System

Abstract

A system is provided.SOLUTION: A system includes means for collecting profile data from a user and suggesting an optimal task scheduling method based on the data, means for analyzing task progress data of the user and generating an efficient task management advice based on the analysis, and means for calculating a task reminding timing and displaying an alert to the user at the timing.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] People with ADHD traits, such as inattention, hyperactivity, and impulsivity, face significant challenges in task management and planning in business settings. This makes it difficult to prioritize tasks, schedule tasks, and keep track of deadlines, resulting in reduced productivity. Conventional task management tools lack support specifically tailored to these traits, necessitating personalized assistance tailored to each individual user. Furthermore, progress analysis and reminder features are designed for typical users, making them difficult for people with ADHD to use. This invention aims to address these challenges and enable business people with ADHD to efficiently manage tasks and improve their productivity. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including the following means.

[0006] 1. A means for collecting profile data from users and proposing optimal task scheduling methods based on the data.

[0007] 2. A means for analyzing a user's task progress data and generating efficient task management advice based on the analysis.

[0008] 3. A means for calculating the task reminder timing and displaying an alert to the user at that timing.

[0009] Furthermore, the following means are provided to flexibly respond to user needs.

[0010] 4. Means for receiving user feedback and regenerating the scheduling method based on said feedback.

[0011] 5. A means of suggesting the regenerated scheduling method to the user.

[0012] In addition, the following means are included to optimize the user's task management.

[0013] 6. Means for receiving a user's advice rating and regenerating improvement advice based on said rating.

[0014] 7. A means of notifying the user of regenerated remediation advice.

[0015] This will enable business people with ADHD traits to efficiently manage tasks in accordance with their own characteristics, thereby improving productivity in the business world.

[0016] "User" refers to the person who uses this system, and primarily refers to individuals with ADHD traits who have difficulty managing tasks in business situations.

[0017] "Profile data" refers to data that includes basic information about a user and personal characteristics related to task management, and is data on which a scheduling method is generated.

[0018] A "task scheduling method" is a plan or schedule that optimizes the priority and execution time of individual tasks based on user profile data.

[0019] "Task progress data" refers to data relating to the progress and completion status of tasks that is entered by the user on a daily basis, and which is analyzed to generate improvement advice.

[0020] "Advice" refers to suggestions or instructions that are based on the analysis results of task progress data and are intended to enable the user to manage tasks efficiently.

[0021] "Reminder timing" refers to the appropriate timing to display an alert to the user based on the deadline and importance of the task.

[0022] An "alert" is a message or warning that is notified to the user at the time of task reminder, and is displayed to prompt the user to perform the task.

[0023] "Feedback" refers to the evaluation or additional information provided by the user regarding the system's suggestions or advice, and the scheduling method or advice is regenerated based on this.

[0024] "Analysis" is the process of evaluating and examining collected data using statistical or machine learning techniques to derive useful information and patterns. [Brief explanation of the drawings]

[0025] [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

[0026] 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.

[0027] First, the terms used in the following description will be explained.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] [First embodiment]

[0034] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0035] 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.

[0036] 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).

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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."

[0046] This invention provides an AI task coaching system that helps business people with ADHD manage their tasks effectively. The system's basic function is to collect user profile data and propose optimal task scheduling methods. It also has the ability to analyze the user's task progress data, generate advice for efficient task management, calculate task reminder timing, and display alerts at appropriate times.

[0047] Program processing overview

[0048] Profile data collection and analysis

[0049] When a user logs in to the system for the first time, they enter profile data such as basic information, the type of work they do, and how they manage their past tasks.

[0050] The terminal transmits this profile data to the server.

[0051] The server analyzes the received profile data and generates an optimal task schedule based on the user's characteristics. For example, a schedule could be created where important tasks are scheduled in the morning and lighter tasks are scheduled in the afternoon.

[0052] The terminal displays the generated schedule proposal to the user.

[0053] Evaluation and feedback of schedule proposals

[0054] The user evaluates the proposed schedule and chooses whether to accept it.

[0055] If accepted, the server saves this schedule and uses it for future task management.

[0056] If there is negative feedback, the server receives the feedback and displays the regenerated schedule on the terminal.

[0057] Collecting and analyzing task progress data

[0058] The user inputs daily task progress into the terminal, for example, recording the start time, end time, and progress of the task.

[0059] The terminal transmits this data to the server in real time.

[0060] The server analyzes the progress data and generates advice to encourage efficient task management. For example, advice such as "Since there are many meetings, shorten the meeting time and allocate it to other tasks" is generated.

[0061] The terminal notifies the user of the generated advice.

[0062] Advice ratings and feedback

[0063] The user evaluates the advice and decides whether to accept it.

[0064] If accepted, the server saves the advice and reflects it in future task management.

[0065] If there is negative feedback, the server receives the feedback and displays the regenerated advice on the terminal.

[0066] Calculate reminder timing and display alerts

[0067] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task, for example, "set a reminder 30 minutes before the deadline."

[0068] When the set reminder time arrives, the device will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[0069] The user confirms the reminder and performs the task.

[0070] Specific examples

[0071] 1. When a user logs in for the first time, they enter their basic information and profile data, including their name, job title, and work patterns.

[0072] 2. The device sends this profile data to the server, which analyzes it and generates a suggestion such as "Put important tasks in the morning and lighter tasks in the afternoon."

[0073] 3. The device displays the suggestions to the user, and the user provides feedback such as "I would like to do important tasks in the afternoon." The server receives the feedback and generates the suggestions again.

[0074] 4. The user inputs their daily task progress into the terminal and reports a situation such as "meetings are too long." The server analyzes this and generates advice such as "shorten the meeting time and allocate time to other tasks."

[0075] 5. The device notifies the user of the advice and asks them to evaluate whether they accept it. If they accept it, the server saves the settings.

[0076] 6. The server calculates the task reminder timing, for example, "30 minutes before the deadline." The device displays the reminder, and the user executes the task.

[0077] As described above, the system of the present invention enables users with ADHD characteristics to improve their task management skills through appropriate scheduling, progress management, and reminders.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] When a user logs in for the first time, they enter basic information (such as name, job title, and job description).

[0081] Step 2:

[0082] The terminal sends the user's basic information to the server.

[0083] Step 3:

[0084] The server analyzes the received basic information and generates an optimal task scheduling method based on the user's characteristics and profile. For example, consider a method such as "schedule important tasks in the morning."

[0085] Step 4:

[0086] The terminal displays the generated task scheduling method to the user, and the user confirms the proposal.

[0087] Step 5:

[0088] The user evaluates the proposed schedule and either accepts it or provides feedback. If accepted, the device sends the selection to the server.

[0089] Step 6:

[0090] The server saves the user's selection and reflects it in future task management. If the user provides feedback, the server generates a new schedule based on the feedback and sends it to the terminal again.

[0091] Step 7:

[0092] The user inputs daily task progress data (start time, end time, progress, etc.) into the terminal.

[0093] Step 8:

[0094] The device transmits task progress data to the server in real time.

[0095] Step 9:

[0096] The server analyzes the received task progress data and generates advice to encourage the user to manage their tasks efficiently. For example, consider the advice "reduce meeting time and allocate time to other tasks."

[0097] Step 10:

[0098] The terminal notifies the user of the generated advice.

[0099] Step 11:

[0100] The user evaluates the advice and chooses whether to accept it, and if so, the device sends the choice to the server.

[0101] Step 12:

[0102] The server saves the user's selection and reflects it in future task management. If the user provides feedback, the server generates new advice based on the feedback and sends it to the device again.

[0103] Step 13:

[0104] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task. For example, consider a method that "sets a reminder 30 minutes before the deadline."

[0105] Step 14:

[0106] When the device reaches the set reminder time, it will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[0107] Step 15:

[0108] The user checks the reminder and executes the task. Through this process, the user manages tasks efficiently and improves productivity.

[0109] Example 1

[0110] 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."

[0111] In today's business environment, it is extremely difficult for business people with ADHD to efficiently manage their tasks. Conventional task management systems perform uniform scheduling and progress management without fully considering the characteristics of each user, making it impossible to provide optimal support to each individual user. For this reason, there is a need for an individually optimized task management support system that allows business people with ADHD to perform at their best.

[0112] 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.

[0113] In this invention, the server includes means for collecting profile data from a user and proposing an optimal task scheduling method based on the data, means for analyzing the user's task progress data and generating efficient task management advice based on the analysis, means for calculating task reminder timings and displaying alerts to the user at those timings, means for receiving user feedback and regenerating a task schedule based on the feedback, means for collecting daily task progress data, analyzing progress based on the data, and generating improvement advice, means for notifying the user of the task management advice, receiving an evaluation of the advice, and means for regenerating the improvement advice based on the evaluation. This enables effective and flexible task management, allowing business people with ADHD characteristics to acquire an optimal task management method and maximize their performance.

[0114] "Profile data" is data that indicates the characteristics of a user, such as basic information about the user, the type of work, and past task management methods.

[0115] A "task scheduling method" is a method for determining task priorities and time allocations based on profile data of a specific user.

[0116] "Task progress data" refers to information such as the start time, end time, and progress status of each task.

[0117] "Advice for efficient task management" is a suggestion based on task progress data to help users carry out their work more effectively.

[0118] "Remind timing" refers to the time at which an alert is displayed to the user, determined based on the deadline and importance of the task.

[0119] An "alert" is a message that is sent to the user at a set reminder timing, urging the user to perform a specific task.

[0120] "Feedback" refers to evaluations and comments that users make in response to the system's suggestions and advice.

[0121] "Regeneration" is the process by which the system creates new suggestions and advice based on feedback and evaluation.

[0122] This invention relates to an AI task coaching system that helps business people with ADHD manage their tasks effectively. The system's basic function is to collect user profile data and propose optimal task scheduling methods. It also has the ability to analyze the user's task progress data, generate advice for efficient task management, calculate task reminder timing, and display alerts at appropriate times.

[0123] The hardware required to implement this system includes the terminals used by users (e.g., PCs and smartphones), the communication infrastructure for sending and receiving data, and the server for analyzing and processing the data.The software includes an application that provides the user interface and an AI algorithm that analyzes data and generates schedules.

[0124] A specific example of the operation of the system of the present invention will be described below.

[0125] Profile data collection and analysis

[0126] 1. When a user logs in for the first time, they enter their basic information (such as name, position, work style, etc.) and information about their past task management methods.

[0127] 2. The device transmits the entered profile data to the server in real time.

[0128] 3. The server analyzes the received profile data and generates an optimal task schedule based on the user's characteristics, such as "set important tasks in the morning and lighter tasks in the afternoon."

[0129] 4. The terminal displays the generated schedule proposal to the user.

[0130] Evaluation and feedback of schedule proposals

[0131] 1. The user reviews the proposed schedule and evaluates whether it is acceptable or not. If necessary, the user can also enter feedback on the schedule into the terminal.

[0132] 2. The device sends the user's feedback to the server.

[0133] 3. The server receives the feedback and regenerates the schedule as needed, which is then displayed to the user again via the terminal.

[0134] Collecting and analyzing task progress data

[0135] 1. The user enters daily task progress data (start time, end time, progress status, etc.) into the terminal.

[0136] 2. The device transmits this data to the server in real time.

[0137] 3. The server analyzes the progress data and generates advice to encourage efficient task management. For example, advice such as "shorten meeting time and allocate time to other tasks" could be considered.

[0138] 4. The device notifies the user of the generated advice.

[0139] Advice ratings and feedback

[0140] 1. The user checks the advice and evaluates whether or not they accept it. If they accept it, they can modify their task management in accordance with the advice.

[0141] 2. The device sends the user's rating and feedback to the server, which then generates new advice based on the feedback, if necessary.

[0142] Calculate reminder timing and display alerts

[0143] 1. The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task, for example, "set a reminder 30 minutes before the deadline."

[0144] 2. When the set reminder time arrives, the device will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[0145] 3. The user acknowledges the alert and performs the task.

[0146] Specific examples

[0147] Prompt Sentence Examples

[0148] "When a user with ADHD traits logs into a task management system for the first time, they enter their profile data (name, job title, type of work, past task management methods). The server then analyzes the data and suggests a schedule that assigns important tasks in the morning and lighter tasks in the afternoon. Please explain the program's process for making appropriate suggestions."

[0149] This allows business people with ADHD characteristics to receive individually optimized task management support, enabling them to work efficiently and effectively. The present invention adaptively regenerates schedules and advice based on user feedback and performs sequential optimization, thereby achieving even greater performance improvements.

[0150] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0151] System program processing flow

[0152] Step 1: Collect profile data

[0153] Specific behavior:

[0154] When a user logs in to the system for the first time, they enter their basic information (name, job title, work patterns) and information about their past task management methods.

[0155] Input and Output:

[0156] The profile data (basic information and task management method) entered by the user is the input, which becomes the raw data for the next process.

[0157] Step 2: Submit profile data

[0158] Specific behavior:

[0159] The terminal transmits the profile data entered by the user to the server in real time.

[0160] Input and Output:

[0161] The profile data is input to the terminal, and is sent to the server, after which the profile data is output to the server.

[0162] Step 3: Analyze profile data and generate schedule

[0163] Specific behavior:

[0164] The server then uses AI algorithms to analyze the received profile data, identifying user characteristics (such as times when they tend to concentrate and past task management trends).

[0165] Input and Output:

[0166] The input is the profile data entered into the server, and the output is the optimal task schedule generated through analysis by the AI ​​algorithm. For example, a suggestion such as "set important tasks in the morning and lighter tasks in the afternoon" is generated.

[0167] Step 4: View schedule suggestions

[0168] Specific behavior:

[0169] The terminal displays the schedule proposal sent from the server to the user, including specific task start and end times and the priority of each task.

[0170] Input and Output:

[0171] The input is the schedule proposal sent from the server, and the output is the schedule proposal displayed on the terminal.

[0172] Step 5: Evaluate and provide feedback on the proposed schedule

[0173] Specific behavior:

[0174] The user checks the proposed schedule and evaluates whether to accept it. If necessary, the user inputs feedback on the proposal into the terminal. The terminal then transmits the user's feedback to the server.

[0175] Input and Output:

[0176] User feedback is the input, and if the server regenerates a schedule based on that feedback, the regenerated schedule is the output.

[0177] Step 6: Enter and submit task progress data

[0178] Specific behavior:

[0179] Users input their daily task progress (start time, end time, progress status, etc.) into the terminal, which then transmits this data to the server in real time.

[0180] Input and Output:

[0181] Task progress data entered by the user is the input, and after the operation of transmitting the data to the server, the task progress data is output to the server.

[0182] Step 7: Analyze progress data and generate advice

[0183] Specific behavior:

[0184] The server analyzes the received task progress data, identifying which tasks are progressing as planned and which are behind schedule, and generates advice for efficient task management based on the analysis results.

[0185] Input and Output:

[0186] The input is task progress data entered into the server, and the output is task management advice generated through analysis. For example, advice such as "shorten meeting time and allocate time to other tasks" is generated.

[0187] Step 8: Advice Notification

[0188] Specific behavior:

[0189] The device will then notify the user of the advice sent from the server, including specific improvements and next steps to take.

[0190] Input and Output:

[0191] The advice sent from the server is the input, and the activity of displaying it on the terminal is the output.

[0192] Step 9: Evaluate and provide feedback on the advice

[0193] Specific behavior:

[0194] The user checks the received advice and evaluates whether or not it is acceptable. If it is acceptable, the user can adjust the task management according to the advice. The terminal sends the user's evaluation and feedback to the server.

[0195] Input and Output:

[0196] The user's ratings and feedback are the input, and when the server regenerates the advice, the regenerated advice is the output.

[0197] Step 10: Calculate the reminder timing

[0198] Specific behavior:

[0199] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task, for example, "set a reminder 30 minutes before the deadline."

[0200] Input and Output:

[0201] The user's task list (task information) is the input, and the reminder timing is the calculation result and the output.

[0202] Step 11: Displaying alerts

[0203] Specific behavior:

[0204] When the set reminder time arrives, the device displays an alert to the user. For example, it may notify the user that "an important meeting will be held in 30 minutes." The user can confirm the alert and carry out the task.

[0205] Input and Output:

[0206] The reminder timing is the input and the alert displayed to the user is the output.

[0207] Through these steps, the system provides individually optimized task management support for business people with ADHD traits, achieving efficient and effective task management for users.

[0208] (Application example 1)

[0209] 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."

[0210] Existing systems are insufficient for business people with ADHD to effectively manage their tasks. In particular, the use of wearable devices can enhance real-time task management and reminder functions, but there are a lack of concrete implementation examples and proposals in this field.

[0211] 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.

[0212] In this invention, the server includes means for collecting profile data from a user and proposing an optimal task scheduling method based on the data, means for analyzing the user's task progress data and generating advice for efficient task management based on the analysis, means for calculating task reminder timings and displaying alerts to the user at those timings, means for the user to input profile data and task progress by voice using a wearable device, and means for displaying advice and alerts on the wearable device, thereby enabling the user to manage tasks effectively in real time.

[0213] "Profile data" is personal information necessary for task management, such as the user's basic information, type of work, and past task management methods.

[0214] "Task scheduling" is a method for optimizing the order in which tasks are executed and the allocation of time based on user profile data.

[0215] "Task progress data" is information about the process of executing a task, such as the start time, end time, and progress status of the user's daily task.

[0216] "Advice" is a recommendation or suggestion for a user to efficiently complete a task.

[0217] "Remind timing" is the optimal timing to display a reminder to the user based on the deadline and importance of the task.

[0218] An "alert" is a notification that is displayed to the user when a specific reminder timing is reached.

[0219] A "wearable device" is a computing device that is worn on the body, including smart glasses and smart watches.

[0220] "Voice input" is a method in which a user inputs information into a device by voice.

[0221] "User" refers to individuals who use this system, particularly business people with ADHD characteristics.

[0222] The "server" is a central processing unit that analyzes data received from users and performs task scheduling and advice generation.

[0223] This invention provides an AI task coaching system to help business people with ADHD manage their tasks effectively. This system uses a wearable device (e.g., smart glasses) to provide real-time support for task management.

[0224] Hardware and software used

[0225] Hardware: Use wearable devices such as smart glasses that allow users to receive assistance with task management while working, with minimal impact on vision.

[0226] Software: The server-side data analysis system is based on Python and uses Pandas and Scikit-learn for data analysis. WebSocket is used for real-time data communication, and MySQL (registered trademark) is used as the database.

[0227] Data processing and calculation

[0228] Collection and analysis of profile data upon first login

[0229] 1. The device collects profile data through user voice input.

[0230] Example: A user speaks, "I'm a virtual store owner and I prefer to do important tasks in the afternoon."

[0231] 2. The smart glasses send this data to the server.

[0232] 3. The server analyzes the received profile data and generates an optimal task schedule.

[0233] 4. The smart glasses display the generated schedule suggestions to the user.

[0234] Collecting and analyzing task progress data

[0235] 1. Users use smart glasses to report their daily task progress via voice.

[0236] Example: A user reports verbally, "Today's meeting time has been shortened by 30 minutes."

[0237] 2. The smart glasses transmit these data to the server in real time.

[0238] 3. The server analyzes the progress data and generates advice for efficient task management, such as reducing meeting time and allocating time to other tasks.

[0239] 4. The smart glasses notify the user of the generated advice.

[0240] Calculate reminder timing and display alerts

[0241] 1. The server monitors the user's task list and calculates the reminder timing based on the deadline and importance of each task.

[0242] Example: Set a reminder alert 30 minutes before the deadline.

[0243] 2. The smart glasses will display an alert to the user when the set reminder time arrives.

[0244] Examples of concrete examples and prompts

[0245] 1. When a user logs in for the first time, they say, "I'm a virtual store owner and I want to do important tasks in the afternoon."

[0246] 2. The smart glasses send this profile data to a server, which analyzes the data and generates a task schedule such as "check inventory in the morning and process orders in the afternoon."

[0247] 3. The smart glasses display the generated schedule to the user, who then confirms by saying "That's OK."

[0248] Example prompts

[0249] Enter your user profile:

[0250] Example: "I'm a virtual store owner and I prefer to do important tasks in the afternoon."

[0251] As described above, the present invention is a system that supports users with ADHD characteristics in effectively managing tasks in real time and carrying out their work efficiently, and is designed to maximize the user experience using wearable devices.

[0252] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0253] Step 1: Collect user profile data

[0254] Users use smart glasses to input their profile data by voice.

[0255] Input: Spoken information from the user (e.g., "I'm a virtual store owner and I prefer to do important tasks in the afternoon.")

[0256] Output: Speech-to-text profile data

[0257] Specific operation: The voice recognition function of the smart glasses recognizes voice input as text data.

[0258] Step 2: Submit profile data

[0259] The terminal (smart glasses) transmits the collected profile data to the server.

[0260] Input: Textual profile data

[0261] Output: Profile data sent to the server

[0262] Specific operation: The smart glasses use a network connection to send profile data to a server.

[0263] Step 3: Analyze the profile data

[0264] The server analyzes the received profile data and generates an optimal task schedule based on the user's characteristics.

[0265] Input: Profile data sent to the server

[0266] Output: The generated optimal task schedule

[0267] Specific operation: A data analysis system (e.g., Pandas, Scikit-learn) on the server analyzes the profile data and generates a schedule using a scheduling algorithm.

[0268] Step 4: View the task schedule

[0269] The terminal (smart glasses) displays the generated task schedule to the user.

[0270] Input: The optimal task schedule sent by the server

[0271] Output: The task schedule as seen by the user

[0272] Specific operation: The display function of the smart glasses displays schedule information.

[0273] Step 5: Enter and submit task progress data

[0274] The user inputs the task progress status into the smart glasses by voice, and the smart glasses send the data to the server.

[0275] Input: Task progress (e.g., "Today's meeting time was shortened by 30 minutes")

[0276] Output: Task progress data sent to the server

[0277] Specific operation: The voice recognition function of the smart glasses converts voice input into text and sends it to a server via the network.

