suggestions, etc.

[0867] "Stress level" is a numerical representation of the degree of stress a user feels, and is evaluated based on activity data and emotional data.

[0868] "Mental care" refers to counseling and relaxation suggestions provided to maintain the user's psychological health.

[0869] "Task sharing" refers to the fair allocation of tasks that are jointly performed by multiple household members, with the aim of optimizing the burden among family members.

[0870] "Communication platform" refers to a digital environment or application for sharing, coordinating, and managing tasks and other information among family members.

[0871] The system of the present invention supports task management, mental care, and emotion recognition in a user's daily life. This system includes a wearable device, a server, a user device, and an emotion engine, and these components function in cooperation with each other.

[0872] Data Collection Phase

[0873] The wearable device:

[0874] The wearable device worn by the user has built-in sensors, cameras, and microphones. This hardware is used to record the user's behavioral history (e.g., number of steps, distance traveled) and activity data (e.g., heart rate, sleep data) in real time. It also collects emotional data such as facial expressions and vocal tone. This data is sent to a server at regular intervals (e.g., every hour) via Bluetooth or Wi-Fi.

[0875] Data Receipt and Confirmation Phase

[0876] The server:

[0877] The server receives the data sent from the wearable device and checks its completeness and accuracy before storing it in the database, verifies whether there are any outliers or missing data, and requests a retransmission if an anomaly is detected.

[0878] HP calculation phase

[0879] The server:

[0880] The server calculates the user's maximum stamina and current remaining stamina based on the data collected from the wearable device. This uses the user's basic health data and past history data. The calculation is performed using a specific algorithm, and the user's remaining stamina is updated in real time.

[0881] Emotion Recognition Phase

[0882] The server:

[0883] The emotion engine in the server analyzes the user's facial expression and voice tone to recognize their emotion. For example, if the facial expression indicates sadness, it will be recognized as "sadness." Voice tone is also analyzed in the same way, and the overall emotional state is determined.

[0884] Task Management Phase

[0885] The server:

[0886] The server uses an algorithm to calculate the amount of stamina consumed for each task and generates an optimal task schedule based on the user's remaining stamina and emotional state. For example, cleaning is set to consume 10 HP, and cooking is set to consume 15 HP. The generated task schedule is sent to the user's device.

[0887] Task Schedule Notifications

[0888] User device processes:

[0889] The system receives task schedules sent to the user's device and notifies the user using push notifications and alerts. The notifications include the specific task execution order and recommended time. For example, if the user's remaining stamina is 70 and the emotion engine recognizes "stress," it will suggest "light stretching" or "relaxing housework" to reduce stress.

[0890] Stress level assessment and mental health care

[0891] The server:

[0892] The server evaluates the user's stress level based on activity and emotional data. If a high stress level is detected, counseling and relaxation methods are offered. For example, specific advice such as "Try 10 minutes of meditation" is provided.

[0893] Task allocation and communication phase

[0894] The server:

[0895] The server aggregates the physical strength and emotional data of all users in the household and proposes fair task sharing. The task sharing among users is displayed and shared on the communication platform. For example, if user A has low physical strength and feels "anxious," a suggestion is made that user B should share more of the important tasks for the day.

[0896] User Action:

[0897] Users can provide feedback on the proposed task allocation. For example, they can comment, "I'm not feeling well today, so I'd like to ask User B to clean up."

[0898] Realigning the proposed allocation

[0899] The server:

[0900] Based on the user's feedback, the server readjusts the task sharing proposal and displays the updated content again on the communication platform.

[0901] Ongoing data monitoring and follow-up

[0902] The wearable device:

[0903] The wearable device continuously collects behavioral history, activity data, and emotional data and transmits them to a server.

[0904] The server:

[0905] The server analyzes the received data in real time and generates follow-ups and new suggestions based on the user's situation. For example, if the user's recent data shows that they frequently feel "tired," the server will make new suggestions to reduce the burden, such as notifying them to "reduce their activity today and take a rest."

[0906] Specific examples

[0907] For example, after User A has finished his or her daily activities, he or she sends data to the server via a wearable device. The server analyzes the data and calculates that User A's current remaining stamina is 70. Furthermore, if the emotion engine recognizes "stress," it will suggest "light stretching" or "easy cooking" in the task schedule for the next day. At home, suggestions are displayed on the communication platform for User B to share many important tasks.

[0908] Prompt Sentence Examples

[0909] "I'm feeling stressed today. My current stamina is at 70. Please suggest a task schedule for tomorrow."

[0910] As described above, the system of the present invention can perform household task management, mental care, and emotion recognition in an integrated manner throughout each phase.

[0911] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0912] Step 1:

[0913] Data Collection Phase

[0914] Processing performed by the device (wearable device):

[0915] Input: The wearable device collects the user's behavioral history (e.g., steps taken, distance traveled), activity data (e.g., heart rate, sleep data), and emotional data (e.g., facial expressions, voice tone) in real time.

[0916] Data processing or calculation: Various biometric data is recorded using the built-in sensors, camera, and microphone.

[0917] Output: The collected data is encoded at regular intervals (e.g., every hour) and sent to a server via Bluetooth or Wi-Fi.

[0918] Specific operation: After data collection, the wearable device's communication function is activated and the data is uploaded to the server using a secure protocol.

[0919] Step 2:

[0920] Data Receipt and Confirmation Phase

[0921] The server:

[0922] Input: Behavioral history, activity data, and emotion data sent from the wearable device.

[0923] Data processing or data calculations performed: Checking the completeness and accuracy of received data, verifying that there are no outliers or missing data.

[0924] Output: The data whose accuracy has been confirmed is stored in a database, and if there is an abnormality, a resend request is made.

[0925] What happens: After receiving the data, the server's validation algorithm checks the integrity of the data and, if necessary, requests that the data be resent.

[0926] Step 3:

[0927] HP calculation phase

[0928] The server:

[0929] Input: Verified activity data and behavioral history data.

[0930] Data processing or calculation: The server's algorithm calculates the maximum stamina and current remaining stamina based on the user's basic health data (age, gender, height, weight, etc.) and past history data.

[0931] Output: Calculated maximum health and current health remaining data.

[0932] Specific operation: The server periodically evaluates the user's physical strength status and updates the remaining physical strength data based on that.

[0933] Step 4:

[0934] Emotion Recognition Phase

[0935] The server:

[0936] Input: Emotion data (facial expressions, vocal tones) sent from the wearable device.

[0937] Data processing or data calculation performed: Using an emotion engine, the user's emotional state is analyzed using facial recognition technology and voice analysis technology.

[0938] Output: Recognized user emotional state data (e.g., "happiness", "anxiety", "anger", "stress").

[0939] What it does: The server's emotion engine analyzes facial expressions and vocal tone, and then uses the resulting data to label emotions.

[0940] Step 5:

[0941] Task Management Phase

[0942] The server:

[0943] Input: User's remaining energy data, emotional state data, and energy consumption data for each task.

[0944] Data processing or calculation: The amount of energy consumed by a task is calculated using an algorithm, and an optimal task schedule is generated based on the user's current remaining energy and emotional state.

[0945] Output: The generated task schedule data.

[0946] Specific operation: The server predicts the amount of energy required for each task and dynamically generates a schedule based on the user's energy and emotional state.

[0947] Step 6:

[0948] Task Schedule Notifications

[0949] Processing performed by the terminal (user terminal):

[0950] Input: Task schedule data sent from the server.

[0951] Data processing or data calculation to be performed: Converting task schedule information into a format for notifying the user.

[0952] Output: Task schedule information as push notifications and alerts.

[0953] Specific operation: The user terminal notifies the user visually or audibly based on the schedule data received from the server. The notification includes the specific task content, execution time, and order.

[0954] Step 7:

[0955] Stress level assessment and mental health care

[0956] The server:

[0957] Input: Activity data and emotion data.

[0958] Data processing or data calculation to be performed: Based on the collected data, the user's stress level is evaluated, and if a high stress level is detected, suggestions for counseling or relaxation methods are generated.

[0959] Output: Stress level assessment results and mental care recommendations.

[0960] Specific operation: The server periodically analyzes data, and if any abnormalities are found in the stress level, it launches a suggestion function to notify the user of countermeasures.

[0961] Step 8:

[0962] Task allocation and communication phase

[0963] The server:

[0964] Input: Physical and emotional data of all users in the household.

[0965] Data processing or data calculation: Aggregate each user's data, calculate a fair task share, and generate a proposal.

[0966] Output: Fairly allocated task sharing proposal data.

[0967] Specific operation: The server processes the data of all household members, calculates a fair task distribution, and then creates a proposal to display on the communication platform.

[0968] User Action:

[0969] Input: Task sharing proposal data received from the server.

[0970] Data processing or calculation to be performed: Enter feedback on the proposal.

[0971] Output: Updated feedback data.

[0972] Specific behavior: Users can send comments and correction requests to the proposal on the communication platform.

[0973] Step 9:

[0974] Realigning the proposed allocation

[0975] The server:

[0976] Input: Feedback data from users.

[0977] Data processing or data calculations performed: Readjust task allocation proposals based on feedback.

[0978] Output: Updated task sharing proposal data.

[0979] Specific operation: The server collects user feedback, reapplies the task allocation calculation algorithm, and displays the updated proposal again on the communication platform.

[0980] Step 10:

[0981] Ongoing data monitoring and follow-up

[0982] Processing performed by the device (wearable device):

[0983] Input: User behavior history, activity data, and emotion data.

[0984] Data processing or calculation: Data is collected in real time and periodically sent to the server.

[0985] Output: Latest behavioral history, activity data, and emotion data.

[0986] Specific operation: The wearable device stores the collected data in a buffer and transmits the data to the server at regular intervals.

[0987] The server:

[0988] Input: Data sent from the wearable device.

[0989] Data processing or calculation: Analyzing data in real time and generating follow-ups or new suggestions according to the user's situation.

[0990] Output: Follow-up and new suggestion data for the user.

[0991] Specific operation: Based on the data analysis results, the server generates the follow-up that is most appropriate for the user's situation and notifies the user's device. For example, if the user frequently feels tired, the server will make new suggestions to reduce the user's fatigue.

[0992] (Application example 2)

[0993] 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."

[0994] In modern society, stress and fatigue affect many people's lives, resulting in an increasing number of health problems. Furthermore, it is difficult to suggest meals that are tailored to the user's health condition and manage appropriate task schedules in daily life. Furthermore, while efficient and fair management of household task allocation is required, it is difficult to adjust this.

[0995] 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 behavioral history and activity amount data from the wearable device, means for calculating the user's maximum and remaining physical strength based on the behavioral history and activity amount data, means for calculating the physical strength consumption for each task and proposing an optimal task schedule to the user, means for assessing the user's stress level and generating counseling and mental care suggestions as needed, means for optimizing task allocation among family members and displaying the suggestions on a communication platform, and means for proposing optimal food menus and delivery times based on the user's physical strength and emotional state and placing orders in cooperation with a food delivery service. This allows the user to receive optimal meal suggestions and manage tasks based on their own health condition, and enables efficient and fair allocation of household tasks.

[0996] A "wearable device" is a device that can collect behavioral history, activity data, and emotional data in real time when worn by the user.

[0997] "Behavioral history data" refers to data related to the user's daily activities, including the number of steps taken, distance traveled, and the duration of specific activities.

[0998] "Activity data" refers to data related to the user's physical activity, including, for example, heart rate, calories burned, exercise amount, and sleep patterns.

[0999] "Maximum stamina" is the theoretical maximum stamina value that a user can have based on past activity data and health data.

[1000] "Remaining physical strength" is a value indicating the user's current physical strength, and is a value indicating the remaining physical strength calculated from the activity amount data and the behavior history data.

[1001] The "Task Schedule" is a plan that suggests tasks such as daily activities and household chores to the user in an optimized order and time based on the user's physical strength and emotional state.

[1002] "Stress level" is an indicator that shows the user's current mental burden and degree of stress.

[1003] "Mental care" refers to methods and suggestions to support the user's mental health, including relaxation techniques and counseling.

[1004] "Task sharing" refers to the efficient allocation of tasks such as housework among multiple users within a household.

[1005] A "communication platform" is a digital environment where multiple users can share information and stay in touch.

[1006] A "food menu" is a meal suggestion that is optimized for a user based on their health and emotional state.

[1007] "Delivery timing" refers to the time or date set so that food is delivered to the user at the optimal time.

[1008] A "food delivery service" is a service that delivers food selected by a user to a specified location.

[1009] "Placing an order" means purchasing food based on the user's selections and arranging delivery through a delivery service.

[1010] The present invention is implemented by a system including a wearable terminal, a server, a user terminal, and an emotion engine, which provides task management, mental care, emotion recognition, and food delivery optimization in a user's daily life in an integrated manner.

[1011] First, the user wears a wearable device. This device collects behavioral history data, activity data, and emotional data in real time and transmits them to a server at regular intervals (e.g., every hour). The wearable device is equipped with a heart rate monitor, accelerometer, gyro sensor, microphone, and camera.

[1012] The server receives the data sent from the wearable device, checks its completeness and accuracy, verifies the data for any outliers or missing data, and then calculates the user's maximum and remaining stamina. This calculation is performed by the HP calculation module and is based on the user's heart rate, activity level, sleep data, etc.

[1013] The server then uses an emotion engine to analyze the collected emotion data and recognize the user's emotional state. For example, if the user expresses anxiety, it can identify this as "anxiety." This emotional state is integrated with HP data to calculate the energy consumption for each task and propose an optimal task schedule for the user. This schedule is then sent to the user's device, along with the specific task order and recommended time.

[1014] The server also evaluates the user's stress level and generates counseling and mental care suggestions as needed. By using an emotion engine to recognize emotions and assessing stress levels based on activity data, the server determines relaxation methods and the need for counseling.

[1015] One of the features of this system is its integration with food delivery services. The server suggests the optimal food menu and delivery timing based on the user's physical strength and emotional state. The suggested menu is then automatically ordered in collaboration with the food delivery service. For example, if the user is tired, it will suggest an easy-to-digest meal or a menu with a relaxing effect. A notification will appear on the user's smartphone saying, "A menu with a relaxing effect has been ordered."

[1016] When it comes to dividing up household tasks, the server aggregates the HP and emotional data of all users in the household and proposes fair task division. These proposals are displayed on the communication platform, and each user can review them and provide feedback. For example, User A can add a comment saying, "I'm not feeling well today, so I'd like User B to clean up." Based on this feedback, the server readjusts the task division proposal and displays the updated content again on the communication platform.

[1017] For example, if User A is very tired and the emotion engine recognizes "stress," the system will suggest a "menu with a relaxing effect" (for example, herbal tea and an easy-to-digest seafood salad) and immediately order it through a delivery service. A notification will appear on the user's smartphone saying, "You have ordered a menu with a relaxing effect."

[1018] In addition, the following are specific examples of prompts that the emotion engine uses to analyze emotion data using a generative AI model:

[1019] Example prompt sentence:

[1020] "If a user is feeling tired or stressed, suggest a relaxing menu and order it through a delivery service. For example, a herbal tea and a digestive seafood salad."

[1021] This system allows users to receive optimal meal suggestions and manage tasks based on their own health status, and also allows for efficient and fair division of household tasks.

[1022] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1023] Step 1:

[1024] A user puts on a wearable device and starts their daily activities. The wearable device collects behavioral history and activity data such as heart rate, number of steps, sleep data, and emotional data (e.g., facial expressions, voice tone) in real time. The input is the user's biometric and behavioral data, and the output is that these data are temporarily stored within the device.

[1025] Step 2:

[1026] The device sends the collected data to a cloud server at regular intervals (e.g., every hour). The input here is the data stored on the device, and the output is a series of biometric and behavioral data sent to the server.

[1027] Step 3:

[1028] The server receives the data sent from the device and checks the completeness and accuracy of the data. It verifies whether there are any outliers or missing data. The input is the collected data, and the output is the accurate data with any outliers or missing data corrected.

[1029] Step 4:

[1030] The server calculates the user's maximum stamina (HP) and current remaining stamina based on the received data. Using the HP calculation module, it calculates stamina based on heart rate, activity level, sleep data, etc. The input is the processed data stored on the server, and the output is the user's maximum stamina and remaining stamina.

[1031] Step 5:

[1032] The server uses an emotion engine to analyze the collected emotion data and recognize the user's emotional state. It identifies emotions based on prompt sentences using a generative AI model. The input is the emotion data stored on the server, and the output is the user's emotional state (e.g., "anxiety" or "stress").

[1033] Step 6:

[1034] The server uses an algorithm to calculate the energy consumption for each task and proposes an optimal task schedule to the user. The input is the calculated energy data and emotional state, and the output is a task schedule. This schedule is sent to the user's device.

[1035] Step 7:

[1036] The user terminal receives the task schedule sent from the server and notifies the user. The schedule includes the specific task order and recommended time. The input is the task schedule received from the server, and the output is task notification information.

[1037] Step 8:

[1038] The server evaluates the user's stress level based on the activity data and emotion data, and generates counseling and mental care suggestions as needed. The input is activity data and emotion data, and the output is counseling and mental care suggestions.

[1039] Step 9:

[1040] The server aggregates the HP and emotional data of all users in the home and proposes fair task sharing. This proposal is displayed on the communication platform. The input is the HP and emotional data of multiple users, and the output is a task sharing proposal.

[1041] Step 10:

[1042] Users provide feedback on the proposed task distribution. For example, User A can add a comment saying, "I'm not feeling well today, so I'd like User B to clean up." The input is the user feedback, and the output is the adjustment data sent to the server.

[1043] Step 11:

[1044] The server readjusts the task allocation proposal based on the user feedback and displays the updated content on the communication platform again. The input is the user feedback, and the output is the readjusted task allocation proposal.

[1045] Step 12:

[1046] In cooperation with the food delivery service, the server proposes the optimal food menu and delivery timing based on the user's physical strength and emotional state. The proposed menu is then linked to the food delivery service, and an order is placed. The input is the user's physical strength and emotional state, and the output is the food delivery order information.

[1047] Step 13:

[1048] The server continuously monitors the user's condition and periodically generates follow-ups and new suggestions, including suggestions for reducing stress based on the latest emotional and activity data. The input is new data collected in real time, and the output is the latest suggestion information.

[1049] 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.

[1050] 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.

[1051] 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.

[1052] [Third embodiment]

[1053] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1054] 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.

[1055] 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).

[1056] 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.

[1057] 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.

[1058] 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).

[1059] 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.

[1060] 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.

[1061] 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.

[1062] 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.

[1063] 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.

[1064] 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."

[1065] The system of the present invention includes a wearable device, a server, and a user device. These components work together to provide task management and mental care for the user's daily life.

[1066] Data Collection Phase

[1067] Processing performed by the device (wearable device):

[1068] The user wears a wearable device, which collects the user's behavioral history and activity data (e.g., number of steps, heart rate, sleep data). The collected data is sent to a server at regular intervals (e.g., every hour).

[1069] Data Receipt and Confirmation Phase

[1070] The server:

[1071] The server receives the data sent from the terminal and checks the integrity and accuracy of the data, thereby ensuring the authenticity of the data.

[1072] HP calculation phase

[1073] The server:

[1074] The server calculates the user's maximum stamina (HP) based on the received data. It also calculates the user's current remaining HP based on the user's current activity level and saves this data. For example, if a user has walked 7,000 steps and their average heart rate is 80 BPM, their current HP will be calculated as 70 out of 100.

[1075] Task Management Phase

[1076] The server:

[1077] The server uses an algorithm to calculate the HP consumption of each household task. For example, cleaning consumes 10 HP, and cooking consumes 15 HP. Based on this information, the server generates an optimal task schedule that takes into account the user's current remaining HP. This schedule is then sent to the user's device.

[1078] Mental care phase

[1079] The server:

[1080] The server evaluates the user's stress level based on their activity data. For example, if a high stress level is detected from data from the past week, the server will suggest counseling to the user. It will also automatically generate mental care suggestions to alleviate stress (e.g., recommending relaxing yoga).

[1081] Task allocation and communication phase

[1082] The server:

[1083] The server aggregates the home page data of the entire family and proposes fair task allocations, ensuring that the burden within the household is distributed evenly. For example, it may suggest that user A be in charge of cleaning and user B be in charge of laundry. These suggestions are displayed on the family communication platform and can be viewed by each member.

[1084] User Action:

[1085] Users can review the proposed division of labor and communicate with their family members to make adjustments. For example, User A can provide feedback such as, "I'm not feeling well today, so I'd like User B to clean."

[1086] Ongoing Support Phase

[1087] The device:

[1088] The wearable device continuously collects behavioral history and activity data and sends it to a server, which keeps the data up to date.

[1089] The server:

[1090] The server analyzes data in real time, monitors the user's condition, and periodically follows up on mental health care and task management, automatically creating new suggestions and advice and notifying the user.

[1091] Specific examples

[1092] For example, after User A has finished their daily activities, they send data to the server via their wearable device. Based on that data, the server calculates that User A's current HP is 70. It then generates a task schedule for tomorrow, suggesting "cooking in the morning, cleaning in the afternoon." At the same time, it determines that User A's stress level is high based on recent activity data and suggests activities that will help them relax. Furthermore, within the home, a suggestion that User A will be in charge of cleaning and User B will be in charge of laundry is displayed on the communication platform, and User A can check the schedule and make adjustments as necessary.

[1093] As described above, the present invention enables efficient household task management and mental care throughout each phase.

[1094] The processing flow will be explained below.

[1095] Step 1: Wearable device data collection

[1096] Device: The user wears a wearable device that records real-time activity history and activity data, including the number of steps taken, heart rate, and sleep duration.

[1097] Step 2: Sending data

[1098] Terminal: The collected data is sent to the server at regular intervals (for example, every hour). Data communication is carried out using a secure protocol.

[1099] Step 3: Receiving and verifying data

[1100] Server: Receives data sent from the device and checks the data for completeness and accuracy, verifying that there are no outliers or missing data.

[1101] Step 4: Calculate Health (HP)

[1102] Server: Based on the received data, calculate the user's maximum stamina and current remaining stamina. The maximum stamina is calculated based on the user's basic health data and past history data.

[1103] Step 5: Calculate the HP cost of the task

[1104] Server: The HP cost for each task (e.g. cleaning, cooking, laundry) is calculated by an algorithm. For example, cleaning is set to 10 HP, cooking to 15 HP, etc.

[1105] Step 6: Generate a task schedule

[1106] Server: Generates the optimal task schedule for the user, taking into account the current remaining HP and task priority. The generated schedule is sent to the user's device.

[1107] Step 7: Notification of scheduled tasks

[1108] Device: The user device receives the task schedule sent from the server and notifies the user. The schedule includes the specific task order and recommended times.

[1109] Step 8: Assess your stress levels and take care of yourself

[1110] Server: Evaluates the user's stress level based on activity data and generates mental health advice as needed. If a high stress level is detected, counseling or relaxation suggestions are automatically provided.

[1111] Step 9: Optimize task distribution

[1112] Server: Aggregates the home page data of all users in the household and calculates a fair task allocation. For example, it suggests that user A be in charge of cleaning and user B be in charge of laundry.

[1113] Step 10: View the proposal

[1114] Server: Display task sharing proposals on the family communication platform so that all users can view them.

[1115] Step 11: User feedback

[1116] User: Provide feedback on the proposed task distribution, for example, adding a comment like "I'm not feeling well today, so I'd like someone else to clean up."

[1117] Step 12: Recalibrate your sharing proposal

[1118] Server: Based on user feedback, the task sharing proposal is re-adjusted and the updated content is displayed again on the communication platform.

[1119] Step 13: Ongoing data monitoring and follow-up

[1120] Device: Continuously collects behavioral history and activity data and sends it to the server.

[1121] Server: Analyzes data in real time, periodically generates follow-ups and new suggestions tailored to the user's situation, and notifies the user.

[1122] Through this process, household tasks and mental care can be managed efficiently, reducing the burden and stress on the entire household.

[1123] Example 1

[1124] 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."

[1125] The purpose of this invention is to provide a system that efficiently manages daily tasks and provides mental care within a home or community. A particular challenge is how to fairly distribute the burden, taking into account the physical strength and stress level of each user. Furthermore, it is necessary to achieve more flexible and adaptive support by providing real-time data analysis and appropriate feedback.

[1126] 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.

[1127] In this invention, the server includes means for collecting movement records and activity data from the wearable device, means for calculating the user's maximum and current physical strength based on the movement records and activity data, means for calculating the amount of physical strength consumed for each task and proposing an optimal work plan to the user, means for evaluating the user's stress level and generating counseling and mental care suggestions as needed, means for optimizing work allocation within the community and displaying the suggestions on a communication platform, means for aggregating the physical strength data of each member of the community and proposing fair work allocation, means for receiving user feedback and readjusting the work based on the feedback, and means for analyzing data in real time, automatically creating new mental care and work plans, and notifying the user. This enables efficient daily task management and mental care within the home or community, and fair distribution of the burden.

[1128] A "wearable device" is an electronic device that can be worn by a user and is used to record movements and collect activity data.

[1129] "Movement record" refers to historical information about the user's daily physical movements and actions.

[1130] "Activity data" refers to information that quantifies the amount of physical activity a user performs in their daily life.

[1131] "Maximum stamina" refers to the maximum stamina that the user possesses, and is usually a number calculated based on a certain standard.

[1132] "Current amount" is a numerical value that indicates the user's current remaining stamina.

[1133] A "work plan" is a suggested schedule of tasks for a user to perform throughout the day.

[1134] "Stamina consumption" is a numerical value that indicates the amount of stamina consumed when performing each task.

[1135] "Stress level" is a numerical value or evaluation value that indicates the degree of mental and physical stress of the user.

[1136] "Counselling" refers to professional advice and support provided to reduce users' stress levels.

[1137] "Mental care" refers to suggestions and activities offered to maintain or improve the mental health of users.

[1138] A "communications platform" is a digital interface for exchanging information, typically an application or web service over the internet.

[1139] "Equitable division of labor" is the allocation of work within a household or community so that each member shares responsibility equally.

[1140] "Feedback" refers to opinions, impressions, and situational information collected from users.

[1141] "Analyzing data in real time" means processing collected data in real time and analyzing it to obtain immediate results.

[1142] "Notifying the user" means that work plans and mental care suggestions are sent from the server to the user's device and displayed.

[1143] The system of the present invention combines a wearable device, a server, and a user terminal to provide task management and mental care for a user in their daily life. Detailed embodiments of the system are described below.

[1144] Hardware Configuration

[1145] Wearable devices:

[1146] Wearable devices can be worn by users to record movement and collect activity data. They include an accelerometer, heart rate monitor, and GPS sensor, and record data such as steps taken, heart rate, and location information in real time.

[1147] server:

[1148] The server receives and analyzes the data sent from the wearable device, and also performs processes such as calculating maximum stamina and current physical capacity, proposing work plans and mental care, and adjusting work allocation.

[1149] User device:

[1150] The user terminal is a device that receives notifications sent from the server, such as a smartphone or tablet, and allows interaction with the user through an interface.

[1151] Software Configuration

[1152] Data collection modules:

[1153] A software module installed in a wearable device collects data from various sensors, such as the number of steps taken from an accelerometer and the heart rate from a heart rate monitor.

[1154] Data Analysis Module:

[1155] A software module installed on the server analyzes the collected data, calculates the maximum stamina and current stock, and calculates the amount of stamina consumed for each task to generate an optimal work plan.

[1156] Stress Assessment Module:

[1157] The software module installed on the server evaluates the user's stress level from collected activity data and automatically generates counseling and mental care suggestions as needed.

[1158] Work allocation adjustment module:

[1159] This module aggregates the physical fitness data of each member of a family or community and proposes fair division of labor. It displays the proposals using a communication platform and can also readjust based on feedback.