[0278] Step 6: Analyze task progress data

[0279] The server analyzes the received task progress data and generates advice for efficient task management.

[0280] Input: Task progress data sent to the server

[0281] Output: Generated advice for efficient task management

[0282] How it works: A data analysis system on the server analyzes task progress data and generates advice using an AI model.

[0283] Step 7: Advice Notification

[0284] The terminal (smart glasses) notifies the user of the generated advice.

[0285] Input: Task management advice sent from the server

[0286] Output: Advice displayed to the user

[0287] Specific operation: The display function of the smart glasses displays advice information.

[0288] Step 8: Calculate reminder timing and set alerts

[0289] The server monitors the user's task list, calculates reminder timing based on the deadline and importance of each task, and sets alerts.

[0290] Input: Task list stored on the server

[0291] Output: Calculated reminder timings and configured alerts

[0292] Specific operation: A timing calculation algorithm on the server determines the reminder timing based on the task deadline and importance, and sets an alert.

[0293] Step 9: Viewing Alerts

[0294] The device (smart glasses) displays an alert to the user based on the set reminder timing.

[0295] Input: Alert information sent from the server

[0296] Output: The reminder alert that is displayed to the user

[0297] Specific operation: The display function of the smart glasses displays the alert information.

[0298] 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.

[0299] This invention provides an AI task coaching system to help business people with ADHD manage their tasks efficiently. The system's basic function is to collect user profile data and propose optimal task scheduling methods. It also analyzes the user's task progress data, generates advice for efficient task management, calculates task reminder timing, and displays alerts at appropriate times. Furthermore, by combining it with an emotion engine, the system recognizes the user's emotional state and improves the accuracy of task management based on this.

[0300] Program processing overview

[0301] Profile data collection and analysis

[0302] When a user logs in to the system for the first time, they enter profile data such as basic information, the type of work they do, and how they manage their past tasks.

[0303] The terminal transmits this profile data to the server.

[0304] The server generates an optimal task scheduling method based on the received profile data, taking into account the user's characteristics. For example, it considers a schedule such as "setting important tasks in the morning and doing creative work in the afternoon."

[0305] The terminal displays the generated task scheduling method to the user.

[0306] Evaluation and feedback of schedule proposals

[0307] The user evaluates the proposed schedule and chooses whether to accept it.

[0308] If accepted, the server saves the schedule and uses it for future task management.

[0309] If there is negative feedback, the server receives the feedback and displays the regenerated schedule on the terminal.

[0310] Collecting and analyzing task progress data

[0311] The user inputs daily task progress data (start time, end time, progress status, etc.) into the terminal.

[0312] The terminal transmits task progress data to the server in real time.

[0313] The server analyzes the progress data and generates advice to encourage the user to manage their tasks efficiently. For example, consider the advice "reduce meeting time and allocate time to other tasks."

[0314] The terminal notifies the user of the generated advice.

[0315] Advice ratings and feedback

[0316] The user evaluates the advice and decides whether to accept it.

[0317] If accepted, the server saves the advice and reflects it in future task management.

[0318] If there is negative feedback, the server receives the feedback and displays the regenerated advice on the terminal.

[0319] Calculate reminder timing and display alerts

[0320] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task. For example, consider a method of "setting a reminder 30 minutes before the deadline."

[0321] When the set reminder time arrives, the device will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[0322] The user confirms the reminder and performs the task.

[0323] Introducing the Emotion Engine

[0324] The device uses an emotion engine to recognize the user's emotional state, for example, by using facial expression recognition and voice analysis techniques to detect the user's emotions.

[0325] The server analyzes the user's emotional data and adjusts task scheduling methods and advice accordingly, such as "if the user is feeling stressed, add relaxation time to the schedule."

[0326] The device also adjusts the tone of alerts and reminders based on information obtained from the emotion engine. For example, if the user is feeling anxious, the device may use a gentler tone.

[0327] Specific examples

[0328] 1. When a user logs in for the first time, they enter basic information and profile data, including their name, job title, and work patterns.

[0329] 2. The device sends the profile data to the server, which analyzes the data and generates a suggestion such as "focus on important tasks in the morning and do lighter work in the afternoon."

[0330] 3. The device displays the suggestions, and the user provides feedback such as "I want to do important tasks in the afternoon." The server receives the feedback and generates the suggestions again.

[0331] 4. The user enters their task progress data daily, reporting, for example, "too much time spent in meetings." The server analyzes this and generates advice such as "shorten your meeting time and allocate more time to other tasks."

[0332] 5. The device notifies the user of the advice and asks them to evaluate whether they accept it. If they accept it, the server saves the settings.

[0333] 6. The server calculates the task reminder timing and sets it to "Set a reminder 30 minutes before the deadline." The device displays the reminder and the user performs the task.

[0334] 7. The emotion engine recognizes the user's emotions and makes adjustments such as adding relaxation time to the schedule if the user is tense.

[0335] This process allows users to achieve task management that is optimized for their own characteristics and emotional state, improving productivity.

[0336] The processing flow will be explained below.

[0337] MODE FOR CARRYING OUT THE INVENTION (PROCESSING STEPS)

[0338] Step 1:

[0339] When a user logs in for the first time, they enter basic information (such as name, job title, and job description).

[0340] Step 2:

[0341] The terminal sends the user's basic information to the server.

[0342] Step 3:

[0343] The server analyzes the received basic information and generates an optimal task scheduling method based on the user's characteristics and profile. For example, it may suggest a schedule such as "set important tasks in the morning and do creative work in the afternoon."

[0344] Step 4:

[0345] The terminal displays the generated task scheduling method to the user, and the user confirms the proposal.

[0346] Step 5:

[0347] The user evaluates the proposed schedule and chooses whether to accept it, and if so, the terminal sends the choice to the server.

[0348] Step 6:

[0349] The server saves the user's selection and reflects it in future task management. If the user provides feedback, the server regenerates a new schedule based on the feedback information and sends it to the terminal again.

[0350] Step 7:

[0351] The user inputs daily task progress data (start time, end time, progress, etc.) into the terminal.

[0352] Step 8:

[0353] The device transmits task progress data to the server in real time.

[0354] Step 9:

[0355] The server analyzes the received task progress data and generates advice to encourage the user to manage their tasks more efficiently. For example, the advice generated is "reduce meeting time and allocate more time to other tasks."

[0356] Step 10:

[0357] The terminal notifies the user of the generated advice.

[0358] Step 11:

[0359] The user evaluates the advice and chooses whether to accept it, and if so, the terminal sends the choice to the server.

[0360] Step 12:

[0361] The server saves the user's selection and reflects it in future task management. If the user provides feedback, the server regenerates advice based on the feedback information and sends it to the device again.

[0362] Step 13:

[0363] The device uses an emotion engine to recognize the user's emotional state. The emotion engine detects the user's emotions through facial expression recognition and voice analysis technology.

[0364] Step 14:

[0365] The server analyzes the user's emotional data and adjusts task scheduling and advice accordingly. For example, if the user is feeling stressed, it may add relaxation time to the schedule.

[0366] Step 15:

[0367] The device will adjust the tone of alerts and reminders based on information it gets from the emotion engine. For example, if the user is feeling anxious, the notification will be delivered in a calmer tone.

[0368] Step 16:

[0369] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task. For example, consider a method that "sets a reminder 30 minutes before the deadline."

[0370] Step 17:

[0371] When the device reaches the set reminder time, it will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[0372] Step 18:

[0373] The user checks the reminder and executes the task. Through this process, the user manages tasks efficiently and improves productivity.

[0374] Example 2

[0375] 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."

[0376] This invention relates to a system for efficient task management for business people with ADHD, and aims to provide a task scheduling method and advice optimized for the user's characteristics and emotional state by effectively utilizing the user's profile data, task progress data, and emotional state. In particular, it aims to solve the problem of improving productivity by taking into account the efficiency of task management and the user's emotional state.

[0377] 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.

[0378] In this invention, the server includes means for collecting profile data from a user and proposing an optimal task scheduling method based on the data, means for analyzing the user's task progress data and generating advice for efficient task management based on the analysis, and means for recognizing the user's emotional state and adjusting the task scheduling method and alert expression based on the emotional data, thereby enabling optimal task management that takes into account the user's characteristics and emotional state.

[0379] A "user" is a person or individual who uses the system.

[0380] "Profile data" is data about a user's basic information, work patterns, and past task management methods.

[0381] A "task scheduling method" is a method for arranging tasks and allocating time optimally based on user characteristics.

[0382] "Task progress data" refers to data recorded by a user, such as the start time, end time, and progress of daily tasks.

[0383] "Reminder timing" is a specific time or time frame for displaying an alert to the user based on the deadline or importance of the task.

[0384] "Emotional state" refers to a state that indicates the user's psychological state or mood, and is data that is detected using facial expression recognition and voice analysis technology.

[0385] "Alert expressions" are the wording and manner in which notifications and reminders are given to users, and are adjusted based on their emotional state.

[0386] "Feedback" refers to opinions and evaluations that users provide in response to suggestions and advice from the system.

[0387] "Regeneration" is the process of recreating new task scheduling methods and advice based on feedback and evaluation.

[0388] A "server" is a computer system that receives data sent by a user, analyzes it, generates results, and sends them to a terminal.

[0389] A "terminal" is a device through which a user inputs data, and is a device that transmits and receives data to and from a server.

[0390] The present invention is an AI task coaching system that enables business people with ADHD to efficiently manage their tasks. An embodiment of this system will be described in detail below.

[0391] Hardware and Software

[0392] Hardware

[0393] Server: High-performance computer system

[0394] Device: The computer, tablet, or smartphone used by the user

[0395] software

[0396] Profile Data Collection Tool: A form for users to enter basic information

[0397] Data Analysis Algorithms: An analytical tool for generating task scheduling methods

[0398] Emotion Engine: Facial expression recognition and speech analysis techniques to recognize a user's emotional state

[0399] Notification system: a notification tool to display task reminders and alerts

[0400] How it works

[0401] Profile data collection and analysis

[0402] 1. When a user logs in for the first time, they enter basic information and profile data into the system, including their name, job title, work patterns, and past task management methods.

[0403] 2. The device sends the entered profile data to the server.

[0404] 3. The server analyzes the received profile data and generates an optimal task scheduling method based on the user's characteristics. For example, consider a schedule that "sets important tasks in the morning and creative work in the afternoon."

[0405] 4. The terminal displays the generated task scheduling method to the user.

[0406] Collecting and analyzing task progress data

[0407] 1. The user inputs daily task progress data (start time, end time, progress, etc.) into the terminal.

[0408] 2. The device sends task progress data to the server in real time.

[0409] 3. The server analyzes the progress data and generates advice to encourage the user to manage their tasks more efficiently. For example, consider the advice "reduce meeting time and allocate time to other tasks."

[0410] 4. The device notifies the user of the generated advice.

[0411] Calculate reminder timing and display alerts

[0412] 1. The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task, for example, "set a reminder 30 minutes before the deadline."

[0413] 2. When the set reminder time arrives, the device will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[0414] 3. The user confirms the reminder and performs the specified task.

[0415] Introducing the Emotion Engine

[0416] 1. The device uses an emotion engine to recognize the user's emotional state. It uses facial expression recognition and voice analysis technology to detect the user's emotions.

[0417] 2. The server analyzes the user's emotional data and adjusts task scheduling methods and advice accordingly, such as "if the user is feeling stressed, add relaxation time to the schedule."

[0418] 3. The device also adjusts the tone of alerts and reminders based on the information it obtains from the emotion engine. For example, if the user is feeling anxious, the device will use a gentler tone.

[0419] Specific examples

[0420] 1. When a user logs in for the first time, they enter basic information and profile data, including their name, job title, and work patterns.

[0421] 2. The device sends the profile data to the server, which analyzes the data and generates a suggestion such as "focus on important tasks in the morning and do lighter work in the afternoon."

[0422] 3. The device displays the suggestions, and the user provides feedback such as "I want to do important tasks in the afternoon." The server receives the feedback and generates the suggestions again.

[0423] 4. The user inputs task progress data daily and reports that they spend too much time in meetings. The server analyzes this and generates advice such as "shorten your meeting time and allocate it to other tasks."

[0424] 5. The device notifies the user of the advice and asks them to evaluate whether they accept it. If they accept it, the server saves the settings.

[0425] 6. The server calculates the task reminder timing and sets a reminder 30 minutes before the deadline. The device displays the reminder and the user executes the task.

[0426] 7. The emotion engine recognizes the user's emotions and makes adjustments such as adding relaxation time to the schedule if the user is tense.

[0427] This process allows users to achieve task management that is optimized for their own characteristics and emotional state, improving productivity.

[0428] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0429] Step 1:

[0430] When a user logs in for the first time, they enter their profile data, including their name, job title, work patterns, and past task management methods. The entered data is saved on the device.

[0431] Step 2:

[0432] The device sends the saved profile data to the server. This is the process of transferring input data as packets to the server. After successful transmission, a data reception confirmation message is displayed on the device.

[0433] Step 3:

[0434] The server analyzes the received profile data. It uses a data mining algorithm for analysis to generate a task scheduling method based on the user's characteristics. For example, if it discovers from the data that the user is most focused in the morning, it generates a schedule that "sets important tasks in the morning and creative work in the afternoon." The generated task scheduling method is obtained as output.

[0435] Step 4:

[0436] The device displays the task scheduling method received from the server to the user. The proposed scheduling method is visually displayed using a simple and easy-to-understand UI. Important tasks and timelines are highlighted for the user's confirmation.

[0437] Step 5:

[0438] The user evaluates the proposed schedule and chooses whether to accept it. The user enters their feedback into an input form and saves it on the device. The user's selection is recorded in the database as an "evaluation."

[0439] Step 6:

[0440] The device sends the evaluation results and feedback to the server, which receives and stores the feedback data. The evaluation data is used for later regeneration.

[0441] Step 7:

[0442] The server analyzes the feedback and generates a regenerated task scheduling method. The schedule is recalculated based on the feedback data, and an improved schedule is generated. For example, a schedule is created based on the user's request to "perform important tasks in the afternoon." The regenerated schedule is output.

[0443] Step 8:

[0444] The terminal displays the regenerated task scheduling method to the user. The new schedule is visualized to the user and the modifications are clearly indicated. The user can check the schedule again.

[0445] Step 9:

[0446] The user inputs daily task progress data, including the start time, end time, and progress of the task. The progress data is saved on the device.

[0447] Step 10:

[0448] The device sends the saved task progress data to the server in real time. The data is transferred in packets in real time, and the progress status is updated on the server. A receipt confirmation message is displayed on the device.

[0449] Step 11:

[0450] The server analyzes the progress data. It uses a data analysis algorithm to analyze the progress and generate efficient task management advice. For example, the advice generated might be "reduce meeting time and allocate time to other tasks." The generated advice is then output.

[0451] Step 12:

[0452] The device notifies the user of the generated advice, which is displayed through a visual notification system and can be viewed by the user.

[0453] Step 13:

[0454] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task. The server determines the optimal timing for reminders through data analysis, for example, "set a reminder 30 minutes before the deadline." The reminder timing is then output.

[0455] Step 14:

[0456] When the device reaches the set reminder time, it will display an alert to the user. Using the alert notification system, specific reminders such as "I have an important meeting in 30 minutes" will be displayed.

[0457] Step 15:

[0458] The user confirms the reminder and performs the specified task. After confirming the reminder notification, the task is started at the appropriate time.

[0459] Step 16:

[0460] The device recognizes the user's emotional state. The emotion engine uses facial expression recognition and voice analysis technology to detect the user's emotional data. The detected emotional state is stored on the device.

[0461] Step 17:

[0462] The server analyzes the emotional data and adjusts the task scheduling method and advice based on this. If the user feels stressed based on the emotional data analysis, the server will make adjustments such as "adding relaxation time to the schedule." The adjusted schedule and advice are then output.

[0463] Step 18:

[0464] The device adjusts the tone of alerts and reminders based on information it gets from the emotion engine. For example, if the user is feeling anxious, the notification system will display an alert in a gentle tone. The adjusted reminder will then be displayed to the user.

[0465] (Application example 2)

[0466] 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."

[0467] Conventional task management systems only provide general scheduling and reminder functions, and are unable to accommodate individual users' characteristics and emotional states, resulting in insufficient efficient task management and stress reduction. Business people with ADHD and those working in food delivery services face particular challenges, making it difficult to optimize schedules and manage emotions due to the lack of individualized support.

[0468] 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 profile data from the user and proposing an optimal task scheduling method based on the data, means for analyzing the user's task progress data and generating advice for efficient task management based on the analysis, means for calculating task reminder timing and displaying an alert to the user at that timing, and means for recognizing the user's emotional state and adjusting task management based on the recognition. This enables optimal task scheduling and emotional management according to the characteristics of each user.

[0469] "Profile data" is data that includes basic information about the user, past task management methods, types of work, and so on.

[0470] A "task scheduling method" refers to a schedule for efficiently managing a user's tasks that is generated based on collected profile data.

[0471] "Task progress data" is data recorded by a user on the progress of daily tasks, and includes start time, end time, progress status, and the like.

[0472] An "alert" is a warning or reminder that is sent to the user based on the reminder timing of a specific task.

[0473] "Emotional State" refers to a user's current psychological and emotional state, which is detected using emotion recognition technology.

[0474] "Stress level" is the degree of stress determined by the user's emotional state.

[0475] "Relaxation suggestions" refer to suggestions to relieve tension and relax when the user feels high stress levels.

[0476] "Regeneration of scheduling method" is the process of reviewing the existing schedule based on user feedback and generating a new optimized schedule.

[0477] "Regeneration of improvement advice" is a process of reviewing existing advice based on user evaluation and generating new, more effective advice.

[0478] "Real-time emotional data analysis" is the process of analyzing a user's emotional state in real time and providing appropriate advice and schedules based on the results.

[0479] The present invention is a task management system specialized for food delivery services. Specific embodiments of this system will be described below.

[0480] First, the system collects profile data from the user (driver) and proposes an optimal task scheduling method based on that data. When the user logs in for the first time, they enter basic information such as their name, years of experience, and past delivery style into their device (smartphone or tablet). This generates the optimal delivery route and time allocation. The device sends this data to the server, which then generates a schedule based on the profile data and proposes it to the user.

[0481] As a concrete example, a user inputs "Name: Taro, Years of experience: 5 years, Task: ['Delivery 1', 'Delivery 2', 'Delivery 3']". The server generates a schedule such as "Finish short deliveries first in the morning, and propose a route to avoid traffic congestion in the afternoon" and displays it on the terminal. If the user provides feedback such as "I would like to make an important delivery in the afternoon", the server regenerates and proposes a new schedule.

[0482] The next step is the collection and analysis of task progress data. Users enter their daily task progress into the device, which then sends this information to the server in real time. The server analyzes the progress data and generates advice for efficient delivery management. For example, it may provide specific advice to the device, such as "traffic congestion is expected at the next delivery destination, so we suggest taking an alternative route."

[0483] The server calculates the timing of task reminders and displays alerts based on the importance and deadline of each task, and displays an alert on the device at that timing. For example, the user will be notified with a reminder such as "Pickup time in 15 minutes."

[0484] Additionally, it will include a function that recognizes the user's emotional state and adjusts task management accordingly. The device uses emotion recognition technology to detect the user's stress level using a facial recognition camera and voice analysis technology. The server analyzes the emotional data and makes relaxation suggestions if it determines that stress is high. For example, flexible responses such as "Your current stress level is high, so we suggest taking a five-minute break" will be possible.

[0485] The hardware used includes smartphones or tablets, cameras, and microphones, while the software uses tools such as Python libraries (including a hypothetical emotion recognition library).

[0486] Example prompt sentence:

[0487] "Please enter your driver's basic information (name, years of experience, tasks)"

[0488] "Update the current task progress (task ID, status)"

[0489] "Please enter your facial and voice data (for emotion recognition)"

[0490] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0491] Step 1:

[0492] When a user logs in for the first time, they enter basic information into the terminal. Specifically, they enter profile data such as their name, years of experience, past delivery style, and task list, and this information is sent to the server. The entered data becomes the basis for optimizing scheduling that reflects the user's unique characteristics (input: profile data, output: user characteristic data).

[0493] Step 2:

[0494] The server analyzes the received profile data and generates an optimal task scheduling method based on the user's characteristics. Specifically, it allocates tasks by time period based on years of experience and past delivery history (data processing: profile data analysis, output: optimal schedule proposal).

[0495] Step 3:

[0496] The device displays the generated task scheduling method to the user. The user checks the proposal and provides feedback as needed. Specifically, the user inputs feedback such as "I would like to do important tasks in the afternoon" (input: feedback, output: user evaluation data).

[0497] Step 4:

[0498] The server receives the user's feedback and regenerates the scheduling method. Specifically, it adjusts the schedule based on the user's requests and generates a new optimized schedule (data processing: feedback analysis, output: regenerated schedule).

[0499] Step 5:

[0500] The user inputs daily task progress data into the terminal. Specifically, the start time, end time, progress status, etc. of each task are entered, and this is sent to the server (input: task progress data, output: progress status data).

[0501] Step 6:

[0502] The server analyzes the progress data and generates advice for efficient task management. Specifically, it generates advice such as, "The next delivery destination is in a congested traffic area, so we suggest an alternative route" (data processing: progress data analysis, output: efficiency advice).

[0503] Step 7:

[0504] The terminal notifies the user of the generated advice, who then checks it and evaluates whether to accept it or not (input: advice evaluation, output: evaluation data).

[0505] Step 8:

[0506] The server receives the user's evaluation and regenerates advice based on the evaluation. Specifically, it readjusts the advice based on requests such as "Please suggest a route that takes less time" (data processing: evaluation analysis, output: regenerated advice).

[0507] Step 9:

[0508] The server calculates the reminder timing for each task and displays an alert on the device. Specifically, a specific reminder such as "Pickup time in 15 minutes" is set, and notifications are sent at the appropriate time (data processing: reminder timing calculation, output: alert notification).

[0509] Step 10:

[0510] To recognize the user's emotional state, the device uses a camera and microphone to analyze facial expressions and voice, and sends the obtained emotional data to a server (input: emotional data, output: analysis results).

[0511] Step 11:

[0512] The server analyzes the received emotional data and adjusts task management based on the user's emotional state. Specifically, if the user is feeling high stress, it suggests relaxation (data processing: emotional data analysis, output: relaxation suggestions).

[0513] 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.

[0514] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0515] 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.

[0516] [Second embodiment]

[0517] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0518] 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.

[0519] 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).

[0520] 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.

[0521] 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.

[0522] 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).

[0523] 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.

[0524] 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.

[0525] 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.

[0526] 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.

[0527] 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.

[0528] 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."

[0529] This invention provides an AI task coaching system that helps business people with ADHD manage their tasks effectively. The system's basic function is to collect user profile data and propose optimal task scheduling methods. It also has the ability to analyze the user's task progress data, generate advice for efficient task management, calculate task reminder timing, and display alerts at appropriate times.

[0530] Program processing overview

[0531] Profile data collection and analysis

[0532] When a user logs in to the system for the first time, they enter profile data such as basic information, the type of work they do, and how they manage their past tasks.

[0533] The terminal transmits this profile data to the server.

[0534] The server analyzes the received profile data and generates an optimal task schedule based on the user's characteristics. For example, a schedule could be created where important tasks are scheduled in the morning and lighter tasks are scheduled in the afternoon.

[0535] The terminal displays the generated schedule proposal to the user.

[0536] Evaluation and feedback of schedule proposals

[0537] The user evaluates the proposed schedule and chooses whether to accept it.

[0538] If accepted, the server saves this schedule and uses it for future task management.

[0539] If there is negative feedback, the server receives the feedback and displays the regenerated schedule on the terminal.

[0540] Collecting and analyzing task progress data

[0541] The user inputs daily task progress into the terminal, for example, recording the start time, end time, and progress of the task.

[0542] The terminal transmits this data to the server in real time.

[0543] The server analyzes the progress data and generates advice to encourage efficient task management. For example, advice such as "Since there are many meetings, shorten the meeting time and allocate it to other tasks" is generated.

[0544] The terminal notifies the user of the generated advice.

[0545] Advice ratings and feedback

[0546] The user evaluates the advice and decides whether to accept it.

[0547] If accepted, the server saves the advice and reflects it in future task management.

[0548] If there is negative feedback, the server receives the feedback and displays the regenerated advice on the terminal.

[0549] Calculate reminder timing and display alerts

[0550] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task, for example, "set a reminder 30 minutes before the deadline."

[0551] When the set reminder time arrives, the device will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[0552] The user confirms the reminder and performs the task.

[0553] Specific examples

[0554] 1. When a user logs in for the first time, they enter their basic information and profile data, including their name, job title, and work patterns.

[0555] 2. The device sends this profile data to the server, which analyzes it and generates a suggestion such as "Put important tasks in the morning and lighter tasks in the afternoon."

[0556] 3. The device displays the suggestions to the user, and the user provides feedback such as "I would like to do important tasks in the afternoon." The server receives the feedback and generates the suggestions again.

[0557] 4. The user inputs their daily task progress into the terminal and reports a situation such as "meetings are too long." The server analyzes this and generates advice such as "shorten the meeting time and allocate time to other tasks."

[0558] 5. The device notifies the user of the advice and asks them to evaluate whether they accept it. If they accept it, the server saves the settings.

[0559] 6. The server calculates the task reminder timing, for example, "30 minutes before the deadline." The device displays the reminder, and the user executes the task.

[0560] As described above, the system of the present invention enables users with ADHD characteristics to improve their task management skills through appropriate scheduling, progress management, and reminders.

[0561] The processing flow will be explained below.

[0562] Step 1:

[0563] When a user logs in for the first time, they enter basic information (such as name, job title, and job description).

[0564] Step 2:

[0565] The terminal sends the user's basic information to the server.

[0566] Step 3:

[0567] The server analyzes the received basic information and generates an optimal task scheduling method based on the user's characteristics and profile. For example, consider a method such as "schedule important tasks in the morning."

[0568] Step 4:

[0569] The terminal displays the generated task scheduling method to the user, and the user confirms the proposal.

[0570] Step 5:

[0571] The user evaluates the proposed schedule and either accepts it or provides feedback. If accepted, the device sends the selection to the server.

[0572] Step 6:

[0573] The server saves the user's selection and reflects it in future task management. If the user provides feedback, the server generates a new schedule based on the feedback and sends it to the terminal again.

[0574] Step 7:

[0575] The user inputs daily task progress data (start time, end time, progress, etc.) into the terminal.

[0576] Step 8:

[0577] The device transmits task progress data to the server in real time.

[0578] Step 9:

[0579] The server analyzes the received task progress data and generates advice to encourage the user to manage their tasks efficiently. For example, consider the advice "reduce meeting time and allocate time to other tasks."

[0580] Step 10:

[0581] The terminal notifies the user of the generated advice.

[0582] Step 11:

[0583] The user evaluates the advice and chooses whether to accept it, and if so, the device sends the choice to the server.

[0584] Step 12:

[0585] The server saves the user's selection and reflects it in future task management. If the user provides feedback, the server generates new advice based on the feedback and sends it to the device again.

[0586] Step 13:

[0587] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task. For example, consider a method that "sets a reminder 30 minutes before the deadline."

[0588] Step 14:

[0589] When the device reaches the set reminder time, it will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[0590] Step 15:

[0591] The user checks the reminder and executes the task. Through this process, the user manages tasks efficiently and improves productivity.

[0592] Example 1

[0593] 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."

[0594] In today's business environment, it is extremely difficult for business people with ADHD to efficiently manage their tasks. Conventional task management systems perform uniform scheduling and progress management without fully considering the characteristics of each user, making it impossible to provide optimal support to each individual user. For this reason, there is a need for an individually optimized task management support system that allows business people with ADHD to perform at their best.

[0595] 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.

[0596] In this invention, the server includes means for collecting profile data from a user and proposing an optimal task scheduling method based on the data, means for analyzing the user's task progress data and generating efficient task management advice based on the analysis, means for calculating task reminder timings and displaying alerts to the user at those timings, means for receiving user feedback and regenerating a task schedule based on the feedback, means for collecting daily task progress data, analyzing progress based on the data, and generating improvement advice, means for notifying the user of the task management advice, receiving an evaluation of the advice, and means for regenerating the improvement advice based on the evaluation. This enables effective and flexible task management, allowing business people with ADHD characteristics to acquire an optimal task management method and maximize their performance.

[0597] "Profile data" is data that indicates the characteristics of a user, such as basic information about the user, the type of work, and past task management methods.

[0598] A "task scheduling method" is a method for determining task priorities and time allocations based on profile data of a specific user.

[0599] "Task progress data" refers to information such as the start time, end time, and progress status of each task.

[0600] "Advice for efficient task management" is a suggestion based on task progress data to help users carry out their work more effectively.

[0601] "Remind timing" refers to the time at which an alert is displayed to the user, determined based on the deadline and importance of the task.

[0602] An "alert" is a message that is sent to the user at a set reminder timing, urging the user to perform a specific task.

[0603] "Feedback" refers to evaluations and comments that users make in response to the system's suggestions and advice.

[0604] "Regeneration" is the process by which the system creates new suggestions and advice based on feedback and evaluation.

[0605] This invention relates to an AI task coaching system that helps business people with ADHD manage their tasks effectively. The system's basic function is to collect user profile data and propose optimal task scheduling methods. It also has the ability to analyze the user's task progress data, generate advice for efficient task management, calculate task reminder timing, and display alerts at appropriate times.

[0606] The hardware required to implement this system includes the terminals used by users (e.g., PCs and smartphones), the communication infrastructure for sending and receiving data, and the server for analyzing and processing the data.The software includes an application that provides the user interface and an AI algorithm that analyzes data and generates schedules.

[0607] A specific example of the operation of the system of the present invention will be described below.

[0608] Profile data collection and analysis

[0609] 1. When a user logs in for the first time, they enter their basic information (such as name, position, work style, etc.) and information about their past task management methods.

[0610] 2. The device transmits the entered profile data to the server in real time.

[0611] 3. The server analyzes the received profile data and generates an optimal task schedule based on the user's characteristics, such as "set important tasks in the morning and lighter tasks in the afternoon."

[0612] 4. The terminal displays the generated schedule proposal to the user.

[0613] Evaluation and feedback of schedule proposals

[0614] 1. The user reviews the proposed schedule and evaluates whether it is acceptable or not. If necessary, the user can also enter feedback on the schedule into the terminal.

[0615] 2. The device sends the user's feedback to the server.

[0616] 3. The server receives the feedback and regenerates the schedule as needed, which is then displayed to the user again via the terminal.

[0617] Collecting and analyzing task progress data

[0618] 1. The user enters daily task progress data (start time, end time, progress status, etc.) into the terminal.

[0619] 2. The device transmits this data to the server in real time.

[0620] 3. The server analyzes the progress data and generates advice to encourage efficient task management. For example, advice such as "shorten meeting time and allocate time to other tasks" could be considered.

[0621] 4. The device notifies the user of the generated advice.

[0622] Advice ratings and feedback

[0623] 1. The user checks the advice and evaluates whether or not they accept it. If they accept it, they can modify their task management in accordance with the advice.

[0624] 2. The device sends the user's rating and feedback to the server, which then generates new advice based on the feedback, if necessary.

[0625] Calculate reminder timing and display alerts

[0626] 1. The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task, for example, "set a reminder 30 minutes before the deadline."

[0627] 2. When the set reminder time arrives, the device will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[0628] 3. The user acknowledges the alert and performs the task.

[0629] Specific examples

[0630] Prompt Sentence Examples

[0631] "When a user with ADHD traits logs into a task management system for the first time, they enter their profile data (name, job title, type of work, past task management methods). The server then analyzes the data and suggests a schedule that assigns important tasks in the morning and lighter tasks in the afternoon. Please explain the program's process for making appropriate suggestions."

[0632] This allows business people with ADHD characteristics to receive individually optimized task management support, enabling them to work efficiently and effectively. The present invention adaptively regenerates schedules and advice based on user feedback and performs sequential optimization, thereby achieving even greater performance improvements.

[0633] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0634] System program processing flow

[0635] Step 1: Collect profile data

[0636] Specific behavior:

[0637] When a user logs in to the system for the first time, they enter their basic information (name, job title, work patterns) and information about their past task management methods.

[0638] Input and Output:

[0639] The profile data (basic information and task management method) entered by the user is the input, which becomes the raw data for the next process.

[0640] Step 2: Submit profile data

[0641] Specific behavior:

[0642] The terminal transmits the profile data entered by the user to the server in real time.

[0643] Input and Output:

[0644] The profile data is input to the terminal, and is sent to the server, after which the profile data is output to the server.

[0645] Step 3: Analyze profile data and generate schedule

[0646] Specific behavior:

[0647] The server then uses AI algorithms to analyze the received profile data, identifying user characteristics (such as times when they tend to concentrate and past task management trends).

[0648] Input and Output:

[0649] The input is the profile data entered into the server, and the output is the optimal task schedule generated through analysis by the AI ​​algorithm. For example, a suggestion such as "set important tasks in the morning and lighter tasks in the afternoon" is generated.

[0650] Step 4: View schedule suggestions

[0651] Specific behavior:

[0652] The terminal displays the schedule proposal sent from the server to the user, including specific task start and end times and the priority of each task.

[0653] Input and Output:

[0654] The input is the schedule proposal sent from the server, and the output is the schedule proposal displayed on the terminal.

[0655] Step 5: Evaluate and provide feedback on the proposed schedule

[0656] Specific behavior:

[0657] The user checks the proposed schedule and evaluates whether to accept it. If necessary, the user inputs feedback on the proposal into the terminal. The terminal then transmits the user's feedback to the server.

[0658] Input and Output:

[0659] User feedback is the input, and if the server regenerates a schedule based on that feedback, the regenerated schedule is the output.

[0660] Step 6: Enter and submit task progress data

[0661] Specific behavior:

[0662] Users input their daily task progress (start time, end time, progress status, etc.) into the terminal, which then transmits this data to the server in real time.

[0663] Input and Output:

[0664] Task progress data entered by the user is the input, and after the operation of transmitting the data to the server, the task progress data is output to the server.

[0665] Step 7: Analyze progress data and generate advice

[0666] Specific behavior:

[0667] The server analyzes the received task progress data, identifying which tasks are progressing as planned and which are behind schedule, and generates advice for efficient task management based on the analysis results.

[0668] Input and Output:

[0669] The input is task progress data entered into the server, and the output is task management advice generated through analysis. For example, advice such as "shorten meeting time and allocate time to other tasks" is generated.

[0670] Step 8: Advice Notification

[0671] Specific behavior:

[0672] The device will then notify the user of the advice sent from the server, including specific improvements and next steps to take.

[0673] Input and Output:

[0674] The advice sent from the server is the input, and the activity of displaying it on the terminal is the output.

[0675] Step 9: Evaluate and provide feedback on the advice

[0676] Specific behavior:

[0677] The user checks the received advice and evaluates whether or not it is acceptable. If it is acceptable, the user can adjust the task management according to the advice. The terminal sends the user's evaluation and feedback to the server.

[0678] Input and Output:

[0679] The user's ratings and feedback are the input, and when the server regenerates the advice, the regenerated advice is the output.

[0680] Step 10: Calculate the reminder timing

[0681] Specific behavior:

[0682] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task, for example, "set a reminder 30 minutes before the deadline."

[0683] Input and Output:

[0684] The user's task list (task information) is the input, and the reminder timing is the calculation result and the output.

[0685] Step 11: Displaying alerts

[0686] Specific behavior:

[0687] When the set reminder time arrives, the device displays an alert to the user. For example, it may notify the user that "an important meeting will be held in 30 minutes." The user can confirm the alert and carry out the task.

[0688] Input and Output:

[0689] The reminder timing is the input and the alert displayed to the user is the output.

[0690] Through these steps, the system provides individually optimized task management support for business people with ADHD traits, achieving efficient and effective task management for users.

[0691] (Application example 1)

[0692] 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."

[0693] Existing systems are insufficient for business people with ADHD to effectively manage their tasks. In particular, the use of wearable devices can enhance real-time task management and reminder functions, but there are a lack of concrete implementation examples and proposals in this field.

[0694] 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.

[0695] In this invention, the server includes means for collecting profile data from a user and proposing an optimal task scheduling method based on the data, means for analyzing the user's task progress data and generating advice for efficient task management based on the analysis, means for calculating task reminder timings and displaying alerts to the user at those timings, means for the user to input profile data and task progress by voice using a wearable device, and means for displaying advice and alerts on the wearable device, thereby enabling the user to manage tasks effectively in real time.

[0696] "Profile data" is personal information necessary for task management, such as the user's basic information, type of work, and past task management methods.

[0697] "Task scheduling" is a method for optimizing the order in which tasks are executed and the allocation of time based on user profile data.

[0698] "Task progress data" is information about the process of executing a task, such as the start time, end time, and progress status of the user's daily task.

[0699] "Advice" is a recommendation or suggestion for a user to efficiently complete a task.

[0700] "Remind timing" is the optimal timing to display a reminder to the user based on the deadline and importance of the task.

[0701] An "alert" is a notification that is displayed to the user when a specific reminder timing is reached.

[0702] A "wearable device" is a computing device that is worn on the body, including smart glasses and smart watches.

[0703] "Voice input" is a method in which a user inputs information into a device by voice.

[0704] "User" refers to individuals who use this system, particularly business people with ADHD characteristics.

[0705] The "server" is a central processing unit that analyzes data received from users and performs task scheduling and advice generation.

[0706] This invention provides an AI task coaching system to help business people with ADHD manage their tasks effectively. This system uses a wearable device (e.g., smart glasses) to provide real-time support for task management.

[0707] Hardware and software used

[0708] Hardware: Use wearable devices such as smart glasses that allow users to receive assistance with task management while working, with minimal impact on vision.

[0709] Software: The server-side data analysis system is based on Python and uses Pandas and Scikit-learn for data analysis. WebSocket is used for real-time data communication, and MySQL is used as the database.

[0710] Data processing and calculation

[0711] Collection and analysis of profile data upon first login

[0712] 1. The device collects profile data through user voice input.

[0713] Example: A user speaks, "I'm a virtual store owner and I prefer to do important tasks in the afternoon."

[0714] 2. The smart glasses send this data to the server.

[0715] 3. The server analyzes the received profile data and generates an optimal task schedule.

[0716] 4. The smart glasses display the generated schedule suggestions to the user.

[0717] Collecting and analyzing task progress data

[0718] 1. Users use smart glasses to report their daily task progress via voice.

[0719] Example: A user reports verbally, "Today's meeting time has been shortened by 30 minutes."

[0720] 2. The smart glasses transmit these data to the server in real time.

[0721] 3. The server analyzes the progress data and generates advice for efficient task management, such as reducing meeting time and allocating time to other tasks.

[0722] 4. The smart glasses notify the user of the generated advice.

[0723] Calculate reminder timing and display alerts

[0724] 1. The server monitors the user's task list and calculates the reminder timing based on the deadline and importance of each task.

[0725] Example: Set a reminder alert 30 minutes before the deadline.

[0726] 2. The smart glasses will display an alert to the user when the set reminder time arrives.

[0727] Examples of concrete examples and prompts

[0728] 1. When a user logs in for the first time, they say, "I'm a virtual store owner and I want to do important tasks in the afternoon."

[0729] 2. The smart glasses send this profile data to a server, which analyzes the data and generates a task schedule such as "check inventory in the morning and process orders in the afternoon."

[0730] 3. The smart glasses display the generated schedule to the user, who then confirms by saying "That's OK."

[0731] Example prompts

[0732] Enter your user profile:

[0733] Example: "I'm a virtual store owner and I prefer to do important tasks in the afternoon."

[0734] As described above, the present invention is a system that supports users with ADHD characteristics in effectively managing tasks in real time and carrying out their work efficiently, and is designed to maximize the user experience using wearable devices.

[0735] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0736] Step 1: Collect user profile data

[0737] Users use smart glasses to input their profile data by voice.

[0738] Input: Spoken information from the user (e.g., "I'm a virtual store owner and I prefer to do important tasks in the afternoon.")

[0739] Output: Speech-to-text profile data

[0740] Specific operation: The voice recognition function of the smart glasses recognizes voice input as text data.

[0741] Step 2: Submit profile data

[0742] The terminal (smart glasses) transmits the collected profile data to the server.

[0743] Input: Textual profile data

[0744] Output: Profile data sent to the server

[0745] Specific operation: The smart glasses use a network connection to send profile data to a server.

[0746] Step 3: Analyze the profile data

[0747] The server analyzes the received profile data and generates an optimal task schedule based on the user's characteristics.

[0748] Input: Profile data sent to the server

[0749] Output: The generated optimal task schedule

[0750] Specific operation: A data analysis system (e.g., Pandas, Scikit-learn) on the server analyzes the profile data and generates a schedule using a scheduling algorithm.

[0751] Step 4: View the task schedule

[0752] The terminal (smart glasses) displays the generated task schedule to the user.

[0753] Input: The optimal task schedule sent by the server

[0754] Output: The task schedule as seen by the user

[0755] Specific operation: The display function of the smart glasses displays schedule information.

[0756] Step 5: Enter and submit task progress data

[0757] The user inputs the task progress status into the smart glasses by voice, and the smart glasses send the data to the server.

[0758] Input: Task progress (e.g., "Today's meeting time was shortened by 30 minutes")

[0759] Output: Task progress data sent to the server

[0760] Specific operation: The voice recognition function of the smart glasses converts voice input into text and sends it to a server via the network.

[0761] Step 6: Analyze task progress data

[0762] The server analyzes the received task progress data and generates advice for efficient task management.

[0763] Input: Task progress data sent to the server

[0764] Output: Generated advice for efficient task management

[0765] How it works: A data analysis system on the server analyzes task progress data and generates advice using an AI model.

[0766] Step 7: Advice Notification

[0767] The terminal (smart glasses) notifies the user of the generated advice.

[0768] Input: Task management advice sent from the server

[0769] Output: Advice displayed to the user

[0770] Specific operation: The display function of the smart glasses displays advice information.

[0771] Step 8: Calculate reminder timing and set alerts

[0772] The server monitors the user's task list, calculates reminder timing based on the deadline and importance of each task, and sets alerts.

[0773] Input: Task list stored on the server

[0774] Output: Calculated reminder timings and configured alerts

[0775] Specific operation: A timing calculation algorithm on the server determines the reminder timing based on the task deadline and importance, and sets an alert.

[0776] Step 9: Viewing Alerts

[0777] The device (smart glasses) displays an alert to the user based on the set reminder timing.

[0778] Input: Alert information sent from the server

[0779] Output: The reminder alert that is displayed to the user

[0780] Specific operation: The display function of the smart glasses displays the alert information.

[0781] 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.

[0782] This invention provides an AI task coaching system to help business people with ADHD manage their tasks efficiently. The system's basic function is to collect user profile data and propose optimal task scheduling methods. It also analyzes the user's task progress data, generates advice for efficient task management, calculates task reminder timing, and displays alerts at appropriate times. Furthermore, by combining it with an emotion engine, the system recognizes the user's emotional state and improves the accuracy of task management based on this.

[0783] Program processing overview

[0784] Profile data collection and analysis

[0785] When a user logs in to the system for the first time, they enter profile data such as basic information, the type of work they do, and how they manage their past tasks.

[0786] The terminal transmits this profile data to the server.

[0787] The server generates an optimal task scheduling method based on the received profile data, taking into account the user's characteristics. For example, it considers a schedule such as "setting important tasks in the morning and doing creative work in the afternoon."