[1160] Specific examples

[1161] For example, after completing a day's activities, User A sends data to a server via a wearable device. Based on that data, the server calculates that User A's current available physical energy is 70. It then generates a work plan for the next day and notifies the user's device with suggestions such as "cook in the morning and clean in the afternoon." At the same time, if the server determines that the user's stress level is high based on recent activity data, it also makes mental care suggestions such as "do 30 minutes of relaxing yoga." Furthermore, a suggestion for dividing household tasks, with User A doing the cleaning and User B doing the laundry, is displayed on the communication platform.

[1162] Prompt Sentence Examples

[1163] Example prompt:

[1164] "Please specifically design a system that uses wearable devices and a server to manage the user's daily tasks and provide mental care. In particular, please provide a detailed explanation of each phase: data collection, HP calculation, task management, and mental care."

[1165] As described above, close cooperation between the user, server, and wearable device makes it possible to achieve efficient daily task management and mental care.

[1166] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1167] Step 1: Data collection

[1168] Processing performed by the device (wearable device):

[1169] Wearable devices use built-in sensors (e.g., accelerometer, heart rate monitor, GPS) to record and collect user movement and activity data. Specifically, when a user walks, the accelerometer counts the number of steps, and the heart rate monitor measures the heart rate. The collected data (e.g., number of steps: 5000, heart rate: 75 BPM) is temporarily stored in the device.

[1170] Input: User's physical activity (e.g., walking, exercising)

[1171] Output: Collected motion records and activity data (e.g., steps, heart rate)

[1172] Step 2: Data Transfer

[1173] The device:

[1174] The wearable device transmits the collected motion records and activity data to a server at regular intervals (e.g., every hour). Data transmission uses a secure communication protocol (e.g., HTTPS).

[1175] Input: Collected motion and activity data (e.g., steps, heart rate)

[1176] Output: Data sent to the server

[1177] Step 3: Data reception and confirmation

[1178] The server:

[1179] The server receives the data sent by the terminal and checks the data for completeness and accuracy using a checksum algorithm. If there is a problem with any part of the received data, it requests a retransmission.

[1180] Input: Data sent from the terminal

[1181] Output: Verified data (e.g., clean step and heart rate data)

[1182] Step 4: HP Calculation

[1183] The server:

[1184] The server calculates the user's maximum stamina and current stamina based on the confirmed data. For example, it runs an algorithm to calculate current stamina based on the number of steps and heart rate data for that day. For example, if the maximum stamina is 100, the current stamina is calculated to be 80 based on the number of steps 5000 and heart rate 75 BPM.

[1185] Input: Verified activity data (e.g., steps, heart rate)

[1186] Output: User's current stamina (e.g. 80 / 100)

[1187] Step 5: Task Management

[1188] The server:

[1189] The server calculates the amount of stamina consumed for each task. For example, cleaning requires 10 HP, cooking requires 15 HP, and generates an optimal task plan based on the user's current stamina and sends it to the user's device.

[1190] Input: Current stamina (e.g. 80 / 100), task list and consumption (e.g. cleaning 10 HP, cooking 15 HP)

[1191] Output: Work plan (e.g. cooking in the morning, cleaning in the afternoon)

[1192] Step 6: Stress assessment and mental health advice

[1193] The server:

[1194] The server analyzes the collected activity data and evaluates the user's stress level. For example, if it determines that the stress level is high based on data from the past week, it will automatically generate mental care suggestions, such as a relaxing 30-minute yoga session, and send them to the user's device.

[1195] Input: Activity data (e.g., number of steps taken in the past week, heart rate)

[1196] Output: Mental care suggestions (e.g. yoga for relaxation)

[1197] Step 7: Assign tasks and communicate

[1198] The server:

[1199] The server collects the physical fitness data of each household member and proposes fair division of labor. The proposals are displayed on the communication platform and can be confirmed by the user and the other members. For example, it may be suggested that User A be in charge of cleaning and User B be in charge of laundry.

[1200] Input: Physical fitness data of household members

[1201] Output: Fair work sharing proposal (e.g., User A cleans, User B does laundry)

[1202] Step 8: Gather feedback and refine

[1203] User Action:

[1204] Users can send feedback on the provided work assignments through the communication platform, for example, sending a message such as, "I'm not feeling well today, so I'd like you to do the cleaning for me."

[1205] Input: User feedback

[1206] Output: Server receiving feedback

[1207] The server:

[1208] The server re-adjusts the division of labor based on the collected feedback and displays the updated proposal again on the communication platform.

[1209] Input: User feedback

[1210] Output: Rebalanced work assignments (e.g., User B now cleans)

[1211] Step 9: Ongoing support

[1212] The device:

[1213] The wearable device continuously collects motion records and activity data and periodically transmits them to a server.

[1214] Input: User's ongoing activity

[1215] Output: Latest collected motion records and activity data

[1216] The server:

[1217] The server analyzes the data in real time, keeping it up to date and providing appropriate follow-up care and task management.

[1218] Input: Latest activity data

[1219] Output: Real-time suggestions and notifications (e.g. break suggestions, task update notifications)

[1220] (Application example 1)

[1221] 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."

[1222] In conventional factories, it has been difficult to grasp the workload and stress levels of workers and efficiently manage work tasks based on this information. Furthermore, optimization of work tasks within factories and mental care for workers are often insufficient, resulting in decreased productivity and increased mental strain on workers. To solve these problems, the present invention provides a comprehensive task management and mental care system based on worker activity data.

[1223] 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.

[1224] In this invention, the server includes means for collecting behavioral history and activity data from the wearable device, means for calculating the user's maximum and remaining physical strength based on the behavioral history and activity data, means for calculating the physical strength consumption for each task and proposing an optimal task schedule to the user, means for evaluating the user's stress level and generating counseling and mental care suggestions as needed, means for generating an optimal task schedule for factory robots based on the worker's activity data and allocating tasks, and means for analyzing the worker's stress level and providing mental care suggestions as needed. This enables efficient task management that takes into account the physical strength and mental health of workers, improving productivity, and reducing the mental burden on workers.

[1225] A "wearable device" is a device worn on the body that constantly monitors and collects the user's behavioral history and activity data.

[1226] "Behavioral history" refers to a record of various actions taken by a user, such as the number of steps taken, distance traveled, and daily activity patterns.

[1227] "Activity data" is data that quantitatively indicates the user's physical activity, and specific examples include the number of steps taken, heart rate, and calorie consumption.

[1228] "Maximum stamina" is a numerical representation of the user's total stamina, and is an indicator of the maximum amount of activity possible in a day.

[1229] "Remaining physical strength" is a numerical value that indicates the user's current physical strength, and indicates the current level of fatigue and the remaining amount of activity that can be performed.

[1230] A "task schedule" is a plan that optimally arranges each task, taking into account the user's physical strength, stress level, and priority.

[1231] "Stress level" indicates the user's mental stress and fatigue level using numerical values ​​and indicators to evaluate the user's mental state.

[1232] "Counseling" refers to suggestions and interventions to provide psychological support and consultation to users experiencing high stress levels.

[1233] "Mental care" refers to specific activities and suggestions for maintaining and improving the user's mental health, including stress reduction and relaxation methods.

[1234] "Worker" refers to an employee or worker who works in a factory or business.

[1235] A "factory robot" is a machine or automated device used to automate work in a factory, performing tasks in place of humans.

[1236] "Task sharing" means allocating multiple tasks fairly to each member, with the aim of efficiently dividing up work.

[1237] The present invention provides a system for improving the efficiency of task management and mental care within a factory based on worker activity data. The system includes a wearable device, a server, and a user terminal.

[1238] Hardware and Software Use

[1239] Wearable devices

[1240] Wearable devices are devices that collect workers' behavioral history and activity data in real time. These devices have built-in sensors such as pedometers, heart rate monitors, and sleep monitors, and periodically send data to a server. Possible devices used include smartwatches and fitness trackers.

[1241] server

[1242] The server receives the data sent from the wearable device and checks its completeness and accuracy. Based on the collected data, it calculates the worker's HP (maximum and remaining stamina) and evaluates their stress level. Furthermore, it calculates the stamina consumption for each task based on the worker's current HP and generates an optimal task schedule. It also makes mental health care suggestions as needed. Cloud services such as Amazon Web Services (AWS) and Google Cloud Platform can be used for server-side processing.

[1243] User terminal

[1244] User devices, such as smartphones, tablets, and PCs, are used by workers and managers to check task schedules and mental health advice and provide feedback.

[1245] Data processing and calculation

[1246] After receiving the data transmitted from the wearable device, the server performs the following processing.

[1247] HP Calculation: Calculates the maximum stamina and current remaining stamina based on data such as the worker's steps, heart rate, sleep time, etc. For example, calculate the HP of a worker who has walked 7,000 steps and save that data.

[1248] Task Schedule Generation: Based on the current HP of the workers, calculate the HP required for each task and generate an optimal task schedule. For example, assign a worker 20 HP of packaging work, 15 HP of inspection work, and 30 HP of assembly work.

[1249] Stress level assessment: Based on the collected data, the stress level of the worker is assessed and mental health care recommendations are made as necessary. For example, if stress is determined to be high based on heart rate data from the past week, counseling is recommended.

[1250] Optimizing task sharing: Optimizing task sharing among family members and adjusting the workload fairly, which will improve the efficiency of work within the factory.

[1251] Specific examples

[1252] In an actual use case, Worker A at a factory wears a wearable device while working. The device collects data such as the number of steps taken each day, heart rate, and sleep time, and sends it to the server. After receiving this data, the server calculates Worker A's current HP and generates a task schedule for the next day based on that. For example, it may make adjustments such as "allocating 20 HP to packaging, 15 HP to inspection, and 30 HP to assembly." It may also determine from recent data that Worker A's stress level is rising and suggest relaxing activities.

[1253] Prompt Sentence Examples

[1254] "Please input the following end-user data into the deep learning model to generate an optimal factory task schedule.

[1255] Number of steps: 7000

[1256] Heart rate: 75 BPM

[1257] Sleep time: 6 hours

[1258] Task suggestions:

[1259] Packaging work (20HP)

[1260] Inspection work (15HP)

[1261] Assembly work (30HP)

[1262] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1263] Step 1:

[1264] Data Collection Phase

[1265] The wearable device collects the worker's behavioral history and activity data. This data includes the number of steps, heart rate, sleep time, etc. The collected data is sent to a server at regular intervals (e.g., every hour). The input is the worker's daily activity data, and the output is the data sent to the server.

[1266] Step 2:

[1267] Data Receipt and Confirmation Phase

[1268] The server receives the data sent from the wearable device and performs a data verification process to verify the completeness and accuracy of the received data. The input is the data from the wearable device and the output is the verified worker activity data.

[1269] Step 3:

[1270] HP calculation phase

[1271] The server calculates the worker's maximum stamina (HP) and current remaining stamina based on the confirmed data. For example, the server calculates the worker's current HP by subtracting the worker's maximum stamina from 100 based on the number of steps, heart rate, and sleep time. The input is the confirmed activity data, and the output is the calculated remaining HP.

[1272] Step 4:

[1273] Task schedule generation phase

[1274] The server calculates the stamina consumption required for each task based on the worker's current HP and generates an optimal task schedule. The server inputs the stamina consumption for each task and adjusts and calculates the schedule based on the worker's HP. The input is the worker's remaining stamina and task data, and the output is the optimal task schedule.

[1275] Step 5:

[1276] Task Schedule Notification Phase

[1277] The server sends the generated task schedule to the user terminal. Workers and managers can check this schedule using the user terminal and provide feedback as needed. The input is the optimal task schedule, and the output is a schedule notification to the user terminal.

[1278] Step 6:

[1279] Stress Level Assessment Phase

[1280] The server evaluates the worker's stress level from the collected activity data. For example, it determines whether stress is increasing from trends in heart rate and sleep time, and makes recommendations for counseling or mental care as necessary. The input is activity data, and the output is the evaluated stress level and recommendations.

[1281] Step 7:

[1282] Feedback reflection phase

[1283] The server receives feedback from users and adjusts task schedules and mental health care suggestions based on that feedback. For example, if a worker provides feedback such as "I'm not feeling well, so I'd like to reduce my workload," the server takes that into account when generating a new schedule. The input is the feedback data, and the output is the adjusted task schedule.

[1284] Step 8:

[1285] Data update and save phase

[1286] The server continuously receives new data, analyzes and stores it, and always manages based on the latest data. The input is new activity data collected in real time, and the output is an updated database and continuous recommendations.

[1287] 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.

[1288] The system of the present invention includes a wearable terminal, a server, a user terminal, and an emotion engine. These components work together to provide task management, mental care, and emotion recognition in the user's daily life.

[1289] Data Collection Phase

[1290] Processing performed by the device (wearable device):

[1291] The user wears a wearable device, which records real-time activity data (e.g., number of steps, heart rate, sleep data) and emotional data (e.g., facial expressions, voice tone). The collected data is sent to a server at regular intervals (e.g., every hour).

[1292] Data Receipt and Confirmation Phase

[1293] The server:

[1294] The server receives the data sent from the device and checks its completeness and accuracy, verifying that there are no outliers or missing data.

[1295] HP calculation phase

[1296] The server:

[1297] The server calculates the user's maximum HP and current remaining HP based on the received data. The maximum HP is calculated based on the user's basic health data and past history data.

[1298] Emotion Recognition Phase

[1299] The server:

[1300] The server uses an emotion engine to analyze the collected emotion data and recognize the user's emotional state. For example, if the user expresses anxiety, it will recognize it as "anxiety."

[1301] Task Management Phase

[1302] The server:

[1303] The server uses an algorithm to calculate the HP consumption of each household task. For example, cleaning consumes 10 HP, and cooking consumes 15 HP. Furthermore, based on the emotional data recognized by the emotion engine, the server generates an optimal task schedule that takes into account the user's current remaining HP and emotional state. This schedule is then sent to the user's device.