[0788] The terminal displays the generated task scheduling method to the user.

[0789] Evaluation and feedback of schedule proposals

[0790] The user evaluates the proposed schedule and chooses whether to accept it.

[0791] If accepted, the server saves the schedule and uses it for future task management.

[0792] If there is negative feedback, the server receives the feedback and displays the regenerated schedule on the terminal.

[0793] Collecting and analyzing task progress data

[0794] The user inputs daily task progress data (start time, end time, progress status, etc.) into the terminal.

[0795] The terminal transmits task progress data to the server in real time.

[0796] The server analyzes the progress data and generates advice to encourage the user to manage their tasks efficiently. For example, consider the advice "reduce meeting time and allocate time to other tasks."

[0797] The terminal notifies the user of the generated advice.

[0798] Advice ratings and feedback

[0799] The user evaluates the advice and decides whether to accept it.

[0800] If accepted, the server saves the advice and reflects it in future task management.

[0801] If there is negative feedback, the server receives the feedback and displays the regenerated advice on the terminal.

[0802] Calculate reminder timing and display alerts

[0803] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task. For example, consider a method of "setting a reminder 30 minutes before the deadline."

[0804] When the set reminder time arrives, the device will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[0805] The user confirms the reminder and performs the task.

[0806] Introducing the Emotion Engine

[0807] The device uses an emotion engine to recognize the user's emotional state, for example, by using facial expression recognition and voice analysis techniques to detect the user's emotions.

[0808] The server analyzes the user's emotional data and adjusts task scheduling methods and advice accordingly, such as "if the user is feeling stressed, add relaxation time to the schedule."

[0809] The device also adjusts the tone of alerts and reminders based on information obtained from the emotion engine. For example, if the user is feeling anxious, the device may use a gentler tone.

[0810] Specific examples

[0811] 1. When a user logs in for the first time, they enter basic information and profile data, including their name, job title, and work patterns.

[0812] 2. The device sends the profile data to the server, which analyzes the data and generates a suggestion such as "focus on important tasks in the morning and do lighter work in the afternoon."

[0813] 3. The device displays the suggestions, and the user provides feedback such as "I want to do important tasks in the afternoon." The server receives the feedback and generates the suggestions again.

[0814] 4. The user enters their task progress data daily, reporting, for example, "too much time spent in meetings." The server analyzes this and generates advice such as "shorten your meeting time and allocate more time to other tasks."

[0815] 5. The device notifies the user of the advice and asks them to evaluate whether they accept it. If they accept it, the server saves the settings.

[0816] 6. The server calculates the task reminder timing and sets it to "Set a reminder 30 minutes before the deadline." The device displays the reminder and the user performs the task.

[0817] 7. The emotion engine recognizes the user's emotions and makes adjustments such as adding relaxation time to the schedule if the user is tense.

[0818] This process allows users to achieve task management that is optimized for their own characteristics and emotional state, improving productivity.

[0819] The processing flow will be explained below.

[0820] MODE FOR CARRYING OUT THE INVENTION (PROCESSING STEPS)

[0821] Step 1:

[0822] When a user logs in for the first time, they enter basic information (such as name, job title, and job description).

[0823] Step 2:

[0824] The terminal sends the user's basic information to the server.

[0825] Step 3:

[0826] The server analyzes the received basic information and generates an optimal task scheduling method based on the user's characteristics and profile. For example, it may suggest a schedule such as "set important tasks in the morning and do creative work in the afternoon."

[0827] Step 4:

[0828] The terminal displays the generated task scheduling method to the user, and the user confirms the proposal.

[0829] Step 5:

[0830] The user evaluates the proposed schedule and chooses whether to accept it, and if so, the terminal sends the choice to the server.

[0831] Step 6:

[0832] The server saves the user's selection and reflects it in future task management. If the user provides feedback, the server regenerates a new schedule based on the feedback information and sends it to the terminal again.

[0833] Step 7:

[0834] The user inputs daily task progress data (start time, end time, progress, etc.) into the terminal.

[0835] Step 8:

[0836] The device transmits task progress data to the server in real time.

[0837] Step 9:

[0838] The server analyzes the received task progress data and generates advice to encourage the user to manage their tasks more efficiently. For example, the advice generated is "reduce meeting time and allocate more time to other tasks."

[0839] Step 10:

[0840] The terminal notifies the user of the generated advice.

[0841] Step 11:

[0842] The user evaluates the advice and chooses whether to accept it, and if so, the terminal sends the choice to the server.

[0843] Step 12:

[0844] The server saves the user's selection and reflects it in future task management. If the user provides feedback, the server regenerates advice based on the feedback information and sends it to the device again.

[0845] Step 13:

[0846] The device uses an emotion engine to recognize the user's emotional state. The emotion engine detects the user's emotions through facial expression recognition and voice analysis technology.

[0847] Step 14:

[0848] The server analyzes the user's emotional data and adjusts task scheduling and advice accordingly. For example, if the user is feeling stressed, it may add relaxation time to the schedule.

[0849] Step 15:

[0850] The device will adjust the tone of alerts and reminders based on information it gets from the emotion engine. For example, if the user is feeling anxious, the notification will be delivered in a calmer tone.

[0851] Step 16:

[0852] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task. For example, consider a method that "sets a reminder 30 minutes before the deadline."

[0853] Step 17:

[0854] When the device reaches the set reminder time, it will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[0855] Step 18:

[0856] The user checks the reminder and executes the task. Through this process, the user manages tasks efficiently and improves productivity.

[0857] Example 2

[0858] 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."

[0859] This invention relates to a system for efficient task management for business people with ADHD, and aims to provide a task scheduling method and advice optimized for the user's characteristics and emotional state by effectively utilizing the user's profile data, task progress data, and emotional state. In particular, it aims to solve the problem of improving productivity by taking into account the efficiency of task management and the user's emotional state.

[0860] 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.

[0861] In this invention, the server includes means for collecting profile data from a user and proposing an optimal task scheduling method based on the data, means for analyzing the user's task progress data and generating advice for efficient task management based on the analysis, and means for recognizing the user's emotional state and adjusting the task scheduling method and alert expression based on the emotional data, thereby enabling optimal task management that takes into account the user's characteristics and emotional state.

[0862] A "user" is a person or individual who uses the system.

[0863] "Profile data" is data about a user's basic information, work patterns, and past task management methods.

[0864] A "task scheduling method" is a method for arranging tasks and allocating time optimally based on user characteristics.

[0865] "Task progress data" refers to data recorded by a user, such as the start time, end time, and progress of daily tasks.

[0866] "Reminder timing" is a specific time or time frame for displaying an alert to the user based on the deadline or importance of the task.

[0867] "Emotional state" refers to a state that indicates the user's psychological state or mood, and is data that is detected using facial expression recognition and voice analysis technology.

[0868] "Alert expressions" are the wording and manner in which notifications and reminders are given to users, and are adjusted based on their emotional state.

[0869] "Feedback" refers to opinions and evaluations that users provide in response to suggestions and advice from the system.

[0870] "Regeneration" is the process of recreating new task scheduling methods and advice based on feedback and evaluation.

[0871] A "server" is a computer system that receives data sent by a user, analyzes it, generates results, and sends them to a terminal.

[0872] A "terminal" is a device through which a user inputs data, and is a device that transmits and receives data to and from a server.

[0873] The present invention is an AI task coaching system that enables business people with ADHD to efficiently manage their tasks. An embodiment of this system will be described in detail below.

[0874] Hardware and Software

[0875] Hardware

[0876] Server: High-performance computer system

[0877] Device: The computer, tablet, or smartphone used by the user

[0878] software

[0879] Profile Data Collection Tool: A form for users to enter basic information

[0880] Data Analysis Algorithms: An analytical tool for generating task scheduling methods

[0881] Emotion Engine: Facial expression recognition and speech analysis techniques to recognize a user's emotional state

[0882] Notification system: a notification tool to display task reminders and alerts

[0883] How it works

[0884] Profile data collection and analysis

[0885] 1. When a user logs in for the first time, they enter basic information and profile data into the system, including their name, job title, work patterns, and past task management methods.

[0886] 2. The device sends the entered profile data to the server.

[0887] 3. The server analyzes the received profile data and generates an optimal task scheduling method based on the user's characteristics. For example, consider a schedule that "sets important tasks in the morning and creative work in the afternoon."

[0888] 4. The terminal displays the generated task scheduling method to the user.

[0889] Collecting and analyzing task progress data

[0890] 1. The user inputs daily task progress data (start time, end time, progress, etc.) into the terminal.

[0891] 2. The device sends task progress data to the server in real time.

[0892] 3. The server analyzes the progress data and generates advice to encourage the user to manage their tasks more efficiently. For example, consider the advice "reduce meeting time and allocate time to other tasks."

[0893] 4. The device notifies the user of the generated advice.

[0894] Calculate reminder timing and display alerts

[0895] 1. The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task, for example, "set a reminder 30 minutes before the deadline."

[0896] 2. When the set reminder time arrives, the device will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[0897] 3. The user confirms the reminder and performs the specified task.

[0898] Introducing the Emotion Engine

[0899] 1. The device uses an emotion engine to recognize the user's emotional state. It uses facial expression recognition and voice analysis technology to detect the user's emotions.

[0900] 2. The server analyzes the user's emotional data and adjusts task scheduling methods and advice accordingly, such as "if the user is feeling stressed, add relaxation time to the schedule."

[0901] 3. The device also adjusts the tone of alerts and reminders based on the information it obtains from the emotion engine. For example, if the user is feeling anxious, the device will use a gentler tone.

[0902] Specific examples

[0903] 1. When a user logs in for the first time, they enter basic information and profile data, including their name, job title, and work patterns.

[0904] 2. The device sends the profile data to the server, which analyzes the data and generates a suggestion such as "focus on important tasks in the morning and do lighter work in the afternoon."

[0905] 3. The device displays the suggestions, and the user provides feedback such as "I want to do important tasks in the afternoon." The server receives the feedback and generates the suggestions again.

[0906] 4. The user inputs task progress data daily and reports that they spend too much time in meetings. The server analyzes this and generates advice such as "shorten your meeting time and allocate it to other tasks."

[0907] 5. The device notifies the user of the advice and asks them to evaluate whether they accept it. If they accept it, the server saves the settings.

[0908] 6. The server calculates the task reminder timing and sets a reminder 30 minutes before the deadline. The device displays the reminder and the user executes the task.

[0909] 7. The emotion engine recognizes the user's emotions and makes adjustments such as adding relaxation time to the schedule if the user is tense.

[0910] This process allows users to achieve task management that is optimized for their own characteristics and emotional state, improving productivity.

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

[0912] Step 1:

[0913] When a user logs in for the first time, they enter their profile data, including their name, job title, work patterns, and past task management methods. The entered data is saved on the device.

[0914] Step 2:

[0915] The device sends the saved profile data to the server. This is the process of transferring input data as packets to the server. After successful transmission, a data reception confirmation message is displayed on the device.

[0916] Step 3:

[0917] The server analyzes the received profile data. It uses a data mining algorithm for analysis to generate a task scheduling method based on the user's characteristics. For example, if it discovers from the data that the user is most focused in the morning, it generates a schedule that "sets important tasks in the morning and creative work in the afternoon." The generated task scheduling method is obtained as output.

[0918] Step 4:

[0919] The device displays the task scheduling method received from the server to the user. The proposed scheduling method is visually displayed using a simple and easy-to-understand UI. Important tasks and timelines are highlighted for the user's confirmation.

[0920] Step 5:

[0921] The user evaluates the proposed schedule and chooses whether to accept it. The user enters their feedback into an input form and saves it on the device. The user's selection is recorded in the database as an "evaluation."

[0922] Step 6:

[0923] The device sends the evaluation results and feedback to the server, which receives and stores the feedback data. The evaluation data is used for later regeneration.

[0924] Step 7:

[0925] The server analyzes the feedback and generates a regenerated task scheduling method. The schedule is recalculated based on the feedback data, and an improved schedule is generated. For example, a schedule is created based on the user's request to "perform important tasks in the afternoon." The regenerated schedule is output.

[0926] Step 8:

[0927] The terminal displays the regenerated task scheduling method to the user. The new schedule is visualized to the user and the modifications are clearly indicated. The user can check the schedule again.

[0928] Step 9:

[0929] The user inputs daily task progress data, including the start time, end time, and progress of the task. The progress data is saved on the device.

[0930] Step 10:

[0931] The device sends the saved task progress data to the server in real time. The data is transferred in packets in real time, and the progress status is updated on the server. A receipt confirmation message is displayed on the device.

[0932] Step 11:

[0933] The server analyzes the progress data. It uses a data analysis algorithm to analyze the progress and generate efficient task management advice. For example, the advice generated might be "reduce meeting time and allocate time to other tasks." The generated advice is then output.

[0934] Step 12:

[0935] The device notifies the user of the generated advice, which is displayed through a visual notification system and can be viewed by the user.

[0936] Step 13:

[0937] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task. The server determines the optimal timing for reminders through data analysis, for example, "set a reminder 30 minutes before the deadline." The reminder timing is then output.

[0938] Step 14:

[0939] When the device reaches the set reminder time, it will display an alert to the user. Using the alert notification system, specific reminders such as "I have an important meeting in 30 minutes" will be displayed.

[0940] Step 15:

[0941] The user confirms the reminder and performs the specified task. After confirming the reminder notification, the task is started at the appropriate time.

[0942] Step 16:

[0943] The device recognizes the user's emotional state. The emotion engine uses facial expression recognition and voice analysis technology to detect the user's emotional data. The detected emotional state is stored on the device.

[0944] Step 17:

[0945] The server analyzes the emotional data and adjusts the task scheduling method and advice based on this. If the user feels stressed based on the emotional data analysis, the server will make adjustments such as "adding relaxation time to the schedule." The adjusted schedule and advice are then output.

[0946] Step 18:

[0947] The device adjusts the tone of alerts and reminders based on information it gets from the emotion engine. For example, if the user is feeling anxious, the notification system will display an alert in a gentle tone. The adjusted reminder will then be displayed to the user.

[0948] (Application example 2)

[0949] 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."

[0950] Conventional task management systems only provide general scheduling and reminder functions, and are unable to accommodate individual users' characteristics and emotional states, resulting in insufficient efficient task management and stress reduction. Business people with ADHD and those working in food delivery services face particular challenges, making it difficult to optimize schedules and manage emotions due to the lack of individualized support.

[0951] 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 profile data from the user and proposing an optimal task scheduling method based on the data, means for analyzing the user's task progress data and generating advice for efficient task management based on the analysis, means for calculating task reminder timing and displaying an alert to the user at that timing, and means for recognizing the user's emotional state and adjusting task management based on the recognition. This enables optimal task scheduling and emotional management according to the characteristics of each user.

[0952] "Profile data" is data that includes basic information about the user, past task management methods, types of work, and so on.

[0953] A "task scheduling method" refers to a schedule for efficiently managing a user's tasks that is generated based on collected profile data.

[0954] "Task progress data" is data recorded by a user on the progress of daily tasks, and includes start time, end time, progress status, and the like.

[0955] An "alert" is a warning or reminder that is sent to the user based on the reminder timing of a specific task.

[0956] "Emotional State" refers to a user's current psychological and emotional state, which is detected using emotion recognition technology.

[0957] "Stress level" is the degree of stress determined by the user's emotional state.

[0958] "Relaxation suggestions" refer to suggestions to relieve tension and relax when the user feels high stress levels.

[0959] "Regeneration of scheduling method" is the process of reviewing the existing schedule based on user feedback and generating a new optimized schedule.

[0960] "Regeneration of improvement advice" is a process of reviewing existing advice based on user evaluation and generating new, more effective advice.

[0961] "Real-time emotional data analysis" is the process of analyzing a user's emotional state in real time and providing appropriate advice and schedules based on the results.

[0962] The present invention is a task management system specialized for food delivery services. Specific embodiments of this system will be described below.

[0963] First, the system collects profile data from the user (driver) and proposes an optimal task scheduling method based on that data. When the user logs in for the first time, they enter basic information such as their name, years of experience, and past delivery style into their device (smartphone or tablet). This generates the optimal delivery route and time allocation. The device sends this data to the server, which then generates a schedule based on the profile data and proposes it to the user.

[0964] As a concrete example, a user inputs "Name: Taro, Years of experience: 5 years, Task: ['Delivery 1', 'Delivery 2', 'Delivery 3']". The server generates a schedule such as "Finish short deliveries first in the morning, and propose a route to avoid traffic congestion in the afternoon" and displays it on the terminal. If the user provides feedback such as "I would like to make an important delivery in the afternoon", the server regenerates and proposes a new schedule.

[0965] The next step is the collection and analysis of task progress data. Users enter their daily task progress into the device, which then sends this information to the server in real time. The server analyzes the progress data and generates advice for efficient delivery management. For example, it may provide specific advice to the device, such as "traffic congestion is expected at the next delivery destination, so we suggest taking an alternative route."

[0966] The server calculates the timing of task reminders and displays alerts based on the importance and deadline of each task, and displays an alert on the device at that timing. For example, the user will be notified with a reminder such as "Pickup time in 15 minutes."

[0967] Additionally, it will include a function that recognizes the user's emotional state and adjusts task management accordingly. The device uses emotion recognition technology to detect the user's stress level using a facial recognition camera and voice analysis technology. The server analyzes the emotional data and makes relaxation suggestions if it determines that stress is high. For example, flexible responses such as "Your current stress level is high, so we suggest taking a five-minute break" will be possible.

[0968] The hardware used includes smartphones or tablets, cameras, and microphones, while the software uses tools such as Python libraries (including a hypothetical emotion recognition library).

[0969] Example prompt sentence:

[0970] "Please enter your driver's basic information (name, years of experience, tasks)"

[0971] "Update the current task progress (task ID, status)"

[0972] "Please enter your facial and voice data (for emotion recognition)"

[0973] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0974] Step 1:

[0975] When a user logs in for the first time, they enter basic information into the terminal. Specifically, they enter profile data such as their name, years of experience, past delivery style, and task list, and this information is sent to the server. The entered data becomes the basis for optimizing scheduling that reflects the user's unique characteristics (input: profile data, output: user characteristic data).

[0976] Step 2:

[0977] The server analyzes the received profile data and generates an optimal task scheduling method based on the user's characteristics. Specifically, it allocates tasks by time period based on years of experience and past delivery history (data processing: profile data analysis, output: optimal schedule proposal).

[0978] Step 3:

[0979] The device displays the generated task scheduling method to the user. The user checks the proposal and provides feedback as needed. Specifically, the user inputs feedback such as "I would like to do important tasks in the afternoon" (input: feedback, output: user evaluation data).

[0980] Step 4:

[0981] The server receives the user's feedback and regenerates the scheduling method. Specifically, it adjusts the schedule based on the user's requests and generates a new optimized schedule (data processing: feedback analysis, output: regenerated schedule).

[0982] Step 5:

[0983] The user inputs daily task progress data into the terminal. Specifically, the start time, end time, progress status, etc. of each task are entered, and this is sent to the server (input: task progress data, output: progress status data).

[0984] Step 6:

[0985] The server analyzes the progress data and generates advice for efficient task management. Specifically, it generates advice such as, "The next delivery destination is in a congested traffic area, so we suggest an alternative route" (data processing: progress data analysis, output: efficiency advice).

[0986] Step 7:

[0987] The terminal notifies the user of the generated advice, who then checks it and evaluates whether to accept it or not (input: advice evaluation, output: evaluation data).

[0988] Step 8:

[0989] The server receives the user's evaluation and regenerates advice based on the evaluation. Specifically, it readjusts the advice based on requests such as "Please suggest a route that takes less time" (data processing: evaluation analysis, output: regenerated advice).

[0990] Step 9:

[0991] The server calculates the reminder timing for each task and displays an alert on the device. Specifically, a specific reminder such as "Pickup time in 15 minutes" is set, and notifications are sent at the appropriate time (data processing: reminder timing calculation, output: alert notification).

[0992] Step 10:

[0993] To recognize the user's emotional state, the device uses a camera and microphone to analyze facial expressions and voice, and sends the obtained emotional data to a server (input: emotional data, output: analysis results).

[0994] Step 11:

[0995] The server analyzes the received emotional data and adjusts task management based on the user's emotional state. Specifically, if the user is feeling high stress, it suggests relaxation (data processing: emotional data analysis, output: relaxation suggestions).

[0996] 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.

[0997] 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.

[0998] 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.

[0999] [Third embodiment]

[1000] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1001] 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.

[1002] 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).

[1003] 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.

[1004] 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.

[1005] 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).

[1006] 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.

[1007] 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.

[1008] 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.

[1009] 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.

[1010] 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.

[1011] 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."

[1012] This invention provides an AI task coaching system that helps business people with ADHD manage their tasks effectively. The system's basic function is to collect user profile data and propose optimal task scheduling methods. It also has the ability to analyze the user's task progress data, generate advice for efficient task management, calculate task reminder timing, and display alerts at appropriate times.

[1013] Program processing overview

[1014] Profile data collection and analysis

[1015] When a user logs in to the system for the first time, they enter profile data such as basic information, the type of work they do, and how they manage their past tasks.

[1016] The terminal transmits this profile data to the server.

[1017] The server analyzes the received profile data and generates an optimal task schedule based on the user's characteristics. For example, a schedule could be created where important tasks are scheduled in the morning and lighter tasks are scheduled in the afternoon.

[1018] The terminal displays the generated schedule proposal to the user.

[1019] Evaluation and feedback of schedule proposals

[1020] The user evaluates the proposed schedule and chooses whether to accept it.

[1021] If accepted, the server saves this schedule and uses it for future task management.

[1022] If there is negative feedback, the server receives the feedback and displays the regenerated schedule on the terminal.

[1023] Collecting and analyzing task progress data

[1024] The user inputs daily task progress into the terminal, for example, recording the start time, end time, and progress of the task.