[1304] Task Schedule Notifications

[1305] Device:

[1306] The user's device receives the task schedule sent from the server and notifies the user. The schedule includes the specific task order and recommended time. For example, if the user's HP is 70 and the emotion engine recognizes "stress," it will prioritize tasks that will reduce stress, such as suggesting "light stretching" or "relaxing housework."

[1307] Stress level assessment and mental health care

[1308] The server:

[1309] The server evaluates the user's stress level based on activity data and emotional data. If a high stress level is detected, it generates suggestions for counseling or mental care. Specifically, if the emotion engine recognizes "stress," it suggests relaxation methods or counseling.

[1310] Task allocation and communication phase

[1311] The server:

[1312] The server aggregates the HP and emotional data of all users in the household and proposes fair task sharing. These proposals are displayed on the family communication platform. For example, if user A is feeling "anxious," user B will be suggested to take on a larger share of the important tasks for the day.

[1313] User Action:

[1314] Users can provide feedback on the proposed task allocation. For example, User A can add a comment saying, "I'm not feeling well today, so I'd like User B to clean up."

[1315] Realigning the proposed allocation

[1316] The server:

[1317] Based on user feedback, the task sharing proposal will be re-adjusted and the updated content will be displayed again on the communication platform.

[1318] Ongoing data monitoring and follow-up

[1319] The device:

[1320] The wearable device continuously collects behavioral history, activity data, and emotional data and transmits them to a server.

[1321] The server:

[1322] The server analyzes the data in real time and periodically generates follow-ups and new suggestions tailored to the user's situation and notifies them. For example, if the user has recently been feeling "fatigue," it will suggest measures to reduce the user's stress.

[1323] Specific examples

[1324] For example, after User A has finished their daily activities, they send data to the server via their wearable device. Based on that data, the server calculates that User A's current HP is 70. Furthermore, if the emotion engine recognizes "stress," it will prioritize stress-reducing tasks such as "light stretching" and "easy cooking" in the task schedule for the next day. At home, a suggestion is displayed on the communication platform that User B should take on more of the important tasks, and User A can check the content and make adjustments as necessary.

[1325] In this way, the present invention can perform household task management, mental care, and emotion recognition in an integrated manner throughout each phase.

[1326] The processing flow will be explained below.

[1327] Step 1: Wearable device data collection

[1328] Device: The user wears a wearable device, which collects real-time behavioral history and activity data (e.g., steps, heart rate, sleep data) and emotional data (e.g., facial expressions, voice tone).

[1329] Step 2: Sending data

[1330] Terminal: The collected data is sent to the server at regular intervals (for example, every hour). Data communication is carried out using a secure protocol.

[1331] Step 3: Receiving and verifying data

[1332] Server: Receives data sent from the device and checks the data for completeness and accuracy, verifying that there are no outliers or missing data.

[1333] Step 4: Calculate Health (HP)

[1334] Server: Based on the received data, calculate the user's maximum stamina and current remaining stamina. The maximum stamina is calculated based on the user's basic health data and past history data.

[1335] Step 5: Emotion Recognition

[1336] Server: The server uses an emotion engine to analyze the collected emotion data and recognize the user's emotional state, for example, "anxiety" or "joy" based on the user's facial expressions and voice tone.

[1337] Step 6: Calculate the HP cost of the task

[1338] Server: The HP cost for each household task is calculated by an algorithm, for example, cleaning is set to 10 HP, cooking is set to 15 HP, etc.

[1339] Step 7: Generate a task schedule

[1340] Server: Generates an optimal task schedule for the user, taking into account the user's current remaining HP and the emotional state provided by the emotion engine. For example, if the user's HP is 70 and the emotion engine detects "stress," it prioritizes tasks that reduce stress. The generated schedule is sent to the user's device.

[1341] Step 8: Notification of scheduled tasks

[1342] Device: The user device receives the task schedule sent from the server and notifies the user. The schedule includes the specific task order and recommended times.

[1343] Step 9: Stress level assessment and mental health recommendations

[1344] Server: Evaluates the user's stress level based on activity data and emotional data, and generates mental health advice as needed. For example, if the emotion engine detects "stress," it will automatically suggest counseling or relaxation techniques.

[1345] Step 10: Optimize task distribution

[1346] Server: Aggregates HP data and emotional data from all users in the household and proposes fair task sharing. For example, if user A is feeling anxious, user B will be suggested to share more of the important tasks for that day. The proposal is displayed on the family communication platform.

[1347] Step 11: View the proposal

[1348] Server: Display the task sharing proposal on the family communication platform so that all users can view the contents.

[1349] Step 12: User feedback

[1350] Users: Provide feedback on the proposed task distribution. For example, User A can add a comment saying, "I'm not feeling well today, so I'd like User B to clean up."

[1351] Step 13: Recalibrate your sharing proposal

[1352] Server: Based on user feedback, the task sharing proposal is re-adjusted and the updated content is displayed again on the communication platform.

[1353] Step 14: Ongoing data monitoring and follow-up

[1354] Device: Continuously collects behavioral history, activity data, and emotional data, and sends them to the server.

[1355] Server: Analyzes data in real time, periodically generates follow-ups and new suggestions based on the user's situation, and notifies the user. For example, if the user has recently been feeling "tired," the server will suggest measures to reduce the user's stress.

[1356] Through this process, household task management, mental care, and emotional awareness can be integrated, reducing the burden and stress on the entire household.

[1357] Example 2

[1358] 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."

[1359] Conventional home task management systems often provide a uniform task schedule without fully considering the user's physical condition or emotional state. This can result in excessive burden on users and problems such as insufficient mental care and stress management. Furthermore, because the allocation of tasks within the home is done without taking into account each individual's physical condition or emotional state, it can easily lead to a sense of unfairness. By solving these issues, we aim to realize a healthier and stress-free home life.

[1360] 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.

[1361] In this invention, the server includes: means for collecting behavior history and activity amount data from the wearable device;

[1362] A means for calculating the user's maximum stamina and remaining stamina based on the behavior history and activity amount data;

[1363] A means of emotion recognition by analyzing the user's facial expressions and vocal tone;

[1364] A method for calculating the amount of energy consumed for each task using an algorithm and proposing an optimal task schedule taking into account the emotional state and remaining energy;

[1365] a means for notifying a user terminal of the proposed task schedule;

[1366] A means for evaluating a user's stress level based on their activity data and emotional data, and generating suggestions for counseling and mental care;

[1367] The system also includes a means for optimizing task allocation among family members and displaying the proposed results on the communication platform. This enables optimal task scheduling and fair task allocation that takes into account the user's physical condition and emotional state. Furthermore, by providing appropriate mental care and stress management, a healthy and stress-free family life can be achieved.

[1368] A "wearable device" is a small electronic device that can be worn by the user and has built-in sensors that collect behavioral history, activity data, emotional data, and other information.

[1369] "Behavioral history" refers to the history of various activities and movements undertaken by a user, including detailed information such as time and location.

[1370] "Activity data" is data that quantifies the physical movements and amount of exercise a user performs in their daily life, and examples include the number of steps taken, heart rate, calories burned, and sleep data.

[1371] "Maximum stamina" is the theoretical maximum stamina of a user, calculated by an algorithm based on the user's basic health status and past history data.

[1372] "Remaining physical strength" is a numerical value that indicates the user's current physical strength, and is calculated in real time based on behavioral history and activity data.

[1373] "Facial expressions" refers to the movement of a user's facial muscles and the movements of the eyes, mouth, eyebrows, etc., and are used as data for analyzing emotional states.

[1374] "Voice tone" refers to characteristics such as pitch, volume, and rhythm of the user's speaking voice, and is used as data for analyzing emotional state.

[1375] "Emotion recognition" is a technology that analyzes collected data such as facial expressions and voice tone to identify a user's emotional state.

[1376] A "task" refers to a specific task or activity that a user must perform in their daily life, such as cleaning, cooking, or laundry.

[1377] A "task schedule" is a planning table that suggests the optimal order and start time of tasks, taking into account the user's remaining physical energy and emotional state.

[1378] "Notifications" refers to the form of alerts or push notifications generated by the server to inform users of task schedules, mental health suggestions, etc.

[1379] "Stress level" is a numerical representation of the degree of stress a user feels, and is evaluated based on activity data and emotional data.

[1380] "Mental care" refers to counseling and relaxation suggestions provided to maintain the user's psychological health.

[1381] "Task sharing" refers to the fair allocation of tasks that are jointly performed by multiple household members, with the aim of optimizing the burden among family members.

[1382] "Communication platform" refers to a digital environment or application for sharing, coordinating, and managing tasks and other information among family members.

[1383] The system of the present invention supports task management, mental care, and emotion recognition in a user's daily life. This system includes a wearable device, a server, a user device, and an emotion engine, and these components function in cooperation with each other.

[1384] Data Collection Phase

[1385] The wearable device:

[1386] The wearable device worn by the user has built-in sensors, cameras, and microphones. This hardware is used to record the user's behavioral history (e.g., number of steps, distance traveled) and activity data (e.g., heart rate, sleep data) in real time. It also collects emotional data such as facial expressions and vocal tone. This data is sent to a server at regular intervals (e.g., every hour) via Bluetooth or Wi-Fi.

[1387] Data Receipt and Confirmation Phase

[1388] The server:

[1389] The server receives the data sent from the wearable device and checks its completeness and accuracy before storing it in the database, verifies whether there are any outliers or missing data, and requests a retransmission if an anomaly is detected.

[1390] HP calculation phase

[1391] The server:

[1392] The server calculates the user's maximum stamina and current remaining stamina based on the data collected from the wearable device. This uses the user's basic health data and past history data. The calculation is performed using a specific algorithm, and the user's remaining stamina is updated in real time.

[1393] Emotion Recognition Phase

[1394] The server:

[1395] The emotion engine in the server analyzes the user's facial expression and voice tone to recognize their emotion. For example, if the facial expression indicates sadness, it will be recognized as "sadness." Voice tone is also analyzed in the same way, and the overall emotional state is determined.

[1396] Task Management Phase

[1397] The server:

[1398] The server uses an algorithm to calculate the amount of stamina consumed for each task and generates an optimal task schedule based on the user's remaining stamina and emotional state. For example, cleaning is set to consume 10 HP, and cooking is set to consume 15 HP. The generated task schedule is sent to the user's device.

[1399] Task Schedule Notifications

[1400] User device processes:

[1401] The system receives task schedules sent to the user's device and notifies the user using push notifications and alerts. The notifications include the specific task execution order and recommended time. For example, if the user's remaining stamina is 70 and the emotion engine recognizes "stress," it will suggest "light stretching" or "relaxing housework" to reduce stress.

[1402] Stress level assessment and mental health care

[1403] The server:

[1404] The server evaluates the user's stress level based on activity and emotional data. If a high stress level is detected, counseling and relaxation methods are offered. For example, specific advice such as "Try 10 minutes of meditation" is provided.

[1405] Task allocation and communication phase

[1406] The server:

[1407] The server aggregates the physical strength and emotional data of all users in the household and proposes fair task sharing. The task sharing among users is displayed and shared on the communication platform. For example, if user A has low physical strength and feels "anxious," a suggestion is made that user B should share more of the important tasks for the day.

[1408] User Action:

[1409] Users can provide feedback on the proposed task allocation. For example, they can comment, "I'm not feeling well today, so I'd like to ask User B to clean up."

[1410] Realigning the proposed allocation

[1411] The server:

[1412] Based on the user's feedback, the server readjusts the task sharing proposal and displays the updated content again on the communication platform.

[1413] Ongoing data monitoring and follow-up

[1414] The wearable device:

[1415] The wearable device continuously collects behavioral history, activity data, and emotional data and transmits them to a server.

[1416] The server:

[1417] The server analyzes the received data in real time and generates follow-ups and new suggestions based on the user's situation. For example, if the user's recent data shows that they frequently feel "tired," the server will make new suggestions to reduce the burden, such as notifying them to "reduce their activity today and take a rest."

[1418] Specific examples

[1419] For example, after User A has finished his or her daily activities, he or she sends data to the server via a wearable device. The server analyzes the data and calculates that User A's current remaining stamina is 70. Furthermore, if the emotion engine recognizes "stress," it will suggest "light stretching" or "easy cooking" in the task schedule for the next day. At home, suggestions are displayed on the communication platform for User B to share many important tasks.

[1420] Prompt Sentence Examples

[1421] "I'm feeling stressed today. My current stamina is at 70. Please suggest a task schedule for tomorrow."

[1422] As described above, the system of the present invention can perform household task management, mental care, and emotion recognition in an integrated manner throughout each phase.

[1423] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1424] Step 1:

[1425] Data Collection Phase

[1426] Processing performed by the device (wearable device):

[1427] Input: The wearable device collects the user's behavioral history (e.g., steps taken, distance traveled), activity data (e.g., heart rate, sleep data), and emotional data (e.g., facial expressions, voice tone) in real time.

[1428] Data processing or calculation: Various biometric data is recorded using the built-in sensors, camera, and microphone.

[1429] Output: The collected data is encoded at regular intervals (e.g., every hour) and sent to a server via Bluetooth or Wi-Fi.

[1430] Specific operation: After data collection, the wearable device's communication function is activated and the data is uploaded to the server using a secure protocol.

[1431] Step 2:

[1432] Data Receipt and Confirmation Phase

[1433] The server:

[1434] Input: Behavioral history, activity data, and emotion data sent from the wearable device.

[1435] Data processing or data calculations performed: Checking the completeness and accuracy of received data, verifying that there are no outliers or missing data.

[1436] Output: The data whose accuracy has been confirmed is stored in a database, and if there is an abnormality, a resend request is made.

[1437] What happens: After receiving the data, the server's validation algorithm checks the integrity of the data and, if necessary, requests that the data be resent.

[1438] Step 3:

[1439] HP calculation phase

[1440] The server:

[1441] Input: Verified activity data and behavioral history data.

[1442] Data processing or calculation: The server's algorithm calculates the maximum stamina and current remaining stamina based on the user's basic health data (age, gender, height, weight, etc.) and past history data.

[1443] Output: Calculated maximum health and current health remaining data.

[1444] Specific operation: The server periodically evaluates the user's physical strength status and updates the remaining physical strength data based on that.

[1445] Step 4:

[1446] Emotion Recognition Phase

[1447] The server:

[1448] Input: Emotion data (facial expressions, vocal tones) sent from the wearable device.

[1449] Data processing or data calculation performed: Using an emotion engine, the user's emotional state is analyzed using facial recognition technology and voice analysis technology.

[1450] Output: Recognized user emotional state data (e.g., "happiness", "anxiety", "anger", "stress").

[1451] What it does: The server's emotion engine analyzes facial expressions and vocal tone, and then uses the resulting data to label emotions.

[1452] Step 5:

[1453] Task Management Phase

[1454] The server:

[1455] Input: User's remaining energy data, emotional state data, and energy consumption data for each task.

[1456] Data processing or calculation: The amount of energy consumed by the task is calculated using an algorithm, and an optimal task schedule is generated based on the user's current remaining energy and emotional state.

[1457] Output: The generated task schedule data.

[1458] Specific operation: The server predicts the amount of energy required for each task and dynamically generates a schedule based on the user's energy and emotional state.

[1459] Step 6:

[1460] Task Schedule Notifications

[1461] Processing performed by the terminal (user terminal):

[1462] Input: Task schedule data sent from the server.

[1463] Data processing or data calculation to be performed: Converting task schedule information into a format for notifying the user.

[1464] Output: Task schedule information as push notifications and alerts.

[1465] Specific operation: The user terminal notifies the user visually or audibly based on the schedule data received from the server. The notification includes the specific task content, execution time, and order.

[1466] Step 7:

[1467] Stress level assessment and mental health care

[1468] The server:

[1469] Input: Activity data and emotion data.

[1470] Data processing or data calculation to be performed: Based on the collected data, the user's stress level is evaluated, and if a high stress level is detected, suggestions for counseling or relaxation methods are generated.

[1471] Output: Stress level assessment results and mental care recommendations.

[1472] Specific operation: The server periodically analyzes data, and if any abnormalities are found in the stress level, it launches a suggestion function to notify the user of countermeasures.

[1473] Step 8:

[1474] Task allocation and communication phase

[1475] The server:

[1476] Input: Physical and emotional data of all users in the household.

[1477] Data processing or data calculation: Aggregate each user's data, calculate a fair task share, and generate a proposal.

[1478] Output: Fairly allocated task sharing proposal data.

[1479] Specific operation: The server processes the data of all household members, calculates a fair task distribution, and then creates a proposal to display on the communication platform.

[1480] User Action:

[1481] Input: Task sharing proposal data received from the server.

[1482] Data processing or calculation to be performed: Enter feedback on the proposal.

[1483] Output: Updated feedback data.

[1484] Specific behavior: Users can send comments and correction requests to the proposal on the communication platform.

[1485] Step 9:

[1486] Realigning the proposed allocation

[1487] The server:

[1488] Input: Feedback data from users.

[1489] Data processing or data calculations performed: Readjust task allocation proposals based on feedback.

[1490] Output: Updated task sharing proposal data.

[1491] Specific operation: The server collects user feedback, reapplies the task allocation calculation algorithm, and displays the updated proposal again on the communication platform.

[1492] Step 10:

[1493] Ongoing data monitoring and follow-up

[1494] Processing performed by the device (wearable device):

[1495] Input: User behavior history, activity data, and emotion data.

[1496] Data processing or calculation: Data is collected in real time and periodically sent to the server.

[1497] Output: Latest behavioral history, activity data, and emotion data.

[1498] Specific operation: The wearable device stores the collected data in a buffer and transmits the data to the server at regular intervals.

[1499] The server:

[1500] Input: Data sent from the wearable device.

[1501] Data processing or calculation: Analyzing data in real time and generating follow-ups or new suggestions according to the user's situation.

[1502] Output: Follow-up and new suggestion data for the user.

[1503] Specific operation: Based on the data analysis results, the server generates the follow-up that is most appropriate for the user's situation and notifies the user's device. For example, if the user frequently feels tired, the server will make new suggestions to reduce the user's fatigue.

[1504] (Application example 2)

[1505] 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."

[1506] In modern society, stress and fatigue affect many people's lives, resulting in an increasing number of health problems. Furthermore, it is difficult to suggest meals that are tailored to the user's health condition and manage appropriate task schedules in daily life. Furthermore, while efficient and fair management of household task allocation is required, it is difficult to adjust this.

[1507] 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 behavioral history and activity amount data from the wearable device, means for calculating the user's maximum and remaining physical strength based on the behavioral history and activity amount data, means for calculating the physical strength consumption for each task and proposing an optimal task schedule to the user, means for assessing the user's stress level and generating counseling and mental care suggestions as needed, means for optimizing task allocation among family members and displaying the suggestions on a communication platform, and means for proposing optimal food menus and delivery times based on the user's physical strength and emotional state and placing orders in cooperation with a food delivery service. This allows the user to receive optimal meal suggestions and manage tasks based on their own health condition, and enables efficient and fair allocation of household tasks.

[1508] A "wearable device" is a device that can collect behavioral history, activity data, and emotional data in real time when worn by the user.

[1509] "Behavioral history data" refers to data related to the user's daily activities, including the number of steps taken, distance traveled, and the duration of specific activities.

[1510] "Activity data" refers to data related to the user's physical activity, including, for example, heart rate, calories burned, exercise amount, and sleep patterns.

[1511] "Maximum stamina" is the theoretical maximum stamina value that a user can have based on past activity data and health data.

[1512] "Remaining physical strength" is a value indicating the user's current physical strength, and is a value indicating the remaining physical strength calculated from the activity amount data and the behavior history data.

[1513] The "Task Schedule" is a plan that suggests tasks such as daily activities and household chores to the user in an optimized order and time based on the user's physical strength and emotional state.

[1514] "Stress level" is an indicator that shows the user's current mental burden and degree of stress.

[1515] "Mental care" refers to methods and suggestions to support the user's mental health, including relaxation techniques and counseling.

[1516] "Task sharing" refers to the efficient allocation of tasks such as housework among multiple users within a household.

[1517] A "communication platform" is a digital environment where multiple users can share information and stay in touch.

[1518] A "food menu" is a meal suggestion that is optimized for a user based on their health and emotional state.

[1519] "Delivery timing" refers to the time or date set so that food is delivered to the user at the optimal time.

[1520] A "food delivery service" is a service that delivers food selected by a user to a specified location.

[1521] "Placing an order" means purchasing food based on the user's selections and arranging delivery through a delivery service.

[1522] The present invention is implemented by a system including a wearable terminal, a server, a user terminal, and an emotion engine, which provides task management, mental care, emotion recognition, and food delivery optimization in a user's daily life in an integrated manner.

[1523] First, the user wears a wearable device. This device collects behavioral history data, activity data, and emotional data in real time and transmits them to a server at regular intervals (e.g., every hour). The wearable device is equipped with a heart rate monitor, accelerometer, gyro sensor, microphone, and camera.

[1524] The server receives the data sent from the wearable device, checks its completeness and accuracy, verifies the data for any outliers or missing data, and then calculates the user's maximum and remaining stamina. This calculation is performed by the HP calculation module and is based on the user's heart rate, activity level, sleep data, etc.

[1525] The server then uses an emotion engine to analyze the collected emotion data and recognize the user's emotional state. For example, if the user expresses anxiety, it can identify this as "anxiety." This emotional state is integrated with HP data to calculate the energy consumption for each task and propose an optimal task schedule for the user. This schedule is then sent to the user's device, along with the specific task order and recommended time.

[1526] The server also evaluates the user's stress level and generates counseling and mental care suggestions as needed. By using an emotion engine to recognize emotions and assessing stress levels based on activity data, the server determines relaxation methods and the need for counseling.

[1527] One of the features of this system is its integration with food delivery services. The server suggests the optimal food menu and delivery timing based on the user's physical strength and emotional state. The suggested menu is then automatically ordered in collaboration with the food delivery service. For example, if the user is tired, it will suggest an easy-to-digest meal or a menu with a relaxing effect. A notification will appear on the user's smartphone saying, "A menu with a relaxing effect has been ordered."

[1528] When it comes to dividing up household tasks, the server aggregates the HP and emotional data of all users in the household and proposes fair task division. These proposals are displayed on the communication platform, and each user can review them and provide feedback. For example, User A can add a comment saying, "I'm not feeling well today, so I'd like User B to clean up." Based on this feedback, the server readjusts the task division proposal and displays the updated content again on the communication platform.

[1529] For example, if User A is very tired and the emotion engine recognizes "stress," the system will suggest a "menu with a relaxing effect" (for example, herbal tea and an easy-to-digest seafood salad) and immediately order it through a delivery service. A notification will appear on the user's smartphone saying, "You have ordered a menu with a relaxing effect."

[1530] In addition, the following are specific examples of prompts that the emotion engine uses to analyze emotion data using a generative AI model:

[1531] Example prompt sentence:

[1532] "If a user is feeling tired or stressed, suggest a relaxing menu and order it through a delivery service. For example, a herbal tea and a digestive seafood salad."

[1533] This system allows users to receive optimal meal suggestions and manage tasks based on their own health status, and also allows for efficient and fair division of household tasks.

[1534] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1535] Step 1:

[1536] A user puts on a wearable device and starts their daily activities. The wearable device collects behavioral history and activity data such as heart rate, number of steps, sleep data, and emotional data (e.g., facial expressions, voice tone) in real time. The input is the user's biometric and behavioral data, and the output is that these data are temporarily stored within the device.

[1537] Step 2:

[1538] The device sends the collected data to a cloud server at regular intervals (e.g., every hour). The input here is the data stored on the device, and the output is a series of biometric and behavioral data sent to the server.

[1539] Step 3:

[1540] The server receives the data sent from the device and checks the completeness and accuracy of the data. It verifies whether there are any outliers or missing data. The input is the collected data, and the output is the accurate data with any outliers or missing data corrected.

[1541] Step 4:

[1542] The server calculates the user's maximum stamina (HP) and current remaining stamina based on the received data. Using the HP calculation module, it calculates stamina based on heart rate, activity level, sleep data, etc. The input is the processed data stored on the server, and the output is the user's maximum stamina and remaining stamina.

[1543] Step 5:

[1544] The server uses an emotion engine to analyze the collected emotion data and recognize the user's emotional state. It identifies emotions based on prompt sentences using a generative AI model. The input is the emotion data stored on the server, and the output is the user's emotional state (e.g., "anxiety" or "stress").

[1545] Step 6:

[1546] The server uses an algorithm to calculate the energy consumption for each task and proposes an optimal task schedule to the user. The input is the calculated energy data and emotional state, and the output is a task schedule. This schedule is sent to the user's device.

[1547] Step 7:

[1548] The user terminal receives the task schedule sent from the server and notifies the user. The schedule includes the specific task order and recommended time. The input is the task schedule received from the server, and the output is task notification information.

[1549] Step 8:

[1550] The server evaluates the user's stress level based on the activity data and emotion data, and generates counseling and mental care suggestions as needed. The input is activity data and emotion data, and the output is counseling and mental care suggestions.

[1551] Step 9:

[1552] The server aggregates the HP and emotional data of all users in the home and proposes fair task sharing. This proposal is displayed on the communication platform. The input is the HP and emotional data of multiple users, and the output is a task sharing proposal.

[1553] Step 10:

[1554] Users provide feedback on the proposed task distribution. For example, User A can add a comment saying, "I'm not feeling well today, so I'd like User B to clean up." The input is the user feedback, and the output is the adjustment data sent to the server.

[1555] Step 11:

[1556] The server readjusts the task allocation proposal based on the user feedback and displays the updated content on the communication platform again. The input is the user feedback, and the output is the readjusted task allocation proposal.

[1557] Step 12:

[1558] In cooperation with the food delivery service, the server proposes the optimal food menu and delivery timing based on the user's physical strength and emotional state. The proposed menu is then linked to the food delivery service, and an order is placed. The input is the user's physical strength and emotional state, and the output is the food delivery order information.

[1559] Step 13:

[1560] The server continuously monitors the user's condition and periodically generates follow-ups and new suggestions, including suggestions for reducing stress based on the latest emotional and activity data. The input is new data collected in real time, and the output is the latest suggestion information.

[1561] 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.

[1562] 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.

[1563] 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.

[1564] [Fourth embodiment]

[1565] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1566] 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.

[1567] 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).

[1568] 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.

[1569] 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.

[1570] 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).

[1571] 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.

[1572] 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.

[1573] 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.

[1574] 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.

[1575] 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.

[1576] 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.

[1577] 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."

[1578] The system of the present invention includes a wearable device, a server, and a user device. These components work together to provide task management and mental care for the user's daily life.

[1579] Data Collection Phase

[1580] Processing performed by the device (wearable device):

[1581] The user wears a wearable device, which collects the user's behavioral history and activity data (e.g., number of steps, heart rate, sleep data). The collected data is sent to a server at regular intervals (e.g., every hour).

[1582] Data Receipt and Confirmation Phase

[1583] The server:

[1584] The server receives the data sent from the terminal and checks the integrity and accuracy of the data, thereby ensuring the authenticity of the data.

[1585] HP calculation phase

[1586] The server:

[1587] The server calculates the user's maximum stamina (HP) based on the received data. It also calculates the user's current remaining HP based on the user's current activity level and saves this data. For example, if a user has walked 7,000 steps and their average heart rate is 80 BPM, their current HP will be calculated as 70 out of 100.

[1588] Task Management Phase

[1589] The server:

[1590] The server uses an algorithm to calculate the HP consumption of each household task. For example, cleaning consumes 10 HP, and cooking consumes 15 HP. Based on this information, the server generates an optimal task schedule that takes into account the user's current remaining HP. This schedule is then sent to the user's device.