[1025] The terminal transmits this data to the server in real time.

[1026] The server analyzes the progress data and generates advice to encourage efficient task management. For example, advice such as "Since there are many meetings, shorten the meeting time and allocate it to other tasks" is generated.

[1027] The terminal notifies the user of the generated advice.

[1028] Advice ratings and feedback

[1029] The user evaluates the advice and decides whether to accept it.

[1030] If accepted, the server saves the advice and reflects it in future task management.

[1031] If there is negative feedback, the server receives the feedback and displays the regenerated advice on the terminal.

[1032] Calculate reminder timing and display alerts

[1033] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task, for example, "set a reminder 30 minutes before the deadline."

[1034] When the set reminder time arrives, the device will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[1035] The user confirms the reminder and performs the task.

[1036] Specific examples

[1037] 1. When a user logs in for the first time, they enter their basic information and profile data, including their name, job title, and work patterns.

[1038] 2. The device sends this profile data to the server, which analyzes it and generates a suggestion such as "Put important tasks in the morning and lighter tasks in the afternoon."

[1039] 3. The device displays the suggestions to the user, and the user provides feedback such as "I would like to do important tasks in the afternoon." The server receives the feedback and generates the suggestions again.

[1040] 4. The user inputs their daily task progress into the terminal and reports a situation such as "meetings are too long." The server analyzes this and generates advice such as "shorten the meeting time and allocate time to other tasks."

[1041] 5. The device notifies the user of the advice and asks them to evaluate whether they accept it. If they accept it, the server saves the settings.

[1042] 6. The server calculates the task reminder timing, for example, "30 minutes before the deadline." The device displays the reminder, and the user executes the task.

[1043] As described above, the system of the present invention enables users with ADHD characteristics to improve their task management skills through appropriate scheduling, progress management, and reminders.

[1044] The processing flow will be explained below.

[1045] Step 1:

[1046] When a user logs in for the first time, they enter basic information (such as name, job title, and job description).

[1047] Step 2:

[1048] The terminal sends the user's basic information to the server.

[1049] Step 3:

[1050] The server analyzes the received basic information and generates an optimal task scheduling method based on the user's characteristics and profile. For example, consider a method such as "schedule important tasks in the morning."

[1051] Step 4:

[1052] The terminal displays the generated task scheduling method to the user, and the user confirms the proposal.

[1053] Step 5:

[1054] The user evaluates the proposed schedule and either accepts it or provides feedback. If accepted, the device sends the selection to the server.

[1055] Step 6:

[1056] The server saves the user's selection and reflects it in future task management. If the user provides feedback, the server generates a new schedule based on the feedback and sends it to the terminal again.

[1057] Step 7:

[1058] The user inputs daily task progress data (start time, end time, progress, etc.) into the terminal.

[1059] Step 8:

[1060] The device transmits task progress data to the server in real time.

[1061] Step 9:

[1062] The server analyzes the received task progress data and generates advice to encourage the user to manage their tasks efficiently. For example, consider the advice "reduce meeting time and allocate time to other tasks."

[1063] Step 10:

[1064] The terminal notifies the user of the generated advice.

[1065] Step 11:

[1066] The user evaluates the advice and chooses whether to accept it, and if so, the device sends the choice to the server.

[1067] Step 12:

[1068] The server saves the user's selection and reflects it in future task management. If the user provides feedback, the server generates new advice based on the feedback and sends it to the device again.

[1069] Step 13:

[1070] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task. For example, consider a method that "sets a reminder 30 minutes before the deadline."

[1071] Step 14:

[1072] When the device reaches the set reminder time, it will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[1073] Step 15:

[1074] The user checks the reminder and executes the task. Through this process, the user manages tasks efficiently and improves productivity.

[1075] Example 1

[1076] 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."

[1077] In today's business environment, it is extremely difficult for business people with ADHD to efficiently manage their tasks. Conventional task management systems perform uniform scheduling and progress management without fully considering the characteristics of each user, making it impossible to provide optimal support to each individual user. For this reason, there is a need for an individually optimized task management support system that allows business people with ADHD to perform at their best.

[1078] 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.

[1079] In this invention, the server includes means for collecting profile data from a user and proposing an optimal task scheduling method based on the data, means for analyzing the user's task progress data and generating efficient task management advice based on the analysis, means for calculating task reminder timings and displaying alerts to the user at those timings, means for receiving user feedback and regenerating a task schedule based on the feedback, means for collecting daily task progress data, analyzing progress based on the data, and generating improvement advice, means for notifying the user of the task management advice, receiving an evaluation of the advice, and means for regenerating the improvement advice based on the evaluation. This enables effective and flexible task management, allowing business people with ADHD characteristics to acquire an optimal task management method and maximize their performance.

[1080] "Profile data" is data that indicates the characteristics of a user, such as basic information about the user, the type of work, and past task management methods.

[1081] A "task scheduling method" is a method for determining task priorities and time allocations based on profile data of a specific user.

[1082] "Task progress data" refers to information such as the start time, end time, and progress status of each task.

[1083] "Advice for efficient task management" is a suggestion based on task progress data to help users carry out their work more effectively.

[1084] "Remind timing" refers to the time at which an alert is displayed to the user, determined based on the deadline and importance of the task.

[1085] An "alert" is a message that is sent to the user at a set reminder timing, urging the user to perform a specific task.

[1086] "Feedback" refers to evaluations and comments that users make in response to the system's suggestions and advice.

[1087] "Regeneration" is the process by which the system creates new suggestions and advice based on feedback and evaluation.

[1088] This invention relates to an AI task coaching system that helps business people with ADHD manage their tasks effectively. The system's basic function is to collect user profile data and propose optimal task scheduling methods. It also has the ability to analyze the user's task progress data, generate advice for efficient task management, calculate task reminder timing, and display alerts at appropriate times.

[1089] The hardware required to implement this system includes the terminals used by users (e.g., PCs and smartphones), the communication infrastructure for sending and receiving data, and the server for analyzing and processing the data.The software includes an application that provides the user interface and an AI algorithm that analyzes data and generates schedules.

[1090] A specific example of the operation of the system of the present invention will be described below.

[1091] Profile data collection and analysis

[1092] 1. When a user logs in for the first time, they enter their basic information (such as name, position, work style, etc.) and information about their past task management methods.

[1093] 2. The device transmits the entered profile data to the server in real time.

[1094] 3. The server analyzes the received profile data and generates an optimal task schedule based on the user's characteristics, such as "set important tasks in the morning and lighter tasks in the afternoon."

[1095] 4. The terminal displays the generated schedule proposal to the user.

[1096] Evaluation and feedback of schedule proposals

[1097] 1. The user reviews the proposed schedule and evaluates whether it is acceptable or not. If necessary, the user can also enter feedback on the schedule into the terminal.

[1098] 2. The device sends the user's feedback to the server.

[1099] 3. The server receives the feedback and regenerates the schedule as needed, which is then displayed to the user again via the terminal.

[1100] Collecting and analyzing task progress data

[1101] 1. The user enters daily task progress data (start time, end time, progress status, etc.) into the terminal.

[1102] 2. The device transmits this data to the server in real time.

[1103] 3. The server analyzes the progress data and generates advice to encourage efficient task management. For example, advice such as "shorten meeting time and allocate time to other tasks" could be considered.

[1104] 4. The device notifies the user of the generated advice.

[1105] Advice ratings and feedback

[1106] 1. The user checks the advice and evaluates whether or not they accept it. If they accept it, they can modify their task management in accordance with the advice.

[1107] 2. The device sends the user's rating and feedback to the server, which then generates new advice based on the feedback, if necessary.

[1108] Calculate reminder timing and display alerts

[1109] 1. The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task, for example, "set a reminder 30 minutes before the deadline."

[1110] 2. When the set reminder time arrives, the device will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[1111] 3. The user acknowledges the alert and performs the task.

[1112] Specific examples

[1113] Prompt Sentence Examples

[1114] "When a user with ADHD traits logs into a task management system for the first time, they enter their profile data (name, job title, type of work, past task management methods). The server then analyzes the data and suggests a schedule that assigns important tasks in the morning and lighter tasks in the afternoon. Please explain the program's process for making appropriate suggestions."

[1115] This allows business people with ADHD characteristics to receive individually optimized task management support, enabling them to work efficiently and effectively. The present invention adaptively regenerates schedules and advice based on user feedback and performs sequential optimization, thereby achieving even greater performance improvements.

[1116] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1117] System program processing flow

[1118] Step 1: Collect profile data

[1119] Specific behavior:

[1120] When a user logs in to the system for the first time, they enter their basic information (name, job title, work patterns) and information about their past task management methods.

[1121] Input and Output:

[1122] The profile data (basic information and task management method) entered by the user is the input, which becomes the raw data for the next process.

[1123] Step 2: Submit profile data

[1124] Specific behavior:

[1125] The terminal transmits the profile data entered by the user to the server in real time.

[1126] Input and Output:

[1127] The profile data is input to the terminal, and is sent to the server, after which the profile data is output to the server.

[1128] Step 3: Analyze profile data and generate schedule

[1129] Specific behavior:

[1130] The server then uses AI algorithms to analyze the received profile data, identifying user characteristics (such as times when they tend to concentrate and past task management trends).

[1131] Input and Output:

[1132] The input is the profile data entered into the server, and the output is the optimal task schedule generated through analysis by the AI ​​algorithm. For example, a suggestion such as "set important tasks in the morning and lighter tasks in the afternoon" is generated.

[1133] Step 4: View schedule suggestions

[1134] Specific behavior:

[1135] The terminal displays the schedule proposal sent from the server to the user, including specific task start and end times and the priority of each task.

[1136] Input and Output:

[1137] The input is the schedule proposal sent from the server, and the output is the schedule proposal displayed on the terminal.

[1138] Step 5: Evaluate and provide feedback on the proposed schedule

[1139] Specific behavior:

[1140] The user checks the proposed schedule and evaluates whether to accept it. If necessary, the user inputs feedback on the proposal into the terminal. The terminal then transmits the user's feedback to the server.

[1141] Input and Output:

[1142] User feedback is the input, and if the server regenerates a schedule based on that feedback, the regenerated schedule is the output.

[1143] Step 6: Enter and submit task progress data

[1144] Specific behavior:

[1145] Users input their daily task progress (start time, end time, progress status, etc.) into the terminal, which then transmits this data to the server in real time.

[1146] Input and Output:

[1147] Task progress data entered by the user is the input, and after the operation of transmitting the data to the server, the task progress data is output to the server.

[1148] Step 7: Analyze progress data and generate advice

[1149] Specific behavior:

[1150] The server analyzes the received task progress data, identifying which tasks are progressing as planned and which are behind schedule, and generates advice for efficient task management based on the analysis results.

[1151] Input and Output:

[1152] The input is task progress data entered into the server, and the output is task management advice generated through analysis. For example, advice such as "shorten meeting time and allocate time to other tasks" is generated.

[1153] Step 8: Advice Notification

[1154] Specific behavior:

[1155] The device will then notify the user of the advice sent from the server, including specific improvements and next steps to take.

[1156] Input and Output:

[1157] The advice sent from the server is the input, and the activity of displaying it on the terminal is the output.

[1158] Step 9: Evaluate and provide feedback on the advice

[1159] Specific behavior:

[1160] The user checks the received advice and evaluates whether or not it is acceptable. If it is acceptable, the user can adjust the task management according to the advice. The terminal sends the user's evaluation and feedback to the server.

[1161] Input and Output:

[1162] The user's ratings and feedback are the input, and when the server regenerates the advice, the regenerated advice is the output.

[1163] Step 10: Calculate the reminder timing

[1164] Specific behavior:

[1165] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task, for example, "set a reminder 30 minutes before the deadline."

[1166] Input and Output:

[1167] The user's task list (task information) is the input, and the reminder timing is the calculation result and the output.

[1168] Step 11: Displaying alerts

[1169] Specific behavior:

[1170] When the set reminder time arrives, the device displays an alert to the user. For example, it may notify the user that "an important meeting will be held in 30 minutes." The user can confirm the alert and carry out the task.

[1171] Input and Output:

[1172] The reminder timing is the input and the alert displayed to the user is the output.

[1173] Through these steps, the system provides individually optimized task management support for business people with ADHD traits, achieving efficient and effective task management for users.

[1174] (Application example 1)

[1175] 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."

[1176] Existing systems are insufficient for business people with ADHD to effectively manage their tasks. In particular, the use of wearable devices can enhance real-time task management and reminder functions, but there are a lack of concrete implementation examples and proposals in this field.

[1177] 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.

[1178] In this invention, the server includes means for collecting profile data from a user and proposing an optimal task scheduling method based on the data, means for analyzing the user's task progress data and generating advice for efficient task management based on the analysis, means for calculating task reminder timings and displaying alerts to the user at those timings, means for the user to input profile data and task progress by voice using a wearable device, and means for displaying advice and alerts on the wearable device, thereby enabling the user to manage tasks effectively in real time.

[1179] "Profile data" is personal information necessary for task management, such as the user's basic information, type of work, and past task management methods.

[1180] "Task scheduling" is a method for optimizing the order in which tasks are executed and the allocation of time based on user profile data.

[1181] "Task progress data" is information about the process of executing a task, such as the start time, end time, and progress status of the user's daily task.

[1182] "Advice" is a recommendation or suggestion for a user to efficiently complete a task.

[1183] "Remind timing" is the optimal timing to display a reminder to the user based on the deadline and importance of the task.

[1184] An "alert" is a notification that is displayed to the user when a specific reminder timing is reached.

[1185] A "wearable device" is a computing device that is worn on the body, including smart glasses and smart watches.

[1186] "Voice input" is a method in which a user inputs information into a device by voice.

[1187] "User" refers to individuals who use this system, particularly business people with ADHD characteristics.

[1188] The "server" is a central processing unit that analyzes data received from users and performs task scheduling and advice generation.

[1189] This invention provides an AI task coaching system to help business people with ADHD manage their tasks effectively. This system uses a wearable device (e.g., smart glasses) to provide real-time support for task management.

[1190] Hardware and software used

[1191] Hardware: Use wearable devices such as smart glasses that allow users to receive assistance with task management while working, with minimal impact on vision.

[1192] Software: The server-side data analysis system is based on Python and uses Pandas and Scikit-learn for data analysis. WebSocket is used for real-time data communication, and MySQL is used as the database.

[1193] Data processing and calculation

[1194] Collection and analysis of profile data upon first login

[1195] 1. The device collects profile data through user voice input.

[1196] Example: A user speaks, "I'm a virtual store owner and I prefer to do important tasks in the afternoon."

[1197] 2. The smart glasses send this data to the server.

[1198] 3. The server analyzes the received profile data and generates an optimal task schedule.

[1199] 4. The smart glasses display the generated schedule suggestions to the user.

[1200] Collecting and analyzing task progress data

[1201] 1. Users use smart glasses to report their daily task progress via voice.

[1202] Example: A user reports verbally, "Today's meeting time has been shortened by 30 minutes."

[1203] 2. The smart glasses transmit these data to the server in real time.

[1204] 3. The server analyzes the progress data and generates advice for efficient task management, such as reducing meeting time and allocating time to other tasks.

[1205] 4. The smart glasses notify the user of the generated advice.

[1206] Calculate reminder timing and display alerts

[1207] 1. The server monitors the user's task list and calculates the reminder timing based on the deadline and importance of each task.

[1208] Example: Set a reminder alert 30 minutes before the deadline.

[1209] 2. The smart glasses will display an alert to the user when the set reminder time arrives.

[1210] Examples of concrete examples and prompts

[1211] 1. When a user logs in for the first time, they say, "I'm a virtual store owner and I want to do important tasks in the afternoon."

[1212] 2. The smart glasses send this profile data to a server, which analyzes the data and generates a task schedule such as "check inventory in the morning and process orders in the afternoon."

[1213] 3. The smart glasses display the generated schedule to the user, who then confirms by saying "That's OK."

[1214] Example prompts

[1215] Enter your user profile:

[1216] Example: "I'm a virtual store owner and I prefer to do important tasks in the afternoon."

[1217] As described above, the present invention is a system that supports users with ADHD characteristics in effectively managing tasks in real time and carrying out their work efficiently, and is designed to maximize the user experience using wearable devices.

[1218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1219] Step 1: Collect user profile data

[1220] Users use smart glasses to input their profile data by voice.

[1221] Input: Spoken information from the user (e.g., "I'm a virtual store owner and I prefer to do important tasks in the afternoon.")

[1222] Output: Speech-to-text profile data

[1223] Specific operation: The voice recognition function of the smart glasses recognizes voice input as text data.

[1224] Step 2: Submit profile data

[1225] The terminal (smart glasses) transmits the collected profile data to the server.

[1226] Input: Textual profile data

[1227] Output: Profile data sent to the server

[1228] Specific operation: The smart glasses use a network connection to send profile data to a server.

[1229] Step 3: Analyze the profile data

[1230] The server analyzes the received profile data and generates an optimal task schedule based on the user's characteristics.

[1231] Input: Profile data sent to the server

[1232] Output: The generated optimal task schedule

[1233] Specific operation: A data analysis system (e.g., Pandas, Scikit-learn) on the server analyzes the profile data and generates a schedule using a scheduling algorithm.

[1234] Step 4: View the task schedule

[1235] The terminal (smart glasses) displays the generated task schedule to the user.

[1236] Input: The optimal task schedule sent by the server

[1237] Output: The task schedule as seen by the user

[1238] Specific operation: The display function of the smart glasses displays schedule information.

[1239] Step 5: Enter and submit task progress data

[1240] The user inputs the task progress status into the smart glasses by voice, and the smart glasses send the data to the server.

[1241] Input: Task progress (e.g., "Today's meeting time was shortened by 30 minutes")

[1242] Output: Task progress data sent to the server

[1243] Specific operation: The voice recognition function of the smart glasses converts voice input into text and sends it to a server via the network.

[1244] Step 6: Analyze task progress data

[1245] The server analyzes the received task progress data and generates advice for efficient task management.

[1246] Input: Task progress data sent to the server

[1247] Output: Generated advice for efficient task management

[1248] How it works: A data analysis system on the server analyzes task progress data and generates advice using an AI model.

[1249] Step 7: Advice Notification

[1250] The terminal (smart glasses) notifies the user of the generated advice.

[1251] Input: Task management advice sent from the server

[1252] Output: Advice displayed to the user

[1253] Specific operation: The display function of the smart glasses displays advice information.

[1254] Step 8: Calculate reminder timing and set alerts

[1255] The server monitors the user's task list, calculates reminder timing based on the deadline and importance of each task, and sets alerts.

[1256] Input: Task list stored on the server

[1257] Output: Calculated reminder timings and configured alerts

[1258] Specific operation: A timing calculation algorithm on the server determines the reminder timing based on the task deadline and importance, and sets an alert.

[1259] Step 9: Viewing Alerts

[1260] The device (smart glasses) displays an alert to the user based on the set reminder timing.

[1261] Input: Alert information sent from the server

[1262] Output: The reminder alert that is displayed to the user

[1263] Specific operation: The display function of the smart glasses displays the alert information.

[1264] 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.

[1265] This invention provides an AI task coaching system to help business people with ADHD manage their tasks efficiently. The system's basic function is to collect user profile data and propose optimal task scheduling methods. It also analyzes the user's task progress data, generates advice for efficient task management, calculates task reminder timing, and displays alerts at appropriate times. Furthermore, by combining it with an emotion engine, the system recognizes the user's emotional state and improves the accuracy of task management based on this.

[1266] Program processing overview

[1267] Profile data collection and analysis

[1268] When a user logs in to the system for the first time, they enter profile data such as basic information, the type of work they do, and how they manage their past tasks.

[1269] The terminal transmits this profile data to the server.

[1270] The server generates an optimal task scheduling method based on the received profile data, taking into account the user's characteristics. For example, it considers a schedule such as "setting important tasks in the morning and doing creative work in the afternoon."

[1271] The terminal displays the generated task scheduling method to the user.

[1272] Evaluation and feedback of schedule proposals

[1273] The user evaluates the proposed schedule and chooses whether to accept it.

[1274] If accepted, the server saves the schedule and uses it for future task management.

[1275] If there is negative feedback, the server receives the feedback and displays the regenerated schedule on the terminal.

[1276] Collecting and analyzing task progress data

[1277] The user inputs daily task progress data (start time, end time, progress status, etc.) into the terminal.

[1278] The terminal transmits task progress data to the server in real time.

[1279] The server analyzes the progress data and generates advice to encourage the user to manage their tasks efficiently. For example, consider the advice "reduce meeting time and allocate time to other tasks."

[1280] The terminal notifies the user of the generated advice.

[1281] Advice ratings and feedback

[1282] The user evaluates the advice and decides whether to accept it.

[1283] If accepted, the server saves the advice and reflects it in future task management.

[1284] If there is negative feedback, the server receives the feedback and displays the regenerated advice on the terminal.

[1285] Calculate reminder timing and display alerts

[1286] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task. For example, consider a method of "setting a reminder 30 minutes before the deadline."

[1287] When the set reminder time arrives, the device will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[1288] The user confirms the reminder and performs the task.

[1289] Introducing the Emotion Engine

[1290] The device uses an emotion engine to recognize the user's emotional state, for example, by using facial expression recognition and voice analysis techniques to detect the user's emotions.

[1291] The server analyzes the user's emotional data and adjusts task scheduling methods and advice accordingly, such as "if the user is feeling stressed, add relaxation time to the schedule."

[1292] The device also adjusts the tone of alerts and reminders based on information obtained from the emotion engine. For example, if the user is feeling anxious, the device may use a gentler tone.

[1293] Specific examples

[1294] 1. When a user logs in for the first time, they enter basic information and profile data, including their name, job title, and work patterns.

[1295] 2. The device sends the profile data to the server, which analyzes the data and generates a suggestion such as "focus on important tasks in the morning and do lighter work in the afternoon."

[1296] 3. The device displays the suggestions, and the user provides feedback such as "I want to do important tasks in the afternoon." The server receives the feedback and generates the suggestions again.

[1297] 4. The user enters their task progress data daily, reporting, for example, "too much time spent in meetings." The server analyzes this and generates advice such as "shorten your meeting time and allocate more time to other tasks."

[1298] 5. The device notifies the user of the advice and asks them to evaluate whether they accept it. If they accept it, the server saves the settings.

[1299] 6. The server calculates the task reminder timing and sets it to "Set a reminder 30 minutes before the deadline." The device displays the reminder and the user performs the task.

[1300] 7. The emotion engine recognizes the user's emotions and makes adjustments such as adding relaxation time to the schedule if the user is tense.

[1301] This process allows users to achieve task management that is optimized for their own characteristics and emotional state, improving productivity.

[1302] The processing flow will be explained below.

[1303] MODE FOR CARRYING OUT THE INVENTION (PROCESSING STEPS)

[1304] Step 1:

[1305] When a user logs in for the first time, they enter basic information (such as name, job title, and job description).

[1306] Step 2:

[1307] The terminal sends the user's basic information to the server.

[1308] Step 3:

[1309] The server analyzes the received basic information and generates an optimal task scheduling method based on the user's characteristics and profile. For example, it may suggest a schedule such as "set important tasks in the morning and do creative work in the afternoon."

[1310] Step 4:

[1311] The terminal displays the generated task scheduling method to the user, and the user confirms the proposal.

[1312] Step 5:

[1313] The user evaluates the proposed schedule and chooses whether to accept it, and if so, the terminal sends the choice to the server.

[1314] Step 6:

[1315] The server saves the user's selection and reflects it in future task management. If the user provides feedback, the server regenerates a new schedule based on the feedback information and sends it to the terminal again.

[1316] Step 7:

[1317] The user inputs daily task progress data (start time, end time, progress, etc.) into the terminal.

[1318] Step 8:

[1319] The device transmits task progress data to the server in real time.

[1320] Step 9:

[1321] The server analyzes the received task progress data and generates advice to encourage the user to manage their tasks more efficiently. For example, the advice generated is "reduce meeting time and allocate more time to other tasks."

[1322] Step 10:

[1323] The terminal notifies the user of the generated advice.

[1324] Step 11:

[1325] The user evaluates the advice and chooses whether to accept it, and if so, the terminal sends the choice to the server.

[1326] Step 12:

[1327] The server saves the user's selection and reflects it in future task management. If the user provides feedback, the server regenerates advice based on the feedback information and sends it to the device again.

[1328] Step 13:

[1329] The device uses an emotion engine to recognize the user's emotional state. The emotion engine detects the user's emotions through facial expression recognition and voice analysis technology.

[1330] Step 14:

[1331] The server analyzes the user's emotional data and adjusts task scheduling and advice accordingly. For example, if the user is feeling stressed, it may add relaxation time to the schedule.

[1332] Step 15:

[1333] The device will adjust the tone of alerts and reminders based on information it gets from the emotion engine. For example, if the user is feeling anxious, the notification will be delivered in a calmer tone.

[1334] Step 16:

[1335] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task. For example, consider a method that "sets a reminder 30 minutes before the deadline."

[1336] Step 17:

[1337] When the device reaches the set reminder time, it will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[1338] Step 18:

[1339] The user checks the reminder and executes the task. Through this process, the user manages tasks efficiently and improves productivity.

[1340] Example 2

[1341] 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."

[1342] This invention relates to a system for efficient task management for business people with ADHD, and aims to provide a task scheduling method and advice optimized for the user's characteristics and emotional state by effectively utilizing the user's profile data, task progress data, and emotional state. In particular, it aims to solve the problem of improving productivity by taking into account the efficiency of task management and the user's emotional state.

[1343] 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.

[1344] In this invention, the server includes means for collecting profile data from a user and proposing an optimal task scheduling method based on the data, means for analyzing the user's task progress data and generating advice for efficient task management based on the analysis, and means for recognizing the user's emotional state and adjusting the task scheduling method and alert expression based on the emotional data, thereby enabling optimal task management that takes into account the user's characteristics and emotional state.

[1345] A "user" is a person or individual who uses the system.

[1346] "Profile data" is data about a user's basic information, work patterns, and past task management methods.

[1347] A "task scheduling method" is a method for arranging tasks and allocating time optimally based on user characteristics.

[1348] "Task progress data" refers to data recorded by a user, such as the start time, end time, and progress of daily tasks.

[1349] "Reminder timing" is a specific time or time frame for displaying an alert to the user based on the deadline or importance of the task.

[1350] "Emotional state" refers to a state that indicates the user's psychological state or mood, and is data that is detected using facial expression recognition and voice analysis technology.

[1351] "Alert expressions" are the wording and manner in which notifications and reminders are given to users, and are adjusted based on their emotional state.

[1352] "Feedback" refers to opinions and evaluations that users provide in response to suggestions and advice from the system.

[1353] "Regeneration" is the process of recreating new task scheduling methods and advice based on feedback and evaluation.

[1354] A "server" is a computer system that receives data sent by a user, analyzes it, generates results, and sends them to a terminal.

[1355] A "terminal" is a device through which a user inputs data, and is a device that transmits and receives data to and from a server.

[1356] The present invention is an AI task coaching system that enables business people with ADHD to efficiently manage their tasks. An embodiment of this system will be described in detail below.

[1357] Hardware and Software

[1358] Hardware

[1359] Server: High-performance computer system

[1360] Device: The computer, tablet, or smartphone used by the user

[1361] software

[1362] Profile Data Collection Tool: A form for users to enter basic information

[1363] Data Analysis Algorithms: An analytical tool for generating task scheduling methods

[1364] Emotion Engine: Facial expression recognition and speech analysis techniques to recognize a user's emotional state

[1365] Notification system: a notification tool to display task reminders and alerts

[1366] How it works

[1367] Profile data collection and analysis

[1368] 1. When a user logs in for the first time, they enter basic information and profile data into the system, including their name, job title, work patterns, and past task management methods.

[1369] 2. The device sends the entered profile data to the server.

[1370] 3. The server analyzes the received profile data and generates an optimal task scheduling method based on the user's characteristics. For example, consider a schedule that "sets important tasks in the morning and creative work in the afternoon."

[1371] 4. The terminal displays the generated task scheduling method to the user.

[1372] Collecting and analyzing task progress data

[1373] 1. The user inputs daily task progress data (start time, end time, progress, etc.) into the terminal.

[1374] 2. The device sends task progress data to the server in real time.

[1375] 3. The server analyzes the progress data and generates advice to encourage the user to manage their tasks more efficiently. For example, consider the advice "reduce meeting time and allocate time to other tasks."

[1376] 4. The device notifies the user of the generated advice.

[1377] Calculate reminder timing and display alerts

[1378] 1. The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task, for example, "set a reminder 30 minutes before the deadline."

[1379] 2. When the set reminder time arrives, the device will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[1380] 3. The user confirms the reminder and performs the specified task.

[1381] Introducing the Emotion Engine

[1382] 1. The device uses an emotion engine to recognize the user's emotional state. It uses facial expression recognition and voice analysis technology to detect the user's emotions.

[1383] 2. The server analyzes the user's emotional data and adjusts task scheduling methods and advice accordingly, such as "if the user is feeling stressed, add relaxation time to the schedule."

[1384] 3. The device also adjusts the tone of alerts and reminders based on the information it obtains from the emotion engine. For example, if the user is feeling anxious, the device will use a gentler tone.

[1385] Specific examples

[1386] 1. When a user logs in for the first time, they enter basic information and profile data, including their name, job title, and work patterns.

[1387] 2. The device sends the profile data to the server, which analyzes the data and generates a suggestion such as "focus on important tasks in the morning and do lighter work in the afternoon."

[1388] 3. The device displays the suggestions, and the user provides feedback such as "I want to do important tasks in the afternoon." The server receives the feedback and generates the suggestions again.

[1389] 4. The user inputs task progress data daily and reports that they spend too much time in meetings. The server analyzes this and generates advice such as "shorten your meeting time and allocate it to other tasks."

[1390] 5. The device notifies the user of the advice and asks them to evaluate whether they accept it. If they accept it, the server saves the settings.

[1391] 6. The server calculates the task reminder timing and sets a reminder 30 minutes before the deadline. The device displays the reminder and the user executes the task.

[1392] 7. The emotion engine recognizes the user's emotions and makes adjustments such as adding relaxation time to the schedule if the user is tense.

[1393] This process allows users to achieve task management that is optimized for their own characteristics and emotional state, improving productivity.

[1394] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1395] Step 1:

[1396] When a user logs in for the first time, they enter their profile data, including their name, job title, work patterns, and past task management methods. The entered data is saved on the device.

[1397] Step 2:

[1398] The device sends the saved profile data to the server. This is the process of transferring input data as packets to the server. After successful transmission, a data reception confirmation message is displayed on the device.

[1399] Step 3:

[1400] The server analyzes the received profile data. It uses a data mining algorithm for analysis to generate a task scheduling method based on the user's characteristics. For example, if it discovers from the data that the user is most focused in the morning, it generates a schedule that "sets important tasks in the morning and creative work in the afternoon." The generated task scheduling method is obtained as output.

[1401] Step 4:

[1402] The device displays the task scheduling method received from the server to the user. The proposed scheduling method is visually displayed using a simple and easy-to-understand UI. Important tasks and timelines are highlighted for the user's confirmation.

[1403] Step 5:

[1404] The user evaluates the proposed schedule and chooses whether to accept it. The user enters their feedback into an input form and saves it on the device. The user's selection is recorded in the database as an "evaluation."

[1405] Step 6:

[1406] The device sends the evaluation results and feedback to the server, which receives and stores the feedback data. The evaluation data is used for later regeneration.

[1407] Step 7:

[1408] The server analyzes the feedback and generates a regenerated task scheduling method. The schedule is recalculated based on the feedback data, and an improved schedule is generated. For example, a schedule is created based on the user's request to "perform important tasks in the afternoon." The regenerated schedule is output.

[1409] Step 8:

[1410] The terminal displays the regenerated task scheduling method to the user. The new schedule is visualized to the user and the modifications are clearly indicated. The user can check the schedule again.

[1411] Step 9:

[1412] The user inputs daily task progress data, including the start time, end time, and progress of the task. The progress data is saved on the device.

[1413] Step 10:

[1414] The device sends the saved task progress data to the server in real time. The data is transferred in packets in real time, and the progress status is updated on the server. A receipt confirmation message is displayed on the device.

[1415] Step 11:

[1416] The server analyzes the progress data. It uses a data analysis algorithm to analyze the progress and generate efficient task management advice. For example, the advice generated might be "reduce meeting time and allocate time to other tasks." The generated advice is then output.

[1417] Step 12:

[1418] The device notifies the user of the generated advice, which is displayed through a visual notification system and can be viewed by the user.

[1419] Step 13:

[1420] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task. The server determines the optimal timing for reminders through data analysis, for example, "set a reminder 30 minutes before the deadline." The reminder timing is then output.

[1421] Step 14:

[1422] When the device reaches the set reminder time, it will display an alert to the user. Using the alert notification system, specific reminders such as "I have an important meeting in 30 minutes" will be displayed.

[1423] Step 15:

[1424] The user confirms the reminder and performs the specified task. After confirming the reminder notification, the task is started at the appropriate time.

[1425] Step 16:

[1426] The device recognizes the user's emotional state. The emotion engine uses facial expression recognition and voice analysis technology to detect the user's emotional data. The detected emotional state is stored on the device.

[1427] Step 17:

[1428] The server analyzes the emotional data and adjusts the task scheduling method and advice based on this. If the user feels stressed based on the emotional data analysis, the server will make adjustments such as "adding relaxation time to the schedule." The adjusted schedule and advice are then output.

[1429] Step 18:

[1430] The device adjusts the tone of alerts and reminders based on information it gets from the emotion engine. For example, if the user is feeling anxious, the notification system will display an alert in a gentle tone. The adjusted reminder will then be displayed to the user.

[1431] (Application example 2)

[1432] 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."

[1433] Conventional task management systems only provide general scheduling and reminder functions, and are unable to accommodate individual users' characteristics and emotional states, resulting in insufficient efficient task management and stress reduction. Business people with ADHD and those working in food delivery services face particular challenges, making it difficult to optimize schedules and manage emotions due to the lack of individualized support.

[1434] 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 profile data from the user and proposing an optimal task scheduling method based on the data, means for analyzing the user's task progress data and generating advice for efficient task management based on the analysis, means for calculating task reminder timing and displaying an alert to the user at that timing, and means for recognizing the user's emotional state and adjusting task management based on the recognition. This enables optimal task scheduling and emotional management according to the characteristics of each user.

[1435] "Profile data" is data that includes basic information about the user, past task management methods, types of work, and so on.

[1436] A "task scheduling method" refers to a schedule for efficiently managing a user's tasks that is generated based on collected profile data.

[1437] "Task progress data" is data recorded by a user on the progress of daily tasks, and includes start time, end time, progress status, and the like.

[1438] An "alert" is a warning or reminder that is sent to the user based on the reminder timing of a specific task.

[1439] "Emotional State" refers to a user's current psychological and emotional state, which is detected using emotion recognition technology.

[1440] "Stress level" is the degree of stress determined by the user's emotional state.

[1441] "Relaxation suggestions" refer to suggestions to relieve tension and relax when the user feels high stress levels.

[1442] "Regeneration of scheduling method" is the process of reviewing the existing schedule based on user feedback and generating a new optimized schedule.

[1443] "Regeneration of improvement advice" is a process of reviewing existing advice based on user evaluation and generating new, more effective advice.

[1444] "Real-time emotional data analysis" is the process of analyzing a user's emotional state in real time and providing appropriate advice and schedules based on the results.

[1445] The present invention is a task management system specialized for food delivery services. Specific embodiments of this system will be described below.

[1446] First, the system collects profile data from the user (driver) and proposes an optimal task scheduling method based on that data. When the user logs in for the first time, they enter basic information such as their name, years of experience, and past delivery style into their device (smartphone or tablet). This generates the optimal delivery route and time allocation. The device sends this data to the server, which then generates a schedule based on the profile data and proposes it to the user.

[1447] As a concrete example, a user inputs "Name: Taro, Years of experience: 5 years, Task: ['Delivery 1', 'Delivery 2', 'Delivery 3']". The server generates a schedule such as "Finish short deliveries first in the morning, and propose a route to avoid traffic congestion in the afternoon" and displays it on the terminal. If the user provides feedback such as "I would like to make an important delivery in the afternoon", the server regenerates and proposes a new schedule.

[1448] The next step is the collection and analysis of task progress data. Users enter their daily task progress into the device, which then sends this information to the server in real time. The server analyzes the progress data and generates advice for efficient delivery management. For example, it may provide specific advice to the device, such as "traffic congestion is expected at the next delivery destination, so we suggest taking an alternative route."

[1449] The server calculates the timing of task reminders and displays alerts based on the importance and deadline of each task, and displays an alert on the device at that timing. For example, the user will be notified with a reminder such as "Pickup time in 15 minutes."

[1450] Additionally, it will include a function that recognizes the user's emotional state and adjusts task management accordingly. The device uses emotion recognition technology to detect the user's stress level using a facial recognition camera and voice analysis technology. The server analyzes the emotional data and makes relaxation suggestions if it determines that stress is high. For example, flexible responses such as "Your current stress level is high, so we suggest taking a five-minute break" will be possible.

[1451] The hardware used includes smartphones or tablets, cameras, and microphones, while the software uses tools such as Python libraries (including a hypothetical emotion recognition library).

[1452] Example prompt sentence:

[1453] "Please enter your driver's basic information (name, years of experience, tasks)"

[1454] "Update the current task progress (task ID, status)"

[1455] "Please enter your facial and voice data (for emotion recognition)"

[1456] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1457] Step 1:

[1458] When a user logs in for the first time, they enter basic information into the terminal. Specifically, they enter profile data such as their name, years of experience, past delivery style, and task list, and this information is sent to the server. The entered data becomes the basis for optimizing scheduling that reflects the user's unique characteristics (input: profile data, output: user characteristic data).

[1459] Step 2:

[1460] The server analyzes the received profile data and generates an optimal task scheduling method based on the user's characteristics. Specifically, it allocates tasks by time period based on years of experience and past delivery history (data processing: profile data analysis, output: optimal schedule proposal).

[1461] Step 3:

[1462] The device displays the generated task scheduling method to the user. The user checks the proposal and provides feedback as needed. Specifically, the user inputs feedback such as "I would like to do important tasks in the afternoon" (input: feedback, output: user evaluation data).

[1463] Step 4:

[1464] The server receives the user's feedback and regenerates the scheduling method. Specifically, it adjusts the schedule based on the user's requests and generates a new optimized schedule (data processing: feedback analysis, output: regenerated schedule).

[1465] Step 5:

[1466] The user inputs daily task progress data into the terminal. Specifically, the start time, end time, progress status, etc. of each task are entered, and this is sent to the server (input: task progress data, output: progress status data).

[1467] Step 6:

[1468] The server analyzes the progress data and generates advice for efficient task management. Specifically, it generates advice such as, "The next delivery destination is in a congested traffic area, so we suggest an alternative route" (data processing: progress data analysis, output: efficiency advice).

[1469] Step 7:

[1470] The terminal notifies the user of the generated advice, who then checks it and evaluates whether to accept it or not (input: advice evaluation, output: evaluation data).

[1471] Step 8:

[1472] The server receives the user's evaluation and regenerates advice based on the evaluation. Specifically, it readjusts the advice based on requests such as "Please suggest a route that takes less time" (data processing: evaluation analysis, output: regenerated advice).

[1473] Step 9:

[1474] The server calculates the reminder timing for each task and displays an alert on the device. Specifically, a specific reminder such as "Pickup time in 15 minutes" is set, and notifications are sent at the appropriate time (data processing: reminder timing calculation, output: alert notification).

[1475] Step 10:

[1476] To recognize the user's emotional state, the device uses a camera and microphone to analyze facial expressions and voice, and sends the obtained emotional data to a server (input: emotional data, output: analysis results).

[1477] Step 11:

[1478] The server analyzes the received emotional data and adjusts task management based on the user's emotional state. Specifically, if the user is feeling high stress, it suggests relaxation (data processing: emotional data analysis, output: relaxation suggestions).

[1479] 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.

[1480] 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.

[1481] 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.

[1482] [Fourth embodiment]

[1483] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1484] 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.

[1485] 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).

[1486] 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.

[1487] 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.

[1488] 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).

[1489] 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.

[1490] 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.

[1491] 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.

[1492] 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.

[1493] 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.

[1494] 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.

[1495] 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."

[1496] This invention provides an AI task coaching system that helps business people with ADHD manage their tasks effectively. The system's basic function is to collect user profile data and propose optimal task scheduling methods. It also has the ability to analyze the user's task progress data, generate advice for efficient task management, calculate task reminder timing, and display alerts at appropriate times.

[1497] Program processing overview

[1498] Profile data collection and analysis

[1499] When a user logs in to the system for the first time, they enter profile data such as basic information, the type of work they do, and how they manage their past tasks.

[1500] The terminal transmits this profile data to the server.

[1501] The server analyzes the received profile data and generates an optimal task schedule based on the user's characteristics. For example, a schedule could be created where important tasks are scheduled in the morning and lighter tasks are scheduled in the afternoon.

[1502] The terminal displays the generated schedule proposal to the user.

[1503] Evaluation and feedback of schedule proposals

[1504] The user evaluates the proposed schedule and chooses whether to accept it.

[1505] If accepted, the server saves this schedule and uses it for future task management.

[1506] If there is negative feedback, the server receives the feedback and displays the regenerated schedule on the terminal.

[1507] Collecting and analyzing task progress data

[1508] The user inputs daily task progress into the terminal, for example, recording the start time, end time, and progress of the task.

[1509] The terminal transmits this data to the server in real time.

[1510] The server analyzes the progress data and generates advice to encourage efficient task management. For example, advice such as "Since there are many meetings, shorten the meeting time and allocate it to other tasks" is generated.

[1511] The terminal notifies the user of the generated advice.

[1512] Advice ratings and feedback

[1513] The user evaluates the advice and decides whether to accept it.

[1514] If accepted, the server saves the advice and reflects it in future task management.

[1515] If there is negative feedback, the server receives the feedback and displays the regenerated advice on the terminal.

[1516] Calculate reminder timing and display alerts

[1517] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task, for example, "set a reminder 30 minutes before the deadline."

[1518] When the set reminder time arrives, the device will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[1519] The user confirms the reminder and performs the task.

[1520] Specific examples

[1521] 1. When a user logs in for the first time, they enter their basic information and profile data, including their name, job title, and work patterns.

[1522] 2. The device sends this profile data to the server, which analyzes it and generates a suggestion such as "Put important tasks in the morning and lighter tasks in the afternoon."

[1523] 3. The device displays the suggestions to the user, and the user provides feedback such as "I would like to do important tasks in the afternoon." The server receives the feedback and generates the suggestions again.

[1524] 4. The user inputs their daily task progress into the terminal and reports a situation such as "meetings are too long." The server analyzes this and generates advice such as "shorten the meeting time and allocate time to other tasks."

[1525] 5. The device notifies the user of the advice and asks them to evaluate whether they accept it. If they accept it, the server saves the settings.

[1526] 6. The server calculates the task reminder timing, for example, "30 minutes before the deadline." The device displays the reminder, and the user executes the task.

[1527] As described above, the system of the present invention enables users with ADHD characteristics to improve their task management skills through appropriate scheduling, progress management, and reminders.

[1528] The processing flow will be explained below.

[1529] Step 1:

[1530] When a user logs in for the first time, they enter basic information (such as name, job title, and job description).

[1531] Step 2:

[1532] The terminal sends the user's basic information to the server.