[1591] Mental care phase

[1592] The server:

[1593] The server evaluates the user's stress level based on their activity data. For example, if a high stress level is detected from data from the past week, the server will suggest counseling to the user. It will also automatically generate mental care suggestions to alleviate stress (e.g., recommending relaxing yoga).

[1594] Task allocation and communication phase

[1595] The server:

[1596] The server aggregates the home page data of the entire family and proposes fair task allocations, ensuring that the burden within the household is distributed evenly. For example, it may suggest that user A be in charge of cleaning and user B be in charge of laundry. These suggestions are displayed on the family communication platform and can be viewed by each member.

[1597] User Action:

[1598] Users can review the proposed division of labor and communicate with their family members to make adjustments. For example, User A can provide feedback such as, "I'm not feeling well today, so I'd like User B to clean."

[1599] Ongoing Support Phase

[1600] The device:

[1601] The wearable device continuously collects behavioral history and activity data and sends it to a server, which keeps the data up to date.

[1602] The server:

[1603] The server analyzes data in real time, monitors the user's condition, and periodically follows up on mental health care and task management, automatically creating new suggestions and advice and notifying the user.

[1604] Specific examples

[1605] For example, after User A has finished their daily activities, they send data to the server via their wearable device. Based on that data, the server calculates that User A's current HP is 70. It then generates a task schedule for tomorrow, suggesting "cooking in the morning, cleaning in the afternoon." At the same time, it determines that User A's stress level is high based on recent activity data and suggests activities that will help them relax. Furthermore, within the home, a suggestion that User A will be in charge of cleaning and User B will be in charge of laundry is displayed on the communication platform, and User A can check the schedule and make adjustments as necessary.

[1606] As described above, the present invention enables efficient household task management and mental care throughout each phase.

[1607] The processing flow will be explained below.

[1608] Step 1: Wearable device data collection

[1609] Device: The user wears a wearable device that records real-time activity history and activity data, including the number of steps taken, heart rate, and sleep duration.

[1610] Step 2: Sending data

[1611] Terminal: The collected data is sent to the server at regular intervals (for example, every hour). Data communication is carried out using a secure protocol.

[1612] Step 3: Receiving and verifying data

[1613] Server: Receives data sent from the device and checks the data for completeness and accuracy, verifying that there are no outliers or missing data.

[1614] Step 4: Calculate Health (HP)

[1615] Server: Based on the received data, calculate the user's maximum stamina and current remaining stamina. The maximum stamina is calculated based on the user's basic health data and past history data.

[1616] Step 5: Calculate the HP cost of the task

[1617] Server: The HP cost for each task (e.g. cleaning, cooking, laundry) is calculated by an algorithm. For example, cleaning is set to 10 HP, cooking to 15 HP, etc.

[1618] Step 6: Generate a task schedule

[1619] Server: Generates the optimal task schedule for the user, taking into account the current remaining HP and task priority. The generated schedule is sent to the user's device.

[1620] Step 7: Notification of scheduled tasks

[1621] Device: The user device receives the task schedule sent from the server and notifies the user. The schedule includes the specific task order and recommended times.

[1622] Step 8: Assess your stress levels and take care of yourself

[1623] Server: Evaluates the user's stress level from activity data and generates mental care suggestions as needed. If a high stress level is detected, counseling and relaxation suggestions are automatically provided.

[1624] Step 9: Optimize task distribution

[1625] Server: Aggregates the home page data of all users in the household and calculates a fair task allocation. For example, it suggests that user A be in charge of cleaning and user B be in charge of laundry.

[1626] Step 10: View the proposal

[1627] Server: Display task sharing proposals on the family communication platform so that all users can view them.

[1628] Step 11: User feedback

[1629] User: Provide feedback on the proposed task distribution, for example, adding a comment like "I'm not feeling well today, so I'd like someone else to clean up."

[1630] Step 12: Recalibrate your sharing proposal

[1631] Server: Based on user feedback, the task sharing proposal is re-adjusted and the updated content is displayed again on the communication platform.

[1632] Step 13: Ongoing data monitoring and follow-up

[1633] Device: Continuously collects behavioral history and activity data and sends it to the server.

[1634] Server: Analyzes data in real time, periodically generates follow-ups and new suggestions tailored to the user's situation, and notifies the user.

[1635] Through this process, household tasks and mental care can be managed efficiently, reducing the burden and stress on the entire household.

[1636] Example 1

[1637] 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."

[1638] The purpose of this invention is to provide a system that efficiently manages daily tasks and provides mental care within a home or community. A particular challenge is how to fairly distribute the burden, taking into account the physical strength and stress level of each user. Furthermore, it is necessary to achieve more flexible and adaptive support by providing real-time data analysis and appropriate feedback.

[1639] 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.

[1640] In this invention, the server includes means for collecting movement records and activity data from the wearable device, means for calculating the user's maximum and current physical strength based on the movement records and activity data, means for calculating the amount of physical strength consumed for each task and proposing an optimal work plan to the user, means for evaluating the user's stress level and generating counseling and mental care suggestions as needed, means for optimizing work allocation within the community and displaying the suggestions on a communication platform, means for aggregating the physical strength data of each member of the community and proposing fair work allocation, means for receiving user feedback and readjusting the work based on the feedback, and means for analyzing data in real time, automatically creating new mental care and work plans, and notifying the user. This enables efficient daily task management and mental care within the home or community, and fair distribution of the burden.

[1641] A "wearable device" is an electronic device that can be worn by a user and is used to record movements and collect activity data.

[1642] "Movement record" refers to historical information about the user's daily physical movements and actions.

[1643] "Activity data" refers to information that quantifies the amount of physical activity a user performs in their daily life.

[1644] "Maximum stamina" refers to the maximum stamina that the user possesses, and is usually a number calculated based on a certain standard.

[1645] "Current amount" is a numerical value that indicates the user's current remaining stamina.

[1646] A "work plan" is a suggested schedule of tasks for a user to perform throughout the day.

[1647] "Stamina consumption" is a numerical value that indicates the amount of stamina consumed when performing each task.

[1648] "Stress level" is a numerical value or evaluation value that indicates the degree of mental and physical stress of the user.

[1649] "Counselling" refers to professional advice and support provided to reduce users' stress levels.

[1650] "Mental care" refers to suggestions and activities offered to maintain or improve the mental health of users.

[1651] A "communications platform" is a digital interface for exchanging information, typically an application or web service over the internet.

[1652] "Equitable division of labor" is the allocation of work within a household or community so that each member shares responsibility equally.

[1653] "Feedback" refers to opinions, impressions, and situational information collected from users.

[1654] "Analyzing data in real time" means processing collected data in real time and analyzing it to obtain immediate results.

[1655] "Notifying the user" means that work plans and mental care suggestions are sent from the server to the user's device and displayed.

[1656] The system of the present invention combines a wearable device, a server, and a user terminal to provide task management and mental care for a user in their daily life. Detailed embodiments of the system are described below.

[1657] Hardware Configuration

[1658] Wearable devices:

[1659] Wearable devices can be worn by users to record movement and collect activity data. They include an accelerometer, heart rate monitor, and GPS sensor, and record data such as steps taken, heart rate, and location information in real time.

[1660] server:

[1661] The server receives and analyzes the data sent from the wearable device, and also performs processes such as calculating maximum stamina and current physical capacity, proposing work plans and mental care, and adjusting work allocation.

[1662] User device:

[1663] The user terminal is a device that receives notifications sent from the server, such as a smartphone or tablet, and allows interaction with the user through an interface.

[1664] Software Configuration

[1665] Data collection modules:

[1666] A software module installed in a wearable device collects data from various sensors, such as the number of steps taken from an accelerometer and the heart rate from a heart rate monitor.

[1667] Data Analysis Module:

[1668] A software module installed on the server analyzes the collected data, calculates the maximum stamina and current stock, and calculates the amount of stamina consumed for each task to generate an optimal work plan.

[1669] Stress Assessment Module:

[1670] The software module installed on the server evaluates the user's stress level from collected activity data and automatically generates counseling and mental care suggestions as needed.

[1671] Work allocation adjustment module:

[1672] This module aggregates the physical fitness data of each member of a family or community and proposes fair division of labor. It displays the proposals using a communication platform and can also readjust based on feedback.

[1673] Specific examples

[1674] For example, after completing a day's activities, User A sends data to a server via a wearable device. Based on that data, the server calculates that User A's current available physical energy is 70. It then generates a work plan for the next day and notifies the user's device with suggestions such as "cook in the morning and clean in the afternoon." At the same time, if the server determines that the user's stress level is high based on recent activity data, it also makes mental care suggestions such as "do 30 minutes of relaxing yoga." Furthermore, a suggestion for dividing household tasks, with User A doing the cleaning and User B doing the laundry, is displayed on the communication platform.

[1675] Prompt Sentence Examples

[1676] Example prompt:

[1677] "Please specifically design a system that uses wearable devices and a server to manage the user's daily tasks and provide mental care. In particular, please provide a detailed explanation of each phase: data collection, HP calculation, task management, and mental care."

[1678] As described above, close cooperation between the user, server, and wearable device makes it possible to achieve efficient daily task management and mental care.

[1679] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1680] Step 1: Data collection

[1681] Processing performed by the device (wearable device):

[1682] Wearable devices use built-in sensors (e.g., accelerometer, heart rate monitor, GPS) to record and collect user movement and activity data. Specifically, when a user walks, the accelerometer counts the number of steps, and the heart rate monitor measures the heart rate. The collected data (e.g., number of steps: 5000, heart rate: 75 BPM) is temporarily stored in the device.

[1683] Input: User's physical activity (e.g., walking, exercising)

[1684] Output: Collected motion records and activity data (e.g., steps, heart rate)

[1685] Step 2: Data Transfer

[1686] The device:

[1687] The wearable device transmits the collected motion records and activity data to a server at regular intervals (e.g., every hour). Data transmission uses a secure communication protocol (e.g., HTTPS).

[1688] Input: Collected motion and activity data (e.g., steps, heart rate)

[1689] Output: Data sent to the server

[1690] Step 3: Data reception and confirmation

[1691] The server:

[1692] The server receives the data sent by the terminal and checks the data for completeness and accuracy using a checksum algorithm. If there is a problem with any part of the received data, it requests a retransmission.

[1693] Input: Data sent from the terminal

[1694] Output: Verified data (e.g., clean step and heart rate data)

[1695] Step 4: HP Calculation

[1696] The server:

[1697] The server calculates the user's maximum stamina and current stamina based on the confirmed data. For example, it runs an algorithm to calculate current stamina based on the number of steps and heart rate data for that day. For example, if the maximum stamina is 100, the current stamina is calculated to be 80 based on the number of steps 5000 and heart rate 75 BPM.

[1698] Input: Verified activity data (e.g., steps, heart rate)

[1699] Output: User's current stamina (e.g. 80 / 100)

[1700] Step 5: Task Management

[1701] The server:

[1702] The server calculates the amount of stamina consumed for each task. For example, cleaning requires 10 HP, cooking requires 15 HP, and generates an optimal task plan based on the user's current stamina and sends it to the user's device.

[1703] Input: Current stamina (e.g. 80 / 100), task list and consumption (e.g. cleaning 10 HP, cooking 15 HP)

[1704] Output: Work plan (e.g. cooking in the morning, cleaning in the afternoon)

[1705] Step 6: Stress assessment and mental health advice

[1706] The server:

[1707] The server analyzes the collected activity data and evaluates the user's stress level. For example, if it determines that the stress level is high based on data from the past week, it will automatically generate mental care suggestions, such as a relaxing 30-minute yoga session, and send them to the user's device.

[1708] Input: Activity data (e.g., number of steps taken in the past week, heart rate)

[1709] Output: Mental care suggestions (e.g. yoga for relaxation)

[1710] Step 7: Assign tasks and communicate

[1711] The server:

[1712] The server collects the physical fitness data of each household member and proposes fair division of labor. The proposals are displayed on the communication platform and can be confirmed by the user and the other members. For example, it may be suggested that User A be in charge of cleaning and User B be in charge of laundry.

[1713] Input: Physical fitness data of household members

[1714] Output: Fair work sharing proposal (e.g., User A cleans, User B does laundry)

[1715] Step 8: Gather feedback and refine

[1716] User Action:

[1717] Users can send feedback on the provided work assignments through the communication platform, for example, sending a message such as, "I'm not feeling well today, so I'd like you to do the cleaning for me."

[1718] Input: User feedback

[1719] Output: Server receiving feedback

[1720] The server:

[1721] The server re-adjusts the division of labor based on the collected feedback and displays the updated proposal again on the communication platform.

[1722] Input: User feedback

[1723] Output: Rebalanced work assignments (e.g., User B now cleans)

[1724] Step 9: Ongoing support

[1725] The device:

[1726] The wearable device continuously collects motion records and activity data and periodically transmits them to a server.

[1727] Input: User's ongoing activity

[1728] Output: Latest collected motion records and activity data

[1729] The server:

[1730] The server analyzes the data in real time, keeping it up to date and providing appropriate follow-up care and task management.

[1731] Input: Latest activity data

[1732] Output: Real-time suggestions and notifications (e.g. break suggestions, task update notifications)

[1733] (Application example 1)

[1734] 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."

[1735] In conventional factories, it has been difficult to grasp the workload and stress levels of workers and efficiently manage work tasks based on this information. Furthermore, optimization of work tasks within factories and mental care for workers are often insufficient, resulting in decreased productivity and increased mental strain on workers. To solve these problems, the present invention provides a comprehensive task management and mental care system based on worker activity data.

[1736] 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.

[1737] In this invention, the server includes means for collecting behavioral history and activity data from the wearable device, means for calculating the user's maximum and remaining physical strength based on the behavioral history and activity data, means for calculating the physical strength consumption for each task and proposing an optimal task schedule to the user, means for evaluating the user's stress level and generating counseling and mental care suggestions as needed, means for generating an optimal task schedule for factory robots based on the worker's activity data and allocating tasks, and means for analyzing the worker's stress level and providing mental care suggestions as needed. This enables efficient task management that takes into account the physical strength and mental health of workers, improving productivity, and reducing the mental burden on workers.