[1533] Step 3:

[1534] The server analyzes the received basic information and generates an optimal task scheduling method based on the user's characteristics and profile. For example, consider a method such as "schedule important tasks in the morning."

[1535] Step 4:

[1536] The terminal displays the generated task scheduling method to the user, and the user confirms the proposal.

[1537] Step 5:

[1538] The user evaluates the proposed schedule and either accepts it or provides feedback. If accepted, the device sends the selection to the server.

[1539] Step 6:

[1540] The server saves the user's selection and reflects it in future task management. If the user provides feedback, the server generates a new schedule based on the feedback and sends it to the terminal again.

[1541] Step 7:

[1542] The user inputs daily task progress data (start time, end time, progress, etc.) into the terminal.

[1543] Step 8:

[1544] The device transmits task progress data to the server in real time.

[1545] Step 9:

[1546] The server analyzes the received task progress data and generates advice to encourage the user to manage their tasks efficiently. For example, consider the advice "reduce meeting time and allocate time to other tasks."

[1547] Step 10:

[1548] The terminal notifies the user of the generated advice.

[1549] Step 11:

[1550] The user evaluates the advice and chooses whether to accept it, and if so, the device sends the choice to the server.

[1551] Step 12:

[1552] The server saves the user's selection and reflects it in future task management. If the user provides feedback, the server generates new advice based on the feedback and sends it to the device again.

[1553] Step 13:

[1554] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task. For example, consider a method that "sets a reminder 30 minutes before the deadline."

[1555] Step 14:

[1556] When the device reaches the set reminder time, it will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[1557] Step 15:

[1558] The user checks the reminder and executes the task. Through this process, the user manages tasks efficiently and improves productivity.

[1559] Example 1

[1560] 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."

[1561] In today's business environment, it is extremely difficult for business people with ADHD to efficiently manage their tasks. Conventional task management systems perform uniform scheduling and progress management without fully considering the characteristics of each user, making it impossible to provide optimal support to each individual user. For this reason, there is a need for an individually optimized task management support system that allows business people with ADHD to perform at their best.

[1562] 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.

[1563] In this invention, the server includes means for collecting profile data from a user and proposing an optimal task scheduling method based on the data, means for analyzing the user's task progress data and generating efficient task management advice based on the analysis, means for calculating task reminder timings and displaying alerts to the user at those timings, means for receiving user feedback and regenerating a task schedule based on the feedback, means for collecting daily task progress data, analyzing progress based on the data, and generating improvement advice, means for notifying the user of the task management advice, receiving an evaluation of the advice, and means for regenerating the improvement advice based on the evaluation. This enables effective and flexible task management, allowing business people with ADHD characteristics to acquire an optimal task management method and maximize their performance.

[1564] "Profile data" is data that indicates the characteristics of a user, such as basic information about the user, the type of work, and past task management methods.

[1565] A "task scheduling method" is a method for determining task priorities and time allocations based on profile data of a specific user.

[1566] "Task progress data" refers to information such as the start time, end time, and progress status of each task.

[1567] "Advice for efficient task management" is a suggestion based on task progress data to help users carry out their work more effectively.

[1568] "Remind timing" refers to the time at which an alert is displayed to the user, determined based on the deadline and importance of the task.

[1569] An "alert" is a message that is sent to the user at a set reminder timing, urging the user to perform a specific task.

[1570] "Feedback" refers to evaluations and comments that users make in response to the system's suggestions and advice.

[1571] "Regeneration" is the process by which the system creates new suggestions and advice based on feedback and evaluation.

[1572] This invention relates to an AI task coaching system that helps business people with ADHD manage their tasks effectively. The system's basic function is to collect user profile data and propose optimal task scheduling methods. It also has the ability to analyze the user's task progress data, generate advice for efficient task management, calculate task reminder timing, and display alerts at appropriate times.

[1573] The hardware required to implement this system includes the terminals used by users (e.g., PCs and smartphones), the communication infrastructure for sending and receiving data, and the server for analyzing and processing the data.The software includes an application that provides the user interface and an AI algorithm that analyzes data and generates schedules.

[1574] A specific example of the operation of the system of the present invention will be described below.

[1575] Profile data collection and analysis

[1576] 1. When a user logs in for the first time, they enter their basic information (such as name, position, work style, etc.) and information about their past task management methods.

[1577] 2. The device transmits the entered profile data to the server in real time.

[1578] 3. The server analyzes the received profile data and generates an optimal task schedule based on the user's characteristics, such as "set important tasks in the morning and lighter tasks in the afternoon."

[1579] 4. The terminal displays the generated schedule proposal to the user.

[1580] Evaluation and feedback of schedule proposals

[1581] 1. The user reviews the proposed schedule and evaluates whether it is acceptable or not. If necessary, the user can also enter feedback on the schedule into the terminal.

[1582] 2. The device sends the user's feedback to the server.

[1583] 3. The server receives the feedback and regenerates the schedule as needed, which is then displayed to the user again via the terminal.

[1584] Collecting and analyzing task progress data

[1585] 1. The user enters daily task progress data (start time, end time, progress status, etc.) into the terminal.

[1586] 2. The device transmits this data to the server in real time.

[1587] 3. The server analyzes the progress data and generates advice to encourage efficient task management. For example, advice such as "shorten meeting time and allocate time to other tasks" could be considered.

[1588] 4. The device notifies the user of the generated advice.

[1589] Advice ratings and feedback

[1590] 1. The user checks the advice and evaluates whether or not they accept it. If they accept it, they can modify their task management in accordance with the advice.

[1591] 2. The device sends the user's rating and feedback to the server, which then generates new advice based on the feedback, if necessary.

[1592] Calculate reminder timing and display alerts

[1593] 1. The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task, for example, "set a reminder 30 minutes before the deadline."

[1594] 2. When the set reminder time arrives, the device will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[1595] 3. The user acknowledges the alert and performs the task.

[1596] Specific examples

[1597] Prompt Sentence Examples

[1598] "When a user with ADHD traits logs into a task management system for the first time, they enter their profile data (name, job title, type of work, past task management methods). The server then analyzes the data and suggests a schedule that assigns important tasks in the morning and lighter tasks in the afternoon. Please explain the program's process for making appropriate suggestions."

[1599] This allows business people with ADHD characteristics to receive individually optimized task management support, enabling them to work efficiently and effectively. The present invention adaptively regenerates schedules and advice based on user feedback and performs sequential optimization, thereby achieving even greater performance improvements.

[1600] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1601] System program processing flow

[1602] Step 1: Collect profile data

[1603] Specific behavior:

[1604] When a user logs in to the system for the first time, they enter their basic information (name, job title, work patterns) and information about their past task management methods.

[1605] Input and Output:

[1606] The profile data (basic information and task management method) entered by the user is the input, which becomes the raw data for the next process.

[1607] Step 2: Submit profile data

[1608] Specific behavior:

[1609] The terminal transmits the profile data entered by the user to the server in real time.

[1610] Input and Output:

[1611] The profile data is input to the terminal, and is sent to the server, after which the profile data is output to the server.

[1612] Step 3: Analyze profile data and generate schedule

[1613] Specific behavior:

[1614] The server then uses AI algorithms to analyze the received profile data, identifying user characteristics (such as times when they tend to concentrate and past task management trends).

[1615] Input and Output:

[1616] The input is the profile data entered into the server, and the output is the optimal task schedule generated through analysis by the AI ​​algorithm. For example, a suggestion such as "set important tasks in the morning and lighter tasks in the afternoon" is generated.

[1617] Step 4: View schedule suggestions

[1618] Specific behavior:

[1619] The terminal displays the schedule proposal sent from the server to the user, including specific task start and end times and the priority of each task.

[1620] Input and Output:

[1621] The input is the schedule proposal sent from the server, and the output is the schedule proposal displayed on the terminal.

[1622] Step 5: Evaluate and provide feedback on the proposed schedule

[1623] Specific behavior:

[1624] The user checks the proposed schedule and evaluates whether to accept it. If necessary, the user inputs feedback on the proposal into the terminal. The terminal then transmits the user's feedback to the server.

[1625] Input and Output:

[1626] User feedback is the input, and if the server regenerates a schedule based on that feedback, the regenerated schedule is the output.

[1627] Step 6: Enter and submit task progress data

[1628] Specific behavior:

[1629] Users input their daily task progress (start time, end time, progress status, etc.) into the terminal, which then transmits this data to the server in real time.

[1630] Input and Output:

[1631] Task progress data entered by the user is the input, and after the operation of transmitting the data to the server, the task progress data is output to the server.

[1632] Step 7: Analyze progress data and generate advice

[1633] Specific behavior:

[1634] The server analyzes the received task progress data, identifying which tasks are progressing as planned and which are behind schedule, and generates advice for efficient task management based on the analysis results.

[1635] Input and Output:

[1636] The input is task progress data entered into the server, and the output is task management advice generated through analysis. For example, advice such as "shorten meeting time and allocate time to other tasks" is generated.

[1637] Step 8: Advice Notification

[1638] Specific behavior:

[1639] The device will then notify the user of the advice sent from the server, including specific improvements and next steps to take.

[1640] Input and Output:

[1641] The advice sent from the server is the input, and the activity of displaying it on the terminal is the output.

[1642] Step 9: Evaluate and provide feedback on the advice

[1643] Specific behavior:

[1644] The user checks the received advice and evaluates whether or not it is acceptable. If it is acceptable, the user can adjust the task management according to the advice. The terminal sends the user's evaluation and feedback to the server.

[1645] Input and Output:

[1646] The user's ratings and feedback are the input, and when the server regenerates the advice, the regenerated advice is the output.

[1647] Step 10: Calculate the reminder timing

[1648] Specific behavior:

[1649] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task, for example, "set a reminder 30 minutes before the deadline."

[1650] Input and Output:

[1651] The user's task list (task information) is the input, and the reminder timing is the calculation result and the output.

[1652] Step 11: Displaying alerts

[1653] Specific behavior:

[1654] When the set reminder time arrives, the device displays an alert to the user. For example, it may notify the user that "an important meeting will be held in 30 minutes." The user can confirm the alert and carry out the task.

[1655] Input and Output:

[1656] The reminder timing is the input and the alert displayed to the user is the output.

[1657] Through these steps, the system provides individually optimized task management support for business people with ADHD traits, achieving efficient and effective task management for users.

[1658] (Application example 1)

[1659] 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."

[1660] Existing systems are insufficient for business people with ADHD to effectively manage their tasks. In particular, the use of wearable devices can enhance real-time task management and reminder functions, but there are a lack of concrete implementation examples and proposals in this field.

[1661] 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.

[1662] In this invention, the server includes means for collecting profile data from a user and proposing an optimal task scheduling method based on the data, means for analyzing the user's task progress data and generating advice for efficient task management based on the analysis, means for calculating task reminder timings and displaying alerts to the user at those timings, means for the user to input profile data and task progress by voice using a wearable device, and means for displaying advice and alerts on the wearable device, thereby enabling the user to manage tasks effectively in real time.

[1663] "Profile data" is personal information necessary for task management, such as the user's basic information, type of work, and past task management methods.

[1664] "Task scheduling" is a method for optimizing the order in which tasks are executed and the allocation of time based on user profile data.

[1665] "Task progress data" is information about the process of executing a task, such as the start time, end time, and progress status of the user's daily task.

[1666] "Advice" is a recommendation or suggestion for a user to efficiently complete a task.

[1667] "Remind timing" is the optimal timing to display a reminder to the user based on the deadline and importance of the task.

[1668] An "alert" is a notification that is displayed to the user when a specific reminder timing is reached.

[1669] A "wearable device" is a computing device that is worn on the body, including smart glasses and smart watches.

[1670] "Voice input" is a method in which a user inputs information into a device by voice.

[1671] "User" refers to individuals who use this system, particularly business people with ADHD characteristics.

[1672] The "server" is a central processing unit that analyzes data received from users and performs task scheduling and advice generation.

[1673] This invention provides an AI task coaching system to help business people with ADHD manage their tasks effectively. This system uses a wearable device (e.g., smart glasses) to provide real-time support for task management.

[1674] Hardware and software used

[1675] Hardware: Use wearable devices such as smart glasses that allow users to receive assistance with task management while working, with minimal impact on vision.

[1676] Software: The server-side data analysis system is based on Python and uses Pandas and Scikit-learn for data analysis. WebSocket is used for real-time data communication, and MySQL is used as the database.

[1677] Data processing and calculation

[1678] Collection and analysis of profile data upon first login

[1679] 1. The device collects profile data through user voice input.

[1680] Example: A user speaks, "I'm a virtual store owner and I prefer to do important tasks in the afternoon."

[1681] 2. The smart glasses send this data to the server.

[1682] 3. The server analyzes the received profile data and generates an optimal task schedule.

[1683] 4. The smart glasses display the generated schedule suggestions to the user.

[1684] Collecting and analyzing task progress data

[1685] 1. Users use smart glasses to report their daily task progress via voice.

[1686] Example: A user reports verbally, "Today's meeting time has been shortened by 30 minutes."

[1687] 2. The smart glasses transmit these data to the server in real time.

[1688] 3. The server analyzes the progress data and generates advice for efficient task management, such as reducing meeting time and allocating time to other tasks.

[1689] 4. The smart glasses notify the user of the generated advice.

[1690] Calculate reminder timing and display alerts

[1691] 1. The server monitors the user's task list and calculates the reminder timing based on the deadline and importance of each task.

[1692] Example: Set a reminder alert 30 minutes before the deadline.

[1693] 2. The smart glasses will display an alert to the user when the set reminder time arrives.

[1694] Examples of concrete examples and prompts

[1695] 1. When a user logs in for the first time, they say, "I'm a virtual store owner and I want to do important tasks in the afternoon."

[1696] 2. The smart glasses send this profile data to a server, which analyzes the data and generates a task schedule such as "check inventory in the morning and process orders in the afternoon."

[1697] 3. The smart glasses display the generated schedule to the user, who then confirms by saying "That's OK."

[1698] Example prompts

[1699] Enter your user profile:

[1700] Example: "I'm a virtual store owner and I prefer to do important tasks in the afternoon."

[1701] As described above, the present invention is a system that supports users with ADHD characteristics in effectively managing tasks in real time and carrying out their work efficiently, and is designed to maximize the user experience using wearable devices.

[1702] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1703] Step 1: Collect user profile data

[1704] Users use smart glasses to input their profile data by voice.

[1705] Input: Spoken information from the user (e.g., "I'm a virtual store owner and I prefer to do important tasks in the afternoon.")

[1706] Output: Speech-to-text profile data

[1707] Specific operation: The voice recognition function of the smart glasses recognizes voice input as text data.

[1708] Step 2: Submit profile data

[1709] The terminal (smart glasses) transmits the collected profile data to the server.

[1710] Input: Textual profile data

[1711] Output: Profile data sent to the server

[1712] Specific operation: The smart glasses use a network connection to send profile data to a server.

[1713] Step 3: Analyze the profile data

[1714] The server analyzes the received profile data and generates an optimal task schedule based on the user's characteristics.

[1715] Input: Profile data sent to the server

[1716] Output: The generated optimal task schedule

[1717] Specific operation: A data analysis system (e.g., Pandas, Scikit-learn) on the server analyzes the profile data and generates a schedule using a scheduling algorithm.

[1718] Step 4: View the task schedule

[1719] The terminal (smart glasses) displays the generated task schedule to the user.

[1720] Input: The optimal task schedule sent by the server

[1721] Output: The task schedule as seen by the user

[1722] Specific operation: The display function of the smart glasses displays schedule information.

[1723] Step 5: Enter and submit task progress data

[1724] The user inputs the task progress status into the smart glasses by voice, and the smart glasses send the data to the server.

[1725] Input: Task progress (e.g., "Today's meeting time was shortened by 30 minutes")

[1726] Output: Task progress data sent to the server

[1727] Specific operation: The voice recognition function of the smart glasses converts voice input into text and sends it to a server via the network.

[1728] Step 6: Analyze task progress data

[1729] The server analyzes the received task progress data and generates advice for efficient task management.

[1730] Input: Task progress data sent to the server

[1731] Output: Generated advice for efficient task management

[1732] How it works: A data analysis system on the server analyzes task progress data and generates advice using an AI model.

[1733] Step 7: Advice Notification

[1734] The terminal (smart glasses) notifies the user of the generated advice.

[1735] Input: Task management advice sent from the server

[1736] Output: Advice displayed to the user

[1737] Specific operation: The display function of the smart glasses displays advice information.

[1738] Step 8: Calculate reminder timing and set alerts

[1739] The server monitors the user's task list, calculates reminder timing based on the deadline and importance of each task, and sets alerts.

[1740] Input: Task list stored on the server

[1741] Output: Calculated reminder timings and configured alerts

[1742] Specific operation: A timing calculation algorithm on the server determines the reminder timing based on the task deadline and importance, and sets an alert.

[1743] Step 9: Viewing Alerts

[1744] The device (smart glasses) displays an alert to the user based on the set reminder timing.

[1745] Input: Alert information sent from the server

[1746] Output: The reminder alert that is displayed to the user

[1747] Specific operation: The display function of the smart glasses displays the alert information.

[1748] 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.

[1749] This invention provides an AI task coaching system to help business people with ADHD manage their tasks efficiently. The system's basic function is to collect user profile data and propose optimal task scheduling methods. It also analyzes the user's task progress data, generates advice for efficient task management, calculates task reminder timing, and displays alerts at appropriate times. Furthermore, by combining it with an emotion engine, the system recognizes the user's emotional state and improves the accuracy of task management based on this.

[1750] Program processing overview

[1751] Profile data collection and analysis

[1752] When a user logs in to the system for the first time, they enter profile data such as basic information, the type of work they do, and how they manage their past tasks.

[1753] The terminal transmits this profile data to the server.

[1754] The server generates an optimal task scheduling method based on the received profile data, taking into account the user's characteristics. For example, it considers a schedule such as "setting important tasks in the morning and doing creative work in the afternoon."

[1755] The terminal displays the generated task scheduling method to the user.

[1756] Evaluation and feedback of schedule proposals

[1757] The user evaluates the proposed schedule and chooses whether to accept it.

[1758] If accepted, the server saves the schedule and uses it for future task management.

[1759] If there is negative feedback, the server receives the feedback and displays the regenerated schedule on the terminal.

[1760] Collecting and analyzing task progress data

[1761] The user inputs daily task progress data (start time, end time, progress status, etc.) into the terminal.

[1762] The terminal transmits task progress data to the server in real time.

[1763] The server analyzes the progress data and generates advice to encourage the user to manage their tasks efficiently. For example, consider the advice "reduce meeting time and allocate time to other tasks."

[1764] The terminal notifies the user of the generated advice.

[1765] Advice ratings and feedback

[1766] The user evaluates the advice and decides whether to accept it.

[1767] If accepted, the server saves the advice and reflects it in future task management.

[1768] If there is negative feedback, the server receives the feedback and displays the regenerated advice on the terminal.

[1769] Calculate reminder timing and display alerts

[1770] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task. For example, consider a method of "setting a reminder 30 minutes before the deadline."

[1771] When the set reminder time arrives, the device will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[1772] The user confirms the reminder and performs the task.

[1773] Introducing the Emotion Engine

[1774] The device uses an emotion engine to recognize the user's emotional state, for example, by using facial expression recognition and voice analysis techniques to detect the user's emotions.

[1775] The server analyzes the user's emotional data and adjusts task scheduling methods and advice accordingly, such as "if the user is feeling stressed, add relaxation time to the schedule."

[1776] The device also adjusts the tone of alerts and reminders based on information obtained from the emotion engine. For example, if the user is feeling anxious, the device may use a gentler tone.

[1777] Specific examples

[1778] 1. When a user logs in for the first time, they enter basic information and profile data, including their name, job title, and work patterns.

[1779] 2. The device sends the profile data to the server, which analyzes the data and generates a suggestion such as "focus on important tasks in the morning and do lighter work in the afternoon."

[1780] 3. The device displays the suggestions, and the user provides feedback such as "I want to do important tasks in the afternoon." The server receives the feedback and generates the suggestions again.

[1781] 4. The user enters their task progress data daily, reporting, for example, "too much time spent in meetings." The server analyzes this and generates advice such as "shorten your meeting time and allocate more time to other tasks."

[1782] 5. The device notifies the user of the advice and asks them to evaluate whether they accept it. If they accept it, the server saves the settings.

[1783] 6. The server calculates the task reminder timing and sets it to "Set a reminder 30 minutes before the deadline." The device displays the reminder and the user performs the task.

[1784] 7. The emotion engine recognizes the user's emotions and makes adjustments such as adding relaxation time to the schedule if the user is tense.

[1785] This process allows users to achieve task management that is optimized for their own characteristics and emotional state, improving productivity.

[1786] The processing flow will be explained below.

[1787] MODE FOR CARRYING OUT THE INVENTION (PROCESSING STEPS)

[1788] Step 1:

[1789] When a user logs in for the first time, they enter basic information (such as name, job title, and job description).

[1790] Step 2:

[1791] The terminal sends the user's basic information to the server.

[1792] Step 3:

[1793] The server analyzes the received basic information and generates an optimal task scheduling method based on the user's characteristics and profile. For example, it may suggest a schedule such as "set important tasks in the morning and do creative work in the afternoon."

[1794] Step 4:

[1795] The terminal displays the generated task scheduling method to the user, and the user confirms the proposal.

[1796] Step 5:

[1797] The user evaluates the proposed schedule and chooses whether to accept it, and if so, the terminal sends the choice to the server.

[1798] Step 6:

[1799] The server saves the user's selection and reflects it in future task management. If the user provides feedback, the server regenerates a new schedule based on the feedback information and sends it to the terminal again.

[1800] Step 7:

[1801] The user inputs daily task progress data (start time, end time, progress, etc.) into the terminal.

[1802] Step 8:

[1803] The device transmits task progress data to the server in real time.

[1804] Step 9:

[1805] The server analyzes the received task progress data and generates advice to encourage the user to manage their tasks more efficiently. For example, the advice generated is "reduce meeting time and allocate more time to other tasks."