[1738] A "wearable device" is a device worn on the body that constantly monitors and collects the user's behavioral history and activity data.

[1739] "Behavioral history" refers to a record of various actions taken by a user, such as the number of steps taken, distance traveled, and daily activity patterns.

[1740] "Activity data" is data that quantitatively indicates the user's physical activity, and specific examples include the number of steps taken, heart rate, and calorie consumption.

[1741] "Maximum stamina" is a numerical representation of the user's total stamina, and is an indicator of the maximum amount of activity possible in a day.

[1742] "Remaining physical strength" is a numerical value that indicates the user's current physical strength, and indicates the current level of fatigue and the remaining amount of activity that can be performed.

[1743] A "task schedule" is a plan that optimally arranges each task, taking into account the user's physical strength, stress level, and priority.

[1744] "Stress level" indicates the user's mental stress and fatigue level using numerical values ​​and indicators to evaluate the user's mental state.

[1745] "Counseling" refers to suggestions and interventions to provide psychological support and consultation to users experiencing high stress levels.

[1746] "Mental care" refers to specific activities and suggestions for maintaining and improving the user's mental health, including stress reduction and relaxation methods.

[1747] "Worker" refers to an employee or worker who works in a factory or business.

[1748] A "factory robot" is a machine or automated device used to automate work in a factory, performing tasks in place of humans.

[1749] "Task sharing" means allocating multiple tasks fairly to each member, with the aim of efficiently dividing up work.

[1750] The present invention provides a system for improving the efficiency of task management and mental care within a factory based on worker activity data. The system includes a wearable device, a server, and a user terminal.

[1751] Hardware and Software Use

[1752] Wearable devices

[1753] Wearable devices are devices that collect workers' behavioral history and activity data in real time. These devices have built-in sensors such as pedometers, heart rate monitors, and sleep monitors, and periodically send data to a server. Possible devices used include smartwatches and fitness trackers.

[1754] server

[1755] The server receives the data sent from the wearable device and checks its completeness and accuracy. Based on the collected data, it calculates the worker's HP (maximum and remaining stamina) and evaluates their stress level. Furthermore, it calculates the stamina consumption for each task based on the worker's current HP and generates an optimal task schedule. It also makes mental health care suggestions as needed. Cloud services such as Amazon Web Services (AWS) and Google Cloud Platform can be used for server-side processing.

[1756] User terminal

[1757] User devices, such as smartphones, tablets, and PCs, are used by workers and managers to check task schedules and mental health advice and provide feedback.

[1758] Data processing and calculation

[1759] After receiving the data transmitted from the wearable device, the server performs the following processing.

[1760] HP Calculation: Calculates the maximum stamina and current remaining stamina based on data such as the worker's steps, heart rate, sleep time, etc. For example, calculate the HP of a worker who has walked 7,000 steps and save that data.

[1761] Task Schedule Generation: Based on the current HP of the workers, calculate the HP required for each task and generate an optimal task schedule. For example, assign a worker 20 HP of packing work, 15 HP of inspection work, and 30 HP of assembly work.

[1762] Stress level assessment: Based on the collected data, the stress level of the worker is assessed and mental health care suggestions are made as necessary. For example, if stress is determined to be high based on heart rate data from the past week, counseling is suggested.

[1763] Optimizing task sharing: Optimizing task sharing among family members and adjusting the workload fairly, which will improve the efficiency of work within the factory.

[1764] Specific examples

[1765] In an actual use case, Worker A at a factory wears a wearable device while working. The device collects data such as the number of steps taken each day, heart rate, and sleep time, and sends it to the server. After receiving this data, the server calculates Worker A's current HP and generates a task schedule for the next day based on that. For example, it may make adjustments such as "allocating 20 HP to packaging, 15 HP to inspection, and 30 HP to assembly." It may also determine from recent data that Worker A's stress level is rising and suggest relaxing activities.

[1766] Prompt Sentence Examples

[1767] "Please input the following end-user data into the deep learning model to generate an optimal factory task schedule.

[1768] Number of steps: 7000

[1769] Heart rate: 75 BPM

[1770] Sleep time: 6 hours

[1771] Task suggestions:

[1772] Packaging work (20HP)

[1773] Inspection work (15HP)

[1774] Assembly work (30HP)

[1775] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1776] Step 1:

[1777] Data Collection Phase

[1778] The wearable device collects the worker's behavioral history and activity data. This data includes the number of steps, heart rate, sleep time, etc. The collected data is sent to a server at regular intervals (e.g., every hour). The input is the worker's daily activity data, and the output is the data sent to the server.

[1779] Step 2:

[1780] Data Receipt and Confirmation Phase

[1781] The server receives the data sent from the wearable device and performs a data verification process to verify the completeness and accuracy of the received data. The input is the data from the wearable device and the output is the verified worker activity data.

[1782] Step 3:

[1783] HP calculation phase

[1784] The server calculates the worker's maximum stamina (HP) and current remaining stamina based on the confirmed data. For example, the server calculates the worker's current HP by subtracting the worker's maximum stamina from 100 based on the number of steps, heart rate, and sleep time. The input is the confirmed activity data, and the output is the calculated remaining HP.

[1785] Step 4:

[1786] Task schedule generation phase

[1787] The server calculates the stamina consumption required for each task based on the worker's current HP and generates an optimal task schedule. The server inputs the stamina consumption for each task and adjusts and calculates the schedule based on the worker's HP. The input is the worker's remaining stamina and task data, and the output is the optimal task schedule.

[1788] Step 5:

[1789] Task Schedule Notification Phase

[1790] The server sends the generated task schedule to the user terminal. Workers and managers can check this schedule using the user terminal and provide feedback as needed. The input is the optimal task schedule, and the output is a schedule notification to the user terminal.

[1791] Step 6:

[1792] Stress Level Assessment Phase

[1793] The server evaluates the worker's stress level from the collected activity data. For example, it determines whether stress is increasing from trends in heart rate and sleep time, and makes recommendations for counseling or mental care as necessary. The input is activity data, and the output is the evaluated stress level and recommendations.

[1794] Step 7:

[1795] Feedback reflection phase

[1796] The server receives feedback from users and adjusts task schedules and mental health care suggestions based on that feedback. For example, if a worker provides feedback such as "I'm not feeling well, so I'd like to reduce my workload," the server takes that into account when generating a new schedule. The input is the feedback data, and the output is the adjusted task schedule.

[1797] Step 8:

[1798] Data update and save phase

[1799] The server continuously receives new data, analyzes and stores it, and always manages based on the latest data. The input is new activity data collected in real time, and the output is an updated database and continuous recommendations.

[1800] 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.

[1801] The system of the present invention includes a wearable terminal, a server, a user terminal, and an emotion engine. These components work together to provide task management, mental care, and emotion recognition in the user's daily life.

[1802] Data Collection Phase

[1803] Processing performed by the device (wearable device):

[1804] The user wears a wearable device, which records real-time activity data (e.g., number of steps, heart rate, sleep data) and emotional data (e.g., facial expressions, voice tone). The collected data is sent to a server at regular intervals (e.g., every hour).

[1805] Data Receipt and Confirmation Phase

[1806] The server:

[1807] The server receives the data sent from the device and checks its completeness and accuracy, verifying that there are no outliers or missing data.

[1808] HP calculation phase

[1809] The server:

[1810] The server calculates the user's maximum HP and current remaining HP based on the received data. The maximum HP is calculated based on the user's basic health data and past history data.

[1811] Emotion Recognition Phase

[1812] The server:

[1813] The server uses an emotion engine to analyze the collected emotion data and recognize the user's emotional state. For example, if the user expresses anxiety, it will recognize it as "anxiety."

[1814] Task Management Phase

[1815] The server:

[1816] The server uses an algorithm to calculate the HP consumption of each household task. For example, cleaning consumes 10 HP, and cooking consumes 15 HP. Furthermore, based on the emotional data recognized by the emotion engine, the server generates an optimal task schedule that takes into account the user's current remaining HP and emotional state. This schedule is then sent to the user's device.

[1817] Task Schedule Notifications

[1818] Device:

[1819] The user's device receives the task schedule sent from the server and notifies the user. The schedule includes the specific task order and recommended time. For example, if the user's HP is 70 and the emotion engine recognizes "stress," it will prioritize tasks that will reduce stress, such as suggesting "light stretching" or "relaxing housework."

[1820] Stress level assessment and mental health care

[1821] The server:

[1822] The server evaluates the user's stress level based on activity data and emotional data. If a high stress level is detected, it generates suggestions for counseling or mental care. Specifically, if the emotion engine recognizes "stress," it suggests relaxation methods or counseling.

[1823] Task allocation and communication phase

[1824] The server:

[1825] The server aggregates the HP and emotional data of all users in the household and proposes fair task sharing. These proposals are displayed on the family communication platform. For example, if user A is feeling "anxious," user B will be suggested to take on a larger share of the important tasks for the day.

[1826] User Action:

[1827] Users can provide feedback on the proposed task allocation. For example, User A can add a comment saying, "I'm not feeling well today, so I'd like User B to clean up."

[1828] Realigning the proposed allocation

[1829] The server:

[1830] Based on user feedback, the task sharing proposal will be re-adjusted and the updated content will be displayed again on the communication platform.

[1831] Ongoing data monitoring and follow-up

[1832] The device:

[1833] The wearable device continuously collects behavioral history, activity data, and emotional data and transmits them to a server.

[1834] The server:

[1835] The server analyzes the data in real time and periodically generates follow-ups and new suggestions tailored to the user's situation and notifies them. For example, if the user has recently been feeling "fatigue," it will suggest measures to reduce the user's stress.

[1836] Specific examples

[1837] For example, after User A has finished their daily activities, they send data to the server via their wearable device. Based on that data, the server calculates that User A's current HP is 70. Furthermore, if the emotion engine recognizes "stress," it will prioritize stress-reducing tasks such as "light stretching" and "easy cooking" in the task schedule for the next day. At home, a suggestion is displayed on the communication platform that User B should take on more of the important tasks, and User A can check the content and make adjustments as necessary.

[1838] In this way, the present invention can perform household task management, mental care, and emotion recognition in an integrated manner throughout each phase.

[1839] The processing flow will be explained below.

[1840] Step 1: Wearable device data collection

[1841] Device: The user wears a wearable device, which collects real-time behavioral history and activity data (e.g., steps, heart rate, sleep data) and emotional data (e.g., facial expressions, voice tone).

[1842] Step 2: Sending data

[1843] Terminal: The collected data is sent to the server at regular intervals (for example, every hour). Data communication is carried out using a secure protocol.

[1844] Step 3: Receiving and verifying data

[1845] Server: Receives data sent from the device and checks the data for completeness and accuracy, verifying that there are no outliers or missing data.

[1846] Step 4: Calculate Health (HP)

[1847] Server: Based on the received data, calculate the user's maximum stamina and current remaining stamina. The maximum stamina is calculated based on the user's basic health data and past history data.

[1848] Step 5: Emotion Recognition

[1849] Server: The server uses an emotion engine to analyze the collected emotion data and recognize the user's emotional state, for example, "anxiety" or "joy" based on the user's facial expressions and voice tone.

[1850] Step 6: Calculate the HP cost of the task

[1851] Server: The HP cost for each household task is calculated by an algorithm, for example, cleaning is set to 10 HP, cooking is set to 15 HP, etc.

[1852] Step 7: Generate a task schedule

[1853] Server: Generates an optimal task schedule for the user, taking into account the user's current remaining HP and the emotional state provided by the emotion engine. For example, if the user's HP is 70 and the emotion engine detects "stress," it prioritizes tasks that reduce stress. The generated schedule is sent to the user's device.

[1854] Step 8: Notification of scheduled tasks

[1855] Device: The user device receives the task schedule sent from the server and notifies the user. The schedule includes the specific task order and recommended times.

[1856] Step 9: Stress level assessment and mental health recommendations

[1857] Server: Evaluates the user's stress level based on activity data and emotional data, and generates mental health advice as needed. For example, if the emotion engine detects "stress," it will automatically suggest counseling or relaxation techniques.

[1858] Step 10: Optimize task distribution

[1859] Server: Aggregates HP data and emotional data from all users in the household and proposes fair task sharing. For example, if user A is feeling anxious, user B will be suggested to share more of the important tasks for that day. The proposal is displayed on the family communication platform.

[1860] Step 11: View the proposal

[1861] Server: Display the task sharing proposal on the family communication platform so that all users can view the contents.

[1862] Step 12: User feedback

[1863] Users: Provide feedback on the proposed task distribution. For example, User A can add a comment saying, "I'm not feeling well today, so I'd like User B to clean up."

[1864] Step 13: Recalibrate your sharing proposal

[1865] Server: Based on user feedback, the task sharing proposal is re-adjusted and the updated content is displayed again on the communication platform.

[1866] Step 14: Ongoing data monitoring and follow-up

[1867] Device: Continuously collects behavioral history, activity data, and emotional data, and sends them to the server.

[1868] Server: Analyzes data in real time, periodically generates follow-ups and new suggestions based on the user's situation, and notifies the user. For example, if the user has recently been feeling "tired," the server will suggest measures to reduce the user's stress.

[1869] Through this process, household task management, mental care, and emotional awareness can be integrated, reducing the burden and stress on the entire household.

[1870] Example 2

[1871] 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."

[1872] Conventional home task management systems often provide a uniform task schedule without fully considering the user's physical condition or emotional state. This can result in excessive burden on users and problems such as insufficient mental care and stress management. Furthermore, because the allocation of tasks within the home is done without taking into account each individual's physical condition or emotional state, it can easily lead to a sense of unfairness. By solving these issues, we aim to realize a healthier and stress-free home life.

[1873] 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.