[1806] Step 10:

[1807] The terminal notifies the user of the generated advice.

[1808] Step 11:

[1809] The user evaluates the advice and chooses whether to accept it, and if so, the terminal sends the choice to the server.

[1810] Step 12:

[1811] The server saves the user's selection and reflects it in future task management. If the user provides feedback, the server regenerates advice based on the feedback information and sends it to the device again.

[1812] Step 13:

[1813] The device uses an emotion engine to recognize the user's emotional state. The emotion engine detects the user's emotions through facial expression recognition and voice analysis technology.

[1814] Step 14:

[1815] The server analyzes the user's emotional data and adjusts task scheduling and advice accordingly. For example, if the user is feeling stressed, it may add relaxation time to the schedule.

[1816] Step 15:

[1817] The device will adjust the tone of alerts and reminders based on information it gets from the emotion engine. For example, if the user is feeling anxious, the notification will be delivered in a calmer tone.

[1818] Step 16:

[1819] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task. For example, consider a method that "sets a reminder 30 minutes before the deadline."

[1820] Step 17:

[1821] When the device reaches the set reminder time, it will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[1822] Step 18:

[1823] The user checks the reminder and executes the task. Through this process, the user manages tasks efficiently and improves productivity.

[1824] Example 2

[1825] 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."

[1826] This invention relates to a system for efficient task management for business people with ADHD, and aims to provide a task scheduling method and advice optimized for the user's characteristics and emotional state by effectively utilizing the user's profile data, task progress data, and emotional state. In particular, it aims to solve the problem of improving productivity by taking into account the efficiency of task management and the user's emotional state.

[1827] 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.

[1828] In this invention, the server includes means for collecting profile data from a user and proposing an optimal task scheduling method based on the data, means for analyzing the user's task progress data and generating advice for efficient task management based on the analysis, and means for recognizing the user's emotional state and adjusting the task scheduling method and alert expression based on the emotional data, thereby enabling optimal task management that takes into account the user's characteristics and emotional state.

[1829] A "user" is a person or individual who uses the system.

[1830] "Profile data" is data about a user's basic information, work patterns, and past task management methods.

[1831] A "task scheduling method" is a method for arranging tasks and allocating time optimally based on user characteristics.

[1832] "Task progress data" refers to data recorded by a user, such as the start time, end time, and progress of daily tasks.

[1833] "Reminder timing" is a specific time or time frame for displaying an alert to the user based on the deadline or importance of the task.

[1834] "Emotional state" refers to a state that indicates the user's psychological state or mood, and is data that is detected using facial expression recognition and voice analysis technology.

[1835] "Alert expressions" are the wording and manner in which notifications and reminders are given to users, and are adjusted based on their emotional state.

[1836] "Feedback" refers to opinions and evaluations that users provide in response to suggestions and advice from the system.

[1837] "Regeneration" is the process of recreating new task scheduling methods and advice based on feedback and evaluation.

[1838] A "server" is a computer system that receives data sent by a user, analyzes it, generates results, and sends them to a terminal.

[1839] A "terminal" is a device through which a user inputs data, and is a device that transmits and receives data to and from a server.

[1840] The present invention is an AI task coaching system that enables business people with ADHD to efficiently manage their tasks. An embodiment of this system will be described in detail below.

[1841] Hardware and Software

[1842] Hardware

[1843] Server: High-performance computer system

[1844] Device: The computer, tablet, or smartphone used by the user

[1845] software

[1846] Profile Data Collection Tool: A form for users to enter basic information

[1847] Data Analysis Algorithms: An analytical tool for generating task scheduling methods

[1848] Emotion Engine: Facial expression recognition and speech analysis techniques to recognize a user's emotional state

[1849] Notification system: a notification tool to display task reminders and alerts

[1850] How it works

[1851] Profile data collection and analysis

[1852] 1. When a user logs in for the first time, they enter basic information and profile data into the system, including their name, job title, work patterns, and past task management methods.

[1853] 2. The device sends the entered profile data to the server.

[1854] 3. The server analyzes the received profile data and generates an optimal task scheduling method based on the user's characteristics. For example, consider a schedule that "sets important tasks in the morning and creative work in the afternoon."

[1855] 4. The terminal displays the generated task scheduling method to the user.

[1856] Collecting and analyzing task progress data

[1857] 1. The user inputs daily task progress data (start time, end time, progress, etc.) into the terminal.

[1858] 2. The device sends task progress data to the server in real time.

[1859] 3. The server analyzes the progress data and generates advice to encourage the user to manage their tasks more efficiently. For example, consider the advice "reduce meeting time and allocate time to other tasks."

[1860] 4. The device notifies the user of the generated advice.

[1861] Calculate reminder timing and display alerts

[1862] 1. The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task, for example, "set a reminder 30 minutes before the deadline."

[1863] 2. When the set reminder time arrives, the device will display an alert to the user, for example, notifying them that they have an important meeting in 30 minutes.

[1864] 3. The user confirms the reminder and performs the specified task.

[1865] Introducing the Emotion Engine

[1866] 1. The device uses an emotion engine to recognize the user's emotional state. It uses facial expression recognition and voice analysis technology to detect the user's emotions.

[1867] 2. The server analyzes the user's emotional data and adjusts task scheduling methods and advice accordingly, such as "if the user is feeling stressed, add relaxation time to the schedule."

[1868] 3. The device also adjusts the tone of alerts and reminders based on the information it obtains from the emotion engine. For example, if the user is feeling anxious, the device will use a gentler tone.

[1869] Specific examples

[1870] 1. When a user logs in for the first time, they enter basic information and profile data, including their name, job title, and work patterns.

[1871] 2. The device sends the profile data to the server, which analyzes the data and generates a suggestion such as "focus on important tasks in the morning and do lighter work in the afternoon."

[1872] 3. The device displays the suggestions, and the user provides feedback such as "I want to do important tasks in the afternoon." The server receives the feedback and generates the suggestions again.

[1873] 4. The user inputs task progress data daily and reports that they spend too much time in meetings. The server analyzes this and generates advice such as "shorten your meeting time and allocate it to other tasks."

[1874] 5. The device notifies the user of the advice and asks them to evaluate whether they accept it. If they accept it, the server saves the settings.

[1875] 6. The server calculates the task reminder timing and sets a reminder 30 minutes before the deadline. The device displays the reminder and the user executes the task.

[1876] 7. The emotion engine recognizes the user's emotions and makes adjustments such as adding relaxation time to the schedule if the user is tense.

[1877] This process allows users to achieve task management that is optimized for their own characteristics and emotional state, improving productivity.

[1878] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1879] Step 1:

[1880] When a user logs in for the first time, they enter their profile data, including their name, job title, work patterns, and past task management methods. The entered data is saved on the device.

[1881] Step 2:

[1882] The device sends the saved profile data to the server. This is the process of transferring input data as packets to the server. After successful transmission, a data reception confirmation message is displayed on the device.

[1883] Step 3:

[1884] The server analyzes the received profile data. It uses a data mining algorithm for analysis to generate a task scheduling method based on the user's characteristics. For example, if it discovers from the data that the user is most focused in the morning, it generates a schedule that "sets important tasks in the morning and creative work in the afternoon." The generated task scheduling method is obtained as output.

[1885] Step 4:

[1886] The device displays the task scheduling method received from the server to the user. The proposed scheduling method is visually displayed using a simple and easy-to-understand UI. Important tasks and timelines are highlighted for the user's confirmation.

[1887] Step 5:

[1888] The user evaluates the proposed schedule and chooses whether to accept it. The user enters their feedback into an input form and saves it on the device. The user's selection is recorded in the database as an "evaluation."

[1889] Step 6:

[1890] The device sends the evaluation results and feedback to the server, which receives and stores the feedback data. The evaluation data is used for later regeneration.

[1891] Step 7:

[1892] The server analyzes the feedback and generates a regenerated task scheduling method. The schedule is recalculated based on the feedback data, and an improved schedule is generated. For example, a schedule is created based on the user's request to "perform important tasks in the afternoon." The regenerated schedule is output.

[1893] Step 8:

[1894] The terminal displays the regenerated task scheduling method to the user. The new schedule is visualized to the user and the modifications are clearly indicated. The user can check the schedule again.

[1895] Step 9:

[1896] The user inputs daily task progress data, including the start time, end time, and progress of the task. The progress data is saved on the device.

[1897] Step 10:

[1898] The device sends the saved task progress data to the server in real time. The data is transferred in packets in real time, and the progress status is updated on the server. A receipt confirmation message is displayed on the device.

[1899] Step 11:

[1900] The server analyzes the progress data. It uses a data analysis algorithm to analyze the progress and generate efficient task management advice. For example, the advice generated might be "reduce meeting time and allocate time to other tasks." The generated advice is then output.

[1901] Step 12:

[1902] The device notifies the user of the generated advice, which is displayed through a visual notification system and can be viewed by the user.

[1903] Step 13:

[1904] The server monitors the user's task list and calculates the timing of reminders based on the deadline and importance of each task. The server determines the optimal timing for reminders through data analysis, for example, "set a reminder 30 minutes before the deadline." The reminder timing is then output.

[1905] Step 14:

[1906] When the device reaches the set reminder time, it will display an alert to the user. Using the alert notification system, specific reminders such as "I have an important meeting in 30 minutes" will be displayed.

[1907] Step 15:

[1908] The user confirms the reminder and performs the specified task. After confirming the reminder notification, the task is started at the appropriate time.

[1909] Step 16:

[1910] The device recognizes the user's emotional state. The emotion engine uses facial expression recognition and voice analysis technology to detect the user's emotional data. The detected emotional state is stored on the device.

[1911] Step 17:

[1912] The server analyzes the emotional data and adjusts the task scheduling method and advice based on this. If the user feels stressed based on the emotional data analysis, the server will make adjustments such as "adding relaxation time to the schedule." The adjusted schedule and advice are then output.

[1913] Step 18:

[1914] The device adjusts the tone of alerts and reminders based on information it gets from the emotion engine. For example, if the user is feeling anxious, the notification system will display an alert in a gentle tone. The adjusted reminder will then be displayed to the user.

[1915] (Application example 2)

[1916] 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."

[1917] Conventional task management systems only provide general scheduling and reminder functions, and are unable to accommodate individual users' characteristics and emotional states, resulting in insufficient efficient task management and stress reduction. Business people with ADHD and those working in food delivery services face particular challenges, making it difficult to optimize schedules and manage emotions due to the lack of individualized support.

[1918] 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 profile data from the user and proposing an optimal task scheduling method based on the data, means for analyzing the user's task progress data and generating advice for efficient task management based on the analysis, means for calculating task reminder timing and displaying an alert to the user at that timing, and means for recognizing the user's emotional state and adjusting task management based on the recognition. This enables optimal task scheduling and emotional management according to the characteristics of each user.

[1919] "Profile data" is data that includes basic information about the user, past task management methods, types of work, and so on.

[1920] A "task scheduling method" refers to a schedule for efficiently managing a user's tasks that is generated based on collected profile data.

[1921] "Task progress data" is data recorded by a user on the progress of daily tasks, and includes start time, end time, progress status, and the like.

[1922] An "alert" is a warning or reminder that is sent to the user based on the reminder timing of a specific task.

[1923] "Emotional State" refers to a user's current psychological and emotional state, which is detected using emotion recognition technology.

[1924] "Stress level" is the degree of stress determined by the user's emotional state.

[1925] "Relaxation suggestions" refer to suggestions to relieve tension and relax when the user feels high stress levels.

[1926] "Regeneration of scheduling method" is the process of reviewing the existing schedule based on user feedback and generating a new optimized schedule.

[1927] "Regeneration of improvement advice" is a process of reviewing existing advice based on user evaluation and generating new, more effective advice.

[1928] "Real-time emotional data analysis" is the process of analyzing a user's emotional state in real time and providing appropriate advice and schedules based on the results.

[1929] The present invention is a task management system specialized for food delivery services. Specific embodiments of this system will be described below.

[1930] First, the system collects profile data from the user (driver) and proposes an optimal task scheduling method based on that data. When the user logs in for the first time, they enter basic information such as their name, years of experience, and past delivery style into their device (smartphone or tablet). This generates the optimal delivery route and time allocation. The device sends this data to the server, which then generates a schedule based on the profile data and proposes it to the user.

[1931] As a concrete example, a user inputs "Name: Taro, Years of experience: 5 years, Task: ['Delivery 1', 'Delivery 2', 'Delivery 3']". The server generates a schedule such as "Finish short deliveries first in the morning, and propose a route to avoid traffic congestion in the afternoon" and displays it on the terminal. If the user provides feedback such as "I would like to make an important delivery in the afternoon", the server regenerates and proposes a new schedule.

[1932] The next step is the collection and analysis of task progress data. Users enter their daily task progress into the device, which then sends this information to the server in real time. The server analyzes the progress data and generates advice for efficient delivery management. For example, it may provide specific advice to the device, such as "traffic congestion is expected at the next delivery destination, so we suggest taking an alternative route."

[1933] The server calculates the timing of task reminders and displays alerts based on the importance and deadline of each task, and displays an alert on the device at that timing. For example, the user will be notified with a reminder such as "Pickup time in 15 minutes."

[1934] Additionally, it will include a function that recognizes the user's emotional state and adjusts task management accordingly. The device uses emotion recognition technology to detect the user's stress level using a facial recognition camera and voice analysis technology. The server analyzes the emotional data and makes relaxation suggestions if it determines that stress is high. For example, flexible responses such as "Your current stress level is high, so we suggest taking a five-minute break" will be possible.

[1935] The hardware used includes smartphones or tablets, cameras, and microphones, while the software uses tools such as Python libraries (including a hypothetical emotion recognition library).

[1936] Example prompt sentence:

[1937] "Please enter your driver's basic information (name, years of experience, tasks)"

[1938] "Update the current task progress (task ID, status)"

[1939] "Please enter your facial and voice data (for emotion recognition)"

[1940] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1941] Step 1:

[1942] When a user logs in for the first time, they enter basic information into the terminal. Specifically, they enter profile data such as their name, years of experience, past delivery style, and task list, and this information is sent to the server. The entered data becomes the basis for optimizing scheduling that reflects the user's unique characteristics (input: profile data, output: user characteristic data).

[1943] Step 2:

[1944] The server analyzes the received profile data and generates an optimal task scheduling method based on the user's characteristics. Specifically, it allocates tasks by time period based on years of experience and past delivery history (data processing: profile data analysis, output: optimal schedule proposal).

[1945] Step 3:

[1946] The device displays the generated task scheduling method to the user. The user checks the proposal and provides feedback as needed. Specifically, the user inputs feedback such as "I would like to do important tasks in the afternoon" (input: feedback, output: user evaluation data).

[1947] Step 4:

[1948] The server receives the user's feedback and regenerates the scheduling method. Specifically, it adjusts the schedule based on the user's requests and generates a new optimized schedule (data processing: feedback analysis, output: regenerated schedule).

[1949] Step 5:

[1950] The user inputs daily task progress data into the terminal. Specifically, the start time, end time, progress status, etc. of each task are entered, and this is sent to the server (input: task progress data, output: progress status data).

[1951] Step 6:

[1952] The server analyzes the progress data and generates advice for efficient task management. Specifically, it generates advice such as, "The next delivery destination is in a congested traffic area, so we suggest an alternative route" (data processing: progress data analysis, output: efficiency advice).

[1953] Step 7:

[1954] The terminal notifies the user of the generated advice, who then checks it and evaluates whether to accept it or not (input: advice evaluation, output: evaluation data).

[1955] Step 8:

[1956] The server receives the user's evaluation and regenerates advice based on the evaluation. Specifically, it readjusts the advice based on requests such as "Please suggest a route that takes less time" (data processing: evaluation analysis, output: regenerated advice).

[1957] Step 9:

[1958] The server calculates the reminder timing for each task and displays an alert on the device. Specifically, a specific reminder such as "Pickup time in 15 minutes" is set, and notifications are sent at the appropriate time (data processing: reminder timing calculation, output: alert notification).

[1959] Step 10:

[1960] To recognize the user's emotional state, the device uses a camera and microphone to analyze facial expressions and voice, and sends the obtained emotional data to a server (input: emotional data, output: analysis results).

[1961] Step 11:

[1962] The server analyzes the received emotional data and adjusts task management based on the user's emotional state. Specifically, if the user is feeling high stress, it suggests relaxation (data processing: emotional data analysis, output: relaxation suggestions).

[1963] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1964] 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.

[1965] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1966] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1967] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1968] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1969] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1970] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1971] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1972] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1973] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1974] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1975] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1976] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1977] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1978] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1979] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1980] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1981] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1982] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1983] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1984] The following is further disclosed regarding the above embodiment.

[1985] (Claim 1)

[1986] means for collecting profile data from a user and proposing an optimal task scheduling method based on the data;

[1987] means for analyzing a user's task progress data and generating advice for efficient task management based on the analysis;

[1988] means for calculating a timing for reminding a task and displaying an alert to a user at that timing;

[1989] A system including:

[1990] (Claim 2)

[1991] means for receiving user feedback and regenerating the scheduling method based on the feedback;

[1992] means for suggesting the regenerated scheduling method to a user;

[1993] The system of claim 1 further comprising:

[1994] (Claim 3)

[1995] means for receiving a user's advice rating and regenerating improvement advice based on the rating;

[1996] means for notifying a user of the regenerated improvement advice;

[1997] The system of claim 1 further comprising:

[1998] "Example 1"

[1999] (Claim 1)

[2000] means for collecting profile data from a user and proposing an optimal task scheduling method based on the data;

[2001] means for analyzing a user's task progress data and generating advice for efficient task management based on the analysis;

[2002] means for calculating a timing for reminding a task and displaying an alert to a user at that timing;

[2003] means for receiving user feedback and regenerating a task schedule based on the feedback;

[2004] a means for collecting daily task progress data, analyzing the progress based on the data, and generating improvement advice;

[2005] means for notifying a user of task management advice and receiving an evaluation of the advice;

[2006] a means for regenerating improvement advice based on the evaluation;

[2007] A system including:

[2008] (Claim 2)

[2009] means for receiving user feedback and regenerating the scheduling method based on the feedback;

[2010] means for suggesting the regenerated scheduling method to a user;

[2011] The system of claim 1 further comprising:

[2012] (Claim 3)

[2013] means for receiving a user's advice rating and regenerating improvement advice based on the rating;

[2014] means for notifying a user of the regenerated improvement advice;

[2015] The system of claim 1 further comprising:

[2016] "Application Example 1"

[2017] (Claim 1)

[2018] means for collecting profile data from a user and proposing an optimal task scheduling method based on the data;

[2019] means for analyzing a user's task progress data and generating advice for efficient task management based on the analysis;

[2020] means for calculating a timing for reminding a task and displaying an alert to a user at that timing;

[2021] a means for a user to input profile data and task progress by voice using a wearable device;

[2022] means for displaying advice and alerts on the wearable device;

[2023] A system including:

[2024] (Claim 2)

[2025] means for receiving user feedback and regenerating the scheduling method based on the feedback;

[2026] means for suggesting the regenerated scheduling method to a user;

[2027] The system of claim 1 further comprising:

[2028] (Claim 3)

[2029] means for receiving a user's advice rating and regenerating improvement advice based on the rating;

[2030] means for notifying a user of the regenerated improvement advice;

[2031] The system of claim 1 further comprising:

[2032] "Example 2: Combining Emotion Engines"

[2033] (Claim 1)

[2034] means for collecting profile data from a user and proposing an optimal task scheduling method based on the data;

[2035] means for analyzing a user's task progress data and generating advice for efficient task management based on the analysis;

[2036] means for calculating a timing for reminding a task and displaying an alert to a user at that timing;

[2037] means for recognizing a user's emotional state and adjusting task scheduling methods and alert expressions based on this emotional data;

[2038] A system including:

[2039] (Claim 2)

[2040] means for receiving user feedback and regenerating the scheduling method based on the feedback;

[2041] means for suggesting the regenerated scheduling method to a user;

[2042] The system of claim 1 further comprising:

[2043] (Claim 3)

[2044] means for receiving a user's advice rating and regenerating improvement advice based on the rating;

[2045] means for notifying a user of the regenerated improvement advice;

[2046] The system of claim 1 further comprising:

[2047] "Application example 2 when combining emotion engines"

[2048] (Claim 1)

[2049] means for collecting profile data from a user and proposing an optimal task scheduling method based on the data;

[2050] means for analyzing a user's task progress data and generating advice for efficient task management based on the analysis;

[2051] means for calculating a timing for reminding a task and displaying an alert to a user at that timing;

[2052] means for recognizing a user's emotional state and adjusting task management based on said recognition;

[2053] A system including:

[2054] (Claim 2)

[2055] means for receiving user feedback and regenerating the scheduling method based on the feedback;

[2056] means for suggesting the regenerated scheduling method to a user;

[2057] a means for suggesting relaxation activities according to a stress level based on the user's emotional state;

[2058] The system of claim 1 further comprising:

[2059] (Claim 3)

[2060] means for receiving a user's advice rating and regenerating improvement advice based on the rating;

[2061] means for notifying a user of the regenerated improvement advice;

[2062] means for analyzing emotion data in real time and generating advice adapted to the user based on the data;

[2063] The system of claim 1 further comprising: [Explanation of symbols]

[2064] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting profile data from a user and proposing an optimal task scheduling method based on the data; means for analyzing a user's task progress data and generating advice for efficient task management based on the analysis; means for calculating a timing for reminding a task and displaying an alert to a user at that timing; A system including:

2. means for receiving user feedback and regenerating the scheduling method based on the feedback; means for suggesting the regenerated scheduling method to a user; The system of claim 1 further comprising:

3. means for receiving a user's advice rating and regenerating improvement advice based on the rating; means for notifying a user of the regenerated improvement advice; The system of claim 1 further comprising:

Images