[1874] In this invention, the server includes: means for collecting behavior history and activity amount data from the wearable device;

[1875] A means for calculating the user's maximum stamina and remaining stamina based on the behavior history and activity amount data;

[1876] A means of emotion recognition by analyzing the user's facial expressions and vocal tone;

[1877] A method for calculating the amount of energy consumed for each task using an algorithm and proposing an optimal task schedule taking into account the emotional state and remaining energy;

[1878] a means for notifying a user terminal of the proposed task schedule;

[1879] A means for evaluating a user's stress level based on their activity data and emotional data, and generating suggestions for counseling and mental care;

[1880] The system also includes a means for optimizing task allocation among family members and displaying the proposed results on the communication platform. This enables optimal task scheduling and fair task allocation that takes into account the user's physical condition and emotional state. Furthermore, by providing appropriate mental care and stress management, a healthy and stress-free family life can be achieved.

[1881] A "wearable device" is a small electronic device that can be worn by the user and has built-in sensors that collect behavioral history, activity data, emotional data, and other information.

[1882] "Behavioral history" refers to the history of various activities and movements undertaken by a user, including detailed information such as time and location.

[1883] "Activity data" is data that quantifies the physical movements and amount of exercise a user performs in their daily life, and examples include the number of steps taken, heart rate, calories burned, and sleep data.

[1884] "Maximum stamina" is the theoretical maximum stamina of a user, calculated by an algorithm based on the user's basic health status and past history data.

[1885] "Remaining physical strength" is a numerical value that indicates the user's current physical strength, and is calculated in real time based on behavioral history and activity data.

[1886] "Facial expressions" refers to the movement of a user's facial muscles and the movements of the eyes, mouth, eyebrows, etc., and are used as data for analyzing emotional states.

[1887] "Voice tone" refers to characteristics such as pitch, volume, and rhythm of the user's speaking voice, and is used as data for analyzing emotional state.

[1888] "Emotion recognition" is a technology that analyzes collected data such as facial expressions and voice tone to identify a user's emotional state.

[1889] A "task" refers to a specific task or activity that a user must perform in their daily life, such as cleaning, cooking, or laundry.

[1890] A "task schedule" is a planning table that suggests the optimal order and start time of tasks, taking into account the user's remaining physical energy and emotional state.

[1891] "Notifications" refers to the form of alerts or push notifications generated by the server to inform users of task schedules, mental health suggestions, etc.

[1892] "Stress level" is a numerical representation of the degree of stress a user feels, and is evaluated based on activity data and emotional data.

[1893] "Mental care" refers to counseling and relaxation suggestions provided to maintain the user's psychological health.

[1894] "Task sharing" refers to the fair allocation of tasks that are jointly performed by multiple household members, with the aim of optimizing the burden among family members.

[1895] "Communication platform" refers to a digital environment or application for sharing, coordinating, and managing tasks and other information among family members.

[1896] The system of the present invention supports task management, mental care, and emotion recognition in a user's daily life. This system includes a wearable device, a server, a user device, and an emotion engine, and these components function in cooperation with each other.

[1897] Data Collection Phase

[1898] The wearable device:

[1899] The wearable device worn by the user has built-in sensors, cameras, and microphones. This hardware is used to record the user's behavioral history (e.g., number of steps, distance traveled) and activity data (e.g., heart rate, sleep data) in real time. It also collects emotional data such as facial expressions and vocal tone. This data is sent to a server at regular intervals (e.g., every hour) via Bluetooth or Wi-Fi.

[1900] Data Receipt and Confirmation Phase

[1901] The server:

[1902] The server receives the data sent from the wearable device and checks its completeness and accuracy before storing it in the database, verifies whether there are any outliers or missing data, and requests a retransmission if an anomaly is detected.

[1903] HP calculation phase

[1904] The server:

[1905] The server calculates the user's maximum stamina and current remaining stamina based on the data collected from the wearable device. This uses the user's basic health data and past history data. The calculation is performed using a specific algorithm, and the user's remaining stamina is updated in real time.

[1906] Emotion Recognition Phase

[1907] The server:

[1908] The emotion engine in the server analyzes the user's facial expression and voice tone to recognize their emotion. For example, if the facial expression indicates sadness, it will be recognized as "sadness." Voice tone is also analyzed in the same way, and the overall emotional state is determined.

[1909] Task Management Phase

[1910] The server:

[1911] The server uses an algorithm to calculate the amount of stamina consumed for each task and generates an optimal task schedule based on the user's remaining stamina and emotional state. For example, cleaning is set to consume 10 HP, and cooking is set to consume 15 HP. The generated task schedule is sent to the user's device.

[1912] Task Schedule Notifications

[1913] User device processes:

[1914] The system receives task schedules sent to the user's device and notifies the user using push notifications and alerts. The notifications include the specific task execution order and recommended time. For example, if the user's remaining stamina is 70 and the emotion engine recognizes "stress," it will suggest "light stretching" or "relaxing housework" to reduce stress.

[1915] Stress level assessment and mental health care

[1916] The server:

[1917] The server evaluates the user's stress level based on activity and emotional data. If a high stress level is detected, counseling and relaxation methods are offered. For example, specific advice such as "Try 10 minutes of meditation" is provided.

[1918] Task allocation and communication phase

[1919] The server:

[1920] The server aggregates the physical strength and emotional data of all users in the household and proposes fair task sharing. The task sharing among users is displayed and shared on the communication platform. For example, if user A has low physical strength and feels "anxious," a suggestion is made that user B should share more of the important tasks for the day.

[1921] User Action:

[1922] Users can provide feedback on the proposed task allocation. For example, they can comment, "I'm not feeling well today, so I'd like to ask User B to clean up."

[1923] Realigning the proposed allocation

[1924] The server:

[1925] Based on the user's feedback, the server readjusts the task sharing proposal and displays the updated content again on the communication platform.

[1926] Ongoing data monitoring and follow-up

[1927] The wearable device:

[1928] The wearable device continuously collects behavioral history, activity data, and emotional data and transmits them to a server.

[1929] The server:

[1930] The server analyzes the received data in real time and generates follow-ups and new suggestions based on the user's situation. For example, if the user's recent data shows that they frequently feel "tired," the server will make new suggestions to reduce the burden, such as notifying them to "reduce their activity today and take a rest."

[1931] Specific examples

[1932] For example, after User A has finished his or her daily activities, he or she sends data to the server via a wearable device. The server analyzes the data and calculates that User A's current remaining stamina is 70. Furthermore, if the emotion engine recognizes "stress," it will suggest "light stretching" or "easy cooking" in the task schedule for the next day. At home, suggestions are displayed on the communication platform for User B to share many important tasks.

[1933] Prompt Sentence Examples

[1934] "I'm feeling stressed today. My current stamina is at 70. Please suggest a task schedule for tomorrow."

[1935] As described above, the system of the present invention can perform household task management, mental care, and emotion recognition in an integrated manner throughout each phase.

[1936] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1937] Step 1:

[1938] Data Collection Phase

[1939] Processing performed by the device (wearable device):

[1940] Input: The wearable device collects the user's behavioral history (e.g., steps taken, distance traveled), activity data (e.g., heart rate, sleep data), and emotional data (e.g., facial expressions, voice tone) in real time.

[1941] Data processing or calculation: Various biometric data is recorded using the built-in sensors, camera, and microphone.

[1942] Output: The collected data is encoded at regular intervals (e.g., every hour) and sent to a server via Bluetooth or Wi-Fi.

[1943] Specific operation: After data collection, the wearable device's communication function is activated and the data is uploaded to the server using a secure protocol.

[1944] Step 2:

[1945] Data Receipt and Confirmation Phase

[1946] The server:

[1947] Input: Behavioral history, activity data, and emotion data sent from the wearable device.

[1948] Data processing or data calculations performed: Checking the completeness and accuracy of received data, verifying that there are no outliers or missing data.

[1949] Output: The data whose accuracy has been confirmed is stored in a database, and if there is an abnormality, a resend request is made.

[1950] What happens: After receiving the data, the server's validation algorithm checks the integrity of the data and, if necessary, requests that the data be resent.

[1951] Step 3:

[1952] HP calculation phase

[1953] The server:

[1954] Input: Verified activity data and behavioral history data.

[1955] Data processing or calculation: The server's algorithm calculates the maximum stamina and current remaining stamina based on the user's basic health data (age, gender, height, weight, etc.) and past history data.

[1956] Output: Calculated maximum health and current health remaining data.

[1957] Specific operation: The server periodically evaluates the user's physical strength status and updates the remaining physical strength data based on that.

[1958] Step 4:

[1959] Emotion Recognition Phase

[1960] The server:

[1961] Input: Emotion data (facial expressions, vocal tones) sent from the wearable device.

[1962] Data processing or data calculation performed: Using an emotion engine, the user's emotional state is analyzed using facial recognition technology and voice analysis technology.

[1963] Output: Recognized user emotional state data (e.g., "happiness", "anxiety", "anger", "stress").

[1964] What it does: The server's emotion engine analyzes facial expressions and vocal tone, and then uses the resulting data to label emotions.

[1965] Step 5:

[1966] Task Management Phase

[1967] The server:

[1968] Input: User's remaining energy data, emotional state data, and energy consumption data for each task.

[1969] Data processing or calculation: The amount of energy consumed by a task is calculated using an algorithm, and an optimal task schedule is generated based on the user's current remaining energy and emotional state.

[1970] Output: The generated task schedule data.

[1971] Specific operation: The server predicts the amount of energy required for each task and dynamically generates a schedule based on the user's energy and emotional state.

[1972] Step 6:

[1973] Task Schedule Notifications

[1974] Processing performed by the terminal (user terminal):

[1975] Input: Task schedule data sent from the server.

[1976] Data processing or data calculation to be performed: Converting task schedule information into a format for notifying the user.

[1977] Output: Task schedule information as push notifications and alerts.

[1978] Specific operation: The user terminal notifies the user visually or audibly based on the schedule data received from the server. The notification includes the specific task content, execution time, and order.

[1979] Step 7:

[1980] Stress level assessment and mental health care

[1981] The server:

[1982] Input: Activity data and emotion data.

[1983] Data processing or data calculation to be performed: Based on the collected data, the user's stress level is evaluated, and if a high stress level is detected, suggestions for counseling or relaxation methods are generated.

[1984] Output: Stress level assessment results and mental care recommendations.

[1985] Specific operation: The server periodically analyzes data, and if any abnormalities are found in the stress level, it launches a suggestion function to notify the user of countermeasures.

[1986] Step 8:

[1987] Task allocation and communication phase

[1988] The server:

[1989] Input: Physical and emotional data of all users in the household.

[1990] Data processing or data calculation: Aggregate each user's data, calculate a fair task share, and generate a proposal.

[1991] Output: Fairly allocated task sharing proposal data.

[1992] Specific operation: The server processes the data of all household members, calculates a fair task distribution, and then creates a proposal to display on the communication platform.

[1993] User Action:

[1994] Input: Task sharing proposal data received from the server.

[1995] Data processing or calculation to be performed: Enter feedback on the proposal.

[1996] Output: Updated feedback data.

[1997] Specific behavior: Users can send comments and correction requests to the proposal on the communication platform.

[1998] Step 9:

[1999] Realigning the proposed allocation

[2000] The server:

[2001] Input: Feedback data from users.

[2002] Data processing or data calculations performed: Readjust task allocation proposals based on feedback.

[2003] Output: Updated task sharing proposal data.

[2004] Specific operation: The server collects user feedback, reapplies the task allocation calculation algorithm, and displays the updated proposal again on the communication platform.

[2005] Step 10:

[2006] Ongoing data monitoring and follow-up

[2007] Processing performed by the device (wearable device):

[2008] Input: User behavior history, activity data, and emotion data.

[2009] Data processing or calculation: Data is collected in real time and periodically sent to the server.

[2010] Output: Latest behavioral history, activity data, and emotion data.

[2011] Specific operation: The wearable device stores the collected data in a buffer and transmits the data to the server at regular intervals.

[2012] The server:

[2013] Input: Data sent from the wearable device.

[2014] Data processing or calculation: Analyzing data in real time and generating follow-ups or new suggestions according to the user's situation.

[2015] Output: Follow-up and new suggestion data for the user.

[2016] Specific operation: Based on the data analysis results, the server generates the follow-up that is most appropriate for the user's situation and notifies the user's device. For example, if the user frequently feels tired, the server will make new suggestions to reduce the user's fatigue.

[2017] (Application example 2)

[2018] 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."

[2019] In modern society, stress and fatigue affect many people's lives, resulting in an increasing number of health problems. Furthermore, it is difficult to suggest meals that are tailored to the user's health condition and manage appropriate task schedules in daily life. Furthermore, while efficient and fair management of household task allocation is required, it is difficult to adjust this.

[2020] 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 behavioral history and activity amount data from the wearable device, means for calculating the user's maximum and remaining physical strength based on the behavioral history and activity amount data, means for calculating the physical strength consumption for each task and proposing an optimal task schedule to the user, means for assessing the user's stress level and generating counseling and mental care suggestions as needed, means for optimizing task allocation among family members and displaying the suggestions on a communication platform, and means for proposing optimal food menus and delivery times based on the user's physical strength and emotional state and placing orders in cooperation with a food delivery service. This allows the user to receive optimal meal suggestions and manage tasks based on their own health condition, and enables efficient and fair allocation of household tasks.

[2021] A "wearable device" is a device that can collect behavioral history, activity da...

Claims

1. A means for collecting behavior history and activity amount data from a wearable device; A means for calculating the user's maximum stamina and remaining stamina based on the behavior history and activity amount data; A method for calculating the amount of physical energy consumed for each task and proposing the optimal task schedule to the user. A means of assessing the user's stress level and generating counseling or mental health care recommendations as needed; A means to optimize task sharing among family members and display suggestions on a communication platform; A system including:

2. 10. The system of claim 1, further comprising means for automatically calculating an equitable burden adjustment based on the division of tasks among family members.

3. 10. The system of claim 1, further comprising means for receiving user feedback and readjusting the task distribution proposal based thereon.

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